Výsledky vyhledávání - "Ashton, Neil"

Report

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

Autor: Ashton, Neil, Mockett, Charles, Fuchs, Marian, Fliessbach, Louis, Hetmann, Hendrik, Knacke, Thilo, Schonwald, Norbert, Skaperdas, Vangelis, Fotiadis, Grigoris, Walle, Astrid, Hupertz, Burkhard, Maddix, Danielle

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-fide

Externí odkaz: http://arxiv.org/abs/2408.11969

Zobrazit plný text záznamu

Report

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

Autor: Ashton, Neil, Maddix, Danielle C., Gundry, Samuel, Shabestari, Parisa M.

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

Externí odkaz: http://arxiv.org/abs/2407.20801

Zobrazit plný text záznamu

Report

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

Autor: Ashton, Neil, Angel, Jordan B., Ghate, Aditya S., Kenway, Gaetan K. W., Wong, Man Long, Kiris, Cetin, Walle, Astrid, Maddix, Danielle C., Page, Gary

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 Computa

Externí odkaz: http://arxiv.org/abs/2407.19320

Zobrazit plný text záznamu