تقرير
DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
| العنوان: | DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics |
|---|---|
| المؤلفون: | Ashton, Neil, Mockett, Charles, Fuchs, Marian, Fliessbach, Louis, Hetmann, Hendrik, Knacke, Thilo, Schonwald, Norbert, Skaperdas, Vangelis, Fotiadis, Grigoris, Walle, Astrid, Hupertz, Burkhard, Maddix, Danielle |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Fluid Dynamics, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.11969 |
| رقم الأكسشن: | edsarx.2408.11969 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |