A proposal on accuracy estimation method for the sampling-based uncertainty analysis with Monte Carlo simulation technique. Issue 2 (1st February 2016)

• Record Type: Journal Article
• Authors: Kim, Song Hyun; Song, Myung Sub; Sun, Gwang Min; Shin, Chang Ho
• Is Part Of: Journal of nuclear science and technology. Volume 53:Issue 2(2016)
• Page Start: 295
• Page End: 301
• Publication Date: 2016-02-01
• Subjects: sensitivity and uncertainty analysis -- S/U analysis -- sampling-based -- Monte Carlo -- eigenvalue -- stochastic uncertainty -- error propagation -- cross section uncertainty; Nuclear engineering -- Periodicals; Nuclear physics -- Periodicals; Nuclear energy -- Periodicals; 621.4805
• DOI: 10.1080/00223131.2015.1038665
• Languages: English
• ISSNs: 0022-3131

Abstract

Abstract : The sampling-based uncertainty analysis method is a stochastic approach to estimate response uncertainties caused by the uncertainty in the input parameters. Conventionally, to minimize the effects caused by the Monte Carlo stochastic uncertainty, lots of particle histories have been used for the uncertainty analysis. However, this can cause inefficiencies in the uncertainty analysis. To optimize the calculation efficiency, how the Monte Carlo stochastic uncertainty influences the response uncertainty should be clearly verified. In this study, a method to estimate the accuracy of the response uncertainty is proposed by introducing a standard error and an error propagation theory. Using the proposed method, response uncertainties and standard errors of the multiplication factors for three benchmark problems are evaluated by the Monte Carlo method. Our results show that the proposed method can accurately estimate the accuracy of the response uncertainty caused by the input uncertainty in using the Monte Carlo simulation method. The proposed method can be directly utilized to estimate the accuracy of the sampling-based uncertainty analysis using the Monte Carlo simulation method. Also, it is expected that the proposed method will contribute to an increase in the calculation efficiency in the sampling-based sensitivity and uncertainty analysis.