Artificial Neural Network- Genetic ‎Algorithm based Optimization of Baffle ‎Assisted Jet Array Impingement Cooling ‎with Cross-Flow

Abstract

The objective of this research is to numerically investigate heat transfer and pressure drop characteristic of a baffle assisted multi-jet impingement of air on a heated plate subjected to constant heat flux and cross flow. Two baffle configurations were considered for the present study. An array of jets with 3 x 3 configurations discharging from round orifices of diameter D=5 mm and with jet-to-heated plate distance ranging from 2D to 3.5D were studied. SST k-ω turbulence model was used for numerical simulation to examine the effect of blow ratio and baffle clearance on heat transfer and pressure drop characteristics. Blow ratios of 0.25, 0.5, 0.75 and 1.0 and baffle clearances of 1 mm, 2 mm, and 3mm were considered for CFD simulations. The split baffle configuration with baffle clearance of 3 mm is found to be more advantageous when both heat transfer and pressure drop are considered. However, the segmented baffle configuration with a baffle clearance of 1 mm gave better results for heat transfer alone. The present study also deals with determination of optimal operating parameters with the help of Genetic Algorithm and Artificial Neural Network. A pareto front was obtained for selecting the desired value of heat transfer or pressure drop. It was found that Artificial Neural Network based predictions strongly agree with CFD simulation results, and hence seems to be very useful in arriving at the optimum values of operating parameters.