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.

翻译：临界热流密度（CHF）标志着轻水反应堆中沸腾危机的起始点，界定了安全的热工水力运行极限。为支持OECD/NEA AI/ML CHF基准的第二阶段（该阶段引入了空间变化的功率分布），本研究汇编并数字化了一个涵盖均匀与非均匀轴向加热条件的广泛CHF数据集。加热分布曲线从技术报告中提取，通过插值映射到一致的轴向网格上，经由能量平衡检验进行验证，并以机器可读格式编码以确保基准兼容性。经典的CHF关联式在均匀加热条件下表现出显著误差，且在应用于非均匀分布时预测能力明显下降；而现代表格化方法虽提供了改进但仍不完美的预测结果。仅基于均匀数据训练的神经网络在该工况下表现良好，但无法推广至空间变化场景，这凸显了需要显式纳入轴向功率分布模型的必要性。通过提供这些经过整理的数据集及基线建模结果，本研究为CHF基准下一阶段的高级迁移学习策略、严格的不确定性量化以及设计优化工作奠定了基础。