He jishu (Sep 2022)
Research on adaptive RBF neural network prediction method for core thermal-hydraulic parameters of fast reactor
Abstract
BackgroundAlterations in thermal parameters directly affect the safety of reactors. Accurately predicting the trends of key thermal parameters under various working conditions can greatly improve reactor safety, thereby effectively preventing the occurrence of nuclear power plant accidents. The thermal and hydraulic characteristic parameters in the reactor are affected by many factors.PurposeThis study aims to explore the prediction method of core thermal-hydraulic parameters of fast reactor and determine the feasibility of neural network prediction.MethodsThe China experimental fast reactor (CEFR) was selected as the research object, and the maximum temperature of the fuel cladding surface and mass flow rate were used as predictor variables. After data samples were generated through the Subchannel code (named Subchanflow), two widely used adaptive neural networks were employed to perform the thermal parameter forecast analysis of CEFR fuel assembly under steady-state conditions, and CEFR 1/2 core was taken as subject to carry out single-step and continuous predictive analysis of thermal parameters under transient conditions.ResultsThe results show that, compared with adaptive BP neural network, the adaptive RBF neural network exhibits a better fitting ability and higher forecasting accuracy, and its maximum error under steady-state conditions is 0.5%. Under transient conditions, some local points have poor forecasting accuracy, however, the adaptive RBF neural network is generally excellent at predicting temperature and mass flow. The average relative error of temperature does not exceed 1%, and the average relative error of flow does not exceed 6%.ConclusionsThe adaptive RBF neural network model can provide real-time forecasting in a short time under unstable flow conditions, and its forecasting results have certain reference value.
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