Dergiler / Firat University Journal of Experimental and Computational Engineering / 2026 / Cilt: 1 Sayı: 1

Modeling of a solar air collector heat transfer coefficient with regression algorithms

Ebru Kavak Akpınar(FIRAT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0003-0666-9189
Mehmet Daş(FIRAT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-4143-9226
Sayfa
14–23
Yayın Tarihi
2022-02-24
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

SAC heat transfer coefficient values were modeled with a mean absolute error of 0.6.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Solar air collectors (SAC) are thermodynamic systems that convert solar energy into useful fluid energy.
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Temel Bulgular & Veri:The obtained values and the thermal performance of the SAC were examined during the sunshine period.
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Sonuç & Katkı:SAC heat transfer coefficient values were modeled with a mean absolute error of 0.6.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Solar air collector · Heat transfer coefficient · Solar energy

Abstract

Solar air collectors (SAC) are thermodynamic systems that convert solar energy into useful fluid energy. SACs are very common in heating, cooling, food drying, and many other low-temperature applications. In this study, the heat transfer coefficient in the SAC was calculated according to Newton's cooling law. The obtained values and the thermal performance of the SAC were examined during the sunshine period. At the same time, SAC heat transfer coefficient models were made with the help of regression algorithms (multiple linear regression, simple linear regression) using SAC temperature data obtained by experimental measurements (inlet fluid, outlet fluid, and absorber plate) and solar radiation data on the SAC. As a result of the modeling, linear mathematical equations expressing the heat transfer coefficient for SACs were obtained. Obtained experimental and model data were compared. SAC heat transfer coefficient values were modeled with a mean absolute error of 0.6.

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Bu Makaleye Atıf Yap

Akpınar, E. K., Daş, M. (2022). Modeling of a solar air collector heat transfer coefficient with regression algorithms. Firat University Journal of Experimental and Computational Engineering, Cilt: 1 Sayı: 1, 14–23. https://doi.org/10.5505/fujece.2022.43153

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Kaynakça (20)

  1. Huang MY, Wang MLi, Keovisar V, Li X, Kong D, Yu Q. "Comparative study on energy and exergy properties of solar photovoltaic/thermal air collector based on amorphous silicon cells". Applied Thermal Engineering, 185, 116376, 2021.
  2. Arslan E, Aktaş M. "4E analysis of infrared-convective dryer powered solar photovoltaic thermal collector". Solar Energy, 208, 46–57, 2020.
  3. Tagnamas Z, Lamsyehe H, Moussaoui H, Bahammou Y, Kouhila M, Idlimam A, Lamharrar A. "Energy and exergy analyses of carob pulp drying system based on a solar collector". Renewable Energy, 163, 495–503, 2021.
  4. Ceylan I, Gürel AE. "Solar-assisted fluidized bed dryer integrated with a heat pump for mint leaves". Applied Thermal Engineering, 106, 899–905, 2016.
  5. Akpinar EK, Koçyiĝit F. "Energy and exergy analysis of a new flat-plate solar air heater having different obstacles on absorber plates". Applied Energy, 87, 3438–3450, 2010.
  6. Tuncer AD, Khanlari A, Sözen A, Gürbüz EY, Şirin C, Gungor A. "Energy-exergy and enviro-economic survey of solar air heaters with various air channel modifications". Renewable Energy, 160, 67–85, 2020.
  7. Komolafe CA, Oluwaleye IO, Awogbemi O, Osueke CO. "Experimental investigation and thermal analysis of solar air heater having rectangular rib roughness on the absorber plate". Case Studies in Thermal Engineering, 14, 100442, 2019.
  8. Sudhakar P, Cheralathan M. "Thermal performance enhancement of solar air collector using a novel V-groove absorber plate with pin-fins for drying agricultural products: an experimental study". Journal of Thermal Analysis and Calorimetry, 140, 2397–2408, 2020.
  9. Norouzian R. The Palgrave Handbook of Applied Linguistics Research Methodology. 1nd ed. Palgrave Macmillan, London, Springer, 2018.
  10. Vafaei LE, Sah M. "Predicting efficiency of flat-plate solar collector using a fuzzy inference system". Procedia computer science, 120, 221-228, 2017.
  11. Çakmak G, Yıldız C. "The prediction of seedy grape drying rate using a neural network method". Computers and Electronics in Agriculture, 75(1), 132-138, 2011.
  12. Caner M, Gedik E, Keçebaş A. "Investigation on thermal performance calculation of two type solar air collectors using artificial neural network". Expert Systems with Applications, 38(3), 1668-1674, 2011.
  13. Ghritlahre HK, Prasad RK. Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique". Thermal Science and Engineering Progress, 6, 226-235, 2018.
  14. Holman JP. Experimental methods for engineers. Eight Edit, McGraw-Hill, New York, 2012.
  15. Acır A, Ata İ. "A study of heat transfer enhancement in a new solar air heater having circular type turbulators". Journal of the Energy Institute, 89, 606–616, 2016.
  16. Devecioglu AG, Oruc V. "Experimental investigation of thermal performance of a new solar air collector with porous surface". Energy Procedia. 113, 251–258, 2017.
  17. Acir A, Ata I, Canli ME. "Investigation of effect of the circular ring turbulators on heat transfer augmentation and fluid flow characteristic of solar air heater". Experimental Thermal and Fluid Science, 77, 45–54, 2016. https://doi.org/10.1016/j.expthermflusci.2016.04.012.
  18. Acır A, Canlı ME, Ata İ, Çakıroğlu R. "Parametric optimization of energy and exergy analyses of a novel solar air heater with grey relational analysis". Applied Thermal Engineering, 122, 330–338, 2017.
  19. Sreehari E, Pradeep Ghantasala GS. "Climate changes prediction using simple linear regression". Journal of Computational and Theoretical Nanoscience, 16, 655–658, 2019.
  20. Mahaboob B, Praveen JP, Appa Rao BV, Harnath Y, Narayana C, Prakash GB. "A study on multiple linear regression using matrix calculus". Advances in Mathematics: Scientific Journal, 9, 4863–4872, 2020.