基于改进DMD和LSTM的铅铋快堆上腔室热分层降阶分析模型研究  

Reduced-order analysis model for thermal stratification in the upper plenum of lead-bismuth fast reactors based on improved DMD and LSTM

作　　者：温从义 邓俊杰 刘紫静[1] 李卫 赵鹏程[1] WEN Congyi;DENG Junjie;LIU Zijing;LI Wei;ZHAO Pengcheng(School of Nuclear Science and Technology,University of South China,Hengyang 421000,China)

机构地区：[1]南华大学核科学技术学院,衡阳421000

出　　处：《核技术》2025年第4期139-149,共11页Nuclear Techniques

基　　金：核反应堆系统设计技术重点实验室运行基金(No.KFKT-05-FW-HT-20220014)资助。

摘　　要：事故紧急停堆后铅铋快堆上腔室发生的热分层现象对堆内结构完整性和自然循环余热排出能力具有显著影响。基于计算流体动力学(Computational Fluid Dynamics,CFD)方法开展热分层现象研究,存在计算资源开销大、耗时长等问题,而现有的标准动态模态分解(Dynamic Mode Decomposition,DMD)方法对热分层现象的预测效果不佳。本文首先基于CFD程序FLUENT得到高精度全阶快照;然后在截断DMD的基础上,依据特征频率压缩时间步样本,并结合长短期记忆(Long Short-Term Memory,LSTM)神经网络构建热分层降阶模型。通过与CFD全阶热分层模拟对热分层现象进行对比分析,该热分层降阶模型能较好地模拟上腔室温度分布、实现对热分层现象的快速预测。[Background]The thermal stratification in the upper plenum of lead-bismuth fast reactor after emergency shutdown has a significant impact on the structural integrity of the reactor and the residual heat removal capacity of the natural circulation.The research on thermal stratification based on Computational Fluid Dynamics(CFD)method has the problems of large computational overhead and time-consuming whilst the existing standard dynamic mode decomposition(DMD)method has poor forecasting results on thermal stratification.[Purpose]This study aims to solve this problem by proposing a thermal stratification model reduction method for the upper plenum of lead-bismuth fast reactor.[Methods]Firstly,the high-precision full-order snapshot was obtained on the basis of the CFD program FLUENT.Then,based on the truncated DMD,the time step samples were compressed according to the characteristic frequency,and the thermal stratification reduction model was constructed by combining the Long Short-Term Memory(LSTM)neural network with DMD.Finally,three methods,i.e.,standard DMD,improved DMD and improved DMD-LSTM,were comparatively analyzed in terms of temperature oscillation error and computation time.[Results]Computational results show that the thermal stratification model reduction method based on improved DMD and LSTM in the upper plenum of the lead-bismuth fast reactor achieves best performance,with root mean square error reduced by 46.60%and 30.45%respectively,compared to standard DMD and improved DMD.The computational time of the improved DMD and LSTM is only 4.4%of FLUENT's,significantly improving efficiency and enabling faster emergency response in lead-bismuth reactors.[Conclusions]Results of this study verify that the thermal stratification model reduction method proposed in this paper can better simulate the temperature distribution in the upper plenum and realize the rapid prediction of the thermal stratification phenomenon.

关 键 词：动态模态分解 长短期记忆神经网络 铅铋快堆上腔室 热分层 温度振荡 

分 类 号：TL33[核科学技术—核技术及应用]