IMPROVEMENT OF CENTRIFUGAL WET SCRUBBER … Ali Thesis.pdf · scrubbers, European Fluid ... 3.3.2...

© 2017 Ali, Hassan i

IMPROVEMENT OF CENTRIFUGAL WET

SCRUBBER DESIGN THROUGH

LABORATORY EXPERIMENTATION AND

COMPUTATIONAL FLUID DYNAMICS

by

Hassan Ali

BEng Mechanical Engineering (HONS)

Submitted in fulfilment of the requirement for the degree of

Doctor of Philosophy

School of Chemistry, Physics and Mechanical Engineering

Science and Engineering Faculty

Queensland University of Technology

2017

© 2017 Ali, Hassan iii

STATEMENT OF ORIGINAL AUTHORSHIP

I certify that the work contained in this thesis has not been previously submitted for any other

award. I also certify that this thesis is my original work and to the best of my knowledge and

belief, it contains no material previously published or written by another person except where

due reference is made.

Signature:

Date:

QUT Verified Signature

© 2017 Ali, Hassan iv

KEYWORDS

Computational Fluid Dynamics, CFD, Wet Scrubbers, Flue gas scrubbing, Sugar milling, Navier-

Stokes equations, Turbulence modelling, Liquid sheet atomisation, Dust collection.

© 2017 Ali, Hassan vi

ACKNOWLEDGEMENTS

I wish to thank my supervisors, Dr Anthony Mann, Dr Floren Plaza and Dr Phillip Hobson, for

giving me this opportunity and for their guidance throughout my candidature, as well as Sugar

Research Australia for providing funding for this project. I would also like to thank the QUT

staff at Banyo, especially Neil McKenzie, for helping with the experiments and Barry Hume

for his help whenever I needed. The assistance of QUT High Performance Computing staff is

also acknowledged. I would also like to thank Mark Hayne from the QUT mechanical

workshops and Dr Peter Woodfield from Griffith University for lending me their high-speed

cameras. Thanks to Ms Diane Kolomeitz for proofreading this thesis. Last but not least, I would

like to thank all my colleagues at the Centre of Tropical Crops and Biocommodities.

© 2017 Ali, Hassan vii

ABSTRACT

Experiments and Computational Fluid Dynamics (CFD) modelling were used to attain

an improved understanding of the complex multiphase flow processes inside fixed vane

centrifugal wet scrubbers. It helped identify the sources of problems reported by industrial

users of this scrubber type and modify the scrubber design for improved performance.

An extensive experimental program was developed to explore the underlying physics of

the flow processes in greater detail. A purpose-built experimental rig was setup, which

included a scaled replica of the prototype scrubber. Experiments were performed at varying

inflow conditions to obtain benchmark measurements for validation of the CFD model. This

included velocity and pressure measurements of the airflow for quantitative validation and

high-speed photography of the liquid distribution for qualitative validation of the CFD

predictions.

Breakup of the liquid sheet inside the test scale scrubber was analysed using high-speed

photography to gain an improved understanding of the liquid breakup process. Invaluable

insights into liquid sheet breakup were gained; this process has been studied for several

decades, but essential understanding has thus far eluded researchers. Details of liquid bag

formation, growth and burst were obtained and the creation of a high pressure zone inside a

liquid bag was identified.

CFD simulations included modelling of the general gas flow, gas-liquid interaction, dust

particle-liquid droplet interaction, wall film formation and separation, as well as liquid droplet

breakup and coalescence via the Eulerian-Eulerian and the Eulerian-Lagrangian approaches.

The Eulerian-Lagrangian method was found to deliver greater flexibility at the simulation

stage and detailed secondary phase (water) properties for post-processing, while the Eulerian-

© 2017 Ali, Hassan viii

Eulerian method provided better insights during post-processing at the overall scale. This

motivated the development of a code to represent the volume fraction of the liquid phase based

on the predictions of the Eulerian-Lagrangian approach and thereby use the post-processing

benefits of both the methods without having to spend any additional computational power in

performing the Eulerian-Eulerian simulations.

The CFD model was also upgraded to predict the scrubber dust capture efficiency while

taking into account the effect of droplet carryover, a phenomenon which has not been

considered previously in numerical simulations of wet scrubbers. It was found that

disregarding the effects of droplet carryover leads to an over-estimate of the dust capture

efficiency.

Once the simulation results had been validated via comparison to experimental findings,

design modifications were made to the scrubber scaled model for improved scrubber

performance. CFD was also used to ensure that the design changes did not result in an

increased pressure drop, which is of major concern to the industry. The project was able to

achieve all its specified aims and goals including a significant reduction in scrubber droplet

carryover as well as reduced blockages in the scrubber inlet. The findings were shared with

participating sugar mills and scrubber manufacturers for comments and implementation in

factory scrubbers. The feedback reported improved scrubber performance after the suggested

design modifications had been implemented.

© 2017 Ali, Hassan ix

List of publications and conference presentations

Journal papers

Ali, H., Plaza, F. and Mann, A. (2017), Numerical prediction of dust capture efficiency of a

centrifugal wet scrubber, American Institute of Chemical Engineers (AIChE) Journal.

Accepted for publication.

Ali, H., Plaza, F. and Mann, A. (2017) ‘Flow visualization and modelling of scrubbing liquid

flow patterns inside a centrifugal wet scrubber for improved design’, Chemical Engineering

Science, 173, pp. 98–109. doi: https://doi.org/10.1016/j.ces.2017.06.047.

Conference papers

Ali, H., Mann, A., Plaza, F., Inside a wet scrubber, Proceedings of the Australian Society of

Sugar Cane Technologists, Mackay (2016). 345-357.

Conference proceedings

Ali, H. (2016), Eulerian modelling of the scrubbing liquid distribution in centrifugal wet

scrubbers, European Fluid Mechanics Conference, Seville.

Ali, H. (2016), CFD modelling of a centrifugal wet scrubber, Australian Heat and Mass

Transfer Conference, Brisbane.

Webinars

Ali, H., Improved Modelling of Wet Scrubbers (2), Sugar Research Australia, Delivered

(22nd February 2017). Available at http://www.webcasts.com.au/sugar-research-webinars.

Mann, A., Ali, H., Improved Modelling of Wet Scrubbers (1), Sugar Research Australia,

Delivered (10th September 2014). Available at http://www.webcasts.com.au/sugar-research-

webinars.

© 2017 Ali, Hassan x

TABLE OF CONTENTS

STATEMENT OF ORIGINAL AUTHORSHIP ................................................................................................................. III

KEYWORDS ....................................................................................................................................................... IV

ACKNOWLEDGEMENTS ........................................................................................................................................ VI

ABSTRACT ....................................................................................................................................................... VII

TABLE OF CONTENTS ........................................................................................................................................... X

LIST OF FIGURES .............................................................................................................................................. XIV

NOTATION ...................................................................................................................................................... XX

1 CHAPTER 1: INTRODUCTION .............................................................................. 1

BACKGROUND ...................................................................................................................................... 1

AIMS AND OBJECTIVES ........................................................................................................................... 7

CONTEXT OF THE STUDY ......................................................................................................................... 7

THESIS OUTLINE ................................................................................................................................... 9

2 CHAPTER 2: LITERATURE REVIEW................................................................ 11

EMPIRICAL RELATIONS, MECHANISMS AND EXPERIMENTS ............................................................................ 11

2.1.1 Mechanisms of dust collection .................................................................................................. 12 2.1.1.1 Inertial impaction ............................................................................................................................. 13 2.1.1.2 Interception ...................................................................................................................................... 14 2.1.1.3 Diffusion ........................................................................................................................................... 14 2.1.1.4 Collection on the scrubber wall ........................................................................................................ 15

2.1.2 Empirical relations for collection efficiency ............................................................................... 16

2.1.3 Empirical relations and experiments for droplet diameter ....................................................... 18

2.1.4 Empirical relations and experiments for pressure drop............................................................. 19

CFD MODELLING ................................................................................................................................ 22

2.2.1 Turbulence modelling ................................................................................................................ 22 2.2.1.1 Standard k−𝜺 model ........................................................................................................................ 22 2.2.1.2 Reynolds Stress Model (RSM) ........................................................................................................... 24 2.2.1.3 Other turbulence models ................................................................................................................. 25

2.2.2 Multi-phase modelling .............................................................................................................. 26 2.2.2.1 Use of the Eulerian-Eulerian and Eulerian-Lagrangian approaches .................................................. 26 2.2.2.2 Drag Coefficient ................................................................................................................................ 30 2.2.2.3 Use of Inertial Impaction parameter in CFD ..................................................................................... 31 2.2.2.4 Inclusion of thermal aspects to simulations ..................................................................................... 32

FLOW VISUALISATION .......................................................................................................................... 33

2.3.1 Primary Breakup ........................................................................................................................ 34 2.3.1.1 Modelling of primary breakup .......................................................................................................... 38

2.3.2 Secondary breakup .................................................................................................................... 40 2.3.2.1 Minimum water droplet size ............................................................................................................ 41 2.3.2.2 Modelling of secondary breakup ...................................................................................................... 41

2.3.3 Droplet-film collision ................................................................................................................. 43

SUMMARY OF THE LITERATURE REVIEW ................................................................................................... 44

3 CHAPTER 3: METHODOLOGY ........................................................................... 46

RESEARCH DESIGN .............................................................................................................................. 47

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EXPERIMENTAL SETUP .......................................................................................................................... 47

3.2.1 Test rig fabrication .................................................................................................................... 48

3.2.2 Data acquisition ........................................................................................................................ 54 3.2.2.1 Velocity and pressure measurements .............................................................................................. 54 3.2.2.2 Scrubbing liquid measurements ....................................................................................................... 56 3.2.2.3 Design modifications ........................................................................................................................ 57 3.2.2.4 High Speed Photography .................................................................................................................. 57

COMPUTATIONAL FLUID DYNAMICS (CFD) .............................................................................................. 60

3.3.1 Introduction ............................................................................................................................... 60

3.3.2 Theory for single phase CFD ...................................................................................................... 61 3.3.2.1 Turbulence modelling ....................................................................................................................... 62

3.3.3 Theory for Multi-phase CFD....................................................................................................... 63 3.3.3.1 Eulerian-Eulerian Approach .............................................................................................................. 64 3.3.3.2 Eulerian-Lagrangian Approach ......................................................................................................... 65 3.3.3.3 Other Relevant sub-models .............................................................................................................. 66

Near-Wall Treatment ................................................................................................................... 66 Drag Law ...................................................................................................................................... 67 One-way and two-way coupling .................................................................................................. 69 Droplet breakup........................................................................................................................... 69 Heat transfer ................................................................................................................................ 70 Species transport modelling ........................................................................................................ 71 Wall film modelling ...................................................................................................................... 71 Conversion of discrete particles to volume fraction .................................................................... 73

3.3.3.4 Simulation of dust particles trajectories and collection ................................................................... 74 CFD SETUP ....................................................................................................................................... 77

3.4.1.1 Mesh generation .............................................................................................................................. 77 3.4.1.2 Single-phase flow setup .................................................................................................................... 81 3.4.1.3 Multi-phase flow setup..................................................................................................................... 83

4 CHAPTER 4: EXPERIMENTAL RESULTS AND DISCUSSION ...................... 87

VELOCITY MEASUREMENTS ................................................................................................................... 88

4.1.1 Velocity across traverses ........................................................................................................... 89

4.1.2 Air velocity in vanes ................................................................................................................... 91

PRESSURE DROP MEASUREMENTS .......................................................................................................... 95

4.2.1 Original design .......................................................................................................................... 95

4.2.2 Pressure plate ............................................................................................................................ 97

ENTRAINED WATER ............................................................................................................................. 98

5 CHAPTER 5: CFD RESULTS AND DISCUSSION ............................................ 101

SINGLE-PHASE MODELLING ................................................................................................................. 101

5.1.1 Simulated velocity profiles ...................................................................................................... 101

5.1.2 Pressure drop comparisons ..................................................................................................... 111

MULTI-PHASE MODELLING .................................................................................................................. 112

5.2.1 Pressure drop comparisons ..................................................................................................... 112

5.2.2 Predicted scrubbing liquid distribution using Eulerian-Eulerian and Eulerian-Lagrangian

approaches .............................................................................................................................. 114

5.2.3 Liquid wall film behaviour ....................................................................................................... 121 5.2.3.1 Droplet-film collision ...................................................................................................................... 121 5.2.3.2 Film stripping and separation ......................................................................................................... 122

FULL SCALE SCRUBBER SIMULATION RESULTS .......................................................................................... 126

6 CHAPTER 6: FLOW VISUALISATION ............................................................. 130

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MULTIMODE SHEET BREAKUP .............................................................................................................. 130

6.1.1 Liquid bag formation and breakup .......................................................................................... 132

6.1.2 Cylindrical ligament breakup ................................................................................................... 135

BREAKUP TIME (REAL AND DIMENSIONLESS) ........................................................................................... 137

RESULTING DROPLET PROPERTIES ......................................................................................................... 142

6.3.1 Droplet shape and drag ........................................................................................................... 142

COMPARISON OF DROPLET VELOCITY .................................................................................................... 143

SUMMARY....................................................................................................................................... 144

7 CHAPTER 7: SCRUBBER PERFORMANCE AND RECOMMENDED DESIGN

CHANGES ................................................................................................................ 145

FACTORS AFFECTING SCRUBBING EFFICIENCY IN A CENTRIFUGAL WET SCRUBBER ............................................ 145

7.1.1 Pressure plate .......................................................................................................................... 146

7.1.2 Water Bath .............................................................................................................................. 147

7.1.3 Bottom cone breakwater......................................................................................................... 153

COLLECTION EFFICIENCY SIMULATION RESULTS ........................................................................................ 157

8 CHAPTER 8: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 164

CHAPTER SUMMARIES........................................................................................................................ 164

MILESTONES CRITERIA AND COMPLETION DATES ..................................................................................... 167

PROJECT ACHIEVEMENTS .................................................................................................................... 168

FUTURE WORK AND RECOMMENDATIONS .............................................................................................. 172

9 BIBLIOGRAPHY .................................................................................................... 174

10 CHAPTER 10: APPENDICES ............................................................................... 190

APPENDIX 1: FLOW RATE CALCULATIONS ............................................................................................... 190

APPENDIX 2: AIR VELOCITY DISTRIBUTION ............................................................................................. 192

APPENDIX 3: TRANSPORT EQUATIONS FOR TURBULENCE MODELS ............................................................... 194

APPENDIX 4: CONTOURS OF Y+ ........................................................................................................... 195

APPENDIX 5: LIST AND DESCRIPTION OF ATTACHED VIDEO FILMS ................................................................ 197

APPENDIX 6: USER DEFINED FUNCTIONS ............................................................................................... 198

© 2017 Ali, Hassan xiii

LIST OF TABLES

Table 3-1. Mass and momentum source terms comparison for wall film modelling. 71

Table 3-2. Dust particle diameter and mass fraction. 75

Table 3-3. Inlet air mass flow rate simulated for the SSM. 83

Table 5-1. Centrifugal wet scrubber design data. 126

Table 8-1. Project milestone description and achievement dates. 167

© 2017 Ali, Hassan xiv

LIST OF FIGURES

Figure 1-1. Typical pressure drop generated in collection devices and collection

efficiencies of dust particles of various sizes (USEPA, 1995). 3

Figure 1-2. A typical centrifugal wet scrubber design (MikroPul, 2009). 6

Figure 1-3. A typical venturi scrubber design (MikroPul, 2009) 6

Figure 2-1. Drag coefficient (𝑪𝒅) vs aerosol Reynolds number (𝑹𝒆𝒑) (Wu et al.) 13

Figure 2-2. Dust particle motion based on the particle diameter. 15

Figure 2-3. Collection of a dust particle on the wetted scrubber wall. 16

Figure 2-4. Side-view schematic of liquid sheet breakup flowing over the

distribution cone edge. 34

Figure 2-5. Regimes of liquid sheet disintegration (Vujanovic 2010). 36

Figure 2-6. Spatial evolution of liquid sheet with gas-to-liquid density ratio of

1/1000 and Weber numbers (a) 500, (b) 400, (c)=300 (Movassat,

2007). 37

Figure 2-7. Schematic of a liquid jet entering a crossflow (Sedarsky et al. 2010). 38

Figure 2-8. Droplet break-up mechanisms (Pilch & Erdman 1987). 41

Figure 3-1. Schematic of the project methodology. 46

Figure 3-2. Schematic of the centrifugal wet scrubber test rig. 50

Figure 3-3. SSM dimensions as fabricated (Plan view). 51

Figure 3-4. SSM vane dimensions as fabricated. 52

Figure 3-5. Inlet dimensions of SSM (Top view), (a) Inlet type A and (b) Inlet

type B. 53

Figure 3-6. Velocity (hot wire anemometer) and static/differential pressure

sensor. 54

Figure 3-7. Velocity and pressure measurement traverse locations (in yellow)

across the test rig. 56

Figure 3-8. High speed cameras (a) X-Stream XS-4, (b) Hi-Spec 1. 58

Figure 3-9. Schematic of test-rig operation. 60

Figure 3-10. Schematic of the boundary layer approach (flow direction is left

to right). 67

Figure 3-11. Schematic of the wall film separation mechanism. 73

Figure 3-12. Dust particle size distribution. 75

Figure 3-13. Schematic of a CFD setup. 77

Figure 3-14. Tetrahedral to hexahedral mesh transition. 78

Figure 3-15. Wireframe view of the surface mesh in the bottom section of the

scrubber. 80

Figure 3-16. Mesh on a plane passing through the scrubber inlet after adaption

at wall boundaries. 80

© 2017 Ali, Hassan xv

Figure 3-17. Polyhedral mesh cross-section across different planes (a) Plane

passing through the middle of the scrubber, (b) scrubber inlet,

(c) plane passing through scrubbing vanes, (d) outlet. 81

Figure 4-1. (a) Side elevation and (b) plan views of the SSM. Four main

identified zones include bottom cone (zone A), below scrubbing

vanes (zone B), above scrubbing vanes (zone C) and above demister

vanes (zone D). 88

Figure 4-2. Positions of holes drilled in zones B, C, D. 89

Figure 4-3. Measured velocity components and velocity magnitudes (m/s) along

a traverse parallel to the longitudinal axis of the SSM inlet, with

locations of 10 mm and 380 mm distances shown on the sketch for

inlet type A. 89


a traverse perpendicular to the longitudinal axis of the SSM, with

the locations of 10 mm and 380 mm distances shown on the sketch

for inlet type A. 90


a traverse parallel to the longitudinal axis of the SSM, with the

locations of 10 mm and 380 mm distances shown on the sketch for

inlet type B. 90


a traverse perpendicular to the longitudinal axis of the SSM, with

the locations of 10 mm and 380 mm distances shown on the sketch

for inlet type B. 91

Figure 4-7. Maximum measured velocity magnitudes at a total air flow of 0.287

kg/s through the scale model scrubbing vanes. 93

Figure 4-8. Maximum measured velocity magnitudes at a total air flow rate of

0.287 kg/s through the scale model demisting vanes. 93

Figure 4-9. Measured velocity magnitude (m/s) along the traverse as shown

above the water distribution cone, with the locations of 10 mm and

380 mm distances shown on the sketch for inlet type A. 94

Figure 4-10. Measured velocity magnitude (m/s) along the traverse as shown

above the demister vanes, with the locations of 10 mm and 380 mm

distances shown on the sketch for inlet type A. 94

Figure 4-11. Total pressure drop (Pa) vs average inlet velocity (m/s) measured

running test rig without and with water addition at a rate of 0.13

L/s for inlet type A. 96

Figure 4-12. Total pressure drop (Pa) vs average Inlet velocity (m/s) measured

running the test rig without and with water at a rate of 0.13 L/s for

inlet type B. 96

Figure 4-13. Scrubbing vanes with a pressure plate in a FSS (a) View from above

the scrubbing vanes, (b) view from below the scrubbing vanes. 97

© 2017 Ali, Hassan xvi

Figure 4-14. Pressure drop vs average inlet velocity through the SSM with inlet

type B and a pressure plate located below scrubbing vanes in

comparison to original design (without pressure plate). 98

Figure 4-15. Measured water mass vs Average inlet velocity in the SSM. 100

Figure 4-16. Pressure drop vs average inlet velocity for two different water

addition rates. 100

Figure 5-1. Predicted and measured velocity profiles approximately 18 cm

below the scrubbing vanes. 102






below the scrubbing vanes on the shown traverse. 105

Figure 5-5. Predicted and measured velocity profiles 1 cm above the water

distribution cone across the shown traverse. 106

Figure 5-6. Predicted and measured velocity profiles 3 cm above the demisting

vanes across the shown traverse. 106

Figure 5-7. Plan views of the predicted velocity magnitudes at an air flow rate

of (a) 0.175 kg/s (b) 0.20 kg/s (c) 0.25 kg/s (d) 0.287 kg/s through

the scrubber vanes of the SSM. 107

Figure 5-8. Plan views of the predicted velocity magnitude at an air flow rate

of (a) 0.175 kg/s (b) 0.20 kg/s (c) 0.25 kg/s (d) 0.287 kg/s through

demisting vanes of the SSM. 108

Figure 5-9. Plan views of the predicted velocity magnitude at air flow rate of

(a) 0.175 kg/s (b) 0.20 kg/s (c) 0.225 kg/s (d) 0.287 kg/s through

a plane passing through the middle of the scrubber. 109

Figure 5-10. Simulated contours of velocity magnitude in a vertical plane

normal to and passing through the scrubbing vanes for varying

pressure plate diameters (a) no pressure plate, (b) 0.20 m pressure

plate diameter, 0.225 m pressure plate diameter, 0.25 m pressure

plate diameter. 110

Figure 5-11. Vector plot on a plane passing through the middle of the scrubber. 111

Figure 5-12. Predicted pressure drop across the SSM for each of the cases

presented in Figure 5-10. 112

Figure 5-13. Measured and predicted total pressure drop (Pa) across SSM with

water addition of 0.13 L/s for inlet type A. 113

Figure 5-14. Measured and predicted total pressure drop (Pa) across the SSM

with water addition of 0.13 L/s for inlet type B. 113

Figure 5-15. Scrubbing liquid distribution in the SSM (a) test rig (b) iso-value

from simulations. 114

Figure 5-16. Modelled iso-volume plot showing the distribution of the secondary

phase (0.001 m diameter) at air flow rate (a) 0.175 kg/s, (b) 0.20 kg/s

and (c) 0.287 kg/s. 115

© 2017 Ali, Hassan xvii

Figure 5-17. a) Predicted water droplet distribution in the SSM at air flow rate

of 0.175 kg/s. b) Predicted iso-surface of water with volume

fraction of 0.001 and the resulting liquid film on the SSM walls. 116

Figure 5-18. a) Predicted water droplet distribution at an air flow rate of 0.20

kg/s. b) Predicted iso-surface of water with volume fraction of

0.001 and the resulting liquid film on the SSM walls. 117







Figure 5-21. Comparison of scrubbing liquid distribution entering the air flow

(a) experiments, (b) Eulerian-Lagrangian, (c) Eulerian-Eulerian. 120

Figure 5-22. High speed photographs of a droplet (diameter 2.75 mm)

rebounding from the demister vane wall due to a small

impingement angle. 122

Figure 5-23 (a) Photograph showing the liquid film on a demister vane surface

of the SSM (b) Modelled liquid film on the demister vanes and wall

of the SSM (image produced using frontal face culling). 123

Figure 5-24. Schematic of steps involved in film separation and droplet re-

entrainment. 124

Figure 5-25. (a) Predicted droplet distribution in the SSM looking in the

direction of air flow from the inlet duct. (b) Predicted droplet

distribution in the SSM with the plane of view rotated clockwise

approximately 30° (looking from above) from that used in (a).

Legends in parts (a) and (b) show the modelled droplet diameters

in m. (c) Actual droplet distribution in the SSM looking in the

direction of air flow from the inlet duct. (d) Close-up view of the

droplets separating from the inside wall of the inlet duct. 125

Figure 5-26. Predicted gas density (left) and gas temperature (right) across a

vertical and a horizontal plane of the FSS. 127

Figure 5-27. Wall film separation (a) SSM experiments (Ali, Mann and Plaza,

2016), (b) FSS simulations. 129

Figure 6-1. Macro-scale growth of liquid sheet in the SSM. 132

Figure 6-2. Liquid bag growth and burst in the SSM (droplet trails represent

the direction of travel. 134

Figure 6-3. Cylindrical ligament breakup in the SSM. 135

Figure 6-4. Multimode sheet breakup in the SSM. 136

Figure 6-5. Liquid sheet breakup time (s) vs gas velocity (m/s). 138

Figure 6-6. Stages of bag breakup for a single water drop (a) 𝒅𝒐=3.1 mm, We=

13.5 (Krzeczkowski 1980) and (b) 𝒅𝒐=4.0 mm, We= 13.78 (SSM

experiments). 139

© 2017 Ali, Hassan xviii

Figure 6-7. Plot of the dimensionless breakup time vs the Weber number from

SSM experiments. 141

Figure 6-8. Evolution of a 4 mm droplet shape after getting detached from a

ligament across a time span of 0.042 s (Frames displayed at equal

time intervals). 142

Figure 6-9. (a) 1.2 mm drop, (b) 0.62 mm drop (bottom droplet in the

sequence). 143

Figure 6-10. (a) Initial and (b) final image of droplet positions captured by high-

speed photography. (c) Predicted velocity vectors for a 1000 µm

diameter droplet. Images and predictions are for the scrubber scale

model with an average air inlet velocity of 6.2 m/s. 144

Figure 7-1. Water distribution in Zone B represented via contours of volume

fraction greater than 0.001 on a plane passing through the middle

of the scrubber (a) with pressure plate, (b) without pressure plate. 147

Figure 7-2. Predicted volume fraction of water above the scrubbing vanes

representing the extent of the water bath at air mass flow rate of

(a) 0.25 kg/s and (b) 0.32 kg/s 148

Figure 7-3. Water accumulation around demister vanes at high gas flow rates. 149

Figure 7-4. Predicted contours of volume fraction of 550 µm droplets in the

scrubber scale model with the existing spacing between the

scrubbing and demisting vanes on the left and the proposed raised

demisting vanes on the right. 151

Figure 7-5. Water distribution in SSM before and after addition of the

breakwater and modelling results for the later. 152

Figure 7-6. Iso-metric view a scrubber body with the suggested position of a

vertical breakwater. 153

Figure 7-7. Top view of the iso-volume surfaces of the combined Eulerian

liquid phases. 153

Figure 7-8. Scrubber scale model operating a) without breakwater plate b)

with breakwater plate. 154

Figure 7-9. Dust deposit build-up at inlet of a factory scrubber. 155

Figure 7-10. Contours of velocity magnitude after deposit build-up inside the

SSM. 155

Figure 7-11. Test geometry (a) without lips and (b) with lips. 156

Figure 7-12. Contours of film thickness. 157

Figure 7-13. Predicted dust particle tracks in FSS (limited to 25 tracks to aid

visibility). 160

Figure 7-14. Simulated grade efficiency comparison with published data. 161

Figure 7-15. Simulated collection efficiency and droplet carryover vs the mass

flow rate of carrier phase. 162

Figure 7-16. Simulated grade efficiency comparison for the FSS with and

without accounting for droplet carryover. 163

Figure 8-1. Lists the achievements of the project in terms of both the

contribution to knowledge and the significance to the industry. 171

© 2017 Ali, Hassan xix

Figure 10-1. Y+ for the SSM, scale-able wall function was used. 195

Figure 10-2. Y+ values for the FSS, enhanced wall treatment was used. 196

© 2017 Ali, Hassan xx

NOTATION

A surface area (m2)

Cd coefficient of drag

d0 initial diameter (m)

dd droplet diameter (m)

dp dust particle diameter (m)

D32 Sauter mean diameter (m)

E total energy (J)

F force (N)

Fc centrifugal force (N)

FD drag force (N)

g gravity (N)

hj sensible enthalpy (J/kg)

I turbulence intensity (%)

J diffusive flux (J/m2·s1)

k turbulence kinetic energy (m2/s2)

keff effective thermal conductivity (W/(m. k))

L particle to fluid mass ratio

mf wall-film mass flow (kg/s)

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min mass flow in (kg/s)

mout mass flow out (kg/s)

mp mass of particle (kg)

ncol collection efficiency

Nu Nusselt number

Oh Ohnesorge number

Pr Prandtl number

Pe Peclet number

Q volume flow rate (m3/s)

Qg volume flow rate gas (m3/s)

Ql volume flow rate liquid (m3/s)

r radius (m)

Re Reynolds number

Rep Aerosol Reynolds number

St Stokes number

t0 particle response time (s)

T dimensionless time

u′ velocity fluctuation (m/s)

��, V velocity (m/s)

V a axial velocity (m/s)

V avg average velocity (m/s)

© 2017 Ali, Hassan xxii

V f wall film velocity (m/s)

V mag velocity magnitude (m/s)

V rel relative velocity (m/s)

V t tangential velocity (m/s)

Vc volume of cell (m3)

Vf volume fraction of phase ‘f’

W Work done (J)

We Weber number

Greek symbols

α volume fraction

ε turbulence dissipation rate (m2/s3)

Ɛ Dimensionless parameter (based on cyclone geometry)

μ viscosity (kg/m. s)

ρg gas density (kg/ m3)

ρl liquid density (kg/ m3)

τ stress (N/m2)

τ𝑒𝑓𝑓 deviatoric stress tensor (N/m2)

σ surface tension (N/m)

Subscripts

avg average

© 2017 Ali, Hassan xxiii

o initial diameter (m)

𝑑 droplet

p dust particle

c critical

g gas

l liquid

mag magnitude

rel relative

u, v, w velocity components

x, y, z coordinate directions

Abbreviations

CAD Computer-aided design

CFD Computational fluid dynamics

DEM Discrete element method

DNS Direct numerical simulation

ELSA Eulerian-Lagrangian spray atomisation

FPS Frames per second

FSS Full scale scrubber

HSC High speed camera

ID Induced draft (fan)

LSM Level-set method

© 2017 Ali, Hassan xxiv

PBM Population balance model

RSM Reynolds stress model

SSM Scrubber scale model

TAB Taylor analogy breakup

RNG Re-normalisation group

UDF User defined function

UDM User define memory

VOF Volume of fluid (method)

© 2017 Ali, Hassan 1

1 CHAPTER 1: INTRODUCTION

Centrifugal wet scrubbers are a type of dust collection device used to extract dust

particles from exhaust gases generated by industrial boilers. This research aims to deliver

an improved centrifugal wet scrubber design by increasing the current understanding of

the complex multiphase flow processes occurring inside the scrubbers.

