期刊
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
卷 47, 期 1, 页码 1095-1107出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13369-021-06004-9
关键词
Hybrid nanoengine oil; Temperature; Volume fraction; Shear rate; Artificial neural network; Mathematical correlation
The study investigates the rheological behavior of ZnO-MWCNT/10W40 nanofluid and proposes a mathematical relation to calculate the NF viscosity. Results show a direct relationship between dynamic viscosity and VF, and an inverse relationship with temperature and SR. Sensitivity analysis indicates that viscosity is primarily dependent on temperature.
This investigation reports the rheological behavior of ZnO-MWCNT/10W40 nanofluid at the temperature range of 5 degrees C-45 degrees C, in the volume fraction (VF) of 0.05%-1%, and in the shear rate (SR) of 666.5 (1/s) to 11,997 (1/s). ZnO and multiwall carbon nanotube were mixed by a volume ratio of 60:40 and dispersed into 10W40 lubricant by a two-phase mixing method. In order to use the sets data of the current experimental research for future goals, a mathematical relation was proposed to calculate the NF viscosity. The results show a direct relationship between the dynamic viscosity and the VF of nanoparticles and the relation inversely to temperature and SR. The performed sensitivity analysis also showed that the viscosity is dependent on temperature in comparison with other independent variables. In fact, based on sensitivity analysis nanoparticles, VF and SR introduced in the next ranks of importance, respectively. In the ANN design, the R-2 value is equal to 0.991. For more precise estimation of the hybrid NF dynamic viscosity, an optimum artificial neural network (ANN) modeling selected from 400 different structures was employed. The comparison of the experimental results with the ANN ones reported an appropriate accuracy of ANN results.
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