A weak-intrusive stochastic finite element method for stochastic structural dynamics analysis

被引:13
作者
Zheng, Zhibao [1 ,4 ]
Beer, Michael [2 ,5 ,6 ,7 ,8 ]
Dai, Hongzhe [3 ]
Nackenhorst, Udo [1 ,4 ]
机构
[1] Leibniz Univ Hannover, Inst Mech & Computat Mech, Appelstr 9a, D-30167 Hannover, Germany
[2] Leibniz Univ Hannover, Inst Risk & Reliabil, Callinstr 34, D-30167 Hannover, Germany
[3] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China
[4] Leibniz Univ Hannover, Int Res Training Grp 2657, Appelstr 11-11a, D-30167 Hannover, Germany
[5] Univ Liverpool, Inst Risk & Uncertainty, Peach St, Liverpool L69 7ZF, England
[6] Univ Liverpool, Sch Engn, Peach St, Liverpool L69 7ZF, England
[7] Tongji Univ, Int Joint Res Ctr Resilient Infrastruct, 1239 Siping Rd, Shanghai 200092, Peoples R China
[8] Tongji Univ, Int Joint Res Ctr Engn Reliabil & Stochast Mech, 1239 Siping Rd, Shanghai 200092, Peoples R China
关键词
Stochastic structural dynamics; Stochastic finite element method; Weak -intrusive approach; Curse of dimensionality; GENERALIZED SPECTRAL DECOMPOSITION; POLYNOMIAL CHAOS; ORDER REDUCTION; RANDOM-FIELDS; EQUATIONS; UNCERTAINTIES; SYSTEMS;
D O I
10.1016/j.cma.2022.115360
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents a weak-intrusive stochastic finite element method for solving stochastic structural dynamics equations. In this method, the stochastic solution is decomposed into the summation of a series of products of random variables, spatial vectors and temporal functions. An iterative algorithm is proposed to compute each triplet of the random variable, spatial vector and temporal function one by one. The original stochastic dynamics problem is firstly transformed into spatial-temporal coupled problems (i.e. deterministic structural dynamics equations), which can be solved efficiently by existing FEM solvers. Based on the solution of the spatial-temporal coupled problem, the original problem is then transformed into stochastic-temporal coupled problems (i.e. one-dimensional second-order stochastic ordinary differential equations), which are solved by a proposed sampling method. All random sources are embedded into the stochastic-temporal coupled problems. The proposed sampling method can solve the stochastic-temporal problems of hundreds of dimensions with low computational costs. Thus the curse of dimensionality in high-dimensional stochastic spaces is avoided with great success. Three numerical examples, including lowand high-dimensional stochastic problems, are used to demonstrate the good accuracy and the high efficiency of the proposed method. (c) 2022 Elsevier B.V. All rights reserved.
引用
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页数:26
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