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Condensers are extensively used heat exchangers in automobiles and air conditioning systems. Optimization of heat transfer and pressure drop inside condensers is an important area of concern for the designers. In the present study, condensation  characteristics inside smooth horizontal tubes is optimized using teaching-learning based optimization (TLBO) algorithm and response surface methodology (RSM). Refrigerant mass velocity (G), vapor quality (x) and tube internal diameter (D i ) are taken as design parameters. Heat transfer coefficient (h) and pressure drop (ΔP) values of refrigerants are calculated based on the mathematical models and served as objective functions for RSM. The same mathematical models are applied to formulate a multi-objective optimization problem with an aim to maximize heat transfer coefficient and minimize pressure drop and is solved using TLBO. Two different refrigerants have been considered to display the application of the approaches. Of the two methods applied, TLBO seems to give better optimum results.

Meta-heuristic and RSM approach for Multi-objective Optimization of Plain Flow Refrigerant Vapor Condensation inside Tubes