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

Kazuma Kobayashi, Brandon Bloss, Alexander Foutch, Brenden Kelly, Ayodeji Alajo, Carlos H. C. Giraldo, Dinesh Kumar and Syed Alam ()
Additional contact information
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

A chapter in Handbook of Smart Energy Systems, 2023, pp 1217-1238 from Springer

Abstract: 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.

Keywords: Digital twin; Artificial intelligence; Accident-tolerant fuel; Multi-criteria Decision-making; Accelerate fuel qualification (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_160

Ordering information: This item can be ordered from
http://www.springer.com/9783030979409

DOI: 10.1007/978-3-030-97940-9_160

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().