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

In: Handbook of Smart Energy Systems

Author

Listed:
  • 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)

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.

Suggested Citation

  • Kazuma Kobayashi & Dinesh Kumar & Matthew Bonney & Syed Alam, 2023. "Practical Applications of Gaussian Process with Uncertainty Quantification and Sensitivity Analysis for Digital Twin for Accident-Tolerant Fuel," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 503-514, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_191
    DOI: 10.1007/978-3-030-97940-9_191
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