(170h) Direct Numerical Simulation of 1000 Deformable Capsules In Channel Flow
AIChE Annual Meeting
2008
2008 Annual Meeting
Engineering Sciences and Fundamentals
Novel Numerical Methods In Fluid Mechanics
Monday, November 17, 2008 - 5:00pm to 5:15pm
Direct numerical
simulations are considered on the motion of
large ensembles (more than 1000 in number) of deformable particles
in a channel bounded by two parallel walls.
Particles are modeled as capsules, that is, liquid drops surrounded by
infinitesimally thin
hyperelastic membranes. Here we assume that the
membrane follows the
neo-Hookean constitutive law. The undisturbed flow, in absence
of the particles, is a parabolic flow driven by a constant
pressure gradient (Poiseuille flow). The particle Reynolds number,
based on the centerline velocity of the Poiseuille flow,
the undeformed particle diameter, and suspending fluid viscosity,
can vary from 0.01 to 10. The capillary number, based on the
suspending fluid viscosity, centerline velocity and
surface tension or membrane elastic modulus, ranges
from 0.05 to 0.5. Particle volume fraction ranges from 5 to 30%.
The ratio of the channel height to particle diameter varies from
1.25 to 15. The liquids interior and exterior
of the particles are Newtonian.
The numerical methodology is based on
a mixed finite-difference/Fourier transform method for
the flow solver and a
front-tracking method for the deformable particles.
In the simulations, the flow field is resolved using up to 280X280X280
grid points, and each particle surface is resolved by 1280 marker points.
Instantaneous snapshots of particle distribution from the simulations
are analyzed to study the interaction between the deformable
particles in a multi-particle environment.
Results are presented on
the time-dependent particle velocity and
trajectory, mean and time-dependent apparent viscosity of the suspension,
mean fluid and particle velocity across the channel, and
pair-wise distribution function.
Particles near the walls are seen to deform more and align with the
mean flow direction, whereas those near the channel center have less deformation.
Two competing mechanisms, namely, deformation-induced migration, and
dispersion due to multi-particle interaction, are studied for
varying Reynolds number, volume fraction, and size ratio. Fluctuations
in particle velocity and trajectory are analyzed. DNS results show that
fluctuations are higher for intermediate volume fractions (10-15%).
Time-averaged velocity in presence of the
particles deviates from the parabolic profile, and shows a 'plug' profile with decreasing velocity
as the volume fraction and size ratio are increased.
The effective viscosity of the suspensions shows nearly an
order of magnitude variation over the range of the parameters considered.
The effective viscosity increases with increasing particle volume fraction,
and the size ratio, in agreement with previous experimental results.
The DNS results are then used to validate the two-phase model of suspension
in a pressure-driven flow in which the flow is divided in to
a particle-depleted layer near the wall and a particle-rich layer
near the center of the channel. The thichness of the particle-depleted layer,
and the variation of viscosity over the channel cross-section are
estimated from the DNS results as functions of volume fraction, capillary number,
and size ratio. It is shown that the two-phase model underpredicts
the mean velocity obtained from the DNS results. The underprediction is
due to overestimation of the local viscosity in the vicinity of the
interface between the particle-depleted and particle-rich regions.
We then propose a three-layer model in which the flow is divided in to
a particle-depleted layer near the wall, a particle-rich layer
near the center, and a transition regime between the two layers
where viscosity varies linearly rather than a step-like manner assumed
in the two-phase model. A closed-form expression for mean velocity is obtained
for the three-layer model. The values of the parameters of the model
are directly obtained from the DNS data. The predicted velocity using the
three-layer model gives excellent agreement with the DNS results.