3 citations · 6 across the 6 of their papers we have counts for
3 papers · 1 filter
DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
Neil Ashton, Charles Mockett, Marian Fuchs +9
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…
AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics
Neil Ashton, Danielle C. Maddix, Samuel Gundry +1
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 ne…
WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics
Neil Ashton, Jordan B. Angel, Aditya S. Ghate +6
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…