keyword stringclasses 7 values | repo_name stringlengths 8 98 | file_path stringlengths 4 244 | file_extension stringclasses 29 values | file_size int64 0 84.1M | line_count int64 0 1.6M | content stringlengths 1 84.1M ⌀ | language stringclasses 14 values |
|---|---|---|---|---|---|---|---|
3D | feos-org/feos | crates/feos/src/uvtheory/eos/wca/attractive_perturbation_uvb3.rs | .rs | 14,774 | 438 | use super::WeeksChandlerAndersen;
use super::attractive_perturbation::one_fluid_properties;
use super::hard_sphere::{
WCA_CONSTANTS_ETA_A_UVB3, WCA_CONSTANTS_ETA_B_UVB3, dimensionless_diameter_q_wca,
};
use crate::uvtheory::parameters::*;
use feos_core::StateHD;
use nalgebra::DVector;
use num_dual::DualNum;
use std::f64::consts::PI;
const C_WCA: [[f64; 6]; 6] = [
[
-0.2622378162,
0.6585817423,
5.5318022309,
0.6902354794,
-3.6825190645,
-1.7263213318,
],
[
-0.1899241690,
-0.5555205158,
9.1361398949,
0.7966155658,
-6.1413017045,
4.9553415149,
],
[
0.1169786415,
-0.2216804790,
-2.0470861617,
-0.3742261343,
0.9568416381,
10.1401796764,
],
[
0.5852642702,
2.0795520346,
19.0711829725,
-2.3403594600,
2.5833371420,
432.3858674425,
],
[
-0.6084232211,
-7.2376034572,
19.0412933614,
3.2388986513,
75.4442555789,
-588.3837110653,
],
[
0.0512327656,
6.6667943569,
47.1109947616,
-0.5011125797,
-34.8918383146,
189.5498636006,
],
];
// Constants for delta B2 RSAP
const C_B2_RSAP: [[f64; 4]; 4] = [
[-0.063550989, 6.206829830, -37.45829549, 40.72849774],
[1.519053409, 13.14989643, 85.35058674, 374.1906360],
[0.693456220, 9.459946180, -53.28984218, 315.8199084],
[0.007492596, 0.546171170, 7.979562575, -119.6126395],
];
// // Constants for B3 Model for Mie nu-6 fluids
const K_LJ_B3: [f64; 16] = [
-3.9806, 79.565, 0.5489, 5.3632, 1.4245, 57.292, 0.0, 1.0031, -39.755, -81.213, 0.6987, 30.156,
-23.692, -85.006, 0.7762, 12.798,
];
const P_B3: [f64; 4] = [0.80844, -0.09541, 0.47525, -2.83283];
const L_B3: [f64; 4] = [4.9485, -21.3, 7.0, 3.2162];
const M_B3: [f64; 4] = [0.11853, 0.078556, -0.55039, 0.009163];
const CU_WCA: [f64; 8] = [26.454, 1.8045, 1.7997, 161.96, 11.605, 12., 0.4, 2.0];
#[derive(Debug, Clone)]
pub(super) struct AttractivePerturbationB3;
impl AttractivePerturbationB3 {
/// Helmholtz energy for attractive perturbation
pub fn helmholtz_energy_density<D: DualNum<f64> + Copy>(
&self,
parameters: &UVTheoryPars,
state: &StateHD<D>,
) -> D {
let p = parameters;
let t = state.temperature;
let density = state.partial_density.sum();
let d = WeeksChandlerAndersen::diameter_wca(p, t);
let x = &state.molefracs;
// vdw effective one fluid properties
let (rep_x, att_x, sigma_x, weighted_sigma3_ij, epsilon_k_x, d_x) =
one_fluid_properties(p, x, t);
let t_x = state.temperature / epsilon_k_x;
let rho_x = density * sigma_x.powi(3);
let rm_x = (rep_x / att_x).powd((rep_x - att_x).recip());
let mean_field_constant_x = mean_field_constant(rep_x, att_x, rm_x);
let q_vdw = dimensionless_diameter_q_wca(t_x, rep_x, att_x);
let i_wca =
correlation_integral_wca(rho_x, mean_field_constant_x, rep_x, att_x, d_x, q_vdw, rm_x);
let delta_a1u = state.partial_density.sum() / t_x * i_wca * 2.0 * PI * weighted_sigma3_ij;
let u_fraction_wca = u_fraction_wca(
rep_x,
density * x.dot(&p.sigma.map(|s| D::from(s.powi(3)))),
t_x,
);
let b21u = delta_b12u(t_x, mean_field_constant_x, weighted_sigma3_ij, q_vdw, rm_x);
let b2bar = residual_virial_coefficient(p, x, state.temperature);
let b3bar = residual_third_virial_coefficient(p, x, state.temperature, &d);
let db31u = delta_b31u(t_x, weighted_sigma3_ij, rm_x, rep_x, att_x, d_x);
let alpha =
(-rho_x * rep_x.recip() * CU_WCA[0] * (t_x.powi(2).recip() * CU_WCA[1] + 1.0)).exp();
density
* (delta_a1u
+ (-u_fraction_wca + 1.0)
* ((b2bar - b21u) * density + density.powi(2) * alpha * (b3bar - db31u) * 0.5))
}
}
fn delta_b12u<D: DualNum<f64> + Copy>(
t_x: D,
mean_field_constant_x: D,
weighted_sigma3_ij: D,
q_x: D,
rm_x: D,
) -> D {
(-mean_field_constant_x - (rm_x.powi(3) - q_x.powi(3)) * 1.0 / 3.0) / t_x
* 2.0
* PI
* weighted_sigma3_ij
}
fn residual_virial_coefficient<D: DualNum<f64> + Copy>(
p: &UVTheoryPars,
x: &DVector<D>,
t: D,
) -> D {
let mut delta_b2bar = D::zero();
for (i, xi) in x.iter().enumerate() {
for (j, xj) in x.iter().enumerate() {
let t_ij = t / p.eps_k_ij[(i, j)];
let rep_ij = p.rep_ij[(i, j)];
let att_ij = p.att_ij[(i, j)];
let q_ij = dimensionless_diameter_q_wca(t_ij, D::from(rep_ij), D::from(att_ij));
delta_b2bar +=
*xi * *xj * p.sigma_ij[(i, j)].powi(3) * delta_b2(t_ij, rep_ij, att_ij, q_ij);
}
}
delta_b2bar
}
fn residual_third_virial_coefficient<D: DualNum<f64> + Copy>(
p: &UVTheoryPars,
x: &DVector<D>,
t: D,
d: &DVector<D>,
) -> D {
let mut delta_b3bar = D::zero();
for (i, xi) in x.iter().enumerate() {
for (j, xj) in x.iter().enumerate() {
let t_ij = t / p.eps_k_ij[(i, j)];
let rep_ij = p.rep_ij[(i, j)];
let att_ij = p.att_ij[(i, j)];
let q_ij = dimensionless_diameter_q_wca(t_ij, D::from(rep_ij), D::from(att_ij));
// No mixing rule defined for B3 yet! The implemented rule is just taken from B2 and not correct!
let rm_ij = (rep_ij / att_ij).powd((rep_ij - att_ij).recip());
let d_ij = (d[i] / p.sigma[i] + d[j] / p.sigma[j]) * 0.5;
// Recheck mixing rule!
delta_b3bar += *xi
* *xj
* p.sigma_ij[(i, j)].powi(6)
* delta_b3(t_ij, rm_ij, rep_ij, att_ij, d_ij, q_ij);
}
}
delta_b3bar
}
fn correlation_integral_wca<D: DualNum<f64> + Copy>(
rho_x: D,
mean_field_constant_x: D,
rep_x: D,
att_x: D,
d_x: D,
q_x: D,
rm_x: D,
) -> D {
let c = coefficients_wca(rep_x, att_x, d_x);
(q_x.powi(3) - rm_x.powi(3)) * 1.0 / 3.0 - mean_field_constant_x
+ mie_prefactor(rep_x, att_x) * (c[0] * rho_x + c[1] * rho_x.powi(2) + c[2] * rho_x.powi(3))
/ (c[3] * rho_x + c[4] * rho_x.powi(2) + c[5] * rho_x.powi(3) + 1.0)
}
/// U-fraction with low temperature correction omega
fn u_fraction_wca<D: DualNum<f64> + Copy>(rep_x: D, reduced_density: D, t_x: D) -> D {
let omega = if t_x.re() < 175.0 {
(-t_x * CU_WCA[5] * (reduced_density - CU_WCA[6]).powi(2)).exp()
* ((t_x * CU_WCA[7]).tanh().recip() - 1.0).powi(2)
} else {
(-t_x * CU_WCA[5] * (reduced_density - CU_WCA[6]).powi(2)).exp() * 0.0
};
-(-(reduced_density.powi(2) * ((rep_x + CU_WCA[4]).recip() * CU_WCA[3] + CU_WCA[2]) + omega))
.exp()
+ 1.0
}
// Coefficients for IWCA
fn coefficients_wca<D: DualNum<f64> + Copy>(rep: D, att: D, d: D) -> [D; 6] {
let rep_inv = rep.recip();
let rs_x = (rep / att).powd((rep - att).recip());
let tau_x = -d + rs_x;
let c1 = rep_inv.powi(2) * C_WCA[0][2]
+ C_WCA[0][0]
+ rep_inv * C_WCA[0][1]
+ (rep_inv.powi(2) * C_WCA[0][5] + rep_inv * C_WCA[0][4] + C_WCA[0][3]) * tau_x;
let c2 = rep_inv.powi(2) * C_WCA[1][2]
+ C_WCA[1][0]
+ rep_inv * C_WCA[1][1]
+ (rep_inv.powi(2) * C_WCA[1][5] + rep_inv * C_WCA[1][4] + C_WCA[1][3]) * tau_x;
let c3 = rep_inv.powi(2) * C_WCA[2][2]
+ C_WCA[2][0]
+ rep_inv * C_WCA[2][1]
+ (rep_inv.powi(2) * C_WCA[2][5] + rep_inv * C_WCA[2][4] + C_WCA[2][3]) * tau_x;
let c4 = rep_inv.powi(2) * C_WCA[3][2]
+ C_WCA[3][0]
+ rep_inv * C_WCA[3][1]
+ (rep_inv.powi(2) * C_WCA[3][5] + rep_inv * C_WCA[3][4] + C_WCA[3][3]) * tau_x;
let c5 = rep_inv.powi(2) * C_WCA[4][2]
+ C_WCA[4][0]
+ rep_inv * C_WCA[4][1]
+ (rep_inv.powi(2) * C_WCA[4][5] + rep_inv * C_WCA[4][4] + C_WCA[4][3]) * tau_x;
let c6 = rep_inv.powi(2) * C_WCA[5][2]
+ C_WCA[5][0]
+ rep_inv * C_WCA[5][1]
+ (rep_inv.powi(2) * C_WCA[5][5] + rep_inv * C_WCA[5][4] + C_WCA[5][3]) * tau_x;
[c1, c2, c3, c4, c5, c6]
}
// Residual second virial coefficient from Revised series approximation RSAP
fn factorial(num: u64) -> u64 {
(1..=num).product()
}
fn delta_b2<D: DualNum<f64> + Copy>(reduced_temperature: D, rep: f64, att: f64, q: D) -> D {
let rm = (rep / att).powd((rep - att).recip());
let beta = reduced_temperature.recip();
let b20 = q.powi(3) * 2.0 / 3.0 * PI;
let y = beta.exp() - 1.0;
let c1 = rep.recip() * C_B2_RSAP[0][1]
+ (rep.powi(2)).recip() * C_B2_RSAP[0][2]
+ (rep.powi(3)).recip() * C_B2_RSAP[0][3]
+ C_B2_RSAP[0][0];
let c2 = rep.recip() * C_B2_RSAP[1][1]
+ (rep.powi(2)).recip() * C_B2_RSAP[1][2]
+ (rep.powi(3)).recip() * C_B2_RSAP[1][3]
+ C_B2_RSAP[1][0];
let c3 = rep.recip() * C_B2_RSAP[2][1]
+ (rep.powi(2)).recip() * C_B2_RSAP[2][2]
+ (rep.powi(3)).recip() * C_B2_RSAP[2][3]
+ C_B2_RSAP[2][0];
let c4 = rep.recip() * C_B2_RSAP[3][1]
+ (rep.powi(2)).recip() * C_B2_RSAP[3][2]
+ (rep.powi(3)).recip() * C_B2_RSAP[3][3]
+ C_B2_RSAP[3][0];
let mut sum_beta = beta;
for i in 2..16 {
let k = factorial(i as u64) as f64 * i as f64;
sum_beta += beta.powi(i) / k
}
(b20 - rm.powi(3) * 2.0 / 3.0 * PI - c1) * y - sum_beta * c2 - beta * c3 - beta.powi(2) * c4
}
fn delta_b31u<D: DualNum<f64> + Copy>(
t_x: D,
weighted_sigma3_ij: D,
rm_x: D,
rep_x: D,
att_x: D,
d_x: D,
) -> D {
let nu = rep_x;
let tau = rm_x - d_x;
let k1 = nu.recip() * C_WCA[0][1]
+ (nu.powi(2)).recip() * C_WCA[0][2]
+ C_WCA[0][0]
+ (nu.recip() * C_WCA[0][4] + (nu.powi(2)).recip() * C_WCA[0][5] + C_WCA[0][3]) * tau;
t_x.recip() * 4.0 * mie_prefactor(rep_x, att_x) * PI * k1 * weighted_sigma3_ij.powi(2)
}
fn delta_b3<D: DualNum<f64> + Copy>(
t_x: D,
rm_x: f64,
rep_x: f64,
_att_x: f64,
d_x: D,
q_x: D,
) -> D {
let beta = t_x.recip();
let b30 = (q_x.powi(3) * PI / 6.0).powi(2) * 10.0;
let b31 = ((t_x + K_LJ_B3[2])
.powf(((rep_x - 12.0) / (rep_x - 6.0) * M_B3[0] + 1.0) * K_LJ_B3[3]))
.recip()
* K_LJ_B3[1]
* ((rep_x - 12.0) / (rep_x - L_B3[0]) * P_B3[0] + 1.0)
+ K_LJ_B3[0];
let b32 = ((t_x + K_LJ_B3[6])
.powf(((rep_x - 12.0) / (rep_x - 6.0) * M_B3[1] + 1.0) * K_LJ_B3[7]))
.recip()
* K_LJ_B3[5]
* ((rep_x - 12.0) / (rep_x - L_B3[1]) * P_B3[1] + 1.0)
+ K_LJ_B3[4];
let b33 = ((t_x + K_LJ_B3[10])
.powf(((rep_x - 12.0) / (rep_x - 6.0) * M_B3[2] + 1.0) * K_LJ_B3[11]))
.recip()
* K_LJ_B3[9]
* ((rep_x - 12.0) / (rep_x - L_B3[2]) * P_B3[2] + 1.0)
+ K_LJ_B3[8];
let b34 = ((t_x + K_LJ_B3[14])
.powf(((rep_x - 12.0) / (rep_x - 6.0) * M_B3[3] + 1.0) * K_LJ_B3[15]))
.recip()
* K_LJ_B3[13]
* ((rep_x - 12.0) / (rep_x - L_B3[3]) * P_B3[3] + 1.0)
+ K_LJ_B3[12];
let b3 = b30 + b31 * beta + b32 * beta.powi(2) + b33 * beta.powi(3) + b34 * beta.powi(4);
// Watch out: Not defined for mixtures!
let tau = -d_x + rm_x;
let tau2 = tau * tau;
let rep_inv = rep_x.recip();
let c1_eta_a = tau
* (rep_inv * WCA_CONSTANTS_ETA_A_UVB3[0][1] + WCA_CONSTANTS_ETA_A_UVB3[0][0])
+ tau2 * (rep_inv * WCA_CONSTANTS_ETA_A_UVB3[0][3] + WCA_CONSTANTS_ETA_A_UVB3[0][2]);
let c1_eta_b = tau * WCA_CONSTANTS_ETA_B_UVB3[0][0] + tau2 * WCA_CONSTANTS_ETA_B_UVB3[0][1];
let b30_uv = (d_x.powi(3) * PI / 6.0).powi(2) * 10.0
- d_x.powi(3) * 5.0 / 9.0
* PI.powi(2)
* ((-q_x.powi(3) + rm_x.powi(3)) * (c1_eta_a + 1.0)
- (-d_x.powi(3) + rm_x.powi(3)) * (c1_eta_b + 1.0));
b3 - b30_uv
}
#[cfg(test)]
mod test {
use super::*;
use crate::uvtheory::Perturbation::WeeksChandlerAndersenB3 as WCAB3;
use crate::uvtheory::parameters::utils::methane_parameters;
use approx::assert_relative_eq;
use nalgebra::dvector;
#[test]
fn test_attractive_perturbation_uvb3() {
let reduced_temperature = 4.0;
let reduced_density = 0.5;
let p = UVTheoryPars::new(&methane_parameters(12.0, 6.0), WCAB3);
let state = StateHD::new(
reduced_temperature * p.epsilon_k[0],
p.sigma[0].powi(3) / reduced_density,
&dvector![1.0],
);
let x = &state.molefracs;
let (rep_x, att_x, sigma_x, weighted_sigma3_ij, epsilon_k_x, d_x) =
one_fluid_properties(&p, &state.molefracs, state.temperature);
let t_x = state.temperature / epsilon_k_x;
let rho_x = state.partial_density.sum() * sigma_x.powi(3);
let rm_x = (rep_x / att_x).powd((rep_x - att_x).recip());
let mean_field_constant_x = mean_field_constant(rep_x, att_x, rm_x);
// Effective Diameters:
let q_vdw = dimensionless_diameter_q_wca(t_x, rep_x, att_x);
assert_relative_eq!(q_vdw, 0.9606854684075393, epsilon = 1e-10);
assert_relative_eq!(d_x, 0.934655265184067, epsilon = 1e-10);
//u-fraction:
let u_fraction_wca = u_fraction_wca(
rep_x,
state.partial_density.sum() * (x * &p.sigma.map(|s| s.powi(3))).sum(),
t_x,
);
assert_relative_eq!(u_fraction_wca, 0.8852775506870431, epsilon = 1e-10);
// delta a1u
let i_wca =
correlation_integral_wca(rho_x, mean_field_constant_x, rep_x, att_x, d_x, q_vdw, rm_x);
let delta_a1u = state.partial_density.sum() / t_x * i_wca * 2.0 * PI * weighted_sigma3_ij;
assert!(delta_a1u == -0.8992910890819197);
// Virial coeffecients:
let b2bar = residual_virial_coefficient(&p, x, state.temperature) / p.sigma[0].powi(3);
assert_relative_eq!(b2bar, -1.6142316456384618, epsilon = 1e-12);
let b21u = delta_b12u(t_x, mean_field_constant_x, weighted_sigma3_ij, q_vdw, rm_x)
/ p.sigma[0].powi(3);
assert_relative_eq!(b21u, -1.5103749286162982, epsilon = 1e-10);
let db3 = delta_b3(t_x, rm_x, rep_x, att_x, d_x, q_vdw);
assert_relative_eq!(db3, -0.6591980196661884, epsilon = 1e-10);
// Full attractive perturbation:
let a = AttractivePerturbationB3.helmholtz_energy_density(&p, &state)
/ state.partial_density.sum();
assert_relative_eq!(-0.9027781694834115, a, epsilon = 1e-5);
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/uvtheory/eos/wca/attractive_perturbation.rs | .rs | 16,309 | 474 | use super::WeeksChandlerAndersen;
use super::hard_sphere::dimensionless_diameter_q_wca;
use crate::uvtheory::parameters::*;
use feos_core::StateHD;
use nalgebra::DVector;
use num_dual::DualNum;
use std::f64::consts::PI;
const C_WCA: [[f64; 6]; 6] = [
[
-0.2622378162,
0.6585817423,
5.5318022309,
0.6902354794,
-3.6825190645,
-1.7263213318,
],
[
-0.1899241690,
-0.5555205158,
9.1361398949,
0.7966155658,
-6.1413017045,
4.9553415149,
],
[
0.1169786415,
-0.2216804790,
-2.0470861617,
-0.3742261343,
0.9568416381,
10.1401796764,
],
[
0.5852642702,
2.0795520346,
19.0711829725,
-2.3403594600,
2.5833371420,
432.3858674425,
],
[
-0.6084232211,
-7.2376034572,
19.0412933614,
3.2388986513,
75.4442555789,
-588.3837110653,
],
[
0.0512327656,
6.6667943569,
47.1109947616,
-0.5011125797,
-34.8918383146,
189.5498636006,
],
];
/// Constants for WCA u-fraction.
const CU_WCA: [f64; 3] = [1.4419, 1.1169, 16.8810];
/// Constants for WCA effective inverse reduced temperature.
const C2: [[f64; 2]; 3] = [
[1.45805207053190E-03, 3.57786067657446E-02],
[1.25869266841313E-04, 1.79889086453277E-03],
[0.0, 0.0],
];
#[derive(Debug, Clone)]
pub struct AttractivePerturbation;
impl AttractivePerturbation {
/// Helmholtz energy for attractive perturbation, eq. 52
pub fn helmholtz_energy_density<D: DualNum<f64> + Copy>(
&self,
parameters: &UVTheoryPars,
state: &StateHD<D>,
) -> D {
let p = parameters;
let x = &state.molefracs;
let t = state.temperature;
let density = state.partial_density.sum();
// vdw effective one fluid properties
let (rep_x, att_x, sigma_x, weighted_sigma3_ij, epsilon_k_x, d_x) =
one_fluid_properties(p, x, t);
let t_x = state.temperature / epsilon_k_x;
let rho_x = density * sigma_x.powi(3);
let rm_x = (rep_x / att_x).powd((rep_x - att_x).recip());
let mean_field_constant_x = mean_field_constant(rep_x, att_x, rm_x);
let q_vdw = dimensionless_diameter_q_wca(t_x, rep_x, att_x);
let i_wca =
correlation_integral_wca(rho_x, mean_field_constant_x, rep_x, att_x, d_x, q_vdw, rm_x);
let delta_a1u = state.partial_density.sum() / t_x * i_wca * 2.0 * PI * weighted_sigma3_ij;
// state.partial_density.sum() / t_x * i_wca * 2.0 * PI * weighted_sigma3_ij;
let u_fraction_wca =
u_fraction_wca(rep_x, density * x.dot(&p.sigma.map(|s| D::from(s.powi(3)))));
let b21u = delta_b12u(t_x, mean_field_constant_x, weighted_sigma3_ij, q_vdw, rm_x);
let b2bar = residual_virial_coefficient(p, x, state.temperature);
density * (delta_a1u + (-u_fraction_wca + 1.0) * (b2bar - b21u) * density)
}
}
// (S43) & (S53)
fn delta_b12u<D: DualNum<f64> + Copy>(
t_x: D,
mean_field_constant_x: D,
weighted_sigma3_ij: D,
q_x: D,
rm_x: D,
) -> D {
(-mean_field_constant_x - (rm_x.powi(3) - q_x.powi(3)) * 1.0 / 3.0) / t_x
* 2.0
* PI
* weighted_sigma3_ij
}
fn residual_virial_coefficient<D: DualNum<f64> + Copy>(
p: &UVTheoryPars,
x: &DVector<D>,
t: D,
) -> D {
let mut delta_b2bar = D::zero();
for (i, xi) in x.iter().enumerate() {
for (j, xj) in x.iter().enumerate() {
//let q_ij = (q[i] / p.sigma[i] + q[j] / p.sigma[j]) * 0.5;
let t_ij = t / p.eps_k_ij[(i, j)];
let rep_ij = p.rep_ij[(i, j)];
let att_ij = p.att_ij[(i, j)];
let q_ij = dimensionless_diameter_q_wca(t_ij, D::from(rep_ij), D::from(att_ij));
// Recheck mixing rule!
delta_b2bar +=
*xi * *xj * p.sigma_ij[(i, j)].powi(3) * delta_b2(t_ij, rep_ij, att_ij, q_ij);
}
}
delta_b2bar
}
fn correlation_integral_wca<D: DualNum<f64> + Copy>(
rho_x: D,
mean_field_constant_x: D,
rep_x: D,
att_x: D,
d_x: D,
q_x: D,
rm_x: D,
) -> D {
let c = coefficients_wca(rep_x, att_x, d_x);
(q_x.powi(3) - rm_x.powi(3)) * 1.0 / 3.0 - mean_field_constant_x
+ mie_prefactor(rep_x, att_x) * (c[0] * rho_x + c[1] * rho_x.powi(2) + c[2] * rho_x.powi(3))
/ (c[3] * rho_x + c[4] * rho_x.powi(2) + c[5] * rho_x.powi(3) + 1.0)
}
/// U-fraction according to Barker-Henderson division.
/// Eq. 15
fn u_fraction_wca<D: DualNum<f64> + Copy>(rep_x: D, reduced_density: D) -> D {
(reduced_density * CU_WCA[0]
+ reduced_density.powi(2) * (rep_x.recip() * CU_WCA[2] + CU_WCA[1]))
.tanh()
}
pub(super) fn one_fluid_properties<D: DualNum<f64> + Copy>(
p: &UVTheoryPars,
x: &DVector<D>,
t: D,
) -> (D, D, D, D, D, D) {
let d = WeeksChandlerAndersen::diameter_wca(p, t);
// &p.sigma;
let mut epsilon_k = D::zero();
let mut weighted_sigma3_ij = D::zero();
let mut rep = D::zero();
let mut att = D::zero();
let mut d_x_3 = D::zero();
for (i, xi) in x.iter().enumerate() {
d_x_3 += *xi * d[i].powi(3);
for (j, xj) in x.iter().enumerate() {
let _y = *xi * *xj * p.sigma_ij[(i, j)].powi(3);
weighted_sigma3_ij += _y;
epsilon_k += _y * p.eps_k_ij[(i, j)];
rep += *xi * *xj * p.rep_ij[(i, j)];
att += *xi * *xj * p.att_ij[(i, j)];
}
}
//let dx = (x * &d.map(|v| v.powi(3))).sum().powf(1.0 / 3.0);
let sigma_x = x.dot(&p.sigma.map(|v| D::from(v.powi(3)))).powf(1.0 / 3.0);
let dx = d_x_3.powf(1.0 / 3.0) / sigma_x;
(
rep,
att,
sigma_x,
weighted_sigma3_ij,
epsilon_k / weighted_sigma3_ij,
dx,
)
}
// Coefficients for IWCA from eq. (S55)
fn coefficients_wca<D: DualNum<f64> + Copy>(rep: D, att: D, d: D) -> [D; 6] {
let rep_inv = rep.recip();
let rs_x = (rep / att).powd((rep - att).recip());
let tau_x = -d + rs_x;
let c1 = rep_inv.powi(2) * C_WCA[0][2]
+ C_WCA[0][0]
+ rep_inv * C_WCA[0][1]
+ (rep_inv.powi(2) * C_WCA[0][5] + rep_inv * C_WCA[0][4] + C_WCA[0][3]) * tau_x;
let c2 = rep_inv.powi(2) * C_WCA[1][2]
+ C_WCA[1][0]
+ rep_inv * C_WCA[1][1]
+ (rep_inv.powi(2) * C_WCA[1][5] + rep_inv * C_WCA[1][4] + C_WCA[1][3]) * tau_x;
let c3 = rep_inv.powi(2) * C_WCA[2][2]
+ C_WCA[2][0]
+ rep_inv * C_WCA[2][1]
+ (rep_inv.powi(2) * C_WCA[2][5] + rep_inv * C_WCA[2][4] + C_WCA[2][3]) * tau_x;
let c4 = rep_inv.powi(2) * C_WCA[3][2]
+ C_WCA[3][0]
+ rep_inv * C_WCA[3][1]
+ (rep_inv.powi(2) * C_WCA[3][5] + rep_inv * C_WCA[3][4] + C_WCA[3][3]) * tau_x;
let c5 = rep_inv.powi(2) * C_WCA[4][2]
+ C_WCA[4][0]
+ rep_inv * C_WCA[4][1]
+ (rep_inv.powi(2) * C_WCA[4][5] + rep_inv * C_WCA[4][4] + C_WCA[4][3]) * tau_x;
let c6 = rep_inv.powi(2) * C_WCA[5][2]
+ C_WCA[5][0]
+ rep_inv * C_WCA[5][1]
+ (rep_inv.powi(2) * C_WCA[5][5] + rep_inv * C_WCA[5][4] + C_WCA[5][3]) * tau_x;
[c1, c2, c3, c4, c5, c6]
}
fn delta_b2<D: DualNum<f64> + Copy>(reduced_temperature: D, rep: f64, att: f64, q: D) -> D {
let rm = (rep / att).powf(1.0 / (rep - att)); // Check mixing rule!!
let rc = 5.0;
let alpha = mean_field_constant(rep, att, rc);
let beta = reduced_temperature.recip();
let y = beta.exp() - 1.0;
let yeff = y_eff(reduced_temperature, rep, att);
-(yeff * (rc.powi(3) - rm.powi(3)) / 3.0 + y * (-q.powi(3) + rm.powi(3)) / 3.0 + beta * alpha)
* 2.0
* PI
}
fn y_eff<D: DualNum<f64> + Copy>(reduced_temperature: D, rep: f64, att: f64) -> D {
// optimize: move this part to parameter initialization
let rc = 5.0;
let rs = (rep / att).powf(1.0 / (rep - att));
let c0 = 1.0
- 3.0 * (mean_field_constant(rep, att, rs) - mean_field_constant(rep, att, rc))
/ (rc.powi(3) - rs.powi(3));
let c1 = C2[0][0] + C2[0][1] / rep;
let c2 = C2[1][0] + C2[1][1] / rep;
let c3 = C2[2][0] + C2[2][1] / rep;
//exponents
let a = 1.05968091375869;
let b = 3.41106168592999;
let c = 0.0;
// (S58)
let beta = reduced_temperature.recip();
let beta_eff = beta
* (-(beta.powf(a) * c1 + beta.powf(b) * c2 + beta.powf(c) * c3 + 1.0).recip() * c0 + 1.0);
beta_eff.exp() - 1.0
}
#[cfg(test)]
#[expect(clippy::excessive_precision)]
mod test {
use super::*;
use crate::uvtheory::Perturbation::WeeksChandlerAndersen as WCA;
use crate::uvtheory::parameters::utils::{methane_parameters, test_parameters_mixture};
use approx::assert_relative_eq;
use nalgebra::dvector;
#[test]
fn test_attractive_perturbation() {
// m = 24, t = 4.0, rho = 1.0
let reduced_temperature = 4.0;
let reduced_density = 1.0;
let p = UVTheoryPars::new(&methane_parameters(24.0, 6.0), WCA);
let state = StateHD::new(
reduced_temperature * p.epsilon_k[0],
p.sigma[0].powi(3) / reduced_density,
&dvector![1.0],
);
let x = &state.molefracs;
let (rep_x, att_x, sigma_x, weighted_sigma3_ij, epsilon_k_x, d_x) =
one_fluid_properties(&p, &state.molefracs, state.temperature);
dbg!(epsilon_k_x);
let t_x = state.temperature / epsilon_k_x;
let rho_x = state.partial_density.sum() * sigma_x.powi(3);
let rm_x = (rep_x / att_x).powd((rep_x - att_x).recip());
let mean_field_constant_x = mean_field_constant(rep_x, att_x, rm_x);
dbg!(t_x);
let q_vdw = dimensionless_diameter_q_wca(t_x, rep_x, att_x);
let b21u = delta_b12u(t_x, mean_field_constant_x, weighted_sigma3_ij, q_vdw, rm_x)
/ p.sigma[0].powi(3);
assert_relative_eq!(b21u, -1.02233215790525, epsilon = 1e-12);
let i_wca =
correlation_integral_wca(rho_x, mean_field_constant_x, rep_x, att_x, d_x, q_vdw, rm_x);
let delta_a1u = state.partial_density.sum() / t_x * i_wca * 2.0 * PI * weighted_sigma3_ij;
assert_relative_eq!(delta_a1u, -1.52406840346272, epsilon = 1e-6);
let u_fraction_wca = u_fraction_wca(
rep_x,
state.partial_density.sum() * x.dot(&p.sigma.map(|s| s.powi(3))),
);
let b2bar = residual_virial_coefficient(&p, x, state.temperature) / p.sigma[0].powi(3);
dbg!(b2bar);
assert_relative_eq!(b2bar, -1.09102560732964, epsilon = 1e-12);
dbg!(u_fraction_wca);
assert_relative_eq!(u_fraction_wca, 0.997069754340431, epsilon = 1e-5);
let a_test = delta_a1u
+ (-u_fraction_wca + 1.0)
* (b2bar - b21u)
* p.sigma[0].powi(3)
* state.partial_density.sum();
dbg!(a_test);
let a = AttractivePerturbation.helmholtz_energy_density(&p, &state)
/ state.partial_density.sum();
dbg!(a);
assert_relative_eq!(-1.5242697155023, a, epsilon = 1e-5);
}
#[test]
fn test_attractive_perturbation_wca_mixture() {
let molefracs = dvector![0.40000000000000002, 0.59999999999999998];
let reduced_temperature = 1.0;
let reduced_density = 0.90000000000000002;
let p = UVTheoryPars::new(
&test_parameters_mixture(
dvector![12.0, 12.0],
dvector![6.0, 6.0],
dvector![1.0, 1.0],
dvector![1.0, 0.5],
),
WCA,
);
let state = StateHD::new(reduced_temperature, 1.0 / reduced_density, &molefracs);
let (rep_x, att_x, sigma_x, weighted_sigma3_ij, epsilon_k_x, d_x) =
one_fluid_properties(&p, &state.molefracs, state.temperature);
// u-fraction
let phi_u = u_fraction_wca(rep_x, reduced_density);
assert_relative_eq!(phi_u, 0.99750066585468078, epsilon = 1e-6);
// Delta B21u
let rm_x = (rep_x / att_x).powd((rep_x - att_x).recip());
let mean_field_constant_x = mean_field_constant(rep_x, att_x, rm_x);
let t_x = state.temperature / epsilon_k_x;
dbg!(t_x);
let q_vdw = dimensionless_diameter_q_wca(t_x, rep_x, att_x);
dbg!(q_vdw);
let delta_b21u = delta_b12u(t_x, mean_field_constant_x, weighted_sigma3_ij, q_vdw, rm_x);
dbg!(delta_b21u);
assert_relative_eq!(delta_b21u, -3.9309384983526585, epsilon = 1e-6);
// delta a1u
let rho_x = state.partial_density.sum() * sigma_x.powi(3);
let i_wca =
correlation_integral_wca(rho_x, mean_field_constant_x, rep_x, att_x, d_x, q_vdw, rm_x);
let delta_a1u = state.partial_density.sum() / state.temperature
* i_wca
* 2.0
* PI
* weighted_sigma3_ij
* epsilon_k_x;
assert_relative_eq!(delta_a1u, -4.7678301069070645, epsilon = 1e-6);
// Second virial coefficient
let delta_b2 = residual_virial_coefficient(&p, &state.molefracs, state.temperature)
/ p.sigma[0].powi(3);
dbg!(delta_b2);
assert_relative_eq!(delta_b2, -4.7846399638747954, epsilon = 1e-6);
// Full attractive contribution
let a = AttractivePerturbation.helmholtz_energy_density(&p, &state) / reduced_density;
assert_relative_eq!(a, -4.7697504236074844, epsilon = 1e-5);
}
#[test]
fn test_attractive_perturbation_wca_mixture_different_sigma() {
let molefracs = dvector![0.40000000000000002, 0.59999999999999998];
let reduced_temperature = 1.5;
let density = 0.10000000000000001;
let volume = 1.0 / density;
let p = UVTheoryPars::new(
&test_parameters_mixture(
dvector![12.0, 12.0],
dvector![6.0, 6.0],
dvector![1.0, 2.0],
dvector![1.0, 0.5],
),
WCA,
);
let state = StateHD::new(reduced_temperature, volume, &molefracs);
let (rep_x, att_x, sigma_x, weighted_sigma3_ij, epsilon_k_x, d_x) =
one_fluid_properties(&p, &state.molefracs, state.temperature);
// u-fraction
let density = state.partial_density.sum();
let x = &state.molefracs;
let phi_u = u_fraction_wca(rep_x, density * x.dot(&p.sigma.map(|s| s.powi(3))));
assert_relative_eq!(phi_u, 0.89210738762113795, epsilon = 1e-5);
// delta b2
let b2bar = residual_virial_coefficient(&p, x, state.temperature) / p.sigma[0].powi(3);
assert_relative_eq!(b2bar, -12.106977583257606, epsilon = 1e-12);
//delta b21u
let rm_x = (rep_x / att_x).powd((rep_x - att_x).recip());
let mean_field_constant_x = mean_field_constant(rep_x, att_x, rm_x);
let t_x = state.temperature / epsilon_k_x;
let q_vdw = dimensionless_diameter_q_wca(t_x, rep_x, att_x);
let delta_b21u = delta_b12u(t_x, mean_field_constant_x, weighted_sigma3_ij, q_vdw, rm_x);
assert_relative_eq!(delta_b21u, -10.841841323394299, epsilon = 1e-6);
let a_ufrac = (-phi_u + 1.0) * (b2bar - delta_b21u) * density;
assert_relative_eq!(a_ufrac, -0.0136498856091876, epsilon = 1e-6);
// delta b20
dbg!(d_x);
// delta a1u
let rho_x = state.partial_density.sum() * sigma_x.powi(3);
assert_relative_eq!(d_x, 0.95196953178057431, epsilon = 1e-6);
let i_wca =
correlation_integral_wca(rho_x, mean_field_constant_x, rep_x, att_x, d_x, q_vdw, rm_x);
dbg!(weighted_sigma3_ij);
dbg!(epsilon_k_x);
let delta_a1u = state.partial_density.sum() / state.temperature
* i_wca
* 2.0
* PI
* weighted_sigma3_ij
* epsilon_k_x;
assert_relative_eq!(delta_a1u, -1.3182160310774731, epsilon = 1e-6);
// Full attractive contribution
let a = AttractivePerturbation.helmholtz_energy_density(&p, &state) * volume;
assert_relative_eq!(a, -1.3318659166866607, epsilon = 1e-5);
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/uvtheory/eos/wca/reference_perturbation.rs | .rs | 3,727 | 100 | use super::WeeksChandlerAndersen;
use super::hard_sphere::{
dimensionless_diameter_q_wca, packing_fraction, packing_fraction_a, packing_fraction_b,
};
use crate::uvtheory::parameters::*;
use feos_core::StateHD;
use num_dual::DualNum;
use std::f64::consts::PI;
#[derive(Debug, Clone)]
pub(super) struct ReferencePerturbation;
impl ReferencePerturbation {
/// Helmholtz energy for perturbation reference (Mayer-f), eq. 29
pub fn helmholtz_energy_density<D: DualNum<f64> + Copy>(
&self,
parameters: &UVTheoryPars,
state: &StateHD<D>,
) -> D {
let p = parameters;
let n = p.sigma.len();
let x = &state.molefracs;
let d = WeeksChandlerAndersen::diameter_wca(p, state.temperature);
//let q = diameter_q_wca(&p, state.temperature);
let eta = packing_fraction(&state.partial_density, &d);
let eta_a = packing_fraction_a(p, eta, state.temperature);
let eta_b = packing_fraction_b(p, eta, state.temperature);
let mut a = D::zero();
for i in 0..n {
for j in 0..n {
let rs_ij = ((p.rep[i] / p.att[i]).powf(1.0 / (p.rep[i] - p.att[i]))
+ (p.rep[j] / p.att[j]).powf(1.0 / (p.rep[j] - p.att[j])))
* 0.5; // MIXING RULE not clear!!!
let d_ij = (d[i] + d[j]) * 0.5; // (d[i] * p.sigma[i] + d[j] * p.sigma[j]) * 0.5;
let t_ij = state.temperature / p.eps_k_ij[(i, j)];
let rep_ij = p.rep_ij[(i, j)];
let att_ij = p.att_ij[(i, j)];
let q_ij = dimensionless_diameter_q_wca(t_ij, D::from(rep_ij), D::from(att_ij))
* p.sigma_ij[(i, j)];
a += x[i]
* x[j]
* ((-eta_a[(i, j)] * 0.5 + 1.0) / (-eta_a[(i, j)] + 1.0).powi(3)
* (-q_ij.powi(3) + (rs_ij * p.sigma_ij[(i, j)]).powi(3))
- ((-eta_b[(i, j)] * 0.5 + 1.0) / (-eta_b[(i, j)] + 1.0).powi(3))
* (-d_ij.powi(3) + (rs_ij * p.sigma_ij[(i, j)]).powi(3)))
}
}
-a * state.partial_density.sum().powi(2) * 2.0 / 3.0 * PI
}
}
#[cfg(test)]
#[expect(clippy::excessive_precision)]
mod test {
use super::*;
use crate::uvtheory::Perturbation::WeeksChandlerAndersen as WCA;
use crate::uvtheory::parameters::utils::{test_parameters, test_parameters_mixture};
use approx::assert_relative_eq;
use nalgebra::dvector;
#[test]
fn test_delta_a0_wca_pure() {
// m = 12.0, t = 4.0, rho = 1.0
let reduced_temperature = 4.0;
let reduced_density = 1.0;
let p = test_parameters(24.0, 6.0, 1.0, 1.0, WCA);
let state = StateHD::new(reduced_temperature, 1.0 / reduced_density, &dvector![1.0]);
let a = ReferencePerturbation.helmholtz_energy_density(&p, &state) / reduced_density;
assert_relative_eq!(a, 0.258690311450425, epsilon = 1e-10);
}
#[test]
fn test_delta_a0_wca_mixture() {
let molefracs = dvector![0.40000000000000002, 0.59999999999999998];
let reduced_temperature = 1.0;
let reduced_density = 0.90000000000000002;
let p = UVTheoryPars::new(
&test_parameters_mixture(
dvector![12.0, 12.0],
dvector![6.0, 6.0],
dvector![1.0, 1.0],
dvector![1.0, 0.5],
),
WCA,
);
let state = StateHD::new(reduced_temperature, 1.0 / reduced_density, &molefracs);
let a = ReferencePerturbation.helmholtz_energy_density(&p, &state) / reduced_density;
assert_relative_eq!(a, 0.308268896386771, epsilon = 1e-6);
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/uvtheory/eos/wca/reference_perturbation_uvb3.rs | .rs | 3,604 | 93 | use super::WeeksChandlerAndersen;
use super::hard_sphere::{
dimensionless_diameter_q_wca, packing_fraction, packing_fraction_a_uvb3,
packing_fraction_b_uvb3,
};
use crate::uvtheory::parameters::*;
use feos_core::StateHD;
use num_dual::DualNum;
use std::f64::consts::PI;
#[derive(Debug, Clone)]
pub(super) struct ReferencePerturbationB3;
impl ReferencePerturbationB3 {
/// Helmholtz energy for perturbation reference (Mayer-f), eq. 29
pub fn helmholtz_energy_density<D: DualNum<f64> + Copy>(
&self,
parameters: &UVTheoryPars,
state: &StateHD<D>,
) -> D {
let p = parameters;
let n = p.sigma.len();
let x = &state.molefracs;
let d = WeeksChandlerAndersen::diameter_wca(p, state.temperature);
//let q = diameter_q_wca(&p, state.temperature);
let eta = packing_fraction(&state.partial_density, &d);
let eta_a = packing_fraction_a_uvb3(p, eta, state.temperature);
let eta_b = packing_fraction_b_uvb3(p, eta, state.temperature);
let mut a = D::zero();
for i in 0..n {
for j in 0..n {
let rs_ij = ((p.rep[i] / p.att[i]).powf(1.0 / (p.rep[i] - p.att[i]))
+ (p.rep[j] / p.att[j]).powf(1.0 / (p.rep[j] - p.att[j])))
* 0.5; // MIXING RULE not clear!!!
let d_ij = (d[i] + d[j]) * 0.5; // (d[i] * p.sigma[i] + d[j] * p.sigma[j]) * 0.5;
let t_ij = state.temperature / p.eps_k_ij[(i, j)];
let rep_ij = p.rep_ij[(i, j)];
let att_ij = p.att_ij[(i, j)];
let q_ij = dimensionless_diameter_q_wca(t_ij, D::from(rep_ij), D::from(att_ij))
* p.sigma_ij[(i, j)];
a += x[i]
* x[j]
* ((-eta_a[(i, j)] * 0.5 + 1.0) / (-eta_a[(i, j)] + 1.0).powi(3)
* (-q_ij.powi(3) + (rs_ij * p.sigma_ij[(i, j)]).powi(3))
- ((-eta_b[(i, j)] * 0.5 + 1.0) / (-eta_b[(i, j)] + 1.0).powi(3))
* (-d_ij.powi(3) + (rs_ij * p.sigma_ij[(i, j)]).powi(3)))
}
}
-a * state.partial_density.sum().powi(2) * 2.0 / 3.0 * PI
}
}
#[cfg(test)]
mod test {
use super::*;
use crate::uvtheory::Perturbation::WeeksChandlerAndersenB3 as WCAB3;
use crate::uvtheory::parameters::utils::test_parameters;
use approx::assert_relative_eq;
use nalgebra::dvector;
#[test]
fn test_delta_a0_uvb3_pure() {
// #temp = 2.0, rho = 0.5, nu = 12
// Hard sphere adhs 1.3491645849732654
// Delta a0 0.1130778070897391
let reduced_temperature = 2.0;
let reduced_density = 0.5;
let p = test_parameters(12.0, 6.0, 1.0, 1.0, WCAB3);
let state = StateHD::new(reduced_temperature, 1.0 / reduced_density, &dvector![1.0]);
let a = ReferencePerturbationB3.helmholtz_energy_density(&p, &state) / reduced_density;
dbg!(a);
assert_relative_eq!(a, 0.1130778070897391, epsilon = 1e-10);
// #temp = 3.0, rho = 1.1, nu = 20
// Hard sphere adhs 5.458989212531771
//Delta a0 0.3405167374787895
let reduced_temperature = 3.0;
let reduced_density = 1.1;
let p = test_parameters(20.0, 6.0, 1.0, 1.0, WCAB3);
let state = StateHD::new(reduced_temperature, 1.0 / reduced_density, &dvector![1.0]);
let a = ReferencePerturbationB3.helmholtz_energy_density(&p, &state) / reduced_density;
assert_relative_eq!(a, 0.3405167374787895, epsilon = 1e-10);
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/ideal_gas/dippr.rs | .rs | 11,206 | 299 | use feos_core::parameter::Parameters;
use feos_core::{FeosResult, IdealGas};
use nalgebra::DVector;
use num_dual::DualNum;
use quantity::{JOULE, KELVIN, KILO, MOL, MolarEntropy, Temperature};
use serde::{Deserialize, Serialize};
/// Parameters for DIPPR equations # 100, 107, and 127 for isobaric
/// heat capacities of ideal gases.
///
/// All equations use units $\[T\]=\text{K}$ and $\[c_p\]=\text{J/kmol/K}$.
#[derive(Serialize, Deserialize, Debug, Clone)]
pub enum Dippr {
/// Technically, DIPPR eq. # 100 is
/// $$c_p = A + BT + CT^2 + DT^3 + ET^4 + FT^5 + GT^6$$
/// This implementation works with an arbitrary number of expansion terms.
DIPPR100(Vec<f64>),
/// $$c_p = A + B\left[\frac{C/T}{\sinh(C/T)}\right]^2 + D\left[\frac{E/T}{\cosh(E/T)}\right]^2$$
DIPPR107([f64; 5]),
/// $$c_p = A+B\left[\frac{\left(\frac{C}{T}\right)^2\exp\left(\frac{C}{T}\right)}{\left(\exp\frac{C}{T}-1 \right)^2}\right]+D\left[\frac{\left(\frac{E}{T}\right)^2\exp\left(\frac{E}{T}\right)}{\left(\exp\frac{E}{T}-1 \right)^2}\right]+F\left[\frac{\left(\frac{G}{T}\right)^2\exp\left(\frac{G}{T}\right)}{\left(\exp\frac{G}{T}-1 \right)^2}\right]$$
DIPPR127([f64; 7]),
}
impl Dippr {
/// Create parameters for Eq. # 100.
pub fn eq100(coefs: &[f64]) -> Self {
Self::DIPPR100(coefs.to_vec())
}
/// Create parameters for Eq. # 107.
pub fn eq107(a: f64, b: f64, c: f64, d: f64, e: f64) -> Self {
Self::DIPPR107([a, b, c, d, e])
}
/// Create parameters for Eq. # 127.
pub fn eq127(a: f64, b: f64, c: f64, d: f64, e: f64, f: f64, g: f64) -> Self {
Self::DIPPR127([a, b, c, d, e, f, g])
}
fn c_p(&self, t: f64) -> f64 {
match self {
Self::DIPPR100(coefs) => coefs.iter().rev().fold(0.0, |acc, c| t * acc + c),
Self::DIPPR107([a, b, c, d, e]) => {
let ct = c / t;
let et = e / t;
a + b * (ct / ct.sinh()).powi(2) + d * (et / et.cosh()).powi(2)
}
Self::DIPPR127([a, b, c, d, e, f, g]) => {
let ct = c / t;
let et = e / t;
let gt = g / t;
let fun = |x: f64| x * x * x.exp() / (x.exp() - 1.0).powi(2);
a + b * fun(ct) + d * fun(et) + f * fun(gt)
}
}
}
fn c_p_integral<D: DualNum<f64> + Copy>(&self, t: D) -> D {
match self {
Self::DIPPR100(coefs) => coefs
.iter()
.enumerate()
.rev()
.fold(D::zero(), |acc, (i, &c)| t * (acc + c / (i + 1) as f64)),
Self::DIPPR107([a, b, c, d, e]) => {
let t_inv = t.recip();
let ct = t_inv * *c;
let et = t_inv * *e;
t * *a + ct.tanh().recip() * (b * c) - et.tanh() * (d * e)
}
Self::DIPPR127([a, b, c, d, e, f, g]) => {
let t_inv = t.recip();
let ct = t_inv * *c;
let et = t_inv * *e;
let gt = t_inv * *g;
let fun = |p: f64, x: D| ((x.exp() - 1.0) * p).recip() * (p * p);
fun(*c, ct) * *b + fun(*e, et) * *d + fun(*g, gt) * *f + t * *a
}
}
}
fn c_p_t_integral<D: DualNum<f64> + Copy>(&self, t: D) -> D {
match self {
Self::DIPPR100(coefs) => {
coefs
.iter()
.enumerate()
.skip(1)
.rev()
.fold(D::zero(), |acc, (i, &c)| t * (acc + c / i as f64))
+ t.ln() * coefs[0]
}
Self::DIPPR107([a, b, c, d, e]) => {
let t_inv = t.recip();
let ct = t_inv * *c;
let et = t_inv * *e;
t.ln() * *a + (t * ct.tanh()).recip() * (b * c)
- ct.sinh().ln() * *b
- et.tanh() * t_inv * (d * e)
+ et.cosh().ln() * *d
}
Self::DIPPR127([a, b, c, d, e, f, g]) => {
let t_inv = t.recip();
let ct = t_inv * *c;
let et = t_inv * *e;
let gt = t_inv * *g;
let fun = |p: f64, x: D| {
(((x.exp() - 1.0) * t).recip() + t_inv) * p - (x.exp() - 1.0).ln()
};
fun(*c, ct) * *b + fun(*e, et) * *d + fun(*g, gt) * *f + t.ln() * *a
}
}
}
}
pub type DipprParameters = Parameters<Dippr, (), ()>;
impl Dippr {
pub fn new(parameters: DipprParameters) -> Vec<Self> {
parameters
.pure
.into_iter()
.map(|p| p.model_record)
.collect()
}
/// Directly calculates the molar ideal gas heat capacity from the DIPPR equations.
pub fn molar_isobaric_heat_capacity(
dippr: &[Dippr],
temperature: Temperature,
molefracs: &DVector<f64>,
) -> FeosResult<MolarEntropy> {
let t = temperature.convert_to(KELVIN);
let c_p: f64 = molefracs.iter().zip(dippr).map(|(x, r)| x * r.c_p(t)).sum();
Ok(c_p * (JOULE / (KILO * MOL * KELVIN)))
}
}
const RGAS: f64 = 8.31446261815324 * 1000.0;
const T0: f64 = 298.15;
impl IdealGas for Dippr {
fn ln_lambda3<D: DualNum<f64> + Copy>(&self, temperature: D) -> D {
let t = temperature;
let h = self.c_p_integral(t) - self.c_p_integral(T0);
let s = self.c_p_t_integral(t) - self.c_p_t_integral(T0);
(h - t * s) / (t * RGAS) + temperature.ln()
}
fn ideal_gas_model(&self) -> &'static str {
"Ideal gas (DIPPR)"
}
}
#[cfg(test)]
mod tests {
use approx::assert_relative_eq;
use feos_core::parameter::{Identifier, PureRecord};
use feos_core::{Contributions, EquationOfState, StateBuilder};
use num_dual::first_derivative;
use quantity::*;
use super::*;
#[test]
fn eq100() -> FeosResult<()> {
let record = PureRecord::new(
Identifier::default(),
0.0,
Dippr::eq100(&[276370., -2090.1, 8.125, -0.014116, 0.0000093701]),
);
let dippr = Dippr::new(DipprParameters::new_pure(record.clone())?);
let eos = EquationOfState::ideal_gas(dippr.clone());
let temperature = 300.0 * KELVIN;
let volume = METER.powi::<3>();
let state = StateBuilder::new(&&eos)
.temperature(temperature)
.volume(volume)
.total_moles(MOL)
.build()?;
let t = temperature.convert_to(KELVIN);
let c_p_direct = record.model_record.c_p(t);
let (_, c_p) = first_derivative(|t| record.model_record.c_p_integral(t), t);
let (_, c_p_t) = first_derivative(|t| record.model_record.c_p_t_integral(t), t);
println!("{c_p_direct} {c_p} {}", c_p_t * t);
assert_relative_eq!(c_p_direct, c_p, max_relative = 1e-10);
assert_relative_eq!(c_p_direct, c_p_t * t, max_relative = 1e-10);
println!(
"{} {}",
Dippr::molar_isobaric_heat_capacity(&dippr, temperature, &state.molefracs)?,
state.molar_isobaric_heat_capacity(Contributions::IdealGas)
);
let reference = 75355.81000000003 * JOULE / (KILO * MOL * KELVIN);
assert_relative_eq!(
reference,
Dippr::molar_isobaric_heat_capacity(&dippr, temperature, &state.molefracs)?,
max_relative = 1e-10
);
assert_relative_eq!(
reference,
state.molar_isobaric_heat_capacity(Contributions::IdealGas),
max_relative = 1e-10
);
Ok(())
}
#[test]
fn eq107() -> FeosResult<()> {
let record = PureRecord::new(
Identifier::default(),
0.0,
Dippr::eq107(33363., 26790., 2610.5, 8896., 1169.),
);
let dippr = Dippr::new(DipprParameters::new_pure(record.clone())?);
let eos = EquationOfState::ideal_gas(dippr.clone());
let temperature = 300.0 * KELVIN;
let volume = METER.powi::<3>();
let state = StateBuilder::new(&&eos)
.temperature(temperature)
.volume(volume)
.total_moles(MOL)
.build()?;
let t = temperature.convert_to(KELVIN);
let c_p_direct = record.model_record.c_p(t);
let (_, c_p) = first_derivative(|t| record.model_record.c_p_integral(t), t);
let (_, c_p_t) = first_derivative(|t| record.model_record.c_p_t_integral(t), t);
println!("{c_p_direct} {c_p} {}", c_p_t * t);
assert_relative_eq!(c_p_direct, c_p, max_relative = 1e-10);
assert_relative_eq!(c_p_direct, c_p_t * t, max_relative = 1e-10);
println!(
"{} {}",
Dippr::molar_isobaric_heat_capacity(&dippr, temperature, &state.molefracs)?,
state.molar_isobaric_heat_capacity(Contributions::IdealGas)
);
let reference = 33585.90452768923 * JOULE / (KILO * MOL * KELVIN);
assert_relative_eq!(
reference,
Dippr::molar_isobaric_heat_capacity(&dippr, temperature, &state.molefracs)?,
max_relative = 1e-10
);
assert_relative_eq!(
reference,
state.molar_isobaric_heat_capacity(Contributions::IdealGas),
max_relative = 1e-10
);
Ok(())
}
#[test]
fn eq127() -> FeosResult<()> {
let record = PureRecord::new(
Identifier::default(),
0.0,
Dippr::eq127(
3.3258E4, 3.6199E4, 1.2057E3, 1.5373E7, 3.2122E3, -1.5318E7, 3.2122E3,
),
);
let dippr = Dippr::new(DipprParameters::new_pure(record.clone())?);
let eos = EquationOfState::ideal_gas(dippr.clone());
let temperature = 20.0 * KELVIN;
let volume = METER.powi::<3>();
let state = StateBuilder::new(&&eos)
.temperature(temperature)
.volume(volume)
.total_moles(MOL)
.build()?;
let t = temperature.convert_to(KELVIN);
let c_p_direct = record.model_record.c_p(t);
let (_, c_p) = first_derivative(|t| record.model_record.c_p_integral(t), t);
let (_, c_p_t) = first_derivative(|t| record.model_record.c_p_t_integral(t), t);
println!("{c_p_direct} {c_p} {}", c_p_t * t);
assert_relative_eq!(c_p_direct, c_p, max_relative = 1e-10);
assert_relative_eq!(c_p_direct, c_p_t * t, max_relative = 1e-10);
println!(
"{} {}",
Dippr::molar_isobaric_heat_capacity(&dippr, temperature, &state.molefracs)?,
state.molar_isobaric_heat_capacity(Contributions::IdealGas)
);
let reference = 33258.0 * JOULE / (KILO * MOL * KELVIN);
assert_relative_eq!(
reference,
Dippr::molar_isobaric_heat_capacity(&dippr, temperature, &state.molefracs)?,
max_relative = 1e-10
);
assert_relative_eq!(
reference,
state.molar_isobaric_heat_capacity(Contributions::IdealGas),
max_relative = 1e-10
);
Ok(())
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/ideal_gas/mod.rs | .rs | 158 | 6 | //! Collection of ideal gas models.
mod dippr;
mod joback;
pub use dippr::{Dippr, DipprParameters};
pub use joback::{Joback, JobackParameters, JobackRecord};
| Rust |
3D | feos-org/feos | crates/feos/src/ideal_gas/joback.rs | .rs | 13,930 | 423 | //! Implementation of the ideal gas heat capacity (de Broglie wavelength)
//! of [Joback and Reid, 1987](https://doi.org/10.1080/00986448708960487).
use feos_core::parameter::{FromSegments, Parameters};
use feos_core::{FeosResult, IdealGas, ReferenceSystem};
use nalgebra::DVector;
use num_dual::*;
use quantity::{MolarEntropy, Temperature};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
/// Coefficients used in the Joback model.
///
/// Contains an additional fourth order polynomial coefficient `e`
/// which is not used in the original publication but is used in
/// parametrization for additional molecules in other publications.
#[derive(Serialize, Deserialize, Debug, Clone, Default)]
pub struct JobackRecord {
pub a: f64,
pub b: f64,
pub c: f64,
pub d: f64,
pub e: f64,
}
impl JobackRecord {
/// Creates a new `JobackRecord`
pub fn new(a: f64, b: f64, c: f64, d: f64, e: f64) -> Self {
Self { a, b, c, d, e }
}
}
/// Implementation of the combining rules as described in
/// [Joback and Reid, 1987](https://doi.org/10.1080/00986448708960487).
impl FromSegments for JobackRecord {
fn from_segments(segments: &[(Self, f64)]) -> FeosResult<Self> {
let mut a = -37.93;
let mut b = 0.21;
let mut c = -3.91e-4;
let mut d = 2.06e-7;
let mut e = 0.0;
segments.iter().for_each(|(s, n)| {
a += s.a * n;
b += s.b * n;
c += s.c * n;
d += s.d * n;
e += s.e * n;
});
Ok(Self { a, b, c, d, e })
}
}
pub type JobackParameters = Parameters<JobackRecord, (), ()>;
/// The ideal gas contribution according to
/// [Joback and Reid, 1987](https://doi.org/10.1080/00986448708960487).
///
/// The thermal de Broglie wavelength is calculated by integrating
/// the heat capacity with the following reference values:
///
/// - T = 289.15 K
/// - p = 1e5 Pa
/// - V = 1e-30 A³
#[derive(Clone)]
pub struct Joback<D = f64>(pub [D; 5]);
impl Joback<f64> {
pub fn new(parameters: JobackParameters) -> Vec<Self> {
parameters
.pure
.into_iter()
.map(|r| {
let m = &r.model_record;
Self([m.a, m.b, m.c, m.d, m.e])
})
.collect()
}
}
impl Joback {
/// Directly calculates the molar ideal gas heat capacity from the Joback model.
pub fn molar_isobaric_heat_capacity(
joback: &[Joback],
temperature: Temperature,
molefracs: &DVector<f64>,
) -> FeosResult<MolarEntropy> {
let t = temperature.to_reduced();
let c_p: f64 = molefracs
.iter()
.zip(joback)
.map(|(x, p)| {
let [a, b, c, d, e] = p.0;
x * (a + b * t + c * t.powi(2) + d * t.powi(3) + e * t.powi(4))
})
.sum();
Ok(c_p / RGAS * quantity::RGAS)
}
}
impl<D: DualNum<f64> + Copy> IdealGas<D> for Joback<D> {
fn ln_lambda3<D2: DualNum<f64, Inner = D> + Copy>(&self, temperature: D2) -> D2 {
let [a, b, c, d, e] = self.0.each_ref().map(D2::from_inner);
let t = temperature;
let t2 = t * t;
let t4 = t2 * t2;
let f = (temperature * KB / (P0 * A3)).ln();
let h = (t2 - T0_2) * 0.5 * b
+ (t * t2 - T0_3) * c / 3.0
+ (t4 - T0_4) * d / 4.0
+ (t4 * t - T0_5) * e / 5.0
+ (t - T0) * a;
let s = (t - T0) * b
+ (t2 - T0_2) * 0.5 * c
+ (t2 * t - T0_3) * d / 3.0
+ (t4 - T0_4) * e / 4.0
+ (t / T0).ln() * a;
(h - t * s) / (t * RGAS) + f
}
fn ideal_gas_model(&self) -> &'static str {
"Ideal gas (Joback)"
}
}
const A: [f64; 22] = [
19.5, -0.909, -23.0, -66.2, 23.6, -8.0, -28.0, 32.37, -6.03, -20.5, -6.03, -20.5, -2.14, -8.25,
30.9, 6.45, 45.0, 24.591, 24.1, 24.5, 25.7, 26.9,
];
const B: [f64; 22] = [
-0.00808, 0.095, 0.204, 0.427, -0.0381, 0.105, 0.208, -0.007, 0.0854, 0.162, 0.0854, 0.162,
0.0574, 0.101, -0.0336, 0.067, -0.07128, 0.0318, 0.0427, 0.0402, -0.0691, -0.0412,
];
const C: [f64; 22] = [
0.000153, -5.44e-05, -0.000265, -0.000641, 0.000172, -9.63e-05, -0.000306, 0.00010267, -8e-06,
-0.00016, -8e-06, -0.00016, -1.64e-06, -0.000142, 0.00016, -3.57e-05, 0.000264, 5.66e-05,
8.04e-05, 4.02e-05, 0.000177, 0.000164,
];
const D: [f64; 22] = [
-9.67e-08, 1.19e-08, 1.2e-07, 3.01e-07, -1.03e-07, 3.56e-08, 1.46e-07, -6.641e-08, -1.8e-08,
6.24e-08, -1.8e-08, 6.24e-08, -1.59e-08, 6.78e-08, -9.88e-08, 2.86e-09, -1.515e-07, -4.29e-08,
-6.87e-08, -4.52e-08, -9.88e-08, -9.76e-08,
];
const GROUPS: [&str; 22] = [
"CH3", "CH2", ">CH", ">C<", "=CH2", "=CH", "=C<", "C≡CH", "CH2_hex", "CH_hex", "CH2_pent",
"CH_pent", "CH_arom", "C_arom", "CH=O", ">C=O", "OCH3", "OCH2", "HCOO", "COO", "OH", "NH2",
];
impl<D: DualNum<f64> + Copy> Joback<D> {
pub fn from_groups(group_counts: [D; 22]) -> Self {
let a: D = A.into_iter().zip(group_counts).map(|(a, g)| g * a).sum();
let b: D = B.into_iter().zip(group_counts).map(|(b, g)| g * b).sum();
let c: D = C.into_iter().zip(group_counts).map(|(c, g)| g * c).sum();
let d: D = D.into_iter().zip(group_counts).map(|(d, g)| g * d).sum();
Self([a - 37.93, b + 0.21, c - 3.91e-4, d + 2.06e-7, D::zero()])
}
pub fn from_group_counts(group_counts: &HashMap<&str, D>) -> Self {
Self::from_groups(GROUPS.map(|g| *group_counts.get(g).unwrap_or(&D::zero())))
}
}
const RGAS: f64 = 6.022140857 * 1.38064852;
const T0: f64 = 298.15;
const T0_2: f64 = T0 * T0;
const T0_3: f64 = T0 * T0_2;
const T0_4: f64 = T0_2 * T0_2;
const T0_5: f64 = T0 * T0_4;
const P0: f64 = 1.0e5;
const A3: f64 = 1e-30;
const KB: f64 = 1.38064852e-23;
#[cfg(test)]
mod tests {
use approx::assert_relative_eq;
use feos_core::{
Contributions, EquationOfState, State, StateBuilder,
parameter::{ChemicalRecord, GroupCount, Identifier, PureRecord, SegmentRecord},
};
use nalgebra::dvector;
use quantity::*;
use std::collections::HashMap;
use super::*;
#[test]
fn paper_example() -> FeosResult<()> {
let segments_json = r#"[
{
"identifier": "-Cl",
"a": 33.3,
"b": -0.0963,
"c": 0.000187,
"d": -9.96e-8,
"e": 0.0,
"molarweight": 35.453
},
{
"identifier": "-CH=(ring)",
"a": -2.14,
"b": 5.74e-2,
"c": -1.64e-6,
"d": -1.59e-8,
"e": 0.0,
"molarweight": 13.01864
},
{
"identifier": "=CH<(ring)",
"a": -8.25,
"b": 1.01e-1,
"c": -1.42e-4,
"d": 6.78e-8,
"e": 0.0,
"molarweight": 13.01864
}
]"#;
let segment_records: Vec<SegmentRecord<JobackRecord, ()>> =
serde_json::from_str(segments_json).expect("Unable to parse json.");
let segment_map: HashMap<_, _> =
segment_records.iter().map(|s| (&s.identifier, s)).collect();
let (_, segments, _) = GroupCount::into_groups(ChemicalRecord::new(
Identifier::default(),
vec![
String::from("-Cl"),
String::from("-Cl"),
String::from("-CH=(ring)"),
String::from("-CH=(ring)"),
String::from("-CH=(ring)"),
String::from("-CH=(ring)"),
String::from("=CH<(ring)"),
String::from("=CH<(ring)"),
],
None,
));
// .segment_map(&segment_records)?;
let joback_segments: Vec<_> = segments
.into_iter()
.map(|(s, n)| (segment_map[&s].model_record.clone(), n))
.collect();
let jr = JobackRecord::from_segments(&joback_segments)?;
assert_relative_eq!(
jr.a,
33.3 * 2.0 - 2.14 * 4.0 - 8.25 * 2.0 - 37.93,
epsilon = 1e-10
);
assert_relative_eq!(
jr.b,
-0.0963 * 2.0 + 5.74e-2 * 4.0 + 1.01e-1 * 2.0 + 0.21,
epsilon = 1e-10
);
assert_relative_eq!(
jr.c,
0.000187 * 2.0 - 1.64e-6 * 4.0 - 1.42e-4 * 2.0 - 3.91e-4,
epsilon = 1e-10
);
assert_relative_eq!(
jr.d,
-9.96e-8 * 2.0 - 1.59e-8 * 4.0 + 6.78e-8 * 2.0 + 2.06e-7,
epsilon = 1e-10
);
assert_relative_eq!(jr.e, 0.0);
let pr = PureRecord::new(Identifier::default(), 1.0, jr);
let joback = Joback::new(JobackParameters::new_pure(pr)?);
let eos = EquationOfState::ideal_gas(joback);
let state = State::new_pure(&&eos, 1000.0 * KELVIN, 1.0 * MOL / METER.powi::<3>())?;
assert!(
((state.molar_isobaric_heat_capacity(Contributions::IdealGas)
/ (JOULE / MOL / KELVIN))
.into_value()
- 224.6)
.abs()
< 1.0
);
Ok(())
}
#[test]
fn c_p_comparison() -> FeosResult<()> {
let record1 = PureRecord::new(
Identifier::default(),
1.0,
JobackRecord::new(1.0, 0.2, 0.03, 0.004, 0.005),
);
let record2 = PureRecord::new(
Identifier::default(),
1.0,
JobackRecord::new(-5.0, 0.4, 0.03, 0.002, 0.001),
);
let joback = Joback::new(JobackParameters::new_binary(
[record1, record2],
None,
vec![],
)?);
let eos = EquationOfState::ideal_gas(joback.clone());
let temperature = 300.0 * KELVIN;
let volume = METER.powi::<3>();
let moles = &dvector![1.0, 3.0] * MOL;
let state = StateBuilder::new(&&eos)
.temperature(temperature)
.volume(volume)
.moles(&moles)
.build()?;
println!(
"{} {}",
Joback::molar_isobaric_heat_capacity(&joback, temperature, &state.molefracs)?,
state.molar_isobaric_heat_capacity(Contributions::IdealGas)
);
assert_relative_eq!(
Joback::molar_isobaric_heat_capacity(&joback, temperature, &state.molefracs)?,
state.molar_isobaric_heat_capacity(Contributions::IdealGas),
max_relative = 1e-10
);
Ok(())
}
}
#[cfg(test)]
pub mod test_ad {
use super::{Joback, JobackParameters, JobackRecord};
use approx::assert_relative_eq;
use feos_core::{Contributions::IdealGas, EquationOfState, FeosResult, State};
use feos_core::{Residual, StateHD};
use nalgebra::{SVector, U1};
use num_dual::DualNum;
use quantity::{KELVIN, KILO, METER, MOL};
pub fn joback() -> FeosResult<(Joback<f64>, Vec<Joback>)> {
let a = 1.5;
let b = 3.4e-2;
let c = 180.0e-4;
let d = 2.2e-6;
let e = 0.03e-8;
let eos = Joback::new(JobackParameters::from_model_records(vec![
JobackRecord::new(a, b, c, d, e),
])?);
let params = [a, b, c, d, e];
let eos_ad = Joback(params);
Ok((eos_ad, eos))
}
#[derive(Clone, Copy)]
struct NoResidual;
impl<D: DualNum<f64> + Copy> Residual<U1, D> for NoResidual {
fn components(&self) -> usize {
1
}
type Real = Self;
type Lifted<D2: DualNum<f64, Inner = D> + Copy> = Self;
fn re(&self) -> Self::Real {
*self
}
fn lift<D2: DualNum<f64, Inner = D> + Copy>(&self) -> Self::Lifted<D2> {
*self
}
fn compute_max_density(&self, _: &SVector<D, 1>) -> D {
D::from(1.0)
}
fn reduced_helmholtz_energy_density_contributions(
&self,
_: &StateHD<D, U1>,
) -> Vec<(&'static str, D)> {
vec![]
}
fn reduced_residual_helmholtz_energy_density(&self, _: &StateHD<D, U1>) -> D {
D::from(0.0)
}
}
#[test]
fn test_joback() -> FeosResult<()> {
let (joback_ad, joback) = joback()?;
let eos = EquationOfState::ideal_gas(joback);
let eos_ad = EquationOfState::new([joback_ad], NoResidual);
let temperature = 300.0 * KELVIN;
let density = 2.3 * KILO * MOL / (METER * METER * METER);
let state = State::new_pure(&&eos, temperature, density)?;
let a_feos = state.molar_helmholtz_energy(IdealGas);
let mu_feos = state.chemical_potential(IdealGas);
let p_feos = state.pressure(IdealGas);
let s_feos = state.molar_entropy(IdealGas);
let h_feos = state.molar_enthalpy(IdealGas);
let state_ad = State::new_pure(&eos_ad, temperature, density)?;
let a_ad = state_ad.molar_helmholtz_energy(IdealGas);
let mu_ad = state_ad.chemical_potential(IdealGas);
let p_ad = state_ad.pressure(IdealGas);
let s_ad = state_ad.molar_entropy(IdealGas);
let h_ad = state_ad.molar_enthalpy(IdealGas);
println!("\nMolar Helmholtz energy:\n{a_feos}");
println!("{a_ad}");
assert_relative_eq!(a_feos, a_ad, max_relative = 1e-14);
println!("\nChemical potential:\n{}", mu_feos.get(0));
println!("{}", mu_ad.get(0));
assert_relative_eq!(mu_feos.get(0), mu_ad.get(0), max_relative = 1e-14);
println!("\nPressure:\n{p_feos}");
println!("{p_ad}");
assert_relative_eq!(p_feos, p_ad, max_relative = 1e-14);
println!("\nMolar entropy:\n{s_feos}");
println!("{s_ad}");
assert_relative_eq!(s_feos, s_ad, max_relative = 1e-14);
println!("\nMolar enthalpy:\n{h_feos}");
println!("{h_ad}");
assert_relative_eq!(h_feos, h_ad, max_relative = 1e-14);
Ok(())
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/estimator.rs | .rs | 3,484 | 109 | //! The [`Estimator`] struct can be used to store multiple [`DataSet`]s for convenient parameter
//! optimization.
use super::{DataSet, FeosError, Loss};
use feos_core::Residual;
use ndarray::{Array1, ArrayView1, Axis, arr1, concatenate};
// use quantity::si::SIArray1;
use std::fmt;
use std::fmt::Display;
use std::fmt::Write;
/// A collection of [`DataSet`]s and weights that can be used to
/// evaluate an equation of state versus experimental data.
pub struct Estimator<E: Residual> {
data: Vec<Arc<dyn DataSet<E>>>,
weights: Vec<f64>,
losses: Vec<Loss>,
}
impl<E: Residual> Estimator<E> {
/// Create a new `Estimator` given `DataSet`s and weights.
///
/// The weights are normalized and used as multiplicator when the
/// cost function across all `DataSet`s is evaluated.
pub fn new(data: Vec<Arc<dyn DataSet<E>>>, weights: Vec<f64>, losses: Vec<Loss>) -> Self {
Self {
data,
weights,
losses,
}
}
/// Add a `DataSet` and its weight.
pub fn add_data(&mut self, data: &Arc<dyn DataSet<E>>, weight: f64, loss: Loss) {
self.data.push(data.clone());
self.weights.push(weight);
self.losses.push(loss);
}
/// Returns the cost of each `DataSet`.
///
/// Each cost contains the inverse weight.
pub fn cost(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let w = arr1(&self.weights) / self.weights.iter().sum::<f64>();
let predictions = self
.data
.iter()
.enumerate()
.map(|(i, d)| Ok(d.cost(eos, self.losses[i])? * w[i]))
.collect::<Result<Vec<_>, FeosError>>()?;
let aview: Vec<ArrayView1<f64>> = predictions.iter().map(|pi| pi.view()).collect();
Ok(concatenate(Axis(0), &aview)?)
}
/// Returns the properties as computed by the equation of state for each `DataSet`.
pub fn predict(&self, eos: &Arc<E>) -> Result<Vec<Array1<f64>>, FeosError> {
self.data.iter().map(|d| d.predict(eos)).collect()
}
/// Returns the relative difference for each `DataSet`.
pub fn relative_difference(&self, eos: &Arc<E>) -> Result<Vec<Array1<f64>>, FeosError> {
self.data
.iter()
.map(|d| d.relative_difference(eos))
.collect()
}
/// Returns the mean absolute relative difference for each `DataSet`.
pub fn mean_absolute_relative_difference(
&self,
eos: &Arc<E>,
) -> Result<Array1<f64>, FeosError> {
self.data
.iter()
.map(|d| d.mean_absolute_relative_difference(eos))
.collect()
}
/// Returns the stored `DataSet`s.
pub fn datasets(&self) -> Vec<Arc<dyn DataSet<E>>> {
self.data.to_vec()
}
/// Representation as markdown string.
pub fn _repr_markdownn_(&self) -> String {
let mut f = String::new();
write!(f, "| target | input | datapoints |\n|:-|:-|:-|").unwrap();
for d in self.data.iter() {
write!(
f,
"\n|{}|{}|{}|",
d.target_str(),
d.input_str().join(", "),
d.datapoints()
)
.unwrap();
}
f
}
}
impl<E: Residual> Display for Estimator<E> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
for d in self.data.iter() {
writeln!(f, "{d}")?;
}
Ok(())
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/mod.rs | .rs | 1,051 | 40 | //! Utilities for working with experimental data.
use feos_core::{DensityInitialization, FeosError};
mod dataset;
pub use dataset::DataSet;
#[expect(clippy::module_inception)]
mod estimator;
pub use estimator::Estimator;
mod loss;
pub use loss::Loss;
// Properties
mod vapor_pressure;
pub use vapor_pressure::VaporPressure;
mod liquid_density;
pub use liquid_density::{EquilibriumLiquidDensity, LiquidDensity};
mod binary_vle;
pub use binary_vle::{BinaryPhaseDiagram, BinaryVleChemicalPotential, BinaryVlePressure};
mod viscosity;
pub use viscosity::Viscosity;
mod thermal_conductivity;
pub use thermal_conductivity::ThermalConductivity;
mod diffusion;
pub use diffusion::Diffusion;
/// Different phases of experimental data points.
#[derive(Clone, Copy, PartialEq)]
pub enum Phase {
Vapor,
Liquid,
}
impl From<Phase> for DensityInitialization {
fn from(value: Phase) -> Self {
match value {
Phase::Liquid => DensityInitialization::Liquid,
Phase::Vapor => DensityInitialization::Vapor,
}
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/dataset.rs | .rs | 2,411 | 69 | //! The [`DataSet`] trait provides routines that can be used for
//! optimization of parameters of equations of state given
//! a `target` which can be values from experimental data or
//! other models.
use super::Loss;
use feos_core::{FeosError, Residual};
use ndarray::Array1;
use std::fmt;
/// Utilities for working with experimental data.
///
/// Functionalities in the context of optimizations of
/// parameters of equations of state.
pub trait DataSet<E: Residual>: Send + Sync {
/// Return target quantity.
fn target(&self) -> &Array1<f64>;
/// Return the description of the target quantity.
fn target_str(&self) -> &str;
/// Return the descritions of the input quantities needed to compute the target.
fn input_str(&self) -> Vec<&str>;
/// Evaluation of the equation of state for the target quantity.
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError>;
/// Evaluate the cost function.
fn cost(&self, eos: &Arc<E>, loss: Loss) -> Result<Array1<f64>, FeosError> {
let mut cost = self.relative_difference(eos)?;
loss.apply(&mut cost);
let datapoints = cost.len();
Ok(cost / datapoints as f64)
}
/// Returns the number of experimental data points.
fn datapoints(&self) -> usize {
self.target().len()
}
/// Returns the relative difference between the equation of state and the experimental values.
fn relative_difference(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let prediction = &self.predict(eos)?;
let target = self.target();
Ok((prediction - target) / target)
}
/// Returns the mean of the absolute relative difference between the equation of state and the experimental values.
fn mean_absolute_relative_difference(&self, eos: &Arc<E>) -> Result<f64, FeosError> {
Ok(self
.relative_difference(eos)?
.into_iter()
.filter(|&x| x.is_finite())
.enumerate()
.fold(0.0, |mean, (i, x)| mean + (x.abs() - mean) / (i + 1) as f64))
}
}
impl<E: Residual> fmt::Display for dyn DataSet<E> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(
f,
"DataSet(target: {}, input: {}, datapoints: {}",
self.target_str(),
self.input_str().join(", "),
self.datapoints()
)
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/thermal_conductivity.rs | .rs | 2,519 | 80 | use super::{DataSet, FeosError, Phase};
use feos_core::{DensityInitialization, EntropyScaling, ReferenceSystem, Residual, State};
use itertools::izip;
use ndarray::{Array1, arr1};
use quantity::{self, KELVIN, METER, Moles, Pressure, Temperature, WATT};
/// Store experimental thermal conductivity data.
#[derive(Clone)]
pub struct ThermalConductivity {
pub target: Array1<f64>,
unit: quantity::ThermalConductivity,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
initial_density: Vec<DensityInitialization>,
}
impl ThermalConductivity {
/// Create a new data set for experimental thermal conductivity data.
pub fn new(
target: quantity::ThermalConductivity<Array1<f64>>,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
phase: Option<&Vec<Phase>>,
) -> Self {
let n = temperature.len();
let unit = WATT / METER / KELVIN;
Self {
target: (target / unit).into_value(),
unit,
temperature,
pressure,
initial_density: phase.map_or(vec![DensityInitialization::None; n], |phase| {
phase.iter().map(|&p| p.into()).collect()
}),
}
}
/// Return temperature.
pub fn temperature(&self) -> &Temperature<Array1<f64>> {
&self.temperature
}
/// Return pressure.
pub fn pressure(&self) -> &Pressure<Array1<f64>> {
&self.pressure
}
}
impl<E: Residual + EntropyScaling> DataSet<E> for ThermalConductivity {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"thermal conductivity"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature", "pressure"]
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let moles = Moles::from_reduced(arr1(&[1.0]));
izip!(&self.temperature, &self.pressure, &self.initial_density)
.map(|(t, p, &initial_density)| {
State::new_npt(eos, t, p, &moles, initial_density)?
.thermal_conductivity()
.map(|lambda| lambda.convert_to(self.unit))
})
.collect()
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(1);
// m.insert("temperature".to_owned(), self.temperature());
// m.insert("pressure".to_owned(), self.pressure());
// m
// }
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/viscosity.rs | .rs | 2,429 | 80 | use super::{DataSet, FeosError, Phase};
use feos_core::{DensityInitialization, EntropyScaling, ReferenceSystem, Residual, State};
use itertools::izip;
use ndarray::{Array1, arr1};
use quantity::{MILLI, Moles, PASCAL, Pressure, SECOND, Temperature};
/// Store experimental viscosity data.
#[derive(Clone)]
pub struct Viscosity {
pub target: Array1<f64>,
unit: quantity::Viscosity,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
initial_density: Vec<DensityInitialization>,
}
impl Viscosity {
/// Create a new data set for experimental viscosity data.
pub fn new(
target: quantity::Viscosity<Array1<f64>>,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
phase: Option<&Vec<Phase>>,
) -> Self {
let n = temperature.len();
let unit = MILLI * PASCAL * SECOND;
Self {
target: (target / unit).into_value(),
unit,
temperature,
pressure,
initial_density: phase.map_or(vec![DensityInitialization::None; n], |phase| {
phase.iter().map(|&p| p.into()).collect()
}),
}
}
/// Return temperature.
pub fn temperature(&self) -> &Temperature<Array1<f64>> {
&self.temperature
}
/// Return pressure.
pub fn pressure(&self) -> &Pressure<Array1<f64>> {
&self.pressure
}
}
impl<E: Residual + EntropyScaling> DataSet<E> for Viscosity {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"viscosity"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature", "pressure"]
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let moles = Moles::from_reduced(arr1(&[1.0]));
izip!(&self.temperature, &self.pressure, &self.initial_density)
.map(|(t, p, &initial_density)| {
State::new_npt(eos, t, p, &moles, initial_density)?
.viscosity()
.map(|viscosity| viscosity.convert_to(self.unit))
})
.collect()
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(1);
// m.insert("temperature".to_owned(), self.temperature());
// m.insert("pressure".to_owned(), self.pressure());
// m
// }
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/diffusion.rs | .rs | 2,464 | 82 | use super::{DataSet, FeosError, Phase};
use feos_core::{DensityInitialization, EntropyScaling, ReferenceSystem, Residual, State};
use itertools::izip;
use ndarray::{Array1, arr1};
use quantity::{self, CENTI, METER, Moles, Pressure, SECOND, Temperature};
use typenum::P2;
/// Store experimental diffusion data.
#[derive(Clone)]
pub struct Diffusion {
pub target: Array1<f64>,
unit: quantity::Diffusivity,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
initial_density: Vec<DensityInitialization>,
}
impl Diffusion {
/// Create a new data set for experimental diffusion data.
pub fn new(
target: quantity::Diffusivity<Array1<f64>>,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
phase: Option<&Vec<Phase>>,
) -> Self {
let n = temperature.len();
let unit = (CENTI * METER).powi::<P2>() / SECOND;
Self {
target: (target / unit).into_value(),
unit,
temperature,
pressure,
initial_density: phase.map_or(vec![DensityInitialization::None; n], |phase| {
phase.iter().map(|&p| p.into()).collect()
}),
}
}
/// Return temperature.
pub fn temperature(&self) -> &Temperature<Array1<f64>> {
&self.temperature
}
/// Return pressure.
pub fn pressure(&self) -> &Pressure<Array1<f64>> {
&self.pressure
}
}
impl<E: Residual + EntropyScaling> DataSet<E> for Diffusion {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"diffusion"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature", "pressure"]
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let moles = Moles::from_reduced(arr1(&[1.0]));
izip!(&self.temperature, &self.pressure, &self.initial_density)
.map(|(t, p, &initial_density)| {
State::new_npt(eos, t, p, &moles, initial_density)?
.diffusion()
.map(|lambda| lambda.convert_to(self.unit))
})
.collect()
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(1);
// m.insert("temperature".to_owned(), self.temperature());
// m.insert("pressure".to_owned(), self.pressure());
// m
// }
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/vapor_pressure.rs | .rs | 3,508 | 107 | use super::{DataSet, FeosError};
use feos_core::{Contributions, PhaseEquilibrium, ReferenceSystem, Residual, SolverOptions, State};
use ndarray::{Array1, arr1};
use quantity::{PASCAL, Pressure, Temperature};
/// Store experimental vapor pressure data.
#[derive(Clone)]
pub struct VaporPressure {
pub target: Array1<f64>,
unit: Pressure,
temperature: Temperature<Array1<f64>>,
max_temperature: Temperature,
datapoints: usize,
extrapolate: bool,
solver_options: SolverOptions,
}
impl VaporPressure {
/// Create a new data set for vapor pressure.
///
/// If the equation of state fails to compute the vapor pressure
/// (e.g. when it underestimates the critical point) the vapor
/// pressure can be estimated.
/// If `extrapolate` is `true`, the vapor pressure is estimated by
/// calculating the slope of ln(p) over 1/T.
/// If `extrapolate` is `false`, it is set to `NAN`.
pub fn new(
target: Pressure<Array1<f64>>,
temperature: Temperature<Array1<f64>>,
extrapolate: bool,
critical_temperature: Option<Temperature>,
solver_options: Option<SolverOptions>,
) -> Self {
let datapoints = target.len();
let max_temperature = critical_temperature
.unwrap_or(temperature.into_iter().reduce(|a, b| a.max(b)).unwrap());
let target_unit = PASCAL;
Self {
target: (target / target_unit).into_value(),
unit: target_unit,
temperature,
max_temperature,
datapoints,
extrapolate,
solver_options: solver_options.unwrap_or_default(),
}
}
/// Return temperature.
pub fn temperature(&self) -> Temperature<Array1<f64>> {
self.temperature.clone()
}
}
impl<E: Residual> DataSet<E> for VaporPressure {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"vapor pressure"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature"]
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
if self.datapoints == 0 {
return Ok(arr1(&[]));
}
let critical_point =
State::critical_point(eos, None, Some(self.max_temperature), self.solver_options)
.or_else(|_| State::critical_point(eos, None, None, self.solver_options))?;
let tc = critical_point.temperature;
let pc = critical_point.pressure(Contributions::Total);
let t0 = 0.9 * tc;
let p0 = PhaseEquilibrium::pure(eos, t0, None, self.solver_options)?
.vapor()
.pressure(Contributions::Total);
let b = (pc / p0).into_value().ln() / (1.0 / tc - 1.0 / t0);
let a = pc.to_reduced().ln() - (b / tc).into_value();
Ok((0..self.datapoints)
.map(|i| {
let t = self.temperature.get(i);
if let Some(p) = PhaseEquilibrium::vapor_pressure(eos, t)[0] {
(p / self.unit).into_value()
} else if self.extrapolate {
(a + (b / t).into_value()).exp() / self.unit.to_reduced()
} else {
f64::NAN
}
})
.collect())
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(1);
// m.insert("temperature".to_owned(), self.temperature());
// m
// }
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/pt_data.rs | .rs | 171 | 10 | use feos_core::StateVec;
trait BulkPropertyCalculation {
fn name(&self) -> String;
fn calcualte(&self, states: StateVec) -> Array1<f64>;
}
pub struct PTData {}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/strategy.rs | .rs | 135 | 5 | use feos_core::{EquationOfState, IdealGas, Residual};
pub struct Strategy<R: Residual, I: IdealGas> {
eos: EquationOfState<R, I>
} | Rust |
3D | feos-org/feos | crates/feos/src/estimator/binary_vle.rs | .rs | 12,337 | 376 | use super::{DataSet, FeosError, Phase};
use feos_core::{
Contributions, DensityInitialization, PhaseDiagram, PhaseEquilibrium, ReferenceSystem,
Residual, State, TemperatureOrPressure,
};
use itertools::izip;
use ndarray::{Array1, ArrayView1, Axis, arr1, s};
use quantity::{_Dimensionless, MolarEnergy, Moles, PASCAL, Pressure, Quantity, RGAS, Temperature};
use std::fmt;
use std::iter::FromIterator;
use std::ops::Sub;
/// Store experimental binary VLE data for the calculation of chemical potential residuals.
#[derive(Clone)]
pub struct BinaryVleChemicalPotential {
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
liquid_molefracs: Array1<f64>,
vapor_molefracs: Array1<f64>,
target: Array1<f64>,
}
impl BinaryVleChemicalPotential {
pub fn new(
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
liquid_molefracs: Array1<f64>,
vapor_molefracs: Array1<f64>,
) -> Self {
let target = Array1::ones(temperature.len() * 2);
Self {
temperature,
pressure,
liquid_molefracs,
vapor_molefracs,
target,
}
}
}
impl<E: Residual> DataSet<E> for BinaryVleChemicalPotential {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"chemical potential"
}
fn input_str(&self) -> Vec<&str> {
vec![
"temperature",
"pressure",
"liquid molefracs",
"vapor molefracs",
]
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let mut prediction = Vec::new();
for (&xi, &yi, t, p) in izip!(
&self.liquid_molefracs,
&self.vapor_molefracs,
&self.temperature,
&self.pressure
) {
let liquid_moles = Moles::from_reduced(arr1(&[xi, 1.0 - xi]));
let liquid = State::new_npt(eos, t, p, &liquid_moles, DensityInitialization::Liquid)?;
let mu_res_liquid = liquid.residual_chemical_potential();
let vapor_moles = Moles::from_reduced(arr1(&[yi, 1.0 - yi]));
let vapor = State::new_npt(eos, t, p, &vapor_moles, DensityInitialization::Vapor)?;
let mu_res_vapor = vapor.residual_chemical_potential();
let kt = RGAS * t;
let rho_frac = (&liquid.partial_density / &vapor.partial_density).into_value();
prediction.push(mu_res_liquid.get(0) - mu_res_vapor.get(0) + kt * rho_frac[0].ln());
prediction.push(mu_res_liquid.get(1) - mu_res_vapor.get(1) + kt * rho_frac[1].ln());
}
let prediction = (MolarEnergy::from_vec(prediction) / MolarEnergy::from_reduced(500.0))
.into_value()
+ 1.0;
Ok(prediction)
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(4);
// m.insert("temperature".to_owned(), self.temperature.clone());
// m.insert("pressure".to_owned(), self.pressure.clone());
// m.insert(
// "liquid_molefracs".to_owned(),
// &self.liquid_molefracs * SIUnit::reference_moles() / SIUnit::reference_moles(),
// );
// m.insert(
// "vapor_molefracs".to_owned(),
// &self.vapor_molefracs * SIUnit::reference_moles() / SIUnit::reference_moles(),
// );
// m
// }
}
/// Store experimental binary VLE data for the calculation of pressure residuals.
#[derive(Clone)]
pub struct BinaryVlePressure {
target: Array1<f64>,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
unit: Pressure,
molefracs: Array1<f64>,
phase: Phase,
}
impl BinaryVlePressure {
pub fn new(
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
molefracs: Array1<f64>,
phase: Phase,
) -> Self {
let unit = PASCAL;
Self {
target: (&pressure / unit).into_value(),
temperature,
pressure,
unit,
molefracs,
phase,
}
}
}
impl<E: Residual> DataSet<E> for BinaryVlePressure {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"pressure"
}
fn input_str(&self) -> Vec<&str> {
let mut vec = vec!["temperature", "pressure"];
vec.push(match self.phase {
Phase::Vapor => "vapor molefracs",
Phase::Liquid => "liquid molefracs",
});
vec
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let options = Default::default();
self.molefracs
.iter()
.enumerate()
.map(|(i, &xi)| {
let vle = (match self.phase {
Phase::Vapor => PhaseEquilibrium::dew_point(
eos,
self.temperature.get(i),
&arr1(&[xi, 1.0 - xi]),
Some(self.pressure.get(i)),
None,
options,
),
Phase::Liquid => PhaseEquilibrium::bubble_point(
eos,
self.temperature.get(i),
&arr1(&[xi, 1.0 - xi]),
Some(self.pressure.get(i)),
None,
options,
),
})?;
Ok(vle
.vapor()
.pressure(Contributions::Total)
.convert_to(self.unit))
})
.collect()
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(4);
// m.insert("temperature".to_owned(), self.temperature.clone());
// m.insert("pressure".to_owned(), self.pressure.clone());
// m.insert(
// (match self.phase {
// Phase::Vapor => "vapor_molefracs",
// Phase::Liquid => "liquid_molefracs",
// })
// .to_owned(),
// &self.molefracs * SIUnit::reference_moles() / SIUnit::reference_moles(),
// );
// m
// }
}
/// Store experimental binary phase diagrams for the calculation of distance residuals.
#[derive(Clone)]
pub struct BinaryPhaseDiagram<TP: TemperatureOrPressure, U> {
specification: TP,
temperature_or_pressure: Quantity<Array1<f64>, U>,
liquid_molefracs: Option<Array1<f64>>,
vapor_molefracs: Option<Array1<f64>>,
npoints: Option<usize>,
target: Array1<f64>,
}
impl<TP: TemperatureOrPressure, U> BinaryPhaseDiagram<TP, U> {
pub fn new(
specification: TP,
temperature_or_pressure: Quantity<Array1<f64>, U>,
liquid_molefracs: Option<Array1<f64>>,
vapor_molefracs: Option<Array1<f64>>,
npoints: Option<usize>,
) -> Self {
let count = liquid_molefracs.as_ref().map_or(0, |x| 2 * x.len())
+ vapor_molefracs.as_ref().map_or(0, |x| 2 * x.len());
let target = Array1::ones(count);
Self {
specification,
temperature_or_pressure,
liquid_molefracs,
vapor_molefracs,
npoints,
target,
}
}
}
impl<TP: TemperatureOrPressure + Sync + Send + fmt::Display, U: Copy + Sync + Send, E: Residual>
DataSet<E> for BinaryPhaseDiagram<TP, U>
where
Quantity<Array1<f64>, U>: FromIterator<TP::Other>,
U: Sub<U, Output = _Dimensionless>,
{
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"distance"
}
fn input_str(&self) -> Vec<&str> {
let mut vec = vec![TP::IDENTIFIER, TP::Other::IDENTIFIER];
if self.liquid_molefracs.is_some() {
vec.push("liquid molefracs")
}
if self.vapor_molefracs.is_some() {
vec.push("vapor molefracs")
}
vec
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let mut res = Vec::new();
let dia = PhaseDiagram::binary_vle(
eos,
self.specification,
self.npoints,
None,
Default::default(),
)?;
let x_liq = dia.liquid().molefracs();
let x_vec_liq = x_liq.index_axis(Axis(1), 0);
let x_vap = dia.vapor().molefracs();
let x_vec_vap = x_vap.index_axis(Axis(1), 0);
let tp_vec = dia.vapor().iter().map(|s| TP::from_state(s)).collect();
for (x_exp, x_vec) in [
(&self.liquid_molefracs, x_vec_liq),
(&self.vapor_molefracs, x_vec_vap),
] {
if let Some(x_exp) = x_exp {
res.extend(predict_distance(
x_vec,
&tp_vec,
x_exp,
&self.temperature_or_pressure,
));
}
}
Ok(Array1::from_vec(res))
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(4);
// if self
// .specification
// .has_unit(&SIUnit::reference_temperature())
// {
// m.insert(
// "temperature".to_owned(),
// SIArray1::from_vec(vec![self.specification]),
// );
// m.insert("pressure".to_owned(), self.temperature_or_pressure.clone());
// } else {
// m.insert(
// "pressure".to_owned(),
// SIArray1::from_vec(vec![self.specification]),
// );
// m.insert(
// "temperature".to_owned(),
// self.temperature_or_pressure.clone(),
// );
// };
// if let Some(liquid_molefracs) = &self.liquid_molefracs {
// m.insert(
// "liquid_molefracs".to_owned(),
// liquid_molefracs * SIUnit::reference_moles() / SIUnit::reference_moles(),
// );
// }
// if let Some(vapor_molefracs) = &self.vapor_molefracs {
// m.insert(
// "vapor_molefracs".to_owned(),
// vapor_molefracs * SIUnit::reference_moles() / SIUnit::reference_moles(),
// );
// }
// m
// }
}
fn predict_distance<U: Copy + Sub<U, Output = _Dimensionless>>(
x_vec: ArrayView1<f64>,
tp_vec: &Quantity<Array1<f64>, U>,
x_exp: &Array1<f64>,
tp_exp: &Quantity<Array1<f64>, U>,
) -> Vec<f64> {
let mut res = Vec::new();
for (tp, &x) in tp_exp.into_iter().zip(x_exp.into_iter()) {
let y = 1.0;
let y_vec = (tp_vec / tp).into_value();
let dx = &x_vec.slice(s![1..]) - &x_vec.slice(s![..-1]);
let dy = &y_vec.slice(s![1..]) - &y_vec.slice(s![..-1]);
let x_vec = x_vec.slice(s![..-1]);
let y_vec = y_vec.slice(s![..-1]);
let t = ((x - &x_vec) * &dx + (y - &y_vec) * &dy) / (&dx * &dx + &dy * &dy);
let x0 = &t * dx + x_vec;
let y0 = &t * dy + y_vec;
let k = x0
.iter()
.zip(y0.iter())
.enumerate()
.filter(|(i, _)| 0.0 < t[*i] && t[*i] < 1.0)
.map(|(i, (xx, yy))| (i, (xx - x) * (xx - x) + (yy - y) * (yy - y)))
.reduce(|(k1, d1), (k2, d2)| if d1 < d2 { (k1, d1) } else { (k2, d2) });
let (x0, y0) = match k {
None => {
let point = t
.iter()
.zip(t.iter().skip(1))
.enumerate()
.find(|&(_, (t1, t2))| *t1 > 1.0 && *t2 < 0.0);
if let Some((point, _)) = point {
(x_vec[point + 1], y_vec[point + 1])
} else {
let n = t.len();
if t[0].abs() < t[n - 1].abs() {
(x_vec[0], y_vec[0])
} else {
(x_vec[n - 1], y_vec[n - 1])
}
}
}
Some((k, _)) => (x0[k], y0[k]),
};
res.push(x0 - x + 1.0);
res.push(y0);
}
res
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/loss.rs | .rs | 2,253 | 72 | use ndarray::Array1;
/// Functions to apply to residuals for robust regression.
///
/// These functions are applied to the resiudals as part of the `cost` function:
/// $\text{cost}(r) = \sqrt{f^2 \rho(z)}$,
/// where $r$ is the residual, $\rho$ is the loss function,
/// $f$ is the scaling factor, and $z = \frac{r^2}{f^2}$.
#[derive(Clone, Debug, Copy)]
pub enum Loss {
/// Linear: $\rho(z) = z$
Linear,
/// Soft L1: $\rho(z) = 2 (\sqrt{1 + z} - 1)$
SoftL1(f64),
/// Huber: $\rho(z) = z$ if $z <= 1$, else $2 \sqrt{z} - 1$
Huber(f64),
/// Cauchy: $\rho(z) = \ln(1 + z)$
Cauchy(f64),
/// Arctan: $\rho(z) = \arctan(z)$
Arctan(f64),
}
impl Loss {
pub fn softl1(scaling_factor: f64) -> Self {
Self::SoftL1(scaling_factor)
}
pub fn huber(scaling_factor: f64) -> Self {
Self::Huber(scaling_factor)
}
pub fn cauchy(scaling_factor: f64) -> Self {
Self::Cauchy(scaling_factor)
}
pub fn arctan(scaling_factor: f64) -> Self {
Self::Arctan(scaling_factor)
}
/// Apply function to array of residuals.
pub fn apply(&self, res: &mut Array1<f64>) {
match self {
Self::Linear => (),
Self::SoftL1(s) => {
let s2 = s * s;
let s2_inv = 1.0 / s2;
res.mapv_inplace(|ri| {
(s2 * (2.0 * (((ri * ri * s2_inv) + 1.0).sqrt() - 1.0))).sqrt()
})
}
Self::Huber(s) => {
let s2 = s * s;
let s2_inv = 1.0 / s2;
res.mapv_inplace(|ri| {
if ri * ri * s2_inv <= 1.0 {
ri
} else {
(s2 * (2.0 * (ri / s).abs() - 1.0)).sqrt()
}
})
}
Self::Cauchy(s) => {
let s2 = s * s;
let s2_inv = 1.0 / s2;
res.mapv_inplace(|ri| (s2 * (1.0 + (ri * ri * s2_inv)).ln()).sqrt())
}
Self::Arctan(s) => {
let s2 = s * s;
let s2_inv = 1.0 / s2;
res.mapv_inplace(|ri| (s2 * (ri * ri * s2_inv).atan()).sqrt())
}
}
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/impl_dataset.rs | .rs | 10,165 | 332 | /// Store experimental vapor pressure data and compare to the equation of state.
#[derive(Clone)]
pub struct VaporPressure {
pub target: SIArray1,
temperature: SIArray1,
max_temperature: SINumber,
datapoints: usize,
std_parameters: Vec<f64>,
}
impl VaporPressure {
/// Create a new vapor pressure data set.
///
/// Takes the temperature as input and possibly parameters
/// that describe the standard deviation of vapor pressure as
/// function of temperature. This standard deviation can be used
/// as inverse weights in the cost function.
pub fn new(
target: SIArray1,
temperature: SIArray1,
std_parameters: Vec<f64>,
) -> Result<Self, FitError> {
let datapoints = target.len();
let max_temperature = *temperature
.to_reduced(SIUnit::reference_temperature())?
.max()
.map_err(|_| FitError::IncompatibleInput)?
* SIUnit::reference_temperature();
Ok(Self {
target,
temperature,
max_temperature,
datapoints,
std_parameters,
})
}
/// Return temperature.
pub fn temperature(&self) -> SIArray1 {
self.temperature.clone()
}
/// Returns inverse standard deviation as weights for cost function.
fn weight_from_std(&self, reduced_temperature: &Array1<f64>) -> Array1<f64> {
reduced_temperature.map(|t| {
1.0 / ((-self.std_parameters[0] * t + self.std_parameters[1]).exp()
+ self.std_parameters[2])
})
}
}
impl<E: EquationOfState> DataSet<E> for VaporPressure {
fn target(&self) -> SIArray1 {
self.target.clone()
}
fn target_str(&self) -> &str {
"vapor pressure"
// r"$p^\text{sat}$"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature"]
}
fn predict(&self, eos: &Rc<E>) -> Result<SIArray1, FitError>
{
let tc =
State::critical_point(eos, None, Some(self.max_temperature), VLEOptions::default())?
.temperature;
let unit = self.target.get(0);
let mut prediction = Array1::zeros(self.datapoints) * unit;
for i in 0..self.datapoints {
let t = self.temperature.get(i);
if t < tc {
let state = PhaseEquilibrium::pure_t(
eos,
self.temperature.get(i),
None,
VLEOptions::default(),
);
if let Ok(s) = state {
prediction.try_set(i, s.liquid().pressure(Contributions::Total))?;
} else {
println!(
"Failed to compute vapor pressure, T = {}",
self.temperature.get(i)
);
prediction.try_set(i, f64::NAN * unit)?;
}
} else {
prediction.try_set(i, f64::NAN * unit)?;
}
}
Ok(prediction)
}
fn cost(&self, eos: &Rc<E>) -> Result<Array1<f64>, FitError>
{
let tc_inv = 1.0
/ State::critical_point(eos, None, Some(self.max_temperature), VLEOptions::default())?
.temperature;
let reduced_temperatures = (0..self.datapoints)
.map(|i| (self.temperature.get(i) * tc_inv).into_value()?)
.collect();
let mut weights = self.weight_from_std(&reduced_temperatures);
weights /= weights.sum();
let prediction = &self.predict(eos)?;
let mut cost = Array1::zeros(self.datapoints);
for i in 0..self.datapoints {
if prediction.get(i).is_nan() {
cost[i] = weights[i]
* 5.0
* (self.temperature.get(i) - 1.0 / tc_inv)
.to_reduced(SIUnit::reference_temperature())?;
} else {
cost[i] = weights[i]
* ((self.target.get(i) - prediction.get(i)) / self.target.get(i))
.into_value()?
}
}
Ok(cost)
}
fn get_input(&self) -> HashMap<String, SIArray1> {
let mut m = HashMap::with_capacity(1);
m.insert("temperature".to_owned(), self.temperature());
m
}
}
/// Store experimental data of liquid densities and compare to the equation of state.
#[derive(Clone)]
pub struct LiquidDensity {
pub target: SIArray1,
temperature: SIArray1,
pressure: SIArray1,
datapoints: usize,
}
impl LiquidDensity {
/// A new data set for liquid densities with pressures and temperatures as input.
pub fn new(
target: SIArray1,
temperature: SIArray1,
pressure: SIArray1,
) -> Result<Self, FitError> {
let datapoints = target.len();
Ok(Self {
target,
temperature,
pressure,
datapoints,
})
}
/// Returns temperature of data points.
pub fn temperature(&self) -> SIArray1 {
self.temperature.clone()
}
/// Returns pressure of data points.
pub fn pressure(&self) -> SIArray1 {
self.pressure.clone()
}
}
impl<E: EquationOfState + MolarWeight> DataSet<E> for LiquidDensity {
fn target(&self) -> SIArray1 {
self.target.clone()
}
fn target_str(&self) -> &str {
"liquid density"
// r"$\rho^\text{liquid}$"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature", "pressure"]
}
fn predict(&self, eos: &Rc<E>) -> Result<SIArray1, FitError> {
assert_eq!(1, eos.components());
let moles = arr1(&[1.0]) * SIUnit::reference_moles();
let unit = self.target.get(0);
let mut prediction = Array1::zeros(self.datapoints) * unit;
for i in 0..self.datapoints {
let state = State::new_npt(
eos,
self.temperature.get(i),
self.pressure.get(i),
&moles,
DensityInitialization::Liquid,
);
if let Ok(s) = state {
prediction.try_set(i, s.mass_density())?;
} else {
prediction.try_set(i, 1.0e10 * unit)?;
}
}
Ok(prediction)
}
fn cost(&self, eos: &Rc<E>) -> Result<Array1<f64>, FitError>
{
let n_inv = 1.0 / self.datapoints as f64;
let prediction = &self.predict(eos)?;
let mut cost = Array1::zeros(self.datapoints);
for i in 0..self.datapoints {
cost[i] = n_inv
* ((self.target.get(i) - prediction.get(i)) / self.target.get(i)).into_value()?
}
Ok(cost)
}
fn get_input(&self) -> HashMap<String, SIArray1> {
let mut m = HashMap::with_capacity(2);
m.insert("temperature".to_owned(), self.temperature());
m.insert("pressure".to_owned(), self.pressure());
m
}
}
/// Store experimental data of liquid densities at VLE and compare to the equation of state.
#[derive(Clone)]
pub struct EquilibriumLiquidDensity {
pub target: SIArray1,
temperature: SIArray1,
max_temperature: SINumber,
datapoints: usize,
}
impl EquilibriumLiquidDensity {
/// A new data set of liquid densities at VLE given temperatures.
pub fn new(
target: SIArray1,
temperature: SIArray1,
) -> Result<Self, FitError> {
let datapoints = target.len();
let max_temperature = *temperature
.to_reduced(SIUnit::reference_temperature())?
.max()
.map_err(|_| FitError::IncompatibleInput)?
* SIUnit::reference_temperature();
Ok(Self {
target,
temperature,
max_temperature,
datapoints,
})
}
/// Returns the temperature of data points.
pub fn temperature(&self) -> SIArray1 {
self.temperature.clone()
}
}
impl<E: EquationOfState + MolarWeight> DataSet<E>
for EquilibriumLiquidDensity
{
fn target(&self) -> SIArray1 {
self.target.clone()
}
fn target_str(&self) -> &str {
"liquid density (equilibrium)"
// r"$\rho^\text{liquid}_\text{equil}$"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature"]
}
fn predict(&self, eos: &Rc<E>) -> Result<SIArray1, FitError>
{
let tc =
State::critical_point(eos, None, Some(self.max_temperature), VLEOptions::default())?
.temperature;
let unit = self.target.get(0);
let mut prediction = Array1::zeros(self.datapoints) * unit;
for i in 0..self.datapoints {
let t: SINumber = self.temperature.get(i);
if t < tc {
let state: PhaseEquilibrium<U, E, 2> =
PhaseEquilibrium::pure_t(eos, t, None, VLEOptions::default())?;
prediction.try_set(i, state.liquid().mass_density())?;
} else {
prediction.try_set(i, f64::NAN * unit)?;
}
}
Ok(prediction)
}
fn cost(&self, eos: &Rc<E>) -> Result<Array1<f64>, FitError>
{
let tc =
State::critical_point(eos, None, Some(self.max_temperature), VLEOptions::default())?
.temperature;
let n_inv = 1.0 / self.datapoints as f64;
let prediction = &self.predict(eos)?;
let mut cost = Array1::zeros(self.datapoints);
for i in 0..self.datapoints {
if prediction.get(i).is_nan() {
cost[i] = n_inv
* 5.0
* (self.temperature.get(i) - tc).to_reduced(SIUnit::reference_temperature())?;
} else {
cost[i] = n_inv
* ((self.target.get(i) - prediction.get(i)) / self.target.get(i))
.into_value()?
}
}
Ok(cost)
}
fn get_input(&self) -> HashMap<String, SIArray1> {
let mut m = HashMap::with_capacity(2);
m.insert("temperature".to_owned(), self.temperature());
m
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/estimator/liquid_density.rs | .rs | 4,420 | 155 | use super::{DataSet, FeosError};
use feos_core::{
DensityInitialization, Molarweight, PhaseEquilibrium, ReferenceSystem, Residual, SolverOptions,
State,
};
use ndarray::{Array1, arr1};
use quantity::{KILOGRAM, METER, MassDensity, Moles, Pressure, Temperature};
use typenum::P3;
/// Liquid mass density data as function of pressure and temperature.
#[derive(Clone)]
pub struct LiquidDensity {
/// mass density
pub target: Array1<f64>,
/// unit of mass density
unit: MassDensity,
/// temperature
temperature: Temperature<Array1<f64>>,
/// pressure
pressure: Pressure<Array1<f64>>,
}
impl LiquidDensity {
/// A new data set for liquid densities with pressures and temperatures as input.
pub fn new(
target: MassDensity<Array1<f64>>,
temperature: Temperature<Array1<f64>>,
pressure: Pressure<Array1<f64>>,
) -> Self {
let unit = KILOGRAM / METER.powi::<P3>();
Self {
target: (target / unit).to_reduced(),
unit,
temperature,
pressure,
}
}
/// Returns temperature of data points.
pub fn temperature(&self) -> &Temperature<Array1<f64>> {
&self.temperature
}
/// Returns pressure of data points.
pub fn pressure(&self) -> &Pressure<Array1<f64>> {
&self.pressure
}
}
impl<E: Residual + Molarweight> DataSet<E> for LiquidDensity {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"liquid density"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature", "pressure"]
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
let moles = Moles::from_reduced(arr1(&[1.0]));
Ok(self
.temperature
.into_iter()
.zip(&self.pressure)
.map(|(t, p)| {
let state = State::new_npt(eos, t, p, &moles, DensityInitialization::Liquid);
if let Ok(s) = state {
(s.mass_density() / self.unit).into_value()
} else {
f64::NAN
}
})
.collect())
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(2);
// m.insert("temperature".to_owned(), self.temperature());
// m.insert("pressure".to_owned(), self.pressure());
// m
// }
}
/// Store experimental data of liquid densities calculated for phase equilibria.
#[derive(Clone)]
pub struct EquilibriumLiquidDensity {
pub target: Array1<f64>,
/// unit of mass density
unit: MassDensity,
/// temperature
temperature: Temperature<Array1<f64>>,
/// options for VLE solver
solver_options: SolverOptions,
}
impl EquilibriumLiquidDensity {
/// A new data set for liquid densities with pressures and temperatures as input.
pub fn new(
target: MassDensity<Array1<f64>>,
temperature: Temperature<Array1<f64>>,
vle_options: Option<SolverOptions>,
) -> Self {
let unit = KILOGRAM / METER.powi::<P3>();
Self {
target: (target / unit).to_reduced(),
unit,
temperature,
solver_options: vle_options.unwrap_or_default(),
}
}
/// Returns temperature of data points.
pub fn temperature(&self) -> &Temperature<Array1<f64>> {
&self.temperature
}
}
impl<E: Residual + Molarweight> DataSet<E> for EquilibriumLiquidDensity {
fn target(&self) -> &Array1<f64> {
&self.target
}
fn target_str(&self) -> &str {
"equilibrium liquid density"
}
fn input_str(&self) -> Vec<&str> {
vec!["temperature"]
}
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, FeosError> {
Ok(self
.temperature
.into_iter()
.map(|t| {
if let Ok(state) = PhaseEquilibrium::pure(eos, t, None, self.solver_options) {
(state.liquid().mass_density() / self.unit).into_value()
} else {
f64::NAN
}
})
.collect())
}
// fn get_input(&self) -> HashMap<String, SIArray1> {
// let mut m = HashMap::with_capacity(2);
// m.insert("temperature".to_owned(), self.temperature());
// m
// }
}
| Rust |
3D | feos-org/feos | crates/feos/src/saftvrmie/mod.rs | .rs | 354 | 9 | //! Statistical Associating Fluid Theory for Variable Range interactions of the generic Mie form (SAFT-VR Mie)
//!
//! [Lafitte et al. (2013)](https://doi.org/10.1063/1.4819786)
mod eos;
pub(crate) mod parameters;
pub use eos::{SaftVRMie, SaftVRMieOptions};
pub use parameters::{SaftVRMieBinaryRecord, SaftVRMieParameters, SaftVRMieRecord, test_utils};
| Rust |
3D | feos-org/feos | crates/feos/src/saftvrmie/parameters.rs | .rs | 19,006 | 617 | use crate::hard_sphere::{HardSphereProperties, MonomerShape};
use feos_core::parameter::{CombiningRule, Parameters, PureRecord};
use nalgebra::{DMatrix, DVector};
use num_dual::DualNum;
use num_traits::Zero;
use serde::{Deserialize, Serialize};
/// 10-point Gauss-Legendre quadrature [position, weight]
const GLQ10: [[f64; 2]; 10] = [
[-0.1488743389816312, 0.2955242247147529],
[0.1488743389816312, 0.2955242247147529],
[-0.4333953941292472, 0.2692667193099963],
[0.4333953941292472, 0.2692667193099963],
[-0.6794095682990244, 0.219086362515982],
[0.6794095682990244, 0.219086362515982],
[-0.8650633666889845, 0.1494513491505806],
[0.8650633666889845, 0.1494513491505806],
[-0.9739065285171717, 0.0666713443086881],
[0.9739065285171717, 0.0666713443086881],
];
/// SAFT-VR Mie pure-component parameters.
#[derive(Serialize, Deserialize, Clone, Default)]
pub struct SaftVRMieRecord {
/// Segment number
pub m: f64,
/// Segment diameter in units of Angstrom
pub sigma: f64,
/// Energetic parameter in units of Kelvin
pub epsilon_k: f64,
/// Repulsive Mie exponent
pub lr: f64,
/// Attractive Mie exponent
pub la: f64,
}
impl SaftVRMieRecord {
pub fn new(m: f64, sigma: f64, epsilon_k: f64, lr: f64, la: f64) -> Self {
Self {
m,
sigma,
epsilon_k,
lr,
la,
}
}
}
#[derive(Debug, Serialize, Deserialize, Clone, Copy, PartialEq)]
pub struct SaftVRMieAssociationRecord {
/// Association radius parameter
pub rc_ab: f64,
/// Association energy parameter in units of Kelvin
pub epsilon_k_ab: f64,
}
impl SaftVRMieAssociationRecord {
pub fn new(rc_ab: f64, epsilon_k_ab: f64) -> Self {
Self {
rc_ab,
epsilon_k_ab,
}
}
}
impl CombiningRule<SaftVRMieRecord> for SaftVRMieAssociationRecord {
fn combining_rule(
pure_i: &SaftVRMieRecord,
pure_j: &SaftVRMieRecord,
parameters_i: &Self,
parameters_j: &Self,
) -> Self {
let rc_ab = (parameters_i.rc_ab * pure_i.sigma + parameters_j.rc_ab * pure_j.sigma) * 0.5;
let epsilon_k_ab = (parameters_i.epsilon_k_ab * parameters_j.epsilon_k_ab).sqrt();
Self {
rc_ab,
epsilon_k_ab,
}
}
}
/// SAFT-VR Mie binary interaction parameters.
#[derive(Serialize, Deserialize, Clone, Copy, Default)]
pub struct SaftVRMieBinaryRecord {
/// Binary dispersion energy interaction parameter
#[serde(skip_serializing_if = "f64::is_zero")]
#[serde(default)]
pub k_ij: f64,
/// Binary interaction parameter for repulsive exponent
#[serde(skip_serializing_if = "f64::is_zero")]
#[serde(default)]
pub gamma_ij: f64,
}
impl SaftVRMieBinaryRecord {
pub fn new(k_ij: Option<f64>, gamma_ij: Option<f64>) -> Self {
let k_ij = k_ij.unwrap_or_default();
let gamma_ij = gamma_ij.unwrap_or_default();
Self { k_ij, gamma_ij }
}
}
/// Parameter set required for the SAFT-VR Mie equation of state.
pub type SaftVRMieParameters =
Parameters<SaftVRMieRecord, SaftVRMieBinaryRecord, SaftVRMieAssociationRecord>;
/// The SAFT-VR Mie parameters in an easier accessible format.
pub struct SaftVRMiePars {
pub m: DVector<f64>,
pub sigma: DVector<f64>,
pub epsilon_k: DVector<f64>,
pub lr: DVector<f64>,
pub la: DVector<f64>,
pub sigma_ij: DMatrix<f64>,
pub epsilon_k_ij: DMatrix<f64>,
pub lr_ij: DMatrix<f64>,
pub la_ij: DMatrix<f64>,
pub c_ij: DMatrix<f64>,
pub alpha_ij: DMatrix<f64>,
}
impl SaftVRMiePars {
pub fn new(parameters: &SaftVRMieParameters) -> Self {
let n = parameters.pure.len();
let [m, sigma, epsilon_k] = parameters.collate(|pr| [pr.m, pr.sigma, pr.epsilon_k]);
let [lr, la] = parameters.collate(|pr| [pr.lr, pr.la]);
let [k_ij, gamma_ij] = parameters.collate_binary(|br| [br.k_ij, br.gamma_ij]);
let mut sigma_ij = DMatrix::zeros(n, n);
let mut e_k_ij = DMatrix::zeros(n, n);
let mut epsilon_k_ij = DMatrix::zeros(n, n);
let mut lr_ij = DMatrix::zeros(n, n);
let mut la_ij = DMatrix::zeros(n, n);
let mut c_ij = DMatrix::zeros(n, n);
let mut alpha_ij = DMatrix::zeros(n, n);
for i in 0..n {
for j in 0..n {
sigma_ij[(i, j)] = 0.5 * (sigma[i] + sigma[j]);
e_k_ij[(i, j)] = (sigma[i].powi(3) * sigma[j].powi(3)).sqrt()
/ sigma_ij[(i, j)].powi(3)
* (epsilon_k[i] * epsilon_k[j]).sqrt();
epsilon_k_ij[(i, j)] = (1.0 - k_ij[(i, j)]) * e_k_ij[(i, j)];
lr_ij[(i, j)] =
(1.0 - gamma_ij[(i, j)]) * ((lr[i] - 3.0) * (lr[j] - 3.0)).sqrt() + 3.0;
la_ij[(i, j)] = ((la[i] - 3.0) * (la[j] - 3.0)).sqrt() + 3.0;
c_ij[(i, j)] = lr_ij[(i, j)] / (lr_ij[(i, j)] - la_ij[(i, j)])
* (lr_ij[(i, j)] / la_ij[(i, j)])
.powf(la_ij[(i, j)] / (lr_ij[(i, j)] - la_ij[(i, j)]));
alpha_ij[(i, j)] =
c_ij[(i, j)] * ((la_ij[(i, j)] - 3.0).recip() - (lr_ij[(i, j)] - 3.0).recip())
}
}
Self {
m,
sigma,
epsilon_k,
lr,
la,
sigma_ij,
epsilon_k_ij,
lr_ij,
la_ij,
c_ij,
alpha_ij,
}
}
}
impl SaftVRMiePars {
#[inline]
pub fn hs_diameter_ij<D: DualNum<f64> + Copy>(
&self,
i: usize,
j: usize,
inverse_temperature: D,
) -> D {
let lr = self.lr_ij[(i, j)];
let la = self.la_ij[(i, j)];
let c_eps_t = inverse_temperature * self.c_ij[(i, j)] * self.epsilon_k_ij[(i, j)];
// perform integration in reduced distances, then multiply sigma
// r0 is dimensionless
let r0 = lower_integratal_limit(la, lr, c_eps_t);
let width = (-r0 + 1.0) * 0.5;
GLQ10.iter().fold(r0, |d, &[x, w]| {
let r = width * x + width + r0;
let u = beta_u_mie(r, la, lr, 1.0, c_eps_t);
let f_u = -(-u).exp_m1();
d + width * f_u * w
}) * self.sigma_ij[(i, j)]
}
}
/// Find lower limit for integration of the temperature dependent diameter
///
/// Method of Aasen et al.
/// Using starting value proposed in Clapeyron.jl (Andrés Riedemann)
fn lower_integratal_limit<D: DualNum<f64> + Copy>(la: f64, lr: f64, c_eps_t: D) -> D {
// initial value from repulsive contribution
let k = (-c_eps_t.recip() * f64::EPSILON.ln()).ln();
let mut r = (-k / lr).exp();
// Halley's method
for _ in 1..5 {
let [u, u_du, du_d2u] = mie_potential_halley(r, la, lr, c_eps_t);
let dr = u_du / (-u_du / du_d2u * 0.5 + 1.0);
// if dr.re() < f64::EPSILON {
if u.re() < 0.0 {
return r;
}
r -= dr;
}
r // error instead?
}
/// Calculate the fractions f / df and df / d2f used for Halley's method,
///
/// f is the function to find the root of.
/// Here, f = -beta u_mie(r) - ln(EPS)
#[inline]
fn mie_potential_halley<D: DualNum<f64> + Copy>(r: D, la: f64, lr: f64, c_eps_t: D) -> [D; 3] {
let ri = r.recip();
let plr = ri.powf(lr);
let pla = ri.powf(la);
let u = plr - pla;
let dplr = plr * (-lr) * ri;
let dpla = pla * (-la) * ri;
let du_dr = dplr - dpla;
let d2u_dr2 = (dplr * (-lr - 1.0) - dpla * (-la - 1.0)) * ri;
let f = -c_eps_t * u - f64::EPSILON.ln();
let df = -c_eps_t * du_dr;
let d2f = -c_eps_t * d2u_dr2;
[f, f / df, df / d2f]
}
/// Dimensionless Mie potential (divided by kT)
#[inline]
fn beta_u_mie<D: DualNum<f64> + Copy>(r: D, la: f64, lr: f64, sigma: f64, c_eps_t: D) -> D {
let ri = r.recip() * sigma;
(ri.powf(lr) - ri.powf(la)) * c_eps_t
}
impl HardSphereProperties for SaftVRMiePars {
fn monomer_shape<N: DualNum<f64>>(&self, _: N) -> MonomerShape<'_, N> {
MonomerShape::NonSpherical(self.m.map(N::from))
}
fn hs_diameter<D: DualNum<f64> + Copy>(&self, temperature: D) -> DVector<D> {
let t_inv = temperature.recip();
DVector::from_fn(self.m.len(), |i, _| self.hs_diameter_ij(i, i, t_inv))
}
}
/// Utilities for running tests
#[doc(hidden)]
pub mod test_utils {
use super::*;
use feos_core::parameter::{AssociationRecord, Identifier};
use std::collections::HashMap;
/// Parameters from Lafitte et al. (2013)
pub fn test_parameters() -> HashMap<&'static str, SaftVRMieParameters> {
let mut parameters = HashMap::new();
let id = Identifier::default();
parameters.insert(
"methane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
16.031,
SaftVRMieRecord::new(1.0, 3.7412, 153.36, 12.65, 6.0),
))
.unwrap(),
);
parameters.insert(
"ethane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
30.047,
SaftVRMieRecord::new(1.4373, 3.7257, 206.12, 12.4, 6.0),
))
.unwrap(),
);
parameters.insert(
"propane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
44.063,
SaftVRMieRecord::new(1.6845, 3.9056, 239.89, 13.006, 6.0),
))
.unwrap(),
);
parameters.insert(
"n-butane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
58.078,
SaftVRMieRecord::new(1.8514, 4.0887, 273.64, 13.65, 6.0),
))
.unwrap(),
);
parameters.insert(
"pentane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
72.094,
SaftVRMieRecord::new(1.9606, 4.2928, 321.94, 15.847, 6.0),
))
.unwrap(),
);
parameters.insert(
"hexane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
86.11,
SaftVRMieRecord::new(2.1097, 4.423, 354.38, 17.203, 6.0),
))
.unwrap(),
);
parameters.insert(
"heptane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
100.125,
SaftVRMieRecord::new(2.3949, 4.4282, 358.51, 17.092, 6.0),
))
.unwrap(),
);
parameters.insert(
"octane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
114.141,
SaftVRMieRecord::new(2.6253, 4.4696, 369.18, 17.378, 6.0),
))
.unwrap(),
);
parameters.insert(
"nonane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
128.157,
SaftVRMieRecord::new(2.8099, 4.5334, 387.55, 18.324, 6.0),
))
.unwrap(),
);
parameters.insert(
"decane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
142.172,
SaftVRMieRecord::new(2.9976, 4.589, 400.79, 18.885, 6.0),
))
.unwrap(),
);
parameters.insert(
"dodecane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
170.203,
SaftVRMieRecord::new(3.2519, 4.7484, 437.72, 20.862, 6.0),
))
.unwrap(),
);
parameters.insert(
"pentadecane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
212.25,
SaftVRMieRecord::new(3.9325, 4.7738, 444.51, 20.822, 6.0),
))
.unwrap(),
);
parameters.insert(
"eicosane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
282.329,
SaftVRMieRecord::new(4.8794, 4.8788, 475.76, 22.926, 6.0),
))
.unwrap(),
);
parameters.insert(
"methanol",
SaftVRMieParameters::new_pure(PureRecord::with_association(
id.clone(),
32.026,
SaftVRMieRecord::new(1.5283, 3.3063, 167.72, 8.6556, 6.0),
vec![AssociationRecord::new(
Some(SaftVRMieAssociationRecord::new(0.41314, 2904.7)),
1.0,
1.0,
0.0,
)],
))
.unwrap(),
);
parameters.insert(
"ethanol",
SaftVRMieParameters::new_pure(PureRecord::with_association(
id.clone(),
46.042,
SaftVRMieRecord::new(1.96, 3.4914, 168.15, 7.6134, 6.0),
vec![AssociationRecord::new(
Some(SaftVRMieAssociationRecord::new(0.34558, 2833.7)),
1.0,
1.0,
0.0,
)],
))
.unwrap(),
);
parameters.insert(
"1-propanol",
SaftVRMieParameters::new_pure(PureRecord::with_association(
id.clone(),
60.058,
SaftVRMieRecord::new(2.3356, 3.5612, 227.66, 10.179, 6.0),
vec![AssociationRecord::new(
Some(SaftVRMieAssociationRecord::new(0.35377, 2746.2)),
1.0,
1.0,
0.0,
)],
))
.unwrap(),
);
parameters.insert(
"1-butanol",
SaftVRMieParameters::new_pure(PureRecord::with_association(
id.clone(),
74.073,
SaftVRMieRecord::new(2.4377, 3.7856, 278.92, 11.66, 6.0),
vec![AssociationRecord::new(
Some(SaftVRMieAssociationRecord::new(0.32449, 2728.1)),
1.0,
1.0,
0.0,
)],
))
.unwrap(),
);
parameters.insert(
"tetrafluoromethane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
87.994,
SaftVRMieRecord::new(1.0, 4.3372, 232.62, 42.553, 5.1906),
))
.unwrap(),
);
parameters.insert(
"hexafluoroethane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
137.99,
SaftVRMieRecord::new(1.8529, 3.9336, 211.46, 19.192, 5.7506),
))
.unwrap(),
);
parameters.insert(
"perfluoropropane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
187.987,
SaftVRMieRecord::new(1.9401, 4.2983, 263.26, 22.627, 5.7506),
))
.unwrap(),
);
parameters.insert(
"perfluorobutane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
237.984,
SaftVRMieRecord::new(2.1983, 4.4495, 290.49, 24.761, 5.7506),
))
.unwrap(),
);
parameters.insert(
"perfluoropentane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
287.981,
SaftVRMieRecord::new(2.3783, 4.6132, 328.56, 29.75, 5.7506),
))
.unwrap(),
);
parameters.insert(
"perfluorohexane",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
337.978,
SaftVRMieRecord::new(2.5202, 4.7885, 349.3, 30.741, 5.7506),
))
.unwrap(),
);
parameters.insert(
"fluorine",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
37.997,
SaftVRMieRecord::new(1.3211, 2.9554, 96.268, 11.606, 6.0),
))
.unwrap(),
);
parameters.insert(
"carbon dioxide",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
43.99,
SaftVRMieRecord::new(1.5, 3.1916, 231.88, 27.557, 5.1646),
))
.unwrap(),
);
parameters.insert(
"benzene",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
78.047,
SaftVRMieRecord::new(1.9163, 4.0549, 372.59, 14.798, 6.0),
))
.unwrap(),
);
parameters.insert(
"toluene",
SaftVRMieParameters::new_pure(PureRecord::new(
id.clone(),
92.063,
SaftVRMieRecord::new(1.9977, 4.2777, 409.73, 16.334, 6.0),
))
.unwrap(),
);
parameters
}
}
#[cfg(test)]
mod test {
use approx::assert_relative_eq;
use num_dual::Dual2;
use test::test_utils::test_parameters;
use super::*;
#[test]
fn test_mie_potential() {
let la = 6.0;
let lr = 12.0;
let c_eps_t = Dual2::from_re(4.0);
let r = 0.9;
let rd = Dual2::from_re(r).derivative();
let u = beta_u_mie(rd, la, lr, 1.0, c_eps_t);
let [_, u_du, du_d2u] = mie_potential_halley(r, la, lr, 4.0);
assert_relative_eq!((-u.re - f64::EPSILON.ln()) / -u.v1, u_du);
assert_relative_eq!(u.v1 / u.v2, du_d2u);
}
#[test]
fn hs_diameter_ethane() {
let temperature = 50.0;
let ethane = SaftVRMiePars::new(&test_parameters()["ethane"]);
let d_hs = ethane.hs_diameter(temperature);
dbg!(&d_hs);
assert_relative_eq!(
3.694019351651498,
d_hs[0],
max_relative = 1e-9,
epsilon = 1e-9
)
}
#[test]
fn test_zero_integrant() {
let temperature = 50.0;
let ethane = SaftVRMiePars::new(&test_parameters()["ethane"]);
let la = ethane.la[0];
let lr = ethane.lr[0];
let c_eps_t = ethane.c_ij[(0, 0)] * ethane.epsilon_k[0] / temperature;
let r0 = lower_integratal_limit(la, lr, c_eps_t);
assert_relative_eq!(
(-beta_u_mie(r0, la, lr, 1.0, c_eps_t)).exp(),
f64::EPSILON,
max_relative = 1e-15,
epsilon = 1e-15
)
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/saftvrmie/eos/mod.rs | .rs | 4,996 | 151 | use super::parameters::{SaftVRMieAssociationRecord, SaftVRMieParameters, SaftVRMiePars};
use crate::association::{Association, AssociationStrength};
use crate::hard_sphere::{HardSphere, HardSphereProperties};
use feos_core::{Molarweight, ResidualDyn, StateHD, Subset};
use nalgebra::DVector;
use num_dual::DualNum;
use quantity::MolarWeight;
use std::f64::consts::{FRAC_PI_6, PI};
// pub(super) mod association;
pub(crate) mod dispersion;
use dispersion::{Properties, helmholtz_energy_density_disp, helmholtz_energy_density_disp_chain};
/// Customization options for the SAFT-VR Mie equation of state.
#[derive(Copy, Clone)]
pub struct SaftVRMieOptions {
pub max_eta: f64,
pub max_iter_cross_assoc: usize,
pub tol_cross_assoc: f64,
}
impl Default for SaftVRMieOptions {
fn default() -> Self {
Self {
max_eta: 0.5,
max_iter_cross_assoc: 50,
tol_cross_assoc: 1e-10,
}
}
}
/// SAFT-VR Mie equation of state.
pub struct SaftVRMie {
pub parameters: SaftVRMieParameters,
pub params: SaftVRMiePars,
pub options: SaftVRMieOptions,
pub chain: bool,
pub association: Option<Association>,
}
impl SaftVRMie {
pub fn new(parameters: SaftVRMieParameters) -> Self {
Self::with_options(parameters, SaftVRMieOptions::default())
}
pub fn with_options(parameters: SaftVRMieParameters, options: SaftVRMieOptions) -> Self {
let params = SaftVRMiePars::new(¶meters);
let chain = params.m.iter().any(|&m| m > 1.0);
let association = (!parameters.association.is_empty())
.then(|| Association::new(options.max_iter_cross_assoc, options.tol_cross_assoc));
Self {
parameters,
params,
options,
chain,
association,
}
}
}
impl ResidualDyn for SaftVRMie {
fn components(&self) -> usize {
self.params.m.len()
}
fn compute_max_density<D: DualNum<f64> + Copy>(&self, molefracs: &DVector<D>) -> D {
let msigma3 = self
.params
.m
.component_mul(&self.params.sigma.map(|v| v.powi(3)));
(msigma3.map(D::from).dot(molefracs) * FRAC_PI_6).recip() * self.options.max_eta
}
fn reduced_helmholtz_energy_density_contributions<D: DualNum<f64> + Copy>(
&self,
state: &StateHD<D>,
) -> Vec<(&'static str, D)> {
let mut a = Vec::with_capacity(4);
let (a_hs, _, d) = HardSphere.helmholtz_energy_density_and_properties(&self.params, state);
a.push(("Hard Sphere", a_hs));
let properties = Properties::new(&self.params, state, &d);
if self.chain {
let a_disp_chain =
helmholtz_energy_density_disp_chain(&self.params, &properties, state);
a.push(("Dispersion + Chain", a_disp_chain));
} else {
let a_disp = helmholtz_energy_density_disp(&self.params, &properties, state);
a.push(("Dispersion", a_disp));
}
if let Some(assoc) = self.association.as_ref() {
a.push((
"Association",
assoc.helmholtz_energy_density(
&self.params,
&self.parameters.association,
state,
&d,
),
));
}
a
}
}
impl Subset for SaftVRMie {
fn subset(&self, component_list: &[usize]) -> Self {
Self::with_options(self.parameters.subset(component_list), self.options)
}
}
impl Molarweight for SaftVRMie {
fn molar_weight(&self) -> MolarWeight<DVector<f64>> {
self.parameters.molar_weight.clone()
}
}
impl AssociationStrength for SaftVRMiePars {
type Record = SaftVRMieAssociationRecord;
fn association_strength_ij<D: DualNum<f64> + Copy>(
&self,
temperature: D,
comp_i: usize,
comp_j: usize,
assoc_ij: &Self::Record,
) -> D {
let diameter = self.hs_diameter(temperature);
let di = diameter[comp_i];
let dj = diameter[comp_j];
let d = (di + dj) * 0.5;
// temperature dependent association volume
// rc and rd are dimensioned in units of Angstrom
let rc = assoc_ij.rc_ab;
// rd is the distance between an association site and the segment centre.
// It is fixed at 0.4 sigma, leading to 0.4 * 0.5 = 0.2 in the combining rule.
let rd = (self.sigma[comp_i] + self.sigma[comp_j]) * 0.2;
let v = d * d * PI * 4.0 / (72.0 * rd.powi(2))
* ((d.recip() * (rc + 2.0 * rd)).ln()
* (6.0 * rc.powi(3) + 18.0 * rc.powi(2) * rd - 24.0 * rd.powi(3))
+ (-d + rc + 2.0 * rd)
* (d.powi(2) + d * rc + 22.0 * rd.powi(2)
- 5.0 * rc * rd
- d * 7.0 * rd
- 8.0 * rc.powi(2)));
v * (temperature.recip() * assoc_ij.epsilon_k_ab).exp_m1()
}
}
| Rust |
3D | feos-org/feos | crates/feos/src/saftvrmie/eos/dispersion.rs | .rs | 14,613 | 388 | use crate::saftvrmie::parameters::SaftVRMiePars;
use feos_core::StateHD;
use nalgebra::{DMatrix, DVector};
use num_dual::{Dual, DualNum};
use num_traits::Zero;
use std::f64::consts::{FRAC_PI_6, PI};
#[derive(Debug)]
pub struct Properties<D> {
/// Temperature dependent diameter
diameter: DVector<D>,
/// total number density of segments
pub segment_density: D,
/// mole fraction of segments
pub segment_molefracs: DVector<D>,
/// mean segment number
mean_segment_number: D,
/// mixture packing fraction using d(T)
zeta_x: D,
/// mixture packing fraction using sigma
pub zeta_x_bar: D,
/// k-values for HS pair correlation fn
k0: [D; 4],
}
impl<D: DualNum<f64> + Copy + Zero> Properties<D> {
pub(super) fn new(
parameters: &SaftVRMiePars,
state: &StateHD<D>,
diameter: &DVector<D>,
) -> Self {
let n = parameters.m.len();
let x = &state.molefracs;
let mean_segment_number: D = x.component_mul(¶meters.m.map(D::from)).sum();
let xs = x.component_mul(¶meters.m.map(D::from)) / mean_segment_number;
// Set eps to one -> get partial derivatives w.r.t segment density
let segment_density = state
.partial_density
.component_mul(¶meters.m.map(D::from))
.sum();
// diameter
let d_ij = DMatrix::from_fn(n, n, |i, j| (diameter[i] + diameter[j]) * 0.5);
let d3_ij = d_ij.map(|d| d.powi(3));
// segment packing fraction
let mut zeta_x = D::zero();
let mut zeta_x_bar = D::zero();
for i in 0..n {
zeta_x += xs[i].powi(2) * d3_ij[(i, i)];
zeta_x_bar += xs[i].powi(2) * parameters.sigma_ij[(i, i)].powi(3);
for j in i + 1..n {
zeta_x += xs[i] * xs[j] * d3_ij[(i, j)] * 2.0;
zeta_x_bar += xs[i] * xs[j] * parameters.sigma_ij[(i, j)].powi(3) * 2.0;
}
}
zeta_x *= segment_density * FRAC_PI_6;
zeta_x_bar *= segment_density * FRAC_PI_6;
let frac_1mzeta3 = (-zeta_x + 1.0).powi(3).recip();
let z = zeta_x;
let z2 = z * z;
let z3 = z2 * z;
let k0 =
-(-z + 1.0).ln() + z * (-z * 39.0 + z2 * 9.0 - z3 * 2.0 + 42.0) * frac_1mzeta3 / 6.0;
let k1 = z * frac_1mzeta3 * 0.5 * (z3 + z * 6.0 - 12.0);
let k2 = -z2 * 3.0 / 8.0 * frac_1mzeta3 * (-zeta_x + 1.0);
let k3 = z * frac_1mzeta3 / 6.0 * (-z3 + z * 3.0 + 3.0);
Self {
diameter: diameter.clone(),
segment_density,
segment_molefracs: xs,
mean_segment_number,
zeta_x,
zeta_x_bar,
k0: [k0, k1, k2, k3],
}
}
}
pub(super) const PHI: [[f64; 7]; 6] = [
[
7.5365557, -37.60463, 71.745953, -46.83552, -2.467982, -0.50272, 8.0956883,
],
[-359.44, 1825.6, -3168.0, 1884.2, -0.82376, -3.1935, 3.709],
[1550.9, -5070.1, 6534.6, -3288.7, -2.7171, 2.0883, 0.0],
[
-1.19932, 9.063632, -17.9482, 11.34027, 20.52142, -56.6377, 40.53683,
],
[
-1911.28, 21390.175, -51320.7, 37064.54, 1103.742, -3264.61, 2556.181,
],
[
9236.9, -129430.0, 357230.0, -315530.0, 1390.2, -4518.2, 4241.6,
],
];
/// First, second and third order perturbations for dispersive interactions
pub fn helmholtz_energy_density_disp<D: DualNum<f64> + Copy>(
parameters: &SaftVRMiePars,
properties: &Properties<D>,
state: &StateHD<D>,
) -> D {
let p = ¶meters;
let n = p.sigma.len();
let xs = &properties.segment_molefracs;
let t_inv = state.temperature.inv();
let zeta_x = properties.zeta_x;
let k_hs = (zeta_x - 1.0).powi(4)
/ ((zeta_x + zeta_x.powi(2) - zeta_x.powi(3)) * 4.0 + zeta_x.powi(4) + 1.0);
let zeta_x_bar = properties.zeta_x_bar;
let zx5 = zeta_x_bar.powi(5);
let zx8 = zeta_x_bar.powi(8);
let mut a1 = D::zero();
let mut a2 = D::zero();
let mut a3 = D::zero();
for i in 0..n {
// parameters
let eps_k = p.epsilon_k[i];
let sig = p.sigma[i];
let la = p.la[i];
let lr = p.lr[i];
let c = p.c_ij[(i, i)];
let di = properties.diameter[i];
let d3 = di.powi(3);
let x0 = di.recip() * sig;
let pref = properties.segment_density * d3 * eps_k * 2.0 * PI * c;
let a1s_b_la = a1s_b_ij(zeta_x, x0, la);
let a1s_b_lr = a1s_b_ij(zeta_x, x0, lr);
let a1s_b_2la = a1s_b_ij(zeta_x, x0, 2.0 * la);
let a1s_b_lalr = a1s_b_ij(zeta_x, x0, la + lr);
let a1s_b_2lr = a1s_b_ij(zeta_x, x0, 2.0 * lr);
let a1_ii = pref * (a1s_b_la - a1s_b_lr);
let a2_ii = pref * eps_k * c * k_hs * 0.5 * (a1s_b_2la - a1s_b_lalr * 2.0 + a1s_b_2lr);
// note indices of f(i, alpha) are shifted due to 0-indexing.
let alpha = parameters.alpha_ij[(i, i)];
let a3_ii = -zeta_x_bar
* f(3, alpha)
* (zeta_x_bar * (zeta_x_bar * f(5, alpha) + f(4, alpha))).exp()
* eps_k.powi(3);
let xii = zeta_x_bar * f(0, alpha) + zx5 * f(1, alpha) + zx8 * f(2, alpha);
// accumulate contributions
let xs_ii2 = xs[i] * xs[i];
a1 += a1_ii * xs_ii2;
a2 += a2_ii * xs_ii2 * (xii + 1.0);
a3 += a3_ii * xs_ii2;
for j in i + 1..n {
// parameters
let eps_k = p.epsilon_k_ij[(i, j)];
let sig = p.sigma_ij[(i, j)];
let la = p.la_ij[(i, j)];
let lr = p.lr_ij[(i, j)];
let c = p.c_ij[(i, j)];
let dij = (di + properties.diameter[j]) * 0.5;
let d3 = dij.powi(3);
let x0 = dij.recip() * sig;
let pref = properties.segment_density * d3 * eps_k * 2.0 * PI * c;
let a1s_b_la = a1s_b_ij(zeta_x, x0, la);
let a1s_b_lr = a1s_b_ij(zeta_x, x0, lr);
let a1s_b_2la = a1s_b_ij(zeta_x, x0, 2.0 * la);
let a1s_b_lalr = a1s_b_ij(zeta_x, x0, la + lr);
let a1s_b_2lr = a1s_b_ij(zeta_x, x0, 2.0 * lr);
let a1_ij = pref * (a1s_b_la - a1s_b_lr);
let a2_ij = pref * eps_k * c * k_hs * 0.5 * (a1s_b_2la - a1s_b_lalr * 2.0 + a1s_b_2lr);
// note indices of f(i, alpha) are shifted due to 0-indexing.
let alpha = parameters.alpha_ij[(i, j)];
let a3_ij = -zeta_x_bar
* f(3, alpha)
* (zeta_x_bar * (zeta_x_bar * f(5, alpha) + f(4, alpha))).exp()
* eps_k.powi(3);
let xij = zeta_x_bar * f(0, alpha) + zx5 * f(1, alpha) + zx8 * f(2, alpha);
let xs_ij = xs[i] * xs[j];
// accumulate contributions
a1 += a1_ij * xs_ij * 2.0;
a2 += a2_ij * xs_ij * (xij + 1.0) * 2.0;
a3 += a3_ij * xs_ij * 2.0;
}
}
state.partial_density.sum()
* properties.mean_segment_number
* (a1 * t_inv + a2 * t_inv.powi(2) + a3 * t_inv.powi(3))
}
/// Combine dispersion and chain contributions
pub fn helmholtz_energy_density_disp_chain<D: DualNum<f64> + Copy>(
parameters: &SaftVRMiePars,
properties: &Properties<D>,
state: &StateHD<D>,
) -> D {
let p = ¶meters;
let n = p.sigma.len();
let k = &properties.k0;
let xs = &properties.segment_molefracs;
let t_inv = state.temperature.inv();
// wrap rho_s in Dual to calculate da1/drho_s and da2/drho_s
// for chain contribution on the fly.
let rho_s_dual = Dual::from_re(properties.segment_density).derivative();
let zeta_x = properties.zeta_x;
let zeta_x_dual = if properties.segment_density.is_zero() {
rho_s_dual * 0.0
} else {
Dual::from_re(zeta_x / properties.segment_density) * rho_s_dual
};
// let zeta_x_dual = Dual::from_re(zeta_x / properties.segment_density) * rho_s_dual;
let k_hs_dual = (zeta_x_dual - 1.0).powi(4)
/ ((zeta_x_dual + zeta_x_dual.powi(2) - zeta_x_dual.powi(3)) * 4.0
+ zeta_x_dual.powi(4)
+ 1.0);
// non-dual things
let zeta_x_bar = properties.zeta_x_bar;
let zx5 = zeta_x_bar.powi(5);
let zx8 = zeta_x_bar.powi(8);
let k_hs = k_hs_dual.re;
let mut a1 = D::zero();
let mut a2 = D::zero();
let mut a3 = D::zero();
let mut a_chain = D::zero();
for i in 0..n {
// parameters
let m = p.m[i];
let eps_k = p.epsilon_k[i];
let sig = p.sigma[i];
let la = p.la[i];
let lr = p.lr[i];
let c = p.c_ij[(i, i)];
let di = properties.diameter[i];
// calculate a1 and a2 using Dual(D)
// this is only done in the outer loop
let d3 = Dual::from_re(di.powi(3));
let x0 = Dual::from_re(di.recip() * sig);
let pref = rho_s_dual * d3 * eps_k * 2.0 * PI * c;
let a1s_b_la = a1s_b_ij(zeta_x_dual, x0, la);
let a1s_b_lr = a1s_b_ij(zeta_x_dual, x0, lr);
let a1s_b_2la = a1s_b_ij(zeta_x_dual, x0, 2.0 * la);
let a1s_b_lalr = a1s_b_ij(zeta_x_dual, x0, la + lr);
let a1s_b_2lr = a1s_b_ij(zeta_x_dual, x0, 2.0 * lr);
let a1_ii = pref * (a1s_b_la - a1s_b_lr);
let a2_ii = pref * eps_k * c * k_hs_dual * 0.5 * (a1s_b_2la - a1s_b_lalr * 2.0 + a1s_b_2lr);
// note indices of f(i, alpha) are shifted due to 0-indexing.
let alpha = parameters.alpha_ij[(i, i)];
let a3_ii = -zeta_x_bar
* f(3, alpha)
* (zeta_x_bar * (zeta_x_bar * f(5, alpha) + f(4, alpha))).exp()
* eps_k.powi(3);
// chi is not Dual(D), since it is not needed in chain
let xii = zeta_x_bar * f(0, alpha) + zx5 * f(1, alpha) + zx8 * f(2, alpha);
// accumulate contributions
let xs_ii2 = xs[i] * xs[i];
a1 += a1_ii.re * xs_ii2;
a2 += a2_ii.re * xs_ii2 * (xii + 1.0);
a3 += a3_ii * xs_ii2;
// calculate chain using D
// use dual-parts of a1_ii and a2_ii for derivatives
// and real-parts of a1s_b-terms.
let x0 = x0.re;
let pref = d3.re * eps_k * 2.0 * PI;
let g_hs = (k[0] + k[1] * x0 + k[2] * x0.powi(2) + k[3] * x0.powi(3)).exp();
let g1 = a1_ii.eps * 3.0 / pref - (a1s_b_la.re * la - a1s_b_lr.re * lr) * c;
let g2_mca = a2_ii.eps * 3.0 / pref / eps_k
- (a1s_b_2lr.re * lr - a1s_b_lalr.re * (la + lr) + a1s_b_2la.re * la)
* k_hs
* c.powi(2);
let beta_eps = t_inv * eps_k;
let gamma = zeta_x_bar
* beta_eps.exp_m1()
* 10.0
* (-(10.0 * (0.57 - alpha)).tanh() + 1.0)
* (-zeta_x_bar * 6.7 - zeta_x_bar.powi(2) * 8.0).exp();
let g2 = g2_mca * (gamma + 1.0);
let ln_g_mie = g_hs.ln() + (beta_eps * g1 + beta_eps.powi(2) * g2) / g_hs;
a_chain += -state.molefracs[i] * (m - 1.0) * ln_g_mie;
for j in i + 1..n {
// parameters
let eps_k = p.epsilon_k_ij[(i, j)];
let sig = p.sigma_ij[(i, j)];
let la = p.la_ij[(i, j)];
let lr = p.lr_ij[(i, j)];
let c = p.c_ij[(i, j)];
let dij = (di + properties.diameter[j]) * 0.5;
let d3 = dij.powi(3);
let x0 = dij.recip() * sig;
let pref = properties.segment_density * d3 * eps_k * 2.0 * PI * c;
let a1s_b_la = a1s_b_ij(zeta_x, x0, la);
let a1s_b_lr = a1s_b_ij(zeta_x, x0, lr);
let a1s_b_2la = a1s_b_ij(zeta_x, x0, 2.0 * la);
let a1s_b_lalr = a1s_b_ij(zeta_x, x0, la + lr);
let a1s_b_2lr = a1s_b_ij(zeta_x, x0, 2.0 * lr);
let a1_ij = pref * (a1s_b_la - a1s_b_lr);
let a2_ij = pref * eps_k * c * k_hs * 0.5 * (a1s_b_2la - a1s_b_lalr * 2.0 + a1s_b_2lr);
// note indices of f(i, alpha) are shifted due to 0-indexing.
let alpha = parameters.alpha_ij[(i, j)];
let a3_ij = -zeta_x_bar
* f(3, alpha)
* (zeta_x_bar * (zeta_x_bar * f(5, alpha) + f(4, alpha))).exp()
* eps_k.powi(3);
let xij = zeta_x_bar * f(0, alpha) + zx5 * f(1, alpha) + zx8 * f(2, alpha);
// accumulate contributions
let xs_ij = xs[i] * xs[j];
a1 += a1_ij * xs_ij * 2.0;
a2 += a2_ij * xs_ij * (xij + 1.0) * 2.0;
a3 += a3_ij * xs_ij * 2.0;
}
}
state.partial_density.sum()
* (properties.mean_segment_number * (a1 * t_inv + a2 * t_inv.powi(2) + a3 * t_inv.powi(3))
+ a_chain)
}
#[inline]
pub(super) fn zeta_eff<D: DualNum<f64> + Copy>(zeta: D, lambda: f64) -> D {
let li = 1. / lambda;
let li2 = li * li;
let li3 = li * li2;
let c = [
li * C[0][1] + li2 * C[0][2] + li3 * C[0][3] + C[0][0],
li * C[1][1] + li2 * C[1][2] + li3 * C[1][3] + C[1][0],
li * C[2][1] + li2 * C[2][2] + li3 * C[2][3] + C[2][0],
li * C[3][1] + li2 * C[3][2] + li3 * C[3][3] + C[3][0],
];
// zeta * c[0] + zeta.powi(2) * c[1] + zeta.powi(3) * c[2] + zeta.powi(4) * c[3]
zeta * (zeta * (zeta * (zeta * c[3] + c[2]) + c[1]) + c[0])
}
/// Sutherland potential for mixtures (Eq. A 16) divided by 2 PI rho_s d_ij^3 epsilon_k_ij
#[inline]
fn a1s_ij<D: DualNum<f64> + Copy>(zeta_x: D, lambda: f64) -> D {
let zeta_eff = zeta_eff(zeta_x, lambda);
-(-zeta_eff * 0.5 + 1.0) / ((-zeta_eff + 1.0).powi(3) * (lambda - 3.0))
}
/// Eq. A 12 of Lafitte divided by 2 PI rho_s d_ij^3 epsilon_k_ij
#[inline]
fn b_ij<D: DualNum<f64> + Copy>(zeta_x: D, x0: D, lambda: f64) -> D {
let x0_3ml = x0.powf(3.0 - lambda);
let i = -(x0_3ml - 1.0) / (lambda - 3.0);
let j = -(x0.powf(4.0 - lambda) * (lambda - 3.0) - x0_3ml * (lambda - 4.0) - 1.0)
/ ((lambda - 3.0) * (lambda - 4.0));
((-zeta_x * 0.5 + 1.0) * i - zeta_x * (zeta_x + 1.0) * 4.5 * j) * (-zeta_x + 1.0).powi(-3)
}
/// Calculates x0^l (a1s_ij + b_ij) without prefactor C
#[inline]
fn a1s_b_ij<D: DualNum<f64> + Copy>(zeta_x: D, x0: D, lambda: f64) -> D {
x0.powf(lambda) * (a1s_ij(zeta_x, lambda) + b_ij(zeta_x, x0, lambda))
}
fn f(k: usize, alpha: f64) -> f64 {
let alpha2 = alpha * alpha;
let alpha3 = alpha * alpha2;
let phi = PHI[k];
(alpha * phi[1] + alpha2 * phi[2] + alpha3 * phi[3] + phi[0])
/ (alpha * phi[4] + alpha2 * phi[5] + alpha3 * phi[6] + 1.0)
}
const C: [[f64; 4]; 4] = [
[0.81096, 1.7888, -37.578, 92.284],
[1.0205, -19.341, 151.26, -463.50],
[-1.9057, 22.845, -228.14, 973.92],
[1.0885, -6.1962, 106.98, -677.64],
];
| Rust |
3D | feos-org/feos | crates/feos/benches/state_properties.rs | .rs | 4,371 | 136 | #![allow(clippy::type_complexity)]
use criterion::{Criterion, criterion_group, criterion_main};
use feos::core::parameter::IdentifierOption;
use feos::core::{Contributions, Residual, State};
use feos::pcsaft::{PcSaft, PcSaftParameters};
use nalgebra::{DVector, dvector};
use quantity::*;
/// Evaluate a property of a state given the EoS, the property to compute,
/// temperature, volume, moles, and the contributions to consider.
fn property<E: Residual, T, F: Fn(&State<E>, Contributions) -> T>(
(eos, property, t, v, n, contributions): (
&E,
F,
Temperature,
Volume,
&Moles<DVector<f64>>,
Contributions,
),
) -> T {
let state = State::new_nvt(eos, t, v, n).unwrap();
property(&state, contributions)
}
/// Evaluate a property with of a state given the EoS, the property to compute,
/// temperature, volume, moles.
fn property_no_contributions<E: Residual, T, F: Fn(&State<E>) -> T>(
(eos, property, t, v, n): (&E, F, Temperature, Volume, &Moles<DVector<f64>>),
) -> T {
let state = State::new_nvt(eos, t, v, n).unwrap();
property(&state)
}
fn properties_pcsaft(c: &mut Criterion) {
let parameters = PcSaftParameters::from_json(
vec!["methane", "ethane", "propane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = PcSaft::new(parameters);
let t = 300.0 * KELVIN;
let density = 71.18 * KILO * MOL / METER.powi::<3>();
let v = 100.0 * MOL / density;
let x = dvector![1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0];
let m = &x * 100.0 * MOL;
let mut group = c.benchmark_group("state_properties_pcsaft_methane_ethane_propane");
group.bench_function("a", |b| {
b.iter(|| property_no_contributions((&&eos, State::residual_helmholtz_energy, t, v, &m)))
});
group.bench_function("compressibility", |b| {
b.iter(|| {
property((
&&eos,
State::compressibility,
t,
v,
&m,
Contributions::Total,
))
})
});
group.bench_function("ln_phi", |b| {
b.iter(|| property_no_contributions((&&eos, State::ln_phi, t, v, &m)))
});
group.bench_function("c_v", |b| {
b.iter(|| {
property_no_contributions((
&&eos,
State::residual_molar_isochoric_heat_capacity,
t,
v,
&m,
))
})
});
group.bench_function("partial_molar_volume", |b| {
b.iter(|| property_no_contributions((&&eos, State::partial_molar_volume, t, v, &m)))
});
}
fn properties_pcsaft_polar(c: &mut Criterion) {
let parameters = PcSaftParameters::from_json(
vec!["acetone", "butanal", "dimethyl ether"],
"../../parameters/pcsaft/gross2006.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = PcSaft::new(parameters);
let t = 300.0 * KELVIN;
let density = 71.18 * KILO * MOL / METER.powi::<3>();
let v = 100.0 * MOL / density;
let x = dvector![1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0];
let m = &x * 100.0 * MOL;
let mut group = c.benchmark_group("state_properties_pcsaft_polar");
group.bench_function("a", |b| {
b.iter(|| property_no_contributions((&&eos, State::residual_helmholtz_energy, t, v, &m)))
});
group.bench_function("compressibility", |b| {
b.iter(|| {
property((
&&eos,
State::compressibility,
t,
v,
&m,
Contributions::Total,
))
})
});
group.bench_function("ln_phi", |b| {
b.iter(|| property_no_contributions((&&eos, State::ln_phi, t, v, &m)))
});
group.bench_function("c_v", |b| {
b.iter(|| {
property_no_contributions((
&&eos,
State::residual_molar_isochoric_heat_capacity,
t,
v,
&m,
))
})
});
group.bench_function("partial_molar_volume", |b| {
b.iter(|| property_no_contributions((&&eos, State::partial_molar_volume, t, v, &m)))
});
}
criterion_group!(bench, properties_pcsaft, properties_pcsaft_polar);
criterion_main!(bench);
| Rust |
3D | feos-org/feos | crates/feos/benches/state_creation.rs | .rs | 5,772 | 185 | #![allow(clippy::type_complexity)]
use criterion::{Criterion, criterion_group, criterion_main};
use feos::core::{
Contributions, DensityInitialization, PhaseEquilibrium, Residual, State, TemperatureOrPressure,
parameter::IdentifierOption,
};
use feos::pcsaft::{PcSaft, PcSaftParameters};
use nalgebra::DVector;
use quantity::*;
/// Evaluate NPT constructor
fn npt<E: Residual>(
(eos, t, p, n, rho0): (
&E,
Temperature,
Pressure,
&Moles<DVector<f64>>,
DensityInitialization,
),
) {
State::new_npt(eos, t, p, n, Some(rho0)).unwrap();
}
/// Evaluate critical point constructor
fn critical_point<E: Residual>((eos, n): (&E, Option<&DVector<f64>>)) {
State::critical_point(eos, n, None, None, Default::default()).unwrap();
}
/// Evaluate critical point constructor for binary systems at given T or p
fn critical_point_binary<E: Residual, TP: TemperatureOrPressure>((eos, tp): (&E, TP)) {
State::critical_point_binary(eos, tp, None, None, None, Default::default()).unwrap();
}
/// VLE for pure substance for given temperature or pressure
fn pure<E: Residual, TP: TemperatureOrPressure>((eos, t_or_p): (&E, TP)) {
PhaseEquilibrium::pure(eos, t_or_p, None, Default::default()).unwrap();
}
/// Evaluate temperature, pressure flash.
fn tp_flash<E: Residual>((eos, t, p, feed): (&E, Temperature, Pressure, &Moles<DVector<f64>>)) {
PhaseEquilibrium::tp_flash(eos, t, p, feed, None, Default::default(), None).unwrap();
}
fn bubble_point<E: Residual>((eos, t, x): (&E, Temperature, &DVector<f64>)) {
PhaseEquilibrium::bubble_point(
eos,
t,
x,
None,
None,
(Default::default(), Default::default()),
)
.unwrap();
}
fn dew_point<E: Residual>((eos, t, y): (&E, Temperature, &DVector<f64>)) {
PhaseEquilibrium::dew_point(
eos,
t,
y,
None,
None,
(Default::default(), Default::default()),
)
.unwrap();
}
fn bench_states<E: Residual>(c: &mut Criterion, group_name: &str, eos: &E) {
let ncomponents = eos.components();
let x = DVector::from_element(ncomponents, 1.0 / ncomponents as f64);
let n = &x * 100.0 * MOL;
let crit = State::critical_point(eos, Some(&x), None, None, Default::default()).unwrap();
let vle = if ncomponents == 1 {
PhaseEquilibrium::pure(eos, crit.temperature * 0.95, None, Default::default()).unwrap()
} else {
PhaseEquilibrium::tp_flash(
eos,
crit.temperature,
crit.pressure(Contributions::Total) * 0.95,
&crit.moles,
None,
Default::default(),
None,
)
.unwrap()
};
let mut group = c.benchmark_group(group_name);
group.bench_function("new_npt_liquid", |b| {
b.iter(|| {
npt((
eos,
vle.liquid().temperature,
vle.liquid().pressure(Contributions::Total) * 1.01,
&n,
DensityInitialization::Liquid,
))
})
});
group.bench_function("new_npt_vapor", |b| {
b.iter(|| {
npt((
eos,
vle.vapor().temperature,
vle.vapor().pressure(Contributions::Total) * 0.99,
&n,
DensityInitialization::Vapor,
))
})
});
group.bench_function("critical_point", |b| {
b.iter(|| critical_point((eos, Some(&x))))
});
if ncomponents == 2 {
group.bench_function("critical_point_binary_t", |b| {
b.iter(|| critical_point_binary((eos, crit.temperature)))
});
group.bench_function("critical_point_binary_p", |b| {
b.iter(|| critical_point_binary((eos, crit.pressure(Contributions::Total))))
});
}
if ncomponents != 1 {
group.bench_function("tp_flash", |b| {
b.iter(|| {
tp_flash((
eos,
crit.temperature,
crit.pressure(Contributions::Total) * 0.99,
&n,
))
})
});
group.bench_function("bubble_point", |b| {
b.iter(|| bubble_point((eos, vle.liquid().temperature, &vle.liquid().molefracs)))
});
group.bench_function("dew_point", |b| {
b.iter(|| dew_point((eos, vle.vapor().temperature, &vle.vapor().molefracs)))
});
} else {
group.bench_function("pure_t", |b| {
b.iter(|| pure((eos, vle.vapor().temperature)))
});
group.bench_function("pure_p", |b| {
b.iter(|| pure((eos, vle.vapor().pressure(Contributions::Total))))
});
}
}
fn pcsaft(c: &mut Criterion) {
let parameters = PcSaftParameters::from_json(
vec!["methane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = PcSaft::new(parameters);
bench_states(c, "state_creation_pcsaft_methane", &&eos);
let parameters = PcSaftParameters::from_json(
vec!["methane", "ethane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = PcSaft::new(parameters);
bench_states(c, "state_creation_pcsaft_methane_ethane", &&eos);
let parameters = PcSaftParameters::from_json(
vec!["methane", "ethane", "propane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = PcSaft::new(parameters);
bench_states(c, "state_creation_pcsaft_methane_ethane_propane", &&eos);
}
criterion_group!(bench, pcsaft);
criterion_main!(bench);
| Rust |
3D | feos-org/feos | crates/feos/benches/dual_numbers.rs | .rs | 8,062 | 227 | //! Benchmarks for the evaluation of the Helmholtz energy function
//! for a given `StateHD` for different types of dual numbers.
