Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6663
Title: Estimation of neutronic performance of a high power density hybrid reactor by multilayer perceptron neural networks
Authors: Übeyli, Mustafa
Übeyli, Elif Derya
Keywords: neutronic parameters
hybrid reactor
multilayer perceptron neural networks (MLPNNs)
least mean squares (LMS) algorithm
Issue Date: 2008
Publisher: Springer
Abstract: Artificial neural networks (ANNs) have recently been utilized in the nuclear technology applications since they are fast, precise and flexible vehicles to modeling, simulation and optimization. This paper presents a new approach based on multilayer perceptron neural networks (MLPNNs) for the estimation of some important neutronic parameters (net Pu-239 production, tritium breeding ratio, cumulative fissile fuel enrichment, and fission rate) of a high power density fusion-fission (hybrid) reactor using light water reactor (LWR) spent fuel. A comparison of the results obtained by the MLPNNs and those found by using the code (Scale 4.3) was carried out. The results pointed out that the MLPNNs trained with least mean squares (LMS) algorithm could provide an accurate computation of the main neutronic parameters for the high power density reactor.
URI: https://doi.org/10.1007/s10894-008-9135-4
https://hdl.handle.net/20.500.11851/6663
ISSN: 0164-0313
1572-9591
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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