The analyses of dynamic response and reliability for failure-dependent stochastic micro-resonator with thermoelastic coupling effects

Yan, Binbin; Ma, Juan; Wu, Di; Wriggers, Peter

doi:10.1016/j.apm.2019.09.040

Cited by 5 publications

(1 citation statement)

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“…Numerous computational procedures have been developed for solving the random static problems [ 25 , 26 ], as well as other engineering applications, including reliability problems [ 27 ]. Normally, there are two categories to implement SFEM: simulation approaches (e.g., the Monte-Carlo method), which are capable of offering the probabilistic features of the concerned structural responses based on the statistics of samples obtained from the simulation [ 28 , 29 , 30 ]; and non-simulation methods, which approach the statistical characteristics of the structural outputs by carrying out various numerical methods [ 31 ]. Development of SFEM in structural engineering, however, has not yet deeply and adequately extended to eigenvalue problems despite their importance in many applications, including the dynamic response of structures.…”

Section: Introductionmentioning

confidence: 99%

Uncertain Dynamic Characteristic Analysis for Structures with Spatially Dependent Random System Parameters

Zhou

et al. 2023

Materials

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This work presents a robust non-deterministic free vibration analysis for engineering structures with random field parameters in the frame of stochastic finite element method. For this, considering the randomness and spatial correlation of structural physical parameters, a parameter setting model based on random field theory is proposed to represent the random uncertainty of parameters, and the stochastic dynamic characteristics of different structural systems are then analyzed by incorporating the presented parameter setting model with finite element method. First, Gauss random field theory is used to describe the uncertainty of structural material parameters, the random parameters are then characterized as the standard deviation and correlation length of the random field, and the random field parameters are then discretized with the Karhunen–Loeve expansion method. Moreover, based on the discretized random parameters and finite element method, structural dynamic characteristics analysis is addressed, and the probability distribution density function of the random natural frequency is estimated based on multi-dimensional kernel density estimation method. Finally, the random field parameters of the structures are quantified by using the maximum likelihood estimation method to verify the effectiveness of the proposed method and the applicability of the constructed model. The results indicate that (1) for the perspective of maximum likelihood estimation, the parameter setting at the maximum value point is highly similar to the input parameters; (2) the random field considering more parameters reflects a more realistic structure.

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