摘要
核电厂安全运行的关键是实现其运行参数的精准预测。近年来,数据驱动方法表现出了强大的预测能力,然而,测量数据的不充分限制了其预测性能。本研究将基于迁移学习框架,开发了一种以多组仿真工况预训练,再利用测量数据微调的预测模型构建方法。首先通过仿真数据训练门控循环单元(GRU)神经网络,再使用部分测量数据微调模型,以预测运行工况的未来状态。使用PKLⅢ热工水力台架的B3.1实验的测量数据,及与之相近的9组RELAP5仿真数据,验证了方法的可行性。运用该方法预测得出蒸汽压力、蒸汽温度、下降管流体温度、出口温度、入口温度和质量流量的相对误差分别能够达到0.358%、0.065%、0.020%、0.065%、0.028%和1.705%。最后通过5组数值实验对比说明了方法各模块的有效性。
The key to the safe operation of nuclear power plants is to achieve accurate prediction of their operating parameters.In recent years,data-driven methods have shown strong predictive capabilities.However,insufficient measurement data limits their predictive performance.Based on the transfer learning framework,this study develops a prediction model construction method that is pre-trained with multiple sets of simulation conditions and then fine-tuned with measured data.First,the Gated Recurrent Unit(GRU)neural network is trained with simulation data,and then the model is fine-tuned using part of the measurement data to predict the future state of the operating conditions.The feasibility of the method is verified using the measurement data of the B3.1 experiment on the PKLⅢthermal hydraulic bench and 9 sets of similar RELAP5 simulation data.Using this method,the relative errors of steam pressure,steam temperature,downcomer fluid temperature,outlet temperature,inlet temperature and mass flow rate can reach 0.358%,0.065%,0.020%,0.065%,0.028%and 1.705%,respectively.Finally,five sets of numerical experiments are used to compare and illustrate the effectiveness of each module of the method.
作者
浦克
宋厚德
刘晓晶
宋美琪
Pu Ke;Song Houde;Liu Xiaojing;Song Meiqi(College of Smart Energy,Shanghai Jiao Tong University,Shanghai,200240,China;Shanghai Digital Nuclear Reactor Technology Integration Innovation Center,Shanghai,200240,China;School of Nuclear Science and Engineering,Shanghai Jiao Tong University,Shanghai,200240,China)
出处
《核动力工程》
北大核心
2025年第2期261-271,共11页
Nuclear Power Engineering
