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

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 degrees 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 degrees 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. (C) 2017 Elsevier Ltd. All rights reserved.