Development of Neural Network for Predicting Local Power Distributions in BWR Fuel Bundles Considering Burnable Neutron Absorber

この論文にアクセスする

この論文をさがす

著者

抄録

A neural network model is under development to predict the local power distribution in a BWR fuel bundle as a high speed simulator of precise nuclear physical analysis model. The relation between 235U enrichment of fuel rods and local peaking factor (LPF) has been learned using a two-layered neural network model ENET. The training signals used were 33 patterns having considered a line symmetry of a 8×8 assembly lattice including 4 water rods. The ENET model is used in the first stage and a new model GNET which learns the change of LPFs caused by burnable neutron absorber Gadolinia, is added to the ENET in the second stage. Using this two-staged model EGNET, total number of training signals can be decreased to 99. These training signals are for zero-burnup cases. The effect of Gadolinia on LPF has a large nonliniality and the GNET should have three layers. This combined model of EGNET can predict the training signals within 0.02 of LPF error, and the LPF of a high power rod is predictable within 0.03 error for Gadolinia rod distributions different from the training signals when the number of Gadolinia rods is less than 10. The computing speed of EGNET is more than 100 times faster than that of a precise nuclear analysis model, and EGNET is suitable for scoping survey analysis.

収録刊行物

  • Journal of nuclear science and technology  

    Journal of nuclear science and technology 32(3), 170-179, 1995-03-25 

    Atomic Energy Society of Japan

参考文献:  9件

参考文献を見るにはログインが必要です。ユーザIDをお持ちでない方は新規登録してください。

各種コード

  • NII論文ID(NAID)
    10002072120
  • NII書誌ID(NCID)
    AA00703720
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    00223131
  • データ提供元
    CJP書誌  J-STAGE 
ページトップへ