Tritium breeding ratio optimization in simple multi-layer blanket with genetic algorithm

被引:1
|
作者
Lim, Soobin [1 ]
Chung, Kyoung-Jae [1 ]
Hwang, Y. S. [1 ]
机构
[1] Seoul Natl Univ, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
Genetic algorithm; Tritium breeding ratio; Optimization; DESIGN;
D O I
10.1016/j.fusengdes.2024.114365
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
Investigation on tritium production via fast neutrons generated by magnetically confined plasma in a tokamak device is conducted for optimization of tritium breeding ratio (TBR). The blanket is configured as a 1-dimensional multiple layer of materials for the wall, moderation, reflection, and tritium productions, and genetic algorithm is adopted to select the optimal material on each order and location. The configuration selected in the process is evaluated in aspect of tritium production to incoming neutron ratio. To construct the algorithm, the ratio of tritium production is parametrized by neutron energies from 0 to 14 MeV for the materials with Geant4 Monte Carlo simulation toolkit, and the simulated tritium in the algorithm are evaluated for the selection of parent for the next generation. Result configuration from the algorithm is put back to the Geant4 simulation for verification, and TBR is evaluated with blanket designs in other facilities.
引用
收藏
页数:7
相关论文
共 50 条
  • [21] Construction of multi-layer feedforward binary neural network by a genetic algorithm
    Chow, CK
    Lee, T
    PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 2562 - 2567
  • [22] A Multi-Layer Perceptron Approach to Financial Distress Prediction with Genetic Algorithm
    Sreedharan, Meenu
    Khedr, Ahmed M.
    El Bannany, Magdi
    AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2020, 54 (06) : 475 - 482
  • [23] Streaming Multi-layer Ensemble Selection using Dynamic Genetic Algorithm
    Luong, Anh Vu
    Nguyen, Tien Thanh
    Liew, Alan Wee-Chung
    2021 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2021), 2021, : 298 - 305
  • [24] A Multi-Layer Perceptron Approach to Financial Distress Prediction with Genetic Algorithm
    Ahmed M. Meenu Sreedharan
    Magdi Khedr
    Automatic Control and Computer Sciences, 2020, 54 : 475 - 482
  • [25] Multi-layer optimization with backpressure and genetic algorithms for multi-hop wireless networks
    Yi Shi
    Yalin E. Sagduyu
    Jason H. Li
    Wireless Networks, 2014, 20 : 1265 - 1273
  • [26] Multi-layer optimization with backpressure and genetic algorithms for multi-hop wireless networks
    Shi, Yi
    Sagduyu, Yalin E.
    Li, Jason H.
    WIRELESS NETWORKS, 2014, 20 (06) : 1265 - 1273
  • [27] Application of particle swarm optimization and genetic algorithm methods for maximizing the phase velocity in the multi-layer nanoscale system
    Zhang, Yuanyuan
    MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES, 2023, 51 (04) : 2302 - 2322
  • [28] Adaptive Parameters Optimization Method for Photovoltaic Simulation System Based on Multi-layer Recurrent Learning and Genetic Algorithm
    Wang, Feng
    Wang, Jiaojiao
    Zhou, Juntao
    Li, Haifeng
    Zheng, Quanchao
    2024 7TH ASIA CONFERENCE ON ENERGY AND ELECTRICAL ENGINEERING, ACEEE 2024, 2024, : 350 - 355
  • [29] Validation of Multi-Layer Network Optimization
    Peng, Yu
    Lin, Rongping
    Li, Fan
    Xing, Chang
    Guo, Jun
    Hu, Wenjie
    Abramov, Vyacheslav
    Addie, Ronald G.
    Zukerman, Moshe
    2016 18TH INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON), 2016,
  • [30] A Survey of Multi-layer Network Optimization
    Rozic, Ciril
    Klonidis, Dimitrios
    Tomkos, Ioannis
    20TH INTERNATIONAL CONFERENCE ON OPTICAL NETWORK DESIGN AND MODELING (ONDM 2016), 2016,