Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT-Al2O3 nanoparticles by correlation and ANN methods using experimental data

In the present paper, the effects of temperature and volume fraction on thermal conductivity of SWCNT-Al2O3/EG hybrid nanofluid are investigated. Single-walled carbon nanotube with outer diameter of 1-2 nm and aluminum oxide nanoparticles with mean diameter of 20 nm with the ratio of 30 and 70%, respectively, were dispersed in the base fluid. The measurements were conducted on samples with volume fractions of 0.04, 0.08, 0.15, 0.3, 0.5, 0.8, 1.5 and 2.5. In order to investigate the effects of temperature on thermal conductivity of the nanofluid, this characteristic was measured in five different temperatures of 30, 35, 40, 45 and 50 A degrees C. The results indicate that enhancement of nanoparticles' thickness in low volume fractions and at any temperature causes a considerable increment in thermal conductivity of the nanofluid. In this study, the highest enhancement of thermal conductivity was 41.2% which was achieved at the temperature of 50 A degrees C and volume fraction of 2.5%. Based on the experimental data, an experimental correlation and a neural network are presented and for thermal conductivity of the nanofluid in terms of volume fraction and temperature. Comparing outputs of the experimental correlation and the designed artificial neural network with experimental data, the maximum error values for the experimental correlation and the artificial neural network were, respectively, 2.6 and 1.94% which indicate the excellent accuracy of both methods in prediction of thermal conductivity.