//! These should give an idea about the expected slow-down depending
//! on the dual number type used without the overhead of the `State`
//! creation.
use criterion::{Criterion, criterion_group, criterion_main};
use feos::core::parameter::IdentifierOption;
use feos::core::{ReferenceSystem, Residual, State, StateHD};
use feos::pcsaft::{
PcSaft, PcSaftAssociationRecord, PcSaftBinaryRecord, PcSaftParameters, PcSaftRecord,
};
use feos_core::parameter::PureRecord;
use nalgebra::{DVector, Dyn, dvector};
use num_dual::{Dual2_64, Dual3_64, Dual64, DualNum, HyperDual64};
use quantity::*;
/// Helper function to create a state for given parameters.
/// - temperature is 80% of critical temperature,
/// - volume is critical volume,
/// - molefracs (or moles) for equimolar mixture.
fn state_pcsaft(n: usize, eos: &PcSaft) -> State<&PcSaft> {
let moles = DVector::from_element(n, 1.0 / n as f64) * 10.0 * MOL;
let molefracs = (&moles / moles.sum()).into_value();
let cp = State::critical_point(&eos, Some(&molefracs), None, None, Default::default()).unwrap();
let temperature = 0.8 * cp.temperature;
State::new_nvt(&eos, temperature, cp.volume, &moles).unwrap()
}
/// Residual Helmholtz energy given an equation of state and a StateHD.
fn a_res<D: DualNum<f64> + Copy, E: Residual<Dyn, D>>((eos, state): (&E, &StateHD<D>)) -> D {
eos.reduced_residual_helmholtz_energy_density(state)
}
/// Benchmark for evaluation of the Helmholtz energy for different dual number types.
fn bench_dual_numbers<E: Residual>(c: &mut Criterion, group_name: &str, state: State<E>) {
let mut group = c.benchmark_group(group_name);
group.bench_function("a_f64", |b| {
b.iter(|| a_res((&state.eos, &derive0(&state))))
});
group.bench_function("a_dual", |b| {
b.iter(|| a_res((&state.eos.lift(), &derive1(&state, Derivative::DV))))
});
group.bench_function("a_dual2", |b| {
b.iter(|| a_res((&state.eos.lift(), &derive2(&state, Derivative::DV))))
});
group.bench_function("a_hyperdual", |b| {
b.iter(|| {
a_res((
&state.eos.lift(),
&derive2_mixed(&state, Derivative::DV, Derivative::DV),
))
})
});
group.bench_function("a_dual3", |b| {
b.iter(|| a_res((&state.eos.lift(), &derive3(&state, Derivative::DV))))
});
}
/// Benchmark for the PC-SAFT equation of state
fn pcsaft(c: &mut Criterion) {
// methane
let parameters = PcSaftParameters::from_json(
vec!["methane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = &PcSaft::new(parameters);
bench_dual_numbers(c, "dual_numbers_pcsaft_methane", state_pcsaft(1, eos));
// water (4C, polar)
let parameters = PcSaftParameters::from_json(
vec!["water_4C_polar"],
"../../parameters/pcsaft/rehner2020.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = &PcSaft::new(parameters);
bench_dual_numbers(
c,
"dual_numbers_pcsaft_water_4c_polar",
state_pcsaft(1, eos),
);
// methane, ethane, propane
let parameters = PcSaftParameters::from_json(
vec!["methane", "ethane", "propane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = &PcSaft::new(parameters);
bench_dual_numbers(
c,
"dual_numbers_pcsaft_methane_ethane_propane",
state_pcsaft(3, eos),
);
}
/// Benchmark for the PC-SAFT equation of state.
/// Binary system of methane and co2 used to model biogas.
fn methane_co2_pcsaft(c: &mut Criterion) {
type Pure = PureRecord<PcSaftRecord, PcSaftAssociationRecord>;
let methane = Pure::from_json(
&["methane"],
"../../parameters/pcsaft/gross2001.json",
IdentifierOption::Name,
)
.unwrap()
.pop()
.unwrap();
let co2 = Pure::from_json(
&["carbon dioxide"],
"../../parameters/pcsaft/gross2005_fit.json",
IdentifierOption::Name,
)
.unwrap()
.pop()
.unwrap();
let k_ij = -0.0192211646;
let br = PcSaftBinaryRecord::new(k_ij);
let parameters = PcSaftParameters::new_binary([methane, co2], Some(br), vec![]).unwrap();
let eos = &PcSaft::new(parameters);
// 230 K, 50 bar, x0 = 0.15
let temperature = 230.0 * KELVIN;
let density = 24.16896 * KILO * MOL / METER.powi::<3>();
let volume = 10.0 * MOL / density;
let x = dvector![0.15, 0.85];
let moles = &x * 10.0 * MOL;
let state = State::new_nvt(&eos, temperature, volume, &moles).unwrap();
bench_dual_numbers(c, "dual_numbers_pcsaft_methane_co2", state);
}
criterion_group!(bench, pcsaft, methane_co2_pcsaft);
criterion_main!(bench);
enum Derivative {
/// Derivative with respect to system volume.
DV,
/// Derivative with respect to temperature.
#[expect(dead_code)]
DT,
/// Derivative with respect to component `i`.
#[expect(dead_code)]
DN(usize),
}
/// Creates a [StateHD] cloning temperature, volume and moles.
fn derive0<E>(state: &State<E>) -> StateHD<f64> {
let total_moles = state.total_moles.into_reduced();
StateHD::new(
state.temperature.into_reduced(),
state.volume.into_reduced() / total_moles,
&(state.moles.to_reduced() / total_moles),
)
}
/// Creates a [StateHD] taking the first derivative.
fn derive1<E>(state: &State<E>, derivative: Derivative) -> StateHD<Dual64> {
let state = derive0(state);
let mut t = Dual64::from(state.temperature);
let mut v = Dual64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(Dual64::from);
match derivative {
Derivative::DT => t = t.derivative(),
Derivative::DV => v = v.derivative(),
Derivative::DN(i) => n[i] = n[i].derivative(),
}
StateHD::new(t, v, &n)
}
/// Creates a [StateHD] taking the first and second (partial) derivatives.
fn derive2<E>(state: &State<E>, derivative: Derivative) -> StateHD<Dual2_64> {
let state = derive0(state);
let mut t = Dual2_64::from(state.temperature);
let mut v = Dual2_64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(Dual2_64::from);
match derivative {
Derivative::DT => t = t.derivative(),
Derivative::DV => v = v.derivative(),
Derivative::DN(i) => n[i] = n[i].derivative(),
}
StateHD::new(t, v, &n)
}
/// Creates a [StateHD] taking the first and second (partial) derivatives.
fn derive2_mixed<E>(
state: &State<E>,
derivative1: Derivative,
derivative2: Derivative,
) -> StateHD<HyperDual64> {
let state = derive0(state);
let mut t = HyperDual64::from(state.temperature);
let mut v = HyperDual64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(HyperDual64::from);
match derivative1 {
Derivative::DT => t = t.derivative1(),
Derivative::DV => v = v.derivative1(),
Derivative::DN(i) => n[i] = n[i].derivative1(),
}
match derivative2 {
Derivative::DT => t = t.derivative2(),
Derivative::DV => v = v.derivative2(),
Derivative::DN(i) => n[i] = n[i].derivative2(),
}
StateHD::new(t, v, &n)
}
/// Creates a [StateHD] taking the first, second, and third derivative with respect to a single property.
fn derive3<E>(state: &State<E>, derivative: Derivative) -> StateHD<Dual3_64> {
let state = derive0(state);
let mut t = Dual3_64::from(state.temperature);
let mut v = Dual3_64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(Dual3_64::from);
match derivative {
Derivative::DT => t = t.derivative(),
Derivative::DV => v = v.derivative(),
Derivative::DN(i) => n[i] = n[i].derivative(),
};
StateHD::new(t, v, &n)
}
| Rust |
3D | feos-org/feos | crates/feos/benches/dft_pore.rs | .rs | 4,373 | 133 | //! Benchmarks for the calculation of density profiles
//! in pores at different conditions.
use criterion::{Criterion, criterion_group, criterion_main};
use feos::core::parameter::IdentifierOption;
use feos::core::{PhaseEquilibrium, State, StateBuilder};
use feos::dft::adsorption::{ExternalPotential, Pore1D, PoreSpecification};
use feos::dft::{DFTSolver, Geometry};
use feos::gc_pcsaft::{GcPcSaftFunctional, GcPcSaftParameters};
use feos::hard_sphere::{FMTFunctional, FMTVersion};
use feos::pcsaft::{PcSaftFunctional, PcSaftParameters};
use nalgebra::dvector;
use quantity::{ANGSTROM, KELVIN, NAV};
fn fmt(c: &mut Criterion) {
let mut group = c.benchmark_group("DFT_pore_fmt");
let func = &FMTFunctional::new(dvector![1.0], FMTVersion::WhiteBear);
let pore = Pore1D::new(
Geometry::Cartesian,
10.0 * ANGSTROM,
ExternalPotential::HardWall { sigma_ss: 1.0 },
None,
None,
);
let bulk = State::new_pure(&func, KELVIN, 0.75 / NAV / ANGSTROM.powi::<3>()).unwrap();
group.bench_function("liquid", |b| {
b.iter(|| pore.initialize(&bulk, None, None).unwrap().solve(None))
});
}
fn pcsaft(c: &mut Criterion) {
let mut group = c.benchmark_group("DFT_pore_pcsaft");
let parameters = PcSaftParameters::from_json(
vec!["butane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let func = &PcSaftFunctional::new(parameters);
let pore = Pore1D::new(
Geometry::Cartesian,
20.0 * ANGSTROM,
ExternalPotential::LJ93 {
sigma_ss: 3.0,
epsilon_k_ss: 100.0,
rho_s: 0.08,
},
None,
None,
);
let vle = PhaseEquilibrium::pure(&func, 300.0 * KELVIN, None, Default::default()).unwrap();
let bulk = vle.liquid();
group.bench_function("butane_liquid", |b| {
b.iter(|| pore.initialize(bulk, None, None).unwrap().solve(None))
});
let bulk = State::new_pure(&func, 300.0 * KELVIN, vle.vapor().density * 0.2).unwrap();
group.bench_function("butane_vapor", |b| {
b.iter(|| pore.initialize(&bulk, None, None).unwrap().solve(None))
});
let parameters = PcSaftParameters::from_json(
vec!["butane", "pentane"],
"../../parameters/pcsaft/gross2001.json",
None,
IdentifierOption::Name,
)
.unwrap();
let func = &PcSaftFunctional::new(parameters);
let vle = PhaseEquilibrium::bubble_point(
&func,
300.0 * KELVIN,
&dvector![0.5, 0.5],
None,
None,
Default::default(),
)
.unwrap();
let bulk = vle.liquid();
group.bench_function("butane_pentane_liquid", |b| {
b.iter(|| pore.initialize(bulk, None, None).unwrap().solve(None))
});
let bulk = StateBuilder::new(&func)
.temperature(300.0 * KELVIN)
.partial_density(&(&vle.vapor().partial_density * 0.2))
.build()
.unwrap();
group.bench_function("butane_pentane_vapor", |b| {
b.iter(|| pore.initialize(&bulk, None, None).unwrap().solve(None))
});
}
fn gc_pcsaft(c: &mut Criterion) {
let mut group = c.benchmark_group("DFT_pore_gc_pcsaft");
group.sample_size(20);
let parameters = GcPcSaftParameters::from_json_segments_hetero(
&["butane"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
let func = GcPcSaftFunctional::new(parameters);
let pore = Pore1D::new(
Geometry::Cartesian,
20.0 * ANGSTROM,
ExternalPotential::LJ93 {
sigma_ss: 3.0,
epsilon_k_ss: 100.0,
rho_s: 0.08,
},
None,
None,
);
let vle = PhaseEquilibrium::pure(&&func, 300.0 * KELVIN, None, Default::default()).unwrap();
let bulk = vle.liquid();
let solver = DFTSolver::new(None)
.picard_iteration(None, None, Some(1e-5), None)
.anderson_mixing(None, None, None, None, None);
group.bench_function("butane_liquid", |b| {
b.iter(|| {
pore.initialize(bulk, None, None)
.unwrap()
.solve(Some(&solver))
})
});
}
criterion_group!(bench, fmt, pcsaft, gc_pcsaft);
criterion_main!(bench);
| Rust |
3D | feos-org/feos | crates/feos/benches/dual_numbers_saftvrmie.rs | .rs | 6,598 | 175 | //! Benchmarks for the evaluation of the Helmholtz energy function
//! for a given `StateHD` for different types of dual numbers.
//! These should give an idea about the expected slow-down depending
//! on the dual number type used without the overhead of the `State`
//! creation.
use criterion::{Criterion, criterion_group, criterion_main};
use feos::core::{Residual, State, StateHD};
use feos::hard_sphere::HardSphereProperties;
use feos::saftvrmie::{SaftVRMie, test_utils::test_parameters};
use feos_core::ReferenceSystem;
use nalgebra::{DVector, Dyn};
use num_dual::{Dual2_64, Dual3_64, Dual64, DualNum, HyperDual64};
use quantity::*;
/// Helper function to create a state for given parameters.
/// - temperature is 80% of critical temperature,
/// - volume is critical volume,
/// - molefracs (or moles) for equimolar mixture.
fn state_saftvrmie(n: usize, eos: &SaftVRMie) -> State<&SaftVRMie> {
let molefracs = DVector::from_element(n, 1.0 / n as f64);
let cp = State::critical_point(&eos, Some(&molefracs), None, None, Default::default()).unwrap();
let temperature = 0.8 * cp.temperature;
State::new_nvt(&eos, temperature, cp.volume, &(molefracs * 10. * MOL)).unwrap()
}
/// Residual Helmholtz energy given an equation of state and a StateHD.
fn a_res<D: DualNum<f64> + Copy, E: Residual<Dyn, D>>((eos, state): (&E, &StateHD<D>)) -> D {
eos.reduced_residual_helmholtz_energy_density(state)
}
fn d_hs<D: DualNum<f64> + Copy>(inp: (&SaftVRMie, D)) -> D {
inp.0.params.hs_diameter(inp.1)[0]
}
/// Benchmark for evaluation of the Helmholtz energy for different dual number types.
fn bench_dual_numbers(c: &mut Criterion, group_name: &str, state: State<&SaftVRMie>) {
let mut group = c.benchmark_group(group_name);
group.bench_function("d_f64", |b| {
b.iter(|| d_hs((state.eos, derive0(&state).temperature)))
});
group.bench_function("d_dual", |b| {
b.iter(|| d_hs((state.eos, derive1(&state, Derivative::DT).temperature)))
});
group.bench_function("d_dual2", |b| {
b.iter(|| d_hs((state.eos, derive2(&state, Derivative::DT).temperature)))
});
group.bench_function("d_hyperdual", |b| {
b.iter(|| {
d_hs((
state.eos,
derive2_mixed(&state, Derivative::DT, Derivative::DT).temperature,
))
})
});
group.bench_function("d_dual3", |b| {
b.iter(|| d_hs((state.eos, derive3(&state, Derivative::DT).temperature)))
});
group.bench_function("a_f64", |b| {
b.iter(|| a_res((&state.eos, &derive0(&state))))
});
group.bench_function("a_dual", |b| {
b.iter(|| a_res((&state.eos, &derive1(&state, Derivative::DV))))
});
group.bench_function("a_dual2", |b| {
b.iter(|| a_res((&state.eos, &derive2(&state, Derivative::DV))))
});
group.bench_function("a_hyperdual", |b| {
b.iter(|| {
a_res((
&state.eos,
&derive2_mixed(&state, Derivative::DV, Derivative::DV),
))
})
});
group.bench_function("a_dual3", |b| {
b.iter(|| a_res((&state.eos, &derive3(&state, Derivative::DV))))
});
}
/// Benchmark for the SAFT VR Mie equation of state
fn saftvrmie(c: &mut Criterion) {
let parameters = test_parameters().remove("ethane").unwrap();
let eos = &SaftVRMie::new(parameters);
bench_dual_numbers(c, "dual_numbers_saftvrmie_ethane", state_saftvrmie(1, eos));
}
criterion_group!(bench, saftvrmie);
criterion_main!(bench);
enum Derivative {
/// Derivative with respect to system volume.
DV,
/// Derivative with respect to temperature.
DT,
/// Derivative with respect to component `i`.
#[expect(dead_code)]
DN(usize),
}
/// Creates a [StateHD] cloning temperature, volume and moles.
fn derive0<E>(state: &State<E>) -> StateHD<f64> {
let total_moles = state.total_moles.into_reduced();
StateHD::new(
state.temperature.into_reduced(),
state.volume.into_reduced() / total_moles,
&(state.moles.to_reduced() / total_moles),
)
}
/// Creates a [StateHD] taking the first derivative.
fn derive1<E>(state: &State<E>, derivative: Derivative) -> StateHD<Dual64> {
let state = derive0(state);
let mut t = Dual64::from(state.temperature);
let mut v = Dual64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(Dual64::from);
match derivative {
Derivative::DT => t = t.derivative(),
Derivative::DV => v = v.derivative(),
Derivative::DN(i) => n[i] = n[i].derivative(),
}
StateHD::new(t, v, &n)
}
/// Creates a [StateHD] taking the first and second (partial) derivatives.
fn derive2<E>(state: &State<E>, derivative: Derivative) -> StateHD<Dual2_64> {
let state = derive0(state);
let mut t = Dual2_64::from(state.temperature);
let mut v = Dual2_64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(Dual2_64::from);
match derivative {
Derivative::DT => t = t.derivative(),
Derivative::DV => v = v.derivative(),
Derivative::DN(i) => n[i] = n[i].derivative(),
}
StateHD::new(t, v, &n)
}
/// Creates a [StateHD] taking the first and second (partial) derivatives.
fn derive2_mixed<E>(
state: &State<E>,
derivative1: Derivative,
derivative2: Derivative,
) -> StateHD<HyperDual64> {
let state = derive0(state);
let mut t = HyperDual64::from(state.temperature);
let mut v = HyperDual64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(HyperDual64::from);
match derivative1 {
Derivative::DT => t = t.derivative1(),
Derivative::DV => v = v.derivative1(),
Derivative::DN(i) => n[i] = n[i].derivative1(),
}
match derivative2 {
Derivative::DT => t = t.derivative2(),
Derivative::DV => v = v.derivative2(),
Derivative::DN(i) => n[i] = n[i].derivative2(),
}
StateHD::new(t, v, &n)
}
/// Creates a [StateHD] taking the first, second, and third derivative with respect to a single property.
fn derive3<E>(state: &State<E>, derivative: Derivative) -> StateHD<Dual3_64> {
let state = derive0(state);
let mut t = Dual3_64::from(state.temperature);
let mut v = Dual3_64::from(state.partial_density.sum().recip());
let mut n = state.molefracs.map(Dual3_64::from);
match derivative {
Derivative::DT => t = t.derivative(),
Derivative::DV => v = v.derivative(),
Derivative::DN(i) => n[i] = n[i].derivative(),
};
StateHD::new(t, v, &n)
}
| Rust |
3D | feos-org/feos | crates/feos/benches/contributions.rs | .rs | 3,299 | 91 | //! Benchmarks for the evaluation of the first derivative of the
//! Helmholtz energy function for various binary mixtures.
//! The mixtures contain fluids of different polarities that are
//! modeled using additional Helmholtz energy contributions.
//! It is supposed to demonstrate the expected reduction in
//! performance when more complex physical interactions are
//! modeled.
use ::num_dual::Dual64;
use criterion::{Criterion, criterion_group, criterion_main};
use feos::core::parameter::IdentifierOption;
use feos::core::{DensityInitialization, Residual, State};
use feos::pcsaft::{PcSaft, PcSaftAssociationRecord, PcSaftParameters, PcSaftRecord};
use feos_core::ReferenceSystem;
use feos_core::parameter::PureRecord;
use nalgebra::dvector;
use num_dual::Dual;
use quantity::*;
type Pure = PureRecord<PcSaftRecord, PcSaftAssociationRecord>;
/// Benchmark for the PC-SAFT equation of state
fn pcsaft(c: &mut Criterion) {
let mut group = c.benchmark_group("pcsaft");
// non-polar components
let mut records = Pure::from_json(
&["hexane", "heptane"],
"../../parameters/pcsaft/gross2001.json",
IdentifierOption::Name,
)
.unwrap();
let hexane = records.remove(0);
let heptane = records.remove(0);
// dipolar components
records = Pure::from_json(
&["acetone", "dimethyl ether"],
"../../parameters/pcsaft/gross2006.json",
IdentifierOption::Name,
)
.unwrap();
let acetone = records.remove(0);
let dme = records.remove(0);
// quadrupolar components
records = Pure::from_json(
&["carbon dioxide", "acetylene"],
"../../parameters/pcsaft/gross2005_literature.json",
IdentifierOption::Name,
)
.unwrap();
let co2 = records.remove(0);
let acetylene = records.remove(0);
// associating components
records = Pure::from_json(
&["ethanol", "1-propanol"],
"../../parameters/pcsaft/gross2002.json",
IdentifierOption::Name,
)
.unwrap();
let ethanol = records.remove(0);
let propanol = records.remove(0);
let t = 300.0 * KELVIN;
let p = BAR;
let moles = dvector![1.0, 1.0] * MOL;
for comp1 in &[hexane, acetone, co2, ethanol] {
for comp2 in [&heptane, &dme, &acetylene, &propanol] {
let params =
PcSaftParameters::new_binary([comp1.clone(), comp2.clone()], None, vec![]).unwrap();
let eos = PcSaft::new(params);
let state =
State::new_npt(&&eos, t, p, &moles, Some(DensityInitialization::Liquid)).unwrap();
let temperature = Dual64::from(state.temperature.into_reduced()).derivative();
let molar_volume = Dual::from(1.0 / state.density.into_reduced());
let moles = state.moles.to_reduced().map(Dual::from);
// let state_hd = state.derive1(Derivative::DT);
let name1 = comp1.identifier.name.as_deref().unwrap();
let name2 = comp2.identifier.name.as_deref().unwrap();
let mix = format!("{name1}_{name2}");
group.bench_function(mix, |b| {
b.iter(|| (&eos).residual_helmholtz_energy(temperature, molar_volume, &moles))
});
}
}
}
criterion_group!(bench, pcsaft);
criterion_main!(bench);
| Rust |
3D | feos-org/feos | crates/feos/tests/main.rs | .rs | 129 | 7 | #[cfg(feature = "gc_pcsaft")]
mod gc_pcsaft;
#[cfg(feature = "pcsaft")]
mod pcsaft;
#[cfg(feature = "saftvrmie")]
mod saftvrmie;
| Rust |
3D | feos-org/feos | crates/feos/tests/gc_pcsaft/mod.rs | .rs | 21 | 3 | mod binary;
mod dft;
| Rust |
3D | feos-org/feos | crates/feos/tests/gc_pcsaft/binary.rs | .rs | 2,746 | 80 | use approx::assert_relative_eq;
#[cfg(feature = "dft")]
use feos::gc_pcsaft::GcPcSaftFunctional;
use feos::gc_pcsaft::{GcPcSaft, GcPcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{Contributions, FeosResult, State};
use nalgebra::dvector;
use quantity::{KELVIN, METER, MOL};
#[test]
fn test_binary() -> FeosResult<()> {
let parameters = GcPcSaftParameters::from_json_segments_hetero(
&["ethanol", "methanol"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
#[cfg(feature = "dft")]
let parameters_func = GcPcSaftParameters::from_json_segments_hetero(
&["ethanol", "methanol"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = &GcPcSaft::new(parameters);
#[cfg(feature = "dft")]
let func = &GcPcSaftFunctional::new(parameters_func);
let molefracs = dvector![0.5, 0.5];
let cp = State::critical_point(&eos, Some(&molefracs), None, None, Default::default())?;
#[cfg(feature = "dft")]
let cp_func = State::critical_point(&func, Some(&molefracs), None, None, Default::default())?;
println!("{}", cp.temperature);
#[cfg(feature = "dft")]
println!("{}", cp_func.temperature);
assert_relative_eq!(
cp.temperature,
536.4129479522177 * KELVIN,
max_relative = 1e-14
);
#[cfg(feature = "dft")]
assert_relative_eq!(
cp_func.temperature,
536.4129479522177 * KELVIN,
max_relative = 1e-14
);
Ok(())
}
#[test]
fn test_polar_term() -> FeosResult<()> {
let parameters1 = GcPcSaftParameters::from_json_segments_hetero(
&["CCCOC(C)=O", "CCCO"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Smiles,
)?;
let parameters2 = GcPcSaftParameters::from_json_segments_hetero(
&["CCCO", "CCCOC(C)=O"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Smiles,
)?;
let eos1 = &GcPcSaft::new(parameters1);
let eos2 = &GcPcSaft::new(parameters2);
let moles = dvector![0.5, 0.5] * MOL;
let p1 = State::new_nvt(&eos1, 300.0 * KELVIN, METER.powi::<3>(), &moles)?
.pressure(Contributions::Total);
let p2 = State::new_nvt(&eos2, 300.0 * KELVIN, METER.powi::<3>(), &moles)?
.pressure(Contributions::Total);
println!("{p1} {p2}");
assert_eq!(p1, p2);
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/gc_pcsaft/dft.rs | .rs | 8,502 | 263 | #![allow(clippy::excessive_precision)]
#![cfg(feature = "dft")]
use approx::assert_relative_eq;
use feos::gc_pcsaft::{GcPcSaft, GcPcSaftFunctional, GcPcSaftParameters};
use feos_core::parameter::{ChemicalRecord, Identifier, IdentifierOption, SegmentRecord};
use feos_core::{PhaseEquilibrium, State, StateBuilder, Verbosity};
use feos_dft::adsorption::{ExternalPotential, Pore1D, PoreSpecification};
use feos_dft::interface::PlanarInterface;
use feos_dft::{DFTSolver, Geometry};
use nalgebra::dvector;
use quantity::*;
use std::error::Error;
#[test]
#[allow(non_snake_case)]
fn test_bulk_implementation() -> Result<(), Box<dyn Error>> {
// correct for different k_B in old code
let KB_old = 1.38064852e-23 * JOULE / KELVIN;
let NAV_old = 6.022140857e23 / MOL;
let parameters = GcPcSaftParameters::from_json_segments_hetero(
&["propane"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
let parameters_func = GcPcSaftParameters::from_json_segments_hetero(
&["propane"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
let eos = GcPcSaft::new(parameters);
let func = GcPcSaftFunctional::new(parameters_func);
let t = 200.0 * KELVIN;
let v = 0.002 * METER.powi::<3>() * NAV / NAV_old;
let n = dvector![1.5] * MOL;
let state_eos = State::new_nvt(&&eos, t, v, &n)?;
let state_func = State::new_nvt(&&func, t, v, &n)?;
let p_eos = state_eos.pressure_contributions();
let p_func = state_func.pressure_contributions();
println!("{:29}: {}", p_eos[0].0, p_eos[0].1);
println!("{:29}: {}", p_func[0].0, p_func[0].1);
println!();
println!("{:29}: {}", p_eos[1].0, p_eos[1].1);
println!("{:29}: {}", p_func[1].0, p_func[1].1);
println!();
println!("{:29}: {}", p_eos[2].0, p_eos[2].1);
println!("{:29}: {}", p_func[2].0, p_func[2].1);
println!();
println!("{:29}: {}", p_eos[3].0, p_eos[3].1);
println!("{:29}: {}", p_func[3].0, p_func[3].1);
assert_relative_eq!(
p_eos[0].1,
1.2471689792172869 * MEGA * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
assert_relative_eq!(
p_eos[1].1,
280.0635060891395938 * KILO * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
assert_relative_eq!(
p_eos[2].1,
-141.9023918353318550 * KILO * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
assert_relative_eq!(
p_eos[3].1,
-763.2289230004602132 * KILO * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
assert_relative_eq!(
p_func[0].1,
1.2471689792172869 * MEGA * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
assert_relative_eq!(
p_func[1].1,
280.0635060891395938 * KILO * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
assert_relative_eq!(
p_func[2].1,
-141.9023918353318550 * KILO * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
assert_relative_eq!(
p_func[3].1,
-763.2289230004602132 * KILO * PASCAL * KB / KB_old,
max_relative = 1e-14,
);
Ok(())
}
#[test]
fn test_bulk_association() -> Result<(), Box<dyn Error>> {
let segment_records =
SegmentRecord::from_json("../../parameters/pcsaft/sauer2014_hetero.json")?;
let ethylene_glycol = ChemicalRecord::new(
Identifier::default(),
vec!["OH".into(), "CH2".into(), "CH2".into(), "OH".into()],
None,
);
let eos_parameters = GcPcSaftParameters::from_segments_hetero(
vec![ethylene_glycol.clone()],
&segment_records,
None,
)?;
let eos = GcPcSaft::new(eos_parameters);
let func_parameters =
GcPcSaftParameters::from_segments_hetero(vec![ethylene_glycol], &segment_records, None)?;
let func = GcPcSaftFunctional::new(func_parameters);
let t = 200.0 * KELVIN;
let v = 0.002 * METER.powi::<3>();
let n = dvector![1.5] * MOL;
let state_eos = State::new_nvt(&&eos, t, v, &n)?;
let state_func = State::new_nvt(&&func, t, v, &n)?;
let p_eos = state_eos.pressure_contributions();
let p_func = state_func.pressure_contributions();
for (s, x) in &p_eos {
println!("{s:18}: {x:21.16}");
}
for (s, x) in &p_func {
println!("{s:26}: {x:21.16}");
}
assert_relative_eq!(p_eos[4].1, p_func[4].1, max_relative = 1e-14);
Ok(())
}
#[test]
#[allow(non_snake_case)]
fn test_dft() -> Result<(), Box<dyn Error>> {
// correct for different k_B in old code
let KB_old = 1.38064852e-23 * JOULE / KELVIN;
let NAV_old = 6.022140857e23 / MOL;
let parameters = GcPcSaftParameters::from_json_segments_hetero(
&["propane"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
let func = GcPcSaftFunctional::new(parameters);
let t = 200.0 * KELVIN;
let w = 150.0 * ANGSTROM;
let points = 2048;
let tc = State::critical_point(&&func, None, None, None, Default::default())?.temperature;
let vle = PhaseEquilibrium::pure(&&func, t, None, Default::default())?;
let profile = PlanarInterface::from_tanh(&vle, points, w, tc, false).solve(None)?;
println!(
"hetero {} {} {}",
profile.surface_tension.unwrap(),
vle.vapor().density,
vle.liquid().density,
);
assert_relative_eq!(
vle.vapor().density,
12.8820179191167643 * MOL / METER.powi::<3>() * NAV_old / NAV,
max_relative = 1e-13,
);
assert_relative_eq!(
vle.liquid().density,
13.2705903446123212 * KILO * MOL / METER.powi::<3>() * NAV_old / NAV,
max_relative = 1e-13,
);
assert_relative_eq!(
profile.surface_tension.unwrap(),
21.1478901897016272 * MILLI * NEWTON / METER * KB / KB_old,
max_relative = 6e-5,
);
Ok(())
}
#[test]
#[allow(non_snake_case)]
fn test_dft_assoc() -> Result<(), Box<dyn Error>> {
let parameters = GcPcSaftParameters::from_json_segments_hetero(
&["1-pentanol"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
let func = &GcPcSaftFunctional::new(parameters);
let t = 300.0 * KELVIN;
let w = 100.0 * ANGSTROM;
let points = 4096;
let vle = PhaseEquilibrium::pure(&func, t, None, Default::default())?;
let profile = PlanarInterface::from_tanh(&vle, points, w, 600.0 * KELVIN, false).solve(None)?;
println!(
"hetero {} {} {}",
profile.surface_tension.unwrap(),
vle.vapor().density,
vle.liquid().density,
);
let solver = DFTSolver::new(Some(Verbosity::Iter))
.picard_iteration(None, None, Some(1e-5), Some(0.05))
.anderson_mixing(None, None, None, None, None);
let bulk = StateBuilder::new(&func)
.temperature(t)
.pressure(5.0 * BAR)
.build()?;
Pore1D::new(
Geometry::Cartesian,
20.0 * ANGSTROM,
ExternalPotential::LJ93 {
epsilon_k_ss: 10.0,
sigma_ss: 3.0,
rho_s: 0.08,
},
None,
None,
)
.initialize(&bulk, None, None)
.unwrap()
.solve(Some(&solver))?;
Ok(())
}
#[test]
#[allow(non_snake_case)]
fn test_dft_newton() -> Result<(), Box<dyn Error>> {
let params = GcPcSaftParameters::from_json_segments_hetero(
&["propane"],
"../../parameters/pcsaft/gc_substances.json",
"../../parameters/pcsaft/sauer2014_hetero.json",
None,
IdentifierOption::Name,
)
.unwrap();
let func = GcPcSaftFunctional::new(params);
let t = 200.0 * KELVIN;
let w = 150.0 * ANGSTROM;
let points = 512;
let tc = State::critical_point(&&func, None, None, None, Default::default())?.temperature;
let vle = PhaseEquilibrium::pure(&&func, t, None, Default::default())?;
let solver = DFTSolver::new(Some(Verbosity::Iter))
.picard_iteration(None, Some(10), None, None)
.newton(None, None, None, None);
PlanarInterface::from_tanh(&vle, points, w, tc, false).solve(Some(&solver))?;
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/mod.rs | .rs | 150 | 9 | mod critical_point;
mod dft;
mod properties;
mod stability_analysis;
mod state_creation_mixture;
mod state_creation_pure;
mod tp_flash;
mod vle_pure;
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/tp_flash.rs | .rs | 2,063 | 66 | use approx::assert_relative_eq;
use feos::pcsaft::{PcSaft, PcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{Contributions, FeosResult, PhaseEquilibrium, SolverOptions};
use nalgebra::dvector;
use quantity::*;
use std::error::Error;
fn read_params(components: Vec<&str>) -> FeosResult<PcSaftParameters> {
PcSaftParameters::from_json(
components,
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)
}
#[test]
fn test_tp_flash() -> Result<(), Box<dyn Error>> {
let propane = PcSaft::new(read_params(vec!["propane"])?);
let butane = PcSaft::new(read_params(vec!["butane"])?);
let t = 250.0 * KELVIN;
let p_propane = PhaseEquilibrium::pure(&&propane, t, None, Default::default())?
.vapor()
.pressure(Contributions::Total);
let p_butane = PhaseEquilibrium::pure(&&butane, t, None, Default::default())?
.vapor()
.pressure(Contributions::Total);
let x1 = 0.5;
let p = x1 * p_propane + (1.0 - x1) * p_butane;
let y1 = (x1 * p_propane / p).into_value();
let z1 = 0.5 * (x1 + y1);
println!("{p_propane} {p_butane} {x1} {y1} {z1}");
let mix = PcSaft::new(read_params(vec!["propane", "butane"])?);
let options = SolverOptions::new().max_iter(100).tol(1e-12);
let vle = PhaseEquilibrium::tp_flash(
&&mix,
t,
p,
&(dvector![z1, 1.0 - z1] * MOL),
None,
options,
None,
)?;
println!(
"x1: {}, y1: {}",
vle.liquid().molefracs[0],
vle.vapor().molefracs[0]
);
assert_relative_eq!(
vle.vapor().pressure(Contributions::Total),
vle.liquid().pressure(Contributions::Total),
max_relative = 1e-10
);
assert_relative_eq!(
&vle.vapor()
.molefracs
.component_mul(&vle.vapor().ln_phi().map(f64::exp)),
&vle.liquid()
.molefracs
.component_mul(&vle.liquid().ln_phi().map(f64::exp)),
max_relative = 1e-10
);
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/state_creation_mixture.rs | .rs | 4,391 | 138 | use approx::assert_relative_eq;
use feos::ideal_gas::{Joback, JobackParameters};
use feos::pcsaft::{PcSaft, PcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{Contributions, EquationOfState, FeosResult, StateBuilder};
use nalgebra::dvector;
use quantity::*;
use std::error::Error;
fn propane_butane_parameters() -> FeosResult<(PcSaftParameters, Vec<Joback>)> {
let saft = PcSaftParameters::from_json(
vec!["propane", "butane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let joback = Joback::new(JobackParameters::from_json(
vec!["propane", "butane"],
"tests/pcsaft/test_parameters_joback.json",
None,
IdentifierOption::Name,
)?);
Ok((saft, joback))
}
#[test]
fn pressure_entropy_molefracs() -> Result<(), Box<dyn Error>> {
let (saft_params, joback) = propane_butane_parameters()?;
let saft = PcSaft::new(saft_params);
let eos = EquationOfState::new(joback, saft);
let pressure = BAR;
let temperature = 300.0 * KELVIN;
let x = dvector![0.3, 0.7];
let state = StateBuilder::new(&&eos)
.temperature(temperature)
.pressure(pressure)
.molefracs(&x)
.build()?;
let molar_entropy = state.molar_entropy(Contributions::Total);
let state = StateBuilder::new(&&eos)
.pressure(pressure)
.molar_entropy(molar_entropy)
.molefracs(&x)
.build()?;
assert_relative_eq!(
state.molar_entropy(Contributions::Total),
molar_entropy,
max_relative = 1e-8
);
assert_relative_eq!(state.temperature, temperature, max_relative = 1e-10);
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-8
);
Ok(())
}
#[test]
fn volume_temperature_molefracs() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_butane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let volume = 1.5e-3 * METER.powi::<3>();
let moles = MOL;
let x = dvector![0.3, 0.7];
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.volume(volume)
.total_moles(moles)
.molefracs(&x)
.build()?;
assert_relative_eq!(state.volume, volume, max_relative = 1e-10);
Ok(())
}
#[test]
fn temperature_partial_density() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_butane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let x = dvector![0.3, 0.7];
let partial_density = x.clone() * MOL / METER.powi::<3>();
let density = partial_density.sum();
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.partial_density(&partial_density)
.build()?;
assert_relative_eq!(x, state.molefracs, max_relative = 1e-10);
assert_relative_eq!(density, state.density, max_relative = 1e-10);
// Zip::from(&state.partial_density.to_reduced(reference))
// .and(&partial_density.into_value()?)
// .for_each(|&r1, &r2| assert_relative_eq!(r1, r2, max_relative = 1e-10));
Ok(())
}
#[test]
fn temperature_density_molefracs() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_butane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let x = dvector![0.3, 0.7];
let density = MOL / METER.powi::<3>();
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.density(density)
.molefracs(&x)
.build()?;
state
.molefracs
.iter()
.zip(&x)
.for_each(|(&l, &r)| assert_relative_eq!(l, r, max_relative = 1e-10));
assert_relative_eq!(state.density, density);
Ok(())
}
#[test]
fn temperature_pressure_molefracs() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_butane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let pressure = BAR;
let x = dvector![0.3, 0.7];
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.pressure(pressure)
.molefracs(&x)
.build()?;
state
.molefracs
.iter()
.zip(&x)
.for_each(|(&l, &r)| assert_relative_eq!(l, r, max_relative = 1e-10));
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/properties.rs | .rs | 1,541 | 55 | use approx::assert_relative_eq;
use feos::pcsaft::{PcSaft, PcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{Residual, StateBuilder};
use nalgebra::dvector;
use quantity::*;
use std::error::Error;
#[test]
fn test_dln_phi_dp() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["propane", "butane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let saft = PcSaft::new(params);
let t = 300.0 * KELVIN;
let p = BAR;
let h = 1e-1 * PASCAL;
let s = StateBuilder::new(&&saft)
.temperature(t)
.pressure(p)
.molefracs(&dvector![0.5, 0.5])
.vapor()
.build()?;
let sh = StateBuilder::new(&&saft)
.temperature(t)
.pressure(p + h)
.molefracs(&dvector![0.5, 0.5])
.vapor()
.build()?;
let ln_phi = s.ln_phi()[0];
let ln_phi_h = sh.ln_phi()[0];
let dln_phi_dp = s.dln_phi_dp().get(0);
let dln_phi_dp_h = (ln_phi_h - ln_phi) / h;
assert_relative_eq!(dln_phi_dp, dln_phi_dp_h, max_relative = 1e-6);
Ok(())
}
#[test]
fn test_virial_is_not_nan() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["water_np"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let saft = &PcSaft::new(params);
let virial_b = saft.second_virial_coefficient(300.0 * KELVIN, &None);
assert!(!virial_b.is_nan());
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/dft.rs | .rs | 13,894 | 368 | #![allow(clippy::excessive_precision)]
#![cfg(feature = "dft")]
use approx::assert_relative_eq;
use feos::hard_sphere::FMTVersion;
use feos::ideal_gas::{Joback, JobackParameters};
use feos::pcsaft::{PcSaft, PcSaftFunctional, PcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{Contributions, EquationOfState, FeosResult, PhaseEquilibrium, State, Verbosity};
use feos_dft::interface::PlanarInterface;
use feos_dft::{DFTSolver, PdgtFunctionalProperties};
use nalgebra::dvector;
use ndarray::Axis;
use quantity::*;
use std::error::Error;
fn parameters(comp: &str) -> FeosResult<PcSaftParameters> {
PcSaftParameters::from_json(
vec![comp],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)
}
#[test]
#[allow(non_snake_case)]
fn test_bulk_implementations() -> Result<(), Box<dyn Error>> {
// correct for different k_B in old code
let KB_old = 1.38064852e-23 * JOULE / KELVIN;
let NAV_old = 6.022140857e23 / MOL;
let eos = PcSaft::new(parameters("water_np")?);
let func_pure = PcSaftFunctional::new(parameters("water_np")?);
let func_full =
PcSaftFunctional::new_full(parameters("water_np")?, FMTVersion::KierlikRosinberg);
let t = 300.0 * KELVIN;
let v = 0.002 * METER.powi::<3>() * NAV / NAV_old;
let n = dvector![1.5] * MOL;
let state = State::new_nvt(&&eos, t, v, &n)?;
let state_pure = State::new_nvt(&&func_pure, t, v, &n)?;
let state_full = State::new_nvt(&&func_full, t, v, &n)?;
let p = state.pressure_contributions();
let p_pure = state_pure.pressure_contributions();
let p_full = state_full.pressure_contributions();
println!("{}: {}", p[0].0, p[0].1);
println!("{}: {}", p_pure[0].0, p_pure[0].1);
println!("{}: {}", p_full[0].0, p_full[0].1);
println!();
println!("{:20}: {}", p[1].0, p[1].1);
println!("{:20}: {}", p_pure[1].0, p_pure[1].1);
println!("{:20}: {}", p_full[1].0, p_full[1].1);
println!();
println!("{:21}: {}", p[2].0, p[2].1);
println!("{:21}: {}", p_pure[2].0, p_pure[2].1);
println!("{:21}: {}", p_full[2].0, p_full[2].1);
println!();
println!("{:21}: {}", p[3].0, p[3].1);
println!("{:21}: {}", p_pure[3].0, p_pure[3].1 + p_pure[4].1);
println!("{:21}: {}", p_full[3].0, p_full[3].1 + p_full[5].1);
println!();
println!("{:21}: {}", p[4].0, p[4].1);
println!("{:21}: {}", p_full[4].0, p_full[4].1);
let ideal_gas = 1.8707534688259309 * MEGA * PASCAL * KB / KB_old;
assert_relative_eq!(p[0].1, ideal_gas, max_relative = 1e-14,);
assert_relative_eq!(p_pure[0].1, ideal_gas, max_relative = 1e-14,);
assert_relative_eq!(p_full[0].1, ideal_gas, max_relative = 1e-14,);
let hard_sphere = 54.7102253882827583 * KILO * PASCAL * KB / KB_old;
assert_relative_eq!(p[1].1, hard_sphere, max_relative = 1e-14,);
assert_relative_eq!(p_full[1].1, hard_sphere, max_relative = 1e-14,);
let hard_chains = -2.0847750028499230 * KILO * PASCAL * KB / KB_old;
assert_relative_eq!(p[2].1, hard_chains, max_relative = 1e-14,);
assert_relative_eq!(p_pure[2].1 + p_pure[4].1, hard_chains, max_relative = 2e-13,);
assert_relative_eq!(p_full[2].1 + p_full[5].1, hard_chains, max_relative = 2e-13,);
let dispersion = -262.895932352779993 * KILO * PASCAL * KB / KB_old;
assert_relative_eq!(p[3].1, dispersion, max_relative = 1e-14,);
assert_relative_eq!(p_pure[3].1, dispersion, max_relative = 1e-14,);
assert_relative_eq!(p_full[3].1, dispersion, max_relative = 1e-14,);
let association = -918.3899928262694630 * KILO * PASCAL * KB / KB_old;
assert_relative_eq!(p[4].1, association, max_relative = 1e-14,);
assert_relative_eq!(p_full[4].1, association, max_relative = 1e-14,);
assert_relative_eq!(p_pure[1].1, hard_sphere + association, max_relative = 1e-14,);
Ok(())
}
#[test]
#[allow(non_snake_case)]
fn test_dft_propane() -> Result<(), Box<dyn Error>> {
// correct for different k_B in old code
let KB_old = 1.38064852e-23 * JOULE / KELVIN;
let NAV_old = 6.022140857e23 / MOL;
let func_pure = PcSaftFunctional::new(parameters("propane")?);
let func_full =
PcSaftFunctional::new_full(parameters("propane")?, FMTVersion::KierlikRosinberg);
let func_full_vec = PcSaftFunctional::new_full(parameters("propane")?, FMTVersion::WhiteBear);
let t = 200.0 * KELVIN;
let w = 150.0 * ANGSTROM;
let points = 2048;
let tc = State::critical_point(&&func_pure, None, None, None, Default::default())?.temperature;
let vle_pure = PhaseEquilibrium::pure(&&func_pure, t, None, Default::default())?;
let vle_full = PhaseEquilibrium::pure(&&func_full, t, None, Default::default())?;
let vle_full_vec = PhaseEquilibrium::pure(&&func_full_vec, t, None, Default::default())?;
let profile_pure = PlanarInterface::from_tanh(&vle_pure, points, w, tc, false).solve(None)?;
let profile_full = PlanarInterface::from_tanh(&vle_full, points, w, tc, false).solve(None)?;
let profile_full_vec =
PlanarInterface::from_tanh(&vle_full_vec, points, w, tc, false).solve(None)?;
let _ = (&func_pure).solve_pdgt(&vle_pure, 198, 0, None)?;
println!(
"pure {} {} {} {}",
profile_pure.surface_tension.unwrap(),
vle_pure.vapor().density,
vle_pure.liquid().density,
(&func_pure).solve_pdgt(&vle_pure, 198, 0, None)?.1
);
println!(
"full {} {} {} {}",
profile_full.surface_tension.unwrap(),
vle_full.vapor().density,
vle_full.liquid().density,
(&func_full).solve_pdgt(&vle_full, 198, 0, None)?.1
);
println!(
"vec {} {} {} {}",
profile_full_vec.surface_tension.unwrap(),
vle_full_vec.vapor().density,
vle_full_vec.liquid().density,
(&func_full_vec).solve_pdgt(&vle_full_vec, 198, 0, None)?.1
);
let vapor_density = 12.2557486248527745 * MOL / METER.powi::<3>() * NAV_old / NAV;
assert_relative_eq!(
vle_pure.vapor().density,
vapor_density,
max_relative = 1e-13,
);
assert_relative_eq!(
vle_full.vapor().density,
vapor_density,
max_relative = 1e-13,
);
assert_relative_eq!(
vle_full_vec.vapor().density,
vapor_density,
max_relative = 1e-13,
);
let liquid_density = 13.8941749145544549 * KILO * MOL / METER.powi::<3>() * NAV_old / NAV;
assert_relative_eq!(
vle_pure.liquid().density,
liquid_density,
max_relative = 1e-13,
);
assert_relative_eq!(
vle_full.liquid().density,
liquid_density,
max_relative = 1e-13,
);
assert_relative_eq!(
vle_full_vec.liquid().density,
liquid_density,
max_relative = 1e-13,
);
let surface_tension = 19.9931025166113692 * MILLI * NEWTON / METER * KB / KB_old;
let surface_tension_kr = 19.9863313312996169 * MILLI * NEWTON / METER * KB / KB_old;
assert_relative_eq!(
profile_pure.surface_tension.unwrap(),
surface_tension,
max_relative = 1e-4,
);
assert_relative_eq!(
profile_full.surface_tension.unwrap(),
surface_tension_kr,
max_relative = 1e-4,
);
assert_relative_eq!(
profile_full_vec.surface_tension.unwrap(),
surface_tension,
max_relative = 1e-4,
);
let surface_tension_pdgt = 20.2849756479219039 * MILLI * NEWTON / METER * KB / KB_old;
let surface_tension_pdgt_kr = 20.2785079953823342 * MILLI * NEWTON / METER * KB / KB_old;
assert_relative_eq!(
(&func_pure).solve_pdgt(&vle_pure, 198, 0, None)?.1,
surface_tension_pdgt,
max_relative = 1e-10,
);
assert_relative_eq!(
(&func_full).solve_pdgt(&vle_full, 198, 0, None)?.1,
surface_tension_pdgt_kr,
max_relative = 1e-10,
);
assert_relative_eq!(
(&func_full_vec).solve_pdgt(&vle_full_vec, 198, 0, None)?.1,
surface_tension_pdgt,
max_relative = 1e-10,
);
Ok(())
}
#[test]
#[allow(non_snake_case)]
fn test_dft_propane_newton() -> Result<(), Box<dyn Error>> {
let func = PcSaftFunctional::new(parameters("propane")?);
let t = 200.0 * KELVIN;
let w = 150.0 * ANGSTROM;
let points = 512;
let tc = State::critical_point(&&func, None, None, None, Default::default())?.temperature;
let vle = PhaseEquilibrium::pure(&&func, t, None, Default::default())?;
let solver = DFTSolver::new(Some(Verbosity::Iter)).newton(None, None, None, None);
PlanarInterface::from_tanh(&vle, points, w, tc, false).solve(Some(&solver))?;
let solver = DFTSolver::new(Some(Verbosity::Iter)).newton(Some(true), None, None, None);
PlanarInterface::from_tanh(&vle, points, w, tc, false).solve(Some(&solver))?;
Ok(())
}
#[test]
#[allow(non_snake_case)]
fn test_dft_water() -> Result<(), Box<dyn Error>> {
// correct for different k_B in old code
let KB_old = 1.38064852e-23 * JOULE / KELVIN;
let NAV_old = 6.022140857e23 / MOL;
let func_pure = PcSaftFunctional::new(parameters("water_np")?);
let func_full_vec = PcSaftFunctional::new_full(parameters("water_np")?, FMTVersion::WhiteBear);
let t = 400.0 * KELVIN;
let w = 120.0 * ANGSTROM;
let points = 2048;
let tc = State::critical_point(&&func_pure, None, None, None, Default::default())?.temperature;
let vle_pure = PhaseEquilibrium::pure(&&func_pure, t, None, Default::default())?;
let vle_full_vec = PhaseEquilibrium::pure(&&func_full_vec, t, None, Default::default())?;
let profile_pure = PlanarInterface::from_tanh(&vle_pure, points, w, tc, false).solve(None)?;
let profile_full_vec =
PlanarInterface::from_tanh(&vle_full_vec, points, w, tc, false).solve(None)?;
println!(
"pure {} {} {}",
profile_pure.surface_tension.unwrap(),
vle_pure.vapor().density,
vle_pure.liquid().density
);
println!(
"vec {} {} {}",
profile_full_vec.surface_tension.unwrap(),
vle_full_vec.vapor().density,
vle_full_vec.liquid().density
);
let vapor_density = 75.8045715345905222 * MOL / METER.powi::<3>() * NAV_old / NAV;
assert_relative_eq!(
vle_pure.vapor().density,
vapor_density,
max_relative = 1e-13,
);
assert_relative_eq!(
vle_full_vec.vapor().density,
vapor_density,
max_relative = 1e-13,
);
let liquid_density = 47.8480850281608454 * KILO * MOL / METER.powi::<3>() * NAV_old / NAV;
assert_relative_eq!(
vle_pure.liquid().density,
liquid_density,
max_relative = 1e-13,
);
assert_relative_eq!(
vle_full_vec.liquid().density,
liquid_density,
max_relative = 1e-13,
);
let surface_tension = 70.1419567481980408 * MILLI * NEWTON / METER * KB / KB_old;
assert_relative_eq!(
profile_pure.surface_tension.unwrap(),
surface_tension,
max_relative = 1e-4,
);
assert_relative_eq!(
profile_full_vec.surface_tension.unwrap(),
surface_tension,
max_relative = 1e-4,
);
let surface_tension_pdgt = 72.9496195135527188 * MILLI * NEWTON / METER * KB / KB_old;
assert_relative_eq!(
(&func_pure).solve_pdgt(&vle_pure, 198, 0, None)?.1,
surface_tension_pdgt,
max_relative = 1e-10,
);
assert_relative_eq!(
(&func_full_vec).solve_pdgt(&vle_full_vec, 198, 0, None)?.1,
surface_tension_pdgt,
max_relative = 1e-10,
);
Ok(())
}
#[test]
fn test_entropy_bulk_values() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["water_np"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let joback = Joback::new(JobackParameters::from_json(
vec!["water_np"],
"tests/pcsaft/test_parameters_joback.json",
None,
IdentifierOption::Name,
)?);
let func = EquationOfState::new(joback, PcSaftFunctional::new(params));
let vle = PhaseEquilibrium::pure(&&func, 350.0 * KELVIN, None, Default::default())?;
let profile = PlanarInterface::from_pdgt(&vle, 2048, false)?.solve(None)?;
let s_res = profile.profile.entropy_density(Contributions::Residual)?;
let s_tot = profile.profile.entropy_density(Contributions::Total)?;
println!(
"Density:\n{}",
profile.profile.density.index_axis(Axis(0), 0).to_owned()
);
println!(
"liquid: {}, vapor: {}",
profile.vle.liquid().density,
profile.vle.vapor().density
);
println!("\nResidual:\n{s_res:?}");
println!(
"liquid: {:?}, vapor: {:?}",
profile.vle.liquid().entropy(Contributions::Residual) / profile.vle.liquid().volume,
profile.vle.vapor().entropy(Contributions::Residual) / profile.vle.vapor().volume
);
println!("\nTotal:\n{s_tot:?}");
println!(
"liquid: {:?}, vapor: {:?}",
profile.vle.liquid().entropy(Contributions::Total) / profile.vle.liquid().volume,
profile.vle.vapor().entropy(Contributions::Total) / profile.vle.vapor().volume
);
assert_relative_eq!(
s_res.get(0),
profile.vle.liquid().entropy(Contributions::Residual) / profile.vle.liquid().volume,
max_relative = 1e-8,
);
assert_relative_eq!(
s_res.get(2047),
profile.vle.vapor().entropy(Contributions::Residual) / profile.vle.vapor().volume,
max_relative = 1e-8,
);
assert_relative_eq!(
s_tot.get(0),
profile.vle.liquid().entropy(Contributions::Total) / profile.vle.liquid().volume,
max_relative = 1e-8,
);
assert_relative_eq!(
s_tot.get(2047),
profile.vle.vapor().entropy(Contributions::Total) / profile.vle.vapor().volume,
max_relative = 1e-8,
);
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/vle_pure.rs | .rs | 2,488 | 81 | use approx::assert_relative_eq;
use feos::pcsaft::{PcSaft, PcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{Contributions, PhaseEquilibrium, SolverOptions};
use quantity::*;
use std::error::Error;
#[test]
fn vle_pure_temperature() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["propane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let saft = PcSaft::new(params);
let temperatures = [
170.0 * KELVIN,
200.0 * KELVIN,
250.0 * KELVIN,
300.0 * KELVIN,
350.0 * KELVIN,
];
let options = SolverOptions {
verbosity: feos_core::Verbosity::Iter,
..Default::default()
};
for &t in temperatures.iter() {
let state = PhaseEquilibrium::pure(&&saft, t, None, options)?;
assert_relative_eq!(state.vapor().temperature, t, max_relative = 1e-10);
assert_relative_eq!(
state.vapor().pressure(Contributions::Total),
state.liquid().pressure(Contributions::Total),
max_relative = 1e-8
);
}
Ok(())
}
#[test]
fn vle_pure_pressure() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["propane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let saft = PcSaft::new(params);
let pressures = [0.1 * BAR, 1.0 * BAR, 10.0 * BAR, 30.0 * BAR, 44.0 * BAR];
for &p in pressures.iter() {
let state = PhaseEquilibrium::pure(&&saft, p, None, Default::default())?;
println!(
"liquid-p: {} vapor-p: {} p:{}",
state.liquid().pressure(Contributions::Total),
state.vapor().pressure(Contributions::Total),
p
);
println!(
"liquid-T: {} vapor-T: {}",
state.liquid().temperature,
state.vapor().temperature
);
assert_relative_eq!(
state.liquid().pressure(Contributions::Total),
p,
max_relative = 1e-8
);
assert_relative_eq!(
state.vapor().pressure(Contributions::Total),
state.liquid().pressure(Contributions::Total),
max_relative = 1e-8
);
assert_relative_eq!(
state.vapor().temperature,
state.liquid().temperature,
max_relative = 1e-10
);
}
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/critical_point.rs | .rs | 1,492 | 49 | use approx::assert_relative_eq;
use feos::pcsaft::{PcSaft, PcSaftParameters};
use feos_core::State;
use feos_core::parameter::IdentifierOption;
use nalgebra::dvector;
use quantity::*;
use std::error::Error;
#[test]
fn test_critical_point_pure() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["propane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let saft = PcSaft::new(params);
let t = 300.0 * KELVIN;
let cp = State::critical_point(&&saft, None, Some(t), None, Default::default())?;
assert_relative_eq!(cp.temperature, 375.12441 * KELVIN, max_relative = 1e-8);
assert_relative_eq!(
cp.density,
4733.00377 * MOL / METER.powi::<3>(),
max_relative = 1e-6
);
Ok(())
}
#[test]
fn test_critical_point_mix() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["propane", "butane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let saft = PcSaft::new(params);
let t = 300.0 * KELVIN;
let molefracs = dvector![0.5, 0.5];
let cp = State::critical_point(&&saft, Some(&molefracs), Some(t), None, Default::default())?;
assert_relative_eq!(cp.temperature, 407.93481 * KELVIN, max_relative = 1e-8);
assert_relative_eq!(
cp.density,
4265.50745 * MOL / METER.powi::<3>(),
max_relative = 1e-6
);
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/stability_analysis.rs | .rs | 1,529 | 52 | use feos::pcsaft::{PcSaft, PcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{DensityInitialization, PhaseEquilibrium, SolverOptions, State};
use nalgebra::dvector;
use quantity::*;
use std::error::Error;
#[test]
fn test_stability_analysis() -> Result<(), Box<dyn Error>> {
let params = PcSaftParameters::from_json(
vec!["water_np", "hexane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let mix = PcSaft::new(params);
let unstable = State::new_npt(
&&mix,
300.0 * KELVIN,
1.0 * BAR,
&(dvector![0.5, 0.5] * MOL),
Some(DensityInitialization::Liquid),
)?;
let options = SolverOptions {
verbosity: feos_core::Verbosity::Iter,
..Default::default()
};
let check = unstable.stability_analysis(options)?;
assert!(!check.is_empty());
let params = PcSaftParameters::from_json(
vec!["propane", "butane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let mix = PcSaft::new(params);
let vle = PhaseEquilibrium::bubble_point(
&&mix,
300.0 * KELVIN,
&dvector![0.5, 0.5],
Some(6.0 * BAR),
None,
(options, options),
)?;
let vapor_check = vle.vapor().stability_analysis(options)?;
let liquid_check = vle.liquid().stability_analysis(options)?;
assert!(vapor_check.is_empty());
assert!(liquid_check.is_empty());
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/pcsaft/state_creation_pure.rs | .rs | 13,218 | 444 | use approx::assert_relative_eq;
use feos::ideal_gas::{Joback, JobackParameters};
use feos::pcsaft::{PcSaft, PcSaftParameters};
use feos_core::parameter::IdentifierOption;
use feos_core::{
Contributions, EquationOfState, FeosResult, PhaseEquilibrium, State, StateBuilder, Total,
};
use quantity::*;
use std::error::Error;
fn propane_parameters() -> FeosResult<(PcSaftParameters, Vec<Joback>)> {
let saft = PcSaftParameters::from_json(
vec!["propane"],
"tests/pcsaft/test_parameters.json",
None,
IdentifierOption::Name,
)?;
let joback = Joback::new(JobackParameters::from_json(
vec!["propane"],
"tests/pcsaft/test_parameters_joback.json",
None,
IdentifierOption::Name,
)?);
Ok((saft, joback))
}
#[test]
fn temperature_volume() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let volume = 1.5e-3 * METER.powi::<3>();
let moles = MOL;
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.volume(volume)
.total_moles(moles)
.build()?;
assert_relative_eq!(state.volume, volume, max_relative = 1e-10);
Ok(())
}
#[test]
fn temperature_density() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let density = MOL / METER.powi::<3>();
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.density(density)
.build()?;
assert_relative_eq!(state.density, density, max_relative = 1e-10);
Ok(())
}
#[test]
fn temperature_total_moles_volume() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let total_moles = MOL;
let volume = METER.powi::<3>();
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.volume(volume)
.total_moles(total_moles)
.build()?;
assert_relative_eq!(state.volume, volume, max_relative = 1e-10);
assert_relative_eq!(state.total_moles, total_moles, max_relative = 1e-10);
Ok(())
}
#[test]
fn temperature_total_moles_density() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_parameters()?.0);
let temperature = 300.0 * KELVIN;
let total_moles = MOL;
let density = MOL / METER.powi::<3>();
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.density(density)
.total_moles(total_moles)
.build()?;
assert_relative_eq!(state.density, density, max_relative = 1e-10);
assert_relative_eq!(state.total_moles, total_moles, max_relative = 1e-10);
assert_relative_eq!(state.volume, total_moles / density, max_relative = 1e-10);
Ok(())
}
// Pressure constructors
#[test]
fn pressure_temperature() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_parameters()?.0);
let pressure = BAR;
let temperature = 300.0 * KELVIN;
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.pressure(pressure)
.build()?;
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
Ok(())
}
#[test]
fn pressure_temperature_phase() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_parameters()?.0);
let pressure = BAR;
let temperature = 300.0 * KELVIN;
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.pressure(pressure)
.liquid()
.build()?;
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
Ok(())
}
#[test]
fn pressure_temperature_initial_density() -> Result<(), Box<dyn Error>> {
let saft = PcSaft::new(propane_parameters()?.0);
let pressure = BAR;
let temperature = 300.0 * KELVIN;
let state = StateBuilder::new(&&saft)
.temperature(temperature)
.pressure(pressure)
.initial_density(MOL / METER.powi::<3>())
.build()?;
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
Ok(())
}
#[test]
fn pressure_enthalpy_vapor() -> Result<(), Box<dyn Error>> {
let (saft_params, joback) = propane_parameters()?;
let saft = PcSaft::new(saft_params);
let eos = EquationOfState::new(joback, saft);
let pressure = 0.3 * BAR;
let molar_enthalpy = 2000.0 * JOULE / MOL;
let state = StateBuilder::new(&&eos)
.pressure(pressure)
.molar_enthalpy(molar_enthalpy)
.vapor()
.build()?;
assert_relative_eq!(
state.molar_enthalpy(Contributions::Total),
molar_enthalpy,
max_relative = 1e-10
);
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
let state = StateBuilder::new(&&eos)
.volume(state.volume)
.temperature(state.temperature)
.moles(&state.moles)
.build()?;
assert_relative_eq!(
state.molar_enthalpy(Contributions::Total),
molar_enthalpy,
max_relative = 1e-10
);
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
Ok(())
}
#[test]
fn density_internal_energy() -> Result<(), Box<dyn Error>> {
let (saft_params, joback) = propane_parameters()?;
let saft = PcSaft::new(saft_params);
let eos = EquationOfState::new(joback, saft);
let pressure = 5.0 * BAR;
let temperature = 315.0 * KELVIN;
let total_moles = 2.5 * MOL;
let state = StateBuilder::new(&&eos)
.pressure(pressure)
.temperature(temperature)
.total_moles(total_moles)
.build()?;
let molar_internal_energy = state.molar_internal_energy(Contributions::Total);
let state_nvu = StateBuilder::new(&&eos)
.volume(state.volume)
.molar_internal_energy(molar_internal_energy)
.total_moles(total_moles)
.build()?;
assert_relative_eq!(
molar_internal_energy,
state_nvu.molar_internal_energy(Contributions::Total),
max_relative = 1e-10
);
assert_relative_eq!(temperature, state_nvu.temperature, max_relative = 1e-10);
assert_relative_eq!(state.density, state_nvu.density, max_relative = 1e-10);
Ok(())
}
#[test]
fn pressure_enthalpy_total_moles_vapor() -> Result<(), Box<dyn Error>> {
let (saft_params, joback) = propane_parameters()?;
let saft = PcSaft::new(saft_params);
let eos = EquationOfState::new(joback, saft);
let pressure = 0.3 * BAR;
let molar_enthalpy = 2000.0 * JOULE / MOL;
let total_moles = 2.5 * MOL;
let state = StateBuilder::new(&&eos)
.pressure(pressure)
.molar_enthalpy(molar_enthalpy)
.total_moles(total_moles)
.vapor()
.build()?;
assert_relative_eq!(
state.molar_enthalpy(Contributions::Total),
molar_enthalpy,
max_relative = 1e-10
);
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
let state = StateBuilder::new(&&eos)
.volume(state.volume)
.temperature(state.temperature)
.total_moles(state.total_moles)
.build()?;
assert_relative_eq!(
state.molar_enthalpy(Contributions::Total),
molar_enthalpy,
max_relative = 1e-10
);
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
Ok(())
}
#[test]
fn pressure_entropy_vapor() -> Result<(), Box<dyn Error>> {
let (saft_params, joback) = propane_parameters()?;
let saft = PcSaft::new(saft_params);
let eos = EquationOfState::new(joback, saft);
let pressure = 0.3 * BAR;
let molar_entropy = -2.0 * JOULE / MOL / KELVIN;
let state = StateBuilder::new(&&eos)
.pressure(pressure)
.molar_entropy(molar_entropy)
.vapor()
.build()?;
assert_relative_eq!(
state.molar_entropy(Contributions::Total),
molar_entropy,
max_relative = 1e-10
);
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
let state = StateBuilder::new(&&eos)
.volume(state.volume)
.temperature(state.temperature)
.moles(&state.moles)
.build()?;
assert_relative_eq!(
state.molar_entropy(Contributions::Total),
molar_entropy,
max_relative = 1e-10
);
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
Ok(())
}
#[test]
fn temperature_entropy_vapor() -> Result<(), Box<dyn Error>> {
let (saft_params, joback) = propane_parameters()?;
let saft = PcSaft::new(saft_params);
let eos = EquationOfState::new(joback, saft);
let pressure = 3.0 * BAR;
let temperature = 315.15 * KELVIN;
let total_moles = 3.0 * MOL;
let state = StateBuilder::new(&&eos)
.temperature(temperature)
.pressure(pressure)
.total_moles(total_moles)
.build()?;
let s = State::new_nts(
&&eos,
temperature,
state.molar_entropy(Contributions::Total),
&state.moles,
None,
)?;
assert_relative_eq!(
state.molar_entropy(Contributions::Total),
s.molar_entropy(Contributions::Total),
max_relative = 1e-10
);
assert_relative_eq!(state.density, s.density, max_relative = 1e-10);
Ok(())
}
fn assert_multiple_states<E: Total>(
states: &[(&State<E>, &str)],
pressure: Pressure,
enthalpy: MolarEnergy,
entropy: MolarEntropy,
density: Density,
max_relative: f64,
) {
for (s, name) in states {
println!("State: {name}");
assert_relative_eq!(s.density, density, max_relative = max_relative);
assert_relative_eq!(
s.pressure(Contributions::Total),
pressure,
max_relative = max_relative
);
assert_relative_eq!(
s.molar_enthalpy(Contributions::Total),
enthalpy,
max_relative = max_relative
);
assert_relative_eq!(
s.molar_entropy(Contributions::Total),
entropy,
max_relative = max_relative
);
}
}
#[test]
fn test_consistency() -> Result<(), Box<dyn Error>> {
let (saft_params, joback) = propane_parameters()?;
let saft = PcSaft::new(saft_params);
let eos = EquationOfState::new(joback, saft);
let temperatures = [350.0 * KELVIN, 400.0 * KELVIN, 450.0 * KELVIN];
let pressures = [1.0 * BAR, 2.0 * BAR, 3.0 * BAR];
for (&temperature, &pressure) in temperatures.iter().zip(pressures.iter()) {
let state = StateBuilder::new(&&eos)
.pressure(pressure)
.temperature(temperature)
.build()?;
assert_relative_eq!(
state.pressure(Contributions::Total),
pressure,
max_relative = 1e-10
);
println!(
"temperature: {}\npressure: {}\ndensity: {}",
temperature, pressure, state.density
);
let molar_enthalpy = state.molar_enthalpy(Contributions::Total);
let molar_entropy = state.molar_entropy(Contributions::Total);
let density = state.density;
let state_tv = StateBuilder::new(&&eos)
.temperature(temperature)
.density(density)
.build()?;
let vle = PhaseEquilibrium::pure(&&eos, temperature, None, Default::default());
let eos = &eos;
let builder = if let Ok(ps) = vle {
let p_sat = ps.liquid().pressure(Contributions::Total);
if pressure > p_sat {
StateBuilder::new(&eos).liquid()
} else {
StateBuilder::new(&eos).vapor()
}
} else {
StateBuilder::new(&eos).vapor()
};
let state_ts = builder
.clone()
.temperature(temperature)
.molar_entropy(molar_entropy)
.build()?;
let state_ps = builder
.clone()
.pressure(pressure)
.molar_entropy(molar_entropy)
.build()?;
dbg!("ph");
let state_ph = builder
.clone()
.pressure(pressure)
.molar_enthalpy(molar_enthalpy)
.build()?;
dbg!("th");
let state_th = builder
.clone()
.temperature(temperature)
.molar_enthalpy(molar_enthalpy)
.build()?;
dbg!("assertions");
assert_multiple_states(
&[
(&state_ph, "p, h"),
(&state_tv, "T, V"),
(&state_ts, "T, s"),
(&state_th, "T, h"),
(&state_ps, "p, s"),
],
pressure,
molar_enthalpy,
molar_entropy,
density,
1e-7,
);
}
Ok(())
}
| Rust |
3D | feos-org/feos | crates/feos/tests/saftvrmie/mod.rs | .rs | 25 | 2 | mod critical_properties;
| Rust |
3D | feos-org/feos | crates/feos/tests/saftvrmie/critical_properties.rs | .rs | 3,184 | 65 | use approx::assert_relative_eq;
use feos::saftvrmie::{SaftVRMie, test_utils};
use feos_core::{SolverOptions, State};
use quantity::*;
use std::collections::HashMap;
/// Critical data reported in Lafitte et al.
pub fn critical_data() -> HashMap<&'static str, (Temperature, Pressure, MassDensity)> {
let mut data = HashMap::new();
let kg_m3 = KILOGRAM / METER.powi::<3>();
let mpa = MEGA * PASCAL;
let k = KELVIN;
_ = data.insert("methane", (195.30 * k, 5.15 * mpa, 154.45 * kg_m3));
_ = data.insert("ethane", (311.38 * k, 5.49 * mpa, 205.84 * kg_m3));
_ = data.insert("propane", (376.20 * k, 4.77 * mpa, 219.98 * kg_m3));
_ = data.insert("n-butane", (432.68 * k, 4.27 * mpa, 228.04 * kg_m3));
_ = data.insert("pentane", (476.44 * k, 3.81 * mpa, 238.14 * kg_m3));
_ = data.insert("hexane", (515.29 * k, 3.44 * mpa, 241.16 * kg_m3));
_ = data.insert("heptane", (547.33 * k, 3.06 * mpa, 233.76 * kg_m3));
_ = data.insert("octane", (576.72 * k, 2.77 * mpa, 227.83 * kg_m3));
_ = data.insert("nonane", (602.20 * k, 2.52 * mpa, 224.89 * kg_m3));
_ = data.insert("decane", (626.37 * k, 2.31 * mpa, 219.85 * kg_m3));
_ = data.insert("dodecane", (668.75 * k, 1.99 * mpa, 214.26 * kg_m3));
_ = data.insert("pentadecane", (720.98 * k, 1.61 * mpa, 194.23 * kg_m3));
_ = data.insert("eicosane", (786.33 * k, 1.21 * mpa, 174.91 * kg_m3));
_ = data.insert("methanol", (547.73 * k, 12.21 * mpa, 260.59 * kg_m3));
_ = data.insert("ethanol", (554.41 * k, 8.83 * mpa, 247.96 * kg_m3));
_ = data.insert("1-propanol", (560.43 * k, 6.89 * mpa, 256.34 * kg_m3));
_ = data.insert("1-butanol", (583.97 * k, 5.50 * mpa, 253.86 * kg_m3));
_ = data.insert(
"tetrafluoromethane",
(232.77 * k, 4.14 * mpa, 644.29 * kg_m3),
);
_ = data.insert("hexafluoroethane", (295.46 * k, 3.24 * mpa, 634.96 * kg_m3));
_ = data.insert("perfluoropropane", (347.88 * k, 2.79 * mpa, 648.71 * kg_m3));
_ = data.insert("perfluorobutane", (386.86 * k, 2.38 * mpa, 635.61 * kg_m3));
_ = data.insert("perfluoropentane", (421.36 * k, 2.13 * mpa, 634.72 * kg_m3));
_ = data.insert("fluorine", (146.20 * k, 5.66 * mpa, 559.47 * kg_m3));
_ = data.insert("carbon dioxide", (307.00 * k, 7.86 * mpa, 472.15 * kg_m3));
_ = data.insert("benzene", (568.33 * k, 5.51 * mpa, 307.69 * kg_m3));
_ = data.insert("toluene", (600.25 * k, 4.73 * mpa, 301.21 * kg_m3));
data
}
#[test]
fn critical_properties_pure() {
let t0 = Some(500.0 * KELVIN);
critical_data().iter().for_each(|(name, data)| {
dbg!(name);
let mut parameters = test_utils::test_parameters();
let option = SolverOptions::default();
let p = parameters.remove(name).unwrap();
let eos = SaftVRMie::new(p);
let cp = State::critical_point(&&eos, None, t0, None, option).unwrap();
assert_relative_eq!(cp.temperature, data.0, max_relative = 2e-3);
assert_relative_eq!(
cp.pressure(feos_core::Contributions::Total),
data.1,
max_relative = 5e-3
);
assert_relative_eq!(cp.mass_density(), data.2, max_relative = 1e-2);
})
}
| Rust |
3D | feos-org/feos | examples/pc_saft_phase_diagram_to_dict.ipynb | .ipynb | 256,739 | 716 | {
"cells": [
{
"cell_type": "markdown",
"id": "fa749966-5842-432a-8760-5d98ac6b0671",
"metadata": {},
"source": [
"# The `to_dict` method for `PhaseDiagram` and `StateVec` objects\n",
"\n",
"The `to_dict` method returns a Python dictionary containing a selection of properties.\n",
"It can be used to conveniently create a `pandas.DataFrame` (to then store or plot data)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8e96c4be-b089-41ec-9b8d-1cc30f324f36",
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import si_units as si\n",
"\n",
"sns.set_context('poster')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 8)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "844b71b0-8cc4-4c7a-a217-d850407652c8",
"metadata": {},
"outputs": [],
"source": [
"pc_saft_parameters = feos.Parameters.from_json(substances = ['water'], pure_path = \"../parameters/pcsaft/esper2023.json\")\n",
"dippr_parameters = feos.Parameters.from_json(substances = ['water'], pure_path = \"../parameters/ideal_gas/poling2000.json\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "045e7402-fe3c-4abc-862f-c450a4f38739",
"metadata": {},
"outputs": [],
"source": [
"eos = feos.EquationOfState.pcsaft(pc_saft_parameters).dippr(dippr_parameters)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9050cf98-9ff4-45fe-8c91-58ab20c69f4b",
"metadata": {},
"outputs": [],
"source": [
"vle = feos.PhaseDiagram.pure(\n",
" eos=eos,\n",
" min_temperature=100*si.CELSIUS,\n",
" npoints = 1000,\n",
")\n",
"\n",
"spinodal = feos.PhaseDiagram.spinodal(\n",
" eos=eos,\n",
" molefracs=np.array([1.0]),\n",
" min_temperature=100*si.CELSIUS,\n",
" npoints = 1000,\n",
")\n",
"\n",
"# isotherm at T = 0.95 * T_c\n",
"t = 0.95 * feos.State.critical_point(eos).temperature\n",
"isotherm = []\n",
"for density in si.linspace(vle.vapor.density[0], vle.liquid.density[0], 1000):\n",
" state = feos.State(eos, t, density=density)\n",
" isotherm.append({\n",
" \"density\": density / (si.MOL / si.METER**3),\n",
" \"pressure\": state.pressure() / si.PASCAL,\n",
" \"mass density\": state.mass_density() / si.KILOGRAM * si.METER**3,\n",
" \"specific enthalpy\": state.specific_enthalpy() / (si.KILO * si.JOULE / si.KILOGRAM),\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "25f25f4c-ef65-4697-a2df-6c997605da7a",
"metadata": {},
"outputs": [],
"source": [
"df_vle = pd.DataFrame(vle.to_dict())\n",
"df_spinodal = pd.DataFrame(spinodal.to_dict())\n",
"df_isotherm = pd.DataFrame(isotherm)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e5e03dd4-1bc1-4bb4-bde1-4497486ec94e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"sns.lineplot(data=df_vle, x=\"mass density vapor\", y=\"pressure\", color=colors[0], label=\"vle\")\n",
"sns.lineplot(data=df_vle, x=\"mass density liquid\", y=\"pressure\", color=colors[0])\n",
"sns.lineplot(data=df_spinodal, x=\"mass density vapor\", y=\"pressure vapor\", color=colors[1], label=\"spinodal\")\n",
"sns.lineplot(data=df_spinodal, x=\"mass density liquid\", y=\"pressure liquid\", color=colors[1])\n",
"sns.lineplot(data=df_isotherm, x=\"mass density\", y=\"pressure\", color=colors[2], label=f\"T = {t / si.KELVIN:.2f} K\")\n",
"\n",
"plt.legend(frameon=False, bbox_to_anchor=(1,1))\n",
"plt.xlabel(r\"$\\rho$ / kg / m$^3$\")\n",
"plt.ylabel(r\"$p$ / Pa\")\n",
"plt.ylim(0, 4e7);"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c439be31-9364-491e-9022-f9f8940bc16f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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uEBE5B5lUikCll6PTcAlubm4YPXo0/vrrL+Tk5GDv3r0YPHgwAODYsWNITU0FcL0I4eVV9/8mKpXK9PjYsWP1ugdRY8aGjkRELsi8oSN7LhARkSuYOHGi6XHlxo6VH1c+py5CQ0NNjzMzM+t1D6LGjMUFIiIXZN7QkTMXiIjIFfTo0QPR0dEAgG3btqG0tBRarRYbN24EAAQFBaF///71undcXJzp8e7duxucK1Fjw+ICEZELspi5wJ4LRETkIsaPv95IsqKiAv/++y927NiB4uJiANe3rKzch6Eu7rjjDri5uQEAfvnlF+Tn51snYaJGgsUFIiIXZN5zgcsiiIjIVUyYMAESiQTA9eUQq1evNj1X3yURwPVlEdOnTwcAZGdn45FHHjH1cahOYmIi3njjjXqPSeRM2NCRiMgFmc9cqKhgl2oiInINkZGRiIuLw+HDh3HkyBHTTIUOHTogJiamQfd+/vnnkZSUhL179yIhIQFjxozBsGHD0LNnTwQFBUEQBBQUFOD8+fM4dOgQUlJSIJPJ8Pbbb1vjpRE5FIsLREQuyLznAmcuEBGRK5kwYQIOHz4MQRCg11//HdiQWQs3yOVyfPvtt/jss8+wePFi6HQ6bNq0CZs2bar2msqNIIkaMy6LICJyQRZbUbLnAhERuZDRo0fDw8PDdCyXyzFu3Dir3FuhUOCll17C5s2b8dRTT6FXr14ICgqCQqGAu7s7QkJC0KdPHzz66KNYvHgxtm7dapVxiRyNMxeIiFyQUqkQHXPmAhERuRIvLy+cPHmyTtdERkYiKSmp1udHRETg6aefrmNmRI0XZy4QEbmgqnaLEATBQdkQERERUWPH4gIRkQsyXxYhCIBWa3BQNkRERETU2LG4QETkgpTulqviKrg0goiIiIjqicUFIiIXZD5zAeB2lERERERUfywuEBG5IJlMCoWbTBSrKOfMBSIiIiKqHxYXiIhclIeH2XaU5Zy5QERERET1w+ICEZGL8jDbjrKcxQUiIiIiqicWF4iIXJTSQ1xcYENHIiIiIqovFheIiFyU0mxZBGcuEBEREVF9sbhAROSiPMxnLrC4QERERET1xOICEZGLUiq5LIKIiIiIrIPFBSIiF2W+WwSXRRARERFRfbG4QETkoswbOrK4QERERET1xeICEZGLslgWUc5lEURERERUPywuEBG5KPNlEWzoSERERET1xeICEZGLstgtgg0diYiIiKieWFwgInJR7LlARERERNbC4gIRkYtSmi2L0GoNMBiMDsqGiIiIiBozFheIiFyUh1lDR4BLI4iIiIioflhcICJyUebLIgA2dSQiIiKi+mFxgYjIRSkUUshkElGMfReIiIhsZ9iwYYiJicGwYcMcnYrNvPzyy4iJiUFMTAzS0tJsOtaCBQtMYx06dMimY9GtsbhAROSiJBKJxewFLosgIiIiovpgcYGIyIUpleKmjpy5QERERET1Ib/1KURE1FR5mM9cYHGBiIjIZrZv3+7oFIhshjMXiIhcmHlxobycyyKIiIiIqO5YXCAicmHmyyI4c4GIiIiI6oPLIoiIXJh5Q0f2XCAioqbIaDRiw4YN2LhxI86dO4e8vDwIggA/Pz/4+/sjOjoaffr0wdixY+Hv72+6buXKlXjllVcAAB988AEmT56M+Ph4LF26FEeOHEF2djY8PDzQrl07jB8/HlOmTIFUWv33t8OGDcO1a9cQERFR5RKJBQsWYOHChQCAxYsXo0+fPjhz5gx+//13HD58GNnZ2VCpVGjXrh0mTZqEiRMn1jjeDVlZWVi6dCn27duH1NRUlJWVwc/PD23atMHw4cMxdepUKJXKW96noqICS5Yswb///ouUlBQYjUaEhYVhyJAhmD59OsLCwm55DwAoLS3Frl27cOjQISQkJJhy8vDwQHBwMHr06IG7774bsbGxtbofOQcWF4iIXJhFzwXuFkFERE1MQUEBHn/8cZw8edLiuezsbGRnZyMpKQmbNm1CRUUFHn744WrvtWjRInz88ccwGAymmEajweHDh3H48GGsWLEC3333HXx9fa2S+/fff4/PP/9cNJ5WqzWNt23bNnzxxReQy6v/WPf333/j3XffRXl5uSiek5ODnJwcHDhwAD/99BMWLFiALl26VHuf1NRUPPzww7hy5YoofunSJVy6dAkrVqzA559/fsvXpNVq0b9/f2g0GovnSkpKUFJSgkuXLmH58uW455578MYbb9T4+sh58L8SEZELU3pwWQQRETVtr7/+uqmwEBYWhrFjxyI6Oho+Pj4oLy9HSkoKTp48iWPHjtV4nx07dmDLli1QKBS466670KNHD0ilUiQkJGDFihUoKSnBiRMn8H//939YunRpgz8QL1u2DOvXr0dAQAAmTZqEmJgYSKVSnDhxAsuXL4dWq8XWrVvx448/4vHHH6/yHsuXL8drr71mOh4wYACGDx8Of39/pKWlYe3atbhw4QIyMjIwY8YM/PHHH2jfvr3FfYqLi/Hggw/i2rVrAIDQ0FBMmTIFrVu3RllZGXbt2oWtW7fimWeeQUxMTI2vSxAEaDQaNGvWDH379kX79u0RHBwMpVKJ4uJixMfH499//0VRURH++usveHl5Ye7cuQ34SZK9sLhAROTCPJRmyyI4c4GIyK4EowGG0nxHp2F3Mq8ASKQym4+Tl5eHbdu2AQC6d++OX3/9Fe7u7lWem5+fj4KCgmrvtXnzZgQGBmLRokVo166dKT5+/HjMmjULDz74IFJSUnDq1Cn8/PPPePTRRxuU+/r169G7d298/fXX8Pb2NsXvvPNOjB49GjNnzoTBYMCiRYvw0EMPwc3NTXT9tWvX8N577wEAJBIJ3n33Xdx1112icx566CG88cYbWLFiBdRqNV588UWsWbPGYqnFp59+aios9OrVC9999x28vLxMz9999934999/8fzzz+Pw4cM1vi65XI7vv/8egwYNqnJJx9SpU/Hcc8/hiSeewIkTJ7Bo0SJMmzYNERERtfipkSOxuEBE5MIsei6otQ7KhIjI9ZQcXo7s356GoTjb0anYncwnGMEPfAnv3lNtOk5qaiqMRiOA6x/KqyssAEBAQAACAgJqvN+7774rKizcEBoaivnz52PKlCkwGo349ddfMXPmTIsP/HXh5+eHL7/8UlRYuKF3794YNWoU/vnnHxQUFCA+Ph49e/YUnbNkyRLTUoj77rvPorAAXP+g//bbbyM+Ph7nz5/H+fPnsX37dowYMcJ0Tn5+PlauXAkA8PT0xOeffy4qLNwwevRoU2GlJjKZDIMHD67xHH9/f3z00UcYOXIkDAYD1q5diyeeeKLGa8jxuFsEEZEL81CZFxe4LIKIyF6yfnnMJQsLAGAozkbWL4/ZfBwPDw/T44SEhAbdq1WrVhg2bFi1z3fs2BH9+/cHAOTm5t5ymcWtTJgwQdRc0tyNsQDgwoULFs9v3rwZwPVZC4888ki195HL5aI+E1u2bBE9v3PnTuh0198fjBs3DkFBQdXea9asWbVqMFkbLVq0MI116tQpq9yTbIszF4iIXJjKrLigZs8FIiJqQtq0aYOQkBBkZWVhxYoVMBqNmDp1Krp16waZrG7LMip/mK/pnL179wIA4uPj0a9fv3rlDQDdunWr8fnQ0FDT4+LiYtFzeXl5pmUM0dHRt1xSMGjQINNj88aXp0+fNj2+1esJDg5GmzZtcP78+RrPA67vYLFmzRocPHgQFy9eRHFxsUXTyRsyMzNveT9yPBYXiIhcmEolnq6p1Rig1xshl3NiGxGRrYXM+s7ll0XYfByZDO+88w5mz54NrVaLVatWYdWqVfDy8kLXrl3Ro0cP9OvXDz169IBEIqnxXi1atLjleJXPyc5u2H/XmmYtABAtuTDfeSEnJ8f0ODo6+pZjBQYGwtvbGyUlJaJrAfHrqM3PICoq6pbFhT///BMffvhhtcUEc6WlpbU6jxyLxQUiIhdmviwCAMrLdfD2rn5NKhERWYd376nw6jWZDR1tbPDgwVixYgUWLlyI7du3Q6fTobS0FPv27cO+ffuwYMECREZG4umnn8aECROqvU/lJRbVUalUpsdlZWUNyrshywsqfxivnFNNVCoVSkpKoFarRfHKx0ql8pb3udXPaePGjXjzzTdNx927d0dcXBwiIyPh7e0tKpq8/vrryM/PN/XNIOfG4gIRkQvz8KiiuKBmcYGIyF4kUhnkPtWvYSfraNeuHb788kuo1WocP37ctPXk0aNHodVqkZaWhrlz5yI1NRWzZ8+u8h61+Za98gdxT09Pq+VfV5UbLpoXC6pz4zzzYkTl44qKilve51Y/p/nz5wO4Pqtk4cKFNfaxqLyNJjk/znslInJhcrkUbu7ib47U3DGCiIiaKJVKhYEDB2L27Nn45ZdfcODAATzzzDOm57/99luLZQE3XL169Zb3r3xOcHBwwxOup8pNF69cuXLL8/Py8lBSUgLAMu+QkJA63aumn1NqaqrpHiNGjKixsFBaWoqioqJbjkfOg8UFIiIXpzKbvaDmjhFEROQivLy88OSTT2L48OEAAJ1OV+3OBPv27bvl/fbv3296HBsba50k6yEwMNDUxDE5OdnU3LE6e/bsMT3u2rWr6LnKr+PgwYM13ic7OxuXLl2q9vnc3FzT46ioqFvmxOUQjQuLC0RELs7DrKkjt6MkIiJXExkZaXqs1+urPOfy5cvYuXNntfdITEw0FReCgoLQs2dPq+ZYV6NGjQIACIKAn376qdrz9Ho9fv75Z9PxyJEjRc8PHjwYCsX1LyLWr18vKhCY+/XXX2EwGKp9vnI/hppmOGi1WnzzzTfVPk/OicUFIiIXZ74dZTmXRRARUROxZ88eLFq0qMbp9Xl5edi8ebPpuH379tWeO2/ePFy8eNEinpWVhWeffdb0wXrGjBmmD+SO8sADD5g+zP/xxx9YuXKlxTl6vR5vvfUWkpKSAFzvTTF06FDROQEBAZg8eTKA60sVnnvuuSqbVW7ZsgWLFi2qMadWrVqZejhs374dJ06csDinoqICL774oiknajzY0JGIyMWZ7xjBZRFERNRU5OTk4IMPPsCnn36K3r17o2vXrmjevDlUKhUKCwuRlJSEDRs2mIoPY8aMqXbrxpEjR2LLli2YPHkyJk2ahO7du0Mmk+Hs2bP4+++/UVxcDOD6soKHHnrIXi+xWhEREZg3bx5ee+01GI1GvPLKK9iwYQOGDx8OPz8/pKenY82aNaZtI1UqFT755JMqd6n4z3/+gz179iA9PR2HDx/GHXfcgbvuugutWrVCWVkZdu/ejc2bN8PX1xcxMTE4fPhwlTm5ubnh/vvvx48//gidTofp06dj0qRJiI2NhYeHBy5evIjVq1cjIyMD/fr1Q3JyMjIzM236cyLrYXGBiMjFWcxcKGdxgYiImgaJRALgei+FG1tPVmfUqFH44IMPqn1+6NCh6NWrFz766CP8+eef+PPPPy3O6d69O7799lvI5c7xMWvq1KkAgPfeew/l5eXYu3cv9u7da3FeaGgoFixYUO2sDR8fH/z666945JFHcOXKFWRkZGDBggWic/z8/PD5559jzZo1Neb0zDPPICkpCXv27IFOp8OyZcuwbNky0Tm9e/fG/PnzMWXKlLq8XHIw5/hbT0REDqMy67nAmQtERNRUTJw4Ea1bt8aBAwdw6tQpXLp0CdnZ2dBoNFAqlQgPD0fXrl0xYcIE9O7d+5b3e/DBB9GjRw8sXboUhw8fRk5ODjw8PNCuXTuMHz8eU6ZMqfKbf0eaOnUqbrvtNixduhR79+5FWloaysrK4OvrizZt2mD48OG4++67oVQqa7xPVFQU1q5diyVLlmDjxo1ISUmBIAgIDQ3FkCFDMGPGDISFhd2yuODm5obvv/8eK1euxOrVq5GYmIiKigoEBASgXbt2GDduHMaPH+90P0e6NYkgCIKjk3AVR48exbRp00zHv//+O3r16uXAjIiIgB1bL2LjhkTTcUyHIDz8aB8HZkRE5Dh8v0aVrVy5Eq+88goA4IMPPjD1HiAiSywHERG5OPOeC9wtgoiIiIjqisUFIiIXZ95zgcsiiIiIiKiuWFwgInJxHmY9FzhzgYiIiIjqisUFIiIXV9VuEWzHQ0RERER1weICEZGLMy8uGI0CNBq9g7IhIiIiosaIW1ESEbk484aOwPW+C0qlZZyIiMiVTJ48mTtEENUSZy4QEbk4d3c5pFKJKKYuY98FIiIiIqo9FheIiFycRCKx3I6ynMUFIiIiIqo9FheIiAgeHmbFBbXWQZkQERERUWPUpHsunD59GvHx8YiPj8eFCxdQUFCAgoIC6HQ6+Pj4oHXr1ujTpw8mTZqEiIgIR6dLROQw5k0d1dyOkoiIiIjqoEkXFx588EGo1eoqn8vLy0NeXh4OHz6M7777DrNnz8Zjjz1m5wyJiJyDSuUmOmZxgYiIiIjqokkXFwAgMDAQsbGxiImJQWRkJLy9vaHX63Ht2jXs3LkTx48fh1arxWeffQadTofZs2c7OmUiIruzmLlQxmURRERERFR7Tbq48Ndff6Ft27aQSCRVPv/YY49h9erVePnllyEIAr755htMnToVISEhds6UiMixVJ7imQtlLC4QERERUR006YaO7dq1q7awcMPEiRMxZMgQAIBer8eePXvskBkRkXPx9DJbFsHiAhERERHVQZMuLtRW27ZtTY9zc3MdmAkRkWN4cuZCkyAIAnILM5GRcxWCIDg6HSIiInIhTXpZRG1duXLF9LhZs2YOzISIyDFYXGg8CjVqpJYUILW0AGnFuShJi4f0ygn4ZZxD26xEBGmKIQFwTa7EuZD2KAjrAH1Ud3hF90CkTxBaeAeghU8gvBTujn4pRERE1IS4fHFh+/bt2Lp1KwDA3d3dtESCiMiVmPdcUJdxtwhnYTAacTI3FVtTE7EtLRFn8zPgbtBhYvoJTE09ggBd1bsi+egr0OfaSeDaSeDoH8hx88Ki6AHYFNoZgkSGuJAWGNuiM0a36IRIL3+7viYiIiJqelymuHDkyBEUFRUBALRaLTIzM7Fv3z7s3bsXACCXy/HWW2/dcuZCeno60tPT65VDUlJSva4jIrI185kL5eU6GAxGyGRcPecoiQWZWHbhKFZeOoncitLrQUHA7dln8dilndUWFaoTpC3Fi+c3YXz6Sbzb4U4czhJwOCsF/z28Hn1CWmJWh34Y1aITFFKZDV4NERERNXUuU1z45JNPcOrUKYu4RCJBXFwcnn76acTFxd3yPitWrMDChQttkSIRkcOYN3QEALVaB29vTp23J53RgH9SzuCHhL04mZsqes5PW4YXkzahX/6lBo0RU5qF747/irc7jMfhwFYAgENZyTiUlYxQlQ+e6jIE98f0hrvMZd4iEBERkRW4/DuHkJAQDBgwAC1atHB0KkREDqNSKSxiZaVaFhfspEKvw58XjuLr+J1ILyuyeL59cTreSliDIG1pldervYOhaR4LRas+8GvTH1KFOwouHoTm0kG4XzkGz6IM0fkqgw7vJKzCux3GYU9QjCmeqS7G64fW4uv4XXip5yhMbt0NUglnrxAREdGtuUxxYdmyZabHarUaV69exbZt2/DLL79g/vz5pv/t37+/A7MkInIMmUwKDw8Fystv9lpgU0fbMxiN+OviUXx2Yisy1cVVnjMoJwnzzm2Am2CweE7VeSSaTX4bylaWM++iOw01PS6L34Ts35+DLvPm8jyFYMQbZ9fhnY7A7koFBgDIUBfh2T3LsDjxID4ZMAUx/iH1fYlERETkIlymuFCZSqVC+/bt0b59e4wfPx73338/srOz8eijj2LFihWIiYmp9topU6agX79+9Ro3KSkJb7/9dn3TJiKyKZWnuLigZnHBpvalX8RbRzbgbH5GtedMzb+Ix8+ugwTibSVlPiEImfkNvHpMqNVYnl1GocVbR5D1y6MoOfjnzftAwBvnN2FhWAxW6y2vO55zFWPWfokXe4zEo50GQSblLAYiajzS0tIwfPhwq9xr0qRJ+PDDD61yL0fT6/XYtGkTtmzZgoSEBOTm5gIAAgMD0bx5c8TFxWHo0KHo0KFDne/98ssvY9WqVabj2bNnY86cOfXK8+DBg5g5c6Zpa+WIiAhs3769XveqzpUrV/Dnn39iz549yMjIgNFoRHBwMPr374+77767Xj+DGzQaDSZMmIDk5GRTbPHixejTp0+Dcq7897o2PxOdToeXXnoJGzZsAAB4e3vj22+/Ra9evRqUR1VcsrhQWfPmzfHCCy/gpZdegk6nw7fffov58+dXe354eDjCw8PtmCERkX14erohL/dmk0DOXLCNa6WFeOPQWmy6erbac3oGReF5QxHCdq8GzAoLHh2GIuyJpZD7BNdpXKm7J0IfXQKphy+Kdnx3M67X4Lljv2PG7FX45MJxHMi8LLpOazTgvaMbsenqWSy47R409w6o07hEROQ8Tp48iddffx3nz5+3eE6tViM1NRX79+/HmTNn8PXXX9fp3rt27RIVFhqivLwcr732mqmwYAt//fUX3n//fVRUVIjiKSkpSElJwV9//YUnn3wSs2fPrtf9v/zyS1FhwREqKiowZ84c7N69G8D1AtKPP/6Ijh072mQ8ly8uAMBtt91menz48GEHZkJE5DjmO0awuGBdRsGIX88dxIfH/kWZvuqfbf/QVniu+wh0SjmMzO+eAczeVPkOfgTBM76CpJ7NFiVSKYJnfAUYDSja9aMpbihMR+TGD7Fs9nJsT0vCfw+vR3Jxrujao9lXMHbdQnw95D4MCm9br/GJiOwpMDAQX331VbXPnz9/Hl988QUAoG3btnj22WerPTcsLMza6dndnj17MHv2bNOH6Z49e+K2225DWFgYFAoF8vPzce7cOezatavO9y4tLcWbb74J4PoscbW6bjsamfvf//6H1NRUq9yrKmvWrMEbb7wBAJBKpRg7diz69esHuVyO48ePY9WqVdBqtViwYAHc3Nzw6KOP1un+8fHx+OWXXwBY5+dRHyUlJXjsscdw7NgxANe/JP/ll18QHR1tszFZXADg5eVlenxju0oiIldjvmNEWSmLC9ZyuSgHL+z9G0eyr1T5fIxfCN7sPQ63RbSFOmk30n6YCQhG0Tn+Y+ei2dT3IZFIGpSLRCJB8PSF0GZfQvm5HaZ46bFVKDnwO4b3fwADwlrjkxNb8P2ZPRAqzZwo0KgxbfPPeKXnaDze+bYG50JEZEseHh4YMWJEtc97e3ubHvv7+9d4bmN35coVzJkzBxUVFfD19cVnn32GgQMHVnmuIAjIysqq0/0//vhjZGRkICwsDKNHjzZ9sK6P48eP4/fffwcAPPPMM/jggw/qfa+q5Ofnm5aqS6VSLFy4ULR8ZuLEiZg8eTJmzpyJ8vJyfPHFFxgxYgRatWpVq/vrdDq8+uqrMBgMGD58OEpKSuz+BXZeXh4efvhhnDt3DgDQunVr/PzzzwgNDbXpuFw8ietTX24ICOB0TyJyTSqzmQvsudBwgiBg6fnDGLX2yyoLC4FKT3zYbxI2TXgat0W0hTYjCelfTgYMOtF5/qNfsEph4QaJXIGwx36D1CtQFM/+7Rnoi3OglCvwetxYrBj7GFp4i88xCgLeO7oRrx5YDYNRXAAhIiLn9Nprr6G8vBwymQzffvtttYUF4HoRui4fQg8cOGBqnv/mm2/C09Oz3nlqNBq8+uqrMBqNGDVqlE0KPj/99BNKS6/vvjRt2rQq+3J069YNzzzzDIDrPSpqmgFj7ptvvsH58+fh5eVlms1hT+np6bj//vtNhYXOnTvjt99+s3lhAWBxAQDw5583m1v16NHDgZkQETkOl0VYV4FGjUd3/Ia5+1aiXK+zeP6+dnHYPfk/eKB9H8ilMhjKi3Ht8wkwlhWIzvMb/hSa3fOR1WcJyP1CEfKgeD2tUV2IvFU33wj1DonGv+PnYFSU5drMJUmH8NiO31FRxWsjIiLncfLkSdM353feeadVP++Ul5fj9ddfhyAIGDt2LIYOHXrri2pwo0+Bj48PXn/9dStlKbZx40bT4wcffLDa86ZOnQqVSgUA2L59u0VvhqokJibi+++/BwD85z//QUiIfXdbunTpEu677z7Tl+d9+vTBr7/+arcv0JtsceGPP/7AwYMHa2wCYjAY8P3332Pp0qWm2P3332+P9IiInA6LC9ZzJu8a7li7EBuvJFg818I7EH+N/j98MmAKfN09APz/Kag//x90WRdE53p2vxNB0+bbbPmBd9xd8O59tyhWtPMHaFLjb57jpsQPwx7Ai91vhwTiPP69moBHti9hgYGIyIn9/fffpscTJtRul6HautEbwc/PD/PmzWvQvSr3KXjxxRcRFBRkjRRFLl68iGvXrgG4vlSgefPm1Z7r5eWFnj17Arje7PJWSxv0ej1effVV6HQ69OzZE/fee6/1Eq+FM2fOYNq0acjMzAQADB8+HD/++KOoBYCtNdmeC6dOncJ///tfhIWFoX///mjXrh0CAwOhUChQUlKC8+fPY9u2baa/XADw2GOPoXfv3g7MmojIccyXRbC4UD/LLxzDywdWQWOw3NtxekwfvB53B1QK8c+6aNs3KD3ytyjm3qIHwh7/HRKpzKb5NrvnY5SeXAdBW349IBiRu+I1RDy7xnSOVCLFM92Go7VvEJ7e/Re0RoPpuZ3XzuPRHb/hh2HT4V7PRpNERGQ7R44cAXB9uUNsbCxKSkqwZMkSbN68GVevXoUgCAgODkZcXBzuvfdedO7cuVb3rdwbYe7cuWjWrFm9c6zcp6B3796YOnVqve9Vk8q7ZHTp0uWW53fp0gV79uwxXVt5IwBzP/30ExISEuDm5oZ33nnHrn2JDh06hCeeeAJlZWUArveNeO+99yCX2/f3cpN/F5CRkYEVK1bUeI63tzeef/55zlogIpdmPnNBXcZvo+tCa9Djv4fXY3HiQYvnAtw98enAKRhZxfKCiuSjyP7jeVFMqvJD+OzlkLrXf91qbSkCmyNg7FzkrX7LFCs7uR4VV05C2aKb6NxxLWMRoPTEw9sWo0SnMcW3pyXh8R2/47uh0+DGAgNRnRiNAtRq1yvmqlRukEqbblPYS5cuWW0bwrCwMHTq1Kle15aUlJimyHt7e+Pq1at48sknkZGRITrvxvaLy5cvx/Tp0/HKK69AJqu+uF25N0K/fv0wZcqUeuV3w40+Be7u7jb9YH758s3tliMjI295fuVzavrveenSJSxcuBAA8Pjjj6N169YNyLJutm/fjmeffRYazfXfyzNmzMCrr77qkKbLTfYdwGuvvYbhw4fjyJEjOHfuHK5evYqCggLo9XqoVCoEBgYiJiYGgwYNwujRo0XdYomIXJF5cUGj0UOvN0Aut+03501BgUaNR7YtwaEsyzcePYOi8O3QaQjz9LV4zqhRI+PbBywaOIb+3y9QBEXbKl0L/qOfR8GWBTCW5Zti+es/QPhTf1mc2z+sNZaOegT3b/pRVGDYknoOz+1djgW33QOppMmuuiSyqtMn07F6xRmUuuDuPF5ebpg4pTNiu4U7OhWb+Oeff0wfNhtq0qRJ+PDDD+t1bU5Ojumx0WjEo48+ipycHERGRmLy5Mlo2bIlSktLsWfPHmzZsgWCIGDJkiUArn+eqs4XX3yB5ORkKJVK084L9VW5T8FTTz1l060SS0pKTI/9/f1veb6fn1+V11ZmNBrx6quvQqvVol27dnXetrIhcnJyMGfOHOj112dLzpkzB7Nnz7bb+OaabHHBy8sLt99+O26//XZHp0JE1CiYFxcAoKxMB19fFhdqklycixlbFiG5ONfiuZkd+uGNuDuq/TY/d8VrFn0W/Mf8B17dx9sk1+pIlV7wH/Us8la+YYqVHl0BbXoi3MLbW5zfPag5fhv5EO7f9BPK9Dc/FK25fArNvfzxcs/RdsmbqLH7+6/TqKiwXELlCkpLtfj7r9NNtrjgLIqLi02PS0tLUVpaikGDBmHhwoVQKpWm5+6++25s3boVTz/9NAwGA5YsWYJx48ahW7duFvc8ffo0Fi1aBOD6h9moqKh651e5T0H79u3x8MMP1/tetaFWq02P3d3db3l+5Z/RjSUH5n799VecPHkSUqkU7777LhQKRcMTrSWt9ubv4GbNmuGee+6x29hVabLFBSIiqhsPlQISCVC5D666TAtfX2X1F7m4w1kpeHjbYhRo1KK4UqbARwMmY0rr7tVeq07ajcItX4qva9UHzaa8a5Ncb8VvxGwUbPwfjOVF1wOCgMLt3yD4gS+qPL9ncAv8NvIhTNv8M9SVCgwLT+9EpJc/HojpY4+0iYic0pw5czBnzhxHpwGj2ZbBPj4++PTTT0Ufmm8YMWIEpk+fbiocLF682KK4oNVqMW/ePBgMBnTq1AmzZs1qUH4//vgjEhISIJPJ8O6779q9R0BDXb16FV98cf335LRp09C1a1e7ju/j4wOVSoXMzEzk5uZi+vTpWLx4MYKDg+2axw2ct0hERAAAqVQCD5W42l5aoqnmbNp0JQH3bfrRorAQovLBqrGP11hYMGrKkPXjw6JKjkShROj//QKJ3H7feFQmU/nCd+hjoljxvsUwaqr+pgYA4kKi8d3QaZCZLYOYd2AN9qVftEmeRE3JXffEwsvLctaYK/DycsNd98Q6Oo0mz9NT3Ltn9OjRoqn+5ip/833woGUPoRu9EWQyGd55550a+zLcyqVLl/DVV18BAKZPn16rBosNdWNrSQCmHgU1qbz9pPnPUhAEzJs3D+Xl5QgPD8dzzz1nvURrydvbG0uWLEFYWBiA630hZsyYgaysLLvnAnDmAhERVeLl5S5q5OiK64Br4++Lx/DC3hUwCOJvhDoGhGHRiJkIr6K/QmV5a96BLueyKNZsyrtwC4uxeq514Tf0MRRs/MRU9DCWF6Pk4B/wHfxItdcMjYzBB/0nYu6+laaYQTDiyV1/YOP4p2/5syByZbHdwtE5NowNHclmfH3F/wbfqjFkq1atoFKpoFarkZeXh7KyMtOH6sTERPzwww8AgJkzZ9a7ySQg7lMQERGBZ555pt73qovKffYKCgpueX5hYWGV1wLA0qVLTdtT/ve//7UoPthLVFQUlixZgunTpyMjI8NUYFi8eDFCQkLsmguLC0REZOLl7Y7srFLTcWkpZy6Y+zFhL/57eL1FfHhke3w95D54Kmpew6m5dhYFm+aLYsq2A+A38mmr5lkfiqBoeHYZjbLTG02xwu3f1VhcAID72/VGakkBFpzeYYrlVZTh8R2/4+8xj3IHCaIaSKUSeHndeu03NS7OsltESEiIqVgAWH5Aroq3t7fp/NLSUtOH5pUrV0Kn00EqlUKhUODrr7+u8vobW1/eeHzjvJYtW2LMmDEArm/rePLkSQBA27ZtTUsxzFVuolhSUiIa85FHHoGbW91m/rRq1cr0OC0t7ZbnVz6nZcuWoueWL18OAAgKCkJCQgISEhKqvMe1a9dMj9esWYNjx44BAAYOHIjYWOvM3mnevDmWLFmCGTNmID09HSkpKZg+fTqWLFli1wIDf9sTEZGJt7f4DS6XRdwkCAI+O7kV809us3ju3ra98GH/SZBLa54eKggCsn97GjBUauAmUyD0oR8gucW19uI77HFRcUFz5Tg0aWfgHlnzvucv9rgd5woysDU10RQ7nnMVbx3egPf6TbBZvkREzshZdouQSCSIiYnBiRMnAFS/40FlpaU3v2SoXIwQbsxqMxrx7bff1mr8Q4cO4dChQwCA4cOHm4oLQqVlgTt37sTOnTtvea/i4mJTfwMAeOCBB+pcXGjXrp3pcXx8/C3Pr3xO27ZtRc/deA05OTmivGqyYsUK02OVSmW14gIgLjBcu3YNV65cMfVgCA0Ntdo4NWHPBSIiMjFf+1tS4npTdasiCALeO7qxysLCY51vwycDptyysAAAJYf+RPm5HaJYwJj/OHw5RGWesWMgDxDv/V1yyHJLSnNSiRRfDLoHLbwDRPFfEw9gzeVTVs2RiIhqb/DgwabH1X27fsPly5dNuyLcmPXQlLRp0wbh4dd3KLl06VKNsxfKyspMsww8PDzQu3dvu+TYEJGRkVi8eDEiIiIAwFRgyMjIsMv4nLlAREQmXuYzF7gswlRY+PbMbovnXu45Ck91GQKJ5Nbrhg3lxcj580VRTB4YhYA7X7FartYgkcrg3ftuFPz7mSlWcvBPBE5++5av09fdA98PfQDjN3wNTaXZGa8eWIW44BYI9/KzVdpERE7FWXaLAIA77rgDCxYsgMFgwL///osXXnih2qaOf/11s5h82223iZ6bN28e5s2bd8vxFixYYJq1MXv27Cp/Dh06dEBSUtIt75WWlobhw4cDACIiIrB9+/ZbXnMrY8aMwU8//QQAWLRoEV577bUqz1u2bJlpeciwYcPg4eEhen7NmjW1Gm/69Omm3gyLFy9Gnz623U0pMjLSNIMhLS0NV69eNc1guFFYsRXOXCAiIhPzmQuuviyiusKCBBJ80G8iZscOrVVhAQDy170PQ6H4m4Pg++dD6u6YBlA18e4j3idbl3MZmuSjtbq2U2A4Puw3SRQr0lbgub3LYTRrgElERLYXFRWFu+66C8D1pQUvvvhilTslbN26FUuWLAEAyGSyBm8z6QgxMTGmP9XNSnj44YdNfSR+//13bNtmOSvx1KlTpqUOcrkcTz31lO2StoGIiAgsWbIEzZs3BwCkpqZi+vTpSE9Pt+m4LC4QEZGJxcwFFy4uCIKA94/+W2VhYf6guzC9fd9a30uXdxWFm78UxTxjx8Czh3P2InCP7glFSBtRrPjQn7W+fmrbnpjYqpsoti/jEn46u88a6RERUR09//zzaN26NQBg9+7dGDt2LL755hv8888/WL58OebMmYPZs2fDYDAAAF544QXT+U1NYGAgXn/9dQDX+0fMnj0bL7zwAlauXInVq1fjjTfewPTp01FeXg7g+iyUxvizCA8Px5IlSxAVFQXg+iyQBx54QNRg0tq4LIKIiEwsGjqWaiEIQq2/nW9KPj2xBd+c2SWK3Sgs3NWmZ53ulbfyDQj6SoUamRxB98932p+rRCKBd+97kL/uPVOs7PgaCPd+Wuuc3+s7AYezkpFeVmSKfXhsEwZHtEM7P/tujUVE5Or8/Pzw888/49lnn8WJEyeQlpaGzz//3OI8hUKB//znP5g5c6bdc7SnSZMmoby8HB9++CE0Gg3Wr1+P9evFO0HJZDI8/vjjePzxxx2UZcOFhYWZlkhcuXIF165dMy2RiIyMvPUN6ogzF4iIyMR8OzS93oiKCn01Zzdd35/Zgy9Oidd11rewUHHlJIr3/yaK+Q19HG6hbau5wjl4x00RHetykqHNSKzmbEu+7h6YP+huSHCzGKEx6DF330oujyAicoDQ0FAsXboU//vf/zBs2DCEhYVBoVDA29sb7du3x0MPPYRNmzY1+cLCDffffz/Wrl2LWbNmoU2bNvD09IRKpUJ0dDTuvfde/P3333j6acdvE91QoaGhWLJkCaKjowFc3xpzxowZSE1NtfpYEqHyPiBkU0ePHsW0adNMx7///jt69erlwIyIiMS0WgNee2mjKPbiK0MQFOzloIzs78/zR/CffStEMQkk+GzgXZjatm6FBQBI+3Q01Ge2mI6lSm9Ef3wBcp+gBudqS4IgIPmFaOjzb65ZbXb3RwgY+5863eedwxvwXcIeUez9fhMxow7LSojsie/XiIjqhzMXiIjIxM1NBjd38ZaKpaWusx3lPylnMHf/Sov4B/0n1quwUJawVVRYAAD/sXOdvrAAXF8a4Rk7VhQrO7Whzvf5T4+RaOEdKIq9f3QjMiotlyAiIqLGj8UFIiISMV8a4SpNHfekX8DsXX/AaDah79VeY/BATN23jRIEAXkr3xTFZH7h8B/1bEPStCvPruLiQvmFfTCUFdbpHh5yBT7qL949olSnwRuH1jY0PSIiInIiLC4QEZGIZVPHpl9cOJ2bhoe3LYHWaBDFn+wyGE92GVyve6rjN6Hi0kFRLHDiG5C6q+qdp72pOg6HRF7p74PRgPLzu6u/oBoDw9vgbrNeFRuvJGBP+oWGpkhEREROgsUFIiIS8fJ2Ex2XNPGZC2mlBZi59Veo9eLlHw/E9MErPUfX656CICBv9VuimCKoFXwHzqxvmg4hdVdB2aafKKZO3FXN2TV7vfcdCFR6imJvHloHnVlBh4iIiBonFheIiEjEcllE0+25UKQpx4wtvyC7vEQUH98yFu/1nVDvrSLLTm9ExeXDoljA+FchkSvqnaujqDoMER2X17O44O+uwks9R4li5wuzsTjxYDVXEBERUWPC4gIREYl4uciyCI1Bj0e2L8H5wmxRvH9oK3w+6G7IpPX7FVnlrIXg1vDpP73euTqSR3vxshDN1ZMwlObX6173tOmFLoERothnJ7Ygr6K03vkRERGRc2BxgYiIRLy8xMsimmJDR0EQ8OK+FTiQeVkUb+cXjB+GTYebTF7ve6vjN0GTfFQUCxg/D5IG3NORlK36QKJQ3gwIAsrP76n+ghrIpFK83edOUaxIW4FPj2+p5goiIiJqLFhcICIiEfOGjk2x58KnJ7Zg5aUToliwhzcW3z4Lvu4eDbp3/j+fiI4VIW3g029ag+7pSFKFO5Rt+oti6nM7632/uJBoTGzVTRRbev4ILhfl1PueRERE5HgsLhARkYj5soiy0qbVc+Hvi8fxxantophK7oZfR8xEpJd/g+5dfvkwyhN3imIBY+c22lkLN6jMlkZUXDrUoPu92msMlLKb/ScMghEfH9/coHsSERGRY7G4QEREIuYNHSsq9NBpm0ZH/2PZVzF33wpRTCqR4Jsh96NLs4hqrqq9gg0fi45lfuHw7v9Ag+/raMo2fUXHmqsnIOjrX3QK9/TFwx0HiGLrU+JxKjet3vckIiIix2JxgYiIRLx93C1iTWFpRHpZER7Zvhhas60P3+s7AcObt2/w/bUZSSg9vloU8x/5DKQKy59nY6OM7iU6FvRaaFJPN+ieT3YZDF838RKUD47+26B7EhERkeOwuEBERCJKpRwKhfjXQ3FxhYOysY5yvRYPb1uMnHLxrgQPdeiP6e37VnNV3eRv/BQQBNOx1MMXvkMftcq9HU3m6QdFaIwoVnH5SIPu6evugdmxQ0SxvRkXsSf9QoPuS0RERI7B4gIREYlIJBJ4+yhFseKixltcEAQBL+z9G/F510Tx28Lb4o3ed1hlDH1BOkr2/yaK+Q1/AjIPH6vc3xkoW8WJjisuH27wPWd26I8wla8oNv/kNgiVijRERETUOLC4QEREFnzMigslxY13WcSC0zuwNlk8hb+lTzN8PeQ+yKUyq4xRsHWhqAeBRO4Ov9uftsq9nYWyVW/RcUVyw2YuAICHXIFnug0TxQ5npVhsEUpERETOj8UFIiKy4OMr7hPQWJdFbEtNxCfHt4hiPm5K/DJ8BvzcVVYZw6hRo2jnD+IxBj4IuW+IVe7vLMxnLmgzEmEsL2nwfe9u0xMRnn6i2OcntzX4vkRERGRfLC4QEZEF85kLxUWNb+bClZI8PL37Twio1AdBIsHCwfehjV+w1cYpPvA7jGX5opj/yGesdn9n4R4ZC1Se6SEI0FxLaPB93WRyi94L+zMv41BmcoPvTURERPbD4gIREVnw8WncMxfK9Vr83/bfUKQV5/1qzzEYFhlTzVV1JwgCCrcuFMVUnUfCLbzhu084G6mbEm6h7UQxTVq8Ve59d9teCFWJ+1N8cWq7Ve5NRERE9sHiAhERWbBo6NiIei4IgoCX96/C2fwMUfzO6Fg81nmQVccqP7cD2rQzopj/yKbVa6Eyt8jOomPz115f7jI5nuoyRBTbnX4Bx3OuWuX+REREZHssLhARkQUfX/OGjo1n5sLixINYcemEKNbWNxifDpwCiURi1bEKtnwpOlaEtoOq8yirjuFM3CO7iI41ViouAMB97eIQ4uEtin1/Zo/V7k9ERES2xeICERFZMG/oqFbroNMZHJRN7R3LvoL/Hl4vinkp3PHj8OnwVLhXc1X9aLMvoeykeCy/EbMhkTbdX63uEZ1Ex5q0eKttG6mUK/BIp4Gi2D9XzuBqSX41VxAREZEzabrvgIiIqN7MGzoCzr8dZW55KR7d8Tt0RnERZP6gqWjtG2T18Yq2fwtU+mAt9fCB74AZVh/Hmbg1F89cMJbmwVCUabX7T4vpA69KRSCjIODHhL1Wuz8RERHZDosLRERkQamUQ6EQ/4pw5qaORsGIp3f/hSx1sSj+ZJfBGNOiczVXNWA8bQWK9iwSxXxuewhSs2n9TY2iWUtI3D1FMWsujfBxU+L+duItL/+8cBQFGrXVxiAiIiLbYHGBiIgsSCQSy6aORc5bXPjq9C7sTr8gig0Ia425PUbaZLzSoysstp/0G/q4TcZyJhKpFG7hHUQxXeaFas6un4c7DoRMcvPtiVqvxe9Jh6w6BhEREVkfiwtERFQl86URzrpjxKHMZHxyYrMoFuLhja8G3we5VGaTMYt2fi869ugwDG6hbW0ylrNxC2kjOtZmWbe4EOHlh3Etxcsvfj67HxqD3qrjEBERkXWxuEBERFUyb+rojDtG5FeU4aldf8BYufeBRIKFQ+5DMw8vm4ypuZaA8vPiPgB+Qx+1yVjOSBEiLqJos85bfYzHOom3DM0uL8GGlHirj0NERETWw+ICERFVyWLmQpFzzVwwCkY8s3sZMs36LDzfbQT6hbay2bhFO38QHct8guHVY4LNxnM25jMXdFkXrT5GbLNIi/+Gv547YPVxiIiIyHpYXCAioir5+IhnLjhbQ8fvzuzBjmtJotjAsDaYEzvUZmMaNWoU710sivkMmgWJ3M1mYzob85kLupxkCHqd1ceZ2aGf6PhYzlWcybtm9XGIiIjIOlhcICKiKlk0dHSingvHsq/gw2ObRLEgDy8sGHwPZFLb/WorObwMxvKimwGJBL6DH7HZeM7Izay4AKMButwUq48zMqojQlQ+otjiRDZ2JCIiclYsLhARUZV8fJ1zt4gCjRpP7vwDBsFoikkgwYLb7kWQjbeCNF8Soep0O9yCbbcEwxnJvAIg9QwQxazd1BEAFFIZprXrLYqtunwCRZpyq49FREREDcfiAhERVcnXT1xcKC/XQatxbMd+QRDw4t4VuFZWKIo/020YBoa3qfoiK9FcS0DFpYOimN/Qx2w6prMyn72gy0m2yTj3x/QWbUtZrtfh70vHbTIWERERNQyLC0REVCU/s5kLAFBY6NjZC39cOIJ/ryaIYv1CW+G5rsNtPnbxnl9FxzK/MHh2G2fzcZ2RvFmU6Fifn2qTcUJVPhjdopMotjjxIIRKu4MQERGRc2BxgYiIquTmLoeHSiGKFRY6bkr65aIcvHlonSgW4O6JBYPvtWmfBQAQDHoU7/9NFPPpPx0Smdym4zorRYBZcSHvqs3GerB9X9HxpaIc7M+4ZLPxiIiIqH5YXCAiomr5mS2NKHJQcUFr0GP2rj9RbrYrwf8GTkGoWdM/WyiL3wRDcZYo5jNwhs3HdVbywOaiY11+ms3G6hfaCm19g0WxPy8ctdl4REREVD8sLhARUbV8/TxEx4UFjlkW8dnJrThttg3hjPZ9cXtUR7uMX7x3kehY2aoP3MM72GVsZ6QIEBcXbDlzQSKRYFqMuLHjP1fOsLEjERGRk2FxgYiIquVnXlxwwMyF/RmX8NXpXaJYG98gvB431i7jG0rzUHpCvBzDlWctAIA80GxZRME1CAbbNfuc1LobFFKZ6Vhj0GNt8imbjUdERER1x+ICERFVy3JZhH1nLhRq1Hhm9zIIuNnATyGVYeHge+Ehd7NLDsUH/wQMN5djSOTu8O5zr13GdlYKs+ICBCP0hek2Gy9Q6YXbm4tninBpBBERkXNhcYGIiKplsSzCjjMXBEHAy/tXIUNdJIq/1GMUOgdG2C2P4r3iXSK8ek6EzNPPbuM7I6lXICQKceHJVjtG3HBP216i41O5aTiXn2nTMYmIiKj2WFwgIqJq+fk7bubC3xePY31KvCg2IKw1Hu080G45aFLjoUk5Jor5DHzQbuM7K4lEYrE0QmfDvgsAMDiiLULMmncuu8jZC0RERM6CxQUiIqqWec8FjUaP8nJdNWdbz9WSfLx2cI0o5uvmgc8H3Q2pxH6/uor3LRYdy/0joOo0wm7jOzO5v3j2iKEoq5ozrTSeVIapbXqIYisunoDWhr0eiIiIqPZYXCAiomr5mvVcAGy/HaVRMOK5PctRpteK4h8PmIwwT1+bjl2ZYNCjeP/vopjPgOmQVGos6Mrk3kGiY72NiwsAcHcb8dKIfE0Zdl07b/NxiYiI6NZYXCAiomrJ5TJ4eYkbJxbaeGnEjwn7cCgrWRS7p20v3BHdxabjmlOf3QZDsfgDs88A194lojKZb4jo2PxnZQutfJshLriFKLbq8kmbj0tERES3xuICERHVyKKpY4HtZi6cL8zCR8c3iWLNvfzxVp87bTZmdUoO/iE6VrbqDbewGLvn4axkPubFhWy7jDupdXfR8ear51Cq09hlbCIiIqoeiwtERFQjezV11BkNeHb3MmgqraGXQILPBk2Fl8LdJmNWx6gtR8mxVaKYd9/77JqDs5P7mC2LsFNxYVx0F8gr9d2oMOjw75UEu4xNRERE1WNxgYiIamTe1LGgQG2TcRac2oHTeddEsUc6DUC/0FY2Ga8mZSfXQ6govRmQSOHde6rd83BmljMXbL8sAgAClJ4YHNFOFOPSCCIiIsdjcYGIiGrkH6ASHRfYYFnE6dw0fHlquyjW1jcYc3uMsvpYtVFstiRC1WEo5H5hDsnFWVn2XMiGIAh2GXtiq26i4z3pF5BTXmKXsYmIiKhqLC4QEVGN/APMZi7kWXfmQoVeh2f3LINeMJpiMokU8wdNhYdcYdWxasNQVgD16Y2imHffe+2eh7OTm81cEPRaGNWFdhl7VFRHqOQ3G40aBQFrk0/bZWwiIiKqGosLRERUowCzmQtFRRXQ643VnF13n5zYgvOF4vX6s2OHoFtQc6uNURelR1dCqLQNpkTuBq+ekx2SizOT+QRbxOzV1FGlcMOoqI6i2KpLJ+0yNhEREVVNbqsbl5WVITExEQUFBSgrK6v1VMmJEyfaKiUiIqoH85kLggAUFZYjsJlng+99KDMZ35/ZI4p1DgjHM12HNfje9VV88E/RsWfXsZB5+jkmGScmdfOAxM0DgvbmMhmDnWYuANeXRlTutXAyNxWpJflo7h1gtxyIiIjoJqsXF9atW4fffvsN8fHxdV57KZFIWFwgInIyHh4KKJVyVFTc3MUhP7/hxYUynQbP710OATd/V7hJZfj8trvhJrNZ7btG+oJ0lCfuEMW4S0T1pCo/GCoVF4xlBXYb+7aItvB3V6FAc3OZzj9XzuCxzrfZLQciIiK6yWrLIioqKvD4449j7ty5OH36NIxGIwRBqPIPgBqfIyIi5yGRSCybOuY3vO/C+0c34kpJvij2Yo+RaO8f2uB711fJ4WXXp2b8f1KlNzy73uGwfJydTOUnOrbnzAWFVGaxNGJDyhm7jU9ERERiVvtqaN68edi5cycAwN3dHX369EFaWhouX75smpFQVlaGa9euISkpCXq9HhKJBB4eHhg5ciQkEom1UiEiIisLCPBARnqx6Ti/gcWF/RmX8GviQVEsLrgFHu00qEH3bSjzJRFePSdB6uZRzdkkVfmLju3V0PGGO6K74M8LR03Hx3OuIr20EOFefnbNg4iIiKxUXDh16hQ2bNgAiUSCqKgo/Pzzz4iIiMA777yDy5cvAwA++OAD0/mlpaVYtmwZvvrqK6jVauTl5WH+/Pnw8vKyRjpERGRlljMX6r8dpVqnxYv7VohiSpkCnw2aCpnUcX2GdblXoEk+Iopxl4iqCYIAjcaAMrcIFEmjAAASCJDnlUFZoYO7u9wuXxoMCGsNXzclirQVptg/V87gkU4DbT42ERERiVmluLBq1SrT4/fffx8RERE1nu/l5YWHHnoIw4YNw4wZM7B37168+uqr+PLLL62RDhERWVmA+XaUDZi58PHxTRbLIV7uOQotfZrV+57WUHJEXPCQegZA1cFxjSWdhVqtxZWUAlxNKUR2Vglyc8uQm6uGTmsA8ADg+8DNk/cD2L8JcrkUfv4eCA7xQnCwF5q38EN0ywB4e7tbNTc3mRwjozpi+cXjphiLC0RERI5hleLCsWPHAABRUVHo2bNnra+Ljo7GRx99hFmzZmHLli3YuXMnhgwZYo2UiIjIisxnLtR3WcSRrBT8dHa/KNYruAVmdehf79yspfSouLjg1WMCJHKFg7JxHEEQkJZahIT4TJxNyEJmRkmd76HXG5GbU4bcnDKcRZYpHhTsiQ4dQ9CpSyhaRPtDKm347IY7oruIigtHsq4gS12MEJVPg+9NREREtWeV4kJ2djYkEgk6dOggileeEqnVauHm5mZxbb9+/dC2bVtcvHgRa9euZXGBiMgJBQSKiwvFRRro9QbI5bJa36Ncr8MLe/8W7Q7hLpPjfwPvcuhyCADQ5aWi4pK4B4R33F0OysYxCgrKceTgVRw9kobCgvove6lJTnYZcrIvY/fOy/D2cUevuEjE9YlCs6D67zwyKLwtvBTuKNVpAAACBPx7JQEPduhnrbSJiIioFqxSXCgrKwMA+Pn5ieLu7jenP5aWliIgoOq9pzt27IgLFy4gISHBGukQEZGV+QdYNjUsyC9HUHDte+V8dmIrLhfnimIvdL8drX2DGpxfQ5UeXSk6lqr8oOrY9JdECIKAy5fysGv7ZSQlZsOemzaVFGuwY9sl7Nh2CW3bNcOQYa3Rpl2zOvdqcJfJcXvzDlh1+aQptj4lnsUFIiIiO7NKcUGpVKKsrAx6vV4U9/G5OSUxPT292uLCjS0os7OzrZEOERFZmVKpgEqlgFqtM8Xy89W1Li6czEnFdwm7RbGuzSLxqJOsjS+pckmE5Wy7pkIQBJw7m43tWy7g6pXCW54vl0sR2dwPzVv4ITjYE82CPOHn5wH9iT9R+PtjkPz/+SiylgMQMPtfFBdXIDenDNlZpbiWVoQrVwqg1Riqvf+F87m4cD4XEZG+GDGqLTp2CqlTkeGO6C6i4sLhrBQUVJTBX1n/GRFERERUN1YpLoSFheHixYsoLCwUxaOjo02PT548ic6dO1d5/cWLF62RBhER2VBAoApqdZHpOC+3dn0XNAY9Xtj7N4yVvhZXSGX4bOBUyKW1X1ZhK7qCa6i4sE8U8+o1xUHZ2F7q1UKsX3MWyZfzazwvsJkKnbuEomPnUDSP8q1yCUyxtwpF/3+ZiwSAXFeMgEAVAgJViG558wsFg8GI9GvFOJuQhYT4zGr7OFxLK8KvPx1Fy1YBuGN8B0S18K/yPHODI9pBKVOgwnC9+GUQjNh+7TymtO5eq+uJiIio4axSXGjXrh0uXLiA5ORkUTw2Ntb0zcNff/2Fe++9F3K5eMi9e/fi7NmzkEgkaN68uTXSISIiGwhs5om01JvFhdycslpd9+Wp7UgqzBLFnu06DDH+IVbNr75Kj64SHUs9fKHqNMJB2dhOUWE5Nq5PxPFj16o9R6mUo3vPCPTuG4XwCJ9bzh6Quol7cQiaqv9OyGRSNI/yQ/MoP4waE4PMjBIcOXQVx46mQV2mszg/+XI+Fn6+Dz16RWDchI7w8qp5lwkPuQK3hbfB5tRzptiWq2dZXCAiIrIjq3TQ6tWrFwAgOTlZNHshLCwMPXv2hCAIuHjxIp588kmcPXsWOp0OJSUlWL16Nf7zn/+Yzh86dKg10iEiIhswb7qXl3vr4kJCXjq+Or1TFOsUEIYnY4dYMbOGKT3yt+jYs/udkCqsu2WiIxmNAg7uv4L/fbSr2sKCv78HJk7pjNf+OwKT7uqCiEjfWi1LkJgVF4za2s1mCQ3zxp0TO2HemyMweWoXi4ahNxw/eg2ffrATRw5dNS2hrM7tUR1FxzuvnYfWoK/mbCIiIrI2q8xcGDx4MCQSCQRBwM6dOzFx4kTTcy+88ALuv/9+AMCePXuwZ8+eKu/h7++PBx980BrpEBGRDTRrJi4u5N6iuKAzGvD83uXQC0ZTTC6R4rOBU6FwguUQAKAvzED5hb2iWFPaJSInpxQr/jqNy5eqXgLh7++B20e3Q/eeEZDJ6v59g9Rd/HdCqGVx4QaFQoa+/Vsgrk9znDqRjk3/JKHAbKcKtVqH5X+exumTGbj7/m7w9q668DM8sr3ouFSnwcHMZNwW0bZOOREREVH9WGXmQnh4OGbOnIkxY8YgP1/8BqZ79+545513IJPJIAhClX8CAgLw9ddfV9vwkYiIHM985kJ+nhpGY/XfJn8TvwsJ+Rmi2FOxQ9ApMNwm+dVH6bHVqLxFglTpDVWn2x2XkJUIgoAjh1Lx+ad7qiwsKJVyjB3XHv95ZQh69W5er8ICUMXMBU3ZLWcYVEUmk6JHr0j855UhGDehI5RKy+8+khJz8NnHu3AuIauKOwDBKm90DxIvr9ycerbOuRAREVH9WGXmAgC89NJL1T531113oXv37li0aBEOHjyI7OxsSKVSREZGYtiwYXjwwQdZWCAicnKBzcQfJA0GAYUF5VVOab9UlIPPT24TxWL8QvBMV+fa3rGkqiURbkoHZWMd5eU6rFwej1Mn0qt8vlfvSIy9s8Mt+xjUhtTd7L+90QAYdEA9d9pQKGS4bUgrdOsejnVrzlq8hrJSLX758QgGDIrGHeM7Qi4XF0VGNu+IEzmppuMtV8/hnT7j67y9JREREdWd1YoLt9K6dWu888479hqOiIiszNPTDUqlHBUVN9ex5+aWWRQXjIIRc/ethNZ4c+tBqUSC/w28C24yu/3auSVDSS7Kk8TbY3o38l0iMtKL8evPR5GfZ7k8wT/AA1OmxqJd+yCrjSdxt9zq0ahRQ9bAbTx9fJWYNqMHevSKwN9/nUZJsUb0/L49KUi/VozpM3vCq9IyidujOuCj45tMx9fKCnGuIAMdA5xntgwREVFTZZVlEURE1PRJJBIEmvddqGLHiKXnj+BQlnj3oEc6DkC3IOfaEaj05HqgUj8IiZsKqi6jHJhRw8SfysDCL/ZVWVjo0y8Kz88dbNXCAmC5WwRQ974LNenQMQTPvzgYnTpb7iySfDkfX362B2mphaZYjF8ImnuJt6/ccvUciIiIyPZYXCAiolq71Y4RmepivH90oyjW3Msf/+k+0ua51VXpibWiY88uoyB183BQNvVnNArYtDEJSxYdg05rED3n4aHA9Jk9MeXuWLi7W3/WiHnPBeB63wVr8vRyw4yHemHy1C6QK8RvWwoLK/D1gv04ffL68gmJRILbm3cQnbMlNdGq+RAREVHV6v1OIysrC0uXLsWBAweQmpqK0tJSeHt7IzIyEv369cP999+PkBDn2MOciIisw7y4YD5z4fWDa1CsrRDFPuw/CSpFw6bJW5tRo4b6zGZRzKvHeAdlU386rQF//H4CZ05nWjwX1cIP0x7sCX9/2xVMJFUsfxD0mirObOA4Egn69m+ByOZ+WPzzERQW3vw7ptcZ8fvi4ygp0WDAoJa4PaoDfj633/T8qdw05FeUIUBpuYSDiIiIrKdeMxeWL1+OkSNH4vvvv0d8fDwKCgqg0+mQn5+P+Ph4fP/99xg5ciT+/PNPa+dLREQOVNN2lBuvnMHGKwmi56e07o7BEe3skltdqBO2QNBW2vJQKoNn1zscl1A9qMu0+OHbg1UWFuL6NMfjs/vZtLAAXP/QD7M+GoJBX83ZDRfZ3BdPPz8ILVuLm0ALArBmZQL+/ScRccHR8JArbj4HAbvTL9gsJyIiIrquzsWF1atX4/XXX4dGc/2bCfMtp24cazQavPXWW1i9enXDsyQiIqdQ3XaURZpyvHZgjei5AHdPvNl7nD3TqzXzJREeMbdB5hXooGzqrrCgHN8s2I+U5AJRXCqVYMLkTrjrnljI5TK75CKRmk2CNOhsOp6XtzsefaIv+vSLsnhu+5aLWPf3WQwIbS2K70w7b9OciIiIqI7LIkpLS/Huu+8CuP5thSAIaN++Pbp37w4fHx8UFxfj+PHjSEpKMj3/7rvvYsSIEfDy8rLJCyAiIvsxn7lgMAjIz1Pj0wubkVVeInruv33GOeVUdMGgR+mJdaKYV/fGsyQiK6sEP35zCEVF4uUnSqUc02f1Qtt2zeyaj0SmgKC7mYtgtN3MhRtkMikmT+0CPz8PbNqYJHruyKFUtG0Tjm0eiRD+/w6Uu9LPwygYIZWw1RQREZGt1Km4sHr1apSWlkIikcDb2xuffPIJBg8ebHHezp07MXfuXBQXF6OsrAyrV6/GAw88YLWkiYjIMTy93ODp6YayMq0ptifpEn67cEh03pCIdpjUqpuds6ud8ov7YSzNE8UaS3EhM6MY3319EGWlWlHcx9cdDz/aB2HhPvZPymJZhG1nLtwgkUgwfGRbeHm7YeXyeFSeSFl0UYu+fu1wsPl5CBIgp7wU5/Iz0SmQW1ISERHZSp1K+Pv332yQ9P7771dZWACAIUOG4P3336/yOiIiatyCQ8Qz0f46dkx07CFX4IN+E6+vx3dCZcfFSyLcm3eFIijaMcnUQfq1Ynz71QGLwkJwiBeeemagYwoLuD5zQcSGPReq0qdfC0yf1QtyufgtTcvCYPRNbQfJ/y867LjGpRFERES2VKfiQmLi9e2cWrRogREjRtR47ogRI9CiRQsIgoCkpKQazyUiosbDvLigLzKKjl/qMQrNvcUN95yFIAgoPS7uDeHZY4KDsqm9tNQifP/1AajLxLMColr44ck5/W3euLFGUsfMXKisc5dQPPhQ1QWGPmltAQHYeY3vRYiIiGypTsWFwsJCSCQSdOnSpVbnx8bGmq4jIqKmwby44FOhMj3u2iwSszr0t3dKtaZNOwNdzmVRzNm3oExLLcIP3xyEWi3+0B7dMgCPPN4XKk/HbvNpPnPBlrtF1CSmQ3CVBYZWBSHokdESRzOvoMRsm1QiIiKynjoVF9RqNQDAx6d2Uy9vnHfjOiIiavxCQrxFx74aD0AA5BIpPhkwGTKp8zbNM98lQh4YBfeobo5JphayMkvw43cHUV4uLiy0ahOIhx/rDaWyTq2TbEIis+9uETWprsDQPjcCbbPDsC/jkoMyIyIiavrq9Q7QWdfREhGR7ZnPXFAY5fDQueGJLoPRMcC5G+aZL4nw6jHBaX+n5eep8cO3By2WQrRp2wwP/V9vuLs7vrAAwHJZhB12i6hJTIdgTJ/VE1Kp+L9r98yW2LH3goOyIiIiavqc9+slIiJySmXyCuilBlEsRhaKZ7oOc1BGtaMvSIcmRdx80ll3iSgqqsD33xxEcZFGFG/TNhCzHomDm5vMQZlZMp+54KhlEZV16BiCu+/rahE3HpXhbEKWAzIiIiJq+lhcICKiOnnj8DoUuYuXu90bGgelXFHNFc6h7PQ/omOphy882g1yUDbVKyvT4sdvDiI/T/wzjmrhhwcfjoPCiQoLACDaAxKAxEmWxfToFYm42yNFMQkk+H3xMWSkFzsoKyIioqarXnMqT58+jYULF9bqvBtqcz4AzJ49uz4pERGRHWy6koBNV8+ir7ItAstv9l7wULs7MKvaKT0lLi6ouoyExMkKIjqtAb/+dARZWaWieFi4Dx561ImWQogIZsfOs8xkyphYLDtzDC0zQkwxndaIX348gjnPDYS3t/P/vSUiImos6vUuJT4+HvHx8bU698Za1q+++qpW57O4QETknEp1Grx28HpDxGL3ctFz2dmlVV3iNIw6DdQJW0Uxr65jHZRN1YxGAX8uPYmU5AJRvFmQJx55vA9UKsfuClEdwSjeihRO1MNCKpEiKM4Dybuz0bIw2BQvLCjHrz8dwWNP9YNC4WQzQYiIiBqpes1dFATBJn+IiMh5/e/EFmSoiwDAYllEZkaJU/87Xp60G4Km7GZAIoGqyxjHJVSFDWvPIv5Uhijm66vE/z3R18m/YXfemQsA0D+8NQ5FXkC2qkgUv3qlEMv/POXUf2+JiIgakzrNXIiLi7NVHjZRWlqKffv24dChQzh79ixSUlJQUlICd3d3BAcHIzY2FuPGjcOgQYOctls4EZEzOJN3DT+d3Wc6LvQoEz1frtahqLACfv4e9k6tVspObRAdK1v2htwnyEHZWNqz6zL27EoWxZRKOR56tDf8nfRnamL+4dxJei7cMCCsNYxSAXuiEzHqQld46ZSm504eT0dYuA+GDm/jwAyJiIiahjoVF5YsWWKrPKzul19+wfz586HRaCye0+v1SE5ORnJyMtasWYNevXrhk08+QXi4c2+hRkTkCAajES/tXwVjpQ+ReqUBbu4yaDU3d43ISC92yuKCIAgoM+u34OlESyLOnM7A+jVnRTGZTILps3ohLNzHQVnVhVlDRyebuRDtHYhwT1+klxVhV8uzGHkxFgrjzbc//25IRPMoP7Rp28yBWRIRETV+zvX1ghUlJyebCgshISGYNGkSXnvtNcyfPx8ffvgh7r33XqhUKgDA0aNHMX36dOTl5TkyZSIip7Q48SBO5aaJYnO6DkV4uK8o5qwd+HWZ56HLviSKeXa7w0HZiKVfK8Ifv5+0+PL/rnu6om27RvJh1zx5J5sJKJFIMCCsNQCgSKnGvqgkCJUKIoIA/L74OIoKy6u7BREREdVCky0uSCQSDBw4ED///DN27tyJDz/8ENOnT8fYsWMxadIkvPXWW1i/fj1atmwJAEhLS8Onn37q4KyJiJxLproYHx3fJIq19g3CE10GIzzCWxRPd9LigvmsBZlfONyjujkmmUpKSjRY9OMR6LQGUXz02Bj0jIus5ion5OTFBQAYEHZz2UO6TwHOh6eLni8r1eK3X49DrzeaX0pERES11GSLC8899xx++uknDBgwANJq1n9GRETg888/Nx1v3LgR5eX85oKI6IY3D61DqU68vOzD/pPgLpNbTNl31pkLpWb9Fjxjxzi8z45eb8Din4+isLBCFO/VuzmGjmhs6/+du6EjAPT//zMXbjgWmIyINuK/v1dSCrBhrXh5ChEREdVeky0u+Pn51eq89u3bm2YvlJeX48qVKzbMioio8diWmogNKeJth+9u0xP9QlsBgEVxITenDFqzb+EdzaAuQvn5PaKYo7egFAQBK5fH40qKeMvJ6Jb+mDy1s8MLH3XlzFtR3hDu6YtWPpWWmUgAaR8jAgJVovP27UnB6ZPpICIiorprssWFuvDy8jI9rqoBJBGRq1HrtJh3cLUo5u+uwmtxNz+Yh4Z6iz5HCgKQlVlipwxrR52wBTDoTccSuRtUnUY4MCNgz65kHD0s7mHh5++BGbN6QS6XOSir+hMMOtGxRO7moExqNsBs9sLB/MuYMasn5ArxW6G//zqNgnzxVqtERER0ay5fXNBqtUhJSTEdc8cIIiJg/sltSCstFMVejxuLAKWn6djNXY7AZp6ic5yt74J5vwWP9oMhVXpVc7btJZ3Ltph67+Ymw8yH4+Dl7e6grBpG0IuL8hJZ4yguHM1KQXCYFyZN6SKKV1To8cdvJ2AwsP8CERFRXdRpK8qmaP369Sgpuf5NW6dOnRAUVPO+5+np6UhPr9+UyaSkpHpdR0RkT+fyM/FDgngpQd/QlpjapqfFueHhPsjNKTMdZ1xznuKCYDSi7PRGUcyzq+N2icjLLcPS305Y9D+8d1o3hEc0hi0nq6HXig4lCucskvQJbSk6LtNrcTY/A716R+LC+RycPH7zd3tKcgG2bbmAkaNj7J0mERFRo+XSxYX8/HzRDhFPPPHELa9ZsWIFFi5caMu0iIgcxigY8fL+ldALN7+1VUhl+LDfpCp7AYRF+OD0qQzTcVpqoT3SrBVNyjEYirNFMU8H9VvQag1YsugYytXiJQSjxsagc2yYQ3KyFouZC3LnLC4EeXijpU8zJBfnmmKHs1IQ2ywSk6d2wdWUQuRXWg6xbfMFtG3bDC1bBzoiXSIiokbHZZdFaLVazJkzB3l5eQCAESNG4Pbbb3dwVkREjrU06QiO5VwVxZ7sMhht/IKrPL95cz/Rcfq1YqeZTl4WL95CUxEaA7fg1tWcbTuCIGDV8nikm83q6NI1DMMa3c4QYoIgQDCfueCkPRcAoHdItOj4YGYyAECpVOC+6d0hld4soAkC8MdvJ6BWi18fERERVc0liwtGoxGvvvoqjh49CgCIiorC+++/7+CsiIgcK1tdgg+OiZcRRHsHYk7s0GqviWzuKzrW643ISHeOpo7mxQXP2NEOyePg/is4dlTcwDE42AtT7+3a6HaGMGdeWACcd+YCAPQNES+NOJKdAuH/r1NpEe2PkaPbiZ4vLKzA2lUJdsuPiIioMXO5ZRGCIODNN9/EunXrAFxv4PjLL7/A19f3FldeN2XKFPTr169eYyclJeHtt9+u17VERLb21pH1KNJWiGIf9J8IpVxR7TUqTzcENlMhL/fmdPK01EKLooO9GcoKUXH5kCjm2Xmk3fO4klJg8eHUzV2GGQ/1glLZBH4FV1VccNKeCwDQOzRadJxXUYZLRTmmmTlDhrfB+fO5uHwxz3TO8aPX0Dk2DJ27hNozVSIiokanCbyzqT1BEPDf//4Xy5YtAwCEhobi119/RWRkZK3vER4ezh0liKjJ2XXtPNZcPiWKTWrVDYPC297y2uZRfqLiQurVQvTt38LqOdaF+uw2wGgwHUsUSni0H2zXHEpKNFiy6CgMBnEHx3vu64bgEMftWGFN5v0WAOfdLQIAorwCEKLyQZb65hKVQ1kppuKCVCrBvdO64bOPdqGi4uYWpiuXnUZ0S394eTlv4YSIiMjR6rQsIicnx1Z52JwgCHjrrbfw559/AgBCQkKwePFiREVFOTgzIiLHKtfr8OqBNaKYr5sH3uw9rlbXN4/yEx2nXi20Umb1Z74kwiNmEKRuHnYb32Aw4vfFx1FcJP7wPXhoK3Tp2rgbOFZmrKq44MQzFyQSCfqY9V04nJUsOvbz88D4SZ1EsdJSLVb9fca0hIKIiIgs1am4MHjwYNx999345ptvkJiYaKucrO5GYeGPP/4AAAQHB2Px4sVo0cKx36wRETmDBae240pJnij2aq8xaOZRu2/XzZs6ZmWWQKvRV32yHQiCAPWZzaKYZ+dRds1h87/nRVPrAaB1m0CMvqO9XfOwuUbWcwEAepv1XThkVlwAgJ5xkejYOUQUiz+VgVMn6rcVNRERkSuoU3HBaDQiPj4eX375JSZNmoRhw4bhnXfewb59+6DT6W59AwcwLywEBQVh8eLFiI6OdmxiRERO4HxhFr45s1sUiwtugfva9ar1PcIjfS267F9LK7JajnWlzUiEPj9VFFN1sV+/hcRz2dix9aIo5uunxLQZPSCTNa0+ykZtuUXMmWcuAEAfs+JCWmkh0ksLRTGJRIIpU7tApRL3G1m14gyKi8R9SYiIiOi6Or3LGT58OJRK5fWtpwQB6enpWLp0KR555BH07dsXzzzzDNasWYPCwkIbpVt3b7/9tkVhoWXLlre4ioio6TMKRryyfxV0lXoTyCVSfNh/MqSS2v96cHOTISTUWxS76sClEWqzJRHygEi4hXe0y9iFBeX48/cTophUKsEDD/aEl7dzf+iuD0FTJjqWuHlAIpU5KJvaifEPhq/ZEplDWSkW53n7KDHxri6iWLlahzXcPYKIiKhKdSoufPXVVzh06BC+++473HvvvQgJCTEVGsrKyrB582a8/PLLGDhwIB544AH89NNPSE62nG5oL++88w6WLl0K4GZhoVWrVg7Lh4jImSy7cMziQ9VjnW9DjH9I1RfUIKqFn+g4JTm/AZk1jHm/BVXnkXbZ8vFGnwV1mXgm39g7O6BFtL/Nx3cEo6ZUdCx193RQJrUnlUgRFyJeFnkkO6XKc7t1D0dsN3GPjPhTGUg4k2mr9IiIiBqtOu8W4ebmhsGDB2Pw4MH473//i7Nnz2L79u3Yvn07zp49CwDQ6/U4duwYjh07hk8//RRRUVEYNmwYhg8fjh49ekAqtf200Pnz5+O3334DcH1644wZM3D58mVcvny5xus6duzI3SCIqMnLryjDe0c3imJRXgF4ttuwet0vumUADh24ajpOuVwAQRDs8qG+MqO2HOVJ4mUe9tqC8t8NibiSUiCKdeoSgkGDm+5sOWOFuLggcW8cu2DEBUdja+rN3lHHsq9Ue+7EKZ1x8UKuqGi0+u8zaN0mEEpl9du0EhERuZoGb0XZsWNHdOzYEbNnz0ZWVhZ27NiBHTt24ODBg9BorneRvnr1KhYtWoRFixbBx8cHgwcPxtChQzFo0CB4ednmjcjx48dNjwVBwP/+979aXffBBx9g8uTJNsmJiMhZvHd0Iwo0anGs3wR4yOu3jWDLVgGi47IyLXKyy+y+5WJ50m4Iukpr4iVSqDqNsPm4ZxOysGuHuHgdEKDC3fd1s3uBxZ4sZi4oG0dxoWeweKeocwWZUOu0UCks//57ebnjzgmd8NfSk6ZYUVEF/t2QiIlTulicT0RE5KoaXFyoLCQkBPfeey/uvfdelJeXY9++fdixYwd27dqF3NxcAEBRURHWrVuHdevWQS6XIy4uDsOGDcOwYcM4Y4CIyA4OZSbjrwtHRbFx0V0wNDKm3vf0D/CAr68SRZWa3SVfzrN7caHMbJcIZes+kHnadklCQb5a9METAGQyKaY92AMeHk37m23zmQvSRjJzITYwEjKJFAbBCAAwCgJO5qaif1jrKs/v0SsCx4+m4cL5XFPswL4r6N4zsskueSEiIqorqxYXKvPw8MCIESMwYsQICIKA06dPm5ZPXLhwAQCg0+lw4MABHDhwAO+99x7atm1rKjTExsY2aPwlS5ZY42UQETUpWoMerx5YLYp5KdzxZu9xDbqvRCJBdKsA0VZ9KZcL0Keffbf8Vcebb0Fp2yURev31PgvlanGfhXETOqB5lJ9Nx3YG5g0dpUrn77kAACqFGzoGhCE+75opdjyn+uKCRCLB5Kld8NnHu6DTXS9ICALw91+n8cwLgyCXN61dQIiIiOrDLr8NJRIJunbtiueeew7r1q3D1q1bMW/ePPTv3x8ymczUFPLChQv47rvvcM8992DQoEH2SI2IyKX8kLAXSYVZotiLPUYizNO3wfc2XxqRfNm+TR11eanQpp8VxVQ2Li5sXH8OV68UimKxXcPQf2C0Tcd1Fo215wIA9AhqLjo+XkPfBQAIbOaJ20eLZ/dkZZZg945LVs+NiIioMXJIqT0yMhLTp0/Hzz//jIMHD2L+/Pm488474ePjYyo03FhGQURE1pFako/5J7eJYl0CI/Bg+75Wub95cSE/X43CgnKr3Ls21GfEu0RIPf2hbBVns/HOxGdizy7xjkiBzVSYck9sk+6zUJnlbhGNqLgQLJ5VczwnFYIg1HjNoMEtER7hI4pt23IBBXb8e05EROSsHD6Pz8vLC2PGjMEnn3yCAwcOYMmSJZg1axZatLDvVFoioqZMEAS8fmgtKgw3p+9LIMEH/SZCLpVZZYyQUG+oVOIeA5XXqNtaWcJW0bGq0whIrPTazOXnqbHMrM+CXC7FAw/2bPJ9Fiqz6LnQSBo6ApYzF3IrSnG1tObZNjKZFHfdE4vKtSOdzoj1qxNskSIREVGj4vDiQmVSqRRxcXF46aWX8O+//zo6HSKiJmPT1bOirfcAYEb7Puhm9gGrIaRSCVq3bSaKXTifY7X710QwGlF+boco5mmjXSL0egN++/UYKir0ovj4SZ0QEdnw5SWNibGiRHQsdW8cPRcAINo7EAFm+R7PTr3ldZHN/dC3v/gLkPjTmTifaJ+/60RERM7KqYoLRERkfWU6DV4/uFYUC/Lwwtweo6w+Vtt24uLCxfO5MBprnmpuDZrU0zCUiGdJqDrapriwbvVZpKUWiWLdeoSjT7+oaq5ouozqQtGxVOXnkDzqQyKRoEewuLh2LKfmvgs3jBoTA5WneIbK6pVnoNcbrJYfERFRY8PiAhFRE/fZia3IUIs/DL/Zexx83T2sPpZ5caG0VIvMjJJqzrYe9VlxLwlFcGsogqKtPs7J49dwYJ/4A2izIE9Mnuo6fRYqMy8uyFSNa1vGnkHiGQgncm49cwEAVJ5uGDuugyiWm1OG3TuTq7mCiIio6WNxgYioCTubn44fz+4TxQaFt8GEll1tMl5gM08EBKpEMXssjVCb91voONzqY2Rnl+LvZadFMYVCiukze0KptNnOzk7NUFYgOpZ6Na7ignnfhYS8dJTrddWcLdard3NEtfATxdjckYiIXBmLC0RETZRRMOKV/athEIymmLtMjvf6TrTpt+zmsxdsvRbdqNOg/PxeUUzVcZhVx9BqDfht0TFoNeJp75Pu6oKwcJ9qrmr6zIsLjW3mQteg5pBW+v+CXjAiPu9ara6VSiWYOKWzuLmj1oD1a85WfxEREVETxuICEVET9cf5oziWc1UUe6rLELTybVbNFdbRLiZIdHz5Uh7Ky2v3bXB9VFw6CEGrvhmQSKDqMNRq9xcEAav+jrdY3hHXpzl69bZeQ8zGyFgm3l1B6tm4igteCnfE+IWIYifM/j9Tk8jmfujTz6y546kMXEhic0ciInI9LC4QETVBueWleP/oRlEs2jsQT3YZbPOx28YEQSa7+evFYBBsOntBnSDut+Ae1R0yb+sVUI4cSsWxI2miWFi4NyZO7my1MRojo7YCgq5CFJM1suICAHQPEjfiPJ1bu5kLN4wea9nccd2as3ZpZEpERORMWFwgImqC3j3yD4q04rXf7/ebCKVcUc0V1qNUytGmbaAodjYhy2bjqc9tFx1bc0lE+rVirF55RhRzd5fjgQd7QuEms9o4jZFRXWARk3kGOCCTholtFiE6PpWbVs2ZVVN5umHMHe1FscyMEhw5XLvmkERERE0FiwtERE3MvoxL+PvScVFsQquuuC2ird1y6NhZPNU88Ww2DAZjNWfXn0FdhIrLh0UxVSfrNHMsL9dhyaKj0OvEeU+9NxZBwV5WGaMxM++3ADSurShv6BoYKTpOKclDoUZdzdlVi+sThbBwb1Fs0z9JqKjQNzg/IiKixoLFBSKiJkRr0GPegdWimI+bEm/GjbNrHh06iYsL5eU6JF/Or+bs+itP2gUYbzZZlMjd4NF2YIPvKwgC/v7rNPJyxR8yBwyKRmy38Abfvykwn7kgVXpDImt8u2bE+IfATSqehXImL71O95BKJRg3vqMoVlqiwc5tFxucHxERUWPB4gIRURPy3Zk9uFgk7m/wUo9RCFZ5V3OFbfj5eSAi0lcUO3Wibh/YakN9VrwkQtl2AKTuqmrOrr19e1IQfypDFGse5Yc7zD5AujJDaeNu5niDm0yODgFholhdl0YA13uNdOgYLIrt3nkZBfl1mwVBRETUWFn9K4aCggLs2rULSUlJKCwshEKhQFBQEEJDQ9G/f39ERETc+iZERFRnV0ry8PkpcXPDrs0i8UBMH4fk06VrGK6lFZmO409lYMLkzpDLrVfXNm/mqOrQ8H4LyZfzLbYT9FAp8MCDPayae2NnKM0VHTfGfgs3dG0WKSoonK7ldpTm7hjfEUmJOaZmjnq9ERs3JOL+6T2skicREZEzs1pxQavVYv78+fj999+h01W/5VirVq1w991347777oObm5u1hicicmmCIGDegTXQGG6u8ZZKJPig30TIpI75QNytezj+3ZBoOlardTiflIOOZksm6ktfkA5turgI0NB+C4UF5Vjyy1GLTv/33t8N/gENnxHRlBiKxTNkZD7B1Zzp/GIDxV98nK7HzAUACA7xQr8BLbBvT4opdvJ4Ogbe1hJRLRrnzA4iIqLasso7TkEQ8NRTT2HRokXQarUQBKHaP5cvX8aHH36IESNGYO3atdYYnojI5W24cgY7r50XxWa274fYZpHVXGF7AYEqtIgWf6A6ebx+3whXRX1WPGtB6uELZXTPet9PpzXg15+PorRUK4oPHdHGoocEAfpi8Q4gct/G+zMy//9JamkBCirK6nWvEaPawcPDbGvK1WchCNyakoiImjarzFxYsWIF9uzZA4lEAgBwd3fHbbfdhpiYGHh4eKCwsBCXL1/G0aNHUVR0fYpsdnY2XnrpJRw7dgyvvfYaFArbb49GRNQUlWgr8OahdaJYiIc3Xuwx0kEZ3dS9ZwSupNxs/JdwJgsVFToolQ3/N9+834JH+8H1bigoCAJWLD8tWsYBADHtgzBqTEy9c2zKDMXZomOZd+OdudDOLxjuMrlo5s/pvGsYHNGuzvfy9HTD8JFtRUtrrqQUIP5UBpuBEhFRk2aV4sLq1atNj7t164YvvvgCISGW32AYjUbs2rULv/32G/bt2wdBELBs2TKkp6fj22+/hUzm2nuGExHVx6cntiBLXSyKvdXnTni7KR2U0U2xXcOwdlWCaZmBTmvAiWPX0G9AdIPuKwiCxcwFVacR9b7fnl3JOH5UPKsisJkK903vDqlUUu/7NmUGs5kLskY8c0EulaFzQDiO5Vw1xU7n1q+4AAD9B0bjwL4U0W4j//6ThE5dQiGTsW8HERE1TVb5DXfhwgUAgIeHB77++usqCwsAIJVKMXToUPz000/45ptv4O/vD0EQsHfvXnzyySfWSIWIyKWcybuGX87tF8UGR7TDHdFdHJSRmJe3Ozp0En+jfWDflQZPEddlnoe+QFwM8OxYv34LF5JysGGtuHeDm7sMMx+Og0rF3kDV0Zv1XJA34p4LgOXSiPrsGHGDXC7F2HEdRLHcnDIcOZRa73sSERE5O6sUF9RqNSQSCfr27YuAgNp1ix46dCiWLVuGwMBACIKA3377DZcuXbJGOkRELsFgNOKl/atgrPRB3V0mx3t9J5iWqTmDvv1biI4zM0pw9Uphg+6pTtwpOpb5hUERVvflC1lZJfjt1+Mwr3XcN607QkLtu31nY2MoMpu50MiLC12bmTV1zKt/cQEAOseGonmUnyi2dfN5aLWGBt2XiIjIWVmluODn5wcAaNasWZ2ua968Od566y0AgMFgwN9//22NdIiIXMLv5w9bfLv6dOxQRPsEOiijqrVtF4SAQPFOC/v3pjTonuXndoqOVe2H1LmgUlKiwc/fH0Z5uXiHo9tHtUOnLqENyq+pE4xGGErMd4tovMsiAMuZC+llRcgtL633/SQSCcaMay+KFRdpsG9Pcr3vSURE5MysUlyIjo4GAGRmZtb52hEjRiAy8vov9L1791ojHSKiJi9bXYIPj/0rirX2DcLjXQY7KKPqSaUS9O0XJYqdOpGOgnx1NVfUTBAEqBN3iWKqDkPqdA+tRo9ffjiMgvxyUbxzl1AMH9m2Xnm5EkNpHiAYRbHGviyitU8QlDJxo9Gz+RkNumebts3QLiZIFNu57RLUam01VxARETVeViku9O3bF4Ig4PDhwygpKanz9V26dIEgCEhPT7dGOkRETd7bRzagWFshir3fbyLc67lbgq3F9YmCQnHzV47RKGDXjsv1upcuI8mimaBH+yG1vt5oFPDHbyeQlireGaJ5lB/ufYANHGvDUJJtEZN5B1VxZuMhk0rR3l88Y6WhxQUAGH2HePZCebkOO7dxGSgRETU9VikuTJw4EQqFAhqNBp9//nmdr/f2vr6uVSplB2UiolvZk34Bqy+fFMUmt+6OAWGtHZNQLXh6uaF3X/HshcOHrqK4qKKaK6pn3m9B7h8BRXDtXrvRKGDFstNIOCMuTgQEqDDzkTi4uXHXotowFIlnKko9AyCRN/4tpTsFhImOE/Ib/qVHZHNfdO0u3oJy755kFBWWV3MFERFR42SVT/MRERF4/vnnIQgCli5diiVLltTp+suXr397FR7O/Z+JiGpSodfh1QNrRDFfNyVejxvroIxq77ahrUWzAvQ6I7ZsOl/n+5gXFzzaD65VvwVBELBudYJFx34PlQIPPdob3t7udc7FVenyxb0+5AGR1ZzZuHQ0Ky5YY+YCAIwc087i7/7WzRescm8iIiJnYZXiwqOPPoqioiJ07doVgiDg/fffxyuvvIL8/PxbXnvixAkcO3YMEokEt99+uzXSISJqsr45swvJxbmi2Cs9xyDIw/l3NvD390Cv3uIPoUcOpSI7q/ZN8wRBQLl5v4VaLonYtDEJ+/akiGIymRQzZvVCcIhXrXMgQJ8v3gZU7t80vhzoFCB+HReLclCh11Vzdu0FBXlZzNw5cigVOdn1bxhJRETkbKxSXNi9eze+++47nD59GhKJBIIgYPXq1bj99tvxxhtv4NChQ9BoNKJrDAYD1q9fjyeeeAKCICA8PBzTpk2zRjpERE3S5aJcLDy9UxTrERSF+2PiHJNQPdw+qp1F74W1qxIgmO8FWQ1tRiIMxeL1/h63aOYoCAI2b0zC9i0XRXGpVIIHZvZA6zbOtbtGY6AvEM9cUPg3jZkL7QNCIcHNGQYGwYjzhVk1XFF7w0e2tfi7v/nfus/cISIiclZW6/x1442hRCIxTU8tKyvD8uXLsXz5cshkMoSHh8PPzw96vR4pKSkoLy+HIAjo1q0bPvroI/j7+1srHSKiJkUQBLx2cA00Br0pJpNI8WH/iZBKGk+/Gl8/Dwwa3Arbt978oH8+KQcnjl1Dj163/oBqvgWlPCASiqBW1Z5vNApYv+Ys9u4Wb/8nkQD3TuuGTp255WR96JvosggvhTuifQJFs4MS8jMstqmsD19fJQbe1hI7KjVzPH0yHcNvb4vQMOefeURERHQrVnlH+sMPP+D555/HmDFj0KJFC9PsBeD6G2JBEKDX65Gamor4+HicO3cOarUagiDAzc0NUVFR2LZtG/bv31+rpRRERK5mbfJp7E4Xr9F+uGN/dAxofNPRhwxvDV9fpSi2dnUCCmvR4M6y38KQavstGAxG/P3XaYvCAgBMuTsW3XpE1D5pEtEXmC+LaDo/S1s0dbxh8LDWUCpvfq8jCMDWevQdISIickZWmbkwaNAgDBo0yHSsVquRlJSEc+fOmf5cuHDBYmmERCKBTqfDunXrsG7dOlM8MDAQMTExaN++velPq1atIJOxizcRuZ4iTTneOrxeFAtV+eD57o2zT41SqcCEKZ2x+Oejppi6TIeli4/jsaf6QSaruu5ddb+FwVWeqy7T4rdfj+PiBXF/CokEmHRXF4v171Q35ssimsrMBeB6U8f1KfGmY2s1dQQAlcoNAwe3xNZNNwuFp09lICO9GGHhPlYbh4iIyBFssiG6SqVC9+7d0b17d1PMYDDg8uXLpmJDYmIizp07h8LCQovr8/LysH//fuzfv98Uc3Nzw6lTp2yRLhGRU/vw2L/ILi8Rxd7uMx5eisa7u0HnLqHo0jUM8adufnBLSS7AyuXxuOue2CpnI2jTz8FQkiOKeVTRzDEjvRiLfzmKvFy1KC6VSnDPtG7ozhkLDWLUVsBQIi7ayJtIzwXAsqnj2fwMGAWj1ZYfDRrcCnt3JaOi4uYSpy2bzmPGrF5WuT8REZGj2KS4UBWZTIa2bduibdu2GD9+vCmemZmJc+fO4ezZs6aCQ1pamsX1Wq3WXqkSETmNo1lXsCTpkCg2PLI9xrTo5KCMrGfK3V2QllqIgvybyyGOHEqFUinHHeM7irbuA6rotxAYBUVQS9Ox0Shg355kbFyfCL3eKD5XLsUDD/ZEx84h1n8hLkZfeM0i1tRmLlRWqtMgtbQALbyt0/jTw0OB24a2wuaNN5dDnDmdifRrRQiP8LXKGERERI5gt+JCdUJDQxEaGoqhQ4eaYqWlpaIlFefOncPFixdruAsRUdOjMxrw0v6VopiHXIH3+k2ots9AY6JSuWH6zJ74+sv9omLAnl3JKC7WYOo9sXBzv/lryrzfgqr9YNPP4VpaEdauSkDyZcu+Pd4+7njwoV6IasGmwdZg3m9BqvSGzKPpTOkPVfkgwN0T+ZoyUywhL8NqxQUAGHhbS+zZlYxy9c1tLrf8ex4PPtx4dn4hIiIy5/DiQlW8vLwQFxeHuLibv2R1uobvM01E1Jh8d2YPksy2wftP99sR6dV0PiRHNvfDtAd7YMkvx2A03tyO8tSJdKReKcSdEzuiQ6cQSCRAeZK434JH+8G4eqUAe3Yl4/TJdFS1m2VEpC9mPtwLvn4etn4pLkOflyo6bkrNHIHr/aA6BoRhb8bNLzUS8tMxNrqz1cZQKhUYPKQV/v0n6eYYZ7KQllqIyOZ+VhuHiIjInpyyuFAVhULh6BSIiOwmpTgP809uFcU6BYTh4Y4DHJSR7XTqHIqp93bF8j9PiQoM+flq/PrzUTQL8kRMlAwyTSwUinJoJF4okLVA1s4I5Obvq/KeEgkwaEgrjB4bA7mczYCtSZebIjqWBza95pgdA0JFxYXEgkyrj9F/UEvs3nUZ6jLx7IVZ/9fb6mMRERHZQ6MpLhARuQpBEDDvwGpoDDcbvkkgwUf9J0MubZoflHvGRcLLyw2//XocGo1e9FxuThlycwB4PiO+KL/qXjz+/h6Yel9XtGnbzEbZujZdzmXRsSK4lYMysZ32/qGi4/OF2VYfQ6mUY/DQ1ti4PtEUO3c2G1evFHAJDxERNUrWaX1MRERWs/ryKexKvyCKzezQD92CmjsoI/uI6RCMp18YiOiWAfW63s1NhlFjY/Cfl4ewsGBDupwU0bGiWcuqT2zE2vmJG3+mFOehXG/95Zn9B0bD08tNFNuy6Xw1ZxMRETk3FheIiJxIgUaNtw6vF8VCVT6Y22OkgzKyr6AgLzw+ux/undYNISFetbrGz0+JMePa49U3hmP47W2hcGuaszuchcXMhaBoxyRiQ+bFBQECLhVZf/aCu7scQ4a1FsWSzuUgLbXQ6mMRERHZGpdFEBE5kQ+O/ovcilJR7J2+4+HtpnRQRvYnlUrQo1ckuvWIQFpqIc4cSsClXZtRKg2GUSKHXNAitFM3RLQKRbuYIEQ297PYtpJsQ9DroM8XbxetCGp6yyJUCjdEeQXgaunN3UcSC7LQOdD6zSv7DYjGzu2XUFZ6c5nP9q0XMWNWL6uPRUREZEssLhAROYlDmclYev6wKDYqqiPGtLBel/rGRCqVIKqFP3wunUGbsvdMcXlgFFo9nuzAzFyXLv8qIBhFMUVQ01sWAQAx/iGi4sJ5s51brMXNTYbbhrQS9V44czoTmRklCA3ztsmYREREtsBlEURETkBr0OPl/atEMZXcDe/0Ge+gjJxH+fk9omOPdgMdlAnpssVLIqQevpB5Ns3mg+ZLI5IKbFNcAIB+A1rAw0O8K9aObRerOZuIiMg5sbhAROQEvj2zGxfM1nTP7TES4V5+jknISQiCgPLze0UxFhccR5crnjHSFHeKuCHGX1xcsNXMBQBQKhUYcFu0KHby+DXk5pTZbEwiIiJrY3GBiMjBLhfl4otT20Wx2MAIzOrQ30EZOQ997hWLNf4e7QY5KBuy3Cki2iF52EOM2cyF1NIClOk0Nhtv4KCWcHO/2YxUEICd2zl7gYiIGg8WF4iIHEgQBLx6YDU0Br0pJpVI8GH/SZBJ+U+0+ZIImXczuIV3cFA2ZLlTRNOdudDaNwhSibhR6PlC6+8YcYPK0w39B0SLYseOpKGwoNxmYxIREVkT37kSETnQiksnsDdD/O3kQx36I7ZZpIMyci7mSyKUbQdAIuHOEI6iyzgvOm7KyyKUcgVa+jQTxZIKMm065qAhrSBX3HxrZjAI2LXjkk3HJCIishYWF4iIHKSgogxvH94gioV7+uLFHiMdlJHzUSeJZy6ouCTCYQSjEdoscXHBLSzGQdnYRzu/YNFxkg37LgCAt7c7+vSNEsUOHbyKkhLbLccgIiKyFhYXiIgc5N2jG5GvETdse7fvBHgq3B2UkXPRF2dDl5kkirGZo+PoC9IgaMVT9N1Cm3ZxIcY/VHR83oY7RtwweGhryGQ3Z+fodUbs2Xm5hiuIiIicA4sLREQOcCDzMv66cFQUG9OiE0ZGdXRQRs7HfEmExN0T7i26Oygb0maICz0SpRdkfmEOysY+zJs6Jtp45gIA+Pl7oGeceFnU/n0pKCvT2nxsIiKihmBxgYjIzir0Ory0b6Uo5qVwx9t9xjsoI+dksQVlm36QyOQOyoa0mWZLIkJjmnz/C/PtKLPUxSjWVth83CHD2qDyj1arMeDAvhSbj0tERNQQLC4QEdnZl6e243Jxrig2t8dIhHn6Oigj52RRXGC/BYfSZiSKjt1C2zkoE/uJ9g6ETCJ+q3SpKMfm4zYL8kTX7uGi2L7dKdBpDTYfm4iIqL5YXCAisqOz+Rn4On6XKNatWXM82L6fgzJyTobyYmiunBDF2G/Bscz7X7iFtXdQJvbjJpMjyjtAFLtUZLvtKCsbOryN6LisTIsjh1PtMjYREVF9sLhARGQnBqMRL+5bAb1gNMXkEik+GTAFMin/Oa6s4uIBoNLPCTIFlK37OC4hgjbDfKeIpj9zAQBa+4q3o7xUlFvNmdYVFu6DmPZBotjunZdhMBiruYKIiMix+G6WiMhOfj63D6dy00Sxp2KHoENAaDVXuC7zJRHKlr0gdfNwUDZk1JRBny/+1ryp7xRxQ2tf8XaUF+00cwEABg9rLTrOz1PjzOlMu41PRERUFywuEBHZwdWSfHx8fLMo1sY3CE93HeagjJwb+y04F/NmjgCgCGnrgEzsz3zmwmU7zVwAgNZtAhHZXNyLZef2SxAEwW45EBER1RaLC0RENiYIAl7Zvwrlep0o/vGAKXDn7gcWjDoNKi4dEsXYb8GxtGlnRMfyZtGQuqsclI19tTGbuZBcnAuD0T5LEyQSCYaYzV64llaEixfy7DI+ERFRXbC4QERkYysuHceu9Aui2Iz2fdE7JNoxCTk5TfIRCHrNzYBEAo+2AxyXEEFjVlxwj+zsoEzsz3zmgtZoQGppgd3G7xwbhsBm4kLOru2X7DY+ERFRbbG4QERkQ7nlpfjv4Q2iWKjKBy/3HO2gjJyf+ZIIt8gukHn6OSYZAgBo0uJFx+6RXRyUif0FuHvCz2yWhj22o7xBKpXgtiHi2Qvnk3KQfq3IbjkQERHVBosLREQ29ObhdSjUqEWx9/tNhI+b0kEZOT/1+T2iYy6JcDzzZRFuLjRzQSKRoLWPePaCPZs6AkCvuEh4ermJYjs5e4GIiJwMiwtERDayLTURay6fEsXGRXfByKiODsrI+QlGIyouHBDFVCwuOJShrAD6gmuimHtz15m5AABt/MRbQtprO8obFG4yDBzUUhQ7fTID+fnqaq6g/9fefYe3WZ39A/9qWJL3jkc8YsfZy3GcvQcQCDskhBEIZZUwSiFlvb+2b2nftlBSwqZAoYEyA20JhDAyCNmJHWcnzva2470lW9Lz+8O14iPvpaPx/VxXr+q5dZ5Ht4JlS7fOuQ8RETkeiwtERP2gptGEp3f/W4gF6rzx7ORrJWXkGhryj8NaL073NrC4IJX9kghovKCLGConGUkSA+yLC46duQAAU6fHQ6fT2I6tVgXbfzzn8DyIiIjaw+ICEVE/+HP6t8ivFT8k/2bSIgzw8ZeUkWuoP7NLONaGxsMreKCkbAgAGnLslkREj4BK6yUpGzmSAuXOXAAAH18dJk2JE2L79uagrrbB4bkQERG1hcUFIqI+llaUhbUn9gix6VGDsTRpgqSMXIfxtFhc8B4yVVIm1Kx1M0fP6bfQbLBdcaHEWNOql4ojzJyTCLVaZTtubLBg7+5sh+dBRETUFhYXiIj6kMlixq92fgEFii2m12jx3LQboVKpOjiTAKD+jNhvwTuJW1DK1nobSs/qtwAA8QGh0KrEt0yO3DGiWXCwN8YmRwmxnTvOw2y2OjwXIiIieywuEBH1oZcPbcFpu/XYq8ZfhkEBoZIych3mqotoLDojxAycuSCVYrXClHtYiHnSThHNvNQaxPmHCLHzVaVScpk5O1E4rqo04fChfCm5EBERtcTiAhFRHzlWmo/XDv8oxEaHROPeUWxI2BX2SyJUel+P/JbcmTQWnoJirBFihvgUSdnIFe8vFgizquUUF2LjgjAoQSx0bP/xPBRFaecMIiIix2BxgYioDzRaLXhsxzqYlUvTkzUqNV6YsRhataaDM6lZqyURg6dApdFKyoYAwHghXTjWBEVDGxQpKRu57GcfXZA0cwEAZs0Rt6XMy63E+XNlkrIhIiJqwuICEVEfeP3wjzhWViDEHho7B6NDudNBV9kXFwxJXBIhm31xwTDIM2ctAEC83bKIrGp5H+ZHjo5ESKiPEOO2lEREJBuLC0REvXSyvBBrDm0RYsOCIvDIuHmSMnI91kYTTOfThJg3iwvSmS4cEI4Ngzx3x5OEgDDhWNayCABQq1WYMUucvXD8WBFKimslZURERMTiAhFRr5itFjy+43M0Wi22mFqlwuoZN0HPKf1dZso6AMVsuhRQqWAYPEVeQgTFaoUxO0OI6TlzwabUWIvqBqOkbIDUSbEwGC79jlEUYMdP56XlQ0RExOICEVEvvHVsBw6V5Aqx+0fNQnJ4rKSMXFO9XTNH3cBR0PgGyUmGALTTzNGDZy7E+odABXE7WZmzFwwGLSZNjRNiaftyUFfXICkjIiLydCwuEBH10JmKi1id8YMQGxwYjsfHL5CUkesynhGLC95J0yRlQs1aN3OMgjYoSlI28uk1Wgz0CxRiMps6AsD0mQlQqy8VPBoaLNi3O1tiRkRE5MlYXCAi6gGL1YrHd3wOk8Vsi6mgwurpN8Gg9ZKYmetRFAX1p+12ihjCfguyGbPs+i146BaULdlvR3lBYlNHAAgO9saYcWLBZ+eOC7BYrO2cQURE1H9YXCAi6oH3TuxCerH4DeE9o6YjNSJeUkauq7H4HCxVRULMwJkL0tk32NR78JKIZoPsiwtVJZIyuWTmbLGxY2WFEYcPFbQzmoiIqP+wuEBE1E3nq0rw5/TvhFi8fyieSLlcUkauzWjXb0ETMABeAwZLyoYAQDE3wGhXXDAkpErKxnnEB4jFBZnbUTaLiw9G/KBgIbZjG7elJCIix2NxgYioG6yKFat2fAGjpVGIvzBjMby1OklZubb6M+KSCEPSVKhUqnZGkyOYsg9BaRR3QuDWoG3NXJDbc6HZrDmJwnFOdiWys8olZUNERJ6KxQUiom54/+Re7C0St3u7c/hUTI1MbOcM6oz9ThFs5iiffcFHFzUcGr+QdkZ7jkEB4r9BYV0VjObGdkY7zsjREQgKMgixndsvyEmGiIg8FosLRERdlFVdij+mbRRisX7BeCZ1oaSMXJ+lrhINeUeFmPcQFhdkq7fbvcOQNEVSJs7FvqGjAgXZNfKXRmg0akydMUiIHT6Yj+oqY9snEBER9QMWF4iIusBiteKx7etQZxb3kP/L9MXw9dJLysr1Gc/tBRTFdqzS6qDnrgTSGc/sEY65JKKJr5ce4d5+Qiyn2jmWH0yaEget16W3dRaLgj3clpKIiByIxQUioi74+/Gd2Ft0QYjdNnQSZkQnyUnITdgvidAPSoVaZ2hnNDlCY1kuzGU5Qoy7d1wS4yc2T8ytcY7igq+vDuNTBgqxPbuyYDZzW0oiInIMFheIiDpxuuIinjsg7g4R5xeCX09aJCkj92E8Y99vgd+Qy2a067eg9gmCLmq4pGycT4yvWFzIcZLiAgBMnzlIOK6uMuHIYW5LSUREjsHiAhFRB8xWCx7d/hlMFrMtpoIKq2feBD8uh+gVxWpB/dm9QswwhMUF2Vrt3jF4MlRqvl1oZj9zIa+mQk4ibYgeGIhBCWLTyV1s7EhERA7CdwtERB147fCPOFSSK8TuGTWdu0P0gYa841CMNUKMO0XI13r3DhZ8WorxCxKOnWnmAgDMmDVIOM66UI7cnAopuRARkWdhcYGIqB1HS/Pw4sHNQmxwYDieSLlCUkbupf6s2DRQGzYI2sAISdkQ0LR7h+lCuhDzHjJdUjbOqdXMhVrnKi6MGhOJwEBuS0lERI7H4gIRURtMFjN+8dNnMCuXmqGpVSqsmbkU3loviZm5D6PdkgjvwZMlZULN6k/tAFr8zKu0Ohg4c0FgX1worq9BvblRUjataTRqTJkeL8QOHshHTbVJUkZEROQpWFwgImrDXzM2IbOiSIg9NGYOxofHSsrI/dgXFwyDp0jKhJrVn9wmHBsGT4Fa5y0pG+dkvywCAPJrKxyeR0cmT4mDRtNyW0or9u7htpRERNS/WFwgIrKTfjELbxwVP2SNCI7Eo8nzJWXkfix1lWgoOCHEDJy5IF1dpvhz7z18tqRMnJevlx7Beh8h5mx9F/z89UgeHy3E9uzMgsXCbSmJiKj/sLhARNRCvbkBj25fB6ui2GJeag1emrUUOo1WYmbuxXh+P9Di31il1UEflywvIfpvv4UDQsyHxYU2xdotjch1suICAEy3a+xYWWnEsSOFcpIhIiKPwOICEVELf0z7FuerSoTYY8kLMDIkup0zqCfsl0To48ZDza09pWrdb0EPw2D2W2jLQLulEc5YXIiJDUL8ILEIwsaORETUn1hcICL6r215p/DeCXEbvvHhsXhgzCxJGbkvo91OEVwSIV/b/RYM7Yz2bK1nLlTISaQT02YOEo7PnytDfl6lnGSIiMjtsbhARASg3FiLx7avE2J6jRZrZi6FVq2RlJV7UhQFxnP7hBiLC/LVnfxROGa/hfYNdIFlEQAwZmwU/P3FGUGcvUBERP2FxQUi8niKouCJXf9CUX21EH8m9UoMDgyXlJX7aiw+B0u1uPTEmztFSGWuKoYpi/0WusoVei4AgFarxpRp9ttS5qG+3nm2ziQiIvfh1t3JLBYLzp49i6NHj+LYsWM4evQoTp48CaPRCAC44YYb8Oc//1lylkQk22dn0rEx65gQmz1wKH42YpqkjNyb8Yy4JEITMADasPh2RpMj1B37QWywqfeF9xD+/LfHfjvKorpqNFot8HLCWU6Tp8Zh8w+nYbU2/fdtbLQifV8OZsxOlJwZERG5G7cuLjz66KP4/vvvZadBRE7sQlUpfrNnvRAL1vvgrzNugkqlkpSVe7Nv5mgYPJn/1pLVHRX/VvoMnwOVVicpG+cX5RskHCtQcLGuulWjR2cQEGjA6DGROHyowBbbsysb02cl8HVHRER9yq2XRVgsFuE4KCgIgwYNkpMMETkds9WCX/z0KWrNDUL8+ek3IsInQFJW7q/ert8Cl0TIpSgKao/+IMR8xlwhKRvXEKTzht5ua9qCWudtlDhlujgz6OLFGpw9UyopGyIiclduPXNh7NixGDx4MEaNGoVRo0YhNjYW//rXv/D000/LTo2InMCrh39EenG2EFs2JBVXxo+WlJH7szbUw5SdIcTYzFGuhpzDsFQWCjFfFhc6pFKpEOkTiKzqSx/QC+uct7gwOCkUAwb44eLFGltsz84sJA0Jk5gVERG5G7cuLvz85z+XnQIROakDxdl48eBmIRbvH4rfTb5GUkaewZSVAVjMlwIqFQyDUuUlRKg98p1w7BWeCF1EkqRsXEeUb4BdcaFKYjYdU6lUmDItDuv/c9wWO3qkEFWVRgQEcrtRIiLqG269LIKIqC21jSY8su1TWBSrLaZRqfHyrJvh66Xv4EzqLft+C7qBo6H29peUDQFArX2/hTGXS8rEtUT6BArHzrwsAgAmTIqFl9elt31Wq4L9e3MkZkRERO6GxQUi8ji/27cBF6rF9ca/GDcPEwbEScrIc9SfE4sL3lwSIZXVWIP6UzuEmO9oFhe6ItKuL4szz1wAAG9vLySnDBRie3ZnwWKxtnMGERFR97C4QEQeZWPWUXx0SmwoOD48Fo+MmyspI8/S1k4RJE/tke8AS+OlgEYL7xF8LXRFlK/dzAUn7rnQbKpdY8fKCiNOHr8oKRsiInI3bt1zoT/k5+cjPz+/R+dmZmb2cTZE1B15NRVYteNzIeaj1eHlWTdD64T707sbc3k+zKViA00WF+SqPfiVcOwzfDY03twppStazVyode6ZCwAQExuE2LhA5GRfKoTs3pWFUWMiJWZFRETugsWFbvriiy/w6quvyk6DiLrJbLXgoW0fo7LBKMR/N/kaJASwY7oj2C+JUHsHQBc1QlI2pFjMqDn0jRDzHX+dpGxcj/3MhcK6SiiKApVKJSmjrpkybRBysg/Zjk+dLEZpSS1Cw3wlZkVERO6AyyKIyCO8eHAz9l/MEmLXDBqLZUO4U4GjGM+Ky1EMiROhUvPPkCz1Z3bBWiP2HvFLvlpSNq7HvqFjg9WCMlOtpGy6btz4aHh7ewmxPbuy2hlNRETUdXxXR0Rub1fBWbx8aKsQi/ULxnPTb3T6bxndSat+C4lTJGVCAFCbIS6J0MeOg1dYfDujyd4Abz+o7X5/uMLSCJ1Og9RJMUJs/74cNDZaJGVERETugssiumnx4sWYOnVqj87NzMzEs88+28cZEVFHyoy1ePinT6FAscU0KjVenX0LAnTc391RFIsZxvP7hZhh8CRJ2ZCiKKjJWC/EfFOulZSNa9KqNQj39kdRi10iCuuqMCo0WmJWXTNlWjy2bztvO66rbcThgwWYMDGmg7OIiIg6xuJCN0VHRyM62vnfOBBR0weox3d8Lrz5B4BfpVzObScdrCH/OJSGOiFmSGQzR1kaCk6iseiMEPMbf42kbFxXpE+A8PuloNb5d4wAgPABfkgaEoYzp0tssT27slhcICKiXuGyCCJyW++e2IUfck4IsZnRSVg5ZpakjDyX8Zw4a0EbNgjagHBJ2VBN+n+EY23wQOjjU+Qk48Lsd4woqnf+ZRHN7LelzLpQjvw818mfiIicD4sLROSWjpbm4f/2i53wQw2+WDNzKdQq/upztFZLIhInSsqEAKBm3zrh2Hf8tew/0gPh3v7CcUl9jaRMum/k6Aj4B+iF2O6dF+QkQ0REboHvsInI7dQ2mrDyx4/RYBUblK2ZuRQRdt80kmMYz6cJx4YEFhdkaSjIhCnnkBDzn7REUjauLczbTzgudqHigkajxuQp4vKwjAN5MBrNkjIiIiJXx+ICEbkVRVHwzO7/4FxViRC/f9RMzI0ZJikrz2ZtqIcp94gQMyRwC1BZqvd/LhxrAiPhPXSGpGxcW7hBLC6UGF2nuAAAk6bGoeWElQaTBYcy8uQlRERELo3FBSJyKx+f3o8vzmYIsXFhMXhywhWSMiJT9kHA0uLbUJUahkETpOXj6artlkT4py6GSq2RlI1rc+WZCwAQFOSNEaMihNi+PdmSsiEiIlfn1rtF5OTk4PPPxW9oMjMzbbePHz+OF198Ubh/ypQpPd5qkojkOlaaj1/vEbfX8/PS49XZy6DTuPWvO6dmvyRCFz0CartvfMkxTPkn0GA3i8SPSyJ6rHXPhWpJmfTcpClxOH60yHack12JvNxKDIwJlJgVERG5Ird+t52fn48333yz3fszMzOFYgMAaLVaFheIXFBVgxH3b/0QJou4Xnj1jJuQEBAmKSsCWu8UwSUR8tTYL4kIioL3kOmSsnF9YXZFslpzA+oaG+DjpZOUUfcNGx6OwCADKiuMtti+Pdm44aYxErMiIiJXxGURROTyFEXBqh2f40J1qRC/e+R0LBrEN8iytWrmyJ0ipFAUBdV7PxVi/hNvgkrNtwI9Fe7degZOsdG1Zi9oNGpMnBwrxA6k5aHBxMaORETUPW49c2Hy5MmtZiYQkft598QufJN1VIiND4/F/6ReKSkjamaprUBjofh7mDtFyGG6kI6G/BNCjLtE9I6flx56jVaYMVVSX4N4/1CJWXXfxMlx2Pz9aShK07HJZMahg/mYODmu4xOJiIha4NcVROTSDhRn4w/7vxFiQXofvDnnNvZZcAKmrHThWKXVQR87VlI2nq1q5wfCsVd4AgyDuQywN1QqVavZC67W1BEAgoO9MWzEACG2dzcbOxIRUfewuEBELqvcWIsHtn6ERqtFiL80cykG+gXJSYoExnN2zRxjx0GldZ316O5CMTegeu8nQsx/2u1cEtEHWjd1dL3iAgBMniLOUsjOqkBBfpWkbIiIyBXxXQURuSSrYsWj29chr7ZCiD80dg7mxw6XkxS1Yjy/TzhmvwU5ag9vhKW6RIgFTF8uKRv3Em7X1NHVei40Gz5yAPwD9EKMsxeIiKg7WFwgIpf00qEt2Jx7UohNiUzAqvGXScqI2tKqmSN3ipDCfkmEYch06AYMlpSNewmzm7ngissigKbGjpPseiwcSMtFQ4OlnTOIiIhELC4QkcvZnHMSf83YLMTCvf3w2uxboFVrJGVF9swVBTCX5QoxNnN0PEtNKWoOfi3EAqbfLikb9xNm8BWOy4y1kjLpvYlTYqFSXTo2Gs04fDBfXkJERORSWFwgIpdyrrIED//0CRQotphGpcars29BhE+AxMzInvH8fuFYbfCHLmqYpGw8V9WeTwBLo+1YpdXDf+JSiRm5lyC9j3BcYaqXlEnvhYT4YOiwcCHGpRFERNRVLC4QkcuobTTh3i0foKrBKMT/J/VKTI/iFG9nY9/MUT9oAlScWeJQiqKg8se3hJjv+Gug8Q2Sk5AbCrYrLpSbXHfmAgBMmioujci6UI7CAtfsI0FERI7F4gIRuQRFUbBqxxfIrCgS4tcljMO9o2ZIyoo6Yj9zgf0WHM94Zjcaco8KscDZ90jKxj0FG+yLC3WSMukbI0dFwM9fbOy4bw9nLxARUedYXCAil/C3o9vx1YXDQmx4cCT+Mn0xVC0XCZNTUBSldTNH7hThcPazFrzCE+Ezcr6kbNxTkM59lkUATY0dJ06OFWLpabloZGNHIiLqBIsLROT0duSfwR/TNwqxQJ0B78xbDh8vnaSsqCONxedgrS0TYmzm6FiWmjJU71snxALn3AuVmn/6+5L9zIU6cwNMFrOkbPrGJLviQn1dI44cLpCUDRERuQq+wyAip5ZXU4EHfvwIVuVSA0cVVHh51jIMCgiVmBl1xHhOXBKh8Q+HNjSundHUH6p2fgClsUV/Eo0XAmaukJaPu7LvuQC4/tKI0DBfDBkaJsTY2JGIiDrD4gIROa26xgb8bPPaVm/UHx+/APNjh0vKirqiVb+FxIlcvuJAiqKgctvbQsx/wg3QBgyQlJH7CtR5t4pVuHhxAQAm2zV2PH+uDEVFbOxIRETtY3GBiJySVbHi0e2f4ViZOBX38tgReGTcXElZUVeZ7PstcEmEQ9Ud34yG/BNCLHDufZKycW8atRqBOoMQc/WZCwAwcnQkfP3EZWf79+ZIyoaIiFwBiwtE5JT+enAzvskSu9wnBoRhzayboVbxV5czUyxmGC8cEGJ67hThUBXfvyQce0UOg/fwOXKS8QBBel/huNzo+sUFrVaN1IkxQuzA/lxYLFZJGRERkbPjO3Qicjrrzx3CmoObhVigzoB/LLgTAXbfEJLzacg/DqVB/HDFmQuO01CQidpD3wix4Msf5rKUfmTfd8EdZi4AwMTJ4tKImpoGnDhW1M5oIiLydCwuEJFTOVSSi1/uEDvca1RqvDHnNiQGhkvKirrDfgtKbdggaAP4385Ryn94RThW+wYjYPodkrLxDO5aXBgQ4YdBCcFCjEsjiIioPSwuEJHTKKyrws82v99qG7f/nXQ1Zg0cIikr6i7jhXTh2DBogqRMPI+lpgxVO9YKscDZ90JtN22f+laAXpxRVdNylw4XN9FuW8qTJy6istJ9nh8REfUdFheIyCnUmxtx9+b3UVRXJcRvHzYZK0ZMlZQV9YTJrt+Cgf0WHKbyp7+LS1LUGgTNXykvIQ/h7yUWF6obTJIy6Xtjk6Oh02tsx4oCpO/PlZgRERE5KxYXiEg6RVHwq51f4FCJ+IZ1amQifj/lWq4VdyGKuRGm7ENCTD8oRVI2nsXaaEL59y8LMf+JN8ErNLadM6iv+HnphePaRvcpLuj1WoxLjhZi+/dmQ1EUSRkREZGzYnGBiKRbfXAT/nPuoBCL9w/BW3Nvg5da0/ZJ5JQa8o9DMYsfrAzxLC44QtXOtbBU5AuxoMt/ISkbz+JvV1yodqNlEQAwaYrY2LG0pA7nz5VJyoaIiJwViwtEJNVnp9Na7Qzh56XHu/PvRLCB68RdjX2/Ba/wBGj8QiRl4zkUixnlG/4ixLyHz4H34MmSMvIsvnbFhRo3mrkAAHHxQRgwwE+I7d/Dxo5ERCRicYGIpNmRfwZP7PyXEFOrVHht9i0YFhwhKSvqDfvigp6zFhyieu+naCw+J8RCrn5KUjaex19n39DRvYoLKpUKE6eIy2sOH8pHfX2jpIyIiMgZsbhARFKcLC/EvVs+gFmxCvH/m3Id5scOl5QV9VarZo7cKaLfKVYryr7+sxDTJ0yEz6gFkjLyPPY9F6ob3GtZBABMSI2BWn2p/01joxWHMvI7OIOIiDwNiwtE5HCFdVW444f3UG337d7PR8/C8uFTJGVFvcVmjnLUZqxHQ/5xIRZ6zdNshOpA9rtFuNvMBQDw89dj5ChxRtn+vVwaQUREl7C4QEQOVdtowl2b1iK/tlKILxo0Bs+kLpSUFfWFNps5srjQrxSrFaX/eVaI6QaOgm/yNZIy8kx+Ovdu6NgsdbK4NCInuwKFBVXtjCYiIk/D4gIROYzZasHKHz/GkdI8IT4hPA5rZi6FWsVfSa6s7WaOoZKy8Qw1aZ/DlCPOFgm5+imo1HwtOZL9zIV6cyPMVoukbPrPsOHh8A8QCyn72NiRiIj+i+8+iMghFEXBk7v+hc25J4V4vH8o3l1wB7y1XpIyo75itOu3wGaO/UuxmFHyr98KMV30SPhPvllSRp7LvucC4J5LIzQaNVInibMXDqTnwmx2v0IKERF1H4sLROQQf0r/Fp+eFr/ZDtL74IPLViDU4NfOWeRK2MzRsap2fYDGwlNCLPTG30Gl1kjKyHP5aHWtYvVm99xJIXVSjHBcV9uI40eLJGVDRETOhMUFIup3bx/bjtePbBNieo0W782/A4mB4ZKyor7U1MzxoBBjM8f+Y200teq1oB80AX4TbpCUkWfTa7StYiaLWUIm/S883A8Jg0OEGBs7EhERwOICEfWzL85m4Hf7NggxjUqNN+fciokRg+QkRX2OzRwdq/LHt2EuzRZiYYt/zx0iJGmruGC0uOfMBQCYNDlOOD6VWYyK8npJ2RARkbNgcYGI+s2W3Ew8vn1dq/jz02/EZXEjJWRE/cW+maM2bBCbOfYTS205Sv/zOyHmPXQmfEZfLikjUqlUrQoMRjddFgEAY8ZFQa+/9HwVBUjbz9kLRESejsUFIuoXB4qzcf/Wf8KsWIX4UxMW4uYhqZKyov5i38yRsxb6T+mXv4e1tkyIhd30B85akMygEZvSuuuyCADQ6TRITokWYml7c2G1KpIyIiIiZ8DiAhH1ueNlBVj+/XutGprdM3I6HhwzW1JW1J/YzNExGgpPoWLza0LML3UxvIfOkJQRNTPYz1xw42URADBxsrhrRFlZHc6dLZWUDREROQMWF4ioT52puIhbvnsHlQ3i+tsbEpPxm0mL+O2qG1IsZphyDgkxPYsL/aL40yeAFt+Iq7Q6hC39s8SMqJlB6zkzFwAgNi4IkVH+Qmzfnux2RhMRkSdgcYGI+kxWdSlu/u4dlBprhfjsgUOxesZNUKv4K8cdNeQfh9JoFGJcFtH36o5vRm3GV0Is6PJHoRuQKCkjasl+WYQ791wAmvpM2M9eOHq4EHV1DZIyIiIi2fhOn4j6RH5tJZZ9+w6K6qqE+OSIBLwz73bo2uimTu7BeD5NOGYzx75nbTSh6P2HhZgmYABCrnlaUkZkz76ho7vPXACAlAkx0GguzUYzm604lFEgMSMiIpKJxQUi6rXi+mos+/Zt5NSUC/HksFisvWwFvLU6SZmRI7CZY/8r37gajYWZQiz0xt9B4x0gKSOyZ78swt17LgCAr58OI0dHCrF07hpBROSxWFwgol4pN9bilu/+jnNVJUJ8ZEgU/nn5XfDz0kvKjByFzRz7V8PFsyj76v+EmD4hFYGz7paUEbVFa7fsq9FqkZSJY6VOjBGOs7MqUFRULSkbIiKSicUFIuqxcmMtln33Dk6WFwrxpMBwfHT53QjS+0jKjByl7WaOnLnQVxRFwcUPHhZ7WqjUiLjzdajUGnmJUSv2PWWsimdsyzh0eDj8/MUicvq+XEnZEBGRTCwuEFGPlBlrcfN37+BYmbi+Nt4/BB9fcQ/CvP0kZUaO1HYzR85c6Cs1+9eh7sh3QixowYP8N3ZCGrudcDyluKDRqJGSOlCIpaflwmKxSsqIiIhkYXGBiLqt1FiDpd++jeN2hYVo30B8csU9iPINlJQZOZrxQrpwrA2NZzPHPmKuKsbFDx4RYpqgKITe+KykjKgj9jMXLIrnfLi2XxpRXWXC6VMl7YwmIiJ3xeICEXVLSX0Nlm58u9VSiIG+QVh35X2I9Q+RlBnJ0KqZYwK/Ue8rFz94CJbqYiE24Na/somjk1J76MwFAIiMCkBMrFhUTtvHxo5ERJ6GxQUi6rLi+mos/fYtZFYUCfEYv6bCQrw/v7H2NKasDOFYH89+C32het9nqNn/uRDzTbkOfhOXSMqIOuOpyyKapU6KFY6PHSlCXV2DpGyIiEgGFheIqEsK66qwdOPbOFVxUYjH+AVh3cL7EMcZCx5HsVpgyhabORrix0vKxn2YK4tQ9P5DQkztG4KIO16Hyu4DLDkPT14WAQDJ46Oh0Vz6N7BYrDiUkS8xIyIicjQWF4ioU9nVZVj8zZs4XSkWFmL9gvH5lfdzKYSHaiw8DaWhTojpWVzoFUVRUPSPn8NaUyrEByx/BdqgSElZUVdo1J65W0QzH18dRo2OEGJp3DWCiMijsLhARB06VVGEG755E1nVZUI8zi8En195P2L8giVlRrIZs8UlEZqgKGgDI9oZTV1RufkN1GasF2J+qTfCf/LNkjKirlJDnFXiaTMXAGDCJLGxY052BYoKqyVlQ0REjsbiAhG161BJLhZ/8zcU1VUJ8UH+ofj8yvsw0C9ITmLkFExZB4VjQ1yylDzchSnnMIo/WSXENP5hGHDHa1wO4QIUiDMV7Bs8eoKhw8Lh768XYmzsSETkOVhcIKI27S48h5u/fRvlJnHa+4jgSPx70c8RzcKCxzNl2zdz5JKInrKa6lDwxq1QzCYhHnHPe9AGDJCUFXWH2SrOVNCqPO8tlkajRkrqQCF2IC0PFovnzeIgIvJEnveXj4g6tSnnBG7//l3UNIofdCaEx2Hdlfch3NtfUmbkLBRFgdFu5oKeMxd67OKHj6Ih/4QQC7riUfiNu0pSRtRd9ssgNGqNpEzkmjBR3DWiutqEU5nF7YwmIiJ3wuICEQk+OrUPd2/+ACaLWYjPjE7CR1fcjSC9j6TMyJmYy3JbNR3kzIWeqdz2d1T99Hchpo8fj7Cb/igpI+oJ+5kL9ltTeorIKH/ExgUKsXQ2diQi8ggsLhARgKZvol/I+AFP7PxXq2/gFsaNwj8WrICvl76ds8nT2C+JUHsHwis8QVI2rst4bj8ufiBuO6nS+yLqgY+g5uvNpdj/3tSqPfctVuokcfbCsaNFqKttkJQNERE5iuf+5SMim0arBY/v+BxrDm5udd+SpBS8OfdW6DVaCZmRs7Jv5qiPS2bTwW4yV11E/qs3QTGLH7oiVrwJXeRQSVlRT5mtFuFY44E9F5qNGx8NjebS87dYrDiYkS8xIyIicgTP/ctHRACAmkYTVmxai8/OpLe676Gxc/DXGUug9dC1w9Q+Y5Z9M8dkOYm4KMXciILXb4G5TJwuHnT5LxAw9VZJWVFvtJ654Lm/N318dBg1RtyWNn0/d40gInJ3LC4QebDCuios2fgWtuWdEuJqlQp/nHo9npqwkN9GU5tM2QeFYwOLC12mKAqK3n8Q9Sd/FOLew2YjfOlzcpKiXmts1XPBs99i2S+NyMmuRGFBtaRsiIjIETz7Lx+RBztUkotFX72KI6V5Qtyg8cI785bjjuFTJGVGzs5SUwpzabYQ08exmWNXlX/zl1YNHLXBAxG18mOotF6SsqLeqrNb3uLt4f8thwwNg3+A2DckjbMXiIjcGosLRB7oq/OHceM3b6KorkqIh+h98enCe3F53EhJmZErsJ+1oNLqoYsaLicZF1O97zOUrHtaiKm8DIh6+HNoAyPaOYtcQb1dccFHq5OUiXPQaNSYkBojxA6k5cFisbZzBhERuToWF4g8iFWxYnXGD3jgx49abTUZ7x+K/yx6ABMGxEnKjlyF0a6Zoy52DL9x74L607tQ+NYKMahSIfK+9+GdOElKTtR37Gcu+Hp5dnEBACZMFIsLNdUmnDpZLCkbIiLqbywuEHmIenMDVv74MV5sY0eIqZGJ+PrqlUgMDJOQGbkak10zR0NcspxEXIgp+xDyXrwGitkkxMOWPgf/iYslZUV9qbaRMxfsRUT6IzYuSIil7c9tezAREbk8FheIPEBWdSmu3/AGvr5wpNV9tw2dhI+uuBvBBl8JmZErsi8ucKeIjjUUnkLuCwthrasQ4oFz70fwwsfkJEV9SlEU1JsbhZg3iwsAWjd2PH60CLW1De2MJiIiV8biApGb25xzEletfwXHygqEuFqlwu8mX4M/T7sBXh68ZRp1j9VUi4bCTCHGZo7tayzNRu7zl8NSdVGI+467CgNuf5m7sbgJo6URChQhxmURTcaNj4JWe+ntpsVixaGMfIkZERFRf2FxgchNNfdXWLFpLSobjMJ9AToDPrjsLtw9cjo/3FC3mHIOA0qLD1EqNfSxY+Ul5MQay/OQ+5crYC4TO+R7D5uNqAc/g0qjlZQZ9bWaRlOrGJdFNPHx0WHUmEghlraPu0YQEbkjFheI3FC5qQ53/rAWLx7c3OrbtCGBA7B+0UrMHjhUUnbkykz2zRyjhkGt95GTjBNrLM1G7p/morHwlBDXJ6Qi+tH/QK3zlpQZ9YdyU12rWBBfFzapdo0dc3MqUVhQ1c5oIiJyVSwuELmZ9IvZuGr9K9ial9nqvqsHjcFX1zyIpKABEjIjd2C077fAZo6tNBafR86f5qLx4lkhroseiZjHv4HGO0BSZtRfyoy1wnGAzsDlZi0MGRaOgEC9EEvbx8aORETuhsUFIjdhVax49fCPuPGbN5FTUy7cp1Gp8ZuJi/DGnFvh56Vv5wpEnTNlHxSO9fHst9BSQ9EZ5PxpLswlF4S4V+QwxPzqO2j8QuUkRv2qzCjOXAjWs0FuS2q1CikTxNkLGel5sFiskjIiIqL+wOICkRu4WFeN275/F39O/xYWRXyzFmbwwycL78F9o2eyvwL1imJuREOuuOOIgTtF2BjPpyHnDzNa9VjQRY9E7FNboA2OlpQZ9bcykzhzIcTAJRH2JkwSiwvV1SacOVUiKRsiIuoPLC4QubituZm47Ms12J5/ptV9kyIGYeO1D2NqZKKEzMjdNBScgGIWt5DjThFNag9vRM6f58FSXSzE9bHjEPPUFmiDIts5k9xBud3MhRDOXGglIsIfsXGBQiw9jUsjiIjcCVtVE7mousYG/F/aRqw9ubvVfSqo8IvkeXh03Dxoue6X+oh9vwVtaBw0fiGSsnEeldv/gaL37gOsFiGuHzQBMau+5b+RByiurxaOOXOhbSmpMcjJrrQdHz1SCKOxEQaDl8SsiIior3DmApELSivKwuVfvtRmYSHCJwCfLrwHq8ZfxsIC9Sn7nSI8vZmjYrWi5IvfoOjvd7cqLPiMmo+YJzexsOAh8msrheMon8B2Rnq25PEDoVZfWp5nbrTiyKECiRkREVFf4swFIhdispixOuMHvHn0J1gVpdX9C2KH468zliDEwCm51PfsmzkaPLiZo6W+CoV/W47ag1+3us9/6q2IvPvvUGl1EjIjGfJrK4TjaL8gKXk4O18/HYaPHIDjR4tssfT9uZg4OU5iVkRE1FdYXCByEUdK8vDo9s+QWVHU6j6DxgvPpC7EXSOmsWkj9QvFam1jp4hkKbnI1lB4Gvkv34CG/BOt7gu+6lcIu+mPUKk5MdCT5NkVFwb6BknJwxVMSI0RigvnzpahvKwOwSFcSkJE5OpYXCBycnWNDVid8QPeOb6z1U4QAJASHoc1M5cgMTBcQnbkKRpLzsNaXyXEPLGZY036f1D497thrasQ71BrMOC2lxA0/wEpeZE89eZGlBrF3SKiWVxo14hRA+Dt7YX6+kZb7EB6HuZfNkRiVkRE1BdYXCByYltyM/HM7n8jt6ai1X1eag0eH38Zfj56JnsrUL+z77eg9guFNiSm7cFuyNpoQslnT6Lih1da3af2C0X0g5/BZ8QcxydG0uXVlLeKRfuy50J7tFoNxo2Pxp5dWbZY+v5czFuQxJl3REQujsUFIidUXF+N/937Nb48f6jN+0eGRGHNzKUYGRLl4MzIU5lyDgvHhrhkj/kg0FB0BgWv3wJT1oFW9+lix2LgI/+GV/ggxydGTuFMpbj9aJjBD/46g6RsXMOEiTFCcaGkuBY52RWIiw+WmBUREfUWiwtETsRsteD9k3uwOuMHVDYYW92vU2vw8Li5eHDMHOg0fPmS45hyxEKXPnaspEwcR1EUVG57B8WfrIJirGl1v/+kpYi4+x2o9Wyg6snOVF4UjpOCuEStM3HxQQgN80FpSZ0tlr4/l8UFIiIXx08nRE5iR/4Z/HbvV202bASAKZEJeG7ajRjM3gokgSlbnLmgj3Pv4kJjWS6K3r0XdUe/b3WfysuA8NvWIHD2PR4ze4Pad7pCLC4MCRwgKRPXoVKpMGFiDL7feMoWO5SRj2uuHwmtlsv8iIhcFYsLRJLlVJfh2f0bsDHrWJv3B+q88euJV+HmIan8IENSWGorYC7NEmL62HGSsul/VTvfx8V/PgprfWWr+3RRwxG18hPoY8dIyIyckf2yiKQgFhe6ImWCWFyoq2vEyeMXMXosl/sREbkqFheIJKkw1eH1I9vw9+M7YbKY2xxzfWIyfjtpEcK9/R2cHdElplxx1gI0XtBFj5CTTD+r3v8FCt++q837AmbdjQG3vchlEGTTYDHjZHmhEBvK4kKXhIT6IGFwCM6fLbPF0tNyWVwgInJhLC4QOVi9uRH/OLELrx7+EZUN9W2OGRUShWcnX4vJkQkOzo6oNfslEbroEVBpdZKy6V+1B79qFdMERSPyZ2/Bd+yVEjIiZ3ayvLBVcXhM6EBJ2bielNQYobhw8vhF1NY2wNfXPX+/EBG5OxYXiBzEbLXg8zMHsDpjEwrqWk+3BoBgvQ+enHAFbhkyERq12sEZErXNk5o5GpKmomrnB7bjgOl3IPzWv0Ljy0Zz1FpGcY5wnBgQhiC9j6RsXM/YcVH48oujMJutAACLRcGhjHxMmzFIbmJERNQjLC4Q9TOz1YL/nDuElw9twbmqkjbHaFRq3DF8Ch4fv4BvTMnp2G9D6c79FgLn3Ae13hfGCwfgN/4a+IyYKzslcmIHirOF4+TwWEmZuCZvby+MGhOJQxn5ttiBtFwWF4iIXBSLC0T9pNFqwb/OZuDlQ1uRVV3a7rirB43BEymXI5G7QJATUixmNOQeFWLuvFOESqVCwLTbETDtdtmpkJNTFAU7Cs4KsfFhLC50V0rqQKG4kJ1VgYsXazBggJ/ErIiIqCdYXCDqYyaLGZ+fOYDXDv+I7JqydsdNi0zEM6lX8psucmoNRaehNBqFmDvPXCDqqtOVF1FUVyXEZkYnScrGdQ0dFg4/Px1qahpssQNpuVh41XCJWRERUU+wuEDUR8qMtXj/5B6sPbkbxfU17Y4bHRKNp1IXYnb0EG4tSU7PlC32W9AERUEbwFk2RD/lnRaOo30DMZgz0LpNo1Fj/ISB2L7tvC12IC0Ply8cBrWafyOJiFwJiwtEvXS2shhvH9uBdWfS291SEgDGhcXgl8nzMT9mOIsK5DJa91tw3yURRN2xMeuYcDyLBeMemzAxRiguVJTX4/y5UgxOCpOYFRERdReLC0Q9YLZasDnnJP6ZuQ9b8zI7HJsSHodfJs/HnIFD+caTXE6rnSLikuUkQuRECmorsa/oghBbEDtCTjJuICo6AJFR/igsqLbF0vfnsbhARORiWFwg6ob82kp8fGofPj61H4V2a23tzYhKwsoxszEzOolFBXJZpmzOXCCy99X5w1Cg2I4DdAbMjRkmMSPXplKpMGFiDDasP2GLHTlUgOsXj4ZOp5GYGRERdQeLC0SdMFnM2JJ7EutOp2NT7klYFaXdsV5qDa5LGId7R83AqNBoB2ZJ1PfMVcWwVOQLMX0cmzmSZ7MqVnyQuVeIXRE3EnoN31L1RnLKQHzz1Qk0/4k1mcw4drQQ41MGyk2MiIi6jH8JidqgKArSL2bji7MHsP78YVQ21Hc4PlBnwO3DpmDFiKmI8g10UJZE/ct+SYTKywBdxBBJ2RA5h215p3G+qkSILUmaICkb9xEYaMCQoeE4lVlsix3Yn8viAhGRC2Fxgei/FEVBZkURvr5wBP8+exBZ1aWdnpMcFovlwyfj2oSx8NbqHJAlkePYL4nQxYyGit/OkgdTFAWvHt4qxIYHR2JqZKKkjNxLysSBQnHhVGYxqiqNCAg0SMyKiIi6iu8SyaMpioJDJbn4JusoNmYda/VtVFt8tTrcMHg8bh82CaND+Y0Kua9WzRzZb4E83Na8U9hr18jxrhHT2Fenj4weHQmdXoMGkwUAoChAxoE8zJ47WHJmRETUFSwukMepNzdid+E5bM3NxHfZx5BfW9ml8yZHDMKNg1NwXeI4+Hnp+zlLIvlab0PJfgvkuYzmRjy772shFuMXhJuSUiRl5H50ei3GjotC2r5cW+xAWi6LC0RELoLFBXJ7iqLgfFUJtuRmYmveKewpPAeTxdylcxMDwrB48HjcMHg84vxD+jlTIuehmBvQkH9CiOnjOHOBPNdLh7bgTGWxEHs8+TI2cuxjKakxQnGhIL8a+XlViB4YIDErIiLqCv5FJLejKApyasqxu/Ac9hSew57C88ipKe/y+QN9g3Bl/Chcn5iMcWExnO5KHqkh/wRgaRRi+hgWF8gzbc3NxKuHfxRiY0MH4sbB4+Uk5MYSB4ciKMiAigqjLZaelovogSMlZkVERF3hMcWFzZs348svv8TRo0dRXFwMPz8/xMfHY8GCBVi2bBn8/Pxkp0g9ZLFacariIg4UZ2Nv0XnsKTzX5aUOzRIDwnDVoNG4Mn40xoYOZEGBPJ59vwVtaDw0vkFykiGS6HhZAR7c9jEUXNqG2EutweoZS6BRqyVm5p7UahVSUmOwZdMZW+xgeh6uuno4NBr+exMROTO3Ly7U1tZi1apV2LJlixAvKytDWVkZMjIy8M9//hNr1qxBcnKynCSpyxRFQX5tJTJKcnCwOAcZxTk4UpqHOnNDt66jUakxYUAc5g4chsvjRmJo0AAWFIhasN8pgksiyBMdLc3DLd/9HVUNRiG+avxlGBESKSkr92dfXKiuNuHMqRIMGzFAYlZERNQZty4uWCwW/OIXv8D27dsBAGFhYViyZAmSkpJQWVmJr7/+GgcOHEBBQQHuu+8+fPzxxxg8mE2DnEVdYwMyK4pwsrwQJ8oKmv6/vBDlproeXS/C2x9zYoZi7sBhmBk9BIF67z7OmMh9sJkjebr/nDuIVTu+gNFuedA1g8Zi5ZjZkrLyDAMi/BAbF4ic7EuzENPTcllcICJycm5dXFi3bp2tsJCUlIS1a9ciLCzMdv9tt92G5557Du+++y4qKyvxm9/8Bh9++KGsdD2SoigorKvCuaoSnK8qwfmqUpyvLMbpymJcqCoVpqF2V6jBF1MiEzElIgFTIhMxPDiCsxOIukBRFG5DSR6roLYS/7vva2y4cKTVfZMjErB6xk38W+IAKakxQnHh6JFCGI2NMBi8JGZFREQdcdvigsViwauvvmo7fv7554XCQrNVq1Zh9+7dOHHiBNLS0rBjxw7MmDHDkam6NUVRUGaqRV5NBfJqK5BXU4H82grk1lTgQnUpzleVoN7c2PmFuiDOLwTjw2MxOTIBUyITMCSQSx2IesJSUQBLdYkQ08dx5gK5t/NVJXj3+C58dGpfmzsKTYlMwNoFK+DjpZOQnedJHj8QX/3nOKzWpi8ZzI1WHDlUgImT4yRnRkRE7XHb4sL+/ftRXNy0ZdSkSZMwatSoNsdpNBosX74czzzzDABgw4YNLC50gcliRml9DYqNNSipr0FxfTVKmm//9//zayuRX1vR5W0fuyNQ543k8Fgkh8UgJTwO48JiEObNppxEfcF+1oLK4Aev8ERJ2RD1j+Ztin/MO4UNF45ib9H5dsfeNnQSfj/lWui47aTD+PrpMGLkABw7WmSLpe/PZXGBiMiJue1fyZ9++sl2e9asWR2ObXl/y/M8zYWqUqQXZ6PSVIeqBiMqG+pR1VCPClP9f28bUWlqilU3mhySk1qlwiD/UIwIicLw4AiMCI7C8OBIxPuHcFYCUT9p1cwxZgxU7IpPLk5RFOwqOIv9F7NwpDQPR0rzOt1ZKFjvg99NvoZbTkqSMjFGKC6cO1uGsrI6hIT4SMyKiIja47bFhVOnTtlujxkzpsOx4eHhiIqKQkFBAUpKSlBWVoaQkJD+TtGprD2xG/+z50tpj2/QeCEhIBSJgeFN/x8QhmHBkRgaNADeWk5BJXIk9lsgd7Tyx4/x1YXDnQ9E01aTy4akYlXKZQg1cFacLCNGDoC3jxfq6y4tnzx/tozFBSIiJ+W2xYXz5y9Nb4yJiel0fExMDAoKCgAA586da7e4kJ+fj/z8/B7ldOiQ+IY9MzOzR9fpD6/9+DnUdR1/g9MbapUaoQZfhHn7IczgizBvf0R4+yPKNwjRvoEIMfhAhf/ORFAAVAKNlYU4hsJ+y4mI2laYcRCNVQbbcbApFDlpaRIzIuqdkvoabNi+BZ3Nv/HXGTA7eiiuTRiLMJ0fzh89ifYXS5AjhIZX41DGpfdd57P0UFT9+97A/v1ZXV3PdqkiIvI0bltcqK6utt0ODg7udHxQUFCb59r74osvhEaRvfHss8/2yXX6Sn9vzFjz3/9d6OfHIaK+0KIoe+wzAJ9Jy4SoL3Tlb5wZwGb8gM39nQz12I49jn/MnJwcxz8oEZELcttFtC2rzHq9vtPxLcfU1tb2S05ERERERERE7shtiwtERERERERE5BhuuyzCx8cHlZVNPQRMJhO02o6fqsl0afcDX1/fdsctXrwYU6dO7VFOJSUlyMjIQGBgIAIDAxEXFwdv7/5ejOA8MjMzhaUgv/nNbzBs2DCJGRHJx9cFkYivCZKtvr4e2dnZtuN58+ZJzIaIyHW4bXHB39/fVlwoLy/vsGAAABUVFcK57YmOjkZ0dHSP81q4cGGPz3U3w4YNQ2pqquw0iJwKXxdEIr4miIiIXIPbLotISEiw3c7Nze10fMsxiYmJ/ZITERERERERkTty2+LC0KFDbbePHDnS4diSkhLbNpShoaHtbkNJRERERERERK25bXFh5syZtts//fRTh2O3bdtmuz179ux+y4mIiIiIiIjIHbltcWHSpEkIDw8HAOzbtw/Hjh1rc5zFYsEHH3xgO77qqqsckh8RERERERGRu3Db4oJGo8HKlSttx08++SRKS0tbjXvhhRdw4sQJAEBKSoow44GIiIiIiIiIOue2u0UAwNKlS7Fp0ybs3LkTp0+fxnXXXYclS5YgKSkJFRUV2LBhA9LT0wEAAQEBwtZXRERERERERNQ1bl1c0Gq1ePnll7Fq1Sps3boVxcXFeP3111uNi4yMxIsvvoghQ4ZIyJKIiIiIiIjItbl1cQEA/Pz88Oabb2LTpk348ssvceTIEZSWlsLX1xdxcXG47LLLsGzZMvj7+8tOlYiIiIiIiMgluX1xodmCBQuwYMEC2WkQERERERERuR23behIRERERERERI7B4gIRERERERER9QqLC0RERERERETUKywuEBEREREREVGveExDR5IvOjoaDz30kHBM5On4uiAS8TVBRETkmlSKoiiykyAiIiIiIiIi18VlEURERERERETUKywuEBEREREREVGvsLhARERERERERL3C4gIRERERERER9Qp3i6A2nTx5Ejt37sSBAwdw+vRpXLx4EWazGQEBARg8eDCmTZuGxYsXY8CAAV2+ptVqxVdffYUNGzbg5MmTKCsrQ1BQEBITE7Fw4ULcdNNN0Ol0Xb7eiRMn8Omnn2L37t24ePEi1Go1oqOjMXPmTCxbtgxxcXFdvlZ1dTU+/fRTbNq0CVlZWaipqUF4eDhGjx6N66+/HvPmzevytcg9lZeX49ixYzh69Kjt//Pz8233b968GTExMd26Jl8TRJds3rwZX375JY4ePYri4mL4+fkhPj4eCxYswLJly+Dn5yc7RSIiIuoAd4sgQVVVFZYsWYILFy50Otbb2xu/+tWvcNttt3U6tri4GI888ggOHDjQ7pghQ4bglVdeQUJCQqfXe/nll/Hmm2/CYrG0eb/BYMD/+3//D0uWLOn0WgcOHMAvf/lLFBYWtjtmwYIF+Mtf/gIfH59Or0fuJzMzE9dee22HY7pbXOBrgqhJbW0tVq1ahS1btrQ7JioqCmvWrEFycrLjEiMiIqJuYXGBBMXFxZgxYwYAQKPRYMKECUhNTUVsbCy8vb2Rn5+Pb7/9FocPH7ad88QTT+Duu+9u95q1tbW47bbbcOLECQBAbGwsbrrpJsTGxuLixYv497//jczMTADAwIED8dlnnyEsLKzd673xxhtYs2YNAECv1+P6669HSkoKzGYzdu/ejW+++QZWqxUqlQqrV6/GokWL2r3W6dOnccstt6C6uhoAMGHCBCxatAiBgYE4c+YM1q1bh5KSEgDAnDlz8MYbb0Ct5moiT3PixAlcf/31tmONRoPExETk5OTAaDQC6F5xga8JoiYWiwX3338/tm/fDgAICwvDkiVLkJSUhMrKSnz99de2AlxgYCA+/vhjDB48WGbKRERE1A4WF0hQXFyMq6++GitWrOhw2cNbb72F1atXAwC8vLywYcMGxMfHtzl29erVeOuttwAAkyZNwptvvglfX1/b/Y2NjXjiiSfwzTffAACuvfZa/OUvf2nzWmfOnMG1114Li8UCHx8f/OMf/8C4ceOEMZs2bcLDDz8Mq9WKgIAAfP/99wgODm7zesuWLUNGRgYA4Gc/+xmefPJJ4f6SkhLceeedOHPmDADgD3/4Q5e++SX3kp2djddeew2jRo3C6NGjMWLECHh7e2PevHnIy8sD0L3iAl8TRE0++eQT/Pa3vwUAJCUlYe3ata0Kac899xzeffddAEBqaio+/PBDh+dJREREnePXTSQICgrCli1b8MADD3TYT+G+++7DggULADR9EFq/fn2b4yoqKrB27VoATd+ovvDCC8KHKKCpOPGHP/wB4eHhAICvvvoKZ8+ebfN6r776qm3a9yOPPNLqQxTQNF27ealGVVUV3nvvvTavtW3bNtuHqBEjRmDVqlWtxoSFheG5554THt9qtbZ5PXJfcXFxeO6553DHHXcgJSUF3t7ePb4WXxNETSwWC1599VXb8fPPP9/mDJ1Vq1ZhxIgRAIC0tDTs2LHDYTkSERFR17G4QAIvL69WH3Tac9VVV9luN0/htrd582aYTCbb+IiIiDbH+fr6YunSpQAARVGwcePGVmPq6uqwdetWAICPj49tfFvuvPNO2+0NGza0Oab5W2EAuOOOO6DRaNocN3r0aEycOBEAUFhYiLS0tHYfl6gzfE0QNdm/fz+Ki4sBNM3gGTVqVJvjNBoNli9fbjtu7+eXiIiI5GJxgXqsZefu5nXn9n766Sfb7VmzZnV4vdmzZ7d5XrP9+/fbHic1NbXDIkhsbCwSExMBALm5uW1+69vyMVo+dlta5t5WbkRdxdcEUZPuvBb480ZEROT8WFygHms5WyE6OrrNMadOnbLdHjNmTIfXGzlypO2b0tOnT8O+HUjLx+vsWvZjWuYBNK0bLysrs+UeGhra42sRdQdfE0RNuvNaCA8PR1RUFADxZ5WIiIicB4sL1CONjY344osvbMdz5sxpNcZqtSI7OxtA07TW5jeG7fHy8rJNEa+rq0NRUZFw//nz5223u9I4r+WYluf29bWIuoqvCaJLevMzd+7cuX7JiYiIiHqOxQXqkb/97W+4cOECgKbGb20VF+rq6mA2mwEA/v7+0Gq1nV43KCjIdruqqkq4r3lrPADtdrpv71otz7W/dleu1XKMfV5EXcXXBNElffnzS0RERPKxuEDd9uOPP+K1114D0PTN6rPPPtvmPve1tbW223q9vkvXbjmu5flA0wez7lzPYDB06Vo6na5XeRF1FV8TRJd09+eXP3NERETOrfOvzcjpLF++HPv27euTa/31r3/FokWLujz+yJEjeOyxx2xbzz355JMYO3Zsn+RC1FMyXxNERERERMSZC9QNJ0+exD333GP7xmjlypXC9mD2Wnaub956rzMtx9l3vvfx8enW9VruYNHRtRoaGnqVF1FX8TVBdEl3f375M0dEROTcOHPBBV1xxRUYPnx4n1wrISGhS+MyMzOxYsUKVFRUAADuv/9+/OIXv+jwHB8fH2i1WpjNZlRXV8NsNne6xrz5+gAQEBAg3Ofv72+7XV5e3mnOLa/V8lz7a3flWi3H2OdF8sl4TfQEXxNEl/j7+6OyshJA089TZwWDjn5+iYiISD4WF1zQ7bff7tDHay4sNH+YuPfee/HYY491ep5arUZcXBzOnTsHi8WCgoICxMbGtju+sbHR1g3fx8fH1iW/WcsPfbm5uZ0+fssx9h8Y+/JaJJ+jXxM9xdcE0SUJCQm2n6Pc3NxOd4xo+TOXmJjYr7kRERFR93FZBHWoubDQvKf4z372M6xatarL5w8dOtR2+8iRIx2OPX78OCwWCwAgKSkJKpVKuH/YsGFdvpb9mJZ5AEBYWBhCQkIAAPn5+SgtLe3ytYYMGdLpYxO1h68JoibdeS2UlJSgoKAAABAaGmr7WSUiIiLnweICtcu+sHDXXXfhySef7NY1Zs6cabu9ffv2Dsdu27bNdnv27Nmt7p84caKt231aWlqH3cJzcnJs+6APHDgQgwcP7jC3n376qcPcWt7fVm5EXcXXBFGT7vy8dfZaICIiIvlYXKA2nT59WigsrFixAk899VS3rzN//nzb9mEbNmywTfG2V1tbi88++wwAoFKpcOWVV7Ya4+Pjgzlz5gBo2sKseXxb1q5da7vdXuf/lvG1a9faviG2d/ToUezfvx8AEBERgdTU1HYfl6gzfE0QNZk0aRLCw8MBAPv27cOxY8faHGexWPDBBx/Yjq+66iqH5EdERETdw+ICtXLmzBnceeedQmHh6aef7tG1goODbTtKmEwmrFq1qtW3q2azGb/+9a9RXFwMALj66qvb/FYVAB588EGo1U0/ti+//DIOHz7casymTZvw4YcfAmhq+nXXXXe1ea3Zs2dj3LhxAIATJ05g9erVrcaUlJQIszUefvhh2+MT9QRfE0RNNBoNVq5caTt+8skn21yO88ILL+DEiRMAgJSUFGHGAxERETkPlaIoiuwkyHkUFhZi8eLFKCkpAQCMHDkSDz74YKfnGQwGzJgxo837ampqcOuttyIzMxMAEBsbi6VLlyImJgYXL17Ev/71L9t90dHR+PTTTzFgwIB2H+vVV1/FK6+8AgDQ6/W44YYbMH78eFitVuzcuRPffPMNrFYrVCoVnnvuOVx33XXtXiszMxO33norampqAAATJkzAokWLEBQUhDNnzmDdunW2D3gzZ87E3/72N2g0mk7/Pcj9rFu3rlWjww8//BDV1dUAmppK+vn5Cff/8pe/bPNafE0QNTGbzbjvvvuwc+dOAEB4eDiWLFmCpKQkVFRUYMOGDUhPTwfQtCvJRx99xB4fRERETorFBRLs3bsXd9xxR7fPGzhwILZs2dLu/UVFRXjkkUdw8ODBdsckJSXh5Zdfbvcb2pZefPFFvP322+1O2zYYDHj66aexbNmyTq+VlpaGxx57rN3p6QAwb948vPDCC9xb3YMtX74c+/bt69Y5zQWCtvA1QdSkpqYGq1atwtatW9sdExkZiRdffBEpKSkOzIyIiIi6g8UFEvRXcQEArFYr1q9fj6+//hqZmZkoLy9HYGAgEhMTsXDhQixZsgQ6na7Lj3ns2DF8+umn2Lt3Ly5evAiVSoWoqCjMmjULy5YtQ3x8fJevVVVVhU8//RQ//PADsrOzUVtbi9DQUIwZMwbXXXcdFixY0OVrkXvq6+ICwNcEUUubNm3Cl19+iSNHjqC0tBS+vr6Ii4vDZZddhmXLlsHf3192ikRERNQBFheIiIiIiIiIqFfYhYuIiIiIiIiIeoXFBSIiIiIiIiLqFRYXiIiIiIiIiKhXWFwgIiIiIiIiol5hcYGIiIiIiIiIeoXFBSIiIiIiIiLqFRYXiIiIiIiIiKhXWFwgIiIiIiIiol5hcYGIiIiIiIiIeoXFBSIiIiIiIiLqFRYXiIiIiIiIiKhXWFwgIiIiIiIiol7Ryk6AiMgVmM1mpKSkwGQyQavV4sCBA9Dr9bLT6pFhw4bZbmdmZkrMxLE89XkTEREROQJnLhARdcHZs2dhMpkAAImJidIKCw8++CCGDRuG4cOHo6ioSEoOLS1fvhzDhg3DsGHD8Morr/Tb4zjb8yYiIiIiEYsLRERdcOLECdvt4cOHS8mhoaEBu3btAgCMGjUKERERUvJwNE993kRERESuhMUFIqIuaFlcGDFihJQcdu/ejbq6OgDAvHnzpOQgg6c+byIiIiJXwuICEVEXHD9+3HZbVnFhy5Ytttue9CHbU583ERERkSthcYGIqAtOnjxpuy2ruLB161YAQHR0tLQcZPDU501ERETkSlhcICLqRG5uLqqqqgAAUVFRCAoKcngOx44dszUynDNnjsMfXxZPfd5ERERErobFBSKiTrTXzHH37t146qmnsHDhQiQnJyMlJQU333wzPvnkE1it1j7Nofnbe8BxSwOsVit+97vf2XaDGD9+PLZv3+6Qx27m7M/bYrFg3bp1WLFiBaZNm4YxY8Zg3rx5eOCBB/DDDz9AURQA4q4ae/fudcjzICIiInIkrewEiIicnX0zx9zcXPz617+27WDQ0sGDB3Hw4EHs3r0ba9asgUql6pMcmvsO+Pj4YPLkyX1yzY40NDTgiSeewMaNGwEAQUFB+Nvf/obk5OR+f+yWnPl5FxYWYuXKlTh27JgQz8vLQ15eHrZs2YL58+fj+eef7/e8iYiIiGRjcYGIqBMtmzkaDAYsXboUpaWl8Pf3R2pqKsLCwlBcXIy9e/eivr4eAPDtt9/i66+/xjXXXNPrxy8qKrJ9gJ05cyZ0Ol2vr9mR2tpaPPTQQ7biSUREBN59910kJSX16+Pac+bnXV5ejjvvvBMXLlywxeLi4jB27FjodDqcO3cOhw4dwubNm/HMM8/0a95EREREzoDFBSKiTrScufDaa6/BarVi1apVuOOOO6DX6233FRUV4c4778T58+cBABs2bOiT4oIjd0soKyvDfffdhyNHjgAAEhIS8O677yI6OrpfH7ctzvy8//jHP9oKC3q9Hn/4wx9w7bXXCmOOHz+ORx99FN99912/F0aIiIiIZGPPBSKiDpSXl6OwsNB2rCgK3nnnHdx7771CYQFo+qZ71apVtuPmIkNvNfcd0Gg0mDVrVp9csy35+fm49dZbbR+wR48ejY8++khKYQFw3ud99uxZrF+/3nb8pz/9qVVhAQBGjhyJ9957D35+fmhoaOif5ImIiIicBIsLREQdaDlrAQCefvppTJkypd3xQ4YMsd3ui6aOdXV12LNnDwAgOTkZISEhvb5mW06fPo1ly5bZCiJTp07F+++/32+P1xlnft6ff/657fb48eOxaNGidscOHDgQd911V98lTEREROSkWFwgIupAy+JCYmIibrnllg7HV1dX224HBwf3+vF37doFk8kEAJg7d26vr9eWjIwM3HbbbbYtH6+44gq89dZb8PX17ZfH6wpnft779u2z3W5rxoK96667rneJEhEREbkAFheIiDrQspnjTTfd1OnuD2fPnrXdHjRoUK8fv7/7Dmzbtg133XUXKisrAQA333wz1qxZI71HgLM+b0VRkJmZaTseN25cp48VGxvbJ4UmIiIiImfGho5ERB1oOXNh+vTpnY4/efKk7fbQoUN79dhWqxXbtm0DAMTHx2Pw4MG9ul5bVq5cCbPZDAC477778Pjjj/f5Y3SXMz/v6upqNDY22o6joqK6dF5kZCTKy8u7nygRERGRi+DMBSKidhiNRmFHgJb9FNrTshgxcuTIXj3+4cOHUVJSAqD/dkvQai/VmC9cuCB8cJbFmZ93XV2dcGwwGLp0no+PT9eTIyIiInJBLC4QEbUjMzMTFosFQNMsBI1G0+k5LYsLI0aM6NXjO2IrxjfffNP2Afn777/HY489Jr3A4MzP275IYDQau/R49fX13U+SiIiIyIWwuEBE1I6W/Ra6MguhoKAAFRUVAJqmy/d2nX3zVoyBgYFISUnp1bXaM3Xq1DY/aDcvGZDBmZ+3v78/vLy8bMcttyntSFfHEREREbkqFheIiNrR3VkILYsRvZ21kJOTg1OnTgEAZs2aJUzj72ttfdD+5S9/KaXA4OzPW6VSCb00Dh061Onj5ObmoqysrPcJExERETkxFheIiNrR3f4Jfdlvofnbe6D/lga05CwzGFzheU+ePNl2+6uvvur0MdavX9/7RImIiIicHIsLRERtsFgstm/Q1Wp1l3Z+6MuZC80fsr28vDBz5sxeXaur7D9of/fddw4vMLjC8168eLHtdnp6OjZu3NjutQsKCvDuu+/2bcJERERETojFBSKiNpw/f97WrC8hIQHe3t6dntNXMxdqamqwf/9+AEBqair8/f17fK3umjp1Kt544w0pBQZXed5JSUm4+uqrbcdPPfUUvv7661bjTp48iRUrVqC6uho6na7/kiciIiJyAiwuEBG1obuzECoqKpCfnw8ACAoKQnR0dI8f+6effrLtXOCIpQH2pk2bJqXA4ErP+3/+538QGxsLoGnHiMcffxyXX345fvWrX+GZZ57BsmXLcP311+PChQu44oorkJycbDtXreafXiIiInI/fIdDRNSG3vRbcIUtKDszbdo0vP76651+0FYUxXa7tx+aXel5h4SE4P333xf+W2dlZWH9+vX44osvkJGRAUVRMH/+fPzxj38Utrn08/NzzJMhIiIiciAWF4iI2iBrpwiLxYLt27cDAIYOHYqYmJgeX6u3pk+f3ukHbZPJZLvt6+vb48dytecNANHR0fj888/x+9//HpMnT0ZISAi8vLwQHR2NuXPn4pVXXsFrr70GPz8/VFZW2s5z5HIPIiIiIkfpvz2+iIhc2D/+8Y9ujb/77rtx99139/px09PTUVFRAaD/vr3PzMzs8tjp06d3uN1ic64AEBAQ0OOcXO15N9NqtVi6dCmWLl3a7pj6+npkZ2cDAHx8fHq1ZIaIiIjIWXHmAhGRE2m5FePcuXMlZtK5+vp6W58JABg8eHCPr+VKz7u7vv/+e9ush5EjR7LnAhEREbklvsMhInIizX0HwsLCMG7cOMnZdGzr1q22D80+Pj4YNmxYj6/lSs+7OyorK/HSSy/ZjlvuMkFERETkTrgsgojIiXz33XeyU+iSrKwsvPDCC7bj+fPn23oU9ISrPO+WHn30USxcuBBz586FXq9vdX96ejp+/etfIy8vDwAQERGBa665xtFpEhERETkEiwtERNQla9euxdmzZ5GXl4e9e/fadkDQ6/V45JFHJGfneIcPH8bGjRvh4+ODkSNHIiYmBnq9HlVVVTh+/DiysrJsY728vPCnP/2JO0UQERGR22JxgYiIumTTpk3Yt2+fEPPx8cFLL72EuLg4SVnJV1dXh7S0NKSlpbV5f3h4OJ5//nlMmzbNwZkREREROQ6LC0RE1GVqtRoBAQGIiYnBjBkzsHz5coSFhclOS4q1a9di06ZNSEtLQ3Z2NsrLy1FRUQEvLy8EBwdjxIgRmDlzJq6//vpeLRkhIiIicgUqRVEU2UkQERERERERkevibhFERERERERE1CssLhARERERERFRr7C4QERERERERES9wuICEREREREREfUKiwtERERERERE1CssLhARERERERFRr7C4QERERERERES9wuICEREREREREfUKiwtERERERERE1CssLhARERERERFRr7C4QERERERERES98v8B81lbiCitSQwAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"sns.lineplot(data=df_vle, x=\"specific enthalpy vapor\", y=\"pressure\", color=colors[0], sort=False, label=\"vle\")\n",
"sns.lineplot(data=df_vle, x=\"specific enthalpy liquid\", y=\"pressure\", color=colors[0], sort=False)\n",
"sns.lineplot(data=df_spinodal, x=\"specific enthalpy vapor\", y=\"pressure vapor\", color=colors[1], sort=False, label=\"spinodal\")\n",
"sns.lineplot(data=df_spinodal, x=\"specific enthalpy liquid\", y=\"pressure liquid\", color=colors[1], sort=False)\n",
"sns.lineplot(data=df_isotherm, x=\"specific enthalpy\", y=\"pressure\", color=colors[2], sort=False, label=f\"T = {t / si.KELVIN:.2f} K\")\n",
"\n",
"plt.legend(frameon=False, bbox_to_anchor=(1,1))\n",
"plt.xlabel(r\"$h$ / kJ / kg\")\n",
"plt.ylabel(r\"$p$ / Pa\")\n",
"plt.ylim(0, 4e7);"
]
},
{
"cell_type": "markdown",
"id": "a245a174-3f75-4148-9fbb-06c0618cad0a",
"metadata": {},
"source": [
"## Using `to_dict` for `StateVec`s.\n",
"\n",
"If you are only interested in one phase of the phase diagram, you can use the `StateVec` objects. They store `State`s of each phase separately and provide methods to conveniently calculate a property for all states. Alternatively, you can use `to_dict` to calculate multiple properties at once."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1dc3c339-5923-4798-8c96-f105b085da62",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 3351984.99511485, 3363256.01942792, 3374551.5372812 ,\n",
" 3385871.5763197 , 3397216.16423726, 3408585.32877665,\n",
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" 3664981.62269616, 3676923.82320398, 3688891.2815469 ,\n",
" 3700884.02677849, 3712902.08800815, 3724945.49440144,\n",
" 3737014.27518038, 3749108.45962367, 3761228.07706697,\n",
" 3773373.15690319, 3785543.72858276, 3797739.8216139 ,\n",
" 3809961.46556287, 3822208.69005431, 3834481.52477141,\n",
" 3846779.99945631, 3859104.14391028, 3871453.98799404,\n",
" 3883829.56162802, 3896230.89479267, 3908658.01752868,\n",
" 3921110.95993732, 3933589.7521807 , 3946094.42448202,\n",
" 3958625.0071259 , 3971181.53045863, 3983764.02488847,\n",
" 3996372.5208859 , 4009007.04898394, 4021667.63977844,\n",
" 4034354.32392833, 4047067.13215591, 4059806.0952472 ,\n",
" 4072571.24405211, 4085362.60948484, 4098180.22252412,\n",
" 4111024.11421351, 4123894.31566163, 4136790.85804258,\n",
" 4149713.77259611, 4162663.09062797, 4175638.84351017,\n",
" 4188641.06268134, 4201669.77964694, 4214725.02597962,\n",
" 4227806.83331947, 4240915.23337436, 4254050.25792018,\n",
" 4267211.93880122, 4280400.30793038, 4293615.39728956,\n",
" 4306857.23892988, 4320125.86497203, 4333421.30760654,\n",
" 4346743.59909414, 4360092.771766 , 4373468.85802405,\n",
" 4386871.89034134, 4400301.90126226, 4413758.92340294,\n",
" 4427242.98945147, 4440754.13216829, 4454292.3843864 ,\n",
" 4467857.77901181, 4481450.34902375, 4495070.12747497,\n",
" 4508717.14749214, 4522391.44227612, 4536093.04510224,\n",
" 4549821.98932069, 4563578.30835677, 4577362.03571127,\n",
" 4591173.20496073, 4605011.84975781, 4618878.00383161,\n",
" 4632771.70098794, 4646692.9751097 , 4660641.8601572 ,\n",
" 4674618.39016845, 4688622.59925952, 4702654.52162486,\n",
" 4716714.19153762, 4730801.64335002, 4744916.9114936 ,\n",
" 4759060.03047965, 4773231.03489947, 4787429.95942475,\n",
" 4801656.83880787, 4815911.70788227, 4830194.60156278,\n",
" 4844505.55484595, 4858844.60281038, 4873211.7806171 ,\n",
" 4887607.12350987, 4902030.66681557, 4916482.4459445 ,\n",
" 4930962.49639074, 4945470.85373255, 4960007.55363264,\n",
" 4974572.63183853, 4989166.12418299, 5003788.0665843 ,\n",
" 5018438.49504661, 5033117.44566036, 5047824.95460258,\n",
" 5062561.05813728, 5077325.79261576, 5092119.19447706,\n",
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" 10378897.74174466, 10403380.1735219 , 10427906.5075385 ,\n",
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" 13118118.01770103, 13147572.20751993, 13177080.8206985 ,\n",
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" 13475188.61287279, 13505304.43906821, 13535476.26020418,\n",
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" 13656726.20416624, 13687180.03820293, 13717690.68703696,\n",
" 13748258.28962253, 13778882.98558722, 13809564.91523712,\n",
" 13840304.2195618 , 13871101.04023957, 13901955.51964252,\n",
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" 14308376.77090551, 14340053.8228378 , 14371790.75601866,\n",
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" 14499340.39756169, 14531379.06621825, 14563478.5534381 ,\n",
" 14595639.0182811 , 14627860.6206361 , 14660143.52122777,\n",
" 14692487.88162293, 14724893.86423788, 14757361.63234492,\n",
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" 14986379.90079877, 15019347.93572218, 15052379.2652922 ,\n",
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" 15487626.03115808, 15521562.00755921, 15555563.9642129 ,\n",
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" 16214392.8966071 , 16249755.23973199, 16285187.73073156,\n",
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" 16427623.42691619, 16463409.85879572, 16499267.72836437,\n",
" 16535197.25525618, 16571198.66047269, 16607272.16639581,\n",
" 16643417.9968016 , 16679636.37687391, 16715927.53321803,\n",
" 16752291.6938749 , 16788729.08833518, 16825239.94755377,\n",
" 16861824.50396449, 16898482.99149464, 16935215.6455803 ,\n",
" 16972022.70318133, 17008904.40279695, 17045860.98448144,\n",
" 17082892.68985974, 17119999.76214393, 17157182.44614917,\n",
" 17194440.98831067, 17231775.63670013, 17269186.64104298,\n",
" 17306674.25273556, 17344238.72486271, 17381880.31221557,\n",
" 17419599.27130956, 17457395.86040277, 17495270.33951452,\n",
" 17533222.97044407, 17571254.01678998, 17609363.74396948,\n",
" 17647552.41923792, 17685820.31170922, 17724167.69237567,\n",
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" 17878357.58375701, 17917106.53749824, 17955936.64601675,\n",
" 17994848.19405055, 18033841.46839001, 18072916.75790166,\n",
" 18112074.35355147, 18151314.54842905, 18190637.63777208,\n",
" 18230043.91899097, 18269533.69169436, 18309107.25771433,\n",
" 18348764.92113273, 18388506.98830741, 18428333.76789932,\n",
" 18468245.57089958, 18508242.7106573 , 18548325.50290813,\n",
" 18588494.2658022 , 18628749.31993411, 18669090.98837195,\n",
" 18709519.59668797, 18750035.47298865, 18790638.94794646,\n",
" 18831330.35483125, 18872110.02954245, 18912978.31064215,\n",
" 18953935.53938818, 18994982.05976836, 19036118.21853469,\n",
" 19077344.36523888, 19118660.8522678 , 19160068.03488027,\n",
" 19201566.2712437 , 19243155.92247202, 19284837.35266423,\n",
" 19326610.92894308, 19368477.02149536, 19410436.00361206,\n",
" 19452488.2517298 , 19494634.14547295, 19536874.06769619,\n",
" 19579208.4045285 , 19621637.54541717, 19664161.8831734 ,\n",
" 19706781.81401831, 19749497.73762994, 19792310.05719119,\n",
" 19835219.17943849, 19878225.51471194, 19921329.47700561,\n",
" 19964531.4840196 , 20007831.95721268, 20051231.32185599,\n",
" 20094730.00708816, 20138328.44597087, 20182027.0755465 ,\n",
" 20225826.33689559, 20269726.6751973 , 20313728.53978887,\n",
" 20357832.38422865, 20402038.66635835, 20446347.84836806,\n",
" 20490760.39686193, 20535276.78292532, 20579897.48219354,\n",
" 20624622.97492201, 20669453.74605764, 20714390.28531215,\n",
" 20759433.08723692, 20804582.65129898, 20849839.48195946,\n",
" 20895204.08875313, 20940676.98637006, 20986258.69473889,\n",
" 21031949.73911209, 21077750.65015314, 21123661.96402586,\n",
" 21169684.2224853 , 21215817.97297131, 21262063.76870391,\n",
" 21308422.16878101, 21354893.7382784 , 21401479.04835232,\n",
" 21448178.67634413, 21494993.20588748, 21541923.22701882,\n",
" 21588969.33628921, 21636132.13688053, 21683412.23872319,\n",
" 21730810.25861737, 21778326.82035739, 21825962.55485901,\n",
" 21873718.10028983, 21921594.10220332, 21969591.21367617,\n",
" 22017710.09544934, 22065951.41607225, 22114315.85205168,\n",
" 22162804.08800384, 22211416.81681086, 22260154.73978132,\n",
" 22309018.5668154 , 22358009.01657422, 22407126.81665404,\n",
" 22456372.7037654 , 22505747.42391688, 22555251.73260453,\n",
" 22604886.3950062 , 22654652.18618202, 22704549.89127982,\n",
" 22754580.30574762, 22804744.23555151, 22855042.49740017,\n",
" 22905475.91897631, 22956045.3391749 , 23006751.6083488 ,\n",
" 23057595.58856164, 23108578.15384859, 23159700.19048579,\n",
" 23210962.59726714, 23262366.28579102, 23313912.18075562,\n",
" 23365601.22026365, 23417434.35613728, 23469412.55424368,\n",
" 23521536.79483064, 23573808.07287402, 23626227.39843699,\n",
" 23678795.79704093, 23731514.31004985, 23784383.99506781,\n",
" 23837405.92635044, 23890581.19523086, 23943910.91056123,\n",
" 23997396.19916995, 24051038.2063361 , 24104838.09628076,\n",
" 24158797.05267747, 24212916.27918097, 24267196.9999777 ,\n",
" 24321640.46035549, 24376247.92729754, 24431020.69009849,\n",
" 24485960.06100559, 24541067.37588573, 24596343.99491976,\n",
" 24651791.30332506, 24707410.7121092 , 24763203.65885467,\n",
" 24819171.60853741, 24875316.05438136, 24931638.5187493 ,\n",
" 24988140.554075 , 25044823.74383536, 25101689.70356881,\n",
" 25158740.08193894, 25215976.56184923, 25273400.86160998,\n",
" 25331014.73616128, 25388819.97835604, 25446818.42030566,\n",
" 25505011.93479406, 25563402.43676263, 25621991.88487218,\n",
" 25680782.28314673, 25739775.68270434, 25798974.18358129,\n",
" 25858379.93665642, 25917995.14568195, 25977822.06942984,\n",
" 26037863.02396023, 26098120.38502369, 26158596.59060504,\n",
" 26219294.14362109, 26280215.61478364, 26341363.64564282,\n",
" 26402740.95182192, 26464350.32646399, 26526194.64390433,\n",
" 26588276.86359123, 26650600.03427485, 26713167.29848932,\n",
" 26775981.89735501, 26839047.1757302 , 26902366.58774581,\n",
" 26965943.70276096, 27029782.21178122, 27093885.93438558,\n",
" 27158258.82621735, 27222904.98709694, 27287828.66982601,\n",
" 27353034.28976023, 27418526.4352385 , 27484309.87896966,\n",
" 27550389.59049384, 27616770.74985042, 27683458.76260772,\n",
" 27750459.27643505, 27817778.19942374, 27885421.72040468,\n",
" 27953396.33154815, 28021708.85358789, 28090366.46407374,\n",
" 28159376.72913661, 28228747.63934661, 28298487.650369 ,\n",
" 28368605.72927587, 28439111.40756421, 28510014.84218379,\n",
" 28581326.88620012, 28653059.17113926, 28725224.20361896,\n",
" 28797835.47961994, 28870907.62076274, 28944456.53835616,\n",
" 29018499.63294442, 29093056.03988202, 29168146.93555284,\n",
" 29243795.92496318, 29320029.54081883, 29396877.89904942,\n",
" 29474375.58014303, 29552562.84742695, 29631487.38871485,\n",
" 29711206.9120235 , 29791793.22473821, 29873339.10873857,\n",
" 29955971.09223247, 30039876.92026505, 30125381.83609614,\n",
" 30213398.57119887]) Pa"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pressures of vapor spinodal\n",
"spinodal.vapor.pressure"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "debd7cca-8b99-485c-8d25-9de8ea21a02c",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>density</th>\n",
" <th>molar entropy</th>\n",
" <th>molar enthalpy</th>\n",
" <th>temperature</th>\n",
" <th>specific entropy</th>\n",
" <th>pressure</th>\n",
" <th>specific enthalpy</th>\n",
" <th>mass density</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>53476.954297</td>\n",
" <td>-0.060682</td>\n",
" <td>-38.296278</td>\n",
" <td>373.150000</td>\n",
" <td>-3.369185</td>\n",
" <td>100461.912545</td>\n",
" <td>-2126.271605</td>\n",
" <td>963.173424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>53461.088499</td>\n",
" <td>-0.060625</td>\n",
" <td>-38.274789</td>\n",
" <td>373.455093</td>\n",
" <td>-3.365992</td>\n",
" <td>101563.880437</td>\n",
" <td>-2125.078481</td>\n",
" <td>962.887665</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>53445.215431</td>\n",
" <td>-0.060567</td>\n",
" <td>-38.253296</td>\n",
" <td>373.760186</td>\n",
" <td>-3.362801</td>\n",
" <td>102675.897451</td>\n",
" <td>-2123.885157</td>\n",
" <td>962.601775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53429.335057</td>\n",
" <td>-0.060510</td>\n",
" <td>-38.231799</td>\n",
" <td>374.065278</td>\n",
" <td>-3.359612</td>\n",
" <td>103798.034688</td>\n",
" <td>-2122.691631</td>\n",
" <td>962.315754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>53413.447342</td>\n",
" <td>-0.060453</td>\n",
" <td>-38.210299</td>\n",
" <td>374.370371</td>\n",
" <td>-3.356426</td>\n",
" <td>104930.363581</td>\n",
" <td>-2121.497903</td>\n",
" <td>962.029600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" density molar entropy molar enthalpy temperature specific entropy \\\n",
"0 53476.954297 -0.060682 -38.296278 373.150000 -3.369185 \n",
"1 53461.088499 -0.060625 -38.274789 373.455093 -3.365992 \n",
"2 53445.215431 -0.060567 -38.253296 373.760186 -3.362801 \n",
"3 53429.335057 -0.060510 -38.231799 374.065278 -3.359612 \n",
"4 53413.447342 -0.060453 -38.210299 374.370371 -3.356426 \n",
"\n",
" pressure specific enthalpy mass density \n",
"0 100461.912545 -2126.271605 963.173424 \n",
"1 101563.880437 -2125.078481 962.887665 \n",
"2 102675.897451 -2123.885157 962.601775 \n",
"3 103798.034688 -2122.691631 962.315754 \n",
"4 104930.363581 -2121.497903 962.029600 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# selected properties of the VLE-liquid branch\n",
"pd.DataFrame(vle.liquid.to_dict()).head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f424fae8-8967-4809-9e40-12e4b5156f44",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# DataFrames work nicely with seaborn:\n",
"\n",
"plt.figure(figsize=(8,6))\n",
"sns.lineplot(\n",
" data=pd.DataFrame(vle.liquid.to_dict()),\n",
" x=\"specific enthalpy\", \n",
" y=\"pressure\", \n",
" color=colors[0], \n",
" sort=False, \n",
" label=\"vle\"\n",
")\n",
"sns.lineplot(\n",
" data=pd.DataFrame(spinodal.liquid.to_dict()),\n",
" x=\"specific enthalpy\", \n",
" y=\"pressure\", \n",
" color=colors[1], \n",
" sort=False, \n",
" label=\"spinodal\"\n",
")\n",
"plt.legend(frameon=False)\n",
"plt.xlabel(r\"$h$ / kJ / kg\")\n",
"plt.ylabel(r\"$p$ / Pa\")\n",
"plt.ylim(0, 3.5e7);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/pcsaft_phase_diagram.ipynb | .ipynb | 159,340 | 584 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pure substance phase diagrams\n",
"\n",
"## Goal of this notebook\n",
"\n",
"- Learn how to generate and work with phase diagrams.\n",
"- Learn what `PhaseEquilibrium` objects are and how to use them."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read parameters from json for a single substance and generate `PcSaft` object"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|hexane|86.177|3.0576|3.7983|236.77|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"110-54-3\",\n",
" \"name\": \"hexane\",\n",
" \"iupac_name\": \"hexane\",\n",
" \"smiles\": \"CCCCCC\",\n",
" \"inchi\": \"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" \"formula\": \"C6H14\"\n",
" },\n",
" \"molarweight\": 86.177,\n",
" \"m\": 3.0576,\n",
" \"sigma\": 3.7983,\n",
" \"epsilon_k\": 236.77\n",
" }\n",
"]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.from_json(\n",
" ['hexane'], \n",
" '../parameters/pcsaft/gross2001.json'\n",
")\n",
"parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pcsaft = feos.EquationOfState.pcsaft(parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## The `PhaseDiagram` object\n",
"\n",
"A `PhaseDiagram` object contains multiple thermodyanmic states at phase equilibrium.\n",
"**For a single substance** it can be constructed via the `PhaseDiagram.pure` method by providing \n",
"\n",
"- an equation of state,\n",
"- the minimum temperature, and\n",
"- the number of points to compute.\n",
"\n",
"The points are evenly distributed between the defined minimum temperature and the critical temperature which is either computed (default) or can be provided via the `critical_temperature` argument.\n",
"\n",
"Furthermore, we can define options for the numerics:\n",
"- `max_iter` to define the maximum number of iterations for the phase equilibrium calculations, and\n",
"- `tol` to set the tolerance used for deterimation of phase equilibria.\n",
"\n",
"A `Verbosity` object can be provided via the `verbosity` argument to print intermediate calculations to the screen.\n",
"\n",
"Starting from the minimum temperature, phase equilibria are computed in sequence using prior results (i.e. at prior temperature) as input for the next iteration."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"phase_diagram = feos.PhaseDiagram.pure(pcsaft, min_temperature=200*si.KELVIN, npoints=501)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|200.00000 K|12.21572 mmol/m³|"
],
"text/plain": [
"T = 200.00000 K, ρ = 12.21572 mmol/m³"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"phase_diagram.vapor[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stored information\n",
"\n",
"A `PhaseDiagram` object contains the following *fields*:\n",
"\n",
"- `states`: a list (with length of `npoints`) of `PhaseEquilibrium` objects at the different temperatures,\n",
"- `vapor` and `liquid`: so-called `StateVec` objects that can be used to compute properties for the vapor and liquid phase, respectively.\n",
"\n",
"The `to_dict` *method* can be used to conveniently generate a `pandas.DataFrame` object (see below)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building a `pandas.DataFrame`: the `to_dict` method\n",
"\n",
"\n",
"The `PhaseDiagramPure` object contains some physical properties (such as densities, temperatures and pressures) as well as the `PhaseEquilibrium` objects at each temperature.\n",
"\n",
"Before we take a look at these objects, a useful tool when working with phase diagrams is the `to_dict` method. It generates a Python dictionary of some properties (with hard-coded units, see the docstring of `to_dict`). This dictionary can readily be used to generate a `pandas.DataFrame`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>temperature</th>\n",
" <th>pressure</th>\n",
" <th>density liquid</th>\n",
" <th>density vapor</th>\n",
" <th>molar enthalpy liquid</th>\n",
" <th>molar enthalpy vapor</th>\n",
" <th>molar entropy liquid</th>\n",
" <th>molar entropy vapor</th>\n",
" <th>mass density liquid</th>\n",
" <th>mass density vapor</th>\n",
" <th>specific enthalpy liquid</th>\n",
" <th>specific enthalpy vapor</th>\n",
" <th>specific entropy liquid</th>\n",
" <th>specific entropy vapor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200.000000</td>\n",
" <td>20.312763</td>\n",
" <td>8539.589591</td>\n",
" <td>0.012216</td>\n",
" <td>-37.322054</td>\n",
" <td>-0.000147</td>\n",
" <td>-0.074718</td>\n",
" <td>-1.879119e-07</td>\n",
" <td>735.916212</td>\n",
" <td>0.001053</td>\n",
" <td>-433.086018</td>\n",
" <td>-0.001703</td>\n",
" <td>-0.867028</td>\n",
" <td>-0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>200.638669</td>\n",
" <td>21.816282</td>\n",
" <td>8532.663964</td>\n",
" <td>0.013078</td>\n",
" <td>-37.281784</td>\n",
" <td>-0.000157</td>\n",
" <td>-0.074497</td>\n",
" <td>-2.001300e-07</td>\n",
" <td>735.319382</td>\n",
" <td>0.001127</td>\n",
" <td>-432.618726</td>\n",
" <td>-0.001819</td>\n",
" <td>-0.864466</td>\n",
" <td>-0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201.277337</td>\n",
" <td>23.418684</td>\n",
" <td>8525.749142</td>\n",
" <td>0.013994</td>\n",
" <td>-37.241570</td>\n",
" <td>-0.000167</td>\n",
" <td>-0.074277</td>\n",
" <td>-2.130365e-07</td>\n",
" <td>734.723484</td>\n",
" <td>0.001206</td>\n",
" <td>-432.152081</td>\n",
" <td>-0.001943</td>\n",
" <td>-0.861915</td>\n",
" <td>-0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201.916006</td>\n",
" <td>25.125608</td>\n",
" <td>8518.845002</td>\n",
" <td>0.014967</td>\n",
" <td>-37.201412</td>\n",
" <td>-0.000179</td>\n",
" <td>-0.074058</td>\n",
" <td>-2.266637e-07</td>\n",
" <td>734.128506</td>\n",
" <td>0.001290</td>\n",
" <td>-431.686083</td>\n",
" <td>-0.002073</td>\n",
" <td>-0.859376</td>\n",
" <td>-0.000003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202.554674</td>\n",
" <td>26.942961</td>\n",
" <td>8511.951422</td>\n",
" <td>0.015999</td>\n",
" <td>-37.161309</td>\n",
" <td>-0.000191</td>\n",
" <td>-0.073841</td>\n",
" <td>-2.410451e-07</td>\n",
" <td>733.534438</td>\n",
" <td>0.001379</td>\n",
" <td>-431.220733</td>\n",
" <td>-0.002212</td>\n",
" <td>-0.856848</td>\n",
" <td>-0.000003</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" temperature pressure density liquid density vapor \\\n",
"0 200.000000 20.312763 8539.589591 0.012216 \n",
"1 200.638669 21.816282 8532.663964 0.013078 \n",
"2 201.277337 23.418684 8525.749142 0.013994 \n",
"3 201.916006 25.125608 8518.845002 0.014967 \n",
"4 202.554674 26.942961 8511.951422 0.015999 \n",
"\n",
" molar enthalpy liquid molar enthalpy vapor molar entropy liquid \\\n",
"0 -37.322054 -0.000147 -0.074718 \n",
"1 -37.281784 -0.000157 -0.074497 \n",
"2 -37.241570 -0.000167 -0.074277 \n",
"3 -37.201412 -0.000179 -0.074058 \n",
"4 -37.161309 -0.000191 -0.073841 \n",
"\n",
" molar entropy vapor mass density liquid mass density vapor \\\n",
"0 -1.879119e-07 735.916212 0.001053 \n",
"1 -2.001300e-07 735.319382 0.001127 \n",
"2 -2.130365e-07 734.723484 0.001206 \n",
"3 -2.266637e-07 734.128506 0.001290 \n",
"4 -2.410451e-07 733.534438 0.001379 \n",
"\n",
" specific enthalpy liquid specific enthalpy vapor specific entropy liquid \\\n",
"0 -433.086018 -0.001703 -0.867028 \n",
"1 -432.618726 -0.001819 -0.864466 \n",
"2 -432.152081 -0.001943 -0.861915 \n",
"3 -431.686083 -0.002073 -0.859376 \n",
"4 -431.220733 -0.002212 -0.856848 \n",
"\n",
" specific entropy vapor \n",
"0 -0.000002 \n",
"1 -0.000002 \n",
"2 -0.000002 \n",
"3 -0.000003 \n",
"4 -0.000003 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(phase_diagram.to_dict(feos.Contributions.Residual))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1500x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
"ax[0].set_title(f\"saturation pressure of {parameters.pure_records[0].identifier.name}\")\n",
"sns.lineplot(y=df.pressure, x=1.0/df.temperature, ax=ax[0])\n",
"\n",
"# axis and styling \n",
"ax[0].set_yscale('log')\n",
"ax[0].set_xlabel(r'$\\frac{1}{T}$ / K$^{-1}$')\n",
"ax[0].set_ylabel(r'$p$ / Pa')\n",
"ax[0].set_xlim(0.002, 0.005)\n",
"ax[0].set_ylim(1e1, 1e7)\n",
"\n",
"ax[1].set_title(r\"$T$-$\\rho$-diagram of {}\".format(parameters.pure_records[0].identifier.name))\n",
"sns.lineplot(y=df.temperature, x=df['density vapor'], ax=ax[1], color=colors[0])\n",
"sns.lineplot(y=df.temperature, x=df['density liquid'], ax=ax[1], color=colors[0])\n",
"\n",
"# axis and styling \n",
"ax[1].set_ylabel(r'$T$ / K')\n",
"ax[1].set_xlabel(r'$\\rho$ / mol/m³')\n",
"ax[1].set_ylim(200, 600)\n",
"ax[1].set_xlim(0, 9000)\n",
"\n",
"sns.despine(offset=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `PhaseEquilibrium` object\n",
"\n",
"A `PhaseEquilibrium` object contains two thermodynamic states (`State` objects) that are in thermal, mechanical and chemical equilibrium.\n",
"The `PhaseEquilibrium.pure` constructor can be used to compute a phase equilibrium for a single substance. We have to provide the equation of state and either temperature or pressure. Optionally, we can also provide `PhaseEquilibrium` object which can be used as starting point for the calculation which possibly speeds up the computation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|300.00000 K|8.86860 mol/m³|\n",
"|phase 2|300.00000 K|7.51850 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 300.00000 K, ρ = 8.86860 mol/m³\n",
"phase 1: T = 300.00000 K, ρ = 7.51850 kmol/m³"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.pure(\n",
" pcsaft, \n",
" temperature_or_pressure=300.0*si.KELVIN\n",
")\n",
"vle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The two equilibrium states can be extracted via the `liquid` and `vapor` getters, respectively. Returned are `State` objects, for which we can now compute any property that is available for the `State` object and the given equation of state."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"saturation pressure p_sat(T = 300 K) = 0.22 bar\n",
"enthalpy of vaporization h_lv (T = 300 K) = 365.83 kJ/kg\n"
]
}
],
"source": [
"liquid = vle.liquid\n",
"vapor = vle.vapor\n",
"\n",
"assert(abs((liquid.pressure() - vapor.pressure()) / si.BAR) < 1e-10)\n",
"print(f'saturation pressure p_sat(T = {liquid.temperature}) = {liquid.pressure() / si.BAR:6.2f} bar')\n",
"print(f'enthalpy of vaporization h_lv (T = {liquid.temperature}) = {(vapor.specific_enthalpy(feos.Contributions.Residual) - liquid.specific_enthalpy(feos.Contributions.Residual)) / (si.KILO*si.JOULE/si.KILOGRAM):6.2f} kJ/kg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to compute a boiling temperature or saturation pressure without needing the `PhaseEquilibrium` object you can use the\n",
"\n",
"- `PhaseEquilibrium.boiling_temperature` and\n",
"- `PhaseEquilibrium.vapor_pressure`\n",
"\n",
"methods. Note that these methods return lists (even for pure substance systems) where each entry contains the pure substance property."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$300\\,\\mathrm{K}$"
],
"text/plain": [
"299.9999999999178 K"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feos.PhaseEquilibrium.boiling_temperature(pcsaft, liquid.pressure())[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `states` method returns all `PhaseEquilibrium` objects from `PhaseDiagram`\n",
"\n",
"Once a `PhaseDiagram` object is created, we can access all underlying `PhaseEquilibrium` objects via the `states` field.\n",
"In the following cell, we compute the enthalpy of vaporization by iterating through all states and calling the `specific_enthalpy` method on the vapor and liquid states of the `PhaseEquilibrium` object, respectively.\n",
"\n",
"Note that this is merely an example to show how to compute any property of the states. The total value of enthalpy of vaporization may not be correct, depending on the ideal gas model used."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Add enthalpy of vaporization to dataframe\n",
"df['hlv'] = [\n",
" (vle.vapor.specific_enthalpy(feos.Contributions.Residual) - vle.liquid.specific_enthalpy(feos.Contributions.Residual)) \n",
" / (si.KILO * si.JOULE / si.KILOGRAM) \n",
" for vle in phase_diagram.states\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 6))\n",
"sns.lineplot(y=df.hlv, x=df.temperature, ax=ax)\n",
"\n",
"# axis and styling \n",
"ax.set_xlabel(r'$T$ / K')\n",
"ax.set_ylabel(r'$\\Delta_{LV} h$ / kJ / kg')\n",
"ax.set_xlim(200, 600)\n",
"ax.set_ylim(0, 500)\n",
"\n",
"sns.despine(offset=10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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| Unknown |
3D | feos-org/feos | examples/pcsaft_entropy_scaling.ipynb | .ipynb | 103,392 | 636 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entropy scaling of pure substances\n",
"\n",
"## Goal\n",
"\n",
"- Learn how to compute dynamic properties (viscosity in this example)\n",
"- Compare substance specific parameters against homo-segmented group contribution\n",
"- Compare viscosity to NIST data (generated in NIST's [webapp](https://webbook.nist.gov/chemistry/fluid/))\n",
"\n",
"## Import needed packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"import si_units as si\n",
"\n",
"sns.set_context(\"talk\")\n",
"sns.set_palette(\"Dark2\")\n",
"sns.set_style(\"ticks\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PC-SAFT (individual component parameters)\n",
"\n",
"First, we read parameters adjusted to hexane saturation pressure and liquid densities (for the regular SAFT parameters) and to viscosity (for correlation). "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|viscosity|\n",
"|-|-|-|-|-|-|\n",
"|hexane|86.177|3.0576|3.7983|236.77|[-1.2035,-2.5958,-0.4816,-0.0865]|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"110-54-3\",\n",
" \"name\": \"hexane\",\n",
" \"iupac_name\": \"hexane\",\n",
" \"smiles\": \"CCCCCC\",\n",
" \"inchi\": \"InChI=1S/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" \"formula\": \"C6H14\"\n",
" },\n",
" \"molarweight\": 86.177,\n",
" \"m\": 3.0576,\n",
" \"sigma\": 3.7983,\n",
" \"epsilon_k\": 236.77,\n",
" \"viscosity\": [\n",
" -1.2035,\n",
" -2.5958,\n",
" -0.4816,\n",
" -0.0865\n",
" ]\n",
" }\n",
"]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.from_json(\n",
" [\"hexane\"], \"../parameters/pcsaft/loetgeringlin2018.json\"\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PC-SAFT homo-GC\n",
"\n",
"For transparency, we build parameters by hand. You can read a detailed explanation about PC-SAFT parameters in the \"working with parameters\" tutorial."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"hexane = feos.ChemicalRecord(\n",
" identifier=feos.Identifier(\n",
" cas=\"110-54-3\",\n",
" name=\"hexane\",\n",
" iupac_name=\"hexane\",\n",
" smiles=\"CCCCCC\",\n",
" inchi=\"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" formula=\"C6H14\"\n",
" ),\n",
" segments=['CH3', 'CH2', 'CH2', 'CH2', 'CH2', 'CH3']\n",
")\n",
"\n",
"ch3 = feos.SegmentRecord(\n",
" 'CH3', \n",
" molarweight=15.0345, \n",
" m=0.61198,\n",
" sigma=3.7202,\n",
" epsilon_k=229.90,\n",
" viscosity=[-8.6878e-3, -1.7951e-1, -12.2359e-2, -0.01245]\n",
")\n",
"\n",
"ch2 = feos.SegmentRecord(\n",
" 'CH2', \n",
" molarweight=14.02658, \n",
" m=0.45606,\n",
" sigma=3.8900,\n",
" epsilon_k=239.01,\n",
" viscosity=[-0.9194e-3, -1.3316e-1, -4.2657e-2, -0.01245]\n",
")\n",
"\n",
"segment_records = {r.identifier: r for r in [ch3, ch2]}\n",
"\n",
"def from_segments(chemical_record, segment_records):\n",
" m = 0\n",
" s3 = 0\n",
" eps = 0\n",
" mw = 0\n",
" viscosity = np.zeros(4)\n",
" for s in chemical_record.segments:\n",
" segment = segment_records[s]\n",
" mw += segment.molarweight\n",
" m += segment.model_record[\"m\"]\n",
" s3 += segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3\n",
" eps += segment.model_record[\"m\"] * segment.model_record[\"epsilon_k\"]\n",
" v = segment.model_record[\"viscosity\"]\n",
" viscosity += np.array([\n",
" v[0] * segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3, \n",
" v[1] * segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3, \n",
" v[2], \n",
" v[3]\n",
" ])\n",
" viscosity[1] /= s3**0.45\n",
" \n",
" # We have to shift the \"A\" parameter because the implemented reference\n",
" # is eta_CE according to eq. 3 of Loetgerin-Lin (2018)\n",
" # A = A(GC) + log(sqrt(1/m)) = -log(m)/2\n",
" viscosity[0] += np.log(np.sqrt(1/m))\n",
" return feos.PureRecord(chemical_record.identifier, mw, m=m, sigma=np.cbrt(s3 / m), epsilon_k=eps / m, viscosity=list(viscosity))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build equations of state\n",
"\n",
"We instantiate an equation of state for each parameter set. `saft` uses substance specific parameters while `saft_gc` uses homo GC parameters both for SAFT as well as correlation parameters."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"parameters_gc = feos.Parameters.new_pure(from_segments(hexane, segment_records))\n",
"saft_gc = feos.EquationOfState.pcsaft(parameters_gc)\n",
"saft = feos.EquationOfState.pcsaft(parameters)\n",
"\n",
"m_gc = parameters_gc.pure_records[0].model_record[\"m\"]\n",
"m = parameters.pure_records[0].model_record[\"m\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Substance specific: [-1.2035, -2.5958, -0.4816, -0.0865]\n",
"Segments : [-1.2034921145837285, -2.536713016411593, -0.415346, -0.0747]\n"
]
}
],
"source": [
"print(\"Substance specific: \", parameters.pure_records[0].model_record[\"viscosity\"])\n",
"print(\"Segments : \", parameters_gc.pure_records[0].model_record[\"viscosity\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare methods to NIST data (T = 450 K)\n",
"\n",
"We will compute the residual entropy, viscosity and logarithmic reduced viscosity and compare to literature data (for which the entropy is computed with parameters fitted to the component, not GC)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Temperature (K)</th>\n",
" <th>Pressure (MPa)</th>\n",
" <th>Density (mol/m3)</th>\n",
" <th>Volume (m3/mol)</th>\n",
" <th>Internal Energy (kJ/mol)</th>\n",
" <th>Enthalpy (kJ/mol)</th>\n",
" <th>Entropy (J/mol*K)</th>\n",
" <th>Cv (J/mol*K)</th>\n",
" <th>Cp (J/mol*K)</th>\n",
" <th>Sound Spd. (m/s)</th>\n",
" <th>Joule-Thomson (K/MPa)</th>\n",
" <th>Viscosity (Pa*s)</th>\n",
" <th>Therm. Cond. (W/m*K)</th>\n",
" <th>Phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>450.0</td>\n",
" <td>0.01</td>\n",
" <td>2.6774</td>\n",
" <td>0.373500</td>\n",
" <td>45.229</td>\n",
" <td>48.964</td>\n",
" <td>154.33</td>\n",
" <td>193.37</td>\n",
" <td>201.76</td>\n",
" <td>212.47</td>\n",
" <td>11.204</td>\n",
" <td>0.000009</td>\n",
" <td>0.029374</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>450.0</td>\n",
" <td>0.11</td>\n",
" <td>29.9840</td>\n",
" <td>0.033351</td>\n",
" <td>45.065</td>\n",
" <td>48.734</td>\n",
" <td>134.03</td>\n",
" <td>193.94</td>\n",
" <td>203.09</td>\n",
" <td>209.04</td>\n",
" <td>11.510</td>\n",
" <td>0.000009</td>\n",
" <td>0.029276</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>450.0</td>\n",
" <td>0.21</td>\n",
" <td>58.3290</td>\n",
" <td>0.017144</td>\n",
" <td>44.896</td>\n",
" <td>48.496</td>\n",
" <td>128.27</td>\n",
" <td>194.53</td>\n",
" <td>204.55</td>\n",
" <td>205.48</td>\n",
" <td>11.842</td>\n",
" <td>0.000009</td>\n",
" <td>0.029196</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>450.0</td>\n",
" <td>0.31</td>\n",
" <td>87.8260</td>\n",
" <td>0.011386</td>\n",
" <td>44.720</td>\n",
" <td>48.249</td>\n",
" <td>124.64</td>\n",
" <td>195.15</td>\n",
" <td>206.15</td>\n",
" <td>201.76</td>\n",
" <td>12.207</td>\n",
" <td>0.000009</td>\n",
" <td>0.029136</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>450.0</td>\n",
" <td>0.41</td>\n",
" <td>118.6100</td>\n",
" <td>0.008431</td>\n",
" <td>44.536</td>\n",
" <td>47.992</td>\n",
" <td>121.90</td>\n",
" <td>195.80</td>\n",
" <td>207.93</td>\n",
" <td>197.87</td>\n",
" <td>12.608</td>\n",
" <td>0.000010</td>\n",
" <td>0.029098</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Temperature (K) Pressure (MPa) Density (mol/m3) Volume (m3/mol) \\\n",
"0 450.0 0.01 2.6774 0.373500 \n",
"1 450.0 0.11 29.9840 0.033351 \n",
"2 450.0 0.21 58.3290 0.017144 \n",
"3 450.0 0.31 87.8260 0.011386 \n",
"4 450.0 0.41 118.6100 0.008431 \n",
"\n",
" Internal Energy (kJ/mol) Enthalpy (kJ/mol) Entropy (J/mol*K) \\\n",
"0 45.229 48.964 154.33 \n",
"1 45.065 48.734 134.03 \n",
"2 44.896 48.496 128.27 \n",
"3 44.720 48.249 124.64 \n",
"4 44.536 47.992 121.90 \n",
"\n",
" Cv (J/mol*K) Cp (J/mol*K) Sound Spd. (m/s) Joule-Thomson (K/MPa) \\\n",
"0 193.37 201.76 212.47 11.204 \n",
"1 193.94 203.09 209.04 11.510 \n",
"2 194.53 204.55 205.48 11.842 \n",
"3 195.15 206.15 201.76 12.207 \n",
"4 195.80 207.93 197.87 12.608 \n",
"\n",
" Viscosity (Pa*s) Therm. Cond. (W/m*K) Phase \n",
"0 0.000009 0.029374 vapor \n",
"1 0.000009 0.029276 vapor \n",
"2 0.000009 0.029196 vapor \n",
"3 0.000009 0.029136 vapor \n",
"4 0.000010 0.029098 vapor "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature = pd.read_csv(\"../examples/data/hexane_nist.csv\", delimiter=\"\\t\")\n",
"literature.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We loop through experimental data, read temperature, pressure and the phase (liquid or vapor) and generate `State` objects for the experimental conditions. Then, we compute the residual molar entropy and the logarithmic reduced viscosity."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pressure</th>\n",
" <th>s*_res/m</th>\n",
" <th>viscosity</th>\n",
" <th>ln_viscosity_reduced</th>\n",
" <th>source</th>\n",
" <th>rel.dev.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.01</td>\n",
" <td>-0.000526</td>\n",
" <td>0.000009</td>\n",
" <td>-1.170829</td>\n",
" <td>literature</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.01</td>\n",
" <td>-0.000526</td>\n",
" <td>0.000009</td>\n",
" <td>-1.202136</td>\n",
" <td>saft</td>\n",
" <td>-3.082130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.01</td>\n",
" <td>-0.000531</td>\n",
" <td>0.000009</td>\n",
" <td>-1.202146</td>\n",
" <td>homo-GC</td>\n",
" <td>-4.154342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.11</td>\n",
" <td>-0.005862</td>\n",
" <td>0.000009</td>\n",
" <td>-1.172124</td>\n",
" <td>literature</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.11</td>\n",
" <td>-0.005862</td>\n",
" <td>0.000009</td>\n",
" <td>-1.188299</td>\n",
" <td>saft</td>\n",
" <td>-1.604523</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pressure s*_res/m viscosity ln_viscosity_reduced source rel.dev.\n",
"0 0.01 -0.000526 0.000009 -1.170829 literature 0.000000\n",
"1 0.01 -0.000526 0.000009 -1.202136 saft -3.082130\n",
"2 0.01 -0.000531 0.000009 -1.202146 homo-GC -4.154342\n",
"3 0.11 -0.005862 0.000009 -1.172124 literature 0.000000\n",
"4 0.11 -0.005862 0.000009 -1.188299 saft -1.604523"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = []\n",
"for i, row in literature.iterrows():\n",
" t = row['Temperature (K)'] * si.KELVIN\n",
" p = row['Pressure (MPa)'] * si.MEGA * si.PASCAL\n",
" viscosity_lit = row['Viscosity (Pa*s)'] * si.PASCAL * si.SECOND\n",
" \n",
" # literature\n",
" state = feos.State(saft, temperature=t, pressure=p, total_moles=si.MOL, density_initialization=row.Phase)\n",
" s = state.molar_entropy(feos.Contributions.Residual)\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m,\n",
" \"viscosity\": viscosity_lit / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": np.log(viscosity_lit/ state.viscosity_reference()),\n",
" \"source\": \"literature\",\n",
" \"rel.dev.\": 0.0\n",
" }\n",
" )\n",
" \n",
" # individual parameters\n",
" viscosity = state.viscosity()\n",
" ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m,\n",
" \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
" \"source\": \"saft\",\n",
" \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
" }\n",
" )\n",
" \n",
" # homo GC\n",
" state = feos.State(saft_gc, temperature=t, pressure=p, total_moles=si.MOL)\n",
" s = state.molar_entropy(feos.Contributions.Residual)\n",
" viscosity = state.viscosity()\n",
" ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m_gc,\n",
" \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
" \"source\": \"homo-GC\",\n",
" \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
" }\n",
" )\n",
"\n",
"# gather everything in a data frame\n",
"data = pd.DataFrame(results)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1500x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(15, 4), gridspec_kw={'wspace': 0.35})\n",
"sns.scatterplot(data=data, x='s*_res/m', y='ln_viscosity_reduced', hue='source', ax=ax[0])\n",
"ax[0].set_xlabel(r\"$\\frac{s_{res}}{R \\cdot m}$\", fontsize=22)\n",
"ax[0].set_ylabel(r\"$\\ln\\left(\\frac{\\eta}{\\eta^{CE}}\\right)$\", fontsize=22)\n",
"ax[0].legend(frameon=False)\n",
"\n",
"sns.lineplot(data=data, x='pressure', y='viscosity', hue='source', ax=ax[1])\n",
"ax[1].set_xlabel(r\"$p$ / MPa\", fontsize=22)\n",
"ax[1].set_ylabel(r\"$\\eta$ / Pa*s\", fontsize=22)\n",
"ax[1].legend(frameon=False)\n",
"sns.despine()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Viscosity hexane compared to NIST data at T = 450 K\n",
"MARD (individual): 0.81 %\n",
"MARD (homo-GC) : 3.70 %\n"
]
}
],
"source": [
"# check mean absolute relative deviation in percent\n",
"mard = data.groupby('source')['rel.dev.'].apply(lambda x: np.mean(np.abs(x)))\n",
"print('Viscosity hexane compared to NIST data at T = 450 K')\n",
"print(f'MARD (individual): {mard.saft:.2f} %')\n",
"print(f'MARD (homo-GC) : {mard[\"homo-GC\"]:.2f} %')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/core_working_with_units.ipynb | .ipynb | 15,904 | 533 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with SI-units\n",
"\n",
"## Goal\n",
"\n",
"> Learn how to work with `SINumber` and `SIArray` objects which represent physical quantities, i.e. one or more floating point numbers with an associated unit.\n",
"\n",
"## The `si-units` module\n",
"\n",
"Most interfaces in `FeOs` use dimensioned quantities as input. For example, to define a thermodynamic state at given temperature, pressure and amount of substance, all of these properties have to be multiplied by an apropriate unit before we can call the function that creates the state.\n",
"\n",
"`FeOs` uses the [quantity](https://itt-ustutt.github.io/quantity/) crate which generates the `si_units` Python module. To have consistency throughout the ecosystem, we recommend importing the package as `import si_units as si`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import si_units as si\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `si` module contains units according to [the Standard Interational System of Units](https://en.wikipedia.org/wiki/International_System_of_Units) (SI), constants and prefixes.\n",
"A **scalar** floating point number multiplied or divided by a unit has the **`SIObject`** data type."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"temperature : 300.15 K\n",
"data type : <class 'si_units._core.SIObject'>\n"
]
}
],
"source": [
"temperature = 300.15 * si.KELVIN\n",
"print(f\"temperature : {temperature}\")\n",
"print(f\"data type : {type(temperature)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The representation of a `SIObject` (e.g. using `print`) uses a prefix (e.g. `k` for `kilo`) so that the numerical value is convenient. For example 1000 g will be represented as 1 kg. Internally, all values are stored with respect to the basic SI unit, i.e. `METER`, `KILOGRAM`, `SECOND`, `AMPERE`, `MOL`, `KELVIN`, and `CANDELA`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1\\,\\mathrm{kg}$"
],
"text/plain": [
"1 kg"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1000 * si.GRAM"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"v = 0.07486757864516083 m³\n"
]
}
],
"source": [
"# volume of an ideal gas\n",
"t = 300.15 * si.KELVIN\n",
"p = 0.5 * si.BAR\n",
"n = 1.5 * si.MOL\n",
"v = n * si.RGAS * t / p\n",
"print(f\"v = {v}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use division to transform a `SIObject` to a `float` for the desired unit:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t = 27.00 °C\n",
"data type = <class 'float'>\n"
]
}
],
"source": [
"# temperature in Celsius\n",
"t_c = t / si.CELSIUS\n",
"print(f\"t = {t_c:3.2f} °C\")\n",
"print(f\"data type = {type(t_c)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mathematical operations and interaction with `numpy`\n",
"\n",
"The `SINumber` object supports some mathematical operations, i.e. we can\n",
"\n",
"- add or subtract two objects, **if they have the same unit**,\n",
"- multiply or divide two objects, which returns a floating point number if they have the same unit,\n",
"- raise an object to a power, and\n",
"- take the square and cubic root, which only works if the units have the correct exponents to allow for the operation."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressure : 265 kPa\n",
"temperature : 40 K\n",
"velocity : 100 m/s\n",
"velocity (cm/min) : 600000.0\n",
"acceleration : 9.81 m s^-2\n"
]
}
],
"source": [
"# addition\n",
"pressure = 2.5 * si.BAR + 15_000 * si.PASCAL\n",
"print('pressure :', pressure)\n",
"\n",
"# subtraction\n",
"temperature = 543.15 * si.KELVIN - 230.0 * si.CELSIUS\n",
"print('temperature :', temperature)\n",
"\n",
"# division\n",
"velocity = 360_000 * si.METER / si.HOUR\n",
"print('velocity :', velocity)\n",
"\n",
"# division as transformation to target unit\n",
"v_cm_minute = velocity / (si.CENTI * si.METER / si.MINUTE) # this is a floating point number\n",
"print(f'velocity (cm/min) : {v_cm_minute:8.1f}')\n",
"\n",
"# power\n",
"acceleration = 9.81 * si.METER / si.SECOND**2\n",
"print('acceleration :', acceleration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the square and cubic root we can use the `numpy` functions, `numpy.sqrt` and `numpy.cbrt`, respectively, or use the methods provided by `si_units`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"speed : 1 m/s\n",
"length : 3 nm\n"
]
}
],
"source": [
"# square root\n",
"speed = np.sqrt(si.METER**2 / si.SECOND**2)\n",
"print('speed :', speed)\n",
"\n",
"# cubic root\n",
"box_length = (27_000 * si.ANGSTROM**3).cbrt()\n",
"print('length :', box_length)\n",
"\n",
"# both numpy and methods of SINumbers work\n",
"assert(np.sqrt(si.METER**2 / si.SECOND**2) == (si.METER**2 / si.SECOND**2).sqrt())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The mathematical functions from the `math` module do not work because it is written in C and `SINumber`s are not compatible with the C function arguments.\n",
"If you are having trouble with errors, consider transforming your properties to floats (by division with the proper unit) and then multiplying the correct unit to the result."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ERROR: must be real number, not si_units._core.SIObject\n"
]
}
],
"source": [
"from math import sqrt\n",
"\n",
"try:\n",
" sqrt(si.METER**2 / si.SECOND**2)\n",
"except Exception as e:\n",
" print(\"ERROR:\", e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you try to perform arithmetic operations with `SINumber`s that are not allowed (e.g. adding two properties with different units), an error is raised."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "Inconsistent units Pa and K",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43msi\u001b[49m\u001b[43m.\u001b[49m\u001b[43mPASCAL\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[43msi\u001b[49m\u001b[43m.\u001b[49m\u001b[43mKELVIN\u001b[49m\n",
"\u001b[31mRuntimeError\u001b[39m: Inconsistent units Pa and K"
]
}
],
"source": [
"si.PASCAL + si.KELVIN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can enforce correct units in our interfaces using the `has_unit` method:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p1 = 1.4550309581768168 MPa\n",
"ERROR: Please provide the molar density, e.g. in units of mol/m³.\n"
]
}
],
"source": [
"MOL_M3 = si.MOL / si.METER**3 # we can create own units for future use\n",
"\n",
"def ideal_gas_pressure(density, temperature):\n",
" if not density.has_unit(MOL_M3):\n",
" raise ValueError(\"Please provide the molar density, e.g. in units of mol/m³.\")\n",
" else:\n",
" return density * temperature * si.RGAS\n",
" \n",
"try:\n",
" p1 = ideal_gas_pressure(0.5 * si.KILO * si.MOL / si.METER**3, 350 * si.KELVIN)\n",
" print('p1 = ', p1)\n",
" p2 = ideal_gas_pressure(0.5 * si.KILOGRAM / si.METER**3, 350 * si.KELVIN)\n",
" print('p2 = ', p2)\n",
"except Exception as e:\n",
" print(\"ERROR:\", e) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Arrays of quantities\n",
"\n",
"The \"number\" part of the `SIObject` can be any type that supports the necessary operations. Aside from floats, those are, e.g., **numpy arrays** or **tensors**."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressures = array([100000., 200000.]) Pa\n",
"data type = <class 'si_units._core.SIObject'>\n"
]
}
],
"source": [
"ps = np.array([1.0, 2.0]) * si.BAR\n",
"print(f\"pressures = {ps}\")\n",
"print(f\"data type = {type(ps)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The most important functionalities of array or tensor datatypes are also implemented for `SIObject`.\n",
"For example, you can index into an array of dimension one."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"200 kPa\n"
]
}
],
"source": [
"print(ps[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several useful numpy methods to create arrays. Most of them can be simply multiplied by units to convert them to `SIObject` objects.\n",
"Some of these functions, such as `linspace` and `logspace` can directly be constructed using `si.linspace` and `si.logspace`, respectively."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressures (numpy) = array([100000., 125000., 150000., 175000., 200000.]) Pa\n",
"pressures (si) = array([100000., 125000., 150000., 175000., 200000.]) Pa\n"
]
}
],
"source": [
"ps_np = np.linspace(1, 2, 5) * si.BAR\n",
"ps_si = si.linspace(1 * si.BAR, 2 * si.BAR, 5)\n",
"print(f\"pressures (numpy) = {ps_np}\")\n",
"print(f\"pressures (si) = {ps_si}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just like scalars, division by a unit that matches the property stored in an array yields a numpy ndarray."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressures = [0.1 0.125 0.15 0.175 0.2 ] MPa\n",
"data type = <class 'numpy.ndarray'>\n"
]
}
],
"source": [
"ps_mpa = ps_np / (si.MEGA*si.PASCAL)\n",
"print(f\"pressures = {ps_mpa} MPa\")\n",
"print(f\"data type = {type(ps_mpa)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Derived units, constants and prefixes\n",
"\n",
"In conjunction to the base units, the `si` module also exports derived units (e.g. `HOUR = 3600 * SECOND`), constants and prefixes (such as `KILO` or `FEMTO`).\n",
"Of course, we could multiply by a floating point number instead of using prefixes (e.g. `cm = 1e-2 * METER` vs. `cm = CENTI * METER`) but we think using prefixes make the code more readable.\n",
"\n",
"For a complete overview of exported units constants and prefixes, take a look at the [documentation of the `si_units` Python package](https://itt-ustutt.github.io/quantity/base/)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hyperfine transition frequency of Cs: 9.19263177 GHz\n",
"Speed of light : 2.99792458e8 m/s\n",
"Planck constant : 6.62607015e-34 Js\n",
"Elementary charge : 1.602176634e-19 C\n",
"Boltzmann constant : 1.380649e-23 J/K\n",
"Avogradro constant : 6.02214076e23 mol^-1\n",
"Luminous efficacy : 683 lm/W\n"
]
}
],
"source": [
"# The seven constants that inform the base units\n",
"print('Hyperfine transition frequency of Cs:', si.DVCS)\n",
"print('Speed of light :', si.CLIGHT)\n",
"print('Planck constant :', si.PLANCK)\n",
"print('Elementary charge :', si.QE)\n",
"print('Boltzmann constant :', si.KB)\n",
"print('Avogradro constant :', si.NAV)\n",
"print('Luminous efficacy :', si.KCD)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gravitational constant: 6.6743e-11 m³/kg/s²\n",
"Ideal gas constant : 8.31446261815324 J/mol/K\n"
]
}
],
"source": [
"# Derived constants\n",
"print('Gravitational constant:', si.G)\n",
"print('Ideal gas constant :', si.RGAS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"In `feos`, most interfaces use dimensioned units in form of `SIObject` from the `si-units` Python library.\n",
"This enables us to write functions that can check for proper units and thus circumvent unit errors that could occur, e.g. providing mass densities instead of molar densities.\n",
"\n",
"In this example, we learned how to create and use these objects in conjunction with methods provided by `numpy`.\n",
"\n",
"## Concluding remkars\n",
"\n",
"Hopefully you found this example helpful. If you have comments, critique or feedback, please let us know and consider [opening an issue on github](https://github.com/feos-org/feos/issues)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/core_dual_numbers.ipynb | .ipynb | 21,918 | 486 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# On Dual Numbers in `FeOs`\n",
"\n",
"In `FeOs`, we use [generalized dual numbers](https://www.frontiersin.org/articles/10.3389/fceng.2021.758090/full) to compute partial derivatives of the Helmholtz energy.\n",
"In this notebook, we take a closer look at how that works.\n",
"\n",
"We will make use of the `EquationOfState.python()` feature: we use a simple version of the Peng-Robinson equation of state implemented as Python class which is registered in `FeOs` as equation of state. \n",
"If you want to learn how to implement an equation of state as Python class in conjunction with `FeOs`, take a look at the example implementation in the examples section.\n",
"In short - a class that implements a `helmholtz_energy` function that takes a `StateD` (or *any* state object, where the internal data types can be any generalized dual number)\n",
"and returns the Helmholtz energy (plus some minor functions), can be used with `FeOs`. We use the equation of state implemented in Python simply because we can add `print` statements and inspect the data types at runtime.\n",
"\n",
"## Dual Numbers\n",
"\n",
"We won't go into much detail regarding the theory of generalized dual numbers. Suffice it to say that generalized dual numbers are mathematical constructs consisting of one real value and one or more non-real values (just like complex numbers) that enable evaluation of arithmetic operations such that the operation *and* the derivative(s) can simultaneously be computed.\n",
"We call these *generalized* dual numbers because they can have a different number of *non-real* values. For example a *dual number* has a real and a single non-real value and can be used to compute first (partial) derivatives, while a *hyper-dual number* has a real and three non-real values and can be used to compute first and second (partial) derivatives.\n",
"We can - in principle - create numbers that allow computation of an arbitrarily high derivative at the cost of execution speed.\n",
"\n",
"Similar to numerical differentiation and complex step differentiation, a derivative is computed by defining a \"step size\". For complex step and dual number differentiation this step is introduced in the non-real part. Derivatives of dual numbers, however, are exact (to machine precision) and independent of the step size used. Hence, we use unity as step size.\n",
"\n",
"For example, when we feed a temperature as `Dual64` data type\n",
"\n",
"```python\n",
"print(temperature)\n",
"300 + [1]ε\n",
"```\n",
"\n",
"into a function, its return value will be a `Dual64` as well. The non-real part of the function result *is* the derivative with respect to temperature.\n",
"\n",
"## Do we need to create dual numbers by hand?\n",
"\n",
"No. If you use already implemented equations of state, you will not even recognize that dual numbers are used.\n",
"However, if you use Python or Rust to implement you own equation of state, it is useful to know how `FeOs` creates and uses dual numbers.\n",
"It's easier to look at an example than to talk about it. Below, you find the implementation of the Peng-Robinson equation of state where we added some `print` statement to the `helmholtz_energy` function to keep track of the values and data types of the thermodynamic state that enters the Helmholtz energy computation."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import numpy as np\n",
" \n",
"SQRT2 = np.sqrt(2)\n",
"\n",
"class PyPengRobinson: \n",
" def __init__(self, critical_temperature, critical_pressure, acentric_factor, molar_weight, delta_ij=None):\n",
" \"\"\"Peng-Robinson Equation of State\n",
" \n",
" Parameters\n",
" ----------\n",
" critical_temperature : SIArray1\n",
" critical temperature of each component.\n",
" critical_pressure : SIArray1\n",
" critical pressure of each component.\n",
" acentric_factor : np.array[float] \n",
" acentric factor of each component (dimensionless).\n",
" molar_weight: SIArray1\n",
" molar weight of each component.\n",
" delta_ij : np.array[[float]], optional\n",
" binary parameters. Shape=[n, n], n = number of components.\n",
" defaults to zero for all binary interactions.\n",
" \n",
" Raises\n",
" ------\n",
" ValueError: if the input values have incompatible sizes.\n",
" \"\"\"\n",
" self.n = len(critical_temperature)\n",
" if len(set((len(critical_temperature), len(critical_pressure), len(acentric_factor)))) != 1:\n",
" raise ValueError(\"Input parameters must all have the same lenght.\")\n",
" \n",
" if self.n == 1:\n",
" self.tc = (critical_temperature / si.KELVIN)[0]\n",
" self.pc = (critical_pressure / si.PASCAL)[0]\n",
" self.omega = acentric_factor[0]\n",
" self.mw = (molar_weight / si.GRAM * si.MOL)[0]\n",
" else:\n",
" self.tc = critical_temperature / si.KELVIN\n",
" self.pc = critical_pressure / si.PASCAL\n",
" self.omega = acentric_factor\n",
" self.mw = molar_weight / si.GRAM * si.MOL\n",
" \n",
" self.a_r = 0.45724 * critical_temperature**2 * si.RGAS / critical_pressure / si.ANGSTROM**3 / si.NAV / si.KELVIN\n",
" self.b = 0.07780 * critical_temperature * si.RGAS / critical_pressure / si.ANGSTROM**3 / si.NAV\n",
" self.kappa = 0.37464 + (1.54226 - 0.26992 * acentric_factor) * acentric_factor\n",
" self.delta_ij = np.zeros((self.n, self.n)) if delta_ij is None else delta_ij\n",
" \n",
" def helmholtz_energy(self, state):\n",
" \"\"\"Return Helmholtz energy.\n",
" \n",
" Parameters\n",
" ----------\n",
" state : StateHD\n",
" The thermodynamic state.\n",
" \n",
" Returns\n",
" -------\n",
" helmholtz_energy: float | any dual number\n",
" The return type depends on the input types.\n",
" \"\"\"\n",
" x = state.molefracs\n",
" tr = 1.0 / self.tc * state.temperature\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n",
" ak_mix = 0.0\n",
" if self.n > 1:\n",
" for i in range(self.n):\n",
" for j in range(self.n):\n",
" ak_mix += np.sqrt(ak[i] * ak[j]) * (x[i] * x[j] * (1.0 - self.delta_ij[i, j]))\n",
" else:\n",
" ak_mix = ak\n",
" b = np.sum(x * self.b)\n",
" v = 1.0 / state.density\n",
" a = state.density * (np.log(v / (v - b)) - ak_mix / (b * SQRT2 * 2.0 * state.temperature)\n",
" * np.log((v * (SQRT2 - 1.0) + b) / (v * (SQRT2 + 1.0) - b)))\n",
" \n",
" # some print statements to inspect data types\n",
" print()\n",
" print(\"data type : \", type(state.temperature))\n",
" print(\"temperature: \", state.temperature)\n",
" print(\"molefracs : \", state.molefracs)\n",
" print(\"density : \", state.density)\n",
" print(\"A/kT : \", a)\n",
" return a\n",
" \n",
" def components(self) -> int: \n",
" \"\"\"Number of components.\"\"\"\n",
" return self.n\n",
" \n",
" def subset(self, i: list[int]):\n",
" \"\"\"Return new equation of state containing a subset of all components.\"\"\"\n",
" if self.n > 1:\n",
" tc = self.tc[i] \n",
" pc = self.pc[i]\n",
" mw = self.mw[i]\n",
" omega = self.omega[i]\n",
" return PyPengRobinson(tc*si.KELVIN, pc*si.PASCAL, omega, mw*si.GRAM/si.MOL)\n",
" else:\n",
" return self\n",
" \n",
" def molar_weight(self) -> si.SIObject:\n",
" if isinstance(self.mw, float):\n",
" return np.array([self.mw]) * si.GRAM / si.MOL\n",
" else:\n",
" return self.mw * si.GRAM / si.MOL\n",
" \n",
" def max_density(self, molefracs: np.ndarray[float]) -> float:\n",
" b = np.sum(molefracs * self.b)\n",
" return 0.9 / b\n",
" \n",
"\n",
"class PyPerfectGas:\n",
" def __init__(self):\n",
" \"\"\"Dummy implementation for an ideal gas with constant heat capacity\"\"\"\n",
" pass\n",
" \n",
" def ln_lambda3(self, temperature):\n",
" return temperature/temperature\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# parameters for propane\n",
"tc = si.array(369.96 * si.KELVIN)\n",
"pc = si.array(4250000.0 * si.PASCAL)\n",
"omega = np.array([0.153])\n",
"molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n",
"\n",
"# create an instance of our python class and hand it over to rust\n",
"eos = feos.EquationOfState.python_residual(PyPengRobinson(tc, pc, omega, molar_weight)).python_ideal_gas([PyPerfectGas()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Properties and dual numbers\n",
"\n",
"After initializing the equation of state for a single substance (in the above cell), let us now build a thermodynamic state.\n",
"For the sake of this example, we use the natural variables of the Helmholtz energy ($\\mathbf{N}$, $V$, $T$) as input so that no volume or temperature has to be iterated."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"state = feos.State(eos, temperature=300*si.KELVIN, volume=40744*si.ANGSTROM**3, total_moles=1/si.NAV)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When computing a thermodynamic property for a `State`, `FeOs` internally generates a new object which contains the same information as `State` but with dual numbers as data types.\n",
"Which dual numbers (dual, hyper-dual, etc.) are used depends on the property to compute or rather which partial derivatives are needed.\n",
"`FeOs` then modifies the non-real parts of those properties (temperature, volume or amount of substance) so that the correct derivatives are computed and feeds this \"generalized dual state\" object into the function that computes the Helmholtz energy.\n",
"The result will have the same data type as the state's variables and the needed derivative(s) are extracted and returned.\n",
"\n",
"`FeOs` does all of that independent from the implementation of the equation of state.\n",
"All you have to do - if you implement a Helmholtz energy function - is to write your code knowing that the state's properties are generalized dual numbers.\n",
"In Python, your can write code just like \"regular\" Python because dual numbers implement all arithmetic opartions you'd typically use."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at an example. First, we compute the `molar_helmholtz_energy`.\n",
"Since *no derivatives* are needed to compute the Helmholtz energy, the data types of the `State`'s properties are regular floating point numbers (`float`).\n",
"\n",
"If you are in a running noteboook, execute the cell below and check the output.\n",
"If you run the cell a second time, you will notice that the information about data types and values aren't printed to the screen anymore.\n",
"That's because we *cache* already computed derivatives for a given state.\n",
"A second call to `molar_helmholtz_energy` simply pulls the already computed value from cache and returns it without entering the `helmholtz_energy` function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'float'>\n",
"temperature: 300.0\n",
"molefracs : [1.0]\n",
"density : 2.4543491066169247e-05\n",
"A/kT : [0.00012419]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_207170/809879024.py:1: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" state.helmholtz_energy() / (si.KB * 300*si.KELVIN)\n"
]
},
{
"data": {
"text/plain": [
"-5.554978406564659"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.helmholtz_energy() / (si.KB * 300*si.KELVIN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we compute the `pressure`, for which we compute the first partial derivative of the Helmholtz energy with respect to the volume.\n",
"\n",
"$p = -\\left(\\frac{\\partial A}{\\partial V}\\right)_{\\mathbf{n}, T}$\n",
"\n",
"If you execute the cell below, you'll notice that the data types are now dual numbers (`Dual64`, a dual number with 64bit floating point numbers) with *a single* non-real part.\n",
"This non-real or *dual* part of the volume is unity while all other non-real parts are zero, which means that the first partial derivative with respect to volume will be computed.\n",
"\n",
"`FeOs` modifies the dual parts of the temperature, volume or amount of substance (for a component) when computing derivatives.\n",
"Note that the `density` is calculated as $\\rho = \\frac{\\sum n_i}{V}$ (where $n_i$ is the amount of substance of component $i$) and hence also has a dual part which is different from zero.\n",
"It is also different from unity - the value is calculated according to the artihmetic operation of division between two dual numbers."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'builtins.PyDual64'>\n",
"temperature: 300 + 0ε\n",
"molefracs : [1 + 0ε]\n",
"density : 0.000024543491066169247 + -602382953715129600000ε\n",
"A/kT : 0.00012419216233064458 + -3038285859542560000000ε\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_207170/966462947.py:64: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n"
]
},
{
"data": {
"text/latex": [
"$100\\,\\mathrm{kPa}$"
],
"text/plain": [
"100.00005278190892 kPa"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.pressure()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to the `pressure`, the `entropy` is computed by taking the first partial derivative with respect to temperature.\n",
"Here, the dual part of the density is zero because both dual parts of the volume and the amount of substance are zero, too."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'builtins.PyDual64'>\n",
"temperature: 300 + 1ε\n",
"molefracs : [1 + 0ε]\n",
"density : 0.000024543491066169247 + 0ε\n",
"A/kT : 0.00012419216233064458 + -0.0000006261809548282462ε\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_207170/966462947.py:64: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n"
]
},
{
"data": {
"text/latex": [
"$109.83\\,\\mathrm{\\frac{ J}{molK}}$"
],
"text/plain": [
"109.82501713655553 J/mol/K"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.molar_entropy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at a more involved property.\n",
"For the Joule-Thomson coefficient, we need multiple second partial derivatives:\n",
"\n",
"$\\mu_{JT}=\\left(\\frac{\\partial T}{\\partial p}\\right)_{H,N_i} = -\\frac{1}{C_p} \\left(V + T \\left(\\frac{\\partial p}{\\partial T}\\right)_{\\mathbf{n}, V} \\left(\\frac{\\partial p}{\\partial V}\\right)_{\\mathbf{n}, T}^{-1} \\right)$\n",
"\n",
"We need three evaluations of the Helmholtz energy (that are actually occuring in the computation of $C_p$):\n",
"\n",
"1. $\\left(\\frac{\\partial p}{\\partial T}\\right)_{\\mathbf{n}, V} \\rightarrow -\\left(\\frac{\\partial A}{\\partial T \\partial V}\\right)_\\mathbf{n}$: second partial derivative w.r.t volume and temperature,\n",
"2. $\\left(\\frac{\\partial p}{\\partial V}\\right)_{\\mathbf{n}, T} \\rightarrow -\\left(\\frac{\\partial^2 A}{\\partial V^2}\\right)_{\\mathbf{n}, T}$: second derivative w.r.t volume,\n",
"3. $\\left(\\frac{\\partial^2 A}{\\partial T^2}\\right)_{\\mathbf{n}, V}$: 2nd partial derivative w.r.t temperature.\n",
"\n",
"Since we compute second derivatives, the data type is now `HyperDual64` (one real, 3 non-real values) for mixed partial derivatives and `Dual2_64` (one real, 2 non-real values) for the other partial derivatives."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'builtins.PyHyperDual64'>\n",
"temperature: 300 + 1ε1 + 0ε2 + 0ε1ε2\n",
"molefracs : [1 + 0ε1 + 0ε2 + 0ε1ε2]\n",
"density : 0.000024543491066169247 + 0ε1 + -602382953715129600000ε2 + 0ε1ε2\n",
"A/kT : 0.00012419216233064458 + -0.0000006261809548282462ε1 + -3038285859542560000000ε2 + 15312125055595608000ε1ε2\n",
"\n",
"data type : <class 'builtins.PyDual2_64'>\n",
"temperature: 300 + 0ε1 + 0ε1²\n",
"molefracs : [1 + 0ε1 + 0ε1²]\n",
"density : 0.000024543491066169247 + -602382953715129600000ε1 + 29569161285839853000000000000000000000000000000ε1²\n",
"A/kT : 0.00012419216233064458 + -3038285859542560000000ε1 + 148659416520544790000000000000000000000000000000ε1²\n",
"\n",
"data type : <class 'builtins.PyDual2_64'>\n",
"temperature: 300 + 1ε1 + 0ε1²\n",
"molefracs : [1 + 0ε1 + 0ε1²]\n",
"density : 0.000024543491066169247 + 0ε1 + 0ε1²\n",
"A/kT : 0.00012419216233064458 + -0.0000006261809548282462ε1 + 0.000000004710234326181233ε1²\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_207170/966462947.py:64: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n"
]
},
{
"data": {
"text/latex": [
"$-1.4939\\times10^{-4}\\,\\mathrm{\\frac{ms^{2}K}{kg}}$"
],
"text/plain": [
"-1.4938695195180373e-4 m kg^-1 s^2 K"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.joule_thomson()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"- `FeOs` uses generalized dual numbers which enable the computation of higher order partial derivatives of the Helmholtz energy without the need of implementing these derivatives analytically.\n",
"- When a property is computed, `FeOs` creates a new thermodynamic state with the correct data types according to the partial derivatives needed.\n",
"- If a property was computed before, it is pulled from the state's cache instead of re-evaluating the Helmholtz energy."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/pets_eos_binary_caseI.ipynb | .ipynb | 187,283 | 286 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PeTS Equation of State - Binary Mixture (Pseudo Pure Fluid)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Original publication of the _perturbation theory for truncated and shifted Lennard-Jones fluids_ (PeTS) of M. Heier, S. Stephan, J. Liu, W.G. Chapman, H. Hasse, K. Langenbach, Mol. Phys. **116**, 2083 (2018);\n",
"https://doi.org/10.1080/00268976.2018.1447153"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import feos\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import si_units as si"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Specifying PeTS Parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"epsilon_k = 1.0 * si.KELVIN\n",
"sigma = 1.0 * si.ANGSTROM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition of Reference Data\n",
"\n",
"The molecular simulation reference data is taken from J. Vrabec, G.K. Kedia, G. Fuchs, H. Hasse, Mol. Phys. **104**, 1509 (2006);\n",
"https://doi.org/10.1080/00268970600556774\n",
"\n",
"Critical point reference data is taken from the original publication of M. Heier, S. Stephan, J. Liu, W.G. Chapman, Mol. Phys. **116**, 2083 (2018);\n",
"https://doi.org/10.1080/00268976.2018.1447153"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Data from Vrabec et al. (2006)\n",
"data = np.array([\n",
" [0.64, 0.00217, 0.8176, 0.00351, 5.7118],\n",
" [0.67, 0.00335, 0.8024, 0.00525, 5.5910],\n",
" [0.70, 0.00479, 0.7866, 0.00727, 5.4666],\n",
" [0.73, 0.00697, 0.7704, 0.01036, 5.325 ],\n",
" [0.76, 0.00944, 0.7538, 0.01374, 5.179 ],\n",
" [0.79, 0.01241, 0.7361, 0.01776, 5.022 ],\n",
" [0.82, 0.01640, 0.7181, 0.0233, 4.844 ],\n",
" [0.85, 0.0214, 0.6986, 0.0303, 4.639 ],\n",
" [0.88, 0.0274, 0.6784, 0.0392, 4.413 ],\n",
" [0.91, 0.0336, 0.6556, 0.0483, 4.172 ],\n",
" [0.94, 0.0417, 0.6309, 0.0616, 3.87 ],\n",
" [0.97, 0.0504, 0.6032, 0.0763, 3.56 ],\n",
" [1.00, 0.0606, 0.5712, 0.0960, 3.18 ],\n",
" [1.03, 0.0730, 0.530, 0.127, 2.63 ],\n",
" [1.06, 0.0855, 0.463, 0.168, 1.88 ]]\n",
")\n",
"\n",
"df = pd.DataFrame(data, columns=['T*', 'p*', 'rho^L*', 'rho^V*', 'Delta^LV h*'])\n",
"\n",
"# Critical point data extracted from Heier et al. (2018), figure 1; unclear origin\n",
"T_c = 1.0850094876660341\n",
"p_c = 0.10073800738007378\n",
"rho_c = 0.3194085027726432\n",
"\n",
"# Critical point data extracted from Vrabec et al. (2018)\n",
"T_c_vrabec = 1.0779\n",
"p_c_vrabec = np.exp(3.1664 - 5.9809 / T_c_vrabec + 0.01498 / T_c_vrabec**3)\n",
"rho_c_vrabec = 0.3190\n",
"\n",
"# Critical point data extracted from Heier et al. (2018), figure 1; critical point of original PeTS implementation\n",
"T_c_pets_heier = 1.0884250474383301\n",
"p_c_pets_heier = 0.10184501845018448\n",
"rho_c_pets_heier = 0.3077634011090573"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition of PeTS Equation of State"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"record = feos.PureRecord(feos.Identifier(), 0, epsilon_k=epsilon_k/si.KELVIN, sigma=sigma/si.ANGSTROM)\n",
"pets = feos.EquationOfState.pets(feos.Parameters.new_binary([record]*2))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"cp = feos.State.critical_point_pure(eos=pets)\n",
"\n",
"T_c_pets = cp[0].temperature\n",
"p_c_pets = cp[0].pressure()\n",
"rho_c_pets = cp[0].density\n",
"\n",
"T_c_pets_red = cp[0].temperature / epsilon_k"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase Diagram of Pseudo Pure Fluid (Binary Mixture of the Same Component)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"temps = np.linspace(0.64, 0.975*T_c_pets_red, 1001) # PhaseEquilibrium.bubble_point_tx() not converging to two phases close to critical point\n",
"p_sat = np.zeros(temps.shape)\n",
"rho_sat = np.zeros([temps.shape[0], 2])\n",
"pressure_ic = None\n",
"\n",
"for i, temperature in enumerate(np.nditer(temps)):\n",
" pe = feos.PhaseEquilibrium.bubble_point(eos=pets, temperature_or_pressure=temperature * epsilon_k, liquid_molefracs=np.array([0.5, 0.5]), tp_init=pressure_ic, tol_inner=1e-7)\n",
" p_sat[i] = pe.liquid.pressure() / (epsilon_k * si.KB / sigma**3)\n",
" rho_sat[i, 0] = pe.vapor.density * (si.NAV * sigma**3)\n",
" rho_sat[i, 1] = pe.liquid.density * (si.NAV * sigma**3)\n",
"\n",
" pressure_ic = pe.vapor.pressure() * 1.03"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1,2, figsize=(14,5))\n",
"\n",
"ax[0].plot(temps, p_sat, color='tab:blue', label='this PeTS implementation')\n",
"ax[0].scatter(df['T*'], df['p*'], marker='s', color='tab:orange', label='simulation data Vrabec et al. (2006)')\n",
"ax[0].scatter(T_c_vrabec, p_c_vrabec, marker='o', color='tab:orange', label='critical point Vrabec et al. (2018)')\n",
"ax[0].scatter(T_c, p_c, marker='o', color='tab:red', label='critical point Heier et al. (2018); unclear origin')\n",
"ax[0].scatter(T_c_pets_heier, p_c_pets_heier, marker='o', color='tab:green', label='critical point PeTS Heier et al. (2018)')\n",
"ax[0].scatter(T_c_pets/epsilon_k, p_c_pets/(epsilon_k * si.KB / sigma**3), marker='x', color='tab:red', label='critical point this PeTS implementation')\n",
"ax[0].set_title('Vapor-Liquid Coexistence - Vapor Pressure')\n",
"ax[0].set_xlabel(r'$T* = \\frac{T}{\\frac{\\epsilon}{k_\\mathrm{B}}}$')\n",
"ax[0].set_ylabel(r'$p* = \\frac{p}{\\frac{\\epsilon}{\\sigma^3}}$')\n",
"ax[0].set_xlim(0.6, 1.2)\n",
"ax[0].set_ylim(0.0, 0.11)\n",
"ax[0].legend(loc='upper left')\n",
"ax[0].grid()\n",
"\n",
"ax[1].plot(temps, rho_sat[:,0], color='tab:blue', label='this PeTS implementation')\n",
"ax[1].plot(temps, rho_sat[:,1], color='tab:blue')\n",
"ax[1].scatter(df['T*'], df['rho^L*'], marker='s', color='tab:orange', label='simulation data Vrabec et al. (2006)')\n",
"ax[1].scatter(df['T*'], df['rho^V*'], marker='s', color='tab:orange')\n",
"ax[1].scatter(T_c, rho_c, marker='o', color='tab:red', label='critical point Heier et al. (2018); unclear origin')\n",
"ax[1].scatter(T_c_vrabec, rho_c_vrabec, marker='o', color='tab:orange', label='critical point Vrabec et al. (2018)')\n",
"ax[1].scatter(T_c_pets_heier, rho_c_pets_heier, marker='o', color='tab:green', label='critical point PeTS Heier et al. (2018)')\n",
"ax[1].scatter(T_c_pets/epsilon_k, rho_c_pets*si.NAV*sigma**3, marker='x', color='tab:red', label='critical point this PeTS implementation')\n",
"ax[1].set_title('Vapor-Liquid Coexistence - Saturated Densities')\n",
"ax[1].set_xlabel(r'$T* = \\frac{T}{\\frac{\\epsilon}{k_\\mathrm{B}}}$')\n",
"ax[1].set_ylabel(r'$\\rho* = \\rho \\sigma^3$')\n",
"ax[1].set_xlim(0.6, 1.1)\n",
"ax[1].set_ylim(0.0, 0.9)\n",
"ax[1].legend(loc='center left')\n",
"ax[1].grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Binary Phase Diagram - Pressure-Composition of Pseudo Pure Fluid (Binary Mixture of the Same Component)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"dia_p = feos.PhaseDiagram.binary_vle(eos=pets, temperature_or_pressure=1*epsilon_k)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2000x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1, 2, figsize=(20,5))\n",
"ax[0].scatter(dia_p.liquid.molefracs[:,0], dia_p.liquid.pressure/(epsilon_k * si.KB / sigma**3), color='tab:red', marker='s')\n",
"ax[0].scatter(dia_p.vapor.molefracs[:,0], dia_p.vapor.pressure/(epsilon_k * si.KB / sigma**3), color='tab:blue', marker='x')\n",
"ax[0].set_xlim(0, 1)\n",
"ax[0].set_ylim(0, 0.15)\n",
"ax[0].set_xlabel(r'$x_1, x_2$')\n",
"ax[0].set_ylabel(r'$p* = \\frac{p}{\\frac{\\epsilon}{\\sigma^3}}$')\n",
"\n",
"\n",
"ax[1].plot([0, 1], [0, 1], color='black')\n",
"ax[1].scatter(dia_p.liquid.molefracs[:,0], dia_p.vapor.molefracs[:,0], color='tab:orange', marker='s')\n",
"ax[1].set_xlim(0, 1)\n",
"ax[1].set_ylim(0, 1)\n",
"ax[1].set_xlabel(r'$x_1$')\n",
"ax[1].set_ylabel(r'$y_1$');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/pcsaft_surface_tension.ipynb | .ipynb | 173,492 | 672 | {
"cells": [
{
"cell_type": "markdown",
"id": "135dd79e",
"metadata": {},
"source": [
"# Surface tension using PC-SAFT Helmholtz energy functionals\n",
"\n",
"## Goal of this notebook\n",
"\n",
"- Learn how to compute the surface tension for a planar interface using the PC-SAFT functionals.\n",
"- Learn about the `SurfaceTensionDiagram` that allows convenient calculation of multiple surface tensions."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9ad1bb1d",
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"sns.set_context(\"talk\")\n",
"sns.set_palette(\"Dark2\")\n",
"sns.set_style(\"ticks\")\n",
"\n",
"colors = sns.color_palette(\"Dark2\", 2)"
]
},
{
"cell_type": "markdown",
"id": "c7ec9a5b",
"metadata": {},
"source": [
"### Water parameters for PC-SAFT \n",
"\n",
"In this example we will calculate surface tensions for water using the 2B association scheme. The parameters that we use, [were adjusted to vapor pressures, liquid densities and surface tensions](https://pubs.acs.org/doi/10.1021/acs.jced.0c00684). Parameters are available [here](https://github.com/feos-org/feos/tree/main/parameters/pcsaft)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "837c7770",
"metadata": {},
"outputs": [],
"source": [
"# Equation of state object.\n",
"parameters = feos.Parameters.from_json(\n",
" ['water_2B'], \n",
" '../parameters/pcsaft/rehner2020.json'\n",
")\n",
"pcsaft = feos.HelmholtzEnergyFunctional.pcsaft(parameters)"
]
},
{
"cell_type": "markdown",
"id": "79b57b8b",
"metadata": {},
"source": [
"Let's first compute the critical point. We will make use of the critical temperature later."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d6ed65fa",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|677.34347 K|18.70466 kmol/m³|"
],
"text/plain": [
"T = 677.34347 K, ρ = 18.70466 kmol/m³"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cp = feos.State.critical_point(pcsaft)\n",
"cp"
]
},
{
"cell_type": "markdown",
"id": "2ad0235c",
"metadata": {},
"source": [
"As you can see, the model overestimates the critical temperature."
