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Intelligent Modeling for Nuclear Power Plant Accident Management

Journal Article · · International Journal on Artificial Intelligence Tools
 [1];  [2];  [2];  [3];  [4]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Univ. of New Mexico, Albuquerque, NM (United States)
  2. Univ. of New Mexico, Albuquerque, NM (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  4. Univ. of Maryland, College Park, MD (United States)
+ Show Author Affiliations

This study 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.

Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE), Nuclear Reactor Technologies (NE-7)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1421650
Report Number(s):
SAND--2018-0055J; 659736
Journal Information:
International Journal on Artificial Intelligence Tools, Journal Name: International Journal on Artificial Intelligence Tools Journal Issue: 2 Vol. 27; ISSN 0218-2130
Publisher:
World ScientificCopyright Statement
Country of Publication:
United States
Language:
English

References (4)

Entropy and MDL discretization of continuous variables for Bayesian belief networks journal January 2000
Dynamic operator actions analysis for inherently safe fast reactors and light water reactors journal January 1988
An Introduction to Variational Methods for Graphical Models journal January 1999
Nuclear power plant fault diagnosis using neural networks with error estimation by series association journal January 1996

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Related Subjects

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
Counterfactual Reasoning
Decision Support
Dynamic Bayesian Networks