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Machine Learning for Prediction of Heat Pipe Effectiveness

Author

Listed:
  • Anish Nair

    (Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626126, India)

  • Ramkumar P.

    (Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626126, India)

  • Sivasubramanian Mahadevan

    (Automobile Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India)

  • Chander Prakash

    (School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, India)

  • Saurav Dixit

    (Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
    Division of Research & Innovation, Uttaranchal University, Dehradun 248007, India)

  • Gunasekaran Murali

    (Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia)

  • Nikolai Ivanovich Vatin

    (Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia)

  • Kirill Epifantsev

    (Saint-Petersburg University of Aerospace Instrumentation, 190000 Saint Petersburg, Russia)

  • Kaushal Kumar

    (Department of Mechanical Engineering, K. R. Mangalam University, Gurgaon 122103, India)

Abstract

This paper details the selection of machine learning models for predicting the effectiveness of a heat pipe system in a concentric tube exchanger. Heat exchanger experiments with methanol as the working fluid were conducted. The value of the angle varied from 0° to 90°, values of temperature varied from 50 °C to 70 °C, and the flow rate varied from 40 to 120 litres per min. Multiple experiments were conducted at different combinations of the input parameters and the effectiveness was measured for each trial. Multiple machine learning algorithms were taken into consideration for prediction. Experimental data were divided into subsets and the performance of the machine learning model was analysed for each of the subsets. For the overall analysis, which included all the three parameters, the random forest algorithm returned the best results with a mean average error of 1.176 and root-mean-square-error of 1.542.

Suggested Citation

  • Anish Nair & Ramkumar P. & Sivasubramanian Mahadevan & Chander Prakash & Saurav Dixit & Gunasekaran Murali & Nikolai Ivanovich Vatin & Kirill Epifantsev & Kaushal Kumar, 2022. "Machine Learning for Prediction of Heat Pipe Effectiveness," Energies, MDPI, vol. 15(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3276-:d:806076
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    References listed on IDEAS

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    1. Wang, Zhangyuan & Zhao, Xudong & Han, Zhonghe & Luo, Liang & Xiang, Jinwei & Zheng, Senglin & Liu, Guangming & Yu, Min & Cui, Yu & Shittu, Samson & Hu, Menglong, 2021. "Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study," Applied Energy, Elsevier, vol. 294(C).
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