Prediction of graphite nanofluids' dynamic viscosity by means of artificial neural networks
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Author list: Dalkilic A.S., วebi A., Celen A., Yildiz O., Acikgoz O., Jumpholkul C., Bayrak M., Surana K., Wongwises S.
Publisher: Elsevier
Publication year: 2016
Journal: International Communications in Heat and Mass Transfer (0735-1933)
Volume number: 73
Start page: 33
End page: 42
Number of pages: 10
ISSN: 0735-1933
eISSN: 1879-0178
Languages: English-Great Britain (EN-GB)
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Abstract
Recently, nanofluids have been studied extensively by the researchers as a result of the developments in nanotechnology. It is essential for researchers to know nanofluids' physical properties in order to make calculations regarding their specific research topics. Determination of viscosity issue is an actual one due to its common usage in heat transfer and thermodynamics. In this study, graphite particles are selected to have nanofluid mixture with its base fluid of pure water. Their volumetric concentrations are varied from 0 to 2% in pure water. Once the stabilized nanofluid is prepared by a sonicator and ultrasonic bath, viscosity is measured by a viscosity meter for the temperatures ranging from 20 ฐC to 60 ฐC. Validation of the experiments have been done by means of the comparison of them with the 32 empirical correlations in the literature. Then, Artificial Neural Network (ANN) analyses have been performed in order to have better empirical correlation than those in the literature. Furthermore, detailed information on the preparation nanofluids, measurement of viscosity, a list of measured data, numerical model by Matlab software, and alteration of viscosity with temperature and concentration have been given in the paper. It was concluded that viscosity correlations in the literature can predict different types of nanofluids' viscosity although they have been derived using specific type and diameter of nano particles and their base fluids. ฉ 2016 Elsevier Ltd.
Keywords
Graphite, SEM
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