Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica - Ahmad, M.N. (Springer Science and Business Media B.V., 2021)
Graphene oxide/silica compositeâs rheological behavior was studied in this investigation. This composite was made to reduce the cost of industrial usages. The volume fractions investigated from 0.1 to 1.0 (GO 30âSiO2 70), the shear rates investigated from 12.23 to 122.3Â sâ1, and the temperatures investigated from 25 to 50Â Â°C. To study the characterization of each solid and the composite, the XRD and the FESEM tests were done. The results of the viscosity investigation revealed the non-Newtonian behavior. After that, a numerical study was done to present a correlation and train an artificial neural network model. These numerical studies were done for both 12.23 and 122.3Â sâ1 shear rates. The novel equation tolerances were 1.932 and 1.338 for 12.23 and 122.3Â sâ1 shear rates, while for the artificial neural network model, the tolerances were 1.46196 and 1.25386 for 12.23 and 122.3Â sâ1 shear rates. This means, after the model was trained, the deviation decreased around â0.46999 and â0.08467 for 12.23 and 122.3Â sâ1 shear rates. This nanofluid can be employed in industrial systems. Â© 2021, AkadÃ©miai KiadÃ³, Budapest, Hungary.