V TİPİ HAVALI BİR GÜNEŞ KOLEKTÖRÜNÜN ISIL PERFORMANSININ DENEYSEL OLARAK İNCELENMESİ VE YAPAY SİNİR AĞLARI İLE MODELLENMESİ

Year 2021, Volume: 9 Issue: 4, 1212 - 1223, 20.12.2021

Doğan Burak Saydam , Kamil Neyfel Çerçi , Ertaç Hürdoğan

https://doi.org/10.21923/jesd.935201

Cited By: 2

Abstract

Bu çalışmada, V tipi emici plaka yüzey geometrisine sahip çift geçişli havalı bir güneş kolektörü tasarlanarak imal edilmiş ve kolektörün performansı Osmaniye ili iklim koşullarında deneysel olarak incelenmiştir. Deney düzeneğinde sıcaklık, nem, ışınım ve hava hızı gibi farklı parametreler ölçülmüş ve elde edilen veriler kullanılarak termodinamiğin birinci yasasına göre kolektörün enerji analizi yapılmıştır. Çalışmada ayrıca deneysel olarak elde edilen kolektör çıkış sıcaklığı, üç farklı Yapay Sinir Ağı modeli kullanılarak tahmin edilmiştir. Çalışma sonucunda kolektör giriş ve çıkış sıcaklığı arasında maksimum 36,07°C’lik bir fark olduğu tespit edilmiştir. Isıl verim deney süresince maksimum %71,42 değerine ulaşmış ve ortalama ısıl verim ise %56,21 olarak hesaplanmıştır. Çıkış sıcaklığının tahminlenmesi için oluşturulan her üç model (YSA-1,2,3) için de yakınsamanın çok iyi olduğu fakat LevenbergMarquardt eğitim algoritmasının kullanıldığı YSA-1 modelinin diğer modellere göre deneysel sonuçların tahmininde daha etkili olduğu tespit edilmiştir. Tahminlenen kolektör çıkış sıcaklığı değerleri kullanılarak hesaplanan kolektör ısıl verimi, deneysel verilerle hesaplanan ısıl verim ile uyum içerisindedir.

Keywords

Havalı Güneş Kolektörü, V tipi, Isıl Verim, Tahminleme, Yapay Sinir Ağları

Supporting Institution

-

Project Number

-

Thanks

-

INVESTIGATION OF THERMAL PERFORMANCE OF V TYPE SOLAR AIR COLLECTOR AND MODELING USING ARTIFICIAL NEURAL NETWORKS

Abstract

A double pass air solar collector with V type absorber plate surface geometry was designed and manufactured in this study. The performance of the collector was experimentally investigated in the climatic conditions of Osmaniye. In the experimental setup, different parameters such as temperature, humidity, radiation, and air velocity were measured. The energy analysis of the collector was made according to the first law of thermodynamics using the obtained data. In addition, the experimentally obtained collector outlet temperature was estimated using three different Artificial Neural Network models. As a result of the study, it was determined that there is a temperature difference between the collector inlet and outlet with maximum value of 36,07 °C. Thermal efficiency reached a maximum value of 71,42% during the experiment, and the average thermal efficiency was calculated as 56.21%. It was determined that the convergence was very good for all three models (ANN-1,2,3) created to predict the outlet temperature but, the ANN-1 model, which use LevenbergMarquardt training algorithm, was more effective in predicting the experimental results compared to the other models. The collector thermal efficiency calculated using the estimated collector outlet temperature values follows the thermal efficiency calculated with experimental data.

