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Title: A Preliminary Study on Applicability of Artificial Neural Network for Optimized Reflector Designs
Authors: Kim, Song Hyun
Vu, Thanh Mai
Pyeon, Cheol Ho
Author's alias: 卞, 哲浩
Keywords: Artificial Neural Network
ANN
Reflector Design
Fuel Pattern
Arrangement
Optimization
Nuclear Reactor
Criticality
Issue Date: Dec-2017
Publisher: Elsevier BV
Journal title: Energy Procedia
Volume: 131
Start page: 77
End page: 85
Abstract: The neutron reflector is a material to reflect neutrons into reactor cores. The reflectors are designed with their one purpose such as increasing the criticality, specific flux distribution, and others. Generally, the reflector design has been conducted by the experiences of designers due to the lots of design variables such as material selection and arrangement. In this study, the applicability of the artificial neural network is preliminarily studied for the optimization of the reflector arrangement. For the research, a system of artificial neural network was developed using C++ program language. The feedforward neural network was used with three layers which are input, hidden, and output layers. The back-propagation algorithm was adopted for the training of the neural network. After the construction of the neural network system, the optimization and auto machine learning algorithms was developed by C++ programing language for the preliminary study on the applicability of artificial neural network into the reflector design. The results show that the reflector gives a good performance to obtain the goal responses. It is expected that this system can contribute to dramatically increase the efficiency of the reflector designs.
Description: 5th International Symposium on Innovative Nuclear Energy Systems, INES-5, 31 October – 2November, 2016, Ookayama Campus, Tokyo Institute of Technology, JAPAN
Rights: © 2017 The Authors. Published by Elsevier Ltd. Under a Creative Commons license.
URI: http://hdl.handle.net/2433/231906
DOI(Published Version): 10.1016/j.egypro.2017.09.478
Appears in Collections:Journal Articles

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