【摘要】 高速列车悬挂系统参数与其动力学性能密切相关，对悬挂系统参数进行多目标优化可以有效改善其动力学性能。根据Pearson相关性对各悬挂系统参数与动力学性能之间的相关性，联合采用层次分析法和模糊综合评价法识别出与动力学性能相关性最大的4个悬挂系统关键参数。以4个关键参数作为设计变量，构建面向脱轨系数、轮重减载率、轮轴横向力、舒适度指标及非线性临界速度的克里金(Kriging)近似模型、径向基神经网络(RBF)近似模型和2阶响应面(RSM)近似模型，根据K折交叉验证法计算3种单一近似模型的权重系数后，将3种近似模型根据权重系数拟合成高速列车动力学性能指标的组合近似模型，并对组合近似模型进行精度评价。把组合近似模型作为目标函数，脱轨系数、轮重减载率、轮轴横向力、舒适度指标及非线性临界速度作为目标响应，选取NSGA-Ⅱ优化算法对悬挂系统参数进行寻优。优化结果表明，最优解对5个动力学性能指标的优化率都达到10%以上，很好地改善了高速列车动力学性能。更多还原

【Abstract】 The parameters of the high-speed train suspension system are closely related to the system’s dynamic performance; the multi-objective optimization of these parameters is greatly helpful to improve the system’s dynamic performance. In this article, according to the Pearson correlation, the analysis is conducted on the correlation between the system’s parameters and its dynamic performance; then, four key parameters with the highest correlation to the system’s dynamic performance are identified, with the help of a combination of Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation. With the key parameters as the design variables, efforts are made to construct the Kriging approximation model, the radial basis function neural network(RBF) approximation model, and the second-order response surface(RSM) approximation model, which take aim at such factors as derailment coefficient, wheel load reduction rate, wheel-axle lateral force, comfort index, and nonlinear critical speed. Then, after the weight coefficients of the three single approximation models are calculated by means of the K-fold cross validation method, they are fitted based on the weight coefficients, in order to set up a combined approximation model of the high-speed train’s dynamic performance indicators, and then the accuracy of the combined approximation model is evaluated. With the combined approximation model as the objective function, with such factors as derailment coefficient, wheel load reduction rate, wheel-axle lateral force, comfort index, and nonlinear critical speed as the objective response, the NSGA-Ⅱ optimization algorithm is used to optimize the system’s parameters. The results show that the optimal solution has an optimization rate of over 10% for all five dynamic performance indicators, thus effectively improving the high-speed train’s dynamic performance.更多还原