A development of methodology for assessing the inter-unit common cause failure in multi-unit PSA model

lThe inter-unit CCF is one of the inter-unit dependencies considered in MUPSA.lWe proposed two methods for assessing inter-unit CCF using the existing CCF data.lAs a case study, we performed the MUPSA using inter-unit CCF events of EDGs.lIt is expected that these methods would contribute to developi...

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Veröffentlicht in:Reliability engineering & system safety 2020-11, Vol.203, p.107012, Article 107012
Hauptverfasser: Jang, Seunghyun, Jae, Moosung
Format: Artikel
Sprache:eng
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Zusammenfassung:lThe inter-unit CCF is one of the inter-unit dependencies considered in MUPSA.lWe proposed two methods for assessing inter-unit CCF using the existing CCF data.lAs a case study, we performed the MUPSA using inter-unit CCF events of EDGs.lIt is expected that these methods would contribute to developing the MUPSA model. The inter-unit common cause failure (CCF) is an event that multiple components are failed simultaneously by common factors may existent in nuclear power plants on a site. The inter-unit CCF can be identified as one of the inter-unit dependencies that has an impact on the multi-unit probabilistic safety assessment (MUPSA). For instance, it may adversely affect the accident mitigation in accident scenarios of multiple units. Therefore, the MUPSA model should be developed considering the inter-unit CCF. In this study, we proposed two methods with the Swain's dependency model and the CCF data in the single-unit PSA (SUPSA) to calculate the inter-unit CCF. The Swain's dependency model is used to calculate the conditional probability considering the dependency level between each CCF event in two different units. To perform the case study, the inter-unit CCF events of emergency diesel generators (EDGs) and alternate AC diesel generators (AAC DGs) in 4 units of different pressurized water reactor (PWR) types were analysed in the case of the multi-unit loss of off-site power (MULOOP). It is expected that methodologies in this study will contribute to the multi-unit level 1 PSA model development as well as evaluating the site risk in the future.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2020.107012