Author(s): Liam O'Connor

Abstract: The automotive industry faces increasing pressure to improve fuel efficiency, reduce emissions, and enhance vehicle performance. One of the critical factors influencing these objectives is the aerodynamic efficiency of vehicles, which can be significantly improved through advanced simulation technologies. Automotive computer simulations have emerged as essential tools for optimizing vehicle aerodynamics during the design phase. This paper explores the role of computational fluid dynamics (CFD) and other simulation techniques in predicting and enhancing vehicle aerodynamic performance. The research delves into the application of CFD simulations in the analysis of airflow around vehicle bodies, identification of drag-inducing components, and refinement of vehicle shapes for improved fuel efficiency. It discusses the impact of simulation-based designs on both traditional internal combustion engine (ICE) vehicles and electric vehicles (EVs), focusing on the aerodynamics challenges faced by both types. Moreover, the paper evaluates the effectiveness of simulation models in reducing the number of physical prototypes required, thus lowering development costs and time. Furthermore, this review highlights advancements in simulation tools, such as the integration of machine learning algorithms for predicting aerodynamic behavior, and the use of virtual wind tunnels in the design process. The primary objective of this paper is to provide a comprehensive overview of how automotive simulations contribute to the enhancement of vehicle aerodynamics and, consequently, overall vehicle efficiency. The hypothesis underlying this research suggests that the use of advanced computer simulations leads to significant improvements in vehicle performance and sustainability by optimizing aerodynamic characteristics at the design stage. The findings of this paper aim to provide insights for automotive engineers and manufacturers striving to meet modern demands for energy-efficient vehicles.

DOI: 10.22271/27078205.2026.v7.i1a.66

Pages: 01-04 | Views: 9 | Downloads: 4

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