Accession Number:

ADA558435

Title:

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

Descriptive Note:

Conference paper

Corporate Author:

IOWA UNIV IOWA CITY DEPT OF MECHANICAL AND INDUSTRIAL ENGINEERING

Report Date:

2010-08-01

Pagination or Media Count:

13.0

Abstract:

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 MCS to the accurate surrogate model. Since the surrogate model is used, the computational cost for the stochastic sensitivity analysis is negligible. 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.

Subject Categories:

  • Numerical Mathematics
  • Statistics and Probability
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE