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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.6
- 수록면
- 2,084 - 2,093 (10page)
- DOI
- https://doi.org/10.1016/j.net.2021.12.033
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초록· 키워드
오류제보하기
Probabilistic safety assessment is widely used to quantify the risks of nuclear power plants and theiruncertainties. When the lognormal distribution describes the uncertainties of basic events, the uncertainty of the top event in a fault tree is approximated with the sum of lognormal random variables afterminimal cutsets are obtained, and rare-event approximation is applied. As handling complicated analyticexpressions for the sum of lognormal random variables is challenging, several approximation methods,especially Monte Carlo simulation, are widely used in practice for uncertainty analysis. In this study, atheoretical approach for analyzing the sum of lognormal random variables using an efficient numericalintegration method is proposed for uncertainty analysis in probability safety assessments. The change ofvariables from correlated random variables with a complicated region of integration to independentrandom variables with a unit hypercube region of integration is applied to obtain an efficient numericalintegration. The theoretical advantages of the proposed method over other approximation methods areshown through a benchmark problem. The proposed method provides an accurate and efficient approachto calculate the uncertainty of the top event in probabilistic safety assessment when the uncertainties ofbasic events are described with lognormal random variables
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