Practical Applications of Gaussian Process with Uncertainty Quantification and Sensitivity Analysis for Digital Twin for Accident-Tolerant Fuel

Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney and Syed Alam ()
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Kazuma Kobayashi: Missouri University of Science and Technology
Dinesh Kumar: University of Bristol
Matthew Bonney: University of Sheffield
Syed Alam: Missouri University of Science and Technology

A chapter in Handbook of Smart Energy Systems, 2023, pp 503-514 from Springer

Abstract: Abstract The application of digital twin (DT) technology to the nuclear field is one of the challenges in the future development of nuclear energy. Possible applications of DT technology in the nuclear field are expected to be very wide: operate commercial nuclear reactors, monitor spent fuel storage and disposal facilities, and develop new nuclear systems. As the US Nuclear Regulatory Committee (NRC) recently announced, machine learning (MI) and artificial intelligence (AI) is a new domain in the nuclear field. This chapter focuses on the DT framework for developing advanced nuclear fuel and explains the utilizations of MI-based surrogate model, Gaussian process (GP) regression, in the framework.

Keywords: Machine learning; Kriging; Gaussian process; Modeling methods; Accident tolerant fuel; Nuclear power (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_191

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DOI: 10.1007/978-3-030-97940-9_191

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