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.

Influence on thermal performances of the concentric tube shell assisted heat-pipe heat exchanger with and without adiabatic section has been experimentally investigated. Water is used as heat-carrying fluid and methanol is used as working fluid in the heat pipe.Mass flow rates of hot water ranges from 30 lph to 70 lph, for cold water 15 lph to 35 lph and during the investigation the temperature of the hot water at inlet is ranges 50°C to 60°C. For various mass flow rate and inlet temperature the heat transfer coefficient, heat transfer rate and effectiveness of the heat exchanger are reported. The mass flow rate of hot and cold water is 70lph and 35lph and inlet hot water at 60°C, the effectiveness of heat pipe without adiabatic section is 26.9%.The investigation reveals that there is an increase in effectiveness by 11.2% is obtained in without adiabatic section, compared to with adiabatic section.