BibTeX

@techreport{Boring2016HUNTER,
title = {Integration of Human Reliability Analysis Models into the Simulation-Based Framework for the Risk-Informed Safety Margin Characterization Toolkit},
author = {Ronald Boring and Diego Mandelli and Martin Rasmussen and Sarah Herberger and Thomas Ulrich and Katrina Groth and Curtis Smith},
year = {2016},
date = {2016-06-01},
number = {INL/EXT-16-39015},
address = {Idaho Falls, ID},
institution = {Idaho National Laboratory},
abstract = {This report presents an application of a computation-based human reliability analysis framework called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), a method developed as part of the Risk Informed Safety Margin Characterization (RISMC) pathway within the U.S. Department of Energy’s Light Water Reactor Sustainability Program that aims to extend the life of the currently operating fleet of U.S. commercial nuclear power plants. HUNTER is a flexible hybrid approach that functions as an 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. HUNTER is the human reliability analysis counterpart to the Risk Analysis and Virtual ENvironment (RAVEN) framework used for dynamic probabilistic risk assessment. Although both RAVEN and HUNTER are still under various stages of development, this report presents a successfully integrated and implemented RAVEN-HUNTER initial demonstration. The demonstration in this report centers on a station blackout scenario, using complexity as the sole virtual operator performance-shaping factor (PSF). The implementation of RAVEN-HUNTER can be readily scaled to other nuclear power plant scenarios of interest and include additional PSFs in the future.},
keywords = {Bayesian methods, Bayesian Networks, Dynamic PRA, Human Reliability Analysis (HRA), nuclear power, Performance Shaping Factors (PSFs), Probabilistic risk assessment (PRA)},
pubstate = {published},
tppubtype = {techreport}
}

Abstract

This report presents an application of a computation-based human reliability analysis framework called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), a method developed as part of the Risk Informed Safety Margin Characterization (RISMC) pathway within the U.S. Department of Energy’s Light Water Reactor Sustainability Program that aims to extend the life of the currently operating fleet of U.S. commercial nuclear power plants. HUNTER is a flexible hybrid approach that functions as an 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. HUNTER is the human reliability analysis counterpart to the Risk Analysis and Virtual ENvironment (RAVEN) framework used for dynamic probabilistic risk assessment. Although both RAVEN and HUNTER are still under various stages of development, this report presents a successfully integrated and implemented RAVEN-HUNTER initial demonstration. The demonstration in this report centers on a station blackout scenario, using complexity as the sole virtual operator performance-shaping factor (PSF). The implementation of RAVEN-HUNTER can be readily scaled to other nuclear power plant scenarios of interest and include additional PSFs in the future.