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 |
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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