Intelligent Modeling for Nuclear Power Plant Accident Management

Darling, Michael Christropher; Luger, George F.; Jones, Thomas B.; Denman, Matthew R.; Groth, Katrina M.

doi:10.1142/S0218213018500033

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:

Darling, Michael Christropher ^[1]; Luger, George F. ^[2]; Jones, Thomas B. ^[2]; Denman, Matthew R. ^[3]; Groth, Katrina M. ^[4]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Univ. of New Mexico, Albuquerque, NM (United States)
Univ. of New Mexico, Albuquerque, NM (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of Maryland, College Park, MD (United States)

Publication Date:: Thu Mar 29 00:00:00 EDT 2018

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE Office of Nuclear Energy (NE), Reactor Fleet and Advanced Reactor Development. Nuclear Reactor Technologies

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.

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                    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.  https://doi.org/10.1142/S0218213018500033

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                    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.  https://doi.org/10.1142/S0218213018500033.  https://www.osti.gov/servlets/purl/1421650.

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@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         = {Thu Mar 29 00:00:00 EDT 2018},

  month        = {Thu Mar 29 00:00:00 EDT 2018}

}

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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

Ho, Vincent; Apostolakis, George
Reliability Engineering & System Safety, Vol. 22, Issue 1-4
DOI: 10.1016/0951-8320(88)90067-1

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

Clarke, Ellis J.; Barton, Bruce A.
International Journal of Intelligent Systems, Vol. 15, Issue 1
DOI: 10.1002/(SICI)1098-111X(200001)15:1<61::AID-INT4>3.0.CO;2-O

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
DOI: 10.1109/23.531786

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Title: Intelligent Modeling for Nuclear Power Plant Accident Management

Abstract

Citation Formats

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

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