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Abstract HI217Full Paper + Presentation

Sequential Uncertainty Modeling for Unfolding Phenomena (SUM-UP) method for estimating probabilities of events specific to Level 2 MUPRA

Authors

PrimaryEishiro Higo— Central Research Institute of Electric Power Industry · higo3791@criepi.denken.or.jp
Co-authorh-miura@criepi.denken.or.jp— h-miura@criepi.denken.or.jp Edit Profile
During the Fukushima Daiichi Nuclear Power Plant accident, various events that affected accident mitigation across multiple units—hereafter referred to as multi-unit (MU) events—occurred. Examples of MU events include resource conflicts due to shared equipment and interruptions of outdoor work caused by elevated radiation levels and/or hydrogen explosions. Such MU events can hinder or even prevent mitigation actions for multiple units and therefore need to be incorporated into probabilistic risk assessment (PRA) to quantify site-level risk.
We have been developing a Level 1 multi-unit PRA (MUPRA) methodology, including techniques to evaluate MU events relevant to Level 1 PRA. To extend this framework to Level 2 MUPRA, it is necessary to consider a new class of MU events (L2 MU events) that interrupt or prevent mitigation actions due to phenomena occurring primarily after the loss of containment integrity. In conventional Level 2 single-unit PRA, assessments have largely focused on containment failure within an individual unit, and L2 MU events, which occur after containment failure, have been largely overlooked.
This study proposes an evaluation framework for L2 MU events that enables probabilistic assessment under current technological limitations. In particular, we propose a Sequential Uncertainty Modeling for Unfolding Phenomena (SUM-UP) method to systematically model uncertainty propagation while accounting for the key characteristics of L2 MU events—namely, that they consist of cascading phenomena and may involve phenomena for which analytical methods and knowledge differ in maturity.
Status: The abstract has been accepted!
📄Paper Status: Paper has been uploaded and is under review — View submitted paper
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