publications

2024

1. 

   WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics

   Neil Ashton, Jordan Angel, Aditya Ghate, and 6 more authors

   arxiv.org, 2024

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   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).

   @article{ashton2024windsor,
     title = {WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics},
     year = {2024},
     journal = {arxiv.org},
     author = {Ashton, Neil and Angel, Jordan and Ghate, Aditya and Kenway, Gaetan and Long Wong, Man and Kiris, Cetin and Walle, Astrid and Maddix, Danielle and Page, Gary},
     url = {https://arxiv.org/abs/2407.19320},
     dimensions = {true},
   }

2. 

   AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics

   Neil Ashton, Danielle Maddix, Samuel Gundry, and 1 more author

   arxiv.org, 2024

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   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).

   @article{ashton2024ahmed,
     title = {AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics},
     year = {2024},
     journal = {arxiv.org},
     author = {Ashton, Neil and Maddix, Danielle and Gundry, Samuel and Shabestari, Parisa},
     url = {https://arxiv.org/abs/2407.20801},
     dimensions = {true},
   }

3. 

   DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics

   Neil Ashton, Charles Mockett, Marian Fuchs, and 9 more authors

   arxiv.org, 2024

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   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.

   @article{ashton2024drivaer,
     title = {DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics},
     year = {2024},
     journal = {arxiv.org},
     url = {https://arxiv.org/abs/2408.11969},
     dimensions = {true},
     author = {Ashton, Neil and Mockett, Charles and Fuchs, Marian and Fliessbach, Louis and Hetmann, Hendrik and Knacke, Thilo and Schonwald, Norbert and Skaperdas, Vangelis and Fotiadis, Grigoris and Walle, Astrid and Hupertz, Burkhard and Maddix, Danielle},
   }