Design and Application of CFD - Fluent

QNET 2002Application Challenge – Cyclone Separator

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QNET - CFD

Contract No.: GTC1 - CT99 - 10030 24th May 2002

Application Challenge

Cyclonic SeparatorPresented

by

Dr. Michael Slack

FLUENT EUROPE LTD Sheffield Airport Business Park

Europa Link . Sheffield . S9 1XU . UK


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Contents• Introduction

• how it works• Typical applications

• Relevance to industrial sector• Complexities of the flow and modelling approach which may be

taken.• The Application Challenge• Experimental Test Case• CFD Modelling Solution Strategies

• Development of best practice• Boundary conditions • Validation of turbulence models• Numerical accuracy

• Comparison with experimental results• Conclusions and sensitivity discussion (LES findings)


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The Cyclone• The cyclone is a separation device that induces swirl rotation in a liquid and

therefore imposes an enhanced radial acceleration on a particulate or liquid suspension for the purpose of separation or classification. When applied to liquids they are termed hydrocyclones.

• Uses• Particle Classifier• Phase Separation • Thickener

• Example applications• Mineral Processing (Sorting course material from fine in grinding circuits)• Oil Industry (oil water, sand and gas separation)• Cement Industry (reactor, classifier and dust extraction)


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Slurry In

Coarse

Fine

Underflow

Inlet

Overflow

Inlet

Vortex Finder

Dust in


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Advantages•Easy of use

•High volume throughput

•Simple and Compact mechanical design

•Reliable


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cl

cl

Locus ofzero velocity

+ ve

Tangential velocity

cl

cl

Axial velocity

cl

cl

Inlet

Apex

Axisymmetric Flow patterns

Vortex finder

Showing the tangential and axial velocity distributions in a cyclone basedon published experimental observations

Illustration of how the tangential velocity profile consists of both freeand forced vortex swirl

Tangential velocity profile

0

Wall

Radius

Tang

entia

l vel

ocity

forcedvortex

vr

cons t= tan

freevortex

v r cons t⋅ = tanTransistion


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Cyclone design and operation•Analytical modelling

•Requires simplifying assumptions to linearise the fundamental equations, (Batchelor).

•Physical modelling

•Expensive and slow

•Numerical modelling

•Recent advances in computers and numerical techniques has led to the application of numerical techniques for cyclone design, (Boysan).


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Modelling complexities•Physical

•Large number of influencing parameters

•The flow field is essentially 3-dimensional and can only be assumedaxisymmetric in the simplified case.

•High stream line curvature

•Anisotropic turbulence (high shear associated with free vortex motion in main flow)

•High volume concentrations of dispersed phase.

•Numerical

•The elliptic partial differential equations governing fluid flow are non- linear and when swirl is present strongly coupled.


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Modelling Approaches.• Analytical Modelling

• Improves awareness of the physical mechanics involved but can only be applied to simple geometry's• Bloor, M.I.G. and Ingham, D.B., 1973, 1986 Leeds University.• Batchelor, G. K., 1967, An introduction to fluid dynamics, Cambridge University Press, pp. 543-547.

• Numerical modelling• Allows a wider scope of boundary conditions to be applied and is much more flexible.• Workers in this field: F. Boysan 1984 Pericleous, K. A. and Rhodes, N., 1984, 1986. Davidson, M. R., 1988. Hargreaves, J. H. and Silvester, R. S., 1990. Rajamani, R. K. et al., 1990, 1992. Dyakowski, T. and Williams, R.A., 1993. Fernando Concha A., 1997. Slack, M. D. and Wraith A. E., 1997. Slack, M. D. and F. Boysan. 1998.


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Turbulence Modelling

Engineers are not interested in the details of the turbulent motions but on the mean properties of the flow.

There is then no need to solve equations for instantaneous variables if averaged values are all that is required.

The numerical treatment of the statistical averaged Navier-Stokes equations are required to provide a value for the Reynolds stress at each point in the flow.


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Types of Turbulence model• The choice of turbulence model should be the one best suited to the particular flow problem.

• Which one ?• Prandtl Mixing length model

• Treat the N-S Equations as lamina but solve them for a modified turbulent viscosity derived from aprandtl mixing length model

• Eddy Viscosity Models• 2-equation k-epsilon model.• Renormalisation group [RNG] k-epsilon model.

