Prediction of Contact Angle of Nanofluids by Single-Phase Approaches
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comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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Title
Prediction of Contact Angle of Nanofluids by Single-Phase ApproachesAuthor (s)
Date
2019Publisher
MDPIBibliographic citation
ÇOBANOGLU, Nur; KARADENIZ, Ziya Haktan; ESTELLÉ, Patrice; MARTÍNEZ CUENCA, Raúl; BUSCHMANN, Matthias h. (2019). Prediction of Contact Angle of Nanofluids by Single-Phase Approaches. Energies, v. 12, issue 23.Type
info:eu-repo/semantics/articlePublisher version
https://www.mdpi.com/1996-1073/12/23/4558/htmVersion
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Abstract
Wettability is the ability of the liquid to contact with the solid surface at the surrounding
fluid and its degree is defined by contact angle (CA), which is calculated with balance between
adhesive and cohesive ... [+]
Wettability is the ability of the liquid to contact with the solid surface at the surrounding
fluid and its degree is defined by contact angle (CA), which is calculated with balance between
adhesive and cohesive forces on droplet surface. Thermophysical properties of the droplet, the forces
acting on the droplet, atmosphere surrounding the droplet and the substrate surface are the main
parameters a ecting on CA.With nanofluids (NF), nanoparticle concentration and size and shape can
modify the contact angle and thus wettability. This study investigates the validity of single-phase
CA correlations for several nanofluids with di erent types of nanoparticles dispersed in water.
Geometrical parameters of sessile droplet (height of the droplet, wetting radius and radius of
curvature at the apex) are used in the tested correlations, which are based on force balance acting on
the droplet surface, energy balance, spherical dome approach and empirical expression, respectively.
It is shown that single-phase models can be expressed in terms of Bond number, the non-dimensional
droplet volume and two geometrical similarity simplexes. It is demonstrated that they can be used
successfully to predict CA of dilute nanofluids’ at ambient conditions. Besides evaluation of CA,
droplet shape is also well predicted for all nanofluid samples with 5% error. [-]
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Energies (2019), v. 12, issue 23Rights
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