Experimental Analysis of Factors Affecting Heat Transfer Performance in a Finned-Tube Evaporator

Abstract

This study investigates the thermal performance of evaporators in vapour compression refrigeration systems, with a focus on the design, experimental analysis, and impact of various operating parameters. The evaporator used in the experiments was tested in the Air Condition Laboratory at Al-Mustaqbal University, with key specifications including a total tube length of 19.2 meters, tube diameter of 7 mm, and fin geometry configured for optimal heat transfer. The experimental setup included measuring the evaporator’s performance under various conditions, specifically with refrigerant R-22. The average evaporator temperature was maintained at 5℃, with the refrigerant entering at -1.6℃ and exiting at 3.6℃. The pressure within the evaporator was recorded at 584 kPa, while the condenser operated at 40℃ and 1533.5 kPa. Key thermodynamic parameters, such as the overall heat transfer coefficient (0.466 kJ/m²·s·℃ for copper), were calculated and analyzed. Key findings from the experiments include a direct relationship between the evaporator’s area and its cooling capacity, as expressed by the equation Q=U×Θm×A. Additionally, it was observed that the refrigeration capacity increases with the temperature difference between the refrigerant and the air. The study also found that the coefficient of performance (COP) of the refrigeration cycle improves with an increase in the evaporator’s effect, though it decreases as the evaporator temperature rises. The study concludes that the presence of lubricating oil within the system complicates heat transfer and pressure drop, making the thermal design of the evaporator challenging. Furthermore, it was determined that high-pressure refrigerants enhance evaporator capacity, and the heat transfer capacity is significantly influenced by the temperature differential between the refrigerant and the medium being cooled. The findings contribute to a better understanding of evaporator design and optimization for improved system performance.