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Journals / Firat University Journal of Experimental and Computational Engineering / 2022 / Cilt: 1 - Sayı: 1

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

Ebru Kavak AKPINAR
Mehmet DAŞ
Pages
14–23
Publication Date
2022-02-24
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Solar air collectors (SAC) are thermodynamic systems that convert solar energy into useful fluid energy.
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Key Findings & Data:The obtained values and the thermal performance of the SAC were examined during the sunshine period.
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Conclusion & Impact:SAC heat transfer coefficient values were modeled with a mean absolute error of 0.6.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables: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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Cite This Article

AKPINAR, 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://ajindex.com/articles/852097

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