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arXiv Machine Learning

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

The paper compiles and digitizes a comprehensive dataset of critical heat flux (CHF) measurements for both uniform and non‑uniform axial heating conditions, preparing it for Phase II of the OECD/NEA AI/ML CHF benchmark. Heating profiles were extracted from technical reports, interpolated onto a consistent axial mesh, validated with energy‑balance checks, and encoded in machine‑readable formats. Baseline modeling results show that classical CHF correlations perform poorly, especially for non‑uniform profiles, and that neural networks trained only on uniform data fail to generalize to spatially varying scenarios, highlighting the need for models that explicitly incorporate axial power distributions.

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