Advance Liquid Metal Reactor Discrete Dynamic Event Tree/Bayesian Network Analysis and Incident Management Guidelines (Risk Management for Sodium Fast Reactors)
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 self-correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design 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 beyond design basis seismic event with the intend of exploring two issues : (1) can human intervention either improve or worsen the potential end states and (2) can a Bayesian Network be constructed to infer the state of the reactor to inform (1). ACKNOWLEDGEMENTS The authors would like to acknowledge the U.S. Department of Energy's Office of Nuclear Energy for funding this research through Work Package SR-14SN100303 under the Advanced Reactor Concepts program. The authors also acknowledge the PRA teams at Argonne National Laboratory, Oak Ridge National Laboratory, and Idaho National Laboratory for their continue d contributions to the advanced reactor PRA mission area.
- Publication Date:
- OSTI Identifier:
- Report Number(s):
- DOE Contract Number:
- Resource Type:
- Technical Report
- Research Org:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org:
- USDOE Office of Nuclear Energy (NE), Nuclear Reactor Technologies (NE-7)
- Country of Publication:
- United States
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