BibTeX

@Article{GrothJRR2019,
author = {Groth, Katrina M. and Denman, Matthew R. and Jones, Thomas B. and Darling, Michael C. and Luger, George F.},
title = {Building and using dynamic risk-informed diagnosis procedures for complex system accidents},
journal = {Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability},
year = {2020},
volume = {234},
number = {1},
pages = {193-207},
note = {Accepted 1 September 2018.},
doi = {10.1177/1748006X18803836},
abstract = {Severe accidents pose unique challenges for nuclear power plant operating crews, including limitations in plant status information and lack of detailed diagnosis and response planning support. Advances in severe accident simulation and Dynamic Probabilistic Risk Assessment (PRA) provide an opportunity to garner detailed insight into severe accidents. In this manuscript, we demonstrate how to build and use a framework which leverages dynamic PRA, simulation, and dynamic Bayesian networks to provide real-time diagnostic support for severe accidents in a nuclear power plant. This paper presents a prototype model for diagnosing reactor system states associated with loss of flow and transient overpower accidents after an earthquake or other unpredictable events in a generic sodium fast reactor. We discuss using this framework to create a risk-informed accident management framework called SMART Procedures. This represents a new application of risk assessment, expanding PRA techniques beyond static licensing support into dynamic and real-time software for accident diagnosis and management},
file = {:Journal Papers/GrothJRR2020_SmartProcs_published_version.pdf:PDF;:Journal Papers/GrothJRR2020_SmartProcs_acceptedversion.pdf:PDF},
groups = {SAMGs},
keywords = {Dynamic PRA, accident management, Artificial Intelligence, Bayesian Networks, Decision support systems},
owner = {kgroth},
timestamp = {2020-02-03},
}

Abstract

Accidents pose unique challenges for operating crews in complex systems such as nuclear power plants, presenting limitations in plant status information and lack of detailed monitoring, diagnosis, and response planning support. Advances in severe accident simulation and dynamic probabilistic risk assessment provide an opportunity to garner detailed insight into accident scenarios. In this article, we demonstrate how to build and use a framework which leverages dynamic probabilistic risk assessment, simulation, and dynamic Bayesian networks to provide real-time monitoring and diagnostic support for severe accidents in a nuclear power plant. We use general purpose modeling technology, the dynamic Bayesian network, and adapt it for risk management of complex engineering systems. This article presents a prototype model for monitoring and diagnosing system states associated with loss of flow and transient overpower accidents in a generic sodium fast reactor. We discuss using this framework to create a risk-informed accident management framework called Safely Managing Accidental Reactor Transients procedures. This represents a new application of risk assessment, expanding probabilistic risk assessment techniques beyond static decision support into dynamic, real-time models which support accident diagnosis and management.