DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866916694122823680 |
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| author | Ashton, Neil Mockett, Charles Fuchs, Marian Fliessbach, Louis Hetmann, Hendrik Knacke, Thilo Schonwald, Norbert Skaperdas, Vangelis Fotiadis, Grigoris Walle, Astrid Hupertz, Burkhard Maddix, Danielle |
| author_facet | Ashton, Neil Mockett, Charles Fuchs, Marian Fliessbach, Louis Hetmann, Hendrik Knacke, Thilo Schonwald, Norbert Skaperdas, Vangelis Fotiadis, Grigoris Walle, Astrid Hupertz, Burkhard Maddix, Danielle |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics Ashton, Neil Mockett, Charles Fuchs, Marian Fliessbach, Louis Hetmann, Hendrik Knacke, Thilo Schonwald, Norbert Skaperdas, Vangelis Fotiadis, Grigoris Walle, Astrid Hupertz, Burkhard Maddix, Danielle Fluid Dynamics Computational Engineering, Finance, and Science Machine Learning 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. |
| title | DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics |
| topic | Fluid Dynamics Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2408.11969 |