| --- |
| license: odbl |
| |
| tags: |
| - AirfRANS |
| - computational fluid dynamics |
| - flow field prediction |
| language: |
| - en |
| - zh |
| --- |
| |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">AirfRANS</span> |
| </strong> |
| </p> |
| |
| ## Dataset Description |
|
|
| AirfRANS is a high-fidelity two-dimensional airfoil CFD dataset introduced in a paper from the NeurIPS 2022 Datasets and Benchmarks Track. The data is intended for surrogate modeling of the incompressible steady-state Reynolds-averaged Navier-Stokes (RANS) equations and contains 1,000 subsonic airfoil simulation cases. |
|
|
| The cases cover NACA four-digit and five-digit airfoils, with Reynolds numbers ranging from 2 million to 6 million and angles of attack ranging from -5° to 15°. |
|
|
| Paper: [AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions](https://proceedings.neurips.cc/paper_files/paper/2022/hash/94ab7b23a345f93333eac8748a66c763-Abstract-Datasets_and_Benchmarks.html) |
|
|
| ## Supported Tasks |
|
|
| | Scenario | Description | |
| |---|---| |
| | CFD surrogate modeling | Predict flow fields from airfoil geometry, boundary conditions, and fluid parameters. | |
| | Full-data training | Learn airfoil flow behavior using the complete training set. | |
| | Data-scarce training | Perform surrogate modeling with a limited number of training cases. | |
| | Parameter extrapolation evaluation | Evaluate model generalization to unseen Reynolds numbers or angles of attack. | |
|
|
| ## Dataset Format and Structure |
|
|
| The data is organized by case: |
|
|
| ```text |
| data/Dataset/ |
| manifest.json |
| <case>/ |
| <case>_internal.vtu |
| <case>_freestream.vtp |
| <case>_aerofoil.vtp |
| ``` |
|
|
| Each case contains a volume mesh, far-field boundary data, and airfoil surface data: |
|
|
| | File | VTK Type | Main Fields | Description | |
| |---|---|---|---| |
| | `<case>_internal.vtu` | `UnstructuredGrid` | `U`, `p`, `nut`, `implicit_distance` | Volume-domain velocity, pressure, turbulent kinematic viscosity, and implicit distance. | |
| | `<case>_freestream.vtp` | `PolyData` | `U`, `p`, `nut` | Far-field boundary flow data. | |
| | `<case>_aerofoil.vtp` | `PolyData` | `Normals`, `U`, `p`, `nut` | Airfoil surface normals and flow data. | |
|
|
| `manifest.json` records the dataset splits for tasks such as `full`, `scarce`, `reynolds`, and `aoa`. The VTK files use XML format, and mesh sizes vary by case. |
|
|
| ## How to Use the Dataset |
|
|
| This dataset has been adapted for the `OneScience-Group/Transolver-Airfoil-Design` model. Download the dataset and model: |
|
|
| ```bash |
| hf download --dataset OneScience-Group/airfrans --local-dir ./data |
| ``` |
|
|
|
|
| ## Official OneScience Information |
|
|
| | Platform | OneScience Main 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 | |
|
|
| ## Citation and License |
|
|
| - Original AirfRANS paper: [AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions](https://proceedings.neurips.cc/paper_files/paper/2022/hash/94ab7b23a345f93333eac8748a66c763-Abstract-Datasets_and_Benchmarks.html) |
| - This dataset is organized from the original AirfRANS data and is licensed under the same ODbL-1.0 license. |
|
|