• Second Moment Closure Models• Uses differential transport equations to solve the stresses at each point in the system, eg. The Reynolds

Stress Model [RSM].• Large Eddy LES !

• Current state of the art Research level turbulence model. Transient calculation on a fine mesh to solve the large eddies directly with a sub-grid scale model to represent those eddies smaller than the mesh and faster than the time step.


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Tangential velocity profiles at 0.41 m below the vortex finder predicted usingSKE, RNG and RSM compared with experimental data.

+ k-epsilon, � RNG, –— RSM, ∆ experiment [LDA]


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+ k-epsilon, � RNG, –— RSM, ∆ experiment [LDA]

Axial velocity profiles at 0.41 m below the vortex finder predicted usingSKE, RNG and RSM compared with experimental data.


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0.205m

0.103m

0.103m

0.308m

0.82m

0.154m

0.074m

0.1m

0.04m 0.32m0.35m0.38m0.41m

0.59m0.62m0.66m

0.77m0.80m

0 mRadial Profile measurement locations Tangential Inlet

Boundary (velocity)

Underflow or spigot (wall boundary)

Overflow/vortex finder (Outflow boundary)

Stairmand Cyclone geometry used for application challenge


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Model Inputs

• Air inflow rate 0.08 m3/s• Release Density 1.225 kg/m3

• Viscosity 1.7894e-5 kg m-1

• Inlet Reynolds Number 10e+4 – 10e+5

• Average residence time in cyclone 0.25 seconds


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• LDA tangential and axial Velocity measurements recorded on radial lines at vertical distances 0.32, 0.38, 0.41, 0.59, 0.62,0.66, 0.77, 0.8 m from top of cyclone.

• Closest approach to walls 0.00924 m• Error on peak velocities are estimated to be ± 0.25 m/s• Experimental results were presented by Ayers et al. 1983,

Theoretical modelling of cyclone performance, Filtech Conference

•

Experimental Strategy

Measurements taken along this line

Inflow


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• Highly swirling flow in cyclone separator • Course 40,000 cell hexahedral mesh• High-order upwind scheme was used.• Computed with Reynolds Stress anisotropic turbulence model

with the standard wall functions. • Solved in a Cartesian coordinate system as apposed to a

cylindrical coordinate system therefore requiring higher order discretisation to avoid numerical diffusion.

• PRESTO (Pressure staggered Option) Pressure interpolation scheme. Essential for high swirl.

• QUICK discretisation scheme for • Momentum• Turbulent Kinetic energy• Turbulent disipation rate• Reynolds Stress

•

CFD Modelling strategy


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Pressure contours Path Lines depicting typical airflow patterns expected

Grid


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Tangential Axial

35 m/s

0

14 m/s

0

0 cm

32353841

596266

7780

Tangential and axial profiles predicted at all the experimental axial locations.


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Position 32. Axial & tangential velocity experimental v Predicted .


–— CFD, ∆ experiment [LDA]


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Conclusions DiscussionA best practice approach for cyclonic flow modelling has produced.

This documented has been compiled based on modelling experience in this area.

The RSM turbulence combined with high order discretisation schemes provides an engineering tool suitable for this type of flow problem. And can give satisfactory results on relatively course meshes.

But what about sensitivity to grid density and more advanced turbulence models ? (LES) ?


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Large Eddy Simulation (LES)• The contribution of large, energy containing eddies to

momentum and energy transfer is exactly computed while the effect of small (subgrid) scales is modeled

• LES involves transient, three-dimensional simulations• Computational requirements are intermediate between those

required for RANS and DNS• Very useful in simulating many transient physical processes• Advantage of a Cyclone for LES is the short residence time

of the fluid. Sub grid scales are therefore not as important.


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Problem Description• Hexahedral mesh of 700,000

cells• QUICK discretization scheme • Second-order time marching

scheme• LES-RNG subgrid model• Time step = 100 µs• 4 days of CPU time on an 8-

Processor HP machine


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Contours of Velocity Magnitude from LES

Instantaneous Instantaneous Averaged over T=0.25 s


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LES Cyclone Animations

Velocity magnitude Axial vorticity Velocity vectors


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Comparison between the tangential velocity at various stations as predicted by the RSM model, the time averaged LES results, and the experimental data (∆ Experimental data; RSM model; - - - Time averaged LES results).

Design and Application of CFD - Fluent

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