Araştırma Makalesi
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TERMOELEKTRİK SOĞUTMA MODÜLLERİNİN SOĞUTMA PERFORMANSI: DENEYSEL VE SAYISAL YÖNTEMLER

Yıl 2022, Cilt: 42 Sayı: 2, 233 - 244, 31.10.2022
https://doi.org/10.47480/isibted.1194999

Öz

Termoelektrik modülün ısısal kararlılığını ve soğutma verimini arttırmak için, atımlı frekans modülasyonunda yeni bir atımlı sürücü yöntemi dikdörtgen dalga olan girişin en uygun yapılması ile geliştirilmiştir. Bu sürücü yönteminin amacı termoelektrik soğutma modülünün sıcaklığını daha iyi kontrol etmek ve verimini arttırmaktır. Bu yöntemde, %50 çalışma periyodundaki ortalama akım ve sürücü atımın en büyük değeri aynı büyüklüktedir ve dc ile sürülen Peltier modülün performansı atımla sürülen ile karşılaştırılmıştır. Ölçme sonuçları, termoelektrik modülün verim katsayısının, atımlı frekans modülasyonu sürücü yöntemi ile sabit dc sürücü yöntemi karşılaştırıldığında %102’ye kadar arttığını göstermiştir. Yapay sinir ağları, alınan deneysel verileri çözümlemek ve modülün performansını tahmin etmek için başarı ile kullanılmıştır. Geliştirilen yapay sinir ağı modeli öğrenmede kullanılmayan verilerle denendiğinde, modülün ortalama uyumu %99 ve en büyük tahmin etme hatası %1.38 olmuştur. Deneysel ve tahmin verilerine bağlı olarak doğru ve basit bir analitik denklem MATLAB® eğri uyum programı kullanılarak belirlenmiştir. Analitik denklemin ortalama uyumu 0.99 ve etkin hatası 0.074 olmuştur.

Kaynakça

  • Derebasi N., Eltez M., Guldiken F., Sever A., Kallis K., Kilic H. and Ozmutlu E.N., 2015, Performance of novel thermoelectric cooling module depending on geometrical factors, J. Elec. Mat. 44, 6, 1566 – 1572.
  • Sekiguchi R., Liu Y. and Sano Y., 2018, Thermal equivalent circuit of Peltier divice considered Seebeck effect and driving method improving cooling efficiency of the device, Elec. & Com. in Japan, 101, 5, 73 – 83.
  • Twaha S., Zhu J., Yan Y. and Li B., 2016, A comprehensive review of thermoelectric technology: Materials, applications, modelling and performance improvement, Ren. Sus. Energy Rev., 65, 698 – 726.
  • Song Lv., Zuogin Q., Dengyun H., Xiaoyuan L. and Wei H., 2020, A comprehensive review of strategies and approaches for enhancing the performance of thermoelectric module, Energies, 13, 3142, 2 – 24.
  • Derebasi N., Eltez M., Guldiken F., Sever A., Kallis K. and Kilic H., 2015, Influence of geometrical factors on performance of thermoelectric material using numerical methos, J. Elec. Mat. 44, 6, 2068 – 2073.
  • Baubaris A., Karampasis E., Voglitsis D. and Papanikolaou N., 2017, Experimental survey on active thermoelectric cooling driven by PWM techniques, Panhellenic Conference on Electronics and Telecommunications PACET.
  • Guclu T. and Cuce E., 2019, Thermoelectric Coolers (TECs): From Theory to Practice, J. Electronic Materials, 48, 1, 211 – 230.
  • Laidi M. and Hanini S., 2013, Optimal solar COP prediction of a solar-assisted adsorption refrigeration system working with activated carbon/methanol as working pairs using direct and inverse artificial neural network, Int J Ref., 36, 247 – 257.
  • Sulaiman A. C., Amin N. A. M., Basha M. H., Majid M. S. A., Nasir N. F. M. and Zaman I., 2018, Cooling performance of thermoelectric cooling (TEC) and applications: A review, Matec Web of Conferences, 225, 03021, 1 – 10.
  • Rowe D. M., 2006, Thermoelectrics Handbook Macro to Nano, CRC Taylor & Francis.
  • Riffat S. B. and Ma X., 2003, Thermoelectrics: a review of present and potential applications, Appl. Therm. Eng., 23, 913 – 935.
  • Bar-Cohen A., Solbrekken G. L. and Yazawa K., 2005, Thermoelectric-Powered convective cooling of microprocessors, IEEE Trans.Adv. Packag., 28, 2, 231 – 239.
  • Graupe D., 1997, Principles of Artificial Neural Networks, Singapore: World Scientific.
  • Qnet2000 Help Manual.
  • MATLAB®, Curve Fitting ToolboxTM User’s Guide, R2018b.

COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS

Yıl 2022, Cilt: 42 Sayı: 2, 233 - 244, 31.10.2022
https://doi.org/10.47480/isibted.1194999

Öz

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® Curve Fitting Toolbox. The average correlation of the analytical model was 0.99 and the root-mean-square error was 0.074.

