Experimental evaluation, new correlation proposing and ANN modeling of thermal properties of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles for internal combustion engines applications

Abstract

Thermal conductivity of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles was investigated experimentally at concentration of 0.045 to 1.9% and a temperature of 30–50 °C. ZnO particles (with an average diameter of 10–30 nm) and double wall carbon nanotubes (DWCNT) (internal diameter of 3–5 nm and 5–15 nm external diameter) were mix at a ratio of 90%: 10% and dispersed in ethylene glycol (EG) then its thermal conductivity was measured. The results showed that maximum relative thermal conductivity (TCR) at temperature of 50 °C and the concentration of 1.9%, equivalent to 24.9%. Economic evaluation and qualitative performance showed that nanofluids hybrid compared with ZnO and nanofluids containing MWCNT, in terms of increasing thermal conductivity (TCE) and economically, is quite effective. A new correlation to predict TCR in terms of concentration of nanoparticles and the temperature was proposed. This correlation has a coefficient of determination (R-squared) and the maximum error of 0.9826 and 2.9%, respectively. The greatest sensitivity was calculated at a maximum temperature and solid volume fraction. Based on the TCR data the artificial neural network (ANN) was developed. The best case ANN containing two hidden layer and 3 neurons in each layer was obtained. This ANN has an R-squared and MSE and was equal to 0.9966% AARD and 1.3127e-05 and 0.0489, respectively. The comparison between experimetnal data, correlation and ANN outputs shows the accuracy and capability of ANN in modeling the TCR data.