Predictions of temperature distributions on layered metal plates using artificial neural networks

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dc.contributor.authorAyata, Tahir
dc.contributor.authorCavusoglu, Abdullah
dc.contributor.authorArcaklioglu, Erol
dc.date.accessioned2020-06-25T17:41:28Z
dc.date.available2020-06-25T17:41:28Z
dc.date.issued2006
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractThe temperature distribution influences the amount of energy needed to heat a body. The benefits of using multi-layered metal plates (NIMP) are due to the requirement of a regular temperature distribution on the opposite side with one side heated irregularly. The factors that affect the regular distribution of the temperature in such a structure are the thickness of the layers and the materials themselves, since for different materials, heat conduction coefficients, density and specific heat values change. In this study, the main objective is to find a neural network solution for the problem of the non-regular distribution of temperature on the non-heated side of an irregularly heated NIMP consisting of two layers of Cu/CrNi and Al/CrNi in order to obtain the optimum thickness levels for the layers. To achieve this aim, the results of the finite elements method (FEM) produced by the program package ANSYS have been used to train and test the network. They are the coefficient of heat conduction (K), specific heat (C), density (D), temperature (T) and layer thickness (L), which are used as the input layer, while the outputs are the maximum,minimum and mean temperature values of the materials. The back propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function have been used in the network. By using the weights of the network, formulations have been given for each output. The network has yielded R-2 values of 0.999 and the mean percent errors are smaller than 0.8 for the training data, while the R-2 values are about 0.999 and the mean percent errors are smaller than 0.7 for the test data. The analysis has been extended for different materials and for the different temperature values that have been applied. The Al/CrNi laminated plate has a lower temperature gradient distribution on the upper (or non-heated) surface due to its lesser heat conductivity compared to the Cu/CrNi steel. The thickness of 8 mm provides the best results among the alloys that have been considered. (c) 2005 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.enconman.2005.11.023
dc.identifier.endpage2370en_US
dc.identifier.issn0196-8904
dc.identifier.issn1879-2227
dc.identifier.issue15-16en_US
dc.identifier.scopus2-s2.0-33747701526
dc.identifier.scopusqualityQ1
dc.identifier.startpage2361en_US
dc.identifier.urihttps://doi.org10.1016/j.enconman.2005.11.023
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3722
dc.identifier.volume47en_US
dc.identifier.wosWOS:000238277200031
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergy Conversion And Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural networken_US
dc.subjectheat conductionen_US
dc.subjectlayered plateen_US
dc.subjecttemperature distributionen_US
dc.titlePredictions of temperature distributions on layered metal plates using artificial neural networksen_US
dc.typeArticle

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