Kaynakça

  • Derebasi N., Eltez M., Guldiken F., Sever A., Kallis K., Kilic H. and Ozmutlu E.N., 2015, Performance of novel thermoelectric cooling module depending on geometrical factors, J. Elec. Mat. 44, 6, 1566 – 1572.
  • Sekiguchi R., Liu Y. and Sano Y., 2018, Thermal equivalent circuit of Peltier divice considered Seebeck effect and driving method improving cooling efficiency of the device, Elec. & Com. in Japan, 101, 5, 73 – 83.
  • Twaha S., Zhu J., Yan Y. and Li B., 2016, A comprehensive review of thermoelectric technology: Materials, applications, modelling and performance improvement, Ren. Sus. Energy Rev., 65, 698 – 726.
  • Song Lv., Zuogin Q., Dengyun H., Xiaoyuan L. and Wei H., 2020, A comprehensive review of strategies and approaches for enhancing the performance of thermoelectric module, Energies, 13, 3142, 2 – 24.
  • Derebasi N., Eltez M., Guldiken F., Sever A., Kallis K. and Kilic H., 2015, Influence of geometrical factors on performance of thermoelectric material using numerical methos, J. Elec. Mat. 44, 6, 2068 – 2073.
  • Baubaris A., Karampasis E., Voglitsis D. and Papanikolaou N., 2017, Experimental survey on active thermoelectric cooling driven by PWM techniques, Panhellenic Conference on Electronics and Telecommunications PACET.
  • Guclu T. and Cuce E., 2019, Thermoelectric Coolers (TECs): From Theory to Practice, J. Electronic Materials, 48, 1, 211 – 230.
  • Laidi M. and Hanini S., 2013, Optimal solar COP prediction of a solar-assisted adsorption refrigeration system working with activated carbon/methanol as working pairs using direct and inverse artificial neural network, Int J Ref., 36, 247 – 257.
  • Sulaiman A. C., Amin N. A. M., Basha M. H., Majid M. S. A., Nasir N. F. M. and Zaman I., 2018, Cooling performance of thermoelectric cooling (TEC) and applications: A review, Matec Web of Conferences, 225, 03021, 1 – 10.
  • Rowe D. M., 2006, Thermoelectrics Handbook Macro to Nano, CRC Taylor & Francis.
  • Riffat S. B. and Ma X., 2003, Thermoelectrics: a review of present and potential applications, Appl. Therm. Eng., 23, 913 – 935.
  • Bar-Cohen A., Solbrekken G. L. and Yazawa K., 2005, Thermoelectric-Powered convective cooling of microprocessors, IEEE Trans.Adv. Packag., 28, 2, 231 – 239.
  • Graupe D., 1997, Principles of Artificial Neural Networks, Singapore: World Scientific.
  • Qnet2000 Help Manual.
  • MATLAB®, Curve Fitting ToolboxTM User’s Guide, R2018b.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

İlhan Kahraman Bu kişi benim

Naim Derebaşı Bu kişi benim

Yayımlanma Tarihi 31 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 42 Sayı: 2

Kaynak Göster

APA Kahraman, İ., & Derebaşı, N. (2022). COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS. Isı Bilimi Ve Tekniği Dergisi, 42(2), 233-244. https://doi.org/10.47480/isibted.1194999
AMA Kahraman İ, Derebaşı N. COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS. Isı Bilimi ve Tekniği Dergisi. Ekim 2022;42(2):233-244. doi:10.47480/isibted.1194999
Chicago Kahraman, İlhan, ve Naim Derebaşı. “COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS”. Isı Bilimi Ve Tekniği Dergisi 42, sy. 2 (Ekim 2022): 233-44. https://doi.org/10.47480/isibted.1194999.
EndNote Kahraman İ, Derebaşı N (01 Ekim 2022) COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS. Isı Bilimi ve Tekniği Dergisi 42 2 233–244.
IEEE İ. Kahraman ve N. Derebaşı, “COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS”, Isı Bilimi ve Tekniği Dergisi, c. 42, sy. 2, ss. 233–244, 2022, doi: 10.47480/isibted.1194999.
ISNAD Kahraman, İlhan - Derebaşı, Naim. “COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS”. Isı Bilimi ve Tekniği Dergisi 42/2 (Ekim 2022), 233-244. https://doi.org/10.47480/isibted.1194999.
JAMA Kahraman İ, Derebaşı N. COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS. Isı Bilimi ve Tekniği Dergisi. 2022;42:233–244.
MLA Kahraman, İlhan ve Naim Derebaşı. “COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS”. Isı Bilimi Ve Tekniği Dergisi, c. 42, sy. 2, 2022, ss. 233-44, doi:10.47480/isibted.1194999.
Vancouver Kahraman İ, Derebaşı N. COOLING PERFORMANCE OF THERMOELECTRIC COOLER MODULES: EXPERIMENTAL AND NUMERICAL METHODS. Isı Bilimi ve Tekniği Dergisi. 2022;42(2):233-44.