ANFIS modelling with fuzzy C-mean clustering of experimentally evaluated thermophysical properties of zirconia-water nanofluids

Abstract

Experiments on the thermal conductivity, viscosity, density, and specific heat of water based ZrO2 nanofluids were carried out in this study. The XRD and TEM were used to characterize the ZrO2 nanoparticles after they were produced through sol–gel technique. Temperatures and particle volume loadings of 0.2 to 1.0 % and 20 to 60 °C, respectively, were taken into account for the analysis. For 1.0 % of water-based ZrO2 nanofluids, thermal conductivity is increased by 24.96 % at 60 °C, moreover, under the same particle loading of 1.0 %, the viscosity was increased by 45.57 % at 20 °C over the water data. On the other side, the density is increased and the specific heat is decreased with the mixing of nanoparticles to the base fluid. The effective thermophysical properties of nanofluids were modeled using an intelligent model, adaptive neuro-fuzzy inference system (ANFIS) technique. The results of the ANFIS model outperformed the calculated correlations.