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DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
Contact:
Neil Ashton (contact@caemldatasets.org)
Website:
Summary:
Machine Learning (ML) has the potential to revolutionise the field of automotive aerodynamics, enabling split-second flow predictions early in the design process. However, the lack of open-source training data for realistic road cars, using high-fidelity CFD methods, represents a barrier to their development. To address this, a high-fidelity open-source (CC-BY-SA) public dataset for automotive aerodynamics has been generated, based on 500 parametrically morphed variants of the widely-used DrivAer notchback generic vehicle. Mesh generation and scale-resolving CFD was executed using consistent and validated automatic workflows representative of the industrial state-of-the-art. Geometries and rich aerodynamic data are published in open-source formats. To our knowledge, this is the first large, public-domain dataset for complex automotive configurations generated using high-fidelity CFD.
CFD Solver:
All cases were run using the open-source finite-volume code OpenFOAM v2212 with custom modifications by UpstreamCFD. Please see the paper below for full details on the code and validation:
How to cite this dataset:
In order to cite the use of this dataset please cite the paper below which contains full details on the dataset.
'' @article{ashton2024drivaer, title = {{DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics}}, year = {2024}, journal = {arxiv.org}, url={https://arxiv.org/abs/2408.11969}, author = {Ashton, N., Mockett, C., Fuchs, M., Fliessbach, L., Hetmann, H., Knacke, T., Schonwald, N., Skaperdas, V., Fotiadis, G., Walle, A., Hupertz, B., and Maddix, D} } ''
Files:
Each folder (e.g run1,run2...run"i" etc) corresponds to a different geometry that contains the following files where "i" is the run number:
- geometry stl (~135mb): drivaer_i.stl
- reference values for each geometry: geo_ref_i.csv
- reference geometry for each geometry: geo_parameters_i.csv
- Boundary VTU (~500mb): boundary_i.vtp
- Volume field VTU (~50GB): volume_i.vtu ( please note on HuggingFace this is split into part 1 and part2 - please cat them together to create the volume_i.vtu)
- forces/moments time-averaged (using varying frontal area/wheelbase): force_mom_i.csv
- forces/moments time-averaged (using constant frontal area/wheelbase): force_mom_constref_i.csv
- slices: folder containing .vtp slices in x,y,z that contain flow-field variables
- Images: This folder contains images of various flow variables (e.g. Cp, CpT, UMagNorm) for slices of the domain at X, Y, and Z locations (M signifies minus, P signifies positive), as well as on the surface. It also includes evaluation plots of the time-averaging of the force coefficients (via the tool MeanCalc) and a residual plot illustrating the convergence.
In addition to the files per run folder, there are also:
- openfoam_meshes : this folder contains the OpenFOAM meshes (in OpenFOAM format) used for these simulations. The 0 and system folders are just the default output from ANSA and were not those used in this study. Please refer to the arxiv paper for full details of the CFD setup. We hope that by providing the meshes, groups may wish to expand the dataset as they see fit.
- blind_15additional_cases_passwd_required.zip : 15 cases being prepared for a blind study and password protected for the time being.
- force_mom_all.csv : forces/moments time-averaged (using varying frontal area/wheelbase) for all runs
- force_mom_constref_all.csv : forces/moments time-averaged (using constant frontal area/wheelbase) for all runs
- geo_parameters_all.csv: reference geometry values for each geometry for all runs
How to download:
Please ensure you have enough local disk space before downloading (complete dataset is 30TB) and consider the examples below that provide ways to download just the files you need:
Credits
- CFD solver and workflow development by Charles Mockett, Marian Fuchs, Louis Fliessbach, Henrik Hetmann, Thilo Knacke & Norbert Schonwald (UpstreamCFD)
- Geometry parameterization by Vangelis Skaperdas, Grigoris Fotiadis (BETA-CAE Systems) & Astrid Walle (Siemens Energy)
- Meshing development workflow by Vangelis Skaperdas & Grigoris Fotiadis (BETA-CAE Systems)
- DrivAer advise and consultation by Burkhard Hupertz (Ford)
- Guidance on dataset preparation for ML by Danielle Maddix (Amazon Web Services - now NVIDIA)
- Simulation runs, HPC setup and dataset preparation by Neil Ashton (Amazon Web Services - now NVIDIA)
License
This dataset is provided under the CC BY SA 4.0 license, please see LICENSE.txt for full license text.
version history:
04/03/2025 - Now available on HuggingFace!
11/11/2024 - the 15 of the 17 cases that were missing are being considered for use as a blind study. For the time-being these are available but password protected in the file blind_15additional_cases_passwd_required.zip. Once we setup a benchmarking sysystem we will provide details on how people can test their methods against these 15 blind cases.
08/10/2024 - The OpenFOAM meshes (in OpenFOAM format) that were generated in ANSA have been uploaded to the openfoam_meshes folder. The 0 and system folders are just the default output from ANSA and were not those used in this study. Please refer to the arxiv paper for full details of the CFD setup. We hope that by providing the meshes, groups may wish to expand the dataset as they see fit.
10/09/2024 - Run_0 has been added as a blind study for the AutoCFD4 workshop. Post-workshop the results from this additional run will be uploaded to the dataset.
29/07/2024 - Note: please be aware currently runs 167, 211, 218, 221, 248, 282, 291, 295, 316, 325, 329, 364, 370, 376, 403, 473 are not in the dataset.
03/05/2024 - draft version produced
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