Risk-Informed Safety Margins Characterization (RISMC) Pathway Technical Program Plan

Smith, Curtis; Rabiti, Cristian; Szilard, Ronaldo

doi:10.2172/1472099

Cited by 19 publications

(17 citation statements)

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“…(Risk Analysis in a Virtual ENviroment), under the combined support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) and Light Water Reactor Sustainability (LWRS) programs, is increasing its capabilities to perform stochastic analysis of dynamic systems. This supports the goal of providing the tools needed by the Risk Informed Safety Margin Characterization (RISMC) path-lead [5] under the Department of Energy (DOE) LWRS program. In particular, the development of RAVEN in conjunction with the thermalhydraulic code RELAP-7 [6], will allow the deployment of advanced methodologies for nuclear power plant (NPP) safety analyses at the industrial level.…”

Section: Figuresmentioning

confidence: 54%

Dynamic Event Tree advancements and control logic improvements

Alfonsi

Rabiti

Mandelli

et al. 2015

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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

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Section: Figuresmentioning

confidence: 54%

Dynamic Event Tree advancements and control logic improvements

Alfonsi

Rabiti

Mandelli

et al. 2015

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“…As discussed, the goal of RISMC R&D Pathway is to support plant decisions using the advanced risk-informed margins management strategy with the aim to improve economics, reliability, and sustain safety of current NPPs [5]. The RISMC approach can optimize plant safety and performance via a novel interaction between probabilistic risk simulation and mechanistic codes for plant-level physics.…”

Section: Rismc Methodologymentioning

confidence: 99%

Advanced Validation Risk-Informed Approach for the Flooding Tool Based Upon Smooth Particle Hydrodynamics: Validation and Development Status of NEUTRINO

Yoo

Choi

2018

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This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for any third party's use, or the results of such use, of any information, apparatus, product, or process disclosed in this report, or represents that its use by such third party would not infringe privately owned rights.

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“…One area of the U.S. Department of Energy's (DOE's) Light Water Reactor Sustainability (LWRS) project is the Risk Informed Safety Margin Characterization (RISMC) pathway (Smith, Rabiti & Martineau, 2011). RISMC research centers on understanding not just the frequency of an event like core damage, but also how close the plant is (or is not) to key safety-related events and how the plant might increase the safety margin.…”

Section: Overview Of Risk Informed Safety Margin Characterizationmentioning

confidence: 99%

A Research Roadmap for Computation-Based Human Reliability Analysis

Boring

Mandelli

Joe

et al. 2015

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The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and enhance the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermohydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is often secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computationbased approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full-scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER). HUNTER incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to account for human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.

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Risk-Informed Safety Margins Characterization (RISMC) Pathway Technical Program Plan

Cited by 19 publications

References 0 publications

Dynamic Event Tree advancements and control logic improvements

Dynamic Event Tree advancements and control logic improvements

Advanced Validation Risk-Informed Approach for the Flooding Tool Based Upon Smooth Particle Hydrodynamics: Validation and Development Status of NEUTRINO

A Research Roadmap for Computation-Based Human Reliability Analysis

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