Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
Graphene oxide/silica composite�s 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 30�SiO2 70), the shear rates investigated from 12.23 to 122.3 s�1, and the temperatu...
| Main Authors: | Ahmad, M.N., Mahmood, A.K., Hashim, K.F., Mustakim, F.B., Selamat, A., Bajuri, M.Y., Arshad, N.I. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.30357 / |
| Published: |
Springer Science and Business Media B.V.
2021
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103359687&doi=10.1007%2fs10973-021-10687-5&partnerID=40&md5=17fb7e95ca1161c71b8eacbb6ca72b8c http://eprints.utp.edu.my/30357/ |
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| Summary: |
Graphene oxide/silica composite�s 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 30�SiO2 70), the shear rates investigated from 12.23 to 122.3 s�1, and the temperatures investigated from 25 to 50 °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 122.3 s�1 shear rates. The novel equation tolerances were 1.932 and 1.338 for 12.23 and 122.3 s�1 shear rates, while for the artificial neural network model, the tolerances were 1.46196 and 1.25386 for 12.23 and 122.3 s�1 shear rates. This means, after the model was trained, the deviation decreased around �0.46999 and �0.08467 for 12.23 and 122.3 s�1 shear rates. This nanofluid can be employed in industrial systems. © 2021, Akadémiai Kiadó, Budapest, Hungary. |
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