DrivAerML: High Fidelity Computational Fluid Dynamics Dataset for Road Car External Aerodynamics Contact: Neil Ashton (contact@caemldatasets.org) Website: https://caemldatasets.org Summary: 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. CFD Solver: All cases were run using the open source finite volume code OpenFOAM v2212 with custom modifications…
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