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Title: Intelligent Modeling for Nuclear Power Plant Accident Management

Abstract

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.

Authors:
 [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:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE), Nuclear Reactor Technologies (NE-7)
OSTI Identifier:
1421650
Report Number(s):
SAND-2018-0055J
Journal ID: ISSN 0218-2130; 659736; TRN: US1801541
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
International Journal on Artificial Intelligence Tools
Additional Journal Information:
Journal Volume: 27; Journal Issue: 2; Journal ID: ISSN 0218-2130
Publisher:
World Scientific
Country of Publication:
United States
Language:
English
Subject:
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; Counterfactual Reasoning; Decision Support; Dynamic Bayesian Networks

Citation Formats

Darling, Michael Christropher, Luger, George F., Jones, Thomas B., Denman, Matthew R., and Groth, Katrina M. Intelligent Modeling for Nuclear Power Plant Accident Management. United States: N. p., 2018. Web. doi:10.1142/S0218213018500033.
Darling, Michael Christropher, Luger, George F., Jones, Thomas B., Denman, Matthew R., & Groth, Katrina M. Intelligent Modeling for Nuclear Power Plant Accident Management. United States. doi:10.1142/S0218213018500033.
Darling, Michael Christropher, Luger, George F., Jones, Thomas B., Denman, Matthew R., and Groth, Katrina M. Thu . "Intelligent Modeling for Nuclear Power Plant Accident Management". United States. doi:10.1142/S0218213018500033. https://www.osti.gov/servlets/purl/1421650.
@article{osti_1421650,
title = {Intelligent Modeling for Nuclear Power Plant Accident Management},
author = {Darling, Michael Christropher and Luger, George F. and Jones, Thomas B. and Denman, Matthew R. and Groth, Katrina M.},
abstractNote = {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.},
doi = {10.1142/S0218213018500033},
journal = {International Journal on Artificial Intelligence Tools},
number = 2,
volume = 27,
place = {United States},
year = {2018},
month = {3}
}

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Works referenced in this record:

Dynamic operator actions analysis for inherently safe fast reactors and light water reactors
journal, January 1988


Entropy and MDL discretization of continuous variables for Bayesian belief networks
journal, January 2000


Nuclear power plant fault diagnosis using neural networks with error estimation by series association
journal, January 1996

  • Keehoon Kim, ; Bartlett, E. B.
  • IEEE Transactions on Nuclear Science, Vol. 43, Issue 4
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