MULTI-OBJECTIVE OPTIMIZATION OF FIN AND TUBE HEAT EXCHANGER WITH TRIPLE INCLINED STRAIGHT WINGLETS FOR PERFORMANCE ENHANCEMENT

The present study was undertaken to enhance the performance of fin and tube heat exchangers equipped with vortex generators. For this, we considered three straight winglets grouped together and placed on alternate tubes. The winglet geometry (which includes the angle of inclination and height of the winglets) and their placement in the flow domain were subjected to optimization. The experiments were designed using a Latin hypercube sampling technique consisting of 180 design points, and computational fluid dynamics was used to generate the response against each set of design variables. The fluid domain was discretized using a hexahedra mesh, while the solid domain was discretized using a polyhedral mesh and thin mesher. The resulting data were used to train the artificial neural network, which further acted as the surrogate model for the genetic algorithm (GA) to generate the Pareto front solution points. The GA was set to increase heat transfer (the Colburn factor) and minimize frictional losses (the friction factor). The results revealed that the optimized models significantly increased the heat exchanger's performance and outperformed previously reported studies. An enhancement of 82.54% was observed in comparison to the baseline model (without any winglets). Also, the optimized model performed exceptionally well under off-design conditions, expanding the scope of utilization.