Publication: Cooling performance of thermoelectric cooler modules: Experimental and numerical methods
dc.contributor.author | Kahraman, İlhan | |
dc.contributor.author | Derebaşı, Naim | |
dc.contributor.buuauthor | Kahraman, İlhan | |
dc.contributor.buuauthor | DEREBAŞI, NAİM | |
dc.contributor.department | Fen Edebiyat Fakültesi | |
dc.contributor.department | Fizik Bölümü | |
dc.contributor.orcid | 0000-0003-2546-0022 | |
dc.contributor.orcid | 0000-0003-0662-1636 | |
dc.contributor.researcherid | AAI-2254-2021 | |
dc.contributor.researcherid | HHV-3379-2022 | |
dc.date.accessioned | 2024-10-07T11:57:29Z | |
dc.date.available | 2024-10-07T11:57:29Z | |
dc.date.issued | 2022-01-01 | |
dc.description.abstract | A 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.sponsorship | Gempa Electro Mechanic and Engineering Co | |
dc.description.sponsorship | Kahramansan Teknoloji | |
dc.identifier.doi | 10.47480/isibted.1194999 | |
dc.identifier.endpage | 243 | |
dc.identifier.issn | 1300-3615 | |
dc.identifier.issue | 2 | |
dc.identifier.startpage | 232 | |
dc.identifier.uri | https://doi.org/10.47480/isibted.1194999 | |
dc.identifier.uri | https://dergipark.org.tr/en/pub/isibted/issue/73436/1194999 | |
dc.identifier.uri | https://hdl.handle.net/11452/45988 | |
dc.identifier.volume | 42 | |
dc.identifier.wos | 000890325500005 | |
dc.indexed.wos | WOS.SCI | |
dc.language.iso | en | |
dc.publisher | Turkish Soc Thermal Sciences Technology | |
dc.relation.journal | Isı Bilimi ve Tekniği Dergisi-Journal of Thermal Science and Technology | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | Thermoelectric cooler module | |
dc.subject | Cooling performance | |
dc.subject | Drive method | |
dc.subject | Artificial neural network | |
dc.subject | Thermodynamics | |
dc.subject | Engineering | |
dc.title | Cooling performance of thermoelectric cooler modules: Experimental and numerical methods | |
dc.type | Article | |
dspace.entity.type | Publication | |
local.contributor.department | Fen Edebiyat Fakültesi/Fizik Bölümü | |
relation.isAuthorOfPublication | 0c85f61f-70fa-4f0d-83a0-a3a0ac50e069 | |
relation.isAuthorOfPublication.latestForDiscovery | 0c85f61f-70fa-4f0d-83a0-a3a0ac50e069 |
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