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用谐波叠加法重构随机道路不平顺高程的时域模型
引用本文:张永林.用谐波叠加法重构随机道路不平顺高程的时域模型[J].农业工程学报,2003,19(6):32-35.
作者姓名:张永林
作者单位:武汉工业学院机械工程系,武汉,430023;华中科技大学CAD中心,武汉,430074
基金项目:湖北省科技攻关计划项目暨湖北省教育厅重点科技项目
摘    要:道路时域建模对道路车辆振动时程分析、车辆台架试验、路面损伤以及车路耦合动力学研究均具有重要意义。谐波迭加法是一种既简单直观而又具有严密数学基础的构造道路时域模型的方法。本文基于随机道路的统计量描述,再现了随机道路针对路面车辆的单点激励和单轮辙激励的时间历程样本,完善了所生成样本的标定方法。对双轮辙激励,采用空间相关的两随机过程模拟,将两过程用虚拟的线性系统联接,建立其符合道路相干函数的传递关系,最终得到道路车辆的双轮辙多点时空相关性激励模型。该模拟方法的数学理论正确,适用于任意指定谱特征的随机道路模拟,对

关 键 词:谐波叠加法    道路激励    随机不平顺    时域模型
文章编号:1002-6819(2003)06-0032-04
收稿时间:2002/12/26 0:00:00
修稿时间:2002年12月26

Time domain model of road irregularities simulated using the harmony superposition method
Zhang Yonglin.Time domain model of road irregularities simulated using the harmony superposition method[J].Transactions of the Chinese Society of Agricultural Engineering,2003,19(6):32-35.
Authors:Zhang Yonglin
Abstract:It is of significance to establish the time domain model of road irregularity as the excitation to vehicles in order to perform vibration analysis, platform experiment, road damage prediction and interactive vehicle-road dynamics. The harmony superposition method based on exact mathematical theory is simpler and more efficient than other general road simulation methods. Based on the statistic illustration of stochastic road, a time series sample of road is reconstructed corresponding to a single wheel and a single track as an input excitation with respect to vehicles and the resulting sample is correctly demarcated. Then, spatial correlative stochastic processes were used to simulate bi-track road unevenness, and the two processes were linked by a virtual linear system featured with a transform function consistent with the road coherent function. The time domain model including the temporal and spatial correlativity of road irregularities was developed. This modeling method is correct in theory and can be applied to simulate stochastic road with a specific spectrum and the same stochastic processes as wind, seismism, and so on.
Keywords:harmony superposition  road excitation  stochastic irregularity  time domain model
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