This chapter outlines the background of the research topic (Section 1.1), the aims and

objectives of the study (Section 1.2), the context of the study (Section 1.3) and a framework

of the remaining chapters of this thesis (Section 1.4).

Background

The stated objective of the United Nations Framework Convention on Climate Change

(UNFCCC) is to “stabilise greenhouse gas concentrations in the atmosphere at a level that

would prevent dangerous anthropogenic interference with the climate system” (UNFCCC,

1992).

The fear of global warming has led to the enforcement of stricter restrictions by

governments and international bodies on industrial emissions. With the Paris Climate

Agreement signed in April 2016, the regulations are likely to tighten over the next few years as

we strive towards a greener planet. Although industrialisation has resulted in scientific

knowledge growing twofold over the last century, it has also had a negative impact on the world

ecology and there is a moral obligation on the human race to preserve the planet.

Over the recent decades, there has been a shift to renewable energy resources for electricity

generation, as global fossil fuel reserves have depleted rapidly and the effects of global warming

have become more evident. One such renewable fuel is bagasse, a by-product of the sugar

https://en.wikipedia.org/wiki/Greenhouse_gas

https://en.wikipedia.org/wiki/Human_impact_on_the_environment

https://en.wikipedia.org/wiki/Climate_system


manufacturing process in cane-sugar factories. Bagasse is characterised as a ‘carbon-neutral’

fuel i.e. burning bagasse does not impact the balance of carbon dioxide in the atmosphere,

provided that the sugarcane plant is replanted, which leads to the reabsorption of the carbon

dioxide gas released from burning the bagasse (Sagawa, Yokohama and Imou, 2008).

However, a problem exists as dust particles are produced in the exhaust gases generated

by industrial boilers burning bagasse. Dust removal from these gases before they are released

into the atmosphere is extremely important. Dust particles in exhaust gas streams can have a

diameter range from 1/10𝑡ℎ of a micron to a few microns, making them extremely harmful to

animals and humans alike (Oliveria and Coury 1996).

The United States Environmental Protection Agency (USEPA) has put the acceptable

limit of dust emission from industrial plants to less than 50 μ𝑔/𝑚3 of exhaust gas (USEPA

1995). Dust removal devices such as baghouses, venturi scrubbers, dust cyclones, settling

chambers, spray towers, electrostatic precipitators and centrifugal wet scrubbers are used to

extract the dust from exhaust gases before gas emission into the atmosphere. The separation

efficiency of these dust collecting devices can be defined as

ncol =min − mout

min

(1.1)

where min and mout are the mass flow rate of the dust content entering the dust collection

device and that escaping collection, respectively.

The choice of the scrubber type employed primarily depends on the dust particle size

produced by the industrial process. For particles that are greater than 5 microns in size,

centrifugal wet scrubbers can be used, giving high collection efficiencies (USEPA, 1995).

Whereas in industries where the particle size is smaller than 1 micron in diameter, the

effectiveness of centrifugal wet scrubbers is greatly reduced and high velocity wet scrubbing

systems such as venturi scrubbers, or different removal technologies such as electrostatic


precipitators or baghouses are required (Mussatti and Hemmer, 2002). Baghouse collectors

and electrostatic precipitators have a very high collection efficiency for the extremely small

particle size but are also a high capital cost (Vasarevicius, 2012). The performance of dust

removal devices is measured in terms of pressure drop and collection efficiency (Pak and

Chang, 2006). The optimum design is to achieve the maximum collection efficiency with

the minimum pressure drop and (for water based technologies) droplet carryover (escape of

water droplets from the scrubbing vessel). Figure 1-1 shows the collection efficiencies and

the typical pressure drop generated by collection devices for a range of dust particle sizes.

Figure 1-1. Typical pressure drop generated in collection devices and collection efficiencies

of dust particles of various sizes (USEPA, 1995).

Wet scrubbers work by exposing the exhaust gas from industrial plants to a scrubbing

liquid. The scrubbing liquid can be introduced as sprays, or alternatively, droplets can be

allowed to form inside the scrubber by the high velocity flue gas impacting a liquid

curtain/sheet.


Centrifugal-type wet scrubbers are able to achieve a higher scrubbing efficiency with

comparatively lower gas velocities and pressure drops than venturi scrubbers for particles

greater than 5 microns in diameter. This is because, in addition to allowing the dust particles

to impact water droplets within the scrubber, centrifugal wet scrubbers also have a highly

swirling flow. A reduction in the gas inlet area increases the tangential velocity component

of the flue gas and initiates the swirling motion via a ‘tangential-entry’ flue gas inlet (Figure

1-2 and Figure 3-5). This means that unlike venturi scrubbers, in which the gas flow is

parallel to the venturi walls (Figure 1-3), dust particles in centrifugal wet scrubbers regularly

come in contact with the wetted scrubber walls and get collected via this additional

mechanism. Swirling particles also have a greater residence time inside the scrubber, which

increases the chance of the dust particles encountering a collecting droplet.

After the gas is scrubbed, it passes through a mist eliminator/demister device, which

extracts any entrained droplets from the gas stream. Several designs, such as wire mesh mist

eliminators, packed beds, vane-type and centrifugal mist eliminators are used. Each of these

uses different mechanisms to carry out the mist elimination task by making use of droplet

impingement and coalescence, filtration, gravitational separation or change in the gas

acceleration (Smith, 1987). Centrifugal wet scrubbers make use of droplet inertia to collect

entrained droplets via employing centrifugal demisters; a sub-category of the vane-type

demisters. These demisters work by changing the flue gas direction and increasing the gas

tangential velocity. Liquid droplets are “thrown” towards the scrubber wall due to their

higher inertia as they are unable to follow the gas streamlines, forming a liquid film that

flows down the scrubber wall. Figure 1-2 presents an illustrative summary of a centrifugal

wet scrubber. For a visual description of the gas flow pattern inside a centrifugal wet

scrubber, see the attached video “Gas flow pattern.mp4”.


In factories burning bagasse as a fuel, the mean dust particle size is greater than 5

microns and centrifugal wet scrubbers are an appropriate choice for dust collection.

However, operational problems are persistently reported by factories using centrifugal wet

scrubbers. These problems include:

Water droplet carryover at high gas loads. This causes deposit build-up on the induced

draft (ID) fan blades as well as corrosion of fan blades, leading to excessive vibration

during the ID fan operation. It also causes a decrease in capture efficiency as dust

particles captured inside water droplets are prone to escaping the scrubber along with

the water droplets.

Insufficient scrubbing efficiency at low gas loads. This puts factories under the risk of

failing environmental regulations.

The overflow of water from the bottom conical exit. This can lead to the initiation of

deposit build-up in the gas inlet. The build-up may become so severe that a boiler has

to be taken off-line for scrubber cleaning and maintenance.

Boiler stoppages due to these problems result in a loss of revenue from electricity sales

in addition to maintenance costs and the long-term damage to equipment (Kumar et al.,

2012). Depending on the factory size, the costs can range from $3000 per hour to $30,000

per hour of boiler stoppage (Viana & Wright 1996).

Surprisingly, despite the widespread use and the regular problems faced by centrifugal

wet scrubbers, published investigations on this scrubber type are rare (Ali, Mann and Plaza,

2016) and attempts to improve scrubber performance have been limited to design changes

based on personal experience and rules of thumb.


Figure 1-2. A typical centrifugal wet scrubber design (MikroPul, 2009).

Figure 1-3. A typical venturi scrubber design (MikroPul, 2009).


Aims and objectives

The aim of this study was to gain an increased understanding of the multiphase flow

processes inside fixed-vane centrifugal wet scrubbers and to use this understanding to

enhance the scrubber design for improved performance. Alleviating the financial burden on

factories due to the foretold problems will also help promote the use of bagasse as a

competitive alternate fuel, which additionally has environmental benefits as described in

Section 1.1.

The cost of wet scrubber operation can be reduced via:

Decreasing the pressure drop inside the scrubber to reduce the ID fan power

requirement.

Decreasing droplet carryover, which causes build-up in the induced draft fan

as well as corrosion of fan blades and parts.

Decreasing the build-up of dust in the scrubbers, to ensure the boiler does

not need to be stopped to bring the scrubber back to operational condition.

The project also aimed to bridge the gap between research and its industrial

application. Investors are increasingly interested in financial benefits and reluctant to fund

research projects with little or no immediate benefit to the industry.

Context of the study

During the past two decades, low cost, high speed computing resources have become

more widely available. This has helped establish Computational Fluid Dynamics (CFD) as

an important tool for studying and improving a wide range of industrial fluid flow processes.

CFD can be used to predict the flow of a single fluid such as the air flow through a building

or around an aircraft wing and the results can be used for design optimisation, or multiphase


simulations can be performed, in which the interaction of different phases with each other

can also be taken into account.

In contrast to the large number of studies on venturi scrubbers (Ali, Qi and Mehboob,

2012) and dust cyclones (Narasimha, Brennan and Holtham, 2007) and a few modelling

studies on flue gas desulphurisation towers (Marocco and Inzolu, 2009) which face similar

problems, there are no experimental or modelling studies on centrifugal wet scrubbers.

Furthermore, a major shift was observed in the literature, wherein research focus for

emission control devices moved from fluid flow experimentation for velocity and pressure

measurements in the last century to the use of CFD in the past decade (Ali, Qi and Mehboob,

2012). This shift, however, did not benefit from the use of flow visualisation techniques such

as high speed photography, which experienced a two-fold performance increase at the turn

of the century.

Flow inside a centrifugal wet scrubber is extremely complex due to the interaction

between dust particles, water droplets, bulk water and the highly turbulent flue gas. Further

refinement and tailoring of CFD codes is required to gain a better understanding of the flow

processes inside a centrifugal wet scrubber. This improved understanding will provide the

basis to propose design changes for centrifugal wet scrubbers. Where venturi scrubber and

dust cyclone simulations have been performed, researchers have mainly benefitted from

experimental results available in the literature. In many cases, the relation between the

experimental results and simulations is limited - as will be shown in the literature review

(Chapter 2) - and continuous validation of CFD codes with experiments is extremely

important. According to Slater (2008) no international standards for the verification and

validation of CFD predictions exist, but, for confidence in design making based on CFD

results, some experimental validation should continue.


Thus, this project not only uses CFD as a simulation tool but also validates the CFD

models used; adding functions to the code where necessary to incorporate the physical

aspects of flow inside a centrifugal wet scrubber, which cannot be simulated via the available

CFD models. These updated models were validated by experiments using flow measurement

techniques, high speed photography and/or published data, both qualitatively and

quantitatively.

Thesis Outline

This section lists and gives a brief outline of the following chapters in the thesis.

Chapter 2- Literature Review

Chapter 2 presents the literature available on the subject including the research work

conducted, the development and use of empirical relations for performance measurement and

enhancement, the use of CFD to simulate flow and the inclusion of multi-phase modelling for

simulating flow in dust collection devices. Experiments conducted for flow visualisation of the

liquid sheet and droplet breakup are also discussed.

Chapter 3- Methodology

Chapter 3 details the methodology to carry out the project from the planning to the

implementation stage and delivery to industry including the details of the experimental program

developed and conducted, the CFD approach undertaken and the functions added to the CFD

code.

Chapter 4- Experimental Results and Discussion

Results from experiments including velocity and pressure measurements with and without

the addition of water are given in this chapter.

Chapter 5- CFD results and discussion


Results from CFD simulations are presented and discussed in this chapter. Comparison of

simulation results, together with experimental measurements for validation of the CFD model,

are also presented.

Chapter 6- Flow visualisation

This chapter extends the details of experimental work conducted via introducing the

scrubbing liquid and the use of High Speed Cameras (HSC) to study the liquid distribution and

characteristics.

Chapter 7- Scrubber performance and recommended design changes

Centrifugal wet scrubber performance and design are analysed in this chapter and changes

to scrubber design are suggested. Simulation results for the collection efficiency of centrifugal

wet scrubbers using the upgraded CFD model are also given in this chapter.

Chapter 8- Summary, conclusions and recommendations

Chapter 8 consists of the concluding remarks. It summarises the project and suggests further

work to enhance the findings of this project. The overall project achievements are identified and

a comment on the success of the project goals is made.


2 CHAPTER 2: LITERATURE REVIEW

This chapter presents the literature review on dust collection devices. It is divided

into three main sections. The first section (Section 2.1) details the literature on the

experimental and physical aspects of fluid flow in dust collectors; the sub-sections review

the literature on mechanisms of dust collection (Sub-section 2.1.1), empirical relations and

experiments carried out to determine the collection efficiency (Section 2.1.2), droplet

diameter (Sub-section 2.1.3) and pressure drop (Sub-section 2.1.4) in dust collection

devices.

The second section (Section 2.2) presents the literature on the application of CFD to

simulate flow in dust collection devices. This includes the development, historical and

current use of single-phase (Sub-section 2.2.1) and multi-phase CFD modelling (Sub-

section 2.2.2).

Section 2.3 presents literature on flow visualisation experiments related to dust

collection devices. The literature on breakup mechanisms and modelling of liquid sheets

(primary breakup) and of droplets (secondary breakup) is also detailed in this section.

Empirical relations, mechanisms and experiments

This section reviews the literature on the mechanisms responsible for dust collection in

wet collection devices and empirical relations developed for collection efficiency, pressure drop

and mean droplet diameter. Much of the published research focuses on venturi scrubbers which

have similar mechanisms of dust collection to a centrifugal wet scrubber.


2.1.1 Mechanisms of dust collection

Three basic mechanisms, namely inertial impaction, interception and diffusion,

account for the majority of the dust collection in wet collection devices. Gravitational

settling and electrostatic precipitation are also utilised in some collector types. The type of

collection mechanism responsible for collection mainly depends on the dust particle and

collecting droplet’s size and velocity.

According to Grover et al. (1977), all particles colliding with a water droplet will get

retained by it. Thus, in general terms, a “good” scrubbing liquid distribution inside the

scrubbing vessel can result in a high collection efficiency, by ensuring that all dust particles

entering the device encounter a collecting droplet in their flow path. However, in reality, this

is not the case as very small particles are able to closely follow gas streamlines (Hinds,

1999), ‘looping’ around a collecting droplet/surface, avoiding contact and escaping

collection. On the other hand, larger particles have a lesser tendency to follow the gas

streamlines due to their higher inertia (Ohio EPA, 1998). They continue to travel within the

boundary layer around a droplet and are more likely to get collected upon impact with the

droplet surface. Recently, Mitra et al. (2015) performed experimentation and numerical

modelling to understand the results of particle-droplet collision using 1 mm diameter

particles and approximately 3.4 mm diameter droplets. They observed that it was also

possible for a particle to pass through a droplet rather than be retained or rebound off the

droplet surface.

The tendency of a particle to deviate from a gas streamline or closely follow it depends

on the particle relaxation time (𝑡0), given by equation 2.1 for the Stokes regime (Hinds,

1999).

𝑡0 =𝜌𝑑2

18𝜇𝑔 (2.1)


where 𝜌 is the particle density, 𝑑 is its diameter and 𝜇𝑔 is the dynamic viscosity. In the Stokes

regime viscous forces are dominant over inertial forces and particles are assumed to be

spherical (Figure 2-1). This means that the particle Reynolds number ‘Re’ (ratio of inertial

to viscous forces) is small and particles will closely follow the change in gas direction. The

square law relationship associated with the particle diameter in Stokes regime causes a

significant change in the response time for a small change in the particle diameter (Equation

2.1).

Figure 2-1. Drag coefficient (𝐶𝐷) vs aerosol Reynolds number (𝑅𝑒𝑝) (Wu et al.)

Dependence of collection efficiency on the particle response time is described later,

whereas the mechanisms of dust collection are described in the following sub-sections. As a

rule of thumb, higher relative velocities between the scrubbing liquid and the gas result in a

greater collection efficiency.

2.1.1.1 Inertial impaction

Inertial impaction is the leading mechanism of dust removal from exhaust gas streams

and is dominant for large particles with diameter 5 microns and above (Kim et al., 2001).

While gas molecules loop around a collecting surface, larger particles are able to continue


moving in their initial direction of travel due to their higher inertia (Figure 2-2). Upon

impact, a dust particle possessing at least a critical kinetic energy is able to penetrate the

liquid droplet surface, getting collected inside it (Pemberton, 1960).

2.1.1.2 Interception

Interception is the second most important mechanism of the collection in dust

collectors and is defined as collection of a dust particle via direct contact with a liquid

droplet, whereby a dust particle “adheres” to the surface of the liquid droplet. According to

Wang et al. (2014), even if a particle does follow a gas streamline it may still get collected

if it passes within one particle radius of a collecting surface.

2.1.1.3 Diffusion

Extremely small particles (diameter <1 micron) are collected by the diffusion

mechanism (Kaldor and Phillips, 1976). As a particle’s size continues to decrease, the

diffusive criteria and thus the probability of capture increases, and vice versa. In collection

by diffusion, dust particles are thrown around in a random manner by impacting gas

molecules, a phenomenon known as Brownian motion.

Costa et al. (2005) discovered that the minimum efficiency in a venturi scrubber was

at a particle size of approximately 0.3 microns and the grade efficiency of venturi scrubbers,

in fact, started to increase for particles that were smaller than 0.3 microns in diameter due to

an increasing collection via the diffusion mechanism.

Collection by diffusion does not contribute significantly to the collection efficiency of

centrifugal wet scrubbers, as the dust particle size in industries where centrifugal wet

scrubbers are used is much larger than that needed for collection through diffusion to occur.


Figure 2-2. Dust particle motion based on the particle diameter.

2.1.1.4 Collection on the scrubber wall

Another mechanism that contributes significantly to the collection efficiency of

centrifugal wet scrubbers is the collection of dust particles on the wetted scrubber walls (Figure

2-3). Unlike venturi scrubbers, in which the gas flow is parallel to the venturi walls and the liquid

film on the walls accounts for negligible dust removal (Viswanathan, 1997), this mechanism

plays a major role in collection efficiency of centrifugal wet scrubbers.

Cyclone separators are solely based on this mechanism as well (Licht, 1980). Particles

with a low response time are more prone to change in gas direction, whereas those with a

greater response time have a slower reaction to the change in gas direction. As a particle

approaches a wall, centrifugal force causes it to deviate from the gas streamline and impact

the wall surface, while the gas molecules are able to follow the curved scrubber wall. The

centrifugal force (𝐹𝑐) on a dust particle is given by equation 2.2.

𝐹𝑐 =𝑚𝑝�� 𝑡

2

𝑟 (2.2)

where 𝑚𝑝 is the dust particle mass, �� 𝑡 is its tangential velocity and 𝑟 the radius of the

scrubber.


Figure 2-3. Collection of a dust particle on the wetted scrubber wall.

The combination of these two mechanisms i.e. collection within the scrubber volume

and collection on the scrubber walls makes the dust collection efficiency of centrifugal

scrubbers very high at relatively lower velocity than that in a venturi scrubber for particle

sizes greater than 5 microns.

2.1.2 Empirical relations for collection efficiency

The collection efficiency of a dust particle depends on the following variables:

1) Particle diameter, 2) Particle velocity, 3) Particle Mass, 4) Particle density, 5) Collecting

droplet diameter, 6) Collecting droplet velocity, 7) Gas viscosity.

Based on the experimental data of Walton & Woolcock (1960), inertial impaction has

been studied by various researchers. It is characterised by the particle Stokes number (St), also

known as the inertial impaction parameter, and given by:

𝑆𝑡 =𝜌𝑑𝑑𝑝

2�� 𝑟𝑒𝑙

18𝜇𝑑𝑑 (2.3)

where, 𝜌𝑑 is the particle density, 𝑑𝑝is the particle diameter, �� 𝑟𝑒𝑙 is the relative velocity, 𝜇 is the

gas viscosity and 𝑑𝑑 is the collecting droplet diameter.


The collection efficiency can then be predicted from the following equation (Calvert,

1970).

𝑛𝑐𝑜𝑙 = (𝑆𝑡

𝑆𝑡 + 0.7)2

(2.4)

Over the years, this parameter has been widely used to assess venturi scrubber designs.

Yung et al. (1978) used it, along with the Nukiyama & Tanasawa relation (1938) for mean

droplet size, to estimate the collection efficiency inside the venturi throat. It was also used by

Viswanathan & Amanthanarayanan (1998) to predict the scrubbing efficiency of venturi

scrubbers and by Mohebbi et al. (2003) for an orifice scrubber, since the major collection

mechanism is similar for both the scrubber types. However, since venturi scrubbers are often

employed in industries where the dust particle size is much smaller than that required for inertial

impaction to occur, other mechanisms such as diffusion should also be taken into account for

better estimation of their collection efficiency.

Correlations for the other dust collection mechanisms discussed in Section 2.1.1 were

presented by Costa et al. (2005) for a venturi scrubber and more recently by Wang et al. (2014),

who reported diffusion, interception and gravitational sedimentation correlations for a tray

washing column. The authors, comparing experimental and theoretical results, concluded that

increased scrubbing liquid to gas ratio inside a tray-washing column increased the systems

collection efficiency. Likewise, Lee & Geleseke (1979) derived a formula for the overall

particle collection efficiency by a packed bed based on theoretical analysis. They showed that

the particle deposition via diffusion depends on the Peclet number (Pe) and increases with

decreasing particle size.

Haller et al. (1989) noted that Calvert's (1970) model only produced reasonable

predictions if droplets were uniformly distributed in the venturi. Since this is not the case in real

venturi scrubbers; they developed a new model via experiments that take into consideration the


non-uniformity of liquid droplet size in a venturi. Noting that a uniform liquid distribution is

necessary for an efficient scrubber performance, they developed a new venturi throat design

attempting to achieve an optimum liquid distribution.

A simpler collection efficiency formula was presented by Mussatti and Hemmer (2002),

which calculates the collection efficiency of wet scrubbers by ratios of the area swept free of

particles to the area swept by collecting droplets. As detailed earlier, this approach will over-

predict the collection efficiency as small particles may loop around the water droplets without

making a physical contact with the droplet surface.

2.1.3 Empirical relations and experiments for droplet diameter

Scrubbing liquid can be introduced into a system via an open pipe or spray nozzles

(Mussatti and Hemmer 2002). The final diameter of a collecting water droplet is important in

determining the fate of a dust particle as larger droplets not only have a small collection surface

area, but it is also easier for dust particles to follow a curvilinear path around them. Various

correlations are available in the literature to calculate the mean diameter of water droplets,

especially inside a venturi scrubber. These correlations generally assume that the droplets reach

their terminal velocity within the venturi and the pressure drop across the venturi occurs at a

constant rate.

Droplet diameter to calculate the Stokes number has been most frequently estimated using

the correlation of Nukiyama & Tanasawa (1938), which predicts the Sauter mean diameter

(𝐷32) of the liquid droplet distribution and is given by

𝐷32 =5.85 × 10−4

�� 𝑟𝑒𝑙√

𝜎

𝜌𝑙+ 10−3 (

𝜇

√𝜎𝜌𝑙

)

0.45

(1000𝑄𝑙

𝑄𝑔)

1.5

(2.5)


Boll et al. (1974) suggested that this correlation is only suitable when the flow velocity

is approximately 45.7 m/s or else the effect of the gas velocity is underestimated. They

presented an improved correlation for the Sauter mean diameter prediction given by:

𝐷32 =

4.22 × 10−2 + 5.776 × 10−3 (𝑄𝑙

𝑄𝑔)1.932

𝑉𝑟𝑒𝑙1.602

(2.6)

Several researchers have also used photographic and laser techniques to measure the

droplet size in experimental venturi scrubbers. The breakup of water jets in the venturi throat

to produce collecting droplets was studied by Atkinson & Strauss (1978) and a description

of the breakup process was given i.e. the liquid did not rupture but rather broke down into

segments that then produced the droplets. Similarly, Roberts & Hill (1981) also studied the

liquid breakup in a venturi throat and concluded that the final droplet size depended on the

initial jet diameter. Alonso & Azzopardi (2001) used equipment based on laser diffraction

to measure the droplet size in a venturi scrubber and concluded that the correlation of

Nukiyama & Tanasawa (1938) over-predicted the droplet size while that of Boll et al. (1974)

produced results that compared very well with their own findings. More recently, Costa et

al. (2004) applied a similar laser diffraction method using commercially available apparatus

and concluded that neither of the correlations reported in literature gave a satisfactory

estimate of the droplet size distribution.

