Title: Optimisation of drag coefficient of a car with integrated canards

Authors: Zihou Yuan; Xingren Zheng; Yanming Du; Hongwei Zhang

Addresses: Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China ' Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China ' Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China ' Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China

Abstract: The objective of this study is to investigate the impact of canards geometric design on the airflow field at the rear of a sedan, with the aim of reducing the drag coefficient of the vehicle. Four key design variables were analysed: canard angle (θ), length (L1), radius (R), and thickness (H). Using these variables, Latin hypercube sampling generated 50 DOE points, which were simulated in ANSYS Fluent® to calculate the drag coefficient (C_D). A comprehensive performance comparison was performed. Ultimately, a backpropagation neural network (BPNN) coupled with a genetic algorithm (GA) was adopted as the optimal method for canard design optimisation. A random forest model identified θ as the most influential factor on C_D. The optimised design achieved a 21% reduction in drag, minimised the rear vortex region, and significantly accelerated the optimisation process.

Keywords: computational fluid dynamics; CFD; artificial neural network; ANN; genetic algorithm; GA; automotive aerodynamics; aerodynamic characteristics; parameter optimisation.

DOI: 10.1504/IJVP.2025.147703

International Journal of Vehicle Performance, 2025 Vol.11 No.3, pp.348 - 378

Received: 14 Oct 2024
Accepted: 05 Apr 2025
Published online: 25 Jul 2025 *

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