Improved Algorithms and Coupled Neutron-Photon Transport for Auto-Importance Sampling Method

Abstract

The Auto-Importance Sampling (AIS) method is a Monte Carlo variance reduction technique proposed for deep penetration problems, which can significantly improve computational efficiency without pre-calculations for importance distribution. However, the AIS method is only validated with several simple examples, and cannot be used for coupled neutron-photon transport. This paper presents the improved algorithms for the AIS method, including particle transport, fictitious particles creation and adjustment, fictitious surface geometry, random number allocation and calculation of the estimated relative error. These improvements allow the AIS method to be applicable to complicated deep penetration problems with complex geometry and multiple materials. A coupled Neutron-Photon Auto-Importance Sampling (NP-AIS) method is proposed to solve the deep penetration problems of coupled neutron-photon transport using the improved algorithms. The NUREG/CR-6115 PWR benchmark was calculated by using the methods of NP-AIS, geometry splitting with Russian roulette and the analog Monte Carlo, respectively. The calculation results of NP-AIS were in good agreement with those of geometry splitting with Russian roulette and the benchmark solutions. The computational efficiency of NP-AIS for both neutron and photon was much better than that of geometry splitting with Russian roulette in most cases, and increased by several orders of magnitude compared with that of the analog Monte Carlo.