Uncertainty Analysis by Dimension Reduction Integration and Saddlepoint Approximations

Abstract

Uncertainty analysis, which assesses the impact of the uncertainty of input variables on responses, is an indispensable component in engineering design under uncertainty, such as reliability-based design and robust design. However, uncertainty analysis is an unaffordable computational burden in many engineering problems. In this paper, an uncertainty analysis method is proposed with the purpose of accurately and efficiently estimating the cumulative distribution function (CDF), probability density function (PDF), and statistical moments of a response given the distributions of input variables. The bivariate dimension reduction method and numerical integration are used to calculate the moments of the response; then saddlepoint approximations are employed to estimate the CDF and PDF of the response. The proposed method requires neither the derivatives of the response nor the search of the most probable point, which is needed in the commonly used first and second order reliability methods (FORM and SORM) and the recently developed first order saddlepoint approximation. The efficiency and accuracy of the proposed method is illustrated with three example problems. with the same computational cost, this method is more accurate for reliability assessment and much more efficient for estimating the full range of the distribution of a response than FORM and SORM. This method provides results as accurate as Monte Carlo simulation, with significantly reduced computational effort.

Department(s)

Mechanical and Aerospace Engineering

Sponsor(s)

University of Missouri Research Board
University of Missouri--Rolla. Intelligent Systems Center
National Science Foundation (U.S.)

Keywords and Phrases

Design Engineering; Monte Carlo Methods; Probability; Reliability

International Standard Serial Number (ISSN)

1050-0472

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2006 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Jan 2006

Link to Full Text

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