Adaptive Neuro-Fuzzy Inference System of friction factor and heat transfer nanofluid turbulent flow in a heated tube

In this paper, estimating of hydrodynamics and heat transfer nanofluid flow through heated tube has been conducted by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The CFD data related to three types of nanofluids (Al2O3, SiO2 and TiO2) flow in horizontal tube with 19 mm diameter and 2000 mm length. Heat flux around tube is fixed at 5000 W/m2, the range of Reynolds number is (3000–30,000) and volume concentrations are (1% and 2%). ANFIS model has three input data presented by Reynolds number, volume concentration of nanofluids and materials and two output presented predicting friction factor and Nusselt number in the tube. The simulation results of proposed algorithm have been compared with CFD simulator in which the mean relative errors (MRE) are 0.1232% and 0.1123 for friction factor and Nusselt number respectively. Finally, ANFIS models can predict hydrodynamics and heat transfer of the higher accuracy than the developed correlations.