Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1007.1122

Transverse flow in thin superhydrophobic channels

Francois Feuillebois·Martin Z. Bazant·Olga I. Vinogradova
View original source ↗Read source document ↗Export metadata ↓

Abstract

We provide some general theoretical results to guide the optimization of transverse hydrodynamic phenomena in superhydrophobic channels. Our focus is on the canonical micro- and nanofluidic geometry of a parallel-plate channel with an arbitrary two-component (low-slip and high-slip) coarse texture, varying on scales larger than the channel thickness. By analyzing rigorous bounds on the permeability, over all possible patterns, we optimize the area fractions, slip lengths, geometry and orientation of the surface texture to maximize transverse flow. In the case of two aligned striped surfaces, very strong transverse flows are possible. Optimized superhydrophobic surfaces may find applications in passive microfluidic mixing and amplification of transverse electrokinetic phenomena.

Explore related subjects

physics.flu-dyn

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Francois Feuillebois, Martin Z. Bazant, Olga I. Vinogradova. 2010-07-07. Transverse flow in thin superhydrophobic channels. https://doi.org/10.1103/physreve.82.055301

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Bayesian neural network correction of RANS turbulence models with uncertainty quantification in separated flows

Data-driven correction of turbulence models offers a promising route for improving Reynolds-averaged Navier-Stokes (RANS) predictions, but quantifying uncertainty in such corrections and ensuring generalization across flows remain key challenges. This work presents a Bayesian neural network (BNN) framework for uncertainty-aware correction of RANS models. The BNN represents both epistemic and input-dependent aleatoric uncertainty, interpreted here as irreducible scatter in the feature-to-correction relationship, with the latter propagated as spatially correlated fields through the RANS solver. The framework is trained exclusively on a periodic-hill configuration and evaluated without retraining on five unseen separated-flow configurations. The learned corrections improve the training-flow prediction, but the strong momentum-field benefit obtained from the anisotropy correction does not transfer consistently to the unseen flows. Out-of-distribution under-coverage is already present at the correction-field level, indicating that the loss of calibration is already present in transfer of the learned correction rather than being introduced primarily by CFD propagation. Overall, the framework enables joint propagation of epistemic and aleatoric uncertainty while exposing the present limitations of uncertainty calibration under distribution shift.

physics.flu-dyn↗
arXiv2026

Direct numerical simulation of particle-laden flow in a linear compressor cascade: Unsteady boundary-layer effects on particle-blade interactions

We perform point-particle direct numerical simulations (PP-DNS) of particle-laden flow through a linear compressor cascade subjected to synthetic free-stream turbulence. Monodisperse particles are advanced in a one-way coupled Eulerian-Lagrangian framework with drag-only dynamics. We use PP-DNS collision statistics together with empirical deposition and erosion models to estimate particle deposition and blade erosion. Collision hotspots and regions of predicted deposition are identified near the leading edge and over the pressure side. On the pressure side, for the intermediate Stokes number, the onset of frequent collisions and predicted deposition is spatially associated with elevated boundary-layer intermittency during bypass transition. Nevertheless, for the largest particles, impacts occur farther upstream. On the suction side, sparse collisions and predicted deposition events appear only for the smallest particles and are phase-modulated by separation-induced vortex shedding. Joint distributions of impact velocity and angle show that leading-edge impacts are faster and span wider angles than pressure-side impacts. For the two larger particle sizes, the higher normal impact velocities near the leading edge result in a greater likelihood of rebound than on the pressure side. Therefore, appreciable erosion is predicted primarily near the leading edge under the present conditions. The present results highlight the role of unsteady boundary-layer dynamics in affecting particle-blade interactions in compressor cascades.

physics.flu-dyn↗
arXiv2026

Diffusion Enhancement and Directional Suppression Induced by Reciprocal Flows

Reciprocal flows repeatedly return fluid elements to their initial positions, producing no net advective transport on time average. Nevertheless, their interplay with diffusion gives rise to nontrivial transport. To describe this phenomenon, we develop a general theory of effective diffusion under two-dimensional linear flows with arbitrary time dependence. By analyzing the advection-diffusion equation, we derive exact expressions for the mean square displacement and the effective diffusion coefficients for extensional, simple shear, and rotational flows within a single framework. We show that the reciprocal flows universally induce diffusion enhancement. The diffusion tensor exhibits pronounced anisotropy, which can result in directional diffusion suppression in spite of the direction-averaged diffusion enhancement. Our results provide a general framework for diffusion control by time-dependent flows and can provide new strategies for transport manipulation in microfluidic and biological systems.

physics.flu-dyn↗