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This content will become publicly available on March 29, 2019

Title: Intelligent Modeling for Nuclear Power Plant Accident Management

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.
 [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)
Publication Date:
Report Number(s):
Journal ID: ISSN 0218-2130; 659736; TRN: US1801541
Grant/Contract Number:
Accepted Manuscript
Journal Name:
International Journal on Artificial Intelligence Tools
Additional Journal Information:
Journal Volume: 27; Journal Issue: 2; Journal ID: ISSN 0218-2130
World Scientific
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
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; Counterfactual Reasoning; Decision Support; Dynamic Bayesian Networks
OSTI Identifier: