ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer

Abstract

In this study, the thermal conductivity of SiO2–DWCNT/ethylene glycol hybrid nanofluid has been experimentally investigated on 0.03–1.71% solid volume fraction and temperatures from 30 to 50 °C. SiO2 and DWCNT’s nanoparticles dispersed in EG as base fluid, and its thermal conductivity was measured. The thermal conductivity was obtained 38% more than ethylene glycol thermal conductivity at some temperatures. A new correlation (R 2 = 0.9925) was proposed to predict experimental thermal conductivity ratio as a function of volume concentration and temperature. Also an artificial neural network was designed for thermal conductivity ratio data predicting. The best artificial neural network topology has two hidden layers with five neurons in each layer. Comparing the experimental thermal conductivity ratio with artificial neural network outputs and the correlation shows the high capacity and accuracy of artificial neural network in thermal conductivity ratio data predicting.