]
},
{
"cell_type": "markdown",
"id": "4c00eed3",
"metadata": {},
"source": [
"## Surface tension for single VLE\n",
"\n",
"To compute the surface tension, three steps are needed.\n",
"\n",
"1. We need to compute the vapor liquid equilibrium (VLE) either at given temperature or pressure.\n",
"2. Then, we need to initialize a density profile. We will use a hyperbolic tangent with the VLE bulk densities as limits.\n",
"3. We solve the DFT equations to yield the equilibrium density profile and calculate the surface tension."
]
},
{
"cell_type": "markdown",
"id": "8fa8d790",
"metadata": {},
"source": [
"For the VLE, we use the `PhaseEquilibrium.pure` method. Here for $T = 300$ Kelvin."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f834af33",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|300.00000 K|1.51670 mol/m³|\n",
"|phase 2|300.00000 K|55.38975 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 300.00000 K, ρ = 1.51670 mol/m³\n",
"phase 1: T = 300.00000 K, ρ = 55.38975 kmol/m³"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.pure(pcsaft, 300*si.KELVIN)\n",
"vle"
]
},
{
"cell_type": "markdown",
"id": "21fb88cb",
"metadata": {},
"source": [
"Next, we initialize the density profile. For the surface tension, a 1D DFT calculation in Cartesian coordinates is conducted. Thus, the density profile will be an 1D array (we have a single substance). \n",
"\n",
"To solve the DFT equations, the density has to be discretized which can be controlled by `n_grid`, the number of grid points. The surface tension is not very sensitive w.r.t the number of grid points but you should make sure to pick a large enough value. When in doubt, run multiple calculations varying the number.\n",
"\n",
"We also have to provide a width of the calculation domain, `l_grid`. The domain should be large enough that the bulk densities can be observed in the limits. You can check the resulting density profile to make sure that's the case.\n",
"\n",
"The critical temperature is used to come up with a good initial estimate for the density profile."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "25c9d99d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 379 μs, sys: 10 μs, total: 389 μs\n",
"Wall time: 372 μs\n"
]
}
],
"source": [
"%%time\n",
"interface = feos.PlanarInterface.from_tanh(\n",
" vle=vle, \n",
" n_grid=512, \n",
" l_grid=100*si.ANGSTROM, \n",
" critical_temperature=cp.temperature\n",
")\n",
"initial_density = interface.density / (si.KILO * si.MOL / si.METER**3)"
]
},
{
"cell_type": "markdown",
"id": "303bb746",
"metadata": {},
"source": [
"The above method does not yet run a calculation. If we try to extract the surface tension, it will return `None`. Let's store the initial density profile for a later comparison."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f915e37c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interface.surface_tension == None"
]
},
{
"cell_type": "markdown",
"id": "7a321fe5",
"metadata": {},
"source": [
"To calculate the equilibrium density profile, we have to call the `solve()` method:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "77c08d7f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 9.24 ms, sys: 3.81 ms, total: 13 ms\n",
"Wall time: 12.9 ms\n"
]
}
],
"source": [
"%%time\n",
"surface_tension = interface.solve().surface_tension"
]
},
{
"cell_type": "markdown",
"id": "19402ba2",
"metadata": {},
"source": [
"`solve()` calculates the equilibrium density profile and returns the `PlanarInterface` object so that we can readily extract the `surface_tension`.\n",
"\n",
"The `PlanarInterface.density` contains the equilibrated density profile. Let's compare it to our initial density and zoom into the interesting region between 40 and 60 Angstrom. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "61992d8f",
"metadata": {},
"outputs": [
{
"data": {
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vkxlvkxkvkwfeHmY8jaYipwCJiEjZ8u9RqTwhISEApKWluSSPffv2MWLECI4fP37BuKSkpCsuLA8ePAhA/fr1C+2vU6cOHh4e5OTkn0q9Z88eABITE7n77rsLPTevEM7MzCQxMZFKlSrl67/Yn3d6enq+40OGDGH+/PnMnz+fVatW0blzZ1q3bk3btm2LzP9KDBw4kNmzZzN//nyeeuopR2E8d+5cbDYbN9xwQ4GRUyn7VFhKmWbCjsluhaIefbFATmb+1ew8gHqXcpPTu0j+16EOZ7+K69iGqQWOzfpXO8dgxHJeYZptPPe68rqvOVaxCgYvf4xefhi9/DAnxBCcEEumyUymyUyiyTP3tdFMhin3K9NkJsPkSaqHF0PD82dst9vp++sX2Ip4bsiAAS+TB35mT/zNXvSv35pnWvXKF7MxJpqlR3fhb/bC1+yJn4fn2dde+Ju9CPT0IdDLhyBPH7w9tAm2iLje4827Mbzp9cWOD/AsWKysvevFS3rG8t/urNeS3rUiip2Dvxtmufj6+hZ63JUfMFqtVp588kmOHz9Ox44deeSRRwgPDycgIMAxQtetWzdOnjxZYAXUy5FXvP276MtjMpkICgoiLi7/x/3Jybm/FSQlJbF58+aL3qew1VWL+vM2FvFsbaNGjZg+fToTJ07k77//ZsGCBSxYsMDR99xzz9G1a9eL5lJcLVq0IDw8nD179vDXX385rj1v3jwA+vfv77R7SemhwrKUOVj/OowGI82Dz62cZseO1WZl55kTYLdjwH72v7m9Bntu++wkVrDbCfH2o5KXD2DP/Z/dTnxmKgkZqWfjco+fu1beNc4dq+4biNGQez3sduzYiUlLxG6zOTIz/DufvDzsdryMJvw8PLDbrGCzYrdZybHmYMnJxmC35Z5rt2E8+1XeedhteNht+NgK+WGZepr0E1H5DjU5+1VcB1dPxOgTiMk3EKNvEAafAF6LPU6qhxdpHt6kenjm/tfkRZqHF6lnv5LNPpwy+5JiKfh80Za4I3y2Y0Wx7u9tMvP7HU9RLzAk3/Evdqwix24lyNOXIC8fgrxy/1vJ258QH3/MxoK/pImIFJe3h/mKP9gqrNi8FJ4mj9xHS+SCduzYwcGDB6latSpffPFFgRFJu91e5NYnl8PX15eUlBTOnClsjlZuoZuYmFjoeQBdu3blq6++clo+F9OsWTO++OILMjMz2bZtGxs3bmTx4sXs3buXkSNH8uOPPxY6vfZyDRw4kLFjxzJnzhy6du3Khg0biI6OpkqVKnTu3Nlp95HSQ/+KlTJ9xhS+7xFAzSu89pWeD1DHCdcoit1udxSg2KxgtxX62l6MvnOvrbmFsOO1Fbs1B2w52HMs2K2W3NdWS+5x6/mvc/vttrOvc3Jf262W8+Is+a5ls2Rhs2Riz8nCbsnAbsnGnpOFIScLe87ZPksWWK/8k9ZLZrNiS4vHlhbvOFT8z/AhZ+3nHJxZBVNAKKYKIXhUCKVmRjqDE2JJNPuSZPYh0fPsf82+pJs8Hc/8AmRaLVTwLPgp/OSoP4nJSCnyvkFevlT28SfE258u1RsxqkW3fP1Z1hzsdrtGREVESrm8PS2bN29e6DTXvXv3FpgieiXq1avHtm3bOHDgQKGjfYcPHy4wDRZyRwgB9u/f77RcLoW3tzft27enffv2PPbYYzz66KOsWLGCWbNmObWwvP322/nggw+IjIwkPj7esWhP3759ixxZlbJNhaWUGgaDAUweGMrBp7p2m+1s8ZmFPa8QzTn7Ol9xmncsA1tWGrbMNOzZabmvs9KwZ6bmvs5Ow56Vju1s2xGTmeq0ItYjJ4ucM9HknIl2HKsHPFJEfLbRg3hPP+I8/Tjj6U+clz8sCyI5uDoeFavhEVQdU3BNErOL3oAZIDErncSsdPZymmp+QQX6fz+yk5ErfiTYy4+qfgFU9Qukul9FalcIplaFYGpVqEitCpXcMrVMRKQ8yisKMzOLt5Luv8+LjY0ttP/rr7++ssT+pXPnzmzbto3p06fzwAMPFCiWvvvuu0LPa9OmDaGhoRw/fpzFixfTp08fp+Z1KQwGA9dccw0rVqzg9Ol/r65RuOJ+fwICArjxxhtZuHAhP/74I0uWLHGsmCvlU9n/DV2kFDIYjRg8fcDT5+LBV8hmycKWkYQtPQlbeiLWjNz/2tKTsKYnne1LxJaRhDU9EVt68rm4tHhsGf9+wrR4PG05VMlMosp5z7gmzCn4LMo87wCSfIOJ9w3itFcAJzz9iDb5cNjTjxPeQaSaz31qHeLjX+D8k2m514/PSiM+K42o+JOF5hPs5UfNChWZ2HUwdQNCCo0REZErV6tWLQA2btzI3XffXeznNFu2bInZbGbLli3MnDmTQYMGAZCdnc2kSZNYuHAhZrPZKc9XAgwePJhp06Zx5MgRXn31VV566SVH0TVv3jxmz55d6OI9np6ePPvss7z44ouMHj2a1NRU7rjjjnwrtcbFxbF06VJSUlJ45JGiPoItvldeeYW2bdvSvXt3/P3P/Sw8dOiQ47nHiIjiPcd7Kd+fgQMHsnDhQj7//HNycnJo3749NWs6Yw6clEYqLEXKOaPZC6O5MgRUvqzz7TnZWFPisKbEkpMSizUlNrednPc6Nl+/LS3e8bxucXhnJuOdmUxYfOHPjNp9AsmuWIPUwCr470ojKeMU5rCGeFZtjEdAKCfTi/e8TV7hWcFccHrV3Uv+h8lgpEFgKI0qhhERXI3woDBNrxURuQw333wzP/zwA7/88gtbt26latWqGI1G+vbtS79+/Yo8LyQkhIceeogvvviCV155hYkTJ1K5cmWio6NJSUnhiSeeYO7cuRddMba4QkNDeffdd3nqqaeYNWsWv/76K3Xr1uX06dPExMRw//33s3z58kLv17dvX2JjYxk/fjwvvfQSb731FnXr1sVoNBIXF8fJkycdcc6wbds2Zs6ciclkombNmgQGBpKUlER0dDR2u51GjRoxfPjwYl3rUr4/bdu2pW7duo59NLV3ZfmmwlJErojBwzN36mrFahRnMqndmoM1LR5r8mlyEk+Sk3gCa+JJchJOkJN4IvdYwnFyEk/kPvt6sftnJOGVkYTXiSjYtZyY8/qM/pV4oEo4QyrXJ7ViTeICq3DUN4R9Jk+OpCZyJCWeo6kJZFlzP2329fCk0r/2JLXYrKw5eZAcu40Vx/c6jucVmhGVqhERXJWI4Nz/VvzX+SIikl+bNm0YN24c3377LXv37uXEiRPY7XbatWt30XOffvppqlatyg8//ODYr7Jx48bcd9999OnTh7lz5zo11169ejlWW928eTP79++nXr16jBo1ioEDB7J8+fIiz33kkUfo0qUL33//PevWrWP//v3YbDZCQkLo3r07PXv2pGfPnk7Jc/To0fzxxx9s3LiRmJgYjh07hpeXF82bN+eGG25gyJAh+PgUbxbUpX5/+vfvz4cffkhAQAC9e/d2yvuR0slgL+762OI0/fr1IyoqioiICKf/AyhSVtht1twi88wRLGeOkBN/NPe/Z45giYvGEnsQe9bl7Ztm8PbHq0YLvGq1wLNmC9LDGnG8QhUSbDZ6184/VWhf4mm6zxtX7Gs/EnE9r7S79bLyEhERKW3eeecdpk6dyj333MOrr77q7nTEjTRiKSJXJYPRhDm4BubgGvg07FSg3263Y00+jeX0ASynD5B9+mDu69iDZJ/aiy218OXhAeyZqWTuX03m/tWOY0EGI5WrNOJkrWvwrtsW7/rt8Krdmqp+gUzr9QD7k06zL/E0uxJOsTvhlGOU89/qBYYWODb/4Fb2JsTQNqwO7cPq4mv2vIw/ERERkatLVlaWY7/Mu+66y83ZiLupsBSRUslgMOARGIZHYFihhWdOcizZJ3eRfWIX2Sd2n329m5z4o4Vf0G4j++Rusk/uJmXdzNxjJg+8al5Ds3rtaFu/Pd4N2mMO64/VbuNgchxRZ04SFX8i9+vMSeKz0mgTWrvApecd2MryY7sBMBtNtKlciy7VGnJ9tQZcU6kGJi3LLiIipdDkyZNJSEigVatWNG3a1N3piJtpKqwbaCqsiPvYMlLIOraDrKPbyDqyjawj28k6th37RbY1yWP0r4RveBd8GnfDt3FXPKtHYDAasdvtHE9LpKpvYL5C0W6302L6WBKyCt9bLdDTm05V69O5WkN61Ainhn9Fp7xPERGRkrBr1y7efvtt4uLiOHjwIAaDge+++462bdu6OzVxM41Yiki5YvSpgE/DTvlGOe02K5aY/WQd3UbmoU1kHlxP5qGN2LMLFoO21DOkbppH6qbc5dtzC83O+DTuRmjjrhh9A/PFZ9usjGjWhU2no1l76iAplqx8/UnZmfwWHcVv0VEA9Kvfik+7DHL22xYREXGK5ORk1q9fj9lsJjw8nFGjRqmoFECFpYgIBqMJz6rheFYNp0K7gUDu6rVZx/4h8+C63ELzwDqyT+wqcG5uoTmf1E3zATAFhOHXvDd+Lfrg2+xGvPwqMqpFNwBybFa2xR3nzxP7+PPEPjadPkKO3ZbvevUK2UMzzZKFr4dnsfd6ExERKSnt27dnz5497k5DrkKaCusGmgorUjpZ0xLJ2PcXGbtXkr5nFVmHN8O/CsN8DEa863fAr0Uf/Fr0watWKwznTZNNs2Sx9tQh/jyxj8hjeziYHMeyO/+PxhWr5LvMS2vms/ToLvrVb8XABm0KXSBIRERExJ1UWLqBCkuRssGankTG3rxCc+VFC01TYBX8W9+Bf5u++DbuhsHD7Oiz2+0cTI6jXkBIvpFJm91G25nvEJOR4jjWtnJtBja8ltvqtsDfXJzdQ0VERERKlgpLN1BhKVI2WVPPkPbPUtJ3LCZtxxKsyaeLjDX6BePf6jb8r+2Hb8QNGIsoEHcnnOKG+Z9gp+A/1T4eZm6p3ZyBDdvQoUpdjAatLisiIiLuocLSDVRYipR9dpuNrCNbSNu+mLTti8k8sLbI0UyjdwX8Wt5ChbZ34XfNzRg88u9zGZuRwvyDW5m5bxO7E04Veo26ASEMb3odAxu2wcdD+2SKiIiIa6mwdAMVliLljzX1DKlbfiZ141zSo5Ziz8kuNM7oF0yF9gMJ6Hgv3g065psWa7fb2XHmODP3bWL+wa0kFbJFyrjrBzCw4bUl9j5ERERECqPC0g1UWIqUb9aMZNK2/Urqxrmkbf+t0G1NAMyh9ajQ6V4COt6LZ5WG+foycywsPbqLWfs2sfLEXmx2O6E+/qy960W8TFrwW0RERFxLhaUbqLAUkTy2rHTS//mdlPWzSN28ALsls9A473rtCew6jArtB2P09s/XdyQlnq93/k11vyAeadY5X198ZhrP/DWbkc260r5K3RJ7HyIiIlK+qbB0AxWWIlIYa0YyqRvnkrLmR9J3RUIh/zwbvStQoeM9BHZ7GO/arS56zU+2LueDLUsB6FEjnBda9yaiUjWn5y4iIiLlmwpLN1BhKSIXY4k/RsraGSSv/p7sYzsKjfGqey2BXYcT0H4wRp8KBfqzrDl0mP0usRmp+Y7fUe8anmt1A3UDQkokdxERESl/VFi6gQpLEbkUWUe2kbTya5JXf48tI6lAv8Hbn4CO9xJ0wxN4VWviOG632/n75AE+2Pw7m2KP5DvHw2Dk7kZteaplT6r4BpT4exAREZGyTZueiYhc5bxqXUPlIZ9S7+NjhA3/Bu8GnfL12zNTSfrjS6L/24xjH95E2vbF2G02DAYD11drwPxbRvJNz/sJDwpznJNjt/HdnnVc/9MHvLNxMWmWLFe/LRGRy/Liiy8SHh5e4MP5uXPnEh4ezosvvpjv+LFjxwgPD6dHjx4FrtWjRw/Cw8NZt25dieQ6ZMiQEr3+1STvz/LYsWPuTuWShYeHEx4e7u40Sj0VliIipYTRy5fA64dS6+U/qT12K0G9RmH0CcwXk/7P7xwfdwuH/9uMxMjPsWWlYTAYuLFWU36/4yk+6TKImv4VHfGZVguf7VhB93nj2B5X+n4ZEBERKSlz585lwoQJ7Nq1y92plAoqLEVESiGvms2pfN8nuaOYwybjWbNFvn7LqT2c/nYUB5+uRezMF7DEH8NkNNK/fitW9nuWNzvcQajPudVlM3Is1Div4BQRuVqFhoZSt25dKlQo+Gx5YcxmM3Xr1qVmzZolnFlBVatWpW7duvj4+Lj83lJ8devWpW7dgiunz5s3j4kTJ6qwLCZtdiYiUooZvXwJ7DKMgM4PkrF7BQm/f0La1kWOFWVt6Ykk/PYhCb9/QsB1Qwi++Xk8qzTigSYdGdigDRO3/8Hn/6xiTNubCfb2c/O7ERG5uGeffZZnn3222PFhYWEsXry4BDMq2vvvv++W+8qlcdffj7JGhaWISBlgMBjwbdId3ybdyT59gMSlE0n+cwq2zJTcAKuF5FXfkPznFPyv7U/wrS/iW7sV/2nTm4ENr6V2heAC11x+dDchPv5cE1LDxe9GREREShtNhRURKWM8K9en8r3jqTv+CKF3j8Mj+LzpX3Y7qRt+4sir13Lsw5tI372S2hWCMRgM+a6RkJXOM3/N5rZFnzF2/S9kWXNc/C5ExNVsNhsLFy5k2LBhtG/fnmbNmtGlSxdeeOEFDhw4UOR5qampvPfee/To0YNmzZrRrVs3Xn/9dRISEpgwYQLh4eFMmDAh3zlFLbRzvqIWgylq8Z6iXGjxnvPt3LmTxx9/nA4dOtCiRQvuuOMOfvjhB2w2W4HYdevW5bvm7Nmzueuuu2jdujXh4eEkJycDRS/ec7H3UNwFh/bv389TTz1Fx44dadmyJf3792fZsmWO2JiYGF555RW6du1K8+bN6dOnD99///2F/8AuYMeOHTz66KO0bduWVq1a0a9fP2bPnl2sczdv3szTTz9Nly5daNasGe3bt+fhhx9m1apVhcbn/d158cUXsVgsfPnll9x00000b96cjh078vzzz3Py5MlCz42JiWHs2LH07t2b5s2bc80119CtWzeGDh3KV199hcViyRf/78V78r6/69evB2D06NGOmLy/z7t37yY8PJzWrVuTkZFR5Pt+8803CQ8PZ8yYMcX6cyrNNGIpIlJGmXwCqNj7KYJ6jiR5zY8k/PoB2Sd3O/rT//md9H9+x7tBJyr1fRXfpj0dBebbG3/jTGYaAF9G/cnqUwf5rOvd1AvU3pciZVFGRgZPPvmk45f8ypUrU7VqVaKjo5k/fz6LFy/m008/pWvXrvnOS0hIYMiQIezbtw+DwUCDBg0wGAzMmDGDVatW0b17d3e8nUu2ZcsWJk2ahNFopF69eiQmJrJ7927eeOMNNm/ezIcffljgA7g8r732GtOnTycsLIx69epx9OhRl+S8Y8cOPvvsM4xGI7Vr1+b48eP8888/jBo1inHjxtGkSROGDBlCSkoK9evXx2q1cujQIcaOHUtaWhojRoy4pPtFRkby5JNPYrFY8PPzo169epw+fZqXX36ZvXv3XvDcTz/9lM8++wyAwMBAGjZsyKlTp1i1ahWrVq3iiSeeYNSoUYWea7FYGD58OGvXrqVOnTrUqVOHQ4cOsXDhQjZs2MD8+fMJCgpyxJ84cYIBAwZw5swZzGYztWrVwsfHh5iYGNatW8fatWsZPHgwZrO5yHwrVKhA69at2bt3L6mpqdSpU4fg4HMze6pWrUrjxo1p0aIF27dvZ/HixfTt27fAdbKzs/n5558BGDBgwAX/jMoCFZYiImWcwcOTwM4PEHDd/aRunk/8L++RdWijoz9z/2qOf9Abn/CuVOr3Or7hnelbryXrYw5zICkWgB1njtNn4ae83fEOBjRo4663IuJ0tqx07DnZ7k7jkhk8PDF6+Trtem+88QarVq2iRYsWjB07lsaNGwO5v9R//vnnfPbZZzz//PMsXrw43y/Yb775Jvv27aNWrVp8/vnnNGjQAIBDhw4xcuRIZsyY4bQcS9LEiRPp3r07b7/9tmNRoGXLlvHMM8+waNEi2rdvz8CBAwucd+rUKebNm8eECRO48cYbgdxiwsOj5H/F/vjjjxk8eDDPP/88Xl5eWK1WXnvtNWbNmsX7779PSEgIbdq04c0333S8p4kTJzJhwgQmTZrEPffcU+wFkOLi4njhhRewWCwMGDCAMWPG4O3tjd1uZ+7cuYwZM6bIwnvevHl89tlnhISE8Prrr9OrVy9H35IlSxg9ejQTJkygVatWXHfddQXOX7JkCdWrV2fhwoWOUcUTJ04wfPhwDhw4wJQpU3j66acd8d988w1nzpyhU6dOfPTRR/n+vsbFxbFo0aILFpUATZs2Zfr06QwZMoT169czYsQI+vXrVyBu4MCBbN++nTlz5hRaWC5fvpzExEQaNmzINddcc8F7lgVlqrCcMGECEydOvGDMa6+9xt13313guMViYdq0aSxcuJAjR45gNptp3LgxQ4YMcfxDISJSmhmMRipc2w//Nn1Jj1pG/C/vkbHrD0d/xp6VHHunG74RvWjV7w0W3/4kYzf8wre71wKQnpPN//05m1Un9vN2xzvxN3u5662IOMXpH54mcdlEsBec6njVMxgJ6jWKyveOv+JL7d+/n3nz5hEcHMwXX3xBpUqVHH1ms5knn3ySvXv3snTpUmbNmsWjjz4KwNGjR/nll1+A3AIzr6iE3FU23333XQYNGnTF+blChQoV+OCDD/D29nYc69WrFw8//DATJ07kq6++4q677ipQPFmtVp599tl8vyt6enq6JOf69evz3//+F6Mx98k2k8nE888/z4IFCzh58iQ5OTl8++23+Pqe+wDi0UcfZebMmZw+fZp169blK/IuZMaMGSQnJ1OzZk3eeOMNTCYTkPt8f//+/dm+fXuhHyJYLBY+/vhjAD766CM6dOiQr793797ExMTw1ltv8fXXXxdaWFosFt577718U1WrVavG008/zahRo1ixYkW+wvLQoUMA3HvvvfmKSoCQkBAeeOCBYr3n4rj55pt5++232bhxI9HR0dSuXTtf/5w5c4DyMVoJZfQZy0qVKtG6detCv0JDQwvEZ2VlMXToUD744AP2799PrVq1CAoKYv369TzxxBN8+OGHbngXIiIlw2Aw4NfsBmq+sIyaL/2JT5P8z/CkRy3j6NhOxE/oxytVa/FV9/sI9Dz3y9bcA1voveBTtmnfSynlEpd/VjqLSgC7LTd/J1iyZAl2u52ePXvmKyrPl1eA5D1zBvDXX39ht9tp1KgR7du3L3BOy5YtadGiRYHjV6MBAwbkKyrz3HvvvUBuEZ1XsPxbYSNVrtC/f39HUZknICCAGjVyF1y75ZZb8hWVAB4eHo4C7ciRI8W+V94U6XvuucdRVJ7v/vvvL/S8bdu2cerUKWrXrl2gqMyT93dr06ZNWK3WAv2NGzemZcuWBY7njQD++31Uq1YNgKVLlxZ4ltLZ/Pz8uOWWW7Db7Y4iMk9MTAx///03ZrOZ22+/vUTzuFqUqRHLPF26dOHdd98tdvwHH3zApk2bqFGjBpMnT6ZevXpA7vD1//3f/zF58mRat2590Ye+RURKG5+Gnaj5wlLSd63gzLxXydj7l6MvbduvpG37lZZtB7D45hd5atc61sccBiA65Qx3LJrEi2368Eiz6zEayuTnlFLGBfV8vPSOWBpNBPV83CmX2rNnDwCrV68udFYXQEpK7grTp06dchw7ePAgkDtyVpQGDRqwfft2p+RZkop6D8HBwQQHBxMfH8+hQ4ccvyPmqVixYoFRMVepVatWoceDg4M5cOBAkf15Hx6kp6cX+14X+17XqVMHDw8PcnLyL/SW93crMTGxyL9b9rPbY2VmZpKYmFjgw41/jwLmCQnJfeb/3+9jyJAhzJ8/n/nz57Nq1So6d+5M69atadu27QX/rl6ugQMHMnv2bObPn89TTz3lKLznzp2LzWbjhhtucNvfEVcrk4XlpYiLi3MM3b/11lv5/sHo2bMnw4cPZ9KkSUycOFGFpYiUWb5NuuHTeAXpUUs5M/dVMg+eG5VI3fATbF7A5z0fY2bj6/lwzwZsdjs5dhsTd6zgjnrXUNUv0H3Ji1ymyveOJ2TAW+X+Gcu8ovH48eMcP378grGZmZmO13m/0Bc1ynmxvqvJhfIMCQkhPj6etLS0An3/HhF0JR8fn0KP503XvVh/XkFXHBf7XptMJoKCgoiLi8t3PG913KSkJDZv3nzR+xS2umpRf8b/Hq3N06hRI6ZPn87EiRP5+++/WbBgAQsWLHD0PffccwUWoboSLVq0IDw8nD179vDXX385rj1v3jwgd2S5vCj3hWVkZCQWi4U6deoUOkQ/ePBgJk2aRFRUFEeOHCny0x8RkdIud4rsjfhG3EDatl84M+81sqK35HZaLST9/gk3+31L524jecziySlLJv/rcZ+KSinVjF6+4MRFcEqjvF/cn3/+eYYPH37J5505c6bImKL6ilPcXMqI2pW60HvIK5b8/Pycdr+Lvf8LbV/hDr6+vqSkpBT552S1WklMTCz0PICuXbvy1VdflWSK+TRr1owvvviCzMxMtm3bxsaNG1m8eDF79+5l5MiR/Pjjj4VOr71cAwcOZOzYscyZM4euXbuyYcMGoqOjqVKlCp07d3bafa52ZXLu0u7du3n22We5//77GTlyJB9//DH79u0rNHbr1q0AtGlT+CqHYWFhjrnqebEiImWZwWDAv+Wt1Hp1PVUe+TbfPpi2tAT8fnmb7zZPZXqVKrQPq+vGTEXEGRo2bAhQ5O9KRcmb5XWhPS73799f6PG80bR/j3DlSUpKIiEh4ZLyuRJFvYf4+Hji4+OB3AWJnCXv/RdVqB0+fNhp93KGi32vDx8+XGAaLOSOEELRfw9Kmre3N+3bt+fxxx9n4cKFdOvWDavVyqxZs5x6n9tvvx1vb28iIyOJj493PG/Zt2/fIkdWy6Iy+U537drFokWLWLduHZGRkXz++efcdtttvP322wUeCs77P+6FRiLz+op6aBtyV8vq169fsb4u9A+wiMjVwmA0EtDpXuq8u4uQAW9j9D63LL0t7jDB0/+Po291IfPwuelNCVnp/Hni0n45FRH36tOnD5C7iM+JEyeKfd7111+PwWBg7969bNiwoUD/9u3bi3y+sk6dOkDu72zZ2QWnIv/444/FzsMZ5syZQ1ZWVpF51KhRw6mFZd5zg1u2bCm0f/r06U67lzPkjbpNnz4dm63gM8nfffddoee1adOG0NBQjh8/zuLFi0s0x4sxGAyOBX9Onz5drHPyFnQ6fwp4YQICArjxxhuxWCz8+OOPLFmyxLFibnlSpgrLypUr8+STTzJ79mzWrFnDjh07WLhwIYMHD8ZutzNt2jQ++uijfOckJSUBuZu1FiWvL2+eeGFiY2OJiooq1tfF/nKKiFxNjJ4+BN/6AnXe30tgj5FgPLciYOb+1Rx5vT2nv3+KzJQzPBz5Hff+/g3f7PzbjRmLyKVo3Lgx/fr1IyMjgwceeCDfyq95Dhw4wKeffkpkZKTjWM2aNR1F6csvv5zvg/PDhw/z4osvFrlfYKNGjahWrRpnzpzh/fffz/fB/6+//sqXX3550b0GnSk5OZn//Oc/pKamOo5FRkYyefJkAB5++OEi92m8HN26dcNgMPDHH384tmyB3J0Kxo0bV+j3wJ0GDx5MhQoVOHLkCK+++mq+32XnzZvH7NmzC92709PTk2effRaA0aNH89NPPxVYqTUuLo7p06c7barsK6+8ws8//5zvewm5A0R5zz1GREQU61p5g0sbN2686DOpefucfv7556Snp9OuXTtq1qx5wXPKmjL1jGVheyWFh4fz+uuvU6NGDT788EOmTZvGPffc45jemvfp1IX+8crbj+hCBWFoaGix/5IeOHBAxaWIlDoeAZUJu38iFXuNInbWC6RtXZTbYbeRuGwiJ//+Ht/a12Gr3IRX1v3MweQ4Xmt3Kx7GgkvTi8jV5bXXXiMtLY0lS5YwZMgQQkJCqFatGjk5OZw4ccLx/Nw777yT77xXXnmFPXv2cPDgQW655RbHtNr9+/dTrVo1Bg8eXOholtFo5MUXX+Spp57iu+++Y8GCBdSqVYuYmBhiY2MZNWoU8+bNu+hiQs4yatQoJk2axPXXX0/9+vVJSEhw3Pumm25y+n6ctWvX5r777uO7777jmWee4b333iM0NJRDhw5hsVh4+eWXefXVV516zysRGhrKu+++y1NPPcWsWbP49ddfqVu3LqdPnyYmJob777+f5cuXF/r96tu3L7GxsYwfP56XXnqJt956i7p162I0GomLi+PkyZOOOGfYtm0bM2fOxGQyUbNmTQIDA0lKSiI6OtqxPU5xnyW++eab+eGHH/jll1/YunUrVatWxWg00rdvX/r165cvtm3bttStW9cxw7G87F15vjJVWF7IsGHD+Pbbbzl9+jSRkZGO/Xa8vHI3+L7QPjd5UzQK298oz+DBgxk8eHCxcunXrx9RUVHFTV1E5KriWa0x1f9vAWn/LOX0d09gicmd+uqTkcjLu3/hllM7+KRBL6buWsPRlAS+6H4vPh6uG3kQkUvn5eXlGJGcO3cu27ZtY9euXZhMJqpUqUL37t3p1atXgYVIgoODmTVrFp999hlLlizh0KFDhISEMGjQIJ566im+//77Iu/Zu3dvvvzyS7788kt27drFwYMHCQ8P56WXXuKmm25yjC65QqtWrZgxYwafffYZGzduJD09nUaNGjFo0CDuuecep45W5nnppZeoUaMGs2fPJjo6mqysLNq3b8+oUaMuOJPOXXr16uVYbXXz5s3s37+fevXqMWrUKAYOHMjy5cuLPPeRRx6hS5cufP/996xbt479+/djs9kICQmhe/fu9OzZk549ezolz9GjR/PHH3+wceNGYmJiOHbsGF5eXjRv3pwbbriBIUOGFLli7r+1adOGcePG8e2337J3715OnDiB3W6nXbt2hcb379+fDz/8kICAAHr37u2U91OaGOyXstZwKTdq1CiWLl3Kfffdx5gxYwC4++672bx5M08//TSPPvpooec9+OCDrF69mscee4ynnnrqivPIKywjIiKYO3fuFV9PRMRdbJYsEn77kPif38ZuOTcTw2IwMqtGW76v3ZF2NZvwTc/78fHwdGOmIuIOEyZMYOLEiYwaNYonnnjC3emIlKh33nmHqVOncs8991xVI86uUqaesbyYvOmu569alffweHR0dJHnHTlyJF+siIjkMpq9qHT7S9R+awd+LW5yHDfbbdx7dB1fb5xC0s4/GLp0KumW0rdXoIiISHFkZWU59su866673JyNe5SrwjJvGe0qVao4juXtYVPUpq15Q+jnx4qISH6eletR7emfqfbEnHzbk1TLTGLcthlc89f/ePC3L0i1FFx1UUREpLSbPHkyCQkJtGrViqZNm7o7HbcoN4XlihUrHIXldddd5zjes2dPzGYzhw8fZu3atQXOmzFjBgBNmzZ1LA0tIiIFGQwG/NvcSZ13oqh48/OO1WONwF3HN/Hwb2MZPf0VUrK1eJmIiJR+u3btYsiQIdx0001MmDABg8HgWAW3PCozheW+fft45ZVX2L17d77jNpuNRYsWOb7J3bt3p0WLFo7+vAfMIfch6oMHDzr6IiMj+d///gfA448/XtJvQUSkTDB6+RE68F1qvvwX5qqNHcdrZcTzWORHrPv6EWwauRQRkVIuOTmZ9evXc/ToUcLDw/n0009p27atu9NymzKzeM+uXbu48847AQgKCqJatWqYTCaOHDni2Kvy2muv5fPPPycgICDfuZmZmTzwwANs2bIFk8lEw4YNSU9PdzxbOWzYMF544QWn5arFe0SkvLBlZxA3ZwwJSz7GwLkfN541mlPl4Sl4127lxuxERETEWcpMYZmcnMwPP/zA1q1bOXDgAPHx8WRnZxMYGEjTpk259dZbufXWWzGZCt9PLTs7m6lTp/Lzzz9z5MgRzGYzTZo04b777nP6csEqLEWkvEnf8ycHv7gPz4Rj5w6aPAgZ8DYV+zxTIkv5i4iIiOuUmcKyNFFhKSLlkS0rjdhZL5K0fFK+436tbqfK8CmY/ILck5iIiIhcsTLzjKWIiFzdjF5+hA2ZQI3//J5v5di0LQuJfvVaMg8Xvjq3iIiIXP1UWIqIiEv5Nu1J7Tc2EV+vveNYTtwhjr55PYl/fIkm0oiIiJQ+KixFRMTlDH4V+arjw3xd53qs5D5fac/J4vS0xzj11f3YMlPdnKGIiIhcChWWIiLickaDkc+638vOawfyfIuBxJt9HX0pa37kyBsdyDqxy40ZioiIyKVQYSkiIm7h4+HJ1F5DSazVihFthrItsIajL/vELo680YG07b+5MUMREREpLhWWIiLiNpW8/fn+xmEYAqvw7DWDmF6znaPPnpnK8Y/vIHHFZDdmKCIiIsWhwlJERNyqTkAlpvYaipfZi8n1uvJSRF8yTJ65nTYrp6c+StxPL2tRHxERkauYCksREXG7lqE1+aLbvZgMRtaENOCpawaT6FXB0R+/6B1OfTkEmyXLjVmKiIhIUVRYiojIVaFnzca80+lOAPZXCOPRlvcQF3TuucuUtdM5/mEfrKnxbspQREREiqLCUkRErhr3NGrHky26A+AbWpeg53/Ht2lPR3/GnlUcfaszlthD7kpRRERECqHCUkRErirPtb6BF1r35pfbRtG0ejjVn1lEwHX3O/qzT+7myNhOZB7e5MYsRURE5HwqLEVE5KpiNBh54pruBHr5AGDw8CRs+DdUuvNVR4w1+TTH3ruBzIMb3JWmiIiInEeFpYiIXPUMBgM+t7xIyLD/gckDAFtGEsc+6E3moY1uzk5ERERUWIqIyFXvYFIsty36jPGelag26icwmYHziktNixUREXErFZYiInJV2xp7lJt/nsiexBim7FrNssCaVHt85rniMj2RY+/fqOJSRETEjVRYiojIVS28YhXqVKjkaD//9xyO1W1/trg8Oy02PfHsyOVmd6UpIiJSrqmwFBGRq5qPh5mvetxHoGfuYj4ZORYeifwee7PeVHvsvOIyLYFjH9yo4lJERMQNVFiKiMhVr1aFYD7tMggDBgAOJsfxn7/n4tf6Dqo+NqNgcRm9xZ3pioiIlDsqLEVEpFToWbMxT7Xs4Wj/fHg78w9uo0KbvlQdOT1/cfn+jWQd3+muVEVERModFZYiIlJqPH1NTzpWqedov7x2PifTkqhwbT+qPvojGE0A2NLiOT7+NnKSYtyVqoiISLmiwlJEREoNk9HIuOsH4G/2AiApO5Pn/voJu91Ohbb9qfroD2DInS6bE3eYE5/ciS0r3Z0pi4iIlAsqLEVEpFSpWSGY19vf5mivPLGP7/asA6BCu7sIHfyhoy/z4HpOfXU/dpvN5XmKiIiUJyosRUSk1BnYoA031mwC5K4a62E89+Ms6ManCOwx0tFO3TSPuFkvujxHERGR8kSFpYiIlDoGg4H3ruvHDTWbsOT2p7inUbt8fZXv/Ri/Fjc5jiUs/ojEyC/ckaqIiEi5oMJSRERKpVCfCkzpNZR6gSEF+gwmD6qOnI5XrZaOY6e/e4K07b+5MEMREZHyQ4WliIiUSUafClR7eiEeFavnHrDbODFpMFlHtrk3MRERkTJIhaWIiJQZ6ZZsxm9dRmaOBQBzxepU+7+FGLz9AbBnpnJ8/G1YEo67M00REZEyR4WliIiUCRtjorlxwSd8tGUZH2xZ6jjuXbslVUdOB0Puj7ychOOc+KQvNkuWu1IVEREpc1RYiohImbDw0DYOp5wB4Kt//mRDzGFHn/81N1P5vk8d7azDm4ib+R9XpygiIlJmqbAUEZEyYfS1fagfGAqAHTsvrp6HxWZ19Af1HElgj0cd7cRlE0nZMMfleYqIiJRFKixFRKRM8PHwZNz1AxztPYkxfB31d76Y0MEf5VspNuabh8k+fdBVKYqIiJRZKixFRKTMaFO5dr49LT/aupTjqYmOttHTm6qPzXAs5mPLSOLk5/dgz8l2daoiIiJligpLEREpU0Zf24dgLz8AMnIsvLJuYb5+zyoNCXvgS0c769AGYme+4NIcRUREyhoVliIiUqZU9PJlTNubHe0lR3ay9MjOfDEBHQYT2O1hRztx6aekbprvqhRFRETKHBWWIiJS5gxo0Jr2YXUd7THrFpJuyT/dNfSe8XjWbOFon/r6ISyxh12VooiISJmiwlJERMocg8HAO53uxOPs3pXHUhP5eNvyfDFGTx+qPTYDw9lps7b0RD1vKSIicplUWIqISJnUKCiMEc26ABDs5UejoLACMZ5Vwwkb+rmjnXlwHXGz/+uyHEVERMoKD3cnICIiUlL+r2UPbHY7jzXvQkVvv0JjAjrdS/ruFSSv+gaAhCXj8WnaA/9rbi40XkRERArSiKWIiJRZPh6evNT2piKLyjyV7/0EzxrNHO3TUx/Fmp5U0umJiIiUGSosRUSk3DN6+VJ15I8YPDwByEk4TtzM/7g5KxERkdJDhaWIiJQrCVnpzDmwpcBxr+oRBN8+xtFOWvk/0ndGujI1ERGRUkuFpYiIlAt2u53Z+zbRdc5H/N+qWUSdOVEgJvjm5/Gq1dLRjpnyCLasNBdmKSIiUjqpsBQRkXIhx27j839WEp+Vhh07Yzf8it1uzxdj8DAT9tD/wGgCwBJ7iLifXnZHuiIiIqWKCksRESkXzEYTL117bqXXv07uJ/LYngJx3rVbEXzz84524rIJZOxb7ZIcRURESisVliIiUm70qBFO52oNHO03N/xKjs1aIC749jF4Vm2c27DbifnmYWzZma5KU0REpNRRYSkiIuWGwWBgTNubMWAAYF/SaWbs3VggzujpTdiwyWDIjcs+uZv4hW+6NFcREZHSRIWliIiUK02DqzGwYWtH+8MtS0kpZDTSp2Engno94WjH//o+mdEFV5MVERERFZYiIlIOPdfqRnw8zADEZaby+Y6VhcaFDHgTc2jd3IbNSszXw7HnWFyVpoiISKmhwlJERMqdqn6BjGjWxdH+MupPTqQmFogzevkR9uCXjnbWka3E//qBK1IUEREpVVRYiohIuTSyWRcq+1QAIMuaw/ubfy80zrdpTwK7Dne04xeOJfv0AZfkKCIiUlqosBQRkXLJz+zFc61vcLRXnzpAqiWr0NiQQe9jCqoGgD0nm9gZzxcaJyIiUl6psBQRkXJrUINraRNai+db3cDKfs/ib/YqNM7kG0jooPcc7bTNC0jfudxVaYqIiFz1VFiKiEi5ZTIamX/LSJ5q2RMfD88LxlbocDfeDTo52qd/fAa7NaekUxQRESkVynRhuXLlSsLDwwkPD6dHjx5FxqWlpTF+/Hj69OlDixYt6NChAyNGjGDdunUuzFZERNzBcHavyuLEVb53vKOdfewfklZMLqm0RERESpUyW1impaXx2muvXTQuPj6e/v3788UXX3D8+HHq16+Pl5cXK1asYOjQofzwww8ln6yIiFxV7HZ7oce9615LQOcHHO24ua9gTY13UVYiIiJXrzJbWI4fP54TJ07Qs2fPC8a99NJLHDp0iIiICJYtW8a8efNYsWIFb7zxBna7nbfeeotdu3a5KGsREXGndEs2n+9YSZ+Fn5JZxH6VIf3fwuDtD4AtLZ4zC95wZYoiIiJXpTJZWG7dupUffviBnj170qtXryLjdu7cSWRkJEajkfHjxxMWFgbkTncaNGgQd9xxB1arlUmTJrkqdRERcZNsaw495o/jrY2/ERV/kh/2ri80ziOoCpVue8nRTlw+iazjO12VpoiIyFWpzBWWFouFMWPG4O3tzSuvvHLB2CVLlgDQoUMHateuXaB/0KBBQO6zmunp6c5PVkRErhqeJg9uqNnU0f5s+woycrILjQ268SnMlevnNmxWYn98usjpsyIiIuVBmSssv/zyS/bu3ctTTz1FlSpVLhi7detWAK699tpC+1u0aIGnpydZWVmaDisiUg6MatENL5MHAKczUvh299pC44xmL0IHf+Bop0ctI23rIpfkKCIicjXycHcCznTgwAG+/PJLIiIiGDJkyEXjDx8+DECtWrUK7TebzVStWpXo6GgOHTpEmzZtirzWjBkzmDVrVrHzFBGRq0+YbwBDG3fgq6i/AJi0YyX3hbfHr5D9Lf1a3Y5v056O/SxjZzyHX/PeGC6ybYmIiEhZVGYKS7vdzssvv0xOTg6vv/46JpPpouckJSUBEBgYWGRMXl9ycvIFrxUbG0tUVNQlZCwiIlejx5p34/s960nPyeZMZhpTdq1hVItuBeIMBgOh94wjekwrsNuwxOwnYekEgm961vVJi4iIuFmZKSx//PFHNm/ezJAhQ2jevHmxzsnKygJyRyaL4umZ+8lzZmbmBa8VGhpKREREse574MCBi15PRETcI8THnwebdOKzHSsA+OKfVQxt3IEKnt4FYr1qNCOwx6MkLc9d5C1+wVgCOt2HR2CYCzMWERFxvzJRWMbExDBu3DjCwsL4v//7v2Kf5+XlRUZGBhZL4UvKA2Rn5y7c4O1d8BeK8w0ePJjBgwcX6779+vXT6KaIyFXs0WadmbZ7DamWLBKz0vl659/8X8vCt68K6fsaKWunY0tLwJaZwpl5rxL2wBcuzlhERMS9ysTiPWPHjiU1NZWXX34Zf3//Yp8XEBAAnJsSW5i8vrxYEREp+yp6+/FQ0+sc7a+i/iQxq/DVwU3+lah052uOdtKfU8g+rWfpRUSkfCkTheXOnbn7h73++utcd911+b7eeustAE6ePOk4tnnzZgDq1KkDQHR0dKHXtVgsnDhxIl+siIiUDw9HXE/A2emvydmZTD67oE9hgro/gjm0bm7DmsOZ+W+4IkUREZGrRpkoLPPExcUV+EpNTQXAZrM5juVNfW3ZsiUAmzZtKvR627dvx2Kx4OXlRZMmTVzyHkRE5OoQ5OXLIxGdHa8refsVGWvw8CT4jjGOdsqaH8g6vrPEcxQREblalIlnLCMjI4vsmzt3LqNHj6Z69eoF4nr37s2XX37JunXriI6Opnbt2vn6Z86cCUCXLl3w8yv6FwoRESmbHmp6HR5GU5GL95wvoNN9JPzyPtknd4Pdzpl5r1FtVPG2oRIRESntytSI5aWKiIige/fuWK1Wnn76aU6fPg3kbl0yc+ZMFixYgNFoZOTIkW7OVERE3KGCpzejWnS7aFEJYDCaqNT3VUc7deMcMqO3lGR6IiIiV40yMWJ5Jd5++23uvvtuoqKi6NmzJw0aNCAhIYGTJ09iMBj473//W+xtREREpHzzv3YAXjXfJevoNgDOzH2F6k//7OasRERESl65HrEECA4OZs6cOTz66KNUq1aN/fv3k5GRQZcuXZg6dSpDhgxxd4oiInIVybLmkGrJKrTPYDRSqd/rjnbatl/J2L/GVamJiIi4jcFut9vdnUR5k7ePZUREBHPnznV3OiIiUgxplix+2LOeL6P+ZED91oy+tk+hcXa7naNjryPz4DoAfJr0oOYLS12ZqoiIiMuV+xFLERGR4vhhz3re2PALMenJTNu9hqSsjELjDAYDlfqf224kY1ck6TuLXmRORESkLCh2YWm1Wlm1ahWLFi0iJibGcXzNmjU8//zzjBgxgkmTJjm29xARESlL7glvR+DZRXxSLVl8u3ttkbG+TXvi07ibox039xU0QUhERMqyYi3ek5aWxtChQ4mKisJut+Pt7c3nn39OcnIyTz31FF5eXuTk5LBy5Up++eUXZs2ape05RESkTPE3e/FAk058si139PF/O/9ieMR1+Hh4Fog1GAyE9H+Do291ASBz/xrStv+G/zU3uzRnERERVynWiOXUqVPZvXs3zz33HB9//DFhYWGMHTuWyZMn8/HHH7Nlyxa2bNnCf//7Xw4ePMiUKVNKOm8RERGXe6jpdfh4mAE4k5nG9L0bioz1aXgdvi3OPYd5Zu4r2G22Es9RRETEHYpVWC5evJh+/frx0EMP0adPH0aPHs3Bgwdp27Ytffr0wWg04unpyf3330/37t1ZtmxZSectIiLicsHeftzTqJ2j/cU/q8i25hQZH9Lv3LOWWdFbSN00r0TzExERcZdiFZbHjh2jWbNmjnbjxo0BaN26dYHYdu3aER0d7aT0REREri4jIjpjNpoAOJGWxPyDW4uM9a7TBv82fR3tM/NexW6zlnSKIiIiLleswtJoNGK1nvtB6OXlBYC/v3+BWF9f33yxIiIiZUk1/yD61W/laE/asRKbvegprpX6vQ4GAwDZJ3aRuuGnEs9RRETE1YpVWFauXJlTp0452n5+fowZM4Z69eoViD1+/DjBwcHOy1BEROQq81jzrhjILRb3J8WyOHpnkbFe1SOo0H6Qox2/6D2tECsiImVOsQrLiIgItmzZ4mh7enpy7733EhYWViB29erVNGnSxHkZioiIXGXqB4Zyc51zj4hM3P7HBYvF4FtedLzOOrqN9B1LSjQ/ERERVytWYfnss8/y/PPPXzTuzJkzNGjQgIEDB15xYiIiIlezUc27ARDo6U33GuFYLvDspFfN5vidt9VI/C/vlXR6IiIiLmWwaz6Oy/Xr14+oqCgiIiKYO3euu9MREZHLtPDgNrrXCKeCp/dFYzP2/e3Y1xKg5kt/4tOwU0mmJyIi4jLFGrEUERGRgm6vd02xikrI3dfSp1FnRzt+0bsllZaIiIjLqbAUERFxkeBbX3C8Ttv2C1lHd7gxGxEREee5osLSYrFw2223ERkZ6ax8RERESq1saw7xmWlF9vs274NXzWsc7fhf33dFWiIiIiXuigpLu93Ovn37SE5OdlY+IiIipU66JZv/Rf3FdT99wGvrFxUZZzAYqHjLfxztlHUzscQeckWKIiIiJUpTYUVERK7Q0qO7eG39Ik6mJ7Hw4DZOpiUVGVuh7QDMlevnNmxW4n/70EVZioiIlBwVliIiIlfoljrNqO4XBECO3caUXauLjDWYPKh483OOdvKqKeQkxZR0iiIiIiVKhaWIiMgV8jCaGNb03NYhP+xZR5olq8j4gE73YwqsAoA9J4uE3z8p8RxFRERKkgpLERERJ7i7UTv8zV4AJGVnMnPfxiJjjZ7eVOz9tKOdFPk51vSip8+KiIhc7VRYioiIOEGApzd3N2rraH+982+sNluR8UHdR2D0DQLAlpFMUuTnJZ2iiIhIiVFhKSIi4iQPNb0Oo8EAQHRKPEuO7Cwy1uhTgaCejzvaCb9/gi07o8RzFBERKQkqLEVERJykhn9Fbqnd3NGeHPXnBeODbngCg6cPANbk0yT/ObUk0xMRESkxKixFRESc6JFmnR2vN5yOZnPskSJjPQJCCezykKOdsHgcdpu1RPMTEREpCSosRUREnKhVaE3aVq7taE/+568Lxlfs8wwYTQBYYg+StnVRieYnIiJSEq6osPT09GT58uXceOONzspHRESk1Msbtaxg9qJWhWDsdnuRseaQ2vi36etoJ/z+aYnnJyIi4mweV3qB6tWrOyMPERGRMuPGmk15t1Nf7qzX0rEFyYVUvPEpUjf8BEDG7hVkRm/Fu3bLEs5SRETEeTQVVkRExMlMRiP3hbcvVlEJ4N2gI151z21VkrhUo5YiIlK6XPGIZWGsViuHDx8mOTmZnJycC8a2bdv2gv0iIiJlncFgoOKNT3LqyyEApKydTshd7+ARGObmzERERIrHqYXlqVOnGDduHEuXLiUzM/Oi8QaDgZ07i97jS0REpKyw2mxA7mhmYSq0HUDszBewJp7AnpNN0h9fUunOV1yZooiIyGVz2lTYo0ePMmDAAH7++WcyMjKw2+0X/bKd/SErIiJSVqVasvhf1F90nvMhiw7vKDLO4OFJUM/HHO3EyM+xWbJckaKIiMgVc9qI5bhx44iLiyM4OJhnn32W66+/npCQEEwmk7NuISIiUuq8vn4R0/duAOCrqD+5vW4LDAZDobFB3R4mfuGb2C2ZWJNPk7JuBoHXD3VluiIiIpfFaSOWq1evxmAwMG7cOPr3709YWJiKShERKfeGNu7geL0t7hgbTkcXGWuqEEJAp/sc7cTfP73gViUiIiJXC6cVltnZ2Xh7e9OhQ4eLB4uIiJQTzSpVp1OVeo72lJ2rLxgfdOOTjtdZR7aSsWdVieUmIiLiLE4rLGvUqKFPVUVERAoxrOl1jte/Rv/DibSkImO9qkfgG9HL0U74/ZMSzU1ERMQZnFZY3nTTTWRlZbFmzRpnXVJERKRMuKFmE2r4BwFgtdv4bvfaC8YH3fiU43XaloVknz5YkumJiIhcMacVlsOGDaNx48aMGTOGo0ePOuuyIiIipZ7JaOSBxp0c7e/3rCMjx1JkvF/zPpirNMpt2O0kLptY0imKiIhcEaetCuvt7c2UKVMYM2YMt99+O71796Z58+b4+fld8Lw777zTWSmIiIhctQY3upaPti4lI8dCQlY6Cw9tY1DDawuNNRiNVLzhSU5/NwqA5FXfUKnva5h8AlyZsoiISLE5rbAEOH78OHFxcWRkZLBgwQIWLFhwwXiDwaDCUkREyoUgL1/612/N93vWAfDNzr8Z2KBNkVuPBFx/P3FzXsaWnogtM4XkP6dS8byFfURERK4mTissd+/ezZAhQ8jIyADAbDZTsWJFbTkiIiJy1oNNOjkKy6j4k2w4HU27sDqFxhq9/Ajs9jAJv34AQOLSCQT1ehyDUT9XRUTk6uO0wnLChAmkp6dTs2ZNxo4dS7t27TAanfYIp4iISKkXXjGM66rW5++TB/Dz8ORQclyRhSVAUM/HSFg8DmxWLLEHSdu6CP/Wd7guYRERkWJyWmG5ZcsWDAYD48ePp1mzZs66rIiISJnyRIvu3FCzCQMbXkuAp/cFY82VauHfpi+pG34CIDHycxWWIiJyVXLakGJGRgY+Pj4qKkVERC7g+moNGB5x/UWLyjxBPR9zvE7/ZynZp/aVVGoiIiKXzWmFZe3atcnJycFqtTrrkiIiIuWeT3gXPKs1dbSTVnzlxmxEREQK57TC8s477yQ7O5vIyEhnXVJERKTcMxgMBPZ41NFO+nMKtuwMN2YkIiJSkNMKyyFDhtChQwdeeeUVtmzZ4qzLioiIlFlpliym7VrDyD9+xG63FxkX0Ok+DF65+0Lb0hJIWTfTVSmKiIgUi9MW7/n8889p2bIlO3fu5J577qFNmzY0b94cPz+/C543atQoZ6UgIiJSasRnpnH9nA9Izs4E4MHTnYpcIdbkG0hAx3sd02ATI78gsPMDLspURETk4pxWWE6cONGxybPdbmfjxo1s2rTpouepsBQRkfIo2NuPFpVq8NfJ/QB8s/PvC2890uNRR2GZdWgDmYc24l33WlekKiIiclFOKyzbtm3rrEuJiIiUCw826egoLH+LjuJEaiLV/IMKjfWqdQ3eDTqRuX81kDtqWeWh/7kqVRERkQtyWmH53XffOetSIiIi5UKvmk2o6V+Ro6kJWO02pu1ey+hr+xQZH9TjUU6dLSxT1s0gdPAHmPwquipdERGRIjlt8R4RERG5NCajkQeadHS0p+/dQGaOpch4/7YDMFUIAcCenUHyX9+WeI4iIiLFocJSRETEjQY1vBZvkxmA+Kw0Fh3eUWSs0exFQOdhjnZi5OfYbbYSz1FERORiVFiKiIi4UZCXL3fWu8bR/nb32gvHd38Ezi6WZ4nZR/ou7R8tIiLup8JSRETEzc6fDrs59gg74o4XGWsOrYtfi5sc7aTIz0s0NxERkeJw2uI9V4PffvuN1atXExUVxenTp0lMTMRsNlOnTh26du3K0KFDqVix8EUO0tLS+Oqrr1iyZAknTpzA19eXa665hmHDhtG+fXsXvxMRESlPmlWqTpvQWmyKPQLAtN1r+PD6AUXGB3Z/lLRtvwKQuuVnLPHHMAfXcEmuIiIihSlTI5ZffPEFs2bNYt++fXh6ehIeHk5QUBA7d+7k888/55ZbbmH37t0FzouPj6d///588cUXHD9+nPr16+Pl5cWKFSsYOnQoP/zwgxvejYiIlCf3nzdqufjIzgsu4uPXog8eIXVyGzYrSSu17YiIiLhXmSos7733Xr7//ns2b95MZGQkc+bM4Y8//mDhwoU0atSIM2fO8OyzzxY476WXXuLQoUNERESwbNky5s2bx4oVK3jjjTew2+289dZb7Nq1yw3vSEREyotb6zSnTWgtxrS9mT/7PYu3h7nIWIPRRFD3EY520sr/Yb9AISoiIlLSylRhOXDgQNq2bYvZnP+HcXh4OG+99RYA+/fv58CBA46+nTt3EhkZidFoZPz48YSFhQFgMBgYNGgQd9xxB1arlUmTJrnujYiISLnjZfJgwa2PMaJZFyp6+100PqDzgxg8PAGwJp4kdcuCkk5RRESkSGWqsLyQevXqOV5nZGQ4Xi9ZsgSADh06ULt27QLnDRo0CICVK1eSnp5ewlmKiIgUj0dAKP5tzz2Hmbhci/iIiIj7lJvCctOmTQD4+vpSt25dx/GtW7cCcO211xZ6XosWLfD09CQrK0vTYUVE5KoS1GOk43XG7hVkn9rrxmxERKQ8K1Orwv6bzWYjNjaWv//+mw8//BCA5557Dj+/c1OMDh8+DECtWrUKvYbZbKZq1apER0dz6NAh2rRpU2jcjBkzmDVrVrHyOn8qroiISGEycrKZf3AbXao1pLp/UKEx3g064lmjGdnH/gEgacX/CB38vguzFBERyXVZheXo0aOdcnODwcDbb7/tlGudb+rUqbzzzjv5jrVo0YJ3332XLl265DuelJQEQGBgYJHXy+tLTk4uMiY2NpaoqKjLTVlERMThq3/+5JNtkSRlZ/B4826MvrZPoXEGg4HArg8T+8NTACT/PY1K/cdiNHu5Ml0REZHLKyznzZuHwWDAbrdf1k3zzi2pwjIsLIzWrVtjtVo5ceIEcXFx7Nq1iwULFtCyZUsCAgIcsVlZWQAFFvw5n6dn7uIImZmZRcaEhoYSERFRrPwOHDhwwWuJiEj5ZjBAUnbuegDT927g6ZY9i1wlNqDTvcTNegG7JRNrShxpWxZQod1AV6YrIiJyeYXlnXfeicFgcHYuTnPTTTdx0003Odq7d+9m7NixLFq0iAMHDjBnzhxMJhMAXl5eZGRkYLEUvUx7dnY2AN7e3kXGDB48mMGDBxcrv379+ml0U0REinRXgza8t+l3Mq0W4rPSWHR4BwMatC401uRXEf+2A0hZ/T2QOx1WhaWIiLjaZRWW7777rrPzKFGNGzfmyy+/pFevXuzatYtffvmF22+/HYCAgAAyMjIcU2ILk9d3/kiniIhISQny8uXOetcwY99GAL7dvbbIwhIgqNvDjsIyfedysk8fwLNyfZfkKiIiAuVoVVh/f3/atWsHkG+0sE6dOgBER0cXep7FYuHEiRP5YkVEREra0MYdHa83xx5hR9zxImO9G16HZ7UmjnbSyq9LNDcREZF/KzeFJUBOTg4AVqvVcaxly5bAue1I/m379u1YLBa8vLxo0qRJoTEiIiLO1jykOq1Dz61YPnX3miJjcxfxGe5oJ/85BXtOdonmJyIicr4SKSx37tzJe++9x5AhQ7jlllu45ZZbGDJkCO+//z47d+4siVteVGJiIuvXrwfIVyD27t0bgHXr1hU6ajlz5kwAunTpkm+bEhERkZI2tMm5Ucv5B7eSmJVeZGzAdUMweOQuNmdNPk3qlp9LPD8REZE8Ti0s09PTefbZZ+nfvz9Tp05lw4YNHDhwgAMHDrBhwwamTJlC//79efbZZ0lPL/qH4+VYv349kyZN4tixYwX6oqKieOihh0hJSSEsLIw+fc4t2x4REUH37t2xWq08/fTTnD59GgC73c7MmTNZsGABRqORkSNHFriuiIhISbqldjOCvXI/1Myy5jBrX+GzawBM/pXwv7a/o5208n8lnp+IiEiey1q8pzA2m43HHnuMdevWYbfbCQ0NpUOHDlSpUgWAU6dOsW7dOk6fPs2vv/5KfHw833zzjdNWl01OTuaTTz7hk08+ITQ0lMqVK2MymTh58iSxsbFA7jYkX375ZYGRx7fffpu7776bqKgoevbsSYMGDUhISODkyZMYDAb++9//FnsrEREREWfx9jBzd6O2fLZjBQDTdq9leMR1GA2Ffy4c2HU4KWunA5AetRRL7CHMoXVdlK2IiJRnTiss58+fz9q1a/Hw8ODFF1/knnvuwWjM/4PPZrMxffp03nnnHdauXcuCBQu48847nXL/Vq1aMXr0aNatW8f+/fs5fPgw2dnZBAQE0L59e3r06MGAAQPw9/cvcG5wcDBz5sxh8uTJLF68mP379+Pr60uXLl146KGH6NChg1NyFBERuVRDGrdn0o6V2LETnXKGlcf30b1GeKGxPo27Yg5riCVmH9jtJK36hpD+Y12csYiIlEcGu91ud8aFHnjgAdatW8fo0aO5//77Lxj77bff8vbbb9OhQwemTp3qjNuXKnn7WEZERDB37lx3pyMiIle5B5dNIyr+BEPCO3BPeFsqeRf8kDRP/G8fETfzPwCYgqpS76PDGExO+xxZRESkUE57xnL37t2YTCYGDrz4pswDBw7Ew8ODXbt2Oev2IiIiZdaH1/dn9YD/8MQ13S9YVAIEXHc/mMwAWBNPkrbtF1ekKCIi5ZzTCsu0tDT8/Pzw9va+aKy3tzd+fn5OX8BHRESkLKrk7Y+H0VSsWI+AUPzb3OloJ62YXEJZiYiInOO0wrJixYqkpKRw5syZi8aeOXOG5ORkgoKCnHV7EREROSuo28OO12k7lmA5c8SN2YiISHngtMKyZcuW2O12JkyYcNHYTz/9FLvdTuvWrZ11exERkXIjy5rDkZT4Ivt9GnfHHFovt2G3kbRqiosyExGR8sppheW9997r2Pvx+eefJzo6ukBMdHQ0zz33HDNnzsRgMHDvvfc66/YiIiJlXkx6Mu9sXEzbme8wauWMIuMMRiOBXYc72sl/foPdmuOKFEVEpJxy2jJx7du3Z+jQoUybNo1FixaxaNEiqlatSuXKlQGIiYnh1KlTjvgHHniAdu3aOev2IiIiZd7p9BTHnpbxsWnsiDtO85DqhcYGXD+UuHmvgDWHnPhjpO1YjH/LW12XrIiIlCtOG7EEGD16NC+//DIBAQHY7XZOnDjB1q1b2bp1KydPnsRutxMYGMiYMWN44YUXnHlrERGRMq95SHVah9ZytKfuXlNkrEdQFfxb3e5oJ638ukRzExGR8s3pG1vdd9993HXXXfz999/8888/jsV8KlWqRLNmzbjuuuvw8vJy9m1FRETKhaFNOrI5NncxnvkHtzKm7c0EefkWGhvY5SFSN+bul5y27RdyEk/iEVTVZbmKiEj5USI7Jnt5edGjRw969OhREpcXEREpt26t05zX1y0iPiuNLGsOs/Zt4pFmnQuN9W12Ax7BNcmJPwo2K8l/f0fwLf9xccYiIlIeOHUqrIiIiJQsL5MHdzdq62hP270Wm91WaKzBaCKg8wOOdtKqb7Db7SWdooiIlEMqLEVEREqZIY3bYzQYAIhOOcPK4/uKjA28/gE4G2uJ2UfG3j9dkaKIiJQzJTIV1mq1cvjwYZKTk8nJufDy5m3btr1gv4iIiORXw78ivWo05vejuwD4dvdautcILzTWHFoH34hepP+zFMhdxMc3vIvLchURkfLBqYXlqVOnGDduHEuXLiUzM/Oi8QaDgZ07dzozBRERkXLh/iYdHYXlsqO7OZoST80KwYXGBnYZ5igsUzf8hPXeTzD5BbkqVRERKQecNhX26NGjDBgwgJ9//pmMjAzsdvtFv2y2wp8JERERkQvrUq0BdSpUAsCOne/2rCsy1q/VHRj9z8ZaMklZN90lOYqISPnhtBHLcePGERcXR3BwMM8++yzXX389ISEhmEwmZ91CREREzjIajAxt0oHX1/9CmE8FQn38i441exFw3RASl3wMQNLKbwjqMdJFmYqISHngtMJy9erVGAwGxo0bR4cOHZx1WRERESnCXQ3aUMU3kD61IzAbL/xBbmCXYY7CMit6M5nRW/Cu3coFWYqISHngtKmw2dnZeHt7q6gUERFxkSAvX26r2+KiRSWAV/UIvOuf+xmdtOqbkkxNRETKGacVljVq1NDeWCIiIlexwC7DHK9TVv+ALTvDjdmIiEhZ4rTC8qabbiIrK4s1a9Y465IiIiJyiS70IW+FdgMxePkBYMtIInXjHFelJSIiZZzTCsthw4bRuHFjxowZw9GjR511WRERESmGk2lJfLRlKdfP+ZDErPRCY4w+FajQfpCjnbRqiqvSExGRMs5pi/d4e3szZcoUxowZw+23307v3r1p3rw5fn5+FzzvzjvvdFYKIiIi5ZLVZuPWRZ8Rk54MwKx9m3ikWedCYwO7PETy2ecrM3avIPvUPjyrNHRZriIiUjY5rbAEOH78OHFxcWRkZLBgwQIWLFhwwXiDwaDCUkRE5AqZjEbuatCaidtXADBt91qGR1yH0VBwYpJ3/fZ4VmtK9omdACT9OYXQu952ZboiIlIGOW0q7O7duxkyZAhbt24FwGw2U7lyZapWrVrkV5UqVZx1exERkXLtvvD2GA0GAKJTzrDy+L5C4wwGA4FdH3K0k/+aht2a45IcRUSk7HLaiOWECRNIT0+nZs2ajB07lnbt2mE0Oq1uFRERkQuo4V+RXjUa8/vRXQB8u3st3WuEFxob0Ok+Yme9CFYL1qRTpG3/Ff9Wt7syXRERKWOcVvlt2bIFg8HA+PHj6dChg4pKERERFxvapKPj9bKjuzmaEl9onKlCCP6t73C0k1Z+XeK5iYhI2ea06i8jIwMfHx+aNWvmrEuKiIjIJehcrQF1KlQCwI6d7/asKzL2/Omwadt/IyfhRInnJyIiZZfTCsvatWuTk5OD1Wp11iVFRETkEhgNRoY26eBoT9+7gcwcS6Gxvk174VGpdm7DZiX5729dkaKIiJRRTiss77zzTrKzs4mMjHTWJUVEROQS3dWgDd4mMwAJWeksOryj0DiD0Uhg5wcc7aRV32C32VyRooiIlEFOKyyHDBlChw4deOWVV9iyZYuzLisiIiKXIMjLlzvrXeNoT9u9psjYgM4PwNmVZC2nD5CxZ1VJpyciImWU01aF/fzzz2nZsiU7d+7knnvuoU2bNjRv3hw/P78Lnjdq1ChnpSAiIiLA0MYdmbFvIwDRyfGcTk+hsm+FAnHmSrXwbXYj6TuWAJC06mt8m3RzZaoiIlJGGOx2u90ZF2rcuDGGs5965l0yr30hu3btcsbtS5V+/foRFRVFREQEc+fOdXc6IiJSBr24eh7XVq7NrXWa4+1hLjIuZcNPnPxsEAAGszf1Pj6Gya+iq9IUEZEywmkjlm3btnXWpUREROQKvdupb7Hi/FvdjqlCCNaUOOyWTFLWTCeo12MlnJ2IiJQ1Tissv/vuO2ddSkRERFzE4OFJwHX3k7B4HJA7HVaFpYiIXCqnLd5zOVJSUtx5exEREQECugxzvM46spXMw5vdmI2IiJRGTissP/7440uKT0lJ4cEHH3TW7UVERKQIZzJT+XRbJH+e2Fdov1e1Jng36ORoJ6362lWpiYhIGeG0wvKLL75g+vTpxYpNS0vjoYceIioqylm3FxERkUJ8u3st7Wa9y/ubf2fSjpVFxgV2PTdqmbLmR2xZ6a5IT0REyginFZYmk4k333yTpUuXXjAuLS2N4cOHs337dkJDQ511exERESlEeFAYWdYcAP48sZ+9iTGFxlVoexcGb38AbBnJpG6c47IcRUSk9HNaYfnmm29itVp57rnn2LhxY6Ex6enpPPLII2zZsoWQkBCmTp3qrNuLiIhIIdqF1SEiuKqjPWXn6kLjjN7+BLQf7GgnrfqmxHMTEZGyw2mFZd++fXnmmWfIysri8ccfZ9++/M9xZGRkMGLECDZt2kSlSpWYNm0a9erVc9btRUREpBAGg4EHm5x7fvKnA5tJLGKaa0DXhxyvM/asIvvU3hLPT0REyganrgr7yCOPcN9995GUlMTw4cM5efIkAJmZmTz66KNs2LCB4OBgpkyZQv369Z15axERESnCHfVaUtHLF4CMHAsz9xU+s8i7bls8azRztJNWTXFJfiIiUvo5fbuRl19+mT59+hATE8NDDz1ETEwMI0eOZN26dQQFBfHNN9/QqFEjZ99WREREiuDjYebe8HaO9tRda7DabAXiDAYDgV3OjVom/zUNe47FJTmKiEjpViL7WH7wwQe0a9eOgwcP0rt3b9asWUNgYCBTpkyhcePGJXFLERERuYD7wztgMuT+2D+amsDyY7sLjQvodC8GD08ArMkxpG37xWU5iohI6VUihaXZbGbSpEk0adKEzMxMAgIC+Oabb2jSpElJ3E5EREQuopp/EH1qRzja3xSxiI/JvxL+re90tLWIj4iIFIfH5Zw0ceLEYsU1b96cXbt20aZNG/744w/++OOPAjGjRo26nBRERETkEg1r0olfDu8A4K+T+9mTEEN4xbACcQFdHyJl/SwA0rb/hiXhOOaK1V2aq4iIlC6XXVgaDIZix69YsYIVK1YU2qfCUkRExDXyth6Jis9dXO+HPet4o8PtBeJ8m/TAI6QOOXGHwW4j+c9pVLr9vy7OVkRESpPLKizbtm3r7DxERESkhOVtPfLNrtUMa9KJO+q1LDzOaCSw84OcmfcqAMl/fkPwrS9iMJbIEzQiIlIGXFZh+d133zk7DxEREXGBuxq0YVDDay868yig8wOcmf862G1YYg+RvnM5fs1ucFGWIiJS2uijRxERkXLEZDQW63EWc3AN/Frc5GgnrfxfSaYlIiKlnApLERERKVRgt4cdr1M3LyAn+bQbsxERkauZCksREZFyLCErnXkHthba59fiJkxB1XIbVgvJf33rusRERKRUUWEpIiJSDmXkWHj+7zm0nfkOT6yawa74UwViDCYPArsMc7STVv4Pu93uyjRFRKSUUGEpIiJSDnmbPNgRd5xMqwWAyVF/FhoX2GUYnH0m0xKzj4zdK12Wo4iIlB4qLEVERMohg8HAw806O9rzDm4lJj25QJw5pDa+EedWg01aOdkl+YmISOlSZgpLu93O5s2b+fDDD7n77rtp3749ERERdOjQgWHDhrFw4cILTt9JS0tj/Pjx9OnThxYtWtChQwdGjBjBunXrXPguREREXOe2Os2p4hsAgMVmZdquNYXG5VvEZ+NcrKlnXJKfiIiUHmWmsFy7di133303kydPZvPmzVSoUIHw8HDsdjt///03zz//PI8++ijZ2dkFzo2Pj6d///588cUXHD9+nPr16+Pl5cWKFSsYOnQoP/zwgxvekYiISMnyNHnwYJNOjva3e9aRkVPw56R/y9swBYQBYM/JJvnv712Wo4iIlA5lprC02+3UqFGDl156idWrV7Ns2TLmzp3LunXreO+99/D09GTFihV88sknBc596aWXOHToEBERESxbtox58+axYsUK3njjDex2O2+99Ra7du1yw7sSEREpWfeGt8PXwxOAxKx0Zu/fXCDG4GEmoPMDjnbSyslaxEdERPK57MJyzZo12Gw2Z+ZyRVq0aMHixYu5//77qVSpUr6+O++8k8cffxyAn376KV/eO3fuJDIyEqPRyPjx4wkLy/1E1mAwMGjQIO644w6sViuTJk1y3ZsRERFxkSAvXwY1vNbRnhz1FzZ7wZ/vgV0fcrzOPrGLzP2rXZKfiIiUDpddWD744IN07NiR//znPyxevJj09HRn5nXJ/P39MZvNRfZ36dIFgMTEROLj4x3HlyxZAkCHDh2oXbt2gfMGDRoEwMqVK93+HkVERErC8IjrMJC78uuh5DiWH91dIMazcn18m/Z0tJNWaBEfERE557ILy/DwcJKSkli4cCFPP/20Y7GbmTNnEhsb68wcnSIzM9Px2tvb2/F669atAFx77bX/PgXIHQn19PQkKytL02FFRKRMql2hEn1qN3W0vyxq65HzRi1TNvyENS2xpFMTEZFS4rILywULFhAZGclLL71Eu3btsNlsrFy5ktdee42uXbsycOBAvvrqKw4cOODMfC/bL7/8AkDjxo3x9/d3HD98+DAAtWrVKvQ8s9lM1apVATh06FDJJikiIuImIyK6OF6vPXWI7XHHCsT4tb4To3/u4yb27AxS1vzosvxEROTq5nElJ1erVo0hQ4YwZMgQkpOTWbFiBcuWLeOvv/5i+/bt7Nixg/Hjx1OrVi169uxJjx49aNOmDYazGy27yj///MOMGTMAeOSRR/L1JSUlARAYGFjk+Xl9yckF9/fKM2PGDGbNmlWsfK6WYltERCRPm8q1aBVak9TsLB5udj0Ng8IKxBjNXgReP5SExeMASFw5mcCeI13+c11ERK4+V1RYni8gIIDbb7+d22+/nezsbNauXcuyZcv4448/iI6O5ptvvmHKlClUrFiR7t2706NHD66//nq8vLyclUKh4uLieOKJJ8jJyeGGG27glltuydeflZUFcMHnMz09c1fLO3867b/FxsYSFRXlhIxFRERcz2Aw8E3P+6nk7YfRUPSEpsAuDzkKy+yj28k8tAGfeu1claaIiFylnFZYns/T05MuXbo4FszZvn07y5YtY/ny5Rw4cIA5c+Ywd+5cvL296dSpEz179qRnz54XHDW8HCkpKTz88MOcOHGCiIgI3n333QIxXl5eZGRkYLFYirxO3t6X5z+b+W+hoaFEREQUK68DBw5csEgVERFxh1CfCheN8azWGJ/wLmTsWQXkLuKjwlJEREqksPy3Fi1a0KJFC5555hmio6NZvnw5y5YtY+vWrSxfvpzIyEhOnDjBqFGjnHbPtLQ0hg8fzs6dO2nYsCFff/11vmcr8wQEBJCRkeGYEluYvL6AgIAiYwYPHszgwYOLlVu/fv00uikiIqVWYNeHHIVlyrqZhN79ESafon9GiohI2XfZi/dcrtq1azNs2DB+/PFH/vrrL9588026d++Oj4+P0+6RkZHBiBEj2Lp1K3Xq1HFMwS1MnTp1AIiOji6032KxcOLEiXyxIiIi5UFCVjpbY48WOO5/bX+Mfrk/V+1ZaaSs+cHVqYmIyFXG5YXl+YKDgxkwYACTJk3ioYceuvgJxZCVlcXIkSPZsGED1atXZ+rUqYSGhhYZ37JlSwA2bdpUaP/27duxWCx4eXnRpEkTp+QoIiJyNTuVnsxLaxbQbtY7jFzxIzk2a75+o6cPAdfd72gnRn6J3W53dZoiInIVcWth6WwWi4UnnniCNWvWEBYWxrRp0xxbhRSld+/eAKxbt67QUcuZM2cC0KVLF/z8/JyftIiIyFXGZrPx/Z51ZORYOJqawOLogo9vBHUf4XidfWwHmfv+dmWKIiJylSkzhaXVauXZZ59l5cqVhIaGMm3aNGrWrHnR8yIiIujevTtWq5Wnn36a06dPA2C325k5cyYLFizAaDQycuTIkn4LIiIiV4Vq/kHcWre5o/35P6sKjEh6Vg3Hp0kPRzsx8guX5SciIlcflyze4wq//fYbS5YsAXJXpf3vf/9bZOyYMWNo2rSpo/32229z9913ExUVRc+ePWnQoAEJCQmcPHkSg8HAf//732Kv+CoiIlIWjIjozIKD2wDYFneMv07up3O1hvligno8SsauSABSN84hJ3kcHgGVXZ6riIi4X5kpLPO2BAE4fvw4x48fLzI2JSUlXzs4OJg5c+YwefJkFi9ezP79+/H19aVLly489NBDdOjQocTyFhERuRq1CKlB12oNWXliHwATt68oUFj6t7odU1BVrIknsedkk7xqCsG3vuCOdEVExM0Mdj1t73J5241EREQwd+5cd6cjIiJSqNUnDzBw8WRHe+Gtj9E6tFa+mLh5rxG/YCwAHiF1qPv+XgxGk0vzFBER9yszz1iKiIiIc3WsUo825xWSn21fUSAmsOtwOFtI5sQdJm3HElelJyIiV5ESKSyzs7M5ePAgBw4cID09vSRuISIiIiXMYDAwqkU3R3vJkZ3sSYjJF2MOroF/q9sc7aTIz12VnoiIXEWc+oxldHQ077//PitXrsRqPbfnVZ06dejWrRsDBgygfv36zryliIiIlKCeNRvTuGIVdiecAuCzHSv4tMugfDGB3R8lddN8ANK2/4Yl9jDm0DquTVRERNzKaSOWhw8f5q677iIyMpKcnBzsdrvj69ChQ0ydOpXbbruNl19+mdTUVGfdVkREREqQ0WDk8ebdHO0FB7dxJCU+X4xv056YwxrkNux2klZORkREyhenFZbjx48nOTkZk8nEsGHD+O6775g3bx5fffUVTz75JE2bNsVmszFnzhzuvvtuYmNjnXVrERERKUG31W1O7QrB1KlQiXc63UmYb0C+foPRSFD3Rx3tpJVfY7NkuTpNERFxI6etCtuxY0cSExN59913ueOOOwqNWbt2LS+//DLHjh2jefPmzJw5E6Ox/K0fpFVhRUSktDmSEk91vyBMRfzctqbGc/DpmtgtmQBUefR7Ajrc7coURUTEjZxW1SUnJ+Pp6cltt91WZEyHDh2YNWsWNWvW5J9//mH27NnOur2IiIiUoFoVgossKgFM/sFUaDfQ0U6K/NIVaYmIyFXCaYWlt7c3np6eFx2BDA4O5qWXXsJutzNv3jxn3V5ERETcLLDnSMfrjL1/knXsHzdmIyIiruS0wrJmzZqkpqZy5MiRi8Z27twZT09P9u3b56zbi4iIiAslZKax9tTBfMe867bFq3ZrRzsx8gtXpyUiIm7itMKyb9++2O12xo0bd9FYk8mEt7c3BoPBWbcXERERF4jPTOP19YtoP/s9Hon8gYycbEefwWAgqMcIRztl9ffYMrUSvIhIeeC0wvLee++lUaNGLFmyhNdeew2LxVJk7IEDB0hOTqZBgwbOur2IiIi4yPd71pGek018VhrT927I11ehw90YfQIBsGWmkLzmB3ekKCIiLua0wtLDw4NPPvmEgIAAZs6cyc0338x3333H8ePH88Xt37+fZ555BoAHH3zQWbcXERERFwj29uPeRu0c7S/+WUW2NcfRNnr5EXD9/Y524vJJOGkBehERuYo5da+PunXrMn36dBo0aMDRo0d5++236dWrFx06dOC2226jR48e3HbbbZw8eZK3336b3r17O/P2IiIi4gKPNOuC2WgC4ERaErP3b87Xf/6eltnH/iFj1x8uzU9ERFzP6ZtI1qtXj3nz5vH666/TuHFj7HY7iYmJ7Nu3jxMnTmC320lJSWH8+PE8+uijfPLJJyxdupRjx445OxUREREpAdX8ArmrQRtH+5Nty8k6b9TSs1pjfJvd6GgnLP3UpfmJiIjrGewlPD/l9OnTbN68maioKHbu3ElUVBSJiYnnEjhvAZ8KFSrQpEkTpk2bVpIpuV2/fv2IiooiIiKCuXPnujsdERGRS3YsNYHOcz7EYrMC8FaHOxjapKOjP237bxwfd2tuw2Cgznt78Kxc3x2pioiIC3iU9A0qV65Mnz596NOnj+PYiRMn2LlzJ//88w87d+5k586dxMXFkZyczPr160s6JREREblCNfwrck+jtkzbvRaAT7f/wcCG1+LjYQbAt1lvzFXCsZzaA3Y7iUsnUvne8e5MWURESlCJF5aFqVatGtWqVaNXr16OYzExMY4RTREREbn6jWrRnRn7NpJlzSEmPZkf9qxjeMT1ABiMRire8ASnvxsFQPKfU6jU73VMPgHuTFlEREqI05+xvFxhYWF0796dUaNGuTsVERERKYaqfoHcF97e0f5sx4p8+1oGXDcEo28QcHbrkT+nujZBERFxmaumsBQREZHS5/Hm3fA25U5/jc1IZda+TY4+o7c/gV0ecrQTl03EfvaZTBERKVtUWIqIiMhlq+xbgaFNOlLDP4gPr+vPPeHt8vUH9XocDLm/blhOHyBt2y/uSFNEREqYCksRERG5Is+07Mmqfs8xuFFbx/6WecwhtfFvc6ejnfD7BBdnJyIirqDCUkRERK6In9kLT1PR6wEG3fCk43XGrkiyjm53RVoiIuJCKixFRESkRPk0uh6v2q0d7YSlGrUUESlrVFiKiIiIU8VnpvHepiUkZKUDYDAYqHjjE47+lNU/kJMc6670RESkBKiwFBEREaeZtGMlHWe/x4Ttf/DVP386jvu3G4QpIAwAe04WSSsmuytFEREpASosRURExGks1hzSzu5l+fXOv4nPTAPAaPYiqMejjrjEyEnYz9vzUkRESjcVliIiIuI0D0VcT0UvXwDSc7L5bMdKR19g9xEYPDwBsCaeJGXDT27JUUREnE+FpYiIiDiNv9mLkc27OtpTd63mWGoCAB6BYVRoP9jRl/D7p9jtdpfnKCIizqfCUkRERJzqgcYdCfMNACDLmsN7m5Y4+oJuPLf1SNahDWTuX+Py/ERExPlUWIqIiIhT+Zo9+U/rGx3teQe3sjX2KADetVvhE97F0Rf/6wcuz09ERJxPhaWIiIg43YD6rWlSsYqjPXbDr45prxVvetZxPG3LQrKOR7k8PxERcS4VliIiIuJ0JqORV9rd4miviznE70d2AuB3zS141mjm6EvQqKWISKmnwlJERERKROdqDelePdzRfmvjb1hsVgwGA8E3/8dxPHntdCxx0e5IUUREnESFpYiIiJSYl9vejNFgAMAOHE9NBKBC+0F4hNTJDbLmkLBkvFvyExER51BhKSIiIiUmvGIYI5t15Y32txHZ92nqBFQCwGDyILjPuWctk1b+D2tKnLvSFBGRK6TCUkRERErU6Gv7MKzpdZiNpnzHAzo/gKlCKAD27AwSlk5wR3oiIuIEKixFRETELYxevgTd+JSjnbj8M2wZKW7MSERELpcKSxEREXG5hKx0AIJ6jMToXQEAW1oCSSv/5860RETkMqmwFBEREZdJyc7k3U2LaTvzHTbHHsHkF0Rg9xGO/vjF47BZstyYoYiIXA4VliIiIuIyI/74gYnbV5BptfDmhl+x2+1UvPEpDB6eAFgTT5Cy+ns3ZykiIpdKhaWIiIi4zOPNuzper485zKLDO/CoWI2A6+53HI//7UPsNqs70hMRkcukwlJERERc5rpqDehZo7Gj/dq6n0nJzqTizc+DIffXEsupvaRumu+mDEVE5HKosBQRERGXeq39rXiZPACIyUjhg82/4xnWAP+2/R0x8b++j91ud1eKIiJyiVRYioiIiEvVDQhhVItujvbU3WvYEXec4Jv/4ziWdWgjGbsi3ZCdiIhcDhWWIiIi4nKPNe9GvYAQAGx2Oy+umYe5Vkt8m93giDnz87vuSk9ERC6RCksRERFxOS+TB293vNPR3hZ3jB/2rif4lhccxzJ2RZK+e6UbshMRkUulwlJERETc4vpqDbij3jWO9rubFpNauw0+jTo7jp2Z+6qetRQRKQVUWIqIiIjbvNr2VgI8vQFIzs5k1v5NVOr/hqM/Y++fpEctdVd6IiJSTCosRURExG0q+1bgP617E+Ltz6ddBvF48274hnfBN6KXIyZuzisatRQRucqpsBQRERG3GhLenpX9nqVf/VYYDAYAKvU7N2qZdWgDaVsWuis9EREpBhWWIiIi4lYmo5FAL598x3zqt8ev5a2OdtzcV7HbbK5OTUREikmFpYiIiFyVAu98xfE6+9gOUjfMdmM2IiJyISosRURE5KpyJjOVZ/6czYg9W/FvO8BxPG7e69itOW7MTEREiuLh7gScJTY2lr///pt//vmHHTt2sGvXLrKysmjXrh3ffffdBc+1WCxMmzaNhQsXcuTIEcxmM40bN2bIkCHceOONLnoHIiIicjQlnj4LJ5CUnQHA39f05ZqNc8Fuw3JqD8lrfiDw+qFuzlJERP6tzBSWv/zyC++8884ln5eVlcWDDz7Ipk2bMJlMNGjQgIyMDNavX8/69et5+OGHee6550ogYxEREfm3Gv4VubZybZYf2w3Afw9F8WvbAdjXzwIgfsFYAjrcjcHD051piojIv5SZqbD+/v506tSJESNGMHHiRB577LFinffBBx+wadMmatSowaJFi1i4cCFLly5l0qRJeHp6MnnyZCIjI0s4exEREQEwGAx8cF1/Knr5ApCRY+Gd4MZgyv0s3BJ7iKQ/p7gzRRERKUSZKSwHDBjAlClTeOaZZ7jhhhuoVKnSRc+Ji4tjxowZALz11lvUq1fP0dezZ0+GDx8OwMSJE0smaRERESmgsm8F3unU19H+PSODo017O9rxC9/Clp3pjtRERKQIZaawvByRkZFYLBbq1KlDhw4dCvQPHjwYgKioKI4cOeLq9ERERMqtW+s0p1/9Vo72aP/a2E25019zEo6TtOIrd6UmIiKFKNeF5datWwFo06ZNof1hYWHUqFEjX6yIiIi4xtj2t1PFNwCAE55+rKjd3tEXv+gdbFlp7kpNRET+pVwXlocPHwagVq1aRcbk9R06dMgVKYmIiMhZgV4+jLv+Lkd7Ylgzcjy8ALAmnyZh8Xh3pSYiIv9SZlaFvRxJSUkABAYGFhmT15ecnHzBa82YMYNZs2YV674HDhwoZoYiIiLlW5fqDXmgSUem7lpDgqc/s6u25O6j6wCI/+U9Ajo/gDm4hpuzFBGRcl1YZmVlAWA2m4uM8fTMfZ4jM/PCiwTExsYSFRXlvOREREQEgJeuvYlVx/dxMDmOH2u1p3/cHjwzErFnpxM3ezRVR1x4v2oRESl55bqw9PLKnU5jsViKjMnOzgbA29v7gtcKDQ0lIiKiWPc9cODARQtVERERyeXj4cnHXQbyxMoZvH9df2o2iiBmyggAUtb8SFCPkfg07OTmLEVEyrdyXVgGBOQuCJA3JbYweX15sUUZPHiwYxXZi+nXr59GN0VERC5B69BarOj3LGajCXtYHRIjvyAregsAp3/4P2q9shaDsVwvHSEi4lbl+l/gOnXqABAdHV1kTN42I3mxIiIi4h5mowkAg9FE6D3nFu7JOryJ5L+nuSstERGhnBeWLVu2BGDz5s2F9sfExHDs2LF8sfL/7d15eBRVugbwt3pLZw8xkIWwk7A5gOwoMoTgCIqCgoACDoJGkSsqyIiijojM4Cgo4EUZvMoyOoDsIItAAFFEEMIWEpBAIAkJJGQh6U56rftH6CI7vdLp9Pt7njydOnXq9Nd1ctL9dZ2qIiIicj+/dg/Cv9ftK8bmfT8LptK6L7RHRESu49WJZXx8PJRKJdLT03H48OFq61evXg0A6NixI1q0aHG3wyMiIqJa5JYW4+9NuqJMVn5Wj+nmNeRvmevmqIiIvJdXJ5ZhYWEYPXo0AGDWrFm4ePGitC4xMRFfffUVAGDKlCluiY+IiIhqtiL1MHYWF2J1s15SWcGPC6HP+cONURERea8Gc/Ge7OxsDB8+XFq2XM31+PHj6N27t1T+/PPP44UXXpCWZ8yYgeTkZCQlJWHo0KGIiYmBVquVzq2cOHEiBg0adHdeBBEREVnl1S4D8cvVC1hjMmBIzmmE64oBkwG5q99A09c2uzs8IiKv02COWJpMJhQWFko/Wq0WAGA0GiuVV73Nh1qtxsqVKzF9+nS0adMG6enpKCgoQK9evbBo0SK8+eab7ng5REREVAelTI4v4sYiKKARlrYeIJVrTmyD5vQu9wVGROSlBFEURXcH4W0stxvp1KkTNmzY4O5wiIiIPNavORcxZscyzE/6Fp1vZgEAlJEd0HJOEgSF0s3RERF5jwZzxJKIiIi8T9+I1nirxxB83jYe5ltlhuwUFCZ+4da4iIi8DRNLIiIi8mgv3vsgYu+Nx/bIzlLZtQ3vwVhw1Y1RERF5FyaWRERE5NEEQcD8fiOR2Hk4SuQqAICsrBhpy54Dz/ghIro7mFgSERGRxwtUqTF/8Ev4OiZeKhPO7kHxr9+6MSoiIu/BxJKIiIgahHaNwvHYmHk41qiFVHb929dgLMxxY1RERN6BiSURERE1GA+3vBe9p66DoA4AAJg1Bbi28mVOiSUicjEmlkRERNSgtG7VDY1HzZOWNcc3o/i3NW6MiIio4WNiSURERA1O8IAX4dshTlrOWPEyFvy02o0RERE1bEwsiYiIqMERZDKEP/dvCCo/AICytAgBWz/AN2cPuTkyIqKGiYklERERNUiqJq0RPOJDaXlA7jns2fYJdlw+48aoiIgaJiaWRERE1GA1fugVyNveLy1P/WM33t79NQ7nXHRjVEREDQ8TSyIiImqwBJkMzV74BlCqAQCNDFoknP8R43d/g1+y09wcHRFRw8HEkoiIiBo0VXhbNB75D2k5/noKemSfwV93L8fBq3+4MTIiooaDiSURERE1eCEP/Q/UMQ9IyzPO7UJoyXVM2LMC+7POuzEyIqKGgYklERERNXiCTI6ISf8HQR0AAAgw6fB+8maI+lJM3LMCezNS3RwhEZFnY2JJREREXkEVEYOIiV9Jy201uZh6YS/0ZhMWnUyEWTS7MToiIs/GxJKIiIi8RmCvpxDy0FRp+ZGc05h08zKWPzQBMoEfi4iI7MX/oERERORVGo/+COoKtyAZe3ozfHPOuTEiIiLPx8SSiIiIvIqgUCFyymrIAxuXFxh1yP58FEyaAqlORnE+RFF0U4RERJ6HiSURERF5HWWjpoic/C1wa/qrIfcicpZNgGg2I7UgBw9tXoi3ft0Eg9nk5kiJiDwDE0siIiLySn4d43HPE7OlZc2Jbcjc8iEm7FmOEoMO/zn3G/66ezmKdKVujJKIyDMwsSQiIiKvFTp0Jvy7PCItl26eg77FOdLyT1f/wPAfvsDl4hvuCI+IyGMwsSQiIiKvJchkiEhYCUVYy/IC0YwpSavx7D0RUp0/iq7jsa1LcPRaultiJCLyBEwsiYiIyKvJ/Rsh6n/WQlCoAADm4ly8cPB/8WbMfVKdfJ0Go3cuw4a0JHeFSURUrzGxJCIiIq+nbtkd4c8tlZYNuRfx+K6P8HmvR6CSyQEAerMJU39agzlHfoDeZHRXqERE9RITSyIiIiIAQQ88i7DR/5KWdRkn0X3L37EmfjxCffyl8qXJB/HE9i+RUZzvjjCJiOolJpZEREREt4QOmY5GQ96QlkvP/YSo9TOx5ZEEtAsJl8qvFOdDcetIJhERMbEkIiIiqiRs1DwEPThBWtYkbYV6/dvYOvRljI3tBQD49MGnEOkf7KYIiYjqH4W7AyAiIiKqTwRBQPiEpTCV3IAmaSsA4ObB5ZAHNsZHo+bh2fZ90OmeqGrblRkNUCuUdztcIqJ6gUcsiYiIiKoQ5ApETv4vfGMflMoKtn+M/B3za0wqb5SVIG7jAiw98xNMZvPdDJWIqF5gYklERERUA5nKF1GvboJPsy5SWd6av6Fw39JK9URRxLSD65BRUoA5R7fj8R+W4MyNrLsdLhGRWzGxJCIiIqqF3D8ETadvh7Jxa6ns+oqXcWPzhxBFEQCQrSnCmfyr0vqTeZl4ZOvneP+3rSgx6O56zERE7sDEkoiIiKgOipAINJ2xE/KQ21Ngb2z8O66v+h+IZhOiAkKwe9ireLLNfdJ6syjiq7O/IG7DAuy8nOyOsImI7iomlkRERER3oGrSBs3f+RnKiHZSWVHil8j+39Ew68sQqvbHov6j8d+HJ6FVUJhUJ1tbhOcTV2HinhXIKil0Q+RERHcHE0siIiIiKyjDWqD5rJ+gbt1bKis5thFZnwyGSVMIAHgwKga7h72K17rGQ1XhPpc/ZqQgbuMCLDqZCKPZdLdDJyJyOSaWRERERFaSB4Yh+s3d8O/yiFRWev4gMv75ZxgKyi/Yo1Yo8cZ9D+HHYa+iT0QrqZ7WqMe+zHOQC/z4RUQND/+zEREREdlA5uOPqFc2IOjBCVKZPvMMMuY8AN3VFKmsbUgTfD84AZ/2ewqNfPwAADO7D4YgCHc7ZCIil2NiSURERGQjQaFE+MSvEPrY21KZMT8DGXMfRMnxzbfrCQKeiumOgyPewCcPjEDvCkcwLeYn7caPV85KV5klIvJETCyJiIiI7CAIAsJGzEHjcYuAW0chzZoCXF30JK6tegVmfalUN8THD2Nie1ZrIyU/B5+dSMTEvSvx+A9LsOPyGZjM5rv2GoiInIWJJREREZEDGg2agsiXV0Pw8ZfKivYuwZUP+kKXdbbObT9J+hEiyo9UJuVm4IXE/6D/hvn45uwhaA16l8ZNRORMTCyJiIiIHBTYcyRavH8UPi1u38tSn3kaV2b3QuH+ZbVOc51+3yDER7evVHa5+Abe/W0Leq79J+Yd24kc7U2Xxk5E5AyCyAn9d92TTz6J5ORkdOrUCRs2bHB3OEREROQkZoMOed+/hcIfF1YqD+g5EuETlkLuH1Ljdkm5Gfj3mYP44fJpmKt8NFPK5BjWqgue63g/uoRFuyp0IiKH8IglERERkZPIlD5o8swCRL22BfLAMKm85Og6XH6vG7RnE2vc7r7GzfBF3DP4ZcQMPN/xAfgrVNI6g9mEdWnH8VXyzy6Pn4jIXkwsiYiIiJwsoOujaPFBEnw7DJTKjDcuI/NfD+Hq4pEw5F6qcbtmgaF4v/djODLqLbzT4xFE+gVL60bH9KhWP6ukEHqT0fkvgIjIRpwK6wacCktEROQdRLMJBds/Qd6GdwGzSSoXFD5oNHgaQofOhEwdUOv2BrMJ2y6dxq4ryVgy4GnIhMrHBIb/8AUuFuXhiTZdMbTln9C9SfNqdYiI7gYmlm7AxJKIiMi7lF36Hdf/8yrK0g5XKpeHRKLxU/9AYN9xEGS2JYRpRbn484b5lcoa+wbg4eadMLhFJ9wf0RoqucLh2ImIrMGvtIiIiIhcTN2qB5q98zMiElZCHhIllZsKs5Gz7DlkfPgASi/8alObp29kwadK4phbWoL/nPsN4378Gl1Xf4hXDqzG9vQz0Bh0TnkdRES14RFLN+ARSyIiIu9lLitB/g//QsHO+RANZZXW+bYfgEYPvwb/Lo9adQSzSFeKrZdOYfvlMziUnQajaK6xnkKQYc3gF9A7opVTXgMRUVVMLN2AiSUREREZctORu/ZNlBxdV22dMiIWjf7yKoIeeBYyHz+r2ivQabE3IwU7Lidjf9Z56Cpc1EcuyHDmmfcQqFJX2mbLxZPoGBqJNsGNIQiCYy+IiLwaE0s3YGJJREREFtpzPyFvzUyUXfyt2jpZwD0IiXsJIfEvQxESYX2bBj32ZZ3DzsvJSMxMRUxIODY9OrlSnQKdFp2/mwMRIkJ9/NGtSTP0aNIC3Ro3R9ewZvBTqmppnYioOiaWbsDEkoiIiCoSRRFlF35Fwc4FKDm+Cajy8UxQqOB/3+MI7DUK/l0egUzla3XbRrMJN8o0CPcLqlT+Q/ppvLjv2xq3kQsydAyNRPcm5Ulmp9AotA1pDKVMbvNrIyLvwEuFEREREbmZIAjwjbkfvjH3Q389DYU/LkLRwW8g6jQAANGoR8nRdSg5ug6COgABXR9DYO/R8Lv3L5ApfepsWyGTV0sqAUAE0L5RBFILcqqtM4lmnL6RhdM3sgCUX1Ro1UPPIS66XaV6BrOJySYRAeARS7fgEUsiIiK6E5OmAEX7l6Fg92KYCq/WWEfmG4yAbsMQ0HMk/Nr/uc57YtamUKdFUm4Gfr9+GceuX0FS7hVojPpq9Y6PnoUmfoGVyh7a9BkKdaVoG9IYbYOboG1wY7QNbow2IU0Q7hvI8zaJvAgTywoOHz6Mb775BidPnoRWq0VUVBQGDx6MhIQE+PlZd+K8NZhYEhERkbVEox6a07tQfGQtSpK2QCwrqbmiTA51q57w6zAAvu0HwDfmfsh8/G1+PpPZjHOFOTh2/QqOXb+C5PyrKNSV4ujotyrVKzMa0P4/f6/1SrSBSh+0CgpD88BQNA8MRf+oGPSLamtzPETkGZhY3rJq1SrMnTsXoigiIiICoaGhuHDhAvR6Pdq0aYPvvvsOISEhTnkuJpZERERkD7O+FJqT21F8ZC00J3+AqC+tvbJcCXXrXvBr/2eo2/SGT7MuUIRG23UU0WQ2Q17l9ien87IwZOtiq9v4n84DMLP74EplG9NOYE9GCiL8gxHpF4RI/+DyH79gNPENrPacRFR/8RxLAGfOnME//vEPAMAHH3yAUaNGQRAEXLt2DZMnT0ZycjLeffddLF5s/T9PIiIiImeTqXwR2HMEAnuOgLmsBCUntqHk6DpozybCXFpUubLJgLI/fkHZH7/c3t4/FD7NOsOneRfpURXV8Y7nadaU4HW6JxK/jvwb0m7m4ULhdVwoysWFoutIK8pFbmn1o6rNAkKrlR3LvYzNl07W/FoFAaE+/mjsG4DGvoEI8w3Ap/2eqhSLKIq4dPMGQnx8EaRSQ8HzPYnchoklgCVLlsBsNmP48OEYPXq0VB4eHo4FCxZgyJAh+PHHH5Gamor27du7MVIiIiKicjJ1AIL6jEFQnzEQzSborpyENmU/SlP3ofTcQZjLiqttY9bkozR1P0pT998uFGRQhEZD2bg1lI1blj+GtYSySWsow1pBHtQEQg2JpUyQoVlgKJoFhmJA09hK6wp0WlwsykV6cT4yivNxpTgff7onqlob2ZqiamVSrKKIvLIS5JWVIKUgB4FKn2oJbrFBh/4bPpGWg1RqhKj8EOLjixCf8sdg1e3f+0S0Rpew6EptlBh0MIsi/BUqHiElcoDXJ5YajQYHDx4EAIwaNara+pYtW6JPnz44dOgQdu7cycSSiIiI6h1BJoe6ZTeoW3YDhkyDaDJCdzkJ2tQDKD1/ELorJ2HMz6h5Y9EM440rMN64gtLUGtbLFVAEhUMeFA5FcDjkweFQBEdAHhQOeVATyP0bQeYXArlfCGS3fhr5+KF7kxbo3qRFnXGPiemBe++JQrbmJrI1RcjWFiFbU4SiGqb4hvkGVisr1GkrLd/Ul+GmvgxXajkN9a3ug6sllp8m7cHS5PLPgmq5Ev5KFfwVPuWPSp/yH4VKWp7T+/FK04lFUcT6tOPwkSuhlivKHxVK+MgVUMtvPUrLCqhkCiaw1CB5fWKZkpICvV4PlUqFzp0711ine/fuOHToEE6erHmqBhEREVF9IsgVULfuCXXrnsAjbwAATCX50GWcgu7KSegyTkKXcQr6rGSINVwBthKTEcaCLBgLsqCz9vmVasj8G0HmEwBB5QuZ0heCyheCSn37d6Uvuqh80fXW7zKVLwR/P8gaNYVBpkSR2YybJgOKDAYUGfWQK1TQpuyHIFcAMgUEuQLFxQVoo7kBvQCYBJn0I0KAWQAAASIAEQJEQcA9ZgNMmgIAAiAIEAQZdKVFUJv0ECHAZDbipqEURZbtBKF8WwAQBChlcnzYZ1il12owm/Dawe+t7pvJ9/4Zs3oOqVT29dlf8L+nD0Alk0Mhk996lEEpU0Apk0Epk0N5a51SJscn/UYgxKfyhSX/dWwXykwGyAQZ5IIMckGATCaDDALkggC5THZrnYCYkHAMrHLrmMvFN3D0WjqEW3XkggyyW49yQYCsynLnsGgEqtSV2kgtyEGZ0QBBECAAECBAEG4/AgJkggABAvwUSjQLrDw9utRowDXtTWkbS13h1v4XgNtlAhCk8oWPvHI6ozXoYRTN1eriVjtSbLceq06fFkUR5qr3ka1yWvKtiCqs59WPASaWuHTpEgAgKioKSqWyxjrNmzevVJeIiIjI08gDQuHXYQD8OgyQykSjAYa8dBjyLsFw/SIMuZdu/+RdgllTYNdziYYymAqzYXIwZv9bP5ZJtJlV1isBLLOlwUOfI61K0bO3fqx1fv+/yjMNoTx1giBgh9ksJbB3ojy0GH98Ja+UrfQxmdDNbKyxfk1t5n7/CvKqlD1k1MO6a3IKUMrluCCv/LnXYDahtdFw+3nv8FKuKn0gF24deb31Wkr1ZTDVEUPFNaUyOXRVzu01imYU68sAK/YjAASo1FDJKyeGxfoy6M3W/eXJBAGNqiToJlGsdiS8Ln4KFXwVlfelxqBHmclQyxbVhfr4V0teb5Rpaq1fsWrPZbUcnncDr08si4rK5/YHBwfXWseyzlK3JqtXr8batWutes60tKr/0oiIiIjuPkGhhCoiBqqImBrXmzSFMBZlw1R0Daab12Cs+FiUA2NxHszaQpi1hTBpCwBTzclRgyKaK2dIAOq+9FEVZkPVzaGAbR/KRZO+Whs23VjGBFS9SYwcgE13QTXqqrVh681tzPrKyZMMQO2fyGuKobTalxd+t36sZdJXTyJDbInBUD0G9a0fa5kN1ad+2xRDPeH1iaVOVz6po7ajlQCgUqkq1a1Jbm4ukpOTnRscERERkRvJ/UMg9w8Bojrcsa4oihD1WphuJZpmTQHMOg1EfSnMhlKI+lKI+jLpd7NeW15mKIVZX1rh9zKIhlKIBh1gNkE0GyGajECVR9FsBKo+Wnmkioicz+sTSx+f8u+YDIbaD1fr9fpKdWvSuHFjdOrUyarnTEtLQ1lZmQ1REhEREdVvgiBA8PGHzMcfaNTULTGIoliejJqMAERAFG8/iiJE0VylzHxr+ujtOhDNECsuQwTMZgDi7bq3lqV2Id46kmnl7eGtvo28c9uz/vb1Ntzm3oWvpeL5juV7Xix/vNWXCpkcCqHyhZBKjXoYbvWFVPdWsyIsbZU/ChBwj7rycVajaEZBhWmoVaMWq5QEKNXwV6gqld3Ul0JT4dzlqruoahtRfsHVztPMKimoUB93/L0+8PrE0ppprtZMlx0zZgzGjBlj1XM++eSTPLpJRERE5GSCIADy8gv7kHeyZQpqbWyaEuyiGNo4oY27zeuvddyyZUsAwNWrV2s9annlypVKdYmIiIiIiOg2r08sO3ToAKVSCb1ej1OnTtVY59ixYwCArl273sXIiIiIiIiIPIPXJ5YBAQHo168fANR4Vdf09HQcPnwYADB48OC7GhsREREREZEn8PrEEgBefvllCIKAzZs3Y82aNdKJzdevX8e0adNgNpsxaNAgtG/f3s2REhERERER1T+CaP3loRq05cuXY968eRBFEZGRkWjUqBEuXLgAvV6PVq1a4bvvvkNoaKhTnsty8Z5OnTphw4YNTmmTiIiIiIjIXXjJrFsmTJiAdu3a4euvv8apU6dw48YNREVFYfDgwUhISIC/v623fCUiIiIiIvIOTCwr6Nu3L/r27evuMIiIiIiIiDwKz7EkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIoco3B2AN2rVqlWlRyIiIiIiIk8miKIoujsIIiIiIiIi8lycCktEREREREQOYWJJREREREREDmFiSURERERERA5hYklEREREREQOYWJJREREREREDmFiSURERERERA5hYklEREREREQOYWJJREREREREDmFiSURERERERA5hYklEREREREQOUbg7ALLe9OnTcenSJXeHQURERERE9USrVq0wf/58d4fBxNKTHDt2DNnZ2e4Og4iIiIiI6on8/Hx3hwCAiaVH0Wq1AAC1Wo02bdq4ORrvlJaWhrKyMvaBG7EP3I994H7sA/djH7gX97/7sQ/cz9IHlhzB3ZhYepDo6GgUFRWhTZs22LBhg7vD8UpPPvkkkpOT2QduxD5wP/aB+7EP3I994F7c/+7HPnA/Sx9ER0e7OxQAvHgPEREREREROYiJJRERERERETmEiSURERERERE5hIklEREREREROYSJJRERERERETmEiSURERERERE5hIklEREREREROYSJJRERERERETmEiSURERERERE5hIklEREREREROUTh7gDIeqNGjUJubi4aN27s7lC8FvvA/dgH7sc+cD/2gfuxD9yL+9/92AfuV9/6QBBFUXR3EEREREREROS5OBWWiIiIiIiIHMLEkoiIiIiIiBzCxJKIiIiIiIgcwsSSiIiIiIiIHMKrwt4lBw4cQEJCAgCgadOmSExMrLGeRqPBv//9b+zatQtXr16Fn58funTpgokTJ6J37952P/+NGzfwxRdfYN++fbh+/TqCgoLQs2dPvPjii+jQoYPd7XqSO/VBQUEB9uzZg0OHDiE5ORnZ2dmQyWSIjIxEv379MGHCBERHR9v8vJmZmYiPj6+zTpcuXbB27Vqb2/Y01oyDdu3a1dlGWFgYfvnlF7ue31Xjy1Pcaf/PnDkTGzdutKqtxMRENG3a1OrndmW/1meLFy/G559/Xmed999/H08//XS1coPBgBUrVmDLli24cuUKlEol2rdvj/Hjx+Mvf/mL3TF52ziwpw9KSkqwb98+/Pzzzzh9+jSysrJgNpsRHh6OXr16YcKECYiNjbUrHm8cC/aOg4EDByIrK6vO7U6dOgUfHx+bY3LV+KqP7Nn/1mxjsWrVKvTq1cvqeFzZr57gwIED+P7773HixAkUFhYiODgYzZo1Q+/evfHKK69AoaicnnnSewETy7tAo9Hg/fffv2O9/Px8PPPMM7h06RJUKhXatm2L/Px87N+/HwcOHMC7776LsWPH2vz8ly9fxjPPPIO8vDz4+fkhJiYGOTk52LFjB/bs2YOFCxfeMfHxdNb0wZQpU3Ds2DEAgL+/P9q0aYOysjJkZGRg1apVWL9+PRYuXIj+/fvbHUe3bt1qLI+JibG7TU9h7TiwuPfee6FSqaqVh4SE2PX8rhpfnsKa/d+yZcta/0YBID09Hfn5+YiMjERkZKRdcTi7Xz3FPffcgxYtWtS4rqbLxOt0Ojz33HM4duwY5HI52rZti9LSUhw5cgRHjhzBCy+8gDfeeMPmOLx5HNjSB7Nnz8aWLVsAAGq1Gi1atIAoikhPT8f69euxZcsWzJ49GyNGjLA7Hm8cC7aOA4vY2FgEBATUuE4QBJvjcNX4qu9s2f+RkZF1vh9kZ2cjOzsbarUaHTt2tCseZ/drfWc0GvHWW29J/1siIyPRvn17FBYW4syZM0hKSkJCQkKlxNLj3gtEcrk5c+aIsbGx4uTJk8XY2FgxLi6uxnovvfSSGBsbKz7xxBNiTk6OKIqiaDabxdWrV4uxsbFihw4dxLNnz9r03GazWRw2bJgYGxsrTpo0Sbx586YoiqJoMBjEzz77TIyNjRW7du0qXrt2zbEXWc9Z0wfjxo0Tp02bJh45ckQ0Go1SeXZ2tvjcc8+JsbGxYrdu3cTc3FybnjsjI0OMjY0VY2NjHX4dnszacWDZVxkZGU59fleML09i7f6vjdlsFuPj48XY2FhxwYIFNj+/q/q1vlu0aJEYGxsrvvnmmzZtZ+mvgQMHimlpaVL5nj17xHvvvVeMjY0V9+7da3M83jgO7OmDN954Q0xISBD3798v6nQ6qbygoECcNm2atK9SU1Ntjscbx4K94yAuLk6MjY0VDx8+7NR4XDW+6it7939dxo0bJ8bGxorTp0+3eVtX9Wt9N2vWLDE2NlYcMWKEmJycXGmdVqsV9+zZI+r1+krlnvZewHMsXezEiRP49ttvER8fj0GDBtVa7+zZs0hMTIRMJsOnn36K8PBwAOXf2IwePRrDhg2DyWTCkiVLbHr+vXv3IiUlBYGBgZg/fz4CAwMBAAqFAq+++ip69uwJrVaLr7/+2v4XWc9Z2weLFi3C/Pnz0bNnT8jlcqk8IiICCxcuRGhoKEpKSrBt27a7EXaDYm0fuIqrxpencMb+P3r0KDIyMgAATz75pDPDoyry8vKwevVqAMDcuXPRunVraV18fDyef/55ALB6mpqFt48DW7z99ttYunQp/vznP1c6qhgSEoJ58+YhJiYGJpMJ69atc2OUZA9XjS9vkpmZiaNHjwLg+4G1Dh8+jO+//x5NmzbF8uXLqx3l9fX1RXx8PJRKpVTmie8FTCxdyGAw4N1334VarcZ7771XZ91du3YBAPr06VPjNIXRo0cDKJ+XrdVqrY5hx44dAIDBgwcjODi42vpRo0ZVqtfQ2NIHjRo1qnVdYGAgunbtCgC4dOmSM0Ns8GzpA1dx1fjyBM7a/5ZzL7t161brVCpyjsTERBgMBrRs2RJ9+vSptn7MmDEAgOTkZFy5csXqdr15HNiqrvcDpVIp9QvfDzyPq8aXN9m0aRNEUURkZGSN+5Cq++abbwAAEydOrHX6b1We+F7AcyxdaOnSpTh//jzeeustRERE1Fn3xIkTAIAePXrUuL5z585QqVTQ6XRISUlB9+7drYrh5MmTdbZrKc/JycG1a9ekby0aClv64E50Oh2A8m+V7PXhhx/i4sWLEAQBTZs2Rb9+/TBo0CDIZA33Ox57+2DJkiW4fv06TCYTwsPD0adPHzzyyCM1npN0J64aX57AGWNAq9Vi586dABz/dtqZ/epJUlNTMX36dOTm5sLf3x/t2rXDo48+WuP51Za/19r+DsPDwxEdHY3MzEycOHECzZs3tyoGbx4HgG19cCfOeD/wxrFgbx+sXr0aX3/9NcrKyhAWFoYePXrgscces/oDekWuGl+ewBljQBRFbNq0CQAwfPhwhz6/OLNf6zOdTiddkKtv3764cOEC1qxZg7S0NKhUKnTo0AEjR46sdkE8T3wvYGLpImlpaVi6dCk6deqE8ePH37F+eno6ANT6R6FUKhEZGYnLly/j0qVLVnWyXq+XrrpVW7uRkZFQKpUwGAy4ePFig0osbe2Duly7dg1HjhwBUPtAtMaqVasqLa9ZswYdOnTA4sWL0axZM4dirI8c6YP169dXWt64cSMWLVqExYsXo1OnTja15Yrx5QmcNQZ27doFrVYLX19fDBkyxKGYnNmvniQlJQUpKSnScmJiIr788ks8++yzePPNNytNv7/T36tlXWZmpk1HzLx1HFjY0gd1KS0txd69ewHU/oHPGt44Fuztg+3bt1da3rZtGxYuXIj58+fjgQcesCkGV40vT+CMMeDM0yKc2a/1WWpqKgwGAwDg2LFj+OCDD6RlANi3bx+++uor/POf/8TQoUOlck98L2Bi6QKiKOKdd96B0WjE7NmzrRqoRUVFAFDjdFULy7qbN29aFUdJSQnMZnOd7QqCgKCgINy4ccPqdj2BPX1Qlzlz5sBgMKBt27aIi4uzaVuFQoHHH38cjz76KNq2bYsmTZqgoKAABw4cwGeffYaUlBRMmjQJGzZsaFDf0tnbB/Hx8Rg2bBjat2+PiIgIaDQa/Prrr/j000+RkZGBiRMnYtOmTTZdldQV46u+c+YY2LBhAwDgoYcesvtv1BX96gmaNGmCqVOn4sEHH0R0dDQCAgJw6dIlfPfdd1i9ejVWrFgBhUKBv/3tb9I2rvp79cZxANjXB3X59NNPcePGDYSGhmLkyJE2x+ONY8HePujVqxf69OmDP/3pT4iKioLBYMCxY8ewaNEinD17FpMnT8Z///tfmxJxbxwHzhwDlveD7t2723001xX9Wp/l5uZKv3/wwQfo2LEj3nnnHbRv3x7Z2dn49NNPsWPHDsycOROtW7eWzr/0xPeChjv/zo2+++47HD9+HGPHjsWf/vQnq7axTKupeNJuVZbpMWVlZTa1WXFbZ7TrCezpg9r8+9//xu7du6FUKjFv3jybP6BHRETg448/xoABAxAdHQ2VSoXw8HCMGjUK//3vfxEQEIDLly9j5cqVDsVZ39jbB0uWLMHDDz+MFi1awMfHB6GhoXj00Uexdu1aREVFobCw0OYT1V0xvuo7Z42BjIwMp1ykwRX96glGjx6NKVOmoHPnzggNDYVKpUK7du0we/Zs6RLxK1asQGZmprSNq/5evXEcAPb1QW22bduGFStWACj/wtGeL1q8cSzY2wfz5s3D8OHD0aZNG/j6+iIoKAhxcXFS0qHT6fDxxx/bFIs3jgNnjQGNRiOdn+fI+4Er+rU+02g00u9qtRrLli2Tppu2aNECCxYsQIcOHWAwGPDll19KdT3xvYCJpZNdu3YNCxYsQHh4OF577TWrt7PcBLbiofGq9Ho9gPI/SlvarLitM9qt7+ztg5ps3LgRCxYsgCAImDt3rsNJalUtWrSQbki8e/dup7btTs7sA4vQ0FAkJCQAAPbs2QNRFK3e1hXjqz5z5v63XKShadOmLrlIgyP96ukmTpyIJk2awGg0IjExUSp31d+rt40Da9TWBzX55ZdfMHPmTADA66+/7vQrXHvrWLClDyzUarX0v+23336TjsBYg+OgMlv2f8XTIgYPHuz0WBzp1/qs4ufxJ554otqRQplMhgkTJgAAfv75Z2m2oSe+FzCxdLI5c+agpKQE77zzjk3fZAYFBQFAnYPIss5S904CAgKkk6pra1cURekwt7Xt1nf29kFV27dvx6xZsyCKIv7+979j2LBhTozytvvuuw/A7TnvDYGz+qAqy74qLCxEYWGh1du5YnzVZ87a/xUv0jBs2DCX3bDa3n71dHK5HF26dAEAXL58WSp31d+rt40Da9TWB1UdPXoUU6ZMgcFgQEJCAl566SWXxOONY8HaPqiqW7duAACz2Syd82cNjoPKbNn/lquD/+Uvf3HZqTv29mt9VjGRbNOmTY11LLcS0Wg00tj3xPcCnmPpZGfPngUAzJ49G7Nnz660znI4OTs7WzopefHixejWrRtatmyJa9eu1TqoDQYDrl69CgBo2bKlVbGoVCpERUUhMzMTV65ckQZrRdnZ2dI3Fq1atbKq3frO3j6oaPfu3ZgxYwZMJhPefPNN6aiiK1imIphMJpc9x93mjD6oScVpG7bsL1eMr/rMWfv/yJEjyMzMhCAILr1Xmb392hBYXrvRaJTKWrZsiePHj9f5Ic9yaXlb/l69bRxYq6Y+qCgpKQkJCQkoLS3F+PHjMX36dJfHAnjXWLhTH9S1DWD7+4Erxpcns2b/O+u0CGtjARrOGKh4/8napp9WPKppOWLpie8FPGLpInl5edV+SkpKAJT/wVjKLEmd5R6Jx44dq7G9U6dOwWAwwMfHBx06dLA6Dku7v//+e43rLeUREREO346jvrG1DywOHDiA119/HUajEVOnTsXEiRNdGucff/wBAA1u/wP290FtLPvKx8cHISEhVsfhqvFV3zm6/y3fTvfo0cOlVy22t18bgprGv+Xv9fjx4zVuc+3aNelcKEtda3jrOLiTuv4HnzlzBi+88AK0Wi1GjhyJWbNm3ZVYvG0s2PM+eP78eel3W7Zz1fjyZNbs/4qnRfTu3dtlsdjbr/VZeHi4dCuR2o7CWsorjn1PfC9gYulkiYmJOHfuXI0///znPwEATZs2lcosg/Phhx8GUD6nvKZvENasWQMA6N+/P/z9/a2Ox9Luzp07azzkvXbtWgBwyVx5d7G3DwDg119/xSuvvAKDwYAXX3wRU6ZMcWmsGo0G3333HQA0qEtrO9IHtTEajdINhvv06QOFwvoJF64aX/WVM/Z/xYs0PPHEEy6L1ZF+9XT79++XPtBVHP/x8fFQKpVIT0/H4cOHq223evVqAEDHjh1rvLl1bbxtHFijtj4AgHPnzmHSpEkoLi7GY489hjlz5rhsOjjgvWOhrj6oy7JlywAAbdu2telWaa4aX57Kmv1f9d6VrhwH9vZrfWe5VdfWrVtrPDK8bt06AEDPnj2lse+J7wVMLOuJTp06IS4uDiaTCa+//jquX78OoHwwr1mzBps3b4ZMJsPkyZOrbbt8+XIMHDiwxumagwYNQrt27VBcXIw33ngDxcXFAMqnFyxcuBBHjx6Fr6+vy4/KeYKkpCS8/PLL0Ol0mDBhAqZNm2b1tnX1wbvvvosff/yx2gWU0tLS8PzzzyMzMxN+fn6YNGmSw6/B033yySfYuHGjdFTNIjs7G1OnTsWJEyegUChqTPh37tyJgQMHYuDAgdXWOTK+vJXlIg1+fn5WffFU1/53pF892R9//IH33nsPqamplcrNZjO2bdsmTamMi4tD586dpfVhYWEYPXo0AGDWrFm4ePGitC4xMRFfffUVAHAcWMHePkhPT8fEiRNRWFiIwYMH46OPPrL6RvAcC5XZ2wf/93//h1WrVqGgoKDSdgUFBXjvvfekL76mTp1a7TlPnDgh9UFOTk6ldY6ML09k7/6vqOJpEdZ80VjX/nekXz3ZpEmTEBgYiMzMTHzwwQfSlVlFUcTKlSuxb98+CIIgXcAL8Mz3Au/4OsxD/OMf/8DTTz+N5ORkxMfHo23btigoKEB2djYEQcDbb79d4z19iouLkZWVVWObMpkMCxcuxNixY/HTTz+hf//+aNWqFXJycnDjxg0olUp8/PHHDepbIXvNnDkTWq0Wcrkcp06dqvW8yo4dO+Ldd9+tVFZXH5w6dQpr166FUqlE8+bNERAQgIKCAmlefHBwMD777DNER0c79wV5oIsXL2LZsmWYNWsWmjVrhuDgYBQXF+PSpUsQRRE+Pj748MMPpQsNVKTVamvtA8D+8eWtLNNgH374Yau+saxr/zvSr57MaDRizZo1WLNmDUJCQhAVFQW5XI4rV65IM0h69OiBf/3rX9W2nTFjBpKTk5GUlIShQ4ciJiYGWq1W+r8xceLEGq9KynFQmb19MGfOHOTl5QEArl69inHjxtXYfuPGjbFo0aJKZRwLldnbBzk5OVi5ciXmzp2Lpk2bIjQ0FGVlZbh48SKMRiNkMhmmTZsmHX2pSKfTSX1Q09Ehe8eXJ3Lk/5CF5f2gZ8+eVp0WUdf+d6RfPVloaCgWLVqEyZMnY82aNdi+fTtatmyJnJwc5ObmQhAEzJgxo9oMIk97L2BiWY+EhoZi/fr1WLZsGXbu3IkLFy7Az88P/fv3x6RJk+y+1H+rVq2wZcsWfPHFF9i3bx/Onz+PoKAgPPzww3jppZekG7F6O8t5ZiaTqdb57ABsnp704osv4uDBgzhz5gzy8vJw+fJlqNVqdOrUCf3798fYsWPRuHFjh2JvKJ5++mmEhYXhzJkzuH79OrKysqBUKhETE4O+ffti3Lhxdt+Q2VXjqyGqeJEGZ0yDdWW/1mdNmzbFa6+9hhMnTiAtLQ2XL1+GXq9HcHAw+vfvj6FDh2Lo0KE13htXrVZj5cqVWL58ObZu3Yr09HQolUr06tUL48aNs/tDl7eNA3v7oOIMk1OnTtXZvi28cSzY2wePPvoogPL9f/XqVaSmpkIulyM6Ohq9evXCM888Y/e5wK4aX/WRI/+HAOefFuHKfq3v7r//fmzevBlLly7FoUOHkJqaioCAAAwcOBDPPfccevXqVW0bT3svEERvuVESERERERERuQTPsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIiIiIiIocwsSQiIiIiIiKHMLEkIiIiIiIihzCxJCIi8jDDhg1Du3btcOjQIbu2LyoqQufOndGuXTu0a9cO6enpzg2QiIi8DhNLIiIiD5KVlYXU1FQEBQWhV69edrWxdetW6HQ6aXn9+vXOCo+IiLwUE0siIiIPsnfvXgBA//79oVAo7Gpj3bp1AIDx48cDADZu3AiTyeScAImIyCsxsSQiIvIglsQyPj7eru2Tk5ORkpKCoKAgzJgxA9HR0cjNzcWBAwecGSYREXkZ+77qJCIiohqlpKRg+PDhVtV9/fXX8dJLL1nddlFREX7//XcolUr079/frvgsRyuHDBkCHx8fDB8+HJ9//jnWr1+PgQMH2tUmERERj1gSERE5UXp6OsLCwmr98ff3l+p27NjRprb3798Po9GIPn36ICAgwObYdDodtm3bBgBS8jt8+HAIgoD9+/cjLy/P5jaJiIgAHrEkIiJyqiFDhmDIkCE1rsvKysJf//pXaDQaDBgwAH369LGpbUenwe7atQs3b95EixYt0K1bNwBAs2bN0L17d/z+++/YtGkTnn/+ebvaJiIi78YjlkRERHdBRkYGxo8fj4yMDAwaNAiLFy+GSqWyenu9Xo+DBw9CEAS7p6xapsEOGzasUrnl6CWvDktERPZiYklERORily5dwtixY5GVlYUhQ4Zg4cKFNiWVAPDrr79Cq9Xi3nvvRXh4uM0xZGRk4MiRIxAEoVpiOWTIEKjValy8eBHHjx+3uW0iIiImlkRERC504cIFjB8/HteuXcPjjz+O+fPn23WbEEenwa5fvx6iKKJHjx6Ijo6utC4gIACDBg0CcPuoJhERkS2YWBIREblIamoqxo8fj9zcXIwYMQIfffQR5HK5ze2IoojExEQAkBJAW5jNZmzcuBFA9WmwFpbpsDt27IBGo7H5OYiIyLsJoiiK7g6CiIiooUlOTsbEiRNRWFiIMWPG4P3334cgCHa1dfLkSYwaNQrNmzfH7t27bd7+wIEDSEhIsLr+3LlzMXLkSJufh4iIvBePWBIRETnZyZMnMWHCBBQWFuLZZ5/F7Nmz7U4qAedMg7UFp8MSEZGteLsRIiIiJ/r999+RkJAAjUaDSZMm4W9/+5vDbe7ZsweAfYllfn6+NI120aJF6NevX61109LS8NRTTyEpKQkXL15E69at7QuYiIi8Do9YEhEROcnhw4fxwgsvQKPRYPLkyU5JKtPT05GWloZGjRpJ9560xebNm2EwGBAYGIi4uDj4+/vX+tO5c2cpmeRRSyIisgUTSyIiIif45Zdf8OKLL0Kr1eLVV1/Fa6+95pR2LdNg4+Li7LrwjyVBjI+Pt+oWJ4MHDwZQnpAajUabn4+IiLwTp8ISERE5wWeffYaysjIIgoBvv/0W3377ba11P/nkE/Tt29eqdh05v/LEiRO4cOECgNsJ450MHjwYS5YsQV5eHvbv32/XVWiJiMj7MLEkIiJykMlkwvnz5wGU3xokLy+vzvrNmze3qt38/HwkJSVBrVbjgQcesDkuy9HKwMBAq7dv164d2rRpg7S0NKxbt46JJRERWYW3GyEiIqqn1q1bh1mzZiEuLg5ffvmlu8MhIiKqFc+xJCIiqqcs02B51JCIiOo7ToUlIiKqp7p164aOHTvaff9KIiKiu4VTYYmIiIiIiMghnApLREREREREDmFiSURERERERA5hYklEREREREQOYWJJREREREREDmFiSURERERERA5hYklEREREREQOYWJJREREREREDmFiSURERERERA5hYklEREREREQOYWJJREREREREDvl/Bmc20OoiamoAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"plt.plot(interface.z / si.ANGSTROM, initial_density[0], linestyle=\"dashed\", label=\"initial density\")\n",
"plt.plot(interface.z / si.ANGSTROM, (interface.density / (si.KILO * si.MOL / si.METER**3))[0], label=\"equilibrium density\")\n",
"\n",
"plt.xlim(40, 60)\n",
"plt.ylim(-5, 60)\n",
"plt.ylabel(r\"$\\rho$ / kmol m$^{-3}$\")\n",
"plt.xlabel(r\"$z$ / A\")\n",
"sns.despine(offset=10)\n",
"plt.legend(frameon=False);"
]
},
{
"cell_type": "markdown",
"id": "bc985a88",
"metadata": {},
"source": [
"## Comparison to NIST data using `SurfaceTensionDiagram`\n",
"\n",
"We can use the above steps to calculate multiple VLE's and then - for each VLE - calculate the surface tension. We provide a utility object, the `SurfaceTensionDiagram`, that you can use to do exactly this task. Let's load some experimental (correlation) data obtained from the NIST Webbook and see how the water model compares to that."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "caa1fda7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Temperature (K)</th>\n",
" <th>Pressure (MPa)</th>\n",
" <th>Density (l, mol/l)</th>\n",
" <th>Volume (l, l/mol)</th>\n",
" <th>Internal Energy (l, kJ/mol)</th>\n",
" <th>Enthalpy (l, kJ/mol)</th>\n",
" <th>Entropy (l, J/mol*K)</th>\n",
" <th>Cv (l, J/mol*K)</th>\n",
" <th>Cp (l, J/mol*K)</th>\n",
" <th>Sound Spd. (l, m/s)</th>\n",
" <th>...</th>\n",
" <th>Volume (v, l/mol)</th>\n",
" <th>Internal Energy (v, kJ/mol)</th>\n",
" <th>Enthalpy (v, kJ/mol)</th>\n",
" <th>Entropy (v, J/mol*K)</th>\n",
" <th>Cv (v, J/mol*K)</th>\n",
" <th>Cp (v, J/mol*K)</th>\n",
" <th>Sound Spd. (v, m/s)</th>\n",
" <th>Joule-Thomson (v, K/MPa)</th>\n",
" <th>Viscosity (v, uPa*s)</th>\n",
" <th>Therm. Cond. (v, W/m*K)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>275.0</td>\n",
" <td>0.000698</td>\n",
" <td>55.502</td>\n",
" <td>0.018017</td>\n",
" <td>0.13978</td>\n",
" <td>0.13979</td>\n",
" <td>0.5100</td>\n",
" <td>75.903</td>\n",
" <td>75.915</td>\n",
" <td>1411.4</td>\n",
" <td>...</td>\n",
" <td>3271.50</td>\n",
" <td>42.830</td>\n",
" <td>45.115</td>\n",
" <td>164.06</td>\n",
" <td>25.580</td>\n",
" <td>33.980</td>\n",
" <td>410.33</td>\n",
" <td>558.89</td>\n",
" <td>8.9986</td>\n",
" <td>0.016879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>285.0</td>\n",
" <td>0.001389</td>\n",
" <td>55.479</td>\n",
" <td>0.018025</td>\n",
" <td>0.89678</td>\n",
" <td>0.89681</td>\n",
" <td>3.2139</td>\n",
" <td>75.397</td>\n",
" <td>75.533</td>\n",
" <td>1454.3</td>\n",
" <td>...</td>\n",
" <td>1704.30</td>\n",
" <td>43.078</td>\n",
" <td>45.445</td>\n",
" <td>159.52</td>\n",
" <td>25.736</td>\n",
" <td>34.170</td>\n",
" <td>417.48</td>\n",
" <td>408.81</td>\n",
" <td>9.2941</td>\n",
" <td>0.017535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>295.0</td>\n",
" <td>0.002621</td>\n",
" <td>55.384</td>\n",
" <td>0.018056</td>\n",
" <td>1.65110</td>\n",
" <td>1.65120</td>\n",
" <td>5.8154</td>\n",
" <td>74.765</td>\n",
" <td>75.361</td>\n",
" <td>1487.7</td>\n",
" <td>...</td>\n",
" <td>934.36</td>\n",
" <td>43.324</td>\n",
" <td>45.773</td>\n",
" <td>155.38</td>\n",
" <td>25.898</td>\n",
" <td>34.374</td>\n",
" <td>424.46</td>\n",
" <td>304.01</td>\n",
" <td>9.6018</td>\n",
" <td>0.018214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>305.0</td>\n",
" <td>0.004719</td>\n",
" <td>55.233</td>\n",
" <td>0.018105</td>\n",
" <td>2.40440</td>\n",
" <td>2.40440</td>\n",
" <td>8.3264</td>\n",
" <td>74.038</td>\n",
" <td>75.300</td>\n",
" <td>1513.1</td>\n",
" <td>...</td>\n",
" <td>536.20</td>\n",
" <td>43.568</td>\n",
" <td>46.099</td>\n",
" <td>151.59</td>\n",
" <td>26.069</td>\n",
" <td>34.596</td>\n",
" <td>431.28</td>\n",
" <td>231.32</td>\n",
" <td>9.9196</td>\n",
" <td>0.018918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>315.0</td>\n",
" <td>0.008145</td>\n",
" <td>55.034</td>\n",
" <td>0.018171</td>\n",
" <td>3.15730</td>\n",
" <td>3.15750</td>\n",
" <td>10.7560</td>\n",
" <td>73.235</td>\n",
" <td>75.301</td>\n",
" <td>1531.7</td>\n",
" <td>...</td>\n",
" <td>320.58</td>\n",
" <td>43.811</td>\n",
" <td>46.422</td>\n",
" <td>148.10</td>\n",
" <td>26.252</td>\n",
" <td>34.843</td>\n",
" <td>437.93</td>\n",
" <td>180.66</td>\n",
" <td>10.2460</td>\n",
" <td>0.019646</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Temperature (K) Pressure (MPa) Density (l, mol/l) Volume (l, l/mol) \\\n",
"0 275.0 0.000698 55.502 0.018017 \n",
"1 285.0 0.001389 55.479 0.018025 \n",
"2 295.0 0.002621 55.384 0.018056 \n",
"3 305.0 0.004719 55.233 0.018105 \n",
"4 315.0 0.008145 55.034 0.018171 \n",
"\n",
" Internal Energy (l, kJ/mol) Enthalpy (l, kJ/mol) Entropy (l, J/mol*K) \\\n",
"0 0.13978 0.13979 0.5100 \n",
"1 0.89678 0.89681 3.2139 \n",
"2 1.65110 1.65120 5.8154 \n",
"3 2.40440 2.40440 8.3264 \n",
"4 3.15730 3.15750 10.7560 \n",
"\n",
" Cv (l, J/mol*K) Cp (l, J/mol*K) Sound Spd. (l, m/s) ... \\\n",
"0 75.903 75.915 1411.4 ... \n",
"1 75.397 75.533 1454.3 ... \n",
"2 74.765 75.361 1487.7 ... \n",
"3 74.038 75.300 1513.1 ... \n",
"4 73.235 75.301 1531.7 ... \n",
"\n",
" Volume (v, l/mol) Internal Energy (v, kJ/mol) Enthalpy (v, kJ/mol) \\\n",
"0 3271.50 42.830 45.115 \n",
"1 1704.30 43.078 45.445 \n",
"2 934.36 43.324 45.773 \n",
"3 536.20 43.568 46.099 \n",
"4 320.58 43.811 46.422 \n",
"\n",
" Entropy (v, J/mol*K) Cv (v, J/mol*K) Cp (v, J/mol*K) \\\n",
"0 164.06 25.580 33.980 \n",
"1 159.52 25.736 34.170 \n",
"2 155.38 25.898 34.374 \n",
"3 151.59 26.069 34.596 \n",
"4 148.10 26.252 34.843 \n",
"\n",
" Sound Spd. (v, m/s) Joule-Thomson (v, K/MPa) Viscosity (v, uPa*s) \\\n",
"0 410.33 558.89 8.9986 \n",
"1 417.48 408.81 9.2941 \n",
"2 424.46 304.01 9.6018 \n",
"3 431.28 231.32 9.9196 \n",
"4 437.93 180.66 10.2460 \n",
"\n",
" Therm. Cond. (v, W/m*K) \n",
"0 0.016879 \n",
"1 0.017535 \n",
"2 0.018214 \n",
"3 0.018918 \n",
"4 0.019646 \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature = pd.read_csv(\"data/water_vle_nist.csv\", delimiter=\"\\t\")\n",
"literature.head()"
]
},
{
"cell_type": "markdown",
"id": "96ae9524",
"metadata": {},
"source": [
"For the `SurfaceTensionDiagram`, we need to provide the VLE's. We compute those using the `PhaseDiagram` object (here for 50 temperatures between 275 Kelvin and the critical temperature) from which we get a list of `PhaseEquilibrium`s via the `states` filed. The `SurfaceTensionDiagram` is nice, because we can reuse equilibrium density profiles from prior iterations as input for the next iteration. It's therefore typically faster and more stable than an \"naive\" implementation by hand.\n",
"\n",
"The `SurfaceTensionDiagram` takes the same arguments `n_grind`, `l_grid` and `critical_temperature` as discussed above."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c0a7854c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 514 ms, sys: 11.9 ms, total: 526 ms\n",
"Wall time: 525 ms\n"
]
}
],
"source": [
"%%time\n",
"vles = feos.PhaseDiagram.pure(\n",
" pcsaft, \n",
" 275*si.KELVIN, \n",
" 50,\n",
" critical_temperature=cp.temperature\n",
")\n",
"sfts = feos.SurfaceTensionDiagram(\n",
" vles.states, \n",
" n_grid=512, \n",
" l_grid=100*si.ANGSTROM, \n",
" critical_temperature=cp.temperature\n",
")"
]
},
{
"cell_type": "markdown",
"id": "caa7026e",
"metadata": {},
"source": [
"We now can extract all surface tensions via `surface_tension` as well as the liquid and vapor states via the `liquid` and `vapor` getters, respectively. Let's store the results in a pandas `DataFrame` to make plotting easier."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6626c4c7",
"metadata": {},
"outputs": [],
"source": [
"dft_data = pd.DataFrame(\n",
" np.array([\n",
" sfts.liquid.temperature / si.KELVIN, \n",
" sfts.surface_tension / si.NEWTON * si.METER\n",
" ]).T, \n",
" columns=[\"Temperature (K)\", \"Surf. Tension (l, N/m)\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "279a66a3",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"plt.title(\"Surface tension of water\")\n",
"sns.lineplot(data=literature, x=\"Temperature (K)\", y=\"Surf. Tension (l, N/m)\", label=\"NIST\")\n",
"sns.lineplot(data=dft_data, x=\"Temperature (K)\", y=\"Surf. Tension (l, N/m)\", label=\"PC-SAFT (2B)\")\n",
"sns.scatterplot(x=[cp.temperature / si.KELVIN], y=[0.0], clip_on=False, color=colors[1], label=\"PC-SAFT (2B), critical point\")\n",
"plt.ylabel(r\"$\\gamma$ / Nm$^{-1}$\")\n",
"plt.xlabel(r\"$T$ / K\")\n",
"\n",
"plt.xlim(250, 700)\n",
"plt.ylim(0.0, 0.08)\n",
"sns.despine(offset=10)\n",
"plt.legend(frameon=False);"
]
},
{
"cell_type": "markdown",
"id": "35af1b14",
"metadata": {},
"source": [
"## Concluding remkars\n",
"\n",
"Hopefully you found this example helpful. If you have comments, critique or feedback, please let us know and consider [opening an issue on github](https://github.com/feos-org/feos/issues)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/lj_models.ipynb | .ipynb | 278,305 | 783 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparison of equations of state for model fluids\n",
"\n",
"In this notebook, we take a look at various model fluid equations of state and compare them.\n",
"These EoS are:\n",
"\n",
"- The PeTS equation of state (from `feos.pets`),\n",
"- the uv-theory equation of state (from `feos.uvtheory`), \n",
"- the uv-B3-theory equation of state (from `feos.uvtheory` using `Perturbation.`), and\n",
"- the Thol equation of state (implemented in this notebook as Python class).\n",
"\n",
"The PeTS equation of state is for a Lennard-Jones fluid with cut off distance of $r_c = 2.5 \\sigma$ and a potential shift while the uv-theory and Thol equation of state model the full Lennard-Jones potential. Note that the uv-theory was developed to model Mie potentials and not primarily adjusted to the Lennard-Jones fluid. Thus, we don't expect similar results when comparing uv-theory and Thol to PeTS."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import si_units as si\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette(\"hls\", 8)\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implement the Thol equation of state as Python class"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Parameters for Thol EoS:\n",
"A = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0,\n",
" 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0])\n",
"N = np.array([0.005208073, 2.186252000, -2.161016000, 1.452700000, -2.041792000, 0.186952860, -0.090988445, \n",
" -0.497456100, 0.109014310, -0.800559220, -0.568839000, -0.620862500, -1.466717700, 1.891469000, \n",
" -0.138370100, -0.386964500, 0.126570200, 0.605781000, 1.179189000, -0.477326790, -9.921857500, -0.574793200, 0.003772923])\n",
"T = np.array([1.000, 0.320, 0.505, 0.672, 0.843, 0.898, 1.294, 2.590, 1.786, 2.770, 1.786,\n",
" 1.205, 2.830, 2.548, 4.650, 1.385, 1.460, 1.351, 0.660, 1.496, 1.830, 1.616, 4.970])\n",
"D = np.array([4.0, 1.0, 1.0, 2.0, 2.0, 3.0, 5.0, 2.0, 2.0, 3.0, 1.0,\n",
" 1.0, 1.0, 1.0, 2.0, 3.0, 3.0, 2.0, 1.0, 2.0, 3.0, 1.0, 1.0])\n",
"L = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 1.0, 2.0, 2.0,\n",
" 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
"ETA = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.067, 1.522,\n",
" 8.82, 1.722, 0.679, 1.883, 3.925, 2.461, 28.2, 0.753, 0.82])\n",
"BETA = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.625,\n",
" 0.638, 3.91, 0.156, 0.157, 0.153, 1.16, 1.73, 383, 0.112, 0.119])\n",
"GAMMA = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.71,\n",
" 0.86, 1.94, 1.48, 1.49, 1.945, 3.02, 1.11, 1.17, 1.33, 0.24])\n",
"EPSILON = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2053,\n",
" 0.409, 0.6, 1.203, 1.829, 1.397, 1.39, 0.539, 0.934, 2.369, 2.43])\n",
"\n",
"class Thol:\n",
" def __init__(self):\n",
" self.sigma = 3.7039 * si.ANGSTROM\n",
" self.eps_k = 150.03 * si.KELVIN\n",
" self.tc = 1.32 * self.eps_k / si.KELVIN\n",
" self.rhoc = 0.31 / self.sigma**3 * si.ANGSTROM**3\n",
" \n",
" def components(self): \n",
" return 1\n",
" \n",
" def subset(self, components):\n",
" return self\n",
" \n",
" def molar_weight(self):\n",
" return np.array([1.0])\n",
" \n",
" def max_density(self, moles):\n",
" return 0.04\n",
" \n",
" def helmholtz_energy(self, state):\n",
" \"\"\"\n",
" state (StateHD):\n",
" temperature in Kelvin als Float, Dual oder HD oder HD3, HDD, HDD3,\n",
" partial_density in # / Angstrom^3\n",
" volume in Angstrom^3\n",
" moles in mol\n",
" \"\"\"\n",
" tau = self.tc / state.temperature\n",
" delta = np.sum(state.partial_density) / self.rhoc\n",
" a = 0.0\n",
" \n",
" for i in range(6):\n",
" a = a + N[i] * delta**D[i] * tau**T[i]\n",
" for i in range(6, 12):\n",
" a = a + N[i] * delta**D[i] * tau**T[i] * np.exp(-1.0 * delta**L[i])\n",
" for i in range(12, 23):\n",
" a = a + N[i] * delta**D[i] * tau**T[i] * np.exp(- 1.0 * ETA[i] * (delta - EPSILON[i])**2.0 - 1.0 * BETA[i] * (tau - 1.0 * GAMMA[i])**2.0)\n",
" return a * np.sum(state.density)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instantiate all equations of state"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"sigma = 3.7039\n",
"sigma_a = sigma * si.ANGSTROM\n",
"eps_k = 150.03\n",
"eps_k_k = eps_k * si.KELVIN\n",
"\n",
"parameters = feos.Parameters.new_pure(feos.PureRecord(feos.Identifier(), 0, rep=12.0, att=6.0, sigma=sigma, epsilon_k=eps_k))\n",
"uvtheory_wca = feos.EquationOfState.uvtheory(parameters)\n",
"uvtheory_bh = feos.EquationOfState.uvtheory(parameters, perturbation=\"BH\")\n",
"uvtheory_b3 = feos.EquationOfState.uvtheory(parameters, perturbation=\"WCA_B3\")\n",
"\n",
"parameters = feos.Parameters.new_pure(feos.PureRecord(feos.Identifier(), 0, sigma=sigma, epsilon_k=eps_k))\n",
"pets = feos.EquationOfState.pets(parameters)\n",
"\n",
"thol = feos.EquationOfState.python_residual(Thol())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare Helmholtz energies at single state point"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"s1 = feos.State(uvtheory_wca, temperature=300*si.KELVIN, pressure=1*si.BAR)\n",
"s2 = feos.State(uvtheory_b3, temperature=300*si.KELVIN, pressure=1*si.BAR)\n",
"s3 = feos.State(thol, temperature=300*si.KELVIN, pressure=1*si.BAR)\n",
"s4 = feos.State(pets, temperature=300*si.KELVIN, pressure=1*si.BAR)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.0016213224956115688"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1.molar_helmholtz_energy(feos.Contributions.Residual) / (si.RGAS * 300 * si.KELVIN)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.0016138652202999674"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.molar_helmholtz_energy(feos.Contributions.Residual) / (si.RGAS * 300 * si.KELVIN)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.0016144150962601575"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s3.molar_helmholtz_energy(feos.Contributions.Residual) / (si.RGAS * 300 * si.KELVIN)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.0009407040108813856"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s4.molar_helmholtz_energy(feos.Contributions.Residual) / (si.RGAS * 300 * si.KELVIN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare critical points\n",
"- Reference data for the Lennard-Jones from Potoff and Panagiotopoulos (MC): $T_c^*=1.3120$, $\\rho_c^*=0.316$, $p_c^*=0.1279$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"uvtheory_b3:\n",
"Tc*=1.31329, Error: 0.099%\n",
"pc*=0.1286, Error: 0.548%\n",
"rhoc*=0.31425, Error: 0.553%\n",
"\n",
"uvtheory_wca:\n",
"Tc*=1.30977, Error: 0.17%\n",
"pc*=0.12448, Error: 2.674%\n",
"rhoc*=0.30233, Error: 4.327%\n",
"\n",
"uvtheory_bh:\n",
"Tc*=1.32083, Error: 0.673%\n",
"pc*=0.13486, Error: 5.442%\n",
"rhoc*=0.31865, Error: 0.838%\n",
"\n",
"thol:\n",
"Tc*=1.32, Error: 0.61%\n",
"pc*=0.13006, Error: 1.689%\n",
"rhoc*=0.3132, Error: 0.886%\n",
"\n"
]
}
],
"source": [
"def evaluate_critical_points():\n",
" names = ['uvtheory_b3', 'uvtheory_wca', 'uvtheory_bh', 'thol']\n",
" print('')\n",
" i = 0\n",
" for eos in [uvtheory_b3, uvtheory_wca, uvtheory_bh, thol]:\n",
" rep = 12\n",
" att = 6.0\n",
" sigma = 3.7039 # in Angstrom\n",
" eps_k = 150.03 # eps / kB in K\n",
" print('{}:'.format(names[i]))\n",
" Tc = feos.State.critical_point(eos).temperature / eps_k_k\n",
" print('Tc*={}, Error: {}%'.format(np.round(Tc, 5), np.round(np.abs(Tc - 1.3120 ) / 1.3120 * 100 ,3)))\n",
" pc = feos.State.critical_point(eos).pressure() * (sigma * si.ANGSTROM)**3 / si.KB / eps_k_k\n",
" print('pc*={}, Error: {}%'.format(np.round(pc, 5), np.round(np.abs(pc - 0.1279 ) / 0.1279 * 100 ,3)))\n",
" rhoc = feos.State.critical_point(eos).density * (sigma * si.ANGSTROM)**3 * si.NAV\n",
" print('rhoc*={}, Error: {}%'.format(np.round(rhoc, 5), np.round(np.abs(rhoc - 0.316) / 0.316 * 100 ,3)))\n",
" i += 1\n",
" print('')\n",
" return\n",
"\n",
"evaluate_critical_points()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare Phase diagrams"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11 ms, sys: 0 ns, total: 11 ms\n",
"Wall time: 11 ms\n"
]
}
],
"source": [
"%%time\n",
"vle_uv = feos.PhaseDiagram.pure(uvtheory_wca, 150*si.KELVIN, 500)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 7.9 ms, sys: 0 ns, total: 7.9 ms\n",
"Wall time: 7.92 ms\n"
]
}
],
"source": [
"%%time\n",
"vle_uv_bh = feos.PhaseDiagram.pure(uvtheory_bh, 150*si.KELVIN, 500)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 13.1 ms, sys: 0 ns, total: 13.1 ms\n",
"Wall time: 13 ms\n"
]
}
],
"source": [
"%%time\n",
"vle_uv_b3 = feos.PhaseDiagram.pure(uvtheory_b3, 150*si.KELVIN, 500)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 850 ms, sys: 385 μs, total: 851 ms\n",
"Wall time: 850 ms\n"
]
}
],
"source": [
"%%time\n",
"vle_thol = feos.PhaseDiagram.pure(thol, 150*si.KELVIN, 500)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.88 ms, sys: 754 μs, total: 2.64 ms\n",
"Wall time: 2.64 ms\n"
]
}
],
"source": [
"%%time\n",
"vle_pets = feos.PhaseDiagram.pure(pets, 150*si.KELVIN, 500)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>t</th>\n",
" <th>rhov</th>\n",
" <th>rhol</th>\n",
" <th>p</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.70</td>\n",
" <td>0.002012</td>\n",
" <td>0.843237</td>\n",
" <td>0.001381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.71</td>\n",
" <td>0.002284</td>\n",
" <td>0.839106</td>\n",
" <td>0.001586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.72</td>\n",
" <td>0.002584</td>\n",
" <td>0.834868</td>\n",
" <td>0.001815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.73</td>\n",
" <td>0.002912</td>\n",
" <td>0.830543</td>\n",
" <td>0.002069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>0.74</td>\n",
" <td>0.003271</td>\n",
" <td>0.826147</td>\n",
" <td>0.002350</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" t rhov rhol p\n",
"0 0.70 0.002012 0.843237 0.001381\n",
"10 0.71 0.002284 0.839106 0.001586\n",
"20 0.72 0.002584 0.834868 0.001815\n",
"30 0.73 0.002912 0.830543 0.002069\n",
"40 0.74 0.003271 0.826147 0.002350"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import reference data from the NIST database\n",
"nist_data = pd.read_csv(\"data/lennard_jones_nist.csv\", delim_whitespace=True)\n",
"nist_data = nist_data[::10]\n",
"nist_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the Phase-Diagrams"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s3 = si.NAV * sigma_a**3 # factor to calculate dimensionless density \n",
"colors = sns.color_palette(\"Dark2\", n_colors = 6)\n",
"plt.figure(figsize=(12, 6))\n",
"\n",
"plt.plot(nist_data.rhov, nist_data.t, linestyle=\"\", color=\"k\", marker=\".\", label='MC', alpha=0.5)\n",
"plt.plot(nist_data.rhol, nist_data.t, linestyle=\"\", color=\"k\", marker=\".\", alpha=0.5)\n",
"\n",
"plt.plot(vle_uv.vapor.density * s3 , vle_uv.vapor.temperature / eps_k_k, color=colors[0], label='uv (WCA)')\n",
"plt.plot(vle_uv.liquid.density * s3, vle_uv.vapor.temperature / eps_k_k, color=colors[0])\n",
"\n",
"plt.plot(vle_uv_bh.vapor.density * s3 , vle_uv_bh.vapor.temperature / eps_k_k, color=colors[1], label='uv (BH)')\n",
"plt.plot(vle_uv_bh.liquid.density * s3, vle_uv_bh.vapor.temperature / eps_k_k, color=colors[1])\n",
"\n",
"plt.plot(vle_uv_b3.vapor.density * s3 , vle_uv_b3.vapor.temperature / eps_k_k, color=colors[2], label='uv (B3)')\n",
"plt.plot(vle_uv_b3.liquid.density * s3, vle_uv_b3.vapor.temperature / eps_k_k, color=colors[2])\n",
"\n",
"plt.plot(vle_thol.vapor.density * s3 , vle_thol.vapor.temperature / eps_k_k, color=colors[3], label='Thol')\n",
"plt.plot(vle_thol.liquid.density * s3, vle_thol.vapor.temperature / eps_k_k, color=colors[3])\n",
"\n",
"plt.ylabel(r\"$T*$\")\n",
"plt.xlabel(r\"$\\rho*$\")\n",
"plt.xlim(0., 0.8)\n",
"plt.ylim(1.0, 1.35)\n",
"plt.legend(frameon=False)\n",
"sns.despine(offset=10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Calculate Errors compared to NIST data (MC)**\n",
"- dimensionless vapor density $\\rho^{v*}$\n",
"- dimensionless liquid density $\\rho^{l*}$\n",
"- dimensionless vapor pressure $p^{v*}$"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"def plot_vle_errors():\n",
" nist_data_subset = nist_data.loc[nist_data['t']< 1.30977] #use only data with temperatures where all EoS are subcritical\n",
"\n",
" fig, axs = plt.subplots(3, 1, sharex=True, sharey=True)\n",
"\n",
" for i, row in nist_data_subset.iterrows():\n",
" temp = row['t']\n",
" rhov = row['rhov']\n",
" rhol = row['rhol']\n",
" psat = row['p']\n",
"\n",
" vle_uvb3 = feos.PhaseEquilibrium.pure(uvtheory_b3, temp * eps_k_k)\n",
" rhov_uvb3 = vle_uvb3.vapor.density * si.NAV * (sigma * si.ANGSTROM)**3\n",
" rhol_uvb3 = vle_uvb3.liquid.density * si.NAV * (sigma * si.ANGSTROM)**3\n",
" ps_uvb3 = vle_uvb3.vapor.pressure() / (si.KB * eps_k_k ) * (sigma * si.ANGSTROM)**3\n",
" err_uvb3_rhov = (rhov - rhov_uvb3) / rhov * 100\n",
" err_uvb3_rhol = (rhol - rhol_uvb3) / rhol * 100\n",
" err_uvb3_ps = (psat - ps_uvb3) / psat * 100\n",
"\n",
" vle_uvwca = feos.PhaseEquilibrium.pure(uvtheory_wca, temp * eps_k_k)\n",
" rhov_uvwca = vle_uvwca.vapor.density * si.NAV * (sigma * si.ANGSTROM)**3\n",
" rhol_uvwca = vle_uvwca.liquid.density * si.NAV * (sigma * si.ANGSTROM)**3\n",
" ps_uvwca = vle_uvwca.vapor.pressure() / (si.KB * eps_k_k ) * (sigma * si.ANGSTROM)**3\n",
" err_uvwca_rhov = (rhov - rhov_uvwca) / rhov * 100\n",
" err_uvwca_rhol = (rhol - rhol_uvwca) / rhol * 100\n",
" err_uvwca_ps = (psat - ps_uvwca) / psat * 100\n",
"\n",
"\n",
" vle_uvbh = feos.PhaseEquilibrium.pure(uvtheory_bh, temp * eps_k_k)\n",
" rhov_uvbh = vle_uvbh.vapor.density * si.NAV * (sigma * si.ANGSTROM)**3\n",
" rhol_uvbh = vle_uvbh.liquid.density * si.NAV * (sigma * si.ANGSTROM)**3\n",
" ps_uvbh = vle_uvbh.vapor.pressure() / (si.KB * eps_k_k ) * (sigma * si.ANGSTROM)**3\n",
" err_uvbh_rhov = (rhov - rhov_uvbh) / rhov * 100\n",
" err_uvbh_rhol = (rhol - rhol_uvbh) / rhol * 100\n",
" err_uvbh_ps = (psat - ps_uvbh) / psat * 100\n",
"\n",
"\n",
" axs[0].plot(temp, err_uvb3_rhov, 'r.')\n",
" axs[0].plot(temp, err_uvwca_rhov, 'g.')\n",
" axs[0].plot(temp, err_uvbh_rhov, 'b.')\n",
"\n",
" axs[1].plot(temp, err_uvb3_rhol, 'r.')\n",
" axs[1].plot(temp, err_uvwca_rhol, 'g.')\n",
" axs[1].plot(temp, err_uvbh_rhol, 'b.')\n",
" \n",
" axs[2].plot(temp, err_uvb3_ps, 'r.')\n",
" axs[2].plot(temp, err_uvwca_ps, 'g.')\n",
" axs[2].plot(temp, err_uvbh_ps, 'b.')\n",
" \n",
" \n",
" \n",
" \n",
" axs[0].plot(temp, err_uvb3_rhov, 'r.', label='uv-B3')\n",
" axs[0].plot(temp, err_uvwca_rhov, 'g.', label='uv-WCA')\n",
" axs[0].plot(temp, err_uvbh_rhov, 'b.', label='uv-BH')\n",
"\n",
"\n",
" axs[0].text(0.7, 2.1, '$Y=\\\\rho^{v*}$')\n",
" axs[1].text(0.7, 2, '$Y=\\\\rho^{l*}$')\n",
" axs[2].text(0.7, 2, '$Y=p^{v*}$')\n",
"\n",
" axs[0].legend(frameon=False, bbox_to_anchor=(1.01, 1.05))\n",
" axs[1].set_ylabel('100$(Y^{MC}-Y^{EoS}) / Y^{EoS}$')\n",
"\n",
" plt.xlabel('$T^*$') \n",
" return\n",
"\n",
"plot_vle_errors() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare Third Virial Coefficients"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_third_virial_coeff():\n",
" tvec = np.linspace(0.5, 20, 800)\n",
" \n",
" b3_uv_wca = []\n",
" b3_uv_b3 = []\n",
" b3_thol = []\n",
" for temp in tvec:\n",
" b3_uv_wca.append(uvtheory_wca.third_virial_coefficient(temp * eps_k_k) / (sigma * si.ANGSTROM)**6 / si.NAV**2)\n",
" b3_uv_b3.append(uvtheory_b3.third_virial_coefficient(temp * eps_k_k) / (sigma * si.ANGSTROM)**6 / si.NAV**2)\n",
" b3_thol.append(thol.third_virial_coefficient(temp * eps_k_k) / (sigma * si.ANGSTROM)**6 / si.NAV**2)\n",
"\n",
" plt.plot(1/tvec, b3_uv_wca, '-', label='uv-WCA')\n",
" plt.plot(1/tvec, b3_uv_b3, label='uv-$B_3$')\n",
" plt.plot(1/tvec, b3_thol, '-', label='Thol')\n",
" plt.ylim(-10, 10)\n",
" plt.ylabel('$B_3 / \\\\sigma^6$')\n",
" plt.xlabel('$\\\\varepsilon/(k_BT)$')\n",
" plt.legend(frameon=False)\n",
" return\n",
"plot_third_virial_coeff()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare isochoric heat capacity isotherms"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_heat_capcities(temp):\n",
" dimensionless_density = np.linspace(0.01, 1.0, 100)\n",
" cv_thol = []\n",
" cv_uv_b3 = []\n",
" cv_uv_wca = []\n",
" cv_uv_bh = []\n",
" for rho in dimensionless_density:\n",
" density = rho / (sigma * si.ANGSTROM)**3 / si.NAV\n",
" temperature = temp * eps_k_k\n",
" s_thol = feos.State(thol, density = density, temperature = temperature)\n",
" cv_thol.append(s_thol.molar_isochoric_heat_capacity(feos.Contributions.Residual) / si.RGAS + 3.0 / 2.0)\n",
" s_uv_b3 = feos.State(uvtheory_b3, density = density, temperature = temperature)\n",
" cv_uv_b3.append(s_uv_b3.molar_isochoric_heat_capacity(feos.Contributions.Residual) / si.RGAS + 3.0 / 2.0)\n",
" s_uv_wca = feos.State(uvtheory_wca, density = density, temperature = temperature)\n",
" cv_uv_wca.append(s_uv_wca.molar_isochoric_heat_capacity(feos.Contributions.Residual) / si.RGAS + 3.0 / 2.0)\n",
" s_uv_bh = feos.State(uvtheory_bh, density = density, temperature = temperature)\n",
" cv_uv_bh.append(s_uv_bh.molar_isochoric_heat_capacity(feos.Contributions.Residual) / si.RGAS + 3.0 / 2.0)\n",
" \n",
" \n",
" plt.plot(dimensionless_density, cv_thol, '-', label='Thol')\n",
" plt.plot(dimensionless_density, cv_uv_b3, '-', label='uv-$B_3$')\n",
" plt.plot(dimensionless_density, cv_uv_wca, '-', label='uv-WCA')\n",
" plt.plot(dimensionless_density, cv_uv_bh, '-', label='uv-BH')\n",
" \n",
" plt.xlabel('$\\\\rho^*$')\n",
" plt.ylabel('$C_v/Nk_B$')\n",
" plt.title('$T^*={}$'.format(temp))\n",
" plt.legend(frameon=False)\n",
" return\n",
"plot_heat_capcities(1.5)\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/core_user_defined_eos.ipynb | .ipynb | 295,487 | 1,280 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing an equation of state in python\n",
"\n",
"> In `FeOs`, you can implement your equation of state in python, register it to the Rust backend, and compute properties and phase equilbria as if you implemented it in Rust.\n",
"> In this tutorial, we will implement the Peng-Robinson equation of state."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of contents <a id=\"Table-of-contents\"/>\n",
"\n",
"- [Implementation](#Implementation)\n",
"- [Computing properties](#Computing-properties)\n",
"- [Critical point](#Critical-point)\n",
"- [Phase equilibria and phase diagrams](#Phase-equilibria-and-phase-diagrams)\n",
"- [Mixtures](#Mixtures)\n",
"- [Comparison to Rust implementation](#Comparison-to-Rust-implementation)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import numpy as np\n",
"\n",
"optional = True\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"if optional:\n",
" import matplotlib.pyplot as plt\n",
" import pandas as pd\n",
" import seaborn as sns\n",
"\n",
" sns.set_style(\"ticks\")\n",
" sns.set_palette(\"Dark2\")\n",
" sns.set_context(\"talk\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation <a id=\"Implementation\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"To implement an equation of state in python, we have to define a `class` which has to have the following methods:\n",
"\n",
"```python\n",
"class MyEquationOfState:\n",
" def helmholtz_energy(self, state: StateHD) -> D\n",
" \n",
" def components(self) -> int\n",
" \n",
" def subset(self, indices: List[int]) -> Self\n",
" \n",
" def molar_weight(self) -> SIArray1\n",
" \n",
" def max_density(self, molefracs: np.ndarray[float]) -> float\n",
"``` \n",
"\n",
"- `components(self) -> int`: Returns the number of components (usually inferred from the shape of the input parameters).\n",
"- `molar_weight(self) -> SIArray1`: Returns an `SIArray1` with size equal to the number of components containing the molar mass of each component.\n",
"- `max_density(self, moles: np.ndarray[float]) -> float`: Returns the maximum allowed number density in units of `molecules/Angstrom³`.\n",
"- `subset(self, indices: List[int]) -> self`: Returns a new equation of state with parameters defined in `indices`.\n",
"- `helmholtz_energy(self, state: StateHD) -> Dual`: Returns the helmholtz energy as (hyper)-dual number given a `StateHD`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SQRT2 = np.sqrt(2)\n",
"\n",
"class PyPengRobinson: \n",
" def __init__(\n",
" self, critical_temperature, critical_pressure, \n",
" acentric_factor, molar_weight, delta_ij=None\n",
" ):\n",
" \"\"\"Peng-Robinson Equation of State\n",
" \n",
" Parameters\n",
" ----------\n",
" critical_temperature : SIArray1\n",
" critical temperature of each component.\n",
" critical_pressure : SIArray1\n",
" critical pressure of each component.\n",
" acentric_factor : np.array[float] \n",
" acentric factor of each component (dimensionless).\n",
" molar_weight: SIArray1\n",
" molar weight of each component.\n",
" delta_ij : np.array[[float]], optional\n",
" binary parameters. Shape=[n, n], n = number of components.\n",
" defaults to zero for all binary interactions.\n",
" \n",
" Raises\n",
" ------\n",
" ValueError: if the input values have incompatible sizes.\n",
" \"\"\"\n",
" self.n = len(critical_temperature)\n",
" if len(set((\n",
" len(critical_temperature), \n",
" len(critical_pressure), \n",
" len(acentric_factor)\n",
" ))) != 1:\n",
" raise ValueError(\"Input parameters must all have the same lenght.\")\n",
" \n",
" self.tc = critical_temperature / si.KELVIN\n",
" self.pc = critical_pressure / si.PASCAL\n",
" self.omega = acentric_factor\n",
" self.mw = molar_weight / si.GRAM * si.MOL\n",
"\n",
" self.a_r = (0.45724 * critical_temperature**2 * si.RGAS \n",
" / critical_pressure / si.ANGSTROM**3 / si.NAV / si.KELVIN)\n",
" self.b = (0.07780 * critical_temperature * si.RGAS \n",
" / critical_pressure / si.ANGSTROM**3 / si.NAV)\n",
" self.kappa = (0.37464 \n",
" + (1.54226 - 0.26992 * acentric_factor) * acentric_factor)\n",
" self.delta_ij = (np.zeros((self.n, self.n)) \n",
" if delta_ij is None else delta_ij)\n",
" \n",
" def helmholtz_energy(self, state):\n",
" \"\"\"Return helmholtz energy.\n",
" \n",
" Parameters\n",
" ----------\n",
" state : StateHD\n",
" The thermodynamic state.\n",
" \n",
" Returns\n",
" -------\n",
" helmholtz_energy: float | any dual number\n",
" The return type depends on the input types.\n",
" \"\"\" \n",
" x = state.molefracs\n",
" tr = 1.0 / self.tc * state.temperature\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n",
" ak_mix = 0.0\n",
" if self.n > 1:\n",
" for i in range(self.n):\n",
" for j in range(self.n):\n",
" ak_mix += (np.sqrt(ak[i] * ak[j]) \n",
" * (x[i] * x[j] * (1.0 - self.delta_ij[i, j])))\n",
" else:\n",
" ak_mix = ak[0]\n",
" b = np.sum(x * self.b)\n",
" rho = np.sum(state.partial_density)\n",
" a = rho * (-np.log(1.0 - b * rho) \n",
" - ak_mix / (b * SQRT2 * 2.0 * state.temperature) \n",
" * np.log((1.0 + (1.0 + SQRT2) * b * rho) / (1.0 + (1.0 - SQRT2) * b * rho)))\n",
" return a\n",
" \n",
" def components(self) -> int: \n",
" \"\"\"Number of components.\"\"\"\n",
" return self.n\n",
" \n",
" def subset(self, i: list[int]):\n",
" \"\"\"Return new equation of state containing a subset of all components.\"\"\"\n",
" if self.n > 1:\n",
" tc = self.tc[i] \n",
" pc = self.pc[i]\n",
" mw = self.mw[i]\n",
" omega = self.omega[i]\n",
" return PyPengRobinson(\n",
" si.array(tc * si.KELVIN), \n",
" si.array(pc * si.PASCAL), \n",
" omega, \n",
" si.array(mw * si.GRAM / si.MOL)\n",
" )\n",
" else:\n",
" return self\n",
" \n",
" def molar_weight(self) -> si.SIObject:\n",
" return si.array(self.mw * si.GRAM / si.MOL)\n",
" \n",
" def max_density(self, molefracs: list[float]) -> float:\n",
" b = np.sum(molefracs * self.b)\n",
" return 0.9 / b "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing properties <a id=\"Computing-properties\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"Let's compute some properties. First, we have to instanciate the class and register it to Rust.\n",
"This is done using the `EquationOfState.python` method."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"tc = si.array(369.96 * si.KELVIN)\n",
"pc = si.array(4250000.0 * si.PASCAL)\n",
"omega = np.array([0.153])\n",
"molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n",
"\n",
"# create an instance of our python class and hand it over to rust\n",
"pr = PyPengRobinson(tc, pc, omega, molar_weight)\n",
"eos = feos.EquationOfState.python_residual(pr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Thermodynamic state: the `State` object\n",
"\n",
"Before we can compute a property, we create a `State` object. This can be done in several ways depending on what control variables we need.\n",
"If no total amount of substance is defined, it is set to $n = \\frac{1}{N_{AV}}$.\n",
"For possible input combinations, you can inspect the signature of the constructor using `State?`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mInit signature:\u001b[39m\n",
"feos.State(\n",
" eos,\n",
" temperature=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" volume=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" density=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" partial_density=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" total_moles=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" moles=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molefracs=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" pressure=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molar_enthalpy=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molar_entropy=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molar_internal_energy=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" density_initialization=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" initial_temperature=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
")\n",
"\u001b[31mDocstring:\u001b[39m \n",
"A thermodynamic state at given conditions.\n",
"\n",
"Parameters\n",
"----------\n",
"eos : Eos\n",
" The equation of state to use.\n",
"temperature : SINumber, optional\n",
" Temperature.\n",
"volume : SINumber, optional\n",
" Volume.\n",
"density : SINumber, optional\n",
" Molar density.\n",
"partial_density : SIArray1, optional\n",
" Partial molar densities.\n",
"total_moles : SINumber, optional\n",
" Total amount of substance (of a mixture).\n",
"moles : SIArray1, optional\n",
" Amount of substance for each component.\n",
"molefracs : numpy.ndarray[float]\n",
" Molar fraction of each component.\n",
"pressure : SINumber, optional\n",
" Pressure.\n",
"molar_enthalpy : SINumber, optional\n",
" Molar enthalpy.\n",
"molar_entropy : SINumber, optional\n",
" Molar entropy.\n",
"molar_internal_energy: SINumber, optional\n",
" Molar internal energy\n",
"density_initialization : {'vapor', 'liquid', SINumber, None}, optional\n",
" Method used to initialize density for density iteration.\n",
" 'vapor' and 'liquid' are inferred from the maximum density of the equation of state.\n",
" If no density or keyword is provided, the vapor and liquid phase is tested and, if\n",
" different, the result with the lower free energy is returned.\n",
"initial_temperature : SINumber, optional\n",
" Initial temperature for temperature iteration. Can improve convergence\n",
" when the state is specified with pressure and molar entropy or enthalpy.\n",
"\n",
"Returns\n",
"-------\n",
"State : state at given conditions\n",
"\n",
"Raises\n",
"------\n",
"Error\n",
" When the state cannot be created using the combination of input.\n",
"\u001b[31mType:\u001b[39m type\n",
"\u001b[31mSubclasses:\u001b[39m "
]
}
],
"source": [
"feos.State?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we use input variables other than $\\mathbf{N}, V, T$ (the natural variables of the Helmholtz energy), creating a state is an iterative procedure.\n",
"For example, we can create a state for a give $T, p$, which will result in a iteration of the volume (density)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1.6605\\times10^{-24}\\,\\mathrm{ mol}$"
],
"text/plain": [
"1.6605390671738466e-24 mol"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# If no amount of substance is given, it is set to 1/NAV.\n",
"s = feos.State(eos, temperature=300*si.KELVIN, pressure=1*si.BAR)\n",
"s.total_moles"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1\\,\\mathrm{ mol}$"
],
"text/plain": [
"1 mol"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s_pt = feos.State(\n",
" eos, \n",
" temperature=300*si.KELVIN, \n",
" pressure=1*si.BAR, \n",
" total_moles=1*si.MOL\n",
")\n",
"s_pt.total_moles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Critical point <a id=\"Critical-point\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"To generate a state at critical conditions, we can use the `critical_point` constructor."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Critical point\n",
"temperature: 369.9506174234607 K\n",
"density : 198.1862458057178 kg/m³\n",
"pressure : 4.249677749116937 MPa\n"
]
}
],
"source": [
"s_cp = feos.State.critical_point(eos)\n",
"print(\"Critical point\")\n",
"print(\"temperature: \", s_cp.temperature)\n",
"print(\"density : \", s_cp.mass_density())\n",
"print(\"pressure : \", s_cp.pressure())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase equilibria and phase diagrams<a id=\"Phase-equilibria-and-phase-diagrams\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"We can also create an object, `PhaseEquilibrium`, that contains states that are in equilibrium."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|300.00000 K|488.99014 mol/m³|\n",
"|phase 2|300.00000 K|11.53399 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 300.00000 K, ρ = 488.99014 mol/m³\n",
"phase 1: T = 300.00000 K, ρ = 11.53399 kmol/m³"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.pure(eos, 300.0*si.KELVIN)\n",
"vle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each phase is a `State` object. We can simply access these states and compute properties, just like before."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|300.00000 K|11.53399 kmol/m³|"
],
"text/plain": [
"T = 300.00000 K, ρ = 11.53399 kmol/m³"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle.liquid # the high density phase `State`"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|300.00000 K|488.99014 mol/m³|"
],
"text/plain": [
"T = 300.00000 K, ρ = 488.99014 mol/m³"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle.vapor # the low density phase `State`"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Heat of vaporization: 14.782343503305126 kJ/mol\n",
"for T = 300 K\n",
"and p = 9.95 bar\n"
]
}
],
"source": [
"# we can now easily compute any property:\n",
"print(\"Heat of vaporization: \", vle.vapor.molar_enthalpy(feos.Contributions.Residual) - vle.liquid.molar_enthalpy(feos.Contributions.Residual))\n",
"print(\"for T = {}\".format(vle.liquid.temperature))\n",
"print(\"and p = {:.2f} bar\".format(vle.liquid.pressure() / si.BAR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also easily compute **vapor pressures** and **boiling temperatures**:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"vapor pressure (T = 300 K): 994.7761635610095 kPa\n",
"boiling temperature (p = 3 bar): 247.84035574956758 K\n"
]
}
],
"source": [
"# This also works for mixtures, in which case the pure component properties are computed.\n",
"# Hence, the result is a list - that is why we use an index [0] here.\n",
"print(\"vapor pressure (T = 300 K):\", feos.PhaseEquilibrium.vapor_pressure(eos, 300*si.KELVIN)[0])\n",
"print(\"boiling temperature (p = 3 bar):\", feos.PhaseEquilibrium.boiling_temperature(eos, 2*si.BAR)[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Phase Diagram\n",
"\n",
"We could repeatedly compute `PhaseEquilibrium` states for different temperatures / pressures to generate a phase diagram.\n",
"Because this a common task, there is a object for that as well.\n",
"\n",
"The `PhaseDiagram` object creates multiple `PhaseEquilibrium` objects (`npoints` of those) between a given lower temperature and the critical point."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"dia = feos.PhaseDiagram.pure(eos, 230.0 * si.KELVIN, 500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can access each `PhaseEquilbrium` and then conveniently comput any property we like:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"enthalpy_of_vaporization = [\n",
" (vle.vapor.molar_enthalpy(feos.Contributions.Residual) - vle.liquid.molar_enthalpy(feos.Contributions.Residual)) / (si.KILO * si.JOULE) * si.MOL \n",
" for vle in dia.states\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 4))\n",
"sns.lineplot(x=dia.vapor.temperature / si.KELVIN, y=enthalpy_of_vaporization, ax=ax);\n",
"ax.set_ylabel(r\"$\\Delta^{LV}h$ / kJ / mol\")\n",
"ax.set_xlabel(r\"$T$ / K\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A more convenient way is to create a dictionary. The dictionary can be used to build pandas `DataFrame` objects.\n",
"This is a bit less flexible, because the units of the properties are rigid. You can inspect the method signature to check what units are used."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mSignature:\u001b[39m dia.to_dict(contributions)\n",
"\u001b[31mDocstring:\u001b[39m\n",
"Returns the phase diagram as dictionary.\n",
"\n",
"Parameters\n",
"----------\n",
"contributions : Contributions, optional\n",
" The contributions to consider when calculating properties.\n",
" Defaults to Contributions.Total.\n",
"\n",
"Returns\n",
"-------\n",
"Dict[str, List[float]]\n",
" Keys: property names. Values: property for each state.\n",
"\n",
"Notes\n",
"-----\n",
"- temperature : K\n",
"- pressure : Pa\n",
"- densities : mol / m³\n",
"- mass densities : kg / m³\n",
"- molar enthalpies : kJ / mol\n",
"- molar entropies : kJ / mol / K\n",
"- specific enthalpies : kJ / kg\n",
"- specific entropies : kJ / kg / K\n",
"- xi: liquid molefraction of component i\n",
"- yi: vapor molefraction of component i\n",
"- component index `i` matches to order of components in parameters.\n",
"\u001b[31mType:\u001b[39m builtin_function_or_method"
]
}
],
"source": [
"dia.to_dict?"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>temperature</th>\n",
" <th>pressure</th>\n",
" <th>density liquid</th>\n",
" <th>density vapor</th>\n",
" <th>molar enthalpy liquid</th>\n",
" <th>molar enthalpy vapor</th>\n",
" <th>molar entropy liquid</th>\n",
" <th>molar entropy vapor</th>\n",
" <th>mass density liquid</th>\n",
" <th>mass density vapor</th>\n",
" <th>specific enthalpy liquid</th>\n",
" <th>specific enthalpy vapor</th>\n",
" <th>specific entropy liquid</th>\n",
" <th>specific entropy vapor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>230.000000</td>\n",
" <td>96625.278174</td>\n",
" <td>14125.988947</td>\n",
" <td>52.208491</td>\n",
" <td>-18.921555</td>\n",
" <td>-0.157448</td>\n",
" <td>-0.035166</td>\n",
" <td>-0.000148</td>\n",
" <td>622.902434</td>\n",
" <td>2.302196</td>\n",
" <td>-429.097170</td>\n",
" <td>-3.570554</td>\n",
" <td>-0.797483</td>\n",
" <td>-0.003363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>230.280462</td>\n",
" <td>97830.133956</td>\n",
" <td>14118.006852</td>\n",
" <td>52.811929</td>\n",
" <td>-18.911909</td>\n",
" <td>-0.159193</td>\n",
" <td>-0.035119</td>\n",
" <td>-0.000150</td>\n",
" <td>622.550454</td>\n",
" <td>2.328805</td>\n",
" <td>-428.878435</td>\n",
" <td>-3.610123</td>\n",
" <td>-0.796418</td>\n",
" <td>-0.003399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>230.560924</td>\n",
" <td>99046.729400</td>\n",
" <td>14110.010220</td>\n",
" <td>53.420767</td>\n",
" <td>-18.902255</td>\n",
" <td>-0.160952</td>\n",
" <td>-0.035072</td>\n",
" <td>-0.000152</td>\n",
" <td>622.197833</td>\n",
" <td>2.355653</td>\n",
" <td>-428.659501</td>\n",
" <td>-3.650021</td>\n",
" <td>-0.795354</td>\n",
" <td>-0.003436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>230.841386</td>\n",
" <td>100275.143120</td>\n",
" <td>14101.999011</td>\n",
" <td>54.035036</td>\n",
" <td>-18.892592</td>\n",
" <td>-0.162726</td>\n",
" <td>-0.035025</td>\n",
" <td>-0.000153</td>\n",
" <td>621.844569</td>\n",
" <td>2.382740</td>\n",
" <td>-428.440366</td>\n",
" <td>-3.690251</td>\n",
" <td>-0.794290</td>\n",
" <td>-0.003473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>231.121849</td>\n",
" <td>101515.453964</td>\n",
" <td>14093.973182</td>\n",
" <td>54.654773</td>\n",
" <td>-18.882920</td>\n",
" <td>-0.164515</td>\n",
" <td>-0.034978</td>\n",
" <td>-0.000155</td>\n",
" <td>621.490660</td>\n",
" <td>2.410068</td>\n",
" <td>-428.221030</td>\n",
" <td>-3.730814</td>\n",
" <td>-0.793228</td>\n",
" <td>-0.003510</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" temperature pressure density liquid density vapor \\\n",
"0 230.000000 96625.278174 14125.988947 52.208491 \n",
"1 230.280462 97830.133956 14118.006852 52.811929 \n",
"2 230.560924 99046.729400 14110.010220 53.420767 \n",
"3 230.841386 100275.143120 14101.999011 54.035036 \n",
"4 231.121849 101515.453964 14093.973182 54.654773 \n",
"\n",
" molar enthalpy liquid molar enthalpy vapor molar entropy liquid \\\n",
"0 -18.921555 -0.157448 -0.035166 \n",
"1 -18.911909 -0.159193 -0.035119 \n",
"2 -18.902255 -0.160952 -0.035072 \n",
"3 -18.892592 -0.162726 -0.035025 \n",
"4 -18.882920 -0.164515 -0.034978 \n",
"\n",
" molar entropy vapor mass density liquid mass density vapor \\\n",
"0 -0.000148 622.902434 2.302196 \n",
"1 -0.000150 622.550454 2.328805 \n",
"2 -0.000152 622.197833 2.355653 \n",
"3 -0.000153 621.844569 2.382740 \n",
"4 -0.000155 621.490660 2.410068 \n",
"\n",
" specific enthalpy liquid specific enthalpy vapor specific entropy liquid \\\n",
"0 -429.097170 -3.570554 -0.797483 \n",
"1 -428.878435 -3.610123 -0.796418 \n",
"2 -428.659501 -3.650021 -0.795354 \n",
"3 -428.440366 -3.690251 -0.794290 \n",
"4 -428.221030 -3.730814 -0.793228 \n",
"\n",
" specific entropy vapor \n",
"0 -0.003363 \n",
"1 -0.003399 \n",
"2 -0.003436 \n",
"3 -0.003473 \n",
"4 -0.003510 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dia = pd.DataFrame(dia.to_dict(feos.Contributions.Residual))\n",
"data_dia.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have a dataframe, we can store our results or create a nicely looking plot:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def phase_plot(data, x, y):\n",
" fig, ax = plt.subplots(figsize=(12, 6))\n",
" if x != \"pressure\" and x != \"temperature\":\n",
" xl = f\"{x} liquid\"\n",
" xv = f\"{x} vapor\"\n",
" else:\n",
" xl = x\n",
" xv = x\n",
" if y != \"pressure\" and y != \"temperature\":\n",
" yl = f\"{y} liquid\"\n",
" yv = f\"{y} vapor\"\n",
" else:\n",
" yv = y\n",
" yl = y\n",
" sns.lineplot(data=data, x=xv, y=yv, ax=ax, label=\"vapor\")\n",
" sns.lineplot(data=data, x=xl, y=yl, ax=ax, label=\"liquid\")\n",
" ax.set_xlabel(x)\n",
" ax.set_ylabel(y)\n",
" ax.legend(frameon=False)\n",
