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A rapid approach to generate multi-unit event trees based on tree combinatorial calculation.

Authors :
Peng, Pengcheng
Tong, Jiejuan
Zhao, Jun
Source :
Progress in Nuclear Energy. Aug2023, Vol. 162, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

The Fukushima nuclear accident poses a new challenge for Probabilistic Risk Assessment (PRA) to consider the integrated risk of a nuclear power plant having more than one units. That calls for new technologies in the area of accident sequence modelling which traditionally deals with the accident in one reactor only. It has been shown that multi-unit event trees tend to be huge and consume a lot of manpower. This paper presents a rapid approach to generate multi-unit event trees based on tree combinational calculation, which leverage the existing event trees that have been built in single unit PRA. The combinatorial calculation is driven from the vector representation of event trees and can screen out the invalid combinations automatically by applying the proposed principles. A case study is presented in this paper with a nuclear power plant having 2 reactor modules under the Loss-Of-Offsite-Power initiating event to demonstrate the feasibility of the approach. The proposed approach has been realized by the software MUETBASE which can significantly improve the working efficiency and correctness of multi-unit event tree modelling. • The proposed method can help to evaluate the risk of plants with multiple units. • A multi-unit event tree generation method by combining single-unit ones is proposed. • The combinatorial calculation is realized via a vector representation of event tree. • Principles for screening valid combinations are provided. • A case study is also presented to demonstrate the feasibility of this method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01491970
Volume :
162
Database :
Academic Search Index
Journal :
Progress in Nuclear Energy
Publication Type :
Academic Journal
Accession number :
168585367
Full Text :
https://doi.org/10.1016/j.pnucene.2023.104781