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Research Papers

Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems With Correlated Random Variables

[+] Author and Article Information
Ikjin Lee

Department of Mechanical and Industrial Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242ilee@engineering.uiowa.edu

K. K. Choi1

Department of Mechanical and Industrial Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242kkchoi@engineering.uiowa.edu

Yoojeong Noh

Department of Mechanical and Industrial Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242noh@engineering.uiowa.edu

Liang Zhao

Department of Mechanical and Industrial Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242liazhao@engineering.uiowa.edu

David Gorsich

 U.S. Army RDECOM/TARDEC, Warren, MI 48397-5000david.gorsich@us.army.mil

1

Corresponding author.

J. Mech. Des 133(2), 021003 (Jan 24, 2011) (10 pages) doi:10.1115/1.4003186 History: Received April 08, 2010; Revised November 20, 2010; Published January 24, 2011; Online January 24, 2011

This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are the mean values of the input correlated random variables. Assuming that an accurate surrogate model is available, the proposed method calculates the component reliability, system reliability, or statistical moments and their sensitivities by applying Monte Carlo simulation to the accurate surrogate model. Since the surrogate model is used, the computational cost for the stochastic sensitivity analysis is affordable compared with the use of actual models. The copula is used to model the joint distribution of the correlated input random variables, and the score function is used to derive the stochastic sensitivities of reliability or statistical moments for the correlated random variables. An important merit of the proposed method is that it does not require the gradients of performance functions, which are known to be erroneous when obtained from the surrogate model, or the transformation from X-space to U-space for reliability analysis. Since no transformation is required and the reliability or statistical moment is calculated in X-space, there is no approximation or restriction in calculating the sensitivities of the reliability or statistical moment. Numerical results indicate that the proposed method can estimate the sensitivities of the reliability or statistical moments very accurately, even when the input random variables are correlated.

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Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

Grahic Jump Location
Figure 2

Sample profile for DDO and RBDO

Grahic Jump Location
Figure 1

Shape of constraint and cost functions

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