| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - fluid dynamics |
| - steady-state laminar flow prediction |
| - CFD surrogate modeling |
| frameworks: PyTorch |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">DeepCFD</span> |
| </strong> |
| </p> |
| |
| # Model Overview |
| DeepCFD is a U-Net-based surrogate model developed by the German Aerospace Center for two-dimensional steady-state laminar flows. It uses geometric information to rapidly predict velocity and pressure fields, accelerating the evaluation of channel flows, flows around obstacles, and aerodynamic designs. |
|
|
| Paper: [DeepCFD: Efficient Steady-State Laminar Flow |
| Approximation with Deep Convolutional Neural Networks](https://arxiv.org/abs/2004.08826). |
|
|
| # Model Description |
| DeepCFD uses a U-Net deep convolutional architecture that takes a signed distance field (SDF) and flow-region mask as inputs to rapidly predict velocity and pressure fields for two-dimensional, nonuniform, steady-state laminar flows. |
|
|
| ## Use Cases |
|
|
| | Use Case | Description | |
| | ---------- | ---------------------------------- | |
| | Steady-state laminar flow prediction | Approximate velocity and pressure fields in two-dimensional, nonuniform, steady-state laminar flows | |
| | Channel-flow simulation | Predict flow distributions around randomly shaped obstacles in a channel | |
| | CFD surrogate modeling | Replace conventional CFD workflows such as OpenFOAM to improve inference efficiency | |
| | Aerodynamic and fluid-shape optimization | Evaluate large numbers of candidate geometries for low-speed laminar-flow design | |
|
|
|
|
| # Usage |
|
|
| ## 1. OneCode |
|
|
| Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience: |
|
|
| [Launch OneCode for one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
|
| ## 2. Manual Setup |
|
|
| **Hardware Requirements** |
|
|
| - A GPU or DCU is recommended. |
| - A CPU can be used for import checks and small-scale pipeline validation, but full training and inference will be slow. |
| - DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster, is recommended. |
|
|
|
|
|
|
| ### Download the Model Package |
|
|
| ```bash |
| modelscope download --model OneScience/DeepCFD --local_dir ./DeepCFD |
| cd DeepCFD |
| ``` |
|
|
| ### Set Up the Runtime Environment |
|
|
|
|
| **DCU Environment** |
|
|
| ```bash |
| # Activate DTK and Conda first |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| # Installation with uv is also supported |
| pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| **GPU Environment** |
| ```bash |
| # Activate Conda first |
| conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 |
| conda activate onescience311 |
| # Installation with uv is also supported |
| pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| ### Training Data |
| The OneScience community provides the `deepcfd` dataset for training. Download it with the command below and verify that the data path in `conf/config.yaml` is configured correctly: |
|
|
| ```bash |
| modelscope download --dataset OneScience/deepcfd --local_dir ./data |
| ``` |
|
|
| The project dataset can also be [downloaded from Zenodo](https://zenodo.org/record/3666056/files/DeepCFD.zip?download=1). |
|
|
| The dataset contains two files: |
| - `dataX.pkl`: Geometric input data for 981 channel-flow samples |
| - `dataY.pkl`: Ground-truth CFD solutions for the corresponding samples, computed with the `simpleFOAM` solver |
|
|
| ### Training |
|
|
| Single GPU: |
|
|
| - Training parameters are read from the `root.datapipe`, `root.model`, and `root.training` sections of `config/config.yaml`. The default configuration is intended for a minimal smoke test. |
| - For full training, set `root.datapipe.source.data_dir` to the actual DeepCFD dataset directory and increase the model size, batch size, and number of epochs as needed. |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
| Multiple GPUs: |
|
|
| ```bash |
| torchrun --standalone --nproc_per_node=<num_GPUs> scripts/train.py |
| ``` |
| By default, training saves the best checkpoint to: |
|
|
| ```text |
| ./weight/best_model.pt |
| ``` |
|
|
| ### Model Weights |
|
|
| This repository will provide pretrained DeepCFD weights in the `weights/` directory. The weights will be uploaded soon. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
|
|
| # Official OneScience Resources |
|
|
| | Platform | OneScience Repository | Skills Repository | |
| | --- | --- | --- | |
| | Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills | |
| | GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills | |
|
|
| # Citations and License |
|
|
| - Original DeepCFD paper: [DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks](https://arxiv.org/abs/2004.08826) |
| - This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope. |
|
|
