Derebaşı, NaimEltez, MuhammedGüldiken, FikretSever, AzizKallis, KlausKılıç, HalilÖzmutlu, Emin N.2024-08-082024-08-082015-06-010361-5235https://doi.org/10.1007/s11664-014-3482-xhttps://link.springer.com/article/10.1007/s11664-014-3482-xhttps://hdl.handle.net/11452/43797Bu çalışma, 06-10, Temmuz 2014 tarihlerinde Nashville[Amerika]’da düzenlenen International Conference on Thermoelectrics (ICT) Kongresi‘nde bildiri olarak sunulmuştur.A geometrical shape factor was investigated for optimum thermoelectric performance of a thermoelectric module using finite element analysis. The cooling power, electrical energy consumption, and coefficient of performance were analyzed using simulation with different current values passing through the thermoelectric elements for varying temperature differences between the two sides. A dramatic increase in cooling power density was obtained, since it was inversely proportional to the length of the thermoelectric legs. An artificial neural network model for each thermoelectric property was also developed using input-output relations. The models including the shape factor showed good predictive capability and agreement with simulation results. The correlation of the models was found to be 99%, and the overall prediction error was in the range of 1.5% and 1.0%, which is within acceptable limits. A thermoelectric module was produced based on the numerical results and was shown to be a promising device for use in cooling systems.eninfo:eu-repo/semantics/closedAccessNeural-networkPredictionSystemThermoelectric cooling moduleCooling performanceFinite element methodArtificial neural networkEngineeringMaterials sciencePhysicsArticle0003538137000301566157244610.1007/s11664-014-3482-x