ABSTRACT: In the present study, the effective parameters of water-Al2O3 nanofluid flowing in flat tubes are investigated using the EFAST Sensitivity Analysis (SA) method. The SA is performed using GMDH type artificial neural networks (ANN) which are based on validated numerical data of two phase modeling of nanofluid flow in flat tubes. There are five design variables namely: tube flattening (H), flow rate (Q), wall heat flux (q"), nanoparticle diameter (dp) and nanoparticle volume fraction (φ) and there are two objective functions namely: pressure drop (ΔP) and heat transfer coefficient (h). The results show that among design variables, the tube flattening has the highest effect on variations of pressure drop (74%) and heat transfer coefficient (40%). Except tube flattening, the flow rate and the nanoparticle volume fraction has the highest effect on pressure drop (24%) and heat transfer coefficient (25%) respectively. The effects of all of the design variables on objective functions are shown in the results.
