  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).
