The effect of canard's optimum geometric design on wake control behind the car using Artificial Neural Network and Genetic Algorithm.

Canard is a cutting-edge aerodynamic attachment for lowering the vehicle's drag coefficient by efficiently directing the airflow as well as reducing the lift coefficient by enhancing down-force. This paper aims to simulate the airflow crossing over the car to investigate the effect of canards' geometric design on the rear-body wake using the application of CFD-based optimization. Hence, 7 design variables based on the geometry of the canard are considered, and the objective functions are set to be drag and lift coefficients that are aimed to be minimized.