HYBRID DYNAMIC EVENT TREE SAMPLING STRATEGY IN RAVEN CODE

摘要

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification and data mining analysis. RAVEN is currently equipped with three different sampling strategies: Once-through samplers (Monte Carlo, Latin Hyper Cube, Stratified and Grid Sampler), Adaptive Samplers (Adaptive Point Sampler) and Dynamic Event Tree samplers (Traditional and Adaptive Dynamic Event Trees). The main subject of this paper is about the development of a Dynamic Event Tree (DET) sampler named "Hybrid Dynamic Event Tree " (HDET). As other authors have already reported, among the different type of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event 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 by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the input space characterised by epistemic uncertainties. The consequent Dynamic Event Tree performs the exploration of the aleatory space. In the RAVEN code, a more general approach has been developed, not limiting the exploration of the epistemic space through a Monte Carlo method but using all the once-through sampling strategies RAVEN currently employs. The user can combine a Latin Hyper Cube, Grid, Stratified and Monte Carlo sampling in order to explore the epistemic space, without any limitation. From this pre-sampling, the Dynamic Event Tree sampler starts its aleatory space exploration.