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

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

doi:10.1177/1748006X18803836

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

Journal Article · Wed Oct 17 00:00:00 EDT 2018 · Proceedings of the Institution of Mechanical Engineers. Part O, Journal of Risk and Reliability

DOI:https://doi.org/10.1177/1748006X18803836· OSTI ID:1477896

^[1]; Denman, Matthew R. ^[2]; Darling, Michael C. ^[3]; Jones, Thomas B. ^[4]; Luger, George F. ^[4]

Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
Sandia National Laboratories, Albuquerque, NM, USA
Sandia National Laboratories, Albuquerque, NM, USA, University of New Mexico, Albuquerque, NM, USA
University of New Mexico, Albuquerque, NM, USA

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.

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Sponsoring Organization:: USDOE

OSTI ID:: 1477896

Journal Information:: Proceedings of the Institution of Mechanical Engineers. Part O, Journal of Risk and Reliability, Journal Name: Proceedings of the Institution of Mechanical Engineers. Part O, Journal of Risk and Reliability Journal Issue: 1 Vol. 234; ISSN 1748-006X

Publisher:: SAGE PublicationsCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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