This paper presents a new open-source high-fidelity dataset for Machine Learning (ML) containing 355 geometric variants of the Windsor body, to help the development and testing of ML surrogate models for external automotive aerodynamics. Each Computational Fluid Dynamics (CFD) simulation was run with a GPU-native high-fidelity Wall-Modeled Large-Eddy Simulations (WMLES) using a Cartesian immersed-boundary method using more than 280M cells to ensure the greatest possible accuracy. The dataset contains geometry variants that exhibits a wide range of flow characteristics that are representative of those observed on road-cars. The dataset itself contains the 3D time-averaged volume & boundary data as well as the geometry and force & moment coefficients. This paper discusses the validation of the underlying CFD methods as well as contents and structure of the dataset. To the authors knowledge, this represents the first, large-scale high-fidelity CFD dataset for the Windsor body with a permissive open-source license (CC-BY-SA).

翻译：本文提出了一种新的开源高保真机器学习数据集，包含355种Windsor车体几何变体，旨在促进面向外部汽车空气动力学的机器学习代理模型的开发与测试。每个计算流体力学模拟均采用基于GPU原生高保真壁模型大涡模拟的笛卡尔浸没边界法进行，使用超过2.8亿网格单元以确保最高精度。数据集涵盖的几何变体呈现出广泛流动特性，能够代表实际道路车辆中观察到的典型流动现象。数据集包含三维时均化体积与边界数据、几何信息以及力与力矩系数。本文讨论了底层CFD方法的验证过程，并阐述了数据集的内容与结构。据作者所知，这是首个采用宽松开源许可（CC-BY-SA）的大规模高保真Windsor车体CFD数据集。