Improved adaptive variance reduction algorithm based on RMC code for deep penetration problems

Abstract

An adaptive variance reduction algorithm based on RMC code was developed for deep penetration problems. The adaptive variance reduction algorithm uses the conservation of penetration rate and a predictor–corrector algorithm to quickly determine exponential importance parameters or equal-gradient importance parameters with fast iterative convergence. The adaptive variance reduction algorithm greatly accelerates one-dimensional deep penetration problems. However, the previous algorithm does not have capacity of energy bias nor does it support calculations on three-dimensional models. The adaptive variance reduction algorithm is improved to allow for energy bias and for three-dimensional models. The improved algorithm was applied to the HBR2 benchmark with the average Figure of Merit (FOM) of RMC code improved 243 fold. Therefore, this improved adaptive variance reduction algorithm can efficiently deal with complex engineering shielding problems.