The prediction of critical heat flux (CHF) using machine learning (ML) approaches has become a highly active research activity in recent years, the goal of which is to build models more accurate than current conventional approaches such as empirical correlations or lookup tables (LUTs). Previous work developed and deployed tube-based pure and hybrid ML models in the CTF subchannel code, however, full-scale reactor core simulations require the use of rod bundle geometries. Unlike isolated subchannels, rod bundles experience complex thermal hydraulic phenomena such as channel crossflow, spacer grid losses, and effects from unheated conductors. This study investigates the generalization of ML-based CHF prediction models in rod bundles after being trained on tube-based CHF data. A purely data-driven DNN and two hybrid bias-correction models were implemented in the CTF subchannel code and used to predict CHF location and magnitude in the Combustion Engineering 5-by-5 bundle CHF test series. The W-3 correlation, Bowring correlation, and Groeneveld LUT were used as baseline comparators. On average, all three ML-based approaches produced magnitude and location predictions more accurate than the baseline models, with the hybrid LUT model exhibiting the most favorable performance metrics.

翻译：近年来，利用机器学习方法预测临界热通量已成为高度活跃的研究方向，其目标是构建比当前经验关联式或查值表等传统方法更精确的模型。先前研究在CTF子通道程序中开发并部署了基于圆管的纯机器学习模型与混合机器学习模型，然而全尺寸堆芯模拟需要使用棒束几何结构。与孤立子通道不同，棒束会经历复杂的热工水力现象，如通道横流、定位格架损失以及未加热导体的影响。本研究探讨了基于机器学习临界热通量预测模型在管状临界热通量数据训练后向棒束的泛化能力。我们在CTF子通道程序中实现了纯数据驱动的深度神经网络和两种混合偏差校正模型，并用于预测燃烧工程5×5棒束临界热通量测试系列中的临界热通量位置与幅值。研究以W-3关联式、Bowring关联式和Groeneveld查值表作为基准比较对象。平均而言，所有三种基于机器学习的方法在幅值与位置预测上均比基准模型更准确，其中混合查值表模型展现出最优的性能指标。