" sns.despine();"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"phase_plot(data_dia, \"density\", \"temperature\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"phase_plot(data_dia, \"molar entropy\", \"temperature\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mixtures <a id=\"Mixtures\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"Fox mixtures, we have to add information about the composition, either as molar fraction, amount of substance per component, or as partial densities."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# propane, butane mixture\n",
"tc = np.array([369.96, 425.2]) * si.KELVIN\n",
"pc = np.array([4250000.0, 3800000.0]) * si.PASCAL\n",
"omega = np.array([0.153, 0.199])\n",
"molar_weight = np.array([44.0962, 58.123]) * si.GRAM / si.MOL\n",
"\n",
"pr = PyPengRobinson(tc, pc, omega, molar_weight)\n",
"eos = feos.EquationOfState.python_residual(pr)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|molefracs\n",
"|-|-|-|\n",
"|300.00000 K|40.96869 mol/m³|[0.50000, 0.50000]|"
],
"text/plain": [
"T = 300.00000 K, ρ = 40.96869 mol/m³, x = [0.50000, 0.50000]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = feos.State(\n",
" eos, \n",
" temperature=300*si.KELVIN, \n",
" pressure=1*si.BAR, \n",
" molefracs=np.array([0.5, 0.5]), \n",
" total_moles=si.MOL\n",
")\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As before, we can compute properties by calling methods on the `State` object. Some return vectors or matrices - for example the chemical potential and its derivative w.r.t amount of substance:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ -93.60749754, -120.5269973 ]) J/mol"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.chemical_potential(feos.Contributions.Residual)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 4.90721995, -0.10487987],\n",
" [-0.10487987, 4.85361765]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.dmu_dni() / (si.KILO * si.JOULE / si.MOL**2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Phase equilibria can be built from different constructors. E.g. at critical conditions given composition:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|molefracs\n",
"|-|-|-|\n",
"|401.65562 K|3.99954 kmol/m³|[0.50000, 0.50000]|"
],
"text/plain": [
"T = 401.65562 K, ρ = 3.99954 kmol/m³, x = [0.50000, 0.50000]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s_cp = feos.State.critical_point(eos, molefracs=np.array([0.5, 0.5]))\n",
"s_cp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or at given temperature (or pressure) and composition for bubble and dew points."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|molefracs|\n",
"|-|-|-|-|\n",
"|phase 1|350.00000 K|879.50373 mol/m³|[0.67631, 0.32369]|\n",
"|phase 2|350.00000 K|8.96383 kmol/m³|[0.50000, 0.50000]|\n"
],
"text/plain": [
"phase 0: T = 350.00000 K, ρ = 879.50373 mol/m³, x = [0.67631, 0.32369]\n",
"phase 1: T = 350.00000 K, ρ = 8.96383 kmol/m³, x = [0.50000, 0.50000]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.bubble_point(\n",
" eos, \n",
" 350*si.KELVIN, \n",
" liquid_molefracs=np.array([0.5, 0.5])\n",
")\n",
"vle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to pure substance phase diagrams, there is a constructor for binary systems."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"vle = feos.PhaseDiagram.binary_vle(eos, 350*si.KELVIN, npoints=50)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(18, 6))\n",
"# fig.title(\"T = 350 K, Propane (1), Butane (2)\")\n",
"sns.lineplot(x=vle.liquid.molefracs[:,0], y=vle.liquid.pressure / si.BAR, ax=ax[0])\n",
"sns.lineplot(x=vle.vapor.molefracs[:,0], y=vle.vapor.pressure / si.BAR, ax=ax[0])\n",
"ax[0].set_xlabel(r\"$x_1$, $y_1$\")\n",
"ax[0].set_ylabel(r\"$p$ / bar\")\n",
"ax[0].set_xlim(0, 1)\n",
"ax[0].set_ylim(5, 35)\n",
"# ax[0].legend(frameon=False);\n",
"\n",
"sns.lineplot(x=vle.liquid.molefracs[:,0], y=vle.vapor.molefracs[:,0], ax=ax[1])\n",
"sns.lineplot(x=np.linspace(0, 1, 10), y=np.linspace(0, 1, 10), color=\"black\", alpha=0.3, ax=ax[1])\n",
"ax[1].set_xlabel(r\"$x_1$\")\n",
"ax[1].set_ylabel(r\"$y_1$\")\n",
"ax[1].set_xlim(0, 1)\n",
"ax[1].set_ylim(0, 1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison to Rust implementation <a id=\"Comparison-to-Rust-implementation\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"Implementing an equation of state in Python is nice for quick prototyping and development but when it comes to performance, implementing the equation of state in Rust is the way to go.\n",
"For each non-cached call to the Helmholtz energy, we have to transition between Rust and Python with our Python implementation which generates quite some overhead.\n",
"\n",
"Here are some comparisons between the Rust and our Python implemenation:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# rust\n",
"eos_rust = feos.EquationOfState.peng_robinson(feos.Parameters.from_json([\"propane\"], \"data/peng-robinson.json\"))\n",
"\n",
"# python\n",
"tc = si.array(369.96 * si.KELVIN)\n",
"pc = si.array(4250000.0 * si.PASCAL)\n",
"omega = np.array([0.153])\n",
"molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n",
"eos_python = feos.EquationOfState.python_residual(PyPengRobinson(tc, pc, omega, molar_weight))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# let's first test if both actually yield the same results ;)\n",
"assert abs(feos.State.critical_point(eos_python).pressure() / si.BAR - feos.State.critical_point(eos_rust).pressure() / si.BAR) < 1e-12\n",
"assert abs(feos.State.critical_point(eos_python).temperature / si.KELVIN - feos.State.critical_point(eos_rust).temperature / si.KELVIN) < 1e-12"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import timeit\n",
"\n",
"time_python = timeit.timeit(lambda: feos.State.critical_point(eos_python), number=2_500)\n",
"time_rust = timeit.timeit(lambda: feos.State.critical_point(eos_rust), number=2_500)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Critical point for pure substance\n",
"Python implementation is slower by a factor of 25.\n"
]
}
],
"source": [
"rel_dev = (time_rust - time_python) / time_rust\n",
"print(f\"Critical point for pure substance\")\n",
"print(f\"Python implementation is {'slower' if rel_dev < 0 else 'faster'} by a factor of {abs(time_python / time_rust):.0f}.\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"time_python = timeit.timeit(lambda: feos.PhaseDiagram.pure(eos_python, 300*si.KELVIN, 100), number=100)\n",
"time_rust = timeit.timeit(lambda: feos.PhaseDiagram.pure(eos_rust, 300*si.KELVIN, 100), number=100)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase diagram for pure substance\n",
"Python implementation is slower by a factor of 79.\n"
]
}
],
"source": [
"rel_dev = (time_rust - time_python) / time_rust\n",
"print(f\"Phase diagram for pure substance\")\n",
"print(f\"Python implementation is {'slower' if rel_dev < 0 else 'faster'} by a factor of {abs(time_python / time_rust):.0f}.\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/pcsaft_working_with_parameters.ipynb | .ipynb | 43,095 | 1,328 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with parameters\n",
"\n",
"## Goal\n",
"\n",
"- Read in parameters for pure substances and mixtures from json files, and\n",
"- create parameters for pure substances and mixtures within Python.\n",
"- For both regular parameters as well as via the homo-segmented group contribution method.\n",
"- Learn how to access information stored in parameter objects."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameters and the equation of state\n",
"\n",
"Before we can start to compute physical properties, two steps are needed:\n",
"\n",
"1. Build a parameter object.\n",
"2. Instatiate the equation of state using the parameter object.\n",
"\n",
"In principle, every implementation of an equation of state can manage parameters differently but typically the workflow is similar for each implementation.\n",
"\n",
"We first generate the parameters object, `Parameters`, which we then use to generate the equation of state object using `EquationOfState.pcsaft()`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Read parameters from json file(s)\n",
"\n",
"The easiest way to create the `Parameters` object is to read information from one or more json files.\n",
"\n",
"- To read information from a single file, use `Parameters.from_json`\n",
"- To read information from multiple files, use `Parameters.from_multiple_json`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From a single file\n",
"\n",
"#### Pure substance\n",
"\n",
"Querying a substance from a file requires an *identifier*.\n",
"This identifier can be one of `Name`, `Cas`, `Inchi`, `IupacName`, `Formula`, or `Smiles` with `Name` (common english name) being the default.\n",
"We can change the identifier type usig the `identifier_option` argument with an `IdentifierOption` object. Given a list of identifiers and a path to the parameter file, we can conveniently generate our object."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" }\n",
"]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import feos\n",
"\n",
"# path to parameter file for substances that are non-associating, i.e. defined by three parameters: m, sigma, and epsilon_k.\n",
"file_na = '../parameters/pcsaft/gross2001.json' \n",
"\n",
"# a system containing a single substance, \"methane\", using \"Name\" as identifier (default)\n",
"parameters = feos.Parameters.from_json(['methane'], pure_path=file_na)\n",
"parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" }\n",
"]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a system containing a single substance, \"methane\", using \"Smiles\" (\"C\") as identifier\n",
"parameters = feos.Parameters.from_json(['C'], pure_path=file_na, identifier_option=feos.IdentifierOption.Smiles)\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Mixtures\n",
"\n",
"Reading parameters for more than one substance from a single file is very straight forward: simply add more identifiers to the list.\n",
"Note that the **order** in which which identifiers are provided **is important**. When computing vector valued properties, **the order of the physical properties matches the order of the substances within the parameter object**."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03|\n",
"|hexane|86.177|3.0576|3.7983|236.77|\n",
"|dodecane|170.338|5.306|3.8959|249.21|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"110-54-3\",\n",
" \"name\": \"hexane\",\n",
" \"iupac_name\": \"hexane\",\n",
" \"smiles\": \"CCCCCC\",\n",
" \"inchi\": \"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" \"formula\": \"C6H14\"\n",
" },\n",
" \"molarweight\": 86.177,\n",
" \"m\": 3.0576,\n",
" \"sigma\": 3.7983,\n",
" \"epsilon_k\": 236.77\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"112-40-3\",\n",
" \"name\": \"dodecane\",\n",
" \"iupac_name\": \"dodecane\",\n",
" \"smiles\": \"CCCCCCCCCCCC\",\n",
" \"inchi\": \"InChI=1/C12H26/c1-3-5-7-9-11-12-10-8-6-4-2/h3-12H2,1-2H3\",\n",
" \"formula\": \"C12H26\"\n",
" },\n",
" \"molarweight\": 170.338,\n",
" \"m\": 5.306,\n",
" \"sigma\": 3.8959,\n",
" \"epsilon_k\": 249.21\n",
" }\n",
"]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a system containing a ternary mixture\n",
"parameters = feos.Parameters.from_json(\n",
" ['methane', 'hexane', 'dodecane'], \n",
" pure_path=file_na\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From multiple files\n",
"\n",
"There may be cases where we have to split our parameter information across different files. For example, the `feos` repository has parameters stored in different files where each file corresponds to the parameter's original publication. Constructing the parameter object using multiple different json files is a bit more complicated. We can provide a list tuples, each of which contains the list of substances and the file where parameters are stored.\n",
"\n",
"In the example below, we define a 4 component mixture from three input files:\n",
"\n",
"- methane is read from a file containing non-associating substance parameters.\n",
"- parameters for 1-butanol and water are read from a file containing associating substances, and\n",
"- acetone parameters are read from a file that contains substances modelled with dipolar interactions."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|mu|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03||\n",
"|1-butanol|74.123|2.7515|3.6139|259.59||1|1|0.006692|2544.6|\n",
"|water|18.015|1.0656|3.0007|366.51||1|1|0.034868|2500.7|\n",
"|acetone|58.08|2.7447|3.2742|232.99|2.88|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"7732-18-5\",\n",
" \"name\": \"water\",\n",
" \"iupac_name\": \"oxidane\",\n",
" \"smiles\": \"O\",\n",
" \"inchi\": \"InChI=1/H2O/h1H2\",\n",
" \"formula\": \"H2O\"\n",
" },\n",
" \"molarweight\": 18.015,\n",
" \"m\": 1.0656,\n",
" \"sigma\": 3.0007,\n",
" \"epsilon_k\": 366.51,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.034868,\n",
" \"epsilon_k_ab\": 2500.7,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"67-64-1\",\n",
" \"name\": \"acetone\",\n",
" \"iupac_name\": \"propan-2-one\",\n",
" \"smiles\": \"CC(C)=O\",\n",
" \"inchi\": \"InChI=1/C3H6O/c1-3(2)4/h1-2H3\",\n",
" \"formula\": \"C3H6O\"\n",
" },\n",
" \"molarweight\": 58.08,\n",
" \"m\": 2.7447,\n",
" \"sigma\": 3.2742,\n",
" \"epsilon_k\": 232.99,\n",
" \"mu\": 2.88\n",
" }\n",
"]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# na = non-associating\n",
"# assoc = associating\n",
"file_na = '../parameters/pcsaft/gross2001.json'\n",
"file_assoc = '../parameters/pcsaft/gross2002.json'\n",
"file_dipolar = '../parameters/pcsaft/gross2006.json'\n",
"\n",
"parameters = feos.Parameters.from_multiple_json(\n",
" [\n",
" (['C'], file_na), \n",
" (['CCCCO', 'O'], file_assoc), \n",
" (['CC(C)=O'], file_dipolar)\n",
" ], \n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### With binary interaction parameters\n",
"\n",
"Some mixtures cannot be adequately described with combination rules from pure substance parameters.\n",
"In PC-SAFT, we can use a binary interaction parameter, `k_ij`, to enhance the description of mixture behavior.\n",
"These interaction parameters can be supplied from a json file via the `binary_path` option.\n",
"\n",
"This parameter is not shown in the default representation of the parameter object. You can access the matrix of `k_ij` via the getter, `PcSaftParameters.k_ij`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|\n",
"|1-butanol|74.123|2.7515|3.6139|259.59|1|1|0.006692|2544.6|\n",
"\n",
"|component 1|component 2|k_ij|\n",
"|-|-|-|\n",
"|butane|1-butanol|0.015|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" }\n",
"]\n",
"\n",
"[\n",
" {\n",
" \"id1\": 0,\n",
" \"id2\": 1,\n",
" \"k_ij\": 0.015\n",
" }\n",
"]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file_na = '../parameters/pcsaft/gross2001.json'\n",
"file_assoc = '../parameters/pcsaft/gross2002.json'\n",
"file_binary = '../parameters/pcsaft/gross2002_binary.json'\n",
"\n",
"parameters = feos.Parameters.from_multiple_json(\n",
" [\n",
" (['CCCC'], file_na), \n",
" (['CCCCO',], file_assoc)\n",
" ],\n",
" binary_path=file_binary,\n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Building parameters in Python\n",
"\n",
"Building `Parameters` in Python is a bit more involved since the `Parameters` object is built from multiple intermediate objects.\n",
"We need\n",
"\n",
"- the `Identifier` object that stores information about how a substance can be identified,\n",
"- the `PureRecord` object that bundles identifier and parameters together with the molar weight.\n",
"- the `BinaryRecord` object that contains binary interaction parameters."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Identifier(cas=106-97-8, name=butane, iupac_name=butane, smiles=CCCC, inchi=InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3, formula=C4H10)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"identifier = feos.Identifier(\n",
" cas='106-97-8',\n",
" name='butane',\n",
" iupac_name='butane',\n",
" smiles='CCCC',\n",
" inchi='InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3',\n",
" formula='C4H10'\n",
")\n",
"identifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `PureRecord` contains the identifier, molar weight and model parameters for a pure substance. At the point of creation, it has no information about the equation of state for which the parameters will ultimately be used. Therefore, there cannot be any checks for the validity of the parameters. Those happen when the equation of state is instantiated using, e.g., `EquationOfState.pcsaft()`\n",
"\n",
"For PC-SAFT *mandatory* parameters are\n",
"\n",
"- the number of segments, `m`, which is a dimensionless floating point number,\n",
"- the Lennard-Jones structure parameter (diameter), `sigma`, in units of Angstrom, and\n",
"- the Lennard-Jones energy parameter, `epsilon_k`, in units of Kelvin.\n",
"\n",
"*Optional* parameters are\n",
"\n",
"- the dipole moment, `mu`, in units of Debye used to model dipolar substances,\n",
"- the quadrupole moment, `q`, in units of Debye used to model quadrupolar substances,\n",
"- parameters to model association:\n",
" - `kappa_ab`, `epsilon_k_ab`, `na`, `nb`\n",
"- and parameters for entropy scaling:\n",
" - `viscosity`, `diffusion`, and `thermal_conductivity`\n",
" - each of which is a list containing coefficients for the respective correlation functions."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PureRecord(identifier: {cas: 106-97-8, name: butane, iupac_name: butane, smiles: CCCC, inchi: InChI=1/C4H10/c1-3-4-2/h3-4H2, 1-2H3, formula: C4H10}, molarweight: 58.123, m: 2.3316, sigma: 3.7086, epsilon_k: 222.88)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"butane = feos.PureRecord(identifier, molarweight=58.123, m=2.3316, sigma=3.7086, epsilon_k=222.88)\n",
"butane"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Parameters` for a single component \n",
"\n",
"For a single substance, we can use the `Parameters.new_pure` constructor."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" }\n",
"]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.new_pure(butane)\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Parameters` for binary mixtures\n",
"\n",
"We can create another `PureRecord` for a second component. Then, the `Parameters.new_binary` constructor let's us build the parameters. Optionally, we can also directly provide a `k_ij` value for this system. Here, we build a record for an associating substance."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"butan_1_ol = feos.PureRecord(\n",
" identifier=feos.Identifier(\n",
" cas='71-36-3',\n",
" name='1-butanol',\n",
" iupac_name='butan-1-ol',\n",
" smiles='CCCCO',\n",
" inchi='InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3',\n",
" formula='C4H10O'\n",
" ), \n",
" molarweight=74.123, \n",
" m=2.7515, \n",
" sigma=3.6139, \n",
" epsilon_k=259.59, \n",
" kappa_ab=0.006692, \n",
" epsilon_k_ab=2544.6\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|||\n",
"|1-butanol|74.123|2.7515|3.6139|259.59|0.006692|2544.6|\n",
"\n",
"|component 1|component 2|k_ij|\n",
"|-|-|-|\n",
"|butane|1-butanol|0.015|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6\n",
" }\n",
"]\n",
"\n",
"[\n",
" {\n",
" \"id1\": 0,\n",
" \"id2\": 1,\n",
" \"k_ij\": 0.015\n",
" }\n",
"]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.new_binary([butane, butan_1_ol], k_ij=0.015)\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Parameters` for mixtures with more than two components\n",
"\n",
"For mixtures with more than two components, we can use the `Parameters.from_records` constructor which takes a list of `PureRecords` and optionally binary interaction parameters."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|||\n",
"|1-butanol|74.123|2.7515|3.6139|259.59|0.006692|2544.6|\n",
"\n",
"|component 1|component 2|k_ij|\n",
"|-|-|-|\n",
"|butane|1-butanol|0.015|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6\n",
" }\n",
"]\n",
"\n",
"[\n",
" {\n",
" \"id1\": 0,\n",
" \"id2\": 1,\n",
" \"k_ij\": 0.015\n",
" }\n",
"]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binary = feos.BinaryRecord(feos.Identifier(name='butane'), feos.Identifier(name='1-butanol'), k_ij=0.015)\n",
"\n",
"parameters = feos.Parameters.from_records(\n",
" [butane, butan_1_ol], \n",
" binary_records=[binary]\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Parameters from group contribution methods\n",
"\n",
"An alternative to substance specific parameters are parameters that combine information from functional groups (molecule *segments*). As with regular parameters objects, we can build a `GcParameters` object from json or from Python - using segment information."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From json files\n",
"\n",
"We need at least two files: \n",
"\n",
"- `pure_path`: a file containing the substance identifiers *and* the segments that form the molecule\n",
"- `segments_path`: a file that contains the segments (identifier and model parameters)\n",
"\n",
"As before, we can specify our substance identifier using `identifier_option` and we can provide binary interaction parameters (segment-segment `k_ij`) via the `binary_path` argument."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<GcParameters at 0x7f1a8c3c9470>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pure_path = '../parameters/pcsaft/gc_substances.json'\n",
"segments_path = '../parameters/pcsaft/sauer2014_homo.json'\n",
"\n",
"parameters = feos.GcParameters.from_json_segments(\n",
" ['CCCC', 'CCCCO'], \n",
" pure_path, \n",
" segments_path, \n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From Python\n",
"\n",
"Building parameters in Python follows a similar approach as for regular parameters. To build `GcParameters` from segments, we need to specify:\n",
"\n",
"- The `ChemicalRecord` which contains the `Identifier` and the segments (as list of `str`s),\n",
"- and the `SegmentRecord` which specifies the identifier of the segment (has to be the same as in the list of the `ChemicalRecord`), the molar weight and model parameters.\n",
"\n",
"If both are available, we can use the `GcParameters.from_segments` constructor to build the parameters."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"cr1 = feos.ChemicalRecord(\n",
" identifier=feos.Identifier(\n",
" cas='106-97-8',\n",
" name='butane',\n",
" iupac_name='butane',\n",
" smiles='CCCC',\n",
" inchi='InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3',\n",
" formula='C4H10'\n",
"),\n",
" segments=['CH3', 'CH2', 'CH2', 'CH3']\n",
")\n",
"\n",
"cr2 = feos.ChemicalRecord(\n",
" identifier=feos.Identifier(\n",
" cas='71-36-3',\n",
" name='1-butanol',\n",
" iupac_name='butan-1-ol',\n",
" smiles='CCCCO',\n",
" inchi='InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3',\n",
" formula='C4H10O'\n",
" ),\n",
" segments=['CH3', 'CH2', 'CH2', 'CH2', 'OH']\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each segment has a `SegmentRecord` which can be constructed just like we did before for a substance."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"ch3 = feos.SegmentRecord(\n",
" 'CH3', \n",
" molarweight=15.0345, \n",
" m=0.61198,\n",
" sigma=3.7202,\n",
" epsilon_k=229.90\n",
")\n",
"ch2 = feos.SegmentRecord(\n",
" 'CH2', \n",
" molarweight=14.02658, \n",
" m=0.45606,\n",
" sigma=3.8900,\n",
" epsilon_k=239.01\n",
")\n",
"oh = feos.SegmentRecord(\n",
" 'OH', \n",
" molarweight=17.00734, \n",
" m=0.40200, \n",
" sigma=3.2859, \n",
" epsilon_k=488.66,\n",
" epsilon_k_ab=2517.0, \n",
" kappa_ab=0.006825\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<GcParameters at 0x7f1a8c3c9530>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.GcParameters.from_segments(\n",
" chemical_records=[cr1, cr2], \n",
" segment_records=[ch3, ch2, oh]\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing information from parameter objects\n",
"\n",
"Once the `Parameters` object is constructed, within a jupyter notebook, we get a nice representation in form of a markdown table.\n",
"Sometimes, however you might want to access information not presented in this table or you might want to store information in a variable.\n",
"\n",
"Let's build parameters for the four-component mixture we looked at earlier:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|mu|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03||\n",
"|1-butanol|74.123|2.7515|3.6139|259.59||1|1|0.006692|2544.6|\n",
"|water|18.015|1.0656|3.0007|366.51||1|1|0.034868|2500.7|\n",
"|acetone|58.08|2.7447|3.2742|232.99|2.88|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"7732-18-5\",\n",
" \"name\": \"water\",\n",
" \"iupac_name\": \"oxidane\",\n",
" \"smiles\": \"O\",\n",
" \"inchi\": \"InChI=1/H2O/h1H2\",\n",
" \"formula\": \"H2O\"\n",
" },\n",
" \"molarweight\": 18.015,\n",
" \"m\": 1.0656,\n",
" \"sigma\": 3.0007,\n",
" \"epsilon_k\": 366.51,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.034868,\n",
" \"epsilon_k_ab\": 2500.7,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"67-64-1\",\n",
" \"name\": \"acetone\",\n",
" \"iupac_name\": \"propan-2-one\",\n",
" \"smiles\": \"CC(C)=O\",\n",
" \"inchi\": \"InChI=1/C3H6O/c1-3(2)4/h1-2H3\",\n",
" \"formula\": \"C3H6O\"\n",
" },\n",
" \"molarweight\": 58.08,\n",
" \"m\": 2.7447,\n",
" \"sigma\": 3.2742,\n",
" \"epsilon_k\": 232.99,\n",
" \"mu\": 2.88\n",
" }\n",
"]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file_na = '../parameters/pcsaft/gross2001.json'\n",
"file_assoc = '../parameters/pcsaft/gross2002.json'\n",
"file_dipolar = '../parameters/pcsaft/gross2006.json'\n",
"\n",
"parameters = feos.Parameters.from_multiple_json(\n",
" [\n",
" (['C'], file_na), \n",
" (['CCCCO', 'O'], file_assoc), \n",
" (['CC(C)=O'], file_dipolar)\n",
" ], \n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get `PureRecord`s via `parameters.pure_records`\n",
"\n",
"We have seen above that it is possible to generate parameters in Python using intermediate objects, such as `Identifier`, `PcSaftRecord` and `PureRecord`.\n",
"You can generate these objects for all substances via the `pure_records` method (getter).\n",
"This getter returns `PureRecord` objects which can be further deconstructed to yield the `Identifier` and `PcSaftRecord` objects and the molar weight.\n",
"\n",
"Note that the order in which substances are returned matches the order in which we specified the substances above."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PureRecord(identifier: {cas: 74-82-8, name: methane, iupac_name: methane, smiles: C, inchi: InChI=1/CH4/h1H4, formula: CH4}, molarweight: 16.043, m: 1.0, sigma: 3.7039, epsilon_k: 150.03),\n",
" PureRecord(identifier: {cas: 71-36-3, name: 1-butanol, iupac_name: butan-1-ol, smiles: CCCCO, inchi: InChI=1/C4H10O/c1-2-3-4-5/h5H, 2-4H2, 1H3, formula: C4H10O}, molarweight: 74.123, m: 2.7515, sigma: 3.6139, epsilon_k: 259.59, association_sites: [{kappa_ab: 0.006692, epsilon_k_ab: 2544.6, na: 1.0, nb: 1.0}]),\n",
" PureRecord(identifier: {cas: 7732-18-5, name: water, iupac_name: oxidane, smiles: O, inchi: InChI=1/H2O/h1H2, formula: H2O}, molarweight: 18.015, m: 1.0656, sigma: 3.0007, epsilon_k: 366.51, association_sites: [{kappa_ab: 0.034868, epsilon_k_ab: 2500.7, na: 1.0, nb: 1.0}]),\n",
" PureRecord(identifier: {cas: 67-64-1, name: acetone, iupac_name: propan-2-one, smiles: CC(C)=O, inchi: InChI=1/C3H6O/c1-3(2)4/h1-2H3, formula: C3H6O}, molarweight: 58.08, m: 2.7447, sigma: 3.2742, epsilon_k: 232.99, mu: 2.88)]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters.pure_records"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"σ (methane)\t = 3.7039 A\n",
"σ (1-butanol)\t = 3.6139 A\n",
"σ (water)\t = 3.0007 A\n",
"σ (acetone)\t = 3.2742 A\n"
]
}
],
"source": [
"for pure_record in parameters.pure_records:\n",
" print(f\"σ ({pure_record.identifier.name})\\t = {pure_record.model_record[\"sigma\"]} A\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Identifier(cas=74-82-8, name=methane, iupac_name=methane, smiles=C, inchi=InChI=1/CH4/h1H4, formula=CH4)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get identifier of substance 0\n",
"parameters.pure_records[0].identifier"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16.043"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get molarweight of substance 0\n",
"parameters.pure_records[0].molarweight"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `PureRecord` object can be used to generate a json string which then can conveniently be stored in a file."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'{\"identifier\":{\"cas\":\"74-82-8\",\"name\":\"methane\",\"iupac_name\":\"methane\",\"smiles\":\"C\",\"inchi\":\"InChI=1/CH4/h1H4\",\"formula\":\"CH4\"},\"molarweight\":16.043,\"m\":1.0,\"sigma\":3.7039,\"epsilon_k\":150.03}'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate a json string from identifier of substance 0\n",
"parameters.pure_records[0].to_json_str()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing parameters from the equation of state\n",
"There is some parameter information that is not available from the `Parameters` struct:\n",
"- The matrix of association parameters (because it depends on model-specific combining rules)\n",
"- Parameters for homosegmented GC methods (because they depend on model-specific sum rules)\n",
"\n",
"To access those and all other parameters, we need to build the equation of state first. Then we can access its parameters using the `parameters` attribute."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'m': array([1. , 2.7515, 1.0656, 2.7447]),\n",
" 'sigma': array([3.7039, 3.6139, 3.0007, 3.2742]),\n",
" 'epsilon_k': array([150.03, 259.59, 366.51, 232.99]),\n",
" 'mu': array([0. , 0. , 0. , 2.88]),\n",
" 'na': array([1., 1.]),\n",
" 'nb': array([1., 1.]),\n",
" 'kappa_ab': array([[0.006692 , 0.01527536],\n",
" [0.01527536, 0.034868 ]]),\n",
" 'epsilon_k_ab': array([[2544.6 , 2522.65],\n",
" [2522.65, 2500.7 ]])}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eos = feos.EquationOfState.pcsaft(parameters)\n",
"eos.parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This also allows us to inspect parameters that are calculated from a homosegmented GC method"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'m': array([2.13608, 2.38216]),\n",
" 'sigma': array([3.79456883, 3.75681406]),\n",
" 'epsilon_k': array([233.79002903, 278.79916706]),\n",
" 'k_ij': array([[0., 0.],\n",
" [0., 0.]])}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.GcParameters.from_segments(\n",
" chemical_records=[cr1, cr2], \n",
" segment_records=[ch3, ch2, oh]\n",
")\n",
"eos = feos.EquationOfState.pcsaft(parameters)\n",
"eos.parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"In $\\text{FeO}_\\text{s}$, handling of parameters is not predefined within the underlying library. Each implementation of an equation of state has complete autonomy about the way parameters are handled.\n",
"Howevery, we offer some convenient ways to automatize parameter handling which result in the methods presented in this example.\n",
"\n",
"We looked at different ways to get parameters for the `EquationOfState.pcsaft()` equation of state, i.e.\n",
"\n",
"- how to read parameters for substances from one or more files where we use the json format to store information, and\n",
"- how to generate parameters the same parameters in Python.\n",
"- In a similar fashion, we showed how parameters can be assembled using a homo-GC method.\n",
"\n",
"Once parameters are created, we can retrieve information by extracting the intermediate objects such as `PureRecord`, `Identifier` and `PcSaftRecord`.\n",
"\n",
"## Further reading\n",
"\n",
"- Files of published parameters and further explanations can be found [in the `feos` github repositoy](https://github.com/feos-org/feos/tree/main/parameters/pcsaft).\n",
"\n",
"## Concluding remkars\n",
"\n",
"Hopefully you found this example helpful. If you have comments, critique or feedback, please let us know and consider [opening an issue on github](https://github.com/feos-org/feos/issues)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/gc_pcsaft_functional.ipynb | .ipynb | 225,374 | 210 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The (heterosegmented) group contribution PC-SAFT functional\n",
"In segment-based Helmholtz energy functionals like the one corresponding to the heterosegmented gc PC-SAFT equation of state, the spatial distribution of individual segments is calculated."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interfaces\n",
"In this example, the vapor-liquid interface of pure 1-pentanol is calculated. From the density profile, it becomes apparent how the polar hydroxy group slightly accumulates on the liquid side of the interface."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"parameters = feos.GcParameters.from_json_segments(\n",
" ['1-pentanol'], \n",
" '../parameters/pcsaft/gc_substances.json', \n",
" '../parameters/pcsaft/sauer2014_hetero.json'\n",
")\n",
"func = feos.HelmholtzEnergyFunctional.gc_pcsaft(parameters)\n",
"vle = feos.PhaseEquilibrium.pure(func, 300*si.KELVIN)\n",
"profile = feos.PlanarInterface.from_tanh(vle, 4096, 100*si.ANGSTROM, 600*si.KELVIN).solve()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots()\n",
"\n",
"ax.plot(profile.z/(si.NANO*si.METER), (profile.density*si.NAV*(si.NANO*si.METER)**3).T)\n",
"ax.legend(['CH$_3$'] + ['CH$_2$']*4 + ['OH'])\n",
"ax.set_xlabel('$z~~/~~\\\\mathrm{nm}$')\n",
"ax.set_ylabel('$\\\\rho~~/~~\\\\mathrm{nm}^{-3}$')\n",
"ax.axis([2.5,6,0,8]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Porous media\n",
"The orientation of polar molecules becomes even more apparent in confined media. Shown here, the orientation of 1-pentanol in a non-polar slit pore."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"bulk = feos.State(func, 300*si.KELVIN, pressure=5*si.BAR)\n",
"solver = feos.DFTSolver().picard_iteration(tol=1e-5, damping_coefficient=0.05).anderson_mixing()\n",
"pore = feos.Pore1D(\n",
" feos.Geometry.Cartesian, \n",
" 20*si.ANGSTROM, \n",
" feos.ExternalPotential.LJ93(3.0, 10.0, 0.08)\n",
").initialize(bulk).solve(solver)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots()\n",
"\n",
"ax.plot(pore.z/(si.NANO*si.METER), (pore.density*si.NAV*(si.NANO*si.METER)**3).T)\n",
"ax.legend(['CH$_3$'] + ['CH$_2$']*4 + ['OH'])\n",
"ax.set_xlabel('$z~~/~~\\\\mathrm{nm}$')\n",
"ax.set_ylabel('$\\\\rho~~/~~\\\\mathrm{nm}^{-3}$')\n",
"ax.axis([0,1.2,0,11]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Branched molecules\n",
"The heterosegmented DFT method is able to describe branched molecules. Note how the isomers 1-propanol and 2-propanol show significantly different orientations in a slit pore.\n",
"\n",
"Due to the symmetry of the 2-propanol molecules, only 3 distinct density profiles are visible."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def calc_pore(comp):\n",
" parameters = feos.GcParameters.from_json_segments(\n",
" [comp], \n",
" '../parameters/pcsaft/gc_substances.json', \n",
" '../parameters/pcsaft/sauer2014_hetero.json'\n",
" )\n",
" func = feos.HelmholtzEnergyFunctional.gc_pcsaft(parameters)\n",
" bulk = feos.State(func, 300*si.KELVIN, pressure=5*si.BAR)\n",
" solver = feos.DFTSolver().picard_iteration(tol=1e-5, damping_coefficient=0.05).anderson_mixing()\n",
" return feos.Pore1D(\n",
" feos.Geometry.Cartesian, \n",
" 20*si.ANGSTROM, \n",
" feos.ExternalPotential.LJ93(3.0, 10.0, 0.08)\n",
" ).initialize(bulk).solve(solver)\n",
"\n",
"comps = ['2-propanol', '1-propanol']\n",
"pores = [calc_pore(comp) for comp in comps]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(1,2,figsize=(15,5))\n",
"\n",
"for pore,comp,ax in zip(pores,comps,axes):\n",
" ax.plot(pore.z/(si.NANO*si.METER), (pore.density*si.NAV*(si.NANO*si.METER)**3).T)\n",
" if comp[0] == '2':\n",
" ax.legend(['CH$_3$', 'CH', 'OH', 'CH$_3$'])\n",
" elif comp[0] == '1':\n",
" ax.legend(['CH$_3$'] + ['CH$_2$']*2 + ['OH'])\n",
" ax.set_xlabel('$z~~/~~\\\\mathrm{nm}$')\n",
" ax.set_ylabel('$\\\\rho~~/~~\\\\mathrm{nm}^{-3}$')\n",
" ax.axis([0,1.2,0,15])\n",
" ax.set_title(comp)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/saftvrqmie/hydrogen_deuterium_binary.ipynb | .ipynb | 100,758 | 366 | {
"cells": [
{
"cell_type": "markdown",
"id": "d29be20b-79b2-48a0-a333-c1af140b3287",
"metadata": {},
"source": [
"# Surface tension calculations using DFT for Deuterium-Hydrogen mixtures\n",
"Classical density functional theory for interfacial properties of hydrogen, helium, deuterium, neon and their mixtures ([doi:10.1063/5.0137226](https://doi.org/10.1063/5.0137226))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b1e9c899-f524-4913-bed5-cf01cfb7ad35",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.dft import *\n",
"from feos.saftvrqmie import *\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.optimize import least_squares\n",
"import si_units as si\n",
"import seaborn as sns\n",
"import tqdm\n",
"import json\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')"
]
},
{
"cell_type": "markdown",
"id": "b62a863b-5b13-4b05-aeb7-d62de853b95f",
"metadata": {},
"source": [
"# Set up functional"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3324bf33-e087-4e87-91e9-9db68a0f13b3",
"metadata": {},
"outputs": [],
"source": [
"parameters = SaftVRQMieParameters.from_json([\"deuterium\", \"hydrogen\"], \n",
" \"../../parameters/saftvrqmie/hammer2023.json\", \n",
" binary_path=\"../../parameters/saftvrqmie/aasen2020_binary.json\")\n",
"eos = HelmholtzEnergyFunctional.saftvrqmie(parameters)"
]
},
{
"cell_type": "markdown",
"id": "1cab6ca7-d418-4012-9625-51aa687a9231",
"metadata": {},
"source": [
"## Critcal temperature estimate"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9dd67350-beaa-44d9-bf0f-aaa08ccc88e3",
"metadata": {},
"outputs": [],
"source": [
"cp = State.critical_point(eos, np.array([0.5, 0.5]) * si.MOL, initial_temperature=35*si.KELVIN)"
]
},
{
"cell_type": "markdown",
"id": "6dfe40bf-902a-441f-aa53-e398826ab2e5",
"metadata": {},
"source": [
"# Load experimental data\n",
"Grigorev and Rudenko.\n",
"*Surface tension of liquid hydrogen isotopes and hydrogen-deuterium solutions.*\n",
"Sov. phys. JETP 1965"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3896cf04-ccf6-470a-9a0c-299f7267b02c",
"metadata": {},
"outputs": [],
"source": [
"data = np.loadtxt(\"./data/hydrogen_deuterium_exp_data.dat\", skiprows=1)"
]
},
{
"cell_type": "markdown",
"id": "1f2916a9-0b8f-4eb9-a217-5df15d96ce40",
"metadata": {},
"source": [
"# Map isotherms for T=18.88 K and T=20.44 K"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "89f40dee-3f95-416e-a09c-9b39a6f2b48e",
"metadata": {},
"outputs": [],
"source": [
"dia_18 = PhaseDiagram.binary_vle(eos, 18.88 * si.KELVIN, npoints=21)\n",
"vle0 = [PhaseEquilibrium.bubble_point(eos, 18.88 * si.KELVIN, np.array([1e-10, 1-1e-10]))]\n",
"vle1 = [PhaseEquilibrium.bubble_point(eos, 18.88 * si.KELVIN, np.array([1- 1e-10, 1e-10]))]\n",
"sft_dia_18 = SurfaceTensionDiagram(vle0 + dia_18.states + vle1, n_grid=1024)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f8a36b06-d731-46a1-adac-4873ddb04204",
"metadata": {},
"outputs": [],
"source": [
"dia_20 = PhaseDiagram.binary_vle(eos, 20.44 * si.KELVIN, npoints=21)\n",
"vle0 = [PhaseEquilibrium.bubble_point(eos, 20.44 * si.KELVIN, np.array([1e-10, 1-1e-10]))]\n",
"vle1 = [PhaseEquilibrium.bubble_point(eos, 20.44 * si.KELVIN, np.array([1- 1e-10, 1e-10]))]\n",
"sft_dia_20 = SurfaceTensionDiagram(vle0 + dia_20.states + vle1, n_grid=1024)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "17631d24-f3a7-4b78-a071-197752546e60",
"metadata": {},
"outputs": [],
"source": [
"def surftens_mulero2012(fluid, tr):\n",
" \"\"\"\n",
" Calculate pure fluid surface tension using Mulero et al. 2012 correlation (doi:10.1063/1.4768782)\n",
" Args:\n",
" fluid (str): Component name\n",
" tr (np.ndarray): Reduced temperature\n",
" Returns:\n",
" sigma (np.ndarray): Surface tension (mN/m)\n",
" \"\"\"\n",
" ff = open(\"mulero_2012_parameters.json\", \"r\")\n",
" complist = json.load(ff)\n",
" ff.close()\n",
" sigma = np.zeros_like(tr)\n",
" for i in range(len(complist[fluid][\"sigma\"])):\n",
" sigma[:] += complist[fluid][\"sigma\"][i] * \\\n",
" (1-tr[:])**complist[fluid][\"n\"][i]\n",
" return sigma * si.NEWTON / si.METER / (si.MILLI * si.NEWTON/ si.METER)\n",
"s_h2_20 = surftens_mulero2012(\"hydrogen\", np.array([20.44/33.145]))\n",
"s_d2_20 = surftens_mulero2012(\"deuterium\", np.array([20.44/38.34]))\n",
"s_h2_18 = surftens_mulero2012(\"hydrogen\", np.array([18.88/33.145]))\n",
"s_d2_18 = surftens_mulero2012(\"deuterium\", np.array([18.88/38.34]))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fd157113-4a5f-4003-bd8a-9c6d0134d0e0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"colors = [\"k\",\"g\",\"r\",\"b\",\"grey\",\"orange\",\"cyan\"]\n",
"data_filter = np.isclose(data[:, 1], 20.44, atol=0.2)\n",
"plt.plot(sft_dia_20.liquid.molefracs[:,0]*100, sft_dia_20.surface_tension / (si.MILLI* si.NEWTON / si.METER), label='$T$=20.44 K', color=\"b\")\n",
"plt.plot(data[data_filter, 0], data[data_filter, 2], linestyle=\"None\", marker=\"o\", color=\"b\")\n",
"plt.plot(np.array([0.0]), s_h2_20, linestyle=\"None\", marker=\"*\", color=\"b\")\n",
"plt.plot(np.array([100.0]), s_d2_20, linestyle=\"None\", marker=\"*\", color=\"b\")\n",
"\n",
"data_filter = np.isclose(data[:, 1], 18.88, atol=0.2)\n",
"plt.plot(sft_dia_18.liquid.molefracs[:,0]*100, sft_dia_18.surface_tension / (si.MILLI* si.NEWTON / si.METER), label='$T$=18.88 K', color=\"k\")\n",
"plt.plot(data[data_filter, 0], data[data_filter, 2], linestyle=\"None\", marker=\"o\", color=\"k\")\n",
"plt.plot(np.array([0.0]), s_h2_18, linestyle=\"None\", marker=\"*\", color=\"k\")\n",
"plt.plot(np.array([100.0]), s_d2_18, linestyle=\"None\", marker=\"*\", color=\"k\")\n",
"\n",
"plt.xlabel(r'$x_{\\rm{D}_2}$ (%)')\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"leg = plt.legend(loc='best', frameon=False)"
]
},
{
"cell_type": "markdown",
"id": "50ae5159-6b75-44f1-830c-2d3061b52ef8",
"metadata": {},
"source": [
"# DFT for all experimental data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4262b481-6a26-498a-8d36-476440fdab69",
"metadata": {},
"outputs": [],
"source": [
"states = []\n",
"for i in range(np.shape(data)[0]):\n",
" T = data[i,1]\n",
" c_D2 = data[i,0]\n",
" surftens_exp = data[i,2]\n",
" x = np.array([c_D2, 100.0-c_D2])/100.0\n",
" vle = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x)\n",
" cp = State.critical_point(eos, x * si.MOL, initial_temperature=35.0*si.KELVIN) #, verbosity=Verbosity.Iter)\n",
" states.append([vle, cp, surftens_exp, T])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c22daf4d-994e-4da5-a37c-be7424f6731f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 68/68 [01:03<00:00, 1.08it/s]\n"
]
}
],
"source": [
"sft = []\n",
"for vle, cp, s_exp, T in tqdm.tqdm(states):\n",
" try:\n",
" sft.append({ \n",
" \"dft\": PlanarInterface.from_tanh(vle, 1024, 200 * si.ANGSTROM, cp.temperature).solve().surface_tension / (si.MILLI * si.NEWTON / si.METER),\n",
" \"exp\": s_exp,\n",
" \"temp\": T\n",
" })\n",
" except Exception as e:\n",
" print(e)\n",
" print(vle)"
]
},
{
"cell_type": "markdown",
"id": "3bbd7e8a-691c-478c-b7ef-b296a6c6f879",
"metadata": {},
"source": [
"# Plot error map"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b189bb10-50bf-401b-bd39-c96dcf796efd",
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(sft)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8bf21035-95f0-4265-a700-9221c5015d8b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7ff6d6c3b9a0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 648x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"smax = 4\n",
"err_fan = 0.1\n",
"df = pd.DataFrame(sft)\n",
"plt.scatter(df.exp, df.dft, c=df.temp, cmap='Blues', marker=\"o\")\n",
"plt.plot([0, smax], [0, smax], color=\"black\", linestyle=\"dashed\", alpha=1.0, lw=1.5)\n",
"plt.plot([0, smax*(1+err_fan/2)], [0, smax*(1-err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.plot([0, smax*(1-err_fan/2)], [0, smax*(1+err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"plt.xlabel(r'$\\gamma_{\\rm{Exp.}}$ (mN/m)')\n",
"plt.xlim(1.5, smax)\n",
"plt.ylim(1.5, smax)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"id": "252f6652-d546-4009-9f48-daf64f20b77f",
"metadata": {},
"source": [
"# Calculate mean absolute deviation (MAD)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9fc67b32-541d-4ca3-bfd2-04ba7de892c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAD=5.4 %\n"
]
}
],
"source": [
"mad = 100*np.sum(np.abs((df.dft-df.exp)/df.exp))/df.exp.shape[0]\n",
"print(f\"MAD={mad:.1f} %\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/saftvrqmie/hydrogen_neon_binary.ipynb | .ipynb | 105,664 | 396 | {
"cells": [
{
"cell_type": "markdown",
"id": "01dcbd2f-39fb-4d59-a7e7-5b113098ab53",
"metadata": {},
"source": [
"# Surface tension calculations using DFT for Hydrogen-Neon mixtures\n",
"Classical density functional theory for interfacial properties of hydrogen, helium, deuterium, neon and their mixtures ([doi:10.1063/5.0137226](https://doi.org/10.1063/5.0137226))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b1e9c899-f524-4913-bed5-cf01cfb7ad35",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.dft import *\n",
"from feos.saftvrqmie import *\n",
"import matplotlib.pyplot as plt\n",
"import si_units as si\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import tqdm\n",
"import json\n",
"from math import ceil\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')"
]
},
{
"cell_type": "markdown",
"id": "550efe70-e836-40e2-ba4a-d773efca8197",
"metadata": {},
"source": [
"# Set up functional"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3324bf33-e087-4e87-91e9-9db68a0f13b3",
"metadata": {},
"outputs": [],
"source": [
"parameters = SaftVRQMieParameters.from_json([\"hydrogen\", \"neon\"], \n",
" \"../../parameters/saftvrqmie/hammer2023.json\", \n",
" binary_path=\"../../parameters/saftvrqmie/aasen2020_binary.json\")\n",
"eos = HelmholtzEnergyFunctional.saftvrqmie(parameters)"
]
},
{
"cell_type": "markdown",
"id": "c30c7ad9-7894-4023-ac96-1c24e9b410ed",
"metadata": {},
"source": [
"# Utility functions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "23fe77cd-83fb-4fc4-8538-a84a225fa14f",
"metadata": {},
"outputs": [],
"source": [
"def surftens_mulero2012(fluid, tr):\n",
" \"\"\"\n",
" Calculate pure fluid surface tension using Mulero et al. 2012 correlation (doi:10.1063/1.4768782)\n",
" Args:\n",
" fluid (str): Component name\n",
" tr (np.ndarray): Reduced temperature\n",
" Returns:\n",
" sigma (np.ndarray): Surface tension (mN/m)\n",
" \"\"\"\n",
" ff = open(\"mulero_2012_parameters.json\", \"r\")\n",
" complist = json.load(ff)\n",
" ff.close()\n",
" sigma = np.zeros_like(tr)\n",
" for i in range(len(complist[fluid][\"sigma\"])):\n",
" sigma[:] += complist[fluid][\"sigma\"][i] * \\\n",
" (1-tr[:])**complist[fluid][\"n\"][i]\n",
" return sigma * si.NEWTON / si.METER / (si.MILLI * si.NEWTON/ si.METER)"
]
},
{
"cell_type": "markdown",
"id": "6ab3048d-73da-4480-8463-a24700664a50",
"metadata": {},
"source": [
"# Set experimental data and constants"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "42b237ea-1fa6-40b7-bdab-93acfc714841",
"metadata": {},
"outputs": [],
"source": [
"# Y. P. Blagoi and V. V. Pashkov\n",
"# Surface tension of normal hydrogen solutions in neon\n",
"# Sov. phys. JETP 28, 3 (1969)\n",
"#\n",
"# (Triple point composition is calculated using SAFT-VRQ Mie EoS)\n",
"neon_h2_data = {}\n",
"#\n",
"neon_h2_data[\"24.59\"] = {\"P\": np.array([1.91, 2.03, 2.52, 2.50]) * 1.01325 * si.BAR,\n",
" \"x_H2\": np.array([1.44, 1.57, 2.16, 2.13]) * 0.01,\n",
" \"sigma\": np.array([4.09, 3.70, 3.30, 3.13]) * si.MILLI * si.NEWTON / si.METER,\n",
" \"x_triple\": np.array([0.03107854, 0.96892146])}\n",
"#\n",
"neon_h2_data[\"26.33\"] = {\"P\": np.array([2.06, 2.49, 3.31, 3.99]) * 1.01325 * si.BAR, \n",
" \"x_H2\": np.array([1.22, 1.71, 2.83, 4.11]) * 0.01,\n",
" \"sigma\": np.array([4.15, 3.63, 2.98, 2.45]) * si.MILLI * si.NEWTON / si.METER,\n",
" \"x_triple\": np.array([0.05122161, 0.94877839])}\n",
"#\n",
"neon_h2_data[\"27.15\"] = {\"P\": np.array([2.49, 3.38, 4.09, 4.93]) * 1.01325 * si.BAR, \n",
" \"x_H2\": np.array([1.58, 2.73, 3.84, 5.88]) * 0.01,\n",
" \"sigma\": np.array([3.75, 3.00, 2.60, 1.83]) * si.MILLI * si.NEWTON / si.METER,\n",
" \"x_triple\": np.array([0.06421055, 0.93578945])}\n",
"#\n",
"neon_h2_data[\"29.00\"] = {\"P\": np.array([2.80, 3.36, 3.77, 4.89, 5.90]) * 1.01325 * si.BAR,\n",
" \"x_H2\": np.array([1.20, 1.94, 2.54, 4.48, 7.38]) * 0.01,\n",
" \"sigma\": np.array([3.51, 3.22, 3.00, 2.37, 1.73]) * si.MILLI * si.NEWTON / si.METER,\n",
" \"x_triple\": np.array([0.10680614, 0.89319386])}\n",
"#\n",
"colors = [\"k\",\"g\",\"r\",\"b\",\"grey\",\"orange\",\"cyan\"]\n",
"tc_neon = 44.49"
]
},
{
"cell_type": "markdown",
"id": "300a5e8d-0d5c-4398-9fbc-7dfc9f567586",
"metadata": {},
"source": [
"# Calculate state and surface tension for the experimental points"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "76a4766a-2eeb-47ca-9862-53a3b41f4df1",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4/4 [00:01<00:00, 3.02it/s]\n"
]
}
],
"source": [
"states_per_T = []\n",
"for key in tqdm.tqdm(neon_h2_data):\n",
" T = float(key)\n",
" states_T = []\n",
" for iP, P in enumerate(neon_h2_data[key][\"P\"]):\n",
" x_h2 = neon_h2_data[key][\"x_H2\"][iP]\n",
" x = np.array([x_h2, 1.0-x_h2])\n",
" vle = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x)\n",
" cp = State.critical_point(eos, x * si.MOL, initial_temperature=40.0*si.KELVIN)\n",
" states_T.append([vle, cp, neon_h2_data[key][\"sigma\"][iP], x_h2, T])\n",
" states_per_T.append(states_T)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6e7a30d5-025e-4aee-a12f-b15328e253c3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4/4 [00:21<00:00, 5.42s/it]\n"
]
}
],
"source": [
"sft_per_T = []\n",
"sft = []\n",
"for state_T in tqdm.tqdm(states_per_T):\n",
" sft_T = []\n",
" for vle, cp, s_exp, x_h2, T in state_T:\n",
" try:\n",
" sft_T.append({ \n",
" \"dft\": PlanarInterface.from_tanh(vle, 1024, 200 * si.ANGSTROM, cp.temperature).solve().surface_tension / (si.MILLI * si.NEWTON / si.METER),\n",
" \"exp\": s_exp / (si.MILLI * si.NEWTON / si.METER),\n",
" \"x_h2\": x_h2,\n",
" \"T\": T\n",
" })\n",
" sft.append(sft_T[-1])\n",
" except Exception as e:\n",
" print(e)\n",
" print(vle)\n",
" sft_per_T.append(sft_T)"
]
},
{
"cell_type": "markdown",
"id": "6279a930-8bf1-45f4-8e30-414fe9238433",
"metadata": {},
"source": [
"# Calculate mean absolute deviation (MAD)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4262b481-6a26-498a-8d36-476440fdab69",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAD=7.5 %\n"
]
}
],
"source": [
"df = pd.DataFrame(sft)\n",
"mad = 100*np.sum(np.abs((df.dft-df.exp)/df.exp))/df.exp.shape[0]\n",
"print(f\"MAD={mad:.1f} %\")"
]
},
{
"cell_type": "markdown",
"id": "cf16a581-b859-44a8-a5c7-86e1daf0f2bc",
"metadata": {},
"source": [
"# Plot surface tension error"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bde2f180-b703-4d51-8f7c-c42a2dd7fc46",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"err_fan = 0.1\n",
"groups = df.groupby('T')\n",
"for i, res in enumerate(groups):\n",
" name=res[0]\n",
" group=res[1]\n",
" plt.plot(group.exp, group.dft, \"o\", color=colors[i], label=str(name))\n",
"plt.plot([0, 5], [0, 5], color=\"black\", linestyle=\"dashed\", alpha=1.0, lw=1.5)\n",
"plt.plot([0, 5*(1+err_fan/2)], [0, 5*(1-err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.plot([0, 5*(1-err_fan/2)], [0, 5*(1+err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.xlim(1, 5)\n",
"plt.ylim(1, 5)\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"plt.xlabel(r'$\\gamma_{\\rm{Exp.}}$ (mN/m)')\n",
"leg = plt.legend(loc='best', ncol=2, frameon=False)"
]
},
{
"cell_type": "markdown",
"id": "8b9431c9-0d69-4e3c-808d-0eba7dd233a6",
"metadata": {},
"source": [
"# Calculate isotherms from pure fluid to triple point"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4448eda8-936c-40e1-a48e-304af159cd3b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4/4 [01:16<00:00, 19.06s/it]\n"
]
}
],
"source": [
"dft_sft_per_T = []\n",
"for key in tqdm.tqdm(neon_h2_data):\n",
" T = float(key)\n",
" x_T = neon_h2_data[key][\"x_triple\"] # Triple point\n",
" dft_sft_T = []\n",
" # Add some safety margin for the numerical differences between Thermopack and FEOS\n",
" x_h2 = np.linspace(1.0e-8, x_T[0]-1.0e-3, ceil(x_T[0]/0.005))\n",
" for x in x_h2:\n",
" z = np.array([x, 1.0-x])\n",
" vle = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, z)\n",
" cp = State.critical_point(eos, z * si.MOL, initial_temperature=40.0*si.KELVIN)\n",
" try:\n",
" dft_sft_T.append({ \n",
" \"dft\": PlanarInterface.from_tanh(vle, 1024, 200 * si.ANGSTROM, cp.temperature).solve().surface_tension / (si.MILLI * si.NEWTON / si.METER),\n",
" \"x_h2\": x\n",
" })\n",
" except Exception as e:\n",
" print(x)\n",
" print(T)\n",
" print(cp)\n",
" print(e)\n",
" print(vle)\n",
" dft_sft_per_T.append(dft_sft_T)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b61fa3f1-6bed-4bc2-883c-9becb76264b5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"for i, sft in enumerate(sft_per_T):\n",
" df_T = pd.DataFrame(sft)\n",
" plt.plot(df_T.x_h2, df_T.exp, \"o\", color=colors[i])\n",
"\n",
"labels = []\n",
"for key in neon_h2_data:\n",
" labels.append(\"$T$=\"+key+\" K\")\n",
"\n",
"for i, sft in enumerate(dft_sft_per_T):\n",
" df_T = pd.DataFrame(sft)\n",
" plt.plot(df_T.x_h2, df_T.dft, color=colors[i], label=labels[i])\n",
"\n",
"for i, key in enumerate(neon_h2_data):\n",
" tr = np.array([float(key)/tc_neon])\n",
" s_mulero = surftens_mulero2012(\"neon\", tr)\n",
" plt.plot([0.0], s_mulero, \"*\", color=colors[i])\n",
"\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"plt.xlabel(r'$x_{\\rm{H_2}}$')\n",
"leg = plt.legend(loc='best', ncol=2, frameon=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/saftvrqmie/hydrogen_fh1_vs_fh2.ipynb | .ipynb | 37,453 | 187 | {
"cells": [
{
"cell_type": "markdown",
"id": "06b263a1-b920-4b97-9bdf-238a87893e92",
"metadata": {},
"source": [
"# Surface tension calculations using DFT for Hydrogen\n",
"Comparing Feynman-Hibbs corrections to first and second order"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7f9082f3-e3e2-421b-b09a-a39160a83183",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.dft import *\n",
"from feos.saftvrqmie import *\n",
"import matplotlib.pyplot as plt\n",
"import si_units as si\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import json\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')"
]
},
{
"cell_type": "markdown",
"id": "0c514e92-9e5b-45a5-98b1-81f83eb4c104",
"metadata": {},
"source": [
"# Pure fluid surface tension correlations"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b334936e-587b-44d0-a92e-6dacd5f08e73",
"metadata": {},
"outputs": [],
"source": [
"def surftens_mulero2012(fluid, tr):\n",
" \"\"\"\n",
" Calculate pure fluid surface tension using Mulero 2012 correlation (doi:10.1063/1.4768782)\n",
" Args:\n",
" fluid (str): Component name\n",
" tr (np.ndarray): Reduced temperature\n",
" Returns:\n",
" sigma (np.ndarray): Surface tension (mN/m)\n",
" \"\"\"\n",
" ff = open(\"mulero_2012_parameters.json\", \"r\")\n",
" complist = json.load(ff)\n",
" ff.close()\n",
" sigma = np.zeros_like(tr)\n",
" for i in range(len(complist[fluid][\"sigma\"])):\n",
" sigma[:] += complist[fluid][\"sigma\"][i] * \\\n",
" (1-tr[:])**complist[fluid][\"n\"][i]\n",
" return sigma * si.NEWTON / si.METER / (si.MILLI * si.NEWTON/ si.METER)"
]
},
{
"cell_type": "markdown",
"id": "f445dc40-3c4f-417d-a430-ed4b11d9216f",
"metadata": {},
"source": [
"# Hydrogen parameters and functional"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4c8f318f-56e0-4a48-b21b-1ecab6bc10a3",
"metadata": {},
"outputs": [],
"source": [
"# Critical and normal boiling point temperature\n",
"Tc = 33.145\n",
"Tnb = 20.369\n",
"# First order Feynman-Hibbs corrections\n",
"parameters_fh1 = SaftVRQMieParameters.from_json([\"hydrogen\"], \"../../parameters/saftvrqmie/aasen2019.json\")\n",
"func_fh1 = HelmholtzEnergyFunctional.saftvrqmie(parameters_fh1)\n",
"state_fh1 = State(func_fh1, temperature=Tnb * si.KELVIN, pressure=20.0 * si.BAR)\n",
"model_fh1_tc = State.critical_point(func_fh1).temperature\n",
"# Second order Feynman-Hibbs corrections\n",
"parameters_fh2 = SaftVRQMieParameters.from_json([\"hydrogen\"], \"../../parameters/saftvrqmie/aasen2019_fh2.json\")\n",
"func_fh2 = HelmholtzEnergyFunctional.saftvrqmie(parameters_fh2)\n",
"state_fh2 = State(func_fh2, temperature=Tnb * si.KELVIN, pressure=20.0 * si.BAR)\n",
"model_fh2_tc = State.critical_point(func_fh2).temperature"
]
},
{
"cell_type": "markdown",
"id": "2ee15889-18da-4802-842f-5e042d673ecb",
"metadata": {},
"source": [
"# Calculate and plot surface tension"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c284f4b3-af36-4d50-8391-a7e44148b050",
"metadata": {},
"outputs": [],
"source": [
"# First order Feynman-Hibbs corrections\n",
"dia_fh1 = PhaseDiagram.pure(func_fh1, Tnb * si.KELVIN, npoints=50)\n",
"sft_dia_fh1 = SurfaceTensionDiagram(dia_fh1.states, n_grid=1024, l_grid=200 * si.ANGSTROM , critical_temperature=model_fh1_tc)\n",
"surf_tens_fh1 = sft_dia_fh1.surface_tension / (si.MILLI* si.NEWTON / si.METER)\n",
"# Second order Feynman-Hibbs corrections\n",
"dia_fh2 = PhaseDiagram.pure(func_fh2, Tnb * si.KELVIN, npoints=50)\n",
"sft_dia_fh2 = SurfaceTensionDiagram(dia_fh2.states, n_grid=1024, l_grid=200 * si.ANGSTROM , critical_temperature=model_fh2_tc)\n",
"surf_tens_fh2 = sft_dia_fh2.surface_tension / (si.MILLI* si.NEWTON / si.METER)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4e69ad65-f86a-432a-8c1b-b6a2378ff9ec",
"metadata": {},
"outputs": [],
"source": [
"tr = np.linspace(Tnb/Tc, 1.0, 100)\n",
"s_corr = surftens_mulero2012(\"hydrogen\", tr)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0247cea8-f6cd-436f-9e98-7dc14130e5ec",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"ax.plot(tr * Tc, s_corr, color='tab:red', label=\"Mulero et al. (2012)\", ls=\"--\")\n",
"ax.plot(dia_fh1.liquid.temperature/si.KELVIN, surf_tens_fh1, color='tab:blue', label='FH1-DFT')\n",
"ax.plot(dia_fh2.liquid.temperature/si.KELVIN, surf_tens_fh2, color='tab:green', label='FH2-DFT')\n",
"ax.set_xlim([19.5, 34.0])\n",
"ax.set_ylim([-0.1, 2.2])\n",
"ax.set_xlabel(r'$T$ (K)')\n",
"ax.set_ylabel(r'$\\gamma$ (mN/m)')\n",
"legend = ax.legend(loc='best', frameon=False, fontsize=12, ncol=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/saftvrqmie/quantum_fluid_dft.ipynb | .ipynb | 33,524 | 291 | {
"cells": [
{
"cell_type": "markdown",
"id": "06b263a1-b920-4b97-9bdf-238a87893e92",
"metadata": {},
"source": [
"# Surface tension calculations using DFT for fluids experiencing quantum effects \n",
"Classical density functional theory for interfacial properties of hydrogen, helium, deuterium, neon and their mixtures ([doi:10.1063/5.0137226](https://doi.org/10.1063/5.0137226))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7f9082f3-e3e2-421b-b09a-a39160a83183",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.dft import *\n",
"from feos.saftvrqmie import *\n",
"import matplotlib.pyplot as plt\n",
"import si_units as si\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import json\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')"
]
},
{
"cell_type": "markdown",
"id": "0c514e92-9e5b-45a5-98b1-81f83eb4c104",
"metadata": {},
"source": [
"# Pure fluid surface tension correlations"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b334936e-587b-44d0-a92e-6dacd5f08e73",
"metadata": {},
"outputs": [],
"source": [
"def surftens_mulero2012(fluid, tr):\n",
" \"\"\"\n",
" Calculate pure fluid surface tension using Mulero 2012 correlation (doi:10.1063/1.4768782)\n",
" Args:\n",
" fluid (str): Component name\n",
" tr (np.ndarray): Reduced temperature\n",
" Returns:\n",
" sigma (np.ndarray): Surface tension (mN/m)\n",
" \"\"\"\n",
" ff = open(\"mulero_2012_parameters.json\", \"r\")\n",
" complist = json.load(ff)\n",
" ff.close()\n",
" sigma = np.zeros_like(tr)\n",
" for i in range(len(complist[fluid][\"sigma\"])):\n",
" sigma[:] += complist[fluid][\"sigma\"][i] * \\\n",
" (1-tr[:])**complist[fluid][\"n\"][i]\n",
" return sigma * si.NEWTON / si.METER / (si.MILLI * si.NEWTON/ si.METER) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "58b2d141-7ab7-4938-b888-bc950d942848",
"metadata": {},
"outputs": [],
"source": [
"def surftens(param, tr):\n",
" \"\"\"\n",
" Calculate pure fluid surface tension A(1-tr)**(B+C*tr+D*tr**2)\n",
" Args:\n",
" param (np.ndarray): \n",
" tr (np.ndarray): Reduced temperature\n",
" Returns:\n",
" sigma (np.ndarray): Surface tension (mN/m)\n",
" \"\"\"\n",
" A = param[0]\n",
" exponent = np.zeros_like(tr)\n",
" exponent[:] = param[1]\n",
" for i in range(2,len(param)):\n",
" exponent += param[i]*tr**(i-1)\n",
" sigma = A*(1-tr)**exponent\n",
" return sigma * si.NEWTON / si.METER / (si.MILLI * si.NEWTON/ si.METER)"
]
},
{
"cell_type": "markdown",
"id": "f445dc40-3c4f-417d-a430-ed4b11d9216f",
"metadata": {},
"source": [
"# Pure fluid parameters"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "54513546-2b6a-4f44-9c2f-9f5f59fd9c1d",
"metadata": {},
"outputs": [],
"source": [
"fluid_param = {}\n",
"fluid_param[\"hydrogen\"] = {\"Tc\": 33.145, \"Tnb\": 20.369, \"xlim\": [19.5, 34.0], \"ylim\": [-0.1, 2.2], \"param\": None} #Alternative correlation from Hammer et al. (2023): [0.00480413, 0.67152591, 0.47781899]\n",
"fluid_param[\"para-hydrogen\"] = {\"Tc\": 32.938, \"Tnb\": 20.271, \"xlim\": [19.5, 34.0], \"ylim\": [-0.1, 2.2], \"param\": [0.00471037, 0.62668507, 0.51110266]} #Correlation parameters from Hammer et al. (2023)\n",
"fluid_param[\"neon\"] = {\"Tc\": 44.49, \"Tnb\": 27.1, \"xlim\": [26.0, 46.0], \"ylim\": [-0.1, 5.5], \"param\": None}\n",
"fluid_param[\"deuterium\"] = {\"Tc\": 38.34, \"Tnb\": 23.661, \"xlim\": [22.5, 39.5], \"ylim\": [-0.1, 3.1], \"param\": None}"
]
},
{
"cell_type": "markdown",
"id": "9ab9a113-6e49-48d4-acb5-cee8966087f2",
"metadata": {},
"source": [
"# Select fluid and set up functional"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "aa462031-732f-479b-80c3-2fb66e48d6d2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculating surface tension for \u001b[1mhydrogen\n"
]
}
],
"source": [
"substances = [\"hydrogen\", \"para-hydrogen\", \"neon\", \"deuterium\"]\n",
"substance = substances[0] # Setting fluid\n",
"print(f\"Calculating surface tension for \\033[1m{substance}\")"
]
},
{
"cell_type": "markdown",
"id": "adfa3259-d508-482b-8314-290d3d1781f5",
"metadata": {},
"source": [
"# Set up functional"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4c8f318f-56e0-4a48-b21b-1ecab6bc10a3",
"metadata": {},
"outputs": [],
"source": [
"parameters = SaftVRQMieParameters.from_json([substance], \"../../parameters/saftvrqmie/hammer2023.json\")\n",
"func = HelmholtzEnergyFunctional.saftvrqmie(parameters)\n",
"Tc = fluid_param[substance][\"Tc\"]\n",
"Tnb = fluid_param[substance][\"Tnb\"]\n",
"state = State(func, temperature=Tnb * si.KELVIN, pressure=20.0 * si.BAR)\n",
"model_tc = State.critical_point(func).temperature"
]
},
{
"cell_type": "markdown",
"id": "2ee15889-18da-4802-842f-5e042d673ecb",
"metadata": {},
"source": [
"# Calculate and plot surface tension"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c284f4b3-af36-4d50-8391-a7e44148b050",
"metadata": {},
"outputs": [],
"source": [
"dia = PhaseDiagram.pure(func, Tnb * si.KELVIN, npoints=50)\n",
"sft_dia = SurfaceTensionDiagram(dia.states, n_grid=1024, l_grid=200 * si.ANGSTROM , critical_temperature=model_tc)\n",
"surf_tens = sft_dia.surface_tension / (si.MILLI* si.NEWTON / si.METER)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4e69ad65-f86a-432a-8c1b-b6a2378ff9ec",
"metadata": {},
"outputs": [],
"source": [
"tr = np.linspace(Tnb/Tc, 1.0, 100)\n",
"if fluid_param[substance][\"param\"] is None:\n",
" s_corr = surftens_mulero2012(substance, tr)\n",
" corr_label = \"Mulero et al. (2012)\"\n",
"else:\n",
" s_corr = surftens(fluid_param[substance][\"param\"], tr)\n",
" corr_label = \"Hammer et al. (2023)\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0247cea8-f6cd-436f-9e98-7dc14130e5ec",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"ax.plot(tr * Tc, s_corr, color='tab:red', label=corr_label, ls=\"--\")\n",
"ax.plot(dia.liquid.temperature/si.KELVIN, surf_tens, color='tab:blue', label='DFT')\n",
"xlim = fluid_param[substance][\"xlim\"]\n",
"ylim = fluid_param[substance][\"ylim\"]\n",
"ax.set_xlim(xlim)\n",
"ax.set_ylim(ylim)\n",
"ax.set_xlabel(r'$T$ (K)')\n",
"ax.set_ylabel(r'$\\gamma$ (mN/m)')\n",
"legend = ax.legend(loc='best', frameon=False, fontsize=12, ncol=1)"
]
},
{
"cell_type": "markdown",
"id": "4a3385ce-d12a-4402-b139-10d852df2e2f",
"metadata": {},
"source": [
"# Calculate mean absolute deviation (MAD)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "dc17b6c6-47e5-414e-a432-cec8d01b493a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAD=4.4 %\n"
]
}
],
"source": [
"tr_mad = dia.liquid.temperature / Tc / si.KELVIN\n",
"tr_filer_mad = np.less_equal(tr_mad, 0.95)\n",
"trx_mad = tr_mad[tr_filer_mad]\n",
"if fluid_param[substance][\"param\"] is None:\n",
" s_corr_mad = surftens_mulero2012(substance, trx_mad)\n",
"else:\n",
" print(\"Correlation\")\n",
" s_corr_mad = surftens(fluid_param[substance][\"param\"], trx_mad)\n",
"mad = 100*np.sum(np.abs((surf_tens[tr_filer_mad]-s_corr_mad)/s_corr_mad))/s_corr_mad.shape[0]\n",
"print(f\"MAD={mad:.1f} %\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/saftvrqmie/radial_distribution_function.ipynb | .ipynb | 143,597 | 628 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "66146e02-48f2-4b2d-b2e3-50bda5cf345f",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.dft import *\n",
"from feos.saftvrqmie import *\n",
"\n",
"import si_units as si\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"sns.set_palette(\"Paired\")\n",
"colors = sns.color_palette(\"Dark2\", 3)\n",
"sns.set_context(\"talk\")\n",
"sns.set_style(\"ticks\")"
]
},
{
"cell_type": "markdown",
"id": "88ead60a-eb91-4d29-8f0a-00aaef3a2e71",
"metadata": {},
"source": [
"# Radial distribution function: comparison between Molecular Dynamics and DFT\n",
"\n",
"In this notebook, we compare the radial distribution function (rdf) as obtained from Molecular Dynamics (MD) simulations using LAMMPS and classical DFT.\n",
"The steps needed are:\n",
"\n",
"1. Read parameters of the SAFT-VRQ Mie equation of state from files, including binary parameters $k_{ij}$ and $l_ {ij}$.\n",
"2. Once the parameters are defined, we can initialize the functional.\n",
"3. We calculate the phase diagram of the mixture. We then select thermodynamic conditions for the simulation in the stable liquid phase.\n",
"4. Generate the LAMMPS tables for energies and forces for all interaction pairs. Run the simulation (not done here; we provide the simulation output).\n",
"5. Read in LAMMPS simulation results. Calculate the rdf using 1D DFT in spherical coordinates.\n",
"6. Plot the results."
]
},
{
"cell_type": "markdown",
"id": "31f54a8d-9128-42bf-8ffe-ecfbaec5d370",
"metadata": {
"tags": []
},
"source": [
"## Example system: Deuterium-Neon\n",
"\n",
"In this example, we will take a look at the binary system consisting of deuterium and neon.\n",
"When you run this example, make sure to adjust the relative path to the parameter files.\n",
"Parameters are published in the [FeO$_\\mathrm{s}$ github repository](https://github.com/feos-org/feos) in the `parameters` directory.\n",
"\n",
"As part of the SI you will find LAMMPS input files for all binary systems that we presented in the main article.\n",
"The procedure to calculate the rdf's is the same as presented in this document."
]
},
{
"cell_type": "markdown",
"id": "acefbecf-e020-44cf-bcc9-401773638ef2",
"metadata": {},
"source": [
"### Read in parameters\n",
"\n",
"We can read parameter from a *json* file using the `SaftVRQMieParameters.from_json` method.\n",
"By default, a list of *names* to define a mixture of substances has to be provided.\n",
"We also have to provide a file with the SAFT-VRQ Mie parameters and optionally another file with binary interaction parameters.\n",
"If we wanted to use other identifiers instead of the name (CAS, SMILES, ...), we could change the `IdentifierOption` to the appropriate data type."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0f0a312b-5b2b-40ad-a2b4-f5edd9d30b70",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|$\\sigma$|$\\varepsilon$|$\\lambda_r$|$\\lambda_a$|fh|\n",
"|-|-|-|-|-|-|-|\n",
"|deuterium|4.028209954364|3.0087|39.2388|11|7|1|\n",
"|neon|20.17969806457545|2.7778|37.501|13|6|1|"
],
"text/plain": [
"SaftVRQMieParameters(\n",
"\tmolarweight=[4.028209954364, 20.17969806457545]\n",
"\tm=[1, 1]\n",
"\tsigma=[3.0087, 2.7778]\n",
"\tepsilon_k=[39.2388, 37.501]\n",
"\tlr=[11, 13]\n",
"\tla=[7, 6]\n",
"\tk_ij=\n",
"[[0, 0.13],\n",
" [0.13, 0]]\n",
")"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = SaftVRQMieParameters.from_json(\n",
" [\"deuterium\", \"neon\"], \n",
" \"../../parameters/saftvrqmie/hammer2023.json\",\n",
" \"../../parameters/saftvrqmie/aasen2020_binary.json\",\n",
" identifier_option=IdentifierOption.Name\n",
")\n",
"parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7a677dd4-9271-4b12-bbb8-fe3f3825f2c3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([[0. , 0.13],\n",
" [0.13, 0. ]]),\n",
" array([[0., 0.],\n",
" [0., 0.]]))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters.k_ij, parameters.l_ij"
]
},
{
"cell_type": "markdown",
"id": "81301dbd-b4e7-4db1-80b9-14ee24af7573",
"metadata": {},
"source": [
"### Define the functional\n",
"\n",
"Once the parameters are set, we can define the functional.\n",
"This is done by the `HelmholtzEnergyFunctional.saftvrqmie` method.\n",
"We will use most of the default options in this example; note however that we can include or ignore the non-additive correction to the hard-sphere reference.\n",
"To calculate the phase diagram, we will **include the correction term**. \n",
"Later we will use both cases and compare the effect on the rdf."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1ad06f2f-3190-4d10-897b-8f1b359602b6",
"metadata": {},
"outputs": [],
"source": [
"functional = HelmholtzEnergyFunctional.saftvrqmie(parameters, inc_nonadd_term=True)"
]
},
{
"cell_type": "markdown",
"id": "5f1c838c-1dfd-4d1c-a986-7ff6e81ae82c",
"metadata": {},
"source": [
"### Phase diagram\n",
"\n",
"First, we generate the phase diagram ($p-x,y$ diagram) from which we then select appropriate thermodynamic conditions for our simulation in the stable liquid phase.\n",
"The phase diagram is constructed using the `PhaseDiagram.binary_vle` method.\n",
"We can either provide a temperature or a pressure as input (FeO$_\\mathrm{s}$ checks the actual unit to decide which diagram to compute), in conjunction with the numper of VLE's to be included.\n",
"Here, we use a constant temperature of $T = 32$K.\n",
"We can access all thermodynamic states of the VLE using the `liquid` and `vapor` fields.\n",
"With that, we can generate a nice plot."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e2b7efd-fb69-4275-b168-df0c4dfbddfd",
"metadata": {},
"outputs": [],
"source": [
"temperature = 32 * si.KELVIN\n",
"dia = PhaseDiagram.binary_vle(functional, temperature, npoints=50)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1c41dbca-fdd1-478b-bb2a-944ef77b801c",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 864x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 7))\n",
"plt.title(r\"{} (0), {} (1) at T = {}\".format(\n",
" parameters.pure_records[0].identifier.name,\n",
" parameters.pure_records[1].identifier.name,\n",
" temperature))\n",
"\n",
"plt.plot(dia.liquid.molefracs[:, 0], dia.liquid.pressure / si.BAR, label=\"liquid\")\n",
"plt.plot(dia.vapor.molefracs[:, 0], dia.vapor.pressure / si.BAR, label=\"vapor\")\n",
"plt.xlim(0, 1.0)\n",
"plt.ylim(3, 9)\n",
"plt.xlabel(r\"$x_0$\")\n",
"plt.ylabel(r\"$p$ / bar\")\n",
"plt.legend(frameon=False);"
]
},
{
"cell_type": "markdown",
"id": "565cc63a-2c10-495b-996a-ac012b6b2ae9",
"metadata": {},
"source": [
"We can now specify the thermodynamic condition for our simulation.\n",
"Here, we use a pressure of $p = 8$ bar with a molefraction for deuterium of $x_0 = 0.2$.\n",
"Note that the order of the indices is the order in which we defined our substances when we read our parameters from files.\n",
"\n",
"In LAMMPS, we conduct a NVT simulation, i.e. at constant temperature ($T = 32$ K), volume and number of molecules.\n",
"Thus, given temperature, pressure and the composition, we can calculate the mass density.\n",
"We will use the density and composition to later calculate the appropriate volume of our simulation box for a specific number of molecules, here $N=4000$.\n",
"\n",
"The density for given $T, p, \\mathbf{x}$ can be calculated using the `State` constructor.\n",
"The mass density and the temperature will be inputs for our LAMMPS input file."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "739c6bf6-bbaf-44fb-a75e-a828a35fc5ba",
"metadata": {},
"outputs": [],
"source": [
"pressure = 8 * si.BAR\n",
"x0 = 0.2\n",
"molefracs = np.array([x0, 1-x0])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "97413ca2-802d-48ec-857f-5c6805c74394",
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$812.99\\,\\mathrm{\\frac{kg}{m^{3}}}$"
],
"text/plain": [
"812.9908034253755 kg/m³"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state = State(functional, temperature, pressure=pressure, molefracs=molefracs)\n",
"state.mass_density()"
]
},
{
"cell_type": "markdown",
"id": "8405fa0c-7a8f-4a3a-a2d7-4e34bfbdc6b6",
"metadata": {},
"source": [
"### Generate LAMMPS energy and force tables\n",
"\n",
"In FeO$_\\mathrm{s}$ we provide an utility function to directly export LAMMPS `table` files from the parameters via the `lammps_tables` method of the `SaftVRQMieParameters` object.\n",
"This method will create files for all interaction pairs. Here, three files will be generated, one for deuterium-deuterium interactions, one for neon-neon and one for deuterium-neon.\n",
"\n",
"We have to provide the temperature (32 K), the number of points to store in the table (5000), and the minimum and maximum distance (0.1 and 12 Angstrom, respectively)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3b074123-73bc-4fce-94d1-60ae739afaf6",
"metadata": {},
"outputs": [],
"source": [
"parameters.lammps_tables(temperature, 5000, 0.1 * si.ANGSTROM, 12.0 * si.ANGSTROM)"
]
},
{
"cell_type": "markdown",
"id": "5956e018-19a4-407b-8ef1-6a05af60f3b2",
"metadata": {},
"source": [
"We provide an input file for the LAMMPS simulation that you can use or adjust for your system.\n",
"There are several adjustments necessary, depending on the system of interest.\n",
"\n",
"This is how the LAMMPS input file starts (here it is named `in.binary_deuterium_neon`)\n",
"\n",
"```\n",
"units real\n",
"atom_style atomic\n",
"\n",
"# substance\n",
"variable substance1 string deuterium\n",
"variable substance2 string neon\n",
"variable molarweight1 equal 4.028209954364\n",
"variable molarweight2 equal 20.17969806457545\n",
"\n",
"# thermodynamic state\n",
"variable x1 equal 0.2\n",
"variable initial_density equal 812.99 # kg / m3 @ p = 8 bar\n",
"variable t equal 32\n",
"variable natoms equal 4000\n",
"variable n1 equal v_x1*v_natoms\n",
"variable n2 equal v_natoms-v_n1\n",
"\n",
"variable molarweight_mix equal v_x1*v_molarweight1+(1-v_x1)*v_molarweight2\n",
"variable avogadro equal 6.022*10^23\n",
"variable initial_density_mol equal v_initial_density/v_molarweight_mix*v_avogadro*10^(-27) # N / V\n",
"variable unit_cell_volume equal 1/v_initial_density_mol\n",
"variable box_length equal v_unit_cell_volume^(1/3)*v_natoms^(1/3)\n",
"\n",
"variable rc equal 12.0\n",
"\n",
"```\n",
"\n",
"When changing to other substances, the following changes have to be done\n",
"\n",
"- Adjust the name of the `substance1` and `substance2` variables. The order has to match the order of the parameters for the functional (this will be the filename we generate with `lammps_table`)\n",
"- Adjust the molar weights accordingly. Those will be used to calculate the number density from which the volume of the simulation box is calculated.\n",
"- Adjust the mole fraction `x1`. Note that in this input we start indexing at `1`.\n",
"- Adjust the density (`initial_density`) and temperature variables.\n",
"- Adjust the cut-off radius `rc` above which the potential energies and forces are set to zero. That value should match the upper bound that we used to generate our LAMMPS tables.\n",
"\n",
"The substance names are used for the filenames that are generated during the simulation.\n",
"\n",
"You should always **check** the simulation log or output. **A simple consistency check is the density** reported by LAMMPS which has to match the density we computed from the `State` object above.\n",
"\n",
"*Example log output after equilibration:* \n",
"```\n",
"Step Temp PotEng KinEng Volume Density \n",
" 0 31.487142 -1205.8191 375.3349 138480.42 0.81297093 \n",
" 1000 31.608622 -1204.7169 376.78297 138480.42 0.81297093 \n",
" 2000 31.951485 -1198.6872 380.86999 138480.42 0.81297093 \n",
" 3000 32.182406 -1201.8826 383.62263 138480.42 0.81297093 \n",
" 4000 31.835575 -1200.741 379.48831 138480.42 0.81297093 \n",
" 5000 31.941999 -1206.9745 380.75691 138480.42 0.81297093 \n",
" 6000 31.438711 -1208.0502 374.75759 138480.42 0.81297093 \n",
" 7000 31.62501 -1207.2402 376.97832 138480.42 0.81297093\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "e0fa60a1-aebd-4d8f-936d-a1ef8a718252",
"metadata": {},
"source": [
"### Read simulation results and compute rdf via DFT\n",
"\n",
"Simulation results can be read using `pandas.read_csv`.\n",
"To make parsing easier, we can add a `#` in the fourth line and define it as comment when reading the lammps results."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "61208cab-04c8-408c-a1d1-c97db8701577",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2488791/3543885588.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
" simulation = pd.read_csv(\n"
]
},
{
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.060</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.084</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.108</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" i r g11 c11 g12 c12 g21 c21 g22 c22\n",
"0 1 0.012 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"1 2 0.036 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"2 3 0.060 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"3 4 0.084 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"4 5 0.108 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"simulation = pd.read_csv(\n",
" 'deuterium_neon_32K.rdf', \n",
" delim_whitespace=True, \n",
" comment=\"#\",\n",
")\n",
"simulation.head()"
]
},
{
"cell_type": "markdown",
"id": "e91537d2-3c3f-4129-9621-b0952a0600f5",
"metadata": {},
"source": [
"For DFT, we construct a second functional without the non-additive correction term (called `functional_additive`).\n",
"FeO$_\\mathrm{s}$ provides an object to calculate the rdf, the `PairCorrelation`.\n",
"\n",
"`PairCorrelation` takes a `State` as input, which we generate for given $T, p, \\mathbf{x}$ for both functionals.\n",
"We can then define the index of the substance that acts as fixed particle, the number of grid points and the maximum radial distance.\n",
"Once defined, we call the `solve` method to run the DFT calculation.\n",
"\n",
"After solving the equilibrium density profile, we can access the rdf for each substance given a fixed particle $i$ where $i$ is the index we used to initialize the `PairCorrelation` object.\n",
"To get all rdf for a binary system, two DFT calculations have to be conducted: one for substance 0 and one for substance 1 acting as fixed particle."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1d0d0f49-be2b-4784-a630-9c2343768a63",
"metadata": {},
"outputs": [],
"source": [
"functional_additive = HelmholtzEnergyFunctional.saftvrqmie(parameters, inc_nonadd_term=False)\n",
"state_additive = State(functional_additive, temperature, pressure=pressure, molefracs=molefracs)\n",
"\n",
"state = State(functional, temperature, pressure=pressure, molefracs=molefracs)\n",
"\n",
"rdf0_additive = PairCorrelation(state_additive, 0, 1024, 12 * si.ANGSTROM).solve()\n",
"rdf1_additive = PairCorrelation(state_additive, 1, 1024, 12 * si.ANGSTROM).solve()\n",
"\n",
"rdf0 = PairCorrelation(state, 0, 1024, 12 * si.ANGSTROM).solve()\n",
"rdf1 = PairCorrelation(state, 1, 1024, 12 * si.ANGSTROM).solve()"
]
},
{
"cell_type": "markdown",
"id": "67956e16-8add-4042-8d0b-a66cb0e73ee5",
"metadata": {},
"source": [
"### Plot the results"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d3b96038-d595-4d36-9823-f15458766bfc",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 936x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(13, 7))\n",
"plt.plot(rdf0_additive.r / si.ANGSTROM, rdf0_additive.pair_correlation_function[0], color=colors[0], linestyle='dashed', label=r\"$g_{00}$ (DFT)\")\n",
"plt.plot(rdf0.r / si.ANGSTROM, rdf0.pair_correlation_function[0], color=colors[0], linestyle='dotted', label=r\"$g_{00}$ (DFT, nonadditive)\")\n",
"plt.plot(simulation.r, simulation.g11, color=colors[0], label=r\"$g_{00}$ (MD)\")\n",
"\n",
"plt.plot(rdf1_additive.r / si.ANGSTROM, rdf1_additive.pair_correlation_function[1], color=colors[1], linestyle='dashed', label=r\"$g_{11}$ (DFT)\")\n",
"plt.plot(rdf1.r / si.ANGSTROM, rdf1.pair_correlation_function[1], color=colors[1], linestyle='dotted', label=r\"$g_{11}$ (DFT, nonadditive)\")\n",
"plt.plot(simulation.r, simulation.g22, color=colors[1], label=r\"$g_{11}$ (MD)\")\n",
"\n",
"plt.plot(rdf0_additive.r / si.ANGSTROM, rdf0_additive.pair_correlation_function[1], color=colors[2], linestyle='dashed', label=r\"$g_{01}$ (DFT)\")\n",
"plt.plot(rdf0.r / si.ANGSTROM, rdf0.pair_correlation_function[1], color=colors[2], linestyle='dotted', label=r\"$g_{01}$ (DFT, nonadditive)\")\n",
"plt.plot(simulation.r, simulation.g12, color=colors[2], label=r\"$g_{01}$ (MD)\")\n",
"\n",
"plt.hlines(1.0, 0, 15, color=\"black\", linestyle=\"dashed\", alpha=0.5)\n",
"plt.xlim(2, 12)\n",
"plt.ylim(0, 4.0)\n",
"\n",
"plt.xlabel(r\"r / $\\mathrm{\\AA}$\")\n",
"plt.ylabel(\"$g(r)$\")\n",
"\n",
"plt.legend(frameon=False, loc=\"best\", ncol=3);\n",
"plt.tight_layout()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/saftvrqmie/deuterium_helium_binary.ipynb | .ipynb | 123,588 | 492 | {
"cells": [
{
"cell_type": "markdown",
"id": "535e17ea-fe17-47c0-9b56-358f3f5541fa",
"metadata": {},
"source": [
"# Surface tension calculations using DFT for Helium-Deuterium mixtures\n",
"Classical density functional theory for interfacial properties of hydrogen, helium, deuterium, neon and their mixtures ([doi:10.1063/5.0137226](https://doi.org/10.1063/5.0137226))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b1e9c899-f524-4913-bed5-cf01cfb7ad35",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.dft import *\n",
"from feos.saftvrqmie import *\n",
"import matplotlib.pyplot as plt\n",
"import si_units as si\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import tqdm\n",
"import json\n",
"from scipy.optimize import fsolve\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')"
]
},
{
"cell_type": "markdown",
"id": "4026bf8d-a6b7-42a8-8040-695b73361041",
"metadata": {},
"source": [
"# Set up FEOS functional"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3324bf33-e087-4e87-91e9-9db68a0f13b3",
"metadata": {},
"outputs": [],
"source": [
"parameters = SaftVRQMieParameters.from_json([\"helium\", \"deuterium\"], \n",
" \"../../parameters/saftvrqmie/hammer2023.json\", \n",
" binary_path=\"../../parameters/saftvrqmie/aasen2020_binary.json\")\n",
"eos = HelmholtzEnergyFunctional.saftvrqmie(parameters)"
]
},
{
"cell_type": "markdown",
"id": "26caf1d0-ca66-4753-82db-a4557386efa9",
"metadata": {},
"source": [
"## Critcal temperature estimate"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9dd67350-beaa-44d9-bf0f-aaa08ccc88e3",
"metadata": {},
"outputs": [],
"source": [
"tc_deuterium = 38.34\n",
"cp = State.critical_point(eos, np.array([0.5, 0.5]) * si.MOL, initial_temperature=35*si.KELVIN)"
]
},
{
"cell_type": "markdown",
"id": "2833757d-c7b7-4f1c-8173-a4409698132c",
"metadata": {},
"source": [
"# Utility functions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f68a0bd7-5caa-418b-8dd7-6e62a84259f7",
"metadata": {},
"outputs": [],
"source": [
"def density_error(x_he, T, eos, rho_he_spec):\n",
" \"\"\"Evaluate relative error in partial density for helium\"\"\"\n",
" x = np.array([x_he[0]/1000, 1-x_he[0]/1000])\n",
" vle_guess = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x)\n",
" rho_he = vle_guess.vapor.partial_density[0] / si.MOL * si.METER**3\n",
" error = (rho_he_spec - rho_he)/rho_he_spec\n",
" return error\n",
"\n",
"def surftens_mulero2012(fluid, tr):\n",
" \"\"\"\n",
" Calculate pure fluid surface tension using Mulero et al. 2012 correlation (doi:10.1063/1.4768782)\n",
" Args:\n",
" fluid (str): Component name\n",
" tr (np.ndarray): Reduced temperature\n",
" Returns:\n",
" sigma (np.ndarray): Surface tension (mN/m)\n",
" \"\"\"\n",
" ff = open(\"mulero_2012_parameters.json\", \"r\")\n",
" complist = json.load(ff)\n",
" ff.close()\n",
" sigma = np.zeros_like(tr)\n",
" for i in range(len(complist[fluid][\"sigma\"])):\n",
" sigma[:] += complist[fluid][\"sigma\"][i] * \\\n",
" (1-tr[:])**complist[fluid][\"n\"][i]\n",
" return sigma * si.NEWTON / si.METER / (si.MILLI * si.NEWTON/ si.METER)"
]
},
{
"cell_type": "markdown",
"id": "662d51a3-25c9-4f66-95df-e42d000475a4",
"metadata": {},
"source": [
"# Load experimental data and set some constants\n",
"Paine and Seidel.\n",
"*The adsorption of helium on liquid hydrogen.*\n",
"Phys. B: Condens. Matter, 1994."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "59adc4e1-4b45-4866-9eae-45e9584024cb",
"metadata": {},
"outputs": [],
"source": [
"data = np.loadtxt(\"./data/deuterium_helium_exp_data.dat\", skiprows=1)\n",
"NA = 6.02214857E+23\n",
"Temperatures = np.unique(data[:, 0])"
]
},
{
"cell_type": "markdown",
"id": "28113b38-663f-4c56-95d8-25bb78cde0e1",
"metadata": {},
"source": [
"# Polynomial $x_{\\rm{He}}(\\rho_{\\rm{He}}) \\times 1000$ along Pxy curve"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "26bbc37f-cb82-44ba-b9de-0fe4133cdd3b",
"metadata": {},
"outputs": [],
"source": [
"polynomialfit = {}\n",
"polynomialfit[\"20\"] = np.poly1d([2.77511195e-13, -1.62794362e-08, 1.04061440e-03, 3.55749085e-03])\n",
"polynomialfit[\"21\"] = np.poly1d([3.11050375e-13, -1.78452507e-08, 1.25231735e-03, 3.42794882e-03])\n",
"polynomialfit[\"22\"] = np.poly1d([3.41745522e-13, -1.91263957e-08, 1.49186801e-03, 3.41256793e-03])\n",
"polynomialfit[\"23\"] = np.poly1d([3.80433522e-13, -2.02545613e-08, 1.76336232e-03, 3.12420795e-03])\n",
"polynomialfit[\"24\"] = np.poly1d([4.18253168e-13, -2.08976527e-08, 2.06946383e-03, 2.97772280e-03])\n",
"polynomialfit[\"26\"] = np.poly1d([5.19526320e-13, -2.04475681e-08, 2.80633414e-03, 2.62607082e-03])"
]
},
{
"cell_type": "markdown",
"id": "30763142-e9f3-44a4-96b3-47ed6b7a8321",
"metadata": {},
"source": [
"# Find helium vapor composition, $x_{\\rm{max}}$, given $\\rho_{\\rm{He}} (x_{\\rm{max}})$ = $\\rho_{\\rm{max}}$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3896cf04-ccf6-470a-9a0c-299f7267b02c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6/6 [00:04<00:00, 1.20it/s]\n"
]
}
],
"source": [
"rho_max = 12000.0\n",
"x_max = []\n",
"x_he_fun_of_rho = {}\n",
"for T in tqdm.tqdm(Temperatures):\n",
" x0 = polynomialfit[str(int(T))](rho_max)\n",
" # Solve for rho_he(x) = rho_max\n",
" x = fsolve(density_error, x0, args=(T, eos, rho_max))\n",
" x_max.append(x[0]/1000)"
]
},
{
"cell_type": "markdown",
"id": "c2aafa05-af33-4fa9-b51d-4bdc1fce7d53",
"metadata": {},
"source": [
"# Map isotherms to $x_{\\rm{He}}=x_{\\rm{max}}$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "76a4766a-2eeb-47ca-9862-53a3b41f4df1",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6/6 [00:08<00:00, 1.41s/it]\n"
]
}
],
"source": [
"states = []\n",
"for i, T in enumerate(tqdm.tqdm(Temperatures)):\n",
" xa = np.linspace(1.0e-8, x_max[i], 20)\n",
" T_states = []\n",
" for x in xa:\n",
" x_sol = np.array([x, 1-x])\n",
" state = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x_sol)\n",
" T_states.append(state)\n",
" states.append(T_states)"
]
},
{
"cell_type": "markdown",
"id": "f20b3e86-56f9-4579-9458-77ed1f58b779",
"metadata": {},
"source": [
"# Calculate and plot surface tension along the isotherms"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a5f84762-277a-4bd7-b801-3ffceefb000d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6/6 [01:49<00:00, 18.26s/it]\n"
]
}
],
"source": [
"gamma = []\n",
"for T_states in tqdm.tqdm(states):\n",
" gamma_T = []\n",
" for state in T_states:\n",
" gamma_T.append(PlanarInterface.from_tanh(state, 1024, 200 * si.ANGSTROM, cp.temperature).solve().surface_tension / (si.MILLI * si.NEWTON / si.METER))\n",
" gamma.append(gamma_T)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6b680af8-4e72-4fc4-a3da-843a5700c895",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"colors = [\"k\",\"g\",\"r\",\"b\",\"grey\",\"orange\",\"cyan\"]\n",
"for i, T in enumerate(Temperatures):\n",
" rho = []\n",
" for state in states[i]:\n",
" rho.append(state.vapor.partial_density[0] / si.MOL * si.METER**3)\n",
" plt.plot(np.array(rho)/1000, gamma[i], label=f\"{T:.1f} K\", color=colors[i])\n",
" rho_exp = data[np.isclose(data[:, 0], T, atol=0.001),1] * 1e27 / NA\n",
" sur_tens_exp = data[np.isclose(data[:, 0], T, atol=0.001),2]\n",
" plt.plot(rho_exp/1000, sur_tens_exp, linestyle=\"None\", marker=\"o\", color=colors[i])\n",
" tr = np.array([T/tc_deuterium])\n",
" s_nist = surftens_mulero2012(\"deuterium\", tr)\n",
" plt.plot([0.0], s_nist, \"*\", color=colors[i]) \n",
"\n",
"plt.xlabel(r'$\\rho_{\\rm{He}}$ (kmol/m$^3$)')\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"leg = plt.legend(loc='best', ncol=2, frameon=False)"
]
},
{
"cell_type": "markdown",
"id": "6c47afd2-2565-427e-9758-082a3bb31cef",
"metadata": {},
"source": [
"# Calculate composition given partial helium density of each experimental point"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "42b237ea-1fa6-40b7-bdab-93acfc714841",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6/6 [00:42<00:00, 7.10s/it]\n"
]
}
],
"source": [
"exp_states = []\n",
"for T in tqdm.tqdm(Temperatures):\n",
" # Loop all experiments on current temperature\n",
" data_T = data[np.isclose(data[:,0],T),1:3]\n",
" for j in range(np.shape(data_T)[0]):\n",
" rho_he_j = data_T[j,0] * 1e27 / NA\n",
" surftens_exp = data_T[j,1]\n",
" # Interpolate to get initial guess, x0: rho_he(x0) ~= rho_he_j\n",
" x0 = polynomialfit[str(int(T))](rho_he_j)\n",
" # Solve for rho_he(x) = rho_he_j\n",
" x = fsolve(density_error, x0, args=(T, eos, rho_he_j))\n",
" exp_states.append([x[0]/1000, surftens_exp, T])"
]
},
{
"cell_type": "markdown",
"id": "3b72f673-0ee9-43dd-97bc-d65e935b5918",
"metadata": {},
"source": [
"# Calculate state and surface tension for the experimental points"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "230ef919-038a-4b94-9bdb-1e3f8376ec83",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 69/69 [00:04<00:00, 14.01it/s]\n"
]
}
],
"source": [
"states = []\n",
"for x, s_exp, T in tqdm.tqdm(exp_states):\n",
" x_sol = np.array([x, 1-x])\n",
" state = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x_sol)\n",
" states.append([state, s_exp, T])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "baa71ae1-ebdb-45a3-adaf-ae0a1b204314",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 69/69 [01:03<00:00, 1.09it/s]\n"
]
}
],
"source": [
"sft = []\n",
"for state, s_exp, T in tqdm.tqdm(states):\n",
" sft.append({\n",
" \"dft\": PlanarInterface.from_tanh(state, 1024, 200 * si.ANGSTROM, cp.temperature).solve().surface_tension / (si.MILLI * si.NEWTON / si.METER), \n",
" \"exp\": s_exp,\n",
" \"T\": T}\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "52550387-b342-403b-a984-770ec0caadb6",
"metadata": {},
"source": [
"# Calculate mean absolute deviation (MAD)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "afd35d6c-1ef6-4599-83a2-aaaf2175b585",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAD=1.3 %\n"
]
}
],
"source": [
"df = pd.DataFrame(sft)\n",
"mad = 100*np.sum(np.abs((df.dft-df.exp)/df.exp))/df.exp.shape[0]\n",
"print(f\"MAD={mad:.1f} %\")"
]
},
{
"cell_type": "markdown",
"id": "167de8bc-1090-4455-8221-5cfced6cc0f0",
"metadata": {},
"source": [
"# Plot error map"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bfb4d45e-f9c6-44f6-98fc-a56a95503028",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"smax = 4\n",
"err_fan = 0.1\n",
"groups = df.groupby('T')\n",
"for i, res in enumerate(groups):\n",
" name=res[0]\n",
" group=res[1]\n",
" plt.plot(group.exp, group.dft, \"o\", color=colors[i], label=str(name))\n",
"plt.plot([0, smax], [0, smax], color=\"black\", linestyle=\"dashed\", alpha=1.0, lw=1.5)\n",
"plt.plot([0, smax*(1+err_fan/2)], [0, smax*(1-err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.plot([0, smax*(1-err_fan/2)], [0, smax*(1+err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"plt.xlabel(r'$\\gamma_{\\rm{Exp.}}$ (mN/m)')\n",
"plt.xlim(1, smax)\n",
"plt.ylim(1, smax)\n",
"leg = plt.legend(loc='best', ncol=2, frameon=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/saftvrqmie/hydrogen_helium_binary.ipynb | .ipynb | 113,541 | 494 | {
"cells": [
{
"cell_type": "markdown",
"id": "0b894b1b-ea24-4039-9aea-b034f01c8da2",
"metadata": {},
"source": [
"# Surface tension calculations using DFT for Helium-Para-Hydrogen mixtures\n",
"Classical density functional theory for interfacial properties of hydrogen, helium, deuterium, neon and their mixtures ([doi:10.1063/5.0137226](https://doi.org/10.1063/5.0137226))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b1e9c899-f524-4913-bed5-cf01cfb7ad35",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.dft import *\n",
"from feos.saftvrqmie import *\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import si_units as si\n",
"import pandas as pd\n",
"from scipy.optimize import fsolve\n",
"import seaborn as sns\n",
"import tqdm\n",
"import json\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')"
]
},
{
"cell_type": "markdown",
"id": "da9d36a9-3fee-4297-acf4-793c1f36d713",
"metadata": {},
"source": [
"# Set up FEOS functional"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3324bf33-e087-4e87-91e9-9db68a0f13b3",
"metadata": {},
"outputs": [],
"source": [
"parameters = SaftVRQMieParameters.from_json([\"helium\", \"para-hydrogen\"], \n",
" \"../../parameters/saftvrqmie/hammer2023.json\",\n",
" binary_path=\"../../parameters/saftvrqmie/aasen2020_binary.json\")\n",
"eos = HelmholtzEnergyFunctional.saftvrqmie(parameters)"
]
},
{
"cell_type": "markdown",
"id": "c92b4400-5ba6-4cec-804c-a8b3c78cee2f",
"metadata": {},
"source": [
"## Critcal temperature estimate"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9dd67350-beaa-44d9-bf0f-aaa08ccc88e3",
"metadata": {},
"outputs": [],
"source": [
"cp = State.critical_point(eos, np.array([0.5, 0.5]) * si.MOL, initial_temperature=35*si.KELVIN)"
]
},
{
"cell_type": "markdown",
"id": "e2e9392c-96a6-40ff-a78c-e5c0efa724a2",
"metadata": {},
"source": [
"# Utility functions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f68a0bd7-5caa-418b-8dd7-6e62a84259f7",
"metadata": {},
"outputs": [],
"source": [
"def density_error(x_he, T, eos, rho_he_spec):\n",
" \"\"\"Evaluate relative error in partial density for helium\"\"\"\n",
" x = np.array([x_he[0]/1000, 1-x_he[0]/1000])\n",
" vle_guess = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x)\n",
" rho_he = vle_guess.vapor.partial_density[0] / si.MOL * si.METER**3\n",
" error = (rho_he_spec - rho_he)/rho_he_spec\n",
" return error\n",
"\n",
"def surftens_mulero2012(param, tr):\n",
" \"\"\"\n",
" Calculate pure fluid surface tension A(1-tr)**(B+C*tr+D*tr**2)\n",
" Args:\n",
" param (np.ndarray): \n",
" tr (np.ndarray): Reduced temperature\n",
" Returns:\n",
" sigma (np.ndarray): Surface tension (mN/m)\n",
" \"\"\"\n",
" A = param[0]\n",
" exponent = np.zeros_like(tr)\n",
" exponent[:] = param[1]\n",
" for i in range(2,len(param)):\n",
" exponent += param[i]*tr**(i-1)\n",
" sigma = A*(1-tr)**exponent\n",
" return sigma * si.NEWTON / si.METER / (si.MILLI * si.NEWTON/ si.METER)"
]
},
{
"cell_type": "markdown",
"id": "8fcb7e84-6750-4a3c-860f-eb29e0fbf5e9",
"metadata": {},
"source": [
"# Load experimental data and set some constants\n",
"Paine and Seidel.\n",
"*Surface energy of liquid hydrogen with adsorbed helium.*\n",
"Phys. Rev. B, 1992."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d2def2b2-5ed8-4c6f-aee0-7a8d1b9be033",
"metadata": {},
"outputs": [],
"source": [
"data = np.loadtxt(\"./data/hydrogen_helium_exp_data.dat\", skiprows=1)\n",
"mw_he = 4.002602 # g/mol\n",
"Temperatures = np.unique(data[:, 0])\n",
"tc_hydrogen = 32.938"
]
},
{
"cell_type": "markdown",
"id": "20a21ba7-3bcc-4e1c-b3c0-a4873c8cdb57",
"metadata": {},
"source": [
"# Polynomial $x_{\\rm{He}}(\\rho_{\\rm{He}}) \\times 1000$ along Pxy curve"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4a6c7463-168a-44d8-868a-f1ba30769203",
"metadata": {},
"outputs": [],
"source": [
"polynomialfit = {}\n",
"polynomialfit[\"15\"] = np.poly1d([3.70595985e-13,-2.52616512e-08,1.15152761e-03, 1.49408651e-02])\n",
"polynomialfit[\"17\"] = np.poly1d([4.70776561e-13, -3.14841235e-08, 1.71167557e-03, 1.40996915e-02])\n",
"polynomialfit[\"19\"] = np.poly1d([5.62549441e-13, -3.60963365e-08, 2.41768533e-03, 1.25644080e-02])\n",
"polynomialfit[\"21\"] = np.poly1d([6.20734538e-13, -3.70971572e-08, 3.30504009e-03, 1.23204963e-02])"
]
},
{
"cell_type": "markdown",
"id": "e646e23f-e040-4efd-8e18-f59da758485a",
"metadata": {},
"source": [
"# Find helium vapor composition, $x_{\\rm{max}}$, given $\\rho_{\\rm{He}} (x_{\\rm{max}})$ = $\\rho_{\\rm{max}}$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "42b237ea-1fa6-40b7-bdab-93acfc714841",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4/4 [00:02<00:00, 1.34it/s]\n"
]
}
],
"source": [
"rho_max = 10000.0\n",
"x_max = []\n",
"x_he_fun_of_rho = {}\n",
"for T in tqdm.tqdm(Temperatures):\n",
" x0 = polynomialfit[str(int(T))](rho_max)\n",
" # Solve for rho_he(x) = rho_max\n",
" x = fsolve(density_error, x0, args=(T, eos, rho_max))\n",
" x_max.append(x[0]/1000)"
]
},
{
"cell_type": "markdown",
"id": "ccf17cf7-0231-48b7-9547-6aceb65d8ba8",
"metadata": {},
"source": [
"# Map isotherms to $x_{\\rm{he}}=x_{\\rm{max}}$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3896cf04-ccf6-470a-9a0c-299f7267b02c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/4 [00:00<?, ?it/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4/4 [00:06<00:00, 1.66s/it]\n"
]
}
],
"source": [
"states = []\n",
"for i, T in enumerate(tqdm.tqdm(Temperatures)):\n",
" xa = np.linspace(1.0e-8, x_max[i], 20)\n",
" T_states = []\n",
" for x in xa:\n",
" x_sol = np.array([x, 1-x])\n",
" state = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x_sol)\n",
" T_states.append(state)\n",
" states.append(T_states)"
]
},
{
"cell_type": "markdown",
"id": "9b2d8dca-4a8e-4d7f-85cc-b1e61cc9b181",
"metadata": {},
"source": [
"# Calculate and plot surface tension along the isotherms"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2acf2b6f-4ccb-4e14-a96b-08949a212878",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4/4 [01:12<00:00, 18.08s/it]\n"
]
}
],
"source": [
"gamma = []\n",
"for T_states in tqdm.tqdm(states):\n",
" gamma_T = []\n",
" for state in T_states:\n",
" gamma_T.append(PlanarInterface.from_tanh(state, 1024, 200 * si.ANGSTROM, cp.temperature).solve().surface_tension / (si.MILLI * si.NEWTON / si.METER))\n",
" gamma.append(gamma_T)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2508afe3-0ea7-4df2-a904-0f303c41cfd6",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"colors = [\"k\",\"g\",\"r\",\"b\",\"grey\",\"orange\",\"cyan\"]\n",
"for i, T in enumerate(Temperatures):\n",
" rho = []\n",
" for state in states[i]:\n",
" rho.append(state.vapor.partial_density[0] / si.MOL * si.METER**3)\n",
" plt.plot(np.array(rho)/1000, gamma[i], label=f\"{T:.2f} K\", color=colors[i])\n",
" rho_exp = data[np.isclose(data[:, 0], T, atol=0.001),1] / mw_he * 1e6\n",
" sur_tens_exp = data[np.isclose(data[:, 0], T, atol=0.001),2]\n",
" plt.plot(rho_exp/1000, sur_tens_exp, linestyle=\"None\", marker=\"o\", color=colors[i])\n",
" tr = np.array([T/tc_hydrogen])\n",
" s_corr = surftens_mulero2012([0.00471037, 0.62668507, 0.51110266], tr)\n",
" plt.plot([0.0], s_corr, \"*\", color=colors[i]) \n",
" \n",
"plt.xlabel(r'$\\rho_{\\rm{He}}$ (kmol/m$^3$)')\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"leg = plt.legend(loc='best', ncol=2, frameon=False)"
]
},
{
"cell_type": "markdown",
"id": "8460f1d9-2cc1-4de4-9340-f77fc2ae55c8",
"metadata": {},
"source": [
"# Calculate composition given partial helium density of each experimental point"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "230ef919-038a-4b94-9bdb-1e3f8376ec83",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4/4 [00:22<00:00, 5.58s/it]\n"
]
}
],
"source": [
"exp_states = []\n",
"for T in tqdm.tqdm(Temperatures):\n",
" # Loop all experiments on current temperature\n",
" data_T = data[np.isclose(data[:,0],T),1:3]\n",
" for j in range(np.shape(data_T)[0]):\n",
" rho_he_j = data_T[j,0] / mw_he * 1e6\n",
" surftens_exp = data_T[j,1]\n",
" # Interpolate to get initial guess, x0: rho_he(x0) ~= rho_he_j\n",
" x0 = polynomialfit[str(int(T))](rho_he_j)\n",
" # Solve for rho_he(x) = rho_he_j\n",
" x = fsolve(density_error, x0, args=(T, eos, rho_he_j))\n",
" exp_states.append([x[0]/1000, surftens_exp, T])"
]
},
{
"cell_type": "markdown",
"id": "b4faefd0-c3ef-4fbf-92da-96fd2d32830e",
"metadata": {},
"source": [
"# Calculate state and surface tension for the experimental points"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d161e32a-ae7d-4627-ad99-071bad4bf29e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 32/32 [00:02<00:00, 11.77it/s]\n"
]
}
],
"source": [
"states = []\n",
"for x, s_exp, T in tqdm.tqdm(exp_states):\n",
" x_sol = np.array([x, 1-x])\n",
" state = PhaseEquilibrium.bubble_point(eos, T * si.KELVIN, x_sol)\n",
" states.append([state, s_exp, T])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0dd3e0d3-c50c-4509-ae94-ae20e06ff5ef",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 32/32 [00:28<00:00, 1.11it/s]\n"
]
}
],
"source": [
"sft = []\n",
"for state, s_exp, T in tqdm.tqdm(states):\n",
" sft.append({\n",
" \"dft\": PlanarInterface.from_tanh(state, 1024, 200 * si.ANGSTROM, cp.temperature).solve().surface_tension / (si.MILLI * si.NEWTON / si.METER), \n",
" \"exp\": s_exp,\n",
" \"T\": T}\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "ff8020ed-c320-4687-b7e5-4c63ba98c5c8",
"metadata": {},
"source": [
"# Calculate mean absolute deviation (MAD)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a531f7ac-ba7b-4c40-a851-cdad9dbe972f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAD=8.1 %\n"
]
}
],
"source": [
"df = pd.DataFrame(sft)\n",
"mad = 100*np.sum(np.abs((df.dft-df.exp)/df.exp))/df.exp.shape[0]\n",
"print(f\"MAD={mad:.1f} %\")"
]
},
{
"cell_type": "markdown",
"id": "41985130-7141-4ba0-8e10-b8abcc44561c",
"metadata": {},
"source": [
"# Plot error map"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "4f6ae846-8eff-41e8-9b14-d025a0f2c7d5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9, 6))\n",
"smax = 3\n",
"err_fan = 0.1\n",
"groups = df.groupby('T')\n",
"for i, res in enumerate(groups):\n",
" name=res[0]\n",
" group=res[1]\n",
" plt.plot(group.exp, group.dft, \"o\", color=colors[i], label=str(name))\n",
"plt.plot([0, smax], [0, smax], color=\"black\", linestyle=\"dashed\", alpha=1.0, lw=1.5)\n",
"plt.plot([0, smax*(1+err_fan/2)], [0, smax*(1-err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.plot([0, smax*(1-err_fan/2)], [0, smax*(1+err_fan/2)], color=\"black\", linestyle=\"dotted\", alpha=0.5, lw=1.5)\n",
"plt.ylabel(r'$\\gamma$ (mN/m)')\n",
"plt.xlabel(r'$\\gamma_{\\rm{Exp.}}$ (mN/m)')\n",
"plt.xlim(1, smax)\n",
"plt.ylim(1, smax)\n",
"leg = plt.legend(loc='best', ncol=2, frameon=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/pcsaft/PhaseDiagramBinary.ipynb | .ipynb | 470,176 | 431 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from feos.eos import *\n",
"from feos import *\n",
"\n",
"import si_units as si\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ideal mixture"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = Parameters.from_json(['hexane', 'octane'], '../../parameters/pcsaft/gross2001.json')\n",
"saft = EquationOfState.pcsaft(params)\n",
"dia_p = PhaseDiagram.binary_vle(saft, 300*si.KELVIN)\n",
"dia_t = PhaseDiagram.binary_vle(saft, si.BAR)\n",
"\n",
"f, ax = plt.subplots(1,3,figsize=(20,5))\n",
"ax[0].plot(dia_p.liquid.molefracs[:,0], dia_p.liquid.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.vapor.molefracs[:,0], dia_p.vapor.pressure/si.BAR, '-k')\n",
"ax[0].set_xlim(0,1)\n",
"ax[0].set_xlabel('$x_1,y_1$')\n",
"ax[0].set_ylabel('$p$ / bar')\n",
"\n",
"ax[1].plot(dia_t.liquid.molefracs[:,0], dia_t.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.vapor.molefracs[:,0], dia_t.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].set_xlim(0,1)\n",
"ax[1].set_xlabel('$x_1,y_1$')\n",
"ax[1].set_ylabel('$T$ / K')\n",
"\n",
"ax[2].plot([0,1], [0,1], '--k')\n",
"ax[2].plot(dia_t.liquid.molefracs[:,0], dia_t.vapor.molefracs[:,0], '-g')\n",
"ax[2].plot(dia_p.liquid.molefracs[:,0], dia_p.vapor.molefracs[:,0], '-k')\n",
"ax[2].set_xlim(0,1)\n",
"ax[2].set_ylim(0,1)\n",
"ax[2].set_xlabel('$x_1$')\n",
"ax[2].set_ylabel('$y_1$');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Azeotropic mixture"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = Parameters.from_multiple_json([(['acetone'], '../../parameters/pcsaft/gross2006.json'), (['hexane'], '../../parameters/pcsaft/gross2001.json')])\n",
"saft = EquationOfState.pcsaft(params)\n",
"dia_p = PhaseDiagram.binary_vle(saft, 300*si.KELVIN)\n",
"dia_t = PhaseDiagram.binary_vle(saft, 5*si.BAR)\n",
"\n",
"f, ax = plt.subplots(1,3,figsize=(20,5))\n",
"ax[0].plot(dia_p.liquid.molefracs[:,0], dia_p.liquid.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.vapor.molefracs[:,0], dia_p.vapor.pressure/si.BAR, '-k')\n",
"ax[0].set_xlim(0,1)\n",
"ax[0].set_xlabel('$x_1,y_1$')\n",
"ax[0].set_ylabel('$p$ / bar')\n",
"\n",
"ax[1].plot(dia_t.liquid.molefracs[:,0], dia_t.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.vapor.molefracs[:,0], dia_t.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].set_xlim(0,1)\n",
"ax[1].set_xlabel('$x_1,y_1$')\n",
"ax[1].set_ylabel('$T$ / K')\n",
"\n",
"ax[2].plot([0,1], [0,1], '--k')\n",
"ax[2].plot(dia_t.liquid.molefracs[:,0], dia_t.vapor.molefracs[:,0], '-g')\n",
"ax[2].plot(dia_p.liquid.molefracs[:,0], dia_p.vapor.molefracs[:,0], '-k')\n",
"ax[2].set_xlim(0,1)\n",
"ax[2].set_ylim(0,1)\n",
"ax[2].set_xlabel('$x_1$')\n",
"ax[2].set_ylabel('$y_1$');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Supercritical mixture"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = Parameters.from_json(['carbon dioxide', 'hexane'], '../../parameters/pcsaft/gross2001.json')\n",
"saft = EquationOfState.pcsaft(params)\n",
"T_vec = [350*si.KELVIN, 450*si.KELVIN, 500*si.KELVIN]\n",
"c_vec = ['k', 'r', 'b']\n",
"dia_p = [PhaseDiagram.binary_vle(saft, T) for T in T_vec]\n",
"dia_t = PhaseDiagram.binary_vle(saft, 50*si.BAR)\n",
"\n",
"f, ax = plt.subplots(1,3,figsize=(20,5))\n",
"for d,c,T in zip(dia_p, c_vec, T_vec):\n",
" ax[0].plot(d.liquid.molefracs[:,0], d.liquid.pressure/si.BAR, color=c, label=f'{T}')\n",
" ax[0].plot(d.vapor.molefracs[:,0], d.vapor.pressure/si.BAR, color=c)\n",
"ax[0].set_xlim(0,1)\n",
"ax[0].set_xlabel('$x_1,y_1$')\n",
"ax[0].set_ylabel('$p$ / bar')\n",
"ax[0].legend()\n",
"\n",
"ax[1].plot(dia_t.liquid.molefracs[:,0], dia_t.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.vapor.molefracs[:,0], dia_t.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].set_xlim(0,1)\n",
"ax[1].set_xlabel('$x_1,y_1$')\n",
"ax[1].set_ylabel('$T$ / K')\n",
"\n",
"ax[2].plot([0,1], [0,1], '--k')\n",
"for d,c,T in zip(dia_p, c_vec, T_vec):\n",
" ax[2].plot(d.liquid.molefracs[:,0], d.vapor.molefracs[:,0], color=c, label=f'{T}')\n",
