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In this study, a cladding surface temperature prediction method based on an adaptive RBF neural network was proposed. This method can significantly improve the accuracy and efficiency of the thermal safety evaluation of the lead–bismuth fast reactor. First, based on the sub-channel analysis program SUBCHANFLOW, the core sub-channel model of the small lead–bismuth fast reactor SPALLER-100 was established. Second, the calculated 2000 groups of core power distribution and coolant flow distribution data were used as training samples. The adaptive RBF neural network model was trained to predict the surface temperature of fuel elements in the lead–bismuth fast reactor. Finally, by comparison, the effectiveness and superiority of the adaptive RBF neural network method were proved. The results indicate that the relative error of the maximum temperature of the fuel cladding predicted using the adaptive RBF neural network method was less than 0.5%, which can be used for the rapid prediction of the thermal and hydraulic parameters of the lead–bismuth fast reactor.

Research on Thermal-Hydraulic Parameter Prediction Method of the Small Lead–Bismuth Fast Reactor Core Based on Adaptive RBF Neural Network