Aerodynamic Optimization of a Front Wheel Wake-Related Bodywork on a Novel Electric Formula Car Using Metaheuristic Approach

Aerodynamic drag reduction is a critical part in the design of a novel electric, entry-level, formula car due to the modest energy density provided by the contemporary Lithium-ion battery cells. In order to improve track performance, aerodynamic development must focus on components which do not generate a considerable amount of downforce. Rotating front wheels are identified as the least aerodynamic part of the race car, since it is responsible for the third of the overall drag forces and producing moderate amounts of lift. In the present study, a parameterized wheel pod geometry is used to improve the overall aerodynamic performance of an open-wheel race car. The model is driven by seven parameters, which entails huge flexibility of the bodywork design. First, an unsteady Computational Fluid Dynamics (CFD) simulation was developed and validated to visualize the oscillating flow behavior and obtain averaged surface force measurements. In expectation of a highly turbulent and complex wheel wake structure, the traditional iterative optimization method had to be excluded. The exploration of the optimal bodywork was executed by a single-objective aerodynamic optimization framework, coupled with CFD simulations. The objective target was to minimize overall drag force while the simulation must agree on the preset restrictions and leave downforce unharmed. Simultaneous Hybrid Exploration (SHERPA) search algorithm was applied along with Kriging response surface surrogate modelling. The convergence of the objective history indicated acceptable fidelity of the predictions for the location of the global minimum of the eight-dimensional design space. The race car equipped with the best design wheel pod generated 11.1% less overall drag, while aerodynamic efficiency was improved by 17.1%.