Derebaşı, NaimEltez, MuhammedGüldiken, FikretSever, AzizKallis, KlausKılıç, Halil2024-08-122024-08-122015-06-010361-5235https://doi.org/10.1007/s11664-015-3657-0https://link.springer.com/article/10.1007/s11664-015-3657-0https://hdl.handle.net/11452/43896Bu çalışma, 06-10, Temmuz 2014 tarihlerinde Nashville[Amerika]’da düzenlenen International Conference on Thermoelectrics (ICT) Kongresi‘nde bildiri olarak sunulmuştur.Prediction of the performance of thermoelectric cooling material (figure of merit, ZT) was carried out by simulated results obtained from the finite element method (FEM) as a training dataset with an artificial neural network. A total of 87 input vectors for the ZT obtained from the four thermoelectric cooling (TEC) modules modeled using the FEM analysis were available in the training set to a back-propagation artificial neural network. An average correlation and maximum prediction error were found to be 100% and 0.01%, respectively, for the ZT after training. The standard deviation of the values was 0.05%. A set of test data, different from the training dataset was used to investigate the network performance. The average correlation and maximum prediction error were found to be 99.92% and 0.07%, respectively, for the tested TEC module. A thermoelectric module produced based on the numerical results was shown to be a promising device for use in cooling systems.eninfo:eu-repo/semantics/closedAccessThermoelectric coolingCooling performanceFigure of meritArtificial neural networkScience & technologyTechnologyPhysical sciencesEngineering, electrical & electronicMaterials science, multidisciplinaryPhysics, appliedEngineeringMaterials sciencePhysicsArticle0003538137001002068207344610.1007/s11664-015-3657-01543-186X