Hugging Face's logo

Datasets:
OneScience-Sugon
/
airfrans

Languages:
English
Chinese
Tags:
AirfRANS
computational fluid dynamics
flow field prediction
License:
Dataset card Files
xet
 Community
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    TypeError
Message:      Value.__init__() missing 1 required positional argument: 'dtype'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                      path=dataset,
                      token=hf_token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                      path,
                  ...<4 lines>...
                      **download_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 700, in get_module
                  config_name: DatasetInfo.from_dict(dataset_info_dict)
                               ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
                  return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
                File "<string>", line 20, in __init__
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 170, in __post_init__
                  self.features = Features.from_dict(self.features)
                                  ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1993, in from_dict
                  obj = generate_from_dict(dic)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1574, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                               ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1593, in generate_from_dict
                  return class_type(**{k: v for k, v in obj.items() if k in field_names})
              TypeError: Value.__init__() missing 1 required positional argument: 'dtype'

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

AirfRANS

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

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:

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-Sugon/Transolver-Airfoil-Design model. Download the dataset and model:

hf download --dataset OneScience-Sugon/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

Downloads last month
14
Total file size:
15 GB