首页> 外文会议>ASME biennial conference on engineering systems design and analysis >NUMERICAL ALGORITHM FOR NON-STATIONARY COVARIANCE ANALYSIS OF NONLINEAR MECHANICAL SYSTEM USING EQUIVALENT STOCHASTIC LINEARIZATION
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NUMERICAL ALGORITHM FOR NON-STATIONARY COVARIANCE ANALYSIS OF NONLINEAR MECHANICAL SYSTEM USING EQUIVALENT STOCHASTIC LINEARIZATION

机译:使用等效随机线性化非线性机械系统非平稳协方差分析数值算法

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A quite important topic of structural dynamics is deterministic mechanical systems subjected to stochastic dynamic actions, such as for wind or earthquake. EN such cases structural response have to be properly evaluated by a stochastic approach. Unfortunately for nonlinear mechanical systems only in a very few cases exact solutions are available, and usually simply approximate solutions should be used. A well known one is stochastic equivalent linearization, easy and simple from the conceptual point of view. Moreover it needs of specific numerical techniques to be properly implemented, whose complexity increases in case of non stationary conditions. In this paper a procedure to solve covariance analysis of stochastic linearized systems in case of non stationary excitation is proposed. The no stationary Lyapunov differential matrix covariance equation for the linearized system is solved by using a numerical algorithm that updates linearized system matrix coefficients step by step. This by a predictor-corrector procedure applied to a Euler-implicit integration scheme for the matrix covariance analysis. It is described in details to be simpler implemented by other researchers, and then applied to a typical and important applicative case, that is seismic response of a Bouc Wen Single Degree of Freedom (SDoF) system. Seismic input processes is modeled as linear filtered white noise non stationary separable process. Accuracy and computational costs are analyzed showing the efficiency of the proposed integrating procedure.
机译:结构动力学的一个相当重要的话题是确定随机动态动作的确定性机械系统,例如风或地震。这种情况必须通过随机方法适当地评估结构响应。不幸的是,对于非线性机械系统仅在很少情况下,可以使用精确的解决方案,并且通常应该使用近似解决方案。众所周知的人是随机等效的线性化,从概念的角度轻松简单。此外,它需要适当地实施特定的数值技术,其复杂性在非静止条件下增加。本文提出了一种解决非平稳激励的随机线性化系统的协方差分析的过程。通过使用数值算法通过使用数值算法来解决线性化系统的静止的Lyapunov差分矩阵协方程,该数值算法逐步更新线性化系统矩阵系数。这通过应用于矩阵协方差分析的欧拉隐式积分方案的预测器校正器过程。详细描述了由其他研究人员实现的更简单,然后应用于典型和重要的应用案例,即BOUC WEN单一自由度(SDOF)系统的地震响应。地震输入过程被建模为线性过滤的白噪声非固定式可分离过程。分析了准确性和计算成本,展示了所提出的整合程序的效率。

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