摘要
反应堆不同工况堆芯瞬态热工水力参数准确性直接影响反应堆安全性,快速、准确预测关键热工参数变化趋势有助于提高反应堆安全性。本文提出一种长短期记忆神经网络(Long Short-Term Memory,LSTM)与卷积神经网络(Convolutional Neural Networks,CNN)耦合的Seq2Seq(Sequence to Sequence)神经网络模型,运用小波分解法对热工参数数据预处理,通过子通道程序SUBCHANFLOW生成中国实验快堆(China Experimental Fast Reactor,CEFR)数据样本,使用秩和比法(Rank-sum Ratio,RSR)对结果进行综合评价得出一种最优的预测方案。最后通过基于时间序列的K交叉折叠验证法、自助法对该方案进行泛化能力分析。研究结果表明:耦合CNN-LSTM的Seq2Seq神经网络模型预测性能最优,其具有较高的精度,更强的拟合能力,最大平均相对误差为0.552%。本文构建的模型方法能够快速提取时间序列特征,泛化能力强,对于预测反应堆关键热工参数具有一定参考意义。
[Background]The accuracy of transient thermal-hydraulic parameters within different operational states of a reactor core is crucial for reactor safety.Rapid and precise prediction of key thermal parameter trends is essential for enhancing reactor safety.[Purpose]This study aims to propose a novel prediction method of reactor transient thermal-hydraulic parameters based on Sequence-to-Sequence(Seq2Seq)neural network model for improving the accuracy and speed of predicting thermal parameters to ensure the safe operation of nuclear power plants.[Methods]Firstly,Long Short Term Memory(LSTM)neural network was coupled with the Convolutional Neural Networks(CNN)to form a Seq2Seq(Sequence to Sequence)neural network model,and the wavelet decomposition was applied to preprocessing thermal parameter data.Then,the SUBCHANFLOW sub-channel program was employed to generate data samples from the China experimental fast reactor(CEFR),and results were comprehensively evaluated using the rank-sum ratio(RSR)method to derive an optimal prediction scheme.Finally,the generalization ability of this scheme was further assessed through time-series-based K-fold cross-validation and bootstrapping methods.[Results]The coupled CNN-LSTM Seq2Seq neural network model exhibits superior predictive performance,with high accuracy and robust fitting capabilities.The maximum average relative error recorded is 0.552%.[Conclusions]The developed Seq2Seq model in this study efficiently extracts time series features and demonstrates strong generalization capabilities,providing a valuable reference for predicting critical thermal parameters in reactors.
作者
陈镜宇
刘喜洋
杨腾伟
赵鹏程
刘紫静
CHEN Jingyu;LIU Xiyang;YANG Tengwei;ZHAO Pengcheng;LIU Zijing(School of Nuclear Science and Technology,University of South China,Hengyang 421001,China)
出处
《核技术》
北大核心
2025年第7期232-240,共9页
Nuclear Techniques