In summary of the above literature, it may be stated that the smaller the droplet size,

the greater will be the surface area to volume ratio and the higher will be the capture

efficiency.

2.1.4 Empirical relations and experiments for pressure drop

Several researchers have proposed empirical relations to calculate the pressure drop in dust

collection devices. One of the earliest works is that of Boll (1960), who developed a


mathematical model to evaluate venturi scrubber performance including pressure drop and

particle collection and compared the results with experimental data from a prototype venturi

scrubber. In his work, an assumption of constant diameter for all the droplets inside the scrubber

was made and both the mass exchange between liquid and gas within the venturi and on the

walls was taken into account.

Calvert (1970) similarly studied venturi scrubbers at the University of California and

presented a simpler empirical model than that of Boll (1960) to calculate the pressure drop. This

model assumes a constant gas velocity and droplet diameter and that the liquid momentum

changes at a constant rate from the plane of injection to the plane where the liquid reaches the

gas velocity.

Hesketh (1974) developed an equation for the pressure drop based on a combination of

data obtained for a range of venturi scrubber types and commented that the pressure drop and

collection efficiency of a venturi scrubber are very closely related. In fact, the proposed equation

for collection efficiency in hia work is only dependent on the system pressure drop. In reality,

however, many other factors, especially droplet carryover, come into play as discussed later.

Azzopardi & Govan (1984) developed a model that takes into account the different aspects

of inter-phase interactions and scrubber geometry to calculate the pressure drop. Their model

considers both the methods used to introduce scrubbing liquid into the vessel, i.e. as a spray or

a liquid film and hence has a wider applicability.

Leith et al. (1985) developed a model that takes into consideration the regain of pressure

loss in the diverging section downstream of the venturi throat. According to the authors, this

gives a better estimate than by models which do not consider this recovery and hence tend to

overestimate the pressure drop by up to 25%. The results through this approach produced

improved predictions in comparison to experimental measurements than the models of both Boll

(1960) and Calvert (1970).


Similarly, empirical relations based on both theoretical analysis and experimentation are

also available in the literature for the pressure drop across dust cyclones.

One of the earliest works is that of Shephard & Lapple (1939), in which the pressure drop

is estimated via the following equation:

𝑃 = Ɛ𝜌𝑔 (𝑉𝑔

2

2) (2.7)

where Ɛ is a dimensionless parameter and depends on the cyclone’s geometry, 𝜌𝑔 is the gas

density and 𝑉𝑔 the gas velocity.

Ramachandran et al. (1991) compared their experimental results with those of various

researchers and developed an updated empirical model for the prediction of pressure drop in

cyclones. This model can be used to predict the required cyclone dimensions for fabrication

based on a given pressure drop. The authors also proposed advantages and disadvantages for

each of the cyclone dimensions and concluded that there was no best set of optimum dimensions

and a designer must make a trade-off between pressure drop, collection efficiency and capital

costs.

Fassani & Goldstein (2000) derived an equation of a similar form to that of Shephard &

Lapple (1939) by conducting experiments on a laboratory setup. However, the authors only

extracted a limited number of velocity measurements to propose a pressure drop equation over

a wide range of cyclone inlet velocities and hence the uncertainty is high in their results.

Faulkner & Shaw (2006) presented a formula for a “Lapple” type dust cyclone based on

experiments, citing the importance of correctly predicting the pressure drop at the design stage

to get an estimate of the operating cost, since pressure drop is directly related to the ID fan power

consumption.


Many of the empirical relations available in the literature are applicable to only one type

of collection device and/or a fixed range of cyclone dimensions. Chen & Shi (2007) tried to

overcome this shortcoming by developing a universal model for pressure drop predictions that

take into account each of the different sources of pressure drop in a cyclone and sums them to

give the total pressure drop.

CFD modelling

Similar to the literature on empirical models for dust collector efficiencies, numerical

modelling has also been mostly restricted to simulating flows in venturi scrubbers and dust

cyclones. The use of CFD models has become more common over the past decades and the

reliance on CFD for the design of newer industrial equipment has increased. The application of

CFD to simulate the physics inside dust collectors ranges from the use of the more commonly

applied Eulerian and Lagrangian methods to Particle Source in Cell approach and potentially

Population Balance Models (PBM).

The literature review for CFD modelling has been divided into two parts. Section 2.2.1

details the literature on the application of turbulence modelling, while section 2.2.2 presents the

literature on multiphase modelling for dust collectors. Both turbulence and multiphase modelling

literature is relevant to this project due to the highly turbulent swirling gas flow inside centrifugal

wet scrubbers as well as the presence of multiple phases including the flue gas, dust particles,

liquid water and water vapour.

2.2.1 Turbulence modelling

2.2.1.1 Standard k−𝜺 model

The standard k−𝜀 model has been used as the industry standard, due to its robustness and

ease of application for many years since Launder and Spalding proposed it in 1972. Also known

as the two-equation model, it models the effect of turbulence via solving transport equations for


the turbulence kinetic energy and the turbulence energy dissipation. For many industrial

problems, the standard k−𝜀 model generates acceptable results but modelling predictions

deviate from experimental measurements when the flow is highly swirling, since standard

k−𝜀 model assumes isotropic eddy-viscosity, whereas when the flow is swirling, the eddy-

viscosity is anisotropic.

Mohebbi et al. (2003) used the standard k- 𝜀 model to predict the pressure drop across an

orifice scrubber, concluding that the collection efficiency increased with increasing inlet

velocity. In another published research (Mohebbi et al., 2002), they discussed typically

employed boundary conditions using the standard k- 𝜀 model when no experimental boundary

conditions are available, and validated the results by comparison to the experimental data of

Taheri et al. (1973).

The standard k- 𝜀 model has also been used for gas flow prediction in venturi scrubbers,

as by Pak & Chang (2006). Using the commercial code KIVA, the authors reported useful

insights for droplet dispersion and peak velocity of gas for both wet and dry runs. However, both

the pressure drop and the collection efficiency were under-predicted in their simulations. They

related this to the inaccurate prediction of the collecting droplet size and not taking into account

the liquid film on the venturi wall. In addition to these suggestions, the under-prediction of

collection efficiency may also be due to ignoring the dust collection via the diffusion mechanism.

Rahimi & Abbaspour (2008) used the standard k- 𝜀 model to determine the pressure drop

across a wire mesh-type mist eliminator. An increase in the difference between simulated and

experimental measurements was reported with increasing gas flow rate. Using the simulation

results, the authors also suggested that efficiency of a mist eliminator increases steadily up to a

certain velocity, after which it decreases due to possible carryover. This also proved true for

vane-type mist eliminators, as discussed in the later chapters of this thesis.


2.2.1.2 Reynolds Stress Model (RSM)

The RSM instead of assuming isotropic eddy viscosity, solves the transport equations for

all the Reynolds stresses (Wallin and Johansson, 2000). It is computationally more expensive,

as an additional seven equations are solved, but for simulating flows with high swirl and rotation,

the advantages of this model in terms of the accuracy of the simulated results outweigh the

drawback of increased computational expense and the tendency to be numerically unstable.

Not surprisingly, given the highly swirling nature of flow in a dust cyclone, the RSM

has been used more often for dust cyclones than for venturi scrubbers. Hu et al. (2005) used

an improved RSM to simulate the gas flow in a cyclone through the input of modified RSM

constants in the commercial software ANSYS Fluent. The results were compared with

experimental data generated from a test cyclone, which used glycol droplets as tracer

particles. The authors reported that the results produced with the modified coefficient values

were more reasonable than those reported previously.

Bernardo et al. (2006) used the RSM to simulate the gas-solid flow inside a cyclone

and compared the collection efficiency with different setups having variable inlet duct

angles. An increase in efficiency to 77.2% was reported by the authors when the inlet angle

was 45 degrees, whereas the original inlet duct gave a collection efficiency of only 54.4%.

Wang et al. (2006) used the RSM to simulate the gas flow in a Lapple-type cyclone

separator. A comparison was made between the simulated and experimental results and good

agreement between the two was reported. Factors affecting collection efficiency, such as the

location of a dust particle when it enters a cyclone were identified and an analysis on the inability

of the standard k- 𝜀 model for highly swirling flows was presented.


2.2.1.3 Other turbulence models

Alternative models attempting to overcome drawbacks of the standard k−𝜀 model and

require a lesser computational power than the RSM have also been developed. The realisable

and the Re-Normalisation group (RNG) k- 𝜀 models are two such examples that address the

drawbacks of the standard k- 𝜀 model. The RNG k- 𝜀 model has improvements incorporated for

enhanced prediction of rotating flows (Papageorgakis & Assanis 1999; Majid et al. 2013)

whereas the realisable k-𝜀 model outperforms the standard k- 𝜀 model for a range of benchmark

test flows (Shih et al., 1995).

Hoekstra et al. (1999) compared the performances of the k- ԑ, the RNG k- ԑ and the RSM

and concluded that both the standard k- ԑ and RNG k- ԑ model predicted unrealistic distributions

of axial and tangential velocities. They imposed uniform turbulence quantities at the cyclone

inlet and found that final predictions were insensitive to input values. They concluded that only

the RSM produced suitable predictions for swirling flows, which were in reasonable agreement

with experimental data.

The pressure drops across a cyclone using the RSM and the RNG k- 𝜀 model were

compared with those calculated using empirical models by Gimbun et al. (2005). The

pressure drop predictions using the RSM differed by only 3%, while the RNG k- 𝜀 model

had a deviation of 14%-18% from the measured values.

Other researchers, Guerra & Béttega (2012), used the RNG k- 𝜀 model to account for

the system turbulence in a venturi scrubber. Static pressures were measured at 15 different

locations in each of the venturi setup in their experiments and the readings were compared

with the simulated data to draw the conclusions. The authors remark that the RNG k- 𝜀 model

is more reliable than the standard k- 𝜀 model for a broader range of flows.


A hybrid k- 𝜀 model, which was a combination of the standard k- 𝜀 model and Prandtl’s

mixing length model, was used by Vegini et al. (2008). The authors state that this approach

can model the anisotropic Reynolds stresses effectively. The findings were compared with

experimental results from a test scale cyclone, in which talcum powder was used as the

particulate phase and good agreement was reported.

Another turbulence model is the Shear Stress Transport (SST) model, which is a good

compromise between the accuracy and computational costs. A comparison was made between

the standard k- ԑ model and the SST model by Galletti et al. (2008) and the SST model was

reported to predict recirculation regions better than the standard k-ԑ model.

In short, the enhanced k- ԑ models produce better approximations than the standard k-ԑ

model, but the RSM out-performs all other Reynolds Averaged Navier-Stokes turbulence

models.

2.2.2 Multi-phase modelling

2.2.2.1 Use of the Eulerian-Eulerian and Eulerian-Lagrangian approaches

The two primary methods of simulating multiphase flows are the Eulerian-Eulerian and

the Eulerian-Lagrangian approaches. A brief introduction of these methods follows in this

section before the literature review. The Eulerian-Eulerian approach considers the different

phases as continuous media whereas, in the Eulerian-Lagrangian approach, the secondary phase

is simulated as discrete particles. Both approaches have been widely used as evident from the

literature for simulation of multiphase flow in dust collectors.

The Eulerian-Lagrangian approach was used by Pak & Chang (2006) to model different

aspects of the multiphase flow inside a venturi scrubber, treating the gas phase as a continuum

and both liquid droplets and dust particles as the discrete entities. A dust particle’s collection

probability was calculated if it existed in the same cell as a water droplet. Collection efficiency


for particle sizes smaller than one micron was also simulated using the inertial impaction

parameter alone. Since diffusion is the dominant mechanism for collection of particles in this

size range the simulated efficiency results may be under-predicted.

Wang et al. (2006) compared their simulation results to experimental observations

performed using tracer particles consisting of cement and ceramic balls with different diameters,

to visualise the particle trajectories in a Lapple-type cyclone scrubber. They made an interesting

observation regarding particles within a certain diameter range spinning at a fixed height inside

the cyclone without further descent, due to the upward and downward forces acting on the

particle balancing each other.

Vegini et al. (2008) used the Eulerian-Eulerian approach to predict the pressure drop and

collection efficiency of cyclones connected in series using the CYCLO code. They validated

their simulated results by comparison to experimental data on pressure drop and collection

efficiency of a cyclone presented by Zhao et al. (2004). A deviation of up to 20% between

simulated and experimental results was reported and an assumption of using similar size and

shape for all the particles was held responsible.

Pirker et al. (2008) used a combined Eulerian-Lagrangian approach for a highly loaded

gas cyclone, in which the locally dominating inter-particle collisions were modelled using the

Eulerian approach, and detailed behaviour of particles in diluted areas was simulated using the

Lagrangian approach. They discussed the effect of different mass loadings on pressure drop and

separation efficiency inside a cyclone and commented on the advantages and disadvantages of

the Eulerian and Lagrangian approaches.

Chu & Yu (2008) presented results of the numerical simulation of particulates in a gas

cyclone and a circulating fluidised bed using RSM to solve the continuous phase and the Discrete

Element Method (DEM) for the modelling of the solid phase. The effect of the solid loading on


particle-particle and particle-gas interactions was also discussed. Particulate behaviour, such as

congregation at walls, was successfully reproduced in their simulations.

Later, Chu et al. (2009) applied a similar approach to model the multiphase flow in a dense

medium cyclone. They modelled the continuous phase as a mixture of water, air and magnetite

particles using the Mixture multiphase model in the commercial software ANSYS Fluent,

whereas the motion of the particle phase was modelled using the DEM. The coupling procedure

between CFD and DEM obtained positions and velocities of particles using DEM, which were

then utilised by the CFD model to yield forces on individual particles. These results were then

incorporated back to the DEM to calculate the motion of individual particles. The authors found

this procedure to be cost effective for simulating flows with a dense secondary phase

concentration while ignoring the effect of the secondary phase on the primary phase, which

would immensely increase the computational cost.

Zhu et al. (2008) summarised the different approaches taken for simulations using the

CFD-DEM method over the past two decades and how this approach is applicable to different

types of flows including gas fluidisation and particulate collection systems. The advantages over

the two-fluid method, such as the ability to capture particle cluster mechanisms in addition to

the general flow behaviour were also discussed.

Tao & Kuisheng (2009) used the Lagrangian method to track liquid droplets inside a

circular venturi scrubber for a range of water-to-gas ratios and reported this ratio to have an

important influence on pressure and velocity distributions. The droplets were introduced via

jets in the throat section of the scrubber and trajectory calculations terminated when these

droplets came in contact with the scrubber walls. Dust particle capture by liquid droplets was

not included, which the authors suggested is required in future studies to accurately predict

the performance of venturi scrubbers.


The Volume of Fluid model was used for predicting pressure drop across a venturi

scrubber by Guerra & Béttega (2012). The authors reported good agreement with the

experimental work although they concluded that the VOF method was unable to account for

atomisation of the liquid, which results in droplet deposition on the wall, causing a greater

pressure drop. The model was, however, able to predict the liquid jet curvature accurately. Note,

that the VOF model conserves momentum but loses interface information if volume fraction of

the secondary phase falls below one in a computational cell. Deshpande (2014) reported the use

of a cell size of only eight microns for the VOF simulation of the breakup of a liquid sheet. In

the near future, it remains unclear if the VOF model will be applicable to the simulation of

industrial problems with highly turbulent flow. To date, this model has only been used to model

flows that are much simpler than flows in industrial processes.

Kuang et al. (2014) proposed a new model to overcome the deficiency of the

Lagrangian particle tracking method to account for inter-particle interactions. First, flow was

simulated using the VOF method to model the shape and position of dense medium cyclone

air core. The results obtained were used as initial conditions for the next step, which tracked

the motion of coal particles. The importance of mixture viscosity was stressed in such a

simulation and an improved correlation for the mixture viscosity was added as a User

Defined Function (UDF) to the commercial software ANSYS Fluent, which was used for

the simulations. Good agreement between the calculated and measured results was reported.

The discrete phase modelling approach has also been used to predict the separation

efficiency of curved vane demisters including the effect of droplet breakup and coalescence in

2-D (Ventatesan, Kulasekharab and Iniyan, 2014). Particle collection by vanes was modelled

by terminating trajectory calculations of particles coming in contact with the vane surface. Good

agreement between experimental and predicted separation efficiencies was reported. It should


be noted, however, that droplets also tend to splash and bounce back from a wall surface, which

was not considered in these simulations.

2.2.2.2 Drag Coefficient

Multiphase flow simulations require the solution of additional transport equations for the

secondary phase and handling of these terms treating the mass, momentum and energy exchange

between the phases (Durst, Miloievic and Schönung, 1984). The drag coefficient has a

significant effect on the fluid flow (Yang et al., 2003). Gonclaves et al. (2004) suggested that

under conditions where small droplets are present and the relative velocity between the gas and

droplets is high, it is reasonable to consider that the drag force is the only force present.

According to the assumptions being made for a particular physical case, equations for the drag

between gas-liquid and gas-solid flows can take different forms. It is necessary to accurately

calculate the drag coefficient to predict droplet trajectories. Generally, small droplets can be

assumed to be spherical (Herne, 1930). The Schiller-Newmann drag law is widely used by

researchers simulating two-phase flow (Rahimi & Abbaspour 2008; Galletti et al. 2008; Majid

et al. 2013).

Karimi et al. (2012) compared the results obtained using four different drag coefficients

with the Eulerian-Eulerian approach. An average difference of 25.2% between the predicted and

experimental data was reported and the authors suggested further improvement in the drag

coefficient correlations to better account for the forces between continuous and dispersed phases.

Majid et al. (2013) used the Schiller-Newmann drag coefficient for both liquid droplets

and dust particles to account for the interaction of primary and secondary phases in a venturi

scrubber. The difference between predicted and measured velocities was seen to increase as

the gas flow rate was increased, which may be due to not taking droplet shape change into

account.


In conclusion, dust content in flue gas may be assumed to be spherical for simplicity of

simulations. The assumption of spherical particles improves convergence and reduces the

simulation time. However, for irregularly shaped particles (large droplets), the drag coefficient

may vary greatly and there may be a need to account for this change in shape while performing

the simulations. As a liquid droplet shape changes from a sphere to a disc, the drag on the particle

significantly increases and a drag model, which assumes the particle shape to be spherical, is

therefore inadequate.

The “Dynamic Drag Model” is one such model that accounts for the change in shape of a

droplet from a sphere to a disc linearly using the following equation (Liu, Mather and Reitz,

1993) and can thus be considered to be more realistic.

𝐶𝑑, 𝑑𝑖𝑠𝑘 = 𝐶𝑑, 𝑠𝑝ℎ𝑒𝑟𝑒 (1+2.632y) (2.8)

where 𝐶𝑑, 𝑑𝑖𝑠𝑘 is the drag coefficient of the distorted droplet, 𝐶𝑑, 𝑠𝑝ℎ𝑒𝑟𝑒 is the drag

coefficient of a spherical droplet and y is a measure of droplet distortion.

2.2.2.3 Use of Inertial Impaction parameter in CFD

The inertial impaction parameter identified earlier in Section 2.1 has also been applied in

CFD. Pak & Chang (2006) and Ali et al.( 2013) used it for numerical simulation of the collection

efficiency of venturi scrubbers. However, since Brownian diffusion and interception may

constitute significantly towards the total collection efficiency in venturi scrubbers, the results

reported by these authors may not be a true representation of the collection efficiency of venturi

scrubbers. Nonetheless, it does open the door for further research to follow, using application of

empirical models to CFD codes. In industries where centrifugal wet scrubbers are used, the mean

dust particle diameter is much greater than 5 microns and inertial impaction is certainly the most

dominant mechanism of dust collection. The application of the Stokes number approach to

calculate the collection efficiency is much more realistic in such cases. Mohebbi et al. (2003)


used this parameter for the CFD modelling of an orifice scrubber, using the Eulerian-Lagrangian

approach. The collection efficiency was then calculated using a modified correlation, which was

based on a least square curve fit of the original experimental data of Walton and Woolcock

(1960). The resultant collection efficiency curve at a range of Stokes number agreed better with

experimental results than the collection efficiency relation of Calvert (1970).

More recently, Wang et al. (2016) published simulation results for a wide range of Stokes

and Reynolds numbers and identified a “boundary stopping” effect, which reduces the inertial

impaction efficiency at Stokes number <<1 due to the presence of a boundary layer around

collecting droplets. However, bringing the effects of the boundary layer into account, no

significant deviation was simulated in comparison to the results from other collection efficiency

correlations.

2.2.2.4 Inclusion of thermal aspects to simulations

Evaporation models for single droplets have been developed and tested, however, the

inclusion of the thermal aspects of flow in dust collectors has not been found in the literature.

Fluid flow in industries has a range of temperature conditions causing the gas density to change

significantly and hence making it important to take this change into account for greater accuracy

of simulated results.

Sazhin et al. (2010) developed a model for an evaporating droplet, which they suggest is

better than the models used in commercial codes (KIVA, Fluent, PHOENICS) as their model

also considers the change in droplet diameter as droplet evaporation progresses with time, a trait

which according to them commercial CFD codes lack.

Simulations to model the evaporation of liquid sprays have also been made. Xuening et

al. (2015) recently carried out simulations for evaporation of brine in a spray evaporating tower.


The authors validated their simulation results by measuring the temperature at random points

inside a spray tower and comparing the measurements with simulated results.

An assessment of the ability of the Eulerian-Lagrangian method to model evaporative

cooling via water spray systems was carried out by Montazeri et al. (2015). The modelling results

were compared to experimental results from a wind tunnel and an average deviation of less than

3% was reported between the experimental and predicted inlet and outlet temperatures.

More recently, Xia et al. (2016) performed CFD simulations using the commercial code

ANSYS Fluent 15.0 for the evaporation of a spray inside a dry cooling tower. The authors

presented a comparison of the predicted and measured results for the air and water temperature

at the cooling tower outlet, but relied on the simulated results alone for other flow aspects such

as the droplet trajectories and evaporation rate.

Flow visualisation

Dust collection is primarily dependent on the scrubbing liquid distribution as described in

the literature review. The scrubbing liquid may be introduced into the scrubber as liquid droplets

generated from a nozzle or as a liquid sheet to be atomised by the exhaust gas stream. In the

centrifugal scrubber type investigated in this project, the scrubbing liquid is introduced by means

of a pipe above the distribution cone. It then flows over the distribution cone and breaks down

into droplets with a varying diameter as it comes in contact with the gas crossflow. This process

is identified to consist of the primary breakup, which is the formation of ligaments and bags from

the liquid sheet, followed by the secondary breakup which is the breakup of liquid ligaments into

droplets. These droplets may or may not further disintegrate into smaller droplets which depends

on the parent droplet diameter and relative velocity. This section presents the literature on the

flow visualisation aspect, which includes the primary and secondary breakup of liquid sheets

and jets as well as wall film formation.


Figure 2-4. Side-view schematic of liquid sheet breakup flowing over the distribution

cone edge.

2.3.1 Primary Breakup

Primary breakup is the result of Kelvin-Helmholtz instability (Wahono et al., 2008). The

gas applies a shear force on the liquid, which produces sinuous waves on the liquid surface

(Clark and Dombrowski, 1972). These waves grow in amplitude, causing the liquid to

disintegrate and breakup into smaller ligaments and droplets at the point of the wave with the

highest amplitude. There has been significant research to understand the physics of the breakup

of liquid ligaments, generally, for spray atomisers, but there still exists scope for further

investigation to help develop mathematical models to numerically model primary liquid

breakup. Dumouchel (2008) presented an in-depth review of the current scientific knowledge of

the primary break mechanisms through experimental investigations for a range of liquid streams

such as liquid jets ranging from low to high velocity, liquid sheets, as well as air-assisted liquid

sheets. The author concluded that despite the powerful experimental tools available to study

liquid atomisation, no universal characterisation of atomisation regimes is available and that

ignoring the effects of some important parameters for non-dimensional groups could be the

reason.

Primary breakup

Secondary breakup

Gas flow direction


The breakup mechanisms are most widely characterised by three dimensionless numbers

in the literature, namely the Reynolds number (Re = 𝜌𝑢𝑑

𝜇), the Weber number (We =

𝜌𝑉2𝑑

𝜎) and

the Ohnesorge number (Oh = 𝜇𝑑

(𝜌𝑑𝜎)0.5) (Pilch & Erdman 1987).

Experiments to analyse the breakup of isolated drops based on these parameters, and

utilising photography, have been conducted since the 1960s (Ranger & Nicholls 1969) and the

quality and detail of these experiments has risen due to the availability of improved optical

techniques and high speed cameras. The various breakup regimes for liquid jets have been

studied for a range of Re and Oh numbers (Vujanovic, 2010) and a graphical illustration is

presented in Figure 2-5. Similar regimes have also been identified for isolated droplets. Dai &

Faeth (2001) studied the multimode breakup regime for liquid droplets which exists for the

Weber number between that for the bag breakup regime and the shear breakup regime and

reported the times for the breakup to occur across the various stages. At very low liquid injection

and gas velocities, the liquid breakup is due to oscillation. This is the Rayleigh breakup regime.

At comparatively higher velocities, aerodynamic forces cause oscillation on the jet surface,

which results in jet breakup (First-Wind induced). At even higher velocities, the liquid sheet

breaks down due to the unstable growth of waves on the surface of the sheet (Second-Wind

induced). If aerodynamic forces are very high, the liquid jet will undergo atomisation, which

results in a fine spray being created (Atomisation regime).


Figure 2-5. Regimes of liquid sheet disintegration (Vujanovic 2010).

Since the Weber number is a ratio between disrupting forces to damping forces, a larger

Weber number results in a shorter breakup length. Liquid sheet breakup lengths with different

Weber numbers are shown in Figure 2-6 for an Oh number of 1.


Figure 2-6. Spatial evolution of liquid sheet with gas-to-liquid density ratio of 1/1000 and

Weber numbers (a) 500, (b) 400, (c)=300 (Movassat, 2007).

Ahmed et al. (2008) studied the characteristics of the primary breakup of liquid sheets

by using a high-speed camera. The viscosity of the injected liquid was changed by mixing

corn syrup with water, and four different nozzle diameters were used to produce sheets with

varying Reynolds number, which helped identify the mechanisms responsible for sheet

disintegration for a range of parameters.


Sedarsky et al. (2010) studied the breakup of a liquid jet in crossflow using various

imaging techniques including particle image velocimetry (PIV), high-speed shadow-graphy

as well as ballistic imaging. They recognised that primary breakup can occur due to a

combination of mechanisms that is called “mixed mode breakup”, and presented a schematic

of liquid jet breakup entering an air crossflow, as shown in Figure 2-7.

Figure 2-7. Schematic of a liquid jet entering a crossflow (Sedarsky et al. 2010).

