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.

翻译：机器学习（ML）有望彻底改变汽车空气动力学领域，能够在设计流程早期实现瞬时的流动预测。然而，缺乏基于高保真CFD方法的真实道路汽车开源训练数据，阻碍了相关技术的发展。为此，本研究基于广泛使用的DrivAer阶背式通用车辆的500个参数化变形变体，生成了一个高保真开源（CC-BY-SA）公共数据集，用于汽车空气动力学研究。网格生成与尺度解析CFD计算均采用代表工业先进水平且经过验证的自动化工作流程执行。几何模型与丰富的空气动力学数据均以开源格式发布。据我们所知，这是首个使用高保真CFD方法生成的、面向复杂汽车构型的大型公开数据集。