Entropy analysis and thermal optimization of nanofluid impinging jet using artificial neural network and genetic algorithm

Optimized and informed design of impinging jets can effectively enhance their rate of heat transfer. One practical pathway for such designing is to add nanoparticles to a background fluid. Here, we determine the effects of nanoparticle chemistry, their size, and their total volume fraction in water on the rate of heat transfer. We perform a comprehensive optimization using artificial neural network (ANN) and genetic algorithm (GA) to systematically study the enhanced heat transfer in nanofluids compared to pure water in obtaining a uniform cooling on a constantly heated surface in a turbulent flow. Our results indicate that increasing the size and concentration of nanoparticles enhances the rate of heat transfer. Nanoparticles are found to improve the uniformity of Nusselt distribution in the range of Nu = 57.5-72.5, however, in the range of Nu = 20-35, air is found to be more uniform. Finally, we perform a Thermodynamic analysis to determine the contribution of heat transfer and the frictional forces of the system on the total entropy generation in the optimal point. Results show that the portion of the two sources on entropy generation virtually equal for air, but the effects of heat transfer dominates for water and Al2O3/water nanofluids.