摘要
反应堆在各种工况下堆芯瞬态热工水力参数预测的准确性,直接影响到反应堆的安全性。质量流量和温度作为堆芯热工水力的重要参数,二者常被建模为时间序列预测问题。研究旨在解决瞬时条件下堆芯热工水力参数连续预测的精度问题,检验基于注意力机制的门控循环单元在核心参数预测中的可行性。本文采用1/2中国实验快堆(China Experimental Fast Reactor,CEFR)为研究对象,使用快堆子通道程序SUBCHANFLOW生成瞬态堆芯热工水力参数的时间序列,采用基于软注意力的门控循环单元(Gated Recurrent Unit,GRU)模型预测堆芯的质量流量和温度时间序列。结果表明:相较于自适应径向基(Radial Basis Function,RBF)神经网络,本文使用的软注意力的GRU网络模型预测结果更好,温度在步长为3的情况下平均相对误差不超过0.5%,在15 s内预测效果较好;质量流量在步长为10的情况下平均相对误差不超过5%,且在后续12 s内预测效果较好。本文构建的模型不仅在连续预测过程中表现出更高的预测精度,且能捕捉到动态时间序列中的趋势特点,这对维护反应堆安全,有效防止核电厂事故有极大的用处。基于软注意力的GRU模型能在瞬态反应堆工况下提供一段时间的连续预测,在工程应用中和提高反应堆安全性上具有一定的参考价值。
[Background]The accuracy of transient thermal hydraulic parameter prediction of reactor cores under various working conditions directly affects reactor safety.Mass flow rate and temperature are important parameters of core thermal hydraulics,which are often modeled as time-series prediction problems.[Purpose]This study aims to solve the accuracy problem of continuous prediction of core thermal hydraulic parameters under instantaneous conditions and to test the feasibility of a gated cycle unit based on the attention mechanism in core parameter prediction.[Methods]The 1/2 full core model of China Experimental Fast Reactor(CEFR)core was taken as the research object.The subchannel SUBCHANFLOW program was employed to generate the time series of transient core thermal hydraulic parameters.The gated recurrent unit(GRU)model based on soft attention was used to predict the mass flow and temperature time series of the core.[Results]The results show that,compared with the adaptive radial basis function(RBF)neural network,the GRU network model with soft attention offers better prediction results.The average relative error of temperature is<0.5%when the step size is 3,and the prediction effect is quite good within 15 s.The average relative error of mass flow rate is<5%when the step size is 10,and fairly good prediction effect is achieved in the subsequent 12 s.[Conclusions]The model constructed in this study not only exhibits higher prediction accuracy in the continuous prediction process but also captures the trend characteristics in the dynamic time series,which is of considerable value for maintaining reactor safety and effectively preventing nuclear power plant accidents.The GRU model based on soft attention can provide continuous prediction for a period of time under transient reactor conditions,providing a reference value in engineering applications and improving reactor safety.
作者
淳思琦
冯欢
张安妮
赵鹏程
CHUN Siqi;FENG Huan;ZHANG Anni;ZHAO Pengcheng(School of Nuclear Science and Technology,University of South China,Hengyang 421001,China;Cooperative Innovation Center for Nuclear Fuel Cycle Technology and Equipment,University of South China,Hengyang 421001,China;School of Resources Environment and Safety Engineering,University of South China,Hengyang 421001,China;School of Computer Science,University of South China,Hengyang 421001,China)
出处
《核技术》
EI
CAS
CSCD
北大核心
2024年第1期124-132,共9页
Nuclear Techniques
基金
装备预研教育部联合基金(No.8091B032243)资助。
关键词
门控循环单元
软注意力
快堆
瞬态热工水力
参数预测
Gated recurrent unit
Soft attention
Fast reactor
Transient thermal hydraulics
Parameter prediction method