基于GPR-SVR协同训练的乏燃料衰变热预测研究  

Prediction of Spent Nuclear Fuel Decay Heat Based on GPR-SVR Co-training

作　　者：刘子豪 刘彤 温欣 李懿 王蓓琪 Liu Zihao;Liu Tong;Wen Xin;Li Yi;Wang Beiqi(School of Nuclear Science and Engineering,Shanghai Jiao Tong University,Shanghai,200240,China)

机构地区：[1]上海交通大学核科学与工程学院,上海200240

出　　处：《核动力工程》2025年第2期272-281,共10页Nuclear Power Engineering

基　　金：国家核技术开发科研项目(HNKF202314-48);中核集团“领创科研”项目(CNNC-LCKY-202242)。

摘　　要：在压水堆核电厂中,乏燃料组件的衰变热是堆芯余热的主要来源,准确预测衰变热对于反应堆冷却系统的设计和安全分析至关重要,但传统核素衰变模拟程序计算成本高,而机器学习模型由于数据不足可能存在过拟合问题。本文基于高斯过程回归(GPR)和支持向量回归(SVR)方法建立了协同训练的基础模型,生成了高质量的乏燃料衰变热虚拟数据,并与核电厂实测数据组成了混合数据集,采用混合数据集训练极限学习机(ELM)模型,对乏燃料衰变热进行了预测。结果表明,与常规的机器学习模型相比,协同训练显著提升了衰变热预测的稳定性和准确性。经过混合数据集训练后,ELM模型的预测稳定性提高了39.9%,衰变热预测结果的均方根误差(RMSE)比传统核素衰变模拟程序低25.7%。本研究提出的方法可为解决核工程领域存在的小数据集问题提供新思路。The decay heat released by spent fuel assemblies is the main source of reactor core waste heat in PWR nuclear power plants.Accurate prediction of decay heat is crucial for the design and safety analysis of the nuclear power plant cooling system.However,the calculation cost of traditional nuclide decay simulation code is high,and the machine learning model may have overfitting problems due to insufficient data.This study establishes a co-training model based on Gaussian Process Regression(GPR)and Support Vector Regression(SVR)to generate high-quality virtual decay heat samples.These virtual samples,combined with measured decay heat data,form a mixed dataset,which is used to train an Extreme Learning Machine(ELM)model for decay heat prediction.The results show that,compared with the conventional machine learning model,the cotraining approach significantly enhances the stability and accuracy of decay heat predictions.After training on the mixed dataset,the prediction stability of the ELM model increased by 39.9%,and the RMSE of the predicted decay heat was 25.7%lower than that of the traditional nuclide decay simulation code.This research provides new insights for addressing the small sample problem in the field of nuclear engineering.

关 键 词：衰变热预测 虚拟数据 协同训练 乏燃料 高斯过程回归(GPR) 支持向量回归(SVR) 

分 类 号：TL363[核科学技术—核技术及应用]