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Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube

Author

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  • Suvanjan Bhattacharyya

    (Department of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Pilani Campus, Vidya Vihar, Pilani 333031, Rajasthan, India)

  • Debraj Sarkar

    (Department of Textile Technology, Government College of Engineering and Textile Technology, Berhampore, Murshidabad 742101, West Bengal, India)

  • Rahul Roy

    (Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India)

  • Shramona Chakraborty

    (Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India)

  • Varun Goel

    (Department of Mechanical Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, HP, India)

  • Eydhah Almatrafi

    (Mechanical Engineering Department-Rabigh, Center of Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

In the present study, the heat transfer and thermal performance of a helical corrugation with perforated circular disc solar air-heater tubes are predicted using a machine learning regression technique. This paper describes a statistical analysis of heat transfer by developing an artificial neural network-based machine learning model. The effects of variation in the corrugation angle (θ), perforation ratio (k), corrugation pitch ratio (y), perforated disc pitch ratio (s), and Reynolds number have been analyzed. An artificial neural network model is used for regression analysis to predict the heat transfer in terms of Nusselt number and thermohydraulic efficiency, and the results showed high prediction accuracies. The artificial neural network model is robust and precise, and can be used by thermal system design engineers for predicting output variables. Two different models are trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The models are evaluated to have an accuracy of 97.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce.

Suggested Citation

  • Suvanjan Bhattacharyya & Debraj Sarkar & Rahul Roy & Shramona Chakraborty & Varun Goel & Eydhah Almatrafi, 2021. "Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7477-:d:588544
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    References listed on IDEAS

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    1. Rabbi, Khan Md. & Sheikholeslami, M. & Karim, Anwarul & Shafee, Ahmad & Li, Zhixiong & Tlili, Iskander, 2020. "Prediction of MHD flow and entropy generation by Artificial Neural Network in square cavity with heater-sink for nanomaterial," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Dezan, Daniel J. & Rocha, André D. & Ferreira, Wallace G., 2020. "Parametric sensitivity analysis and optimisation of a solar air heater with multiple rows of longitudinal vortex generators," Applied Energy, Elsevier, vol. 263(C).
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    Cited by:

    1. Roozbeh Vaziri & Akeem Adeyemi Oladipo & Mohsen Sharifpur & Rani Taher & Mohammad Hossein Ahmadi & Alibek Issakhov, 2021. "Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    2. Marcell Kupi & Eszter Szemerédi, 2021. "Impact of the COVID-19 on the Destination Choices of Hungarian Tourists: A Comparative Analysis," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    3. Zálešák, Martin & Klimeš, Lubomír & Charvát, Pavel & Cabalka, Matouš & Kůdela, Jakub & Mauder, Tomáš, 2023. "Solution approaches to inverse heat transfer problems with and without phase changes: A state-of-the-art review," Energy, Elsevier, vol. 278(PB).

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