Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation

Abstract

An accurate artificial neural network (ANN) model and new correlation are developed to predict thermal conductivity of functionalized carbon nanotubes (MWNT-10nm 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 feed-forward 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 and model the thermal conductivity of CNTs-water nanofluid very efficiently and accurately.