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

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. Firstly, ANSYS Fluent is utilized to generate CFD calculations for a series of Design of Experiment (DOE) points. Then, the GMDH-ANN processes the results to elicit the polynomials that demonstrate the relation of design variables and objective functions. A genetic algorithm is next implemented for multi-objective optimization using polynomials as its input, and consequently, Pareto optimal points are achieved. The numerical results show that an appropriate design for canards on the rear bumper causes a potential drag and lifts reduction of 9.62% and 9.6% in comparison to the car without canards. Moreover, the size of the wake behind the car decreases and the differences in the pressure distribution between car fore-body and rear-body is reduced. Finally, the fuel efficiency is potentially enhanced due the changes in car drag coefficient and frontal area.