Development of logical structure for multi-unit probabilistic safety assessment

Lim, Ho-Gon; Kim, Dong-San; Han, Sang Hoon; Yang, Joon-Eon

doi:10.1016/j.net.2018.10.012

Cited by 19 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, f(X, B) in Equation ( 3) can be converted to Equation (14). Please note that the terms on the right-hand side in Equation ( 14) are explicitly mutually exclusive since X c i /X c i = 0.…”

Section: Exclusive Modeling Of One Group Of Partitioning Eventsmentioning

confidence: 99%

“…The initial MUPSA studies [2,3] were performed due to the gradually increasing concern regarding multi-unit nuclear accidents. After the Fukushima accident in 2011, many studies and reports on MUPSA [4][5][6][7][8][9][10][11][12][13][14][15][16] were published. In 2019, the International Atomic Energy Agency (IAEA) published a technical report [17] as part of a safety report series that provides comprehensive guidance for performing MUPSA.…”

Section: Introduction 1multi-unit Probabilistic Safety Assessmentsmentioning

confidence: 99%

See 1 more Smart Citation

A Method to Avoid Underestimated Risks in Seismic SUPSA and MUPSA for Nuclear Power Plants Caused by Partitioning Events

Jung

2021

Energies

View full text Add to dashboard Cite

Seismic probabilistic safety assessment (PSA) models for nuclear power plants (NPPs) have many non-rare events whose failure probabilities are proportional to the seismic ground acceleration. It has been widely accepted that minimal cut sets (MCSs) that are calculated from the seismic PSA fault tree should be converted into exact solutions, such as binary decision diagrams (BDDs), and that the accurate seismic core damage frequency (CDF) should be calculated from the exact solutions. If the seismic CDF is calculated directly from seismic MCSs, it is drastically overestimated. Seismic single-unit PSA (SUPSA) models have random failures of alternating operation systems that are combined with seismic failures of components and structures. Similarly, seismic multi-unit PSA (MUPSA) models have failures of NPPs that undergo alternating operations between full power and low power and shutdown (LPSD). Their failures for alternating operations are modeled using fraction or partitioning events in seismic SUPSA and MUPSA fault trees. Since partitioning events for one system are mutually exclusive, their combinations should be excluded in exact solutions. However, it is difficult to eliminate the combinations of mutually exclusive events without modifying PSA tools for generating MCSs from a fault tree and converting MCSs into exact solutions. If the combinations of mutually exclusive events are not deleted, seismic CDF is underestimated. To avoid CDF underestimation in seismic SUPSAs and MUPSAs, this paper introduces a process of converting partitioning events into conditional events, and conditional events are then inserted explicitly inside a fault tree. With this conversion, accurate CDF can be calculated without modifying PSA tools. That is, this process does not require any other special operations or tools. It is strongly recommended that the method in this paper be employed for avoiding CDF underestimation in seismic SUPSAs and MUPSAs.

show abstract

Section: Exclusive Modeling Of One Group Of Partitioning Eventsmentioning

confidence: 99%

Section: Introduction 1multi-unit Probabilistic Safety Assessmentsmentioning

confidence: 99%

A Method to Avoid Underestimated Risks in Seismic SUPSA and MUPSA for Nuclear Power Plants Caused by Partitioning Events

Jung

2021

Energies

View full text Add to dashboard Cite

show abstract

“…Historically, PSAs and risk models have principally been single-unit oriented, where the focus was the specific reactor under analysis, independent from other reactors on site. In recent years, advancements have been made to adapt the classical PSA models to cover multi-units at the same site [29,30]. Studying the nature of events in the database, we observed several instances of multi-unit occurrence, which can be attributed to one or more of the following reasons:…”

Section: Analysis Of Events At Single-and Multi-unit Sitesmentioning

confidence: 99%