2.3.1.1 Modelling of primary breakup

The Volume Of Fluid (VOF) (Hirt and Nichols, 1981) method in its various forms has

been used to perform direct numerical simulation (DNS) of the primary breakup of liquid jets,

while models based on the theory for breakup of liquid jets originating from atomisers are also

available (Duangkhamchan et al., 2012). The VOF model has a requirement of an extremely

fine mesh, which is at least an order of magnitude smaller than the smallest flow structure

(Gorokhovski & Herrmann 2008). If this condition is not fulfilled, although the momentum

remains conserved, the interface is lost. An addition to the VOF model is the Level Set Method

(LSM) (Osher and Sethian, 1988), which reportedly captures the interface with a better

resolution, however, mass conservation with this method is not satisfactory (Luo et al., 2015).


A combination of the LSM and the VOF was proposed by Sussmann & Puckett (2000), which

combines the advantages of both the methods and gives superior results to either of the two

parent methods. Mesh adaption is commonly used to reduce the computational expense, which

involves refining the grid at the interphase of the two phases throughout the simulation (Fuster

et al., 2009).

Vallet & Borghi (2001) described a new approach that is being increasingly used as a

replacement for the true VOF modelling, formally known as the Eulerian-Lagrangian Spray

Atomisation (ELSA) model. This method involves replacement of the smallest flow structures,

i.e. droplets, by Lagrangian particles of an equal mass and momentum. However, this approach

can only be applied after the droplets have broken up from the larger structures i.e. bags and

ligaments, and secondary breakup has already initiated. A variation was introduced by Kim &

Moin (2011), who used the LSM to track the interface and replaced ligaments with Lagrangian

drops before the droplets were actually produced by the LSM, predicting the size of the droplets

via the stability theory of Yuen (1968) and then calculating the number of droplets produced.

Luo et al. (2015) pointed out the drawbacks of both the VOF and the LSM and presented

a new method known as the Accurate LSM. A droplet-droplet and a droplet-film collision were

simulated, and comparison with experiments showed excellent agreement. The breakup of a

swirling liquid sheet was then simulated using a similar approach, but no experimental

comparison was made. It should be noted that the Weber number and turbulence for colliding

droplets will be much less than that of a disintegrating sheet, which will produce a range of

ligaments and droplet sizes upon atomisation. The new method cannot be truly relied upon

without further experimental validation.

Recently, Behzad et al. (2016) applied the level set approach to study the surface breakup

of a non-turbulent liquid jet being injected into a gaseous crossflow. The length and timescales

of disturbances causing the breakup were compared with theoretical predictions. Yet again, the


simulations are only valid for a certain set of flow conditions due to several assumptions the

authors made such as non-turbulent inflow of the jet as well as the gas cross flow.

Comparisons of results from experimental techniques and modelling have also been made.

Muller et al. (2016) performed high speed photography of primary breakup of a liquid jet and

compared their findings with the simulation results of a similar jet using the VOF model. The

authors reported good agreement of the liquid morphology via experimental visualisation and

the CFD approach undertaken. Although, only primary breakup was modelled and the liquid

interface appears to have been lost during the simulations, as evident from the illustrations

presented in their paper. This is typically a result of the mesh cell size not being fine enough to

capture the smallest flow structures.

2.3.2 Secondary breakup

Resultant droplets from the primary breakup of liquid sheets may or may not break up into

smaller droplets. The probability of further disintegration depends on the droplet Weber number

and the Ohnesorge number. If the Weber number is greater than a critical Weber number, droplet

breakup will occur. The critical Weber number for different breakup mechanisms to occur was

reported by Pilch & Erdman (1987). The authors conducted tests to recognise the Weber number

required for transition of breakup from one regime to another regime and their findings are

reported in the Figure 2-8.


Figure 2-8. Droplet break-up mechanisms (Pilch & Erdman 1987).

2.3.2.1 Minimum water droplet size

The maximum stable diameter of a droplet can be determined by the critical We

required for breakup ( Pilch & Erdman 1987). However, this assumption only gives a crude

estimate of the smallest droplets that will be produced; multistage breakup produces droplets

whose diameter is much less than the maximum stable diameter, which is calculated based

on the critical We number approach reported by Pilch and Erdman (1987). The authors also

presented an improved estimate of the maximum stable diameter given by:

𝑑 = 𝑊𝑒𝑐

𝜎

𝜌𝑉𝑟𝑒𝑙 2 (1 −

𝑉𝑑

𝑉𝑟𝑒𝑙 )−2

(2.9)

2.3.2.2 Modelling of secondary breakup

Secondary breakup is better understood than primary breakup and various models are

available for simulating secondary breakup, predominantly based on the Lagrangian particle

tracking model. The Taylor Analogy Breakup (TAB) model is one such model that has been

used by many researchers over the years. It is based on the fact that a droplet will break up into


smaller droplets when the distorting forces acting on it, such as the drag forces, exceed the

restoring forces i.e. the surface tension (Taylor 1963).

DNS using the VOF and its variants may also be computationally feasible, especially in

studies consisting of a small number of droplets, but in most industrial cases the required

computational power is too high. Lebas et al. (2009) performed the numerical simulation of the

primary breakup and secondary breakup of a cylindrical spray. DNS based on the VOF model

was used and the results were compared to those obtained by the Eulerian-Lagrangian Spray

Atomisation model (ELSA) and experimental measurements. The authors modified the ELSA

model to include simulation of primary breakup of the jet using model constants, which were

obtained from the DNS data. In the ELSA model, droplets smaller than a cut-off size are

converted to Lagrangian particles in an attempt to reduce the computational expense. A similar

strategy was successfully used by Tomar et al. (2010) i.e. identifying droplets via the void

fraction between two regions of the droplet phase in the computational domain and replacing the

droplets by Lagrangian particles.

Shinjo & Umemura (2010) performed the simulation of jet breakup and included both the

primary and secondary breakup in their simulations. They used an extremely fine grid (0.35-

micron cell size) to capture the interface of the full range of droplets produced during the

atomisation process. The total number of grid points ranged from 400 million to 6 billion.

Although this can give useful insights for future development and simulation of the atomisation

process, for industrial flows and practical application, such a simulation will not be feasible in

the near future and consideration of the smaller droplets as Lagrangian entities remains the more

realistic choice for modelling. Upon atomisation, a jet breaks up into droplets, which can be

orders of magnitude smaller than the jet diameter (Tomar et al., 2010), and hence an extremely

small cell size is required. Recently, Deshpande et al. (2015) performed numerical simulations

of the breakup of a liquid sheet using the VOF method. To keep the computational cost


affordable, the domain length was restricted to a maximum of 2000 microns and consequently,

the sheet did not reach the stage of complete atomisation.

2.3.3 Droplet-film collision

Several outcomes can result when a droplet collides with a wall surface. In centrifugal

scrubbers, droplets colliding with scrubber walls generate a liquid film. They may also rebound

or splash and the controlling parameter is the collision Weber number.

While absorption at low Weber numbers and splashing at high Weber numbers has been

studied by several researchers, there is insufficient literature on the rebounding mechanism that

occurs in the transition regime between absorption and splashing. The physical explanation of

its occurrence depends on whether or not impacting droplets are able to squeeze the gas out of

the gap remaining between the two colliding surfaces when collision occurs, as suggested by

Pan & Law 2007. They studied the outcome of droplets of varying diameter colliding with films

of varying thickness and concluded that the film thickness is also important in determining the

fate of colliding droplets.

Consensus on the dynamics of the liquid film separation is scarce. The major reported

research for droplet size generated at breakup of a liquid film (O’Rourke & Amsden 1996;

Friedrich et al. 2008) present opposing views on the inclusion of the surface tension of the liquid

as a significant parameter affecting the size of droplets produced when a liquid film separates

from the wall.

O’Rourke & Amsden (1996) developed a new wall film model, which included the

effect of sharp corners on the liquid film, a region where the thin film approximation does

not hold (O’Rourke & Amsden 1996). This model was implemented in the commercial CFD

code KIVA-3. The authors suggested that the breakup of the wall film at sharp corners cannot

be predicted by the thin film model and a sub-model for this breakup is needed.


Lee & Ryou (2000) developed a model to study the behaviour of droplets forming a

film in a diesel engine. The regimes considered included rebound and deposition, as well as

splash, and the results were compared with the experimental data of several studies. Later,

Lee et al. (2001) also upgraded the model by incorporating the effects on the film of the

droplet impingement forces as well as the film inertia and reported better approximations

than those obtained through their earlier model, which overestimated the spray radius.

Andreassi et al. (2007) simulated the wall film separation in diesel engines using a

similar model in KIVA-3, which was updated via applying empirical relations based on their

experimental data obtained via photography conducted on a test rig constructed in their

project.

Recently, Dinc (2015) studied liquid sprays and the collection of spray droplets on

walls using both the discrete particle method (DPM) and Eulerian wall film (EWF) models.

Based on comparisons between the two models, the author concluded that the EWF model

was the more reliable of the two approaches, as it produced a more uniform distribution of

the film variables. This judgement needs to be further investigated with more in-depth

experimental validation of the wall film models. It is possible that a limited number of

discrete droplet parcels used in the simulations caused the non-uniformity of the film

distribution when the discrete particle method was used.

Summary of the literature review

The literature review presented identifies the growing trend of performing simulations

for engineering design, given the availability of higher computational power in recent years.

It was observed that empirical models for the pressure drop across venturi scrubbers and dust

cyclones exist in the literature, but no such models exist for centrifugal wet scrubbers.

Similarly, computational simulations have also focused only on the flow aspects of dust

cyclones and venturi scrubbers; centrifugal wet scrubbers have not been studied. The reason


could be attributed to the lower availability of experimental data on centrifugal wet scrubbers.

Some CFD has been carried out for centrifugal wet scrubbers but currently, this has been

restricted to the gas-phase alone and there is much room for more in-depth investigations using

CFD to design new technologically advanced centrifugal wet scrubbers. For wet scrubbers,

droplet carryover can affect the overall dust collection efficiency, but this has not been

considered in any research on venturi scrubbers. Similarly, vapour mass fraction in the flue gas

has also been ignored and so has the change in gas density as it cools down inside a wet scrubber.

The difference in inlet and outlet gas temperatures is significant, especially in wet dust collectors,

since the hot flue gas loses its energy to the scrubbing liquid. Hence, this effect must be taken

into account for an improved estimation of the pressure drop and gas velocity profiles.

Overlooking these aspects may significantly affect the prediction accuracy.

There also appears to be very little work done on the physics of capture of dust particles

by water droplets inside wet scrubbers, and most of the available research is focused only on

gas flow simulations. Many of the published articles provide useful insights on the flow

aspects in industrial equipment but the importance of experiments cannot be negated, and

comparison of simulated results with theoretical relations alone cannot be considered

adequate.

Finally, the study of liquid sheet breakup has remained in the focus of the scientific

community for nearly five decades. High speed photography and PIV has been used to study

the breakup mechanisms as well as estimate the size of resulting droplets, but the modelling

of the whole breakup process remains a challenge for researchers. There is a requirement to

study the primary breakup further before a universally acceptable model can be developed.


3 CHAPTER 3: METHODOLOGY

This chapter describes the methodology undertaken in the project. An overview of

the project design is given in Figure 3-1. Section 3.1 introduces the research design, Section

3.2 the experimental approach and Section 3.3 the simulation methodology.

Figure 3-1. Schematic of the project methodology.

Methodology

Experiments Computational Fluid Dynamics

Test rig fabrication

Incorporate particle-droplet interaction

model

CFD model validation

High speed photography

Flow measurements

Communicate results to factories

Simulate design improvement

Single-phase simulations

Multi-phase simulations

Incorporate feedback

Improved scrubber design


Research design

Collection of operational data from industrial scrubbers is difficult. Hence, one of the core

initial aims of this project was the construction of a laboratory scale model of a wet scrubber.

This helped study the fluid flow pattern inside a scrubber and use velocity and pressure

measurements from the scaled model to validate the CFD predictions. Multiphase flow can be

extremely complex and appropriate validation of CFD models is of great importance.

The primary software used for the CFD modelling was ANSYS Fluent, which has been

increasingly used as the leading CFD software in industry. Simulation results, together with

experimental measurements, were used to analyse the multiphase flow inside a centrifugal

scrubber. Once the scrubber design’s flaws were identified, design changes were made to

overcome these inadequacies and the changes were further tested via the validated CFD

model. Through an iterative process, changes were made to the scrubber scale model (SSM)

and the CFD geometry, to develop an increased understanding of the CFD model behaviour

under different flow conditions and to further support the CFD findings.

Design changes, which resulted in performance improvements, were then

communicated with sugar mills and scrubber manufacturers and their feedback incorporated

into further CFD modelling.

Experimental setup

The experimental part of the project has been described in this section. Section 3.2.1 gives

a description of the measurement apparatus and the test rig including the SSM, Section 3.2.2

describes the approach to perform the measurements and Section 3.2.3 describes the flow

visualisation aspect of the experimental work through high speed photography.


A computer-aided design (CAD) was made for a centrifugal wet scrubber type commonly

used in the Australian sugar industry. The experimental test rig consisted of a scaled model of

this full-scale scrubber (FSS).

Achieving the same Reynolds number and thus dynamic similarity for the prototype and

the SSM required an extremely high air mass flow rate inside the SSM. This made dynamic

similarity out of the project scope because of both budget constraints and safety issues.

Furthermore, important scrubber performance parameters of droplet carryover, collection

efficiency and pressure drop are all strongly dependent on the air/gas velocity. The test rig was

thus scaled to run at a gas velocity similar to that of the FSS, rather than having a similar mass

flow rate. The gas flow rate through SSM to attain a similar velocity profile as a FSS was

calculated to be approximately 0.287 kg/s of air, based on data from a FSS with a diameter of

3.6 m and a mass flow rate of 24.25 kg/s (Appendix 1). Since a Reynolds number for the scaled

model having this mass flow rate was calculated to be sufficiently high for the flow to still be

highly turbulent (>40 000), achieving geometric similarity alone was considered adequate.

Secondly, once validated, the CFD model for the SSM could be used to simulate the flow in a

FSS, eliminating the need for achieving dynamic similarity.

3.2.1 Test rig fabrication

The test rig consisted of the following parts:

Scaled scrubber body

Flexible and rigid ducting

Centrifugal fan

Water reservoir

Water pump

High speed camera

LED lights


Velocity (hot wire anemometer) and static/differential pressure measurement probes

which were extendable to reach the desired location inside the SSM (Figure 3-6).

A schematic of the test rig is shown in Figure 3-2, the SSM dimensions in Figure 3-3,

dimensions of the SSM vanes in Figure 3-4 and dimensions of the two different inlets used for

the SSM experiments in Figure 3-5.

The SSM body was made with acrylic, and commercially fabricated using laser cutting

and heat moulding, then connected to a centrifugal fan with a power rating of 1.5 kW via rigid

and flexible ducting. Flexible ducting was used between the SSM outlet and the rigid ducting

connecting to the centrifugal fan to avoid damaging the SSM body due to possible fan vibrations

caused when the test rig was running. The fan speed was controlled using a variable speed

controller, while water flow rate was monitored via a mechanical control valve. To measure the

flow rate of air through the SSM, equation (3.1) was used.

�� = 𝜌𝐴�� 𝑎𝑣𝑔 (3.1)

where 𝜌 is the density of air, A is the SSM inlet area and �� 𝑎𝑣𝑔 is the average air velocity at the

inlet calculated by �� 𝑎𝑣𝑔 = �� /R (see Appendix 2).

The water pump was placed inside a water reservoir below the SSM (Figure 3-2). Since

the water pump only ran at a fixed flow rate, a T-valve was used to direct any excess water back

into the reservoir.


Water pump Water reservoir

Variable speed controller

Centrifugal fan

Water control valve

Scaled scrubber model

Ducting

Laptop

LED lights

High speed camera

Figure 3-2. Schematic of the centrifugal wet scrubber test rig.


Figure 3-3. SSM dimensions as fabricated in mm (Plan view).


Figure 3-4. SSM vane dimensions as fabricated in mm.


(a) (b)

Figure 3-5. Inlet dimensions of SSM in mm (Top view), (a) Inlet type A and (b) Inlet type B.


3.2.2 Data acquisition

This section presents the details of the steps taken to operate the test rig. Velocity and

pressure measurements were made for both dry runs (without water) and wet runs (with water),

followed by high speed photography for the flow visualisation. An illustrative summary of this

section is presented in Figure 3-9.

3.2.2.1 Velocity and pressure measurements

Firstly, velocity and pressure measurements were made at several locations in the test rig,

both without and with water addition. This included measurements taken at the SSM inlet and

outlet, below and above the scrubbing and mist eliminator vanes as well as the centrifugal fan

outlet. Whilst the scrubber was run wet, velocity measurements were restricted to the inlet of the

SSM body to avoid damaging the hot wire anemometer, which was being used to measure the

air velocity.

Figure 3-6. Velocity (hot wire anemometer) and static/differential pressure sensor.

Readings were taken in straight lines at distances of 0.5 cm across horizontal traverses as

shown in Figure 3-7. Holes were drilled into the SSM body to allow insertion of the

measurement probes. At any one point in time, all but a single hole were sealed to minimise any


influence on the flow due to the holes, which could give rise to uncertainty in the measurements.

Axial and circumferential velocity measurements were taken and velocity magnitude was

calculated from these readings using equation 3.2.

�� 𝑚𝑎𝑔 = √�� 𝑡 + �� 𝑎 (3.2)

where �� 𝑚𝑎𝑔 is the velocity magnitude, �� 𝑡 is the tangential velocity and �� 𝑎 the axial velocity.

This process was repeated for each of the centrifugal fan speeds which were controlled via the

variable speed controller.


Figure 3-7. Velocity and pressure measurement traverse locations (in yellow) across the

test rig.

3.2.2.2 Scrubbing liquid measurements

The volume flow rate of water being pumped into the SSM was approximated by

measuring the volume of water filled in one minute in a beaker of known volume placed

underneath the scale model water outlet. At each flow rate, this step was repeated multiple times

and the measured time was averaged to minimise the measurement error and calibrate the T-

valve.


When the test rig was run, scrubbing liquid accumulated inside the SSM and the

accumulated liquid volume was measured by determining the difference in mass of the water

reservoir which was placed at the bottom of the test rig (Figure 3-2) from the instance when

the water pump was turned on till steady state was achieved. Steady state was considered to

have been reached once the mass of water inside the reservoir became constant and the water

mass in the reservoir stopped decreasing further. This meant that the amount of liquid

entering the SSM was equal to that exiting it. At each gas flow rate, the scrubber was allowed

to reach the steady state before any measurements were made.

3.2.2.3 Design modifications

Measurements were also repeated after each of the changes in geometric configuration

of the SSM. The modifications included change in the inlet convergence angle, increased

spacing between scrubbing and mist eliminator vanes, installation of a break-water annulus

between scrubbing and demister vanes and a vertical break-water in the bottom cone. The

modifications to the inlet convergence angle allowed an improved understanding of the

effect of the tangential inlet on the different velocity components of the flue gas. The

remaining changes were made to overcome the problems associated with scrubber operation

in industry.

3.2.2.4 High Speed Photography

High speed photography of flow inside the SSM was used for validation of the multiphase

CFD model. Two high speed cameras IDT X-Stream 4 with a pixel size of 16 x 16 microns and

HiSpec 1 with a pixel size of 14 x 14 microns were used (Figure 3-8), allowing image capture

at up to 10,000 frames per second (FPS). Two lenses compatible with both the cameras were

used, one with a focal length of 8 mm to capture images at close distances and the other with a

35 mm focal length to capture images from greater distances. The commercial software used to


analyse the images obtained were Motion Studio and HiSpec Control 1.2.0.0, while an open

source software Tracker 4.90 was also used.

The aspects of the liquid flow that were captured via high speed photography included:

1) Water sheet breakup flowing over the water distribution cone.

2) Liquid film formation on scrubber walls and vane surfaces.

3) Edge separation of liquid film.

4) Formation of water bath between the two sets of vanes.

5) Water accumulating in the bottom cone.

6) Water seeping back into the gas inlet.

7) Droplet escape from test rig as carryover.

Lens “stopping” was used to increase the depth of field of the images captured by the high-

speed cameras. This involves reducing the ratio of focal length to diameter of the lens pupil via

changing the diameter of the lens pupil. Naturally, a smaller pupil results in less light entering

the lens. Hence, additional LED lighting was placed around the test rig that compensated for the

loss of light entering the high-speed camera lens, both due to high camera shutter speed and the

lens being “stopped”. A total of 10 LED lights with a power rating of 15 watts each were used.

(a) (b)

Figure 3-8. High speed cameras (a) X-Stream XS-4, (b) Hi-Spec 1.


The high-speed photography played an important role in determining validity of the

applied CFD models. Velocity vectors for water droplets from CFD results could be

compared to those obtained from the high-speed images. Objects of known dimensions

inside the test rig were used as references to measure the distance travelled by the water

droplets, while the time taken to travel the distance was calculated based on the shutter speed

of the high-speed camera. The size of the resultant droplets and the liquid film height were

also measured in a similar manner, i.e. comparison to an object with known dimensions in

the SSM.


Figure 3-9. Schematic of test-rig operation.

Computational Fluid Dynamics (CFD)

3.3.1 Introduction

CFD is well established as a valuable tool for fluid flow study and has gained recognition

throughout the world. Due to the difficulty in extracting data from full-scale factory scrubbers,

the project was designed so that CFD could be used for this purpose instead, once the modelled

results had been validated via comparison to experiments. The software chosen to perform CFD

Turn on the centrifugal fan

Set the air flow rate to the desired value

Turn on the water pump

Wait for steady-state

Repeat velocity and pressure measurements

Make velocity and pressure measurements

Turn on LED lights

Adjust high speed camera position and settings

Capture Images

Turn water pump off and gradually decrease the fan speed

to zero.

Dry runs (stage 1)

Wet runs (stage 2)

High speed

photography (stage 3)


modelling was ANSYS Fluent, due to its versatility and ability to allow modifications to include

a vast range of modelling approaches.

This section outlines a brief description and theory of the adopted CFD approach. Single

phase CFD theory is presented in Section 3.3.2, and multiphase CFD theory is presented in

Section 3.3.3. Detailed description has been given for the additional functions which were

incorporated into the CFD code.

3.3.2 Theory for single phase CFD

CFD is based on the governing equations of fluid flow, which include the continuity,

momentum and energy equations, and are based on the principle of conservation of mass,

Newton’s 2nd law and first law of thermodynamics respectively. These equations are solved for

each cell in the geometry, collectively known as a “control volume”. The continuity equation for

fluid flow is based on the conservation of mass principle and is given as below:

𝜕𝜌

𝜕𝑡+

𝜕(𝜌𝑢)

𝜕𝑥+

𝜕(𝜌𝑣)

𝜕𝑦+

𝜕(𝜌𝑤)

𝜕𝑧= 0 (3.3)

where 𝜌 is the density, 𝑢, 𝑣, 𝑤 are the velocity components, 𝑥, 𝑦, 𝑧 are the coordinate

directions.

The general form of the momentum equations can be described as:

𝜌 (𝜕𝑢

𝜕𝑡+ 𝑢. 𝛻𝑢) = −𝛻𝑝 + 𝛻. 𝜏 + 𝑓 (3.4)

where 𝑢 is the fluid velocity, 𝜏 is the stress, 𝑝 is the pressure, 𝑓 represents body forces and t

is the time. For each of the directions x, y and z, there is a separate momentum equation as

momentum is a vector quantity.

The energy equation can be expressed as follows:

𝜕(𝜌𝐸)

𝜕𝑡+ 𝛻. (��(𝜌𝐸 + 𝜌)) = 𝛻(𝑘𝑒𝑓𝑓𝛻𝑇) − ∑ ℎ𝑗𝐽𝑗𝑗 +(𝜏��𝑓𝑓. ��)) (3.5)


where ℎ𝑗 is the sensible enthalpy, 𝑘𝑒𝑓𝑓 is the effective thermal conductivity, 𝐽𝑗is the diffusive

flux of the species and E is the total energy.

3.3.2.1 Turbulence modelling

CFD involves modelling of fluid flow using the Navier-Stokes equations (momentum

equation) together with the continuity and energy equations based on Reynolds averaging

techniques, Large Eddy Simulation (LES) or Direct Numerical Simulation (DNS). The most

robust and established of these methods is the Reynolds averaging technique, which involves

splitting the solution variables into mean and fluctuating parts for each of the flow

components i.e. velocity, pressure and scalar quantities. For example, velocity ‘v’ is replaced

by a sum of mean (𝑣) and fluctuating components (��) of the velocity.

Reynolds decomposition of Navier-Stokes equations in three dimensions results in ten

unknown terms: three velocity components, a pressure term and six Reynolds stresses. To

close the system of equations, approximations for these Reynolds stress terms need to be

made. This is the basis of turbulence modelling. Over the years, several techniques for

modelling these terms have been developed. Reynolds Averaged Navier-Stokes (RANS)

turbulence models range from the basic, so-called zero-equation to one-equation to the more

advanced two-equation (k−𝜀 model) and the seven-equation Reynolds Stress Model (RSM).

Each of these models tries to overcome the limitations of earlier models, but no turbulence

model is the best fit for all applications and all models have limitations in accordance to the

physics of a specific flow problem.

This has been explained in the following words by Cengel & Cimbala (2010):

“Turbulent flow CFD solutions are only as good as appropriateness and validity of the

turbulence model used in the calculation”. Results obtained from the improper application

of turbulence modelling may lead the designer in the wrong direction (Gohara, Strock and

Hall, 1997). The selection criteria used for the turbulence model to be used in this project is


detailed later. A brief description of the numerical approaches used to solve the Reynolds

stresses resulting from the Reynolds averaging technique is given in this section. Two

common approaches are the Boussinesq approach and the Reynolds Stress Model (RSM).

In the Boussinesq approach, the Reynolds stresses are equal to a function of the mean

velocity gradient and a turbulence quantity i.e. turbulence viscosity. The turbulence viscosity

in the k−𝜀 model is computed as a function of the turbulence kinetic energy k, and the

turbulence dissipation rate 𝜀. This is the standard k−𝜀 model. The variant of the standard

k−𝜀 model proposed by Launder and Spalding (1972) and also used in this project was the

Realisable k−𝜀 model proposed by Shih et al. (1995), which solves the turbulent viscosity

in a different manner than the standard k−𝜀 model .

The second approach is the RSM (Launder and Reece, 1975), which is considered the

more accurate of the Reynolds averaged turbulence models. This accuracy though comes at

the cost of increased computational time and complexity of the solution process. Unlike the

k−𝜀 model, the RSM does not assume isotropic eddy viscosity and instead solves a separate

equation for each of the Reynolds stress as well as an equation for the dissipation rate. It was

observed in the literature review that the RSM is more commonly used by researchers to

model highly swirling flows, such as those in a centrifugal wet scrubber or a cyclone

separator.

3.3.3 Theory for Multi-phase CFD

Many fluid flows in the real world consist of a mixture of phases such as gas-liquid,

gas-solid or liquid-solid flows. Two basic methods developed for such multiphase flow

simulations are:

1) Eulerian-Eulerian approach

2) Eulerian-Lagrangian approach


Both these approaches were used in this project and a brief description of the main

features of these approaches is given in this section.

