Research on Fast Prediction Method of Neutron Flux Based on Hybrid Driven Reduced Order Model

The accurate prediction of neutron flux and reactor power is very important for the safe operation of the reactor immediately after the disturbance of reactor parameters. The traditional method combining POD and Galerkin projection has the problem of low accuracy due to cumulative error. In this study, the implicit difference method is used to obtain the exact solution of one-dimensional neutron spatiotemporal diffusion. As the reference data, two LSTM neural network terms are introduced to eliminate the cumulative error and truncation error of POD, and to build a hybrid drive model driven by physics and data. The results show that the root-mean-square error of neutron flux, total power and each order modal coefficient is reduced by 1-2 orders of magnitude after adding the neural network correction term, and the calculation time is significantly reduced under the same order of prediction when the neural network extension term is added. The improved model based on 2nd and 3rd order scaling to 6th order is 13% and 7.6% faster than the original 6th order model, respectively. The hybrid drive model can improve the rapid prediction accuracy of POD, and the results have certain reference value.