Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6664
Title: Estimation of radiation damage at the structural materials of a hybrid reactor by probabilistic neural networks
Authors: Übeyli, Elif Derya
Übeyli, Mustafa
Keywords: Probabilistic neural networks (PNNS)
Radiation damage
Atomic displacement
Helium generation
Hybrid reactor
Issue Date: 2009
Publisher: Pergamon-Elsevier Science Ltd
Abstract: This paper presents a new approach based on probabilistic neural networks (PNNs) for the radiation damage parameters at the structural material of a nuclear fusion-fission (hybrid) reactor. Artificial neural networks (ANNs) have recently been introduced to the nuclear engineering applications as a fast and flexible vehicle to modeling, simulation and optimization. The results of the PNNs implemented for the atomic displacement and the helium generation at the structural material of the reactor and the results available in the literature obtained by using the code (Scale 4.3) were compared. The drawn conclusions confirmed that the proposed PNNs could provide an accurate computation of the radiation damage parameters. (C) 2008 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2008.06.026
https://hdl.handle.net/20.500.11851/6664
ISSN: 0957-4174
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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