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Science & Global Security
The Technical Basis for Arms Control, Disarmament, and Nonproliferation Initiatives
Volume 31, 2023 - Issue 3
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Articles

Utilizing a Virtual Sodium-Cooled Fast Reactor Digital Twin to Aid in Diversion Pathway Analysis for International Safeguards Applications

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Pages 137-161 | Received 17 Oct 2022, Accepted 06 Dec 2023, Published online: 02 Feb 2024
 

Abstract

Digital twin technology can improve the effectiveness of international safeguards inspectors by providing a tool that can perform an accurate acquisition pathway analysis, identify pathway indicators, develop required sensors to detect indicators, and monitor facilities in real time using critical data streams that benefit from this safeguards-by-design approach. Safeguards inspectors are required to visit facilities and verify the nuclear material to ensure no diversion has taken place and to detect facility misuse; however, this analysis and verification effort is time consuming, and with limited funding, it is imperative that time spent at a nuclear facility is focused on key areas. We developed a virtual digital twin of two general sodium-cooled fast reactors and explored diversion and misuse scenarios to determine how a digital twin could provide inspectors with an understanding of how proliferation may occur and where the most likely areas for proliferation would be. For each of the three reactors, an optimization algorithm was able to find core designs that would be difficult to detect via sensors alone; however, a machine learning adapter provided by the digital twin was able to show general trends in where proliferation is likely to take place.

Acknowledgments

This manuscript has been authored by Battelle Energy Alliance, LLC under Contract No. DE-AC07-05ID14517 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. This research made use of the resources of the High-Performance Computing Center at Idaho National Laboratory, which is supported by the Office of Nuclear Energy of the U.S. Department of Energy and the Nuclear Science User Facilities under contract no. DE-AC07-05ID14517. The authors would like to thank the editors and reviewers for their insights and comments. Their efforts significantly added to the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

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5 IAEA, “Safety of Nuclear Power Plants: Design,” International Atomic Energy Agency, Vienna, 2016; IAEA, “Technical Challenges in the Application and Licensing of Digital Instrumentation and Control Systems in Nuclear Power Plants,” International Atomic Energy Agency, Vienna, 2015; IAEA, “Options to Enhance Proliferation Resistance of Innovative Small and Medium Sized Reactors,” International Atomic Energy Agency, Vienna, 2014.

6 “Digital Engineering Strategy,” Department of Defense, Washington, D.C., 2018.

7 V. Yadav et al., “The State of Technology of Application of Digital Twins,” U.S. Nuclear Regulatory Commission, Washington, D.C., 2021; V. Yadav et al., “Technical Challenges and Gaps in Digital-Twin-Enabling Technologies for Nuclear Reactor Applications,” U.S. Nuclear Regulatory Commission, Washington, D.C., 2021.

8 Ibid

9 P. Apte, “Digital Twins of Nuclear Power Plants,” The American Society of Mechanical Engineers, 2021. [Online]. Available: https://www.asme.org/topics-resources/content/digital-twins-of-nuclear-power-plants; L. Thompson, “Argonne to explore how digital twins may transform nuclear energy with $8 million from ARTP-E’s GEMINA program,” Argonne National Laboratory, 2020. [Online]. Available: https://www.anl.gov/article/argonne-to-explore-how-digital-twins-may-transform-nuclear-energy-with-8-million-from-arpaes-gemina; B. Kochunas and X. Huan, “Digital Twin Concepts with Uncertainty for Nuclear Power Applications,” Energies 14 (2021): 4235.

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15 IAEA, “IAEA Safeguards Glossary,” IAEA, Vienna, 2022.

16 Ibid.

17 Ibid.

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19 R. H. Stewart et al., “Examination of Diversion and Misuse Detection for a Generalized Sodium-Cooled Fast Test Reactor,” Journal of Nuclear Material Management 2 (2022).

20 N. Martin, S. Stewart and S. Bays, “A multiphysics model of the versatile test reactor based on the MOOSE framework.”.

21 R. H. Stewart et al., “Examination of Diversion and Misuse Detection for a Generalized Sodium-Cooled Fast Test Reactor.”

22 S. Nomoto, H. Yamamoto, Y. Sekiguchi and Tamura, “Measurement of subassembly outlet coolant temperature in the JOYO experimental fast reactor,” Nuclear Engineering and Design 62 (1980): 233–239; C. Day, “FFTF core and primary sodium circuit instrumentation,” Hanford Engineering Development Laboratory, HEDL-SA-1082, Richland, WA, 1975; A. Chenu, R. Adams, K. Mikityuk and R. Chawla, “Analysis of selected Phenix EOL tests with the FAST code system – Part I: Control-rod-shift experiments,” Annals of Nuclear Energy 49 (2012): 182–190.

23 K. Korsah et al, “Assessment of Sensor Technology for Advanced Reactors,” Oak Ridge National Laboratory, ORNL/TM-2016/337, Oak Ridge, 2016; Nuclear Regulatory Commission, “Westinghouse Technology Systems Manual (R304P), Section 3.1.4, Control Rod Drive Mechanisms,” Nuclear Regulatory Commission, Technical Training Center; C. Day, “FFTF core and primary sodium circuit instrumentation,” Hanford Engineering Developement Laboratory, Richland, 1975.

24 R. H. Stewart et al., “Examination of Diversion and Misuse Detection for a Generalized Sodium-Cooled Fast Test Reactor.”.

25 F. Pedregosa et al, “Scikin-learn: Machine learning in python,” The Journal of Machine Learning Research 12 (2011): 2825–2830.

26 M. Ribeiro, S. Singh and C. Guestrin, ““Why should i trust you?” Explaining the predictions of any classifier.,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, 2016.

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