Experimental investigation of hybrid nano-lubricant for rheological and thermal engineering applications

• Published in: Journal of thermal analysis and calorimetry 2019-10, Vol.138 (2), p.1823-1839
• Main Authors: Rejvani, Mousa, Saedodin, Seyfolah, Vahedi, Seyed Masoud, Wongwises, Somchai, Chamkha, Ali J.
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Chemistry; Chemistry and Materials Science; Deviation; Inorganic Chemistry; Internal combustion engines; Lubricants; Lubrication; Lubrication and lubricants; Measurement Science and Instrumentation; Nanofluids; Nanoparticles; Ostwald ripening; Particle size; Physical Chemistry; Polymer Sciences; Pseudoplasticity; Regression analysis; Regression models; Rheological properties; Sensitivity analysis; Shear rate; Silicon dioxide; Thermal engineering; Viscosity; Viscosity measurement
• ISSN: 1388-6150; 1588-2926
• DOI: 10.1007/s10973-019-08225-5

Nowadays, various types of engine oils are widely used in lubricating and cooling internal combustion engines. In this study, the behavior of MWCNTs–SiO 2 (30–70)/10W40 hybrid nanofluid as part of a new generation of engine oil is investigated experimentally. A mixture of SiO 2 , with 20–30 nm particle diameter, and MWCNT, with 3–5 nm inner and 5–15 nm outer nanoparticle diameter was dispersed into a base fluid of 10W40 engine oil. Then, the viscosity of the product was measured at nanofluid concentrations and temperatures, respectively, ranging from 0.05 to 1% and 5 to 55 °C, for different values of shear rate. Also, a sensitivity analysis to the solid volume fraction was performed at different temperatures. The results show that the behavior of the samples is well fitted with the pseudo-plastic Ostwald de Waele non-Newtonian model. The viscosity of the produced hybrid nano-lubricant is found to be 35% greater than that of pure engine oil. Because of the significant deviation between the measured viscosity and the values predicted by existing classical viscosity models, a new regression model is obtained. The R 2 and adj. R 2 for the model are computed as 0.988 and 0.977, respectively, signifying strong predictability with ± 3% margin of deviation.