OneScience-Group/deepcfd

1---2license: cc-by-4.03#User-Defined Tags4tags:5  - 2D Steady-State Laminar Flow6  - Flow Field Prediction7language:8  - en9  - zh10---11<p align="center">12  <strong>13    <span style="font-size: 30px;">DeepCFD</span>14  </strong>15</p>16 17## Dataset Overview18The DeepCFD dataset is a publicly available two-dimensional steady-state laminar-flow computational fluid dynamics dataset created by a research team affiliated with the German Research Center for Artificial Intelligence (DFKI). The dataset was generated with OpenFOAM's simpleFoam solver and contains approximately 1,000 samples of flow around randomly generated obstacles in a channel. It is suitable for CFD solver acceleration, evaluation of flow-field generalization across different obstacle configurations, prediction-error analysis, and flow-field visualization.19 20 21## Supported Tasks22 23This standardized dataset repository organizes DeepCFD's input geometry and signed distance fields in `dataX.pkl` and the corresponding ground-truth CFD solutions in `dataY.pkl`. Each sample contains steady-state flow variables including computational mesh coordinates, horizontal velocity (U_x), vertical velocity (U_y), and pressure (p). It also provides a signed distance function (SDF) and flow-region class labels to represent obstacle geometry, the computational domain, and boundary conditions. The data can be used as input for training, inference, evaluation, and visualization with the `OneScience/DeepCFD` model.24 25 26 27## Data Format28 29| File | shape | dtype | Channel Description |30|---|---:|---|---|31| `data/dataX.pkl` | `[981, 3, 172, 79]` | `float32` | Channel 0: signed distance to the obstacle surface; Channel 1: fluid-region label; Channel 2: signed distance to the top/bottom surfaces |32| `data/dataY.pkl` | `[981, 3, 172, 79]` | `float32` | Channel 0: horizontal velocity `Ux`; Channel 1: vertical velocity `Uy`; Channel 2: pressure `pressure` |33 34Each sample in this dataset is represented on a `172 × 79` regular grid. The input data consists of the signed distance to the obstacle surface, fluid-region labels, and signed distances to the top/bottom surfaces. The output data consists of the corresponding horizontal velocity `Ux`, vertical velocity `Uy`, and pressure `pressure`.35 36## How to Use the Dataset37 38This dataset is compatible with the `OneScience-Group/DeepCFD` model. During DeepCFD runs, scripts in the model package can organize the data from this repository into the model's working directory.39 40Download the dataset:41 42```bash43hf download --dataset OneScience-Group/deepcfd --local-dir ./data44```45 46## Official OneScience Information47 48| Platform | OneScience Main Repository | Skills Repository |49|---|---|---|50| Gitee  | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |51| GitHub  | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |52 53## Citation and License54 55- Original DeepCFD paper: [DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks](https://arxiv.org/abs/2004.08826)56- This dataset was converted from the original DeepCFD dataset and is licensed under the same CC-BY-4.0 license. The original copyright notice and license text must be retained when using, modifying, or redistributing it.57