Multi-Objective Optimization of Force Convective Heat Transfer in a Stack of Horizontal Channels Using CFD and Genetic Algorithms: a Comparison with Asymptotic Method Results

Abstract

In this paper, by combining the computational fluid dynamics (CFD) and NSGA II algorithm, the forced convective heat transfer flow in a stack of horizontal parallel plates has been multi-objectively optimized. In the optimization process, the distance between the plates in the set of parallel channels has been changed so as to simultaneously optimize the amount of heat transfer between the plates and fluid and the pressure drop of the fluid (maximization of heat transfer and minimization of pressure drop). The Pareto front, which illustrate the changes of the heat transfer from the plates and the pressure drop of fluid simultaneously, have been presented in the results section. This result contains important information regarding the thermal designing of stack of channels subjected to forced convective heat transfer. The Pareto front have been obtained for four different fluids that have different Prandtl (Pr) numbers (mercury, air, water and oil), and the results related to each fluid have been discussed. Finally, the multi-objective optimization results obtained in this paper have been compared with the results of the asymptotic analysis method (which is a single-objective method aimed at increasing the amount of heat transfer from plates) for internal fluid flows; and useful information has been obtained.