發(fā)布時(shí)間：2019-06-14 00:50

【摘要】：重載鐵路運(yùn)輸提速、大軸重、長(zhǎng)編組的發(fā)展需求,對(duì)車-軌-橋系統(tǒng)的安全性和耐久性提出了更高的要求,建立動(dòng)力學(xué)仿真模型研究車-軌-橋系統(tǒng)動(dòng)力學(xué)響應(yīng)并進(jìn)行優(yōu)化,可以為線路設(shè)計(jì)提供相關(guān)設(shè)計(jì)依據(jù)以滿足其發(fā)展需求。本文將車輛視為多剛體系統(tǒng),推導(dǎo)了重載鐵路有砟軌道-橋梁有限單元方程,采用赫茲非線性輪軌關(guān)系建立了重載鐵路車-軌-橋系統(tǒng)垂向動(dòng)力學(xué)模型,編制Matlab程序?qū)崿F(xiàn)了車-軌-橋系統(tǒng)動(dòng)力響應(yīng)的迭代求解,并與實(shí)測(cè)數(shù)據(jù)進(jìn)行對(duì)比驗(yàn)證了模型的可靠性。隨后選取了車輛、軌道、橋梁參數(shù),進(jìn)行車-軌-橋系統(tǒng)動(dòng)力學(xué)仿真分析,研究了不同軌道結(jié)構(gòu)參數(shù)下系統(tǒng)響應(yīng)峰值的變化規(guī)律�；谠撟兓�(guī)律,設(shè)立單目標(biāo)、多目標(biāo)動(dòng)力響應(yīng)優(yōu)化工況,采用Pareto排序的遺傳算法優(yōu)化方法對(duì)車-軌-橋系統(tǒng)動(dòng)力性能進(jìn)行優(yōu)化,得到不同工況下最優(yōu)參數(shù)取值。通過(guò)研究得出以下主要結(jié)論:(1)軌道結(jié)構(gòu)參數(shù)的變化對(duì)車-軌-橋系統(tǒng)各部件響應(yīng)峰值具有不同程度的影響。當(dāng)軌下墊層剛度kp和道床厚度hb發(fā)生變化時(shí),主要影響鋼軌、軌枕、道床垂向加速度、速度響應(yīng)峰值,尤其是垂向加速度響應(yīng)峰值,對(duì)橋梁響應(yīng)峰值影響不大,且對(duì)整個(gè)系統(tǒng)垂向位移響應(yīng)影響不大。(2)單類動(dòng)力響應(yīng)峰值優(yōu)化結(jié)果表明,鋼軌加速度響應(yīng)峰值最小時(shí),軌下墊層剛度kp為160 MN/m,道床厚度hb為0.31m;道砟塊加速度響應(yīng)峰值最小時(shí),軌下墊層剛度kp為60 MN/m,道床厚度化為0.60m。不同優(yōu)化結(jié)果相互比較時(shí),軌下墊層剛度kp或道床厚度hb的最優(yōu)取值存在差異,實(shí)際工程中需兼顧系統(tǒng)響應(yīng)水平。(3)多類動(dòng)力響應(yīng)峰值的優(yōu)化結(jié)果表明,鋼軌加速度響應(yīng)與軌枕加速度響應(yīng),或鋼軌加速度響應(yīng)與道砟塊加速度響應(yīng)組合時(shí),軌下墊層合理剛度kp為180MN/m,道床合理厚度hb為0.39m,軌枕加速度響應(yīng)與道砟塊加速度響應(yīng)組合時(shí),軌下墊層合理剛度kp為160MN/m,道床合理厚度hb為0.60m。本文從車-軌-橋系統(tǒng)動(dòng)力學(xué)響應(yīng)的角度出發(fā),結(jié)合不同優(yōu)化目標(biāo)給出了道床厚度、軌下墊層剛度參考取值。(4)在優(yōu)化效率方面,本研究針對(duì)每個(gè)工況獨(dú)立重復(fù)優(yōu)化3次,3次優(yōu)化結(jié)果保持一致,說(shuō)明了本文所提出的遺傳算法可以實(shí)現(xiàn)車-軌-橋系統(tǒng)動(dòng)力性能的優(yōu)化,且優(yōu)化效率顯著。
[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.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U211;U441.7;U239.4

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