Lightweight Design of the Baffle Structure in a High-Speed-Train Water Tank Based on an Improved Multi-Objective Particle Swarm Optimization Algorithm

Abstract

Lightweight design of high-speed-train water tanks can easily lower the natural frequency of baffles, increasing the risk of resonance and fatigue failure and thus threatening operational safety. To address this issue, a lightweight optimization method that does not reduce the first-order natural frequency of the baffle is proposed, taking a suspended water tank of a certain type of CRH electric multiple unit (EMU) as the research object. First, a two-way fluid–structure interaction (FSI) finite element model was established based on the computational fluid dynamics (CFD) method. The design of experiments method was employed to determine the optimal number of baffles inside the tank, and the response surface methodology was applied to construct quadratic polynomial surrogate models for baffle mass, maximum water tank stress, baffle deformation, and the first-order natural frequency. Analysis of variance was conducted to verify the fitting accuracy and significance of each model. After clarifying the influence of design variables on the response indicators, and to overcome the shortcomings of the standard multi-objective particle swarm optimization (MOPSO) algorithm, such as susceptibility to local optima, simplistic constraint handling, and premature convergence, an improved multi-objective particle swarm optimization (IMOPSO) algorithm integrating chaotic initialization, adaptive parameter adjustment, and a dynamic mutation strategy was proposed. With the first-order natural frequency serving as a constraint, multi-objective optimization of the baffle structure was carried out. Finally, the prediction accuracy of the surrogate models was numerically validated using finite element simulation software. The results show that after optimization, the baffle mass was reduced by 16.13%, the first-order natural frequency increased by 0.41 Hz (by FEM), the maximum water tank stress decreased by 288 Pa (by FEM), and the baffle deformation was reduced by 7.66% according to FEM verification (RSM surrogate-model prediction gave a reduction of 9.07%). The maximum prediction error of the surrogate models was only 1.53%, confirming the effectiveness and feasibility of the proposed method. This study can provide a theoretical basis and engineering reference for the improvement and performance optimization of water tanks on CRH EMUs and other similar tank structures.