An accurate artificial neural network (ANN)model and newcorrelation are developed to predict thermal conductivity of functionalized carbon nanotubes (MWNT-10 nm in diameter)-water nanofluid based on experimental data. Experimental values of thermal conductivity are in six concentrations of nanoparticles from 0.005% up to 1.5%. The temperatures were changed within 10–60 °C. In order to estimate the thermal conductivity, a feedforward three-layer neural network is utilized. The obtained results exhibited that the new correlation and ANN model have a good agreement with the experimental data. The maximum values of deviation and mean square error of neural network outputs were 2% and 8.2E−05, respectively. The findings illustrated that the artificial neural network can estimate andmodel the thermal conductivity of CNTs-water nanofluid very efficiently and accurately.
