High Oil Content Hydrocyclone
Numerical Flow SimulationGelmirez M. Raposo
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Gelmirez M. Raposo
Carlos Alberto Capela Moraes
Luiz Philipe Martinez Marins
João Aguirre
Angela O. Nieckele
Topics
• Problem Description
• Methodology
• Goals
• Results
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• Conclusion and Next Steps
Problem Description
• Hydrocyclone: high oil content (10% to 15%)
– Equipment used
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– Experimental data
– Numerical simulation:
– turbulence models
– flow rates
Problem Description
• Experimental data
– Data acquired with LDV and PIV
• Made by Cenpes (Marins, 2007)
» Tangential and axial velocities profiles
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Problem Description
• Mathematical modeling
– Hypothesis
• Transient
• Isothermal flow
• Constant properties
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– Conservation of:
• Continuity
• Momentum
– Turbulence models
• Reynolds Stress Model (RSM)
• LES (Smagorinsky-Lilly subgrid model)
Methodology
• Finite Volume (Fluent and CFX)
– Transient scheme (second order)
– Inlets: mass flow rate known
– Outlet: mass split (0.35/0.65)
– Walls: no slip condition
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• Test Cases
– 2 turbulence models
– 3 meshesa: Complete model
b and c: Simplified model
– Walls: no slip condition
Mesh a b c
Element
type
hexaedral hexahedral
and prisma
hexahedral
and prisma
cells 2,827,684 1,035,484 1,252,404
quality -
equisize
Skew
quality>0.3 0 to 0.1 >
85% cells
0 to 0.1 >
96% cells
Methodologyb
a
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c
a
Methodology
• Set up’s
Turbulence model RSM LES
Software CFX FLUENT FLUENT
Mesh scheme a b and c c
Momentum and turbulence
discretization scheme
High Resolution QUICK QUICK
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Pressure discretization ---- PRESTO! PRESTO!
Pressure coupling Coupled SIMPLE SIMPLE
Pressure strain correlation 2nd order 2nd order -----
Inlet set up 3.7% turbulent
intensity and
automatic
turbulent length
scale
10% turbulent
intensity and 4.8 m
turbulent length
scale
-----
Near-wall modeling “Scalable” wall
function
Non equilibrium wall
function
Standard wall
function
Goals
• Establish a reliable way to model
hydrocyclones:
– Design mesh saving computational cost
– Turbulence model performance for each
situation
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situation
– Geometrical and operational parameters
influence
Results
0
5
10
15
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
tangencial velocity
Axial position
180 mm
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-5
0
5
10
15
20
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
radial position, m
axial velocity, m/s
radial position, m
LDV
RSM (Mesh a)
RSM (Mesh c)
LES (Mesh c)
Results
0
5
10
15
-0.03 -0.02 -0.01 0 0.01 0.02 0.03
tangencial velocity
Axial position
220 mm
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-5
0
5
10
-0.03 -0.02 -0.01 0 0.01 0.02 0.03
radial position, m
axial velocity, m/s
radial position, m
LDV
RSM (Mesh a)
RSM (Mesh c)
LES (Mesh c)
Results
0
2
4
6
8
10
12
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
tangencial velociy, m/s Axial position 180 mm
RSM (Mesh c)
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-2-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
radial position, m
-2
-1
0
1
2
3
4
5
6
7
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
radial position, m
axial velociy, m/sLDV
Flow rate (m3/h)
6.2
5.5
Results
Axial position 180mm
LES (Mesh c)
0
2
4
6
8
10
12
14
16
tangencial velociy, m/s
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-2
0
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
radial position, m
tangencial velociy, m/s
-4
-2
0
2
4
6
8
10
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
radial position, m
axial velociy, m/s
LDV
Flow rate (m3/h)
6.2
5.5
Conclusion
• Modelling
– Geometry, inlet simplifications
– Turbulent intensity and length scale
• Good agreement with experimental data
– LES, over predicted tangential velocity peak
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– LES, over predicted tangential velocity peak
– RSM mesh a, good agreement with axial velocity
– RSM mesh b, worst results
– RSM mesh c, reasonable results between effort
and precision
• Changing flow rate does not modify core size
Next Steps
• Improve performance by modifying the geometry
• Multiphase simulation
• Acquire more experimental data (PUC-Rio)
• Flow measurement
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• Velocity profiles
• Pressure
• Turbulent quantities
