Estimation of radiation damage at the structural materials of a hybrid reactor by probabilistic neural networks

Authors:
Elif Derya íbeyli;Mustafa íbeyli
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji íniversitesi, 06530 Söğütözü, Ankara, Turkey;Department of Mechanical Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji íniversitesi, 06530 Söğütözü, Ankara, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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.