Empirical study and model development of thermal conductivity improvement and assessment of cost and sensitivity of EG-water based SWCNT-ZnO (30%: 70%) hybrid nanofluid

Abstract

n this research, the effects of temperature and solid volume fraction on thermal conductivity of SWCNT-ZnO (30%–70%)/EG-water (60%–40%) hybrid nano fluid are studied experimentally. In order to present a model, two methods of correlation and artificial neural network modeling were utilized. From the results of the test it was revealed that the increment of temperature and solid volume fraction of nanoparticles in the base fluid would result in thermal conductivity enhancement (TCE). Investigating the sensitivity of relative thermal conductivity of the nanofluid to concentration variations is more than its sensitivity to temperature changes. A comparison of cost and TCE in terms of solid volume fraction signified that using hybrid nanofluid is more convenient from the aspects of cost and TCE improvement. With purpose of modeling the experimental data, a new correlation was presented where its R-squared was only 0.9918 which is an acceptable error. Also an artificial neural network was designed in order to model the experimental data, R-squared and mean-squared errors of this model were 0.9972 and 0.000037887 respectively. The outputs of artificial neural network were exceptionally accurate; this is an indication of the capabilities of such a model.