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非线性随机振动分析的概率密度演化方法
引用本文:彭勇波,李 杰.非线性随机振动分析的概率密度演化方法[J].西南交通大学学报,2014,27(2):220-226.
作者姓名:彭勇波  李 杰
基金项目:国家自然科学基金资助项目(51108344)土木工程防灾国家重点实验室探索性研究课题资助项目(SLDRCE11-B-04)中央高校基本科研业务费专项资金资助项目
摘    要:为深入探讨概率密度演化方法对于非线性随机振动分析的适用性,考察了随机地震动作用下一类硬弹簧Duffing振子的非线性响应,对概率密度演化方法与经典非线性随机振动分析进行了比较研究.结果表明:在弱非线性水平,概率密度演化方法与混沌多项式展开、Monte Carlo模拟的解答一致;在强非线性水平,数值求解误差、人为截断误差放大,概率密度演化方法与混沌多项式展开解答在Monte Carlo模拟解附近上下波动,表明概率密度演化方法与经典非线性随机振动解答在均方特征意义上是等价的. 

关 键 词:概率密度演化方法    混沌多项式展开    Monte  Carlo模拟    随机地震动    Karhunen-Love分解
收稿时间:2012-12-10

Probability Density Evolution Method of Nonlinear Random Vibration Analysis
PENG Yongbo,LI Jie.Probability Density Evolution Method of Nonlinear Random Vibration Analysis[J].Journal of Southwest Jiaotong University,2014,27(2):220-226.
Authors:PENG Yongbo  LI Jie
Abstract:In order to reveal the applicability of the probability density evolution method in nonlinear random vibration analysis, a comparative research of the probability density evolution method and the classical nonlinear random vibration analysis was carried out by investigating the nonlinear responses of a class of randomly base-driven Duffing oscillators using the probability density evolution method (PDEM), the adaptive polynomial chaos expansion (APCE) and the Monte Carlo simulation (MCS). A physically based stochastic ground motion model was employed, and represented by a Karhunen-Love expansion in the application of the APCE. This discrete representation can be viewed as a projection of the physical vector space into the Gaussian vector space. Numerical results reveal that the solution processes of the three approaches are identical to weakly nonlinear systems, while they are approximately identical to strongly nonlinear systems though errors resulted from numerical techniques and artificial truncations are amplified, indicating that the solution of the PDEM is equivalent to that of the classical nonlinear random vibration analysis in the mean-square sense. The PDEM, moreover, goes a step further than the classical nonlinear random vibration analysis since the probability density function of responses and the dynamic reliability of systems can be simultaneously provided by the PDEM. The other methods, however, need much more computational efforts to obtain high order statistics of responses. 
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