A non-contact acoustic pressure-based method for load identification in acoustic-structural interaction system with non-probabilistic uncertainty

被引:25
|
作者
He, Z. C. [1 ,3 ]
Lin, X. Y. [1 ]
Li, Eric [2 ]
机构
[1] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China
[2] Teesside Univ, Sch Sci Engn & Design, Middlesbrough, Cleveland, England
[3] Guangxi Univ Sci & Technol, Guangxi Key Lab Automobile Components & Vehicle T, Liuzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Load identification; Acoustic-structure interaction; Non-contact; Non-probabilistic uncertainty; FINITE-ELEMENT-METHOD; BORNE TRANSMISSION PATHS; INTERVAL-ANALYSIS METHOD; BAYESIAN-APPROACH; INVERSE METHODS; REGULARIZATION; QUANTIFICATION; FORCE; RECONSTRUCTION; PARAMETERS;
D O I
10.1016/j.apacoust.2018.12.034
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
A non-contact acoustic pressure-based method is proposed for load identification in the acoustic structure interaction system involving non-probabilistic uncertainty. The forward problem for load identification is established through the discretized convolution integral relationship of the dynamic loads and the Green's kernel function matrix of the system. The inverse process is constructed by using truncated single value decomposition approach in order to overcome the ill-posedness of the global kernel function matrix. In this work, two non-probabilistic models including ellipsoid model and interval model are proposed to quantify the effects of the system uncertainty. Several numerical examples are investigated to verify the effectiveness of the present methods. The results show that the non-contact acoustic pressure-based method with great convenience for dynamic load identification is accurate and effective. For the system with non-probabilistic uncertainty, the ellipsoid model and interval model are the proper choices to identify the bounds with knowing only the extreme values of the parameters. Moreover, the load bounds derived from the ellipsoid model are more reliable than those derived from the interval model. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:223 / 237
页数:15
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