Abstract

The development of knowledge management in dynamic viscosity enables the effective use of fluid to optimize processes and innovations. By collecting, organizing, sharing and applying knowledge, professionals in various industries can take advantage of the potential of dynamic viscosity for optimal performance. Robust model results were developed to predict the viscosity of SiO2/SAE 50 nanofluid (NF) using RSM with a knowledge management approach in laboratory conditions of temperatures T = 25–50 °C, solid volume fractions in the range of SVF= 0–1.5% and shear rates in the range of SR= 666.5–7998 s−1 has been reported to establish a background in using the mentioned NF at high engine speed. After examining the four statistical models Linear, Quadratic, Cubic and Quartic in terms of statistical parameters extracted from ANOVA and measurement charts of the models, it was determined that the model Quartic has high precision compared to the three models in predicting the desired response. Thus, the values of R2 = 0.9923, Predicted R2 =0.9887 and Adjusted R2 = 0.9906 were reported for the selected model; and the MOD values of the Quartic model are − 7 < MOD < +8. Also, a correlation relationship consisting of three independent parameters has been presented to predict viscosity, and it has been determined that viscosity dependence on SR, and NF behavior is non-Newtonian. Comparison of base fluid (BF) viscosity contour with and without the use of nanoparticles (NPs) at high engine speed has shown that the use of SiO2 NPs has led to an increase in viscosity. Also, the use of SiO2 nanoparticles has led to an increase in viscosity by 33.27% and 25% respectively at the minimum and maximum temperature at high engine speed. Finally, the viscosity optimization was done by minimizing the amount of NP consumption and the maximum viscosity at high engine speed in laboratory conditions, and the optimal value was obtained as 212.063 mPa.sec

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