CFD modeling and multi-objective optimization of compact heat exchanger using CAN method

Thermal modeling and optimal design of compact heat exchanger is presented in this paper. Fin pitch, fin height, cold stream flow length, no-flow length and hot stream flow length were considered as five design parameters. A CFD analysis coupled with artificial neural network was used to develop a relation between Colburn factor and Fanning friction factor for the triangle fin geometry with acceptable precision. Then, fast and elitist non-dominated sorting genetic algorithm (NSGA-II) was applied to obtain the maximum effectiveness and the minimum total pressure drop as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called ‘Pareto-optimal solutions’. It reveals that any geometrical changes which decrease the pressure drop in the optimum situation, lead to a decrease in the effectiveness and vice versa. Finally sensitivity analysis shows the increases of heat transfer surface area necessarily do not increases the pressure drop and it is case sensitive.