The rise in wheel rail equivalent conicity after wheel reprofiling leads to an increase in bogie hunting frequency, elastic vibration modes of carbodies are easily excited, which may induce abnormal carbody vibrations. A modal optimisation design framework for high-speed train carbodies is proposed from the perspective of structural design. A specific high-speed train carbody exhibiting abnormal elastic vibration during service is taken as the research object, and the vibration mechanism is first identified based on operational test data. A convolutional neural network (CNN)-based surrogate model is constructed to establish the nonlinear mapping between sectional thickness parameters, carbody mass, and the first-order diamond modal frequency. The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is then employed to carry out high-dimensional size optimisation of the sectional profiles. The optimised carbody is further evaluated through static strength verification, free modal analysis, and rigid–flexible coupled dynamic simulations under equivalent operating conditions. The results show that the proposed optimisation framework achieves significant weight reduction while effectively increasing the first-order diamond modal frequency, thereby suppressing abnormal elastic vibrations and substantially improving passenger comfort. The proposed method provides a practical and effective solution for the lightweight and modal optimisation design of high-speed train carbodies.
Liu et al. (Tue,) studied this question.