The main subject of this paper is about the development of a Dynamic Event Tree (DEl) sampler named "Hybrid Dynamic Event Tree" HD.E7. As other authors have already reported, among the dlirent type of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Even: Tree is in charge of treating the first class (aleatory) uncertainties: the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated lv an initial MOnte Carlo sampling MCDET1.
From each Monte Carlo sample, a DET analysis is ran (ln total, N :rees1. The Monte carlo employs a pre-sampling of the input space characterized b,v epistemic uacertanties. The consequent Dynamic Event Tree performs the exploration of the aleatc'ry space. In the R4 PEN code, a more general approach has been developed, flat limiting the exploration qf the epistemic space through a Monte Carlo method but using all the once-through sampling strategies RAVEN currently employs The user con combine a Latin Hyper Cube, Grid, Stratfled and Monte carlo sampling in order to explore the epistemic space, without any limitation. From this pre-sampllng, the Dynamic Event Tree sampler starts its aleatory space exploration.Ī computation-based human reliability analysis framework called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) has been developed as part of the Risk Informed Safety Margin Characterization (RISMC) pathway within the U.S. Department of Energy's Light Water Reactor Sustainability Program that aims to extend the life of the currently operating fleet of U.S. HUNTER is a flexible hybrid approach that functions as a framework for dynamic modeling, including a simplified model of human cognition-a virtual operator-that produces relevant outputs such as the human error probability (HEP), time spent on task, or task decisions based on relevant plant evolutions.
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