3.3.3.1 Eulerian-Eulerian Approach

This approach is based on the fact that space can only be occupied by a single phase

at any one time i.e. total volume fraction of all the phases is equal to 1. Several variations of

this method have been developed and studied, including the Volume of Fluid (VOF) method

(Hirt and Nichols, 1981), the mixture model (Bowen, 1976) and the Eulerian model, which

was studied in detail by Mazzei (2008) for application to fluidised suspensions. An

explanation for the Eulerian model for multiphase flows follows herein, whereas the

justification of the use of this approach is given in the CFD results’ chapter. In the Eulerian

model, the momentum and continuity equations are solved for each of the secondary phases.

For multiphase flows, the continuity equation for fluid flow becomes:

𝜕(𝛼𝑞𝜌𝑞)

𝜕𝑡+ 𝛻. (𝛼𝑞𝜌𝑞 ��𝑞) = ∑(��𝑝𝑞 − ��𝑞𝑝)

𝑛

𝑝=1

(3.6)

where 𝛼𝑞 is the volume fraction, 𝜌𝑞 is the density, ��𝑞 is the velocity of phase q, ��𝑝𝑞 is the

mass flow of phase p to phase q, ��𝑞𝑝 is the mass flow of phase q to phase p, n is the number

of phases.

While the momentum equation becomes:

𝜕(𝛼𝑞𝜌𝑞�� 𝑞 )

𝜕𝑡+ 𝛻. (𝛼𝑞𝜌𝑞�� 𝑞 �� 𝑞 )

= −𝛼𝑞𝛻𝑝 + 𝛻𝜏�� + 𝛼𝑞𝜌𝑞�� + ∑(��𝑝𝑞 + ��𝑝𝑞�� 𝑝𝑞 − ��𝑞𝑝�� 𝑞𝑝 )

𝑛

𝑝=1

+ ��

(3.7)

where 𝜏�� is the stress strain tensor for phase q, �� is the gravitational acceleration, ��𝑝𝑞 is the

interaction force between phases, �� 𝑝𝑞 and �� 𝑞𝑝 are the interphase velocities, �� represents the

external body, lift and virtual mass forces.


3.3.3.2 Eulerian-Lagrangian Approach

In the Eulerian-Lagrangian approach, the gas phase is modelled as a continuous phase

whereas the secondary phase is tracked as discrete particles via the application of Newton’s

laws of motion. This approach is feasible when the secondary phase volume fraction is low

(Vie et al., 2014) because a force balance for each particle is solved individually and

computational power considerations need to be made. The type of modelling can either be

one-way coupled i.e. the continuous phase affects the particle phase, or two-way coupled i.e.

both the continuous and discrete phase can apply forces on each other. The selection criterion

for the coupling method between the phases is generally the concentration of the secondary

phase. This method was used to track the water droplets as well as the dust particles and was

tailored accordingly in the current project i.e. two-way coupling when the discrete phase was

water droplets and one-way coupling when it was the dust particles.

The force balance on a single discrete particle derived from Newton’s laws is:

𝑑��𝑝

𝑑𝑡= 𝐹𝐷(�� 𝑔 − �� 𝑝) +

𝑔(𝜌𝑝 − 𝜌)

𝜌𝑝+ �� (3.8)

where 𝐹𝐷(�� 𝑔 − �� 𝑝) is the drag force per unit of particle mass and 𝐹𝐷= 18𝜇𝐶𝐷𝑅𝑒

24𝜌𝑝𝑑𝑝2 , �� 𝑔 is the gas

velocity, �� 𝑑 is the particle velocity, 𝜇 is the gas molecular viscosity, 𝜌 is the gas density, 𝜌𝑝

is the particle density, 𝑑𝑝 is the particle diameter, �� represents other forces.

The relative Reynolds number depends on the relative velocity between the particle

and fluid and is given by 𝑅𝑒 =𝜌𝑑𝑝|�� 𝑔 −�� 𝑝|

𝜇𝑔 .

Other forces such as the lift (existing due to the primary phase velocity gradient) and

virtual mass (significant if the secondary phase density is less than the primary phase


density) forces were not considered in this project as they did not have a significant effect

on the discrete phase in comparison to the drag force and gravity.

3.3.3.3 Other Relevant sub-models

Fluid flow modelling is highly dependent on several factors that affect the production

of turbulence variables, such as the near-wall treatment and the use of appropriate drag laws

and phase-coupling in case of multiphase flows. These sub-models are described in this

section.

Near-Wall Treatment

Velocity gradient is high near the walls. Flow near the wall can either be modelled

using the Navier-Stokes equations or via applying the wall function approach, in which the

boundary layer resides within an ‘inflation layer’ of cells. Regardless of the high velocity

gradient near walls, the change in velocity is highly predictable and has been experimentally

measured by researchers to establish the so-called ‘wall functions’. The “standard” wall

function formulated from the works of Launder & Spalding (1972) is the most widely

adopted approach.

A criterion to estimate the cell size next to the wall for the most appropriate prediction

of flow within the viscous sublayer known as “Y+” is used when using the Reynolds

averaging turbulence models. The suggested value of Y+ in high Re flows ranges from 30-

300, whereas to solve the viscous sublayer, a Y+ of approximately 1 is required. In most real

flows, there is little or no gain due to the extreme reduction in cell size near the wall required

to reach a Y+ of 1. With the use of a suitable wall function, the need to have an extremely

fine mesh near the wall to effectively capture the high velocity gradient region can be

avoided as the velocity profile in this region is highly predictable.


Figure 3-10. Schematic of the boundary layer approach (flow direction is left to right).

Drag Law

Other than gravity, the drag force can be considered to be the single most important

force acting on the secondary phase, especially when the secondary phase size is small. Drag

force is also the main reason for loss of the primary phase momentum and hence an important

reason for pressure drop in wet dust collectors.

Water droplets attain a spherical shape due to surface tension while dust particles

simulated in this project were only a few microns in diameter and can thus be assumed to be

spherical. For spherical particles, the Schiller-Naumann drag law is considered appropriate

as discussed earlier. This draw law is as follows:

𝐶𝑑 = {24(1 + 0.15𝑅𝑒𝑑

0.687)

𝑅𝑒𝑑 𝑅𝑒 ≤ 1000

0.44 𝑅𝑒 > 1000

(3.9)

where Re is the relative Reynolds number calculated via the relative velocity between the

two phases. Unless otherwise stated, the results presented in this thesis use the Schiller-

Naumann drag law.

Wall

Inflation layer cells

𝑼𝒚


The simulations were also compared to the results using the drag law of Sartor &

Abbott (1975) via adding the drag law to the CFD model. The equations for this drag law

are as follows:

𝐶𝐷 = 24

𝑅𝑒𝑑 𝑅𝑒𝑑 < 0.1 (3.10)

𝐶𝐷 = 24

𝑅𝑒𝑑

(1 + 0.0916𝑅𝑒𝑑) 0.1 < 𝑅𝑒𝑑 < 5.0 (3.11)

𝐶𝐷 =24

𝑅𝑒𝑑(1 + 0.158𝑅𝑒𝑑

23) 5.0 < 𝑅𝑒𝑑 < 1000 (3.12)

Whereas for 𝑅𝑒𝑑>1000 the 𝐶𝑑 was assumed to be 0.44. It was observed that the results

using the two different drag laws did not differ to a great extent (Figure 5-13). The reason

can be attributed to the large scale of the simulations. The effect of the use of different drag

laws may be more evident if more detailed simulations are made for smaller flow domains.

However, in a real scrubber, the scrubbing liquid enters the gas cross-flow as a sheet

and not a drop (Chapter 7), and a spherical droplet assumption in this region may

underestimate the pressure drop predictions. To take this into account, the drag coefficient

of the drop for a residence time equal to the sheet breakup time (Figure 6-5) was assumed to

be equal to that of a disc, as suggested by Liu et al. (1993). After sheet breakup, the drag law

for a spherical particle may be used and the Schiller-Naumann drag law was applied. This

approach was undertaken for the FSS simulations. The dynamic drag law follows a similar

approach but uses a linear relation for the drag coefficient between that for a disk and sphere.

It was available as a built-in function in the simulation software and was also used as detailed

in the literature review, producing slightly better prediction results as reported later. For the

FSS simulations, the simulation results using the user defined drag law and the dynamic drag

law did not differ.


One-way and two-way coupling

When water droplets were tracked via the Lagrangian approach, the simulation was

two-way coupled i.e. both the phases could affect the flow of the other phase. This approach

was adopted since it was observed in the experiments that addition of water to the SSM

caused an increase in the gas pressure drop. Whereas, when the dust particles were tracked

the simulation was one-way coupled i.e. the gas phase could influence the dust particle flow

variables, but the dust particles were considered to have no effect on the gas phase.

While droplet-droplet interaction was also considered in the simulations, the particle-

particle interaction between dust particles was not considered to have a significant influence

and hence was not modelled. This judgment was based on the following analysis.

Inter-particle interaction is dependent on the inter-particle spacing as discussed by

Sommerfeld (2000) and is given by the following equation.

𝑆

𝑑𝑝= (

𝜋

6𝛼𝑝)13 (3.13)

𝛼𝑝 =1

𝜌𝑝

𝐿𝜌𝑓+ 1

(3.14)

where L is the ratio of the dust mass flow to the flue gas mass flow, 𝜌𝑝 is the dust particle

density and 𝜌𝑓 is the fluid density. Applying this relation, the ratio 𝑆

𝑑𝑝 was calculated to be

greater than 140 for the current case. Kuan et al. (2007) suggested that the effect of inter-

particle collisions on the fluid flow can be ignored if the inter-particle spacing is greater than

10 and hence the inter-particle spacing was not considered in the simulations.

Droplet breakup

Droplet breakup was simulated using the Taylor Analogy Breakup (TAB) model

(Taylor 1963). In this model, oscillations in a droplet analogous to a spring-mass system are


calculated to predict droplet breakup into smaller droplets when oscillations reach a critical

value and the droplet surface tension cannot hold the drop together anymore. The “child”

droplet diameter is calculated via equating the parent droplet energy to the child droplet

energy, yielding a Sauter mean diameter for a Rossin-Rammler distribution.

When FSS simulations were performed, any water droplets which broke down into

droplets of <100 microns diameter were considered to instantaneously evaporate and were

deleted from the solution domain via a user defined function (Appendix 5). This helped keep

the number of trajectories in the simulation computationally affordable via reducing the

number of droplet parcels being tracked. The mass of the evaporated droplets was added to

the vapour phase mass fraction as a source term (section 3.3.3.3.6).

Heat transfer

Heat transfer between the phases was not considered in the simulations conducted for

the SSM since the experiments were conducted at room temperature. However, it was taken

into account for the FSS; since the inlet gas temperature is high and as the gas loses its heat

to the scrubbing liquid, the gas density changes considerably. Furthermore, the gas also

carries water vapour and the vapour percentage increases as the gas moves through the

scrubber, due to scrubbing liquid evaporation. For both Eulerian-Lagrangian and Eulerian-

Eulerian approaches, the heat transfer coefficient was calculated using the correlation of

Ranz & Marshall (1952), which computes the Nusselt number (Nu) via the equation below:

𝑁𝑢 = 2.0 + 0.6 𝑅𝑒1

2𝑃𝑟1

3 (3.15)

where Re and Pr are the phase’s Reynolds number and the Prandtl number respectively.

The Nusselt number itself is the ratio of the convective to conductive heat transfer at a

fluid boundary while the Prandtl number is the viscous to thermal diffusion rate ratio. The

rate of evaporation is then calculated from a heat balance and the mass fraction of the


evaporated vapour is removed, taken from the liquid phase and added to the gas vapour

species fraction.

Species transport modelling

The Species transport model can be used to simulate the exchange of mass between

components of mixtures i.e. production of water vapour from the liquid water phase. The mass

fraction of an individual component of a mixture can be calculated by the difference in the initial

mass fraction and the rate of production of the component in each cell.

Factory measurement data was used to determine the amount of water vapour in the flue

gas entering the FSS and the vapour fraction was set to 0.25 of the total mass fraction of the gas

at the inlet. To model the change in density of the gas as it cooled inside the scrubber, the gas

was simulated as an ideal incompressible gas. The standard species transport modelling

approach available in ANSYS Fluent was adopted.

Wall film modelling

An important trait of multiphase simulation is the ability to model wall films i.e. liquid

films formed on the surface of walls. This includes formation of wall film due to Lagrangian

parcels impinging on a wall surface or secondary phase coming in contact with the wall and

getting collected to form a liquid film. Similar to flow equations of the fluid domain,

continuity, momentum and energy equations are solved for the wall film in the wall film

model. The table below details the sources of mass and momentum for the wall film:

Table 3-1. Mass and momentum source terms comparison for wall film modelling.

Equation Secondary phase (Lagrangian) Secondary phase (Eulerian)

Mass ��𝑓 = ��𝑝 ��𝑓 = 𝛼𝑑𝜌𝑑�� 𝑠A

Momentum 𝑞 𝑙 = ��𝑓(�� 𝑝 − �� 𝑓) 𝑞 𝑙 = ��𝑓�� 𝑠


where, ��𝑝 is the mass flow rate of the Lagrangian parcel striking the wall, ��𝑓 is the wall

film mass flow, �� 𝑝 is the parcel velocity, �� 𝑓 the film velocity, 𝛼𝑑, 𝜌𝑑 and �� 𝑠 are the secondary

phase volume fraction, density and velocity normal to wall respectively and A is the wall

cell surface area.

Impinging droplets can also splash, or the film can separate from an edge, producing

Lagrangian droplets. Similar to the mass and momentum of impinging droplets added to the

wall film momentum equation as source terms, the mass separating or stripping from the

wall film was subtracted from the wall film momentum equations. Splashing or absorption

depended on the works of O’Rourke and Amsden (1996) and O’Rourke & Amsden (2000)

in the modelling approach undertaken in this project. Resultant droplet diameter as a result

of film stripping depended on film inertia, surface tension and film Weber number. Several

models are available, but the model of O’Rourke was used based on findings reported in

Chapter 5. According to O’Rourke and Amsden (1996), the thin film approximation breaks

down at sharp corners and a sub-model is required for modelling film separation. If film

inertia is small, the film will remain attached to the wall but if the inertia is more than a

critical value, film separation will occur. A schematic of liquid film approaching a sharp

corner is shown in Figure 3-11. O’Rourke and Amsden (1996) also suggested that surface

tension of the liquid had little if any effect on film separation and film inertia and gas shear

were the more dominant forces responsible for separation.


Figure 3-11. Schematic of the wall film separation mechanism.

Conversion of discrete particles to volume fraction

This sub-section describes the conversion process of discrete particles to the

secondary phase volume fraction in a computational cell. The Eulerian-Lagrangian method

was discovered to provide greater flexibility during the simulation stage and detailed

secondary phase (water) properties during post-processing for the processes under

investigation, while the Eulerian-Eulerian method provided superior insights during post-

processing at the macroscale. However, the CFD sub-model developed to account for the

dust particle-water droplet interaction (detailed in the next section) calculated the probability

of dust collection based on the scrubbing liquid volume fraction in a computational cell. This

variable was available for post-processing when the Eulerian-Eulerian approach (as detailed

in section 3.3.3.2) is used but not available with the Eulerian-Lagrangian approach. Hence,

Liquid film Wall

Film flow direction

𝜽 Resultant droplets

Gravity


when the liquid phase was tracked via the Lagrangian approach, it was necessary to convert

the Lagrangian phase to the volume fraction, thereby decreasing the computational expense

of having to repeat Eulerian-Eulerian simulations.

Considering the droplets as spheres, the volume of all the droplets in a cell was

calculated and summed to calculate the volume fraction of the secondary phase in a cell

using equation (3.16). This method posed an advantage since the Lagrangian approach treats

particles as discrete points and hence a particle can only physically be present in a single cell

at any given time.

𝑉𝑓 =∑𝑉𝑑

𝑉𝑐 (3.16)

where 𝑉𝑓 is the volume fraction of secondary phase in a cell, 𝑉𝑑 is the volume of a drop

and 𝑉𝑐 is the volume of the cell.

The UDF (see Appendix 5) was run at the end of a simulation when steady state had

been reached. The calculated secondary phase volume fraction in each cell was then stored

in a user-defined memory (UDM) location and the data was called as and when needed by

the particle-droplet interaction UDF or for post processing.

3.3.3.4 Simulation of dust particles trajectories and collection

Dust particles tracked via the Lagrangian approach were introduced into the CFD

model after a converged solution for the scrubbing liquid inside the SSM was reached.

Particles with a mean diameter of 22 microns following a Rossin-Rammler size distribution

(Figure 3-12) were introduced at a velocity equal to the gas velocity at the inlet. This

distribution was obtained from the analysis of the dust in the exhaust stream of a real boiler

installed with a centrifugal wet scrubber and the data used is given in Table 3-2 and plotted

in Figure 3-12.


Table 3-2. Dust particle diameter and mass fraction.

Particle diameter× 10−6 (m) 11.5 27.0 52.9 88.0 125.0 193.0 324.0 458.0

Mass fraction 0.38 0.25 0.19 0.03 0.045 0.04 0.04 0.025

Figure 3-12. Dust particle size distribution.

As described in Chapter 2, the two mechanisms which are responsible for virtually all

the dust collection in a centrifugal wet scrubber are inertial impaction and collection on the

scrubber wall. The User Defined Function (UDF) to model the collection via these

mechanisms is described here.

The volume fraction of scrubbing liquid was used to determine the probability of a

dust particle arriving at a droplet boundary in a computational cell. This was followed by

generation of a random number and considering collision to occur if the inertial impaction

parameter was greater than the random number generated. Droplet diameter used to calculate

Diameter, d (microns)

0 100 200 300 400 500

Ma

ss

fra

cti

on

> d

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


the inertial impaction parameter was calculated via the equation for Sauter mean diameter

(𝐷32) of droplets in a venturi scrubber presented by Boll et al. 1974.

Collection via inertial impaction is greatly dependent on the surface tension of the

collecting droplet. A dust particle has to overcome the surface tension of the droplet via

utilising its kinetic energy. Assuming that droplet surface deformed by the impacting particle

has a similar shape to the embedded particle, Pemberton (1960) derived an expression for

the required amount of work to be done by a dust particle against the droplet surface tension

to get collected.

𝑊 =8𝜋𝑟2𝜎

3

(3.17)

where r is the radius of the particle and 𝜎 is the surface tension of the droplet. Hence, if

collision occurred and dust particle kinetic energy was more than the critical kinetic energy

using equation 3.18, the particle was considered collected and removed from the simulation.

Whereas if the dust particle failed any of the above tests, it continued to be tracked.

Similarly, when the Eulerian approach was used to model the scrubbing liquid, the

volume fraction information was already available and no conversion was necessary. The

UDF for dust particle collection was tailored to run in a similar manner for the Eulerian

approach. No assumption was necessary for the liquid droplet diameter to calculate the

inertial impaction parameter and the average of the diameters of the Eulerian phases in a cell

was used.

For collection on the scrubber wall, it was assumed that a liquid film exists on the wall

surface as was observed in experiments. If a dust particle hit a wall face, the probability of

collection was based on the same approach as described earlier for when a dust particle

approached a water droplet within a control volume. Whereas, if the dust particle kinetic

energy was less than the critical kinetic energy required for collection, the particle was

reflected back in the gas flow in a direction opposite to the face of the scrubber wall in the


computational cell. The particle tracking continued until it reached another cell where the

test was repeated, or the particle escaped from the scrubber outlet.

Dust particle behaviour was only simulated and no experimental measurements were

conducted to measure the dust collection efficiency of the centrifugal scrubber design. The

results for the particle collection efficiency have thus been compared to published data for

the capture efficiency of other wet collectors. The results presented in this document are in

terms of the number of dust particles getting collected or escaping collection.

CFD Setup

This section describes the steps taken to perform the CFD simulations. A schematic

of the general CFD setup is shown in Figure 3-12.

Figure 3-13. Schematic of a CFD setup.

3.4.1.1 Mesh generation

The computer-aided design (CAD) used for fabricating the SSM was imported to the

ANSYS Workbench platform. This was followed by “cleaning” the geometry to remove any

unnecessary details, which were not important and would slow down the simulations due to

the excessive number of cells needed to map the small geometrical features. Before meshing,

the geometry was ‘sliced’ into smaller sections called “bodies” to allow a hybrid mesh

consisting of both hexahedral and tetrahedral elements to be made (Figure 3-15 and Figure

3-16). An example of the transition from tetrahedral to hexahedral cells from the SSM CFD

mesh is shown in (Figure 3-14). Smaller and simpler bodies can be recognised by the

meshing software for hex mesh generation with minimal user effort. A hex mesh

Geometry

• Computer Aided Design

Meshing

• Flow geometry is divided into cells

Physics

• Flow and boundary conditions are setup

Solve

• Flow equations solved.

Post-processing

• Result analysis via assessment of flow variables.


significantly reduces the total number of cells required to completely mesh a control volume.

A new mesh was generated after each of the design changes to the scaled model was made.

The figures in this section only represent a small portion of the meshed bodies for a general

representation of the meshing process.

Figure 3-14. Tetrahedral to hexahedral mesh transition.

Cell size in the main flow region had a greater effect on the accuracy of the simulated

results. Various grids types and cell sizes with cell counts ranging from 1 million to over 10

million were tested and a mesh with about 2.8 million cells was selected for the SSM. The

coarse mesh in particular, with a cell count of 1.1 million cells, performed poorly especially

in approximating the velocity profile in the low velocity zone in the middle of the vessel.

Increasing the number of cells resulted in an improved approximation of the velocity profile

but this increase in accuracy was not linear with the number of cells and the degree of

improvement decreased with increasing cell count, as shown later (Figure 5-3).

Mesh generation is an iterative process in CFD, the ideal mesh giving the best possible

accuracy with least number of cells. In the first instance, a coarse mesh is created, which is

then refined over several iterations based on simulation result comparison with experimental

measurements. The most recognised way to attain the so-called “mesh independent” solution

is to refine the mesh till simulation results do not change with further mesh refinement. The

approach undertaken in this project was different, as mesh refinement was only conducted

up to a certain extent, in an attempt to minimise the excessive computational cost associated


with an extremely high number of mesh cells. i.e. In other words, the mesh was only refined

until no “noticeable” difference in the simulation results was evident. Figure 5-3 represents

the simulated velocity magnitude using the different meshes for similar boundary conditions.

The mesh selected for the FSS simulations had a cell count of 5 million and cells were

converted to polyhedral cells to reduce the total cell count (Figure 3-17). The process also

resulted in improved aspect ratio of the near-wall cells. The inflation layer size was kept

equal to that in the SSM and the minimum cell face size was limited to the dimension of the

smallest feature i.e. the vane thickness. A solid region was generated in the middle of the

outlet to avoid backflow (Figure 3-17), which could cause solution instabilities to rise.

It was also found that in contrast to having 10-15 inflation layers at walls which is

considered a ‘good’ practice (LEAP CFD, 2012), as few as four inflation layers sufficed to

produce the desired results and the velocity gradient stabilised within the inflation layer. The

grid was adapted by executing commands within the iterative process after a selected number

of time-steps to achieve a value of 30-300 for the Y+ on the walls, on which a no-slip

condition was applied. Inflation layer cell size on vane edges was smaller than the vane edge

size and hence the enhanced wall treatment was applied. This ensured that the standard wall

function was not applied if first cell height of the inflation layer was within the viscous sub-

layer and flow equations were solved in these cells. For the FSS simulations, mesh adaption

was not possible due to the inability of the default algorithm available in the software to

adapt a polyhedral mesh, and first layer cell height was set by re-meshing the geometry after

results for the Y+ value were generated by running the simulation.


Figure 3-15. Wireframe view of the surface mesh in the bottom section of the scrubber. A

slice plane used to separate the bottom cone from the main body (Slice plane 1) and another

to separate the inlet from the main body (Slice plane 2) are labelled.

Figure 3-16. Mesh on a plane passing through the scrubber inlet after adaption at wall

boundaries.

Slice plane 2

Slice plane 1


Figure 3-17. Polyhedral mesh cross-section across different planes (a) Plane passing through

the middle of the scrubber, (b) scrubber inlet, (c) plane passing through scrubbing vanes, (d)

outlet.

3.4.1.2 Single-phase flow setup

Known values of velocity at the scrubber inlet, together with measured pressure drop

were used as boundary conditions and the solution was initialized based on these

measurements.

(a) (b)

(c) (d)


The modelling approach was to attain a steady state solution using the standard k−𝜀

model method before the solver was converted to run on the RSM. A High Performance

Computing (HPC) Linux cluster was used for the simulations and the number of CPUs used

was limited to 8 or 16 for a single simulation.

Standard values of the turbulence model constants in Ansys Fluent were used as they

have been tested and proven to be applicable to a wide range of turbulent flows (Ansys,

2013). After convergence was achieved using first-order discretisation schemes, the solver

was converted to second-order discretisation schemes for improved accuracy. For both

steady and unsteady runs, the convergence criteria for each scaled residual was fixed at 10−4,

except the continuity equation for which it was 10−3 and for the energy equation at 10−6.

Reducing the under-relaxation factors limits the change in flow variables in an iteration and

thereby increases the solution process stability and this method was adopted to achieve

convergence in case of residual divergence. At the first instance, the default values of the

under-relaxation factors were used. In case of residual divergence, the under-relaxation

factor for the particular variable was reduced by ½. For higher air flow rates, the instabilities

in the simulation became increasingly difficult to manage, requiring multiple instances of

under-relaxation factor reduction until the solution stabilised. When multi-phase simulations

were performed, the required time-step size for stability was smaller than that for single

phase simulations (< 0.0001 s and <0.005 s respectively), and the discretisation schemes

were converted to 2nd order as an added benefit of using a smaller time-step size. This

enabled achieving a converged solution at the higher order discretisation schemes using the

RSM for all multiphase simulations.

The mass flow rates of air at the scrubber inlet were set as detailed in Table 3-3.


Table 3-3. Inlet air mass flow rate simulated for the SSM.

Mass flow rate (kg/s) 0.150 0.175 0.200 0.250 0.287 0.320

The turbulence intensity at the inlet was approximated using the mean deviation of

measured velocity at inlet of the SSM as measured with the hot wire anemometer using the

following formula:

A pressure outlet boundary condition was applied to the gas outlet.

3.4.1.3 Multi-phase flow setup

Once the single phase simulations had been completed, the secondary phase was

introduced as either multiple Eulerian phases with different phase diameters or via the

Lagrangian particle tracking method. At this stage, the wall film equations were also solved

and the results were monitored as the simulation progressed.

An important difference between the simulation setup for the SSM and the FSS model

was inclusion of the thermal aspect of the flow. To model this trait, density of air in the

simulations for the SSM was constant, whereas, for the FSS, the gas was assumed to be an

incompressible ideal gas whose density changed with the change in gas temperature. This,

together with the species transport modelling, allowed the FSS simulations to include the

vapour content at the scrubber inlet from the boiler. Secondary phase within the bulk flow

domain and on wall film was also allowed to change phase from liquid to vapour

accordingly.