Keywords

Solar Air Collector, V type, Thermal Efficiency, Estimation, Artificial Neural Networks

Project Number

-

References

• Abu Salam, Z., Keskin M. E. 2018. Yapay Sinir Ağları ile Dibis Barajı'nın Seviye Tahmini. Mühendislik Bilimleri ve Tasarımı Dergisi, 6(4), 564 – 569.
• Abuşka, M., Akgül, M., Altıntaş, V., 2017. Yutucu Plaka Üzerine Konik Yayların Yerleştirildiği Güneş Enerjili Hava Kollektörünün Bulanık Mantık ile Modellenmesi. Politeknik Dergisi, 20 (4), 907-914.
• Acır, A., Canlı, M. E., Ata, İ., Çakıroğlu, R., 2017. Parametric optimization of energy and exergy analyses of a novel solar air heater with grey relational analysis. Applied Thermal Engineering, 122, 330-338.
• Akpinar, E. K., Toraman, S., 2016. Determination of drying kinetics and convective heat transfer coefficients of ginger slices, Heat Mass Transfer, 52, 2271–2281.
• Alta, D., Bilgili, E., Ertekin, C., Yaldiz, O., 2010. Experimental investigation of three different solar air heaters: Energy and exergy analyses. Applied Energy, 87(10), 2953-2973.
• Aylak, B. L., Özdemir, M. H., İnce, M., Oral, O. 2021. Prediction of Turkey’s Electricity Generation by Sources Using Artificial Neural Network and Bidirectional Long Short - Term Memory Mühendislik Bilimleri ve Tasarımı Dergisi,9(2), 425-435.
• Caner, M., Gedik, E., Keçebaş, A., 2011. Investigation on thermal performance calculation of two type solar air collectors using artificial neural network. Expert Systems with Applications, 38(3), 1668-1674.
• Chai, T., Draxler, R. R., 2014. Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)? -Arguments against avoiding RMSE in literature. Geoscientific Model Development Discussions, 7: 1247- 1250.
• Colangelo, G., Favale, E., Miglietta, P., de Risi, A., 2016. Innovation in flat solar thermal collectors: A review of the last ten years experimental results. Renewable and Sustainable Energy Reviews, 57, 1141-1159.
• Çerçi, K., Saydam, D., Hürdoğan, E., 2020. Estimation of the Experimental Drying Performance Parameters Using Polynomial SVM and ANN Models. European Mechanical Science, 4 (3), 123-130.
• Daş, M., Akpinar, E. K. 2018. Mushroom drying in air heated solar collector drying system and modeling of drying performance with artificial neural network. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 11(1), 23-30.
• Daş, M., Akpinar, E. K. 2020. Güneş enerjili bir kurutucudaki ekserjetik faktörlerin hesaplanması ve yapay sinir ağı ile modellenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(2), 593-609.
• Deo, N. S., Chander, S., Saini, J. S., 2016. Performance analysis of solar air heater duct roughened with multigap V-down ribs combined with staggered ribs. Renewable Energy, 91, 484-500.
• Elsheikh, A. H., Sharshir, S. W., Abd Elaziz, M., Kabeel, A. E., Guilan, W., Haiou, Z. 2019. Modeling of solar energy systems using artificial neural network: A comprehensive review. Solar Energy, 180, 622-639.
• Esen, H., Ozgen, F., Esen, M., & Sengur, A. 2009. Artificial neural network and wavelet neural network approaches for modelling of a solar air heater. Expert systems with applications, 36(8), 11240-11248.
• Garcia, R. P., del Rio Oliveira, S., Scalon, V. L., 2019. Thermal efficiency experimental evaluation of solar flat plate collectors when introducing convective barriers. Solar Energy, 182, 278-285.
• Ghritlahre, H. K., Chandrakar, P., Ahmad, A., 2020. Application of ANN model to predict the performance of solar air heater using relevant input parameters. Sustainable Energy Technologies and Assessments, 40, 100764.
• Ghritlahre, H. K., Prasad, R. K., 2018. Application of ANN technique to predict the performance of solar collector systems-A review. Renewable and Sustainable Energy Reviews, 84, 75-88.
