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. Department of Energy's Light Water Reactor Sustainability Program that aims to extend the life of the currently operating fleet of U.S. 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. 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. The consequent Dynamic Event Tree performs the exploration of the aleatc'ry space. The Monte carlo employs a pre-sampling of the input space characterized b,v epistemic uacertanties. From each Monte Carlo sample, a DET analysis is ran (ln total, N :rees1. 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. 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 main subject of this paper is about the development of a Dynamic Event Tree (DEl) sampler named "Hybrid Dynamic Event Tree" HD.E7. RAVEN is currently equ4ped with three dWerent sampling sfrategies: Once-through samplers (Monte Carlo, Latin Hyper Cube, Straqfled and Grid Sampler), Adaptive Samplers (Adaptive Point Sampler) and Dynamic Even: Tree samplers (Traditional and Adaptive Iiynamic Event Trees).
Its main goal is to create a mulli.purpose platform/or the deploying of oil the capabilities needed for Probabilistic Risk Assessment, uncertainty quan:flcatian and data mining analysis.
The RAVEN code has been under development at the Idaho National Laboratoiy since 2012.