Whilst the secondary phase was simulated using the Eulerian approach, four phase

diameters were chosen based on visualisation analysis from the SSM and included water

droplets with diameter 0.0001 m, 0.00055 m, 0.001 m and 0.005 m. Three of these diameters

were chosen as the phase diameters in any one simulation to have a range of diameters, but

𝐼 =𝑢′

�� × 100 (3.18)


yet keep the computational expense affordable. Whereas, when the secondary phase was

introduced as Lagrangian particles, droplet size was fixed at 0.005 m at the edge of the water

distribution cone for the SSM simulations. This was based on the sheet thickness coming-

off the distribution cone as seen in the test-scale scrubber. Droplets were then allowed to

break up into smaller ‘child’ droplets or coalesce upon collision with other droplets based

on the Taylor Analogy Breakup (TAB) model (G. . Taylor, 1963) and O’Rourke’s (O'Rourke

1981) model respectively. The injection type used was “cone”, which allowed a greater

control on the number of parcel streams introduced into the domain. Each parcel represented

a number of particles with the same properties, an approach commonly used in discrete phase

simulations for a reduction in the computational expense. In the experiments, the pressure

drop across the scrubber scale model was observed to increase after the addition of water

and hence Lagrangian modelling of water was two-way coupled i.e. particle phase affected

the carrier phase flow properties and vice versa. The scrubbing liquid flow rate introduced

into the simulation was the same as that in the test rig experiments (~ 0.13 L/s). This flow

rate was scaled from the design water flow rate to the FSS.

The same methodology was used for the FSS simulation except that the initial particle

diameter was set to 0.02 m and if a droplet diameter fell below a critical minimum value

during droplet breakup, the droplet was assumed to undergo instantaneous evaporation,

deleted and its mass added as a source term to the water vapour content in the carrier gas.

This reduced the number of droplets being tracked in the simulation which had a very small

fraction of mass of the total discrete phase and thereby helped to minimise the computational

expense. It was assumed that the final droplet diameter using the current approach (TAB

model) and an approach modelling the change in liquid sheet morphology to droplets (i.e.

the Volume-of-Fluid model) would yield the same final droplet size. This is because droplet

breakup is characterised by the droplet Weber number which primarily controls droplet


breakup in the TAB model. According to Pilch & Erdman (1987), a droplet whose Weber

number is less than 12 will not undergo further breakup when the Ohnesorge number (relates

the viscous forces to the inertial and surface tension forces) is small (< 0.1) as is the case for

the droplets in a centrifugal wet scrubber.

The Phase-Coupled SIMPLE algorithm was used to couple the pressure and velocity

equations, while the time step size was kept variable depending on the global Courant

number, which was restricted to a value of 1 for the whole duration of the multiphase

simulation. This meant that the secondary phase could only flow across one cell in a single

time step, which added to the stability of the solution process. The total volume of the

secondary phase introduced in the simulation was based on scaled values of the scrubbing

liquid inflow of a FSS as recommended by a scrubber manufacturer and equalled 0.127 kg/s

for the SSM. The test rig scale scrubber has a size ratio of approximately 1:9 to a full-scale

scrubber. Scrubbing liquid was introduced from both a pipe above the distribution cone and

directly from the edges of the distribution cone in later simulations in an effort to reduce the

total simulation time, as this was found to have no effect on the overall simulation results.

The number of child droplet parcels born via droplet breakup was set to five, in an

attempt to obtain a smoothed size distribution while keeping the number of parcels tracked

computationally affordable. A higher number would have resulted in an undesirable increase

in the number of parcels produced and hence increased computational expense.

Tracking a dust particle caught in one of the recirculation zones within the scrubber was

avoided by limiting the maximum number of time steps for which the solver would track a

particle. This was estimated after tracking single particles of varying diameters across the

scrubber and allowing for the particle to “loop” around inside the scrubber a “few” times before

exiting through the gas outlet. The particle positions were only updated upon entering or leaving

a computational cell since it was found that multiple updates of the particle position within a


single computational cell did not have any noticeable effect on the particle trajectory. This could

be the result of the computational mesh already being fine enough to generate the required results

without having to track the particle multiple times across each cell.


4 CHAPTER 4: EXPERIMENTAL RESULTS AND

DISCUSSION

This chapter reports the findings of the experimental program. The first section

(4.1) presents the velocity measurements from the SSM at varying air flow rates whereas

the total pressure drop measurements for both dry and wet runs across the SSM are given

in section 4.2.

The simplest but most important measurements made via the test rig were the

velocity and pressure profiles across various SSM traverses, providing input boundary

conditions for flow modelling as well as data for the CFD validation.

A schematic of the SSM is given in Figure 4-1, which introduces the scrubber zones

which will be referred to later in this chapter. For the base case, the air mass flow rate at

the SSM inlet was approximately 0.287 kg/s based on the mass flow of gas into the FSS of

24.25 kg/s. Unless otherwise stated, the results correspond to this inlet air flow rate.


Figure 4-1. (a) Side elevation and (b) plan views of the SSM. Four main identified zones

include bottom cone (zone A), below scrubbing vanes (zone B), above scrubbing vanes (zone

C) and above demister vanes (zone D).

Velocity measurements

Measured air velocity at inlet of the scrubber ranged from 1 m/s near the walls to up to 13

m/s in the inlet centre at different air flow rates. Hence, the inlet air velocity has been reported

as the “average inlet velocity” and was calculated as follows (for details see Appendix 2):

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑖𝑛𝑙𝑒𝑡 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 =𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑎𝑡 𝑖𝑛𝑙𝑒𝑡 𝑐𝑒𝑛𝑡𝑟𝑒

𝑅

The ratio “R” equalled 1.23 for the base case (see Appendix A.2).

The estimated error in measurements is less than ±0.5 m/s for the velocity magnitude

of air across the entire range and is due to a 3% documented uncertainty of the hot-wire

anemometer probe and small fluctuations in measurements due to flow turbulence.

Dirty gas inlet

Scrubbing vanes

Water inlet

Tangential inlet

Dirty water

outlet Zone A

Clean gas outlet

Zone B

Demister vanes

Zone C

Zone D

Dirty gas inlet

Water distribution cone

(a) (b)


4.1.1 Velocity across traverses

Two holes each were drilled approximately halfway up the inlet height, above scrubbing

vanes, above the water distribution cone and above the demisting vanes at locations as shown in

Figure 4-2. Zone B offered the easiest access for the hot wire anemometer to measure individual

velocity components of the flow. Hence, measurements from this zone, in particular, were used

to validate the CFD model and are presented in this section for an air flow rate of 0.287 kg/s.

Figure 4-2. Positions of holes drilled in zones B, C, D.

Figure 4-3. Measured velocity components and velocity magnitudes (m/s) along a traverse

parallel to the longitudinal axis of the SSM inlet, with locations of 10 mm and 380 mm

distances shown on the sketch for inlet type A.

0

5

10

15

20

25

0 100 200 300 400

Vel

oci

ty (

m/s

)

Position along axis (mm)

Velocity magnitude (m/s)

Axial velocity (m/s)

Tangential velocity (m/s)

Holes



perpendicular to the longitudinal axis of the SSM, with the locations of 10 mm and 380 mm

distances shown on the sketch for inlet type A.


parallel to the longitudinal axis of the SSM, with the locations of 10 mm and 380 mm

distances shown on the sketch for inlet type B.

0

5

10

15

20

25

0 100 200 300 400

Ve

loci

ty (

m/s

)





0

5

10

15

20

0 100 200 300 400

Ve

loci

ty (

m/s

)







perpendicular to the longitudinal axis of the SSM, with the locations of 10 mm and 380 mm

distances shown on the sketch for inlet type B.

4.1.2 Air velocity in vanes

Unlike zone B, zones C and D offered limited access to measure velocity components

within the vanes. Therefore, measurement of a single velocity component which was parallel

to the flow direction and between the gaps in the vanes was carried out rather than the

individual velocity component approach (axial and tangential) which was described in

Section 3.2.1.1. This was achieved by rotating and moving the hot-wire anemometer’s probe

to position its orientation perpendicular to the air flow direction in-between the gaps in the

vanes. The maximum measured velocity for inlet Type A at 0.287 kg/s are presented in

Figure 4-7 and Figure 4-8 for the scrubbing and demisting vanes respectively.

Velocity measurements above the water distribution cone and the demister vanes were

also made for a single velocity component (perpendicular to the flow direction) across the

0

5

10

15

20

0 100 200 300 400

Vel

oci

ty (

m/s

)






whole traverse length and the results are presented in Figure 4-9 and Figure 4-10

respectively. The orientation of the hot-wire anemometer probe was rotated halfway of the

traverse to match the flow direction.

The air velocity varied by a factor of 2.9 across the scrubbing vanes and a factor of 2.2

across the demisting vanes with the highest velocities on the inlet duct side of the scale model

for both the sets of vanes. Lower flow velocity was measured towards the inside of the scrubbing

vanes (closest to the central axis) and it increased moving towards the outside of the vanes

(furthest from the central axis). For the demisting vanes, the velocity readings peaked at

approximately 75% along the vane’s length, while low velocity was measured towards both the

inside and outside of the vanes.

This inconsistency of measured velocity through the vanes suggests that the probability of

dust capture also depends on the location of a particle as it enters the scrubber. As a particle

velocity will vary according to the gas velocity in a real scrubber, a low particle velocity will

result in a lesser collection probability. Since air velocity is different across each of the scrubbing

vanes, breakup of the water sheet coming down from the distribution cone will also not be evenly

distributed.


Figure 4-7. Maximum measured velocity magnitudes at a total air flow of 0.287 kg/s through the

scale model scrubbing vanes.

Figure 4-8. Maximum measured velocity magnitudes at a total air flow rate of 0.287 kg/s

through the scale model demisting vanes.


Figure 4-9. Measured velocity magnitude (m/s) along the traverse as shown above the water

distribution cone, with the locations of 10 mm and 380 mm distances shown on the sketch for

inlet type A.

Figure 4-10. Measured velocity magnitude (m/s) along the traverse as shown above the

demister vanes, with the locations of 10 mm and 380 mm distances shown on the sketch for

inlet type A.

0

3

6

9

12

15

0 100 200 300 400

Vel

oci

ty m

agn

itu

de

(m/s

)


0

2

4

6

8

10

0 100 200 300 400

Vel

oci

ty m

agn

itu

de

(m

/s)



Pressure drop measurements

4.2.1 Original design

Pressure drop across the SSM was also measured via the multimeter for both wet and

dry runs. Total pressure drop for the SSM with inlet type A is given in Figure 4-11 and type

B in Figure 4-12.The main sources of pressure drop were the converging inlet and the air

momentum loss to water.

Generally, pressure drop increased for both wet and dry runs as inlet velocity was

increased. Inlet type B had a lower pressure drop for similar inlet velocities than inlet type

A. However, pressure drop due to the loss in air momentum to the water (during wet runs)

was higher when inlet type B was used than the pressure drop when inlet type A was used.

For example, it can be observed from Figure 4-11 that pressure drop for Inlet type A at an

average inlet velocity of 7 m/s is approximately 380 Pa without water, and this increases to

460 Pa with water. On the other hand for Inlet type B, pressure drop at an average inlet

velocity of 7 m/s is only 250 Pa without water and increased to 460 Pa with water (Figure

4-12). This may be due to the greater influence of the air axial velocity when Inlet type B

was used in comparison to that generated through inlet type A (in which the tangential

velocity component was much more dominating). In case of higher tangential velocity (Inlet

type A), pressure drop due to gas inlet’s convergence angle was higher. In Chapter 5, a

comparison of these results with modelling results is also presented.


Figure 4-11. Total pressure drop (Pa) vs average inlet velocity (m/s) measured running test

rig without and with water addition at a rate of 0.13 L/s for inlet type A.

Figure 4-12. Total pressure drop (Pa) vs average Inlet velocity (m/s) measured running the

test rig without and with water at a rate of 0.13 L/s for inlet type B.


4.2.2 Pressure plate

Some scrubbers are installed with a pressure plate, located just below the scrubbing

vanes (Figure 4-13). The reported reason is to attain increased gas speeds thereby increasing

the dust collection efficiency. This modification was also made on the SSM and velocity and

pressure drop readings were made for this setup as well. It was observed that increase in dust

collection efficiency was actually an outcome of the scrubbing liquid being redirected

towards scrubber walls in Zone B and not due to an increased gas speed as the pressure plate

is within the low velocity region of the scrubber. The presence of a wall film increases the

collection efficiency of centrifugal wet scrubbers, as described in Chapter 2. Pressure drop

increased as a result of the pressure plate installation as shown in Figure 4-14. Only one plate

with a diameter of 235 mm was tested in the experiments. However, CFD simulations were

made for a range of plate diameters and are presented in Chapter 5.

Figure 4-13. Scrubbing vanes with a pressure plate in a FSS (a) View from above the scrubbing

vanes, (b) view from below the scrubbing vanes.


Figure 4-14. Pressure drop vs average inlet velocity through the SSM with inlet type B and

a pressure plate located below scrubbing vanes in comparison to original design (without

pressure plate).

Entrained water

When the SSM was run with water, the system did not reach steady state immediately.

As an agitated water bath between scrubbing and demisting vanes formed, flow of water

exiting through the bottom cone of the SSM gradually increased to reach the steady state

when the water flow rate into the scrubber equalled to that exiting it.

It was found that the agitated water bath’s height was dependent on the inlet air flow

rate. Most of the water volume in the SSM was present in the water bath and its mass was

estimated by subtracting the water reservoir pool mass at steady state from the initial

reservoir mass. The accumulated water mass in the SSM was determined in this way for a

range of air flows yielding the curve shown in Figure 4-15. Note that the water bath

continuously sloshes inside the SSM and even after reaching the equilibrium state,

fluctuations in the measured water mass of up to ±25 g were observed.


The accumulated water mass had a parabolic increase with increasing average air inlet

velocity (Figure 4-15). As inlet velocity was increased, the water bath height increased,

finally reaching the demister vanes and resulting in a significant increase in droplet

carryover. It is likely that many of the smaller dust particles are captured in this agitated

water bath in a FSS and droplet carryover will result in the escape of captured dust particles

contained in the entrained droplets.

As expected, a greater pressure drop was also observed for a higher water addition rate

due to a greater loss of the air momentum to the water (Figure 4-16).


Figure 4-15. Measured water mass vs Average inlet velocity in the SSM.

Figure 4-16. Pressure drop vs average inlet velocity for two different water addition rates.

100

200

300

400

500

600

5.0 5.5 6.0 6.5 7.0 7.5 8.0

Pre

ssu

re d

rop

(P

a)

Average Inlet velocity (m/s)

Water rate 0.13 L/s

Water rate 0.15 L/s


5 CHAPTER 5: CFD RESULTS AND DISCUSSION

This section presents the CFD modelling results and comparisons with

experimental measurements from the previous chapter. Results for the single-phase

modelling are presented in Section 5.1 followed by the multi-phase modelling results in

section 5.2.

Single-phase modelling

Single-phase simulations lay an important foundation for multi-phase CFD. Results were

used to provide an “initial” solution for the multi-phase simulation setup, giving a head-start,

stability to the iteration procedure and an improved convergence behaviour. Similar to the multi-

phase simulations, single-phase simulations were also used to validate various aspects of the

computational model such as the correct turbulence model via comparison to velocity and

pressure readings obtained from the test rig.

As described previously (Section 2.2), selection of the correct turbulence model has

utmost importance in CFD. In the following section, the modelling results obtained using

various turbulence models are presented in comparison to experimental measurements made

from the various traverses across the SSM.

5.1.1 Simulated velocity profiles

Figure 5-1 represents the simulated velocity magnitude values across the traverse

(similar to the experimental data presented in Figure 4-3) for various turbulence models

compared with experiments. Figure 5-2 compares the 1st and 2nd order RSM results to the

measured velocity across the same traverse. It was observed that as the inlet air velocity was

increased, the difference between measured and simulated results also increased and only


the RSM was able to produce satisfactory results in cases with high air inlet velocity. The

2nd order RSM predicted the flow with an even greater accuracy than the 1st order RSM but

this approach experienced instabilities during the solution procedure which were controlled

via the procedure described earlier in Section 3.4.

Figure 5-1. Predicted and measured velocity profiles approximately 18 cm below the

scrubbing vanes.



scrubbing vanes.

Various grids including a coarse grid (1.1 million cells), a medium grid (2.2 million

cells) and a fine grid (6.8 million cells) were tested. The medium grid produced reasonably

good agreement with the measurements of the entire flow field with almost three times fewer

cells than the fine grid and was chosen for further simulations. It was decided that the

increased computational cost of using a fine grid was not justified for a small improvement

in the predictions. The meshing process was repeated to modify the first cell height to

achieve the desired Y+ values while the same cell size was applied to the rest of the geometry

volume. Results for the mesh independence test are presented in Figure 5-3. To assess the

solution accuracy at different locations in the SSM using this grid, comparisons with

measured results from a 2nd traverse in zone 2 (Figure 5-4), above the water distribution cone

0

2

4

6

8

10

12

14

16

18

20

0 50 100 150 200 250 300 350

Vel

oci

ty m

agn

itu

de

(m/s

)



(Figure 5-5) and above the demister vanes (Figure 5-6) were also made. The difference in

the measured and predicted velocity magnitudes can be attributed to slight variations of the

hot-wire anemometer probe’s orientation and a limited number of measurements made

across a traverse, resulting in a reduced resolution. However, the trend in the readings was

considered sufficient to judge the simulation model accuracy. Along the flow area of a single

vane, velocity was more evenly distributed for the demister vanes than the scrubbing vanes

(Figure 5-7 and Figure 5-8).


scrubbing vanes.



Figure 5-4. Predicted and measured velocity profiles approximately 18 cm below the scrubbing

vanes on the shown traverse.


Figure 5-5. Predicted and measured velocity profiles 1 cm above the water distribution cone

across the shown traverse.

Figure 5-6. Predicted and measured velocity profiles 3 cm above the demisting vanes across

the shown traverse.

0

2

4

6

8

10

12

14

16

18

0 50 100 150 200 250 300 350

Vel

oci

ty m

agn

itu

de

(m/s

)


0

2

4

6

8

10

0 50 100 150 200 250 300 350

Vel

oci

ty m

agn

itu

de

(m/s

)


Measured Predicted

Measured Predicted


In Figure 5-7, the simulated velocity magnitude in a plane parallel and passing through

the scrubbing vanes is presented at different air in-flow rate and Figure 5-8 presents the

velocity magnitudes across the demisting vanes in a similar manner. Figure 5-9 presents the

velocity magnitude through a plane passing through the middle of the scrubber. The flow

asymmetry is evident from the plots. Scrubbing liquid entering the gas cross flow will behave

differently depending on the local gas velocity. The asymmetry exists due to the tangential

gas inlet being on one side of the vessel in the scrubber design under study in this project

and hence cannot be avoided. Dust particle collection probability is also affected, as a

particle with a higher velocity is more likely to get captured than one with a lower velocity.

Similarly, a water droplet entering a vane with higher axial velocity will have a greater

chance of escaping the scrubber as carryover.

Figure 5-7. Plan views of the predicted velocity magnitudes at an air flow rate of (a) 0.175

kg/s (b) 0.20 kg/s (c) 0.25 kg/s (d) 0.287 kg/s through the scrubber vanes of the SSM.

(a) (b)

(c) (d)


Figure 5-8. Plan views of the predicted velocity magnitude at an air flow rate of (a) 0.175

kg/s (b) 0.20 kg/s (c) 0.25 kg/s (d) 0.287 kg/s through demisting vanes of the SSM.

(a) (b)

(c) (d)


Figure 5-9. Plan views of the predicted velocity magnitude at air flow rate of (a) 0.175 kg/s

(b) 0.20 kg/s (c) 0.225 kg/s (d) 0.287 kg/s through a plane passing through the middle of

the scrubber.

The predicted velocity distribution in Figure 5-10 shows the effect of including a

pressure plate below the scrubbing vanes. With increasing plate diameter, a noticeable

difference in the pressure drop is only evident when the pressure plate diameter exceeded

the low velocity zone diameter and was greater than 0.23 m. The effects of including a

pressure plate on the scrubber performance are further discussed in Chapter 7.


Figure 5-10. Simulated contours of velocity magnitude in a vertical plane normal to and

passing through the scrubbing vanes for varying pressure plate diameters (a) no pressure

plate, (b) 0.20 m pressure plate diameter, 0.225 m pressure plate diameter, 0.25 m pressure

plate diameter.

Low velocity and recirculation zones may lead to deposit build-up and were identified via

the gas phase vector plots from the single phase simulation results. Due to the asymmetric

scrubber geometry, recirculation zones were not observed in the middle of the scrubber in the

CFD simulation results but rather slightly offset towards the gas inlet side of the vessel. CFD

simulations indicated that one recirculation zone was present just below the scrubbing vanes and

terminated left of the water outlet (Figure 5-11). Another was present above the centre cone of

(a) (b)

Low velocity zone

Scrubbing vanes Pressure plate

(c) (d)


the demisting vanes. Real scrubber images obtained from sugar mills showed dust accumulation

on the demisting vane cone, which is in agreement to the presence of the predicted recirculation

zone.

Figure 5-11. Vector plot on a plane passing through the middle of the scrubber.

5.1.2 Pressure drop comparisons

In this section, the single phase simulation results for the predicted pressure drop

with different sized pressure plates are given, whereas the pressure drop predictions for the

multiphase simulations are given in section 5.2.1. The pressure plate diameter measurement

labelled “no pressure plate” in Figure 5.11 corresponds to the diameter of the solid region

from which the scrubbing vanes extrude (for reference see Figure 3-4).

Recirculation zones


Figure 5-12. Predicted pressure drop across the SSM for each of the cases presented in

Figure 5-10.

Multi-phase modelling

In this section, results for the multiphase simulation of both the SSM and the FSS are

presented.

5.2.1 Pressure drop comparisons

Total pressure drop across the SSM using the Eulerian-Eulerian as well as Eulerian-

Lagrangian approaches are shown in Figure 5-13 for inlet type A.

Only a single Eulerian-Eulerian simulation was performed using the Sartor and Abbott

drag law, as the predicted results showed it to slightly underperform when compared to the

Schiller and Naumann drag law.

Figure 5-14 shows the measured and predicted pressure drop using the Eulerian-

Eulerian approach for inlet type B. The measured and predicted values are the closest for the

300

350

400

450

500

550

600

0.10 0.13 0.15 0.18 0.20 0.23 0.25

Pre

ssu

re d

orp

(P

a)

Pressure plate diameter (m)

No pressure

plate


mid-range of the average inlet velocity. The constant to calculate the average inlet velocity

(Appendix 2) was based on measured velocity readings in this range; hence agreeing most

closely to the predictions.

Figure 5-13. Measured and predicted total pressure drop (Pa) across SSM with water

addition of 0.13 L/s for inlet type A.

Figure 5-14. Measured and predicted total pressure drop (Pa) across the SSM with water

addition of 0.13 L/s for inlet type B.

100

200

300

400

500

600

700

800

4 5 6 7 8 9 10

Pre

ssu

re d

rop

(P

a)

Average inlet velocity (m/s)

Sartor and Abbott (1975)


5.2.2 Predicted scrubbing liquid distribution using Eulerian-Eulerian and Eulerian-

Lagrangian approaches

The Eulerian-Eulerian approach limited the number of droplet sizes that could be

simulated because of the increasing computational cost. The simulation time was found to

increase by almost 100% for every additional Eulerian phase. Nonetheless, it still provided

useful information about the scrubbing liquid distribution. A comparison of the scrubbing liquid

distribution between the two sets of vanes from the experiments and the simulations is given in

Figure 5-15, which shows that the scrubbing liquid distribution can accurately be predicted via

CFD simulations. In Figure 5-16, iso-volume plots show the predicted distribution of the

scrubbing liquid at airflow rates ranging from 0.175 kg/s to 0.287 kg/s using the Eulerian-

Eulerian approach. At low airflow rates, many regions of the SSM have low or no scrubbing

liquid volume fraction. As the airflow rate is increased, the scrubbing liquid distribution

improves. There is also an increased volume of scrubbing liquid above the demisting vanes at

the highest flow rate.

Figure 5-15. Scrubbing liquid distribution in the SSM (a) test rig (b) iso-value from

simulations.


(a) (b) (c)

Figure 5-16. Modelled iso-volume plot showing the distribution of the secondary phase

(0.001 m diameter) at air flow rate of (a) 0.175 kg/s, (b) 0.20 kg/s and (c) 0.287 kg/s.

Population balance modelling (PBM) approach can potentially be used to overcome this

deficiency of the Eulerian-Eulerian method (i.e. the restriction on the number of droplet sizes

simulated due to an increasing computational cost) but further progress needs to be made to

increase the reliability and stability of the PBM approach to model complex flows involving

liquids. Ramkrishna & Singh (2014) noted that simulations using the CFD-PBM in literature

lacked validation with experiments and comparison with data if any was somewhat “sketchy”.

Qin et al. (2016) used the CFD-PBM approach for prediction of droplet sizes in rotor-stator

mixing devices and concluded that the functions for droplet breakage and coalescence in PBM

are derived from theory, and accuracy of such models cannot be guaranteed.

To get a detailed view of the droplet size distribution the Eulerian-Lagrangian simulation

results were used. This approach, when coupled with the appropriate droplet breakup model

allowed to obtain an improved resolution of the droplet size range. Figure 5-16 to Figure 5-20

show the scrubbing liquid distribution using the Eulerian-Lagrangian method. The volume


fraction of droplets after conversion from discrete particles to the volume fraction in cells and

the wall film stretch, following the method described in section 3.3.3.3.8, is also shown. The

total number of droplet parcels shown in the figures has been reduced by a factor of 5 to aid

visibility.

(a) (b)

Figure 5-17. a) Predicted water droplet distribution in the SSM at air flow rate of 0.175 kg/s.

b) Predicted iso-surface of water with volume fraction of 0.001 and the resulting liquid film

on the SSM walls.

Tracked droplets

Wall film

Volume fraction of drops


(a) (b)

Figure 5-18. a) Predicted water droplet distribution at an air flow rate of 0.20 kg/s. b)

Predicted iso-surface of water with volume fraction of 0.001 and the resulting liquid film on

the SSM walls.

(a) (b)



the SSM walls.


(a) (b)



the SSM walls.

In Figure 5-21 (a), 400 hi-speed photography images from the SSM have been super-

imposed on top of each other to show the distribution of the water droplets as a liquid sheet

breaks down after entering the air flow; prediction results for the Eulerian-Lagrangian and the

Eulerian-Eulerian method are also shown for comparison.

The importance of gas velocity in determining the scrubbing liquid distribution in a

real scrubber can be observed from the preceding images. At low air velocity, the centrifugal

force on the water droplets is not enough to redirect them to the scrubber walls. However, as

the air velocity increases, the area covered by the wall film below the scrubbing vanes

increases.

At the lowest modelled air flow rate of 0.175 kg/s ((b)

Figure 5-17 (b)), many of the water droplets below the scrubbing vanes remain

unbroken and are at their initial size of 5 mm. However, when air velocity increases, the


diameter of droplets below the scrubbing vanes decreases. As seen in Figure 5-20 with the

highest air flow rate of 0.287 kg/s, virtually all of the droplets below the scrubbing vanes are

smaller than their initial size. At higher air flows, the water bath also rises higher up towards

the demister vanes. This comes at the expense of a greater pressure drop and carryover and

hence there is a need to determine the ideal operating velocity for individual scrubbers at the

design stage. The ideal air flow rate is between 0.25 kg/s and 0.287 kg/s as the optimum

scrubbing liquid distribution can be observed in this range and the droplet carryover remains

relatively low. If the air flow rate continues to increase, there will be a significant droplet

carryover, resulting in a loss of performance.


