Building and using dynamic risk-informed diagnosis procedures for complex system accidents

Katrina M Groth, Matthew R Denman, Michael C Darling, Thomas B Jones and George F Luger

Journal of Risk and Reliability, 2020, vol. 234, issue 1, 193-207

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.

Keywords: Dynamic probabilistic risk assessment; accident management; artificial intelligence; dynamic Bayesian networks; decision support systems (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:234:y:2020:i:1:p:193-207

DOI: 10.1177/1748006X18803836

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