Dynamic Event Tree advancements and control logic improvements

Abstract

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, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees).The main subject of this report is to show the activities that have been recently accomplished:-Migration of the RAVEN/RELAP-7 control logic system into MOOSE, and -Development of advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year a migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework.The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named "Hybrid Dynamic Event Tree" (HDET) and its Adaptive version "Adaptive Hybrid Dynamic Event Tree" (AHDET). Among different types of uncertainties, it is possible to discern them two main types: aleatory and epistemic. The classical Dynamic Event Tree is in charge of treating the first type of uncertainties (i.e., aleatory); the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties is treated by an initial Monte Carlo sampling (MCDET). For each Monte Carlo sample (i.e., sampling of the set of the epistemic variables), a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the input space characterized 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 forward sampling strategies currently available in RAVEN. 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.The Dynamic Event Tree is a good fit to develop a goal oriented sampling strategy. The DET is used to drive a Limit Surface search. The methodology that has been developed by the authors last year performs a Limit Surface search in the aleatory space only. This report documents how this approach has been extended in order to consider the epistemic space interacting with the Hybrid Dynamic Event Tree methodology.iii CONTENTS

Enhancements to the RAVEN code in FY16

“…In FY 15 RAVEN capabilities were extended to provide the tools to the engineers to better understand the reasons/drivers of the system responses by adding advanced sampling strategies (e.g. hybrid dynamic event tree and adaptive hybrid dynamic even tree) [1], advanced static data mining capabilities (e.g. clustering, principal component analysis, manifold learning) [2], and ways to connect multiple reduced order model (able to reproduce scalar figure of merits) in order to create ensemble of models.…”

“…That is, finding points on a discretized domain that bound the limit surface from above and/or below. RAVEN provides acceleration schemes for optimizing such limit surface searches by utilizing surrogate models and a grid-based discretization [1,54]. In d-dimensional space, this amounts to n d evaluations of a grid with n points per dimension.…”