A Chebyshev convex method for mid-frequency analysis of built-up structures with large convex uncertainties. Issue 9 (29th May 2020)

• Record Type: Journal Article
• Authors: Qin, Wu; Yin, Hui; Yu, D.J.; Shangguan, Wen-Bin
• Is Part Of: Engineering computations. Volume 37:Issue 9(2020)
• Page Start: 3431
• Page End: 3453
• Publication Date: 2020-05-29
• Subjects: Chebyshev polynomials -- Built-up structures -- Convex parameters -- FE-SEA -- Mid-frequency analysis; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-08-2019-0379
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: This paper aims to develop an efficient numerical method for mid-frequency analysis of built-up structures with large convex uncertainties. Design/methodology/approach: Based on the Chebyshev polynomial approximation technique, a Chebyshev convex method (CCM) combined with the hybrid finite element/statistical energy analysis (FE-SEA) framework is proposed to fulfil the purpose. In CCM, the Chebyshev polynomials for approximating the response functions of built-up structures are constructed over the uncertain domain by using the marginal intervals of convex parameters; the bounds of the response functions are calculated by applying the convex Monte–Carlo simulation to the approximate functions. A relative improvement method is introduced to evaluate the truncated order of CCM. Findings: CCM has an advantage in accuracy over CPM when the considered order is the same. Furthermore, it is readily to consider the CCM with the higher order terms of the Chebyshev polynomials for handling the larger convex parametric uncertainty, and the truncated order can be effectively evaluated by the relative improvement method. Originality/value: The proposed CCM combined with FE-SEA is the first endeavor to efficiently handling large convex uncertainty in mid-frequency vibro-acoustic analysis of built-up structures. It also has the potential to serve as a powerful tool for other kinds of system analysis when large convex uncertainty is involved.