A new method in beam shaping: Multi-Objective Genetic Algorithm method coupled with a Monte-Carlo based reactor physics code

被引:5
|
作者
Turkmen, Mehmet [1 ]
Ergun, Sule [1 ]
Colak, Uner [2 ]
机构
[1] Hacettepe Univ, Nucl Engn Dept, Beytepe Campus, Ankara, Turkey
[2] Istanbul Tech Univ, Energy Inst, Ayazaga Campus, Istanbul, Turkey
关键词
Piercing beam port; ITU TRIGA Mark II; Boron neutron capture therapy; Monte Carlo method; Genetic Algorithm; NEUTRON-CAPTURE THERAPY; II RESEARCH REACTOR; BNCT; DESIGN;
D O I
10.1016/j.pnucene.2017.05.008
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
In this study, a new technique which uses Multi-Objective Genetic Algorithm method (NSGA-II) coupled with a Monte-Carlo based reactor physics code (MCNP) was proposed to shape the beams of neutron sources. The method was applied for the beam of Piercing Beam Port of the Istanbul Technical University TRIGA Mark II Research and Training Reactor. The beam was shaped according to the requirements of the Boron Capture Neutron Therapy (BNCT) application as described by IAEA technical documents. A set of optimized patterns, consisting of the spectrum shifter, filtering, and collimator sub-patterns, were obtained. Epithermal neutrons were considered for BNCT. With the optimized pattern, the epithermal neutron flux at the port exit is calculated as 4.17 +/- 0.14 x 10(8) n. cm(-2). s(-1) with a current-to-flux ratio of 0.87 +/- 0.03. Thermal and fast neutron fluxes and photon flux are reduced by about 20, 3, and 200 times, respectively, when compared with the bare port values. As a result, BNCT is applicable in this port as the desired neutron fluxes at the port exit are obtained. The method suggested in this study provides promising results and is quite effective in shaping the beam. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:165 / 176
页数:12
相关论文
共 50 条
  • [1] A Multi-Objective Molecular Generation Method Based on Pareto Algorithm and Monte Carlo Tree Search
    Liu, Yifei
    Zhu, Yiheng
    Wang, Jike
    Hu, Renling
    Shen, Chao
    Qu, Wanglin
    Wang, Gaoang
    Su, Qun
    Zhu, Yuchen
    Kang, Yu
    Pan, Peichen
    Hsieh, Chang-Yu
    Hou, Tingjun
    ADVANCED SCIENCE, 2025,
  • [2] APPLICATIONS OF THE INVERSE MONTE-CARLO METHOD IN PHOTON-BEAM PHYSICS
    DUNN, WL
    BOFFI, VC
    OFOGHLUDHA, F
    NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 1987, 255 (1-2): : 147 - 151
  • [3] A Multi-objective Genetic Algorithm based on Nearest Neighbor Method
    Li Wenbin
    Yin Cheng
    2013 FOURTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND ENGINEERING APPLICATIONS, 2013, : 19 - 22
  • [4] New near infrared wavelength selection algorithm based on Monte-Carlo method
    Hong M.
    Wen Q.
    Wen Z.
    Guangxue Xuebao/Acta Optica Sinica, 2010, 30 (12): : 3637 - 3642
  • [5] Proposal and validation of an optimization method using Monte Carlo method for multi-objective functions
    Inage, Sin-ichi
    Ohgi, Shouki
    Takahashi, Yoshinori
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2024, 215 : 146 - 157
  • [6] Multi-Objective Monte-Carlo Tree Search based Aerial Maneuvering Control
    Chen, Xiang
    Zhang, Xinguo
    Wang, Weijia
    Wei, Wenling
    2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), 2016, : 81 - 87
  • [7] Multi-objective optimization of method of characteristics parameters based on genetic algorithm
    Song, Qufei
    Zhang, Chang
    Wu, Yiwei
    Feng, Kuaiyuan
    Guo, Hui
    Gu, Hanyang
    ANNALS OF NUCLEAR ENERGY, 2023, 194
  • [8] EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm
    Chen, Yuanyuan
    Xu, Fengjiao
    Pian, Cong
    Xu, Mingmin
    Kong, Lingpeng
    Fang, Jingya
    Li, Zutan
    Zhang, Liangyun
    GENES, 2021, 12 (02) : 1 - 18
  • [9] Compensation method in genetic algorithm for multi-objective optimization
    Yuan Hua
    Chen Guo-qing
    PROCEEDINGS OF 2005 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1 AND 2, 2005, : 943 - 946
  • [10] A new multi-objective optimization algorithm based on combined swarm intelligence and Monte Carlo simulation
    Zhang, Kangkang
    Song, Yan
    INFORMATION SCIENCES, 2022, 610 : 759 - 776