Machine Learning for Prediction of Heat Pipe Effectiveness.

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

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.

MACHINE learning; HEAT pipes; RANDOM forest algorithms; HEAT exchangers; WORKING fluids; MACHINE performance

Energies (19961073), 2022, Vol 15, Issue 9, pN.PAG

1996-1073

Academic Journal

10.3390/en15093276