• Ghritlahre, H. K., Prasad, R. K., 2018. Development of optimal ANN model to estimate the thermal performance of roughened solar air heater using two different learning algorithms. Annals of Data Science, 5(3), 453-467.
• Holman J.P., 2001. Experimental methods for engineers, McGraw Hill., USA.
• Jafarkazemi, F., Ahmadifard, E., 2013. Energetic and exergetic evaluation of flat plate solar collectors. Renewable energy, 56, 55-63.
• Karim, M. A., Hawlader, M. N. A., 2006. Performance investigation of flat plate, v-corrugated and finned air collectors. Energy, 31(4), 452-470.
• Karim, M. A., Perez, E., Amin, Z. M., 2014. Mathematical modelling of counter flow v-grove solar air collector. Renewable energy, 67, 192-201.
• Karsli, S., 2007. Performance analysis of new-design solar air collectors for drying applications. Renewable Energy, 32(10), 1645-1660.
• Khanlari, A., Sözen, A., Afshari, F., Şirin, C., Tuncer, A. D., Gungor, A., 2020. Drying municipal sewage sludge with v-groove triple-pass and quadruple-pass solar air heaters along with testing of a solar absorber drying chamber. Science of The Total Environment, 709, 136198.
• Kumar, R., Kumar, A., Chauhan, R., Sethi, M., 2016. Heat transfer enhancement in solar air channel with broken multiple V-type baffle. Case Studies in Thermal Engineering, 8, 187-197.
• Leong, K. Y., Ong, H. C., Amer, N. H., Norazrina, M. J., Risby, M. S., Ahmad, K. K., 2016. An overview on current application of nanofluids in solar thermal collector and its challenges. Renewable and Sustainable Energy Reviews, 53, 1092-1105.
• Manjunath, M. S., Karanth, K. V., Sharma, N. Y., 2018. Numerical investigation on heat transfer enhancement of solar air heater using sinusoidal corrugations on absorber plate. International Journal of Mechanical Sciences, 138, 219-228.
• Naphon, P., Kornkumjayrit, K., 2008. Numerical analysis on the fluid flow and heat transfer in the channel with V-shaped wavy lower plate. International Communications in Heat and Mass Transfer, 35(7), 839-843.
• Oztop, H. F., Bayrak, F., Hepbasli, A., 2013. Energetic and exergetic aspects of solar air heating (solar collector) systems. Renewable and Sustainable Energy Reviews, 21, 59-83.
• Özkaya, M. G., Variyenli, H. İ., Korkmaz, M., 2007. Düzlemsel Güneş Kollektörlerinde Farklı Profillerdeki Emici Plakaların Deneysel İncelenmesi. Politeknik Dergisi, 10(2), 173-177.
• Saxena, A., Agarwal, N., Srivastava, G., 2013. Design and performance of a solar air heater with long term heat storage. International Journal of Heat and Mass Transfer, 60, 8-16.
• Saydam, D. B., Çerçi, K. N., Hürdoğan, E., Özalp, C., 2019. Manufacturing of a Finned Type Solar Air Collector and Investigation of Its Performance in Osmaniye Climate Conditions. UEMK 2019 Bildiriler Kitabı 24-25 Ekim 2019, Gaziantep, 1239-1247.
• Sharma, A., Chauhan, R., Singh, T., Kumar, A., Kumar, R., Sethi, M., 2017. Optimizing discrete V obstacle parameters using a novel Entropy-VIKOR approach in a solar air flow channel. Renewable Energy, 106, 310-320.
• Sözen, A., Menlik, T., Ünvar, S. 2008. Determination of efficiency of flat-plate solar collectors using neural network approach. Expert Systems with Applications, 35(4), 1533-1539.
• Ucar, A., Inallı, M., 2006. Thermal and exergy analysis of solar air collectors with passive augmentation techniques. International communications in heat and mass transfer, 33(10), 1281-1290.
• Varol, Y., Oztop, H. F., 2008. A comparative numerical study on natural convection in inclined wavy and flat-plate solar collectors. Building and environment, 43(9), 1535-1544.
• Zulkifle, I., Alwaeli, A. H., Ruslan, M. H., Ibarahim, Z., Othman, M. Y. H., Sopian, K., 2018. Numerical investigation of V-groove air-collector performance with changing cover in Bangi, Malaysia. Case studies in thermal engineering, 12, 587-599.