ML and AI Approaches for Analyzing Nuclear Architecture
An important advancement has been achieved through the introduction of a comprehensive AI and ML benchmark for predicting Critical Heat Flux (CHF). CHF signifies the point at which wall heat transfer in a boiling system begins to decrease significantly. This occurrence is known as the critical boiling transition, boiling crisis, or, depending on operational conditions, departure from nucleate boiling (DNB) or dry out. The increase in wall temperature induced by CHF in a heat transfer-controlled system can accelerate wall corrosion, potentially leading to fuel rod failure in a nuclear reactor core. Predicting CHF accurately is exceptionally challenging due to the intricate dynamics of local fluid movement and heat exchange, despite its critical role as a design limit for reactor safety.
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Future Nuclear Devices and Advancements in Utilization
Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have piqued significant interest among nuclear engineers. However, the practical application and widespread adoption of ML and AI techniques for analyzing nuclear architecture are hindered by the lack of specialized benchmark exercises.
The Task Force on Artificial Intelligence and Machine Learning for Scientific Computing in Nuclear Engineering, established by the Working Party on Scientific Issues and Uncertainty Analyses of the Nuclear Science Committee within the Expert Group on Reactor Systems Multi-Physics (EGMUP), aims to fulfill the NEA’s strategic objective of “contributing to establishing a robust scientific and technical foundation for the advancement of next-generation nuclear systems and the implementation of innovations.” This Task Force will focus on developing standardized exercises targeting essential AI and ML operations across various computing domains, ranging from single physics to multi-scale and multi-physics simulations.
Future Prospects
The active involvement of the international scientific community in leveraging machine learning and artificial intelligence in nuclear engineering underscores a strong commitment to this technological frontier. The Task Force’s ultimate goal is to establish fundamental guidelines for the application of AI and ML in the nuclear engineering domain based on the insights gained from these initiatives.
Traditionally, present CHF models rely on experimental correlations tailored to specific application domains. By leveraging AI and ML techniques with an extensive experimental database provided by the US Nuclear Regulatory Commission (NRC), this benchmark aims to enhance CHF modeling. This improved modeling approach can enhance our comprehension of safety margins, paving the way for new design and operational optimization possibilities.
