A Comparative study of sampling techniques for dynamic probabilistic risk assessment of nuclear power plants

Kubo, Kotaro ; Zheng, X. ; Tanaka, Yoichi; Tamaki, Hitoshi ; Sugiyama, Tomoyuki ; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*

Dynamic probabilistic risk assessment (PRA) is a method for improving the realism and completeness of conventional PRA. However, enormous calculation costs are incurred by these improvements. One solution is to select an appropriate sampling method. In this paper, we applied the Monte Carlo, Latin hypercube, grid-point, and quasi-Monte Carlo sampling methods to the dynamic PRA of a simplified accident sequence and compared the results for each method. Quasi-Monte Carlo sampling was found to be the most effective method in this case.

Language	:	English
Journal	:	Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020)
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Pages	:	p.308 - 315
Publication Year/Month	:	2020/10
Meeting title	:	Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020)
Held date	:	2020/05
Location (city)	:	Chiba
Location (country)	:	Japan
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DOI for research data	:	Link to research data related to this paper/report.
Keywords	:	Probabilistic Risk Assessment; Dynamic PRA; Monte Carlo Method; THALES-2; RAPID
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Registration No. : BB20200097
JAEA Abstracts No. : 48001396
Paper Submission No. : 23636

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