Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 40, Issue 1-6, Pages 35-51Publisher
SPRINGER
DOI: 10.1007/s00158-009-0385-1
Keywords
Reliability-based design optimization; Input model uncertainty; Identification of marginal and joint CDFs; Correlated input variables; Copula; Bayesian method; Goodness-of-fit test
Categories
Funding
- U.S. Army TARDEC [W911NF-07-D-0001]
- Automotive Research Center
Ask authors/readers for more resources
In RBDO, input uncertainty models such as marginal and joint cumulative distribution functions (CDFs) need to be used. However, only limited data exists in industry applications. Thus, identification of the input uncertainty model is challenging especially when input variables are correlated. Since input random variables, such as fatigue material properties, are correlated in many industrial problems, the joint CDF of correlated input variables needs to be correctly identified from given data. In this paper, a Bayesian method is proposed to identify the marginal and joint CDFs from given data where a copula, which only requires marginal CDFs and correlation parameters, is used to model the joint CDF of input variables. Using simulated data sets, performance of the Bayesian method is tested for different numbers of samples and is compared with the goodness-of-fit (GOF) test. Two examples are used to demonstrate how the Bayesian method is used to identify correct marginal CDFs and copula.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available