Experimental and Numerical Investigation on the Aerodynamic Characteristics of High-Speed Pantographs with Supporting Beam Wind Deflectors

Aiming to mitigate the aerodynamic lift force imbalance between pantograph strips,which exacerbates wear and affects the current collection performance of the pantograph-catenary system,a study has been conducted to support the beam deflector optimization using a combination of experimental measurements and computational fluid dynamics(CFD)simulations.The results demonstrate that the size,position,and installation orientation of the wind deflectors significantly influence the amount of force compensation.They also indicate that the front strip deflectors should be installed downwards and the rear strip deflectors upwards,thereby forming a“π”shape.Moreover,the lift force compensation provided by the wind deflectors increases with the size of the deflector.Alternative wind compensation strategies,such as control circuits,are also discussed,putting emphasis on the pros and cons of various pantograph types and wind compensation approaches.

应用元启发式优化和高斯过程回归预测受电弓-接触网系统性能的可行性研究

受电弓接触网系统为高速列车提供电能,正确评估受电弓与接触网之间的接触力(CF)对于稳定供电至关重要.CF的大小和变化范围决定了列车受流质量和安全运行.因此,快速、准确地预测CF具有重要意义.然而,通过实验收集CF数据具有挑战性,并且通过数值模拟获得及时结果并不总是可行的.在本研究中,我们提出了一种结合元启发式优化和高斯过程回归的高效的代理模型方法,来预测受电弓接触网系统接触力统计量。首先,使用有限元法(FEM)建立并验证受电弓接触网模型,用于生成训练和测试数据集.其次,利用高斯过程回归(GPR)进行对接触力的预测.将一种新开发的元启发式优化,即二元饥饿游戏搜索(HGS),应用于特征选择.为了增强BHGS的性能,嵌入了混沌机制,产生了ChaOs-HGSGPR(CHGS-GPR).最后,对CHGS-GPR的预测结果进行了评估.结果发现,所提出的CHGS-GPR对CF的平均值提供了相当准确的预测,并且可以扩展到铁路线路的初步设计、列车运行的实时评估和控制.