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Predicting the core thermal hydraulic parameters with a gated recurrent unit model based on the soft attention mechanism
Nuclear Engineering and Technology ( IF 2.7 ) Pub Date : 2024-03-06 , DOI: 10.1016/j.net.2024.01.045 Anni Zhang , Siqi Chun , Zhoukai Cheng , Pengcheng Zhao
Nuclear Engineering and Technology ( IF 2.7 ) Pub Date : 2024-03-06 , DOI: 10.1016/j.net.2024.01.045 Anni Zhang , Siqi Chun , Zhoukai Cheng , Pengcheng Zhao
Accurately predicting the thermal hydraulic parameters of a transient reactor core under different working conditions is the first step toward reactor safety. Mass flow rate and temperature are important parameters of core thermal hydraulics, which have often been modeled as time series prediction problems. This study aims to achieve accurate and continuous prediction of core thermal hydraulic parameters under instantaneous conditions, as well as test the feasibility of a newly constructed gated recurrent unit (GRU) model based on the soft attention mechanism for core parameter predictions. Herein, the China Experimental Fast Reactor (CEFR) is used as the research object, and CEFR 1/2 core was taken as subject to carry out continuous predictive analysis of thermal parameters under transient conditions., while the subchannel analysis code named SUBCHANFLOW is used to generate the time series of core thermal-hydraulic parameters. The GRU model is used to predict the mass flow and temperature time series of the core. The results show that compared to the adaptive radial basis function neural network, the GRU network model produces better prediction results. The average relative error for temperature is less than 0.5 % when the step size is 3, and the prediction effect is better within 15 s. The average relative error of mass flow rate is less than 5 % when the step size is 10, and the prediction effect is better in the subsequent 12 s. The GRU model not only shows a higher prediction accuracy, but also captures the trends of the dynamic time series, which is useful for maintaining reactor safety and preventing nuclear power plant accidents. Furthermore, it can provide long-term continuous predictions under transient reactor conditions, which is useful for engineering applications and improving reactor safety.
更新日期：2024-03-06
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