Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model

Abstract

As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained multi-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.

Shape Optimization of High-Speed Rail by Biomimetic

“…al. [11] optimized the streamline of the head of CRH380A by using Kriging method, CFD analysis, and multi-objective optimization method. Table 1 shows the result of the force coefficient of the optimized shape and original shape.…”

Experimental and Numerical Study on the Aerodynamic Performance of High-Speed Trains

“…The division of the spatial grid directly affects the accuracy and stability of the results of the calculation (Yao et al, 2012). In order to assess the effect of various spatial grid divisions on the results of the numerical calculation, three different sets of grids are used to verify.…”

Research on Aerodynamic Noise Reduction for High-Speed Trains

“…Grid-independent validation was performed using different numbers of hexahedral meshes combined to form a prism mesh near the surface of the train, with a more highly refined mesh in the wake regions of the tail train and the pantograph to assess the influence of different spatial meshes on the calculation results. With the thickness of the first prism layer defined to satisfy the requirement of the wall function [32], three meshes configurations were considered in the present study. The parameters of the three mesh configurations and the corresponding computational results are listed in Table 1.…”