Multi-Objective Optimization of a Fin with two-Dimensional Heat Transfer Using NSGA-II and ANN

Abstract

Two-dimensional heat transfer in a fin was modeled with acceptable accuracy and optimized. Bezier curve was used to estimate the fin geometry. The finite volume method coupled with the artificial neural network was developed to predict the temperature distribution through the fin with -1.5% to +1% and ± 0.5% accuracy for fin efficiency and rate of heat transfer, respectively. Locations of four control points in the Bezier curve were considered as design variables. Then, fast and elitist non-dominated sorting genetic algorithm (NSGA-II) was applied to find the maximum fin efficiency and the rate of heat transfer as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called ‘Pareto optimal solutions’. The maximum 72 percent for fin efficiency was found with 739W as its rate of heat transfer while the maximum rate of heat transfer was 962.3 W with 57 percent efficiency. In addition, the optimum results of two-dimensional heat transfer were compared with one-dimensional and the average 14.7 percent decreases in fin efficiency and the rate of heat transfer was found that show the deficiency of the one-dimension modeling. In the second case study, the Pareto front was derived for the rate of heat transfer and fin surface area as two objective functions. It was observed that the results of optimum fin configuration in the case of fin efficiency as objective function are the same with the results of fin surface area as objective function.