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首页> 外文期刊>International journal of applied mechanics >STOCHASTIC STRUCTURAL MODAL ANALYSIS INVOLVING UNCERTAIN PARAMETERS USING GENERALIZED POLYNOMIAL CHAOS EXPANSION
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STOCHASTIC STRUCTURAL MODAL ANALYSIS INVOLVING UNCERTAIN PARAMETERS USING GENERALIZED POLYNOMIAL CHAOS EXPANSION

机译:广义多项式混沌扩展的不确定参数随机结构模态分析

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In this paper, the application of generalized polynomial chaos expansion in stochastic structural modal analysis including uncertain parameters is investigated. We review the theory of polynomial chaos and relating error analysis. A general formulation for the representation of modal problems by the polynomial chaos expansion is derived.It shows how the modal frequencies and modal shapes are influenced by the parameter uncertainties. The key issues that arise in the polynomial chaos simulation of modal analysis are discussed for two examples: a discrete 2-DOF system and continuous model of a microsensor. In both cases, the polynomial chaos expansion is used for the approximation of uncertain parameters, eigenfrequencies and eigenvectors. We emphasize the accuracy and time efficiency of the method in estimation of the stochastic modal responses in comparison with the sampling techniques, such as the Monte Carlo simulation.
机译:本文研究了广义多项式混沌展开在包含不确定参数的随机结构模态分析中的应用。我们回顾了多项式混沌理论和相关的误差分析。推导了用多项式混沌展开表示模态问题的一般公式,表明了模态频率和模态形状如何受到参数不确定性的影响。对于两个示例,讨论了模态分析的多项式混沌仿真中出现的关键问题:离散2-DOF系统和微传感器的连续模型。在这两种情况下,都使用多项式混沌展开来逼近不确定参数,特征频率和特征向量。与抽样技术(例如蒙特卡洛模拟)相比,我们强调了该方法在估计随机模态响应中的准确性和时间效率。

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