Optimization of Heat Transfer Coefficient during Condensation of Refrigerant inside Plain Horizontal Tube using Teaching-Learning based Optimization Algorithm

Abstract

Objectives: To predict the optimum value of heat transfer coefficient during condensation of refrigerant inside a smooth horizontal tube using Teaching-Learning based Optimization Algorithm. Methods: Refrigerant vapor quality and mass flux are considered as variables. An objective function is formulated based on the Shah’s correlation for heat transfer coefficient. The optimal results predicted by Teaching-Learning based Optimization Algorithm are validated with experimental data. Results: Refrigerant mass flux and vapor quality are varied from 100 to 500 kg/m2s and 0.1 to 0.9 respectively. The optimal value of heat transfer coefficient, refrigerant mass flux and vapor quality predicted by the algorithm are 7.56 kW/m2K, 493 kg/m2s and 0.87, respectively. Conclusions: The Teaching-Learning based Optimization Technique is capable of predicting the optimal set of values for different design and operating parameters.