Optimization of thermal and flow characteristics of R-404A vapor condensation inside corrugated tubes

• The condensation of R-404A inside corrugated tubes were experimentally investigated.
• Correlations were developed to predict heat transfer coefficient and pressure drop.
• Evolutionary algorithm was used for optimization of artificial neural networks.
• ANNs were used for prediction of heat transfer coefficient and pressure drop.
• Pareto based optimization of R-404A vapor condensation was performed.

In the present study the condensation pressure drop and heat transfer coefficient of R-404A flowing through the corrugated tubes are experimentally investigated. The test section is a horizontal 1 m long double-pipe counter-flow heat exchanger where R-404A flows in the inner tube with internal diameters of 8.7 mm and coolant water flows through the annulus. The pressure drop and heat transfer coefficient are obtained for the plain tube and nine corrugated tubes, mass velocities ranging from 187 to 561 kg/m2 s, vapor quality from 0.18 to 0.85 and average condensing temperature from 29.2 to 35.8 °C. Investigation shows that using the corrugated tube increases the heat transfer coefficient and pressure drop up to 59% and 115% above the plain tube values, respectively. New empirical correlations are developed to predict the heat transfer coefficient and pressure drop inside the corrugated tubes. Finally, artificial neural network and multi-objective genetic algorithm are used for finding optimal operational conditions during the condensation of R-404A refrigerant inside the corrugated tubes.