Multi-objective aerodynamic optimization design of high-speed maglev train nose

Abstract

PurposeThe nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train, and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence, the study analyzes aerodynamic parameters with multi-objective optimization design.Design/methodology/approachThe nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics. Then the modified vehicle modeling function (VMF) parameterization method and surface discretization method are adopted for the parametric design of the nose. For the 12 key design parameters extracted, combined with computational fluid dynamics (CFD), support vector machine (SVR) model and multi-objective particle swarm optimization (MPSO) algorithm, the multi-objective aerodynamic optimization design of high-speed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint. The engineering improvement and wind tunnel test verification of the optimized shape are done.FindingsResults show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train. The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.Originality/valueCompared with the original shape, the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%, and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%, respectively, after adopting the optimized shape modified according to engineering design requirements.

Key technologies and applications of intelligent dispatching command for high-speed railway in China

“…reinforcement learning) (Ding, Zhang, Liu et al. , 2022; Ding, Zhang, Wang, & Yuan, 2022; Wang, Zhang, Zhang, Wang, & Ding, 2022; Yan, Zhang, Ding, & Wang, 2022). In order to realize conflict detection and resolution in the dynamic environment, the model and framework of automatic adjustment are constructed.…”

“…For this problem, the common solving methods mainly refers to operation research, intelligent optimization (evolutionary computation) and AI algorithm (e.g. reinforcement learning) (Ding, Zhang, Liu et al, 2022;Yan, Zhang, Ding, & Wang, 2022). In order to realize conflict detection and resolution in the dynamic environment, the model and framework of automatic adjustment are constructed.…”

A study on aerodynamic noise characteristics of a high-speed maglev train with a speed of 600 km/h

“…Tyll, Liu, and Schetz (1996) comprehensively studied the aerodynamic performance of TR06 maglev train model (scale: 1:12 based on the real train) with three-car formation at a speed of about 241.4 km·h − 1 by using a wind tunnel with moving ground, and obtained the aerodynamic data of the train, which provided a reference basis for future research. Yao, Chen, Ding, and Pang (2021) used the improved vehicle modeling function (VMF) parametric method and the curved surface discrete method to carry out the parametric design on the nose of high-speed maglev train, and the aerodynamic drag coefficient of the whole train decreased by 19.2% after the optimized shape was used. Through a simulation study on the flow field characteristics of a high-speed maglev train on an open track at a speed of 600 km·h − 1 , Zhou, Li, Zhao, and Zhang (2020) found that the thickness of the side boundary layer of the car body increased significantly due to the impact of surface vortex structure shedding, many vortex structures were generated at the train shoulder and in the curved surface change area, and a pair of longitudinal vortexes rotating in the opposite direction appeared in the wake area.…”