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Thermal performance modelling of solar flat plate parallel tube collector using ANN

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  • Mausam, Kuwar
  • Singh, Shiva
  • Ghosh, Subrata Kumar
  • Singh, Ravindra P.

Abstract

This study aimed to investigate the solar flat plate collector (FPC) thermal performance using a hybrid nanofluid made of Cu-MWCNTs-water. The study involved varying the FPC flow rates, inclination angle, and radiation intensity. The ANN and mathematical model has been developed on the basis of experimental data to predict instantaneous efficiency. The concentration, flow rate, angle of inclination, and intensity were input to the network, and instantaneous efficiency was output from the network. The neurons having minimal mean square error (MSE) and coefficient of determination (R2) were selected. An enhancement of 32.25 % was observed using Cu-MWCNTs hybrid nanofluid in instantaneous efficiency. The R2 ranges are 0.8857–0.9533 and 0.94938–0.9989, respectively, showing the proposed correlation and neural network accuracy. The present study is useful in making highly optimised predictions of the instantaneous efficiency in FPC using a hybrid nanofluid for heat transfer, fluid heating, space heating, etc.

Suggested Citation

  • Mausam, Kuwar & Singh, Shiva & Ghosh, Subrata Kumar & Singh, Ravindra P., 2024. "Thermal performance modelling of solar flat plate parallel tube collector using ANN," Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:energy:v:303:y:2024:i:c:s0360544224017134
    DOI: 10.1016/j.energy.2024.131940
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    References listed on IDEAS

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    3. Jarimi, Hasila & Al-Waeli, Ali H.A. & Razak, Tajul Rosli & Abu Bakar, Mohd Nazari & Fazlizan, Ahmad & Ibrahim, Adnan & Sopian, Kamaruzzaman, 2022. "Neural network modelling and performance estimation of dual-fluid photovoltaic thermal solar collectors in tropical climate conditions," Renewable Energy, Elsevier, vol. 197(C), pages 1009-1019.
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