Structural-borne acoustics analysis and multi-objective optimization by using panel acoustic participation and response surface methodology

被引:20
|
作者
Wang, Yongliang [1 ,2 ]
Qin, Xunpeng [1 ,2 ]
Huang, Song [2 ]
Lu, Li [3 ]
Zhang, Qingkai [2 ]
Feng, Jiawei [1 ,2 ]
机构
[1] Hubei Key Lab Adv Technol Automot Parts, Wuhan 430070, Peoples R China
[2] Wuhan Univ Technol, Sch Automot Engn, Wuhan 430070, Peoples R China
[3] Dongfeng Peugeot Citroen Automobile Co LTD, Wuhan 430070, Peoples R China
关键词
Panel acoustic participation; Design of experiment; Response surface methodology; Structural-borne acoustics; Multi-objective optimization; SENSITIVITY-ANALYSIS; DAMPING STRUCTURE; FINITE-ELEMENT; CRASHWORTHINESS; MINIMIZATION; DESIGN; PLATES;
D O I
10.1016/j.apacoust.2016.09.013
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
This paper is aimed to investigate the structural-borne acoustics analysis and multi-objective optimization of an enclosed box structure by using the panel acoustic participation (PAP) and response surface methodology (RSM). The acoustic frequency response function is applied to achieve the critical frequency of interest under each excitation. The PAP analysis is then carried out at all critical frequencies and the remarkable acoustic panels are identified. The correlation coefficient matrix method is proposed for reselecting and grouping the positions of acoustic panels identified to paste damping layer to control noise. With the help of faced central composite design, an efficient set of sample points are generated and then the second-order polynomial functions of sound pressure response at each critical frequency are computed and verified by the adjusted coefficient of multiple determination. The functional relationships between sound pressure responses and the thicknesses of damping layers are investigated, and multi objective optimization of the thicknesses of damping layers is developed. The results indicate that, by using the PAP and RSM, the structural-borne acoustics at critical frequencies are calculated conveniently and controlled effectively. The optimization process of the explicit optimization model proposed in this paper is simple and the computational time is saved. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:139 / 151
页数:13
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