BibTeX

@techreport{SAND2015-8872,
title = {Interim Status Report for Risk Management for SFRs},
author = {Zachary K Jankovsky and Matthew R Denman and Katrina M Groth and Timothy A Wheeler},
year = {2015},
date = {2015-10-01},
number = {SAND2015-8872},
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 passive, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system’s design to take advantage of natural phenomena to manage the accident. Inherently and passively safe designs are laudable, but nonetheless 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 variety of beyond design basis events with the intent of exploring the utility of a Dynamic Bayesian Network to infer the state of the reactor to inform the operators corrective actions. These inferences also serve to identify the instruments most critical to informing an operators actions as candidates for hardening against radiation and other extreme environmental conditions that may exist in an accident. This reduction in uncertainty serves to inform ongoing discussions of how small sodium reactors would be licensed and may serve to reduce regulatory risk and cost for such reactors.},
keywords = {Bayesian Networks, Decision support systems, Dynamic PRA, nuclear power, Probabilistic risk assessment (PRA), risk-informed procedures},
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 passive, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system’s design to take advantage of natural phenomena to manage the accident. Inherently and passively safe designs are laudable, but nonetheless 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 variety of beyond design basis events with the intent of exploring the utility of a Dynamic Bayesian Network to infer the state of the reactor to inform the operators corrective actions. These inferences also serve to identify the instruments most critical to informing an operators actions as candidates for hardening against radiation and other extreme environmental conditions that may exist in an accident. This reduction in uncertainty serves to inform ongoing discussions of how small sodium reactors would be licensed and may serve to reduce regulatory risk and cost for such reactors.