Rendering optimal design under various uncertainties A unified approach and application to brake instability study

被引:3
作者
Lu, Hui [1 ]
Yang, Kun [1 ]
Shangguan, Wen-bin [1 ]
Yin, Hui [1 ]
Yu, D. J. [2 ]
机构
[1] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Peoples R China
[2] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Uncertainty; Reliability; Brake instability; Fuzzy possibility; Unified optimization; FINITE-ELEMENT-METHOD; RELIABILITY-ANALYSIS; INTERVAL; OPTIMIZATION; SYSTEM; SQUEAL; PARAMETERS; REDUCTION;
D O I
10.1108/EC-03-2019-0100
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Purpose The purpose of this paper is to propose a unified optimization design method and apply it to handle the brake squeal instability involving various uncertainties in a unified framework. Design/methodology/approach Fuzzy random variables are taken as equivalent variables of conventional uncertain variables, and a unified response analysis method is first derived based on level-cut technique, Taylor expansion and central difference scheme. Next, a unified reliability analysis method is developed by integrating the unified response analysis and fuzzy possibility theory. Finally, based on the unified reliability analysis method, a unified reliability-based optimization model is established, which is capable of optimizing uncertain responses in a unified way for different uncertainty cases. Findings The proposed method is extended to perform squeal instability analysis and optimization involving various uncertainties. Numerical examples under eight uncertainty cases are provided and the results demonstrate the effectiveness of the proposed method. Originality/value Most of the existing methods of uncertainty analysis and optimization are merely effective in tackling one uncertainty case. The proposed method is able to handle the uncertain problems involving various types of uncertainties in a unified way.
引用
收藏
页码:345 / 367
页数:23
相关论文
共 36 条
[1]   Optimizing performance with multiple responses using cross-evaluation and aggressive formulation in data envelopment analysis [J].
Al-Refaie, Abbas .
IIE TRANSACTIONS, 2012, 44 (04) :262-276
[2]   Overview on the development of fuzzy random variables [J].
Angeles Gil, Maria ;
Lopez-Diaz, Miguel ;
Ralescu, Dan A. .
FUZZY SETS AND SYSTEMS, 2006, 157 (19) :2546-2557
[3]  
[Anonymous], 1999, J SYST ENG
[4]  
[Anonymous], 2009, Comp. Meth. Appl. Mech. Eng., DOI DOI 10.1016/J.CMA.2008.11.007
[5]   Explicit fuzzy analysis of systems with imprecise properties [J].
Balu, A. S. ;
Rao, B. N. .
INTERNATIONAL JOURNAL OF MECHANICS AND MATERIALS IN DESIGN, 2011, 7 (04) :283-289
[6]   Interval optimization of dynamic response for structures with interval parameters [J].
Chen, SH ;
Wu, J .
COMPUTERS & STRUCTURES, 2004, 82 (01) :1-11
[7]   Probabilistic interval analysis for structures with uncertainty [J].
Gao, Wei ;
Song, Chongmin ;
Tin-Loi, Francis .
STRUCTURAL SAFETY, 2010, 32 (03) :191-199
[8]  
Goldberg D.E., 1989, Genetic algorithms in search, optimization, and machine learning
[9]   Automotive disc brake squeal [J].
Kinkaid, NM ;
O'Reilly, OM ;
Papaclopoulos, P .
JOURNAL OF SOUND AND VIBRATION, 2003, 267 (01) :105-166
[10]  
Klir G., 1995, Fuzzy Sets and Fuzzy Logic, V4