Aerodynamic Optimization of a Reduced Scale Model of a Ground Vehicle with a Shape Morphing Technique

Abstract

Aerodynamic performances of ground vehicle continuously improve and a lot of both wind tunnel measurements and Computational Fluid Dynamics (CFD) investigations contribute in the identification of local zones where shape deformation can lead to drag force reduction. Gradient-based optimization with optimal system involving CFD models is one of the powerful methods for shape optimization, but a genetic algorithm applied on the surrogate model can also explore a large design space in a reasonable period of computation time. In this paper, we present an aerodynamic optimization technique using a Kriging model in order to perform CFD simulations of different front air dam geometries situated below the front bumper of a reduced scale road vehicle. A first design-of-experiment (DoE) is undertaken with Large Eddy Simulations (LES), involving height geometric parameters for radial-basis-function of the front air dam, utilizing a Sobol algorithm. Then, a multi-objective-genetic-algorithm (MOGA) is applied on the constituted surrogate model, depending on the geometric parameters of the front air dam, in order to reach a minimum drag coefficient value by considering pressure constraints. Results show that a front air dam can increase the pressure at the rear of the tailgate, especially by slowing the airflow below the underfloor, but an optimum balance is necessary in order to not increase the stagnation pressure on the air dam, leading to the loss of this benefit. The Sobol technique driven by the Kriging model enables the retrieval of optimum airdam shapes found with wind tunnel tests, even with relatively coarse numerical meshes used for CFD simulations.