PRESSURIZED WATER-REACTOR CORE PARAMETER PREDICTION USING AN ARTIFICIAL NEURAL NETWORK

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In pressurized water reactors, the fuel reloading problem has significant meaning in terms of both safety and economics. The local power peaking factor must be kept lower than a predetermined value during a cycle, and the effective multiplication factor must be maximized to extract the maximum energy. If these core parameters could be obtained in a very short time, the optimal fuel reloading patterns would be found more effectively and quickly. A very fast core parameter prediction system is developed using the back propagation neural network. This system predicts the core parameters several hundred times as fast as the reference numerical code, within an error of a few percent. The effects of the variation of the training rate coefficients, the momentum, and the hidden layer units are also discussed.
Publisher
AMER NUCLEAR SOCIETY
Issue Date
1993-01
Language
English
Article Type
Article
Keywords

FUEL-MANAGEMENT; OPTIMIZATION; DESIGN

Citation

NUCLEAR SCIENCE AND ENGINEERING, v.113, no.1, pp.70 - 76

ISSN
0029-5639
URI
http://hdl.handle.net/10203/67081
Appears in Collection
NE-Journal Papers(저널논문)
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