scholarly journals Reliability-Based Design Optimization of a Composite Plate through a Dual Design Space

2021 ◽  
Author(s):  
L. Coelho ◽  
N. Fabbiane ◽  
C. Fagiano ◽  
C. Julien ◽  
D. Lucor
Keyword(s):  
Design Optimization ◽  
Composite Plate ◽  
Design Space ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design

Iterative reliable design space approach for efficient reliability-based design optimization

2019 ◽  
Vol 36 (1) ◽  
pp. 151-169 ◽  
Author(s):  
Chen Jiang ◽  
Haobo Qiu ◽  
Xiaoke Li ◽  
Zhenzhong Chen ◽  
Liang Gao ◽  
...  
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Reliable Design ◽  
Space Approach

Model Uncertainty Approximation Using a Copula-Based Approach for Reliability Based Design Optimization

2016 ◽  
Author(s):  
Hao Pan ◽  
Zhimin Xi ◽  
Ren-Jye Yang
Keyword(s):  
Design Optimization ◽  
Model Uncertainty ◽  
Design Space ◽  
Epistemic Uncertainty ◽  
Minimum Cost ◽  
Vehicle Design ◽  
Model Bias ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Model Inadequacy

Reliability-based design optimization (RBDO) has been widely used to design engineering products with minimum cost function while meeting defined reliability constraints. Although uncertainties, such as aleatory uncertainty and epistemic uncertainty, have been well considered in RBDO, they are mainly considered for model input parameters. Model uncertainty, i.e., the uncertainty of model bias which indicates the inherent model inadequacy for representing the real physical system, is typically overlooked in RBDO. This paper addresses model uncertainty characterization in a defined product design space and further integrates the model uncertainty into RBDO. In particular, a copula-based bias correction approach is proposed and results are demonstrated by two vehicle design case studies.

Reliability-Based Design Optimization in X-Space Using Ensemble of Gaussian Reliability Analyses (EoGRA)

2015 ◽  
Author(s):  
Po Ting Lin ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Reliability Index ◽  
Design Space ◽  
Performance Measure ◽  
Probabilistic Constraints ◽  
Reliability Based Design Optimization ◽  
Design Point ◽  
Reliability Based Design ◽  
Standard Normal

Reliability-Based Design Optimization (RBDO) algorithms have been developed to solve design optimization problems with existence of uncertainties. Traditionally, the original random design space is transformed to the standard normal design space, where the reliability index can be measured in a standardized unit. In the standard normal design space, the Modified Reliability Index Approach (MRIA) measured the minimum distance from the design point to the failure region to represent the reliability index; on the other hand, the Performance Measure Approach (PMA) performed inverse reliability analysis to evaluate the target function performance in a distance of reliability index away from the design point. MRIA was able to provide stable and accurate reliability analysis while PMA showed greater efficiency and was widely used in various engineering applications. However, the existing methods cannot properly perform reliability analysis in the standard normal design space if the transformation to the standard normal space does not exist or is difficult to determine. To this end, a new algorithm, Ensemble of Gaussian Reliability Analyses (EoGRA), was developed to estimate the failure probability using Gaussian-based Kernel Density Estimation (KDE) in the original design space. The probabilistic constraints were formulated based on each kernel reliability analysis for the optimization processes. This paper proposed an efficient way to estimate the constraint gradient and linearly approximate the probabilistic constraints with fewer function evaluations. Some numerical examples with various random distributions are studied to investigate the numerical performances of the proposed method. The results showed EoGRA is capable of finding correct solutions in some problems that cannot be solved by traditional methods.

Sign in / Sign up

Export Citation Format

Share Document