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Abstract

The design of race cars often focuses on the lower portion of the vehicle to achieve aerodynamic advantages by creating an optimal pressure pattern on the car’s underside. In fact, an appropriately shaped underbody can generate significant downforce with minimal aerodynamic drag, allowing for higher speeds during cornering. Unfortunately, no experimental facilities can accurately simulate the airflow around a car while cornering. For this reason, computational fluid dynamics (CFD) has become the primary tool for effectively designing such features. This study focuses on optimizing the shape of a Venturi channel for a highly simplified race car model that embodies the primary characteristics of a Le Mans Hypercar (LHM). We analyze and optimize the forces generated during straight-line travel and cornering, identifying the optimal configurations and their unique characteristics.

Keywords

Venturi effect shape optimization design optimization optimal configuration race car machine learning Kriging multi-dimensional spline multi-objective optimization

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Shape optimization of a race car underbody on straight and curved paths

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