Figure 5-21. Comparison of scrubbing liquid distribution entering the air flow (a) experiments, (b) Eulerian-Lagrangian, (c) Eulerian-Eulerian.

Water distribution cone

10 mm

(a) (b) (c)


5.2.3 Liquid wall film behaviour

This section details the experimental and simulation results for the liquid wall film.

Droplet-film collision is discussed in section 5.2.3.1 and film stripping and separation in section

5.2.3.2.

5.2.3.1 Droplet-film collision

Droplet-film collision can result in several different outcomes depending on the droplet

and the film’s properties, most importantly the film Weber number. In general, a small collision

Weber number resulted in bouncing and a large Weber number resulted in absorption.

In this section, absorption and bouncing of droplets, which depends on droplet size,

film thickness and droplet impact angle, has been compared to the findings of Pan and Law

(2007). Pan and Law (2007) observed that droplets above a certain size were likely to

coalesce onto a film surface while smaller droplets were unable to remove the air between

the film and themselves and hence rebounded instead. They also found that for a particular

Weber number range, the collision outcome was highly dependent on the film thickness.

Increase in the film height by a small value in this range led to a “triple reversal behaviour”

of the droplet as it contacted the film, ranging from absorption, bouncing, absorption and

bouncing again.

If a wall film is not present and a droplet collides with a wall surface, the result as

suggested by experimental visualisation may also be a splash. The presence of a wall film

provides a cushioning to the impacting droplet, absorbing the impact energy to avoid splash.

If splashing does occur, some of the droplet mass was observed to be retained by the liquid

film and only a part of the total volume of the impacting droplet disintegrated into smaller

droplets.


The collision outcome also depends on the impact angle of the droplet with the film or a

wall surface as droplets with similar Weber numbers were observed to get either rebounded or

absorbed into the film in different instances. Figure 5-22 shows high speed photographs of a

droplet rebounding from a demister vane outer wall. Ripples generated on the existing wall film

can be observed after the droplet made contact with the film. Droplets which rebounded from

vane surfaces due to a small impact angle got collected on the vessel wall above the demister

vanes due to a larger impact angle.

Figure 5-22. High speed photographs of a droplet (diameter 2.75 mm) rebounding from the

demister vane wall due to a small impingement angle.

5.2.3.2 Film stripping and separation

Wall film momentum is affected by both the gas shear and secondary phase coming in

contact with the film surface, resulting in film stripping or separation. Both these phenomena

were observed experimentally as well as simulated via the wall film model and the results have

been given in this section, together with the justification of the modelling assumptions and

explanation of the phenomenon. Consistent with the SSM observations, the predicted wall film

height was less than 1 mm on most of the scrubber wall surfaces (Figure 5-23).

Wall Droplet

Vane surface

Point of contact Rebounded droplet

Vane wall


(a) (b)

Figure 5-23 (a) Photograph showing the liquid film on a demister vane surface of the SSM

(b) Modelled liquid film on the demister vanes and wall of the SSM (image produced using

frontal face culling).

Film separation was observed to occur in various regions of the SSM including the

scrubbing and demisting vanes and the inlet wall. As detailed in the literature review, liquid

surface tension has little role in film separation, but it plays an important role in determining

the size of the resulting droplets as was seen via high speed photography in this project. The

film separation process can be divided into two stages i.e. the onset of separation and the

subsequent detachment of ligaments and droplets from the film. The film thickness reaches

a maximum as it flows to a surface edge and soon after the onset of separation due to film

inertia, the separated film experiences an increased shear force from the air flow. This shear

force increases further with the increasing film height and more of the wall film mass rises

above the gas boundary layer. This results in breaking up of the ligaments into droplets of

varying sizes which get re-entrained in the gas flow or fall under gravity depending on the

droplet size. A schematic of these phenomena has been presented in Figure 5-24.

Demister vanes Scrubber wall

Film thickness 0.00075 0.00068 0.00060 0.00053 0.00045 0.00038 0.00031 0.00023 0.00016 0.00008 0.00001

[m]


Figure 5-24. Schematic of steps involved in film separation and droplet re-entrainment.

In the CFD simulations, resultant droplet sizes were simulated to have an equal

diameter to the film thickness at the point of separation. Separation was allowed to occur if

the film Weber number exceeded 10 at the point of separation. This was based on

experimental observations in which droplet diameter was measured via comparison to the

inlet dimension (Figure 5-25). The film Weber number was then calculated using O’Rourke

and Amsdens (1996) hypothesis i.e. separated droplet diameter equals to the film height at

the point of separation. Note that the droplets appear as lines in the image due to a

comparatively low camera shutter speed (Figure 5-25).

Vane surface

Liquid film

Gas flow direction

Ligament growth Resultant droplets

On-set of separation

Gravitational force direction


(a) Head-on view (b) Side view

(c) Head-on view (d) Side view

Figure 5-25. (a) Predicted droplet distribution in the SSM looking in the direction of air flow

from the inlet duct. (b) Predicted droplet distribution in the SSM with the plane of view

rotated clockwise approximately 30° (looking from above) from that used in (a). Legends in

parts (a) and (b) show the modelled droplet diameters in m. (c) Actual droplet distribution

Scrubber inlet Separated droplets

Scrubber inlet

Separated droplets


in the SSM looking in the direction of air flow from the inlet duct. (d) Close-up view of the

droplets separating from the inside wall of the inlet duct.

Full scale scrubber simulation results

FSS simulations were made in a similar manner to the Eulerian-Lagrangian simulations

for the SSM, following the approach described in Section 3.4. To validate the predictions,

measured data available from a scrubber manufacturer for a similar scrubber design was used

(Table 5-1). Velocity distribution and pressure drop comparisons were made with the SSM

simulations with air as the inlet gas for the FSS.

Table 5-1. Centrifugal wet scrubber design data.

Property Numerical value

Inlet gas temperature 188 ˚C

Inlet gas density .698 𝑘𝑔

𝑚3⁄

Outlet gas temperature 75 ˚C

Outlet gas density .892 𝑘𝑔

𝑚3⁄

The simulated FSS had a gas inflow rate of 24.25 kg/s and a scrubbing liquid inflow rate of

15 kg/s. The gas entered the scrubber at a temperature of 185 ˚C and a water vapour mole fraction

of 0.35. The simulation results for the gas density and temperature are shown in Figure 5-26. The

gas outlet temperature was approximately 85 ˚C and the predicted average density at the outlet

was 0.8 𝑘𝑔

𝑚3⁄ . The dimension ratio for the FSS to the SSM is 9.18:1.


Figure 5-26. Predicted gas density (left) and gas temperature (right) across a vertical and a horizontal plane of the FSS.


The wall film model produced similar results to the observations made in the SSM. Edge separation

from the scrubbing vanes is shown in Figure 5-26. The direction of droplet travel is similar to that

observed in the SSM. This can help determine the ideal gas flow rate for a scrubber type to ensure

that the scrubbing liquid is allowed to flow down the vanes to restrict the water bath height above

the scrubbing vanes. In case the gas flow rate is too high, all of the resultant droplets will get re-

entrained in the gas flow and the water bath height will continue to rise, resulting in a higher droplet

carryover.


(a) (b)

Figure 5-27. Wall film separation (a) SSM experiments (Ali, Mann and Plaza, 2016), (b) FSS simulations.

Separated droplets

Scrubbing vane

Scrubbing vanes

Path of re-

entrained

droplet Separated droplets


6 CHAPTER 6: FLOW VISUALISATION

Water flowing over the lower edge of the distribution cone comes in contact with the

moving air and undergoes preliminary disintegration into multiple types of sub-structures

before breaking up into discrete droplets. The appearance and process of formation and

disintegration of liquid structures in the SSM are discussed in this chapter.

This liquid sheet disintegration was observed in the high frame rate images captured

during the experiments and resultant droplet properties were studied and used as inputs

for the CFD model. The visualisation results were also used to validate the secondary phase

CFD model predictions and to enhance the understanding of the liquid sheet breakup to

droplets process. The change in morphology of the liquid phase i.e. from a sheet to discrete

droplets makes the process difficult to numerically simulate and improved understanding

of the process is required to develop models for this purpose.

Images obtained from the high-speed camera were analysed in the software Motion

studio and Hi-Spec Control. An open source image analysis software (Tracker) was also

used for the image processing to calculate the droplet velocity vectors and liquid sheet

breakup times.

Section 6.1 focuses on the multimode sheet breakup phenomena (primary breakup),

Section 6.2 presents details of the sheet breakup time (secondary breakup) and section 6.3

presents the resulting droplet properties.

Multimode sheet breakup

Liquid sheet breakup behaviour is characterised by the Weber number (We), the Reynolds

number (Re) and the Ohnesorge number (Oh). Mayinger and Neumann (1978) suggested that


liquid entering a gas crossflow does not break up into droplets directly. Firstly, sheets with

lifetime in milliseconds are formed, enlarging constantly due to the gas shear, and ultimately

breaking down into droplets with varying sizes.

The water curtain flowing over the distribution cone in the SSM was observed to breakup

via a similar mechanism. It experienced a shear force from the air, leading to the formation of

multiple sub-structures including “umbrella-like” shapes and cylindrical ligaments and finally

disintegrating over a span of milliseconds to create child droplets with a range of sizes. In Figure

6-1 the development over time of a first and second generation wave-like structure is shown.

Initially, the sub-structure type was believed to depend on the air cross flow velocity, but

structures originating from the same point in the SSM and hence experiencing similar air

velocities randomly formed bags or ligaments. This suggests that the sheet span-wise length or

the turbulence intensity may also be important in determining the type of sub-structure formed.

Nonetheless, it was found that the probability of liquid bag formation in the SSM increased with

an increasing air velocity in comparison to the probability of cylindrical ligament formation.

Details of the sub-structures originating from the “flapping” sheet are presented in the following

sections of this chapter.


Figure 6-1. Macro-scale growth of liquid sheet in the SSM.

6.1.1 Liquid bag formation and breakup

Described as a miniature “jellyfish” by Sedarsky et al. (2010), liquid bags may form as a

result of multi-mode breakup. Bag growth then occurs due to the shear force exerted by the

carrier phase. Since the total liquid volume in the bag remains the same over its lifetime, bag

growth results in thinning of the bag surface, leading to bag burst.

As observed in the SSM, once a tear appeared on the bag surface, the tear diameter

increased and droplets were produced when the tear reached the bag’s edges or collided with

another tear. Bag burst also appeared to be insensitive to the bag span-wise length and bags

originating from the same point were seen to burst after reaching random stream-wise heights.

Growth of liquid sheet (wave-like structure)

Initiation of 2nd

sheet Growth of sheet

10 mm

0 ms 11.8 ms 16.4 ms 17.9 ms

20.4 ms 24.1 ms 27.9 ms 33.7 ms

42.3 ms 50.9 ms 63.0 ms 69.4 ms

Air flow direction


Guildenbecher & Lopez-Rivera (2009) noted that liquid bag breakup times were in the

order of milliseconds and spatial dimensions in the order of micrometres. Hence, due to the

extremely small spatial dimensions, no investigators had been able to measure the local flow

field around a liquid bag.

Instability waves forming on the surface are considered to be the main reason for bag burst

(Chapter 2). High-speed photography of bag burst from the SSM suggests that bag burst occurs

due to an air pressure difference between the two sides of the bag. A high-pressure zone forms

on the inside and a local low-pressure zone forms on the outside of the bag. This is evident from

the observation that just after bag burst occurs, the resultant droplets first accelerate in the

direction of bag growth at their parent positions before starting to accelerate in the general gas

flow direction (Figure 6-2). The initial direction of droplet motion was identifiable as shown in

Figure 6-2 by the droplet trails when the high speed camera was operated at a relatively lower

shutter speed.


Figure 6-2. Liquid bag growth and burst in the SSM (droplet trails represent the direction of travel.

Air flow direction

0 ms 0.3 ms 3.7 ms 4.1 ms

4.5 ms 4.8 ms 5.5 ms 7.2 ms

7.6 ms 7.9 ms 8.9 ms 9.6 ms

11.0 ms 13.4 ms 14.8 ms 15.2 ms

Droplet trails


6.1.2 Cylindrical ligament breakup

The second breakup type is the breakup of cylindrical ligaments into droplets. This results

in formation of comparatively larger droplets than those formed via bag burst. Stages of this

breakup type are shown in Figure 6-3. In the first part of the process, blobs form in the cylindrical

ligaments because of the natural tendency of a liquid to attain a spherical shape to minimise its

area. Once the blobs are formed, different segments of the ligament experience a different drag

force and accelerate at varying velocities, resulting in elongation of the connecting “strings”. A

string may pinch-off at multiple locations to produce several droplets or pinch-off at a single

location resulting in the formation of two cylindrical ligaments. Following this, the string

segment still in contact with the blob may break away as a discrete droplet (labelled ‘1’ in Figure

6-3) or coalesce with the parent blob to produce a larger droplet (labelled ‘2’ in Figure 6-3).

Figure 6-3. Cylindrical ligament breakup in the SSM.

A general case of multimode sheet breakup, which forms both types of structures

identified earlier, is shown in Figure 6-4.

Pinch-off locations 2 1

0 ms 8.0 ms 10.4 ms 16.2 ms 21.1 ms


Figure 6-4. Multimode sheet breakup in the SSM.

Cylindrical ligament Larger droplets

Bag

Bag growth

Bag burst

Smaller droplets

0 ms 2.53 ms 5.05 ms 7.58 ms

10.10 ms 12.63 ms 15.16 ms 17.68 ms

10 mm


Breakup time (real and dimensionless)

In this section, breakup time for liquid sheet disintegration measured in this project is

compared to that reported in the literature.

Pilch & Erdman (1987) define primary breakup time as the time when a coherent

droplet ceases to exist and total breakup as the time when all resultant droplets from the

primary breakup undergo no further breakup.

In the current work, bag burst was used to identify the end of the primary breakup.

Many of the resultant droplets from bag burst had a very small response time and quickly

accelerated to the air velocity, making the recognition of the end of the primary

disintegration process easier. In Figure 6-5 the breakup time is plotted against the air velocity

for liquid sheets of approximate span-wise thickness of 10 mm. The experiments were

repeated multiple times for different air flow rates and each measurement has been plotted.

The breakup time of planar liquid sheets from an air-blast nozzle are also shown for

comparison. The data was taken from the experiments of Park et al. (2004) and the

description of sheet breakup given by the authors resembles very closely to that observed in

the SSM experiments. Both the ‘y’ intercept and the gradient of the plots is very similar over

a wide range of air velocity, which may be due to similar breakup mechanisms (bag

formation, growth and burst). A sudden and significant increase in breakup time can be

expected if the We number is smaller than 12 and vibrational breakup is expected to occur.

The measured breakup time for the liquid sheet in the SSM experiments is higher in

comparison to the data of Park et al. (2004) due to a lower air velocity. Since the air velocity

could not be measured inside the SSM when it was run with water addition, velocity

predictions from the CFD modelling are used.


Figure 6-5. Liquid sheet breakup time (s) vs gas velocity (m/s).

In Figure 6-6, different stages of bag breakup from the SSM experiments are compared

with the images for similar experiments performed by Krzeczkowski (1980). Breakup times

are reported across the lifespan of the bag and the difference observed in the two sets of data

is small.


Figure 6-6. Stages of bag breakup for a single water drop (a) 𝑑𝑜=3.1 mm, We= 13.5

(Krzeczkowski 1980) and (b) 𝑑𝑜=4.0 mm, We= 13.78 (SSM experiments).

The breakup time (t) has been reported as a dimensionless time (T) by Pilch & Erdman

(1987) taking the form shown in equation 6.1.

𝑇 =

𝑡𝑉𝑟𝑒𝑙√𝜌𝑔

𝜌𝑙

𝑑0

(6.1)

where 𝑑0 is the initial diameter, 𝜌𝑙and 𝜌𝑔the liquid and gas density respectively and 𝑉𝑟𝑒𝑙 is the

relative velocity between the liquid and gas.

For Weber numbers greater than 350, Pilch & Erdman (1987) report a value of 1.25

for the dimensionless primary breakup time whereas the total dimensionless breakup time

0 ms 4.9 ms 14.2 ms 16.2 ms

17.6 ms 18.5 ms 20.5 ms 21.7 ms

0 ms 11.07 ms 21.48 ms 21.85 ms 27.83 ms

(a)

(b)


for Weber number between 12 and 18 is given by 6(𝑊𝑒 − 12)−0.25 and for Weber numbers

between 18 and 45 by 2.45(𝑊𝑒 − 12)0.25 (Pilch & Erdman 1987). Hsiang & Faeth (1992)

collected research data spanning more than 20 years for the total breakup time and found

that the ratio of the real time to dimensionless time varied very little over a large Weber

number range (approximately 10-106 ) at about 5. Any breakup at Weber number greater

than 350 is characterized as catastrophic breakup and the breakup regimes only change

within a relatively small range of the Weber numbers from 12 to 350 (Chapter 2). This may

be the reason behind the small variation in the real time to dimensionless time ratio over a

large Weber number range.

Smallest droplets which were frequently observed in the high speed photography, had

a diameter of approximately 0.1 mm while the largest droplets were up to 5 mm in diameter.

On the other hand, the calculated minimum droplet size based on the work of Pilch &

Erdman (1987) (Equation 6.2) was much larger than the minimum size observed in this

project, at approximately 2 to 3 mm for the range of air velocities.

d = Wec

σ

ρV 2 (6.2)

Pilch & Erdman's (1987) named this the maximum stable diameter i.e. diameter at

which an existing droplet will cease to undergo any further breakup. The smaller droplets

that existed in the SSM were a result of bag burst, which can result in formation of droplets

with sizes much smaller than the maximum stable diameter. In addition, the velocity of these

small droplets relative to the air is very small, so there is little driving force for vibrational

breakup of droplets as expected and vibrational breakup was not observed in the SSM

experiments. Hsiang & Faeth (1993) had similarly observed that even large child droplets

born as a result of secondary breakup did not breakup any further, even though the droplet

We satisfied the breakup criteria. This as well could be due to the droplets not being in the


region where they experience high shear forces, long enough for vibrational breakup to

occur.

Numerous definitions of drop breakup time are proposed, which is mainly because of

the difficulty in interpreting the experimental observations; for example, a drop in the bag

breakup regime may have already burst but still appear to be a coherent drop (Pilch &

Erdman's (1987).

Bag breakup times and measurements from the SSM experiments were used to produce

the plot of the dimensionless breakup time vs the Weber number as shown in Figure 6-7.

The dimensionless breakup time is very similar to that reported by Pilch and Erdman (1987)

but for a different breakup regime. Further experimentation is needed for the characterisation

of the dimensionless breakup times for a vast range of Weber numbers to cover all the

breakup regimes via the same experimental setup.

Figure 6-7. Plot of the dimensionless breakup time vs the Weber number from SSM

experiments.


Resulting droplet properties

6.3.1 Droplet shape and drag

Large droplets (>3 mm) were seen to oscillate and constantly change shape back and

forth from spherical to elliptical. This may be due to shear forces acting on the drop turning

it into an elliptical shape and the surface tension force attempting to resume a spherical

shape. The change in shape is shown for a droplet with diameter 4mm in Figure 6-8 across

a time span of 0.042 seconds.

Figure 6-8. Evolution of a 4 mm droplet shape after getting detached from a ligament across

a time span of 0.042 s (Frames displayed at equal time intervals).

According to Wang et al. (2016), shape deformation for drops with a diameter smaller

than 2 mm is small and can thus be ignored. However, in this project, high-speed

photography was able to capture droplet interface for droplets with a diameter as small as

0.6 mm and results show that even small droplets undergo a shape change.

Flow

direction


In the Figure 6-9 the shape change is evident for droplets with diameter 1.2 mm and

0.62 mm.

(a)

(b)

Figure 6-9. (a) 1.2 mm drop, (b) 0.62 mm drop (bottom droplet in the sequence).

Comparison of droplet velocity

Droplet velocity from experiments was calculated for selected droplets via analysis of

high-speed images and predicted velocities were compared for the multiphase model

validation. Distance travelled was approximated via comparison to dimensions of known

objects in the SSM. For the calculation, images in a single sequence, which had the

maximum time-period between them were selected to minimise the error, while making sure

that the droplet was in clear focus in both the first and last image. This ensured that any

distance travelled by the droplet towards or away from the camera, which could negatively

affect the velocity calculation, was not taken into account (assuming a small depth of field).

In Figure 6-10, this method has been highlighted by presenting the source images and the

equivalent CFD modelling results. Time between the presented images is 11.3 ms and

distance travelled by droplet approximately 23.7 mm, giving a velocity of 2.1 m/s. Predicted

velocity vectors for a droplet phase of equivalent diameter are also given.

1 2 3 4 5 6

1 2 3 4 5 6


Figure 6-10. (a) Initial and (b) final image of droplet positions captured by high-speed

photography. (c) Predicted velocity vectors for a 1000 µm diameter droplet. Images and

predictions are for the scrubber scale model with an average air inlet velocity of 6.2 m/s.

Summary

In this chapter, results and discussion of the SSM flow visualisation were presented.

Different modes of sheet breakup were identified and where available, the results were compared

to the literature. The agreement between the simulated and measured aspects of the multiphase

flow on both the large scale (general scrubbing liquid distribution) and the small scale (droplet

properties) was good.

(a) (b)

(c)

Water droplet X

y

Demister vane


7 CHAPTER 7: SCRUBBER PERFORMANCE AND

RECOMMENDED DESIGN CHANGES

This chapter details results for dust collection efficiency, using the user-defined

function developed for the dust particle-water droplet interaction. The first section (7.1)

presents the general aspects affecting collection efficiency and recommends design changes

to increase scrubber performance. The second section (7.2) presents the simulated

collection efficiency for different particle sizes and air/gas flow rates for both the SSM and

the FSS.

Factors affecting scrubbing efficiency in a centrifugal wet

scrubber

Scrubbing efficiency greatly depends on the carrier gas velocity, which in turn is

influenced by the reduction in the inlet cross-sectional area as shown in Chapter 4. Individual

velocity components i.e. in tangential and axial directions also play an important role in

determining the collection efficiency of a scrubber as they strongly influence the distribution

of the scrubbing liquid. Higher tangential velocity generated via a greater inlet convergence

angle improves the scrubbing efficiency but at a cost of a greater pressure drop and vice

versa. This increase in scrubbing efficiency is a result of

1) a greater centrifugal force on dust particles

2) a higher particle kinetic energy

3) smaller collecting droplet size (increases the total collection surface area)

4) More water droplets carried to scrubber walls to make a liquid film.


A greater axial velocity will reduce the scrubbing efficiency as it will result in a greater

scrubbing liquid carryover. Both experimental and simulation results showed that the

recommended changes should help improve centrifugal scrubber performance. In particular,

the breakwater in the bottom cone and the drainage slot in the scrubbing vanes were added

to a factory scrubber and improved scrubber performance was reported. The details of these

recommendations are given in the following sections.

7.1.1 Pressure plate

Not all of the scrubbing water seeping down the scrubbing vanes forms a wall film

in Zone B. A considerable amount falls vertically under gravity from within the low velocity

region of the gas in the middle of the scrubber. Ideally, the wall in zone B should be

completely covered with a liquid film for the optimum dust collection efficiency.

The scrubbing liquid can be redirected towards the high gas velocity zone, ensuring

that more scrubbing liquid makes it to the wall to form a wall film. To serve this purpose, a

‘pressure plate’ installed below the scrubbing vanes is usually used (Section 4.2.2). Contrary

to the common belief, it was found that installation of a pressure plate does not lead to an

increase in collection efficiency due to a gas velocity increase but rather due to a greater

amount of scrubbing liquid getting redirected to the scrubber wall in Zone B (Figure 7-1).

This also reduces the “black” carryover as the water bath above the scrubbing vanes carries

a lower dust content as a result of a greater dust particle percentage getting captured below

the scrubbing vanes.

Simulation results, which were presented in Figure 5-10 showed that the diameter of

the pressure plate should be chosen to be within the low gas velocity zone of the scrubber.

For the SSM Inlet type A, this was equal to a maximum of 0.23 m. Any further increase in

the diameter led to a sudden rise in the pressure drop (Figure 5-12).


(a) (b)

Figure 7-1. Water distribution in Zone B represented via contours of volume fraction greater

than 0.001 on a plane passing through the middle of the scrubber (a) with pressure plate, (b)

without pressure plate.

7.1.2 Water Bath

Although the water bath is essential for a high scrubbing efficiency, at high gas

flow rates, it can rise up to the demister vanes and cause an undesirable amount of carryover.

This effect was both observed in experiments as well as simulated. Figure 7-2 compares the

combined volume fraction of three Eulerian phases representing water droplets with

diameter 0.0001, 0.0005 and 0.001 meters at two different air flow rates. Increased height of

water bath can be observed at higher air flow rate through the SSM, while figure 7-3 shows

excessive carryover as observed in the SSM. Although the demister vanes have drains to

stop the accumulation of water around the vanes, at high gas velocities the scrubbing liquid

is not able to drain efficiently, resulting in a water level rise around the demisting vanes

Scrubbing vanes

Pressure plate


(Figure 7-3). As this accumulated water level rises, it may get re-entrained into the gas flow

and escape from the scrubber.

Figure 7-2. Predicted volume fraction of water above the scrubbing vanes representing the

extent of the water bath at air mass flow rate of (a) 0.25 kg/s and (b) 0.32 kg/s

(a)

(b)


Figure 7-3. Water accumulation around demister vanes at high gas flow rates.

To overcome this, the distance between scrubbing and demister vanes can be

increased, making it less likely for the water bath to extend past the demisting vanes at high

gas flow rates. Figure 7-4 shows the simulated volume fraction across a plane passing

through the middle of the scrubber model for the original design and with the increased

distance between the vanes. The predicted volume fraction of droplets above the raised

demister vanes is significantly lower than that in the original position, suggesting a reduction

in droplet carryover with the new design.

A further reduction in the carryover can be achieved by installation of a breakwater

between the vanes (Figure 7-5). This restricts the height of the water bath and stops it from

reaching the demister vanes and thereby greatly reducing the carryover at high air flow rates.

A vertical breakwater between the scrubbing and demisting vanes was also simulated and

produced promising results (Figure 7-6).

Figure 7-7 shows the iso-volume surfaces of the combined Eulerian liquid phases.

Increased scrubbing liquid volume can be observed on the break-water side of the scrubber

body. The suggested location for this vertical breakwater is on the scrubber wall opposite to

the flue gas inlet. This will ensure that the scrubbing liquid draining down from the scrubbing

Demister Vanes

Water accumulation


vanes in this region comes in direct contact with the flue gas and will be helpful to limit the

height of the water bath in case of high inlet gas loading. In scrubbing vessels with a

significant droplet carryover, a drainage hole directly below the vertical breakwater will also

aid in reducing the droplet carryover. The vertical breakwater was not modelled

quantitatively due to mixed feedback received from factories regarding this particular

proposed design change. A factory scrubber experiencing significant droplet carryover was

modified to include the drainage hole and droplet carryover was reportedly reduced.

