High-Dimensional Reliability Method Accounting for Important and Unimportant Input Variables

Author:

Yin Jianhua12,Du Xiaoping3

Affiliation:

1. Department of Mechanical and, Energy Engineering, Indiana University—Purdue University Indianapolis, 723 W. Michigan Street, Indianapolis, IN 46202-5195;

2. School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907

3. Department of Mechanical and Energy Engineering, Indiana University—Purdue University Indianapolis, 723 W. Michigan Street, Indianapolis, IN 46202-5195

Abstract

Abstract Reliability analysis is a core element in engineering design and can be performed with physical models (limit-state functions). Reliability analysis becomes computationally expensive when the dimensionality of input random variables is high. This work develops a high-dimensional reliability analysis method through a new dimension reduction strategy so that the contributions of unimportant input variables are also accommodated after dimension reduction. Dimension reduction is performed with the first iteration of the first-order reliability method (FORM), which identifies important and unimportant input variables. Then a higher order reliability analysis is performed in the reduced space of only important input variables. The reliability obtained in the reduced space is then integrated with the contributions of unimportant input variables, resulting in the final reliability prediction that accounts for both types of input variables. Consequently, the new reliability method is more accurate than the traditional method which fixes unimportant input variables at their means. The accuracy is demonstrated by three examples.

Funder

Directorate for Engineering

Publisher

ASME International

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials

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