A rapid modeling method and accuracy criteria for common-cause failures in Risk Monitor PSA model

Bing Zhang; Shanqi Chen; Zhixian Lin; Shaoxuan Wang; Zhen Wang; Daochuan Ge; Dingqing GUO; Jian Lin; Fang WANG; JIN WANG

doi:https://doi.org/10.1016/j.net.2020.06.021

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자료유형: 학술저널

저자정보: Bing Zhang (China Nuclear Power Engineering Co. Ltd.) Shanqi Chen (Chinese Academy of Sciences) Zhixian Lin (Chinese Academy of Sciences) Shaoxuan Wang (Chinese Academy of Sciences) Zhen Wang (Chinese Academy of Sciences) Daochuan Ge (Chinese Academy of Sciences) Dingqing GUO (China Nuclear Power Engineering Co. Ltd.) Jian Lin (China Nuclear Power Engineering Co. Ltd.) Fang WANG (Chinese Academy of Sciences) JIN WANG (Chinese Academy of Sciences)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제53권 제1호

발행연도: 2021.1

수록면: 103 - 110 (8page)

DOI: https://doi.org/10.1016/j.net.2020.06.021

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In the development of a Risk Monitor probabilistic safety assessment (PSA) model from the basic PSAmodel of a nuclear power plant, the modeling of common-cause failure (CCF) is very important. Atpresent, some approximate modeling methods are widely used, but there lacks criterion of modelingaccuracy and error analysis. In this paper, aiming at ensuring the accuracy of risk assessment andminimizing the Risk Monitor PSA models size, we present three basic issues of CCF model resulted fromthe changes of a nuclear power plant configuration, put forward corresponding modeling methods, andderive accuracy criteria of CCF modeling based on minimum cut sets and risk indicators according to therequirements of risk monitoring. Finally, a nuclear power plant Risk Monitor PSA model is taken as anexample to demonstrate the effectiveness of the proposed modeling method and accuracy criteria, andthe application scope of the idea of this paper is also discussed.

#Nuclear power plant #Probabilistic safety assessment #Risk monitor #Common-cause failure #Criteria for modeling accuracy

참고문헌 (19)

참고문헌 신청

C.H. Shepherd / 2004 / Risk monitors - the state of the art in their development and use at nuclear power plants - produced on behalf of IAEA and OECD/NEA WGRisk :250

F. Wang / 2016 / Risk monitor RiskAngel for risk-informed applications in nuclear power plants / Ann. Nucl. Energy 91:142~147

J. Wang / 2016 / Fault-tree-based instantaneous risk computing core in nuclear power plant risk monitor / Ann. Nucl. Energy 95:35~41

X. Dai / 2017 / Hierarchical modeling of GO-FLOW models for online risk monitoring of nuclear power plants / 2017 25th International Conference on Nuclear Engineering, ICONE, American Society of Mechanical Engineers (ASME)

Z. G. Ma / 2017 / Knowledge-based software design for Defense-in-Depth risk monitor system and application for AP1000 / J. Nucl. Sci. Technol 54(5):552~568

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