Development and Uncertainty Quantification of Machine Learning Models for Critical Heat Flux predictions

Michele Cazzola

Development and Uncertainty Quantification of Machine Learning Models for Critical Heat Flux predictions.

Rel. Tatiana Tommasi, Alberto Ghione, Lucia Sargentini, Riccardo Finotello, Julien Nespoulous. Politecnico di Torino, Master of science program in Computer Engineering, 2025

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Abstract

Critical Heat Flux (CHF) represents a concern for nuclear safety, as it leads to a rapid drop down in the heat transfer between a heated surface and the liquid coolant in the core of nuclear reactors. This could cause several issues to the system, including structural damage and release of radioactive material. The main challenges related to CHF predictions are the highly non-linear relationships with the physical features it depends on, and the two different underlying, microscopic phenomena that lead to CHF: Departure from Nucleate Boiling (DNB) and dryout, occurring under different conditions but not distinguishable externally, both resulting in the same outcome.

Both for these reasons and for the inaccuracy and limited applicability of current physical correlations, the OECD/NEA Expert Group on Reactor Systems Multi-Physics (EGMUP) has established a task force to develop machine learning (ML) strategies for CHF regression, along with uncertainty quantification (UQ) techniques to assess the reliability and the robustness of these models