Research on Adaptive BP Neural Network Prediction Method for Thermal Parameters of China Experimental Fast Reactor

Whether the thermal-hydraulic parameters of China Experimental Fast Reactor (CEFR) core exceed the limit is the standard for evaluating the safe operation of the reactor. For the maximum temperature prediction problem of fuel cladding, after generating the data samples by the core sub-channel analysis code COBRA, an intelligent prediction code based on adaptive BP neural network algorithm was developed in the paper. For a specific single-box component, only the core inlet power and mass flow rate were required to achieve fast and accurate prediction of the fuel cladding maximum temperature. Compared with COBRA, in the scenario of large-scale repetitive calculation, self development code can save a lot of calculation time and rescource, and improve the operating efficiency of fuel cladding design and CEFR operation. The experimental analysis shows that the maximum relative error of BP neural network method is less than 6%, the average prediction relative error is less than 3%, and the calculation efficiency is improved to 300 times of the original code. So the prediction accuracy of the network model is high, and self development code is easy to apply to other parameter predictions of the experimental fast reactor.