Multidisciplinary reliability design optimization using an enhanced saddlepoint approximation in the framework of sequential optimization and reliability analysis

Rong Yuan, Debiao Meng and Haiqing Li

Journal of Risk and Reliability, 2016, vol. 230, issue 6, 570-578

Abstract: For high reliability calculation efficiency and evaluation accuracy, saddlepoint approximation technology has been introduced into design and optimization under uncertainties. When using saddlepoint approximation, there are two prerequisites: all random information is tractable and saddlepoint equations are easy to be solved. However, the above requirements cannot always be met in complex multidisciplinary systems. Random variables sometimes are intractable, or saddlepoint equations are highly nonlinear. To tackle these problems, in this study, an efficient reliability-based multidisciplinary design optimization using the combination method of saddlepoint approximation and third-moment is given. A simplified alternative cumulant generating function can be constructed by saddlepoint approximation and third-moment with the first, second and third moments of a random variable effectively. Then, this cumulant generating function can be utilized to calculate the cumulative distribution function and the probability density function of this random variable approximately. Moreover, to obtain better efficiency, the framework of sequential optimization and reliability analysis is introduced in this study. The corresponding formula of the proposed reliability-based multidisciplinary design optimization is given in detail. Two test problems are solved to show the application of the proposed method.

Keywords: Saddlepoint approximation and third-moment; cumulant generating function; cumulative distribution function; probability density function; sequential optimization; reliability analysis (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:230:y:2016:i:6:p:570-578

DOI: 10.1177/1748006X16673500

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