Study on Quantification Method Based on Monte Carlo Sampling for Multiunit Probabilistic Safety Assessment Models

In Korea, many nuclear power plants operate at a single site based on geographical characteristics, but the population density near the sites is higher than that in other countries. Thus, multiunit accidents are a more important consideration than in other countries and should be addressed appropria...

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Bibliographic Details
Main Authors: Kyemin Oh, Sang Hoon Han, Jin Hee Park, Ho-Gon Lim, Joon Eon Yang, Gyunyoung Heo
Format: Article
Language:English
Published: Elsevier 2017-06-01
Series:Nuclear Engineering and Technology
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1738573317300049
Description
Summary:In Korea, many nuclear power plants operate at a single site based on geographical characteristics, but the population density near the sites is higher than that in other countries. Thus, multiunit accidents are a more important consideration than in other countries and should be addressed appropriately. Currently, there are many issues related to a multiunit probabilistic safety assessment (PSA). One of them is the quantification of a multiunit PSA model. A traditional PSA uses a Boolean manipulation of the fault tree in terms of the minimal cut set. However, such methods have some limitations when rare event approximations cannot be used effectively or a very small truncation limit should be applied to identify accident sequence combinations for a multiunit site. In particular, it is well known that seismic risk in terms of core damage frequency can be overestimated because there are many events that have a high failure probability. In this study, we propose a quantification method based on a Monte Carlo approach for a multiunit PSA model. This method can consider all possible accident sequence combinations in a multiunit site and calculate a more exact value for events that have a high failure probability. An example model for six identical units at a site was also developed and quantified to confirm the applicability of the proposed method.
ISSN:1738-5733