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, Syed Alam

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationHandbook of Smart Energy Systems
Subtitle of host publicationVolume 1-4
PublisherSpringer
Pages2131-2154
Number of pages24
Volume1-4
ISBN (Electronic)9783030979409
ISBN (Print)9783030979393
DOIs
StatePublished - Jan 1 2023
Externally publishedYes

Keywords

  • Accident-tolerant fuel
  • Artificial intelligence
  • Digital twin
  • Machine learning
  • Multiscale modeling
  • Small modular reactor

ASJC Scopus subject areas

  • General Economics, Econometrics and Finance
  • General Business, Management and Accounting
  • General Mathematics
  • General Environmental Science
  • General Energy
  • General Engineering

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