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

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 ofHSTs are summarized and classified as shape-based and disturbance-based parameterizationmethods.Meanwhile,the advantages and limitations of each parameterizationmethod,aswell 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 ofmulti-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 aerodynamicmulti-objective optimization ofHSTs,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.

Shape Optimization of Pressure Vessel Head Based on the Super-ellipse Equation

This paper studies optimal shape design of pressure vessel head subject to internal uniform pressure.The optimization aims at minimizing its maximum stress while the volume of the vessel head remains no less than the standard ellipsoidal head.Super-ellipse curve is selected to describe the middle surface shape of the vessel head because it represents a large family of curves with only two or three parameters and makes the design and manufacture easy.The performance of different elements and element sizes of FEM modeling is carefully studied in view of computational cost,accuracy and noises of von Mises stress.The response surface of the maximum stress vs.shape design parameters is approximated by a Kriging surrogate model with EI criterion for sampling adding,based on the parameter optimization which is carried out to search the optimal shape.Finally,it is shown by numerical comparison that the super-ellipsoidal head is better than the standard ellipsoidal head and the other vessel heads in the literature.