"ax[2].plot(dia_t.liquid.molefracs[:,0], dia_t.vapor.molefracs[:,0], '-g', label=f'{50*si.BAR}')\n",
"ax[2].set_xlim(0,1)\n",
"ax[2].set_ylim(0,1)\n",
"ax[2].set_xlabel('$x_1$')\n",
"ax[2].set_ylabel('$y_1$')\n",
"ax[2].legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Liquid-liquid equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = Parameters.from_multiple_json([(['water'], '../../parameters/pcsaft/gross2002.json'), (['octane'], '../../parameters/pcsaft/gross2001.json')])\n",
"\n",
"saft = EquationOfState.pcsaft(params)\n",
"dia_p = PhaseDiagram.lle(saft, 500*si.KELVIN, np.array([0.5, 0.5])*si.MOL, si.BAR, 5000*si.BAR)\n",
"dia_t = PhaseDiagram.lle(saft, si.BAR, np.array([0.5, 0.5])*si.MOL, 300*si.KELVIN, 364*si.KELVIN)\n",
"\n",
"f, ax = plt.subplots(1,3,figsize=(20,5))\n",
"ax[0].plot(dia_p.liquid.molefracs[:,0], dia_p.liquid.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.vapor.molefracs[:,0], dia_p.vapor.pressure/si.BAR, '-k')\n",
"ax[0].set_xlim(0,1)\n",
"ax[0].set_xlabel('$x_1,y_1$')\n",
"ax[0].set_ylabel('$p$ / bar')\n",
"\n",
"ax[1].plot(dia_t.liquid.molefracs[:,0], dia_t.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.vapor.molefracs[:,0], dia_t.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].set_xlim(0,1)\n",
"ax[1].set_xlabel('$x_1,y_1$')\n",
"ax[1].set_ylabel('$T$ / K')\n",
"\n",
"ax[2].plot([0,1], [0,1], '--k')\n",
"ax[2].plot(dia_t.liquid.molefracs[:,0], dia_t.vapor.molefracs[:,0], '-g')\n",
"ax[2].plot(dia_p.liquid.molefracs[:,0], dia_p.vapor.molefracs[:,0], '-k')\n",
"ax[2].set_xlim(0,1)\n",
"ax[2].set_ylim(0,1)\n",
"ax[2].set_xlabel('$x_1$')\n",
"ax[2].set_ylabel('$y_1$');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Heteroazeotropic mixture"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = Parameters.from_multiple_json([(['acetone'], '../../parameters/pcsaft/gross2006.json'), (['water'], '../../parameters/pcsaft/gross2002.json')])\n",
"\n",
"saft = EquationOfState.pcsaft(params)\n",
"dia_p = PhaseDiagram.binary_vlle(saft, 450*si.KELVIN, (0.005, 0.9), 50*si.BAR, 25*si.BAR, 101)\n",
"dia_t = PhaseDiagram.binary_vlle(saft, si.BAR, (0.001, 0.99), 0*si.CELSIUS, 50*si.CELSIUS, 101)\n",
"\n",
"f, ax = plt.subplots(1,3,figsize=(20,5))\n",
"ax[0].plot(dia_p.vle.liquid.molefracs[:,0], dia_p.vle.liquid.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.vle.vapor.molefracs[:,0], dia_p.vle.vapor.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.lle.vapor.molefracs[:,0], dia_p.lle.vapor.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.lle.liquid.molefracs[:,0], dia_p.lle.liquid.pressure/si.BAR, '-k')\n",
"ax[0].set_xlim(0,1)\n",
"ax[0].set_xlabel('$x_1,y_1$')\n",
"ax[0].set_ylabel('$p$ / bar')\n",
"\n",
"ax[1].plot(dia_t.vle.liquid.molefracs[:,0], dia_t.vle.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.vle.vapor.molefracs[:,0], dia_t.vle.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.lle.liquid.molefracs[:,0], dia_t.lle.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.lle.vapor.molefracs[:,0], dia_t.lle.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].set_xlim(0,1)\n",
"ax[1].set_xlabel('$x_1,y_1$')\n",
"ax[1].set_ylabel('$T$ / K')\n",
"\n",
"ax[2].plot([0,1], [0,1], '--k')\n",
"ax[2].plot(dia_t.vle.liquid.molefracs[:,0], dia_t.vle.vapor.molefracs[:,0], '-g')\n",
"ax[2].plot(dia_p.vle.liquid.molefracs[:,0], dia_p.vle.vapor.molefracs[:,0], '-k')\n",
"ax[2].set_xlim(0,1)\n",
"ax[2].set_ylim(0,1)\n",
"ax[2].set_xlabel('$x_1$')\n",
"ax[2].set_ylabel('$y_1$');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two associating components"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params = Parameters.from_json(['water', '1-butanol'], '../../parameters/pcsaft/gross2002.json')\n",
"\n",
"\n",
"saft = EquationOfState.pcsaft(params)\n",
"dia_p = PhaseDiagram.binary_vlle(saft, 350*si.KELVIN, (0.55, 0.98), si.BAR, 0.5*si.BAR, 101)\n",
"dia_t = PhaseDiagram.binary_vlle(saft, si.BAR, (0.5,0.995), 70*si.CELSIUS, 80*si.CELSIUS, 101)\n",
"\n",
"f, ax = plt.subplots(1,3,figsize=(20,5))\n",
"ax[0].plot(dia_p.vle.liquid.molefracs[:,0], dia_p.vle.liquid.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.vle.vapor.molefracs[:,0], dia_p.vle.vapor.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.lle.vapor.molefracs[:,0], dia_p.lle.vapor.pressure/si.BAR, '-k')\n",
"ax[0].plot(dia_p.lle.liquid.molefracs[:,0], dia_p.lle.liquid.pressure/si.BAR, '-k')\n",
"ax[0].set_xlim(0,1)\n",
"ax[0].set_xlabel('$x_1,y_1$')\n",
"ax[0].set_ylabel('$p$ / bar')\n",
"\n",
"ax[1].plot(dia_t.vle.liquid.molefracs[:,0], dia_t.vle.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.vle.vapor.molefracs[:,0], dia_t.vle.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.lle.liquid.molefracs[:,0], dia_t.lle.liquid.temperature/si.KELVIN, '-g')\n",
"ax[1].plot(dia_t.lle.vapor.molefracs[:,0], dia_t.lle.vapor.temperature/si.KELVIN, '-g')\n",
"ax[1].set_xlim(0,1)\n",
"ax[1].set_xlabel('$x_1,y_1$')\n",
"ax[1].set_ylabel('$T$ / K')\n",
"\n",
"ax[2].plot([0,1], [0,1], '--k')\n",
"ax[2].plot(dia_t.vle.liquid.molefracs[:,0], dia_t.vle.vapor.molefracs[:,0], '-g')\n",
"ax[2].plot(dia_p.vle.liquid.molefracs[:,0], dia_p.vle.vapor.molefracs[:,0], '-k')\n",
"ax[2].set_xlim(0,1)\n",
"ax[2].set_ylim(0,1)\n",
"ax[2].set_xlabel('$x_1$')\n",
"ax[2].set_ylabel('$y_1$');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Binary interaction parameters"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1,3,figsize=(20,5))\n",
"\n",
"for binary_path,ls in zip([None, \"../../parameters/pcsaft/rehner2023_binary.json\"], [':', '-']):\n",
" params = Parameters.from_json([\"2-butanone\", \"ethanol\"], \"../../parameters/pcsaft/esper2023.json\", binary_path)\n",
" eos = EquationOfState.pcsaft(params)\n",
" dia_p = PhaseDiagram.binary_vle(eos, 300*si.KELVIN)\n",
" dia_t = PhaseDiagram.binary_vle(eos, 1.013*si.BAR)\n",
"\n",
" ax[0].plot(dia_p.liquid.molefracs[:,0], dia_p.liquid.pressure/si.BAR, 'k', ls=ls)\n",
" ax[0].plot(dia_p.vapor.molefracs[:,0], dia_p.vapor.pressure/si.BAR, 'k', ls=ls)\n",
" ax[0].set_xlim(0,1)\n",
" ax[0].set_xlabel('$x_1,y_1$')\n",
" ax[0].set_ylabel('$p$ / bar')\n",
"\n",
" ax[1].plot(dia_t.liquid.molefracs[:,0], dia_t.liquid.temperature/si.KELVIN, 'g', ls=ls)\n",
" ax[1].plot(dia_t.vapor.molefracs[:,0], dia_t.vapor.temperature/si.KELVIN, 'g', ls=ls)\n",
" ax[1].set_xlim(0,1)\n",
" ax[1].set_xlabel('$x_1,y_1$')\n",
" ax[1].set_ylabel('$T$ / K')\n",
"\n",
" ax[2].plot([0,1], [0,1], '--k')\n",
" ax[2].plot(dia_t.liquid.molefracs[:,0], dia_t.vapor.molefracs[:,0], 'g', ls=ls)\n",
" ax[2].plot(dia_p.liquid.molefracs[:,0], dia_p.vapor.molefracs[:,0], 'k', ls=ls)\n",
" ax[2].set_xlim(0,1)\n",
" ax[2].set_ylim(0,1)\n",
" ax[2].set_xlabel('$x_1$')\n",
" ax[2].set_ylabel('$y_1$');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/pcsaft/pore_geometry.ipynb | .ipynb | 81,371 | 178 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from feos.dft import *\n",
"from feos.pcsaft import PcSaftParameters\n",
"\n",
"import si_units as si\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"params = PcSaftParameters.from_json([\"methanol\"], \"../../parameters/pcsaft/rehner2020.json\")\n",
"func = HelmholtzEnergyFunctional.pcsaft(params)\n",
"T = 350*si.KELVIN\n",
"state = State(func, T, pressure=si.BAR)\n",
"potential = ExternalPotential.LJ93(3.0, 100.0, 0.08)\n",
"#potential = ExternalPotential.Steele(3.0, 100.0, 0.08)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1080x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1,2,figsize=(15,5))\n",
"\n",
"pore_sizes = [5,10,20]\n",
"slit_pore = Pore1D(Geometry.Cartesian, 100*si.ANGSTROM, potential, 4096).initialize(state)\n",
"cylindrical_pores = [Pore1D(Geometry.Cylindrical, r*si.ANGSTROM, potential).initialize(state) for r in pore_sizes]\n",
"spherical_pores = [Pore1D(Geometry.Spherical, r*si.ANGSTROM, potential).initialize(state) for r in pore_sizes]\n",
"\n",
"for cpore, spore, size in zip(cylindrical_pores, spherical_pores, pore_sizes):\n",
" ax[0].plot(cpore.r/si.ANGSTROM - size, cpore.external_potential.T, label=f'${size}$')\n",
" ax[1].plot(spore.r/si.ANGSTROM - size, spore.external_potential.T, label=f'${size}$')\n",
"for a in ax:\n",
" a.plot(slit_pore.z/si.ANGSTROM-50, slit_pore.external_potential.T, label='$\\infty$')\n",
" a.axis([-6,0,-20,55])\n",
" a.legend()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pmin = 0.2*si.BAR\n",
"pmax = 2.5*si.BAR\n",
"cyl10 = Adsorption1D.equilibrium_isotherm(func, T, si.linspace(pmin, pmax, 151), Pore1D(Geometry.Cylindrical, 10*si.ANGSTROM, potential))\n",
"cyl20 = Adsorption1D.equilibrium_isotherm(func, T, si.linspace(pmin, pmax, 151), Pore1D(Geometry.Cylindrical, 20*si.ANGSTROM, potential))\n",
"#sph10 = Adsorption1D.equilibrium_isotherm(func, T, si.linspace(pmin, pmax, 151), Pore1D(Geometry.Spherical, 10*si.ANGSTROM, potential))\n",
"sph20 = Adsorption1D.equilibrium_isotherm(func, T, si.linspace(pmin, pmax, 151), Pore1D(Geometry.Spherical, 20*si.ANGSTROM, potential))\n",
"car10 = Adsorption1D.equilibrium_isotherm(func, T, si.linspace(pmin, pmax, 151), Pore1D(Geometry.Cartesian, 20*si.ANGSTROM, potential))\n",
"car20 = Adsorption1D.equilibrium_isotherm(func, T, si.linspace(pmin, pmax, 151), Pore1D(Geometry.Cartesian, 40*si.ANGSTROM, potential))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f1c1c3be580>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(cyl10.pressure/si.BAR, cyl10.total_adsorption/(np.pi*100*si.ANGSTROM**3)/(si.KILO*si.MOL/si.METER**3), 'r', label='cyl10')\n",
"plt.plot(cyl20.pressure/si.BAR, cyl20.total_adsorption/(np.pi*400*si.ANGSTROM**3)/(si.KILO*si.MOL/si.METER**3), 'r--', label='cyl20')\n",
"#plt.plot(sph10.pressure/si.BAR, sph10.total_adsorption/(4/3*np.pi*1000*si.ANGSTROM**3)/(si.KILO*si.MOL/si.METER**3), 'b', label='sph10')\n",
"plt.plot(sph20.pressure/si.BAR, sph20.total_adsorption/(4/3*np.pi*8000*si.ANGSTROM**3)/(si.KILO*si.MOL/si.METER**3), 'b--', label='sph20')\n",
"plt.plot(car10.pressure/si.BAR, car10.total_adsorption/(10*si.ANGSTROM**3)/(si.KILO*si.MOL/si.METER**3), 'g', label='car10')\n",
"plt.plot(car20.pressure/si.BAR, car20.total_adsorption/(20*si.ANGSTROM**3)/(si.KILO*si.MOL/si.METER**3), 'g--', label='car20')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1080x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1,2,figsize=(15,5))\n",
"\n",
"ax[0].plot(cyl10.profiles[-1].r/si.ANGSTROM, (cyl10.profiles[-1].density/(si.KILO*si.MOL/si.METER**3)).T, label='cyl10')\n",
"ax[1].plot(cyl20.profiles[-1].r/si.ANGSTROM, (cyl20.profiles[-1].density/(si.KILO*si.MOL/si.METER**3)).T, label='cyl20')\n",
"#ax[0].plot(sph10.profiles[-1].r/si.ANGSTROM, (sph10.profiles[-1].density/(si.KILO*si.MOL/si.METER**3)).T, label='sph10')\n",
"ax[1].plot(sph20.profiles[-1].r/si.ANGSTROM, (sph20.profiles[-1].density/(si.KILO*si.MOL/si.METER**3)).T, label='sph20')\n",
"ax[0].plot(car10.profiles[-1].r/si.ANGSTROM, (car10.profiles[-1].density/(si.KILO*si.MOL/si.METER**3)).T, label='car10')\n",
"ax[1].plot(car20.profiles[-1].r/si.ANGSTROM, (car20.profiles[-1].density/(si.KILO*si.MOL/si.METER**3)).T, label='car20')\n",
"\n",
"ax[0].set_xlim(0,10)\n",
"ax[1].set_xlim(0,20)\n",
"for a in ax:\n",
" a.set_ylim(0,50)\n",
" a.legend()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/pcsaft/fea_adsorption.ipynb | .ipynb | 66,212 | 231 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from feos.dft import ExternalPotential, HelmholtzEnergyFunctional, Adsorption1D, Geometry, Pore1D, State\n",
"from feos.pcsaft import PcSaftParameters\n",
"from si_units import ANGSTROM, KELVIN, BAR, NAV, KILO, METER, MOL, linspace\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"from scipy.spatial import Voronoi, distance\n",
"from itertools import permutations"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class SolidStructure:\n",
"\n",
" N_atom = 0\n",
" dimensions = {\n",
" \"Lx\": 0.0,\n",
" \"Ly\": 0.0,\n",
" \"Lz\": 0.0\n",
" }\n",
" forcefield = ''\n",
" total_mass = 0.0\n",
"\n",
" def __init__(self,structure):\n",
" \n",
" self.name = structure\n",
"\n",
" database = os.path.join(os.getcwd(),'structure_parameters','solid_database.json')\n",
" \n",
" with open(database) as f:\n",
" data = json.load(f)\n",
"\n",
" for i in range(len(data)):\n",
" if data[i]['Name']==self.name:\n",
" self.N_atom = data[i]['N_atom']\n",
" self.dimensions = data[i]['Dimensions']\n",
" self.forcefield = data[i]['Forcefield']\n",
" \n",
" \n",
" def read_structure(self):\n",
" struct_param = os.path.join(os.getcwd(),'structure_parameters')\n",
" structure_df = pd.read_csv(os.path.join(struct_param,'{}.dat'.format(self.name)),names=['x','y','z','Type'], delim_whitespace=True)\n",
" \n",
" filename = os.path.join(struct_param,'{}.dat'.format(self.forcefield))\n",
" forcefield_df = pd.read_csv(filename,names=['Type','sigma','epsilon','mass'], delim_whitespace=True)\n",
"\n",
" coordinates = np.array([structure_df[\"x\"], structure_df[\"y\"], structure_df[\"z\"]])\n",
" \n",
" sigma_ss = np.zeros(len(structure_df[\"x\"]))\n",
" epsilon_k_ss = np.zeros_like(sigma_ss)\n",
" \n",
" for i in range(len(sigma_ss)):\n",
" sigma_ss[i] = forcefield_df.sigma[forcefield_df.Type==structure_df[\"Type\"][i]]\n",
" epsilon_k_ss[i] = forcefield_df.epsilon[forcefield_df.Type==structure_df[\"Type\"][i]]\n",
" \n",
" self.total_mass = np.sum(np.array([forcefield_df.mass[forcefield_df.Type==t]for t in structure_df.Type])) * 1.66054e-27\n",
"\n",
" return coordinates, sigma_ss, epsilon_k_ss "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2498554/267063123.py:30: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
" structure_df = pd.read_csv(os.path.join(struct_param,'{}.dat'.format(self.name)),names=['x','y','z','Type'], delim_whitespace=True)\n",
"/tmp/ipykernel_2498554/267063123.py:33: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
" forcefield_df = pd.read_csv(filename,names=['Type','sigma','epsilon','mass'], delim_whitespace=True)\n",
"/tmp/ipykernel_2498554/267063123.py:41: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
" sigma_ss[i] = forcefield_df.sigma[forcefield_df.Type==structure_df[\"Type\"][i]]\n",
"/tmp/ipykernel_2498554/267063123.py:42: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
" epsilon_k_ss[i] = forcefield_df.epsilon[forcefield_df.Type==structure_df[\"Type\"][i]]\n"
]
}
],
"source": [
"structure = SolidStructure('LTA')\n",
"coordinates, sigma_ss, epsilon_ss = structure.read_structure()\n",
"system_size = [structure.dimensions[\"Lx\"] * ANGSTROM, structure.dimensions[\"Lx\"] * ANGSTROM, structure.dimensions[\"Lx\"] * ANGSTROM]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.84534814234455 3220\n",
"[5.9595, 5.9594999999999985, 5.9595]\n",
"CPU times: user 410 ms, sys: 8.46 ms, total: 419 ms\n",
"Wall time: 418 ms\n"
]
}
],
"source": [
"%%time\n",
"vor = Voronoi(coordinates.T)\n",
"\n",
"minimum_distance = 0.0\n",
"dist = np.zeros(int(vor.vertices.size / 2))\n",
"for i,point in enumerate(vor.vertices):\n",
" if(point[0] >= 0.0 and point[0] <= structure.dimensions[\"Lx\"] and point[1] >= 0.0 and point[1] <= structure.dimensions[\"Ly\"]):\n",
" dist[i] = np.min(distance.cdist(np.array([point]),coordinates.T))\n",
"pore_radius = np.max(dist)\n",
"print(pore_radius,np.argmax(dist))\n",
"pore_center = [vor.vertices[np.argmax(dist)][0], vor.vertices[np.argmax(dist)][1], vor.vertices[np.argmax(dist)][2]]\n",
"print(pore_center)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"params = PcSaftParameters.from_json(['argon'],'structure_parameters/noble_gases.json')\n",
"func = HelmholtzEnergyFunctional.pcsaft(params)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"potential = ExternalPotential.FreeEnergyAveraged(coordinates * ANGSTROM, sigma_ss, epsilon_ss, pore_center, system_size, [51, 51])\n",
"pore = Pore1D(Geometry.Spherical, 5.9595 * ANGSTROM, potential, 128)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"isotherm = Adsorption1D.adsorption_isotherm(func, 298.0 * KELVIN, linspace(1.0e-3 * BAR, 5.0 * BAR, 51), pore)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f03793bec70>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1080x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"f, ax = plt.subplots(1,2,figsize=(15,5))\n",
"ax[0].plot(isotherm.pressure/BAR, isotherm.total_adsorption*NAV)\n",
"ax[0].set_xlabel('$p~~/~~\\\\mathrm{bar}$')\n",
"ax[0].set_ylabel('$N$')\n",
"ax[0].set_xlim(0,5)\n",
"ax[0].set_ylim(0,1)\n",
"\n",
"for profile in isotherm.profiles[::10]:\n",
" ax[1].plot(profile.r/ANGSTROM, (profile.density/(KILO*MOL/METER**3)).T, label=f'{profile.bulk.pressure()/BAR:.2} bar')\n",
"ax[1].set_xlabel('$r~~/~~\\\\mathrm{\\AA}$')\n",
"ax[1].set_ylabel('$\\\\rho~~/~~\\\\mathrm{kmol/m³}$')\n",
"ax[1].set_xlim(0,6)\n",
"ax[1].set_ylim(0,8)\n",
"ax[1].legend()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/pcsaft/Entropy Scaling.ipynb | .ipynb | 68,563 | 612 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entropy scaling of pure substances\n",
"\n",
"## Goal\n",
"\n",
"- Learn how to compute dynamic properties (viscosity in this example)\n",
"- Compare substance specific parameters against homo-segmented group contribution\n",
"- Compare viscosity to NIST data (generated in NIST's [webapp](https://webbook.nist.gov/chemistry/fluid/))\n",
"\n",
"## Import needed packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from feos.pcsaft import *\n",
"from feos.eos import *\n",
"import matplotlib.pyplot as plt\n",
"import si_units as si\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"sns.set_context(\"talk\")\n",
"sns.set_palette(\"Dark2\")\n",
"sns.set_style(\"ticks\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PC-SAFT (individual component parameters)\n",
"\n",
"First, we read parameters adjusted to hexane saturation pressure and liquid densities (for the regular SAFT parameters) and to viscosity (for correlation). "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|$m$|$\\sigma$|$\\varepsilon$|$\\mu$|$Q$|$\\kappa_{AB}$|$\\varepsilon_{AB}$|$N_A$|$N_B$|$N_C$|\n",
"|-|-|-|-|-|-|-|-|-|-|-|-|\n",
"|hexane|86.177|3.0576|3.7983|236.77|-|-|-|-|0|0|0|"
],
"text/plain": [
"<PcSaftParameters at 0x7f8a3dc95850>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = PcSaftParameters.from_json(\n",
" [\"hexane\"], \"../../parameters/pcsaft/loetgeringlin2018.json\"\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PC-SAFT homo-GC\n",
"\n",
"For transparency, we build parameters by hand [following the official tutorial](https://feos-org.github.io/feos/examples/eos/pcsaft/pcsaft_working_with_parameters.html#From-Python)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"hexane = ChemicalRecord(\n",
" identifier=Identifier(\n",
" cas=\"110-54-3\",\n",
" name=\"hexane\",\n",
" iupac_name=\"hexane\",\n",
" smiles=\"CCCCCC\",\n",
" inchi=\"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" formula=\"C6H14\"\n",
" ),\n",
" segments=['CH3', 'CH2', 'CH2', 'CH2', 'CH2', 'CH3']\n",
")\n",
"\n",
"ch3 = SegmentRecord(\n",
" 'CH3', \n",
" molarweight=15.0345, \n",
" model_record=PcSaftRecord(\n",
" m=0.61198, sigma=3.7202, epsilon_k=229.90,\n",
" viscosity=[-8.6878e-3, -1.7951e-1, -12.2359e-2, -0.01245]\n",
" )\n",
")\n",
"\n",
"ch2 = SegmentRecord(\n",
" 'CH2', \n",
" molarweight=14.02658, \n",
" model_record=PcSaftRecord(\n",
" m=0.45606, sigma=3.8900, epsilon_k=239.01,\n",
" viscosity=[-0.9194e-3, -1.3316e-1, -4.2657e-2, -0.01245]\n",
" )\n",
")\n",
"\n",
"segment_records = {r.identifier: r for r in [ch3, ch2]}\n",
"\n",
"def from_segments(chemical_record, segment_records):\n",
" m = 0\n",
" s3 = 0\n",
" eps = 0\n",
" mw = 0\n",
" viscosity = np.zeros(4)\n",
" for s in chemical_record.segments:\n",
" segment = segment_records[s]\n",
" mw += segment.molarweight\n",
" m += segment.model_record.m\n",
" s3 += segment.model_record.m * segment.model_record.sigma**3\n",
" eps += segment.model_record.m * segment.model_record.epsilon_k\n",
" v = segment.model_record.viscosity\n",
" viscosity += np.array([\n",
" v[0] * segment.model_record.m * segment.model_record.sigma**3, \n",
" v[1] * segment.model_record.m * segment.model_record.sigma**3, \n",
" v[2], \n",
" v[3]\n",
" ])\n",
" viscosity[1] /= s3**0.45\n",
" \n",
" # We have to shift the \"A\" parameter because the implemented reference\n",
" # is eta_CE according to eq. 3 of Loetgerin-Lin (2018)\n",
" # A = A(GC) + log(sqrt(1/m)) = -log(m)/2\n",
" viscosity[0] += np.log(np.sqrt(1/m))\n",
" saft_record = PcSaftRecord(m, np.cbrt(s3 / m), eps / m, viscosity=viscosity)\n",
" return PureRecord(chemical_record.identifier, mw, saft_record)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build equations of state\n",
"\n",
"We instantiate an equation of state for each parameter set. `saft` uses substance specific parameters while `saft_gc` uses homo GC parameters both for SAFT as well as correlation parameters."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"parameters_gc = PcSaftParameters.new_pure(from_segments(hexane, segment_records))\n",
"saft_gc = EquationOfState.pcsaft(parameters_gc)\n",
"saft = EquationOfState.pcsaft(parameters)\n",
"\n",
"m_gc = parameters_gc.pure_records[0].model_record.m\n",
"m = parameters.pure_records[0].model_record.m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Substance specific: [-1.2035, -2.5958, -0.4816, -0.0865]\n",
"Segments : [-1.2034921145837285, -2.536713016411593, -0.415346, -0.0747]\n"
]
}
],
"source": [
"print(\"Substance specific: \", parameters.pure_records[0].model_record.viscosity)\n",
"print(\"Segments : \", parameters_gc.pure_records[0].model_record.viscosity)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare methods to NIST data (T = 450 K)\n",
"\n",
"We will compute the residual entropy, viscosity and logarithmic reduced viscosity and compare to literature data (for which the entropy is computed with parameters fitted to the component, not GC)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Temperature (K)</th>\n",
" <th>Pressure (MPa)</th>\n",
" <th>Density (mol/m3)</th>\n",
" <th>Volume (m3/mol)</th>\n",
" <th>Internal Energy (kJ/mol)</th>\n",
" <th>Enthalpy (kJ/mol)</th>\n",
" <th>Entropy (J/mol*K)</th>\n",
" <th>Cv (J/mol*K)</th>\n",
" <th>Cp (J/mol*K)</th>\n",
" <th>Sound Spd. (m/s)</th>\n",
" <th>Joule-Thomson (K/MPa)</th>\n",
" <th>Viscosity (Pa*s)</th>\n",
" <th>Therm. Cond. (W/m*K)</th>\n",
" <th>Phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>450.0</td>\n",
" <td>0.01</td>\n",
" <td>2.6774</td>\n",
" <td>0.373500</td>\n",
" <td>45.229</td>\n",
" <td>48.964</td>\n",
" <td>154.33</td>\n",
" <td>193.37</td>\n",
" <td>201.76</td>\n",
" <td>212.47</td>\n",
" <td>11.204</td>\n",
" <td>0.000009</td>\n",
" <td>0.029374</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>450.0</td>\n",
" <td>0.11</td>\n",
" <td>29.9840</td>\n",
" <td>0.033351</td>\n",
" <td>45.065</td>\n",
" <td>48.734</td>\n",
" <td>134.03</td>\n",
" <td>193.94</td>\n",
" <td>203.09</td>\n",
" <td>209.04</td>\n",
" <td>11.510</td>\n",
" <td>0.000009</td>\n",
" <td>0.029276</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>450.0</td>\n",
" <td>0.21</td>\n",
" <td>58.3290</td>\n",
" <td>0.017144</td>\n",
" <td>44.896</td>\n",
" <td>48.496</td>\n",
" <td>128.27</td>\n",
" <td>194.53</td>\n",
" <td>204.55</td>\n",
" <td>205.48</td>\n",
" <td>11.842</td>\n",
" <td>0.000009</td>\n",
" <td>0.029196</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>450.0</td>\n",
" <td>0.31</td>\n",
" <td>87.8260</td>\n",
" <td>0.011386</td>\n",
" <td>44.720</td>\n",
" <td>48.249</td>\n",
" <td>124.64</td>\n",
" <td>195.15</td>\n",
" <td>206.15</td>\n",
" <td>201.76</td>\n",
" <td>12.207</td>\n",
" <td>0.000009</td>\n",
" <td>0.029136</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>450.0</td>\n",
" <td>0.41</td>\n",
" <td>118.6100</td>\n",
" <td>0.008431</td>\n",
" <td>44.536</td>\n",
" <td>47.992</td>\n",
" <td>121.90</td>\n",
" <td>195.80</td>\n",
" <td>207.93</td>\n",
" <td>197.87</td>\n",
" <td>12.608</td>\n",
" <td>0.000010</td>\n",
" <td>0.029098</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Temperature (K) Pressure (MPa) Density (mol/m3) Volume (m3/mol) \\\n",
"0 450.0 0.01 2.6774 0.373500 \n",
"1 450.0 0.11 29.9840 0.033351 \n",
"2 450.0 0.21 58.3290 0.017144 \n",
"3 450.0 0.31 87.8260 0.011386 \n",
"4 450.0 0.41 118.6100 0.008431 \n",
"\n",
" Internal Energy (kJ/mol) Enthalpy (kJ/mol) Entropy (J/mol*K) \\\n",
"0 45.229 48.964 154.33 \n",
"1 45.065 48.734 134.03 \n",
"2 44.896 48.496 128.27 \n",
"3 44.720 48.249 124.64 \n",
"4 44.536 47.992 121.90 \n",
"\n",
" Cv (J/mol*K) Cp (J/mol*K) Sound Spd. (m/s) Joule-Thomson (K/MPa) \\\n",
"0 193.37 201.76 212.47 11.204 \n",
"1 193.94 203.09 209.04 11.510 \n",
"2 194.53 204.55 205.48 11.842 \n",
"3 195.15 206.15 201.76 12.207 \n",
"4 195.80 207.93 197.87 12.608 \n",
"\n",
" Viscosity (Pa*s) Therm. Cond. (W/m*K) Phase \n",
"0 0.000009 0.029374 vapor \n",
"1 0.000009 0.029276 vapor \n",
"2 0.000009 0.029196 vapor \n",
"3 0.000009 0.029136 vapor \n",
"4 0.000010 0.029098 vapor "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature = pd.read_csv(\"hexane_nist.csv\", delimiter=\"\\t\")\n",
"literature.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pressure</th>\n",
" <th>s*_res/m</th>\n",
" <th>viscosity</th>\n",
" <th>ln_viscosity_reduced</th>\n",
" <th>source</th>\n",
" <th>rel.dev.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.01</td>\n",
" <td>-0.000526</td>\n",
" <td>0.000009</td>\n",
" <td>-1.170829</td>\n",
" <td>literature</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.01</td>\n",
" <td>-0.000526</td>\n",
" <td>0.000009</td>\n",
" <td>-1.202136</td>\n",
" <td>saft</td>\n",
" <td>-3.082130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.01</td>\n",
" <td>-0.000531</td>\n",
" <td>0.000009</td>\n",
" <td>-1.202146</td>\n",
" <td>homo-GC</td>\n",
" <td>-4.154342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.11</td>\n",
" <td>-0.005862</td>\n",
" <td>0.000009</td>\n",
" <td>-1.172124</td>\n",
" <td>literature</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.11</td>\n",
" <td>-0.005862</td>\n",
" <td>0.000009</td>\n",
" <td>-1.188299</td>\n",
" <td>saft</td>\n",
" <td>-1.604523</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pressure s*_res/m viscosity ln_viscosity_reduced source rel.dev.\n",
"0 0.01 -0.000526 0.000009 -1.170829 literature 0.000000\n",
"1 0.01 -0.000526 0.000009 -1.202136 saft -3.082130\n",
"2 0.01 -0.000531 0.000009 -1.202146 homo-GC -4.154342\n",
"3 0.11 -0.005862 0.000009 -1.172124 literature 0.000000\n",
"4 0.11 -0.005862 0.000009 -1.188299 saft -1.604523"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = []\n",
"for i, row in literature.iterrows():\n",
" t = row['Temperature (K)'] * si.KELVIN\n",
" p = row['Pressure (MPa)'] * si.MEGA * si.PASCAL\n",
" viscosity_lit = row['Viscosity (Pa*s)'] * si.PASCAL * si.SECOND\n",
" \n",
" # literature\n",
" state = State(saft, temperature=t, pressure=p, total_moles=si.MOL, density_initialization=row.Phase)\n",
" s = state.molar_entropy(Contributions.Residual)\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m,\n",
" \"viscosity\": viscosity_lit / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": np.log(viscosity_lit/ state.viscosity_reference()),\n",
" \"source\": \"literature\",\n",
" \"rel.dev.\": 0.0\n",
" }\n",
" )\n",
" \n",
" # individual parameters\n",
" viscosity = state.viscosity()\n",
" ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m,\n",
" \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
" \"source\": \"saft\",\n",
" \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
" }\n",
" )\n",
" \n",
" # homo GC\n",
" state = State(saft_gc, temperature=t, pressure=p, total_moles=si.MOL)\n",
" s = state.molar_entropy(Contributions.Residual)\n",
" viscosity = state.viscosity()\n",
" ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m_gc,\n",
" \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
" \"source\": \"homo-GC\",\n",
" \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
" }\n",
" )\n",
"\n",
"# gather everything in a data frame\n",
"data = pd.DataFrame(results)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1080x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(15, 4), gridspec_kw={'wspace': 0.35})\n",
"sns.scatterplot(data=data, x='s*_res/m', y='ln_viscosity_reduced', hue='source', ax=ax[0]);\n",
"ax[0].set_xlabel(r\"$\\frac{s_{res}}{R \\cdot m}$\", fontsize=22)\n",
"ax[0].set_ylabel(r\"$\\ln\\left(\\frac{\\eta}{\\eta^{CE}}\\right)$\", fontsize=22);\n",
"ax[0].legend(frameon=False)\n",
"\n",
"sns.lineplot(data=data, x='pressure', y='viscosity', hue='source', ax=ax[1]);\n",
"ax[1].set_xlabel(r\"$p$ / MPa\", fontsize=22)\n",
"ax[1].set_ylabel(r\"$\\eta$ / Pa*s\", fontsize=22);\n",
"ax[1].legend(frameon=False)\n",
"sns.despine()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Viscosity hexane compared to NIST data at T = 450 K\n",
"MARD (individual): 0.81 %\n",
"MARD (homo-GC) : 3.70 %\n"
]
}
],
"source": [
"# check mean absolute relative deviation in percent\n",
"mard = data.groupby('source')['rel.dev.'].apply(lambda x: np.mean(np.abs(x)))\n",
"print('Viscosity hexane compared to NIST data at T = 450 K')\n",
"print(f'MARD (individual): {mard.saft:.2f} %')\n",
"print(f'MARD (homo-GC) : {mard[\"homo-GC\"]:.2f} %')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/pcsaft/adsorption_isotherms.ipynb | .ipynb | 423,308 | 626 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from feos.dft import *\n",
"from feos import StateVec, PhaseEquilibrium, State\n",
"from feos.parameters import Parameters\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import si_units as si\n",
"import numpy as np\n",
"from concurrent.futures import ProcessPoolExecutor\n",
"\n",
"# the following is only needed if you processpool executor\n",
"# does not start multiple processes\n",
"#import psutil\n",
"#p = psutil.Process()\n",
"#p.cpu_affinity(list(range(psutil.cpu_count())))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"params = Parameters.from_json([\"methanol\"], \"../../parameters/pcsaft/rehner2020.json\")\n",
"func = HelmholtzEnergyFunctional.pcsaft(params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Phase equilibria in pores"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.55 s, sys: 72.1 ms, total: 6.63 s\n",
"Wall time: 6.62 s\n"
]
}
],
"source": [
"%%time\n",
"potential = ExternalPotential.LJ93(3.0, 100.0, 0.08)\n",
"pore = Pore1D(Geometry.Cartesian, 20*si.ANGSTROM, potential)\n",
"solver = DFTSolver().newton()\n",
"\n",
"pmin = 0.2*si.BAR\n",
"pmax = 2.5*si.BAR\n",
"pressure_vec = si.linspace(pmin, pmax, 151)\n",
"pressures = np.array([0.3, 0.6, 1.0, 1.2, 1.5, 2.0]) * si.BAR\n",
"\n",
"isotherm1 = Adsorption1D.adsorption_isotherm(func, 350*si.KELVIN, pressure_vec, pore, solver=solver)\n",
"points1 = Adsorption1D.adsorption_isotherm(func, 350*si.KELVIN, pressures, pore, solver=solver)\n",
"\n",
"isotherm2 = Adsorption1D.desorption_isotherm(func, 350*si.KELVIN, pressure_vec, pore, solver=solver)\n",
"points2 = Adsorption1D.desorption_isotherm(func, 350*si.KELVIN, pressures, pore, solver=solver)\n",
"\n",
"isotherm3 = Adsorption1D.equilibrium_isotherm(func, 350*si.KELVIN, pressure_vec, pore, solver=solver)\n",
"points3 = Adsorption1D.equilibrium_isotherm(func, 350*si.KELVIN, pressures, pore, solver=solver)\n",
"\n",
"equilibrium = Adsorption1D.phase_equilibrium(func, 350*si.KELVIN, pmin, pmax, pore, solver=solver)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(isotherm1.pressure/si.BAR, isotherm1.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"plt.plot(isotherm2.pressure/si.BAR, isotherm2.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"plt.plot(isotherm3.pressure/si.BAR, isotherm3.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"plt.plot(equilibrium.pressure/si.BAR, equilibrium.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2), 'xk')\n",
"plt.gca().set_prop_cycle(None)\n",
"plt.plot(points1.pressure/si.BAR, points1.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2), 'x')\n",
"plt.plot(points2.pressure/si.BAR, points2.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2), 'x')\n",
"plt.plot(points3.pressure/si.BAR, points3.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2), 'x')\n",
"plt.xlabel('p / bar')\n",
"plt.ylabel('N / 1e-6 mol/m²')\n",
"plt.legend(['adsorption path', 'desorption path', 'equilibrium path', 'phase equilibrium']);"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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0c+ZMpaam6uqrr9bJkyez7Pfggw9q37593seoUaNsSoyw4M42aLAHk0UACDG5FThvvPGGJk6caEumggr1IhO5o86DrXIa0kSSmnaznldPY9xhAEGNOi88RPz7LsFhxowZWV5PnDhR5cqV0/Lly3XppZd618fGxqpChQqBjodwldMkER7N77Vue1g+UWr76Nm97AAgAFJSUhQVFVXo9yckJPgwDVA41HmwVfZJIjzqdJFiEqSkPdKO36WalwU+G4AijTqvaAnZFoXExERJUqlSpbKs/+yzz1SmTBk1bNhQzzzzjE6dOpXncZKTk5WUlJTlAXjldkVVYrIIoAg6efKkunfvrhIlSqhixYoaPXr0WfskJydr4MCBqly5sooXL65WrVpp7ty53u07d+7U9ddfr/POO0/FixfXhRdeqB9//NG7fd68ebr44osVHR2tihUr6umnn1ZaWpp3e4cOHdSnTx/169dPZcqUUadOnSRJhmHonXfeUZcuXVSsWDHVrFlTX331lfd9NWrUkCQ1a9ZMhmGoQ4cOks6+WpmcnKxHH31U5cqVU0xMjC655BItXbrUu33u3LkyDEOzZs1SixYtFBsbq7Zt2551O2Jmnqu6U6dOVdu2bRUTE6OGDRtq3rx53n1cLpd69uypGjVqqFixYqpbt67eeOMN7/ahQ4dq0qRJ+uabb7y3N2b+vv7111+6/PLLFRsbqyZNmmjhwoW55kHwo85DQOU0SYQkRcZIF95sLTNZBBD2qPOo82xnhiCXy2Vee+21Zrt27bKsf++998wZM2aYa9asMT/99FOzcuXK5k033ZTnsYYMGWJKOuuRmJjozy8BoeKNpqY5JN40dy7Mefv3/a3t07oHNhcQwk6fPm1u2LDBPH36dMZKt9s0k0/Y83C78529V69eZtWqVc1ff/3VXLNmjXndddeZcXFx5mOPPebd54EHHjDbtm1r/vbbb+bWrVvNV1991YyOjjY3b95smqZpXnvtteZVV11lrlmzxty2bZv53XffmfPmzTNN0zT37NljxsbGmr179zY3btxoTp8+3SxTpow5ZMgQ7/Evu+wys0SJEuYTTzxh/vnnn+aff/5pmqZpSjJLly5tfvDBB+amTZvM559/3nQ6neaGDRtM0zTNJUuWmJLMX3/91dy3b5955MgR0zRNs0ePHuaNN97oPf6jjz5qVqpUyfzxxx/N9evXmz169DDPO+887/5z5swxJZmtWrUy586da65fv95s37692bZt21y/b9u3bzclmeeff7751VdfmRs2bDAfeOABMy4uzjx8+LBpmqaZkpJiDh482Fy6dKn5119/mZ9++qkZGxtrTps2zTRN0zx+/Lh5++23m507dzb37dtn7tu3z0xOTvYeu169eub3339vbtq0ybz11lvNatWqmampqfn+2eZHjn920yUmJlI/+Ah1HgJubOP0em/R2dt2LrK2vVjRNM8cD3w2IMRQ51HnUecVXkg20D388MNmtWrVzN27d+e536xZs0xJ5tatW3Pd58yZM2ZiYqL3sXv3bgo3ZHi9kVWU7VqS8/Z9a6ztw0qZ5vEDgc0GhKgcP/yST1i/S3Y8kk/kK/fx48fNqKgo84svvvCuO3LkiFmsWDFv4bZz507T6XSaf//9d5b3duzY0XzmmWdM0zTNRo0amUOHDs3xHM8++6xZt25d052pmBw/frxZokQJ0+VymaZpFW7NmjU7672SzIcffjjLulatWpm9evUyTTOjeFq5cmWWfTIXbidOnDAjIyPNzz77zLs9JSXFrFSpkjlq1CjTNDMKt19//dW7zw8//GBKyrGgyXzuV155xbsuNTXVPP/8882RI0fm+B7TNM1HHnnEvOWWW3LMmv3YH374oXfd+vXrTUnmxo0bcz12YQRD4VYUUOch4F5vaH0e7F569ja3O+OC7aopgc8GhBjqPOo806TOK6yQu8W1T58++v777zVnzhydf/75ee7bqlUrSdLWrVtz3Sc6Olrx8fFZHoCXmT4gcG7jyzFZBFBkbNu2TSkpKd7PFsm6/a5u3bre12vXrpXL5VKdOnVUokQJ72PevHnatm2bJOnRRx/Viy++qHbt2mnIkCFas2aN9/0bN25UmzZtZBiGd127du104sQJ7dmzx7uuefPmOWZs06bNWa83btxYoK8xNTVV7dq1866LjIzUxRdffNZxGjdu7F2uWLGiJOngwbxntc6cLyIiQi1atMhy3PHjx6t58+YqW7asSpQooffff1+7du3KV/bC5EHwoc6DLbyTguVQ7xmG1CR9sghqPSBsUedR5wWDkJkkwjRN9e3bV9OnT9fcuXO991jnxTODiecHCBRYXmPQeTBZBHDuImOlZ/fad24fOXHihJxOp5YvXy6nM+vfGyVKlJAkPfDAA+rUqZN++OEH/fLLLxoxYoRGjx6tvn375vs8xYsX91nmwoqMjPQuewpN9znMcjh16lQNHDhQo0ePVps2bRQXF6dXX31VixcvtiUPAos6D7bKbZIIj8Z3SHNekrb/JiXukRLybjwGkA11HnUedV6+hExLwiOPPKJPP/1Un3/+ueLi4rR//37t379fp0+flmS1Bg8fPlzLly/Xjh079O2336p79+669NJLs7S2AgWS1yyuHpkni9g+L/f9AOTOMKSo4vY8Ml3FzMsFF1ygyMjILIXE0aNHtXnzZu/rZs2ayeVy6eDBg6pVq1aWR+aZJ6tUqaKHH35Y//vf/zRgwAB98MEHkqT69etr4cKFMk3Tu+/8+fMVFxf3r72JJGnRokVnva5fv74keWcAc7lceX6NUVFRmj9/vnddamqqli5dqgYNGvzr+QuSLy0tTcuXL/fmmz9/vtq2bavevXurWbNmqlWrlvdqtEdUVFSe+RG6qPNgq9wmifA4r5pU7RJJprRmWsBiAWGDOk8SdR513r8LmQa6d955R4mJierQoYMqVqzofUybZn1IRkVF6ddff9XVV1+tevXqacCAAbrlllv03Xff2ZwcIS0/PeiiikuNb7eWl3/s/0wAbFGiRAn17NlTTzzxhGbPnq1169bp3nvvlSNTr9k6derorrvuUvfu3fW///1P27dv15IlSzRixAj98MMPkqR+/frp559/1vbt27VixQrNmTPHW7z07t1bu3fvVt++ffXnn3/qm2++0ZAhQ9S/f/8s58nNl19+qQkTJmjz5s0aMmSIlixZoj59+kiSypUrp2LFimnGjBk6cOCAd5bMzIoXL65evXrpiSee0IwZM7RhwwY9+OCDOnXqlHr27HnO38Px48dr+vTp+vPPP/XII4/o6NGjuv/++yVJtWvX1rJly/Tzzz9r8+bNGjRoUJZZxSSpevXqWrNmjTZt2qTDhw8rNTX1nDMhOFDnwVZmPuq9Jndaz6unSpn+cQ0gPFDnUecFg5C6xTUvVapUyTKNL+AT+elBJ0nN75OWfij9+YN0/IAUV97/2QAE3KuvvqoTJ07o+uuvV1xcnAYMGHBWAfTxxx/rxRdf1IABA/T333+rTJkyat26ta677jpJ1pXNRx55RHv27FF8fLw6d+6s119/XZJUuXJl/fjjj3riiSfUpEkTlSpVSj179tTzzz+fr3zDhg3T1KlT1bt3b1WsWFFTpkzxXhGNiIjQm2++qRdeeEGDBw9W+/bts0xf7/HKK6/I7Xbrnnvu0fHjx9WiRQv9/PPPOu+8887hO5dx7FdeeUWrVq1SrVq19O2336pMmTKSpP/+979auXKl7rjjDhmGoW7duql379766aefvO9/8MEHNXfuXLVo0UInTpzQnDlzVL169XPOBftR58FW3jGH86j3Gtwo/fiEdHiztHeFVDnnMaIAhC7qvHNDnXfuDPPfKqIiJikpSQkJCUpMTGQgYUgjqkrJiVKf5VKZWnnv++GV0p6lUsfBUvsBgckHhKAzZ85o+/btqlGjhmJiYuyOEzYMw9D06dPVtWtXu6OcZceOHapRo4ZWrlyppk2b2h2n0PL6s0v9EBr4OSFH+a33/u8Bae2XUssHpWtfC1w+IIRQ5/kHdZ7/BUOdFzK3uAK28Pagy8evSvP7rOflkzJmAwMAAEBw897i+i9jVXluc133lZSW4t9MAIAihwY6IC/5GYPO48KbpOgE6dhO6a85/s0FAAAA3/i3SSI8al4ulaggnT4qbfnZ/7kAAEVKyIxBB9giv2PQSVJUrHVldcl71mQRtTr6NxsAZBLMI1ZUr149qPMBKOLyM0mEZNWDjW+XFrxpTRZR/3r/ZwMAUecVFfSgA/JSkB50ktT8Xuv5zx+l4/v9EgkAAAA+lJ9JIjyadLOeN/8snTziv0wAgCKHBjogLwXpQSdJ5RtIVVpZ71v5if9yAQAAwDcKckG2fAOpYhPJnWqNRQcAgI/QQAfkJvNED/ntQSdlmixickbBBwAAgOBjmpLSb80y8vlPo6Z3Wc+rPvdLJABA0UQDHZAbM1PjWn5mcfW4sKsUU1JK3CVtm+3rVAAAAPCVzBdT83vHRMNbJUektG+VdGC9X2IBAIoeGuiA3GQu2ArSgy6yWMb4JMs+9m0mAAAA+E7mC7L57UFXvLRUp5O1TC86AICP0EAH5MYsxBVVjxbpt7luniEl7fVdJgAAAPiOmWlIk4LUe57bXNd8IbnSfJsJAFAk0UAH5MadqdhyRBTsvWXrSlXbWo18K5gsAkDB3Hvvveratav3dYcOHdSvXz/v6+rVq2vs2LHe14Zh6Ouvvy7weSZOnKiSJUsWOmewyv79AYBcFfaOidpXSbFlpJMHpW2zfJ8LQNiizjs34Vzn0UAH5KawBZuHpxfdCiaLAFAwb7zxhiZOnJjr9qVLl+qhhx465/Pccccd2rx58zkfxy7hWngCCKDC3jHhjJQa324tr/rMt5kAhDXqvPwpinUeDXRAbgp7y4NH/RukYqWkpD3Slpm+ywUg7CUkJORZkJQtW1axsbHndI7U1FQVK1ZM5cqVO6fjAEBIcxdiDDqPpv+xnjf9JJ36x3eZAIQ16jzkhgY6IDfegs2QDKPg74+MySjcljNZBBAO3G63RowYoRo1aqhYsWJq0qSJvvrqqyz7/Pjjj6pTp46KFSumyy+/XBMnTpRhGDp27JgkaejQoWratGmW94wdO1bVq1f3vs5+60N2OXXt37dvn7p06aJixYqpZs2aWXLt2LFDhmFo2rRpuuyyyxQTE6PPPvvsrCuTOZ23X79+6tChg/d1hw4d1LdvX/Xr10/nnXeeypcvrw8++EAnT57Ufffdp7i4ONWqVUs//fRTrvk9X8Pw4cPVrVs3FS9eXJUrV9b48eOz7DNmzBg1atRIxYsXV5UqVdS7d2+dOHFCkjR37lzdd999SkxMlGEYMgxDQ4cO9b731KlTuv/++xUXF6eqVavq/fffzzMPgCIq8wXZgt4xUaGR9XClSOv+z7e5AAQcdR51nt1ooANy47nloTC95zya32s9b/lFStxzzpGAcGWapk6lnrLlYZpmvnOOGDFCkydP1rvvvqv169fr8ccf191336158+ZJknbv3q2bb75Z119/vVatWqUHHnhATz/9tL++bVkMGjRIt9xyi1avXq277rpLd955pzZu3Jhln6efflqPPfaYNm7cqE6dOhX6XJMmTVKZMmW0ZMkS9e3bV7169dJtt92mtm3basWKFbr66qt1zz336NSpU3ke59VXX1WTJk20cuVKb7aZMzN6HDscDr355ptav369Jk2apNmzZ+vJJ5+UJLVt21Zjx45VfHy89u3bp3379mngwIHe944ePVotWrTQypUr1bt3b/Xq1UubNm0q9NcMIExluWOiEP80apJ+MZbZXIFcUeedO+q8olHnFXDke6AI8fSgK8z4cx5lakvV20s7frfGorv8Wd9kA8LM6bTTavV5K1vOvfg/ixUb+e+3ESQnJ+vll1/Wr7/+qjZt2kiSatasqT/++EPvvfeeLrvsMr3zzju64IILNHr0aElS3bp1tXbtWo0cOdKvX4Mk3XbbbXrggQckScOHD9fMmTM1btw4vf322959+vXrp5tvvvmcz9WkSRM9//zzkqRnnnlGr7zyisqUKaMHH3xQkjR48GC98847WrNmjVq3bp3rcdq1a+ctbOvUqaP58+fr9ddf11VXXeXN61G9enW9+OKLevjhh/X2228rKipKCQkJMgxDFSpUOOvY11xzjXr37i1Jeuqpp/T6669rzpw5qlu37jl//QDCyLnWe41uk2YOkvaukA5ulMrV9102IExQ55076ryswrXOowcdkBtf9KCTMnrRrfhEcqXluSuA4LV161adOnVKV111lUqUKOF9TJ48Wdu2bZMkbdy4Ua1aZS1APUWev2U/T5s2bc66stqiRQufnKtx48beZafTqdKlS6tRo0bedeXLl5ckHTx4MM/j/FvmX3/9VR07dlTlypUVFxene+65R0eOHPnXK7bZM3qKu3/LA6AIOtd6r0RZqXZ6TxV60QEhizovA3WefehBB+TGFz3oJKn+9VJsaen4XutW13rXnHs2IMwUiyimxf9ZbNu588MzJsYPP/ygypUrZ9kWHR2d7/M5HI6zbrdITU3N9/vPRfHixfPcnt9skZGRWV4bhpFlnZE+bqfb7VZh7dixQ9ddd5169eqll156SaVKldIff/yhnj17KiUl5V8HT84p47nkARCmvPXeOfRbaNpN2vSDtGaa1HGI5OSfWEBm1HnUedlR5+WMTw8gN54xSQozHklmEdHWZBELxlmTRdBAB5zFMIx83X5gpwYNGig6Olq7du3SZZddluM+9evX17fffptl3aJFi7K8Llu2rPbv3y/TNL0FzqpVq84536JFi9S9e/csr5s1a1agY5QtW1br1q3Lsm7VqlVnFUG+kv17s2jRItWvb90etnz5crndbo0ePVqO9L+Hv/jiiyz7R0VFyeVyCQAKzfTBBdnanaRipaQTB6S/5ki1r/JNNiBMUOetOud81HlFA7e4ArnxVQ86SWp+n/W8ZaZ0bNe5Hw9AwMXFxWngwIF6/PHHNWnSJG3btk0rVqzQuHHjNGnSJEnSww8/rC1btuiJJ57Qpk2b9Pnnn2vixIlZjtOhQwcdOnRIo0aN0rZt2zR+/Ph/nQkrP7788ktNmDBBmzdv1pAhQ7RkyRL16dOnQMe44oortGzZMk2ePFlbtmzRkCFDzirkfGn+/PkaNWqUNm/erPHjx+vLL7/UY489JkmqVauWUlNTNW7cOP3111/65JNP9O6772Z5f/Xq1XXixAnNmjVLhw8fztctEQCQhac3ybkMaRIRJTW+3Vpe9dm5ZwIQcNR5vkedV3A00AG58dUYdJJU+gKpxqWSTGuyCAAhafjw4Ro0aJBGjBih+vXrq3Pnzvrhhx9Uo0YNSVLVqlX1f//3f/r666/VpEkTvfvuu3r55ZezHKN+/fp6++23NX78eDVp0kRLlizJMitVYQ0bNkxTp05V48aNNXnyZE2ZMkUNGjQo0DE6deqkQYMG6cknn1TLli11/PjxLFdrfW3AgAFatmyZmjVrphdffFFjxozxzjrWpEkTjRkzRiNHjlTDhg312WefacSIEVne37ZtWz388MO64447VLZsWY0aNcpvWQGEKV/c4ipZd0tI0p8/SKePntuxANiCOs+3qPMKzjALMu9wEZCUlKSEhAQlJiYqPj7e7jiw07410nvtpRIVpIE+mLJ53f+kr+6zjvf4Osnpn67EQLA7c+aMtm/frho1aigmJsbuOH43d+5cXX755Tp69KhKlixpd5ygUb16dfXr1y/LDF7BLq8/u9QPoYGfE85ycKP0dmtrvOAn/yr8cUxTeqeddHC9dO0YqWVP32UEQgh1HiTqvMKiBx2QG1/2oJOketdJxctKJ/ZLm2f45pgAAAAoPF/1oDOMjF50zOYKACgEGuiA3HhmgfHFGHSSNT5J07us5WUf++aYAAAAKDxfTBLh0fh26zh/L5MO+eDuCwBAkcIsrkBuvD3ofNiO3byHNH+stG22dHSHdF513x0bQFDq0KHDWVPaQ9qxY4fdEQBAMtMvyPrijokS5awZXDfPsHrRXTXs3I8JIKhR5+WMOq9w6EEH5MaXs7h6lKop1bxckiktn+S74wIAAKDgfH3HhOc21zXTMmpJAADygQY6IDe+HoPOo8V91vPKTyVXqm+PDQAAgPzz9R0TdTpLxc6Tju+T/prjm2MCAIoEGuiA3PijB50k1b1GKlFeOnlQ+vMH3x4bAAAA+eerSSI8IqKlRrdZy0wWAQAoABrogNz4qwedM1Jqdre1vJzJIgAAAGzjy0kiPJp0s543fi+dPua74wIAwhoNdEBuvGOS+OHX5KLukgzpr7nSkW2+Pz4AAAD+nS8nifCo1EwqW19yJUvrvvLdcQEAYY0GOiA3/upBJ1mzt9a+2lpeNsH3xwcAAMC/88eQJoaRcbfEyk99d1wAQFijgQ7Ijb/GoPNo+YD1vPJTKeWUf84BIOCqV6+usWPH2h3DFhMnTlTJkiW9r4cOHaqmTZt6X997773q2rWr93WHDh3Ur1+/Ap9nx44dMgxDq1atKnRWAJDk+0kiPJrcKTkipb0rpf3rfHtsALahzivpfU2d53sRdgcAgpY7zXr2Rw86SarVUSpZTTq2U1r/v4wrrQAQou644w5dc801uW5/4403ZJrmOZ+nSpUq2rdvn8qUKXPOxwJQxPlrSJPiZaS6XaSN31oXY7u84tvjA0CAUef5Hz3ogNx4r6j6qR3b4ZRa3G8tL/3QP+cAwtTQoUM1fPjwHLcNHz5cQ4cODWwgSJKKFSumcuXK5bo9ISEhy5XXwkhJSZHT6VSFChUUEcF1RgDnyB+TRHg0u8d6XjNVSkv2/fGBMEWdF5yo8/yPBjogN95bXP34a9LsbskZZd3+8Pdy/50HCDNOp1ODBw8+q3gbPny4Bg8eLKfTPz1fO3TooD59+qhPnz5KSEhQmTJlNGjQoLOuFp46dUr333+/4uLiVLVqVb3//vtZtj/11FOqU6eOYmNjVbNmTQ0aNEipqane7atXr9bll1+uuLg4xcfHq3nz5lq2bJl3+x9//KH27durWLFiqlKlih599FGdPHkyz+zffPONLrroIsXExKhmzZoaNmyY0tLSvNu3bNmiSy+9VDExMWrQoIFmzpwpwzD09ddfS5Lmzp0rwzB07Ngx73tWrVolwzC0Y8cOSWff+pBd9lsfJCktLS3P72f16tU1fPhwde/eXfHx8XrooYfOuvUhp/N+/fXXMgzD+9pzG8aECRNUtWpVlShRQr1795bL5dKoUaNUoUIFlStXTi+99FKe30cAYcYfk0R41OooxVWSTh+VNv3o++MDYYo6jzqvqNZ5NNABufFnweZRvIx04U3W8tKP/HceIMwMGjRIL7zwQpbizVO0vfDCCxo0aJDfzj1p0iRFRERoyZIleuONNzRmzBh9+GHWXrCjR49WixYttHLlSvXu3Vu9evXSpk2bvNvj4uI0ceJEbdiwQW+88YY++OADvf76697td911l84//3wtXbpUy5cv19NPP63IyEhJ0rZt29S5c2fdcsstWrNmjaZNm6Y//vhDffr0yTXz77//ru7du+uxxx7Thg0b9N5772nixIneIsXtduvmm29WVFSUFi9erHfffVdPPfWUL79tucrP9/O1115TkyZNtHLlynP62W7btk0//fSTZsyYoSlTpuijjz7Stddeqz179mjevHkaOXKknn/+eS1evPhcvywAocKfYw47nFLTbtbyik98f3wgTFHnUecVRljUeSaySExMNCWZiYmJdkeB3VZ+bppD4k1z8k3+Pc+uxdZ5hpczzZNH/HsuIAicPn3a3LBhg3n69OlzPtYLL7xgSjKjoqJMSeYLL7zgg4S5u+yyy8z69eubbrfbu+6pp54y69ev731drVo18+677/a+drvdZrly5cx33nkn1+O++uqrZvPmzb2v4+LizIkTJ+a4b8+ePc2HHnooy7rff//ddDgcuX5PO3bsaL788stZ1n3yySdmxYoVTdM0zZ9//tmMiIgw//77b+/2n376yZRkTp8+3TRN05wzZ44pyTx69Kh3n5UrV5qSzO3bt5umaZoff/yxmZCQ4N0+ZMgQs0mTJt7XPXr0MG+88Ubv6/x+P7t27Zol+/bt201J5sqVK3M8r2ma5vTp083MZc6QIUPM2NhYMykpybuuU6dOZvXq1U2Xy+VdV7duXXPEiBFmTvL6s0v9EBr4OeEs6/5n1WETuvjn+Ie3WscfkmCaR3f55xxAEKHOOxt1HnVeftGDDsiNdww6P/agk6TzW0oVGklpZ6RVn/n3XECYGTRokKKiopSSkqKoqCi/XlH1aN26dZYu9W3atNGWLVvkcrm86xo3buxdNgxDFSpU0MGDB73rpk2bpnbt2qlChQoqUaKEnn/+ee3atcu7vX///nrggQd05ZVX6pVXXtG2bdu821avXq2JEyeqRIkS3kenTp3kdru1ffv2HDOvXr1aL7zwQpb3PPjgg9q3b59OnTqljRs3qkqVKqpUqVKWrysQ8vP9bNGihU/OVb16dcXFxXlfly9fXg0aNJAj0+yN5cuXz/KzAhDm/D2kSekLpGqXSDKl1VP8cw4gTFHnUecVRDjUeTTQAbnx5y0PmRmG1PIBa3nZhIzZxAD8q+HDh3uLtpSUlFwHFA40z20KHoZhyJ3+u71w4ULddddduuaaa/T9999r5cqVeu6555SSkuLdf+jQoVq/fr2uvfZazZ49Ww0aNND06dMlSSdOnNB///tfrVq1yvtYvXq1tmzZogsuuCDHPCdOnNCwYcOyvGft2rXasmWLYmJi8vU1eYobM9O4IZnHU/Gn4sWL57nd4XCcNT5MTtly+rnk9bMCUASYfprFNbOL0ieLWPkJdR5QANR51HlS0arzQqaBbsSIEWrZsqXi4uJUrlw5de3aNct93pJ05swZPfLIIypdurRKlCihW265RQcOHLApMUJeoHrQSVKj26ToeOmfv6S/5vj/fEAYyDwWSXJy8lljlfhL9nErFi1apNq1a+d7wOIFCxaoWrVqeu6559SiRQvVrl1bO3fuPGu/OnXq6PHHH9cvv/yim2++WR9//LEk6aKLLtKGDRtUq1atsx5RUVE5nvOiiy7Spk2bcnyPw+FQ/fr1tXv3bu3bty/L15VZ2bJlJSnLPp7Be8/FuX4/PdmOHz+eZQBlX2RD4FDnwTaBGHO4/g1WnXdsl7Tjd/+dBwgj1HnUeZmzFZU6L2Qa6ObNm6dHHnlEixYt0syZM5Wamqqrr746yw/p8ccf13fffacvv/xS8+bN0969e3XzzTfbmBohLRCzuHpEFZea/sdaZrII4F/lNFBwTgMK+8OuXbvUv39/bdq0SVOmTNG4ceP02GOP5fv9tWvX1q5duzR16lRt27ZNb775pveqqSSdPn1affr00dy5c7Vz507Nnz9fS5cuVf369SVZM4MtWLBAffr00apVq7RlyxZ98803eQ4ePHjwYE2ePFnDhg3T+vXrtXHjRk2dOlXPP/+8JOnKK69UnTp11KNHD61evVq///67nnvuuSzHqFWrlqpUqaKhQ4dqy5Yt+uGHHzR69OiCfOtydK7fT0lq1aqVYmNj9eyzz2rbtm36/PPPNXHixHPOhsChzoNtAnHHRFSs1PAWa3klk0UA/4Y6jzovs6JU54VMA92MGTN077336sILL1STJk00ceJE7dq1S8uXL5ckJSYm6qOPPtKYMWN0xRVXqHnz5vr444+1YMGCs1qHgXwJxBXVzFr0tJ43/yQd2x2YcwIhyuVy5TiLl6d4yzyuha91795dp0+f1sUXX6xHHnlEjz32mB566KF8v/+GG27Q448/rj59+qhp06ZasGBBlq/D6XTqyJEj6t69u+rUqaPbb79dXbp00bBhwyRZ457MmzdPmzdvVvv27dWsWTMNHjw4y7gi2XXq1Enff/+9fvnlF7Vs2VKtW7fW66+/rmrVqkmybh2YPn269+t64IEHzpqGPjIyUlOmTNGff/6pxo0ba+TIkXrxxRcL8q3L0bl+PyWpVKlS+vTTT/Xjjz+qUaNGmjJlioYOHXrO2RA41HmwTaDumPDc5rrhW+n0Uf+eCwhx1HnUeZkVpTrPMLPfzBsitm7dqtq1a2vt2rVq2LChZs+erY4dO+ro0aMqWbKkd79q1aqpX79+evzxx3M8TnJyspKTk72vk5KSVKVKFSUmJio+Pt7fXwaC2cK3pZ+fkRreKt0aoF5tk66Xtv8mtR8odfT/IKiAHc6cOaPt27erRo0a+R4XI1h06NBBTZs21dixY+2OEhCGYWj69Onq2rWr3VGCQl5/dpOSkpSQkED94CPUeQiYZR9L3/eT6l4jdfPjJA6mKb3TVjq4QbrmNeniB/13LsBG1Hmhgzovq2Co80KmB11mbrdb/fr1U7t27dSwYUNJ0v79+xUVFZWlaJOsWTr279+f67FGjBihhIQE76NKlSr+jI5QEsgx6Dw8k0WsmCSlpeS9LwAAYYg6DwFlBmhIE8OQmnkmi/jUv+cCAISkkGyge+SRR7Ru3TpNnTr1nI/1zDPPKDEx0fvYvZtbC5EuULO4Zlb3GimuonTykLTx28CdFwCAIEGdh4Dy3EwUiAuyje+QHJHSvlXS/rX+Px8AIKRE2B2goPr06aPvv/9ev/32m84//3zv+goVKiglJUXHjh3LcnX1wIEDqlChQq7Hi46OVnR0tD8jI1R5e9AFsB3bGSk1v1eaO8KaLKLRrYE7N4B/NXfuXLsjBFSIjoKBEEadh4AL5AXZ4qWletdIG76RVnwiXTPK/+cEkG/UebBbyPSgM01Tffr00fTp0zV79mzVqFEjy/bmzZsrMjJSs2bN8q7btGmTdu3apTZt2gQ6LsKBO32SiED2oJOki3pY59y1QDqwIbDnBgDABtR5sE2ghzTx3Oa6ZpqUeiYw5wQAhISQ6UH3yCOP6PPPP9c333yjuLg473gjCQkJKlasmBISEtSzZ0/1799fpUqVUnx8vPr27as2bdqodevWNqdHSLJjDDpJiq8o1bvWusV12UfStec+vTUAAMGMOg+2cQdoDDqPC66Q4itLSX9Lm36QGt4SmPMCAIJeyPSge+edd5SYmKgOHTqoYsWK3se0adO8+7z++uu67rrrdMstt+jSSy9VhQoV9L///c/G1AhpdoxB5+GZLGL1VCn5eODPDwQA3eoRavgz6z/UebCNGeB6z+GUmv7HWmayCIQxPjMRaoLhz2zI9KDLzzcrJiZG48eP1/jx4wOQCGHPrh50klTjUql0benIFusWCE+DHRAGIiMjJUmnTp1SsWLFbE4D5N+pU6ckZfwZhu9Q58E2ZvqQJoEcc7jpXdJvr0rb5kjHdkklqwbu3ICfUechVAVDnRcyDXRAwNnZg84wrEa5GU9Zk0W06GmtA8KA0+lUyZIldfDgQUlSbGysDP58I4iZpqlTp07p4MGDKlmypJxOGz4XAPiHHWMOl6ohVW8v7fhdWjVF6vBU4M4N+Bl1HkJNMNV5NNABubFjFtfMmtwpzRomHdwg7VwgVW9nTw7ADzyzLnqKNyAUlCxZMs8ZQwGEILvumGh2j9VAt/JT6dIn7Ks3AT+gzkMoCoY6jwY6IDd2zeLqUayk1Og2acUkaekHNNAhrBiGoYoVK6pcuXJKTU21Ow7wryIjI+k5B4SjQE8S4dHgBunHJ6TEXdL2udbkEUCYoM5DqAmWOo8GOiA3do5B53HxQ1YD3YZvpaS9Unwl+7IAfuB0OoPiwxAAUEQFepIIj8hiUuPbrYuwyyfSQIewRJ0HFAx9qYHcuNOsZ7t60ElShYZStXZW8bhsgn05AAAAwpF3kggb6r3mPaznP3+QTnArIAAUdTTQAbnx3PLgsLmj6cUPWc/LPpZSz9ibBQAAIJzYdYurJFVoJFVuYV0UXvVZ4M8PAAgqNNABubF7kgiPetdJ8ZWlU4el9dPtzQIAABBO7OxBJ0nN77Wel0/KGP8YAFAk0UAH5MbuSSI8nBFSy57W8pL3JNO0Nw8AAEC4sLMHnSQ1vFmKipOObpd2/GZPBgBAUKCBDshNMEwS4XHRvZIzWtq7UtqzzO40AAAA4cGuSSI8oopbk0VI1mQRAIAiiwY6IDdumwu2zIqXlhrdai0vec/eLAAAAOHC7ltcpYzbXDd+L504ZF8OAICtaKADchNMPeikjMki1n8tHd9vaxQAAICwEAwXZCs2lipdJLlTpdWf25cDAGArGuiA3ARDwZZZpaZSlVZW8bbsY7vTAAAAhL5gmRQs82QRjDcMAEUSDXRAboKlYMvM04tu+cdSWoq9WQAAAEKdd1Iwm+u9hrdIUSWkf7ZJO/6wNwsAwBZB1PIABJlgmcU1swY3SiUqSCcOSBu+sTsNAABAaLN7kgiP6BJSo9usZSaLAIAiiQY6IDfBNgadJDkjpRb3W8tMFgEAAHBugmGSCA/vZBHfSieP2BoFABB4NNABuQm2Meg8WtwnOSKlPUulv1fYnQYAACB0BVO9V6mpVLGp5EqRVk+xOw0AIMBooANyE4w96CSpRDmp4c3W8pL37c0CAAAQyoKt3vNOFjGRySIAoIihgQ7ITTBdUc3u4v9az+v+TzpxyN4sAAAAocpb7wXJP4sa3SpFFpeObJF2LrA7DQAggILkkwgIQt4xSYLw1+T85lLl5tYtEAwkDAAAUDhmkMzi6hEdZzXSSdR4AFDEBMknERCEgrkHnZTRi27ZR5Ir1d4sAAAAoSiYJonw8NzmuuEb6dQ/tkYBAAQODXRAboJtTJLsLuwqFS8rHd8nbfzO7jQAAAChJxgvyFZqJlVoLLmSpdVT7U4DAAgQGuiA3ARjwZZZRLTU/D5rmckiAAAACi4YL8gaBpNFAEARRAMdkJtgLNiya3G/5IiQdi2U9q22Ow0AAEBoCbZJIjwa3SZFxkqHN0m7FtmdBgAQAEH2SQQEkWDvQSdJ8RWlBjday4vpRQcAAFAgZpDWezHxUsNbrGUmiwCAIoEGOiA3wTyLa2aeySLWfimdPGxvFgAAgFDiuX00GOs9z1Am66czWQQAFAFB+EkEBIlQ6EEnSVUulio2tQYSXvax3WkAAABCRzDXe5Uvkso3smq8VZ/bnQYA4Gc00AG5cadZz8E8Bp1kDSTcure1vPQDKS3F3jwAAAChIpjHHDYMqWVPa3nZBMnttjcPAMCvaKADchOsY5Lk5MKbpBIVpBMHpPX/szsNAABAaAjWSSI8Gt0mRcdL/2yTts+1Ow0AwI+C9JMICAKegs0RYW+O/IiIki5+0FpeOD5jPBUAAADkLtgvyEaXkJrcaS0v/cjeLAAAv6KBDshNqEwS4dH8PikiRtq/Rtq5wO40AAAAwc9b7wVpA50ktUi/zXXTj1Li3/ZmAQD4TYi0PAA2COZBg3NSvHTGFdZFb9ubBQAAIBR4xnUL1ltcJalcPal6e6sxcflEu9MAAPwkiD+JAJsF86DBufFMFvHnD9I/f9mbBQAAINiFSr3X4n7recUkyZVqbxYAgF/QQAfkJtR60ElS2bpSrSslmdLi9+xOAwAAENyCfZIIj3rXSSXKWxOC/fm93WkAAH4Q5J9EgI1C5Ypqdq17Wc8rP5XOJNqbBQAAIJgF+yQRHhFR0kU9rGUmiwCAsEQDHZAb75gkQV6wZXdBR6lsPSnlhLTiE7vTAAAABK9QmCTCo3kPq6ffjt+lg3/anQYA4GM00AG58fagC7FfE8PI6EW3+D3JlWZvHgAAgGAVSkOaJJwv1b3GWl42wd4sAACfC7GWByCAQqlgy67xHVKxUlLiLmnTD3anAQAACE7eHnQh8s+ilj2t59VTpOQT9mYBAPhUiHwSATYI1THoJCmyWMZsXwvftjcLAABAsAq1C7I1OkilakrJSdLaL+1OAwDwoZBqoPvtt990/fXXq1KlSjIMQ19//XWW7ffee68Mw8jy6Ny5sz1hEfpCrWDL7uIHJUektHuR9Pdyu9MAAJAn6jzYwgyRWVw9HA6pRXovumUfSaZpbx4AgM+EyCeR5eTJk2rSpInGjx+f6z6dO3fWvn37vI8pU6YEMCHCSij3oJOkuApSw1us5UXv2JsFAIB/QZ0HW4TSJBEeTf8jRcRI+9dKe5banQYA4CMRdgcoiC5duqhLly557hMdHa0KFSoEKBHClmcGVyl0e9BJ1mQRa6ZK66dLV70gxVeyOxEAADmizoMtQvGOidhS1kXYVZ9JSz+UqlxsdyIAgA+EVA+6/Jg7d67KlSununXrqlevXjpy5Eie+ycnJyspKSnLA/D2npNCZ9DgnFRqKlVrJ7nTpCXv250GAIBzQp0HnwvVOyY8k0Wsny6dzPv3AAAQGkK45eFsnTt31uTJkzVr1iyNHDlS8+bNU5cuXeRyuXJ9z4gRI5SQkOB9VKlSJYCJEbTcmf7MhNIV1Zy07m09L/tYSjllbxYAAAqJOg9+4blrIlTGoPOo3Fyq1ExypUgrP7E7DQDAB0Lskyhvd955p2644QY1atRIXbt21ffff6+lS5dq7ty5ub7nmWeeUWJiovexe/fuwAVG8MrSgy7EG+jqdpHOqy6dOSatZqweAEBoos6DX4TaJBGZeSeLmJB1eBYAQEgKwU+i/KtZs6bKlCmjrVu35rpPdHS04uPjszyAsOpB53BKrR62lhe9QwEHAAgL1HnwiVCcJMKj4S1STIJ0bKe0bZbdaQAA5yisG+j27NmjI0eOqGLFinZHQagJpx50ktTsbik6XjqyhQIOABAWqPPgE6E4SYRHVKzU9G5reemH9mYBAJyzkGqgO3HihFatWqVVq1ZJkrZv365Vq1Zp165dOnHihJ544gktWrRIO3bs0KxZs3TjjTeqVq1a6tSpk73BEXrCZRZXj+g4qdk91vKCcfZmAQAgB9R5sEWoThLh0eJ+63nzz9LRHbZGAQCcm5BqoFu2bJmaNWumZs2aSZL69++vZs2aafDgwXI6nVqzZo1uuOEG1alTRz179lTz5s31+++/Kzo62ubkCDnhMotrZq0fthobt8+T9q2xOw0AAFlQ5yHgTDPjFtdQvSBbppZ0wRWSTHrRAUCIi7A7QEF06NBBpmnmuv3nn38OYBqENXea9RyqxVpOSlaVLuwqrfs/qxfdLR/YnQgAAC/qPAScmfmOiRC+IHvxf6Vts6UVk6UOz0hRxe1OBAAohBD+JAL8yB3itzvkpm1f63nd/0mJe+zNAgAAYKfMDXShfMdE7aul82pIZxKlNdPsTgMAKKQQ/iQC/Mg7HklIdTL9d5WaSdXbW1/fonfsTgMAAGAfd6YhTUL5rgmHQ7r4IWt58fvWrbsAgJBDAx2Qk1Ce0evftHvMel4+UTp9zM4kAAAA9sky5nCI13zN7pIii0uHNkrbf7M7DQCgEGigA3LiueUhlG93yE2tK6Wy9aWUE1YjHQAAQFEULj3oJCkmQWrazVpe/J69WQAAhRKGrQ+AD4RzDzrDyBiLbvG7UlqKvXkAAADskLkHXShPEuHhuc1180/S0R22RgEAFFwYfBIBfmCG6SQRHo1ulUpUkI7vk9Z9ZXcaAACAwMs8Vls41Hxl60o1L7fuBFn6od1pAAAFRAMdkJNw7kEnSRHRUuuHreUF4xhMGAAAFD3uMOtBJ0mt0uu7FZOllJP2ZgEAFEiYfBIBPhbuPegkqfl9UlQJ6eAGaessu9MAAAAElqfeMxzWECDhoPbV0nk1pDOJ0pov7E4DACgAGuiAnLjTJ4kI1x50klSspHRRD2t5wZu2RgEAAAi4cLxjwuGQLn7QWl78HndJAEAIoYEOyIm3B12Y/4q0ftgqSrfPk/attjsNAABA4GTuQRdOmt4lRRaXDm2Utv9mdxoAQD6F2acR4CPheEU1JyWrShfeZC0vGGdvFgAAgEAy0++YCLchTYqVlJp2s5aXvG9rFABA/tFAB+SkKIxB59G2r/W87n/Ssd32ZgEAAAiUcL4ge/FD1vOmH6WjO2yNAgDIHxrogJyEc8GWXaWmUo1LrUbJRe/YnQYAACAwvD3owvCfRGXrSjUvt77GpR/anQYAkA9h+GkE+EBR6kEnSW0fs55XTJJOH7M1CgAAQECE+wXZVg9bzysmSykn7c0CAPhXNNABOfHO4lpEfkVqdZTKNZBSTkjLP7Y7DQAAgP+F6yQRHrWvks6rLp1JlNZ8YXcaAMC/CNNPI+AcFbUedIaRMRbdoneltBR78wAAAPhbuE4S4eFwZoxFt/g9yTTtzQMAyBMNdEBOwv2Wh5w0vFWKqyid2C+t/dLuNAAAAP5VFOq9pndJkcWlQxulHb/bnQYAkAca6ICcFLUedJIUEZUxVsmCcVxlBQAA4a0o1HvFSkpN7rSWF79naxQAQN4i7A4ABKWicEU1J83vlX571brKumWmVOdquxMBAGxw5swZrVmzRgcPHpTbMy5ruhtuuMGmVICPFZUxhy9+SFr2kbTpR+noDmtcOgBA0ClUA92ZM2c0btw4zZkzJ8fCbcWKFT4JB9imKFxRzUmxklYj3cK3pD9ep4EOAIqgGTNmqHv37jp8+PBZ2wzDkMvlsiEV4AfhPkmER7l60gVXSNtmW73oOo+wOxEAIAeFaqDr2bOnfvnlF9166626+OKLZRiGr3MB9ioqV1Rz0uYRq3jbtUDatUiq2truRACAAOrbt69uu+02DR48WOXLl7c7DuA/4T5JRGZtHrEa6FZ8InV4WopJsDsRACCbQjXQff/99/rxxx/Vrl07X+cBgoM7zXouCgVbdvGVpKbdpBWTpT/GSv+ZanciAEAAHThwQP3796dxDuGvKA1pckFHqWw96dCfViNd2z52JwIAZFOo7kGVK1dWXFycr7MAwcMsQgVbTto+JsmQNv8kHdhgdxoAQADdeuutmjt3rt0xAP8rSkOaGIbUure1vPhdyZVmbx4AwFkK1YNu9OjReuqpp/Tuu++qWrVqvs4E2M9zRdVRROdRKVNLanCDtOEbaf5Y6eb37U4EAAiQt956S7fddpt+//13NWrUSJGRkVm2P/roozYlA3ysKPWgk6TGt0uzXpASd0sbv5Ua3mx3IgBAJoVqfWjRooXOnDmjmjVrKjY29qzC7Z9//vFJOMA2RemKam4uedxqoFv7lXT5c9J5NMYDQFEwZcoU/fLLL4qJidHcuXOzjDVsGAYNdAgfnjHoisp42pHFpJY9pXkjpUVv00AHAEGmUA103bp1099//62XX35Z5cuXZ5IIhB93EZnVKy+Vmkk1L5f+miMtGCdd+5rdiQAAAfDcc89p2LBhevrpp+VwFOHPQYS/ojRJhEfLB6Q/Xpf2LJV2L5GqXGx3IgBAukI10C1YsEALFy5UkyZNfJ0HCA5FsWDLSfv+VgPdyk+ky56SSpS1OxEAwM9SUlJ0xx130DiH8FfUbnGVpBLlrFtdV34qLXxLqjLZ7kQAgHSFqrzq1aun06dP+zoLEDyKYsGWk+rtpcrNpbQz0uJ37E4DAAiAHj16aNq0aXbHAPyvqA5p0voR63njd9LRnfZmAQB4FaoH3SuvvKIBAwbopZdeynHw4Pj4eJ+EA2xTVAu27AzDGotu2t3Skg+ldv2kGH6/ASCcuVwujRo1Sj///LMaN258Vp03ZswYm5IBPlZUL8iWb5AxjMni96TOL9udCACgQjbQde7cWZLUsWPHLOtN05RhGHK5XOeeDLBTUS3YclL3WqlMHenwZmn5x1K7x+xOBADwo7Vr16pZs2aSpHXr1mXZxrjDCCtmER5zuE0fq4FuxWSpw1NSTILdiQCgyCtUA93s2bMp0BDevD3oimDBlp3DYfWc+6a3tHC8dPF/pcgYu1MBAPxkzpw5dkcAAsM0reeiWO/V6iiVrScd+lNa8YnUto/diQCgyCtQA92ECRN0ww03qEOHDn6KAwQJd/okEfSgszS6TZrzspS0R1o9RWpxn92JAAA+VrVqVd1www268cYbdfnllysiolDXcYHQUZTvmDAMqXUv6bvHrNtcWz0sOfmdBwA7Fehy0aeffqrzzz9fbdu21ciRI7Vx40Z/5QLsxRh0WUVEZVxZnf+G5EqzNw8AwOc++eQTRUdHq3fv3ipTpozuuOMOffbZZzp27Jjd0QD/KOr1XuM7pNjSUuIu6c/v7E4DAEVegRroZs+erX379ql3795avny5WrVqpdq1a2vAgAH67bff5Pb0OgJCXVG+opqbi7pLxUpJR7dLG7+xOw0AwMcuu+wyjR49Wlu2bNH8+fPVtGlTjRs3ThUqVNAVV1yhsWPH6q+//rI7JuA7Rb3eiywmtXzAWl443t4sAICCNdBJ0nnnnae7775bX3zxhQ4fPqxx48bp9OnTuuuuu1SuXDl1795dX331lU6ePOmPvEBgFPUrqjmJKm7d/iBJf7yeMW4LACDsXHjhhXrmmWe0aNEi7dixQ926ddOsWbPUsGFDNWzYUD/88IPdEYFzV5QnifBo+YDkjJL2LJV2L7E7DQAUaef0aRQVFaXOnTvr7bff1u7duzVjxgxVr15dw4cP15gxY3yVEQi8on5FNTcXPyhFFpf2r5W2zrI7DQAgACpUqKAHH3xQ3333nQ4dOqThw4crOjra7ljAuTPT7/4pyhdkS5STGt1uLdOLDgBs5dORQFu0aKEWLVrohRdeUGpqqi8PDQQWs7jmLLaU1PxeadF4qxdd7SvtTgQACKDixYvrpptusjsG4BtuetBJktr0llZ9Km38Vjq6Uzqvmt2JAKBIKlAD3QsvvPCv+xiGoUGDBikyMrLQoQDbMYtr7to8Ii15X9r5h3UrRJWL7U4EAPABh8MhwzDy3McwDKWlMVEQwgQ96CzlL5RqXi79Ncea0bXzy3YnAoAiqUANdNOnT891m2EY2rRpk86cOaNBgwadczDAVoxBl7uEylKTO6SVn1q96LpNsTsRAMAH8qrzFi5cqDfffJMJwRBeGNIkQ5tHrAa6FZOlDk9JMQl2JwKAIqdA/blXrlyZ4+Pjjz9WuXLllJqaqgcffNBfWfXbb7/p+uuvV6VKlWQYhr7++uss203T1ODBg1WxYkUVK1ZMV155pbZs2eK3PAhjFGx5a9dPkiFt+lHav87uNAAAH7jxxhvPetSrV08TJ07Ua6+9pttuu02bNm3y2/mp8xBwTBKR4YKOUpm6Uspxafkku9MAQJF0Tp9G27dv1913362WLVsqISFB69ev17vvvuurbGc5efKkmjRpovHjcx7AdNSoUXrzzTf17rvvavHixSpevLg6deqkM2fO+C0TwhQ96PJWprZ0YVdr+ffRtkYBAPje3r179eCDD6pRo0ZKS0vTqlWrNGnSJFWr5r+xqajzEHDc4prB4ZDaPWotL3pbSkuxNw8AFEGFaqA7fPiw+vbtq3r16mnfvn1asGCBpk2bptq1a/s6XxZdunTRiy++mOPgxKZpauzYsXr++ed14403qnHjxpo8ebL27t171hVY4F/Rg+7ftR9oPa+fLh3abG8WAIBPJCYm6qmnnlKtWrW0fv16zZo1S999950aNmzo93NT5yHgqPeyanSbFFdROr5PWvul3WkAoMgpUAPdyZMnNWzYMF1wwQVasGCBvvvuO82aNUstW7b0V7582759u/bv368rr8yYVTIhIUGtWrXSwoULc31fcnKykpKSsjwAZnHNhwoNpbrXSjKlP8bYnQYAcI5GjRqlmjVr6vvvv9eUKVO0YMECtW/f3u5Ykqjz4CfUe1lFREutHraWF7yZMWkaACAgCjRJxAUXXKDjx4+rb9++6tatmwzD0Jo1a87ar3Hjxj4LmF/79++XJJUvXz7L+vLly3u35WTEiBEaNmyYX7MhBHFFNX8uHSht+kFa84V02VNSqRp2JwIAFNLTTz+tYsWKqVatWpo0aZImTcp5HKr//e9/AU5GnQc/8TRAUe9laHGf9Ntr0qE/pS2/SHU7250IAIqMAjXQHTx4UJJ1hfXVV1+VaZrebYZhyDRNGYYhl8vl25R+9Mwzz6h///7e10lJSapSpYqNiRAU3IxBly+VL5JqXSlt/dWa0fWGN+1OBAAopO7du8swDLtj+BR1HvLEJBFni0mwGukWvCnNf4MGOgAIoAI10G3fvt1fOc5ZhQoVJEkHDhxQxYoVvesPHDigpk2b5vq+6OhoRUdH+zseQo1JD7p8u/QJq4Fu1efSZU9KCefbnQgAUAgTJ060O0KuqPPgF0wSkbPWvaRF70i7Fki7l0hVLrY7EQAUCQW6XFStWrV8PexQo0YNVahQQbNmzfKuS0pK0uLFi9WmTRtbMiGEeXvQFagNu2iq2lqq3l5yp1pXWgEA8DHqPPgFQ5rkLL6S1PgOa5naDgACptCtD2fOnNGaNWt08OBBubMNIHrDDTecc7CcnDhxQlu3bvW+3r59u1atWqVSpUqpatWq6tevn1588UXVrl1bNWrU0KBBg1SpUiV17drVL3kQxhg0uGAufULa8bu0fJI1u2tc+X9/DwAgKJ05c0bjxo3TnDlzcqzzVqxY4ZfzUuch4EyGNMlV277Sqk+lP3+QDm+RytS2OxEAhL1CNdDNmDFD3bt31+HDh8/a5s8x6JYtW6bLL7/c+9ozpkiPHj00ceJEPfnkkzp58qQeeughHTt2TJdccolmzJihmJgYv+RBGGPQ4IKpcal0/sXSniXWmCWdXrI7EQCgkHr27KlffvlFt956qy6++OKAjUtHnYeAczMGXa7K1ZPqdJE2/yQtGMc4wwAQAIaZeaaHfKpdu7auvvpqDR48+KzZtEJdUlKSEhISlJiYqPj4eLvjwC6f3yFtniHdME66qLvdaULDlpnSZ7dKkbFSv3VS8dJ2JwKAgAmn+iEhIUE//vij2rVrZ3cUnwunnxN8YMYz0qK3pXb9pKuY7fcsOxdKH3eWnFFWbccdEgCKqEDVD4W6XHTgwAH1798/7BrnAC/GJCm4WldKFZtKqaesYhcAEJIqV66suLg4u2MA/sckEXmr2tq6Q8KVIi1+1+40ABD2CtVAd+utt2ru3Lk+jgIEEcYkKTjDsMaik6Ql70unj9kaBwBQOKNHj9ZTTz2lnTt32h0F8C8uyObNMKR2j1nLSz+Sko/bmwcAwlyhxqB76623dNttt+n3339Xo0aNFBkZmWX7o48+6pNwgG0o2Aqn7jVSuQbSwQ1WI91lT9qdCABQQC1atNCZM2dUs2ZNxcbGnlXn/fPPPzYlA3yMC7L/ru41Uula0pGt1mRgbfvYnQgAwlahGuimTJmiX375RTExMZo7d26WwYMNw6CBDqHPe8sDgwYXiMMhtR8g/V9P6zbX1r2kaG6TAoBQ0q1bN/399996+eWXVb58+YBNEgEEHBdk/53DIbV9VPruUau2u/ghKSLK7lQAEJYK1UD33HPPadiwYXr66afloAED4YiCrfAuvEmaO8K60rr0I+mSfnYnAgAUwIIFC7Rw4UI1adLE7iiAf3l60NEInbfGd0hzXpKS/pbW/Z/UtJvdiQAgLBWqdS0lJUV33HEHjXMIX9zyUHgOp9WLTpIWviWlnLI3DwCgQOrVq6fTp0/bHQPwP9O0nqn38hYZI7V62Fqe/0bG9w0A4FOFamHr0aOHpk2b5ussQPCgB925aXSbVLKadPKQtGKS3WkAAAXwyiuvaMCAAZo7d66OHDmipKSkLA8gbFDv5V+L+6WoEtKhjdKWmXanAYCwVKhbXF0ul0aNGqWff/5ZjRs3Pmvw4DFjxvgkHGAbetCdG2ekdMnj0vf9rCutLe6XIqLtTgUAyIfOnTtLkjp27JhlvWmaMgxDLpfLjliA71Hv5V+xklLze627I+a/IdW52u5EABB2CtVAt3btWjVr1kyStG7duizbGEgYYYErqueu6X+k3161xitZ+anUsqfdiQAA+TBnzhy7IwCBQb1XMK17S4vflXb+Ie1ZJp3fwu5EABBWCtVAR+GGsMcsrucuIlpq95j005PS72OkZnfTiw4AQsBll11mdwQgMOhBVzAJlaVGt0urP7dqu26f250IAMIKrQ9ATrii6hsX9ZDiKkpJe6xedAAAAMHCc0GWO4Dy75LHJRnSph+kAxvsTgMAYYUGOiAnXFH1jcgY6ZL+1vLvo6W0ZHvzAAAAeLg9DXTUe/lWto7U4AZr+Y/X7c0CAGGGBjogJ/Sg852LuktxldLHovvE7jQAAAAWLsgWjufi67qvpH/+sjcLAIQRGuiAnFCw+U5kjNTe04tuDL3oACBInTp1yu4IQGBxQbZwKjWVal1p3SI8/w270wBA2ChQA93gwYO1fPlyf2UBgge3PPhWs3syetGtmGx3GgBADsqUKaPrrrtO77//vvbv3293HMD/uCBbeO0HWs+rPpeS9tqbBQDCRIEa6Pbs2aMuXbro/PPPV69evfTTTz8pJSXFX9kA+7jTrGdmcfWN7L3oUs/YmwcAcJY///xTnTp10hdffKHq1aurVatWeumll7R27Vq7owH+4Z0kgnqvwKq1kaq2lVwp0oK37E4DAGGhQJ9GEyZM0P79+zVlyhTFxcWpX79+KlOmjG655RZNnjxZ//zzj79yAoFlcsuDz13UXYqvLB3fy1h0ABCEqlatqr59++rXX3/VgQMH1K9fP61du1bt27dXzZo11a9fP82ePVsul8vuqIBveG9xpYGuUC4dYD0v/1g6ecTeLAAQBgr8aeRwONS+fXuNGjVKmzZt0uLFi9WqVSu99957qlSpki699FK99tpr+vvvv/2RFwgMN7c8+FxEdKZedKPpRQcAQSwhIUHdunXT1KlTdejQIb333ntyuVy67777VLZsWX322Wd2RwTOnacHHfVe4VzQUarYREo9JS1+1+40ABDyzvlyUf369fXkk09q/vz52r17t3r06KHff/9dU6ZM8UU+wB7eMUki7M0RbprdI8WfLx3fx1h0ABAiIiMjddVVV2ncuHHauXOnZs2apTp16tgdCzh3TBJxbgxDap/ei27Je9KZJHvzAECI82l/7rJly6pnz5765ptvNHDgQF8eGggsJonwj8y96P5gLDoACEXNmjVTy5Yt7Y4BnDsmiTh39a6XytSRziRKyz6yOw0AhDQGXABy4i3Y+BXxuWZ3Z+pFN8nuNAAAoKhikohz53BIl6RffF04Xko9bW8eAAhhfBoBOeGWB/+JiM4YVPj3MRRyAADAHtR7vtHoVqlkVenkIWkFE4EBQGHRQAfkhFse/Kvp3VJCFenEfmk5vegAAIANuGPCN5yRUrvHrOX5b0hpKfbmAYAQ5dNPI7fbrV27dvnykIA9uKLqXxFRGYMK/0EvOgAAYAPGHPadpndLxctJSXuktV/anQYAQlKhGug+/vhjde7cWfXr11erVq00cOBA/f333zp06JBq1Kjh64xA4NGDzv+a3iUlVJVOHJCWT7Q7DQAUeeedd55KlSp11qNGjRrq1KmTZs6caXdEwLeo93wnMkZq28da/mNMxsVuAEC+RRRkZ5fLpZtvvlkzZszQtddeqxtuuEFHjx7Vl19+qffff1/jxo3zV04gcDxXUyWuqPpTRJQ1Ft13j0l/vC41v1eKLGZ3KgAossaOHZvj+mPHjmn58uW67rrr9NVXX+n6668PbDDAX5gkwrda3G+NL3xkq7TxW+nCm+xOBAAhpUANdK+//rqWLl2qNWvWqG7dut71brdbY8aM0UMPPeTzgEDAmZmu+DEmiX81+Y/022gpcZe07GOpTW+7EwFAkdWjR488tzdt2lQjRoyggQ7hgyFNfCs6Tmr1sDTvFen30VKDrpJh2J0KAEJGgVofJk6cqFGjRmVpnJMkh8OhgQMH6sUXX5Rpmj4NCARc5i75FGz+FRElXTrQWv7jdSnllL15AAC5uu666/Tnn3/aHQPwHW5x9b1W/5Uii0v710pbuC0eAAqiQA1027ZtU6tWrXLd/sQTT8id+fZAIBRl6UFHweZ3Tf8jlawqnTwoLf3A7jQAgFwkJycrKirK7hiA7zBJhO/FlpJa3m8tzxsp0XkDAPKtQA10xYsX16FDh3LdvmrVKt1///3nHAqwFT3oAssZKV32tLX8x1jpTJKtcQAAOfvoo4/UtGlTu2MAvuPtQceQJj7V9lEpopj09zJp2yy70wBAyCjQGHSXXXaZ3n33XbVt2/asbfv379edd96pLVu2aMKECT4LCAQcPegCr/Ed1i2uR7ZIi96ROjxldyIAKHL69++f4/rExEStWLFCmzdv1m+//RbgVIAfMUmEf5QoZ00YsWi8NHekdEFHxqIDgHwo0KfRkCFD9H//93/q0aOH1q1bpzNnzmjv3r1677331LJlS5UpU8ZfOYHAYRbXwHNGSJc/ay0vfEs69Y+9eQCgCFq5cmWOj8OHD+uqq67SunXr1Lx5c7tjAr7DJBH+0+5RKSJG2rNE+muu3WkAICQUqAdd48aN9dNPP+n+++/Xp59+mnGQiAg99thj6tu3r6pVq+bzkEBAMYurPRp0lcqPkQ6slRa8KV051O5EAFCkzJkzx+4IQGAxSYT/xFWQmt8rLX7XGouuZgd60QHAvyhQA50kXXrppdq8ebOWLFmi7du3Kz4+Xm3atFGpUqV08uRJDRkyxB85gcDhaqo9HA7piuekKXdKi9+TWvWS4srbnQoAAIQraj7/atdPWvaxtGuhtP03qeZldicCgKBWqO5BDodDrVu3Vrdu3XTttdeqVKlSkqxJJGigQ8jjaqp96nSWKjeXUk9Jf4yxOw0AAAhnnjHoqPn8I76i1LyHtTxvlL1ZACAEcP8ekB1XU+1jGNIVg6zlZROkxD325gEAAOHLO0kEt176Tbt+kjNK2vmHtOMPu9MAQFCjgQ7Ijh509qrZQareXnKlcLUVAAD4Dxdl/S+hstTsHmt57iv2ZgGAIEcDHZCdZxZXijV7GIZ0xfPW8spPpSPb7M0DAADCExdlA+OSxyVHpLTjd2nnArvTAEDQooEOyM6dZj0zg6t9qraWal1lFc7zRtqdBgAAhCN60AVGySpSs7utZeo6AMhVWLVADB06VIZhZHnUq1fP7lgINSbFWlC44jnrec0X0sGN9mYBANiOOg8+Rw+6wGnfX3JESH/NlXYttjsNAASlsGqgk6QLL7xQ+/bt8z7++IPBSFFAboq1oFCpmVT/ekmmNOclu9MAAIIAdR58xjQzlo2w+ydR8ClZVWr6H2t5HmPRAUBOIuwO4GsRERGqUKGC3TEQyrxXU8Pu1yP0XP6ctPF7aeN30t6VVqMdAKDIos6Dz3guyEo00AVK+wHSqs+lbbOl3UulKi3tTgQAQSXsPo22bNmiSpUqqWbNmrrrrru0a9euPPdPTk5WUlJSlgeKOCaJCB7l6kuNb7eWZ9OLDgCKOuo8+IyZqYGOuyYC47zqUpM7rWXGogOAs4RVA12rVq00ceJEzZgxQ++88462b9+u9u3b6/jx47m+Z8SIEUpISPA+qlSpEsDECEreHnRh9esRui57ymos3TpT2rXI7jQAAJtQ58GnsvSgo4EuYNoPyKjr9iy3Ow0ABBXDNDMPwBBejh07pmrVqmnMmDHq2bNnjvskJycrOTnZ+zopKUlVqlRRYmKi4uPjAxUVwWTnQunjzlKpC6RHV9idBpL07aPSiklStUuke7+XDMPuRACQRVJSkhISEqgfAog6D+ck+bg04nxr+bn9UmQxe/MUJdN7Sas/l2p3ku76wu40APCvAlXnhXUXoZIlS6pOnTraunVrrvtER0crPj4+ywNFHDN6BZ9Ln5CcUdLOP6zZvwAARR51Hs6J6c5YZgy6wLp0oPU93/Kz9DcXwwHAI6w/jU6cOKFt27apYsWKdkdBKPHc8sDtDsGjZBWpxf3W8qwXss68BgAokqjzcE64xdU+pS+QGqWPMcxYdADgFVYNdAMHDtS8efO0Y8cOLViwQDfddJOcTqe6detmdzSEEnrQBaf2A6TI4tLeFdLGb+1OAwAIMOo8+FTmHnTUfIF36RNWL7rNM6Q9y+xOAwBBIawa6Pbs2aNu3bqpbt26uv3221W6dGktWrRIZcuWtTsaQol3Ftew+vUIfSXKSW0esZZnDZdcafbmAQAEFHUefMrbg85gbFs7lKklNfmPtTznJXuzAECQiLA7gC9NnTrV7ggIB/SgC15t+0rLPpKObJFWfSo1v9fuRACAAKHOg09R79nvsiekNVOlbbOlnQukam3tTgQAtqKLEJAdY9AFr5h4qf1Aa3nuK1LqaXvzAACA0GRyx4TtzqsuNbvHWp79EmMMAyjy+EQCsuOKanBrcb+UUEU6vk9a/J7daQAAQCjigmxwuHSg5IySdv4hbZ9ndxoAsBUNdEB2FGzBLTJGuvxZa/mPMdLpo/bmAQAAoYcLssEh4Xzr4qskzX6RXnQAijQa6IDsKNiCX+M7pHINpDOJ0h9j7U4DAABCjXdSMOo9213SX4ooJu1ZKm2ZaXcaALANDXRAdsziGvwcTqnjYGt58btS0l578wAAgNDivSBLvWe7uPLSxQ9Yy3PoRQeg6OITCciOHnShoU5nqUprKe2MNWEEAABAfjFJRHBp10+KKiHtWy39+b3daQDAFnwiAdkxBl1oMAzpqmHW8spPpcNb7M0DAABCB/VecCleRmr1sLU85+WMO1oAoAihgQ7Ijh50oaNqa6lOF+tnNnu43WkAAECooN4LPm37SNEJ0sEN0obpdqcBgICjgQ7IjiuqoaXjYEmGtOEb6e/ldqcBAAChgHov+BQ7T2rziLU8Z4TkSrM3DwAEGA10QHYMGhxayjeQmtxpLf86lIGFAQDAv/OMQUe9F1xa97Ia6o5skdZ+aXcaAAgoPpGA7LiiGnouf1ZyRknbf5O2zbY7DQAACHZMEhGcYuKldo9Zy/NekVyp9uYBgADiEwnIzs2YJCGnZFWp5QPW8q9DGVgYAADkjQuywevih6TiZaWjO6RVn9udBgAChgY6IDuTgi0ktR8oRcVJ+9dI6/9ndxoAABDMmCQieEUVly553FqeN0pKS7Y3DwAECA10QHbeHnQR9uZAwRQvLbV71Fqe/aKUlmJvHgAAELzoQRfcWtwvxVWUkvZIKybbnQYAAoIGOiA7JokIXa17S8XLSUe3Sysm2Z0GAAAEK3rQBbfIYlL7Adbyb69KKafszQMAAUALBJCdZ/wyrqiGnugS0mVPWstzX5HOJNmbBwAABCfvJBGGvTmQu4t6WOMMnzggLX7X7jQA4Hc00AHZcUU1tDW/VypdSzp1WJo/1u40AAAgGHFBNvhFREmXP2ctzx8rnT5qaxwA8Dca6IDsGJMktDkjpSuHWcsLx0uJf9ubBwAABB8uyIaGRrdJ5RpIZxKl+W/YnQYA/IoGOiA7CrbQV+9aqWobKe2MNOclu9MAAIBgwwXZ0OBwSh0HW8uL3pWS9tmbBwD8iAY6IDsKttBnGNLVL1rLqz6X9q+1Nw8AAAguXJANHXU6S1VaSWmnpd9G2Z0GAPyGBjogO2ZxDQ/nt5AuvFmSKf0yyO40AAAgmHgniaDeC3qGIV051FpePkk6ss3WOADgL3wiAdkxaHD46DhYckRKf82Rtv5qdxoAABAsvHdM8M+hkFCtrVT7autCOsOXAAhTfCIB2XHLQ/goVUO6+CFr+ZfBGcU4AAAo2jw96Kj3QodnLLp1/yftW21vFgDwAxrogOwYgy68XDpQikmQDq6XVk+xOw0AAAgG1Huhp0Ija1ZXSZr1gr1ZAMAPaKADsqMHXXiJLSW1H2gtz35RSjllbx4AAGA/6r3QdPmzkiPCGrpk++92pwEAn6KBDsiOK6rh5+KHpISq0vF90qLxdqcBAAB2Y5KI0FSqptT8Xmt51jDJNG2NAwC+xCcSkB2zuIafyJiMcUv+eEM6ccjePAAAwF5ckA1dlz4hRcZKe5ZKm360Ow0A+AwtEEB2zOIanhreIlVsKqUcl+a9YncaAABgJy7Ihq64ClLrXtbyrBeYBAxA2OATCciOMUnCk8MhXf2itbzsY+nQZnvzAAAA+3BBNrS1fVSKKSkd+lNaM83uNADgEzTQAdlxy0P4qtFeqtPFaoT9dajdaQAAgF24IBvaipWULnncWp4zQkpLtjUOAPgCDXRAdhRs4e2qYVbj66YfpJ0L7E4DAADswCQRoe/ih6S4ilLiLuvuCAAIcXwiAdl5e9Dx6xGWytaVLupuLf/8XMYtLgAAoOjgjonQFxUrXfaUtfzbq1LycXvzAMA5ogUCyM5ND7qw1+EZKaqEtHeFtO4ru9MAAIBA446J8NDsbql0LenUYWn+G3anAYBzQgMdkJ3JFdWwF1c+Y9ySX4dKKadsjQMAAAKMOybCgzNS6jjEWl7wlpS0z948AHAO+EQCsqMHXdHQ5hEpoaqU9Le0YJzdaQAAQCB5xqCj3gt99a+XqrSW0k5Lc16yOw0AFBoNdEB29KArGiKLSVcNtZbnj5WS9tqZBgAABJJ3kgjqvZBnGNLVw63lVZ9JBzbYmwcACokGOiA7bw+6CHtzwP8uvFmq0kpKPSXNesHuNAAAIFC4xTW8VLlYqn+D1fD66xC70wBAofCJBGTnveWBX4+wZxhSpxHW8uop0t8r7M0DAAACg0kiws+VQ60L7Ft+kf6aZ3caACiwsGyBGD9+vKpXr66YmBi1atVKS5YssTsSQombW1yLlPObS43vsJZ/flYyTXvzAADyRJ0Hn6DeCz+lL5Ba3G8tzxwkud325gGAAgq7Brpp06apf//+GjJkiFasWKEmTZqoU6dOOnjwoN3RECq4olr0dBwiRRSTdi2UNnxtdxoAQC6o8+Az3nov7P45VLRd9pQUHS/tWy2t+8ruNABQIGH3iTRmzBg9+OCDuu+++9SgQQO9++67io2N1YQJE+yOhlDBFdWiJ6Gy1O4xa3nmYCn1jL15AAA5os6Dz3h6zFPvhZfiZaRL+lnLs4ZT0wEIKWE1Cn5KSoqWL1+uZ555xrvO4XDoyiuv1MKFC3N8T3JyspKTk72vk5KS/JZv3vKvtWHnAjkMpwyHQw7DaS0bDjmM9NcOhwzDKYfhUIQjItO6CDkchpyOCDkNpwyHU870h8PhlMMRYb02nHI6I2Q4nIpI3xbhjJDDESmnw6kIZ6Scjgg5IpxyGpGKcDrkdEYqwhEhpzPC++xwRljjcxVF9KArkjoM+VHOfWmaddcuadHbUvv+3m0dO3aUy+XS3Llz7QsIAEVcsNd5hTH5x5d14syx9NouozZ0nLXsqQmdinBYdZ7DcFh1odPa5vTWfZHptaFTEY4oOZwZNWGkM8pbIzqdkXJGRKUvOxXhiFRERKS3LoxwRsjhDONaiEkiwlaH577OqOmWvC+1e9S7jZoOQDALqwa6w4cPy+VyqXz58lnWly9fXn/++WeO7xkxYoSGDRsWiHj6ec0kfefYGpBz+YLTNGVIcpiSIVMOKdNrZXntWc683XoY1jbvewxrvZlpOX2/LO8xjUzr04+hrOsy/+cwclqb/p+RdT9H+pkNw5FpH0f6eRwqf2afYhLitXn5tzq+aZ211jDSG1EzPww5DYcMwyGn4UgvoK2H0zDSC2vPskNOh2eb02psNZzeY1iNq4YcDmu70zCsQjv9fU7DqQiHIYfDae1vGFaxnb49In19hDP9/enHt7ZlWudwKsLhUGR6465RVBthc+CMjNLsrafUcZJTs6LGSE3vkuLKq2PHjpo9e7auuOIKuyMCQJEW7HVeYXzx9xTtjLI7Re4MM6P+c3pruYz6MPfaMKOu89SBmeu+rNs91VjmujHrOmt9ttrOlByZazllf51e82Wq8bw1oeFQqTN/K75kvPbuWqm/vx6RtcZTpmVHep2Xvt6ZbZ2RuSbz1nkZ9Z+1zZnx2un0vteZ+XWmes6ZfvwIz8X39BrOqt8c6cd1KNJpHTvCYSjSGZHxXodDUY4Iq34tgrfwZqnpYl6Tmt0txZaipgMQ9MKqga4wnnnmGfXvn9FTJikpSVWqVPHLucrEVlaDxB0yZcqUsjy7jcyvJbdnu+FZTn8Ykjvzfpm2u9O3W8vG2dsL2Bjj8uxveP/nY2YuyzZLiJIUJaX8Jv3zm91p/M4000tkz3OmJlFrnUM5NaNmLrUNM1MTqpFpfbbmVSO9UM76OlPZfVajacZ2R+bt6Q2knu2eAtyR5bXhLZy92z0Np95jeApsQ3Ufv1abDu/S7DVbVWviYb1Zvo+eeHevNixboAtbtFWvUZP0vxV7rPMaSj+WIYeh9HNJToeR53aHI2OdZ7t3X0f6d97wdF7N+l7r65Z3f8PI2F+edcq6zXAoyzpH+u90xj6ZlmmsBRCGAlnnFcb5ZknFJh/Psy7MqAM96z21XvbXyvo6vQbMXhMWpB40DUPp/cyU5n2bLz4vgqAGLC6peElJ26TEbfZkCICc6jzJISPb66z1X/bL6J7X2es4Tz3oqc88+2av6xyS9yJ5ptouS63nubideZvhre8cjqx1XsZrz0Vzp7f2q9PvWm06tEuz125VrQkH9Wb5/npi/BZtWG7VdP8dOVFfLd/jrd2MbLWZd9mReV0OtVwOdV3Ge7K916Fcj2MYRnoNmVGXOQ3P9xNAMHC7A/NZFVYNdGXKlJHT6dSBAweyrD9w4IAqVKiQ43uio6MVHR0diHjqf8dbATlPbtxut9xut9LcaXK5XEpzp8rlSkt/ba1zp79Oc6XJbXr2S5PblSq3yyWX6ZLLnSa32yW32yWX23rtSn/tdlv7uN0umaZLbrdbLjPNena75Dath2m6rf1Nt0y3S25Z+1rb3Rn7pD+Uadk03XLL7d2e8dqUmWW9KbenhM20T8a+6f+Z7vT9rNdumTpjxGhvTI2M/dP3y/LsXc55vbxHyyixzfRzK8u6s8pyZWqitS47n910m2l9eume/towCvaXh+E5Tj4qgOxH9utfU2a25wA4r3+Mjo0srm0bT+raB6yBhaOrNtaJjs9q4JerAxcEgF8Ui3Rq4/DOdsdAIQV7nVcY7z70uy3ndbtcSklLzlIPpqalWjWdy6rt0tJSMmpBV5pcbk8tmKY0V6pcbrdMd5q3rnS70+Q23em1YapV32V+nb5semrF9HrP5akZTdOqA91pWWpAM31fb30nz77WNlNuudNrQLfbqrXc3vouc32Yuaaz1qUaEdobU1OpRmT6sU3vc5aaMnNNl6UezF4DZq//3JKZeX3m2i/TJXcj8+X3TPWekbWJVumvC1LrFbbOC/Ua77wBmWq6+ydJyqjpnvxqjf9ODCAsuZNPBeQ8YdVAFxUVpebNm2vWrFnq2rWrJKtRatasWerTp4+94YKAw5HefT68fuzIxjRNudymUr0NqqbS3C6luV1ymW653G7vc5rbZb12u5VmuuR2m3KZLqWl7+N2u5SWvs7ltopU672mt7h2pTe+pplub3FtbbcKZ5fbKnbTzIwGWJdpyvScw/swM46fqRDPvt3tKbDdVuGdUcCb3temZ99MBbQnW+ZC2p2pyPbsW/bp1prT40Pv9/OuYe/LbRpym7L+AZD+bD2Ufi7J5Ta9y1m3ZVrnNnM9jtudXppneq/1OtM6memv08v/bPsByJsZyBZ/+Bx1nu84nE7FOGPtjoFCMtNrCqu+s2o4V3rNluZOS6/x0us806U0l6e+S6/R0mvCLLVh+v4ZtV721y5vDZl5H5encdVzLM92d9Y6zpV92VvHmZku0HtqPVdGo2umutBTu2VcoM9a21k1ost7Ib7cM601u3tGTXfP8I/Sa7HM9ZoptztbTZapNjt72arFXJnea5pm+uusdaF330zL7vR9qdsA5CTsWmr69++vHj16qEWLFrr44os1duxYnTx5Uvfdd5/d0YCAMAxDEU5DEU6HpEi744Scjh07Znm98q27tGJZaIwdmb1xz1MMmpka99yeBj332esAINhR5wHpt0Q6DTmdDgVv/1D7Za/p1rx9nxYvWmtTmqzMHBrs3Jka9zw1HYDgkJSUpBpj/X+esGugu+OOO3To0CENHjxY+/fvV9OmTTVjxoyzBhQGgOwyDx58Q+/z9NygGVq5fJsuv7yD5syZa3O6f2cYhpx+GzMSAOxHnQcgPzLXdK17mnrjxSVasnidrrjiCs2ePdvueNb4coY1gl/Y/YMcCEMRrsDM6GSYJh1sM0tKSlJCQoISExMVHx9vdxwAAZK5kJs1a5ZOnTig677oqCWv7dDJjSe96wEgJ9QPoYGfExD+std0+/et1PUz7tHGV6npABROoOqHojfvNgDkwOVyZSnYYkuU1+PVrlONp2oorl6sTp9OsjkhAAAA/k32mq5CxWa6N6GBajxVQ6XqFVdqaorNCQEgZ/SoBQBJc+fOPWvdtR1e1NTJP0tP19TlkfS0AAAACHY51XT3XfWm/u/LK6Wna6hn2QaBDwUA+UAPOgDIhcMZoacvfkqS9G3qQa3Z8IXNiQAAAFBQsXEV1LdqZ0nSewfm6+jRv2xOBABno4EOAPLQ6MI7dUNkOUnSyMWvyO1KszkRAAAACuqGDi+rntuh4w5D78x81O44AHAWGugA4F/0u/JNxbpNrXGk6vt5g+2OAwAAgAJyRkRpYJPekqQvTu3QX9vtn80VADKjgQ4A/kXZchfqoTItJUljd3yrkyf225wIAAAABdXqov+qgxEnl2FozO/P2R0HALKggQ4A8uGeq99UFZd0yGnog58fsTsOAAAACqH/pSMUYZqaZ57QwmVv2x0HALxooAOAfIiKjtMTDe6TJE0+vkm7d/5ucyIAAAAUVI3ql+mO2BqSpNfWvCdXarLNiQDAQgMdAORTh1aPq40Rq1TD0GvznrQ7DgAAAArh4avHKc5tarPTrW/mPG13HACQRAMdAOSbYRh6qv0rcpqmZpsntHDJOLsjAQAAoIBKlqyuhyu0lySN2/OLTibttTkRANBABwAFckGNy3VniVqSpJHr3ldqykmbEwEAAKCgunUcrapuQ4edDn00s6/dcQCABjoAKKhencbrPLepbU5pyi+P2R0HAAAABRQZFav+9XtIkiYd36Q9uxfYnAhAUUcDHQAUUEJcZT1W9VpJ0tuHFunQwfU2JwIAAEBBXdGqv1qpmFIMQ2PmMr4wAHvRQAcAhXBTh5fU0B2hkw5Dr//KbREAAAChxjAMPXnJcDlMUzPdiVqy8kO7IwEowmigA4BCcDgj9FyrZ2WYpr5LPaQVaz+zOxIAAAAKqM4FnXR7saqSpFdWjVNaWrLNiQAUVTTQAUAhNWxwm26OrihJemnZq0pLS7E5EQAAAArqkaveUrzbrS0Ot76a84zdcQAUUTTQAcA5eOzqtxXvdmuzw6UvZj9hdxwAAAAUUMlSNdWnfHtJ0lt7flFi4m6bEwEoimigA4BzcF7p2nq0QgdJ0lt/z9KRf7bZGwgAAAAFdtuVY1TLZSjRYWj8L4/YHQdAEUQDHQCco1uvHKP6LoeOOwyN/bmX3XEAAABQQBFRsXq6sVXHfXHyL23561ebEwEoamigA4Bz5IyM1rMtBkqSvk7Zp9Xrp9mcCAAAAAXVqkUvXWnEyWUYGvn7szLdbrsjAShCaKADAB9o2vgedY0sJ0l6ackIuZgwAgAAIOQMuPw1RZmmFuu0Zi96ze44AIoQGugAwEf6Xf224tymNjpc+j8mjAAAAAg551dpqx7x9SVJr/75iZLPJNqcCEBRQQMdAPhI6TJ19UiFSyVJb/79q44xYQQAAEDIeeDq8SrnMvW3U/rkl752xwFQRNBABwA+dMeVY1TH7VCiw6E3mDACAAAg5MSWKKfHa94sSXr/yAod2L/K3kAAigQa6ADAhyIiY/Rsc2vCiP9L3qv167+wOREAAAAK6tpLh6qJGaXTDkNvzOpndxwARQANdADgY80b36PrIsvJNAy9tORluV2pdkcCAABAARgOh55pPUiS9F3aEa1eM9nmRADCHQ10AOAH/a8ar+JuU2sdLk3/daDdcQAAAFBAF9brqq7RFSVJrywbLXdais2JAIQzGugAwA/Klq2nXhXaS5Je3/urjh7ZanMiAAAAFNRjV72l4m5T65xuTZ/1hN1xAIQxGugAwE/+c9Xr3gkjXv/5YbvjAAAAoIDKlK6j3hUulSSN3furEo/tsDcQgLBFAx0A+ElkRIwGtXxakjQ99YBWrvnU5kQAAAAoqG5XjlYtt0PHHA69OaOX3XEAhCka6ADAj5o27KZboipIkl5Y/qpSU0/bnAgAAAAFERlZTM816ydJ+vLMbq3f9I29gQCEJRroAMDP+nV6V+e53NrqcOuzmf3sjgMAAIACatH0Pl3rLCXTMPTywmFyu112RwIQZmigAwA/K1nqAj1epZMk6e0D87V//2qbEwEAAKCgBlw1TsXdbq0xUvX13OfsjgMgzNBABwABcOMVI3WRO0KnHYZe+bWP3XEAAABQQGXLN1bvMhdLksbu/F6Jx3bZnAhAOKGBDgACwOGM1PPthivCNDXLdUy/LXnD7kgAAAAooG6d3lItl6GjDkPjfn7Y7jgAwggNdAAQILXrXKd7iteSJL287kOdPn3U5kQAAAAoiMio4nq2qXU3xBend2n9n9NtTgQgXNBABwAB9HCX91TBZepvp/TBT/+1Ow4AAAAKqOVFD+ka74QRL8jtSrM7EoAwQAMdAARQbInyerrePZKkj5M26K/ts2xOBAAAgIIacNVbinWbWuNI0zdznrY7DoAwQAMdAATYFa2f0GVGCaUZhl6c95RMt9vuSAAAACiAcuUbqXe5NpKk13fPUOKxnTYnAhDqwqqBrnr16jIMI8vjlVdesTsWAGRhOBx65so3FeM2tdRI1vfznrc7EgAEPeo8AMHmP1e/oVpuhzVhxAyGLgFwbsKqgU6SXnjhBe3bt8/76Nu3r92RAOAslSu11H/LtJQkvbb9W666AkA+UOcBCCaRkbF6tlk/SdKXZ/Zow4av7A0EIKSFXQNdXFycKlSo4H0UL17c7kgAkKMencarptvQP05D4356yO44ABD0qPMABJuWTe9Tl4gychuGXlr8otyuVLsjAQhRYddA98orr6h06dJq1qyZXn31VaWl5T2jTnJyspKSkrI8ACAQIqNi9fxFAyRJXyT/rTVrP7M5EQAEN+o8AMFo4NXj0yeMcOnrWU/YHQdAiAqrBrpHH31UU6dO1Zw5c/Tf//5XL7/8sp588sk83zNixAglJCR4H1WqVAlQWgCQWjbpoRuiKsg0DA1dOlKpKSftjgQAQYk6D0CwKle2gXpXaC9JGvP3TP1zZLPNiQCEIsM0TdPuEHl5+umnNXLkyDz32bhxo+rVq3fW+gkTJui///2vTpw4oejo6Bzfm5ycrOTkZO/rpKQkValSRYmJiYqPjz+38ACQD0eP/qUbvr5BxxyG+pVqrp7XT7Q7EoACSkpKUkJCAvVDAVHnAQgXaWnJuvOTVtrkcOnGyHJ68T+z7I4EwEcCVecFfQPdoUOHdOTIkTz3qVmzpqKios5av379ejVs2FB//vmn6tatm6/zUWADsMO3c57Vc7u+U7Tb1PSrPlCV89vYHQlAAVA/FA51HoBwsnr9NN2zdLhMw9CEpgPUssm9dkcC4AOBqh8i/HZkHylbtqzKli1bqPeuWrVKDodD5cqV83EqAPCt6y97Ud9OnqnFjjMaPruf3rt7oQxHWI1CAABnoc4DEE6aXHiHblszQV+k7NWLK8boq/q3KzIq1u5YAEJE2Pzrb+HChRo7dqxWr16tv/76S5999pkef/xx3X333TrvvPPsjgcAeTIcDg2+fLSi3aYWmqf0/R8v2B0JAIIGdR6AUPFol/dVyuXWXw5TE3/pY3ccACEkbBrooqOjNXXqVF122WW68MIL9dJLL+nxxx/X+++/b3c0AMiXqtUu1X/PayJJenXbVzp2bIe9gQAgSFDnAQgVCSWr6Ylq10uS3ju8RLv/XmxzIgChIujHoAs0xiYBYKfUM8d1++fttNVp6saoCnqx20y7IwHIB+qH0MDPCUAgmC6XHvyklRYbyWpnFNc7dy9g6BIghAWqfuBvCQAIIpExcRrS4gkZpqlvUvZr8coP7Y4EAACAAjCcTj1/2ShFmqbmmyf1y4IRdkcCEAJooAOAINO08T26PeZ8SdLwlW8o+UyizYkAAABQENVrXKEHEhpKkkZunqLjSXttTgQg2NFABwBB6LEuH6qsy9ROp/T+Tw/ZHQcAAAAF1LPLu6rmkg45Db01g3oOQN5ooAOAIBSXcL6eqdNNkjQhcb22bv3Z5kQAAAAoiOiYknquySOSpKmndmj9xq9sTgQgmNFABwBB6sq2z6iDEac0w9Cw35+R25VqdyQAAAAUQJvmD+uaiDJyG4ZeWPSiXKnJdkcCEKRooAOAIGU4HHruqrcV6za1ypGqr2YOsDsSAAAACuiJTu8qzm1qg8OlqTMfszsOgCBFAx0ABLEKFZuqb8XLJElj983SoQNrbE4EAACAgihTpq76nX+1JGncgd91cN9KmxMBCEY00AFAkOt25Vhd6I7QcYdDI37uJZmm3ZEAAABQALdeMUqNzSiddDj0ysw+1HMAzkIDHQAEOWdEpIa2f1lO09RMM0mz5o+wOxIAAAAKwOGM0KBLXvTWc3MWvmp3JABBhgY6AAgB9Wp10X3xDSRJL27+XImJu2xOBAAAgIKoV6uLusfXlyS9+OdknTi+z+ZEAIIJDXQAECIevuYDVXcZOuw0NObHB+yOAwAAgALq1eV9ne+SDjoNvfHTg3bHARBEaKADgBARHZOgYc2tmVz/l7JPi1Z+aHMiAAAAFESxYudpSJNHJEnTTu3QqvVTbU4EIFjQQAcAIeSiJj10Z3RlSdKwVW/o1OmjNicCAABAQbRu/rBujCgr0zA0dMkIpaSctDsSgCBAAx0AhJh+10xQBZepPQ5p/I897Y4DAACAAhrY+X2Vcrm1zeHWRzN62x0HQBCggQ4AQkzx+EoaXP8+SdKnxzdrzcavbE4EAACAgihZupaernGTJOmDf5brrx1z7Q0EwHY00AFACGrfZoCuc5aS2zA0ZNFwpaacsjsSAAAACqDzZS+ovYop1TA0dO5Aud0uuyMBsBENdAAQop7s8qFKudza6nDrw58esjsOAAAACsBwODSo41sq5ja10kjWV7OesDsSABvRQAcAIeq80rX1TM1bJEnvH12lrX/9anMiAAAAFETF8y/WY+XbSZJe3/OLDhxcb3MiAHahgQ4AQlinS4eqg0oozTA0ZN6TcqWl2B0JAAAABXDnVW+qsdupEw5DL//8X7vjALAJDXQAEMIMh0PPd3pHJdym1jhS9fkvj9kdCQAAAAXgjIzWkHYvKsI0NdudqF/nv2J3JAA2oIEOAEJc+QpNNaDylZKkcQd+1+49i2xOBAAAgIKoU+c63RdfX5L08qZPlZS0x+ZEAALNME3TtDtEMElKSlJCQoISExMVHx9vdxwAyBfT5dIDn7TSEiNZrVVM79+zSIaDazBAoFA/hAZ+TgCCWfKZY7r18/ba4ZRuiCyn/1z0iN2RAEg6ceKUWre8x+/1Aw102VC4AQhVu3b9oZtnPaxkh6GWZrT6NHtUFzXpbncsoEigfggN/JwABLulqybo/tWv2x0DQCau0y5t7LXR7/VDhN+ODAAIqKpVL9FzVa7R8D0/aqmRrB6rXlWbVW+pdmxFGTJkGIYMSTIMSbLWyVpnGIa1xjDyXm9kvE+Z9zUynqX043v2NbxHy3p8I9txjLzWO7Jk8ebPdt6M146M9ZnXZdnfkfM5DUfW46cfS5lyZD5e1vUO6+s2HDnuf/bx03M6HOn5HVmzZs5pOLJslzdbxnbPOSVrfMKsOTO/zsgrR9ZjeI4phyd/pge9MgEA8KuWTe9X7x2/6n//rBE9aYDgkOoytTEA56EHXTZcWQUQ6vbtXa73f3tWX5/5W2npjVmArxmmKU9zrOdPmfe1mcv6PNZ515tnbz97f+NfjmOcvc4wFCOHpty30gdf/dmoH0IDPycAAFBQgaof6EEHAGGmYqXmGnLnz7p/93x9v3y8TrvOSKYpU5JpmrL+k/V/M2PZc73Gs13Z9s263ZRpKuM43te5H8/zHsmzPtM203Ocs/c/e32mdWbmdZn3zXw+ZTpCtvNKORxbmb7+bOty2C/zVa7sxzazrE9/GDmsy23f7OuMrOvOPnbgGmRNw8j9yr7t7cI5JTMV43YFPAkAAACQHzTQAUCYqlKlnXpVaWd3DNjAdLtlmpkecktuU6YyrzfT17vT15uZ9jel9H0kedeZ6fvKu2/GMb3rZGY5h7zHzrpd3uN5XitjOcu+ZrZzKiOvd30u+ytzTlMOg1t0AQAAEJxooAMAIMwY6ePKAQAAAAgNVO8AAAAAAACAjWigAwAAAAAAAGxEAx0AAAAAAABgIxroAAAAAAAAABvRQAcAAAAAAADYiAY6AAAAAAAAwEY00AEAAAAAAAA2ooEOAAAAAAAAsBENdAAAAAAAAICNaKADAAAAAAAAbBQyDXQvvfSS2rZtq9jYWJUsWTLHfXbt2qVrr71WsbGxKleunJ544gmlpaUFNigAAAAKhDoPAAAUdRF2B8ivlJQU3XbbbWrTpo0++uijs7a7XC5de+21qlChghYsWKB9+/ape/fuioyM1Msvv2xDYgAAAOQHdR4AACjqDNM0TbtDFMTEiRPVr18/HTt2LMv6n376Sdddd5327t2r8uXLS5LeffddPfXUUzp06JCioqLydfykpCQlJCQoMTFR8fHxvo4PAADCEPWDb1DnAQCAYBOo+iFkbnH9NwsXLlSjRo28RZskderUSUlJSVq/fr2NyQAAAHAuqPMAAEC4C5lbXP/N/v37sxRtkryv9+/fn+v7kpOTlZyc7H2dmJgoyWohBQAAyA9P3RBiNyaEDOo8AABgl0DVebY20D399NMaOXJknvts3LhR9erV81uGESNGaNiwYWetr1Klit/OCQAAwtPx48eVkJBgd4ygQJ0HAADCyZEjR/xa59naQDdgwADde++9ee5Ts2bNfB2rQoUKWrJkSZZ1Bw4c8G7LzTPPPKP+/ft7X7vdbv3zzz8qXbq0DMPI17nx75KSklSlShXt3r2bMV+CFD+j4MbPJ7jx8wl+/v4Zmaap48ePq1KlSj4/dqgKxjrv2LFjqlatmnbt2kVDapDi79PQwM8pNPBzCn78jEJDYmKiqlatqlKlSvn1PLY20JUtW1Zly5b1ybHatGmjl156SQcPHlS5cuUkSTNnzlR8fLwaNGiQ6/uio6MVHR2dZV3JkiV9kglni4+P5y+eIMfPKLjx8wlu/HyCnz9/RjT4ZBWsdZ5k/az4XQ1u/H0aGvg5hQZ+TsGPn1FocDj8O41DyIxBt2vXLv3z/+3deVRU9fsH8PfICMOSYJosgqBiKLhlCYGewFIpUFBTTBNo0xYITTMtc8nqaBtohNupxKij2aIikuYC7kgKCBoHkUyOC5BFKoKKzOf3Rz/m6yDLDMydYWber3PmeObO5955nvP4mfvhmbkz//yD0tJS1NXVIS8vDwDg6ekJOzs7jB49Gt7e3oiMjMTHH3+MsrIyvPvuu4iJiWl0YUZERERE7QPXeURERGTujKZBt2jRImzYsEF1/6GHHgIAZGRkICgoCBYWFkhLS8Orr74Kf39/2NraIjo6GkuXLjVUyERERESkAa7ziIiIyNwZTYMuOTkZycnJzY5xd3dHenq6fgIirVhZWWHx4sV8l7sdY43aN9anfWN92j/WqH3T1zqP/w/aP9bIOLBOxoF1av9YI+OgrzrJhNS/E0tERERERERERERNkvYb7oiIiIiIiIiIiKhZbNAREREREREREREZEBt0REREREREREREBsQGHRERERERERERkQGxQUc6k5SUBA8PDygUCvj5+SE7O7vJscnJyZDJZGo3hUKhx2jNy4EDBzB27Fi4uLhAJpNh69atLe6TmZmJIUOGwMrKCp6eni3+uh61jbY1yszMvGcOyWQylJWV6SdgM7Ns2TIMHToU9913H7p164Zx48ahqKioxf1++OEH9O3bFwqFAgMGDOAvjUukNfXhecg0aLP2AFqek0IILFq0CM7OzrC2tsbIkSNRXFwsZQpmQZd1qq2txbx58zBgwADY2trCxcUFUVFRuHTpktRpmDxdz6e7vfLKK5DJZFixYoWOozYvUtSosLAQYWFhsLe3h62tLYYOHYrS0lKpUjALuq5TVVUVYmNj4erqCmtra3h7e2PNmjVSpmAWtKnT6dOn8fTTT8PDw6PZ1zJta98QG3SkE99//z1mz56NxYsXIycnB4MGDUJwcDAqKiqa3KdTp064fPmy6nb+/Hk9Rmxebty4gUGDBiEpKUmj8efOnUNoaChGjBiBvLw8zJo1Cy+99BJ27dolcaTmS9sa1SsqKlKbR926dZMoQvO2f/9+xMTEICsrC7t370ZtbS1Gjx6NGzduNLnPkSNHMGXKFLz44ovIzc3FuHHjMG7cOJw6dUqPkZuH1tQH4HnI2Gm79tBkTn788cf4/PPPsWbNGhw7dgy2trYIDg7GzZs39ZWWydF1naqrq5GTk4OFCxciJycHP//8M4qKihAWFqbPtEyOFPOp3pYtW5CVlQUXFxep0zBpUtSopKQEw4cPR9++fZGZmYn8/HwsXLiQb1i1gRR1mj17Nnbu3Ilvv/0WhYWFmDVrFmJjY5GamqqvtEyOtnWqrq5Gr169sHz5cjg5OenkmI0SRDrg6+srYmJiVPfr6uqEi4uLWLZsWaPj169fL+zt7fUUHd0NgNiyZUuzY9566y3h4+Ojtm3y5MkiODhYwsioniY1ysjIEABEZWWlXmIidRUVFQKA2L9/f5NjIiIiRGhoqNo2Pz8/8fLLL0sdntnTpD48Dxk/bdceLc1JpVIpnJycxCeffKJ6/N9//xVWVlZi48aNEmRgHnRdp8ZkZ2cLAOL8+fO6CdoMSVWnCxcuiO7du4tTp04Jd3d3kZCQoPPYzYUUNZo8ebKYNm2aNAGbKSnq5OPjI5YuXao2ZsiQIWLBggU6jNy8aFunuzX1WtaWY9bjJ+iozW7fvo0TJ05g5MiRqm0dOnTAyJEjcfTo0Sb3q6qqgru7O9zc3BAeHo7Tp0/rI1zSwNGjR9XqCQDBwcHN1pMMY/DgwXB2dsaoUaNw+PBhQ4djNq5evQoAuP/++5scw3lkOJrUB+B5yJi1Zu3R0pw8d+4cysrK1MbY29vDz8+P87aVpKhTY65evQqZTAYHBwedxG1upKqTUqlEZGQk5s6dCx8fH2mCNxNS1EipVGLHjh148MEHERwcjG7dusHPz0+jr8Khxkk1lwICApCamoqLFy9CCIGMjAycOXMGo0ePliYRE9fa/oU+jskGHbXZlStXUFdXB0dHR7Xtjo6OTX4flpeXF77++mts27YN3377LZRKJQICAnDhwgV9hEwtKCsra7Se165dQ01NjYGiors5OztjzZo1+Omnn/DTTz/Bzc0NQUFByMnJMXRoJk+pVGLWrFkYNmwY+vfv3+S4puYRvydQWprWh+ch49aatUdLc7L+X85b3ZGiTg3dvHkT8+bNw5QpU9CpUyfdBG5mpKrTRx99BLlcjri4ON0HbWakqFFFRQWqqqqwfPlyPPnkk/j1118xfvx4TJgwAfv375cmERMn1VxKTEyEt7c3XF1dYWlpiSeffBJJSUl47LHHdJ+EGWhNnfR1THmrnp2ojfz9/eHv76+6HxAQgH79+mHt2rV4//33DRgZkXHw8vKCl5eX6n5AQABKSkqQkJCAlJQUA0Zm+mJiYnDq1CkcOnTI0KFQIzStD89DRMavtrYWEREREEJg9erVhg6H7nLixAmsXLkSOTk5kMlkhg6HGqFUKgEA4eHheOONNwD8d2XGkSNHsGbNGgQGBhoyPLpLYmIisrKykJqaCnd3dxw4cAAxMTFwcXG559N3ZNz4CTpqs65du8LCwgLl5eVq28vLy5v8AsWGOnbsiIceeghnz56VIkTSkpOTU6P17NSpE6ytrQ0UFbXE19eXc0hisbGxSEtLQ0ZGBlxdXZsd29Q80vR1kbSnTX0a4nnIuLRm7dHSnKz/l/NWd6SoU7365tz58+exe/dufnquDaSo08GDB1FRUYEePXpALpdDLpfj/PnzmDNnDjw8PCTJw5RJUaOuXbtCLpfD29tbbUy/fv34K66tJEWdampq8M477yA+Ph5jx47FwIEDERsbi8mTJ+PTTz+VJhETp4v+hVTHZIOO2szS0hIPP/ww9u7dq9qmVCqxd+9etU8nNKeurg4FBQVwdnaWKkzSgr+/v1o9AWD37t0a15MMIy8vj3NIIkIIxMbGYsuWLdi3bx969uzZ4j6cR/rTmvo0xPOQcWnN2qOlOdmzZ084OTmpjbl27RqOHTvGedtKUtQJ+F9zrri4GHv27EGXLl2kScBMSFGnyMhI5OfnIy8vT3VzcXHB3LlzsWvXLumSMVFS1MjS0hJDhw5FUVGR2pgzZ87A3d1dxxmYBynqVFtbi9raWnTooN66sbCwUH0KkrSji/6FZMfU+OckiJqxadMmYWVlJZKTk8Xvv/8uZsyYIRwcHERZWZkQQojIyEgxf/581fj33ntP7Nq1S5SUlIgTJ06IZ555RigUCnH69GlDpWDSrl+/LnJzc0Vubq4AIOLj40Vubq7q187mz58vIiMjVeP/+OMPYWNjI+bOnSsKCwtFUlKSsLCwEDt37jRUCiZP2xolJCSIrVu3iuLiYlFQUCBmzpwpOnToIPbs2WOoFEzaq6++Kuzt7UVmZqa4fPmy6lZdXa0a0/B17vDhw0Iul4tPP/1UFBYWisWLF4uOHTuKgoICQ6Rg0lpTH56HjJ+2aw9N5uTy5cuFg4OD2LZtm8jPzxfh4eGiZ8+eoqamRu/5mQpd1+n27dsiLCxMuLq6iry8PLU5f+vWLYPkaAqkmE8N8Vdc20aKGv3888+iY8eOYt26daK4uFgkJiYKCwsLcfDgQb3nZyqkqFNgYKDw8fERGRkZ4o8//hDr168XCoVCrFq1Su/5mQpt63Tr1i3V32rOzs7izTffFLm5uaK4uFjjY2qCDTrSmcTERNGjRw9haWkpfH19RVZWluqxwMBAER0drbo/a9Ys1VhHR0cREhIicnJyDBC1ecjIyBAA7rnV1yQ6OloEBgbes8/gwYOFpaWl6NWrl1i/fr3e4zYn2tboo48+Er179xYKhULcf//9IigoSOzbt88wwZuBxmoDQG1eNHydE0KIzZs3iwcffFBYWloKHx8fsWPHDv0GbiZaUx+eh0yDNmsPIVqek0qlUixcuFA4OjoKKysr8cQTT4iioiJ9pGLSdFmnc+fONTnnMzIy9JSRadL1fGqIDbq2k6JGX331lfD09BQKhUIMGjRIbN26Veo0TJ6u63T58mXx3HPPCRcXF6FQKISXl5f47LPPhFKp1Ec6JkubOjV17mn4N3Rzx9SETAghtP34HhEREREREREREekGv4OOiIiIiIiIiIjIgNigIyIiIiIiIiIiMiA26IiIiIiIiIiIiAyIDToiIiIiIiIiIiIDYoOOiIiIiIiIiIjIgNigIyIiIiIiIiIiMiA26IiIiIiIiIiIiAyIDToiIi0sWbIEgwcPNnQYRERERHQXQ63RkpOT4eDgoPfnJSLTwwYdEVEDGzZswPDhww0dBhERERHdxZjWaG2NNTExEWPHjkVwcDCmT58OIYQOoyOi9ogNOiKiBrZt24awsDC9Pmdtba1en4+IiIjI2BhijdZabY319ddfh4eHB/78809s3LgR169f12F0RNQesUFHRCYpKCgIsbGxiI2Nhb29Pbp27YqFCxe2+O7jzZs38euvv7a4oFq7di3c3NxgY2ODiIgIXL16VfXYb7/9hlGjRqFr166wt7dHYGAgcnJy1PaXyWRYvXo1wsLCYGtriw8//LD1yRIREREZkfz8fPj5+UGhUOCBBx7AkiVLWtxH0zVavZKSEvTq1QuxsbEQQqguRU1LS4OXlxdsbGwwceJEVFdXY8OGDfDw8EDnzp0RFxeHuro61XEqKysRFRWFzp07w8bGBk899RSKi4u1itXDwwMffPABoqKiYGdnB3d3d6SmpuKvv/5CeHg47OzsMHDgQBw/flztOImJicjOzsbs2bNhZ2enUd5EZLzYoCMik7VhwwbI5XJkZ2dj5cqViI+Px5dfftnsPnv37kX37t3Rt2/fJsecPXsWmzdvxvbt27Fz507k5ubitddeUz1+/fp1REdH49ChQ8jKykKfPn0QEhJyzzufS5Yswfjx41FQUIAXXnihbckSERERGYGLFy9ixIgR8Pf3x8mTJ7FixQp88skn+Prrr5vdT5M1Wr38/HwMHz4cU6dOxRdffAGZTAYAqK6uxueff45NmzZh586dyMzMxPjx45Geno709HSkpKRg7dq1+PHHH1XHeu6553D8+HGkpqbi6NGjEEIgJCSk2asfGos1ISEBw4YNQ25uLkJDQxEZGYmoqChMmzYNOTk56N27N6KiolRvJtfU1AAAFAoFUlJSkJ+f32LeRGTc5IYOgIhIKm5ubkhISIBMJoOXlxcKCgqQkJCA6dOnN7mPJpcj3Lx5E9988w26d+8O4L93N0NDQ/HZZ5/ByckJjz/+uNr4devWwcHBAfv378eYMWNU26dOnYrnn3++DRkSERERGZcvv/wSjo6O96zR4uPjm33DUtNLRo8cOYIxY8ZgwYIFmDNnjtpjtbW1WL16NXr37g0AmDhxIlJSUlBeXg47Ozt4e3tjxIgRyMjIwOTJk1FcXIzU1FQcPnwYAQEBAIDvvvsObm5u2Lp1KyZNmqRxrCEhIXj55ZcBAIsWLcLq1asxdOhQ1THmzZsHf39/lJeXw8nJCZMmTcK1a9fwzz//YNSoUejfv3+LuRORceMn6IjIZD366KOqd0wBwN/fH8XFxWqXLdxNCIHt27e3uPjr0aOHqjlXf1ylUomioiIAQHl5OaZPn44+ffrA3t4enTp1QlVVFUpLS9WO88gjj7Q2NSIiIiKjdPLkyUbXaGfOnGnzGq20tBSjRo3CokWL7mnOAYCNjY2qOQcAjo6O8PDwULt81NHRERUVFQCAwsJCyOVy+Pn5qR7v0qULvLy8UFhYqFWsAwcOVHsOABgwYMA92+qfOy0tDQcOHMCpU6ewbt06yOX8bA2RqWODjojo/2VnZ+POnTuqd0hbKzo6Gnl5eVi5ciWOHDmCvLw8dOnSBbdv31YbZ2tr26bnISIiIjI2lZWVjW5v7nuCNV2jPfDAA/D19cXGjRtx7dq1ex7v2LGj2n2ZTNboNqVS2ezzNKepWO9+nvrmZGPb2vLcRGTc2KAjIpN17Ngxtfv13wdnYWHR6Pht27YhNDS0ycfrlZaW4tKlS2rH7dChA7y8vAAAhw8fRlxcHEJCQuDj4wMrKytcuXKljdkQERERGT8hhGRrNGtra6SlpUGhUCA4OLjNv3zar18/3LlzRy3ev//+G0VFRfD29m5TrEREDbFBR0Qmq7S0FLNnz0ZRURE2btyIxMREzJw5s8nxqampGn23iUKhQHR0NE6ePImDBw8iLi4OERERcHJyAgD06dMHKSkpKCwsxLFjx/Dss8/C2tpaZ3kRERERGTOp1mjAf1co7NixA3K5HE899RSqqqpaHWefPn0QHh6O6dOn49ChQzh58iSmTZuG7t27Izw8vM2xEhHdjQ06IjJZUVFRqKmpga+vL2JiYjBz5kzMmDGj0bElJSU4e/YsgoODWzyup6cnJkyYgJCQEIwePRoDBw7EqlWrVI9/9dVXqKysxJAhQxAZGYm4uDh069ZNZ3kRERERGTOp1mj17Ozs8Msvv0AIgdDQUNy4caPVsa5fvx4PP/wwxowZA39/fwghkJ6efs+lsa2NlYionkw0d7E/EZGRCgoKwuDBg7FixQqNxsfHx2PPnj1IT0+XNjAiIiIiM2bKazRjipWI2h9+go6ICICrqyvefvttQ4dBRERERHcxpjWaMcVKRO0Pf6uZiAhARESEoUMgIiIiogaMaY1mTLESUfvDS1yJiIiIiIiIiIgMiJe4EhERERERERERGRAbdERERERERERERAbEBh0REREREREREZEBsUFHRERERERERERkQGzQERERERERERERGRAbdERERERERERERAbEBh0REREREREREZEBsUFHRERERERERERkQGzQERERERERERERGdD/ARAyLDcEntAwAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1,2,figsize=(15,5))\n",
"ax[0].plot(isotherm1.pressure/si.BAR, isotherm1.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER))\n",
"ax[0].plot(isotherm2.pressure/si.BAR, isotherm2.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER))\n",
"ax[0].plot(isotherm3.pressure/si.BAR, isotherm3.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER))\n",
"ax[0].plot(equilibrium.pressure/si.BAR, equilibrium.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER), 'xk')\n",
"ax[0].set_xlabel('p / bar')\n",
"ax[0].set_ylabel('Ω / mN/m')\n",
"ax[0].legend(['adsorption path', 'desorption path', 'equilibrium path', 'phase equilibrium']);\n",
"\n",
"ax[1].plot(StateVec([p.bulk for p in isotherm1.profiles]).density/(si.KILO*si.MOL/si.METER**3), isotherm1.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER))\n",
"ax[1].plot(StateVec([p.bulk for p in isotherm2.profiles]).density/(si.KILO*si.MOL/si.METER**3), isotherm2.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER))\n",
"ax[1].plot(StateVec([p.bulk for p in isotherm3.profiles]).density/(si.KILO*si.MOL/si.METER**3), isotherm3.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER))\n",
"ax[1].plot(StateVec([p.bulk for p in equilibrium.profiles]).density/(si.KILO*si.MOL/si.METER**3), equilibrium.grand_potential/(si.MILLI*si.NEWTON*si.ANGSTROM**2/si.METER), 'xk')\n",
"ax[1].set_xlim(0,0.1)\n",
"ax[1].set_xlabel('ρ / kmol/m³')\n",
"ax[1].set_ylabel('U / mN/m')\n",
"ax[1].legend(['adsorption path', 'desorption path', 'equilibrium path', 'phase equilibrium']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Enthalpy of adsorption"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(isotherm3.pressure/si.BAR, isotherm3.enthalpy_of_adsorption/(si.KILO*si.JOULE/si.MOL))\n",
"\n",
"vle = PhaseEquilibrium.pure(func, 350*si.KELVIN)\n",
"#h_vap = (vle.vapor.molar_enthalpy() - vle.liquid.molar_enthalpy())/(si.KILO*si.JOULE/si.MOL)\n",
"#plt.plot([pmin/si.BAR, pmax/si.BAR],[h_vap,h_vap])\n",
"plt.xlabel('p / bar')\n",
"plt.ylabel('Δh_ads,Δh_vap / kJ/mol')\n",
"plt.legend(['Δh_ads', 'Δh_vap']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Variation of pore width"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.2 ms, sys: 19.9 ms, total: 26.1 ms\n",
"Wall time: 10.4 s\n"
]
}
],
"source": [
"%%time\n",
"def plt_iso(L):\n",
" isotherm = Adsorption1D.equilibrium_isotherm(func, 350*si.KELVIN, si.linspace(0.5*si.BAR, 1.6*si.BAR, 100), Pore1D(Geometry.Cartesian, L, potential), solver=DFTSolver().newton())\n",
" return isotherm.pressure, isotherm.total_adsorption\n",
"\n",
"L_vec = si.linspace(10*si.ANGSTROM,40*si.ANGSTROM,7)\n",
"with ProcessPoolExecutor(1) as ex:\n",
" isotherms = [i for i in ex.map(plt_iso, L_vec)]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2000x1000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(20,10))\n",
"for p,a in isotherms:\n",
" ax.plot(p/si.BAR, a/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"plt.xlabel('p / bar')\n",
"plt.ylabel('N / 1e-6 mol/m²')\n",
"plt.legend([f'{w/si.ANGSTROM:g} A' for w in L_vec])\n",
"plt.xlim(1,1.6);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Variation of $\\varepsilon$-parameter"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 36.2 ms, sys: 271 ms, total: 307 ms\n",
"Wall time: 2 s\n"
]
}
],
"source": [
"%%time\n",
"def plt_iso(epsilon):\n",
" potential = ExternalPotential.LJ93(3.0, epsilon, 0.08)\n",
" isotherm = Adsorption1D.equilibrium_isotherm(func, 350*si.KELVIN, si.linspace(0.5*si.BAR, 1.6*si.BAR, 100), Pore1D(Geometry.Cartesian, 20*si.ANGSTROM, potential), solver=DFTSolver().newton())\n",
" return isotherm.pressure, isotherm.total_adsorption\n",
"\n",
"eps_vec = np.linspace(70,150,9)\n",
"with ProcessPoolExecutor() as ex:\n",
" isotherms = [i for i in ex.map(plt_iso, eps_vec)]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2000x1000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(20,10))\n",
"for p,a in isotherms:\n",
" ax.plot(p/si.BAR, a/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"plt.xlabel('p / bar')\n",
"plt.ylabel('N / 1e-6 mol/m²')\n",
"plt.legend([f'{e:g}' for e in eps_vec])\n",
"plt.xlim(1,1.6);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Variation of temperature"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 38.6 ms, sys: 303 ms, total: 341 ms\n",
"Wall time: 10.2 s\n"
]
}
],
"source": [
"%%time\n",
"def plt_iso(T):\n",
" potential = ExternalPotential.LJ93(3.0, 150., 0.08)\n",
" psat = PhaseEquilibrium.pure(func, T).liquid.pressure()\n",
" pmin = psat / 100\n",
" pmax = psat * 2\n",
" isotherm = Adsorption1D.equilibrium_isotherm(func, T, si.linspace(pmin, pmax, 200), Pore1D(Geometry.Cartesian, 20*si.ANGSTROM, potential), solver=DFTSolver().newton())\n",
" return isotherm.pressure, isotherm.total_adsorption\n",
"\n",
"T_vec = si.linspace(290*si.KELVIN,430*si.KELVIN,15)\n",
"with ProcessPoolExecutor() as ex:\n",
" isotherms = [i for i in ex.map(plt_iso, T_vec)]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2000x1000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(20,10))\n",
"for p,a in isotherms:\n",
" ax.semilogx(p/si.BAR, a/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"plt.xlabel('p / bar')\n",
"plt.ylabel('N / 1e-6 mol/m²')\n",
"plt.legend([f\"{T/si.KELVIN:g} K\" for T in T_vec])\n",
"plt.xlim(0.05,100);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Binary mixture"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"mix = HelmholtzEnergyFunctional.pcsaft(Parameters.from_json([\"methanol\", \"ethanol\"], \"../../parameters/pcsaft/rehner2020.json\"))\n",
"methanol = HelmholtzEnergyFunctional.pcsaft(Parameters.from_json([\"methanol\"], \"../../parameters/pcsaft/rehner2020.json\"))\n",
"ethanol = HelmholtzEnergyFunctional.pcsaft(Parameters.from_json([\"ethanol\"], \"../../parameters/pcsaft/rehner2020.json\"))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"potential = ExternalPotential.LJ93(3.0, 100.0, 0.08)\n",
"pmin = 0.2*si.BAR\n",
"pmax = 1.8*si.BAR\n",
"solver = DFTSolver().newton(tol=1e-10)\n",
"\n",
"isotherm_ethanol = Adsorption1D.equilibrium_isotherm(ethanol, 350*si.KELVIN, si.linspace(pmin, pmax, 51), Pore1D(Geometry.Cartesian, 15*si.ANGSTROM, potential), solver=solver)\n",
"isotherm_methanol = Adsorption1D.equilibrium_isotherm(methanol, 350*si.KELVIN, si.linspace(pmin, pmax, 51), Pore1D(Geometry.Cartesian, 15*si.ANGSTROM, potential), solver=solver)\n",
"isotherm_mix = Adsorption1D.equilibrium_isotherm(mix, 350*si.KELVIN, si.linspace(pmin, pmax, 51), Pore1D(Geometry.Cartesian, 15*si.ANGSTROM, potential), np.array([0.5, 0.5]), solver=solver)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1,2,figsize=(15,5))\n",
"ax[0].plot(isotherm_methanol.pressure/si.BAR, isotherm_methanol.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"ax[0].plot(isotherm_mix.pressure/si.BAR, isotherm_mix.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"ax[0].plot(isotherm_ethanol.pressure/si.BAR, isotherm_ethanol.total_adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2))\n",
"ax[0].legend(['$x_{EtOH}=0.0$', '$x_{EtOH}=0.5$', '$x_{EtOH}=1.0$']);\n",
"\n",
"ax[1].plot(isotherm_mix.pressure/si.BAR, (isotherm_mix.adsorption/(si.MICRO*si.MOL*si.ANGSTROM**2/si.METER**2)).T)\n",
"ax[1].legend(['methanol', 'ethanol']);"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"equilibrium = Adsorption1D.phase_equilibrium(mix, 350*si.KELVIN, pmin, pmax, Pore1D(Geometry.Cartesian, 15*si.ANGSTROM, potential), np.array([0.5, 0.5]), solver=solver)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 17.0)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(equilibrium.profiles[1].r/si.ANGSTROM, (equilibrium.profiles[1].density/(si.KILO*si.MOL/si.METER**3)).T)\n",
"plt.legend([\"Methanol\", \"Ethanol\"])\n",
"plt.xlim(0,10)\n",
"plt.ylim(0,17)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# External potentials"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Lennard-Jones 9-3"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"rho = 3.0e4\n",
"potential = ExternalPotential.LJ93(3.0, 90, 0.08)\n",
"s = State(func, 350*si.KELVIN, density=rho*si.MOL/si.METER**3)\n",
"profile = Pore1D(Geometry.Cartesian, 20*si.ANGSTROM, potential).initialize(s).solve(solver=solver)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f7db5794a50>]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(profile.r / si.ANGSTROM, (profile.density / (si.MOL / si.METER**3))[0] / 5000)\n",
"plt.plot(profile.r / si.ANGSTROM, profile.external_potential[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Double Well"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"potential = ExternalPotential.DoubleWell(3.0, 25, 150, 0.08)\n",
"s = State(func, 350*si.KELVIN, density=rho*si.MOL/si.METER**3)\n",
"profile = Pore1D(Geometry.Cartesian, 40*si.ANGSTROM, potential).initialize(s).solve(solver=solver)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f7d0e5a7750>]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2IXwAgFMx5gM2IXwAgFOFuLcL7EH4AACnovIBmxA+AMCpGPMBmxA+AMCpmOEUNiF8AIBTMcMpbEL4AACnYswHbEL4AACnYswHbEL4AACnis5wypgPWIvwAQBOReUDNiF8AIBTMeYDNiF8AIBTMcMpbEL4AACnovIBmxA+AMCpGPMBmxA+AMCJDEMyQuZzKh+wWK/Cx5o1azR79mylp6dr5MiRuu2221RRURGzTVtbm0pLS5Wdna20tDSVlJSotrY2ro0GAPRTpOohMeYDlutV+CgrK1Npaam2bNmiV155RcFgUDfddJOam5uj26xYsUIbN27U+vXrVVZWpqqqKi1ZsiTuDQcA9EO4S/ig8gGLuQzDMPq687FjxzRy5EiVlZXpuuuuk9/vV05OjtatW6fbb79dkrRnzx5NnTpV5eXlmjdv3gWP2dDQIJ/PJ7/fr4yMjL42DQBwPq310v8Zaz7/52NSQpKtzcHg15vf736N+fD7/ZKkrKwsSdKOHTsUDAa1cOHC6DZFRUUqLCxUeXl5t8cIBAJqaGiIWQAAAywyu6lE5QOW63P4CIfDevDBB3X11Vdr2rRpkqSamholJSUpMzMzZtvc3FzV1NR0e5w1a9bI5/NFl4KCgr42CQDQU5ExHy6P5HLZ2xY4Tp/DR2lpqXbv3q3nn3++Xw1YtWqV/H5/dDl8+HC/jgcA6AHm+ICN+jTE+YEHHtCLL76o119/XWPGjIm+n5eXp/b2dtXX18dUP2pra5WXl9ftsbxer7xeb1+aAQDoK+b4gI16VfkwDEMPPPCANmzYoFdffVXjx4+PWT9r1iwlJiZq06ZN0fcqKipUWVmp4uLi+LQYANB/3NEWNurVWVdaWqp169bpt7/9rdLT06PjOHw+n1JSUuTz+bRs2TKtXLlSWVlZysjI0PLly1VcXNyjK10AABah8gEb9Sp8/OQnP5EkzZ8/P+b9Z555Rvfcc48k6bHHHpPb7VZJSYkCgYAWLVqkJ598Mi6NBQDECWM+YKNehY+eTAmSnJystWvXau3atX1uFABggHFHW9iIe7sAgBNR+YCNCB8A4EShdvPRw8ymsB7hAwCcKETlA/YhfACAE0UrH8yzBOsRPgDAiToC5iPdLrAB4QMAnIhuF9iI8AEAThTpdkmg2wXWI3wAgBOFIt0uVD5gPcIHADhRtNuFMR+wHuEDAJwoOuCUbhdYj/ABAE7EgFPYiPABAE7EgFPYiPABAE4UYp4P2IfwAQBORLcLbET4AAAnYnp12IjwAQBO1ME8H7AP4QMAnIh5PmAjwgcAOBFXu8BGhA8AcKLomA+6XWA9wgcAOFE0fNDtAusRPgDAibjaBTYifACAE3XQ7QL7ED4AwInodoGNCB8A4ETRq10IH7Ae4QMAnIjKB2xE+AAAJyJ8wEaEDwBwImY4hY0IHwDgRNF7uxA+YD3CBwA4USR8JCTb2w44EuEDAJyoo8185N4usAHhAwCcxjCkEJUP2IfwAQBOE+lykah8wBaEDwBwmkiXiyQlptjXDjgW4QMAnCZS+XC5JXeCvW2BIxE+AMBpooNNkyWXy962wJEIHwDgNNHLbBnvAXsQPgDAabpWPgAbED4AwGmofMBmhA8AcBoqH7AZ4QMAnIbKB2xG+AAAp6HyAZsRPgDAabivC2xG+AAAp+GOtrAZ4QMAnIbKB2xG+AAAp2HMB2xG+AAAp6HyAZsRPgDAaRjzAZsRPgDAaeh2gc0IHwDgNEwyBpsRPgDAaYIt5mNiir3tgGMRPgDAaYKt5mPiMHvbAccifACA0xA+YLNeh4/XX39dt9xyi/Lz8+VyufTCCy/ErDcMQw899JBGjRqllJQULVy4UPv27YtXewEA53D4ZIt+9vpBbf/g5Pk3bG82HwkfsEmvw0dzc7NmzpyptWvXdrv+kUce0RNPPKGnnnpKW7duVWpqqhYtWqS2trZ+NxYA0L39dY362A//rH/5w/u646ly/c+7NefeOFr5YMwH7JHQ2x0WL16sxYsXd7vOMAw9/vjj+ud//mfdeuutkqRf/OIXys3N1QsvvKC77rqrf60FAJwlHDa04ld/U2OgI/rev/7hfS2YmiuP23X2DtEBp1Q+YI+4jvk4dOiQampqtHDhwuh7Pp9Pc+fOVXl5ebf7BAIBNTQ0xCwAgJ576d0a7TrqV2qSR2Vfna/05AR9cKJFb1We6n4HKh+wWVzDR02NWebLzc2NeT83Nze67kxr1qyRz+eLLgUFBfFsEgAMeT99/aAkadm1EzQ2O1Xzp4yUJG2uqOt+h0j4SKLyAXvYfrXLqlWr5Pf7o8vhw4ftbhIADBp7axv1t8P1SnC79Ll5YyVJ10/JkST9ed/x7nei2wU2i2v4yMvLkyTV1tbGvF9bWxtddyav16uMjIyYBQAuJqea2/X8tkq9tLtaobBhd3NirH/T/Afb9UUjlZNuzlg6e1yWJGlPdaPaO8Jn78QkY7BZXMPH+PHjlZeXp02bNkXfa2ho0NatW1VcXBzPjwIAS7xdeUrzv79Z3/jNLt3/n29p2b9vV0eomx90GwRDYW14+6gk6VNXne6yHjM8RRnJCWoPhbWvrjF2p3D49L1dqHzAJr0OH01NTdq5c6d27twpyRxkunPnTlVWVsrlcunBBx/Ud7/7Xf3ud7/Trl279PnPf175+fm67bbb4tx0ABhYR+tbtfTn2+RvDWpc9jAlJ7q1ueKYnio7YHfTJEmbK47peFO7RqQlaX5nV4skuVwuTRvtkyTtPuqP3amj9fRzKh+wSa/Dx5tvvqkrrrhCV1xxhSRp5cqVuuKKK/TQQw9Jkr72ta9p+fLl+sIXvqDZs2erqalJL730kpKTuXsigMHDMAx9/b/eUUNbh2aO8en3/3it/vWT0yVJT5UdlL8laHMLpV93drksuXKMEj2xf86L8swu7H21TbE7BbuEjwTCB+zR6/Axf/58GYZx1vLss89KMhP3d77zHdXU1KitrU1/+tOfNHny5Hi3GwAG1MZ3qvXG/uNKTnTrsTsvV6o3QbddPlpTctPVFOjQhreP2Nq+Y40BvbbHvJrljlljzlo/foTZpfLBiebYFZHxHgkpktv2aw7gUJx5AHCGQEdIj768R5L0D/MnaUJOmiTJ7Xbp7jnm2Ir/esve8PHC20fVETZ0eUGmLslNP2v9uBGpkqRDx88MH8zxAfsRPgDgDL/cUqnDJ1uVk+7V3187PmbdLTPz5XJJu482qNrfeo4jDCzDMLR+h9nlcsdVZ1c9JGlcthk+Dp9sjb1Ch8tscREgfABAF41tQf3oVfNmmCsWTtawpNi7UGSneXVFQaYk6dU955jEa4C9c8SvvbVN8ia4dcvM/G63yc9MUZLHrfZQWFX1XUJS5KZyTDAGGxE+AKCLn7/xgU61BDVhRKo+dY6qwoKp5izOr9kUPiIDTRdPy1NGcmK323jcLo3KNAf6x4SPQOelt96zu2oAqxA+AKBTfUu7/u3P5lTlD944WQme7v9EXjNphCRp+wenFLZ40rHW9pB+t7NKUuzcHt0Z5TPDR01Dl7uKBzqvfklKG5D2AT1B+ACATv/v9YNqDHSoKC9dn5g+6pzbXZafoWFJHvlbg9p75iReA+wPu6rVGOhQYdYwzZuQfd5t833moNKq+i7ho53KB+xH+AAAScebAnrmLx9IklbeOFnu7m5F3ynB49aVhcMlmdUPK/1qu9nlcufsgvO2UVK02yVmYGyk24XKB2xE+AAASU++dkCtwZBmjvHpxktzL7j9VeM6w8ehkwPdtKgDx5q07YOTcruk27uZ2+NMed1VPiLdLlQ+YCPCBwDHO3S8Wf+x5QNJ0pdvmiKX6/wVBen0zdveqrSu8hGpetxQNFK5GReeNTrf103loz0SPqh8wD6EDwCO9y+/f1/BkKGPTs7RdZNzLryDFL13ypFTrapvaR/I5kmSmgMdem5bpSTp7jmFPdpnVGflo9rftfLRYD7S7QIbJVx4EwCIvw9PNGvroZOq8bfJ43Zp0sg0XT1phNK81v5Z+vO+Y/rT+7XyuF361iem9ng/X0qixmYP04cnWvRuVYOu7rwCZqD891tH1NjWofEjUnX9lJE92idytcvJ5na1BUNKTvTQ7YKLAuEDgGUMw9Bf9p/QD16p0FuV9WetT03y6LPFY1V6/aRzzl8RTy3tHfqnDbslSZ+bN1aTRvbuB3lavk8fnmjRrqP+AQ0f4bARHQx779XjLjjQNCJzWKISPS4FQ4ZONLdrdGZKl24XwgfsQ/gAYInyAyf02Ct7te0Dc4BmgtulKwuHa/yIVAXDYb35wSlVnmzRT8sOauPOKn3/jpn6yABXEx59uUKVJ1uU70vWl2/q/Q0wLxudod/vqj77tvVx9qf3a3XoeLMykhNUcuWFB5pGuFwujUjzqtrfpmONATN8cLULLgKEDwAD6s0PTuoHr+zVXw+ckCQlJbj1mbmF+uJHJ2pkl0GThmFo0/t1+t+/f08fnmjRZ57equU3XKIvLbhEnh7+S783NlfU6dm/fiBJWlMyQ+l9qLRMyzfHfbxb1RDPpsUwDEM/3GRO9/7ZeWOV2stuqZx0M3wcbwyYbwQYcAr7ET6AIcQwDPlbgzpa36q6hoCCobDChiHJpYyUBA0flqTs1CTlpHt7dEVHf2w9eEI/enW/3th/XJKU6HHprtmFKr1+kvJ8Z1+p4XK5tPDSXH1kUra+s/E9Pb/9sJ7YtE/bD53UD++6PCao9Nfhky168Fc7ZRjSZ+cV6qM9HGR6psig00PHm9XQFhyQrqL/ea9W71Y1KDXJo/uundDr/UekeSWZ85hIOj3JWBLdLrAP4QM4D8MwFAwZCnSE1BEy1BE2FAobCobCCoUNdYTD6ggbcsmlRI9LiR63EjofE91uJSW45U1w97iP/kKaAh2qbWhTjb9NVfWtqqpvU7W/VUfrW1VV36pqf5ta2kMXPE5Kokdjs4dpXHaqJo5M1aSRaZqUk66JI1PPupFab7S2h/SHXdVat61SOz40L0FNcLt0x1Vj9MANl5hl/wsYlpSg75XM0LwJ2frmhl0qP3hCH3viz3r8zit0zSX974Y51dyu//XsdtW3BDVzjE/f+sSlfT5WVmqSRvmSVe1v096aRl3VefltvITDhh7/k1n1uOfqcRqemtTrY4xIM/c5Fq18RGY4pfIB+zgmfNQ1tunRlyqir7vejcE449YMRte13T/t3M/odt3Zx+t+n7OOGfNZZ2xndP/8zG2N87b3nJ98zv3O197e/Hee+VmGDPOxy/PoMbqu7zyWEbNvNx/ucsl1+qn5GH3d3TpXdINgKKxAMKxAR0jt0efm60BH+KyP6otEj0veBI+8nWHEm9jleYJH3kS3kjxueRPN1y5Jze0damkPqTnQofrWoGr9bWruQbCQpOzUJOVmJMub6Jbb5VLYMNTQGpS/NaiTze1qDYa0p6ZRe2oapXdj9x2dmWKGka5LTlq3P3xNgQ7tr2vSrqN+/XnvMf31wAk1BTokSUket+64aozu/+hEFWT1/g6qt10xWtPH+FT6y7e0p6ZRn/t5/7th6lvadc8z27Svrkl5Gcl68rOz5E3w9OlYEZNz01Xtb1NFbfzDx2/ePqr3qxuU5k3oU9VDMrtdpC6VD652wUXAMeGjqa1D63ccsbsZGAISPS553C4lut3yeFxKcLtkGFJ7KKyOkFkV6TjjZmPBkKFgqEORv//9ke5N0MgMr/IzU5TvSzEfM5M7H1M0ypdsXlJ5Du0dYR2tb9UHJ5p16FizDhxr0r66Jh2oa9KJ5nYdrTcrKWV7j8Xs50tJ1LAkj5IS3OoImWGmsTNodFWQlaJPzSrQHVcVdNu90hsTc9L0QunVWr3xPT23rVJPbNqnbYdO6Pt3zNSY4b0LNIeON2vZs9t18Hizhg9L1H/+/ZweVWIupCgvXWV7j2lvTXzv8dLYFtT3/rhHkrT8hknKHNb7qofUtdulXeoISOGguYIBp7CRY8JHVmqSvn5zUcx7Xbu8z/x3VOw61znXnalrP/r5j3nu/S60rXr8GT0/5vn2Uw/bfd5jnnVI1+kqRGdlomtVwtVZmDDfO/26u+1jqiY6XXXpWi2JbHH2tp1ViW4qEd6ESLeJuS7R45bbdfb/Vt0xDEPtobDZZRMMdVZSwmrvUlGJVFsiVZb2zm0CQXOcRqo3Qalej4YlJSgjOVG5GV7lZiT3esDhmZIS3Bo/IrVzvojYdaea27X/WJP21TZpf12T9h9r0v7aRlX52+TvrJycKSfdqym56Zo3IUvXTc7RtHxf3LqZJCk50aM1S6Zr3oQsffM3u7Tl4End8P0yLf3IWN133QSNTD9/wAmGwvqP8g/16MsVag2GNDozRT+/Z3avL6s9l8m55nEqauMbPp7YtE/HmwKaMCJV9149vs/HiVQ+jjUGTne5SIQP2Mox4SNzWJK+OH+i3c2AQ7hckW4WWT5pVn8MT03S7NSs6NThEc2BDh051RoNSR63S76URGWneuUbNvDzcUjSrZeP1vTRPv3Tht0qP3hCP/vzIT3zlw9002W5uvHSXM0Zn61RGclyu10KdIS0v65Jr+2p03PbDutovTm9+LwJWXririviOnh1Sl5n+KhplGEYcRnIu/NwvX7eOa/Htz5xqZIS+j4ZdcyA07bOS4ITUyXP4DkvMfRw9gG4oFRvQvRH1k4TctK07r65Ktt7TD96db92fHhKf9hVoz/sqpFkVrGSPG61BkPq2vOVk+7VPy64RJ+ZUxjXqowkTRqZJpdLOtUS1PGm9miloa/agiF9+dc7FQobumVmvq4v6tlspucSCR/HGgNSa7355rD4jk0BeovwAWBQcblcmj9lpOZPGal3q/x68Z1qvb73mCpqGjvH1piDctO8CZo9brgWTxulv7s8/7zjYPojOdGjcdmpOnS8WXtrG/sdPtb84X0dONasnHSv/vetl/W7fZH2NAY61N50XEmSlJLZ7+MC/UH4ADBoXZbv02X5Pn395iJ1hMKqbQyoIxRWSpJHOWkDP5dJxOTcNB063qyKmsZ+TbO+4e0j+vfyDyVJj5TM6PMg064ykhOU5HGrPRRW46ljypaklOH9Pi7QH9zVFsCQkOBxa3RmisZmp2pkerJlwUOSpnQOOt3bj0Gn7xyp1zf+e5ck8+qW/na3RLhcrmj1o8VvTvhG+IDdCB8A0E+TO8fD7Onj5bb765p0zzPbFegIa/6UHD24sPf3mTmfyERjgUbCBy4OhA8A6KdI5WNfbaPC4d7NSnf4ZIs+//RWnWxu14wxPv3401fG/V42kcpHR5M56yzhA3YjfABAP40bkapEj0vN7aHoZb09sbe2Ubc/9VdV+ds0ISdVz9wze0AuzY5c8WK0mncUJnzAboQPAOinRI9bE3PMSbt62vVSfuCE7niqXLUNAV0yMk3r/n6estP6d6XMuUQqH+62evMNwgdsRvgAgDiYOipDklRR03De7QzD0L/9+aA++/RW+VuDurwgU+vvL+73VPTnEwkfCYF68w3CB2zGpbYAEAeRSdjeP0/l41hjQN/csEuvvFcrSfrkFaP1r5+crpSkgZmDJCKns6KS3NEZjAgfsBnhAwDioChyxUv12ZUPwzD04jvVeui3u3WqJahEj0vf/NhU3fORcZZcEjyis/IxLNQZjAgfsBnhAwDiINLtcuh4s9qCoeiMqu9XN+i7v39Pf9l/Irrd/71jpi7Nz7CsbTlpXrkUVrrRaN6hkfABmxE+ACAORqZ7lTksUfUtQe2va1KeL1k/eGWvnt9WqbBh3k34ix+dqNLrJ/XrRnF9kZPu1XA1KcEVNt8Y1vdZWIF4IHwAQBy4XC4V5aVry8GTeuyVvdp26KQaAx2SpI/PGKVv3FykgqxhtrQt1ZugwiSzOyiUnCVPQv+nbQf6g/ABAHFSlJehLQdPatOeOknStNEZeugTl2nOePvvIjsxpVkKSIHkHNkTgYDTuNQWAOJk9jgzZOSke/Xo7TP0u9JrLorgIUnjvOZg05akbJtbAlD5AIC4+dj0PG184BpNyElV6gDMVNofoxPM8OFPyBIjPmA3Kh8AECcul0vTx/guuuAhSXkevyTphLjSBfYjfACAA2Qb5k3lao1MexsCiPABAI6QGTbDR1XQuvlFgHMhfACAA6QFzUnOKtvTbG4JQPgAgKEvHFZKS5Uk6f1Wn82NAQgfADD0NdXKHQooZLi0qylNgY6Q3S2CwxE+AGCoq/9QklSjEQoaCaqqb7O5QXA6wgcADHWnzPBRl5AnSTp8ssXO1gCEDwAY8jorH00poyVJR0612tkagPABAENeZ+WjPb1AknT4FJUP2IvwAQBD3alDkiR31jhJdLvAfoQPABjKDEOqe0+SlDzqUknSoePNdrYIIHwAwJDWWCO1npJcHhVMuVyStLe2kcttYSvCBwAMZXXvmo/ZEzV6xHBlDktUMGRob02Tve2Cow1Y+Fi7dq3GjRun5ORkzZ07V9u2bRuojwIAnEut2eWikZead90dbc5wuvPwKRsbBacbkPDxq1/9SitXrtTDDz+st956SzNnztSiRYtUV1c3EB8HADiXml3mY+5lkqR5E7IlSa+8z99j2MdlGIYR74POnTtXs2fP1o9//GNJUjgcVkFBgZYvX65vfOMb5923oaFBPp9Pfr9fGRncfREA+uWx6ZK/UvrcC9LE63XgWJMW/N8yuVzS00uv0qyxWXK7ut/V5TrHCgx6bpc0LCkhrsfsze93fD9ZUnt7u3bs2KFVq1ZF33O73Vq4cKHKy8vP2j4QCCgQCERfNzQ0xLtJAOBM9YfN4OHySGNmS5Im5qTpEzNG6cV3qvW/nn3T5gbCLhNyUvXql+fb9vlxDx/Hjx9XKBRSbm5uzPu5ubnas2fPWduvWbNGq1evjnczumnYfmnt7M4XnWne5TrP887tuj4/7z7qfp8L7t/DfVwuyeU+z9Ldes8F1l9o/x5s4/ZI7oQujwmnX7s8sa9j1id0s+95tnGdsY0nyVwSOh/diZKb8dNAjMrOf/DlXy5506Jvf69khpITPfrjrmo1t3PVC6wX9/DRW6tWrdLKlSujrxsaGlRQUDAAn2RIRvistzCEuBM7Q0nnY4L39POYJbFzXWRbb+w+CV4pcZiUkCwlpphLzPMUKTG587Hr+mGSx/b/SwGn7fsf83Hs1TFvp3kT9P07ZurR22eoPXT672L8O+GB7sX9L+WIESPk8XhUW1sb835tba3y8vLO2t7r9crr9ca7GWcbPl768l5FE4dhnOe5zNddn59zu/Ps06P9e7iPITM8nXfpDFhG6ALre3KMHm4T7pDCoTMeO2JfGxdYH/O6J9t0SKF287hdhYPmErzAuTCQ3AmnQ0lSqvmvzaT0zse0nr32ZkjJmVKyjzAz2FT/TfqPJWaAHT5eyupcho+XsidKI6aYwdUKwTap4iXz+dS/63YTl8slb4LHmvYAXcT9L1tSUpJmzZqlTZs26bbbbpNkDjjdtGmTHnjggXh/XM95EqT03Atvh8EjHJJCQSkU6HxsN5eO9tPPY5ag1NF12+72C5h/tDtazcdgi9TRJgVbzSXmeevpbaNt6pDaG80lHpNIRoJISmQZ3uV15PlwKXWElDrSfEwZ3qXrDpb62/NSy3HzecNR6cM3Yte7PFL2JClvmpQ7TcqbLo2eJQ3Lin9b9r9inocZo83PAC4iA/LPqpUrV2rp0qW66qqrNGfOHD3++ONqbm7WvffeOxAfB6dye8zFqn9JnothnA4l0XDSIrU3S4Em8wcg0CS1N/XsdaAzvEhSoMFc/JU9b487sTOM5EhpI83HyJKWK/lGSxn55o9SggVVRyf54M/m4w3/LGWOlU4eMu+rcvKgdHyf1HpSOl5hLrv/+/R+OVOlwrlSYbHZRZIZh67n8ifNx+l3MB4KF50BCR933nmnjh07poceekg1NTW6/PLL9dJLL501CBUYElyu02M/4iXUIbX5zWmx2+rNx9b6czw/JTUfM5c2v9n11FhtLheSmmOGEN+Y04HEN+Z0l8FA/It8qGo9JdXsNp9f8fmzK62GYU51XrvbnHujdrdUtVM6eUA69r657HjW3HbEFOmSG6VJC6WxH+l9SDy4War8qxlE597fz/8wIP4GZJ6P/mCeD6AfOgKng0jTMam5rsvzY2YgaagyuwQ62i58vGSflDWhM4xM6FzGSzlFBJMz7fmD9PzdUvYl0vJeXMLadEw6vNW8MqWyXKp6O3ZwfGKqNP46afJN0qQbL1wVaTkp/ex66dQH0uz7pI9/v0//OUBv2TrPBwAbJXjNyoVvzPm3MwzzR6rhiBlG/EfMQNJQZc4NceqQGVTa/OaPYdXbZx8jdaQ0curpJWeqNLLIDCxO9EHn+I5x1/Ruv7QcaeonzEUyKygHN0v7/iTt/5PUVCPt/aO5SNLIS6VLbjKXgjnmlVoRde9Lv7nPDB6+AmnBQ/39rwIGBJUPAN1rbzF/xE4ePD1u4eQh6cSB849BySyU8q+U8q8wl1EzzQGyQ92Txeat60uelqbfHp9jGobZRbPvf6R9r0hHtsVWRTxJ5gDW5Eyp5YQ5lkSShmVL9/zeDIWARXrz+034ANB7gSbpWIU5TqGuy9JY1f32WRPNIFI4zxzDkDN1aA2CrD8sPT7NnHjvqwcGrkuq5aR04NXTYaT15BkbuKSij0uL/8+Fq19AnNHtAmBgedOkMbPMpavWU1L1O6e7aqreluo/NAdVnjwg7f4vc7vkTDOERK7uyL/cvHJpsNr3svk4Zs7AjoUZlmVWVabfLoXDZgXq+H7zSqmkNGnUDPMKJ+AiR/gAED8pw6UJHzWXiJaTZgg58qZ5Bcbh7eaVOhV/MJfoftebV3dMWiClnz0h4UVtb2f4mLzIus90u6Xh48wFGGQIHwAG1rAsM1BMWmC+DgXN6siHfzGv7vjgL2bF5N3fmItkTsA1aaE5M+foKy/uSdNaTkoHXjOfT/mYvW0BBgnCBwBreRJPd9lc/Y/mnCZHd5hXdhzYJB19y5wDo3a39JfHzblHpt5iLoXFF1/3zO7/NudWyZtuXu0D4IIYcArg4tJ8whxUufePZndGe9PpdcNGSJfdJs24Sxpz1cVREfnZAunom9JN/yJ9xMZbSAA242oXAENDsM2c8+L930l7fm+OFYnImijNvEua8Sn7xj0c2SH92w3mDQVXvMf9o+BohA8AQ08oKB0qk975tfT+RvP+ORGFH5FmLZUuvTW+09xfyPp7pHc3SDPvlj75lHWfC1yECB8AhrZAk7TnRelvz0kHyyR1/hlLzpQu/7Q0614pZ/LAtqH2Xempa8xJv+7/i3mnWsDBCB8AnKOhSnr7l9Jb/y75D59+f+zVZgi59O/if/dew5B+catZibn0VulTv4jv8YFBiPABwHnCIWn/JmnHM9Lel05PQ56SZXaLzFoq5UyJz2fteFba+CVzevMHtjPXBiDCBwCn8x+V3v5P6a1fmDfPiygslmbd07+xIUd2SM9+XOpolRaulq55MB4tBgY9wgcASJ3VkD9JO/69sxoSMt9P9pmX6175eSn3sp5fslu5RVp3p3nVzaSF0qfXD6171AD9QPgAgDNFx4b8IvauvFkTzdvZT/m4efO7hKSz922qk8rXSuU/lsId0pjZ0udeMO9xA0AS4QMAzi0clg6+eroaEmo/vc7jNWcqzSw07zcTbJVO7DdnYI1UTS5bIv3djwgewBkIHwDQE20NZrfMnhfNWVVbT51729FXSdd9RZp888UxsypwkenN7zf3dgHgXMkZ0rQl5mIY0smDUs07UkO11OY3L9H1FZj3ocmaYHdrgSGD8AEAklnNyJ5oLgAGFMO0AQCApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFjqorurrWEYkqSGhgabWwIAAHoq8rsd+R0/n4sufDQ2NkqSCgoKbG4JAADorcbGRvl8vvNu4zJ6ElEsFA6HVVVVpfT0dLlcrrgeu6GhQQUFBTp8+LAyMjLiemzw/VqB73jg8R0PLL7fgWfXd2wYhhobG5Wfny+3+/yjOi66yofb7daYMWMG9DMyMjI46QcQ3+/A4zseeHzHA4vvd+DZ8R1fqOIRwYBTAABgKcIHAACwlKPCh9fr1cMPPyyv12t3U4Ykvt+Bx3c88PiOBxbf78AbDN/xRTfgFAAADG2OqnwAAAD7ET4AAIClCB8AAMBShA8AAGApx4SPtWvXaty4cUpOTtbcuXO1bds2u5s0ZHz729+Wy+WKWYqKiuxu1qD2+uuv65ZbblF+fr5cLpdeeOGFmPWGYeihhx7SqFGjlJKSooULF2rfvn32NHYQutD3e88995x1Tt988832NHYQWrNmjWbPnq309HSNHDlSt912myoqKmK2aWtrU2lpqbKzs5WWlqaSkhLV1tba1OLBpyff8fz58886j++//36bWhzLEeHjV7/6lVauXKmHH35Yb731lmbOnKlFixaprq7O7qYNGZdddpmqq6ujyxtvvGF3kwa15uZmzZw5U2vXru12/SOPPKInnnhCTz31lLZu3arU1FQtWrRIbW1tFrd0cLrQ9ytJN998c8w5/dxzz1nYwsGtrKxMpaWl2rJli1555RUFg0HddNNNam5ujm6zYsUKbdy4UevXr1dZWZmqqqq0ZMkSG1s9uPTkO5ak++67L+Y8fuSRR2xq8RkMB5gzZ45RWloafR0KhYz8/HxjzZo1NrZq6Hj44YeNmTNn2t2MIUuSsWHDhujrcDhs5OXlGY8++mj0vfr6esPr9RrPPfecDS0c3M78fg3DMJYuXWrceuuttrRnKKqrqzMkGWVlZYZhmOdrYmKisX79+ug277//viHJKC8vt6uZg9qZ37FhGMZHP/pR40tf+pJ9jTqPIV/5aG9v144dO7Rw4cLoe263WwsXLlR5ebmNLRta9u3bp/z8fE2YMEGf+cxnVFlZaXeThqxDhw6ppqYm5pz2+XyaO3cu53Qcbd68WSNHjtSUKVP0xS9+USdOnLC7SYOW3++XJGVlZUmSduzYoWAwGHMOFxUVqbCwkHO4j878jiN++ctfasSIEZo2bZpWrVqllpYWO5p3lovuxnLxdvz4cYVCIeXm5sa8n5ubqz179tjUqqFl7ty5evbZZzVlyhRVV1dr9erVuvbaa7V7926lp6fb3bwhp6amRpK6Pacj69A/N998s5YsWaLx48frwIED+uY3v6nFixervLxcHo/H7uYNKuFwWA8++KCuvvpqTZs2TZJ5DiclJSkzMzNmW87hvunuO5akT3/60xo7dqzy8/P1zjvv6Otf/7oqKir0m9/8xsbWmoZ8+MDAW7x4cfT5jBkzNHfuXI0dO1a//vWvtWzZMhtbBvTNXXfdFX0+ffp0zZgxQxMnTtTmzZu1YMECG1s2+JSWlmr37t2MAxtA5/qOv/CFL0SfT58+XaNGjdKCBQt04MABTZw40epmxhjy3S4jRoyQx+M5axR1bW2t8vLybGrV0JaZmanJkydr//79djdlSIqct5zT1pkwYYJGjBjBOd1LDzzwgF588UW99tprGjNmTPT9vLw8tbe3q76+PmZ7zuHeO9d33J25c+dK0kVxHg/58JGUlKRZs2Zp06ZN0ffC4bA2bdqk4uJiG1s2dDU1NenAgQMaNWqU3U0ZksaPH6+8vLyYc7qhoUFbt27lnB4gR44c0YkTJzine8gwDD3wwAPasGGDXn31VY0fPz5m/axZs5SYmBhzDldUVKiyspJzuIcu9B13Z+fOnZJ0UZzHjuh2WblypZYuXaqrrrpKc+bM0eOPP67m5mbde++9djdtSPjKV76iW265RWPHjlVVVZUefvhheTwe3X333XY3bdBqamqK+dfJoUOHtHPnTmVlZamwsFAPPvigvvvd7+qSSy7R+PHj9a1vfUv5+fm67bbb7Gv0IHK+7zcrK0urV69WSUmJ8vLydODAAX3ta1/TpEmTtGjRIhtbPXiUlpZq3bp1+u1vf6v09PToOA6fz6eUlBT5fD4tW7ZMK1euVFZWljIyMrR8+XIVFxdr3rx5Nrd+cLjQd3zgwAGtW7dOH/vYx5Sdna133nlHK1as0HXXXacZM2bY3Ho541JbwzCMH/3oR0ZhYaGRlJRkzJkzx9iyZYvdTRoy7rzzTmPUqFFGUlKSMXr0aOPOO+809u/fb3ezBrXXXnvNkHTWsnTpUsMwzMttv/Wtbxm5ubmG1+s1FixYYFRUVNjb6EHkfN9vS0uLcdNNNxk5OTlGYmKiMXbsWOO+++4zampq7G72oNHddyvJeOaZZ6LbtLa2Gv/wD/9gDB8+3Bg2bJjxyU9+0qiurrav0YPMhb7jyspK47rrrjOysrIMr9drTJo0yfjqV79q+P1+exveyWUYhmFl2AEAAM425Md8AACAiwvhAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACW+v9Gj0M70LX4EwAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(profile.r / si.ANGSTROM, (profile.density / (si.MOL / si.METER**3))[0] / 5000)\n",
"plt.plot(profile.r / si.ANGSTROM, profile.external_potential[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DFTSolver options"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|solver|max_iter|tol|\n",
"|-|-:|-:|\n",
"|Newton (max_iter_gmres=200)|50|1e-11|"
],
"text/plain": [
"DFTSolver(\n",
" Newton { log: false, max_iter: 50, max_iter_gmres: 200, tol: 1e-11 }\n",
")"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a list of solvers that are called consecutively\n",
"solver = DFTSolver().newton()\n",
"solver"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | examples/pcsaft/parameter_adjustment/adjust_viscosity_correlation.ipynb | .ipynb | 42,906 | 364 | {
"cells": [
{
"cell_type": "markdown",
"id": "d2e9adfb-9962-40f7-8265-4861621e757e",
"metadata": {},
"source": [
"# Adjusting correlation parameters for entropy scaling of viscosity"
]
},
{
"cell_type": "markdown",
"id": "4b54ad7f-d2ee-478c-8dd3-aebb2addec81",
"metadata": {},
"source": [
"## Goal\n",
"\n",
"- Read in experimental data for viscosity of a pure substance.\n",
"- Use the `DataSet`, `Loss` and `Estimator` objects to store and work with experimental data.\n",
"- Define a `cost` function that will be used with `scipy.optimize.least_squares`.\n",
"- Run the optimization."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "805bdfdb-d397-45a3-8354-082a8d13e4c1",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.optimize import least_squares\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from si_units import BAR, KELVIN, KILOGRAM, METER, MILLI, PASCAL, SECOND, RGAS\n",
"\n",
"from feos.eos import EquationOfState, State, Contributions\n",
"from feos.pcsaft import Identifier, PcSaftParameters, PcSaftRecord, PureRecord\n",
"from feos.eos.estimator import Estimator, Loss, DataSet, Phase\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 5)\n",
"plt.rcParams['figure.figsize'] = [12,7]"
]
},
{
"cell_type": "markdown",
"id": "46c5a891-be8b-44d9-8f42-c84adf0cadb4",
"metadata": {},
"source": [
"# `DataSet` objects\n",
"\n",
"FeO$_\\text{s}$ provides a range of data structures that facilitate the adjustment of parameters. One such structure is the `DataSet`, which serves as a storage unit for experimental data. When working with a specific model, a `DataSet` enables the evaluation of a `cost` function. How this cost function is defined depends on the property that you want to predict with your equation of state. In this notebook, we use the viscosity where the cost function is the relative difference between the experimental data and the model prediction.\n",
"\n",
"A DataSet encompasses the following components:\n",
"\n",
"- The `target`: This refers to the experimental data associated with the property for which we intend to adjust parameters. In our case, this is the viscosity.\n",
"- Other properties: These are necessary for determining the thermodynamic conditions. For instance, temperature is required for vapor pressure calculations, while liquid density computations necessitate temperature and pressure. For the viscosity, we use pressure, temperature and the phase (liquid or vapor) as input.\n",
"- Each property available in FeO$_\\text{s}$ is accompanied by a corresponding DataSet constructor."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3c80cd76-4868-4b92-a654-287c7d65b875",
"metadata": {},
"outputs": [],
"source": [
"# read data from file into DataFrame\n",
"data = pd.read_csv(\"data/hexane_viscosity.csv\")\n",
"# map strings for phase to Phase objects\n",
"phase = [Phase.Vapor if p == 'vapor' else Phase.Liquid for p in data.phase]\n",
"\n",
"# construct a `DataSet` for vapor pressure\n",
"dataset = DataSet.viscosity(\n",
" target=data[\"viscosity / mPas\"].values * MILLI * PASCAL * SECOND,\n",
" temperature=data[\"temperature / K\"].values * KELVIN,\n",
" pressure=data[\"pressure / bar\"].values * BAR,\n",
" phase=phase\n",
")"
]
},
{
"cell_type": "markdown",
"id": "30f51ce6-a910-4e6f-93d2-7c02d070143e",
"metadata": {},
"source": [
"For `DataSet.viscosity` we have to supply the experimental data for viscosity, temperature, and pressure.\n",
"\n",
"Optionally, a list of `Phase` objects can be supplied (via the `phase` argument) in which case not the stable phase is calculated for given temperature and pressure but the possibly instable phase according to the input phase."
