Machine Learning and Artificial Intelligence-Driven Multi-scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications

Shamim Hassan, Abid Hossain Khan, Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Shoaib Usman and Syed Alam ()
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Shamim Hassan: Bangladesh University of Engineering and Technology
Abid Hossain Khan: Bangladesh University of Engineering and Technology
Richa Verma: Indian Institute of Technology Delhi
Dinesh Kumar: University of Bristol
Kazuma Kobayashi: Missouri University of Science and Technology
Shoaib Usman: Missouri University of Science and Technology
Syed Alam: Missouri University of Science and Technology

A chapter in Handbook of Smart Energy Systems, 2023, pp 2131-2154 from Springer

Abstract: Abstract The concept of small modular reactor has changed the outlook for tackling future energy crises. This new reactor technology is very promising considering its lower investment requirements, modularity, design simplicity, and enhanced safety features. The application of artificial intelligence-driven multiscale modeling (neutronics, thermal hydraulics, fuel performance, etc.) incorporating Digital Twin and associated uncertainties in the research of small modular reactors is a recent concept. In this work, a comprehensive study is conducted on the multiscale modeling of accident-tolerant fuels. The application of these fuels in the light water-based small modular reactors is explored. This chapter also focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors. Finally, a brief assessment of the research gap on the application of artificial intelligence to the development of high burnup composite accident-tolerant fuels is provided. Necessary actions to fulfill these gaps are also discussed.

Keywords: Multiscale modeling; Artificial intelligence; Accident-tolerant fuel; Machine learning; Digital twin; Small modular reactor (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_149

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DOI: 10.1007/978-3-030-97940-9_149

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