Heat transfer optimization using genetic algorithm and artificial neural network in a heat exchanger with partially filled different high porosity metal foam

Abstract

The metal foam is well known for its high surface area to volume ratio and thus used to transfer heat from the exhaust gas leaving the heat exchanger system. The present work deals with the numerical simulations of a heat exchanger partially filled with three different metal foams made up of Aluminum (Al), Copper (Cu) and Nickel (Ni) having two pore densities namely 20 PPI and 40 PPI, respectively. The hot gas is made to flow through the 8 mm channel in which metal foams are inserted and different heights of foams such as 2 mm, 4 mm, 6 mm and 8 mm are considered for the analysis. The purpose of this study is to optimize thermal performance by increasing heat transfer and decreasing pressure drop which is calculated from the simulations using a commercial software ANSYS FLUENT. In order to achieve this, a optimization technique called Non-dominated Sorting Genetic Algorithm (NSGA-II) is coded in MATLAB by making use of artificial neural network (ANN tool) as an interpolation tool to generate more data based on the already existing data. Finally, Pareto front is obtained for the optimized functional values of heat transfer and drop in pressure after running the code for NSGA-II. From the numerical simulations it is observed that there is 5.68 times enhancement in heat transfer rate when copper metal foam is used for higher inlet velocities, when compared with non-porous channel. From the optimization study, it is found that 50% filled metal foam porous channel is showing enhanced heat transfer rate with decreased pressure drop as depicted in the pareto optimal plot for copper and aluminium.