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Multi-factor and Multi-object Optimization for Foundation Brake Device in Railway Freight Car

机译:铁路货车底盘制动装置的多因素多目标优化

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It is one of the important measures for improving the operational performance of the basic brake device to ensure the safety of railway freight cars. The operational performance of basic brake device includes braking performance and mitigation performance, which are affected by multi-factor such as lever size, sleeve clearance, installation angle and so on. Therefore, a multi-factor and multi-object optimization method that based on iSIGHT and multi-body dynamics simulation is proposed. The object of optimization is to minimize the transverse displacement of the brake beam and the mitigation force, and maximize the transmission efficiency. Firstly, identify the key factors such as the thickness of fixed lever, the width of back groove of middle tie rod, and the inner diameter of bushing 3 and bushing 4 by the main effect value of orthogonal test. Then, the Latin hypercube sampling points are applied for numerical simulation to establish the response surface model. The optimal solution set is obtained by NSGA-II multi-objectives genetic algorithm cyclic approximation optimization. The optimization scheme is selected by the target importance and the distribution probability of the optimal solution set. Finally, the optimization model is validated in RecurDyn. The optimized scheme achievement shows that the transverse displacement of the brake beam is reduced by 31%, the maximum reduction in mitigation power is 36% and the transmission efficiency is increased by 2%.
机译:保证铁路货车安全是提高基本制动装置运行性能的重要措施之一。基本制动装置的操作性能包括制动性能和缓解性能,这些性能受杠杆大小,套筒间隙,安装角度等多方面因素的影响。因此,提出了一种基于iSIGHT和多体动力学仿真的多因素多目标优化方法。优化的目的是使制动梁的横向位移和缓解力最小,并使传动效率最大化。首先,通过正交试验的主要效果值确定关键因素,如固定杆的厚度,中拉杆后槽的宽度以及衬套3和衬套4的内径。然后,将拉丁超立方体采样点应用于数值模拟,以建立响应面模型。通过NSGA-II多目标遗传算法循环逼近优化获得最优解集。根据目标重要性和最佳解决方案集的分布概率选择优化方案。最后,在RecurDyn中验证了优化模型。优化方案的结果表明,制动梁的横向位移降低了31%,最大缓解功率降低了36%,传动效率提高了2%。

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