用机器学习提升核反应堆棒束临界热流密度预测精度
Prediction of Critical Heat Flux in Rod Bundles Using Tube-Based Hybrid Machine Learning Models in CTF
- 基于管状数据训练的混合模型迁移至棒束结构
- 混合模型在位置与数值预测上均优于传统经验公式
- 适合核热工安全模拟与先进反应堆设计参考
近年来，利用机器学习（ML）方法预测临界热流密度（CHF）成为研究热点，目标是构建比传统经验关联式或查表法（LUT）更精确的模型。此前工作已在CTF子通道代码中开发并部署了基于管状结构的纯机器学习与混合模型，但全尺度反应堆核心仿真需采用棒束几何。与孤立子通道不同，棒束存在通道间交叉流动、格架损失及未加热导体效应等复杂热工水力现象。本研究探究了在管状数据上训练的机器学习模型向棒束结构的泛化能力。在CTF子通道代码中实现了纯数据驱动的DNN和两种混合偏差校正模型，用于预测燃烧工程5×5棒束实验系列中的CHF位置与大小。以W-3关联式、Bowring关联式和Groeneveld LUT为基线对比。平均而言，所有三种基于ML的方法在位置与数值预测上均优于基线模型，其中混合LUT模型表现最优。
原文摘要 · Abstract (English)
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.
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