PREDICTION OF THERMAL PERFORMANCE OF DESIGNED DIFFERENT OBSTACLES ON ABSORBER PLATES IN SOLAR AIR COLLECTORS BY SUPPORT VECTOR MACHINE

Öz

 In this study,   energy performance of a new flat plate solar air collector (SAHs) with different obstacles at fin shape and rectangle Type I and  Type II was investigated. The measured parameters were the inlet and outlet temperatures, the absorbing plate temperatures, the ambient temperature, and the solar radiation. Further, the measurements were performed at different values of mass flow rate of air (0.0074, 0.0052, 0.0016 kg/s). The thermal efficiency was calculated based on the measurements The results obtained were trained and tested with the support vector machine (SVM) which is one of the regression analyse methods. 10-fold cross-validation method was used to evaluate the regression performance. The best regression analysis result was obtained using cubic SVM method in which R² was 0.88 in the Type II air collector. Comparison between predicted and experimental results indicates that the proposed SVM model can be used for estimating the efficiency of SAHs with reasonable accuracy.

Anahtar Kelimeler

• Solar Air Collector,Thermal Efficiency,SVM

Kaynakça

1. Koçyiğit H., Yutucu plaka üzerine farklı türde kanatçıkların yerleştirildiği bir havalı kollektörün enerji ve ekserji analizi, Yüksek Lisans, Fırat Üniversitesi, 2008.
2. Güneş Enerjisi Potansiyel Atlası, (2007). www.eiei.gov.tr
3. Karsli, S., Performance analysis of new-design solar air collectors for drying applications, Renewable Energy, 32, (2007), pp.1645-1660.
4. Ucar, A., Inallı, M., Thermal and exergy analysis of solar air collectors with passive augmentation techniques, International Communications in Heat and Mass Transfer, 33 (2006), pp. 1281–1290.
5. Moummi, N., Youcef-Ali, S., Moummi, A., Desmons, J.Y., Energy analysis of a solar air collector with rows of fins, Renewable Energy, 29, (2004), 13, pp. 2053-2064.
6. Hegazy Adel, A., , Optimization of flow-channel depth for conventional flat-plate solar air heaters, Renewable Energy, 7, (1996) 1, pp.15-21.
7. Dincer, I., On energetic, exergetic and environmental aspects of drying systems, International Journal of Energy Research, 26 (2002), 8, pp.717-727.
8. Dincer, I. and Sahin, A.Z., A new model for thermodynamic analysis of a drying process, International Journal of Heat and Mass Transfer, 47 (2004), 4, pp.645-652.

9. Midilli, A., Kucuk, H., , Energy and exergy analysis of solar drying process of Pistachio, Energy, 28, (2003) pp. 539-556.
10. Kurtbas, İ., Durmuş, A., Efficiency and exergy analysis of a new solar air heater, Renewable Energy, 29, (2004), pp. 1489-1501.
11. Aldabbagh L.B.Y., Egelioglu, F., Ikan M., Single and double pass solar air heaters with wire mesh as packing bed, Energy, 35, (2010) pp. 3783–3787.
12. Yeh, H., Ho, C., Hou, J., The improvement of collector efficiency in solar air heaters by simultaneously air flow over and under the absorbing plate, Energy, 24, (1999), 857-871.
13. Karim, Md. A., Hawlader, M.N.A., Performance investigation of flat plate, v-corrugated and finned air collectors, Energy, 31, (2006), pp. 452-470.
14. Hachemi A., Experimental study of thermal performance of offset rectangular plate fin absorber-plates, Renewable Energy, 17 (1999) 3 pp. 371–84.
15. Benli, H., Determination of thermal performance calculation of two different types solar air collectors with the use of ANN, Int. Jo. of Heat and Mass Transfer, 60, (2013) pp. 1-7.
16. Esen H., Özgen F., Esen M., Sengür A., Artificial neural network and wavelet neural network approaches for modelling of a solar air heater, Expert System with Applications, 36, (2009), pp. 11240-11248.
17. Zhong, Z. D., Zhu, X. J., and Cao, G. Y., Modeling a PEMFC by a support vector machine. Journal of Power Sources 160 (2006) 1, pp. 293–298.
18. Drezet, P. M. L., and Harrison, R. F., Support vector machines for system identification, UKACC International Conference on CONTROL’98, 1–4 September (1998)., pp. 688–692.
19. Schölkopf, B., Sung, K., Burges, C. J. C., Girosi, F., Niyogi, P., Poggio, T., and Vapnik, V.N. Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing 45, (1997) pp. 2758–2765.
20. Yıldız , A., Gürlek, G., Güngör, A., ve Özbalta, N., Alüminyum ve Bakır borulu Güneş Kolektörlerinin Enerji ve Ekserji Verimlerinin Deneysel Karşılaştırılması, Mühendis ve Makine Cilt: 48,(2002) Sayı: 569
21. Khazaee, A., Ebrahimzadeh, A., Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features, Biomedical Signal Processing and Control, 5, (2010) 4, pp. 252–263.
22. Ayhan, S., Erdoğmuş, Ş., Destek Vektör Makineleriyle Sınıflandırma Problemlerinin Çözümü İçin Çekirdek Fonksiyonu Seçimi, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 9, (2014) 1, pp. 175–198,
23. Osuna, E., Freund, R., Girosi, F., Support Vector Machines : Training and Applications, Massachusetts Institute of Technology, 9217041 (1997), no. 1602.
24. Mehta, S.S., Lingayat, N.S., Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram, Biomedical Signal Processing and Control, 3, (2008) 4, pp. 341–349.
25. Chatterjee, S., A. S., Hadi, Regression Analysis by Example, 5th ed. Hoboken: John Wiley & Sons, 2012.
26. Diez, D.M., Barr, C.D., Çetinkaya-Rundel, M., OpenIntro Statistics Third Edition, 2nd ed. OpenIntro, 2012.
27. Esen, H., Ozgen, F., Esen, M., Sengur, A., Modelling of a new solar air heater through least-squares support vector machines, Expert Systems with Applications, 36, (2009) 7, pp. 10673–10682.
28. Akbari, A., Arjmandi, M. K., An efficient voice pathology classification scheme based on applying multi-layer linear discriminant analysis to wavelet packet-based features, Biomedical Signal Processing and Control, 10, (2014) 1, pp. 209–223.
29. Wong, T.T., Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation, Pattern Recognition, 48, (2015) 9, pp. 2839–2846
30. Jiang, G., Wang, W., Error estimation based on variance analysis of k-fold cross-validation, Pattern Recognition, 69, (2017), pp. 94–106.