eprintid: 14655
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/46/55
datestamp: 2023-11-10 03:29:14
lastmod: 2023-11-10 03:29:14
status_changed: 2023-11-10 01:57:28
type: article
metadata_visibility: show
creators_name: Ahmad, M.N.
creators_name: Mahmood, A.K.
creators_name: Hashim, K.F.
creators_name: Mustakim, F.B.
creators_name: Selamat, A.
creators_name: Bajuri, M.Y.
creators_name: Arshad, N.I.
title: Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
ispublished: pub
keywords: Fits and tolerances; Graphene; Non Newtonian flow; Shear deformation; Silica; Viscosity; Viscosity measurement, Artificial neural network modeling; Industrial systems; Nanofluids; Non-Newtonian behaviors; Novel equation; Rheological behaviors, Neural networks
note: cited By 0
abstract: 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.
date: 2021
publisher: Springer Science and Business Media B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103359687&doi=10.1007%2fs10973-021-10687-5&partnerID=40&md5=17fb7e95ca1161c71b8eacbb6ca72b8c
id_number: 10.1007/s10973-021-10687-5
full_text_status: none
publication: Journal of Thermal Analysis and Calorimetry
volume: 145
number: 4
pagerange: 2209-2224
refereed: TRUE
issn: 13886150
citation:   Ahmad, M.N. and Mahmood, A.K. and Hashim, K.F. and Mustakim, F.B. and Selamat, A. and Bajuri, M.Y. and Arshad, N.I.  (2021) Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica.  Journal of Thermal Analysis and Calorimetry, 145 (4).  pp. 2209-2224.  ISSN 13886150