Abstract
Aiming at the difficulty of screening the optimization domain in sensitivity analysis and the difficulty of determining the design variables in the stiffness contribution degree evaluation method, a research method combining the Improved Stiffness Contribution Degree (ISCD) with sensitivity analysis is proposed, which can determine the optimization domain under the guidance of the theoretical design, and then screen the design variables, so as to achieve the purpose of simplifying the computation and analysis. Compared with the traditional stiffness contribution degree evaluation method, this method not only improves the theoretical formula and constructs the ISCD parametric analysis process, but also, relative to the traditional sensitivity analysis, the optimization domain has been successfully reduced through the ISCD parametric analysis, which provides a scientific basis for the determination of the design variables. In the study of rail vehicle optimization, the rail vehicle body was decomposed in two levels to determine the optimization domain based on the guidance of this method, and 11 groups of design variables were selected from the reduction range. Then, the BP neural network agent model was constructed, and the multi-objective optimization was carried out by NSGA-II genetic algorithm. The optimized rail vehicle body mass is reduced by 132 kg, while the fully-equipped the first-order bending frequency is improved by 1.029 Hz. The results show that the volume of the optimized domain of the rail vehicle body determined by applying the ISCD analysis method is only 53.47% of the volume of the original rail car body. This approach has effectively eliminated most of the structures with low sensitivity values before the sensitivity analysis, thus significantly reducing the scope of the sensitivity analysis, making the high sensitivity design variables more concentrated, and laying the foundation for the rail vehicle body optimization study.
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