Modeling Fluid Flow through Screens and Filters using CFD

Porous media like screens and filters, filter beds, packed beds, etc. can be modeled using commercial CFD (Computational Fluid Dynamics) codes like FLUENT, STAR CD, and CFX. These codes can be used to model pressure drops through the porous media and predict their performance. The pressure change over the thin porous medium is defined as a combination of Darcy’s Law and an additional inertial loss term.

We are using the dynamic mesh feature and the multiphase capability of FLUENT 6.3.26 (ANSYS, Inc.) to model flow of slurry through an industrial vibrating screen. The viscous and inertial resistances play an important role and are calculated based on the superficial velocity of the fluid flowing through the filter media. The pressure drop across the filter and the degree of non-uniformity of flow can be determined from a computational analysis.

The above picture shows a screen with a slurry layer containing colloidal suspensions on top and air at the bottom. FLUENT 6.3 provides improved accuracy for modeling transient multiphase flows [1]. Here the VOF (Volume of Fluid) model is used to simulate the jets of slurry that rise up above the screen. The screen is modeled as a three-dimensional thin packed bed.

The vibrating screens are used to separate sands from drilling muds. The vibratory motion can be simulated using Fluent’s dynamic mesh feature. When drilling fluids containing colloidal suspensions flow through screens the vibrator frequency and mean conveying velocity depend on the maximum acceleration An normal to the screen, the angle θ the screen makes with the horizontal, the ratio of parallel to normal vibration amplitudes C, the phase angle γ between the parallel and normal vibratory motions, and the coefficients of static and dynamic friction (μs, μd) between the particles and the screen [2].

fv = F (θ, An, C, γ, μs, μd)

It can then be seen that the frequency is inversely related to the velocity if all other factors are held constant thus affecting the filtration rate. Correlations can then be developed to predict the filter performance. It is unlikely that one set of operating parameter values will give optimum performance for all screens and applications hence operating parameters must be determined for different operating conditions.

Experiments are expensive to conduct.  Computer models can help reduce costs to determine the optimum operating conditions.

References

• FLUENT 6.3.26 Documentation
• Hoberock, LL. A Study of Vibratory Screening of Drilling Fluids. J Petroleum Tech. November 1980; 1889-1202.

Author: Vidya Raja, The University of Akron

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