A numerical investigation of developing forced convective heat transfer and pressure drop of nanofluid flow inside a tube subject to a constant wall heat flux boundary condition is presented. The single-phase homogenous and two different two-phase models: Lagrangian-Eulerian model or (discrete phase model) and mixture model are utilized with both constant and temperature dependent properties to further investigate and clarify the differences and evaluate the assumption of the single-phase model. The obtained results were subjected to an intensive comparison with the available experimental data and numerical works in the literature. The influence of some important parameters such as, source and sink terms, injected particle mass flow rate, slip velocity, particle forces, Reynolds number, constant or temperature dependent properties and particle concentration on the heat transfer and flow characteristics of nanofluids were determined and discussed in detail. It was observed that the two phase Lagrangian-Eulerian model (DPM) overestimated the heat transfer coefficient values and the results from the mixture model displayed an unrealistic increase in heat transfer particularly for high particle volume fraction. The proposed single phase approach revealed a very good agreement with the experimental data and the maximum difference in the average heat transfer coefficient between the single-phase and DPM was found to be 5.9% considering variable properties. The results also revealed that increasing the injected particle mass flow rate does not have a significant effect on the heat transfer coefficient values and that the particles move with the same velocity of the fluid. Furthermore, the heat transfer coefficient increases as the particle volume fraction and Reynolds number increases, but it is accompanied by a higher pressure drop and wall shear stress values. DPM model provides a reasonable prediction for the thermal behavior of the nanofluids transport, the single-phase approach with temperature dependent viscosity and thermal conductivity is an accurate way to analyze the transport of nanofluids while requiring less CPU usage and memory for predicting the enhancement in nanofluids convective heat transfer. (C) 2017 Elsevier Ltd. All rights reserved.