重载铁路车—轨—桥系统垂向建模及动力性能优化
发布时间：2019-06-14 00:50
【摘要】：重载铁路运输提速、大轴重、长编组的发展需求,对车-轨-桥系统的安全性和耐久性提出了更高的要求,建立动力学仿真模型研究车-轨-桥系统动力学响应并进行优化,可以为线路设计提供相关设计依据以满足其发展需求。本文将车辆视为多刚体系统,推导了重载铁路有砟轨道-桥梁有限单元方程,采用赫兹非线性轮轨关系建立了重载铁路车-轨-桥系统垂向动力学模型,编制Matlab程序实现了车-轨-桥系统动力响应的迭代求解,并与实测数据进行对比验证了模型的可靠性。随后选取了车辆、轨道、桥梁参数,进行车-轨-桥系统动力学仿真分析,研究了不同轨道结构参数下系统响应峰值的变化规律。基于该变化规律,设立单目标、多目标动力响应优化工况,采用Pareto排序的遗传算法优化方法对车-轨-桥系统动力性能进行优化,得到不同工况下最优参数取值。通过研究得出以下主要结论:(1)轨道结构参数的变化对车-轨-桥系统各部件响应峰值具有不同程度的影响。当轨下垫层刚度kp和道床厚度hb发生变化时,主要影响钢轨、轨枕、道床垂向加速度、速度响应峰值,尤其是垂向加速度响应峰值,对桥梁响应峰值影响不大,且对整个系统垂向位移响应影响不大。(2)单类动力响应峰值优化结果表明,钢轨加速度响应峰值最小时,轨下垫层刚度kp为160 MN/m,道床厚度hb为0.31m;道砟块加速度响应峰值最小时,轨下垫层刚度kp为60 MN/m,道床厚度化为0.60m。不同优化结果相互比较时,轨下垫层刚度kp或道床厚度hb的最优取值存在差异,实际工程中需兼顾系统响应水平。(3)多类动力响应峰值的优化结果表明,钢轨加速度响应与轨枕加速度响应,或钢轨加速度响应与道砟块加速度响应组合时,轨下垫层合理刚度kp为180MN/m,道床合理厚度hb为0.39m,轨枕加速度响应与道砟块加速度响应组合时,轨下垫层合理刚度kp为160MN/m,道床合理厚度hb为0.60m。本文从车-轨-桥系统动力学响应的角度出发,结合不同优化目标给出了道床厚度、轨下垫层刚度参考取值。(4)在优化效率方面,本研究针对每个工况独立重复优化3次,3次优化结果保持一致,说明了本文所提出的遗传算法可以实现车-轨-桥系统动力性能的优化,且优化效率显著。
[Abstract]:The development demand of speed increase, large axle load and long marshalling of heavy load railway transportation puts forward higher requirements for the safety and durability of vehicle-rail-bridge system. The dynamic simulation model is established to study and optimize the dynamic response of vehicle-rail-bridge system, which can provide the relevant design basis for line design to meet its development needs. In this paper, the vehicle is regarded as a multi-rigid-body system, and the finite element equation of ballasted track-bridge of heavy-duty railway is derived. the vertical dynamic model of heavy-duty railway vehicle-rail-bridge system is established by using Hertz nonlinear wheel-rail relationship. The iterative solution of dynamic response of vehicle-rail-bridge system is realized by Matlab program, and the reliability of the model is verified by comparing with the measured data. Then the vehicle, track and bridge parameters are selected to simulate the dynamics of the vehicle-rail-bridge system, and the variation of the peak value of the system response under different track structure parameters is studied. Based on the change law, the single objective and multi-objective dynamic response optimization conditions are set up, and the dynamic performance of the vehicle-rail-bridge system is optimized by using the genetic algorithm optimization method of Pareto ranking, and the optimal parameters under different working conditions are obtained. The main conclusions are as follows: (1) the change of track structure parameters has different degrees of influence on the response peak value of each component of the vehicle-rail-bridge system. When the rail underlying stiffness kp and the track bed thickness hb change, the rail, sleeper, track bed vertical acceleration, velocity response peak value, especially the vertical acceleration response peak value, has little effect on the bridge response peak value, and has little effect on the vertical displacement response of the whole system. (2) the peak value of rail acceleration response is the smallest, and the rail underlying cushion stiffness kp is 160 MN/m,. (2) the peak value of rail acceleration response is the smallest, and the rail underlying layer stiffness kp is 160 MN/m,. The thickness of track bed hb is 0.31m; The peak acceleration response of the ballast block is the smallest, and the stiffness of the underlying layer of the rail kp is 60 MN/m, and the thickness of the track bed is 0.60 m. When different optimization results are compared, the optimal values of rail underlying stiffness kp or track bed thickness hb are different, and the system response level should be taken into account in practical engineering. (3) the optimization results of multiple kinds of dynamic response peaks show that when the rail acceleration response and sleeper acceleration response, or the combination of rail acceleration response and ballast block acceleration response, the reasonable stiffness kp of rail underlying layer is 180mm, and the reasonable thickness hb of track bed is 0.39m. When the acceleration response of sleeper and ballasted block is combined, the reasonable stiffness kp of underlying layer is 160mn m, and the reasonable thickness of track bed hb is 0.60m. In this paper, from the point of view of dynamic response of vehicle-rail-bridge system, combined with different optimization objectives, the reference values of track bed thickness and cushion stiffness under rail are given. (4) in terms of optimization efficiency, the optimization results of three times of independent repeated optimization for each working condition are consistent, which shows that the genetic algorithm proposed in this paper can optimize the dynamic performance of vehicle-rail-bridge system, and the optimization efficiency is remarkable.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U211;U441.7;U239.4
