Development of new variance reduction methods based on weight window technique in RMC code

Abstract

Variance reduction methods in Monte Carlo calculations are essential especially for the design and analysis of some real-world nuclear applications with complex and large geometry. As the most popular variance reduction method, weight window technique has demonstrated its great capability and been implemented in most Monte Carlo particle transport codes. In this work, we propose two new variance reduction methods based on weight window. One is automatic inner iterative fixed source calculation method, which makes it more convenient and efficient to use weight window technique. The other is called adaptive weight window method, which solves one of the most common frustrations when using weight window that the output of zero weight windows and difficulties to obtain a set of variance reduction parameters in some deep penetration problems. These two new methods are verified and compared. The efficiency of newly proposed adaptive weight window method and automatic inner iterative fixed source calculation method was found to be 32,966 and 25,793 times higher than that of an analogue run.