Since the railway vehicle structure has lots of parameters and several complex constraints, this study establishes a method for structural parameter optimization based on sensitivity analysis and surrogate models. Fatigue crack problem of the equipment cabin bottom cover of the EMU is taken as an example to optimize its structural parameters. First, establish the finite element (FE) model of the bottom cover and compare it with the bench test results to verify the accuracy of the load and restraint conditions. The sensitivity analysis method is used to determine the main parameters. The input samples are obtained by Latin hypercube sampling method, and the output samples are obtained by the method jointly developed by ABAQUS+Python and the surrogate model between the input and output samples is obtained by fitting, and its accuracy is verified. According to the design requirements, the optimization objective function and constraint conditions are established, and the optimization result is obtained by optimization algorithm. The results were substituted into the FE model for verification. The results show that the maximum equivalent stress of the bottom cover is reduced from 126.7 MPa to 78.9 MPa under a cyclic aerodynamic load of ±4 kPa, which is 37.7% optimized, and the effect is significant. This method avoids the iterative optimization of the FE model and improves the optimization efficiency.
Four kinds of aluminum alloy welded joints widely used in car-body for high-speed train were experimentally examined to clarify their probabilistic fatigue properties. Based on BS8118 and IIW standard criterions, the probabilistic fatigue limits for these four welded joints, corresponding to loading cycle of 2×106, are evaluated under two different loading modes. In terms of the extrapolation proposed by Eurocode 9 criterion, the corresponding probabilistic S-N curves in very high cycle regime are obtained, combined with grouping method and lifting and lowering method.
Load spectra research for bogie frame requires establishing the load-stress relationship on working condition, which has been omitted by the researchers. With the load-stress of the bogie frame of an intercity Electric Multiple Unit (Hereinafter referred to as EMU) as the research object, an optimization model of the load-stress transfer relationship is established. The load-stress coefficient for EMU bogie frame was calibrated in the laboratory bench and online test was arranged on Dazhou-Chengdu line. Comparison of nonlinear and linear neural networks proves that the linear transitive relation between the load and stress of the bogie frame in the operating process is highly suitable. An optimization model of the load-stress transfer coefficient is obtained. The data calculated with the modified coefficient are closer to the dynamic stress results in the actual operating process than the data calculated with the calibration coefficient. The coefficient of the modified transitive relation is unaffected by operating area, empty load, heavy load, or other conditions in the operating process of the intercity EMU. The real loads in actual situations are obtained. The model of online load-stress relationship that is highly suitable for line stress calculation is finally established. The research is helpful for further damage calculation and inferring the time history signal of the load in load spectra research.
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