本文编号：2498961
[Abstract]:The development demand of speed increase, large axle load and long marshalling of heavy load railway transportation puts forward higher requirements for the safety and durability of vehicle-rail-bridge system. The dynamic simulation model is established to study and optimize the dynamic response of vehicle-rail-bridge system, which can provide the relevant design basis for line design to meet its development needs. In this paper, the vehicle is regarded as a multi-rigid-body system, and the finite element equation of ballasted track-bridge of heavy-duty railway is derived. the vertical dynamic model of heavy-duty railway vehicle-rail-bridge system is established by using Hertz nonlinear wheel-rail relationship. The iterative solution of dynamic response of vehicle-rail-bridge system is realized by Matlab program, and the reliability of the model is verified by comparing with the measured data. Then the vehicle, track and bridge parameters are selected to simulate the dynamics of the vehicle-rail-bridge system, and the variation of the peak value of the system response under different track structure parameters is studied. Based on the change law, the single objective and multi-objective dynamic response optimization conditions are set up, and the dynamic performance of the vehicle-rail-bridge system is optimized by using the genetic algorithm optimization method of Pareto ranking, and the optimal parameters under different working conditions are obtained. The main conclusions are as follows: (1) the change of track structure parameters has different degrees of influence on the response peak value of each component of the vehicle-rail-bridge system. When the rail underlying stiffness kp and the track bed thickness hb change, the rail, sleeper, track bed vertical acceleration, velocity response peak value, especially the vertical acceleration response peak value, has little effect on the bridge response peak value, and has little effect on the vertical displacement response of the whole system. (2) the peak value of rail acceleration response is the smallest, and the rail underlying cushion stiffness kp is 160 MN/m,. (2) the peak value of rail acceleration response is the smallest, and the rail underlying layer stiffness kp is 160 MN/m,. The thickness of track bed hb is 0.31m; The peak acceleration response of the ballast block is the smallest, and the stiffness of the underlying layer of the rail kp is 60 MN/m, and the thickness of the track bed is 0.60 m. When different optimization results are compared, the optimal values of rail underlying stiffness kp or track bed thickness hb are different, and the system response level should be taken into account in practical engineering. (3) the optimization results of multiple kinds of dynamic response peaks show that when the rail acceleration response and sleeper acceleration response, or the combination of rail acceleration response and ballast block acceleration response, the reasonable stiffness kp of rail underlying layer is 180mm, and the reasonable thickness hb of track bed is 0.39m. When the acceleration response of sleeper and ballasted block is combined, the reasonable stiffness kp of underlying layer is 160mn m, and the reasonable thickness of track bed hb is 0.60m. In this paper, from the point of view of dynamic response of vehicle-rail-bridge system, combined with different optimization objectives, the reference values of track bed thickness and cushion stiffness under rail are given. (4) in terms of optimization efficiency, the optimization results of three times of independent repeated optimization for each working condition are consistent, which shows that the genetic algorithm proposed in this paper can optimize the dynamic performance of vehicle-rail-bridge system, and the optimization efficiency is remarkable.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U211;U441.7;U239.4
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