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.

本研究解决了当前汽车空气动力学中缺乏开源训练数据的问题，尤其是在使用高保真CFD方法时。该论文提出了一个高保真的开源公共数据集，基于500种参数变形的DrivAer轿车模型生成，具有重要的工业应用潜力，为未来的机器学习研究提供了基础。

DrivAerML：用于道路汽车外部空气动力学的高保真计算流体动力学数据集