BibTeX

@techreport{SAND2015-2484,
title = {Advance Liquid Metal Reactor Discrete Dynamic Event Tree/Bayesian Network Analysis and Incident Management Guidelines (Risk Management for Sodium Fast Reactors)},
author = {Matthew R Denman and Katrina M Groth and Jeff Cardoni and Tim Wheeler},
year = {2015},
date = {2015-04-01},
number = {SAND2015-2484},
address = {Albuquerque, NM},
institution = {Sandia National Laboratories},
abstract = {Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self-correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system’s design to manage the accident. While inherently and passively safe designs are laudable, extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues: (1) can human intervention either improve or worsen the potential end states and (2) can a Bayesian Network be constructed to infer the state of the reactor to inform (1).},
keywords = {Artificial intelligence, Bayesian Networks, Decision support systems, Dynamic PRA, nuclear power, Probabilistic risk assessment (PRA), risk-informed procedures, uncertainty},
pubstate = {published},
tppubtype = {techreport}
}

Abstract

Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self-correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system’s design to manage the accident. While inherently and passively safe designs are laudable, extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues: (1) can human intervention either improve or worsen the potential end states and (2) can a Bayesian Network be constructed to infer the state of the reactor to inform (1).