DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics

• URL: http://arxiv.org/abs/2408.11969v1
• Date: Wed, 21 Aug 2024 19:47:41 GMT
• Title: DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
• Authors: Neil Ashton, Charles Mockett, Marian Fuchs, Louis Fliessbach, Hendrik Hetmann, Thilo Knacke, Norbert Schonwald, Vangelis Skaperdas, Grigoris Fotiadis, Astrid Walle, Burkhard Hupertz, Danielle Maddix,
• Abstract summary: This dataset is a high-fidelity open-source (CC-BY-SA) public dataset for automotive aerodynamics. It is based on 500 parametrically morphed variants of the widely-used DrivAer notchback generic vehicle. It is the first large, public-domain dataset for complex automotive configurations generated using high-fidelity CFD.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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