Design optimization of hybrid uncertain structures with fuzzy-boundary interval variables

2020 ◽  
Author(s):  
Hui Lü ◽  
Kun Yang ◽  
Xiaoting Huang ◽  
Hui Yin
Keyword(s):  
Design Optimization ◽  
Interval Variables ◽  
Uncertain Structures ◽  
Fuzzy Boundary ◽  
Fuzzy Boundary Interval

An optimization method for brake instability reduction with fuzzy-boundary interval variables

2018 ◽  
Vol 233 (12) ◽  
pp. 3209-3221 ◽  
Author(s):  
Hui Lü ◽  
Qianlang Feng ◽  
Zicheng Cai ◽  
Wen-Bin Shangguan
Keyword(s):  
Reliability Analysis ◽  
Optimization Method ◽  
Response Analysis ◽  
Analysis Model ◽  
Structure Response ◽  
Interval Variables ◽  
Dual Uncertainties ◽  
Reliability Based Optimization ◽  
Fuzzy Boundary ◽  
Fuzzy Boundary Interval

In some special engineering circumstances, it is likely that all parameters of an uncertain automotive structure can only be treated as interval variables due to limited knowledge, but meanwhile their lower and upper bounds can just be modeled as fuzzy variables rather than as deterministic values due to ambiguous information. To handle this dual uncertainties case, a reliability-based optimization method with fuzzy-boundary interval variables is developed in this study, and it is further extended to carry out squeal instability analysis and reduction of brake involving both limited and vague information. In the proposed method, fuzzy-boundary interval variables are utilized to cope with the above dual uncertainties of structure parameters and help to build up the structure response analysis model. First, the structure responses are derived on the basis of α-cut strategy, Taylor series expansion, subinterval analysis, and central difference method. Then, with the aid of fuzzy possibility theory, a reliability analysis model of structure response is developed, which can make use of extra reliability information and thus quantify the reliability more accurately. Next, a reliability-based optimization model involving fuzzy-boundary interval variables is established by integrating the uncertain response analysis model and the reliability analysis model. Finally, the proposed method is extended to carry out automotive brake squeal instability analysis and optimization. The numerical investigations demonstrate the applicability and effectiveness of the proposed method.

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

2017 ◽  
Vol 232 (1) ◽  
pp. 52-64 ◽  
Author(s):  
Fan Yang ◽  
Zhufeng Yue ◽  
Lei Li ◽  
Dong Guan
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Region Of Interest ◽  
Kriging Model ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Learning Function ◽  
Design Variables ◽  
Interval Variables ◽  
Hybrid Reliability

This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

General Robust Design by Semi-Second-Order Taylor Expansion

2008 ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Taylor Expansion ◽  
Random Variables ◽  
Second Order ◽  
Robust Design Optimization ◽  
Integration Strategy ◽  
Interval Variables ◽  
Standard Deviations ◽  
Robustness Assessment

The purpose of robust design optimization is to minimize variations in design performances and therefore to make the design insensitive to uncertainties. Current robust design methods fall into two types — probabilistic robust design and worst-case (interval) robust design. The former method is used when random variables are involved. In this method, robustness is measure by standard deviations of design performances. The later method is used when uncertainties are represented by intervals. The widths of design performances are then used to measure robustness. In many engineering application, both random variables and interval variables may exist simultaneously. In this paper, a general approach to robust design optimization is proposed. The generality comes from the ability to handle both random and interval variables. To alleviate the computational burden, we employ a previously developed general robustness assessment method — semi-second-order Taylor expansion method, to evaluate the maximum and minimum standard deviations of a design performance. An efficient integration strategy of the general robustness assessment and optimization is proposed. With the integration strategy, the number of function calls can be reduced while good accuracy is maintained. A robust shaft design problem is given for demonstration.

An uncertainty-based design optimization strategy with random and interval variables for multidisciplinary engineering systems

Structures ◽  
2021 ◽  
Vol 32 ◽  
pp. 997-1004
Author(s):  
Debiao Meng ◽  
Tianwen Xie ◽  
Peng Wu ◽  
Chao He ◽  
Zhengguo Hu ◽  
...  
Keyword(s):  
Design Optimization ◽  
Engineering Systems ◽  
Optimization Strategy ◽  
Interval Variables

scholarly journals System Reliability Based Design Optimization of Truss Structures with Interval Variables

2019 ◽  
Author(s):  
Mohammad Zaeimi ◽  
Ali Ghoddosain
Keyword(s):  
Design Optimization ◽  
System Reliability ◽  
Truss Structures ◽  
Equivalent Model ◽  
Reliability Based Design Optimization ◽  
Uncertain Variables ◽  
Reliability Based Design ◽  
Interval Variables ◽  
Reliability Method ◽  
Process Reliability

New products ranging from simple components to complex structures should be designed to be optimal and reliable. In this paper, for the first time, a hybrid uncertain model is applied to system reliability based design optimization (RBDO) of trusses. All uncertain variables are described by random distributions but those lack information are defined by variation intervals. For system RBDO of trusses, the first order reliability method, as well as an equivalent model and the branch and bound method, are utilized to determine the system failure probability; and Improved (μ + λ) constrained differential evolution (ICDE) is employed for the optimization process. Reliability assessment of some engineering examples is proposed to verify our results. Moreover, the effect interval variables on the optimum weight of the truss structures are investigated. The results indicate that the optimal weight depends not only on the uncertainty level but also on the equivalent standard deviation; and a falling-rising behavior is observed.

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