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