The prediction of maximum temperature for single chips' cooling using artificial neural networks

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dc.contributor.authorÖzsunar, Abuzer
dc.contributor.authorArcaklıoğlu, Erol
dc.contributor.authorNusret Dur, F.
dc.date.accessioned2020-06-25T17:48:39Z
dc.date.available2020-06-25T17:48:39Z
dc.date.issued2009
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractA CFD simulation usually requires extensive computer storage and lengthy computational time. The application of artificial neural network models to thermal management of chips is still limited. In this study, the main objective is to find a neural network solution for obtaining suitable thickness levels and material for a chip subjected to a constant heat power. To achieve this aim a neural network is trained and tested using the results of the CFD program package Fluent. The back-propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function is employed in the network. By using the weights of the network, various formulations are designed for the output. The network has resulted in R (2) values of 0.999, and the mean% errors smaller than 0.8 and 0.7 for the training and test data, respectively. The analysis is extended for different thickness and input power values. Comparison of some randomly selected results obtained by the neural network model and the CFD program has yielded a maximum error of 1.8%, mean absolute percentage error of 0.55% and R (2) of 0.99994.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1007/s00231-008-0445-x
dc.identifier.endpage450en_US
dc.identifier.issn0947-7411
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-67649130364
dc.identifier.scopusqualityQ2
dc.identifier.startpage443en_US
dc.identifier.urihttps://doi.org/10.1007/s00231-008-0445-x
dc.identifier.urihttps://hdl.handle.net/20.500.12587/4520
dc.identifier.volume45en_US
dc.identifier.wosWOS:000262411700007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.ispartofHeat And Mass Transfer
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleThe prediction of maximum temperature for single chips' cooling using artificial neural networksen_US
dc.typeArticle

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