The development of Machine Learning (ML) methods for Computational Fluid Dynamics (CFD) is currently limited by the lack of openly available training data. This paper presents a new open-source dataset comprising of high fidelity, scale-resolving CFD simulations of 500 geometric variations of the Ahmed Car Body - a simplified car-like shape that exhibits many of the flow topologies that are present on bluff bodies such as road vehicles. The dataset contains simulation results that exhibit a broad set of fundamental flow physics such as geometry and pressure-induced flow separation as well as 3D vortical structures. Each variation of the Ahmed car body were run using a high-fidelity, time-accurate, hybrid Reynolds-Averaged Navier-Stokes (RANS) - Large-Eddy Simulation (LES) turbulence modelling approach using the open-source CFD code OpenFOAM. The dataset contains boundary, volume, geometry, and time-averaged forces/moments in widely used open-source formats. In addition, the OpenFOAM case setup is provided so that others can reproduce or extend the dataset. This represents to the authors knowledge, the first open-source large-scale dataset using high-fidelity CFD methods for the widely used Ahmed car body that is available to freely download with a permissive license (CC-BY-SA).

翻译：目前，机器学习（ML）方法在计算流体动力学（CFD）领域的发展受到公开可用训练数据缺乏的限制。本文提出了一个新的开源数据集，该数据集包含对Ahmed汽车车身（一种简化的类汽车外形，展现了诸如道路车辆等钝体上存在的多种流动拓扑结构）的500种几何变体进行的高保真、尺度解析CFD模拟。数据集中的模拟结果展现了一系列广泛的基元流动物理现象，例如由几何和压力诱导的流动分离以及三维涡旋结构。每种Ahmed汽车车身变体均使用开源CFD代码OpenFOAM，采用高保真、时间精确的混合雷诺平均纳维-斯托克斯（RANS）-大涡模拟（LES）湍流建模方法进行模拟。数据集以广泛使用的开源格式包含了边界、体积、几何以及时均力/力矩数据。此外，还提供了OpenFOAM案例设置，以便他人能够复现或扩展该数据集。据作者所知，这是首个针对广泛使用的Ahmed汽车车身、采用高保真CFD方法的大规模开源数据集，可通过宽松许可（CC-BY-SA）免费下载。