]
},
{
"cell_type": "markdown",
"id": "65c565d7-30a4-446a-8e22-0c225edeb88f",
"metadata": {},
"source": [
"# `Estimator` and `Loss` objects"
]
},
{
"cell_type": "markdown",
"id": "89312d88-4eba-4649-af06-a11d673a88b9",
"metadata": {},
"source": [
"To collect the `DataSet` objects for the properties we wish to adjust parameters for, we can assemble them into an `Estimator` object. The `Estimator` requires the following inputs:\n",
"\n",
"- A list of `DataSet` objects.\n",
"- A corresponding list of `weights`, with each weight assigned to a specific `DataSet`.\n",
"- A list of `losses`, where each `DataSet` has an associated loss function.\n",
"\n",
"In our case, there is only a single `DataSet` so we can ignore the `weights`. Since our data comes from a correlation (from the NIST WebBook), we don't need a loss function."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2e4c20fb-2415-43ac-87c4-68279dcb4e7d",
"metadata": {},
"outputs": [],
"source": [
"estimator = Estimator(data=[dataset],weights=[1],losses=[Loss.linear()])"
]
},
{
"cell_type": "markdown",
"id": "c49e1f8a-93f5-4981-b7ea-410b651058c8",
"metadata": {},
"source": [
"# Defining a cost function\n",
"\n",
"When using `scipy`'s `least_squares` solver, it requires a function to calculate the cost or residuals. The first argument of this function must be the parameter vector that's being optimized. Therefore, it is necessary to define this function in Python before working with `least_squares`.\n",
"\n",
"To simplify the process, we create two functions:\n",
"\n",
"- `eos_from_parameters`: This function constructs the parameters and equation of state based on the current parameter vector. Since FeO$_\\text{s}$ does not allow mutation of an existing `EquationOfState` object, generating a new equation of state is necessary. We can use this function later to generate the equation of state for the final parameters.\n",
"- `cost`: Taking the parameters and estimator as inputs, this function builds the equation of state, calculates the cost of the current model, and returns the result.\n",
"\n",
"These functions aid in the seamless integration of the solver, allowing for the generation of the equation of state and the calculation of the model's cost."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8002fc42-49dd-4166-b85e-7c96f567fa8d",
"metadata": {},
"outputs": [],
"source": [
"# extract PC-SAFT parameters, identifier and molarweight, which are kept constant during optimization\n",
"hexane = PcSaftParameters.from_json([\"hexane\"], \"../../../parameters/pcsaft/esper2023.json\").pure_records[0]\n",
"identifier = hexane.identifier\n",
"saft = hexane.model_record\n",
"mw = hexane.molarweight\n",
"m, sigma, epsilon_k = saft.m, saft.sigma, saft.epsilon_k\n",
"\n",
"def eos_from_parameters(c):\n",
" \"\"\"Returns equation of state (PC-SAFT) for current parameters.\"\"\"\n",
" global m, sigma, epsilon_k, identifier, mw\n",
" model_record = PcSaftRecord(m, sigma, epsilon_k, viscosity=c)\n",
" pure_record = PureRecord(identifier, mw, model_record)\n",
" parameters = PcSaftParameters.from_records([pure_record])\n",
" return EquationOfState.pcsaft(parameters)\n",
"\n",
"def cost(p, estimator):\n",
" \"\"\"Calculates cost function for current parameters.\"\"\"\n",
" return estimator.cost(eos_from_parameters(p))"
]
},
{
"cell_type": "markdown",
"id": "b3d9f5f9-77ff-4523-a3ec-d7a3c05b829a",
"metadata": {},
"source": [
"# Adjust parameters\n",
"\n",
"To begin parameter adjustment, there are two additional requirements:\n",
"\n",
"1. An initial guess for the parameters.\n",
"2. Bounds for the parameters.\n",
"\n",
"It is crucial to note that trying multiple combinations of initial parameters is recommended to avoid getting stuck in a local minimum.\n",
"\n",
"Once these prerequisites are fulfilled, we can invoke `least_squares` with `args=(estimator, )` as an argument for the `cost` function. Please note that the tuple syntax is necessary in this case."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1309b82d-dd20-4e9c-987c-03167cf4a7b8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iteration Total nfev Cost Cost reduction Step norm Optimality \n",
" 0 1 4.7163e-03 6.45e-03 \n",
" 1 2 4.5509e-04 4.26e-03 1.00e+00 2.23e-03 \n",
" 2 3 1.6840e-05 4.38e-04 1.75e+00 2.57e-04 \n",
" 3 4 1.6086e-07 1.67e-05 1.21e+00 1.39e-05 \n",
" 4 5 8.4360e-08 7.65e-08 2.34e-01 7.53e-08 \n",
" 5 6 8.4357e-08 3.66e-12 4.59e-03 2.37e-11 \n",
"`gtol` termination condition is satisfied.\n",
"Function evaluations 6, initial cost 4.7163e-03, final cost 8.4357e-08, first-order optimality 2.37e-11.\n",
"CPU times: user 59.3 ms, sys: 6.51 ms, total: 65.8 ms\n",
"Wall time: 64.5 ms\n"
]
}
],
"source": [
"%%time\n",
"initial_parameters = [0.0]*4 \n",
"fitted_parameters = least_squares(cost, initial_parameters, args=(estimator,), verbose=2).x"
]
},
{
"cell_type": "markdown",
"id": "c7810262-d26d-4a5a-8d3b-5b1d217a9eb5",
"metadata": {},
"source": [
"To provide statistics during the adjustment process, we can utilize the `verbose=2` option, which prints relevant information. However, our primary interest lies in obtaining the optimal parameters. These parameters can be accessed from the `x` field of the `OptimizeResult` object generated by the `least_squares` function.\n",
"\n",
"Once the adjustment is complete, we can investigate the results. A quick and straightforward method for analyzing the optimization results is to use the `Estimator.mean_absolute_relative_difference` method. This method returns the mean absolute relative difference (MARD) for each `DataSet`, without applying losses or weights. It is important to exercise caution when dealing with `NaN` values, as any predictions resulting in `NaN` are filtered out and thus not immediately visible in the reported values.\n",
"\n",
"It's essential to note that the resulting MARD **does not include** an evaluation of the loss functions or weights. Therefore, it may not equal the property that is minimized in the optimization process."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b266f557-40f5-43d6-82eb-f5c393a3b464",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adjusted correlation parameters\n",
"c = [-1.20010418 -2.15742133 0.24509729 0.21828778]\n",
"MARD = 0.2038 %\n"
]
}
],
"source": [
"print(\"Adjusted correlation parameters\")\n",
"print(\"c = {}\".format(fitted_parameters))\n",
"\n",
"mard = estimator.mean_absolute_relative_difference(eos_from_parameters(fitted_parameters)) * 100\n",
"print(\"MARD = {:<5.4} %\".format(mard[0]))"
]
},
{
"cell_type": "markdown",
"id": "342179f3-ca31-4ecf-8fa6-b58680ffd697",
"metadata": {},
"source": [
"# Plot resuts\n",
"\n",
"We can now use our results and calcuate the phase diagram and have a visual inspection of our parameters. Here, we calculate the reduced logarithmic viscosity to use the entropy scaled depiction."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4569e7a0-54ec-4fb7-b735-1dbb4dca268a",
"metadata": {},
"outputs": [],
"source": [
"fitted_eos = eos_from_parameters(fitted_parameters)\n",
"results = []\n",
"for _, (t, p, viscosity, phase) in data.iterrows():\n",
" s = State(fitted_eos, temperature=t*KELVIN, pressure=p*BAR, density_initialization=phase)\n",
" results.append({\n",
" 's_res': s.molar_entropy(Contributions.Residual) / RGAS / m,\n",
" 'ln_eta_nist': np.log(viscosity * MILLI * PASCAL * SECOND / s.viscosity_reference()),\n",
" 'ln_eta_saft': s.ln_viscosity_reduced(),\n",
" 'temperature': t,\n",
" 'pressure': p,\n",
" })\n",
" \n",
"# collect into DataFrame\n",
"results_df = pd.DataFrame(results)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "90dca269-a213-4b2b-aff2-f006b4f522e2",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(\n",
" data=results_df, x='s_res', y='ln_eta_nist', \n",
" color=colors[0], label='NIST'\n",
")\n",
"sns.lineplot(\n",
" data=results_df, x='s_res', y='ln_eta_saft', \n",
" color=colors[1], label='entropy scaling'\n",
")\n",
"\n",
"plt.xlabel(r\"$s^\\text{res}(T, \\rho) / R / m$\")\n",
"plt.ylabel(r\"$\\ln\\frac{\\eta}{\\eta_\\text{CE}}$\")\n",
"plt.legend(frameon=False);"
]
},
{
"cell_type": "markdown",
"id": "96c715a3-dd27-4afd-a146-c5c516e43ce7",
"metadata": {},
"source": [
"# Summary\n",
"\n",
"- The `Estimator` object in FeO$_\\text{s}$ allows the collection of `DataSet` objects for adjusting parameters.\n",
" - The `Estimator` takes a list of `DataSet` objects, weights, and `Loss` objects as inputs.\n",
" - For `DataSet.viscosity`, it calculates the cost of a model by evaluating the relative difference between the model's prediction and experimental data in each `DataSet`.\n",
"- To work with `scipy`'s `least_squares` solver, a cost function is required.\n",
" - Two functions, `eos_from_parameters` and `cost`, are built for this purpose.\n",
" - `eos_from_parameters` constructs the parameters and equation of state for the current parameter vector.\n",
" - `cost` calculates and returns the cost of the current model based on the parameters and estimator.\n",
"- Initial parameter guesses and parameter bounds are necessary for parameter adjustment.\n",
" - Checking multiple combinations of initial parameters is recommended to avoid local minima (not shown in this notebook).\n",
"- For `DataSet.viscosity`, the `Estimator.mean_absolute_relative_difference` method provides the mean absolute relative difference (MARD) without applying weights or losses."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/pcsaft/parameter_adjustment/adjust_kij.ipynb | .ipynb | 171,306 | 634 | {
"cells": [
{
"cell_type": "markdown",
"id": "2a6eb367-6205-4244-a6cd-da994342e3d5",
"metadata": {},
"source": [
"# Adjusting PC-SAFT binary $k_{ij}$ parameter "
]
},
{
"cell_type": "markdown",
"id": "eb4e4769-2ea1-4241-a64f-742cd9147b82",
"metadata": {},
"source": [
"## Goal\n",
"\n",
"- Read in experimental data for binary VLEs\n",
"- Use the `DataSet`, `Loss` and `Estimator` objects to store and work with experimental data.\n",
"- Define a `cost` function that will be used with `scipy.optimize.least_squares`.\n",
"- Run the optimization."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5bbffc77-9ab5-4996-8980-a464c75959b9",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.optimize import least_squares\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from si_units import BAR, KELVIN, KILOGRAM, METER\n",
"\n",
"from feos.eos import EquationOfState, PhaseDiagram\n",
"from feos.pcsaft import Identifier, IdentifierOption, PcSaftParameters, PcSaftRecord, PureRecord\n",
"from feos.eos.estimator import Estimator, Loss, DataSet, Phase\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 5)\n",
"plt.rcParams['figure.figsize'] = [12,7]"
]
},
{
"cell_type": "markdown",
"id": "ec15860d-6c4f-4fbc-bf8f-7243beee6226",
"metadata": {},
"source": [
"## Defining a cost function\n",
"\n",
"We will solve the fitting-problem using `scipy`'s `least_squares` solver, which requires a function to calculate the cost or residuals. The first argument of this function must be the parameter vector that's being optimized. Therefore, it is necessary to define this function in Python before working with `least_squares`.\n",
"\n",
"To simplify the process, we create two functions:\n",
"\n",
"- `eos_from_kij`: This function constructs the equation of state based on the current $k_{ij}$ value. Since FeO$_\\text{s}$ does not allow mutation of an existing `EquationOfState` object, generating a new equation of state is necessary. We can use this function later to generate the equation of state for the final parameter.\n",
"- `cost`: Taking the parameter and estimator (discussed below) as inputs, this function builds the equation of state, calculates the cost of the current model, and returns the result."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7d84054b-62d4-44eb-a548-e6d5e92cfb86",
"metadata": {},
"outputs": [],
"source": [
"def eos_from_kij(kij):\n",
" \"\"\"Returns equation of state (PC-SAFT) for current kij.\"\"\"\n",
" global parameters\n",
" p = PcSaftParameters.new_binary(parameters.pure_records, kij)\n",
" return EquationOfState.pcsaft(p)\n",
"\n",
"def cost(kij, estimator):\n",
" \"\"\"Calculates cost function for current parameters.\"\"\"\n",
" return estimator.cost(eos_from_kij(kij))"
]
},
{
"cell_type": "markdown",
"id": "0bd26bc1-5d56-4a55-a266-c5679e8e97bd",
"metadata": {},
"source": [
"### Utility function - plot experiment and prediciton\n",
"\n",
"The following function takes experimental data and a $k_{ij}$ value as input and produces a phase diagram depicting both experiment and prediction from the equation of state."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b809b90b-7ff4-44ca-9d2b-9b78fdf59a01",
"metadata": {},
"outputs": [],
"source": [
"def plot_comparison(data, kij):\n",
" \"\"\"Generates a plot for each experimental isotherm and PC-SAFT estimate\"\"\"\n",
" for i, (temperature, isotherm) in enumerate(data.groupby('t')):\n",
" # plot experiment\n",
" plt.plot(\n",
" isotherm.x1, isotherm.p, \n",
" color=colors[i], marker='s', linestyle=\"\",\n",
" label=f\"T = {temperature} K\"\n",
" )\n",
" plt.plot(\n",
" isotherm.y1, isotherm.p, \n",
" color=colors[i], marker='s', linestyle=\"\",\n",
" )\n",
"\n",
" # plot PC-SAFT\n",
" vle = PhaseDiagram.binary_vle(\n",
" eos_from_kij(kij), \n",
" temperature*KELVIN, \n",
" npoints=25\n",
" )\n",
" plt.plot(\n",
" vle.liquid.molefracs[:, 0], vle.liquid.pressure / BAR,\n",
" color=colors[i]\n",
" )\n",
" plt.plot(\n",
" vle.vapor.molefracs[:, 0], vle.vapor.pressure / BAR,\n",
" color=colors[i]\n",
" )\n",
"\n",
" plt.xlim(0, 1)\n",
" plt.ylim(0.1, 0.8)\n",
" plt.xlabel(r\"$x_1, y_1$\")\n",
" plt.ylabel(r\"$p$ / bar\")\n",
" plt.legend(\n",
" frameon=False, \n",
" title=r\"$k_{ij} = $\" + f\"{kij:8.4f}\",\n",
" bbox_to_anchor=[1,1]\n",
" );"
]
},
{
"cell_type": "markdown",
"id": "728613b1-fa04-4867-8237-4186faed328a",
"metadata": {},
"source": [
"## Experimental data\n",
"\n",
"We are using experimental data extracted from the openly available supplementary information of the work of [Jaubert et al.](https://doi.org/10.1021/acs.iecr.0c01734). We will be using the binary mixture of 2-propanol and 2,2,4-trimethylpentane as example."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "28a94d65-5509-4104-bf0c-818988504e34",
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"data/binary_vle.csv\", delimiter=\"\\t\")"
]
},
{
"cell_type": "markdown",
"id": "c53e97dc-95db-485d-bf99-cb60e7e9658d",
"metadata": {},
"source": [
"## Compare to PC-SAFT prediction ($k_{ij} = 0.0$)\n",
"\n",
"Before we start, let's inspect how well PC-SAFT predicts this binary mixture. We will use the parameters by Esper et al."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "23b05a20-63c8-47da-9915-3b9eef0b7cda",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|$m$|$\\sigma$|$\\varepsilon$|$\\mu$|$Q$|$\\kappa_{AB}$|$\\varepsilon_{AB}$|$N_A$|$N_B$|$N_C$|\n",
"|-|-|-|-|-|-|-|-|-|-|-|-|\n",
"|2-propanol|60.058|3.95902|2.93004|198.97045|-|-|0.03508|1898.4667|1|1|0|\n",
"|2,2,4-trimethylpentane|114.141|3.13984|4.08786|249.78239|-|-|-|-|0|0|0|"
],
"text/plain": [
"<PcSaftParameters at 0x7f1a78903c70>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = PcSaftParameters.from_json([\"2-propanol\", \"2,2,4-trimethylpentane\"], \"../../../parameters/pcsaft/esper2023.json\")\n",
"pcsaft = EquationOfState.pcsaft(parameters)\n",
"parameters"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aad85cf6-5a0a-4ce8-acb9-c3c9a8ab374e",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_comparison(data, 0.0)"
]
},
{
"cell_type": "markdown",
"id": "38ec44ea-f71d-4557-bc25-6e9d96983442",
"metadata": {},
"source": [
"## Build `DataSet` objects\n",
"\n",
"There is definitely room for improvement. Let's start with the `DataSet` object available in FeO$_\\text{s}$.\n",
"A `DataSet` serves as a storage unit for experimental data and also defines a cost function for that specific data set.\n",
"Below, we will use the `DataSet.binary_phase_diagram` constructor and provide temperature, pressures, as well as liquid and vapor mole fractions.\n",
"The `binary_phase_diagram` data set is one of three options - the others will be discussed below.\n",
"Importantly, this data set will use a *distance* criterion as cost function. I.e. the length of the vector that is orthogonal to the phase envelope and that connects to the experimental datum. \n",
"\n",
"Each `DataSet` with this constructor has to contain data for a *single* pressure or temperature. Here, we group the experimental data according to temperatures and end up with 3 `DataSet` objects - one for each temperature."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fea4598b-7d10-4256-9e71-56ea9844921b",
"metadata": {},
"outputs": [],
"source": [
"isotherms = [\n",
" DataSet.binary_phase_diagram(\n",
" temperature*KELVIN, \n",
" isotherm.p.values * BAR, \n",
" isotherm.x1.values,\n",
" isotherm.y1.values,\n",
" npoints=40\n",
" ) \n",
" for temperature, isotherm in data.groupby('t')\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "d741aab4-cb51-4a91-8da2-76483e0cabc2",
"metadata": {},
"source": [
"## Build `Estimator` object\n",
"\n",
"Next, we use the `Estimator` object. The `Estimator` is a wrapper around multiple `DataSet`s and provides a convenient way to calculate the cost function of multiple `DataSet`s in one function call. Furthermore, we can use the `Estimator` to define weights between `DataSet`s and `Loss` objects for robust regression. In this example, all isotherms have the same weights (each weight is unity) and we perform a simple least squares optimization an thus use `Loss.linear`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cec22449-afab-429a-aee0-a587f7c61570",
"metadata": {},
"outputs": [],
"source": [
"estimator = Estimator(isotherms, weights=[1]*3, losses=[Loss.linear()]*3)"
]
},
{
"cell_type": "markdown",
"id": "fb107c4c-a35d-423c-8e22-11983b23da3a",
"metadata": {},
"source": [
"## Run the optimization\n",
"\n",
"With that, we can perform the optimization. We set the initial $k_{ij}$ value to zero, the bounds to `[-0.5, 0.5]` and we provide the estimator as additional argument to the `cost` function defined on top of this notebook. Once the optimization successfully finished, we can inspect the results."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e1e6ffea-0eb4-4fdd-995a-6a465326be57",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iteration Total nfev Cost Cost reduction Step norm Optimality \n",
" 0 1 5.2773e-05 9.16e-04 \n",
" 1 2 2.5544e-06 5.02e-05 4.13e-02 1.85e-04 \n",
" 2 3 6.8459e-07 1.87e-06 8.62e-03 1.62e-05 \n",
" 3 4 6.6847e-07 1.61e-08 8.77e-04 4.85e-07 \n",
" 4 5 6.6846e-07 1.54e-11 2.79e-05 4.18e-08 \n",
" 5 6 6.6846e-07 1.11e-13 2.41e-06 4.95e-10 \n",
"`gtol` termination condition is satisfied.\n",
"Function evaluations 6, initial cost 5.2773e-05, final cost 6.6846e-07, first-order optimality 4.95e-10.\n",
"CPU times: user 470 ms, sys: 1.99 ms, total: 472 ms\n",
"Wall time: 474 ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2500868/2373019003.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" p = PcSaftParameters.new_binary(parameters.pure_records, kij)\n"
]
}
],
"source": [
"%%time\n",
"initial_kij = [0.0]\n",
"fitted_kij = least_squares(cost, initial_kij, bounds=[-0.5, 0.5], args=(estimator,), verbose=2).x"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0f7e8b0f-3bb5-4038-807c-f44194a9c040",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_comparison(data, fitted_kij[0])"
]
},
{
"cell_type": "markdown",
"id": "f5c38595-ff70-43e5-bb03-f135cf223bdf",
"metadata": {},
"source": [
"# Using other target functions\n",
"\n",
"In the above example, we used `DataSet.binary_phase_diagram` which used the distance criterion as cost. In FeO$_{s}$, we provide two additional cost functions: difference in chemical potentials and difference in pressures."
]
},
{
"cell_type": "markdown",
"id": "e9707d8a-f5a0-457a-acc6-1886833477a5",
"metadata": {},
"source": [
"## Difference in chemical potentials given $T, p, x_1, y_1$\n",
"\n",
"The cost function of `DataSet.binary_vle_chemical_potential` is defined as difference of the chemical potentials of the vapor and liquid phase (for each substance) for given $\\{T, p, \\bar{y}\\}$ and $\\{T, p, \\bar{x}\\}$, respectively. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c068dd2d-b261-4fd3-87ef-11ef937ce989",
"metadata": {},
"outputs": [],
"source": [
"isotherms = [\n",
" DataSet.binary_vle_chemical_potential(\n",
" np.array([temperature]*len(isotherm)) *KELVIN, \n",
" isotherm.p.values * BAR, \n",
" isotherm.x1.values,\n",
" isotherm.y1.values,\n",
" ) \n",
" for temperature, isotherm in data.groupby('t')\n",
"]\n",
"estimator_mu = Estimator(isotherms, weights=[1]*3, losses=[Loss.linear()]*3)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8a349ab6-b51c-487d-ae48-ca34b252089d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iteration Total nfev Cost Cost reduction Step norm Optimality \n",
" 0 1 1.2674e-03 2.39e-02 \n",
" 1 2 7.9041e-05 1.19e-03 4.56e-02 2.07e-03 \n",
" 2 3 6.7858e-05 1.12e-05 4.85e-03 2.37e-05 \n",
" 3 4 6.7856e-05 1.49e-09 5.67e-05 9.92e-09 \n",
"`gtol` termination condition is satisfied.\n",
"Function evaluations 4, initial cost 1.2674e-03, final cost 6.7856e-05, first-order optimality 9.92e-09.\n",
"CPU times: user 21.9 ms, sys: 0 ns, total: 21.9 ms\n",
"Wall time: 21.1 ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2500868/2373019003.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" p = PcSaftParameters.new_binary(parameters.pure_records, kij)\n"
]
}
],
"source": [
"%%time\n",
"initial_kij = [0.0] # \n",
"fitted_kij_mu = least_squares(cost, initial_kij, bounds=[-0.5, 0.5], args=(estimator_mu,), verbose=2).x"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c04615cb-807c-4fa2-9dfe-279d18f24983",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_comparison(data, fitted_kij_mu[0])"
]
},
{
"cell_type": "markdown",
"id": "5dce7aeb-26f9-43fb-b49d-4bd7ae5e2a2c",
"metadata": {},
"source": [
"## Difference in pressure given $\\{T, p, x_i\\}$ or $\\{T, p, y_i\\}$\n",
"\n",
"`DataSet.binary_vle_pressure` uses temperatures, pressures, mole fractions and a flag for the phase (`Phase.Liquid` or `Phase.Vapor`) as input. Depending on the phase it calculates the dew or bubble pressure for each data point. The cost is the difference between the dew/bubble pressure and the provided (input) pressure.\n",
"\n",
"Since we have information about both phases in our experimental data, we can construct 6 `DataSet`s (3 isotherms for 2 phases). Alternatively, we could have splitted the data into two `DataSet`s, one for each phase."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "4e6f9a43-cd47-4996-81de-78147b8af713",
"metadata": {},
"outputs": [],
"source": [
"isotherms = [\n",
" [\n",
" DataSet.binary_vle_pressure(\n",
" np.array([temperature]*len(isotherm)) *KELVIN, \n",
" isotherm.p.values * BAR, \n",
" isotherm.x1.values,\n",
" Phase.Liquid\n",
" ),\n",
" DataSet.binary_vle_pressure(\n",
" np.array([temperature]*len(isotherm)) *KELVIN, \n",
" isotherm.p.values * BAR, \n",
" isotherm.y1.values,\n",
" Phase.Vapor\n",
" ),\n",
" ]\n",
" for temperature, isotherm in data.groupby('t')\n",
"]\n",
"\n",
"from itertools import chain\n",
"estimator_p = Estimator(list(chain.from_iterable(isotherms)), weights=[1]*6, losses=[Loss.linear()]*6)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "157a03af-7d98-479d-b44c-a79f6d737c2d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2500868/2373019003.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" p = PcSaftParameters.new_binary(parameters.pure_records, kij)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iteration Total nfev Cost Cost reduction Step norm Optimality \n",
" 0 1 2.2123e-04 3.56e-03 \n",
" 1 2 8.2940e-06 2.13e-04 5.17e-02 1.51e-04 \n",
" 2 3 8.1416e-06 1.52e-07 1.13e-03 1.64e-06 \n",
" 3 4 8.1416e-06 2.73e-11 1.54e-05 4.11e-08 \n",
" 4 5 8.1416e-06 1.27e-14 3.15e-07 1.09e-09 \n",
"`gtol` termination condition is satisfied.\n",
"Function evaluations 5, initial cost 2.2123e-04, final cost 8.1416e-06, first-order optimality 1.09e-09.\n",
"CPU times: user 875 ms, sys: 7.91 ms, total: 883 ms\n",
"Wall time: 880 ms\n"
]
}
],
"source": [
"%%time\n",
"initial_kij = [0.0] # \n",
"fitted_kij_p = least_squares(cost, initial_kij, bounds=[-0.5, 0.5], args=(estimator_p,), verbose=2).x"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8833ef48-c0aa-420c-8f2a-a7e920946d7f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_comparison(data, fitted_kij_p[0])"
]
},
{
"cell_type": "markdown",
"id": "1512c6fb-1727-48a3-a8e2-c724978c7662",
"metadata": {},
"source": [
"# Evaluating the results\n",
"\n",
"We end up with 3 slightly different values for $k_{ij}$.\n",
"Note that comparing the MARD values of the three different ways we created `DataSet`s is not very informative since each contains a different property that is predicted. E.g. the MARD of the chemical potential cost function contains the differences of chemical potentials of both phases and compares these values to zero. Finally, note that the MARD is not the metric that is minimized."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "815c48a9-702d-4dc8-8ee0-4cd4b27a92f2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adjusted k_ij parameters and MARDs\n",
"MARD (distance, k_ij = 0.05082) = 1.302 %\n",
"MARD (chem. pot., k_ij = 0.05048) = 6.181 %\n",
"MARD (pressure, k_ij = 0.05056) = 3.092 %\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2500868/2373019003.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" p = PcSaftParameters.new_binary(parameters.pure_records, kij)\n"
]
}
],
"source": [
"print(\"Adjusted k_ij parameters and MARDs\")\n",
"\n",
"mard_distance = estimator.mean_absolute_relative_difference(eos_from_kij(fitted_kij)) * 100\n",
"mard_mu = estimator_mu.mean_absolute_relative_difference(eos_from_kij(fitted_kij_mu)) * 100\n",
"mard_p = estimator_p.mean_absolute_relative_difference(eos_from_kij(fitted_kij_p)) * 100\n",
"print(f\"MARD (distance, k_ij = {fitted_kij[0]:>6.4}) = {mard_distance.mean():>8.4} %\")\n",
"print(f\"MARD (chem. pot., k_ij = {fitted_kij_mu[0]:>6.4}) = {mard_mu.mean():>8.4} %\")\n",
"print(f\"MARD (pressure, k_ij = {fitted_kij_p[0]:>6.4}) = {mard_p.mean():>8.4} %\")"
]
},
{
"cell_type": "markdown",
"id": "20d1f5b9-fc65-49c2-8eac-bbd07c662e70",
"metadata": {},
"source": [
"# Summary\n",
"\n",
"- The `Estimator` object in FeO$_\\text{s}$ allows the collection of `DataSet` objects for adjusting parameters.\n",
" - The `Estimator` takes a list of `DataSet` objects, weights, and `Loss` objects as inputs.\n",
" - It calculates the cost of a model where the cost function can be different for each `DataSet`.\n",
"- To work with `scipy`'s `least_squares` solver, a cost function is required.\n",
" - Two functions, `eos_from_kij` and `cost`, are built for this purpose.\n",
" - `eos_from_kij` constructs the parameters and equation of state for the current $k_{ij}$ value.\n",
" - `cost` calculates and returns the cost of the current model based on the parameters and estimator.\n",
"- An initial parameter guess and bounds are necessary for parameter adjustment.\n",
"- The `Estimator.mean_absolute_relative_difference` is probably is not the most instructive metric to judge the quality of the adjusted parameter. It's straight forward to calculate MARDs of composition or pressure/temperature with FeO$_\\text{s}$ which can be used to get an idea of the quality of the adjustment."
]
}
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| Unknown |
3D | feos-org/feos | examples/pcsaft/parameter_adjustment/adjust_non_polar_non_asssociating.ipynb | .ipynb | 86,914 | 435 | {
"cells": [
{
"cell_type": "markdown",
"id": "d2e9adfb-9962-40f7-8265-4861621e757e",
"metadata": {},
"source": [
"# Adjusting PC-SAFT parameters for a non-associating, non-polar substance"
]
},
{
"cell_type": "markdown",
"id": "4b54ad7f-d2ee-478c-8dd3-aebb2addec81",
"metadata": {},
"source": [
"## Goal\n",
"\n",
"- Read in experimental data for vapor pressure and liquid density of a pure substance.\n",
"- Use the `DataSet`, `Loss` and `Estimator` objects to store and work with experimental data.\n",
"- Define a `cost` function that will be used with `scipy.optimize.least_squares`.\n",
"- Run the optimization."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "805bdfdb-d397-45a3-8354-082a8d13e4c1",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.optimize import least_squares\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from si_units import BAR, KELVIN, KILOGRAM, METER\n",
"\n",
"from feos.eos import EquationOfState, PhaseDiagram\n",
"from feos.pcsaft import Identifier, PcSaftParameters, PcSaftRecord, PureRecord\n",
"from feos.eos.estimator import Estimator, Loss, DataSet\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 5)"
]
},
{
"cell_type": "markdown",
"id": "46c5a891-be8b-44d9-8f42-c84adf0cadb4",
"metadata": {},
"source": [
"# `DataSet` objects\n",
"\n",
"FeO$_\\text{s}$ provides a range of data structures that facilitate the adjustment of parameters. One such structure is the `DataSet`, which serves as a storage unit for experimental data. When working with a specific model, a `DataSet` enables the evaluation of a `cost` function. How this cost function is defined, depends on the property that you want to predict with your equation of state. In this notebook, we use vapor pressures and liquid densities and the cost functions are defined as the relative difference between the experimental data and the model prediction.\n",
"\n",
"A DataSet encompasses the following components:\n",
"\n",
"- The `target`: This refers to the experimental data associated with the property for which we intend to adjust parameters. In our case, these properties include vapor pressures and liquid densities. Additional options consist of dynamic properties used to adjust correlation parameters for entropy scaling or VLE (Vapor-Liquid Equilibrium) data of mixtures to fine-tune binary interaction parameters.\n",
"- Other properties: These are necessary for determining the thermodynamic conditions. For instance, temperature is required for vapor pressure calculations, while liquid density computations necessitate temperature and pressure.\n",
"- Each property available in FeO$_\\text{s}$ is accompanied by a corresponding DataSet constructor.\n",
"\n",
"Below, we load (pseudo) experimental data for hexane's vapor pressures and liquid densities. The data, taken from the NIST WebBook, incorporates simulated statistical uncertainties (e.g., measurement errors) by introducing Gaussian noise."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3c80cd76-4868-4b92-a654-287c7d65b875",
"metadata": {},
"outputs": [],
"source": [
"# read data from file into DataFrame\n",
"data_psat = pd.read_csv(\"data/hexane_vapor_pressure.csv\")\n",
"# construct a `DataSet` for vapor pressure\n",
"dataset_psat = DataSet.vapor_pressure(\n",
" target=data_psat[\"vapor_pressure / bar\"].values * BAR,\n",
" temperature=data_psat[\"temperature / K\"].values * KELVIN,\n",
" extrapolate=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "30f51ce6-a910-4e6f-93d2-7c02d070143e",
"metadata": {},
"source": [
"For `DataSet.vapor_pressure` we have to supply the experimental data for vapor pressures and temperatures as input.\n",
"\n",
"Optional parameters are\n",
"- `extrapolate` which allows an extrapolation for experimental data above the model's critical temperature (using an Antoine-like equation),\n",
"- `critical_temperature` when the experimental critical temperature is known,\n",
"- `max_iter` to set the maximum number of iterations for the VLE solver, and\n",
"- `verbosity` to print the solver iterations to the terminal."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "981aab8f-bd41-4939-a27a-02223c5a5ff0",
"metadata": {},
"outputs": [],
"source": [
"# read data from file into DataFrame\n",
"data_rhol = pd.read_csv(\"data/hexane_liquid_density.csv\")\n",
"# construct a `DataSet` for liquid density\n",
"dataset_rhol = DataSet.liquid_density(\n",
" target=data_rhol[\"density / kg/m3\"].values * KILOGRAM / METER**3,\n",
" temperature=data_rhol[\"temperature / K\"].values * KELVIN,\n",
" pressure=data_rhol[\"pressure / bar\"].values * BAR\n",
")"
]
},
{
"cell_type": "markdown",
"id": "53e754f2-6a6f-415b-87f7-8a35e0c7d61b",
"metadata": {},
"source": [
"For `DataSet.liquid_density` we have to supply the experimental data for liquid mass densities, temperatures and pressures as input."
]
},
{
"cell_type": "markdown",
"id": "65c565d7-30a4-446a-8e22-0c225edeb88f",
"metadata": {},
"source": [
"# `Estimator` and `Loss` objects"
]
},
{
"cell_type": "markdown",
"id": "89312d88-4eba-4649-af06-a11d673a88b9",
"metadata": {},
"source": [
"To collect the `DataSet` objects for the properties we wish to adjust parameters for, we can assemble them into an `Estimator` object. The `Estimator` requires the following inputs:\n",
"\n",
"- A list of `DataSet` objects.\n",
"- A corresponding list of `weights`, with each weight assigned to a specific `DataSet`.\n",
"- A list of `losses`, where each `DataSet` has an associated loss function.\n",
"\n",
"The `Estimator` facilitates the calculation of the model's `cost`, which involves the following steps:\n",
"\n",
"1. Iterating through all `DataSets`: For each `DataSet`, the relative difference between the model's prediction and the experimental data is evaluated.\n",
"2. Application of a `Loss` function: The available `Loss` functions in FeO$_\\text{s}$ are identical to those found in [`scipy.optimize.least_squares`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html). Each property can have a different `Loss`. If `Loss.linear()` is used, the optimization simplifies to a regular least-squares problem.\n",
"3. Normalization: The relative differences (with the applied loss functions) are divided by the number of data points in each corresponding `DataSet`.\n",
"4. Weighted cost calculation: The costs of each `DataSet` are weighted based on the provided (normalized) `weights`.\n",
"\n",
"The following example demonstrates the construction of an estimator using vapor pressure and liquid density data. We utilize `weights = [3, 2]` (normalized `weights = [0.6, 0.4]`) and a `Loss.huber(0.05)` loss function, which treats predictions above 5% linearly in the cost function instead of squaring them (outlier treatment).\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2e4c20fb-2415-43ac-87c4-68279dcb4e7d",
"metadata": {},
"outputs": [],
"source": [
"estimator = Estimator(\n",
" data=[dataset_psat, dataset_rhol],\n",
" weights=[3, 2],\n",
" losses=[Loss.huber(0.05), Loss.huber(0.05)]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c49e1f8a-93f5-4981-b7ea-410b651058c8",
"metadata": {},
"source": [
"# Defining a cost function\n",
"\n",
"When using `scipy`'s `least_squares` solver, it requires a function to calculate the cost or residuals. The first argument of this function must be the parameter vector that's being optimized. Therefore, it is necessary to define this function in Python before working with `least_squares`.\n",
"\n",
"To simplify the process, we create two functions:\n",
"\n",
"- `eos_from_parameters`: This function constructs the parameters and equation of state based on the current parameter vector. Since FeO$_\\text{s}$ does not allow mutation of an existing `EquationOfState` object, generating a new equation of state is necessary. We can use this function later to generate the equation of state for the final parameters.\n",
"- `cost`: Taking the parameters and estimator as inputs, this function builds the equation of state, calculates the cost of the current model, and returns the result.\n",
"\n",
"These functions aid in the seamless integration of the solver, allowing for the generation of the equation of state and the calculation of the model's cost."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a61f8330-e31c-4d97-943d-6abe9fad9f1b",
"metadata": {},
"outputs": [],
"source": [
"# define things that are constant during optimization\n",
"identifier = Identifier(\n",
" cas=\"110-54-3\", \n",
" name=\"hexane\", \n",
" iupac_name=\"hexane\", \n",
" smiles=\"CCCCCC\", \n",
" inchi=\"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\", \n",
" formula=\"C6H14\"\n",
")\n",
"molarweight = 86.177 # g / mol\n",
"\n",
"def eos_from_parameters(p):\n",
" \"\"\"Returns equation of state (PC-SAFT) for current parameters.\"\"\"\n",
" global identifier, molarweight\n",
" m, sigma, epsilon_k = p\n",
" model_record = PcSaftRecord(m, sigma, epsilon_k)\n",
" pure_record = PureRecord(identifier, molarweight, model_record)\n",
" parameters = PcSaftParameters.new_pure(pure_record)\n",
" return EquationOfState.pcsaft(parameters)\n",
"\n",
"def cost(p, estimator):\n",
" \"\"\"Calculates cost function for current parameters.\"\"\"\n",
" return estimator.cost(eos_from_parameters(p))"
]
},
{
"cell_type": "markdown",
"id": "b3d9f5f9-77ff-4523-a3ec-d7a3c05b829a",
"metadata": {},
"source": [
"# Adjust parameters\n",
"\n",
"To begin parameter adjustment, there are two additional requirements:\n",
"\n",
"1. An initial guess for the parameters.\n",
"2. Bounds for the parameters.\n",
"\n",
"It is crucial to note that trying multiple combinations of initial parameters is recommended to avoid getting stuck in a local minimum.\n",
"\n",
"Once these prerequisites are fulfilled, we can invoke `least_squares` with `args=(estimator, )` as an argument for the `cost` function. Please note that the tuple syntax is necessary in this case."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1309b82d-dd20-4e9c-987c-03167cf4a7b8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iteration Total nfev Cost Cost reduction Step norm Optimality \n",
" 0 1 9.6023e-05 1.11e-04 \n",
" 1 2 4.1622e-05 5.44e-05 5.08e+01 1.34e-03 \n",
" 2 3 6.2580e-06 3.54e-05 3.32e+00 1.84e-04 \n",
" 3 4 3.4028e-06 2.86e-06 1.40e+00 6.00e-06 \n",
" 4 5 3.2495e-06 1.53e-07 4.42e+00 5.86e-05 \n",
" 5 6 3.2206e-06 2.88e-08 1.05e+00 1.94e-05 \n",
" 6 7 3.2167e-06 3.91e-09 4.65e-01 5.20e-06 \n",
" 7 8 3.2164e-06 3.01e-10 1.33e-01 1.28e-06 \n",
" 8 9 3.2164e-06 1.82e-11 3.21e-02 3.01e-07 \n",
" 9 10 3.2164e-06 1.02e-12 7.61e-03 6.80e-08 \n",
" 10 11 3.2164e-06 5.45e-14 1.78e-03 1.54e-08 \n",
" 11 12 3.2164e-06 2.93e-15 4.12e-04 3.49e-09 \n",
"`gtol` termination condition is satisfied.\n",
"Function evaluations 12, initial cost 9.6023e-05, final cost 3.2164e-06, first-order optimality 3.49e-09.\n",
"CPU times: user 1.25 s, sys: 2.23 ms, total: 1.25 s\n",
"Wall time: 1.26 s\n"
]
}
],
"source": [
"%%time\n",
"initial_parameters = [3.0, 3.0, 300.0] # m, sigma, epsilon_k\n",
"bounds = ([2.0, 2.0, 150.0], [8.0, 5.0, 500.0]) # ([lower bounds], [upper bounds])\n",
"fitted_parameters = least_squares(cost, initial_parameters, bounds=bounds, args=(estimator,), verbose=2).x"
]
},
{
"cell_type": "markdown",
"id": "c7810262-d26d-4a5a-8d3b-5b1d217a9eb5",
"metadata": {},
"source": [
"To provide statistics during the adjustment process, we can utilize the `verbose=2` option, which prints relevant information. However, our primary interest lies in obtaining the optimal parameters. These parameters can be accessed from the `x` field of the `OptimizeResult` object generated by the `least_squares` function.\n",
"\n",
"Once the adjustment is complete, we can investigate the results. A quick and straightforward method for analyzing the optimization results is to use the `Estimator.mean_absolute_relative_difference` method. This method returns the mean absolute relative difference (MARD) for each `DataSet`, without applying losses or weights. It is important to exercise caution when dealing with `NaN` values, as any predictions resulting in `NaN` are filtered out and thus not immediately visible in the reported values.\n",
"\n",
"It's essential to note that the resulting MARD **does not include** an evaluation of the loss functions or weights. Therefore, it does not equal the property that is minimized in the optimization process."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b266f557-40f5-43d6-82eb-f5c393a3b464",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adjusted parameters\n",
"m = 3.024\n",
"sigma = 3.811 A\n",
"epsilon_k = 238.333 K\n",
"\n",
"MARD (including outliers)\n",
"p_sat = 6.3 %\n",
"rho_l = 0.93 %\n"
]
}
],
"source": [
"print(\"Adjusted parameters\")\n",
"print(\"m = {:>8.4}\".format(fitted_parameters[0]))\n",
"print(\"sigma = {:>8.4} A\".format(fitted_parameters[1]))\n",
"print(\"epsilon_k = {:>8.6} K\".format(fitted_parameters[2]))\n",
"print(\"\")\n",
"\n",
"mard = estimator.mean_absolute_relative_difference(eos_from_parameters(fitted_parameters)) * 100\n",
"print(\"MARD (including outliers)\")\n",
"print(\"p_sat = {:<4.2} %\".format(mard[0]))\n",
"print(\"rho_l = {:<4.2} %\".format(mard[1]))"
]
},
{
"cell_type": "markdown",
"id": "342179f3-ca31-4ecf-8fa6-b58680ffd697",
"metadata": {},
"source": [
"# Plot resuts\n",
"\n",
"We can now use our results and calcuate the phase diagram and have a visual inspection of our parameters."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9a75d38a-8f73-402a-9558-e5f3582e982d",
"metadata": {},
"outputs": [],
"source": [
"phase_diagram = PhaseDiagram.pure(eos_from_parameters(fitted_parameters), 250.0 * KELVIN, 201)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f8444e44-ca81-42f5-be85-543640490d89",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1080x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
"ax[0].set_title(\"saturation pressure of hexane\")\n",
"\n",
"sns.lineplot(\n",
" y=phase_diagram.vapor.pressure / BAR,\n",
" x=1.0/phase_diagram.vapor.temperature * KELVIN,\n",
" ax=ax[0]\n",
")\n",
"sns.scatterplot(\n",
" x=1.0 / data_psat[\"temperature / K\"], \n",
" y=data_psat[\"vapor_pressure / bar\"], \n",
" ax=ax[0],\n",
" color=colors[1]\n",
")\n",
"ax[0].set_yscale('log')\n",
"ax[0].set_xlabel(r'$\\frac{1}{T}$ / K$^{-1}$');\n",
"ax[0].set_ylabel(r'$p$ / bar');\n",
"#ax[0].set_xlim()\n",
"#ax[0].set_ylim()\n",
"\n",
"ax[1].set_title(r\"$T$-$\\rho$-diagram of hexane\")\n",
"sns.lineplot(\n",
" y=phase_diagram.vapor.temperature / KELVIN,\n",
" x=phase_diagram.vapor.mass_density / KILOGRAM * METER**3,\n",
" ax=ax[1],\n",
" color=colors[0]\n",
")\n",
"sns.lineplot(\n",
" y=phase_diagram.liquid.temperature / KELVIN,\n",
" x=phase_diagram.liquid.mass_density / KILOGRAM * METER**3,\n",
" ax=ax[1],\n",
" color=colors[0]\n",
")\n",
"\n",
"ax[1].set_ylabel(r'$T$ / K');\n",
"ax[1].set_xlabel(r'$\\rho$ / kg m$^{-3}$');"
]
},
{
"cell_type": "markdown",
"id": "96c715a3-dd27-4afd-a146-c5c516e43ce7",
"metadata": {},
"source": [
"# Summary\n",
"\n",
"- The `Estimator` object in FeO$_\\text{s}$ allows the collection of `DataSet` objects for adjusting parameters.\n",
" - The `Estimator` takes a list of `DataSet` objects, weights, and `Loss` objects as inputs.\n",
" - In our example, it calculates the cost of a model by evaluating the relative difference between the model's prediction and experimental data in each `DataSet`.\n",
"- To work with `scipy`'s `least_squares` solver, a cost function is required.\n",
" - Two functions, `eos_from_parameters` and `cost`, are built for this purpose.\n",
" - `eos_from_parameters` constructs the parameters and equation of state for the current parameter vector.\n",
" - `cost` calculates and returns the cost of the current model based on the parameters and estimator.\n",
"- Initial parameter guesses and parameter bounds are necessary for parameter adjustment.\n",
" - Checking multiple combinations of initial parameters is recommended to avoid local minima (not shown in this notebook).\n",
"- In this example, the `Estimator.mean_absolute_relative_difference` method provides the mean absolute relative difference (MARD) for each `DataSet`, without applying losses or weights.\n",
" - The resulting MARD does not include the effects of loss functions or weights and does not represent the minimized property."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | examples/saftvrmie/validate_lafitte.ipynb | .ipynb | 552,836 | 1,012 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6150a26e-767d-4735-ba5a-ee097684c917",
"metadata": {},
"outputs": [],
"source": [
"from feos import *\n",
"from feos.parameters import *\n",
"\n",
"import si_units as si\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"colors = sns.color_palette(\"Dark2\", 12)\n",
"sns.set_context(\"notebook\")\n",
"sns.set_style(\"ticks\")\n",
"sns.set_palette(\"Dark2\")"
]
},
{
"cell_type": "markdown",
"id": "f0309d4e-cc9b-460c-b9fa-96d3d005664e",
"metadata": {},
"source": [
"# Validation of SAFT-VR Mie implementation\n",
"\n",
"In this notebook, we reproduce some of the figures presented in the original [publication of Lafitte et al. (2013)](https://doi.org/10.1063/1.4819786)."
]
},
{
"cell_type": "markdown",
"id": "25674fe2-eb5c-474d-9943-81bc4a5615aa",
"metadata": {},
"source": [
"# Phase diagrams of LJ and Mie fluids without chain contribution"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "315609a2-a2c6-4ba4-bca0-b0ed1b0d4232",
"metadata": {},
"outputs": [],
"source": [
"exponents6 = [\n",
" (8, 6),\n",
" (10, 6),\n",
" (12, 6),\n",
" (15, 6),\n",
" (20, 6),\n",
" (36, 6)\n",
"]\n",
"\n",
"other_exponents = [\n",
" (12, 6),\n",
" (14, 7),\n",
" (18, 9),\n",
" (24, 12)\n",
"]\n",
"\n",
"def sample_exponents(exponents, npoints):\n",
" to_reduced_density = si.MOL / si.METER**3 * si.NAV * si.ANGSTROM**3\n",
" to_reduced_pressure = si.PASCAL * si.ANGSTROM**3 / si.KB / si.KELVIN\n",
" \n",
" result = {}\n",
" for (lr, la) in exponents:\n",
" record = PureRecord(Identifier(), 0, m=1.0, sigma=1.0, epsilon_k=1.0, lr=lr, la=la) \n",
" eos = EquationOfState.saftvrmie(Parameters.new_pure(record))\n",
" cp = State.critical_point(eos)\n",
" dia = PhaseDiagram.pure(eos, 0.3 * cp.temperature, npoints)\n",
" df = pd.DataFrame(dia.to_dict(Contributions.Residual))\n",
" df['density liquid'] *= to_reduced_density\n",
" df['density vapor'] *= to_reduced_density\n",
" df['pressure'] *= to_reduced_pressure\n",
" result.update({(lr, la): df})\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cd8fdba3-837c-48d2-b7c5-cb9e4cafbc74",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 214 ms, sys: 3.95 ms, total: 218 ms\n",
"Wall time: 217 ms\n"
]
}
],
"source": [
"%%time\n",
"diagrams6 = sample_exponents(exponents6, 250)\n",
"other_diagrams= sample_exponents(other_exponents, 250)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4e2f7f34-38ab-47d2-8273-862a264cbe72",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(2, 1, figsize=(6, 10))\n",
"\n",
"for i, (e, data) in enumerate(diagrams6.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax[0], x='density vapor', y='temperature', color=color, label=f\"{e[0]}-{e[1]}\")\n",
" sns.lineplot(data=data, ax=ax[0], x='density liquid', y='temperature', color=color)\n",
"\n",
"ax[0].set_ylim(0.5, 2.0)\n",
"ax[0].set_xlim(0., 1.)\n",
"ax[0].legend(frameon=False);\n",
"ax[0].set_xlabel(r\"$\\rho^*$\")\n",
"ax[0].set_ylabel(r\"$T^*$\")\n",
"\n",
"for i, (e, data) in enumerate(other_diagrams.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax[1], x='density vapor', y='temperature', color=color, label=f\"{e[0]}-{e[1]}\")\n",
" sns.lineplot(data=data, ax=ax[1], x='density liquid', y='temperature', color=color)\n",
"\n",
"ax[1].set_ylim(0.4, 1.4)\n",
"ax[1].set_xlim(0., 1.)\n",
"ax[1].set_xlabel(r\"$\\rho^*$\")\n",
"ax[1].set_ylabel(r\"$T^*$\")\n",
"ax[1].legend(frameon=False);"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bb96f7f6-3906-4eee-a20c-176116e94bc3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"for i, (e, data) in enumerate(diagrams6.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax, y='pressure', x='temperature', color=color, label=f\"{e[0]}-{e[1]}\")\n",
"\n",
"ax.set_ylim(0.0, 0.2)\n",
"ax.set_xlim(0.4, 1.6)\n",
"ax.legend(frameon=False)\n",
"ax.set_ylabel(r\"$p^*$\")\n",
"ax.set_xlabel(r\"$T^*$\");"
]
},
{
"cell_type": "markdown",
"id": "292f8998-40f9-41d0-87b6-04c1d2911c72",
"metadata": {},
"source": [
"# Lennard-Jones chains"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cfe21a10-e0a2-4528-8661-437af1b7a7dd",
"metadata": {},
"outputs": [],
"source": [
"def sample_chains(inputs, npoints):\n",
" \"\"\"Inputs: List[(m, lr, la)]\"\"\"\n",
" to_reduced_density = si.MOL / si.METER**3 * si.NAV * si.ANGSTROM**3\n",
" to_reduced_pressure = si.PASCAL * si.ANGSTROM**3 / si.KB / si.KELVIN\n",
" \n",
" result = {}\n",
" for (m, lr, la) in inputs:\n",
" record = PureRecord(Identifier(), 0, m=m, sigma=1.0, epsilon_k=1.0, lr=lr, la=la) \n",
" eos = EquationOfState.saftvrmie(Parameters.new_pure(record))\n",
" cp = State.critical_point(eos)\n",
" try:\n",
" dia = PhaseDiagram.pure(eos, 0.4 * cp.temperature, npoints)\n",
" df = pd.DataFrame(dia.to_dict(Contributions.Residual))\n",
" df['density liquid'] *= to_reduced_density * m\n",
" df['density vapor'] *= to_reduced_density * m\n",
" df['pressure'] *= to_reduced_pressure\n",
" except:\n",
" dia = PhaseDiagram.pure(eos, 0.8 * cp.temperature, npoints)\n",
" df = pd.DataFrame(dia.to_dict(Contributions.Residual))\n",
" df['density liquid'] *= to_reduced_density * m\n",
" df['density vapor'] *= to_reduced_density * m\n",
" df['pressure'] *= to_reduced_pressure\n",
" result.update({(m, lr, la): df})\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5702c6e7-0b80-4076-9517-7c21de635e58",
"metadata": {},
"outputs": [],
"source": [
"lj_chains_input = [\n",
" (1, 12, 6),\n",
" (2, 12, 6),\n",
" (4, 12, 6),\n",
" (8, 12, 6)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3f4ebcf7-2de0-482a-9a61-46c9b4186047",
"metadata": {},
"outputs": [],
"source": [
"lj_chains = sample_chains(lj_chains_input, 250)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5e17281f-599d-47ca-b2e5-59d57fca2ccc",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"\n",
"for i, (e, data) in enumerate(lj_chains.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax, x='density vapor', y='temperature', color=color, \n",
" label=f\"m: {e[0]} ({e[1]}-{e[2]})\")\n",
" sns.lineplot(data=data, ax=ax, x='density liquid', y='temperature', color=color)\n",
"\n",
"ax.set_ylim(1.0, 3.5)\n",
"ax.set_xlim(0., 1.)\n",
"ax.legend(frameon=False);\n",
"ax.set_xlabel(r\"$\\rho_s^*$\")\n",
"ax.set_ylabel(r\"$T^*$\");"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "07134921-bba6-416e-9b95-1f5a3a6075c2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"for i, (e, data) in enumerate(lj_chains.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax, y='pressure', x='temperature', color=color, label=f\"m: {e[0]} ({e[1]}-{e[2]})\")\n",
"\n",
"ax.set_ylim(0.0, 0.16)\n",
"ax.set_xlim(0.5, 3.5)\n",
"ax.legend(frameon=False)\n",
"ax.set_ylabel(r\"$p^*$\")\n",
"ax.set_xlabel(r\"$T^*$\");"
]
},
{
"cell_type": "markdown",
"id": "4bda8be1-12a4-4e63-a0de-15e562aeec7d",
"metadata": {},
"source": [
"# Long LJ chains"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "19456823-3b5a-4e2d-8ddf-46f6fb13edd4",
"metadata": {},
"outputs": [],
"source": [
"lj_long_chains_input = [\n",
" (1, 12, 6),\n",
" (2, 12, 6),\n",
" (4, 12, 6),\n",
" (8, 12, 6),\n",
" (20, 12, 6),\n",
" (50, 12, 6),\n",
" (100, 12, 6)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3b105a00-2c89-4703-abb4-f38e8be7d818",
"metadata": {},
"outputs": [],
"source": [
"lj_long_chains = sample_chains(lj_long_chains_input, 250)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4b468940-c7ec-439a-9c90-623c406214f3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"\n",
"for i, (e, data) in enumerate(lj_long_chains.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax, x='density vapor', y='temperature', color=color, \n",
" label=f\"m: {e[0]} ({e[1]}-{e[2]})\")\n",
" sns.lineplot(data=data, ax=ax, x='density liquid', y='temperature', color=color)\n",
"\n",
"ax.set_ylim(1.0, 4.5)\n",
"ax.set_xlim(0., 0.8)\n",
"ax.legend(frameon=False);\n",
"ax.set_xlabel(r\"$\\rho_s^*$\")\n",
"ax.set_ylabel(r\"$T^*$\");"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c5b5cfd0-8b9a-45b3-88f4-040a4fa9785c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"for i, (e, data) in enumerate(lj_long_chains.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax, y='pressure', x=1/data.temperature, color=color, label=f\"m: {e[0]} ({e[1]}-{e[2]})\")\n",
"\n",
"#ax.set_ylim(0.0, 0.16)\n",
"ax.set_xlim(0.2, 1.0)\n",
"ax.legend(frameon=False)\n",
"ax.set_yscale(\"log\")\n",
"ax.set_ylabel(r\"$\\ln p^*$\")\n",
"ax.set_xlabel(r\"$1 / T^*$\");"
]
},
{
"cell_type": "markdown",
"id": "0b86182c-bdff-4568-9c68-33cee035f69b",
"metadata": {},
"source": [
"# Associating LJ sites ($r^c_{ab} = 0.2 \\sigma$, $r^d_{ab} = 0.4 \\sigma$)\n",
"\n",
"- Note: $r^d_{ab} = 0.4 \\sigma$ is set as constant that cannot be changed."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "aa55d6f2-11b3-472a-8e74-1cab14481306",
"metadata": {},
"outputs": [],
"source": [
"def sample_association(inputs, npoints):\n",
" \"\"\"Inputs: List[(lr, la, eps_k_ab)]\"\"\"\n",
" to_reduced_density = si.MOL / si.METER**3 * si.NAV * si.ANGSTROM**3\n",
" to_reduced_pressure = si.PASCAL * si.ANGSTROM**3 / si.KB / si.KELVIN\n",
" \n",
" result = {}\n",
" for (lr, la, eps_k_ab) in inputs:\n",
" record = PureRecord(Identifier(), 0, m=1.0, sigma=1.0, epsilon_k=1.0, lr=lr, la=la, association_sites=[{\"rc_ab\": 0.2, \"epsilon_k_ab\": eps_k_ab, \"na\": 1.0, \"nb\": 1.0}]) \n",
" eos = EquationOfState.saftvrmie(Parameters.new_pure(record))\n",
" cp = State.critical_point(eos)\n",
" dia = PhaseDiagram.pure(eos, 0.6 * cp.temperature, npoints)\n",
" df = pd.DataFrame(dia.to_dict(Contributions.Residual))\n",
" df['density liquid'] *= to_reduced_density\n",
" df['density vapor'] *= to_reduced_density\n",
" df['pressure'] *= to_reduced_pressure\n",
" result.update({(lr, la, eps_k_ab): df})\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "af1c8e61-5a36-4747-9963-8e550934bca0",
"metadata": {},
"outputs": [],
"source": [
"assoc_inputs = [\n",
" (12, 6, 10),\n",
" (12, 6, 20),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "cac78d6c-9981-40cb-98e6-4f82ef01b59c",
"metadata": {},
"outputs": [],
"source": [
"lj_assoc = sample_association(assoc_inputs, 250)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ada2deed-eb86-45ea-b32c-158343f65906",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<>:6: SyntaxWarning: invalid escape sequence '\\e'\n",
"<>:6: SyntaxWarning: invalid escape sequence '\\e'\n",
"/tmp/ipykernel_996906/2770306470.py:6: SyntaxWarning: invalid escape sequence '\\e'\n",
" label=f\"$\\epsilon$: {e[2]} ({e[0]}-{e[1]})\")\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=(5,5))\n",
"\n",
"for i, (e, data) in enumerate(lj_assoc.items()):\n",
" color = colors[i]\n",
" sns.lineplot(data=data, ax=ax, x='density vapor', y='temperature', color=color, \n",
" label=f\"$\\epsilon$: {e[2]} ({e[0]}-{e[1]})\")\n",
" sns.lineplot(data=data, ax=ax, x='density liquid', y='temperature', color=color)\n",
"\n",
"ax.set_ylim(1.0, 1.8)\n",
"ax.set_xlim(0., 0.8)\n",
"ax.legend(frameon=False);\n",
"ax.set_xlabel(r\"$\\rho_s^*$\")\n",
"ax.set_ylabel(r\"$T^*$\");"
]
},
{
"cell_type": "markdown",
"id": "fc248c3c-40e4-4ae6-8744-4eecae1fd8e8",
"metadata": {},
"source": [
"# Compare critical data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "2a708b74-70bf-45fe-91bc-0c395f3f4506",
"metadata": {},
"outputs": [],
"source": [
"critical_data = pd.read_csv(\"lafitte_critical_data.csv\")\n",
"path = \"../../parameters/saftvrmie/lafitte2013.json\"\n",
"\n",
"result = [] \n",
"for _, row in critical_data.iterrows():\n",
" p = Parameters.from_json([row.substance], path)\n",
" eos = EquationOfState.saftvrmie(p)\n",
" cp = State.critical_point(eos)\n",
" try:\n",
" cp.is_stable()\n",
" except:\n",
" cp = State.critical_point(eos, initial_temperature=600*si.KELVIN)\n",
" cp.is_stable()\n",
" tc = row.tc * si.KELVIN\n",
" pc = row.pc * si.MEGA * si.PASCAL\n",
" rhoc = row.rhoc * si.KILOGRAM / si.METER**3\n",
" tce = row.tc_exp * si.KELVIN\n",
" pce = row.pc_exp * si.MEGA * si.PASCAL\n",
" rhoce = row.rhoc_exp * si.KILOGRAM / si.METER**3\n",
" result.append(\n",
" {\n",
" \"substance\": row.substance,\n",
" r\"$\\Delta T_c$ / %\": (tc - cp.temperature) / tc * 100,\n",
" r\"$\\Delta p_c$ / %\": (pc - cp.pressure()) / pc * 100,\n",
" r\"$\\Delta \\rho_c$ / %\": (rhoc - cp.mass_density()) / rhoc * 100,\n",
" #r\"$\\Delta T_c^\\text{exp}$ / %\": (tce - cp.temperature) / tc * 100,\n",
" #r\"$\\Delta p_c^\\text{exp}$ / %\": (pce - cp.pressure()) / pc * 100,\n",
" #r\"$\\Delta \\rho_c^\\text{exp}$ / %\": (rhoce - cp.mass_density()) / rhoc * 100,\n",
" }\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "56134983-54f3-4cea-b48b-85f0cd257d92",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>substance</th>\n",
" <th>$\\Delta T_c$ / %</th>\n",
" <th>$\\Delta p_c$ / %</th>\n",
" <th>$\\Delta \\rho_c$ / %</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>methane</td>\n",
" <td>0.074235</td>\n",
" <td>0.349624</td>\n",
" <td>0.408159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ethane</td>\n",
" <td>0.063685</td>\n",
" <td>0.303040</td>\n",
" <td>0.262340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>propane</td>\n",
" <td>0.054626</td>\n",
" <td>0.218771</td>\n",
" <td>0.244391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>n-butane</td>\n",
" <td>0.049322</td>\n",
" <td>0.322633</td>\n",
" <td>0.376631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>pentane</td>\n",
" <td>0.041264</td>\n",
" <td>0.372420</td>\n",
" <td>0.452599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>hexane</td>\n",
" <td>0.032569</td>\n",
" <td>0.425717</td>\n",
" <td>0.466502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>heptane</td>\n",
" <td>0.021799</td>\n",
" <td>0.236802</td>\n",
" <td>0.516342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>octane</td>\n",
" <td>0.013382</td>\n",
" <td>0.396097</td>\n",
" <td>0.520532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>nonane</td>\n",
" <td>0.009823</td>\n",
" <td>0.204764</td>\n",
" <td>0.479280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>decane</td>\n",
" <td>0.006019</td>\n",
" <td>0.236239</td>\n",
" <td>0.470530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>dodecane</td>\n",
" <td>0.001322</td>\n",
" <td>0.071482</td>\n",
" <td>0.088943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>pentadecane</td>\n",
" <td>0.001082</td>\n",
" <td>0.225868</td>\n",
" <td>0.086090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>eicosane</td>\n",
" <td>0.000570</td>\n",
" <td>-0.160432</td>\n",
" <td>0.085698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>methanol</td>\n",
" <td>0.183407</td>\n",
" <td>0.027326</td>\n",
" <td>0.047228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>ethanol</td>\n",
" <td>0.000055</td>\n",
" <td>0.051359</td>\n",
" <td>0.056039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1-propanol</td>\n",
" <td>0.001504</td>\n",
" <td>0.014485</td>\n",
" <td>0.063254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1-butanol</td>\n",
" <td>0.001073</td>\n",
" <td>-0.006879</td>\n",
" <td>0.065317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>tetrafluoromethane [r14]</td>\n",
" <td>-0.001183</td>\n",
" <td>0.095950</td>\n",
" <td>0.009225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>hexafluoroethane [r116]</td>\n",
" <td>0.001738</td>\n",
" <td>-0.120370</td>\n",
" <td>0.017759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>perfluoropropane [r218]</td>\n",
" <td>0.000942</td>\n",
" <td>-0.094440</td>\n",
" <td>0.018019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>perfluorobutane</td>\n",
" <td>0.001199</td>\n",
" <td>0.110424</td>\n",
" <td>0.020914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>perfluoropentane</td>\n",
" <td>0.000991</td>\n",
" <td>0.002725</td>\n",
" <td>0.021134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>fluorine</td>\n",
" <td>0.002474</td>\n",
" <td>-0.017037</td>\n",
" <td>0.000084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>carbon dioxide</td>\n",
" <td>0.001593</td>\n",
" <td>-0.034393</td>\n",
" <td>0.046387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>benzene</td>\n",
" <td>0.001097</td>\n",
" <td>-0.076523</td>\n",
" <td>0.085332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>toluene</td>\n",
" <td>0.001027</td>\n",
" <td>0.071343</td>\n",
" <td>0.083596</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" substance $\\Delta T_c$ / % $\\Delta p_c$ / % \\\n",
"0 methane 0.074235 0.349624 \n",
"1 ethane 0.063685 0.303040 \n",
"2 propane 0.054626 0.218771 \n",
"3 n-butane 0.049322 0.322633 \n",
"4 pentane 0.041264 0.372420 \n",
"5 hexane 0.032569 0.425717 \n",
"6 heptane 0.021799 0.236802 \n",
"7 octane 0.013382 0.396097 \n",
"8 nonane 0.009823 0.204764 \n",
"9 decane 0.006019 0.236239 \n",
"10 dodecane 0.001322 0.071482 \n",
"11 pentadecane 0.001082 0.225868 \n",
"12 eicosane 0.000570 -0.160432 \n",
"13 methanol 0.183407 0.027326 \n",
"14 ethanol 0.000055 0.051359 \n",
"15 1-propanol 0.001504 0.014485 \n",
"16 1-butanol 0.001073 -0.006879 \n",
"17 tetrafluoromethane [r14] -0.001183 0.095950 \n",
"18 hexafluoroethane [r116] 0.001738 -0.120370 \n",
"19 perfluoropropane [r218] 0.000942 -0.094440 \n",
"20 perfluorobutane 0.001199 0.110424 \n",
"21 perfluoropentane 0.000991 0.002725 \n",
"22 fluorine 0.002474 -0.017037 \n",
"23 carbon dioxide 0.001593 -0.034393 \n",
"24 benzene 0.001097 -0.076523 \n",
"25 toluene 0.001027 0.071343 \n",
"\n",
" $\\Delta \\rho_c$ / % \n",
"0 0.408159 \n",
"1 0.262340 \n",
"2 0.244391 \n",
"3 0.376631 \n",
"4 0.452599 \n",
"5 0.466502 \n",
"6 0.516342 \n",
"7 0.520532 \n",
"8 0.479280 \n",
"9 0.470530 \n",
"10 0.088943 \n",
"11 0.086090 \n",
"12 0.085698 \n",
"13 0.047228 \n",
"14 0.056039 \n",
"15 0.063254 \n",
"16 0.065317 \n",
"17 0.009225 \n",
"18 0.017759 \n",
"19 0.018019 \n",
"20 0.020914 \n",
"21 0.021134 \n",
"22 0.000084 \n",
"23 0.046387 \n",
"24 0.085332 \n",
"25 0.083596 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(result)"
]
},
{
"cell_type": "markdown",
"id": "fcb01a9c-0c60-41c9-8493-50ae890f2c77",
"metadata": {},
"source": [
"# Binary Phase Diagrams of Ethane + n-Decane"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7aa25342",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PureRecord"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p.pure_records[0].__class__"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "e96a4fd1-4238-423b-b8ed-86ce4e5b726d",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|lr|la|\n",
"|-|-|-|-|-|-|-|\n",
"|ethane|30.047|1.4373|3.7257|206.12|12.4|6.0|\n",
"|decane|142.172|2.9976|4.589|400.79|18.885|6.0|\n",
"\n",
"|component 1|component 2|k_ij|\n",
"|-|-|-|\n",
"|ethane|decane|-0.0222|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-84-0\",\n",
" \"name\": \"ethane\",\n",
" \"iupac_name\": \"ethane\",\n",
" \"smiles\": \"CC\",\n",
" \"inchi\": \"InChI=1S/C2H6/c1-2/h1-2H3\"\n",
" },\n",
" \"molarweight\": 30.047,\n",
" \"m\": 1.4373,\n",
" \"sigma\": 3.7257,\n",
" \"epsilon_k\": 206.12,\n",
" \"lr\": 12.4,\n",
" \"la\": 6.0\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"124-18-5\",\n",
" \"name\": \"decane\",\n",
" \"iupac_name\": \"decane\",\n",
" \"smiles\": \"CCCCCCCCCC\",\n",
" \"inchi\": \"InChI=1S/C10H22/c1-3-5-7-9-10-8-6-4-2/h3-10H2,1-2H3\"\n",
" },\n",
" \"molarweight\": 142.172,\n",
" \"m\": 2.9976,\n",
" \"sigma\": 4.589,\n",
" \"epsilon_k\": 400.79,\n",
" \"lr\": 18.885,\n",
" \"la\": 6.0\n",
" }\n",
"]\n",
"\n",
"[\n",
" {\n",
" \"id1\": 0,\n",
" \"id2\": 1,\n",
" \"k_ij\": -0.0222\n",
" }\n",
"]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = \"../../parameters/saftvrmie/lafitte2013.json\"\n",
"p = Parameters.from_json([\"ethane\", \"decane\"], path)\n",
"p = Parameters.new_binary(p.pure_records, k_ij=-0.0222)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "c2ccaba3-88ec-4c83-990c-170fe1951e5c",
"metadata": {},
"outputs": [],
"source": [
"eos = EquationOfState.saftvrmie(p)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "d8ac99d0-e4ce-4775-8067-025b72032806",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i, t in enumerate([444.15*si.KELVIN, 511.15*si.KELVIN]):\n",
" color = colors[i]\n",
" dia = PhaseDiagram.binary_vle(eos, t, npoints=50)\n",
" df = pd.DataFrame(dia.to_dict(Contributions.Residual))\n",
" df.pressure = df.pressure * (si.PASCAL / si.MEGA / si.PASCAL)\n",
" sns.lineplot(data=df, x=\"x0\", y=\"pressure\", sort=False, color=color)\n",
" sns.lineplot(data=df, x=\"y0\", y=\"pressure\", sort=False, color=color, label=f\"T={t}\")\n",
"\n",
"plt.title(\"ethane + n-decane, $k_{ij} = -0.0222$\")\n",
"plt.legend(frameon=False)\n",
"plt.xlabel(r\"$x_\\mathrm{C_2H_6}$\")\n",
"plt.ylabel(\"$P$ / MPa\")\n",
"plt.xlim(0, 1)\n",
"plt.ylim(0, 20);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | docs/rust_api.md | .md | 236 | 9 | # API
The documentation for the Rust crates is hosted on `docs.rs`.
- [`feos-core`](https://docs.rs/feos-core/latest/feos_core/)
- [`feos-dft`](https://docs.rs/feos-dft/latest/feos_dft/)
- [`feos`](https://docs.rs/feos/latest/feos/)
| Markdown |
3D | feos-org/feos | docs/installation.md | .md | 1,398 | 44 | # Installation
`````{tab-set}
````{tab-item} Python
{math}`\text{FeO}_\text{s}` is available on `PyPI`. You can install it using `pip`:
```
pip install feos
```
If you have a Rust compiler installed and want to have the latest development version (github `main` branch), you can build and install the python wheel via
```
pip install git+https://github.com/feos-org/feos
```
````
````{tab-item} Rust
In Rust, the `feos-dft` crate and each equation of state or DFT functional is a separate, optional module behind a feature flag.
To use {math}`\text{FeO}_\text{s}` with all models (including Python bindings), use the `all_models` feature:
```toml
[dependencies]
feos-core = "0.4"
feos = { version="0.4", features = ["all_models"] }
quantity = "0.6"
```
To access the generic implementations for properties and phase equilibria, the `feos-core` crate is also included as dependency. Finally, {math}`\text{FeO}_\text{s}` makes extensive use of SI units provided by the `quantity` crate.
In the following example we use only the PC-SAFT equation of state and Helmholtz energy functionals (without Python bindings):
```toml
[dependencies]
feos-core = "0.4"
feos-dft = "0.4"
feos = { version="0.4", features = ["dft", "pcsaft"] }
quantity = "0.6"
```
To access generic DFT functionalities like interfaces and adsorption isotherms, the `feos-dft` crate is added to the dependencies.
````
`````
| Markdown |
3D | feos-org/feos | docs/index.md | .md | 3,395 | 144 | ---
hide-toc: true
---
# Welcome to {math}`\text{FeO}_\text{s}`
{math}`\text{FeO}_\text{s}` is a **framework** for **thermodynamic equations of state** (EoS) and **classical density functional theory** (DFT).
It is written in **Rust** with a **Python** interface.
## Usage
`````{tab-set}
````{tab-item} Python
```python
import feos
# Build an equation of state
parameters = feos.Parameters.from_json(['methanol'], 'parameters.json')
eos = feos.EquationOfState.pcsaft(parameters)
# Define thermodynamic conditions
critical_point = feos.State.critical_point(eos)
# Compute properties
p = critical_point.pressure()
t = critical_point.temperature
print(f'Critical point for methanol: T={t}, p={p}.')
```
```output
Critical point for methanol: T=531.5 K, p=10.7 MPa.
```
````
````{tab-item} Rust
```rust
// some imports omitted
use feos::core::parameter::{IdentifierOption, Parameters};
use feos::core::{Contributions, State};
use feos::pcsaft::PcSaft;
// Build an equation of state
let parameters = Parameters::from_json(
vec!["methanol"],
"parameters.json",
None,
IdentifierOption::Name,
)?;
let eos = &PcSaft::new(parameters);
// Define thermodynamic conditions
let critical_point = State::critical_point(&eos, None, None, None, Default::default())?;
// Compute properties
let p = critical_point.pressure(Contributions::Total);
let t = critical_point.temperature;
println!("Critical point for methanol: T={}, p={}.", t, p);
```
```output
Critical point for methanol: T=531.5 K, p=10.7 MPa.
```
````
`````
## Getting started
- Learn how to [install the code](installation).
- Browse the [python tutorials](tutorials/index).
- Need something specific? May we have a [recipe](recipes/index) for that.
- Questions or comments? [We are happy to hear from you](help_and_feedback)!
## Want to learn more?
- Delve into the [python](api/index) or [Rust API](rust_api).
- Learn about the underlying theory in our [Theory guide](theory/eos/properties)!
## Features
```{dropdown} Equations of State
:open:
- thermodynamic **properties**
- **phase equilibria** for pure substances and mixtures
- **critical point** calculations for pure substances and mixtures
- **dynamic properties** (entropy scaling)
- **stability analysis**
---
**Implemented equations of state**
- PC-SAFT (incl. group contribution method)
- ePC-SAFT
- uv-Theory
- SAFT-VR-Mie and the extension to quantum fluids SAFT-VRQ-Mie
- PeTS
- Multiparameter Helmholtz energy equations of state for common pure components
```
```{dropdown} Density Functional Theory
:open:
- **interfacial** properties,
- properties in **nanopores** and at **walls**,
- **adsorption isotherms**,
- **solvation free energies**,
- different **dimensions** and **coordinate systems**
```
```{dropdown} Extensibility / Usability
:open:
- Helmholtz energy uses **generalized (hyper-) dual numbers** - no analytical derivatives are needed.
- Interfaces use **dimensioned quantities** - never accidentally mix molar and mass-specific properties.
- Python bindings are written in Rust - **robust type checking** and **error handling**.
```
```{toctree}
:hidden:
installation
help_and_feedback
```
```{toctree}
:caption: Python
:hidden:
tutorials/index
recipes/index
api/index
```
```{toctree}
:caption: Rust
:hidden:
rust_api
```
```{toctree}
:caption: Theory
:hidden:
theory/eos/index
theory/dft/index
theory/models/index
``` | Markdown |
3D | feos-org/feos | docs/help_and_feedback.md | .md | 214 | 6 | # Help & Feedback
If you have feedback or need help, don't hesitate to contact us.
- Open an [issue](https://github.com/feos-org/feos/issues) on github.
- Join our [discord server](https://discord.gg/Wxfh7JUnWh). | Markdown |
3D | feos-org/feos | docs/conf.py | .py | 1,146 | 47 | import os
import sys
import feos
sys.path.append(os.path.abspath(os.path.join(__file__, "../..")))
sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = 'feos'
copyright = '2022, Gernot Bauer, Philipp Rehner'
author = 'Gernot Bauer, Philipp Rehner'
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.doctest',
'sphinx.ext.mathjax',
'sphinx.ext.githubpages',
'sphinx.ext.napoleon',
'nbsphinx',
'myst_parser',
'sphinx_copybutton',
'sphinx_design',
'sphinx_inline_tabs',
]
# -- Options for Markdown files ----------------------------------------------
myst_enable_extensions = [
"colon_fence",
"deflist",
"dollarmath"
]
myst_heading_anchors = 3
napoleon_numpy_docstring = True
autodoc_typehints = "both"
autosummary_generate = True
templates_path = ['_templates']
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
# -- Options for HTML output -------------------------------------------------
html_theme = 'furo'
html_title = f'FeOs v{feos.__version__}'
| Python |
3D | feos-org/feos | docs/rustguide/index.md | .md | 3,611 | 65 | # Guide
Welcome to the {math}`\text{FeO}_\text{s}` Rust guide.
On the following pages we discuss the structure of the {math}`\text{FeO}_\text{s}` project and how to use, compile and extend it.
## Introduction
{math}`\text{FeO}_\text{s}` is primarily developed on Linux but it is tested and runs on Linux, macOS and Windows.
You need a [Rust compiler](https://www.rust-lang.org/tools/install) to compile the code.
For development, [Visual Studio Code](https://code.visualstudio.com/) with the [rust-analyzer](https://rust-analyzer.github.io/) plugin works pretty well, but you should use what you are comfortable in.
## Prerequisites
If you are unfamiliar with Rust a good place to start is the [Rust Programming Language Book](https://doc.rust-lang.org/book/).
To start following our guide, you should understand the following topics:
* how Rust projects, called *crates*, are structured (the *module* system),
* data types and `structs`,
* the Rust *ownership model*,
* `enums` and *pattern matching*,
* *traits*,
With these foundations you should be able to follow the discussion.
Eventually you'll need to learn and understand
* limitations of *traits* and *trait objects*,
* smart pointers (`Rc` and `Box`),
* and the `pyO3` crate if you are interested in the Python interface.
## Project Structure
Some common functionalities of {math}`\text{FeO}_\text{s}` are contained in separate workspace crates so that they can be used as standalone dependencies outside of {math}`\text{FeO}_\text{s}`.
The most important ones are
* `feos-core` (`core` for short): defines traits and structs for equations of state and implements thermodynamic states, phase equilibria and critical point routines.
* `feos-dft` (`dft` for short): builds on `core` and defines traits and structs for classical density functional theory and implements utilities to work with convolutions, external potentials, etc.
These crates offer abstractions for tasks that are common for all equations of state and Helmholtz energy functionals.
Using `core` and `dft`, the following *implementations* of equations of state and functionals are currently available:
* `pcsaft`: the [PC-SAFT](https://pubs.acs.org/doi/abs/10.1021/ie0003887) equation of state and Helmholtz energy functional.
* `gc-pcsaft`: the [hetero-segmented group contribution](https://aip.scitation.org/doi/full/10.1063/1.4945000) method of the PC-SAFT equation of state.
* `uv-theory`: the equation of state based on [uv-Theory](https://aip.scitation.org/doi/full/10.1063/5.0073572).
* `pets`: the [PeTS](https://www.tandfonline.com/doi/full/10.1080/00268976.2018.1447153) equation of state and Helmholtz energy functional.
In addition to that, the `hard_sphere` and `assocation` modules contain implementations of the corresponding Helmholtz energy contributions that are used across multiple models.
To reduce compile times during development, every model is gated by its own `feature`. Specific parts of the library can be built and tested by passing the corresponding feature flags to the Rust compiler.
Due to the particular treatment of procedural macros in Rust, an additional workspace crate `feos-derive` provides procedural macros for the implementation of the `EquationOfState` and `HelmholtzEnergyFunctional` traits for `enum`s used in FFIs like `PyO3`.
## Where to Get Help
{math}`\text{FeO}_\text{s}` is openly developed on [github](https://github.com/feos-org). Each crate has it's own github repository where you can use the *discussion* feature or file an *issue*.
```{eval-rst}
.. toctree::
:maxdepth: 1
:hidden:
core/index
```
| Markdown |
3D | feos-org/feos | docs/rustguide/core/index.md | .md | 1,383 | 37 | # `feos-core`
In this section, we discuss the `feos-core` crate.
We will learn how equations of state are abstracted using traits, how generalized (hyper-) dual numbers are utilized and how thermodynamic states and phase equilibria are defined.
## Setup
To setup your workspace, [fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo) the [`feos` github repository](https://github.com/feos-org/feos) and from that clone the fork.
To build the Rust library, switch to the `feos-core` crate
```
cd feos-core
```
and type:
```
cargo build
```
## Important crates
`feos-core` depends on a number of other important crates. Most notably, we use
- `quantity`: for scalar and vector valued dimensioned quantities. Those are used in almost all user-facing interfaces.
- `num-dual`: for generalized (hyper-) dual numbers. These data types are very important because we use them to be able to compute partial, higher-order derivatives of the Helmholtz energy without needing to implement them analytically.
- `ndarray`: for multidimensional arrays. We use these when mathematical operations are performed on arrays. They are a central data structure for `feos-dft`.
- `pyo3`: for the Python interface. All interfaces to Python are written in pure Rust using `PyO3`. It's awesome.
```{eval-rst}
.. toctree::
:maxdepth: 1
:hidden:
equation_of_state
state
``` | Markdown |
3D | feos-org/feos | docs/tutorials/index.md | .md | 271 | 12 | # Tutorials
You can find each of the tutorials below as jupyter notebook in the github repository in the [`examples` folder](https://github.com/feos-org/feos/tree/main/examples).
