Multi-unit Level 1 probabilistic safety assessment: Approaches and their application to a six-unit nuclear power plant site
Following a surge of interest in multi-unit risk in the last few years, many recent studies have suggested methods for multi-unit probabilistic safety assessment (MUPSA) and addressed several related aspects. Most of the existing studies though focused on two-unit nuclear power plant (NPP) sites or...
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Veröffentlicht in: | Nuclear engineering and technology 2018, 50(8), , pp.1217-1233 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Following a surge of interest in multi-unit risk in the last few years, many recent studies have suggested methods for multi-unit probabilistic safety assessment (MUPSA) and addressed several related aspects. Most of the existing studies though focused on two-unit nuclear power plant (NPP) sites or used rather simplified probabilistic safety assessment (PSA) models to demonstrate the proposed approaches. When considering an NPP site with three or more units, some approaches are inapplicable or yield very conservative results. Since the number of such sites is increasing, there is a strong need to develop and validate practical approaches to the related MUPSA. This article provides several detailed approaches that are applicable to multi-unit Level 1 PSA for sites with up to six or more reactor units. To validate the approaches, a multi-unit Level 1 PSA model is developed and the site core damage frequency is estimated for each of four representative multi-unit initiators, as well as for the case of a simultaneous occurrence of independent single-unit initiators in multiple units. For this purpose, an NPP site with six identical OPR-1000 units is considered, with full-scale Level 1 PSA models for a specific OPR-1000 plant used as the base single-unit models. Keywords: Inter-unit Dependency, Multi-unit Core Damage Frequency, Multi-unit Probabilistic Safety Assessment, Multi-unit Probabilistic Risk Assessment, Multi-unit Risk, Site Core Damage Frequency |
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ISSN: | 1738-5733 2234-358X |
DOI: | 10.1016/j.net.2018.01.006 |