Journal
MATERIALS SCIENCE AND ENGINEERING B-ADVANCED FUNCTIONAL SOLID-STATE MATERIALS
Volume 297, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.mseb.2023.116693
Keywords
HNF; Shear stress; Shear rate; CuO; SAE50 oil; Response surface methodology (RSM
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This study investigated the rheological behavior of a hybrid nanofluid containing MWCNT-CuO nanoparticles in SAE50 base oil. The viscosity of the nanofluid was measured at different solid volume fractions, temperatures, and shear rates. The results showed that the nanofluid exhibited non-Newtonian behavior and the viscosity decreased with increasing temperature. The response surface methodology was used to accurately predict the viscosity based on the three-variable mathematical relationship.
In this study, rheological behavior of hybrid nanofluid (HNF) with MWCNT(35%)-CuO (65%) nanoparticles (NPs) in SAE50 base oil was investigated by response surface methodology (RSM) and laboratory methods. Viscosity of HNF samples with solid volume fractions of SVF = 0.0625% to 1% was measured at temperatures between (T = 25-50 degrees C) and shear rates in the range of SR = 666.5-7998 s-1 using a Brookfield viscometer. The behavior of HNF shows non-Newtonian behavior in terms of relation between SR and shear stress (SS) in all SVFs. The relative viscosity at T = 30 degrees C and SVF = 1% has the highest value and is equal to + 22%. Viscosity changes in terms of temperature show that viscosity decreases with increasing temperature. The maximum viscosity difference at T = 25 degrees C is equal to -13.7 (-2.97). Also, to save time and reduce economic costs and check the accuracy and correctness of experimental data, it was predicted by RSM. Modeling with a three-variable mathematical relationship including T, SVF, and SR with R2 = 0.9986 coefficient was presented. After checking the viscosity with independent variables, it was determined that T effect is greater than other parameters on viscosity. Comparison results between experimental data and the proposed data show a high agreement between the predicted values.
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