Research on the source-detector variance reduction method based on the AIS adjoint Monte Carlo method

被引:2
|
作者
Yisheng, Hao [1 ,2 ]
Rui, Qiu [1 ,2 ]
Zhen, Wu [1 ,2 ,3 ]
Shenshen, Gao [1 ,2 ]
Hui, Zhang [1 ,2 ]
Junli, Li [1 ,2 ]
机构
[1] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Key Lab Particle & Radiat Imaging, Minist Educ, Beijing 100084, Peoples R China
[3] Nuctech Co Ltd, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Adjoint methods; Deep penetration problem; Source-detector problem; Variance reduction method; AIS method; RADIATION-FIELD; CODE;
D O I
10.1016/j.anucene.2023.109916
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
The coupled variance reduction method based on adjoint calculation has a good variance reduction effect when solving the source-detector problem. This paper incorporates the Automatic Importance Sampling (AIS) method into the adjoint Monte Carlo method and then proposes a coupled variance reduction method based on the AIS adjoint Monte Carlo method. The result shows that the total flux deviation between the AIS adjoint Monte Carlo method and the original adjoint method is 0.13%, indicating good agreement and verifying the accuracy of the AIS adjoint method. In this paper, the AIS adjoint Monte Carlo method is applied to the calculation of a com-mercial reactor shielding example, and four sets of source bias and weight window parameters of the AIS adjoint calculation are used as calculation examples. The maximum statistical error is less than 5.00%, and the relative deviation from the measured value is within 20.00%. Generally, the calculation efficiency of AIS-CADIS is slightly better than that of CADIS.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Global variance reduction method based on multi-group Monte Carlo adjoint calculation
    Shi, Tao
    Li, Hui
    Ding, Qianxue
    Wang, Mengqi
    Zheng, Zheng
    Peng, Chao
    Mei, Qiliang
    ANNALS OF NUCLEAR ENERGY, 2021, 151
  • [2] Global variance reduction method based on multi-group Monte Carlo adjoint calculation
    Shi, Tao
    Li, Hui
    Ding, Qianxue
    Wang, Mengqi
    Zheng, Zheng
    Peng, Chao
    Mei, Qiliang
    Shi, Tao (shitao@snerdi.com.cn), 1600, Elsevier Ltd (151):
  • [3] Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method
    Hao, Yi-Sheng
    Wu, Zhen
    Gao, Shen-Shen
    Qiu, Rui
    Zhang, Hui
    Li, Jun-Li
    NUCLEAR SCIENCE AND TECHNIQUES, 2024, 35 (05)
  • [4] Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method
    YiSheng Hao
    Zhen Wu
    ShenShen Gao
    Rui Qiu
    Hui Zhang
    JunLi Li
    NuclearScienceandTechniques, 2024, 35 (05) : 180 - 195
  • [5] VARIANCE REDUCTION TECHNIQUES USING ADJOINT MONTE-CARLO METHOD IN SHIELDING PROBLEM
    UEKI, K
    STEVENS, PN
    JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 1979, 16 (02) : 117 - 131
  • [6] Study on Monte Carlo Variance Reduction Method for Thick Shield and Small Detector Problem
    Gao S.
    Li J.
    Wu Z.
    Ma R.
    Wang X.
    Qiu R.
    Li C.
    Zhang H.
    Yuanzineng Kexue Jishu/Atomic Energy Science and Technology, 2020, 54 (07): : 1294 - 1300
  • [7] Optimization of a Monte Carlo variance reduction method based on sensitivity derivatives
    Liu, Yaning
    Hussaini, M. Yousuff
    Oekten, Giray
    APPLIED NUMERICAL MATHEMATICS, 2013, 72 : 160 - 171
  • [8] Radioactive Source-Detector System: Design and Monte Carlo Opinion
    Ali, Zainab kareem
    Mohammed, Ali N.
    BAGHDAD SCIENCE JOURNAL, 2024, 21 (10) : 3266 - 3276
  • [9] Monte Carlo global variance reduction method combining source bias and weight window
    Zhang Xian
    Liu Shi-Chang
    Wei Jun-Xia
    Li Shu
    Wang Xin
    Shangguan Dan-Hua
    ACTA PHYSICA SINICA, 2024, 73 (04)
  • [10] Monte Carlo model and variance reduction method based on lidar of ship wake
    Liang Shan-Yong
    Wang Jiang-An
    Zhang Feng
    Wu Rong-Hua
    Zong Si-Guang
    Wang Yu-Hong
    Wang Le-Dong
    ACTA PHYSICA SINICA, 2013, 62 (01)