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Cooling performance of thermoelectric cooler modules: Experimental and numerical methods

dc.contributor.authorKahraman, İlhan
dc.contributor.authorDerebaşı, Naim
dc.contributor.buuauthorKahraman, İlhan
dc.contributor.buuauthorDEREBAŞI, NAİM
dc.contributor.departmentBursa Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.
dc.contributor.orcid0000-0003-2546-0022
dc.contributor.orcid0000-0003-0662-1636
dc.contributor.researcheridAAI-2254-2021
dc.contributor.researcheridHHV-3379-2022
dc.date.accessioned2024-10-07T11:57:29Z
dc.date.available2024-10-07T11:57:29Z
dc.date.issued2022-01-01
dc.description.abstractA novel pulse-driving method in which the pulse frequency modulation is was developed by optimising the input power owing to the duty cycle of rectangular wave to enhance the cooling efficiency and thermal stability of the thermoelectric module. The aim of this driving method is to have better control of the thermoelectric cooler module temperature and to improve its coefficient of performance. In this method, the average current and the peak of pulse drive are in the 50% duty cycle with the same magnitude and the performance of Peltier module driving with average dc is compared with the pulse driving. The measurement results show that the coefficient of performance of the thermoelectric module with the pulse-frequency modulation driving method increased up to 102% as compared to the constant dc driving method. An artificial neural network has been successfully used to analyse these experimentally collected data and predict the performance of the module. When the developed artificial neural network model was tested using untrained data, the average correlation of the model was 99% and the overall prediction error was 1.38%. An accurate and simple analytical equation based on the predicted and experimental results was determined using the MATLAB (R) Curve Fitting Toolbox. The average correlation of the analytical model was 0.99 and the root-mean-square error was 0.074.
dc.description.sponsorshipGempa Electro Mechanic and Engineering Co
dc.description.sponsorshipKahramansan Teknoloji
dc.identifier.doi10.47480/isibted.1194999
dc.identifier.endpage243
dc.identifier.issn1300-3615
dc.identifier.issue2
dc.identifier.startpage232
dc.identifier.urihttps://doi.org/10.47480/isibted.1194999
dc.identifier.urihttps://dergipark.org.tr/en/pub/isibted/issue/73436/1194999
dc.identifier.urihttps://hdl.handle.net/11452/45988
dc.identifier.volume42
dc.identifier.wos000890325500005
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherTurkish Soc Thermal Sciences Technology
dc.relation.journalIsı Bilimi ve Tekniği Dergisi-Journal of Thermal Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectThermoelectric cooler module
dc.subjectCooling performance
dc.subjectDrive method
dc.subjectArtificial neural network
dc.subjectThermodynamics
dc.subjectEngineering
dc.titleCooling performance of thermoelectric cooler modules: Experimental and numerical methods
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublication0c85f61f-70fa-4f0d-83a0-a3a0ac50e069
relation.isAuthorOfPublication.latestForDiscovery0c85f61f-70fa-4f0d-83a0-a3a0ac50e069

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