Aerodynamics-guided machine learning for design optimization of electric vehicles.

ABSTRACT: The transition to electric vehicles is driving a fundamental shift in the automobile design process. Changes in constraints afforded by the absence of a combustion engine create new opportunities for modifying vehicle geometries. Current approaches to optimizing vehicle aerodynamics require a vast amount of computational studies and physical experiments, which are expensive when performing parameter sweeps over conceivable geometric configurations, suggesting the need for more efficient surrogate models to assist analysis. Here we analyze a dataset of industry-quality automobile geometries with their associated aerodynamic performance obtained from experimentally validated, high-fidelity large-eddy simulations. We show that a relationship between these geometries and their respective aerod

SUBMITTER: Tran J 

PROVIDER: S-EPMC11579422 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature