Performance enhancement of a fin and tube heat exchanger with the novel arrangement of curved winglets using a multi-objective optimization approach

Abstract

The present study considers performing multi-objective optimization of a fin and tube heat exchanger to enhance its performance. For this, a group of three curved rectangular winglets, placed on alternate sides of the tube, are considered. Two different geometrical arrangements are analyzed. In the first case – referred to as case A – the distance between the three curved blades is varied in a way that the relative gap between the curved blades is the same. In the second case – referred to as case B – the distance between the three curved blades is held fixed at the entry section, but the relative gap is varied at the exit of the curved blades. The experiments are designed using the Latin hypercube sampling technique, and the responses against each set of design variables are calculated using computational fluid dynamics. The conservation equation for mass, momentum and energy are solved along with transition SST (a three equation) turbulence model considering a steady state formulation. The resulting data is used to train the artificial neural network that serves as a surrogate model for GA to generate the Pareto front points. Conclusive results indicate that the optimized models from both cases largely outperform compared to the previously reported studies. Furthermore, the performance of the case B variant is slightly better than for case A.