Abstract
Abstract Experimental investigation and multiobjective optimization are used to find optimum combinations of pressure drop and heat transfer coefficient during R-404A evaporation inside corrugated tubes. The mass velocity of refrigerant was between 280.5 and 561 kg m−2 s−1, the evaporating pressure kept in the range of 4–8 bar, and the heat flux was between 10 and 20 kW m−2. Results indicate that using corrugated tubes enhance both heat transfer coefficient and pressure drop simultaneously. Artificial neural networks and multi-objective genetic algorithm have been employed in this study to detect an optimal working condition. This will be achieved by finding an optimum combination of heat transfer coefficient and pressure drop. The design variables were corrugation pitch, corrugation depth, refrigerant mass velocity, and quality of vapor. It is shown in the results that some informative design aspects involved in the performance of corrugated tube evaporators can be observed by multi-objective Pareto optimization.
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