高速列车车轮型面多目标优化研究

对于动车组车轮磨耗引起的动力学性能降低问题，车轮型面优化是一个很好的解决方案。采用旋转压缩微调法（Rotary-scaling fine-tuning method，RSFT）进行型面生成；建立某型动车组车辆动力学模型，采用该模型计算相应的优化目标和约束条件；利用径向基神经网络-粒子群（Radial-based neural network-particle swarm optimization，RBF-PSO）算法优化出最优廓形。通过对比优化前后车轮型面的动力学性能和磨耗性能，可以发现：优化后车轮型面临界速度为424.6 km/h，增大10.2%；横向平稳性和垂向平稳性指标整体减小，同时提高了曲线通过时的安全性指标，脱轨系数、倾覆系数和轮轴横向力都进一步减小。优化后车轮型面接触点分布相对更加均匀，等效锥度减小。同时优化后车轮型面有效减小车轮磨耗深度，并减小了轮缘根部磨耗，车轮最大磨耗深度减小9.8%。

Optimization of Wheel Profiles for High-speed Trains

Wheel profile optimization is a good solution to the problem of reduced dynamic performance caused by wheel wear of high-speed trains. A rotary-scaling fine-tuning method(RSFT) is used to generate the new wheel profile; a vehicle dynamics model for the certain high-speed trains is established, and the corresponding optimization objectives and constraints are calculated by the model; the optimal profile is optimized using a radial-based neural network-particle swarm optimization(RBF-PSO) algorithm. By comparing the dynamics and wear performance of the wheel profile before and after the optimization, it can be found that:the wheel profile critical speed after optimization is 424.6 km/h, an increase of 10.2%; the lateral and vertical ride indexes are reduced overall, while the safety indexes during curve passing are improved, and the derailment coefficient, overturning coefficient and lateral axle force are further reduced. The optimized wheel profile has a more evenly distribution of contact points and a reduced equivalent conicity. At the same time, the optimized wheel profile effectively reduces the depth of wheel wear and reduces the root wear of the wheel rim, reducing the maximum depth of wheel wear by 9.8%.