Global variance reduction method based on multi-group Monte Carlo adjoint calculation

Abstract

Variance reduction technique is one of the most efficient methods to accelerate Monte Carlo (MC) results convergence. Among all techniques, MC self-couple variance reduction method is highly praised for it does not need any secondary model or code. This method can be applied in high-fidelity geometry simulation. In this paper, a new mesh-based bias parameters generation method based on multi-group Monte Carlo adjoint calculation for global variance reduction has been proposed. Forward flux is used to calculate global adjoint source. Source bias parameters and transport bias parameters are constructed by using global adjoint flux. The mesh definition of mesh-based adjoint flux tally and mesh-based weight window could to be fully consistent with each other. H.B. Robinson-2 in-vessel and ex-vessel neutron dosimetry experiment has been used to validate the calculation efficiency. According to numerical results, the calculation efficiency has been improved about 1–2 orders for H.B. Robinson-2 benchmark and the goal of global variance reduction has been achieved. The mesh-based bias parameters generation method based on multi-group Monte Carlo adjoint calculation would be a strong tool for Monte Carlo global deep-penetration shielding calculation.