Search arXiv⌕ Search

arXiv · 1803.09706

A note on Stokes' problem in dense granular media using the $μ(I)$--rheology

Abstract

The classical Stokes' problem describing the fluid motion due to a steadily moving infinite wall is revisited in the context of dense granular flows of mono-dispersed beads using the recently proposed $μ(I)$--rheology. In Newtonian fluids, molecular diffusion brings about a self-similar velocity profile and the boundary layer in which the fluid motion takes place increases indefinitely with time $t$ as $\sqrt{νt}$, where $ν$ is the kinematic viscosity. For a dense granular visco-plastic liquid, it is shown that the local shear stress, when properly rescaled, exhibits self-similar behaviour at short-time scales and it then rapidly evolves towards a steady-state solution. The resulting shear layer increases in thickness as $\sqrt{ν_g t}$ analogous to a Newtonian fluid where $ν_g$ is an equivalent granular kinematic viscosity depending not only on the intrinsic properties of the granular media such as grain diameter $d$, density $ρ$ and friction coefficients but also on the applied pressure $p_w$ at the moving wall and the solid fraction $ϕ$ (constant). In addition, the $μ(I)$--rheology indicates that this growth continues until reaching the steady-state boundary layer thickness $δ_s = β_w (p_w/ϕρg )$, independent of the grain size, at about a finite time proportional to $β_w^2 (p_w/ρg d)^{3/2} \sqrt{d/g}$, where $g$ is the acceleration due to gravity and $β_w = (τ_w - τ_s)/τ_s$ is the relative surplus of the steady-state wall shear-stress $τ_w$ over the critical wall shear stress $τ_s$ (yield stress) that is needed to bring the granular media into motion... (see article for a complete abstract).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J. John Soundar Jerome, Bastien Di Pierro. 2018-05-14. A note on Stokes' problem in dense granular media using the $μ(I)$--rheology. https://doi.org/10.1017/jfm.2018.250

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

KEEP EXPLORING

Related papers

Particle migration in areas of constricted flow

Cardiovascular diseases are a leading cause of death globally. Among them, some are linked to stenosis, which is an abnormal narrowing of blood vessels, as well as other factors. Smart drug delivery systems based on micro- and nanoparticles are a promising method to offer non/minimal-invasive therapeutic mechanisms. Here we investigate the propensity of particles with different shapes and sizes to drift laterally (marginate) towards an occlusion area in a two-dimensional (2D) parallel plate laminar flow using the Lattice-Boltzmann method (LBM). To verify the outcomes on both sides of the stenosis, a probability of adhesion to the borders was calculated. Analysis was done on the impact of wall-shear stress on both sides of the stenosis. Our results show that rectangular particles migrate in larger amounts and earlier than circular ones.

physics.flu-dyn↗

Manifold-adapted radial basis functions for reduced-order modelling of chaotic flows

Chaotic systems often evolve on a low-dimensional attractor whose geometry varies from one region to another. We propose a non-intrusive reduced-order model that reads this local geometry by clustering and uses it to shape a radial basis library whose kernels adapt to each region. Fitting the reduced velocity onto this library by one global least-squares solve gives an explicit, differentiable vector field that reproduces the long-term statistics without any use of the governing equations. A radial basis field decays away from the data and cannot by itself return an escaped state. The integration is therefore stabilised by a kinematic corrector, whose reported magnitude measures how far each result rests on the learned field. On Lorenz-63 the model recovers the attractor, its marginal densities and its Lyapunov spectrum. On Lorenz-96 its valid prediction time matches typical configurations of neural-network and reservoir-computing forecasters and trails their best-tuned ones, and the invariant measure is reproduced on both the full state and on a reduced observable. On the Kuramoto--Sivashinsky equation and the quasiperiodic Kolmogorov flow the model matches the energy distribution and spectrum of an intrusive quantised-local Galerkin model and improves on a global Galerkin projection of the same reduced dimension. The recovery is dictated by the distance from a state to its nearest kernels, not by the one-step regression error.

physics.flu-dyn↗

Discovery of an explicit closure dispersion model for contrast-agent transport in arteries

At high Peclet number, the classical Taylor-Aris dispersion model becomes inadequate for early-time contrast-agent transport in arteries, while an explicit closure model and a clear physical interpretation of this regime remain lacking. In this study, we develop a novel explicit-closure one-dimensional (1-D) effective dispersion model for this regime, with its functional structures identified through symbolic regression. Analysis of the resulting model reveals that, in the high-Peclet-number regime, axial transport is redistributed between the effective convection flux and the dispersive flux, resulting in a reduction of the effective convective transport velocity in the dispersion model. This redistribution gives rise to a transition from the classical quadratic scaling to a linear scaling of the effective diffusivity with radial Peclet number. Numerical validation demonstrates close agreement with the convection-diffusion model over the investigated high-Peclet-number conditions, while the classical Taylor-Aris model exhibits substantial deviations. Application of the proposed model to averaged flow velocity inversion further demonstrates improved velocity estimation, particularly in the high-Peclet-number regime. These results highlight the importance of accounting for non-classical dispersion for reliable contrast-agent-based arterial blood flow velocimetry and provide new insight into high-Peclet-number mass transport.

physics.flu-dyn↗