Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Abstract

Global variance reduction is a bottleneck in Monte Carlo shielding calculations. The global variance reduction problem requires that the statistical error of the entire space is uniform. This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method, which was implemented in the Monte Carlo program MCShield. The proposed method was validated using the VENUS-III international benchmark problem and a self-shielding calculation example. The results from the VENUS-III benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids, decreasing from 1.08 × 10^–2 to 3.84 × 10^–3, representing a 64.00% reduction. This demonstrates that the grid-AIS method is effective in addressing global issues. The results of the self-shielding calculation demonstrate that the grid-AIS method produced accurate computational results. Moreover, the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.