Single-channel Temperature Prediction of Heat Pipe Reactor Based on Deep Neural Network

Heat pipe reactors have become a strong candidate for nuclear power generation due to their unique design and efficient heat conduction performance. However, accurate monitoring of the core temperature field remains a key challenge. This paper explores a novel method for rapid prediction of the core temperature field based on deep learning technology. By establishing a backpropagation neural network (BPNN) model and training a large amount of core numerical simulation data, it is possible to predict the temperature field of a single channel core section using 6 temperature measurement points. The training results of the model show that selecting the appropriate number of neurons and hidden layers can effectively improve prediction accuracy and reduce the risk of overfitting. The neural network model in this study has an average absolute error of 1.06 K on the test set, demonstrating good predictive ability and a low level of error. Errors are primarily concentrated in corner fuel rods and regions with intense heat exchange.