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Digital Twin for Multi-criteria Decision-Making Framework to Accelerate Fuel Qualification for Accident Tolerant Fuel Concepts

In: Handbook of Smart Energy Systems

Author

Listed:
  • Kazuma Kobayashi

    (Missouri University of Science and Technology)

  • Brandon Bloss

    (Missouri University of Science and Technology)

  • Alexander Foutch

    (Missouri University of Science and Technology)

  • Brenden Kelly

    (Missouri University of Science and Technology)

  • Ayodeji Alajo

    (Missouri University of Science and Technology)

  • Carlos H. C. Giraldo

    (Missouri University of Science and Technology)

  • Dinesh Kumar

    (University of Bristol)

  • Syed Alam

    (Missouri University of Science and Technology)

Abstract

Accident-tolerant fuels and their licensing are one of the top priority strategic areas under “US Nuclear Regulation Committee (NRC) Systems Analysis Research Activities.” In addition, United States Department of Energy (DOE) has given significant attention for the advanced novel fuel, which can increase the burnup while exhibiting superior accident tolerance under “DOE Accident Tolerant Fuel Program” (under Fuel Cycle Research R&D). Therefore, this chapter focuses on the advanced composite accident-tolerant fuel systems. This chapter explains the integration of experiments with computational research efforts and data availability for the challenging qualification effort for accident-tolerant fuel concepts leveraging existing US DOE Accident-Tolerant Fuel Program’s industrial information. An overview of conventional empirical modeling reliance and its limitations in nuclear fuel development is also explained. The explanation of composite accident-tolerant fuel concepts leads to the concept development of digital twin and material twin technologies as a means to accelerate fuel qualification method, which can be leveraged in developing and evaluating accident-tolerant fuel system. Most importantly, digital twin will pave the way for multi-criteria decision-making and risk-informed framework for both US DOE and US NRC for the new generation of accident-tolerant fuel concepts.

Suggested Citation

  • Kazuma Kobayashi & Brandon Bloss & Alexander Foutch & Brenden Kelly & Ayodeji Alajo & Carlos H. C. Giraldo & Dinesh Kumar & Syed Alam, 2023. "Digital Twin for Multi-criteria Decision-Making Framework to Accelerate Fuel Qualification for Accident Tolerant Fuel Concepts," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 1217-1238, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_160
    DOI: 10.1007/978-3-030-97940-9_160
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