Machine Learning for Prediction of Heat Pipe Effectiveness

Anish Nair, Ramkumar P., Sivasubramanian Mahadevan, Chander Prakash, Saurav Dixit, Gunasekaran Murali, Nikolai Ivanovich Vatin, Kirill Epifantsev and Kaushal Kumar
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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
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

Energies, 2022, vol. 15, issue 9, 1-14

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.

Keywords: heat pipe; exchanger; machine learning; effectiveness (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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