Response prediction and fatigue life estimation of thin-walled structures under random excitation using the modified equivalent linearization method

被引:2
|
作者
Wang, Zhi [1 ]
Zhang, Yahui [1 ]
Qin, Zhaohong [2 ]
机构
[1] Dalian Univ Technol, Int Ctr Computat Mech, Dept Engn Mech, State Key Lab Struct Anal Ind Equipment, Dalian 116023, Peoples R China
[2] Beijing Inst Struct & Environm Engn, Sci & Technol Reliabil & Environm Engn Lab, Beijing 100076, Peoples R China
基金
中国国家自然科学基金;
关键词
Geometric nonlinearity; Nonlinear reduced order model; Equivalent linearization; Fatigue life estimation; STOCHASTIC LINEARIZATION; RANDOM VIBRATION; FORMULATION; EXTENSION; MODELS;
D O I
10.1016/j.tws.2023.110824
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper proposes a fast estimation method for the high-cycle fatigue of geometrically nonlinear thin-walled structures under random loading. The core idea of this method is utilizing a modified equivalent linearization method to obtain the statistical properties of the modal displacement response of the thin-walled structures, which is used to generate samples and then transform them into stress samples to determine the peak probability density function of the von Mises stress for fatigue life estimation. There are three critical issues addressed in implementing the method: (1) Using the implicit condensation technique to introduce the membrane softening effect in the regulated equivalent linearization method to improve the computational accuracy of the displacement statistical properties and convert the higher order moments into easily computable second order moments by separating the displacement mean values, so the regulated equations of motion for non-zero mean systems are derived, and fix the problem that the regulated equivalent linearization method does not apply to the prediction of the dynamic response of general thin-walled structures. (2) A nonlinear relationship between modal displacement and stress is derived with the implicit condensation and expansion method to transform the generated displacement samples to stress samples, which are used to determine probability density functions of stress and their derivatives. (3) Simplifying the probability density function of the three-dimensional joint distribution to the probability density function of the two-dimensional joint distribution by taking advantage of the Bayes rule, thus deriving a fast formula for the probability density function of the peak Mises stress.
引用
收藏
页数:18
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