机器学习临界热流密度模型在CTF子通道程序中方形棒束预测中的评估
Assessment of Machine Learning-Based Critical Heat Flux Models in the CTF Subchannel Code for Square Rod Bundle Prediction
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中文总结 AI 辅助
本研究评估了基于机器学习的临界热流密度模型在CTF子通道程序中的方形棒束预测性能，发现管束训练的ML模型可有效迁移至棒束应用，局部混合LUT模型表现最佳，显著优于传统方法。
中文摘要 AI 辅助
临界热流密度（CHF）的预测是核热工水力中一项关键的安全相关量，由于其与燃料性能和反应堆安全直接相关，仍然是一个重要挑战。近期研究表明，相对于传统的经验关联式和查找表（LUTs），机器学习（ML）方法可以显著提高CHF预测精度。然而，大多数基于ML的CHF模型是使用管束数据库开发和评估的，其在与反应堆相关的棒束几何结构中的适用性在很大程度上尚未被探索。本研究利用电力科学研究院（EPRI）棒束CHF数据库，评估了部署在CTF子通道程序中的基于ML的CHF模型。在局部和半局部公式中考虑了纯模型和混合残差校正模型。经过管束训练的ML CHF模型通常能良好地迁移到棒束应用中，并在大多数几何结构和运行条件下优于传统CHF方法。局部混合LUT模型产生了最强的整体性能，而半局部纯ML模型仍具有高度竞争力。与Bowring关联式、W-3关联式和2006年Groeneveld LUT的比较表明，即使模型仅使用管束数据训练，棒束CHF预测也能实现显著改进。这些发现提供了在生产级子通道分析环境中对方形棒束中基于ML的CHF模型进行的首批大规模评估之一，并支持其在反应堆热工水力分析中的更广泛应用。
英文摘要
The prediction of critical heat flux (CHF), a key safety-related quantity in nuclear thermal hydraulics, remains an important challenge due to its direct relationship with fuel performance and reactor safety. Recent studies have demonstrated that relative to traditional empirical correlations and lookup tables (LUTs), machine learning (ML) methods can substantially improve CHF prediction accuracy. Most ML-based CHF models, however, have been developed and evaluated using tube databases, leaving their applicability to reactor-relevant rod bundle geometries largely unexplored. This study evaluates ML-based CHF models deployed within the CTF subchannel code using the Electric Power Research Institute (EPRI) rod bundle CHF database. Both pure and hybrid residual correction models are considered in local and semilocal formulations. The tube-trained ML CHF models generally transferred favorably to rod bundle applications and outperformed traditional CHF methods across most geometries and operating conditions. The local hybrid LUT model produced the strongest overall performance, and the semilocal pure ML model remained highly competitive. Comparison against the Bowring correlation, W-3 correlation, and 2006 Groeneveld LUT demonstrated that substantial improvements in rod bundle CHF prediction are possible even when models are trained exclusively on tube data. These findings provide one of the first large-scale assessments of ML-based CHF models in square rod bundles within a production-level subchannel analysis environment and support their broader application in reactor thermal hydraulic analysis.
发表机构
- North Carolina State University（北卡罗来纳州立大学）
- University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
- Oak Ridge National Laboratory（橡树岭国家实验室）
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