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Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology

In: Handbook of Smart Energy Systems

Author

Listed:
  • Kazuma Kobayashi

    (Missouri University of Science and Technology)

  • Dinesh Kumar

    (University of Bristol
    Missouri University of Science and Technology)

  • Matthew Bonney

    (University of Sheffield)

  • Souvik Chakraborty

    (Indian Institute of Technology Delhi)

  • Kyle Paaren

    (Idaho National Laboratory)

  • Shoaib Usman

    (Missouri University of Science and Technology)

  • Syed Alam

    (Missouri University of Science and Technology)

Abstract

As US Nuclear Regulatory Committee (NRC) recently announced machine learning (ML) and artificial intelligence (AI) will be the main research topics in the nuclear industry. One of the applications is the development of new nuclear fuels using digital twin technology, in which machine learning-based data analysis methods will significantly contribute to accelerate developments. This chapter introduces the ML-based uncertainty quantification and sensitivity analysis methods and shows its actual application to nuclear fuel development codes: a finite element-based nuclear fuel performance code BISON.

Suggested Citation

  • Kazuma Kobayashi & Dinesh Kumar & Matthew Bonney & Souvik Chakraborty & Kyle Paaren & Shoaib Usman & Syed Alam, 2023. "Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 2265-2277, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_205
    DOI: 10.1007/978-3-030-97940-9_205
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