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

TL;DR

This paper explores the use of AI-driven multi-scale modeling, including Digital Twin technology, for designing and optimizing accident-tolerant fuels in small modular reactors, highlighting research gaps and future directions.

Contribution

It introduces a comprehensive approach combining AI, multi-scale modeling, and Digital Twin concepts for accident-tolerant fuel development in SMRs, addressing current research gaps.

Findings

AI enhances fuel design optimization

Multi-scale modeling improves safety assessments

Identifies key research gaps in AI applications

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 multi-scale 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.