Research Progress of Aerodynamic Multi-Objective Optimization on High-Speed Train Nose Shape

Abstract

The aerodynamic optimization design of high-speed trains (HSTs) is crucial for energy conservation, environmental preservation, operational safety, and speeding up. This study aims to review the current state and progress of the aerodynamic multi-objective optimization of HSTs. First, the study explores the impact of train nose shape parameters on aerodynamic performance. The parameterization methods involved in the aerodynamic multiobjective optimization of HSTs are summarized and classified as shape-based and disturbance-based parameterization methods. Meanwhile, the advantages and limitations of each parameterization method, as well as the applicable scope, are briefly discussed. In addition, the NSGA-II algorithm, particle swarm optimization algorithm, standard genetic algorithm, and other commonly used multi-objective optimization algorithms and the improvements in the field of aerodynamic optimization for HSTs are summarized. Second, this study investigates the aerodynamic multi-objective optimization technology for HSTs using the surrogate model, focusing on the Kriging surrogate models, neural network, and support vector regression. Moreover, the construction methods of surrogate models are summarized, and the influence of different sample infill criteria on the efficiency of multi-objective optimization is analyzed. Meanwhile, advanced aerodynamic optimization methods in the field of aircraft have been briefly introduced to guide research on the aerodynamic optimization of HSTs. Finally, based on the summary of the research progress of the aerodynamic multi-objective optimization of HSTs, future research directions are proposed, such as intelligent recognition technology of characteristic parameters, collaborative optimization of multiple operating environments, and sample infill criterion of the surrogate model.