A new correlation for estimating the thermal conductivity and dynamic viscosity of CuO/liquid paraffin nanofluid using neural network method. (March 2018)

• Record Type: Journal Article
• Authors: Karimipour, Arash; Ghasemi, Samad; Darvanjooghi, Mohammad Hossein Karimi; Abdollahi, Ali
• Is Part Of: International communications in heat and mass transfer. Volume 92(2018:Mar.)
• Page Start: 90
• Page End: 99
• Publication Date: 2018-03
• Subjects: Nanofluids -- CuO nanoparticles -- Viscosity -- Thermal conductivity -- Correlation; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Heat -- Transmission; Mass transfer; Periodicals; 621.4022
• DOI: 10.1016/j.icheatmasstransfer.2018.02.002
• Languages: English
• ISSNs: 0735-1933

Abstract

Abstract: In this study the effects of CuO nanoparticles mass fraction and temperature was studied on the dynamic viscosity and thermal conductivity of CuO/viscous paraffin nanofluid. TEM and DLS analysis as well as zeta potential test were performed for obtaining the morphology and nanoparticles stability within the basefluid. The results of TEM and DLS images exhibited that the average CuO nanoparticles diameter was ranged from 15 to 30 nm. Moreover, Zeta potential analysis showed high stability of nanoparticles in the basefluid. In addition, the results showed that with the increment of nanoparticles mass fraction and temperature the ratio of the thermal conductivity of nanofluid to basefluid increased and this parameter increases significantly with the temperature at the temperature below 40 and 70 °C where the mass fraction was chosen below and higher than 2.5 wt%, respectively. Also the results showed that with the increase of temperature the ratio of dynamic viscosity of nanofluid to basefluid decreases insignificantly and with the increment of nanoparticle load this parameter enhances tangibly. Moreover, two separated correlation including temperature and mass fraction of CuO nanoparticle was estimated by using hybrid GMDH-type neural network method for estimating relative dynamic viscosity and thermal conductivity of nanofluid. The results declared that the deviation of the data obtained by correlation from the experimental values was mostly <5% for both the thermal conductivity and dynamic viscosity. Finally, the value of relative Pr number was calculated at various temperature and mass fraction of CuO nanoparticles based on obtained correlations. The results of sensitivity analysis for relative Pr number exhibited that this parameter is more sensitive to mass fraction of nanoparticles in comparison with the temperature.