```{eval-rst}
.. toctree::
:maxdepth: 2
utility/index
eos/index
dft/index
``` | Markdown |
3D | feos-org/feos | docs/tutorials/eos/pcsaft_phase_diagram.ipynb | .ipynb | 159,346 | 584 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pure substance phase diagrams\n",
"\n",
"## Goal of this notebook\n",
"\n",
"- Learn how to generate and work with phase diagrams.\n",
"- Learn what `PhaseEquilibrium` objects are and how to use them."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read parameters from json for a single substance and generate `PcSaft` object"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|hexane|86.177|3.0576|3.7983|236.77|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"110-54-3\",\n",
" \"name\": \"hexane\",\n",
" \"iupac_name\": \"hexane\",\n",
" \"smiles\": \"CCCCCC\",\n",
" \"inchi\": \"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" \"formula\": \"C6H14\"\n",
" },\n",
" \"molarweight\": 86.177,\n",
" \"m\": 3.0576,\n",
" \"sigma\": 3.7983,\n",
" \"epsilon_k\": 236.77\n",
" }\n",
"]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.from_json(\n",
" ['hexane'], \n",
" '../../../parameters/pcsaft/gross2001.json'\n",
")\n",
"parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pcsaft = feos.EquationOfState.pcsaft(parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## The `PhaseDiagram` object\n",
"\n",
"A `PhaseDiagram` object contains multiple thermodyanmic states at phase equilibrium.\n",
"**For a single substance** it can be constructed via the `PhaseDiagram.pure` method by providing \n",
"\n",
"- an equation of state,\n",
"- the minimum temperature, and\n",
"- the number of points to compute.\n",
"\n",
"The points are evenly distributed between the defined minimum temperature and the critical temperature which is either computed (default) or can be provided via the `critical_temperature` argument.\n",
"\n",
"Furthermore, we can define options for the numerics:\n",
"- `max_iter` to define the maximum number of iterations for the phase equilibrium calculations, and\n",
"- `tol` to set the tolerance used for deterimation of phase equilibria.\n",
"\n",
"A `Verbosity` object can be provided via the `verbosity` argument to print intermediate calculations to the screen.\n",
"\n",
"Starting from the minimum temperature, phase equilibria are computed in sequence using prior results (i.e. at prior temperature) as input for the next iteration."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"phase_diagram = feos.PhaseDiagram.pure(pcsaft, min_temperature=200*si.KELVIN, npoints=501)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|200.00000 K|12.21572 mmol/m³|"
],
"text/plain": [
"T = 200.00000 K, ρ = 12.21572 mmol/m³"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"phase_diagram.vapor[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stored information\n",
"\n",
"A `PhaseDiagram` object contains the following *fields*:\n",
"\n",
"- `states`: a list (with length of `npoints`) of `PhaseEquilibrium` objects at the different temperatures,\n",
"- `vapor` and `liquid`: so-called `StateVec` objects that can be used to compute properties for the vapor and liquid phase, respectively.\n",
"\n",
"The `to_dict` *method* can be used to conveniently generate a `pandas.DataFrame` object (see below)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building a `pandas.DataFrame`: the `to_dict` method\n",
"\n",
"\n",
"The `PhaseDiagramPure` object contains some physical properties (such as densities, temperatures and pressures) as well as the `PhaseEquilibrium` objects at each temperature.\n",
"\n",
"Before we take a look at these objects, a useful tool when working with phase diagrams is the `to_dict` method. It generates a Python dictionary of some properties (with hard-coded units, see the docstring of `to_dict`). This dictionary can readily be used to generate a `pandas.DataFrame`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>temperature</th>\n",
" <th>pressure</th>\n",
" <th>density liquid</th>\n",
" <th>density vapor</th>\n",
" <th>molar enthalpy liquid</th>\n",
" <th>molar enthalpy vapor</th>\n",
" <th>molar entropy liquid</th>\n",
" <th>molar entropy vapor</th>\n",
" <th>mass density liquid</th>\n",
" <th>mass density vapor</th>\n",
" <th>specific enthalpy liquid</th>\n",
" <th>specific enthalpy vapor</th>\n",
" <th>specific entropy liquid</th>\n",
" <th>specific entropy vapor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200.000000</td>\n",
" <td>20.312763</td>\n",
" <td>8539.589591</td>\n",
" <td>0.012216</td>\n",
" <td>-37.322054</td>\n",
" <td>-0.000147</td>\n",
" <td>-0.074718</td>\n",
" <td>-1.879119e-07</td>\n",
" <td>735.916212</td>\n",
" <td>0.001053</td>\n",
" <td>-433.086018</td>\n",
" <td>-0.001703</td>\n",
" <td>-0.867028</td>\n",
" <td>-0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>200.638669</td>\n",
" <td>21.816282</td>\n",
" <td>8532.663964</td>\n",
" <td>0.013078</td>\n",
" <td>-37.281784</td>\n",
" <td>-0.000157</td>\n",
" <td>-0.074497</td>\n",
" <td>-2.001300e-07</td>\n",
" <td>735.319382</td>\n",
" <td>0.001127</td>\n",
" <td>-432.618726</td>\n",
" <td>-0.001819</td>\n",
" <td>-0.864466</td>\n",
" <td>-0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201.277337</td>\n",
" <td>23.418684</td>\n",
" <td>8525.749142</td>\n",
" <td>0.013994</td>\n",
" <td>-37.241570</td>\n",
" <td>-0.000167</td>\n",
" <td>-0.074277</td>\n",
" <td>-2.130365e-07</td>\n",
" <td>734.723484</td>\n",
" <td>0.001206</td>\n",
" <td>-432.152081</td>\n",
" <td>-0.001943</td>\n",
" <td>-0.861915</td>\n",
" <td>-0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201.916006</td>\n",
" <td>25.125608</td>\n",
" <td>8518.845002</td>\n",
" <td>0.014967</td>\n",
" <td>-37.201412</td>\n",
" <td>-0.000179</td>\n",
" <td>-0.074058</td>\n",
" <td>-2.266637e-07</td>\n",
" <td>734.128506</td>\n",
" <td>0.001290</td>\n",
" <td>-431.686083</td>\n",
" <td>-0.002073</td>\n",
" <td>-0.859376</td>\n",
" <td>-0.000003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202.554674</td>\n",
" <td>26.942961</td>\n",
" <td>8511.951422</td>\n",
" <td>0.015999</td>\n",
" <td>-37.161309</td>\n",
" <td>-0.000191</td>\n",
" <td>-0.073841</td>\n",
" <td>-2.410451e-07</td>\n",
" <td>733.534438</td>\n",
" <td>0.001379</td>\n",
" <td>-431.220733</td>\n",
" <td>-0.002212</td>\n",
" <td>-0.856848</td>\n",
" <td>-0.000003</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" temperature pressure density liquid density vapor \\\n",
"0 200.000000 20.312763 8539.589591 0.012216 \n",
"1 200.638669 21.816282 8532.663964 0.013078 \n",
"2 201.277337 23.418684 8525.749142 0.013994 \n",
"3 201.916006 25.125608 8518.845002 0.014967 \n",
"4 202.554674 26.942961 8511.951422 0.015999 \n",
"\n",
" molar enthalpy liquid molar enthalpy vapor molar entropy liquid \\\n",
"0 -37.322054 -0.000147 -0.074718 \n",
"1 -37.281784 -0.000157 -0.074497 \n",
"2 -37.241570 -0.000167 -0.074277 \n",
"3 -37.201412 -0.000179 -0.074058 \n",
"4 -37.161309 -0.000191 -0.073841 \n",
"\n",
" molar entropy vapor mass density liquid mass density vapor \\\n",
"0 -1.879119e-07 735.916212 0.001053 \n",
"1 -2.001300e-07 735.319382 0.001127 \n",
"2 -2.130365e-07 734.723484 0.001206 \n",
"3 -2.266637e-07 734.128506 0.001290 \n",
"4 -2.410451e-07 733.534438 0.001379 \n",
"\n",
" specific enthalpy liquid specific enthalpy vapor specific entropy liquid \\\n",
"0 -433.086018 -0.001703 -0.867028 \n",
"1 -432.618726 -0.001819 -0.864466 \n",
"2 -432.152081 -0.001943 -0.861915 \n",
"3 -431.686083 -0.002073 -0.859376 \n",
"4 -431.220733 -0.002212 -0.856848 \n",
"\n",
" specific entropy vapor \n",
"0 -0.000002 \n",
"1 -0.000002 \n",
"2 -0.000002 \n",
"3 -0.000003 \n",
"4 -0.000003 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(phase_diagram.to_dict(feos.Contributions.Residual))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1500x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
"ax[0].set_title(f\"saturation pressure of {parameters.pure_records[0].identifier.name}\")\n",
"sns.lineplot(y=df.pressure, x=1.0/df.temperature, ax=ax[0])\n",
"\n",
"# axis and styling \n",
"ax[0].set_yscale('log')\n",
"ax[0].set_xlabel(r'$\\frac{1}{T}$ / K$^{-1}$')\n",
"ax[0].set_ylabel(r'$p$ / Pa')\n",
"ax[0].set_xlim(0.002, 0.005)\n",
"ax[0].set_ylim(1e1, 1e7)\n",
"\n",
"ax[1].set_title(r\"$T$-$\\rho$-diagram of {}\".format(parameters.pure_records[0].identifier.name))\n",
"sns.lineplot(y=df.temperature, x=df['density vapor'], ax=ax[1], color=colors[0])\n",
"sns.lineplot(y=df.temperature, x=df['density liquid'], ax=ax[1], color=colors[0])\n",
"\n",
"# axis and styling \n",
"ax[1].set_ylabel(r'$T$ / K')\n",
"ax[1].set_xlabel(r'$\\rho$ / mol/m³')\n",
"ax[1].set_ylim(200, 600)\n",
"ax[1].set_xlim(0, 9000)\n",
"\n",
"sns.despine(offset=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `PhaseEquilibrium` object\n",
"\n",
"A `PhaseEquilibrium` object contains two thermodynamic states (`State` objects) that are in thermal, mechanical and chemical equilibrium.\n",
"The `PhaseEquilibrium.pure` constructor can be used to compute a phase equilibrium for a single substance. We have to provide the equation of state and either temperature or pressure. Optionally, we can also provide `PhaseEquilibrium` object which can be used as starting point for the calculation which possibly speeds up the computation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|300.00000 K|8.86860 mol/m³|\n",
"|phase 2|300.00000 K|7.51850 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 300.00000 K, ρ = 8.86860 mol/m³\n",
"phase 1: T = 300.00000 K, ρ = 7.51850 kmol/m³"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.pure(\n",
" pcsaft, \n",
" temperature_or_pressure=300.0*si.KELVIN\n",
")\n",
"vle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The two equilibrium states can be extracted via the `liquid` and `vapor` getters, respectively. Returned are `State` objects, for which we can now compute any property that is available for the `State` object and the given equation of state."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"saturation pressure p_sat(T = 300 K) = 0.22 bar\n",
"enthalpy of vaporization h_lv (T = 300 K) = 365.83 kJ/kg\n"
]
}
],
"source": [
"liquid = vle.liquid\n",
"vapor = vle.vapor\n",
"\n",
"assert(abs((liquid.pressure() - vapor.pressure()) / si.BAR) < 1e-10)\n",
"print(f'saturation pressure p_sat(T = {liquid.temperature}) = {liquid.pressure() / si.BAR:6.2f} bar')\n",
"print(f'enthalpy of vaporization h_lv (T = {liquid.temperature}) = {(vapor.specific_enthalpy(feos.Contributions.Residual) - liquid.specific_enthalpy(feos.Contributions.Residual)) / (si.KILO*si.JOULE/si.KILOGRAM):6.2f} kJ/kg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to compute a boiling temperature or saturation pressure without needing the `PhaseEquilibrium` object you can use the\n",
"\n",
"- `PhaseEquilibrium.boiling_temperature` and\n",
"- `PhaseEquilibrium.vapor_pressure`\n",
"\n",
"methods. Note that these methods return lists (even for pure substance systems) where each entry contains the pure substance property."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$300\\,\\mathrm{K}$"
],
"text/plain": [
"299.9999999999178 K"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feos.PhaseEquilibrium.boiling_temperature(pcsaft, liquid.pressure())[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `states` method returns all `PhaseEquilibrium` objects from `PhaseDiagram`\n",
"\n",
"Once a `PhaseDiagram` object is created, we can access all underlying `PhaseEquilibrium` objects via the `states` field.\n",
"In the following cell, we compute the enthalpy of vaporization by iterating through all states and calling the `specific_enthalpy` method on the vapor and liquid states of the `PhaseEquilibrium` object, respectively.\n",
"\n",
"Note that this is merely an example to show how to compute any property of the states. The total value of enthalpy of vaporization may not be correct, depending on the ideal gas model used."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Add enthalpy of vaporization to dataframe\n",
"df['hlv'] = [\n",
" (vle.vapor.specific_enthalpy(feos.Contributions.Residual) - vle.liquid.specific_enthalpy(feos.Contributions.Residual)) \n",
" / (si.KILO * si.JOULE / si.KILOGRAM) \n",
" for vle in phase_diagram.states\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 6))\n",
"sns.lineplot(y=df.hlv, x=df.temperature, ax=ax)\n",
"\n",
"# axis and styling \n",
"ax.set_xlabel(r'$T$ / K')\n",
"ax.set_ylabel(r'$\\Delta_{LV} h$ / kJ / kg')\n",
"ax.set_xlim(200, 600)\n",
"ax.set_ylim(0, 500)\n",
"\n",
"sns.despine(offset=10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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| Unknown |
3D | feos-org/feos | docs/tutorials/eos/pcsaft_entropy_scaling.ipynb | .ipynb | 103,404 | 636 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entropy scaling of pure substances\n",
"\n",
"## Goal\n",
"\n",
"- Learn how to compute dynamic properties (viscosity in this example)\n",
"- Compare substance specific parameters against homo-segmented group contribution\n",
"- Compare viscosity to NIST data (generated in NIST's [webapp](https://webbook.nist.gov/chemistry/fluid/))\n",
"\n",
"## Import needed packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"import si_units as si\n",
"\n",
"sns.set_context(\"talk\")\n",
"sns.set_palette(\"Dark2\")\n",
"sns.set_style(\"ticks\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PC-SAFT (individual component parameters)\n",
"\n",
"First, we read parameters adjusted to hexane saturation pressure and liquid densities (for the regular SAFT parameters) and to viscosity (for correlation). "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|viscosity|\n",
"|-|-|-|-|-|-|\n",
"|hexane|86.177|3.0576|3.7983|236.77|[-1.2035,-2.5958,-0.4816,-0.0865]|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"110-54-3\",\n",
" \"name\": \"hexane\",\n",
" \"iupac_name\": \"hexane\",\n",
" \"smiles\": \"CCCCCC\",\n",
" \"inchi\": \"InChI=1S/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" \"formula\": \"C6H14\"\n",
" },\n",
" \"molarweight\": 86.177,\n",
" \"m\": 3.0576,\n",
" \"sigma\": 3.7983,\n",
" \"epsilon_k\": 236.77,\n",
" \"viscosity\": [\n",
" -1.2035,\n",
" -2.5958,\n",
" -0.4816,\n",
" -0.0865\n",
" ]\n",
" }\n",
"]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.from_json(\n",
" [\"hexane\"], \"../../../parameters/pcsaft/loetgeringlin2018.json\"\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PC-SAFT homo-GC\n",
"\n",
"For transparency, we build parameters by hand. You can read a detailed explanation about PC-SAFT parameters in the \"working with parameters\" tutorial."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"hexane = feos.ChemicalRecord(\n",
" identifier=feos.Identifier(\n",
" cas=\"110-54-3\",\n",
" name=\"hexane\",\n",
" iupac_name=\"hexane\",\n",
" smiles=\"CCCCCC\",\n",
" inchi=\"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" formula=\"C6H14\"\n",
" ),\n",
" segments=['CH3', 'CH2', 'CH2', 'CH2', 'CH2', 'CH3']\n",
")\n",
"\n",
"ch3 = feos.SegmentRecord(\n",
" 'CH3', \n",
" molarweight=15.0345, \n",
" m=0.61198,\n",
" sigma=3.7202,\n",
" epsilon_k=229.90,\n",
" viscosity=[-8.6878e-3, -1.7951e-1, -12.2359e-2, -0.01245]\n",
")\n",
"\n",
"ch2 = feos.SegmentRecord(\n",
" 'CH2', \n",
" molarweight=14.02658, \n",
" m=0.45606,\n",
" sigma=3.8900,\n",
" epsilon_k=239.01,\n",
" viscosity=[-0.9194e-3, -1.3316e-1, -4.2657e-2, -0.01245]\n",
")\n",
"\n",
"segment_records = {r.identifier: r for r in [ch3, ch2]}\n",
"\n",
"def from_segments(chemical_record, segment_records):\n",
" m = 0\n",
" s3 = 0\n",
" eps = 0\n",
" mw = 0\n",
" viscosity = np.zeros(4)\n",
" for s in chemical_record.segments:\n",
" segment = segment_records[s]\n",
" mw += segment.molarweight\n",
" m += segment.model_record[\"m\"]\n",
" s3 += segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3\n",
" eps += segment.model_record[\"m\"] * segment.model_record[\"epsilon_k\"]\n",
" v = segment.model_record[\"viscosity\"]\n",
" viscosity += np.array([\n",
" v[0] * segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3, \n",
" v[1] * segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3, \n",
" v[2], \n",
" v[3]\n",
" ])\n",
" viscosity[1] /= s3**0.45\n",
" \n",
" # We have to shift the \"A\" parameter because the implemented reference\n",
" # is eta_CE according to eq. 3 of Loetgerin-Lin (2018)\n",
" # A = A(GC) + log(sqrt(1/m)) = -log(m)/2\n",
" viscosity[0] += np.log(np.sqrt(1/m))\n",
" return feos.PureRecord(chemical_record.identifier, mw, m=m, sigma=np.cbrt(s3 / m), epsilon_k=eps / m, viscosity=list(viscosity))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build equations of state\n",
"\n",
"We instantiate an equation of state for each parameter set. `saft` uses substance specific parameters while `saft_gc` uses homo GC parameters both for SAFT as well as correlation parameters."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"parameters_gc = feos.Parameters.new_pure(from_segments(hexane, segment_records))\n",
"saft_gc = feos.EquationOfState.pcsaft(parameters_gc)\n",
"saft = feos.EquationOfState.pcsaft(parameters)\n",
"\n",
"m_gc = parameters_gc.pure_records[0].model_record[\"m\"]\n",
"m = parameters.pure_records[0].model_record[\"m\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Substance specific: [-1.2035, -2.5958, -0.4816, -0.0865]\n",
"Segments : [-1.2034921145837285, -2.536713016411593, -0.415346, -0.0747]\n"
]
}
],
"source": [
"print(\"Substance specific: \", parameters.pure_records[0].model_record[\"viscosity\"])\n",
"print(\"Segments : \", parameters_gc.pure_records[0].model_record[\"viscosity\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare methods to NIST data (T = 450 K)\n",
"\n",
"We will compute the residual entropy, viscosity and logarithmic reduced viscosity and compare to literature data (for which the entropy is computed with parameters fitted to the component, not GC)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Temperature (K)</th>\n",
" <th>Pressure (MPa)</th>\n",
" <th>Density (mol/m3)</th>\n",
" <th>Volume (m3/mol)</th>\n",
" <th>Internal Energy (kJ/mol)</th>\n",
" <th>Enthalpy (kJ/mol)</th>\n",
" <th>Entropy (J/mol*K)</th>\n",
" <th>Cv (J/mol*K)</th>\n",
" <th>Cp (J/mol*K)</th>\n",
" <th>Sound Spd. (m/s)</th>\n",
" <th>Joule-Thomson (K/MPa)</th>\n",
" <th>Viscosity (Pa*s)</th>\n",
" <th>Therm. Cond. (W/m*K)</th>\n",
" <th>Phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>450.0</td>\n",
" <td>0.01</td>\n",
" <td>2.6774</td>\n",
" <td>0.373500</td>\n",
" <td>45.229</td>\n",
" <td>48.964</td>\n",
" <td>154.33</td>\n",
" <td>193.37</td>\n",
" <td>201.76</td>\n",
" <td>212.47</td>\n",
" <td>11.204</td>\n",
" <td>0.000009</td>\n",
" <td>0.029374</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>450.0</td>\n",
" <td>0.11</td>\n",
" <td>29.9840</td>\n",
" <td>0.033351</td>\n",
" <td>45.065</td>\n",
" <td>48.734</td>\n",
" <td>134.03</td>\n",
" <td>193.94</td>\n",
" <td>203.09</td>\n",
" <td>209.04</td>\n",
" <td>11.510</td>\n",
" <td>0.000009</td>\n",
" <td>0.029276</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>450.0</td>\n",
" <td>0.21</td>\n",
" <td>58.3290</td>\n",
" <td>0.017144</td>\n",
" <td>44.896</td>\n",
" <td>48.496</td>\n",
" <td>128.27</td>\n",
" <td>194.53</td>\n",
" <td>204.55</td>\n",
" <td>205.48</td>\n",
" <td>11.842</td>\n",
" <td>0.000009</td>\n",
" <td>0.029196</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>450.0</td>\n",
" <td>0.31</td>\n",
" <td>87.8260</td>\n",
" <td>0.011386</td>\n",
" <td>44.720</td>\n",
" <td>48.249</td>\n",
" <td>124.64</td>\n",
" <td>195.15</td>\n",
" <td>206.15</td>\n",
" <td>201.76</td>\n",
" <td>12.207</td>\n",
" <td>0.000009</td>\n",
" <td>0.029136</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>450.0</td>\n",
" <td>0.41</td>\n",
" <td>118.6100</td>\n",
" <td>0.008431</td>\n",
" <td>44.536</td>\n",
" <td>47.992</td>\n",
" <td>121.90</td>\n",
" <td>195.80</td>\n",
" <td>207.93</td>\n",
" <td>197.87</td>\n",
" <td>12.608</td>\n",
" <td>0.000010</td>\n",
" <td>0.029098</td>\n",
" <td>vapor</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Temperature (K) Pressure (MPa) Density (mol/m3) Volume (m3/mol) \\\n",
"0 450.0 0.01 2.6774 0.373500 \n",
"1 450.0 0.11 29.9840 0.033351 \n",
"2 450.0 0.21 58.3290 0.017144 \n",
"3 450.0 0.31 87.8260 0.011386 \n",
"4 450.0 0.41 118.6100 0.008431 \n",
"\n",
" Internal Energy (kJ/mol) Enthalpy (kJ/mol) Entropy (J/mol*K) \\\n",
"0 45.229 48.964 154.33 \n",
"1 45.065 48.734 134.03 \n",
"2 44.896 48.496 128.27 \n",
"3 44.720 48.249 124.64 \n",
"4 44.536 47.992 121.90 \n",
"\n",
" Cv (J/mol*K) Cp (J/mol*K) Sound Spd. (m/s) Joule-Thomson (K/MPa) \\\n",
"0 193.37 201.76 212.47 11.204 \n",
"1 193.94 203.09 209.04 11.510 \n",
"2 194.53 204.55 205.48 11.842 \n",
"3 195.15 206.15 201.76 12.207 \n",
"4 195.80 207.93 197.87 12.608 \n",
"\n",
" Viscosity (Pa*s) Therm. Cond. (W/m*K) Phase \n",
"0 0.000009 0.029374 vapor \n",
"1 0.000009 0.029276 vapor \n",
"2 0.000009 0.029196 vapor \n",
"3 0.000009 0.029136 vapor \n",
"4 0.000010 0.029098 vapor "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature = pd.read_csv(\"../../../examples/data/hexane_nist.csv\", delimiter=\"\\t\")\n",
"literature.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We loop through experimental data, read temperature, pressure and the phase (liquid or vapor) and generate `State` objects for the experimental conditions. Then, we compute the residual molar entropy and the logarithmic reduced viscosity."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pressure</th>\n",
" <th>s*_res/m</th>\n",
" <th>viscosity</th>\n",
" <th>ln_viscosity_reduced</th>\n",
" <th>source</th>\n",
" <th>rel.dev.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.01</td>\n",
" <td>-0.000526</td>\n",
" <td>0.000009</td>\n",
" <td>-1.170829</td>\n",
" <td>literature</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.01</td>\n",
" <td>-0.000526</td>\n",
" <td>0.000009</td>\n",
" <td>-1.202136</td>\n",
" <td>saft</td>\n",
" <td>-3.082130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.01</td>\n",
" <td>-0.000531</td>\n",
" <td>0.000009</td>\n",
" <td>-1.202146</td>\n",
" <td>homo-GC</td>\n",
" <td>-4.154342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.11</td>\n",
" <td>-0.005862</td>\n",
" <td>0.000009</td>\n",
" <td>-1.172124</td>\n",
" <td>literature</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.11</td>\n",
" <td>-0.005862</td>\n",
" <td>0.000009</td>\n",
" <td>-1.188299</td>\n",
" <td>saft</td>\n",
" <td>-1.604523</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pressure s*_res/m viscosity ln_viscosity_reduced source rel.dev.\n",
"0 0.01 -0.000526 0.000009 -1.170829 literature 0.000000\n",
"1 0.01 -0.000526 0.000009 -1.202136 saft -3.082130\n",
"2 0.01 -0.000531 0.000009 -1.202146 homo-GC -4.154342\n",
"3 0.11 -0.005862 0.000009 -1.172124 literature 0.000000\n",
"4 0.11 -0.005862 0.000009 -1.188299 saft -1.604523"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = []\n",
"for i, row in literature.iterrows():\n",
" t = row['Temperature (K)'] * si.KELVIN\n",
" p = row['Pressure (MPa)'] * si.MEGA * si.PASCAL\n",
" viscosity_lit = row['Viscosity (Pa*s)'] * si.PASCAL * si.SECOND\n",
" \n",
" # literature\n",
" state = feos.State(saft, temperature=t, pressure=p, total_moles=si.MOL, density_initialization=row.Phase)\n",
" s = state.molar_entropy(feos.Contributions.Residual)\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m,\n",
" \"viscosity\": viscosity_lit / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": np.log(viscosity_lit/ state.viscosity_reference()),\n",
" \"source\": \"literature\",\n",
" \"rel.dev.\": 0.0\n",
" }\n",
" )\n",
" \n",
" # individual parameters\n",
" viscosity = state.viscosity()\n",
" ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m,\n",
" \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
" \"source\": \"saft\",\n",
" \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
" }\n",
" )\n",
" \n",
" # homo GC\n",
" state = feos.State(saft_gc, temperature=t, pressure=p, total_moles=si.MOL)\n",
" s = state.molar_entropy(feos.Contributions.Residual)\n",
" viscosity = state.viscosity()\n",
" ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
" results.append(\n",
" {\n",
" \"pressure\": p / si.MEGA / si.PASCAL,\n",
" \"s*_res/m\": s / si.RGAS / m_gc,\n",
" \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
" \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
" \"source\": \"homo-GC\",\n",
" \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
" }\n",
" )\n",
"\n",
"# gather everything in a data frame\n",
"data = pd.DataFrame(results)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1500x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(15, 4), gridspec_kw={'wspace': 0.35})\n",
"sns.scatterplot(data=data, x='s*_res/m', y='ln_viscosity_reduced', hue='source', ax=ax[0])\n",
"ax[0].set_xlabel(r\"$\\frac{s_{res}}{R \\cdot m}$\", fontsize=22)\n",
"ax[0].set_ylabel(r\"$\\ln\\left(\\frac{\\eta}{\\eta^{CE}}\\right)$\", fontsize=22)\n",
"ax[0].legend(frameon=False)\n",
"\n",
"sns.lineplot(data=data, x='pressure', y='viscosity', hue='source', ax=ax[1])\n",
"ax[1].set_xlabel(r\"$p$ / MPa\", fontsize=22)\n",
"ax[1].set_ylabel(r\"$\\eta$ / Pa*s\", fontsize=22)\n",
"ax[1].legend(frameon=False)\n",
"sns.despine()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Viscosity hexane compared to NIST data at T = 450 K\n",
"MARD (individual): 0.81 %\n",
"MARD (homo-GC) : 3.70 %\n"
]
}
],
"source": [
"# check mean absolute relative deviation in percent\n",
"mard = data.groupby('source')['rel.dev.'].apply(lambda x: np.mean(np.abs(x)))\n",
"print('Viscosity hexane compared to NIST data at T = 450 K')\n",
"print(f'MARD (individual): {mard.saft:.2f} %')\n",
"print(f'MARD (homo-GC) : {mard[\"homo-GC\"]:.2f} %')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | docs/tutorials/eos/index.md | .md | 179 | 12 | # Equations of state
```{eval-rst}
.. toctree::
:maxdepth: 1
pcsaft_working_with_parameters
pcsaft_phase_diagram
pcsaft_entropy_scaling
core_user_defined_eos
```
| Markdown |
3D | feos-org/feos | docs/tutorials/eos/core_user_defined_eos.ipynb | .ipynb | 295,505 | 1,280 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing an equation of state in python\n",
"\n",
"> In `FeOs`, you can implement your equation of state in python, register it to the Rust backend, and compute properties and phase equilbria as if you implemented it in Rust.\n",
"> In this tutorial, we will implement the Peng-Robinson equation of state."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of contents <a id=\"Table-of-contents\"/>\n",
"\n",
"- [Implementation](#Implementation)\n",
"- [Computing properties](#Computing-properties)\n",
"- [Critical point](#Critical-point)\n",
"- [Phase equilibria and phase diagrams](#Phase-equilibria-and-phase-diagrams)\n",
"- [Mixtures](#Mixtures)\n",
"- [Comparison to Rust implementation](#Comparison-to-Rust-implementation)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import numpy as np\n",
"\n",
"optional = True\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"if optional:\n",
" import matplotlib.pyplot as plt\n",
" import pandas as pd\n",
" import seaborn as sns\n",
"\n",
" sns.set_style(\"ticks\")\n",
" sns.set_palette(\"Dark2\")\n",
" sns.set_context(\"talk\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation <a id=\"Implementation\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"To implement an equation of state in python, we have to define a `class` which has to have the following methods:\n",
"\n",
"```python\n",
"class MyEquationOfState:\n",
" def helmholtz_energy(self, state: StateHD) -> D\n",
" \n",
" def components(self) -> int\n",
" \n",
" def subset(self, indices: List[int]) -> Self\n",
" \n",
" def molar_weight(self) -> SIArray1\n",
" \n",
" def max_density(self, molefracs: np.ndarray[float]) -> float\n",
"``` \n",
"\n",
"- `components(self) -> int`: Returns the number of components (usually inferred from the shape of the input parameters).\n",
"- `molar_weight(self) -> SIArray1`: Returns an `SIArray1` with size equal to the number of components containing the molar mass of each component.\n",
"- `max_density(self, moles: np.ndarray[float]) -> float`: Returns the maximum allowed number density in units of `molecules/Angstrom³`.\n",
"- `subset(self, indices: List[int]) -> self`: Returns a new equation of state with parameters defined in `indices`.\n",
"- `helmholtz_energy(self, state: StateHD) -> Dual`: Returns the helmholtz energy as (hyper)-dual number given a `StateHD`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SQRT2 = np.sqrt(2)\n",
"\n",
"class PyPengRobinson: \n",
" def __init__(\n",
" self, critical_temperature, critical_pressure, \n",
" acentric_factor, molar_weight, delta_ij=None\n",
" ):\n",
" \"\"\"Peng-Robinson Equation of State\n",
" \n",
" Parameters\n",
" ----------\n",
" critical_temperature : SIArray1\n",
" critical temperature of each component.\n",
" critical_pressure : SIArray1\n",
" critical pressure of each component.\n",
" acentric_factor : np.array[float] \n",
" acentric factor of each component (dimensionless).\n",
" molar_weight: SIArray1\n",
" molar weight of each component.\n",
" delta_ij : np.array[[float]], optional\n",
" binary parameters. Shape=[n, n], n = number of components.\n",
" defaults to zero for all binary interactions.\n",
" \n",
" Raises\n",
" ------\n",
" ValueError: if the input values have incompatible sizes.\n",
" \"\"\"\n",
" self.n = len(critical_temperature)\n",
" if len(set((\n",
" len(critical_temperature), \n",
" len(critical_pressure), \n",
" len(acentric_factor)\n",
" ))) != 1:\n",
" raise ValueError(\"Input parameters must all have the same lenght.\")\n",
" \n",
" self.tc = critical_temperature / si.KELVIN\n",
" self.pc = critical_pressure / si.PASCAL\n",
" self.omega = acentric_factor\n",
" self.mw = molar_weight / si.GRAM * si.MOL\n",
"\n",
" self.a_r = (0.45724 * critical_temperature**2 * si.RGAS \n",
" / critical_pressure / si.ANGSTROM**3 / si.NAV / si.KELVIN)\n",
" self.b = (0.07780 * critical_temperature * si.RGAS \n",
" / critical_pressure / si.ANGSTROM**3 / si.NAV)\n",
" self.kappa = (0.37464 \n",
" + (1.54226 - 0.26992 * acentric_factor) * acentric_factor)\n",
" self.delta_ij = (np.zeros((self.n, self.n)) \n",
" if delta_ij is None else delta_ij)\n",
" \n",
" def helmholtz_energy(self, state):\n",
" \"\"\"Return helmholtz energy.\n",
" \n",
" Parameters\n",
" ----------\n",
" state : StateHD\n",
" The thermodynamic state.\n",
" \n",
" Returns\n",
" -------\n",
" helmholtz_energy: float | any dual number\n",
" The return type depends on the input types.\n",
" \"\"\" \n",
" x = state.molefracs\n",
" tr = 1.0 / self.tc * state.temperature\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n",
" ak_mix = 0.0\n",
" if self.n > 1:\n",
" for i in range(self.n):\n",
" for j in range(self.n):\n",
" ak_mix += (np.sqrt(ak[i] * ak[j]) \n",
" * (x[i] * x[j] * (1.0 - self.delta_ij[i, j])))\n",
" else:\n",
" ak_mix = ak[0]\n",
" b = np.sum(x * self.b)\n",
" rho = np.sum(state.partial_density)\n",
" a = rho * (-np.log(1.0 - b * rho) \n",
" - ak_mix / (b * SQRT2 * 2.0 * state.temperature) \n",
" * np.log((1.0 + (1.0 + SQRT2) * b * rho) / (1.0 + (1.0 - SQRT2) * b * rho)))\n",
" return a\n",
" \n",
" def components(self) -> int: \n",
" \"\"\"Number of components.\"\"\"\n",
" return self.n\n",
" \n",
" def subset(self, i: list[int]):\n",
" \"\"\"Return new equation of state containing a subset of all components.\"\"\"\n",
" if self.n > 1:\n",
" tc = self.tc[i] \n",
" pc = self.pc[i]\n",
" mw = self.mw[i]\n",
" omega = self.omega[i]\n",
" return PyPengRobinson(\n",
" si.array(tc * si.KELVIN), \n",
" si.array(pc * si.PASCAL), \n",
" omega, \n",
" si.array(mw * si.GRAM / si.MOL)\n",
" )\n",
" else:\n",
" return self\n",
" \n",
" def molar_weight(self) -> si.SIObject:\n",
" return si.array(self.mw * si.GRAM / si.MOL)\n",
" \n",
" def max_density(self, molefracs: list[float]) -> float:\n",
" b = np.sum(molefracs * self.b)\n",
" return 0.9 / b "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing properties <a id=\"Computing-properties\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"Let's compute some properties. First, we have to instanciate the class and register it to Rust.\n",
"This is done using the `EquationOfState.python` method."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"tc = si.array(369.96 * si.KELVIN)\n",
"pc = si.array(4250000.0 * si.PASCAL)\n",
"omega = np.array([0.153])\n",
"molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n",
"\n",
"# create an instance of our python class and hand it over to rust\n",
"pr = PyPengRobinson(tc, pc, omega, molar_weight)\n",
"eos = feos.EquationOfState.python_residual(pr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Thermodynamic state: the `State` object\n",
"\n",
"Before we can compute a property, we create a `State` object. This can be done in several ways depending on what control variables we need.\n",
"If no total amount of substance is defined, it is set to $n = \\frac{1}{N_{AV}}$.\n",
"For possible input combinations, you can inspect the signature of the constructor using `State?`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mInit signature:\u001b[39m\n",
"feos.State(\n",
" eos,\n",
" temperature=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" volume=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" density=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" partial_density=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" total_moles=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" moles=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molefracs=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" pressure=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molar_enthalpy=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molar_entropy=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" molar_internal_energy=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" density_initialization=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
" initial_temperature=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
")\n",
"\u001b[31mDocstring:\u001b[39m \n",
"A thermodynamic state at given conditions.\n",
"\n",
"Parameters\n",
"----------\n",
"eos : Eos\n",
" The equation of state to use.\n",
"temperature : SINumber, optional\n",
" Temperature.\n",
"volume : SINumber, optional\n",
" Volume.\n",
"density : SINumber, optional\n",
" Molar density.\n",
"partial_density : SIArray1, optional\n",
" Partial molar densities.\n",
"total_moles : SINumber, optional\n",
" Total amount of substance (of a mixture).\n",
"moles : SIArray1, optional\n",
" Amount of substance for each component.\n",
"molefracs : numpy.ndarray[float]\n",
" Molar fraction of each component.\n",
"pressure : SINumber, optional\n",
" Pressure.\n",
"molar_enthalpy : SINumber, optional\n",
" Molar enthalpy.\n",
"molar_entropy : SINumber, optional\n",
" Molar entropy.\n",
"molar_internal_energy: SINumber, optional\n",
" Molar internal energy\n",
"density_initialization : {'vapor', 'liquid', SINumber, None}, optional\n",
" Method used to initialize density for density iteration.\n",
" 'vapor' and 'liquid' are inferred from the maximum density of the equation of state.\n",
" If no density or keyword is provided, the vapor and liquid phase is tested and, if\n",
" different, the result with the lower free energy is returned.\n",
"initial_temperature : SINumber, optional\n",
" Initial temperature for temperature iteration. Can improve convergence\n",
" when the state is specified with pressure and molar entropy or enthalpy.\n",
"\n",
"Returns\n",
"-------\n",
"State : state at given conditions\n",
"\n",
"Raises\n",
"------\n",
"Error\n",
" When the state cannot be created using the combination of input.\n",
"\u001b[31mType:\u001b[39m type\n",
"\u001b[31mSubclasses:\u001b[39m "
]
}
],
"source": [
"feos.State?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we use input variables other than $\\mathbf{N}, V, T$ (the natural variables of the Helmholtz energy), creating a state is an iterative procedure.\n",
"For example, we can create a state for a give $T, p$, which will result in a iteration of the volume (density)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1.6605\\times10^{-24}\\,\\mathrm{ mol}$"
],
"text/plain": [
"1.6605390671738466e-24 mol"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# If no amount of substance is given, it is set to 1/NAV.\n",
"s = feos.State(eos, temperature=300*si.KELVIN, pressure=1*si.BAR)\n",
"s.total_moles"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1\\,\\mathrm{ mol}$"
],
"text/plain": [
"1 mol"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s_pt = feos.State(\n",
" eos, \n",
" temperature=300*si.KELVIN, \n",
" pressure=1*si.BAR, \n",
" total_moles=1*si.MOL\n",
")\n",
"s_pt.total_moles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Critical point <a id=\"Critical-point\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"To generate a state at critical conditions, we can use the `critical_point` constructor."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Critical point\n",
"temperature: 369.9506174234607 K\n",
"density : 198.1862458057178 kg/m³\n",
"pressure : 4.249677749116937 MPa\n"
]
}
],
"source": [
"s_cp = feos.State.critical_point(eos)\n",
"print(\"Critical point\")\n",
"print(\"temperature: \", s_cp.temperature)\n",
"print(\"density : \", s_cp.mass_density())\n",
"print(\"pressure : \", s_cp.pressure())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase equilibria and phase diagrams<a id=\"Phase-equilibria-and-phase-diagrams\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"We can also create an object, `PhaseEquilibrium`, that contains states that are in equilibrium."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|300.00000 K|488.99014 mol/m³|\n",
"|phase 2|300.00000 K|11.53399 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 300.00000 K, ρ = 488.99014 mol/m³\n",
"phase 1: T = 300.00000 K, ρ = 11.53399 kmol/m³"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.pure(eos, 300.0*si.KELVIN)\n",
"vle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each phase is a `State` object. We can simply access these states and compute properties, just like before."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|300.00000 K|11.53399 kmol/m³|"
],
"text/plain": [
"T = 300.00000 K, ρ = 11.53399 kmol/m³"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle.liquid # the high density phase `State`"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|300.00000 K|488.99014 mol/m³|"
],
"text/plain": [
"T = 300.00000 K, ρ = 488.99014 mol/m³"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle.vapor # the low density phase `State`"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Heat of vaporization: 14.782343503305126 kJ/mol\n",
"for T = 300 K\n",
"and p = 9.95 bar\n"
]
}
],
"source": [
"# we can now easily compute any property:\n",
"print(\"Heat of vaporization: \", vle.vapor.molar_enthalpy(feos.Contributions.Residual) - vle.liquid.molar_enthalpy(feos.Contributions.Residual))\n",
"print(\"for T = {}\".format(vle.liquid.temperature))\n",
"print(\"and p = {:.2f} bar\".format(vle.liquid.pressure() / si.BAR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also easily compute **vapor pressures** and **boiling temperatures**:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"vapor pressure (T = 300 K): 994.7761635610095 kPa\n",
"boiling temperature (p = 3 bar): 247.84035574956758 K\n"
]
}
],
"source": [
"# This also works for mixtures, in which case the pure component properties are computed.\n",
"# Hence, the result is a list - that is why we use an index [0] here.\n",
"print(\"vapor pressure (T = 300 K):\", feos.PhaseEquilibrium.vapor_pressure(eos, 300*si.KELVIN)[0])\n",
"print(\"boiling temperature (p = 3 bar):\", feos.PhaseEquilibrium.boiling_temperature(eos, 2*si.BAR)[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Phase Diagram\n",
"\n",
"We could repeatedly compute `PhaseEquilibrium` states for different temperatures / pressures to generate a phase diagram.\n",
"Because this a common task, there is a object for that as well.\n",
"\n",
"The `PhaseDiagram` object creates multiple `PhaseEquilibrium` objects (`npoints` of those) between a given lower temperature and the critical point."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"dia = feos.PhaseDiagram.pure(eos, 230.0 * si.KELVIN, 500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can access each `PhaseEquilbrium` and then conveniently comput any property we like:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"enthalpy_of_vaporization = [\n",
" (vle.vapor.molar_enthalpy(feos.Contributions.Residual) - vle.liquid.molar_enthalpy(feos.Contributions.Residual)) / (si.KILO * si.JOULE) * si.MOL \n",
" for vle in dia.states\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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yEnAoJQ6HUmLveRYzt6IEm25exKabF6GnpY1BDp4Ic/XFSFc/uLGzDxFRqzBUEpHG09fWwVAnbwx18sb7wRMQX5iDQylxOJgSiwvZSY0GX6+qrcGx9Js4ln4T/z67CzILO8VznEG2btDW4m1yIqKmMFQSUZciCAK6W9ihu4UdXuw5DHcqy3As7QYOpsQiIvU6CqsqGq1/oyAbNwqysfzaMVjpG2OUmx/GuvkjxMkbhuxNTkSkwFBJRF2apb4RJnsGYrJnIOS1NbiQnYSDKXE4nBKLW3/r7JNfWYpNNy9g080LMNLRw0POMoxxD0CYiw8s9I0k+gRERB2DxoTKnJwcnDx5ElFRUbh27RpiY2NRWVmJ4OBgrF+/vtntRowYgbS0tBb3HRkZCX19PrhPpOl0tLQx0METAx08saj/OCQU5eJwShwOpcThTOZtyBvcJi+TVylm9tERtDDI0RNj3AIw2s2fU0cSUZekMaFyz549+OSTT9q8vUwmg4mJSZPvCYLQ5v0SUefVzcwGcwNCMDcgBIWV5TiSeh37k6NxJPV6o97kcrFWMa3ke2d2oLeNC8a6BWCsewC6m9vyHEJEXYLGhEoTExMMHjwYPXv2RM+ePRETE4Ply5e3evv33nsPAwYMUGOFRNSZmesbYopXIKZ4BaJCXo2TGfHYlxyNA8kxyKtoPD/51dxUXM1Nxf9d2g9PMxuMcQvAOI8eCLRxYcAkIo2lMaFy6tSpmDp1quJ1VlaWhNUQkSYz0NFFmKsvwlx9sWTQFFzMScb+pGjsS46+Z7ii20W5WBF1DCuijsHZ2ALjPXpgvEcv9LHlgOtEpFk0JlQSEUlBW0sLwfYeCLb3wHv9x+F6QRb2JUVjf3IMruU1fl47rbQAq6JPYFX0CTgamWOcRw9M8OiJIDs3Bkwi6vQYKu/auHEjVq9ejYqKCtjY2KBfv3545JFHmn3Okojo7wRBgK+lA3wtHfB6YBjSSgqwLzkafyRF4WxmIkSIinUzygrxY8xJ/BhzEvaGpnj4bsDsb+fBsTCJqFNiqLxr7969jV7v3r0b33zzDb744gsMGTLkvttv3LgRmzdvbtWx4uPj21QjEXUuziYWmOM/BHP8hyC7rBj7kqOxJ/EaTmfeRq34V8DMKi/G2tjTWBt7GraGJnjYvQfGe/TEAHsP6GhpS/gJiIhar8uHyuDgYAwcOBA9e/aEk5MTqqurcfHiRXz77beIiYnBvHnz8OuvvyIgIKDF/eTk5CA6OrqdqiaizsbOyFQxbWRueYkiYJ7KuN1oRp+c8hL8FHcGP8WdgY2BCcZ79MAkz0D04y1yIurg2hQq33nnHZUcXBAEfPzxxyrZV1stWbKk0WtDQ0OEhoZi0KBBmD59OqKjo/HZZ59h7dq1Le7H1tb2vsGzXnx8PCoqKu6/IhFpJBtDEzzlMwBP+QxAfkXp3YAZhZPptxqNhZlbUYJ1cWewLu4MnIzN8Ui33pjUrRd6WjuzFzkRdThtCpXbtm2DIAgQG9y+eRD123aEUNkcAwMDvP7663juuedw9uxZFBYWwty8+QGNp02bhmnTprVq3+Hh4byqSUQAACsDY0yXBWO6LBh3KstwIDkGexKv4c/0W6iurVGsl15aiO+ijuO7qOPoZmaDSZ69Malbb3hb2ElYPRHRX9oUKidPntwl/kru27cvAKC2thYpKSkthkoiImVZ6hvhCe9+eMK7Hwoqy7AvKRo7EyJxIuNWo2cwE4py8fWVw/j6ymH4WzliUrfemNitF1xNrSSsnoi6ujaFyr/fMtZUurq6ip9rampaWJOISLUs9I0wTdYf02T9kVNejD2JUdhx+wrOZyc1Wi8mPwMx+Rn45OI+BNm6YaJnbzzi0Qt2RqYSVU5EXVWX76jTkhs3bih+dnBwkLASIurKbA1NMdNvEGb6DUJaSQF2JlzFjttXEZWf3mi9iznJuJiTjP+e242hTt541KsPxroFwEhXT6LKiagrYahswffffw8A6N69O+zt7SWuhoiobpiieT2HY17P4YgvzMGO21exI+Eq4gtzFOvUiiKOpd3AsbQbMNbRw8PuPfBo9z4Y7ODFMTCJSG3UEipjYmKwa9cuREVFIT+/bsoyKysr9OzZExMmTIC/v786DvvAfvzxR+jp6WHChAmwtLRULL9z5w6++uor7N+/HwDw2muvSVUiEVGzvMxtMb/PSLwRGIaY/Axsv30VOxOuIq20QLFOqbwKv8dfwu/xl+BgZIbJnoF41Ksv/Kx494WIVEsQ29qFuwllZWVYtGiRYiDxv++6vnPPuHHj8OGHH8LIyEhVh0ZGRgYmT56seF1VVYWysjLo6Og0mhVn7ty5eO655wAAixcvxk8//QRBEODs7AwrKytUVFTg9u3bkMvl0NLSwvz58xXrq0p97++AgABs3bpVpfsmoq6tVqzF2axEbLl1CXsSr6G4urLJ9fytHPGoVx9M9gyEvZFZO1dJRO2tPbKHyq5U1tbW4qWXXsLZs2chiiJsbW0xcOBAxbOImZmZOHv2LLKzs7F3717k5+dj9erVKutFXlNTg4KCgnuWy+XyRssbjg85fvx4AEBkZCTS09MRFxcHbW1tuLi4IDg4GNOnT4efn59K6iMiag9aghYGOXhikIMnPhw4CYdSYrEl/hIiUm80GgOzvoPP4gt/YKhjdzzavS8edu8BQx3dFvZORNQ8lYXK7du348yZM9DR0cHbb7+N6dOnQ+tvz+7U1tbi119/xSeffIIzZ85gx44dja4uKsPFxQXXr19/oG0CAwMRGBiokuMTEXU0hjq6eKRbLzzSrRfyKkqw4/ZVbIm/jKu5qYp1akURx9Jv4lj6TZjp7cCkbr3xuHc/BNq4dImh44hIdVQWKnfu3AlBELBw4UI89dRTTa6jpaWFGTNmoKamBh9//DG2b9+uslBJRETNszYwwWz/IZjtPwS3CrKxJf4ytsZfbvT8ZVFVBdZfP4v118/Cx8IeT3j3Q7hXH9gYmjS/YyKiu1TWDbD+1vHjjz9+33Uff/xx6OjoIDY2VlWHJyKiVupuYYe3gsbg9GML8dvDz+MJ734w1mk87ND1gix8cH4P+m36GHMO/4SDyTGQ13K8XiJqnsquVJaWlsLY2BgGBgb3XdfAwADGxsYoKytT1eGJiOgBNXz+8oMBj2BP4jVsunkRZ7MSFOvIxVrsT47B/uQY2BmaItyrD57w7sfpIYnoHioLlZaWlsjNzUVeXh6sra1bXDcvLw9FRUWwsbFR1eGJiEgJxrr6eNy7Hx737ofbhbn47dZF/HbrIjLLihTrZJcXY2XUcayMOo4gWzdMk/XHxG69YKyrL2HlRNRRqOz2d2BgIERRxNKlS++77rfffgtRFBVzaxMRUcfhaW6Dt4LG4Oxjb2P9qFmY4NETulrajda5mJOMf57cgqBNH+Nfp7cjOi+9mb0RUVehsiuVM2bMwIEDB7Bp0yaUlpbilVdegbu7e6N1kpKSsHTpUuzevRuCIGDGjBmqOjwREamYtpYWQl18EOrig/yKUmy7fQWbbl5ATH6GYp2S6kr8FHcGP8WdQaCNK2b4BGNSt96cGpKoC1JZqBwwYACeffZZrFu3Drt378bu3bvh6OgIO7u6526ysrKQmZmpWH/mzJkIDg5W1eGJiEiNrAyMMcd/COb4D0FUXhp+vXEeW+MvNxpc/UpuCq7kpuCDc7sR7tUHM3yC4W/lJGHVRNSeVDpN4zvvvANXV1csXboUhYWFSE9PR3p641siFhYWePXVV3mVkoiok+ph7YzFg5zxbr9x2JlwFb/cOIfLOSmK94urK7Eu7gzWxZ1BH1tXzJAFYyKvXhJpPJXP/f3UU0/hsccew8mTJxEVFYW8vDwAgLW1NXr06IEhQ4ZAX58PdRMRdXZGunqYJuuPabL+iMlPxy/Xz91z9fJyTgou56Tgv+d249HufTHTdxC6s+c4kUZSeagEAH19fYwYMQIjRoxQx+6JiKiD8bdywuJBkxVXL3++fg5XchtfvVwbexprY08jxLE7ZvoNwkhXX+j8rQMQEXVeagmVRETUNTW8ehmdl45fbtRdvSxpcPXyRMYtnMi4BSdjczzjOxBPyvrD2oCz9hB1dmoJlTU1NUhMTERRURHkcnmL6/bv318dJRARkcQCrJ3w8aDJeLffw9h2+wrWxp5G3J2/OmymlxZiycX9+PLyITzSrRdm+g1GH1tXCSsmImWoNFRmZmbiyy+/xMGDB1FRUXHf9QVBQExMjCpLICKiDsZYVx9P+QzADFkwzmUlYm3safyRFAW5WAsAqKqtwZb4y9gSfxm9bVww03cQHunWCwY6uhJXTkQPQmWhMiUlBU8++STy8vIgimKrtmntekRE1PkJgoABDt0wwKEbMsuK8Mv1s/jl+jlklxcr1rmam4o3TvyGD87vwQyfYMz0GwwHIzMJqyai1lJZqPzyyy+Rm5sLKysrLFiwACEhIbCxsYG2Nh/CJiKixhyMzLCgzyi82isU+5KisTbuNM5lJSrev1NZhmWREfgu6k880q0XnvMPQU8bZ+kKJqL7UlmoPHXqFARBwJdffomBAweqardERKTB9LR1MNGzNyZ69kZMfjrWxp7B1vjLqKipBgBU19Zga/xlbI2/jAH23fBcQAhGufpBW0tlswwTkYqoLFRWVVXBwMCAgZKIiNrE38oJnw4Jx7/6jcWvN85jTewppJcWKt4/m5WAs1kJcDe1wmz/IXjCux9MdDnuMVFHobI/9VxcXPiMJBERKc1C3wjzeg7HyakLsXz4k/f0CE8qzsd/zu5C/00f48Nze5BackeiSomoIZWFyocffhiVlZU4ffq0qnZJRERdmK6WNiZ69sauCS9jx/h5mODRE1qCoHi/uLoS30X/icG/f4qXI35FVF6ahNUSkcpC5ezZs+Hr64tFixYhJSXl/hsQERG1UpCdO1aGzsCpqQvxQsBQmDa47V0ritiRcBVjdy7F9P0/4s/0m7xzRiQBlT1TaWBggDVr1mDRokWYOHEixowZg549e8LY2LjF7SZPnqyqEoiISMO5mFhiUfB4vNFnJDbdvIDVMSeRVJyveP94+k0cT7+JHlZOmNdzOMZ79OBUkETtRKWDn6elpSE3Nxfl5eXYsWMHduzY0eL6giAwVBIR0QMz0dXHHP8hmOk7CH8kR2PFtWO4mpuqeD8qPx0vH/sV/3fRCs/3GIonvINgqKMnYcVEmk9loTIuLg5PP/00ysvLAQC6urqwtLTkOJVERKQ22lpamODRE+Pde+BU5m2suHYMEWk3FO8nl+TjvTM78OXlQ5jlPwgzfQfB0qDlO2hE1DYqC5VLly5FWVkZXF1d8eGHHyI4OBhaHEeMiIjagSAIGOLohSGOXojJT8eKa8exMyESNXengsyvLMUXlw9h+bVjeMpnAF7sMQz2nKmHSKVUlvouX74MQRDw1VdfYeDAgQyUREQkCX8rJywdPg0np/4Ts/0Gw7DBHOLl8mp8H30Cg3//FO+e3oG0kgLpCiXSMCpLfuXl5TA0NESPHj1UtUsiIqI2czGxxAcDJ+LcY2/jzT6jYKX/123vyho51sWdRsiWz/DPk1uQWJQnYaVEmkFlodLd3R1yuRw1NTWq2iUREZHSLA2M8XpgGM489hbeD54Ae0NTxXvVtTX49cZ5DN/6Bf5xfBNuFWRLWClR56ayUDl58mRUVVXhyJEjqtolERGRyhjp6mFuQAhOTl2IjwdNhrOxheK9GrEWW+IvI3TbV5h3dANi8jOkK5Sok1JZqHz66acxcOBA/Pvf/8bly5dVtVsiIiKVMtDRxTO+A/Hno2/isyGPwt3UWvGeCBG7EiMxesc3mHP4J0TnpUtYKVHnorLe3ytWrEBgYCBiYmIwffp0BAUFtWrw81deeUVVJRAREbWanrYOnpT1x2Pd+2JnQiSWXj2Km4V/3f7enxyD/ckxGOfeAwv6jIKPpb2E1RJ1fCoLlcuWLYNwd05WURRx4cIFXLx48b7bMVQSEZGUdLS0Ee7VB5M9e2NvUjS+vXqk0e3vvUlR+CMpGhO79cL8PiPhZW4rYbVEHZfKQmX//v1VtSsiIqJ2pyX8NZD6geQYfHHlkCJciqibX3xXYiTCPfvg9cAweJhZ32ePRF2LykLl+vXrVbUrIiIiyQiCgDHuARjl5oe9SdH48vJB3LjbK7xWFPF7/CVsu30Fj3Xvi9cDw+BiYilxxUQdA0coJyIiakL9lcuDk17HsuHT4Glmo3ivRqzFxpsXMHTL5/jX6e3IKiuSsFKijoGhkoiIqAXaWlqY7BmII1PewFchj8Hd1ErxXnVtDX6KO4OQLZ/h04v7UVRVIWGlRNJiqCQiImoFHS1tPOYdhIjwBfh0SHijcS7L5dX4NvIohvz+KVZF/YkKebV0hRJJhKGSiIjoAehqaWO6LBjHH30THw6YCBsDE8V7dyrL8MH5PRi+9Qv8dvMiamprJayUqH0xVBIREbWBvrYOZvkPxsmp/8SCPiNhrKOneC+ttABvnPgNo3d8g4PJMRBFUcJKidoHQyUREZESjHX18UbgSJycuhCz/QZDV0tb8d71gizMOvwTwveuxIWsJAmrJFI/hkoiIiIVsDE0wQcDJ+JY+AKEe/WBAEHx3vnsJEzeuwIvHv0FScV5ElZJpD4MlURERCrkZmqFb4c9gf2TXkWos0+j93YnXkPo1i/x0fm97ClOGoehkoiISA38rZywfvQsbB77HHrbuCiWV9XWYGXUcYT8/hl+ijsDeW2NhFUSqU6bQ+Xp06dRy15tRERELRrs6IVdE17CN8OegKORuWJ5fmUp/nV6O0Zt/wZHUq9LWCGRarQ5VM6aNQuDBg3CwoULsW/fPpSVlamyLiIiIo2hJWjhUa8+OP7oArzZZxQMdXQV790szMYzB9dgxoHViLuTKWGVRMpp89zfPj4+uH79Onbu3Ildu3ZBV1cXgwYNwogRIzBixAjY2tqqsk4iIqJOz1BHD68HhmGarD8+u3QAm29ehIi64YaOpd3AifRbeNZ3IBb0GQVzfUOJqyV6MIKoxOBZ6enpOHz4MA4dOoSLFy9CLpdDEAQIgoAePXpg5MiRCAsLg5eXlypr7vTCw8MRHR2NgIAAbN26VepyiIhIItF56fjvud04lXm70XJrA2O8EzQWj3sHQUtg9wdSXntkD6VCZUNFRUWIiIjAoUOHcOLECZSVlUEQ6oZTcHNzQ1hYGEaMGIGgoCDF8q6KoZKIiOqJoogDyTH44Pzee4YbCrRxxYcDJ6KPratE1ZGm6FShsqGqqiqcOXMGhw4dwtGjR5GTk1N3MEGApaUlQkNDMWLECISEhEBfX1/Vh+/wGCqJiOjvKuTV+D76BL6NPILyv80dPs27H94OGgsbQ5NmtiZqWacNlX8XGRmJQ4cO4fDhw4iPj687sCDAwMAAgwcPRlhYGMLCwmBubn6fPWkGhkoiImpOekkBPrqwFzsTIhstN9MzwII+o/Cs70DoNJi1h6g12iN7tMuDGr169cL8+fOxZ88e7N+/HwsXLkSfPn1QWVmJw4cP491338X69evboxQiIqIOzcnEAssfmo5NY5+Dj4W9YnlRVQX+c3YXHt65FBezkyWskKhp7f70r7u7O2bPno0NGzbgxIkT+OijjxAaGgpDQ/ZyIyIiqjfE0Qv7J72G/w54BGZ6BorlsXcyMXnPCvzr9HYUVpZLWCFRY20eUkgVrKysMHXqVEydOlXKMoiIiDokHS1tzPEfgkndeuOTi39g082LAAARIn6KO4N9SdH4T/AETOzWq8t3giXpcZwCIiKiDs7G0ARfhDyGreNehMzCTrE8u7wYLx/7FU8dWI3EorwW9kCkfgyVREREnUSwvQf2TXwNbweNgb72Xzcbj6XfRNj2r7D06lFU1cglrJC6MoZKIiKiTkRPWwev9ArF4clvYLizTLG8skaO/7u0H2N3fovzWYnSFUhdFkMlERFRJ+RhZo2fR83C/4Y/CdsG41feKMhG+N7v8O8zO1FaXSlhhdTVMFQSERF1UoIgYJJnb0RMWYBnfAdCQF1nHREiVseewsjtX+NE+i2Jq6SuQtLe36qUk5ODkydPIioqCteuXUNsbCwqKysRHBx83zEwq6ursW7dOuzcuRPJycnQ1dWFr68vnn76aYwePbqdPgEREVHbmOsb4uNBk/GoVx+8eWILbhZmAwBSSu5g2v4fMF0WjPf6j2s0NBGRqmlMqNyzZw8++eSTB96usrISs2bNwsWLF6GtrY3u3bujvLwc586dw7lz5/Dcc8/hzTffVEPFREREqhVk5459k17DN1cO43/XjqFGrAUAbLhxDkdS4/B/g8MR5uorcZWkqTTm9reJiQkGDx6MF154AcuWLcNLL73Uqu0+++wzXLx4ES4uLti9ezd27tyJgwcPYvny5dDT08P333+PI0eOqLl6IiIi1dDX1sHCoDHY88jL8LdyVCzPLCvCs4fW4rXjm3CnolTCCklTaUyonDp1KtasWYP58+dj1KhRsLa2vu82ubm52LhxIwBg8eLF8PT0VLwXFhaGuXPnAgCWLVumnqKJiIjUpIe1M/Y88goW9h0NvQZzhW+Nv4yR27/GkdTrElZHmqjNt7/9/PyUPriBgQGsrKzg5+eHCRMmYOzYsUrv80EcOXIE1dXV8PDwwMCBA+95f9q0aVi+fDmio6ORnJwMNze3dq2PiIhIGbpa2nit9wiMcQvAghO/40puCgAgq7wYzxxcg6d8BmBR/3Ew1tWXuFLSBG2+UimKotL/ysvLkZaWhsOHD+ONN97Ayy+/jNraWlV+vhZduXIFABAUFNTk+/b29nBxcWm0LhERUWfjY2mPHePn4b1+4xpdtfz5+lmM3vEtLmQlSVgdaYo2X6l85ZVXlD54dXU1cnNzceHCBSQlJeHIkSP49ddfMWPGDKX33RqJiYkA0OIVSDc3N6SmpiIhIaHFfW3cuBGbN29u1XHj4+NbXSMREZEqaGtp4cWew/CQiwz/OL4J0fkZAICk4jyE/7ESL/UcjvmBI6GnrTF9eKmdSRoqG3r//fexceNG7Ny5s91CZWFhIQDA3Ny82XXq3ysqKmpxXzk5OYiOjlZdcURERGrga+mAXRNextdXDmPZtQjUiiJqRRHLIiNwJPU6vhn6BPysHKQukzqhDvPnyBtvvIGNGzfi9u3b7XbMysq6mQZ0dXWbXUdPTw8AUFFR0eK+bG1tERAQ0KrjxsfH33d/RERE6qJ3t4d4mKsv/nF8MxKL8wAAMfkZGL9rKd4OGou5AUOgJWhMf15qB5KGyuLiYpiamgKouyJobm5+3yuCqqSvX/dgcnV1dbPrVFVVAajrVNSSadOmYdq0aa06bnh4OK9qEhGR5ILs3HFg0j/w0YW9+CnuDACgqrYGH5zfg+PpN/HV0Mdga2gqcZXUWajsT5Cvv/76gdYvLi7GrFmzGi1buXIl1q1bp6qS7svMzAzAX7fBm1L/Xv26REREmsRIVw8fD5qM9aNmwb5BgIxIu4HRO77BsbQbElZHnYnKQuXKlSvx66+/tmrd0tJSzJkz556rdX369EFwcLCqSrovDw8PAEBSUvO93pKTkxutS0REpIlCXXxwcPLrGOX615CBOeUlmHFgNT48vxdVNXIJq6POQGWhUltbGx999BEOHjzY4nqlpaWYO3cuIiMjYWtrq6rDt0lgYCAA4NKlS02+n5WVhdTU1EbrEhERaSorA2OsDnsGHw6YCP0GvcC/izqOyXtW4HZhroTVUUenslD50UcfoaamBm+++SYuXLjQ5DplZWV4/vnncfnyZdjY2GDt2rWqOnybhIWFQVdXF4mJiThz5sw979fPtuPv7w93d/f2Lo+IiKjdCYKAWf6DsWvCy/A2t1Msj8xLw8M7v8XW+MsSVkcdmcpC5ZQpUzB//nxUVlbi5Zdfxs2bNxu9X15ejhdeeAEXL16EtbU11q1b12haRCnY2NjgiSeeAAC8++67jXqeHzlyBD/88AMA4OWXX5akPiIiIqn4Wzli78RXMEP212NppfIqvHZ8E945tQ0V8uY7uVLXpNLe388//zyys7Px888/Y+7cudi4cSMcHR1RUVGBF198EefPn4eVlRXWrFkDLy8vVR4aGRkZmDx5suJ1fa/tS5cuYcCAAYrlc+fOxXPPPad4/c9//hPR0dG4fPkyJkyYAG9vb5SVlSmepZw9ezZGjhyp0lqJiIg6A0MdPfzfkHAMc/bGwpNbUFhVNxze+utncSU3Fd+FzoCbqZXEVVJHofIBqN577z2MHTsWWVlZmDNnDrKysjBv3jycPXsWFhYWWL16NWQymaoPi5qaGhQUFCj+lZWVAQDkcnmj5X8fH9LAwAA//fQTFixYAC8vLyQmJuLOnTsIDg7Gt99+i7feekvltRIREXUm4z164o+Jr6G3jYti2bW7t8MPJMdIWBl1JIIoiqKqd1pdXY05c+bg3LlzMDAwQEVFBczNzbF27Vr4+fndfwcarn6cyoCAAGzdulXqcoiIiFqlskaOD87txrq4xv0Q5vUYjreCRkOnwbzi1LG0R/ZQy1D5urq6WL58Ofz8/FBRUQEzMzOsXr2agZKIiKgT09fWweJBk7Fs+DQY6egplq+IOoYn9n2PrLL2m8CEOp42PVO5bNmyVq3Xs2dPxMbGIigoCEePHsXRo0fvWUfVc4gTERGRek32DESAlRNeOPozbhRkAwDOZiVi3M6lWDXiaQTZuUlcIUmhzaFSEIRWrx8REYGIiIgm32OoJCIi6ny8Leywe8IrePv0NsUwQ1nlxXjsj+/w8aDJmCbrL3GF1N7aFCr79+cXhYiIqKsz0tXDN0MfR5CtG/5zdhfkYi2qamvw5sktuJaXjvcHTIAun7PsMtoUKtevX6/qOoiIiKgTEgQBz/oNgo+lPV44+gvyKkoBAOviTiPuTia+C50BG0MTiauk9qCWjjpERETUtQx08MQfj7yKXtbOimVnsxIwbtdSROamSlgZtReGSiIiIlIJJxMLbBn3IsK9+iiWpZcWYsreldh5+6qElVF7YKgkIiIilTHU0cU3Qx/H+8EToC3UxYzKGjleOvYrvrlyGGoYHps6CIZKIiIiUilBEDA3IAS/jJ4NC30jxfLPLh/E639uRmWNXMLqSF0YKomIiEgtQpy6Y+f4l9DNzEaxbEv8ZTy5/wfk3+3QQ5qDoZKIiIjUxtPcBjsnvISBDt0Uy85lJeKR3ctx6+7A6aQZGCqJiIhIrSz1jbBh9Bw83j1IsSypOA+T9izH6czbElZGqsRQSURERGqnp62DL0Km4u2gsYplhVUVmLH/R+xOvCZhZaQqbQ6Vp0+fRm1trSprISIiIg0mCAJe6fUQVobOgL523fwrVbU1mHd0A9bEnJK4OlJWm2bUAYBZs2bB3Nwcw4cPx4gRIzBs2DAYGRndf0MiIiLq0iZ49IS9oSlmHlqHwqpyiBCx6OxOZJUX4a2+YyAIgtQlUhu0+Uqlj48PCgsLsXPnTrzxxhsYOHAgXnjhBWzatAk5OTmqrJGIiIg0TH97D2wfPw9OxuaKZcsiIzD/xG+orq2RsDJqqzaHyh07duDIkSN49913ERwcjNraWhw7dgzvv/8+hg8fjscffxyrVq1CfHy8KuslIiIiDeFtYYcd41+Cr6WDYtlvty5h1qF1KKuukrAyagtBVNHQ9kVFRYiIiMChQ4dw4sQJlJWVKS5fu7m5ISwsDCNGjEBQUFCXv6wdHh6O6OhoBAQEYOvWrVKXQ0REJKnCynLMOfITzmQmKJb1t3PHulGzYKZnIGFlmqM9sofKQmVDVVVVOHPmDA4dOoSjR48qbocLggBLS0uEhoZixIgRCAkJgb6+vqoP3+ExVBIRETVWIa/GP/7cjD0NeoL3tHbGL6Nnw8rAWMLKNEN7ZI82d9RpiZ6eHoYNG4Zhw4YBACIjI3Ho0CEcPnwY8fHx2LJlC7Zu3QoDAwMMHjwYYWFhCAsLg7m5+X32TERERJrIQEcXy4c/iXf0DLHhxjkAwLW8NEz94ztsGDMXDkZmEldI99Mu41T26tUL8+fPx549e7B//34sXLgQffr0QWVlJQ4fPox3330X69evb49SiIiIqIPS1tLC/w2egucDQhTLbhRk49G9K5FSnC9hZdQa7T74ubu7O2bPno0NGzbgxIkT+OijjxAaGgpDQ8P2LoWIiIg6GEEQsKj/eLwRGKZYllScjyl7VyK+kKPLdGRquf3dWlZWVpg6dSqmTp0qZRlERETUgQiCgAV9RsFYRx8fXdgLAMgsK8Kje7/DxrFzG/UWp46D0zQSERFRh/Riz2H4ZNBkCKgbNSa3ogRP7Pse1+9kSVwZNYWhkoiIiDqsp30H4quhj0Hr7nCEeRWleGLf97hRwGDZ0TBUEhERUYc2tXtffD30cUWwzK0oweN/MFh2NAyVRERE1OGFe/XBV38Llk/s+x43C7IlrozqMVQSERFRp/CoVx98GfKY4hnLnPISPL5vFW4X5kpcGQEMlURERNSJTO3eF18NndooWD65/weklxRIWxgxVBIREVHnMrV7EL4IeVTxOq20ANP2/4Dc8hIJqyKGSiIiIup0Hvfuhw8HTFS8vl2UixkHfkRhZbmEVXVtagmVWVlZyM3l8w1ERESkPrP8B2Nh39GK19H5GXj20FqUVVdJWFXXpbIZdURRxHfffYfvv/8eZWVlAABDQ0P4+PjA399f8c/b2xs6OpJO5ENEREQa4tVeoSisqsB3UccBABeyk/DckfVYM/JZ6Gkzb7QnlbX2hg0b8PXXXzdaVlZWhsuXL+PKlSt/HVBHB97e3oqQOX36dFWVQERERF2MIAh4r9/DKK6qwIYb5wAAx9Jv4s2TW/DN0Mch3B2CiNRPZaFy06ZNAIB+/frhrbfegoWFBZKSkhAbG4uYmBjExMQgOTkZ1dXViteCIDBUEhERkVIEQcAngyajpLoCOxMiAQBb4y/DydgcbweNlbi6rkNloTIlJQWCIOCLL76Avb09AMDV1RUhISGKdUpKShAXF4fo6GhER0cjNjZWVYcnIiKiLkxbSwtfDX0cueUlOJV5GwCwLDICTsYWeMZ3oMTVdQ0qC5UmJibQ09NTBMrm1unXrx/69eunqsMSERERAQD0tXXwQ9gzCN+7EnF3MgEA753ZAXtDU4xxD5C4Os2nst7fvXv3RmlpKaqq2OOKiIiIpGGmZ4CfRs2Cg5EZAKBWFPHysY24mJ0scWWaT2Whctq0aZDL5Th06JCqdklERET0wJyMzbF+1GyY6uoDACpqqjHr0DokFedJXJlma1OonDNnDr788kvs27cPKSkpAICQkBBMmDABS5YsQVZWlkqLJCIiInoQflYO+GHE09DV0gYA5FeWYvahn1BSXSlxZZqrTc9Unjx5EqdOnVK8NjU1hZ+fH7y9vVFVVYXJkyfjo48+QlhYmMoKJSIiInoQQ5y647Mhj+L1PzcDAK4XZOG1YxvxQ9jT0BI4qaCqtSlUvvDCC4iJiUFsbCxyc3NRVFSEs2fP4ty5c4p1XnnlFdja2iI0NBQ9e/ZEQEAABz4nIiKidjW1e19cv5OFFVHHAAAHUmLx6aUDHGpIDdqU8N544w3Fz1lZWYiJiUF0dLRi/MnMzLoeV9nZ2di8eTM2b677C0FXVxfdu3dHQEAA/P39ERAQgF69eqngYxARERE17e2gMbhZmIVDKXEA6oYa8rFwwBSvQGkL0zBKXza0t7eHvb09QkNDFcvy8/MRGxvbKGimpKSgqqpKcYUTqBusNCYmRtkSiIiIiJqlraWFpcOmYfKeFbheUNfv482Tv6ObmTUCbV0lrk5zqOVetJWVFYYMGYIhQ4YolpWUlCgCZv3g54mJieo4PBEREVEjpnoGWD3yGYzf9T8UVJahskaOOUfW449HXoWdkanU5WmEVj2lOm/ePCQnKze+k4mJCYKDgzFz5kx89tln2Lt3Ly5duqTUPomIiIhay93UGt+FzoDO3U46WWVFePnYr5DX1khcmWZoVaiMiIjA+PHjsWTJEhQVFans4AYGBirbFxEREdH9DHH0wn+CJyhen868jc8uHZSwIs3RqlC5fft2BAcHY+3atRg9ejR++eUX1NbWqrs2IiIiIpWb6TcIkzx7K17/71oEDiSzj4eyWhUqfXx88OOPP+KHH36AnZ0dPvzwQ0ycOBHHjx9Xd31EREREKiUIAj4dHA5vczvFstf/3MwZd5T0QCN/hoSEYMeOHVi8eDGKi4vxwgsvYO7cuYiPj1dXfUREREQqZ6yrj1UjnoKRjh4AoKiqAs8f+RmVNXKJK+u8Hng4eUEQ8Oijj2L//v145ZVXcOnSJUyaNAkffPAB7ty5o44aiYiIiFTO28IOnw95VPE6Oj8DSy7uk7Cizq3NcxQZGBjg5ZdfxsGDBxEeHo5NmzZh9OjRWLNmDaqrq1VZIxEREZFaTPTsjWd9Bylefx99AkdTr0tYUeel9MSX1tbW+OCDD7Bz504EBQXh008/xYQJE+6/IREREVEH8F7/cfCxsFe8fuPP35BTXixhRZ2TUqEyJSUFR48exapVq7Bq1SpkZ2dDS0tL6TEtiYiIiNqLoY4u/vfQk9DXrpsTJreiBPP//B2iKEpcWefSqhl1kpOTcevWrUb/EhISUFFRAQAQRRG6urro1q0bxo4dC5lMptaiiYiIiFTJ19IBi/qPx3tndgAAjqZdx+rYU5jjP+Q+W1K9VoXK0aNHQxAEiKIILS0tuLq6IiQkBN7e3vD29oZMJoOHhwe0tbXVXS8RERGRWjzrOxARaddxKCUOAPDxhT8wzMkb3hZ299mSgFaGytmzZ0Mmk0Emk6F79+7Q09NTd13taunSpVi2bFmL67z//vt48skn26kiIiIiam+CIOCLkKkYtf0bZJcXo7JGjvknfsP2cfOgraV0NxSN16pQuXDhQnXX0SFYW1vD3d29yfdsbW3buRoiIiJqb9YGJvi/wVMw6/BPAIDLOSlYFf0n5vUcLnFlHV+rQmVXMWzYMCxZskTqMoiIiEhCo9z8Ee7VB1vjLwMAPr98EKNc/dCdt8FbpPJruUeOHME333yj6t0SERERtZv/DngEdoamAHD3NvjvqKmtlbiqjk3lofLWrVtYuXKlqndLRERE1G4s9Y2wZPAUxetLOcn4IeaEhBV1fLz93UBcXBwWLFiAnJwcGBsbw8fHB+PHj4e3t7fUpREREVE7G+3mjymegdh2+woA4NNLBzDWPQDuptbSFtZBMVQ2EBsbi9jYWMXrI0eOYOXKlXjmmWfw1ltvtThk0saNG7F58+ZWHSc+Pl7pWomIiEj9Phg4EScybiGnvASVNXK8d3onfho1E4IgSF1ah9OqUPnRRx/B398ffn5+8Pb2ho6OZmVROzs7vPbaaxg6dChcXFxgYmKChIQEbNiwARs3bsS6deugo6PTYi/4nJwcREdHt2PVREREpG6W+kb4T/AEvHJsI4C6QdH3JkVhvEdPiSvreFqVDn/++WdFItfR0YGXlxf8/Pzg5+cHf39/+Pr6wsTERK2FqtMTTzxxzzIfHx/897//hYuLCz7//HOsW7cO06dPh4uLS5P7sLW1RUBAQKuOFx8fr5iNiIiIiDq2Sd16Y9PNC/gz/RYA4D9nd2G4swwmuvoSV9axCGIrJra8ffs2YmNjERMTg7i4OMTExODOnTt1OxAECIIAZ2dn+Pn5oaCgAOfPn290G7kzq6mpwUMPPYTs7Gy8++67eOaZZ5TeZ3h4OKKjoxEQEICtW7eqoEoiIiJSp9uFORi5/WtU1dYAAJ4LCMF/gidIXFXrtUf2aNWVSk9PT3h6emL8+PGKZVlZWfcEzQMHDgCARj1noK2tjd69e+PgwYNISkqSuhwiIiKSgKe5LV7u9RC+unIYALA65hSmy4I5hWMDbX440t7eHvb29njooYcUy0pKShRBU5Po6uoCAORyucSVEBERkVRe7vkQtsZfRlJxPmrEWnxwbg/Wj54ldVkdhkrHqTQxMUH//v3x7LPPtrhebW0tjhw5gpdeekmVh1ebmzdvAgAcHBwkroSIiIikYqCji/f6j1O8Ppp2HUdSr0tYUcfSrt24ExMT8fvvv2P79u3Iy8trz0O3WUREhCJUDhkyROJqiIiISEpj3QIwyMETpzNvAwA+PLcHQ526Q1er+WEHuwqVz6jzd+Xl5di6dSumT5+Ohx9+GD/++CNyc3PRiv5B7eLmzZv497//jbi4uEbLa2trsXv3bixYsAAAEBoail69eklRIhEREXUQgiDg/eAJEFDXf+RmYTZ+jjsrcVUdg9quVF65cgW///47/vjjD5SVlSlCpKmpKcLCwrB9+3Z1HfqByOVybNq0CZs2bYKFhQWcnJygra2N5ORkFBYWAgD69euHTz/9VOJKiYiIqCMIsHbCNFk//HrjPADgyyuHMLV7X5jqGUhcmbRUGirz8/Oxfft2bNmyBbdv110WFkUROjo6CAkJweTJkzFixAjI5fIOEyqdnZ3x+uuv48qVK4iPj0dSUhKqqqpgbm6OYcOGYcKECZgwYUKLs+kQERFR17Kw72jsvH0VpfIq3Kksw/fRJzC/z0ipy5KU0qFSFEUcO3YMW7ZswdGjR1FTU6O4KtmjRw9MnjwZ48aNg5WVlWKbjtSL2szMDPPmzZO6DCIiIupEbA1N8VyPofj67hBDq6L/xEy/QbAyMJa4Mum0OVQmJydjy5Yt2LZtG3JychRB0snJCY888ggmTZoET09PlRVKRERE1JE8HzAUa2JOobCqHCXVlfjftWNY1KB3eFfT5lA5evRoCIIAURRhbGyMMWPGYPLkyQgODlZlfUREREQdkpmeAV7u9RA+vvAHAGBt7Ck8FxACByMziSuThtK9v4OCgrBr1y58/PHHDJRERETUpczyGwQ7Q1MAQGWNHN/cvR3eFbU5VOrp6UEURVy6dAmjR4/Giy++iD/++ANVVVWqrI+IiIiowzLU0cNrvUcoXm+8eQEZpYUSViSdNofKEydO4L333oOvry/kcjkiIiIwf/58DB48GO+99x7OnTunyjqJiIiIOqTpsv5wNDIHAFTX1uCH6BMSVySNNodKMzMzPPXUU9i2bRu2bt2KJ598EqampigpKcGWLVvw7LPPYsSIEfjqq68QHx+vypqJiIiIOgw9bR083yNE8frn62dxp7JMwoqkoZIZdfz9/fGf//wHJ06cwKeffor+/fsDANLT07Fq1SpMmDABjz76KH7++Wfk5+er4pBEREREHcZ0WTAs9I0AAKXyKvwUe1riitqfSqdp1NPTw8SJE/HTTz/hwIEDePHFF2Fvbw9RFBEdHY3Fixdj2LBhePXVV1V5WCIiIiJJGevqY5bfIMXrH2NOoVzetfqZqG3ub1dXV7z++us4evQoVq1ahdGjR0NbWxtyuRynTp1S12GJiIiIJDHbbzAMdXQBAPmVpYppHLsKtYXKeoIgYNiwYfj2229x/PhxvPXWW/Dy8lL3YYmIiIjalaWBMWbI/hpecVX0n6iprZWwoval9lDZkJWVFWbNmoXdu3dj48aNmDp1ansenoiIiEitnu8xDNpCXbxKLSnA4dQ4iStqP+0aKhsKDAzEhx9+KNXhiYiIiFTOydgcD7sHKF6vie06j/xJFiqJiIiINNEsv8GKn/9Mv4WbBdkSVtN+GCqJiIiIVCjY3gN+lg6K12u7yPBCDJVEREREKiQIAmY2uFq5Nf5SlxheiKGSiIiISMWmeAbCRFcfAFBcXYm9SdESV6R+DJVEREREKmakq4dHuvVSvN5884KE1bQPhkoiIiIiNXiiez/Fzycz4pFcrNlTVTNUEhEREalBkJ0bvMxtFa8337ooYTXqx1BJREREpAaCIODx7kGK11tvXYYoihJWpF4MlURERERqEu7VBwIEAEByST4i89Ikrkh9GCqJiIiI1MTR2BzB9u6K17sSIiWsRr0YKomIiIjUaILHX73AdydGauwtcIZKIiIiIjUa59FDcQs8taQAV3NTJa5IPRgqiYiIiNTI3sgMwfYeite7Eq9JV4waMVQSERERqVnDgdD3aejsOgyVRERERGo2xs1f8XNScR4SinIlrEY9GCqJiIiI1MzR2By+lg6K1xGpNySsRj0YKomIiIjawUPOMsXPEWkMlURERETUBqENQuWpzHhUyKslrEb1GCqJiIiI2kE/ew8Y6egBAMrl1TifnShtQSrGUElERETUDvS1dTDE0UvxWtOeq2SoJCIiImonDZ+rPJZ+U8JKVI+hkoiIiKidNLxSef1OFgoqyySsRrUYKomIiIjaiZe5Laz0jQEAIkRczE6WuCLVYagkIiIiaieCIKC/vbvi9bmsROmKUTGGSiIiIqJ21HAe8Asa1AOcoZKIiIioHQXZ/nWl8lpeOmpqayWsRnUYKomIiIjakb+VI7QEAQBQJq/CbQ2ZB5yhkoiIiKgdGenqobu5reJ1ZF6ahNWoDkMlERERUTvrae2s+DmKoZKIiIiI2qJhqLzGUElEREREbeFn5aj4+VZBjoSVqA5DJREREVE782rwTGVuRQkKK8slrEY1GCqJiIiI2pm9oSmMdfQUr+OLOv/VSoZKIiIionYmCEKjq5W3CxkqiYiIiKgNPBuFys4/ViVDJREREZEEvMxtFD/Ha8AA6AyVRERERBLwMP0rVKaW3JGwEtVgqCQiIiKSgKWBkeJn9v4mIiIiojYx0zNQ/FxUVSFhJarBUElEREQkAXM9Q8XPRVXlEEVRwmqUx1BJREREJIGGoVIu1qJcXi1hNcpjqCQiIiKSgGmD298AUFjVuZ+rZKgkIiIikoC+tg4MtHUVrzv7c5UMlUREREQSMW/UWYdXKomIiIioDcwaddbp3FcqdaQuoCM5c+YM1qxZg6tXr6KsrAxOTk4YO3Ysnn/+eRgZGd1/B0REREQPoOGwQnymUkOsX78eM2fOREREBPT19eHl5YW0tDSsWLECU6dORUFBgdQlEhERkYbRpCuVDJUAoqKi8PHHHwMAPvjgA0RERGDbtm04dOgQAgICEB8fj0WLFklcJREREWkaM30+U6lRli9fjtraWkyaNAlPPPEEBEEAANjb2+PLL7+ElpYWDhw4gLi4OIkrJSIiIk3ScKzKzj5VY5cPlaWlpfjzzz8BAI8//vg973t4eGDgwIEAgH379rVrbURERKTZ+EylBomNjUVVVRX09PTQq1evJtcJCgoCAFy9erU9SyMiIiINp0nPVHb53t8JCQkAACcnJ+jq6ja5jpubW6N1m7Jx40Zs3ry5VceMj49/wCqJiIhIEzW6/d3Jr1R2+VBZWFgIADA3N292nfr36tdtSk5ODqKjo1VbHBEREWk0X0t7xc8ORmYSVqK8Lh8qKysrAaDZq5QAoKen12jdptja2iIgIKBVx4yPj0dFRee+xE1ERETK62vrhs+HPIr4wlzM6zlM6nKU0uVDpb6+PgCgurq62XWqqqoarduUadOmYdq0aa06Znh4OK9qEhEREQRBwDRZf6nLUIku31GnNbe2W3OLnIiIiKgr6/Kh0sPDAwCQnp7e7NXK5OTkRusSERERUWNdPlT6+flBV1cXVVVViIyMbHKdixcvAgACAwPbsTIiIiKizqPLh0oTExOEhIQAQJNDAiUmJuLMmTMAgLFjx7ZrbURERESdRZcPlQDw0ksvQRAE7NixA5s2bYIoigCA7OxszJ8/H7W1tRg5ciR8fX0lrpSIiIioY2KoBNCrVy+8/fbbAIB///vfCA0NxZQpUxAWFobo6Gh069YNH374ocRVEhEREXVcXX5IoXozZ86Ej48PVq9ejcjISOTl5cHJyQljx47F888/D2NjY6lLJCIiIuqwGCobGDRoEAYNGiR1GURERESdDm9/ExEREZHSGCqJiIiISGm8/S2B1NRUAHVzgIeHh0tcDREREWm6+Ph4AH9lEHVgqJRAZWUlAKCiooJzgBMREVG7qc8g6sBQKQErKyvk5+dDX18fLi4ujd6Lj49HRUUFDAwM4OXlJVGFmo1trF5sX/Vi+6oX21e92L7q1VL7pqamorKyElZWVmo7viDWj/RNHUJ4eDiio6MREBCArVu3Sl2ORmIbqxfbV73YvurF9lUvtq96Sd2+7KhDREREREpjqCQiIiIipTFUEhEREZHSGCqJiIiISGkMlURERESkNIZKIiIiIlIaQyURERERKY2hkoiIiIiUxlBJREREREpjqCQiIiIipXHu7w7m8ccfR05ODmxtbaUuRWOxjdWL7atebF/1YvuqF9tXvaRuX879TURERERK4+1vIiIiIlIaQyURERERKY2hkoiIiIiUxlBJREREREpj728VEEURly9fxpEjR3Dx4kXcvn0bJSUlMDU1hb+/PyZPnoxHHnkEgiA02u7OnTs4dOgQTp06hejoaGRkZEBLSwuOjo4ICQnBzJkz4eLi0uQxly5dimXLlrVY1/vvv48nn3xSZZ9TKm1tXwDw8fFpcd82NjY4efJks+/HxMRg1apVOH/+PIqKimBnZ4fQ0FC89NJLsLKyUvqzdQRtbd+3334b27Zta9Uxjhw5AmdnZ8XrrVu34p133mlxm+eeew5vvvnmg3+gDuqPP/5Q/H89OzsbBQUF0NXVhYeHB4YPH45nn30WlpaWTW5bWlqKVatWYf/+/UhPT4eRkRF69+6N2bNnY8CAAS0e98yZM1izZg2uXr2KsrIyODk5YezYsXj++edhZGSkjo8qiba0L8/BrdfW7y/Pwa3TlvbtiOdg9v5WgdOnT2PmzJmK166urjAzM0NaWhoKCgoAAA899BCWLl0KPT09xXrTp0/HxYsXAQDGxsZwc3NDRUUFUlJSIJfLYWRkhG+++QbDhg2755j1JzRra2u4u7s3WdecOXMwcuRI1X1QibS1fYG/Tmg9evS45z0AsLCwwIoVK5o87oEDBzB//nxUV1fD2toaDg4OSEhIQFlZGWxtbfHrr7/C1dVVNR9SQm1t35UrV+LYsWPN7jcxMRH5+flwdHTEkSNHoKX1142R+hOaiYkJZDJZk9tPnDhRY34hA8CkSZMQFxcHPT092NrawtLSEvn5+UhPTwcAWFtbY/Xq1fD19W20XX5+PqZPn46EhATo6emhe/fuyM/PR2ZmJgRBwKJFizBjxowmj7l+/XosXrwYoijCwcEBVlZWuHXrFqqqquDl5YUNGzbAwsJC3R+9XbSlfXkObr22fn95Dm6dtrRvhzwHi6S0kydPiiNGjBDXrVsn5ubmNnpv27ZtYo8ePUSZTCZ++umnjd576qmnxPnz54vnzp0T5XK5YnlGRoY4a9YsUSaTiX379hVzcnLuOea3334rymQy8a233lLPh+pA2tq+oiiKMplMlMlkYkpKygMdMzMzU+zdu7cok8nEr7/+WqyurhZFURSLiorEOXPmiDKZTAwPDxdra2vb/sE6CGXatzm1tbViWFiYKJPJxC+//PKe97ds2SLKZDLxqaeeUrr+zmLTpk3iuXPnxKqqqkbL4+LixAkTJogymUwcN27cPdu9+OKLokwmE6dMmSJmZmaKoljXvhs3bhRlMpno5+cnxsTE3LPdtWvXRF9fX9HHx0fcuHGj4ruamZkpTpkyRZTJZOIrr7yihk8qjba0L8/BrdfW7y/Pwa3T1vZtjlTnYIZKFSguLr7ni9DQihUrRJlMJgYHB4s1NTWK5fn5+c1uU1RUJA4cOFCUyWTimjVr7nm/K53Q2tq+otj2E9pHH30kymQyccaMGfe8V1BQIAYFBYkymUw8fPjwA+23I1KmfZtz9uxZRdsnJibe835XDJUtuXr1qqK9bt26pVgeHR0tymQy0dfXt8l2/Oc//9lsOJw3b54ok8nEhQsX3vNeQkKC6OvrK8pkMjE2Nla1H6YDaq59eQ5WjebaVxR5DlaFltq3OVKdg9lRRwVMTEygq6vb7Pv1t04KCgqQn5+vWN7c81MAYGpqisDAQABAQkKCagrtpNravsrYv38/gLrZCf7O3NwcY8eOBVD3HExnp472rX/Op2/fvs3eGqS/eHp6Kn4uLy9X/Fz/PRw4cGCT7fjEE08AAI4dO4aysjLF8tLSUvz5558Amv4Oe3h4YODAgQCAffv2qeATdGzNtS/PwarRXPsqoyudg++nLe0r1TmYHXXaQUVFheJnAwODVm9XWVkJADA0NGx2nbi4OCxYsAA5OTkwNjaGj48Pxo8fD29v77YX3Mm0pn2XL1+O7Oxs1NTUwN7eHgMHDsS4ceOafMYnIyMDWVlZAID+/fs3ub9+/frht99+w9WrV1XwCTq2B/3+lpWVKYJKeHh4i+ump6fj7bffRkZGBgwMDODp6YkxY8Yofpl3FfXP9RkZGaFbt26K5VeuXAFQ931rSq9evaCnp4fKykrExsYiKCgIABAbG4uqqiro6emhV69eTW4bFBSEU6dOdYnvcHPtez88B7dOa9qX5+C2e9Dvr5TnYIbKdrBnzx4AgK+vL0xMTFq1TVZWFs6dOweg+V8oQN0vj9jYWMXrI0eOYOXKlXjmmWfw1ltvQVtbW4nKO4fWtO+WLVsavd62bRu+/fZbLF26FAEBAY3eS0xMBADo6urCwcGhyf3VPxyekpKC6urqFq/0dXYP+v3dv38/ysrKYGhoiIcffrjFdVNTU5Gamqp4HRERgdWrV2P8+PFYvHhxi7/MO7va2lrk5OTg5MmT+PzzzwEAb775JoyNjRXr1H8X3dzcmtyHrq4uHB0dkZSUhISEBEWorL+y5uTk1Ox3s36fmnoVrjXt2xKeg1v2oO3Lc/CDUeb7K+U5mKFSzaKiorBx40YAwPPPP9/q7T788ENUV1eje/fuCA0Nved9Ozs7vPbaaxg6dChcXFxgYmKChIQEbNiwARs3bsS6deugo6ODhQsXquyzdET3a9+wsDBMmjQJvr6+cHBwQGlpKU6fPo2vvvoKKSkpmD17NrZv3w5HR0fFNvU9ns3NzZscpgiAosdsbW0tSkpKWryN1pm15fu7detWAMCoUaOaDaFmZmaYO3cuQkND4e7uDnNzc6SlpWH79u344YcfsGfPHtTU1OCbb75RzQfpQNauXYtPPvmk0bJevXphyZIl9/QyLiwsBFD3XWxO/XtFRUVt2q5+XU3xIO3bEp6Dm/ag7ctz8INRxfdXynMwQ6Ua5ebm4tVXX4VcLseoUaMwfvz4Vm23atUqHDx4ELq6uliyZEmTf+nWP0vVkI+PD/773//CxcUFn3/+OdatW4fp06c3O85aZ9ea9l2+fHmj1/r6+hg/fjwGDRqERx99FOnp6Vi2bBkWL16sWKf+lldLf/k2vGVTv76macv3NyUlBefPnwfQ8m2XkSNH3jPUSrdu3fDGG2/Ax8cHb7zxBvbt24cLFy60eJWoM7K3t0ffvn1RU1OD9PR05ObmIjY2Fjt27EBgYCDMzMwU6z7Id7HhYwoPsp2mfX8fpH2bw3Nw8x60fXkOfjDKfn+lPgezo46aFBcX47nnnkN6ejoCAgKwZMmSVm23bds2fPnllxAEAYsXL0bPnj0f+NizZ8+GnZ0d5HI5jhw58sDbdwZtbd96VlZWiitvhw4dgthguFZ9fX0AQHV1dbPbV1VV3bO+Jmlr+27fvh2iKMLZ2VnREeRBjRs3Dr179wYAHDx4sE376Mgefvhh/Prrr9i8eTNOnDiB7du3o3fv3ti9ezeeeeYZ1NTUKNZ9kO9iw+ddH2Q7Tfv+Pkj7NoXn4JYp2771eA5umrLtK/U5mKFSDUpLSzF37lzExMTA29sbP/74Y6ueRdu7dy/effddiKKI//znP5g0aVKbjq+tra34QiQlJbVpHx1ZW9v37/r06QOg7lZL/e0WoPFtQbGZuQHq19fS0mrTsTuytravKIrYvn07gLqBfJu7bdUa9f/baOL39+98fX3x3XffwdLSErGxsYpnWAEorkq0dIu6/r2GVzBac2u7NbfINUFL7ft3PAc/uAdp37/jOfj+HqR9O8I5mKFSxcrLy/HCCy/gypUr8PDwwJo1a1r1rMfBgwfxz3/+EzU1NXjrrbeUnkmk/raBXC5Xaj8dTVvbtykNb600/OvPw8MDQN1fyRkZGU1um5KSAgBwcXHRqAfElWnfc+fOITU1FYIg3LfH4f1o6ve3OSYmJggODgYAREdHK5bXfxebO7FXV1crZtyoX7fhz+np6c1e7UlOTr5nO03VXPs2xHNw27WmfZvCc3DrtLZ9O8I5mKFShSorKzFv3jycP38ezs7OWLt2LWxtbe+73bFjx/DGG29ALpfjtddew+zZs5Wu5ebNmwDQbM+5zqit7duc+jbS19dvNFWdk5MT7OzsAAAXLlxoctv65Zo09I2y7Vs/Llq/fv2UnjpNE7+/91N/8m74y7X++1U/pMjfRUZGorq6Gvr6+vDz81Ms9/Pzg66uLqqqqhAZGdnktvX71KTvcEuaat96PAcrr6X2bQ7Pwa3XmvbtCOdghkoVqa6uxquvvorTp0/D3t4e69ata9SbrTmnT5/Gq6++iurqarzwwgt4+eWXla4lIiJC8YUYMmSI0vvrCNravs2Ry+VYs2YNgLqBpXV0GvdZGzNmDABg8+bN92xbWFioGAOsfgDezk7Z9i0tLVUMVjxlyhSlaomLi1MM3K0p39/7KSgoUAxf0zAc1n8Pz5492+TVyk2bNgGoG6C+4VAjJiYmCAkJAdD0dzgxMRFnzpwBoDnf4ZY0174Az8Gq0FL7Nofn4NZrTft2mHOwSufn6aLkcrn46quvijKZTBwyZIh4+/btVm136dIlMTAwUJTJZOLHH3/c6uPduHFDXLRo0T3Tq9XU1Ii7du0S+/btK8pkMvGFF154oM/RUbW1fT/77DNx69atYnFxcaPl6enpiins/P39xStXrtyzbXp6utirVy/FvLP18wI3nHd28uTJGjHvbFvbt6H6Kb8CAwPFkpKSFtctLi4W//GPf4gXL168p/2OHz8uhoSEiDKZTJw4cWKj+Zg7s7Nnz4r/+9//mpyqLioqSgwPDxdlMpk4dOjQe9rvhRdeUMz9nZWVJYpi47m/fX19xaioqHv2e/XqVdHHx+eeub+zsrIUc3+/9NJLavi07a+t7ctzcOu0tX15Dm4dZc4P9TrKOVgQxWaegqVW2717NxYsWAAAcHZ2hr29fbPrLlq0CP7+/gDq/hJLTExs9FB3U/z9/bFo0SLF69jYWEyePBlA3VhdTk5O0NbWRnJysuLh+379+mHFihWtGj6jo2tr+7700ks4fPgwtLW14erqCnNzcxQXFyMhIQGiKEJfXx8fffQRJk6c2OS+9u3bhwULFkAul8Pa2hoODg5ISEhAWVkZbGxssGHDBo2YgrCt7dvQ008/jXPnzmHKlCn37SleVFSkmCXD2NgYrq6u0NPTUwyfAQDe3t5YtWoVnJyc2vqxOpRDhw4proDZ2trCzs4O2trayMjIQE5ODoC6oUS+++67e65E5Ofn48knn0RiYiL09PTQvXt33LlzBxkZGRAEAe+++y6efvrpJo+7du1aLFmyBKIowtHREZaWlrh16xaqqqrQrVs3bNiwAVZWVur98O2gre3Lc3DrtLV9eQ5uHWXOD/U6yjmY41SqQMOhDdLS0pCWltbsusXFxYqf6x+gr6mpwaVLl5rd5u+3BZydnfH666/jypUriI+PR1JSEqqqqmBubo5hw4ZhwoQJmDBhgsbM5NDW9n3yySdhY2ODqKgoZGdnIy0tDbq6uvD29sagQYPw1FNPNTtTCVB3W8XV1RXfffcdLly4gBs3bsDOzg7h4eF46aWXYG1trZoPKLG2tm+9huOitea2i6GhIRYuXIgrV67gxo0bSE9PR1lZGUxMTDBgwACMGTMGU6dO1ahhQvr06YN33nkHZ8+exa1bt5CYmIiqqiqYmZlhwIABGDFiBKZOndpkL1YrKyts2bIF33//Pfbt24dbt27ByMgIw4YNw5w5c1ocNmTmzJnw8fHB6tWrERkZiby8PDg5OWHs2LF4/vnnWz27TEfX1vblObh12tq+PAe3jjLnB6BjnYN5pZKIiIiIlMaOOkRERESkNIZKIiIiIlIaQyURERERKY2hkoiIiIiUxlBJREREREpjqCQiIiIipTFUEhEREZHSGCqJiIiISGkMlURERESkNIZKIiIiIlIaQyURERERKU1H6gKIiDq7d955B1u3bm3Ttr169cJvv/32QNtMmjQJcXFxWLNmDQYPHnzf9ZcuXYply5YBAK5fv97selu2bMGiRYtQU1ODfv36YeXKlTA1NX2g2oio62KoJCJSUmpqKmxsbO5ZXlZWhrKyMgBo8n0ACA4OfqBjpaWlIS4uDmZmZg+8bUvWrl2LJUuWQBRFhIaG4uuvv4aBgYHK9k9Emo+hkohISevXr29y+aJFi7B582Y4OjoiIiJCJcc6fPgwAGDYsGHQ0VHNKfzrr7/GihUrAACPPPIIlixZorJ9E1HXwbMGEZGaREdHAwD8/f1Vts/6UBkWFqb0vkRRxIcffohffvkFAPDUU0/hvffegyAISu+biLoehkoiIjWQy+W4efMmANWFysLCQly4cAG6uroYNmyYUvuSy+V4++23sWvXLgDAyy+/jNdee00VZRJRF8VQSUSkBrdu3UJVVRUA1YXKiIgIyOVyDB06FCYmJm3eT2VlJf7xj3/g6NGjEAQB//rXv/DMM8+opEYi6roYKomI1KD+1jcABAQEqGSfqrj1XVJSgnnz5uHcuXPQ0dHB4sWLMXnyZJXUR0RdG0MlEZEaxMbGAgCsra1hb2+v9P6qqqrw559/QhAEjBgxos37eeaZZxAdHQ19fX18/fXXSu2LiKghDn5ORKQG9Vcq/fz8VLK/06dPo6ysDD169FAqpNbXNXXqVAZKIlIphkoiIhWrra1FXFwcgI516xsA+vTpAwD45Zdf8NNPPyldFxFRPYZKIiIVS0hIUAx6ropOOqIo4siRIwCAkSNHKrWvH374AX379gUALF68GGvXrlW2PCIiAAyVREQqV/88JaCaK5WRkZHIycmBm5sbvL29ldqXiYkJfvjhBwQFBQEAPvnkE6xZs0bpGomIGCqJiFSs/rlFU1NTuLi4KL0/VQ54DgDGxsb44Ycf0L9/fwDAkiVL8OOPP6pk30TUdTFUEhGpWExMDIC6TjqqmJ3m0KFDAFQXKgHAyMgIq1atUswf/umnn+L7779X2f6JqOthqCQiUjFVdtJJTExEfHw8LC0tFc9Cqkp9sBwwYAAA4PPPP8eqVatUegwi6joYKomIVCg1NRUFBQUAVDOcUP2t79DQUGhrayu9v78zNDTEqlWrMGjQIADAF198gZUrV6r8OESk+RgqiYhUqP7WN6CaK5Wqfp6yKQYGBli5ciWGDBkCAPjqq6+wfPlytR2PiDQTQyURkQrVh0pDQ0N4enoqta/8/HxcvnwZBgYGisCnLgYGBlixYgVCQkIAAN988w2WLVum1mMSkWYRRFEUpS6CiIju9fvvv+Pdd99FaGgob0kTUYfHK5VERB1U/a1vZQc8JyJqDzpSF0BERE3r27cv/P391fo8JRGRqvD2NxEREREpjbe/iYiIiEhpDJVEREREpDSGSiIiIiJSGkMlERERESmNoZKIiIiIlMZQSURERERKY6gkIiIiIqUxVBIRERGR0hgqiYiIiEhpDJVEREREpLT/B7/5ZfXRSDjhAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 700x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 4))\n",
"sns.lineplot(x=dia.vapor.temperature / si.KELVIN, y=enthalpy_of_vaporization, ax=ax);\n",
"ax.set_ylabel(r\"$\\Delta^{LV}h$ / kJ / mol\")\n",
"ax.set_xlabel(r\"$T$ / K\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A more convenient way is to create a dictionary. The dictionary can be used to build pandas `DataFrame` objects.\n",
"This is a bit less flexible, because the units of the properties are rigid. You can inspect the method signature to check what units are used."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mSignature:\u001b[39m dia.to_dict(contributions)\n",
"\u001b[31mDocstring:\u001b[39m\n",
"Returns the phase diagram as dictionary.\n",
"\n",
"Parameters\n",
"----------\n",
"contributions : Contributions, optional\n",
" The contributions to consider when calculating properties.\n",
" Defaults to Contributions.Total.\n",
"\n",
"Returns\n",
"-------\n",
"Dict[str, List[float]]\n",
" Keys: property names. Values: property for each state.\n",
"\n",
"Notes\n",
"-----\n",
"- temperature : K\n",
"- pressure : Pa\n",
"- densities : mol / m³\n",
"- mass densities : kg / m³\n",
"- molar enthalpies : kJ / mol\n",
"- molar entropies : kJ / mol / K\n",
"- specific enthalpies : kJ / kg\n",
"- specific entropies : kJ / kg / K\n",
"- xi: liquid molefraction of component i\n",
"- yi: vapor molefraction of component i\n",
"- component index `i` matches to order of components in parameters.\n",
"\u001b[31mType:\u001b[39m builtin_function_or_method"
]
}
],
"source": [
"dia.to_dict?"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>temperature</th>\n",
" <th>pressure</th>\n",
" <th>density liquid</th>\n",
" <th>density vapor</th>\n",
" <th>molar enthalpy liquid</th>\n",
" <th>molar enthalpy vapor</th>\n",
" <th>molar entropy liquid</th>\n",
" <th>molar entropy vapor</th>\n",
" <th>mass density liquid</th>\n",
" <th>mass density vapor</th>\n",
" <th>specific enthalpy liquid</th>\n",
" <th>specific enthalpy vapor</th>\n",
" <th>specific entropy liquid</th>\n",
" <th>specific entropy vapor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>230.000000</td>\n",
" <td>96625.278174</td>\n",
" <td>14125.988947</td>\n",
" <td>52.208491</td>\n",
" <td>-18.921555</td>\n",
" <td>-0.157448</td>\n",
" <td>-0.035166</td>\n",
" <td>-0.000148</td>\n",
" <td>622.902434</td>\n",
" <td>2.302196</td>\n",
" <td>-429.097170</td>\n",
" <td>-3.570554</td>\n",
" <td>-0.797483</td>\n",
" <td>-0.003363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>230.280462</td>\n",
" <td>97830.133956</td>\n",
" <td>14118.006852</td>\n",
" <td>52.811929</td>\n",
" <td>-18.911909</td>\n",
" <td>-0.159193</td>\n",
" <td>-0.035119</td>\n",
" <td>-0.000150</td>\n",
" <td>622.550454</td>\n",
" <td>2.328805</td>\n",
" <td>-428.878435</td>\n",
" <td>-3.610123</td>\n",
" <td>-0.796418</td>\n",
" <td>-0.003399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>230.560924</td>\n",
" <td>99046.729400</td>\n",
" <td>14110.010220</td>\n",
" <td>53.420767</td>\n",
" <td>-18.902255</td>\n",
" <td>-0.160952</td>\n",
" <td>-0.035072</td>\n",
" <td>-0.000152</td>\n",
" <td>622.197833</td>\n",
" <td>2.355653</td>\n",
" <td>-428.659501</td>\n",
" <td>-3.650021</td>\n",
" <td>-0.795354</td>\n",
" <td>-0.003436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>230.841386</td>\n",
" <td>100275.143120</td>\n",
" <td>14101.999011</td>\n",
" <td>54.035036</td>\n",
" <td>-18.892592</td>\n",
" <td>-0.162726</td>\n",
" <td>-0.035025</td>\n",
" <td>-0.000153</td>\n",
" <td>621.844569</td>\n",
" <td>2.382740</td>\n",
" <td>-428.440366</td>\n",
" <td>-3.690251</td>\n",
" <td>-0.794290</td>\n",
" <td>-0.003473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>231.121849</td>\n",
" <td>101515.453964</td>\n",
" <td>14093.973182</td>\n",
" <td>54.654773</td>\n",
" <td>-18.882920</td>\n",
" <td>-0.164515</td>\n",
" <td>-0.034978</td>\n",
" <td>-0.000155</td>\n",
" <td>621.490660</td>\n",
" <td>2.410068</td>\n",
" <td>-428.221030</td>\n",
" <td>-3.730814</td>\n",
" <td>-0.793228</td>\n",
" <td>-0.003510</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" temperature pressure density liquid density vapor \\\n",
"0 230.000000 96625.278174 14125.988947 52.208491 \n",
"1 230.280462 97830.133956 14118.006852 52.811929 \n",
"2 230.560924 99046.729400 14110.010220 53.420767 \n",
"3 230.841386 100275.143120 14101.999011 54.035036 \n",
"4 231.121849 101515.453964 14093.973182 54.654773 \n",
"\n",
" molar enthalpy liquid molar enthalpy vapor molar entropy liquid \\\n",
"0 -18.921555 -0.157448 -0.035166 \n",
"1 -18.911909 -0.159193 -0.035119 \n",
"2 -18.902255 -0.160952 -0.035072 \n",
"3 -18.892592 -0.162726 -0.035025 \n",
"4 -18.882920 -0.164515 -0.034978 \n",
"\n",
" molar entropy vapor mass density liquid mass density vapor \\\n",
"0 -0.000148 622.902434 2.302196 \n",
"1 -0.000150 622.550454 2.328805 \n",
"2 -0.000152 622.197833 2.355653 \n",
"3 -0.000153 621.844569 2.382740 \n",
"4 -0.000155 621.490660 2.410068 \n",
"\n",
" specific enthalpy liquid specific enthalpy vapor specific entropy liquid \\\n",
"0 -429.097170 -3.570554 -0.797483 \n",
"1 -428.878435 -3.610123 -0.796418 \n",
"2 -428.659501 -3.650021 -0.795354 \n",
"3 -428.440366 -3.690251 -0.794290 \n",
"4 -428.221030 -3.730814 -0.793228 \n",
"\n",
" specific entropy vapor \n",
"0 -0.003363 \n",
"1 -0.003399 \n",
"2 -0.003436 \n",
"3 -0.003473 \n",
"4 -0.003510 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dia = pd.DataFrame(dia.to_dict(feos.Contributions.Residual))\n",
"data_dia.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have a dataframe, we can store our results or create a nicely looking plot:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def phase_plot(data, x, y):\n",
" fig, ax = plt.subplots(figsize=(12, 6))\n",
" if x != \"pressure\" and x != \"temperature\":\n",
" xl = f\"{x} liquid\"\n",
" xv = f\"{x} vapor\"\n",
" else:\n",
" xl = x\n",
" xv = x\n",
" if y != \"pressure\" and y != \"temperature\":\n",
" yl = f\"{y} liquid\"\n",
" yv = f\"{y} vapor\"\n",
" else:\n",
" yv = y\n",
" yl = y\n",
" sns.lineplot(data=data, x=xv, y=yv, ax=ax, label=\"vapor\")\n",
" sns.lineplot(data=data, x=xl, y=yl, ax=ax, label=\"liquid\")\n",
" ax.set_xlabel(x)\n",
" ax.set_ylabel(y)\n",
" ax.legend(frameon=False)\n",
" sns.despine();"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"phase_plot(data_dia, \"density\", \"temperature\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"phase_plot(data_dia, \"molar entropy\", \"temperature\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mixtures <a id=\"Mixtures\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"Fox mixtures, we have to add information about the composition, either as molar fraction, amount of substance per component, or as partial densities."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# propane, butane mixture\n",
"tc = np.array([369.96, 425.2]) * si.KELVIN\n",
"pc = np.array([4250000.0, 3800000.0]) * si.PASCAL\n",
"omega = np.array([0.153, 0.199])\n",
"molar_weight = np.array([44.0962, 58.123]) * si.GRAM / si.MOL\n",
"\n",
"pr = PyPengRobinson(tc, pc, omega, molar_weight)\n",
"eos = feos.EquationOfState.python_residual(pr)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|molefracs\n",
"|-|-|-|\n",
"|300.00000 K|40.96869 mol/m³|[0.50000, 0.50000]|"
],
"text/plain": [
"T = 300.00000 K, ρ = 40.96869 mol/m³, x = [0.50000, 0.50000]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = feos.State(\n",
" eos, \n",
" temperature=300*si.KELVIN, \n",
" pressure=1*si.BAR, \n",
" molefracs=np.array([0.5, 0.5]), \n",
" total_moles=si.MOL\n",
")\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As before, we can compute properties by calling methods on the `State` object. Some return vectors or matrices - for example the chemical potential and its derivative w.r.t amount of substance:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ -93.60749754, -120.5269973 ]) J/mol"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.chemical_potential(feos.Contributions.Residual)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 4.90721995, -0.10487987],\n",
" [-0.10487987, 4.85361765]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.dmu_dni() / (si.KILO * si.JOULE / si.MOL**2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Phase equilibria can be built from different constructors. E.g. at critical conditions given composition:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|molefracs\n",
"|-|-|-|\n",
"|401.65562 K|3.99954 kmol/m³|[0.50000, 0.50000]|"
],
"text/plain": [
"T = 401.65562 K, ρ = 3.99954 kmol/m³, x = [0.50000, 0.50000]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s_cp = feos.State.critical_point(eos, molefracs=np.array([0.5, 0.5]))\n",
"s_cp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or at given temperature (or pressure) and composition for bubble and dew points."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|molefracs|\n",
"|-|-|-|-|\n",
"|phase 1|350.00000 K|879.50373 mol/m³|[0.67631, 0.32369]|\n",
"|phase 2|350.00000 K|8.96383 kmol/m³|[0.50000, 0.50000]|\n"
],
"text/plain": [
"phase 0: T = 350.00000 K, ρ = 879.50373 mol/m³, x = [0.67631, 0.32369]\n",
"phase 1: T = 350.00000 K, ρ = 8.96383 kmol/m³, x = [0.50000, 0.50000]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.bubble_point(\n",
" eos, \n",
" 350*si.KELVIN, \n",
" liquid_molefracs=np.array([0.5, 0.5])\n",
")\n",
"vle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to pure substance phase diagrams, there is a constructor for binary systems."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"vle = feos.PhaseDiagram.binary_vle(eos, 350*si.KELVIN, npoints=50)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(18, 6))\n",
"# fig.title(\"T = 350 K, Propane (1), Butane (2)\")\n",
"sns.lineplot(x=vle.liquid.molefracs[:,0], y=vle.liquid.pressure / si.BAR, ax=ax[0])\n",
"sns.lineplot(x=vle.vapor.molefracs[:,0], y=vle.vapor.pressure / si.BAR, ax=ax[0])\n",
"ax[0].set_xlabel(r\"$x_1$, $y_1$\")\n",
"ax[0].set_ylabel(r\"$p$ / bar\")\n",
"ax[0].set_xlim(0, 1)\n",
"ax[0].set_ylim(5, 35)\n",
"# ax[0].legend(frameon=False);\n",
"\n",
"sns.lineplot(x=vle.liquid.molefracs[:,0], y=vle.vapor.molefracs[:,0], ax=ax[1])\n",
"sns.lineplot(x=np.linspace(0, 1, 10), y=np.linspace(0, 1, 10), color=\"black\", alpha=0.3, ax=ax[1])\n",
"ax[1].set_xlabel(r\"$x_1$\")\n",
"ax[1].set_ylabel(r\"$y_1$\")\n",
"ax[1].set_xlim(0, 1)\n",
"ax[1].set_ylim(0, 1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison to Rust implementation <a id=\"Comparison-to-Rust-implementation\"/>\n",
"[↑ Back to top](#Table-of-contents)\n",
"\n",
"Implementing an equation of state in Python is nice for quick prototyping and development but when it comes to performance, implementing the equation of state in Rust is the way to go.\n",
"For each non-cached call to the Helmholtz energy, we have to transition between Rust and Python with our Python implementation which generates quite some overhead.\n",
"\n",
"Here are some comparisons between the Rust and our Python implemenation:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# rust\n",
"eos_rust = feos.EquationOfState.peng_robinson(feos.Parameters.from_json([\"propane\"], \"../../../examples/data/peng-robinson.json\"))\n",
"\n",
"# python\n",
"tc = si.array(369.96 * si.KELVIN)\n",
"pc = si.array(4250000.0 * si.PASCAL)\n",
"omega = np.array([0.153])\n",
"molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n",
"eos_python = feos.EquationOfState.python_residual(PyPengRobinson(tc, pc, omega, molar_weight))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# let's first test if both actually yield the same results ;)\n",
"assert abs(feos.State.critical_point(eos_python).pressure() / si.BAR - feos.State.critical_point(eos_rust).pressure() / si.BAR) < 1e-12\n",
"assert abs(feos.State.critical_point(eos_python).temperature / si.KELVIN - feos.State.critical_point(eos_rust).temperature / si.KELVIN) < 1e-12"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import timeit\n",
"\n",
"time_python = timeit.timeit(lambda: feos.State.critical_point(eos_python), number=2_500)\n",
"time_rust = timeit.timeit(lambda: feos.State.critical_point(eos_rust), number=2_500)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Critical point for pure substance\n",
"Python implementation is slower by a factor of 24.\n"
]
}
],
"source": [
"rel_dev = (time_rust - time_python) / time_rust\n",
"print(f\"Critical point for pure substance\")\n",
"print(f\"Python implementation is {'slower' if rel_dev < 0 else 'faster'} by a factor of {abs(time_python / time_rust):.0f}.\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"time_python = timeit.timeit(lambda: feos.PhaseDiagram.pure(eos_python, 300*si.KELVIN, 100), number=100)\n",
"time_rust = timeit.timeit(lambda: feos.PhaseDiagram.pure(eos_rust, 300*si.KELVIN, 100), number=100)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase diagram for pure substance\n",
"Python implementation is slower by a factor of 80.\n"
]
}
],
"source": [
"rel_dev = (time_rust - time_python) / time_rust\n",
"print(f\"Phase diagram for pure substance\")\n",
"print(f\"Python implementation is {'slower' if rel_dev < 0 else 'faster'} by a factor of {abs(time_python / time_rust):.0f}.\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | docs/tutorials/eos/pcsaft_working_with_parameters.ipynb | .ipynb | 43,658 | 1,344 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with parameters\n",
"\n",
"## Goal\n",
"\n",
"- Read in parameters for pure substances and mixtures from json files, and\n",
"- create parameters for pure substances and mixtures within Python.\n",
"- For both regular parameters as well as via the homo-segmented group contribution method.\n",
"- Learn how to access information stored in parameter objects."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameters and the equation of state\n",
"\n",
"Before we can start to compute physical properties, two steps are needed:\n",
"\n",
"1. Build a parameter object.\n",
"2. Instatiate the equation of state using the parameter object.\n",
"\n",
"In principle, every implementation of an equation of state can manage parameters differently but typically the workflow is similar for each implementation.\n",
"\n",
"We first generate the parameters object, `Parameters`, which we then use to generate the equation of state object using `EquationOfState.pcsaft()`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Read parameters from json file(s)\n",
"\n",
"The easiest way to create the `Parameters` object is to read information from one or more json files.\n",
"\n",
"- To read information from a single file, use `Parameters.from_json`\n",
"- To read information from multiple files, use `Parameters.from_multiple_json`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From a single file\n",
"\n",
"#### Pure substance\n",
"\n",
"Querying a substance from a file requires an *identifier*.\n",
"This identifier can be one of `Name`, `Cas`, `Inchi`, `IupacName`, `Formula`, or `Smiles` with `Name` (common english name) being the default.\n",
"We can change the identifier type usig the `identifier_option` argument with an `IdentifierOption` object. Given a list of identifiers and a path to the parameter file, we can conveniently generate our object."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" }\n",
"]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import feos\n",
"\n",
"# path to parameter file for substances that are non-associating, i.e. defined by three parameters: m, sigma, and epsilon_k.\n",
"file_na = '../../../parameters/pcsaft/gross2001.json' \n",
"\n",
"# a system containing a single substance, \"methane\", using \"Name\" as identifier (default)\n",
"parameters = feos.Parameters.from_json(['methane'], pure_path=file_na)\n",
"parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" }\n",
"]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a system containing a single substance, \"methane\", using \"Smiles\" (\"C\") as identifier\n",
"parameters = feos.Parameters.from_json(['C'], pure_path=file_na, identifier_option=feos.IdentifierOption.Smiles)\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Mixtures\n",
"\n",
"Reading parameters for more than one substance from a single file is very straight forward: simply add more identifiers to the list.\n",
"Note that the **order** in which which identifiers are provided **is important**. When computing vector valued properties, **the order of the physical properties matches the order of the substances within the parameter object**."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03|\n",
"|hexane|86.177|3.0576|3.7983|236.77|\n",
"|dodecane|170.338|5.306|3.8959|249.21|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"110-54-3\",\n",
" \"name\": \"hexane\",\n",
" \"iupac_name\": \"hexane\",\n",
" \"smiles\": \"CCCCCC\",\n",
" \"inchi\": \"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
" \"formula\": \"C6H14\"\n",
" },\n",
" \"molarweight\": 86.177,\n",
" \"m\": 3.0576,\n",
" \"sigma\": 3.7983,\n",
" \"epsilon_k\": 236.77\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"112-40-3\",\n",
" \"name\": \"dodecane\",\n",
" \"iupac_name\": \"dodecane\",\n",
" \"smiles\": \"CCCCCCCCCCCC\",\n",
" \"inchi\": \"InChI=1/C12H26/c1-3-5-7-9-11-12-10-8-6-4-2/h3-12H2,1-2H3\",\n",
" \"formula\": \"C12H26\"\n",
" },\n",
" \"molarweight\": 170.338,\n",
" \"m\": 5.306,\n",
" \"sigma\": 3.8959,\n",
" \"epsilon_k\": 249.21\n",
" }\n",
"]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a system containing a ternary mixture\n",
"parameters = feos.Parameters.from_json(\n",
" ['methane', 'hexane', 'dodecane'], \n",
" pure_path=file_na\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From multiple files\n",
"\n",
"There may be cases where we have to split our parameter information across different files. For example, the `feos` repository has parameters stored in different files where each file corresponds to the parameter's original publication. Constructing the parameter object using multiple different json files is a bit more complicated. We can provide a list tuples, each of which contains the list of substances and the file where parameters are stored.\n",
"\n",
"In the example below, we define a 4 component mixture from three input files:\n",
"\n",
"- methane is read from a file containing non-associating substance parameters.\n",
"- parameters for 1-butanol and water are read from a file containing associating substances, and\n",
"- acetone parameters are read from a file that contains substances modelled with dipolar interactions."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|mu|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03||\n",
"|1-butanol|74.123|2.7515|3.6139|259.59||1|1|0.006692|2544.6|\n",
"|water|18.015|1.0656|3.0007|366.51||1|1|0.034868|2500.7|\n",
"|acetone|58.08|2.7447|3.2742|232.99|2.88|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"7732-18-5\",\n",
" \"name\": \"water\",\n",
" \"iupac_name\": \"oxidane\",\n",
" \"smiles\": \"O\",\n",
" \"inchi\": \"InChI=1/H2O/h1H2\",\n",
" \"formula\": \"H2O\"\n",
" },\n",
" \"molarweight\": 18.015,\n",
" \"m\": 1.0656,\n",
" \"sigma\": 3.0007,\n",
" \"epsilon_k\": 366.51,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.034868,\n",
" \"epsilon_k_ab\": 2500.7,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"67-64-1\",\n",
" \"name\": \"acetone\",\n",
" \"iupac_name\": \"propan-2-one\",\n",
" \"smiles\": \"CC(C)=O\",\n",
" \"inchi\": \"InChI=1/C3H6O/c1-3(2)4/h1-2H3\",\n",
" \"formula\": \"C3H6O\"\n",
" },\n",
" \"molarweight\": 58.08,\n",
" \"m\": 2.7447,\n",
" \"sigma\": 3.2742,\n",
" \"epsilon_k\": 232.99,\n",
" \"mu\": 2.88\n",
" }\n",
"]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# na = non-associating\n",
"# assoc = associating\n",
"file_na = '../../../parameters/pcsaft/gross2001.json'\n",
"file_assoc = '../../../parameters/pcsaft/gross2002.json'\n",
"file_dipolar = '../../../parameters/pcsaft/gross2006.json'\n",
"\n",
"parameters = feos.Parameters.from_multiple_json(\n",
" [\n",
" (['C'], file_na), \n",
" (['CCCCO', 'O'], file_assoc), \n",
" (['CC(C)=O'], file_dipolar)\n",
" ], \n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### With binary interaction parameters\n",
"\n",
"Some mixtures cannot be adequately described with combination rules from pure substance parameters.\n",
"In PC-SAFT, we can use a binary interaction parameter, `k_ij`, to enhance the description of mixture behavior.\n",
"These interaction parameters can be supplied from a json file via the `binary_path` option.\n",
"\n",
"This parameter is not shown in the default representation of the parameter object. You can access the matrix of `k_ij` via the getter, `PcSaftParameters.k_ij`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|\n",
"|1-butanol|74.123|2.7515|3.6139|259.59|1|1|0.006692|2544.6|\n",
"\n",
"|component 1|component 2|k_ij|\n",
"|-|-|-|\n",
"|butane|1-butanol|0.015|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" }\n",
"]\n",
"\n",
"[\n",
" {\n",
" \"id1\": 0,\n",
" \"id2\": 1,\n",
" \"k_ij\": 0.015\n",
" }\n",
"]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file_na = '../../../parameters/pcsaft/gross2001.json'\n",
"file_assoc = '../../../parameters/pcsaft/gross2002.json'\n",
"file_binary = '../../../parameters/pcsaft/gross2002_binary.json'\n",
"\n",
"parameters = feos.Parameters.from_multiple_json(\n",
" [\n",
" (['CCCC'], file_na), \n",
" (['CCCCO',], file_assoc)\n",
" ],\n",
" binary_path=file_binary,\n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Building parameters in Python\n",
"\n",
"Building `Parameters` in Python is a bit more involved since the `Parameters` object is built from multiple intermediate objects.\n",
"We need\n",
"\n",
"- the `Identifier` object that stores information about how a substance can be identified,\n",
"- the `PureRecord` object that bundles identifier and parameters together with the molar weight.\n",
"- the `BinaryRecord` object that contains binary interaction parameters."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Identifier(cas=106-97-8, name=butane, iupac_name=butane, smiles=CCCC, inchi=InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3, formula=C4H10)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"identifier = feos.Identifier(\n",
" cas='106-97-8',\n",
" name='butane',\n",
" iupac_name='butane',\n",
" smiles='CCCC',\n",
" inchi='InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3',\n",
" formula='C4H10'\n",
")\n",
"identifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `PureRecord` contains the identifier, molar weight and model parameters for a pure substance. At the point of creation, it has no information about the equation of state for which the parameters will ultimately be used. Therefore, there cannot be any checks for the validity of the parameters. Those happen when the equation of state is instantiated using, e.g., `EquationOfState.pcsaft()`\n",
"\n",
"For PC-SAFT *mandatory* parameters are\n",
"\n",
"- the number of segments, `m`, which is a dimensionless floating point number,\n",
"- the Lennard-Jones structure parameter (diameter), `sigma`, in units of Angstrom, and\n",
"- the Lennard-Jones energy parameter, `epsilon_k`, in units of Kelvin.\n",
"\n",
"*Optional* parameters are\n",
"\n",
"- the dipole moment, `mu`, in units of Debye used to model dipolar substances,\n",
"- the quadrupole moment, `q`, in units of Debye used to model quadrupolar substances,\n",
"- parameters to model association:\n",
" - `kappa_ab`, `epsilon_k_ab`, `na`, `nb`\n",
"- and parameters for entropy scaling:\n",
" - `viscosity`, `diffusion`, and `thermal_conductivity`\n",
" - each of which is a list containing coefficients for the respective correlation functions."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PureRecord(identifier: {cas: 106-97-8, name: butane, iupac_name: butane, smiles: CCCC, inchi: InChI=1/C4H10/c1-3-4-2/h3-4H2, 1-2H3, formula: C4H10}, molarweight: 58.123, m: 2.3316, sigma: 3.7086, epsilon_k: 222.88)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"butane = feos.PureRecord(identifier, molarweight=58.123, m=2.3316, sigma=3.7086, epsilon_k=222.88)\n",
"butane"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Parameters` for a single component \n",
"\n",
"For a single substance, we can use the `Parameters.new_pure` constructor."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|\n",
"|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" }\n",
"]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.new_pure(butane)\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Parameters` for binary mixtures\n",
"\n",
"We can create another `PureRecord` for a second component. Then, the `Parameters.new_binary` constructor let's us build the parameters. Optionally, we can also directly provide a `k_ij` value for this system. Here, we build a record for an associating substance."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"butan_1_ol = feos.PureRecord(\n",
" identifier=feos.Identifier(\n",
" cas='71-36-3',\n",
" name='1-butanol',\n",
" iupac_name='butan-1-ol',\n",
" smiles='CCCCO',\n",
" inchi='InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3',\n",
" formula='C4H10O'\n",
" ), \n",
" molarweight=74.123, \n",
" m=2.7515, \n",
" sigma=3.6139, \n",
" epsilon_k=259.59,\n",
" association_sites=[{\n",
" \"na\": 1,\n",
" \"nb\": 1,\n",
" \"kappa_ab\": 0.006692, \n",
" \"epsilon_k_ab\": 2544.6\n",
" }]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|\n",
"|1-butanol|74.123|2.7515|3.6139|259.59|1|1|0.006692|2544.6|\n",
"\n",
"|component 1|component 2|k_ij|\n",
"|-|-|-|\n",
"|butane|1-butanol|0.015|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" }\n",
"]\n",
"\n",
"[\n",
" {\n",
" \"id1\": 0,\n",
" \"id2\": 1,\n",
" \"k_ij\": 0.015\n",
" }\n",
"]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.Parameters.new_binary([butane, butan_1_ol], k_ij=0.015)\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Parameters` for mixtures with more than two components\n",
"\n",
"For mixtures with more than two components, we can use the `Parameters.from_records` constructor which takes a list of `PureRecords` and optionally binary interaction parameters."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|\n",
"|butane|58.123|2.3316|3.7086|222.88|\n",
"|1-butanol|74.123|2.7515|3.6139|259.59|1|1|0.006692|2544.6|\n",
"\n",
"|component 1|component 2|k_ij|\n",
"|-|-|-|\n",
"|butane|1-butanol|0.015|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"106-97-8\",\n",
" \"name\": \"butane\",\n",
" \"iupac_name\": \"butane\",\n",
" \"smiles\": \"CCCC\",\n",
" \"inchi\": \"InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3\",\n",
" \"formula\": \"C4H10\"\n",
" },\n",
" \"molarweight\": 58.123,\n",
" \"m\": 2.3316,\n",
" \"sigma\": 3.7086,\n",
" \"epsilon_k\": 222.88\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" }\n",
"]\n",
"\n",
"[\n",
" {\n",
" \"id1\": 0,\n",
" \"id2\": 1,\n",
" \"k_ij\": 0.015\n",
" }\n",
"]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binary = feos.BinaryRecord(feos.Identifier(name='butane'), feos.Identifier(name='1-butanol'), k_ij=0.015)\n",
"\n",
"parameters = feos.Parameters.from_records(\n",
" [butane, butan_1_ol], \n",
" binary_records=[binary]\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Parameters from group contribution methods\n",
"\n",
"An alternative to substance specific parameters are parameters that combine information from functional groups (molecule *segments*). As with regular parameters objects, we can build a `GcParameters` object from json or from Python - using segment information."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From json files\n",
"\n",
"We need at least two files: \n",
"\n",
"- `pure_path`: a file containing the substance identifiers *and* the segments that form the molecule\n",
"- `segments_path`: a file that contains the segments (identifier and model parameters)\n",
"\n",
"As before, we can specify our substance identifier using `identifier_option` and we can provide binary interaction parameters (segment-segment `k_ij`) via the `binary_path` argument."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<GcParameters at 0x7fb34959a6d0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pure_path = '../../../parameters/pcsaft/gc_substances.json'\n",
"segments_path = '../../../parameters/pcsaft/sauer2014_homo.json'\n",
"\n",
"parameters = feos.GcParameters.from_json_segments(\n",
" ['CCCC', 'CCCCO'], \n",
" pure_path, \n",
" segments_path, \n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From Python\n",
"\n",
"Building parameters in Python follows a similar approach as for regular parameters. To build `GcParameters` from segments, we need to specify:\n",
"\n",
"- The `ChemicalRecord` which contains the `Identifier` and the segments (as list of `str`s),\n",
"- and the `SegmentRecord` which specifies the identifier of the segment (has to be the same as in the list of the `ChemicalRecord`), the molar weight and model parameters.\n",
"\n",
"If both are available, we can use the `GcParameters.from_segments` constructor to build the parameters."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"cr1 = feos.ChemicalRecord(\n",
" identifier=feos.Identifier(\n",
" cas='106-97-8',\n",
" name='butane',\n",
" iupac_name='butane',\n",
" smiles='CCCC',\n",
" inchi='InChI=1/C4H10/c1-3-4-2/h3-4H2,1-2H3',\n",
" formula='C4H10'\n",
"),\n",
" segments=['CH3', 'CH2', 'CH2', 'CH3']\n",
")\n",
"\n",
"cr2 = feos.ChemicalRecord(\n",
" identifier=feos.Identifier(\n",
" cas='71-36-3',\n",
" name='1-butanol',\n",
" iupac_name='butan-1-ol',\n",
" smiles='CCCCO',\n",
" inchi='InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3',\n",
" formula='C4H10O'\n",
" ),\n",
" segments=['CH3', 'CH2', 'CH2', 'CH2', 'OH']\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each segment has a `SegmentRecord` which can be constructed just like we did before for a substance."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"ch3 = feos.SegmentRecord(\n",
" 'CH3', \n",
" molarweight=15.0345, \n",
" m=0.61198,\n",
" sigma=3.7202,\n",
" epsilon_k=229.90\n",
")\n",
"ch2 = feos.SegmentRecord(\n",
" 'CH2', \n",
" molarweight=14.02658, \n",
" m=0.45606,\n",
" sigma=3.8900,\n",
" epsilon_k=239.01\n",
")\n",
"oh = feos.SegmentRecord(\n",
" 'OH', \n",
" molarweight=17.00734, \n",
" m=0.40200, \n",
" sigma=3.2859, \n",
" epsilon_k=488.66,\n",
" epsilon_k_ab=2517.0, \n",
" kappa_ab=0.006825\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<GcParameters at 0x7fb3582d9470>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.GcParameters.from_segments(\n",
" chemical_records=[cr1, cr2], \n",
" segment_records=[ch3, ch2, oh]\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing information from parameter objects\n",
"\n",
"Once the `Parameters` object is constructed, within a jupyter notebook, we get a nice representation in form of a markdown table.\n",
"Sometimes, however you might want to access information not presented in this table or you might want to store information in a variable.\n",
"\n",
"Let's build parameters for the four-component mixture we looked at earlier:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|component|molarweight|m|sigma|epsilon_k|mu|na|nb|kappa_ab|epsilon_k_ab|\n",
"|-|-|-|-|-|-|-|-|-|-|\n",
"|methane|16.043|1.0|3.7039|150.03||\n",
"|1-butanol|74.123|2.7515|3.6139|259.59||1|1|0.006692|2544.6|\n",
"|water|18.015|1.0656|3.0007|366.51||1|1|0.034868|2500.7|\n",
"|acetone|58.08|2.7447|3.2742|232.99|2.88|"
],
"text/plain": [
"[\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"74-82-8\",\n",
" \"name\": \"methane\",\n",
" \"iupac_name\": \"methane\",\n",
" \"smiles\": \"C\",\n",
" \"inchi\": \"InChI=1/CH4/h1H4\",\n",
" \"formula\": \"CH4\"\n",
" },\n",
" \"molarweight\": 16.043,\n",
" \"m\": 1.0,\n",
" \"sigma\": 3.7039,\n",
" \"epsilon_k\": 150.03\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"71-36-3\",\n",
" \"name\": \"1-butanol\",\n",
" \"iupac_name\": \"butan-1-ol\",\n",
" \"smiles\": \"CCCCO\",\n",
" \"inchi\": \"InChI=1/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3\",\n",
" \"formula\": \"C4H10O\"\n",
" },\n",
" \"molarweight\": 74.123,\n",
" \"m\": 2.7515,\n",
" \"sigma\": 3.6139,\n",
" \"epsilon_k\": 259.59,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.006692,\n",
" \"epsilon_k_ab\": 2544.6,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"7732-18-5\",\n",
" \"name\": \"water\",\n",
" \"iupac_name\": \"oxidane\",\n",
" \"smiles\": \"O\",\n",
" \"inchi\": \"InChI=1/H2O/h1H2\",\n",
" \"formula\": \"H2O\"\n",
" },\n",
" \"molarweight\": 18.015,\n",
" \"m\": 1.0656,\n",
" \"sigma\": 3.0007,\n",
" \"epsilon_k\": 366.51,\n",
" \"association_sites\": [\n",
" {\n",
" \"kappa_ab\": 0.034868,\n",
" \"epsilon_k_ab\": 2500.7,\n",
" \"na\": 1.0,\n",
" \"nb\": 1.0\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"identifier\": {\n",
" \"cas\": \"67-64-1\",\n",
" \"name\": \"acetone\",\n",
" \"iupac_name\": \"propan-2-one\",\n",
" \"smiles\": \"CC(C)=O\",\n",
" \"inchi\": \"InChI=1/C3H6O/c1-3(2)4/h1-2H3\",\n",
" \"formula\": \"C3H6O\"\n",
" },\n",
" \"molarweight\": 58.08,\n",
" \"m\": 2.7447,\n",
" \"sigma\": 3.2742,\n",
" \"epsilon_k\": 232.99,\n",
" \"mu\": 2.88\n",
" }\n",
"]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file_na = '../../../parameters/pcsaft/gross2001.json'\n",
"file_assoc = '../../../parameters/pcsaft/gross2002.json'\n",
"file_dipolar = '../../../parameters/pcsaft/gross2006.json'\n",
"\n",
"parameters = feos.Parameters.from_multiple_json(\n",
" [\n",
" (['C'], file_na), \n",
" (['CCCCO', 'O'], file_assoc), \n",
" (['CC(C)=O'], file_dipolar)\n",
" ], \n",
" identifier_option=feos.IdentifierOption.Smiles\n",
")\n",
"parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get `PureRecord`s via `parameters.pure_records`\n",
"\n",
"We have seen above that it is possible to generate parameters in Python using intermediate objects, such as `Identifier`, `PcSaftRecord` and `PureRecord`.\n",
"You can generate these objects for all substances via the `pure_records` method (getter).\n",
"This getter returns `PureRecord` objects which can be further deconstructed to yield the `Identifier` and `PcSaftRecord` objects and the molar weight.\n",
"\n",
"Note that the order in which substances are returned matches the order in which we specified the substances above."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PureRecord(identifier: {cas: 74-82-8, name: methane, iupac_name: methane, smiles: C, inchi: InChI=1/CH4/h1H4, formula: CH4}, molarweight: 16.043, m: 1.0, sigma: 3.7039, epsilon_k: 150.03),\n",
" PureRecord(identifier: {cas: 71-36-3, name: 1-butanol, iupac_name: butan-1-ol, smiles: CCCCO, inchi: InChI=1/C4H10O/c1-2-3-4-5/h5H, 2-4H2, 1H3, formula: C4H10O}, molarweight: 74.123, m: 2.7515, sigma: 3.6139, epsilon_k: 259.59, association_sites: [{kappa_ab: 0.006692, epsilon_k_ab: 2544.6, na: 1.0, nb: 1.0}]),\n",
" PureRecord(identifier: {cas: 7732-18-5, name: water, iupac_name: oxidane, smiles: O, inchi: InChI=1/H2O/h1H2, formula: H2O}, molarweight: 18.015, m: 1.0656, sigma: 3.0007, epsilon_k: 366.51, association_sites: [{kappa_ab: 0.034868, epsilon_k_ab: 2500.7, na: 1.0, nb: 1.0}]),\n",
" PureRecord(identifier: {cas: 67-64-1, name: acetone, iupac_name: propan-2-one, smiles: CC(C)=O, inchi: InChI=1/C3H6O/c1-3(2)4/h1-2H3, formula: C3H6O}, molarweight: 58.08, m: 2.7447, sigma: 3.2742, epsilon_k: 232.99, mu: 2.88)]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters.pure_records"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"σ (methane)\t = 3.7039 A\n",
"σ (1-butanol)\t = 3.6139 A\n",
"σ (water)\t = 3.0007 A\n",
"σ (acetone)\t = 3.2742 A\n"
]
}
],
"source": [
"for pure_record in parameters.pure_records:\n",
" print(f\"σ ({pure_record.identifier.name})\\t = {pure_record.model_record[\"sigma\"]} A\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Identifier(cas=74-82-8, name=methane, iupac_name=methane, smiles=C, inchi=InChI=1/CH4/h1H4, formula=CH4)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get identifier of substance 0\n",
"parameters.pure_records[0].identifier"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16.043"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get molarweight of substance 0\n",
"parameters.pure_records[0].molarweight"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `PureRecord` object can be used to generate a json string which then can conveniently be stored in a file."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'{\"identifier\":{\"cas\":\"74-82-8\",\"name\":\"methane\",\"iupac_name\":\"methane\",\"smiles\":\"C\",\"inchi\":\"InChI=1/CH4/h1H4\",\"formula\":\"CH4\"},\"molarweight\":16.043,\"m\":1.0,\"sigma\":3.7039,\"epsilon_k\":150.03}'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate a json string from identifier of substance 0\n",
"parameters.pure_records[0].to_json_str()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing parameters from the equation of state\n",
"There is some parameter information that is not available from the `Parameters` struct:\n",
"- The matrix of association parameters (because it depends on model-specific combining rules)\n",
"- Parameters for homosegmented GC methods (because they depend on model-specific sum rules)\n",
"\n",
"To access those and all other parameters, we need to build the equation of state first. Then we can access its parameters using the `parameters` attribute."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'m': array([1. , 2.7515, 1.0656, 2.7447]),\n",
" 'sigma': array([3.7039, 3.6139, 3.0007, 3.2742]),\n",
" 'epsilon_k': array([150.03, 259.59, 366.51, 232.99]),\n",
" 'mu': array([0. , 0. , 0. , 2.88]),\n",
" 'na': array([1., 1.]),\n",
" 'nb': array([1., 1.]),\n",
" 'kappa_ab': array([[0.006692 , 0.01527536],\n",
" [0.01527536, 0.034868 ]]),\n",
" 'epsilon_k_ab': array([[2544.6 , 2522.65],\n",
" [2522.65, 2500.7 ]])}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eos = feos.EquationOfState.pcsaft(parameters)\n",
"eos.parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This also allows us to inspect parameters that are calculated from a homosegmented GC method"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'m': array([2.13608, 2.38216]),\n",
" 'sigma': array([3.79456883, 3.75681406]),\n",
" 'epsilon_k': array([233.79002903, 278.79916706]),\n",
" 'k_ij': array([[0., 0.],\n",
" [0., 0.]])}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters = feos.GcParameters.from_segments(\n",
" chemical_records=[cr1, cr2], \n",
" segment_records=[ch3, ch2, oh]\n",
")\n",
"eos = feos.EquationOfState.pcsaft(parameters)\n",
"eos.parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"In $\\text{FeO}_\\text{s}$, handling of parameters is not predefined within the underlying library. Each implementation of an equation of state has complete autonomy about the way parameters are handled.\n",
"Howevery, we offer some convenient ways to automatize parameter handling which result in the methods presented in this example.\n",
"\n",
"We looked at different ways to get parameters for the `EquationOfState.pcsaft()` equation of state, i.e.\n",
"\n",
"- how to read parameters for substances from one or more files where we use the json format to store information, and\n",
"- how to generate parameters the same parameters in Python.\n",
"- In a similar fashion, we showed how parameters can be assembled using a homo-GC method.\n",
"\n",
"Once parameters are created, we can retrieve information by extracting the intermediate objects such as `PureRecord`, `Identifier` and `PcSaftRecord`.\n",
"\n",
"## Further reading\n",
"\n",
"- Files of published parameters and further explanations can be found [in the feos github repositoy](https://github.com/feos-org/feos/tree/main/parameters/pcsaft).\n",
"\n",
"## Concluding remkars\n",
"\n",
"Hopefully you found this example helpful. If you have comments, critique or feedback, please let us know and consider [opening an issue on github](https://github.com/feos-org/feos/issues)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.14.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | docs/tutorials/utility/core_working_with_units.ipynb | .ipynb | 15,904 | 533 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with SI-units\n",
"\n",
"## Goal\n",
"\n",
"> Learn how to work with `SINumber` and `SIArray` objects which represent physical quantities, i.e. one or more floating point numbers with an associated unit.\n",
"\n",
"## The `si-units` module\n",
"\n",
"Most interfaces in `FeOs` use dimensioned quantities as input. For example, to define a thermodynamic state at given temperature, pressure and amount of substance, all of these properties have to be multiplied by an apropriate unit before we can call the function that creates the state.\n",
"\n",
"`FeOs` uses the [quantity](https://itt-ustutt.github.io/quantity/) crate which generates the `si_units` Python module. To have consistency throughout the ecosystem, we recommend importing the package as `import si_units as si`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import si_units as si\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `si` module contains units according to [the Standard Interational System of Units](https://en.wikipedia.org/wiki/International_System_of_Units) (SI), constants and prefixes.\n",
"A **scalar** floating point number multiplied or divided by a unit has the **`SIObject`** data type."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"temperature : 300.15 K\n",
"data type : <class 'si_units._core.SIObject'>\n"
]
}
],
"source": [
"temperature = 300.15 * si.KELVIN\n",
"print(f\"temperature : {temperature}\")\n",
"print(f\"data type : {type(temperature)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The representation of a `SIObject` (e.g. using `print`) uses a prefix (e.g. `k` for `kilo`) so that the numerical value is convenient. For example 1000 g will be represented as 1 kg. Internally, all values are stored with respect to the basic SI unit, i.e. `METER`, `KILOGRAM`, `SECOND`, `AMPERE`, `MOL`, `KELVIN`, and `CANDELA`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1\\,\\mathrm{kg}$"
],
"text/plain": [
"1 kg"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1000 * si.GRAM"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"v = 0.07486757864516083 m³\n"
]
}
],
"source": [
"# volume of an ideal gas\n",
"t = 300.15 * si.KELVIN\n",
"p = 0.5 * si.BAR\n",
"n = 1.5 * si.MOL\n",
"v = n * si.RGAS * t / p\n",
"print(f\"v = {v}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use division to transform a `SIObject` to a `float` for the desired unit:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t = 27.00 °C\n",
"data type = <class 'float'>\n"
]
}
],
"source": [
"# temperature in Celsius\n",
"t_c = t / si.CELSIUS\n",
"print(f\"t = {t_c:3.2f} °C\")\n",
"print(f\"data type = {type(t_c)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mathematical operations and interaction with `numpy`\n",
"\n",
"The `SINumber` object supports some mathematical operations, i.e. we can\n",
"\n",
"- add or subtract two objects, **if they have the same unit**,\n",
"- multiply or divide two objects, which returns a floating point number if they have the same unit,\n",
"- raise an object to a power, and\n",
"- take the square and cubic root, which only works if the units have the correct exponents to allow for the operation."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressure : 265 kPa\n",
"temperature : 40 K\n",
"velocity : 100 m/s\n",
"velocity (cm/min) : 600000.0\n",
"acceleration : 9.81 m s^-2\n"
]
}
],
"source": [
"# addition\n",
"pressure = 2.5 * si.BAR + 15_000 * si.PASCAL\n",
"print('pressure :', pressure)\n",
"\n",
"# subtraction\n",
"temperature = 543.15 * si.KELVIN - 230.0 * si.CELSIUS\n",
"print('temperature :', temperature)\n",
"\n",
"# division\n",
"velocity = 360_000 * si.METER / si.HOUR\n",
"print('velocity :', velocity)\n",
"\n",
"# division as transformation to target unit\n",
"v_cm_minute = velocity / (si.CENTI * si.METER / si.MINUTE) # this is a floating point number\n",
"print(f'velocity (cm/min) : {v_cm_minute:8.1f}')\n",
"\n",
"# power\n",
"acceleration = 9.81 * si.METER / si.SECOND**2\n",
"print('acceleration :', acceleration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the square and cubic root we can use the `numpy` functions, `numpy.sqrt` and `numpy.cbrt`, respectively, or use the methods provided by `si_units`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"speed : 1 m/s\n",
"length : 3 nm\n"
]
}
],
"source": [
"# square root\n",
"speed = np.sqrt(si.METER**2 / si.SECOND**2)\n",
"print('speed :', speed)\n",
"\n",
"# cubic root\n",
"box_length = (27_000 * si.ANGSTROM**3).cbrt()\n",
"print('length :', box_length)\n",
"\n",
"# both numpy and methods of SINumbers work\n",
"assert(np.sqrt(si.METER**2 / si.SECOND**2) == (si.METER**2 / si.SECOND**2).sqrt())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The mathematical functions from the `math` module do not work because it is written in C and `SINumber`s are not compatible with the C function arguments.\n",
"If you are having trouble with errors, consider transforming your properties to floats (by division with the proper unit) and then multiplying the correct unit to the result."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ERROR: must be real number, not si_units._core.SIObject\n"
]
}
],
"source": [
"from math import sqrt\n",
"\n",
"try:\n",
" sqrt(si.METER**2 / si.SECOND**2)\n",
"except Exception as e:\n",
" print(\"ERROR:\", e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you try to perform arithmetic operations with `SINumber`s that are not allowed (e.g. adding two properties with different units), an error is raised."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "Inconsistent units Pa and K",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43msi\u001b[49m\u001b[43m.\u001b[49m\u001b[43mPASCAL\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[43msi\u001b[49m\u001b[43m.\u001b[49m\u001b[43mKELVIN\u001b[49m\n",
"\u001b[31mRuntimeError\u001b[39m: Inconsistent units Pa and K"
]
}
],
"source": [
"si.PASCAL + si.KELVIN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can enforce correct units in our interfaces using the `has_unit` method:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p1 = 1.4550309581768168 MPa\n",
"ERROR: Please provide the molar density, e.g. in units of mol/m³.\n"
]
}
],
"source": [
"MOL_M3 = si.MOL / si.METER**3 # we can create own units for future use\n",
"\n",
"def ideal_gas_pressure(density, temperature):\n",
" if not density.has_unit(MOL_M3):\n",
" raise ValueError(\"Please provide the molar density, e.g. in units of mol/m³.\")\n",
" else:\n",
" return density * temperature * si.RGAS\n",
" \n",
"try:\n",
" p1 = ideal_gas_pressure(0.5 * si.KILO * si.MOL / si.METER**3, 350 * si.KELVIN)\n",
" print('p1 = ', p1)\n",
" p2 = ideal_gas_pressure(0.5 * si.KILOGRAM / si.METER**3, 350 * si.KELVIN)\n",
" print('p2 = ', p2)\n",
"except Exception as e:\n",
" print(\"ERROR:\", e) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Arrays of quantities\n",
"\n",
"The \"number\" part of the `SIObject` can be any type that supports the necessary operations. Aside from floats, those are, e.g., **numpy arrays** or **tensors**."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressures = array([100000., 200000.]) Pa\n",
"data type = <class 'si_units._core.SIObject'>\n"
]
}
],
"source": [
"ps = np.array([1.0, 2.0]) * si.BAR\n",
"print(f\"pressures = {ps}\")\n",
"print(f\"data type = {type(ps)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The most important functionalities of array or tensor datatypes are also implemented for `SIObject`.\n",
"For example, you can index into an array of dimension one."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"200 kPa\n"
]
}
],
"source": [
"print(ps[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several useful numpy methods to create arrays. Most of them can be simply multiplied by units to convert them to `SIObject` objects.\n",
"Some of these functions, such as `linspace` and `logspace` can directly be constructed using `si.linspace` and `si.logspace`, respectively."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressures (numpy) = array([100000., 125000., 150000., 175000., 200000.]) Pa\n",
"pressures (si) = array([100000., 125000., 150000., 175000., 200000.]) Pa\n"
]
}
],
"source": [
"ps_np = np.linspace(1, 2, 5) * si.BAR\n",
"ps_si = si.linspace(1 * si.BAR, 2 * si.BAR, 5)\n",
"print(f\"pressures (numpy) = {ps_np}\")\n",
"print(f\"pressures (si) = {ps_si}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just like scalars, division by a unit that matches the property stored in an array yields a numpy ndarray."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressures = [0.1 0.125 0.15 0.175 0.2 ] MPa\n",
"data type = <class 'numpy.ndarray'>\n"
]
}
],
"source": [
"ps_mpa = ps_np / (si.MEGA*si.PASCAL)\n",
"print(f\"pressures = {ps_mpa} MPa\")\n",
"print(f\"data type = {type(ps_mpa)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Derived units, constants and prefixes\n",
"\n",
"In conjunction to the base units, the `si` module also exports derived units (e.g. `HOUR = 3600 * SECOND`), constants and prefixes (such as `KILO` or `FEMTO`).\n",
"Of course, we could multiply by a floating point number instead of using prefixes (e.g. `cm = 1e-2 * METER` vs. `cm = CENTI * METER`) but we think using prefixes make the code more readable.\n",
"\n",
"For a complete overview of exported units constants and prefixes, take a look at the [documentation of the `si_units` Python package](https://itt-ustutt.github.io/quantity/base/)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hyperfine transition frequency of Cs: 9.19263177 GHz\n",
"Speed of light : 2.99792458e8 m/s\n",
"Planck constant : 6.62607015e-34 Js\n",
"Elementary charge : 1.602176634e-19 C\n",
"Boltzmann constant : 1.380649e-23 J/K\n",
"Avogradro constant : 6.02214076e23 mol^-1\n",
"Luminous efficacy : 683 lm/W\n"
]
}
],
"source": [
"# The seven constants that inform the base units\n",
"print('Hyperfine transition frequency of Cs:', si.DVCS)\n",
"print('Speed of light :', si.CLIGHT)\n",
"print('Planck constant :', si.PLANCK)\n",
"print('Elementary charge :', si.QE)\n",
"print('Boltzmann constant :', si.KB)\n",
"print('Avogradro constant :', si.NAV)\n",
"print('Luminous efficacy :', si.KCD)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gravitational constant: 6.6743e-11 m³/kg/s²\n",
"Ideal gas constant : 8.31446261815324 J/mol/K\n"
]
}
],
"source": [
"# Derived constants\n",
"print('Gravitational constant:', si.G)\n",
"print('Ideal gas constant :', si.RGAS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"In `feos`, most interfaces use dimensioned units in form of `SIObject` from the `si-units` Python library.\n",
"This enables us to write functions that can check for proper units and thus circumvent unit errors that could occur, e.g. providing mass densities instead of molar densities.\n",
"\n",
"In this example, we learned how to create and use these objects in conjunction with methods provided by `numpy`.\n",
"\n",
"## Concluding remkars\n",
"\n",
"Hopefully you found this example helpful. If you have comments, critique or feedback, please let us know and consider [opening an issue on github](https://github.com/feos-org/feos/issues)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | docs/tutorials/utility/core_dual_numbers.ipynb | .ipynb | 21,918 | 486 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# On Dual Numbers in `FeOs`\n",
"\n",
"In `FeOs`, we use [generalized dual numbers](https://www.frontiersin.org/articles/10.3389/fceng.2021.758090/full) to compute partial derivatives of the Helmholtz energy.\n",
"In this notebook, we take a closer look at how that works.\n",
"\n",
"We will make use of the `EquationOfState.python()` feature: we use a simple version of the Peng-Robinson equation of state implemented as Python class which is registered in `FeOs` as equation of state. \n",
"If you want to learn how to implement an equation of state as Python class in conjunction with `FeOs`, take a look at the example implementation in the examples section.\n",
"In short - a class that implements a `helmholtz_energy` function that takes a `StateD` (or *any* state object, where the internal data types can be any generalized dual number)\n",
"and returns the Helmholtz energy (plus some minor functions), can be used with `FeOs`. We use the equation of state implemented in Python simply because we can add `print` statements and inspect the data types at runtime.\n",
"\n",
"## Dual Numbers\n",
"\n",
"We won't go into much detail regarding the theory of generalized dual numbers. Suffice it to say that generalized dual numbers are mathematical constructs consisting of one real value and one or more non-real values (just like complex numbers) that enable evaluation of arithmetic operations such that the operation *and* the derivative(s) can simultaneously be computed.\n",
"We call these *generalized* dual numbers because they can have a different number of *non-real* values. For example a *dual number* has a real and a single non-real value and can be used to compute first (partial) derivatives, while a *hyper-dual number* has a real and three non-real values and can be used to compute first and second (partial) derivatives.\n",
"We can - in principle - create numbers that allow computation of an arbitrarily high derivative at the cost of execution speed.\n",
"\n",
"Similar to numerical differentiation and complex step differentiation, a derivative is computed by defining a \"step size\". For complex step and dual number differentiation this step is introduced in the non-real part. Derivatives of dual numbers, however, are exact (to machine precision) and independent of the step size used. Hence, we use unity as step size.\n",
"\n",
"For example, when we feed a temperature as `Dual64` data type\n",
"\n",
"```python\n",
"print(temperature)\n",
"300 + [1]ε\n",
"```\n",
"\n",
"into a function, its return value will be a `Dual64` as well. The non-real part of the function result *is* the derivative with respect to temperature.\n",
"\n",
"## Do we need to create dual numbers by hand?\n",
"\n",
"No. If you use already implemented equations of state, you will not even recognize that dual numbers are used.\n",
"However, if you use Python or Rust to implement you own equation of state, it is useful to know how `FeOs` creates and uses dual numbers.\n",
"It's easier to look at an example than to talk about it. Below, you find the implementation of the Peng-Robinson equation of state where we added some `print` statement to the `helmholtz_energy` function to keep track of the values and data types of the thermodynamic state that enters the Helmholtz energy computation."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import numpy as np\n",
" \n",
"SQRT2 = np.sqrt(2)\n",
"\n",
"class PyPengRobinson: \n",
" def __init__(self, critical_temperature, critical_pressure, acentric_factor, molar_weight, delta_ij=None):\n",
" \"\"\"Peng-Robinson Equation of State\n",
" \n",
" Parameters\n",
" ----------\n",
" critical_temperature : SIArray1\n",
" critical temperature of each component.\n",
" critical_pressure : SIArray1\n",
" critical pressure of each component.\n",
" acentric_factor : np.array[float] \n",
" acentric factor of each component (dimensionless).\n",
" molar_weight: SIArray1\n",
" molar weight of each component.\n",
" delta_ij : np.array[[float]], optional\n",
" binary parameters. Shape=[n, n], n = number of components.\n",
" defaults to zero for all binary interactions.\n",
" \n",
" Raises\n",
" ------\n",
" ValueError: if the input values have incompatible sizes.\n",
" \"\"\"\n",
" self.n = len(critical_temperature)\n",
" if len(set((len(critical_temperature), len(critical_pressure), len(acentric_factor)))) != 1:\n",
" raise ValueError(\"Input parameters must all have the same lenght.\")\n",
" \n",
" if self.n == 1:\n",
" self.tc = (critical_temperature / si.KELVIN)[0]\n",
" self.pc = (critical_pressure / si.PASCAL)[0]\n",
" self.omega = acentric_factor[0]\n",
" self.mw = (molar_weight / si.GRAM * si.MOL)[0]\n",
" else:\n",
" self.tc = critical_temperature / si.KELVIN\n",
" self.pc = critical_pressure / si.PASCAL\n",
" self.omega = acentric_factor\n",
" self.mw = molar_weight / si.GRAM * si.MOL\n",
" \n",
" self.a_r = 0.45724 * critical_temperature**2 * si.RGAS / critical_pressure / si.ANGSTROM**3 / si.NAV / si.KELVIN\n",
" self.b = 0.07780 * critical_temperature * si.RGAS / critical_pressure / si.ANGSTROM**3 / si.NAV\n",
" self.kappa = 0.37464 + (1.54226 - 0.26992 * acentric_factor) * acentric_factor\n",
" self.delta_ij = np.zeros((self.n, self.n)) if delta_ij is None else delta_ij\n",
" \n",
" def helmholtz_energy(self, state):\n",
" \"\"\"Return Helmholtz energy.\n",
" \n",
" Parameters\n",
" ----------\n",
" state : StateHD\n",
" The thermodynamic state.\n",
" \n",
" Returns\n",
" -------\n",
" helmholtz_energy: float | any dual number\n",
" The return type depends on the input types.\n",
" \"\"\"\n",
" x = state.molefracs\n",
" tr = 1.0 / self.tc * state.temperature\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n",
" ak_mix = 0.0\n",
" if self.n > 1:\n",
" for i in range(self.n):\n",
" for j in range(self.n):\n",
" ak_mix += np.sqrt(ak[i] * ak[j]) * (x[i] * x[j] * (1.0 - self.delta_ij[i, j]))\n",
" else:\n",
" ak_mix = ak\n",
" b = np.sum(x * self.b)\n",
" v = 1.0 / state.density\n",
" a = state.density * (np.log(v / (v - b)) - ak_mix / (b * SQRT2 * 2.0 * state.temperature)\n",
" * np.log((v * (SQRT2 - 1.0) + b) / (v * (SQRT2 + 1.0) - b)))\n",
" \n",
" # some print statements to inspect data types\n",
" print()\n",
" print(\"data type : \", type(state.temperature))\n",
" print(\"temperature: \", state.temperature)\n",
" print(\"molefracs : \", state.molefracs)\n",
" print(\"density : \", state.density)\n",
" print(\"A/kT : \", a)\n",
" return a\n",
" \n",
" def components(self) -> int: \n",
" \"\"\"Number of components.\"\"\"\n",
" return self.n\n",
" \n",
" def subset(self, i: list[int]):\n",
" \"\"\"Return new equation of state containing a subset of all components.\"\"\"\n",
" if self.n > 1:\n",
" tc = self.tc[i] \n",
" pc = self.pc[i]\n",
" mw = self.mw[i]\n",
" omega = self.omega[i]\n",
" return PyPengRobinson(tc*si.KELVIN, pc*si.PASCAL, omega, mw*si.GRAM/si.MOL)\n",
" else:\n",
" return self\n",
" \n",
" def molar_weight(self) -> si.SIObject:\n",
" if isinstance(self.mw, float):\n",
" return np.array([self.mw]) * si.GRAM / si.MOL\n",
" else:\n",
" return self.mw * si.GRAM / si.MOL\n",
" \n",
" def max_density(self, molefracs: np.ndarray[float]) -> float:\n",
" b = np.sum(molefracs * self.b)\n",
" return 0.9 / b\n",
" \n",
"\n",
"class PyPerfectGas:\n",
" def __init__(self):\n",
" \"\"\"Dummy implementation for an ideal gas with constant heat capacity\"\"\"\n",
" pass\n",
" \n",
" def ln_lambda3(self, temperature):\n",
" return temperature/temperature\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# parameters for propane\n",
"tc = si.array(369.96 * si.KELVIN)\n",
"pc = si.array(4250000.0 * si.PASCAL)\n",
"omega = np.array([0.153])\n",
"molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n",
"\n",
"# create an instance of our python class and hand it over to rust\n",
"eos = feos.EquationOfState.python_residual(PyPengRobinson(tc, pc, omega, molar_weight)).python_ideal_gas([PyPerfectGas()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Properties and dual numbers\n",
"\n",
"After initializing the equation of state for a single substance (in the above cell), let us now build a thermodynamic state.\n",
"For the sake of this example, we use the natural variables of the Helmholtz energy ($\\mathbf{N}$, $V$, $T$) as input so that no volume or temperature has to be iterated."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"state = feos.State(eos, temperature=300*si.KELVIN, volume=40744*si.ANGSTROM**3, total_moles=1/si.NAV)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When computing a thermodynamic property for a `State`, `FeOs` internally generates a new object which contains the same information as `State` but with dual numbers as data types.\n",
"Which dual numbers (dual, hyper-dual, etc.) are used depends on the property to compute or rather which partial derivatives are needed.\n",
"`FeOs` then modifies the non-real parts of those properties (temperature, volume or amount of substance) so that the correct derivatives are computed and feeds this \"generalized dual state\" object into the function that computes the Helmholtz energy.\n",
"The result will have the same data type as the state's variables and the needed derivative(s) are extracted and returned.\n",
"\n",
"`FeOs` does all of that independent from the implementation of the equation of state.\n",
"All you have to do - if you implement a Helmholtz energy function - is to write your code knowing that the state's properties are generalized dual numbers.\n",
"In Python, your can write code just like \"regular\" Python because dual numbers implement all arithmetic opartions you'd typically use."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at an example. First, we compute the `molar_helmholtz_energy`.\n",
"Since *no derivatives* are needed to compute the Helmholtz energy, the data types of the `State`'s properties are regular floating point numbers (`float`).\n",
"\n",
"If you are in a running noteboook, execute the cell below and check the output.\n",
"If you run the cell a second time, you will notice that the information about data types and values aren't printed to the screen anymore.\n",
"That's because we *cache* already computed derivatives for a given state.\n",
"A second call to `molar_helmholtz_energy` simply pulls the already computed value from cache and returns it without entering the `helmholtz_energy` function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'float'>\n",
"temperature: 300.0\n",
"molefracs : [1.0]\n",
"density : 2.4543491066169247e-05\n",
"A/kT : [0.00012419]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_216511/809879024.py:1: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" state.helmholtz_energy() / (si.KB * 300*si.KELVIN)\n"
]
},
{
"data": {
"text/plain": [
"-5.554978406564659"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.helmholtz_energy() / (si.KB * 300*si.KELVIN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we compute the `pressure`, for which we compute the first partial derivative of the Helmholtz energy with respect to the volume.\n",
"\n",
"$p = -\\left(\\frac{\\partial A}{\\partial V}\\right)_{\\mathbf{n}, T}$\n",
"\n",
"If you execute the cell below, you'll notice that the data types are now dual numbers (`Dual64`, a dual number with 64bit floating point numbers) with *a single* non-real part.\n",
"This non-real or *dual* part of the volume is unity while all other non-real parts are zero, which means that the first partial derivative with respect to volume will be computed.\n",
"\n",
"`FeOs` modifies the dual parts of the temperature, volume or amount of substance (for a component) when computing derivatives.\n",
"Note that the `density` is calculated as $\\rho = \\frac{\\sum n_i}{V}$ (where $n_i$ is the amount of substance of component $i$) and hence also has a dual part which is different from zero.\n",
"It is also different from unity - the value is calculated according to the artihmetic operation of division between two dual numbers."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'builtins.PyDual64'>\n",
"temperature: 300 + 0ε\n",
"molefracs : [1 + 0ε]\n",
"density : 0.000024543491066169247 + -602382953715129600000ε\n",
"A/kT : 0.00012419216233064458 + -3038285859542560000000ε\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_216511/966462947.py:64: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n"
]
},
{
"data": {
"text/latex": [
"$100\\,\\mathrm{kPa}$"
],
"text/plain": [
"100.00005278190892 kPa"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.pressure()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to the `pressure`, the `entropy` is computed by taking the first partial derivative with respect to temperature.\n",
"Here, the dual part of the density is zero because both dual parts of the volume and the amount of substance are zero, too."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'builtins.PyDual64'>\n",
"temperature: 300 + 1ε\n",
"molefracs : [1 + 0ε]\n",
"density : 0.000024543491066169247 + 0ε\n",
"A/kT : 0.00012419216233064458 + -0.0000006261809548282462ε\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_216511/966462947.py:64: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n"
]
},
{
"data": {
"text/latex": [
"$109.83\\,\\mathrm{\\frac{ J}{molK}}$"
],
"text/plain": [
"109.82501713655553 J/mol/K"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.molar_entropy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at a more involved property.\n",
"For the Joule-Thomson coefficient, we need multiple second partial derivatives:\n",
"\n",
"$\\mu_{JT}=\\left(\\frac{\\partial T}{\\partial p}\\right)_{H,N_i} = -\\frac{1}{C_p} \\left(V + T \\left(\\frac{\\partial p}{\\partial T}\\right)_{\\mathbf{n}, V} \\left(\\frac{\\partial p}{\\partial V}\\right)_{\\mathbf{n}, T}^{-1} \\right)$\n",
"\n",
"We need three evaluations of the Helmholtz energy (that are actually occuring in the computation of $C_p$):\n",
"\n",
"1. $\\left(\\frac{\\partial p}{\\partial T}\\right)_{\\mathbf{n}, V} \\rightarrow -\\left(\\frac{\\partial A}{\\partial T \\partial V}\\right)_\\mathbf{n}$: second partial derivative w.r.t volume and temperature,\n",
"2. $\\left(\\frac{\\partial p}{\\partial V}\\right)_{\\mathbf{n}, T} \\rightarrow -\\left(\\frac{\\partial^2 A}{\\partial V^2}\\right)_{\\mathbf{n}, T}$: second derivative w.r.t volume,\n",
"3. $\\left(\\frac{\\partial^2 A}{\\partial T^2}\\right)_{\\mathbf{n}, V}$: 2nd partial derivative w.r.t temperature.\n",
"\n",
"Since we compute second derivatives, the data type is now `HyperDual64` (one real, 3 non-real values) for mixed partial derivatives and `Dual2_64` (one real, 2 non-real values) for the other partial derivatives."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"data type : <class 'builtins.PyHyperDual64'>\n",
"temperature: 300 + 1ε1 + 0ε2 + 0ε1ε2\n",
"molefracs : [1 + 0ε1 + 0ε2 + 0ε1ε2]\n",
"density : 0.000024543491066169247 + 0ε1 + -602382953715129600000ε2 + 0ε1ε2\n",
"A/kT : 0.00012419216233064458 + -0.0000006261809548282462ε1 + -3038285859542560000000ε2 + 15312125055595608000ε1ε2\n",
"\n",
"data type : <class 'builtins.PyDual2_64'>\n",
"temperature: 300 + 0ε1 + 0ε1²\n",
"molefracs : [1 + 0ε1 + 0ε1²]\n",
"density : 0.000024543491066169247 + -602382953715129600000ε1 + 29569161285839853000000000000000000000000000000ε1²\n",
"A/kT : 0.00012419216233064458 + -3038285859542560000000ε1 + 148659416520544790000000000000000000000000000000ε1²\n",
"\n",
"data type : <class 'builtins.PyDual2_64'>\n",
"temperature: 300 + 1ε1 + 0ε1²\n",
"molefracs : [1 + 0ε1 + 0ε1²]\n",
"density : 0.000024543491066169247 + 0ε1 + 0ε1²\n",
"A/kT : 0.00012419216233064458 + -0.0000006261809548282462ε1 + 0.000000004710234326181233ε1²\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_216511/966462947.py:64: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
" ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n"
]
},
{
"data": {
"text/latex": [
"$-1.4939\\times10^{-4}\\,\\mathrm{\\frac{ms^{2}K}{kg}}$"
],
"text/plain": [
"-1.4938695195180373e-4 m kg^-1 s^2 K"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.joule_thomson()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"- `FeOs` uses generalized dual numbers which enable the computation of higher order partial derivatives of the Helmholtz energy without the need of implementing these derivatives analytically.\n",
"- When a property is computed, `FeOs` creates a new thermodynamic state with the correct data types according to the partial derivatives needed.\n",
"- If a property was computed before, it is pulled from the state's cache instead of re-evaluating the Helmholtz energy."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | feos-org/feos | docs/tutorials/utility/index.md | .md | 109 | 12 | # Utility
```{eval-rst}
.. toctree::
:maxdepth: 1
core_working_with_units
core_dual_numbers
```
| Markdown |
3D | feos-org/feos | docs/tutorials/dft/index.md | .md | 103 | 9 | # Density functional theory
```{eval-rst}
.. toctree::
:maxdepth: 1
pcsaft_surface_tension
```
| Markdown |
3D | feos-org/feos | docs/tutorials/dft/pcsaft_surface_tension.ipynb | .ipynb | 173,518 | 672 | {
"cells": [
{
"cell_type": "markdown",
"id": "135dd79e",
"metadata": {},
"source": [
"# Surface tension using PC-SAFT Helmholtz energy functionals\n",
"\n",
"## Goal of this notebook\n",
"\n",
"- Learn how to compute the surface tension for a planar interface using the PC-SAFT functionals.\n",
"- Learn about the `SurfaceTensionDiagram` that allows convenient calculation of multiple surface tensions."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9ad1bb1d",
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"sns.set_context(\"talk\")\n",
"sns.set_palette(\"Dark2\")\n",
"sns.set_style(\"ticks\")\n",
"\n",
"colors = sns.color_palette(\"Dark2\", 2)"
]
},
{
"cell_type": "markdown",
"id": "c7ec9a5b",
"metadata": {},
"source": [
"### Water parameters for PC-SAFT \n",
"\n",
"In this example we will calculate surface tensions for water using the 2B association scheme. The parameters that we use, [were adjusted to vapor pressures, liquid densities and surface tensions](https://pubs.acs.org/doi/10.1021/acs.jced.0c00684). Parameters are available [here](https://github.com/feos-org/feos/tree/main/parameters/pcsaft)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "837c7770",
"metadata": {},
"outputs": [],
"source": [
"# Equation of state object.\n",
"parameters = feos.Parameters.from_json(\n",
" ['water_2B'], \n",
" '../../../parameters/pcsaft/rehner2020.json'\n",
")\n",
"pcsaft = feos.HelmholtzEnergyFunctional.pcsaft(parameters)"
]
},
{
"cell_type": "markdown",
"id": "79b57b8b",
"metadata": {},
"source": [
"Let's first compute the critical point. We will make use of the critical temperature later."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d6ed65fa",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|677.34347 K|18.70466 kmol/m³|"
],
"text/plain": [
"T = 677.34347 K, ρ = 18.70466 kmol/m³"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cp = feos.State.critical_point(pcsaft)\n",
"cp"
]
},
{
"cell_type": "markdown",
"id": "2ad0235c",
"metadata": {},
"source": [
"As you can see, the model overestimates the critical temperature."
]
},
{
"cell_type": "markdown",
"id": "4c00eed3",
"metadata": {},
"source": [
"## Surface tension for single VLE\n",
"\n",
"To compute the surface tension, three steps are needed.\n",
"\n",
"1. We need to compute the vapor liquid equilibrium (VLE) either at given temperature or pressure.\n",
"2. Then, we need to initialize a density profile. We will use a hyperbolic tangent with the VLE bulk densities as limits.\n",
"3. We solve the DFT equations to yield the equilibrium density profile and calculate the surface tension."
]
},
{
"cell_type": "markdown",
"id": "8fa8d790",
"metadata": {},
"source": [
"For the VLE, we use the `PhaseEquilibrium.pure` method. Here for $T = 300$ Kelvin."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f834af33",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|300.00000 K|1.51670 mol/m³|\n",
"|phase 2|300.00000 K|55.38975 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 300.00000 K, ρ = 1.51670 mol/m³\n",
"phase 1: T = 300.00000 K, ρ = 55.38975 kmol/m³"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle = feos.PhaseEquilibrium.pure(pcsaft, 300*si.KELVIN)\n",
"vle"
]
},
{
"cell_type": "markdown",
"id": "21fb88cb",
"metadata": {},
"source": [
"Next, we initialize the density profile. For the surface tension, a 1D DFT calculation in Cartesian coordinates is conducted. Thus, the density profile will be an 1D array (we have a single substance). \n",
"\n",
"To solve the DFT equations, the density has to be discretized which can be controlled by `n_grid`, the number of grid points. The surface tension is not very sensitive w.r.t the number of grid points but you should make sure to pick a large enough value. When in doubt, run multiple calculations varying the number.\n",
"\n",
"We also have to provide a width of the calculation domain, `l_grid`. The domain should be large enough that the bulk densities can be observed in the limits. You can check the resulting density profile to make sure that's the case.\n",
"\n",
"The critical temperature is used to come up with a good initial estimate for the density profile."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "25c9d99d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 367 μs, sys: 10 μs, total: 377 μs\n",
"Wall time: 363 μs\n"
]
}
],
"source": [
"%%time\n",
"interface = feos.PlanarInterface.from_tanh(\n",
" vle=vle, \n",
" n_grid=512, \n",
" l_grid=100*si.ANGSTROM, \n",
" critical_temperature=cp.temperature\n",
")\n",
"initial_density = interface.density / (si.KILO * si.MOL / si.METER**3)"
]
},
{
"cell_type": "markdown",
"id": "303bb746",
"metadata": {},
"source": [
"The above method does not yet run a calculation. If we try to extract the surface tension, it will return `None`. Let's store the initial density profile for a later comparison."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f915e37c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interface.surface_tension == None"
]
},
{
"cell_type": "markdown",
"id": "7a321fe5",
"metadata": {},
"source": [
"To calculate the equilibrium density profile, we have to call the `solve()` method:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "77c08d7f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.8 ms, sys: 1.02 ms, total: 12.8 ms\n",
"Wall time: 12.7 ms\n"
]
}
],
"source": [
"%%time\n",
"surface_tension = interface.solve().surface_tension"
]
},
{
"cell_type": "markdown",
"id": "19402ba2",
"metadata": {},
"source": [
"`solve()` calculates the equilibrium density profile and returns the `PlanarInterface` object so that we can readily extract the `surface_tension`.\n",
"\n",
"The `PlanarInterface.density` contains the equilibrated density profile. Let's compare it to our initial density and zoom into the interesting region between 40 and 60 Angstrom. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "61992d8f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"plt.plot(interface.z / si.ANGSTROM, initial_density[0], linestyle=\"dashed\", label=\"initial density\")\n",
"plt.plot(interface.z / si.ANGSTROM, (interface.density / (si.KILO * si.MOL / si.METER**3))[0], label=\"equilibrium density\")\n",
"\n",
"plt.xlim(40, 60)\n",
"plt.ylim(-5, 60)\n",
"plt.ylabel(r\"$\\rho$ / kmol m$^{-3}$\")\n",
"plt.xlabel(r\"$z$ / A\")\n",
"sns.despine(offset=10)\n",
"plt.legend(frameon=False);"
]
},
{
"cell_type": "markdown",
"id": "bc985a88",
"metadata": {},
"source": [
"## Comparison to NIST data using `SurfaceTensionDiagram`\n",
"\n",
"We can use the above steps to calculate multiple VLE's and then - for each VLE - calculate the surface tension. We provide a utility object, the `SurfaceTensionDiagram`, that you can use to do exactly this task. Let's load some experimental (correlation) data obtained from the NIST Webbook and see how the water model compares to that."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "caa1fda7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Temperature (K)</th>\n",
" <th>Pressure (MPa)</th>\n",
" <th>Density (l, mol/l)</th>\n",
" <th>Volume (l, l/mol)</th>\n",
" <th>Internal Energy (l, kJ/mol)</th>\n",
" <th>Enthalpy (l, kJ/mol)</th>\n",
" <th>Entropy (l, J/mol*K)</th>\n",
" <th>Cv (l, J/mol*K)</th>\n",
" <th>Cp (l, J/mol*K)</th>\n",
" <th>Sound Spd. (l, m/s)</th>\n",
" <th>...</th>\n",
" <th>Volume (v, l/mol)</th>\n",
" <th>Internal Energy (v, kJ/mol)</th>\n",
" <th>Enthalpy (v, kJ/mol)</th>\n",
" <th>Entropy (v, J/mol*K)</th>\n",
" <th>Cv (v, J/mol*K)</th>\n",
" <th>Cp (v, J/mol*K)</th>\n",
" <th>Sound Spd. (v, m/s)</th>\n",
" <th>Joule-Thomson (v, K/MPa)</th>\n",
" <th>Viscosity (v, uPa*s)</th>\n",
" <th>Therm. Cond. (v, W/m*K)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>275.0</td>\n",
" <td>0.000698</td>\n",
" <td>55.502</td>\n",
" <td>0.018017</td>\n",
" <td>0.13978</td>\n",
" <td>0.13979</td>\n",
" <td>0.5100</td>\n",
" <td>75.903</td>\n",
" <td>75.915</td>\n",
" <td>1411.4</td>\n",
" <td>...</td>\n",
" <td>3271.50</td>\n",
" <td>42.830</td>\n",
" <td>45.115</td>\n",
" <td>164.06</td>\n",
" <td>25.580</td>\n",
" <td>33.980</td>\n",
" <td>410.33</td>\n",
" <td>558.89</td>\n",
" <td>8.9986</td>\n",
" <td>0.016879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>285.0</td>\n",
" <td>0.001389</td>\n",
" <td>55.479</td>\n",
" <td>0.018025</td>\n",
" <td>0.89678</td>\n",
" <td>0.89681</td>\n",
" <td>3.2139</td>\n",
" <td>75.397</td>\n",
" <td>75.533</td>\n",
" <td>1454.3</td>\n",
" <td>...</td>\n",
" <td>1704.30</td>\n",
" <td>43.078</td>\n",
" <td>45.445</td>\n",
" <td>159.52</td>\n",
" <td>25.736</td>\n",
" <td>34.170</td>\n",
" <td>417.48</td>\n",
" <td>408.81</td>\n",
" <td>9.2941</td>\n",
" <td>0.017535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>295.0</td>\n",
" <td>0.002621</td>\n",
" <td>55.384</td>\n",
" <td>0.018056</td>\n",
" <td>1.65110</td>\n",
" <td>1.65120</td>\n",
" <td>5.8154</td>\n",
" <td>74.765</td>\n",
" <td>75.361</td>\n",
" <td>1487.7</td>\n",
" <td>...</td>\n",
" <td>934.36</td>\n",
" <td>43.324</td>\n",
" <td>45.773</td>\n",
" <td>155.38</td>\n",
" <td>25.898</td>\n",
" <td>34.374</td>\n",
" <td>424.46</td>\n",
" <td>304.01</td>\n",
" <td>9.6018</td>\n",
" <td>0.018214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>305.0</td>\n",
" <td>0.004719</td>\n",
" <td>55.233</td>\n",
" <td>0.018105</td>\n",
" <td>2.40440</td>\n",
" <td>2.40440</td>\n",
" <td>8.3264</td>\n",
" <td>74.038</td>\n",
" <td>75.300</td>\n",
" <td>1513.1</td>\n",
" <td>...</td>\n",
" <td>536.20</td>\n",
" <td>43.568</td>\n",
" <td>46.099</td>\n",
" <td>151.59</td>\n",
" <td>26.069</td>\n",
" <td>34.596</td>\n",
" <td>431.28</td>\n",
" <td>231.32</td>\n",
" <td>9.9196</td>\n",
" <td>0.018918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>315.0</td>\n",
" <td>0.008145</td>\n",
" <td>55.034</td>\n",
" <td>0.018171</td>\n",
" <td>3.15730</td>\n",
" <td>3.15750</td>\n",
" <td>10.7560</td>\n",
" <td>73.235</td>\n",
" <td>75.301</td>\n",
" <td>1531.7</td>\n",
" <td>...</td>\n",
" <td>320.58</td>\n",
" <td>43.811</td>\n",
" <td>46.422</td>\n",
" <td>148.10</td>\n",
" <td>26.252</td>\n",
" <td>34.843</td>\n",
" <td>437.93</td>\n",
" <td>180.66</td>\n",
" <td>10.2460</td>\n",
" <td>0.019646</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Temperature (K) Pressure (MPa) Density (l, mol/l) Volume (l, l/mol) \\\n",
"0 275.0 0.000698 55.502 0.018017 \n",
"1 285.0 0.001389 55.479 0.018025 \n",
"2 295.0 0.002621 55.384 0.018056 \n",
"3 305.0 0.004719 55.233 0.018105 \n",
"4 315.0 0.008145 55.034 0.018171 \n",
"\n",
" Internal Energy (l, kJ/mol) Enthalpy (l, kJ/mol) Entropy (l, J/mol*K) \\\n",
"0 0.13978 0.13979 0.5100 \n",
"1 0.89678 0.89681 3.2139 \n",
"2 1.65110 1.65120 5.8154 \n",
"3 2.40440 2.40440 8.3264 \n",
"4 3.15730 3.15750 10.7560 \n",
"\n",
" Cv (l, J/mol*K) Cp (l, J/mol*K) Sound Spd. (l, m/s) ... \\\n",
"0 75.903 75.915 1411.4 ... \n",
"1 75.397 75.533 1454.3 ... \n",
"2 74.765 75.361 1487.7 ... \n",
"3 74.038 75.300 1513.1 ... \n",
"4 73.235 75.301 1531.7 ... \n",
"\n",
" Volume (v, l/mol) Internal Energy (v, kJ/mol) Enthalpy (v, kJ/mol) \\\n",
"0 3271.50 42.830 45.115 \n",
"1 1704.30 43.078 45.445 \n",
"2 934.36 43.324 45.773 \n",
"3 536.20 43.568 46.099 \n",
"4 320.58 43.811 46.422 \n",
"\n",
" Entropy (v, J/mol*K) Cv (v, J/mol*K) Cp (v, J/mol*K) \\\n",
"0 164.06 25.580 33.980 \n",
"1 159.52 25.736 34.170 \n",
"2 155.38 25.898 34.374 \n",
"3 151.59 26.069 34.596 \n",
"4 148.10 26.252 34.843 \n",
"\n",
" Sound Spd. (v, m/s) Joule-Thomson (v, K/MPa) Viscosity (v, uPa*s) \\\n",
"0 410.33 558.89 8.9986 \n",
"1 417.48 408.81 9.2941 \n",
"2 424.46 304.01 9.6018 \n",
"3 431.28 231.32 9.9196 \n",
"4 437.93 180.66 10.2460 \n",
"\n",
" Therm. Cond. (v, W/m*K) \n",
"0 0.016879 \n",
"1 0.017535 \n",
"2 0.018214 \n",
"3 0.018918 \n",
"4 0.019646 \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature = pd.read_csv(\"../../../examples/data/water_vle_nist.csv\", delimiter=\"\\t\")\n",
"literature.head()"
]
},
{
"cell_type": "markdown",
"id": "96ae9524",
"metadata": {},
"source": [
"For the `SurfaceTensionDiagram`, we need to provide the VLE's. We compute those using the `PhaseDiagram` object (here for 50 temperatures between 275 Kelvin and the critical temperature) from which we get a list of `PhaseEquilibrium`s via the `states` filed. The `SurfaceTensionDiagram` is nice, because we can reuse equilibrium density profiles from prior iterations as input for the next iteration. It's therefore typically faster and more stable than an \"naive\" implementation by hand.\n",
"\n",
"The `SurfaceTensionDiagram` takes the same arguments `n_grind`, `l_grid` and `critical_temperature` as discussed above."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c0a7854c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 523 ms, sys: 3.87 ms, total: 526 ms\n",
"Wall time: 525 ms\n"
]
}
],
"source": [
"%%time\n",
"vles = feos.PhaseDiagram.pure(\n",
" pcsaft, \n",
" 275*si.KELVIN, \n",
" 50,\n",
" critical_temperature=cp.temperature\n",
")\n",
"sfts = feos.SurfaceTensionDiagram(\n",
" vles.states, \n",
" n_grid=512, \n",
" l_grid=100*si.ANGSTROM, \n",
" critical_temperature=cp.temperature\n",
")"
]
},
{
"cell_type": "markdown",
"id": "caa7026e",
"metadata": {},
"source": [
"We now can extract all surface tensions via `surface_tension` as well as the liquid and vapor states via the `liquid` and `vapor` getters, respectively. Let's store the results in a pandas `DataFrame` to make plotting easier."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6626c4c7",
"metadata": {},
"outputs": [],
"source": [
"dft_data = pd.DataFrame(\n",
" np.array([\n",
" sfts.liquid.temperature / si.KELVIN, \n",
" sfts.surface_tension / si.NEWTON * si.METER\n",
" ]).T, \n",
" columns=[\"Temperature (K)\", \"Surf. Tension (l, N/m)\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "279a66a3",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"plt.title(\"Surface tension of water\")\n",
"sns.lineplot(data=literature, x=\"Temperature (K)\", y=\"Surf. Tension (l, N/m)\", label=\"NIST\")\n",
"sns.lineplot(data=dft_data, x=\"Temperature (K)\", y=\"Surf. Tension (l, N/m)\", label=\"PC-SAFT (2B)\")\n",
"sns.scatterplot(x=[cp.temperature / si.KELVIN], y=[0.0], clip_on=False, color=colors[1], label=\"PC-SAFT (2B), critical point\")\n",
"plt.ylabel(r\"$\\gamma$ / Nm$^{-1}$\")\n",
"plt.xlabel(r\"$T$ / K\")\n",
"\n",
"plt.xlim(250, 700)\n",
"plt.ylim(0.0, 0.08)\n",
"sns.despine(offset=10)\n",
"plt.legend(frameon=False);"
]
},
{
"cell_type": "markdown",
"id": "35af1b14",
"metadata": {},
"source": [
"## Concluding remkars\n",
"\n",
"Hopefully you found this example helpful. If you have comments, critique or feedback, please let us know and consider [opening an issue on github](https://github.com/feos-org/feos/issues)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | docs/api/eos.md | .md | 1,954 | 94 | # Equations of state
The key data structure in FeOs is the `EquationOfState` class that contains all implemented equations of state.
The `State` and `PhaseEquilibrium` objects are used to define thermodynamic conditions and -- once created -- can be used to compute properties.
## The `EquationOfState` class
### Residual Helmholtz energy models
```{eval-rst}
.. currentmodule:: feos
.. autosummary::
EquationOfState.pcsaft
EquationOfState.epcsaft
EquationOfState.gc_pcsaft
EquationOfState.peng_robinson
EquationOfState.pets
EquationOfState.uvtheory
EquationOfState.saftvrmie
EquationOfState.saftvrqmie
```
### Ideal gas models
```{eval-rst}
.. autosummary::
EquationOfState.dippr
EquationOfState.joback
```
### Example: Combine a DIPPR ideal gas model with PC-SAFT
```python
import feos
pc_saft_parameters = feos.Parameters.from_json(
['methane', 'ethane'],
'pc_saft_parameters.json'
)
dippr_parameters = feos.Parameters.from_json(
['methane', 'ethane'],
'dippr_parameters.json'
)
eos = feos.EquationOfState.pcsaft(pc_saft_parameters).dippr(dippr_parameters)
```
### Example: Combine the ideal gas model of Joback & Reid with PC-SAFT
```python
import feos
pc_saft_parameters = feos.Parameters.from_json_smiles(
['CCC', 'CCCC'],
'smarts.json',
'pcsaft_group_parameters.json'
)
joback_parameters = feos.Parameters.from_json_smiles(
['CCC', 'CCCC'],
'smarts.json',
'joback_parameters.json'
)
eos = feos.EquationOfState.pcsaft(pc_saft_parameters).joback(joback_parameters)
```
### Models defined in Python
```{eval-rst}
.. currentmodule:: feos
.. autosummary::
EquationOfState.python_residual
EquationOfState.python_ideal_gas
```
## Data types
```{eval-rst}
.. currentmodule:: feos
.. autosummary::
:toctree: generated/
EquationOfState
Contributions
Verbosity
State
StateVec
PhaseEquilibrium
PhaseDiagram
```
| Markdown |
3D | feos-org/feos | docs/api/ad.md | .md | 3,570 | 80 | # Automatic differentiation
Automatic differentiation (AD) is used all throughout FeOs to calculate Helmholtz energy derivatives and Jacobians/Hessians for numerical solvers.
This section refers specifically to automatic (implicit) differentiation of phase equilibria with respect to model parameters that are crucial for parameter prediction or estimation.
Within Rust most phase equilibrium calculations can be used in an AD context, e.g., in order to calculate derivatives of process models (see [here](https://github.com/feos-org/feos-campd)). The Python interface focuses on the important use case of massively parallel phase equilibrium calculations for parameter estimation or prediction.
## Available equations of state
Only a subset of the models in FeOs can be used to calculate derivatives with respect to model parameters. However, those dedicated implementations also result in unprecedented performance (see example below). Similar to the [`EquationOfState`](eos.md#the-equationofstate-class) class, models with AD capabilities are collected in the `EquationOfStateAD` class.
The currently available models are:
|Model|Description|Parameters pure|Parameters binary|
|-|-|-|-|
|`PcSaftNonAssoc`|The PC-SAFT equation of state including a dipolar contribution but no association|`m`, `sigma`, `epsilon_k`, `mu`|`k_ij`|
|`PcSaftFull`|The PC-SAFT equation of state with a dipolar contribution and association|`m`, `sigma`, `epsilon_k`, `mu`, `kappa_ab`, `epsilon_k_ab`, `na`, `nb`|`k_ij`|
## Properties
Currently the following phase equilibrium properties are available in the AD interface of FeOs. We plan to extend the list in the future.
```{eval-rst}
.. currentmodule:: feos
.. autosummary::
:toctree: generated/
vapor_pressure_derivatives
liquid_density_derivatives
equilibrium_liquid_density_derivatives
bubble_point_pressure_derivatives
dew_point_pressure_derivatives
```
## Examples
The following example calculates pure-component vapor pressures including their derivatives with respect to the core PC-SAFT parameters for 10 Million temperatures in little more than two seconds.
```python
import feos
import numpy as np
n = 10_000_000
fit_params = ["m", "sigma", "epsilon_k"]
# order: m, sigma, epsilon_k, mu
parameters = np.array([[1.5, 3.4, 230.0, 2.3]] * n)
temperature = np.expand_dims(np.linspace(250.0, 400.0, n), 1)
eos = feos.EquationOfStateAD.PcSaftNonAssoc
%time feos.vapor_pressure_derivatives(eos, fit_params, parameters, temperature)
```
```
CPU times: user 1min 39s, sys: 654 ms, total: 1min 40s
Wall time: 2.3 s
```
For the most complex case, a binary mixture of cross-associating mixtures, the following example calculates 100.000 bubble point pressures and their derivative with respect to the binary interaction parameter in 3 seconds.
```python
import feos
import numpy as np
n = 100_000
fit_params = ["k_ij"]
parameters = np.array([[
# Substance 1: m, sigma, epsilon_k, mu, kappa_ab, epsilon_k_ab, na, nb
1.5, 3.4, 230.0, 2.3, 0.01, 1200.0, 1.0, 2.0,
# Substance 2: m, sigma, epsilon_k, mu, kappa_ab, epsilon_k_ab, na, nb
2.3, 3.5, 245.0, 1.4, 0.005, 500.0, 1.0, 1.0,
# k_ij
0.01,
]] * n)
temperature = np.linspace(200.0, 388.0, n)
molefracs = np.array([0.5] * n)
pressure = np.array([1e5] * n)
input = np.stack((temperature, molefracs, pressure), axis=1)
eos = feos.EquationOfStateAD.PcSaftFull
%time feos.bubble_point_pressure_derivatives(eos, fit_params, parameters, input)
```
```
CPU times: user 3min 11s, sys: 15.9 ms, total: 3min 11s
Wall time: 3.17 s
``` | Markdown |
3D | feos-org/feos | docs/api/dft.md | .md | 1,047 | 71 |
# Classical density functional theory
## The `HelmholtzEnergyFunctional` class
Implementations of Helmholtz energy functionals for DFT.
```{eval-rst}
.. currentmodule:: feos
.. autosummary::
:toctree: generated/
HelmholtzEnergyFunctional
```
```{eval-rst}
.. currentmodule:: feos
.. autosummary::
HelmholtzEnergyFunctional.pcsaft
HelmholtzEnergyFunctional.gc_pcsaft
HelmholtzEnergyFunctional.pets
HelmholtzEnergyFunctional.saftvrqmie
HelmholtzEnergyFunctional.fmt
```
## Vapor-liquid interfaces
```{eval-rst}
.. autosummary::
:toctree: generated/
PlanarInterface
SurfaceTensionDiagram
```
## Adsorption
```{eval-rst}
.. autosummary::
:toctree: generated/
ExternalPotential
Geometry
Pore1D
Pore2D
Pore3D
Adsorption1D
Adsorption3D
```
## Solvation
```{eval-rst}
.. autosummary::
:toctree: generated/
PairCorrelation
SolvationProfile
```
## Other data types
```{eval-rst}
.. autosummary::
:toctree: generated/
FMTVersion
DFTSolver
``` | Markdown |
3D | feos-org/feos | docs/api/index.md | .md | 196 | 13 | # API
All functions and classes in FeOS are exported at the package root. Here, they are grouped by application:
```{eval-rst}
.. toctree::
:maxdepth: 2
parameters
eos
dft
ad
``` | Markdown |
3D | feos-org/feos | docs/api/parameters.md | .md | 1,119 | 55 | # Parameter handling
In FeOs parameters can be read from JSON files or created manually by combining pure-component parameters and binary interaction parameters.
## `Parameters` and `GcParameters`
The core data types for parameter handling in FeOs are `Parameters` for regular component-specific parameters and `GcParameters` for group-contribution models.
```{eval-rst}
.. currentmodule:: feos
.. autosummary::
:toctree: generated/
Parameters
GcParameters
```
### Methods to build `Parameters`
```{eval-rst}
.. autosummary::
Parameters.from_records
Parameters.new_pure
Parameters.new_binary
Parameters.from_json
Parameters.from_multiple_json
```
### Methods to build `GcParameters`
```{eval-rst}
.. autosummary::
GcParameters.from_segments
GcParameters.from_json_segments
GcParameters.from_smiles
GcParameters.from_json_smiles
```
## Other data types
```{eval-rst}
.. autosummary::
:toctree: generated/
Identifier
IdentifierOption
PureRecord
SegmentRecord
BinaryRecord
BinarySegmentRecord
ChemicalRecord
SmartsRecord
``` | Markdown |
3D | feos-org/feos | docs/recipes/recipes_critical_point_pure.ipynb | .ipynb | 1,587 | 77 | {
"cells": [
{
"cell_type": "markdown",
"id": "2f323a90-1e4f-4a27-a495-38dbf8dad3e3",
"metadata": {},
"source": [
"# Critical point of a pure substance "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "06f40029-24e4-4f91-b502-6b9265818ed8",
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"\n",
"parameters = feos.Parameters.from_json(\n",
" substances=['methanol'], \n",
" pure_path='../../parameters/pcsaft/gross2002.json'\n",
")\n",
"eos = feos.EquationOfState.pcsaft(parameters)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "38b998ad-4fd0-4a1e-8fa3-991a13fc0860",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"|temperature|density|\n",
"|-|-|\n",
"|531.52541 K|8.11475 kmol/m³|"
],
"text/plain": [
"T = 531.52541 K, ρ = 8.11475 kmol/m³"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"critical_point = feos.State.critical_point(eos)\n",
"critical_point"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | docs/recipes/recipes_phase_equilibrium_pure.ipynb | .ipynb | 2,463 | 109 | {
"cells": [
{
"cell_type": "markdown",
"id": "2f323a90-1e4f-4a27-a495-38dbf8dad3e3",
"metadata": {},
"source": [
"# Phase equilibrium of a pure substance "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "06f40029-24e4-4f91-b502-6b9265818ed8",
"metadata": {},
"outputs": [],
"source": [
"import si_units as si\n",
"import feos\n",
"\n",
"parameters = feos.Parameters.from_json(\n",
" substances=['methanol'], \n",
" pure_path='../../parameters/pcsaft/gross2002.json'\n",
")\n",
"eos = feos.EquationOfState.pcsaft(parameters)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "38b998ad-4fd0-4a1e-8fa3-991a13fc0860",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|350.00000 K|62.68366 mol/m³|\n",
"|phase 2|350.00000 K|23.13883 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 350.00000 K, ρ = 62.68366 mol/m³\n",
"phase 1: T = 350.00000 K, ρ = 23.13883 kmol/m³"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle_t = feos.PhaseEquilibrium.pure(eos, 350 * si.KELVIN)\n",
"vle_t"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7e22455b-7f69-4745-9d92-8e991f45ba81",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"||temperature|density|\n",
"|-|-|-|\n",
"|phase 1|426.13707 K|575.28700 mol/m³|\n",
"|phase 2|426.13707 K|20.09406 kmol/m³|\n"
],
"text/plain": [
"phase 0: T = 426.13707 K, ρ = 575.28700 mol/m³\n",
"phase 1: T = 426.13707 K, ρ = 20.09406 kmol/m³"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vle_p = feos.PhaseEquilibrium.pure(eos, 15 * si.BAR)\n",
"vle_p"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | docs/recipes/recipes_p_sat_t_boil.ipynb | .ipynb | 2,100 | 97 | {
"cells": [
{
"cell_type": "markdown",
"id": "2f323a90-1e4f-4a27-a495-38dbf8dad3e3",
"metadata": {},
"source": [
"# Vapor pressure and boiling temperature of a pure substance "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "06f40029-24e4-4f91-b502-6b9265818ed8",
"metadata": {},
"outputs": [],
"source": [
"import si_units as si\n",
"import feos\n",
"\n",
"parameters = feos.Parameters.from_json(\n",
" substances=['methanol'], \n",
" pure_path='../../parameters/pcsaft/gross2002.json'\n",
")\n",
"eos = feos.EquationOfState.pcsaft(parameters)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "38b998ad-4fd0-4a1e-8fa3-991a13fc0860",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[156.972757835309 kPa]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# returns pure substance vapor pressures\n",
"vapor_pressure = feos.PhaseEquilibrium.vapor_pressure(eos, 350 * si.KELVIN)\n",
"vapor_pressure"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7e22455b-7f69-4745-9d92-8e991f45ba81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[426.13707189627803 K]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# returns pure substance boiling temperatures\n",
"boiling_temperature = feos.PhaseEquilibrium.boiling_temperature(eos, 15 * si.BAR)\n",
"boiling_temperature"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | docs/recipes/recipes_surface_tension_diagram_pure.ipynb | .ipynb | 37,580 | 124 | {
"cells": [
{
"cell_type": "markdown",
"id": "2f323a90-1e4f-4a27-a495-38dbf8dad3e3",
"metadata": {},
"source": [
"# Surface tension diagram of a pure substance"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ef5e5353-7984-4a37-8035-20809c876817",
"metadata": {},
"outputs": [],
"source": [
"import feos\n",
"import si_units as si\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"\n",
"sns.set_context('talk')\n",
"sns.set_palette('Dark2')\n",
"sns.set_style('ticks')\n",
"colors = sns.palettes.color_palette('Dark2', 1)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4fb0714a-7ae2-46bc-833a-0136ac3f7cd9",
"metadata": {},
"outputs": [],
"source": [
"parameters = feos.Parameters.from_json(\n",
" substances=['methanol'], \n",
" pure_path='../../parameters/pcsaft/gross2002.json'\n",
")\n",
"functional = feos.HelmholtzEnergyFunctional.pcsaft(parameters)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "38b998ad-4fd0-4a1e-8fa3-991a13fc0860",
"metadata": {},
"outputs": [],
"source": [
"phase_diagram = feos.PhaseDiagram.pure(functional, 150 * si.KELVIN, 25)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a48aff0e-cdb8-4553-96ff-57328272c184",
"metadata": {},
"outputs": [],
"source": [
"st_diagram = feos.SurfaceTensionDiagram(\n",
" phase_diagram.states,\n",
" n_grid=1024\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1bb266d3-8dbf-4bbf-833d-42bfee960fe8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lineplot(\n",
" y=st_diagram.surface_tension / (si.MILLI * si.NEWTON / si.METER),\n",
" x=st_diagram.liquid.temperature / si.KELVIN\n",
")\n",
"sns.scatterplot(\n",
" y=st_diagram.surface_tension / (si.MILLI * si.NEWTON / si.METER),\n",
" x=st_diagram.liquid.temperature / si.KELVIN,\n",
" clip_on=False\n",
")\n",
"sns.despine(offset=10)\n",
"plt.xlim(100, 600)\n",
"plt.ylim(0, 50)\n",
"plt.xlabel(r'$T$ / K')\n",
"plt.ylabel(r'$\\gamma$ / mN m$^{-1}$');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "feos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | feos-org/feos | docs/recipes/index.md | .md | 554 | 28 | # Recipes
This section contains short code snippets for specific, commonly used tasks.
If you are looking for tutorials with explanations, see the [tutorials](/tutorials/index).
## Critical points and phase equilibria
```{eval-rst}
.. toctree::
:maxdepth: 1
recipes_critical_point_pure
recipes_p_sat_t_boil
recipes_phase_equilibrium_pure
recipes_phase_diagram_pure
recipes_automatic_differentiation
```
## DFT
```{eval-rst}
.. toctree::
:maxdepth: 1
recipes_surface_tension_pure
recipes_surface_tension_diagram_pure
```
| Markdown |
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