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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 |
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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