DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
<|reference_start|>DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics: 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.<|reference_end|>
@article{ashton2024drivaerml:,
    title={DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for
  Road-Car External Aerodynamics},
    author={Neil Ashton, Charles Mockett, Marian Fuchs, and Louis Fliessbach,
  Hendrik Hetmann, Thilo Knacke, Norbert Schonwald, Vangelis Skaperdas,
  Grigoris Fotiadis, Astrid Walle, Burkhard Hupertz, Danielle Maddix},
    journal={arXiv preprint arXiv:2408.11969},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11969},
    primaryClass={physics.flu-dyn cs.CE cs.LG}
}