Multi-Objective Optimization of Nanofluid Flow in Flat Tubes Using CFD, Artificial Neural Networks and Genetic Algorithms


In this article, multi-objective optimization of Al2O3-water nanofluid parameters in flat tubes is performed using Computational Fluid Dynamics (CFD) techniques, Artificial Neural Networks (ANN) and Non-dominated Sorting Genetic Algorithms (NSGA II). At first, nanofluid flow is solved numerically in various flat tubes using CFD techniques and heat transfer coefficient ((h) over bar) and pressure drop (Delta P) in tubes are calculated. In this step, two phase mixture model is applied for nanofluid flow analysis and the flow regime is also laminar. In the next step, numerical data of the previous step will be applied for modeling (h) over bar and Delta P using Grouped Method of Data Handling (GMDH) type ANN. Finally, the modeling achieved by GMDH will be used for Pareto based multi-objective optimization of nanofluid parameters in horizontal flat tubes using NSGA II algorithm. It is shown that the achieved Pareto solution includes important design information on nanofluid parameters in flat tubes.