4.7 Article

A stochastic model updating strategy-based improved response surface model and advanced Monte Carlo simulation

期刊

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 82, 期 -, 页码 323-338

出版社

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2016.05.026

关键词

Stochastic model updating; Response surface model; Monte Carlo simulation; Moving least-squares method; Design of experiment; Experimental static test

资金

  1. National Natural Science Foundation of China [11472147, 51475025]
  2. Fund of Hong Kong Scholars Program [G-YZ90, XJ2015002]
  3. China's Post-Doctoral Science Funding [2015M580037]
  4. Hong Kong SAR Government [514013(B-Q39B)]

向作者/读者索取更多资源

To improve the accuracy and efficiency of computation model for complex structures, the stochastic model updating (SMU) strategy was proposed by combining the improved response surface model (IRSM) and the advanced Monte Carlo (MC) method based on experimental static test, prior information and uncertainties. Firstly, the IRSM and its mathematical model were developed with the emphasis on moving least-square method, and the advanced MC simulation method is studied based on Latin hypercube sampling method as well. And then the SMU procedure was presented with experimental static test for complex structure. The SMUs of simply-supported beam and aeroengine stator system (casings) were implemented to validate the proposed IRSM and advanced MC simulation method. The results show that (1) the SMU strategy hold high computational precision and efficiency for the SMUs of complex structural system; (2) the IRSM is demonstrated to be an effective model due to its SMU time is far less than that of traditional response surface method, which is promising to improve the computational speed and accuracy of SMU; (3) the advanced MC method observably decrease the samples from finite element simulations and the elapsed time of SMU. The efforts of this paper provide a promising SMU strategy for complex structure and enrich the theory of model updating. (C) 2016 Elsevier Ltd. All rights reserved.

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