Browsing by Author "Kallis, Klaus"

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Influence of geometrical factors on performance of thermoelectric material using numerical methods
(Springer, 2015-06-01) Derebaşı, Naim; Eltez, Muhammed; Güldiken, Fikret; Sever, Aziz; Kallis, Klaus; Kılıç, Halil; DEREBAŞI, NAİM; Güldiken, Fikret; Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü; 0000-0003-2546-0022; AAI-2254-2021; CRO-8755-2022
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.
Performance of novel thermoelectric cooling module depending on geometrical factors
(Springer, 2015-06-01) Derebaşı, Naim; Eltez, Muhammed; Güldiken, Fikret; Sever, Aziz; Kallis, Klaus; Kılıç, Halil; Özmutlu, Emin N.; DEREBAŞI, NAİM; Güldiken, Fikret; Özmutlu, Emin N.; Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.; 0000-0003-2546-0022; AAI-2254-2021; CRO-8755-2022; FPR-2739-2022
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.