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Register a new userParticle motion nearby rough surfaces
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Added by
Christina Kurzthaler
Publication date
2020
fields
Physics
and research's language is
English
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We study the hydrodynamic coupling between particles and solid, rough boundaries characterized by random surface textures. Using the Lorentz reciprocal theorem, we derive analytical expressions for the grand mobility tensor of a spherical particle and find that roughness-induced velocities vary nonmonotonically with the characteristic wavelength of the surface. In contrast to sedimentation near a planar wall, our theory predicts continuous particle translation transverse and perpendicular to the applied force. Most prominently, this motion manifests itself in a variance of particle displacements that grows quadratically in time along the direction of the force. This increase is rationalized by surface roughness generating particle sedimentation closer to or farther from the surface, which entails a significant variability of settling velocities.
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The effect of bridge splitting is considered in the case of capillary adhesion: for a fixed total volume of liquid, does having more capillary bridges increase the total adhesion force? Previous studies have shown that the capillary-induced adhesion force between two planar surfaces is only substantially enhanced by bridge splitting in specific circumstances. Here this previous result is reconsidered, and it is shown that bridge splitting may significantly increase the adhesion forces when one of the surfaces is rough. The resistance to shear is also examined, and it is shown that bridge splitting on a rough surface can lead to a steady capillary-induced shear force that scales linearly with translation velocity, even in the absence of contact-line pinning.
The surface texture of materials plays a critical role in wettability, turbulence and transport phenomena. In order to design surfaces for these applications, it is desirable to characterise non-smooth and porous materials by their ability to exchange mass and momentum with flowing fluids. While the underlying physics of the tangential (slip) velocity at a fluid-solid interface is well understood, the importance and treatment of normal (transpiration) velocity and normal stress is unclear. We show that, when slip velocity varies at an interface above the texture, a non-zero transpiration velocity arises from mass conservation. The ability of a given surface texture to accommodate for a normal velocity of this kind is quantified by a transpiration length. We further demonstrate that normal momentum transfer gives rise to a pressure jump. For a porous material, the pressure jump can be characterised by so called resistance coefficients. By solving five Stokes problems, the introduced measures of slip, transpiration and resistance can be determined for any anisotropic non-smooth surface consisting of regularly repeating geometric patterns. The proposed conditions are a subset of effective boundary conditions derived from formal multi-scale expansion. We validate and demonstrate the physical significance of the effective conditions on two canonical problems -- a lid-driven cavity and a turbulent channel flow, both with non-smooth bottom surfaces.
Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a large high-fidelity dataset to train neural networks is deterring their use in practical applications. In this study, we propose a transfer learning framework to model the drag on irregular rough surfaces even with a limited amount of direct numerical simulations. We show that transfer learning of empirical correlations, reported in the literature, can significantly improve the generalization ability of neural networks for drag prediction. The developed framework can be applied to applications where acquiring a large dataset is difficult, but empirical correlations have been reported.
Direct Numerical Simulations of two superposed fluids in a channel with a textured surface on the lower wall have been carried out. A parametric study varying the viscosity ratio between the two fluids has been performed to mimic both {bf idealised} super hydrophobic and liquid infused surfaces and assess its effect on the frictional, form and total drag for three different textured geometries: longitudinal square bars, transversal square bars and staggered cubes. The interface between the two fluids is assumed to be slippery in the streamwise and spanwise directions and not deformable in the vertical direction, corresponding to the ideal case of infinite surface tension. To identify the role of the fluid-fluid interface, an extra set of simulations with a single fluid has been carried out and compared to the results obtained with two fluids of same viscosity separated by the interface. The drag and the maximum wall-normal velocity fluctuations were found to be highly correlated for all the surface configurations, whether they reduce or increase the drag. This implies that the structure of the near-wall turbulence is dominated by the total shear and not by the local boundary condition of super-hydrophobic, liquid--infused or rough surfaces.
This letter reports on the femtosecond laser fabrication of gradient-wettability micro/nano- patterns on Si surfaces. The dynamics of directional droplet spreading on the surface tension gradients developed is systematically investigated and discussed. It is shown that microdroplets on the patterned surfaces spread at a maximum speed of 505 mm/sec, that is the highest velocity demonstrated so far for liquid spreading on a surface tension gradient in ambient conditions. The application of the proposed laser patterning technique for the precise fabrication of surface tension gradients for open microfluidic systems, liquid management in fuel cells and drug delivery is envisaged.
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