In this article, multi-objective optimization of Al2O3-water nanofluid parameters in flat tubes is per- 31 formed using Computational Fluid Dynamics (CFD) techniques, Artificial Neural Networks (ANN) and 32 Non-dominated Sorting Genetic Algorithms (NSGA II). At first, nanofluid flow is solved numerically in 33 34 various flat tubes using CFD techniques and heat transfer coefficient (h) and pressure drop (DP) in tubes are calculated. In this step, two phase mixture model is applied for nanofluid flow analysis and the flow 35 36 regime is also laminar. In the next step, numerical data of the previous step will be applied for modeling h and DP using Grouped Method of Data Handling (GMDH) type ANN. Finally, the modeling achieved by 37 GMDH will be used for Pareto based multi-objective optimization of nanofluid parameters in horizontal 38 flat tubes using NSGA II algorithm. It is shown that the achieved Pareto solution includes important 39 design information on nanofluid parameters in flat tubes.
