BibTeX

@techreport{Boring2015_RISMC,
title = {A Research Roadmap for Computation-Based Human Reliability Analysis},
author = {Ronald Boring and Diego Mandelli and Jeffery Joe and Curtis Smith and Katrina Groth},
year = {2015},
date = {2015-07-01},
number = {INL/EXT-15-36051},
address = {Idaho Falls, ID},
institution = {Idaho National Laboratory},
abstract = {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 computation-based 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.},
keywords = {Bayesian Networks, causal models, Dynamic PRA, Human Reliability Analysis (HRA), nuclear power, Probabilistic risk assessment (PRA)},
pubstate = {published},
tppubtype = {techreport}
}

Abstract

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 computation-based 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.