Fast calculation method of particle transport problem by data-driven neural network

Abstract

The Monte Carlo (MC) method is a powerful tool for solving particle transport problems. However, it is extremely time-consuming to obtain results that meet the specified statistical error requirements, especially for large-scale refined models. This paper focuses on improving the computational efficiency of neutron transport simulations. Accordingly, this study presents a novel method for executing fast calculation of neutron fixed source problems, which have broad applications. This type of particle transport problem aims at obtaining a fixed target tally corresponding to different source distributions for fixed geometry and material. First, a fast simulation is achieved by treating the source distribution as the input to a neural network, with the estimated target tally as the output. This neural network is trained with data from MC simulations of diverse source distributions, ensuring its reusability. Second, since the data acquisition is time consuming, the importance principle of MC method is utilized to efficiently generate training data. This method has been tested on several benchmark models. The relative errors of results from neural networks are less than 5% and the time required to obtain these results is negligible compared to that of original Monte Carlo simulations. In conclusion, this paper has proposed a method that trains neural networks with MC simulation results containing importance data and uses this network to accelerate the computation of neutron fixed source problems.