A novel integrated model to improve the dynamic viscosity of MWCNT-Al2O3 (40:60)/Oil 5W50 hybrid nano-lubricant using artificial neural networks (ANNs)

In this study, a unique incorporated version is presented to enhance the dynamic viscosity of MWCNT- Al2O3 (40:60)/Oil 5W50 hybrid nanofluid (HNF) the usage of the 3 maximum vast and vital powerful parameters corresponding to temperatures, solid volume fractions (SVFs) and shear rates (SRs). An empirical relationship between energy consumption and these characteristics is presented. Thus, ANNs are used to develop a high-level data analysis model to predict the dynamic viscosity of MWCNT-Al2O3 (40:60)/Oil 5W50 HNF. A sensitivity analysis is employed to assess the importance of various parameters of MWCNT- Al2O3 (40:60)/Oil 5W50 HNF dynamic viscosity and the position of temperature, SVF and SR in simulation. It is found that the highest dynamic viscosity values are observed at temperatures below 5 degrees C. In addition, the dynamic viscosity is reduced by SR changes from 0 rpm to 800 rpm. Statistical analysis shows that the model performance is nearly equal, ranging between 0.98, 0.978, and 0.925, and that the errors are less than 2.6 % for the training, testing, and validation phases, respectively. Overall, it could be determined that the ANN simulation can generate the connection between the measured dynamic viscosity and anticipated dynamic viscosity of HNF.

Automated summary

In this study, an integrated version is proposed to enhance the dynamic viscosity of MWCNT-Al2O3 (40:60)/Oil 5W50 hybrid nanofluid. The relationship between energy consumption and key parameters such as temperatures, solid volume fractions, and shear rates is established. ANNs are used to develop a data analysis model for predicting the dynamic viscosity of the hybrid nanofluid. The study finds that the highest dynamic viscosity values are observed at temperatures below 5 degrees C, and the dynamic viscosity decreases with increasing shear rates.