Aggregation of Published Non-Uniform Axial Power Data for Phase II of the OECD/NEA AI/ML Critical Heat Flux Benchmark

TL;DR

This paper compiles and digitizes a comprehensive dataset of critical heat flux measurements under uniform and non-uniform axial power profiles, highlighting the limitations of classical correlations and neural networks trained only on uniform data.

Contribution

It provides curated datasets and baseline modeling results for the OECD/NEA CHF benchmark, emphasizing the need for models that incorporate spatial power variations.

Findings

Classical correlations have large errors under non-uniform heating.

Neural networks trained on uniform data do not generalize to non-uniform profiles.

Curated datasets enable advanced modeling and uncertainty quantification.

Abstract

Critical heat flux (CHF) marks the onset of boiling crisis in light-water reactors, defining safe thermal-hydraulic operating limits. To support Phase II of the OECD/NEA AI/ML CHF benchmark, which introduces spatially varying power profiles, this work compiles and digitizes a broad CHF dataset covering both uniform and non-uniform axial heating conditions. Heating profiles were extracted from technical reports, interpolated onto a consistent axial mesh, validated via energy-balance checks, and encoded in machine-readable formats for benchmark compatibility. Classical CHF correlations exhibit substantial errors under uniform heating and degrade markedly when applied to non-uniform profiles, while modern tabular methods offer improved but still imperfect predictions. A neural network trained solely on uniform data performs well in that regime but fails to generalize to spatially varying scenarios, underscoring the need for models that explicitly incorporate axial power distributions. By providing these curated datasets and baseline modeling results, this study lays the groundwork for advanced transfer-learning strategies, rigorous uncertainty quantification, and design-optimization efforts in the next phase of the CHF benchmark.