Title: Optimisation of drag reduction for passive flow control based on a notchback MIRA model

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

Addresses: Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China; School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China ' Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China; School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China ' Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China; School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China ' Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China; School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China ' Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, Hubei, 430200, China; School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China

Abstract: This paper investigates the application of passive flow control for optimising automotive aerodynamic drag reduction, based on a notchback MIRA model. The study uses a computational fluid dynamics (CFD) simulation, a neural network prediction model and a genetic algorithm to optimise and combine the body geometry features and duckwing add-ons in a step-by-step manner. The results show that optimising the body independently reduces the drag coefficient (CD) by 12.4%, while optimising the duckwing independently reduces it by 21.109%. Combining the optimal parameters of the body and the duckwing in a composite optimisation scheme reduces the drag coefficient of the whole vehicle to 0.24509 - up to 24.09% less than the original model. Flow field analysis shows that composite optimisation significantly improves rear flow field structure, effectively compresses the low-speed region and significantly reduces turbulence intensity while enhancing tail pressure recovery. This study provides an efficient optimisation strategy for the design of automotive aerodynamic drag reduction, which is important for improving vehicle energy efficiency and reducing emissions.

Keywords: computational fluid dynamics; CFD; artificial neural network; ANN; genetic algorithm; GA; automotive aerodynamics; passive flow control; parameter optimisation.

DOI: 10.1504/PCFD.2026.154627

Progress in Computational Fluid Dynamics, An International Journal, 2026 Vol.26 No.4, pp.211 - 238

Received: 17 Jun 2025
Accepted: 24 Sep 2025
Published online: 08 Jul 2026 *

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