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作 者:朱怀志 徐巍[1] ZHU Huaizhi;XU Wei(School of Mechanical Engineering,Hubei Univ.of Tech.,Wuhan 430068,China)
机构地区:[1]湖北工业大学机械工程学院,湖北武汉430068
出 处:《湖北工业大学学报》2023年第5期40-47,共8页Journal of Hubei University of Technology
摘 要:以某轨道工程车为研究对象,利用Solidworks建立转向架刚性模型,对该模型进行网格划分后,使用ANSYS和SIMPACK建立包含柔性转向架的刚柔耦合动力学模型,研究转向架弹性变形对车辆直线运行稳定性、曲线通过性能和车辆运行平稳性的影响。以轴箱定位节点横向、垂向刚度,一系悬挂横向、垂向刚度,二系悬挂横向、垂向刚度为设计变量,设计拉丁超立方试验,利用RBF神经网络和NSGA-Ⅱ算法对横向、垂向平稳性指标,脱轨系数和轮轨横向力进行多目标优化。结果表明:轴箱定位节点横向、垂向刚度为5775 kN/m、11419 kN/m,一系悬挂横向刚度、垂向刚度为6042 kN/m、7834 kN/m,二系悬挂横向、垂向刚度为152 kN/m、158 kN/m时车辆动力学性能得到显著改善。Taking a rail engineering vehicle as the research object,the rigid model of the bogie was established by Solidworks.After meshing the model,the rigid-flexible coupling dynamic model,including the flexible bogie,is established by ANSYS and SIMPACK to study the influence of the elastic deformation of the bogie on the straight running stability,curve-passing performance,and running smoothness of the vehicle.The Latin hypercube test is designed with transverse and vertical stiffness of the axle box positioning joint,primary suspension transverse and vertical stiffness,and secondary suspension transverse and vertical stiffness as design variables.The transverse and vertical smoothness index,derailment coefficient,and wheel-rail transverse force are optimized by the RBF neural network and the NSGA II algorithm.The results show that the transverse and vertical stiffness of the axle box positioning joint is 5775 kN/m and 11419 kN/m,the transverse and vertical stiffness of the first suspension is 6042 kN/m and 7834 kN/m,and that the vehicle dynamic performance is significantly improved when the transverse and vertical stiffness of the second suspension is 152 kN/m and 158 kN/m.
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