A stochastic model updating strategy-based improved response surface model and advanced Monte Carlo simulation. (1st January 2017)

• Record Type: Journal Article
• Authors: Zhai, Xue; Fei, Cheng-Wei; Choy, Yat-Sze; Wang, Jian-Jun
• Is Part Of: Mechanical systems and signal processing. Volume 82(2017)
• Page Start: 323
• Page End: 338
• Publication Date: 2017-01-01
• Subjects: Stochastic model updating -- Response surface model -- Monte Carlo simulation -- Moving least-squares method -- Design of experiment -- Experimental static test; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.05.026
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: 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. Highlights: Improve a response surface model with moving least-square method. Develop an advanced Monte Carlo simulation-based Latin Hypercube sampling method. Propose a stochastic model updating (SMU) strategy by combining the proposed methods, prior information and uncertainties. High computational efficiency and accuracy for the presented approaches and strategy. Enrich and develop the theory and methodology of model updating for complex structure.