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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.

This paper explores the viability of using counterfactual reasoning for impact analyses when understanding and responding to “beyond-design-basis” nuclear power plant accidents. Currently, when a severe nuclear power plant accident occurs, plant operators rely on Severe Accident Management Guidelines. However, the current guidelines are limited in scope and depth: for certain types of accidents, plant operators would have to work to mitigate the damage with limited experience and guidance for the particular situation. We aim to fill the need for comprehensive accident support by using a dynamic Bayesian network to aid in the diagnosis of a nuclear reactor's state and to analyze the impact of possible response measures. The dynamic Bayesian network, DBN, offers an expressive representation of the components and relationships that make up a complex causal system. For this reason, and for its tractable reasoning, the DBN supports a functional model for the intricate operations of nuclear power plants. In this domain, it is also pertinent that a Bayesian network can be composed of both probabilistic and knowledge-based components. Though probabilities can be calculated from simulated models, the structure of the network, as well as the value of some parameters, must be assigned by human experts. Since dynamic Bayesian network-based systems are capable of running better-than-real-time situation analyses, they can support both current event and alternate scenario impact analyses.