Intelligent Modeling for Nuclear Power Plant Accident Management

Journal Article · Thu Mar 29 00:00:00 EDT 2018 · International Journal on Artificial Intelligence Tools

DOI: https://doi.org/10.1142/S0218213018500033 · OSTI ID:1421650

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

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