Improved algorithms and coupled neutron-photon transport for auto-importance sampling method  

作　　者：王鑫 李君利 武祯 邱睿 李春艳 梁漫春 张辉 刚直 徐红 

机构地区：[1]Department of Engineering Physics, Tsinghua University, Beijing 100084, China [2]Key Laboratory of Particle ＆ Radiation Imaging, Ministry of Education, Beijing 100084, China [3]Nuctech Company Limited, Beijing 100084, China [4]State Nuclear Hua Qing (Beijing) Nuclear Power Technology R＆D Center Co. Ltd., Beijing 102209, China

出　　处：《Chinese Physics C》2017年第1期100-109,共10页中国物理C（英文版）

基　　金：Supported by the subject of National Science and Technology Major Project of China(2013ZX06002001-007,2011ZX06004-007);National Natural Science Foundation of China(11275110,11375103)

摘　　要：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 improved algorithms for the AIS method, including particle transport, fictitious particle creation and adjustment, fictitious surface geometry, random number allocation and calculation of the estimated relative error. These improvements allow the AIS method to be applied to complicated deep penetration problems with complex geometry and multiple materials. A Completely coupled Neutron-Photon Auto-Importance Sampling（CNP-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 CNP-AIS, geometry splitting with Russian roulette and analog Monte Carlo, respectively. The calculation results of CNP-AIS are in good agreement with those of geometry splitting with Russian roulette and the benchmark solutions. The computational efficiency of CNP-AIS for both neutron and photon is 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.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 improved algorithms for the AIS method, including particle transport, fictitious particle creation and adjustment, fictitious surface geometry, random number allocation and calculation of the estimated relative error. These improvements allow the AIS method to be applied to complicated deep penetration problems with complex geometry and multiple materials. A Completely coupled Neutron-Photon Auto-Importance Sampling（CNP-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 CNP-AIS, geometry splitting with Russian roulette and analog Monte Carlo, respectively. The calculation results of CNP-AIS are in good agreement with those of geometry splitting with Russian roulette and the benchmark solutions. The computational efficiency of CNP-AIS for both neutron and photon is 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.

关 键 词：Monte Carlo deep penetration auto-importance sampling coupled neutron-photon transport 

分 类 号：O571.5[理学—粒子物理与原子核物理]