Droplet carryover was the greatest for droplet sizes smaller than 100 microns and

negligible for droplet sizes greater than 500 microns. CFD results predicted approximately

20% of the Eulerian phase (water) with a diameter of 100 microns to escape as carryover

when the inlet air mass flow rate was 0.287 kg/s. This was reduced to 13.4% when a

breakwater annulus was installed between the vanes located at a height of 0.075 m above the

edge of the water distribution cone. Increasing the total height between the two sets of vanes

to 0.32 m and installation of a breakwater annulus at 0.12 m above the edge of the water

distribution cone further reduced the carryover to only 2.38% for droplet phase of diameter

100 microns.


Figure 7-4. Predicted contours of volume fraction of 550 µm droplets in the scrubber scale model with the existing spacing between the scrubbing

and demisting vanes on the left and the proposed raised demisting vanes on the right.


Figure 7-5. Water distribution in SSM before and after addition of the breakwater and modelling results for the later.

Breakwater

annulus


Figure 7-6. Iso-metric view a scrubber body with the suggested position of a vertical

breakwater.

Figure 7-7. Top view of the iso-volume surfaces of the combined Eulerian liquid phases.

7.1.3 Bottom cone breakwater

High gas velocities also cause increased swirling of water in the bottom cone causing

the level of scrubbing liquid in Zone A to rise. In a real scrubber, water in the bottom cone

carries a significant quantity of captured dust. Water accumulation in Zone A has a risk of

Inlet

Scrubbing

vanes

Demister

vanes

Vertical

breakwater

Increased drainage

Vertical breakwater


splashing into the gas inlet where it can initiate dust accumulation (Figure 7-9). This effect

is also observable from the contours of film thickness changing with time in the attached

movie, “Wall film flow.mp4”.

The SSM was modified by installing a breakwater in the bottom cone that stopped

scrubbing liquid swirl in this region and enhanced drainage through the water outlet. Figure

7-8 shows the SSM operating with and without the breakwater and the breakwater successful

in preventing build-up of water.

Deposit build-up is a severe problem for industries, as it results in a higher pressure

drop and a greater carryover due to an increased gas speed. For an air flow rate of 0.287 kg/s,

deposit build-up was simulated by modifying the inlet of the SSM to include a solid surface

restricting the air flow, which resulted in an increase in pressure drop of more than 200 Pa

from the case with no deposit build-up. The peak gas velocities reached more than 20 m/s

(Figure 7-10) whereas those for the inlet conditions without build-up were 16 m/s (Figure

5-9).

(a) (b)

Figure 7-8. Scrubber scale model operating a) without breakwater plate b) with breakwater

plate.

Breakwater plate


Figure 7-9. Dust deposit build-up at inlet of a factory scrubber.

Figure 7-10. Contours of velocity magnitude after deposit build-up inside the SSM.

The initiation of deposit build-up inside the scrubber inlet is also caused by the seepage

of the wall film from within the gas boundary layer and into the scrubber inlet. The seepage

can be restricted by installing lips on the scrubber inlet wall. CFD predictions were made on

a test geometry with input conditions similar to those in and around the inlet of a centrifugal

wet scrubber. Two cases were simulated, case ‘A’ without the inlet ‘lips’ and case ‘B’ with

Deposit build-up


the inlet ‘lips’. The two geometries are shown in Figure 7-11 and the corresponding contours

of film thickness in Figure 7-12. Water droplets with a diameter of 5 mm were introduced

from each cell on the face labelled ‘Inlet 2’ with velocity in the negative ‘Z’ direction and a

smaller component in the negative ‘X’ direction. Air velocity in inlet ‘1’ was 7 m/s whereas

that in inlet ‘2’ was 9 m/s. Steady state simulations were performed to achieve a converged

solution followed by a transient run, till the area-weighted average of the wall film mass

became constant. Film seepage into Inlet ‘1’ can be observed from the predictions which

correspond to the formation of build-up in the inlet. A greater film thickness can also be seen

above the top wall of the inlet in case ‘b’ (with lips), which may result in a deposit build-up

above the wall. However, this will not result in a greater pressure drop, unlike the dust

accumulation in the inlet of a real scrubber since the lip height is small and hence still more

favourable.

(a) (b)

Figure 7-11. Test geometry (a) without lips and (b) with lips.

Inlet 1 Inlet 1

Inlet 2 Inlet 2

Outlet Outlet

Lips


(a) (b)

Figure 7-12. Contours of film thickness.

Collection efficiency simulation results

Collection efficiency of wet dust collection devices has been reported using two methods

in the literature, namely the grade efficiency and the overall collection efficiency. The grade

efficiency is defined as the collection efficiency of a specific dust particle size entering the

collection device, whereas the overall collection efficiency is the ratio of the particle mass

successfully removed by the collection device to the dust mass entering the device. Both the

grade efficiency and the overall scrubbing efficiency were simulated for the SSM at a range of

air flow rates and the grade efficiency for the FSS at a gas flow rate of 24.25 kg/s. For the mean

dust particle size, a scrubbing efficiency of more than 99% is reached in the SSM predictions

and for the FSS simulation, the efficiency is approximately 97.5% for the mean particle size

which corresponds to the lower end of the range as reported by scrubber operators. The reduced

simulated efficiency in the FSS simulation may be due to the inlet convergence angle, since the

collection efficiency results for the SSM are for inlet type A (higher tangential velocity), whereas

those for the FSS are simulated with inlet type B (lower tangential velocity). Note that the real

FSS has an inlet type B while inlet type A was additionally used in the SSM experiments and

simulations for a comparison of the effects due to this change.

Film seepage


Simulations for collection efficiency reported in the literature are found lacking on two

fronts. Firstly, the dust collection efficiency may be over-estimated if the effect of droplet

carryover is not taken into account. Droplet carryover can be significant in certain situations, as

discussed earlier, and greatly reduce the overall collection efficiency of a wet collector. The

prediction results presented here also take the reduction in the dust collection efficiency due to

droplet carryover into account. Secondly, since collection efficiency is dependent on the velocity

of the dust particle, merely reporting the grade efficiency does not provide enough information

to evaluate a scrubber design and the overall collection efficiency needs to be reported for

varying mass flow rates of the carrier phase.

Dust particle tracks in Figure 7-13 show the dependence of the collection efficiency on

the particle size. Total number of particle tracks displayed were limited to 25 to aid visibility.

Most of the dust particles get collected below the scrubbing vanes and a majority of the

remaining particles in the water bath.

Figure 7-14 compares the simulated grade efficiency to those from the literature,

including those from Pak & Chang (2006), Haler et al. (1989) and a commercial two-stage

scrubber manufactured by Sly INC (2009). To account for effects of droplet carryover, a

percentage of particles, simulated to be captured and equal to the percentage of droplet carryover,

were assumed to have escaped collection.

The overall collection efficiency at different air flow rates is given in Figure 7-15.

Reduction in the overall collection efficiency at high carrier phase mass flow rate due to

excessive water droplet carryover can also be observed, which corresponds to the “black-rain”

reported by scrubber users. The collection efficiency reaches a maximum at an air flow rate of

approximately 0.28 kg/s and starts to decline after an air flow rate of 0.30 kg/s is reached. In

Figure 7-16, the simulation results for the grade efficiency of the FSS are presented. For dust

particle diameter of 15 microns, a collection efficiency of approximately 99% was simulated.


This trend will continue to increase for particles which are even greater in diameter (>15

microns) and is in agreement to manufacturer claims of “up to 99%” collection efficiency and

personal communication with factory staff stating the collection of “virtually all dust particles”.

The low collection efficiency of the extremely small dust particles in the size distribution

simulated is also analogous to factory staff communicating the poor collection efficiency of soot

particles.


Figure 7-13. Predicted dust particle tracks in FSS (limited to 25 tracks to aid visibility).

15 microns

10 microns

5 microns


Figure 7-14. Simulated grade efficiency comparison with published data.


Figure 7-15. Simulated collection efficiency and droplet carryover vs the mass flow rate of

carrier phase in the SSM.


Figure 7-16. Simulated grade efficiency comparison for with and without accounting for droplet

carryover in the FSS.

Simulation (no carryover) Simulation (with carryover) Simulation (no carryover) Simulation (with carryover)

Gra

de

eff

icie

ncy

%

Particle diameter (micro meters)


8 CHAPTER 8: SUMMARY, CONCLUSIONS AND

RECOMMENDATIONS

Chapter summaries

Chapter 1 presented a brief introduction of dust collection devices, the context of the study,

the aims and objectives of the project and gave an insight to the thesis layout. A background to

the use of dust collection devices was given and the factors influencing the type of scrubber

employed were presented. The increasing use of CFD to simulate industrial flows was also

highlighted, while the scarcity of experimental data for centrifugal type wet scrubbers was

identified as the leading cause for no CFD research having been conducted for this scrubber

type.

Chapter 2 presented the literature review conducted for the project. It was divided into

three main sections, consisting of a section presenting the empirical relations developed for dust

collection devices, a section for the application of CFD to dust collection devices and a section

dedicated to the literature on the flow visualisation aspect of the current project. Inertial

impaction was found to be reported as the most dominant mechanism of dust collection. The use

of CFD models for both single and multiphase flow applied to dust collection device modelling

was also presented. The leading methods for multiphase modelling were found to be the

Eulerian-Eulerian and Eulerian-Lagrangian approaches, while further progress is required for

other models, such as the population balance model before they can be utilised to their full

potential. A review of the experimental work conducted on the breakup of liquid jets in a two-

stage process, namely, the primary breakup and the secondary breakup, was also carried out.

There is much research on the subject but the scope was observed to be limited to the


experimental study and simulation of small-scale geometries or single atomising sheets and jets.

Furthermore, no models were found to simulate the change in morphology of a liquid sheet to

droplets. The VOF method has been used for this purpose but it is an extremely expensive

approach and not suitable for industrial scale simulations.

Chapter 3 outlined the methodology adopted to undertake the project. The setup of the

test rig, which was the backbone of the experimental program, was discussed and the methods

to perform velocity and pressure measurements across the SSM were described. Two high speed

cameras were used in this project and their specifications were also introduced in this chapter. A

chart summarising the steps for the test rig operation was also presented in this section. It also

included details of the CFD model setup. A hybrid mesh was employed, which utilised both

tetrahedral and hexahedral elements and the Y+ value throughout the flow domain was adapted

to suit the turbulence model in use for the particular simulation. The user-defined function to

account for the dust particle-water droplet interaction was also introduced in this section.

Chapter 4 detailed the results for the experimental part of the project. Velocity

measurements across various traverses were given. Similarly, pressure drop measurements were

also detailed for the various air flow rates across the SSM. This was done for both the original

and the modified designs. As expected, the pressure drop across the SSM increased with

increasing air mass flow rate. The reason for increased scrubbing efficiency via the use of a

pressure plate was found to be actually different from the common perception. A separate section

introduced the entrained scrubbing liquid, giving rise to the formation of a water bath above the

scrubbing vanes. The total mass of entrained water inside the SSM was plotted against different

air mass flow rates. It showed that there exists a peak velocity, above which centrifugal wet

scrubbers should not be operated as this results in a significant carryover and hence a loss of

collection efficiency.


Chapter 5 was dedicated to the CFD simulation results. It presented the single and multi-

phase modelling results in different sections of a centrifugal wet scrubber along with a

comparison to experimental measurements and observations from the test rig SSM. The liquid

wall film’s role to reach the optimum collection efficiency was also highlighted. The general

scrubbing liquid distribution simulated via the Eulerian-Eulerian and the Eulerian-Lagrangian

approaches was compared to the experiments. Both the models gave good agreement with the

experiments, while the Eulerian-Lagrangian method had the added advantage of providing

greater detail of the droplet diameter characteristics for post-processing. The results of the new

CFD sub-model as described in Chapter 3 were also given in this chapter, giving the volume

fraction of the discrete phase in each computational cell with much less computational expense

than the Eulerian-Eulerian method.

Chapter 6 presented the procedure and the findings of the flow visualisation performed via

high speed photography. The observations made via the analysis of the high speed photography

were given and the different modes of liquid sheet breakup were discussed. The findings agreed

with the available literature. Additionally, it was observed that droplets created from bag burst

initially moved in the direction of bag growth rather than the continuous phase flow direction,

suggesting the presence of a local high pressure zone on the inside of the bag. The dimensionless

breakup time for liquid sheets was found to be between the values suggested in the literature,

which may be due to the difference in the definition of breakup i.e. primary (initial) breakup or

total (complete) breakup.

In Chapter 7, the findings of the project for the improvement of scrubber performance,

together with the simulation results for the dust collection efficiency were presented. The

proposed design modifications for improved scrubber performance included the installation of a

breakwater annulus between the demister and scrubbing vanes, raising the height of the demister

vanes, a vertical breakwater above the breakwater annulus and a vertical breakwater in the


bottom cone. The predicted carryover of 100 micron droplets in the scrubber reduced from 20%

to 2.38% with the implementation of the suggested design changes. A major problem

experienced in centrifugal wet scrubber use is dust accumulation in the gas inlet, which increases

the pressure drop and droplet carryover. Potential solutions for this problem include the vertical

breakwater in the bottom cone and the installation of ‘lips’ on the inlet walls. The effect of

droplet carryover on the collection efficiency of a wet collecting device was highlighted and

accounted for in the collection efficiency predictions. Ignoring the effects of droplet carryover

results in an over prediction of the collection efficiency and this has not been taken into account

in the literature to date. The suggested changes to limit droplet carryover will also increase the

collection efficiency of the wet scrubber at high gas flow rates.

Milestones criteria and completion dates

The overall objective of this project was to suggest design improvements for fixed vane

centrifugal wet scrubbers, which was successfully achieved. To ensure a timely completion of

the project, several milestones were set in the project plan. In Table 8-1 the achievement criteria

together with the completion date for these milestones are given.

Table 8-1. Project milestone description and achievement dates.

Milestone # Description Date achieved

1 Project started 5/02/2014

2 Complete construction test rig 1/08/2014

3

Upgrade of relevant CFD model

Travel to mill sites for wet scrubber inspection

31/03/2015


4

Complete initial modelling

Complete initial measurements on test rig

1/08/2015

5

Suggest and model design modifications

Communicate results with factory staff and

scrubber manufacturer for comment

Attend and present a paper at the Australian

Society of Sugar Cane Technologists (ASSCT)

conference

1/06/2016

6 Incorporate feedback into further CFD

modelling

1/03/2016

7 Completion of doctoral program 1/06/2016

Project achievements

In this project, an extensive experimental program was planned and carried out, which

resulted in an improved understanding of the flow hydrodynamics inside a centrifugal wet

scrubber. Although CFD modelling is increasingly being used by researchers to simulate

industrial processes, experimentation remains crucial and cannot be disregarded.

At present, modelling approaches always include unavoidable assumptions to simplify the

flow problems. These assumptions may or may not have a significant effect on predictions and

direct comparison to experimental data is extremely important to validate the CFD techniques

which are employed and/or for further improvement of the available CFD models.


The literature review showed that CFD-based research on collection devices mostly makes

comparisons to experimental findings of other researchers, in which case the geometries under

study or the flow parameters are not always similar. This may result in overlooking important

flow aspects for a particular process. The simulations and experiments in this project were both

conducted on an exactly similar geometry. Following the CFD model validation, corrective

measures to overcome problems faced in the centrifugal wet scrubber operation were proposed.

The validated CFD model was then used to assess these corrective measures, which were then

implemented to the test rig SSM. This intensive testing of the CFD models helped gain an

improved understanding of the application of the various CFD modelling techniques to

multiphase flows.

One such example of a flow aspect, which has been previously overlooked, is droplet

carryover. It was found that none of the research conducted on wet collectors took the negative

effects of this phenomenon into account. In this project, for the first time, the effects of droplet

carryover on the collection efficiency were considered in the CFD modelling. It was found that

demisting devices have a peak performance at a specific gas flow rate and a further increase in

gas velocity can result in a significant deterioration of the demisting device performance.

Contrary to the common belief of “higher velocity-higher collection efficiency”, numerical

predictions showed that the collection efficiency declines after reaching a maximum if gas

velocity continues to increase, this being the result of increased droplet carryover.

This was also the first time that all the aspects of the multiphase flow inside a centrifugal

wet scrubber were simulated. Venturi scrubbers and dust cyclones have been studied before but

no experimental or modelling work was found on centrifugal wet scrubbers. Furthermore, no

previous research can be considered to have taken all the aspects of flow inside a wet dust

collector into account.


A major shift observed in the literature was that research focus on emission control devices

moved from experiments to CFD in the past two decades. A significant improvement to the

methods of engineering data collection during this period was the availability of high speed

photography, which experienced a two-fold increase in performance. However, research on dust

emission devices did not benefit from this development. High speed photography has only been

used to study the breakup of a single liquid sheet or jet and the application of this approach to a

large-scale problem had not been exploited previously.

The combination of the experimental and numerical approach undertaken in this project

allowed greater confidence in implementing the changes to factory scrubbers. The project was

successfully able to achieve its goals and positive feedback was received from the factories that

implemented the suggested changes to their centrifugal wet scrubber units.

A CFD tool was developed by the project, and can now be applied, with the capability to

re-design differing versions of centrifugal wet scrubbers, which will improve carry over

performance while still achieving the other required key performance parameters such as

pressure loss and high dust removal. On the whole, the project scheme demonstrates a method

applicable to a wide range of fluid flow problems. A summary of the project achievements and

the significance of contribution is listed in Figure 8-1.


Centrifugal scrubber test rig constructed.

Velocity and pressure measurements made.

Visualisation of the general scrubbing liquid distribution was done.

Detailed study of liquid sheet breakup conducted.

Study of the liquid wall film separation conducted.

Recognized water accumulation in the bottom cone which leads to

overflow in scrubbers.

Helped visualise film seepage as the leading cause for dust

accumulation in the scrubber inlet.

Helped recognize the formation of a water bath above the scrubbing vanes.

Helped identify the mechanisms leading to a high droplet

carryover.

A CFD tool was developed.

First time when all the multiphase flow aspects inside a centrifugal wet

scrubber were modelled.

Both single phase and multiphase simulations were performed.

Turbulence models were tested and validated.

Multiphase modelling techniques were tested and validated.

Film separation was modelled and validated.

Liquid droplet breakup was modelled.

Innovative steps were taken for reduced computational expense whilst

maintaining solution accuracy.

Numerical codes developed for:

Small droplet evaporation and deletion from the flow domain.

Conversion of Lagrangian droplets to represent the volume

fraction of the secondary phase in a computational cell.

Dust particle-water droplet interaction.

Dust particle-wall film interaction.

Significance to industry

Design changes for improved performance were suggested:

Suggestions for maximum pressure plate diameter.

Methods to restrict the water bath height for a decreased droplet carryover.

Vertical breakwater, Horizontal breakwater, Raised demister vanes.

Bottom cone breakwater for enhance drainage.

Lips on inlet surfaces to reduce film seepage into the inlet.

Experimental program Numerical program

Figure 8-1. List of project achievements in terms of both the contribution to knowledge and the industrial significance.


Future work and recommendations

This section includes suggestions and scope for future work using experiments and CFD,

in order to further improve the physical understanding of the mechanisms occurring inside a

centrifugal wet scrubber, and provide further improvements to design.

For the experimental aspect, the use of advanced velocity measurement equipment such

as Laser Doppler Anemometry and Particle Image Velocimetry is suggested. This will help

determine velocity profile in the scrubber with greater precision than that obtained using the

current method.

Experiments should also be performed to measure the collection efficiency of the scrubber

by introducing dust particles and designing a mechanism to measure the percentage of the

particles captured or escaped. The current test rig setup lacked this capability.

In the current project, only a single vane orientation and angle were used in the test rig.

However, a feasibility study of 3-D printing vanes with different angles was carried out. The

testing of such geometries should help ascertain the effect of vane angle on the performance of

the scrubber.

The above may also be simulated along with the combinations of different inlet

convergence angles and distance between the vanes to determine the best combination. The

approaches used and/or developed in this project may be extended to the analysis of venturi

scrubbers and spray towers to include the effect of droplet carryover on the scrubber

performance, in order to attain better estimates of the collection efficiency. It is suggested that

Eulerian modelling for multiphase flow be only carried out when the Eulerian phase size does

not span over a significant size range. This is because, for improved accuracy, a greater number

of separate Eulerian phases will be required, which will significantly increase the computational

cost.


Finally, although several methods are available to model sheet breakup, they lack

extensive validation against experimental results. The change in morphology of a sheet to

droplets, in particular, requires further research, in order to develop improved models. Similarly,

models for liquid wall film are available, but there remains a need to develop a model using

experimental data of a wider range of parameters that affect the wall film thickness and

behaviour.


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© 2017 Ali, Hassan Page 190

10 CHAPTER 10: APPENDICES

Appendix 1: Flow rate calculations

The gas flow rate in the vertical scrubber being considered is approximately 24.25 kg/s. This

gives a Reynolds number of 490000 in the free stream region.

Re =ρvl

μ=

m (d)

μ

Rep =24.25 × 3.607

(1.8035)2 × π × 1.741 × 10−5

𝐑𝐞𝐩 = 𝟒𝟗𝟏𝟔𝟕𝟒

Corresponding air speed in free stream region of SSM for an equivalent Re is as below:

Re =ρvl

μ

491674 =(1.1839)v(0.2)

1.86 × 10−5

𝐯 = 𝟑𝟖. 𝟔𝟒 𝐦/𝐬

This speed was unrealistic to be achieved inside the test rig under the available

resources. Hence, it was decided to progress further using the actual speed of the gas inside

the wet scrubber as the free stream speed to be attained inside the test rig. The primary phase

in the test rig is air and a velocity of 2.03 m/s in the free stream region was calculated.

m = ρAV

24.25 = (1.17)(A)(V)

𝐕 = 𝟐. 𝟎𝟑 𝐦/𝐬

The flow speed at the inlet of the test rig is thus:

V1A1 = V2A2


V1 =2.03×π×0.196×0.196

0.3546×0.11

𝐕𝟏 = 𝟔. 𝟑 𝐦/𝐬

m = ρAV

m = (1.17)(0.039)(6.3)

�� = 𝟎. 𝟐𝟖𝟕 𝐤𝐠/𝐬

where A1is the area of the inlet

A2 is the cross section area of the scrubber cylindrical body


Appendix 2: Air velocity distribution

Average inlet velocity= Measured velocity at inlet centre/R

where R=Mass flow rate from velocity at the inlet centre/Average mass flow rate of inlet

divided into 3 sections as shown in Figure A.1

Figure A. 1. Measured velocity in the SSM inlet at a fan speed of 40 Hz (not drawn to scale).

Area of rectangle A = 0.055 × 0.178= 0.00979

Area of rectangle B = (0.0825 × 0.264) - 0.00979= 0.01199

Area of rectangle C = (0.355 × 0.11) - 0.02178= 0.01738

Average mass flow rate = (0.00979 × 1.18 × 9) + (0.01199 × 1.18 × 7.6) + (0.01738 ×

1.18 × 6.025) = 0.335

Mass flow rate from velocity at the inlet centre = 1.18 × 0.0395 × 9 = 0.414

3.0

6.6

7.0

7.5

7.5

9.0

9.0

9.0

8.5

8.0

7.5 7.5.5.5

7.0

3mm hole for probe insertion

Probing direction

0.11 m

0.3

56

m

A B

C


R = 0.414 / 0.335 = 1.23

Figure A. 2. Measured velocity at centrifugal fan outlet in m/s at a fan speed of 40 Hz.

10.0

10.0

10.5

11.5

12.0

8.0

8.0

9.0

9.3

10.0

10.5

12.0

13.0

4.3

3.2

2.7

2.5

3.0

3.9

5.5

7.0

8.0

9.0

10.3

11.6

2.0

3.0

4.0

10.0

3.0

4.0

5.7

8.0

Fan outlet Fan outlet

Towards motor and test rig

0.2

4

2 m


Appendix 3: Transport equations for turbulence models

Standard k−𝜺 model

In the standard k−𝜀 model the turbulence kinetic energy (k) and the turbulence dissipation

rate (𝜀) are calculated from the following equations.

where and represent the generation of the turbulence kinetic energy due to the mean

velocity gradients and buoyancy respectively, represents is a term representing the effects of

compressibility on turbulence, and are the turbulent Prandtl numbers and 𝐶1, 𝐶2 and

𝐶3 are the model constants.

Reynolds Stress Model

The RSM solves equations for each of the Reynolds stresses and an equation for the

dissipation rate. The exact form of the Reynolds stresses transport equation is as follows:

.


Appendix 4: Contours of Y+

Figure 10-1. Y+ for the SSM, scale-able wall function was used.


Figure 10-2. Y+ values for the FSS, enhanced wall treatment was used.


Appendix 5: List and description of attached video films

Video name ‘Gas flow pattern.mp4’

Shows the predicted motion of a gas molecule after it enters the scrubbing vessel.

Video name ‘SSM.mp4’

Shows the real liquid distribution in the scrubber scaled model at 500 frames per second.

Video name ‘Water droplet flow.mp4’

Shows the predicted scrubbing liquid flow and distribution forming the liquid wall film

shown in the video ‘Wall-film flow.mp4’.

Video name ‘Wall-film flow.mp4’

Shows the predicted time-dependent formation of the liquid wall film for the simulated

droplet flow in the video ‘Water droplet flow.mp4’.


Appendix 6: User Defined Functions

The body force macro has been used to access variables for particle collection since most

of the required data was already available with this macro. Note, that if a particle is not collected

it is assumed to instantaneously accelerate to the gas velocity. UDFs for both the Eulerian and

Lagrangian approaches have been combined and libraries are not repeated for brevity.

#include "udf.h"

#include "dll.h"

#include "random.h"

#include "time.h"

#define water_surf_tension 0.072

#define droplet_sauter_mean 0.001

#define mohebbi_r -0.245

DEFINE_DPM_BODY_FORCE(name,particle_data,i)

{

int x;

float r=0;

float RandCollisionNumber=0;

real relvelxwd=0;

real relvelywd=0;

#if RP_3D

real relvelzwd=0;

#endif

real vmag=0;

real kinectic_energy_on_sigma=0;

real criticalarea=0;

real stokes_number=0;

real stokestopower=0;

real impaction_power=0;

real collection_efficiency=0;

real bforce=0;

real VELPHASEX=0;

real vn=0;

real normal[3];

real ChanceOfCollision=0;

Thread *t=P_CELL_THREAD(particle_data); /*mixture level thread*/

cell_t c=P_CELL(particle_data); /*particle data cell*/

Thread **phase_water;

phase_water=THREAD_SUB_THREADS(t);


set_random_seed(time(NULL));

{

ChanceOfCollision=C_UDMI(c,t,0);

Or for Eulerian approach

ChanceOfCollision=C_VOF(c,phase_water[1])+C_VOF(c,phase_water[2])+C_

VOF(c,phase_water[3]);

RandCollisionNumber=uniform_random();

/*Message0("Randcoll=%.10f %10f \n",ChanceOfCollision,RandCollisionNumber);*/

if (ChanceOfCollision>=RandCollisionNumber)

{

kinectic_energy_on_sigma= ((P_MASS(particle_data) * vmag*vmag)/ 2)

/0.072;

criticalarea= M_PI * P_DIAM(particle_data)*P_DIAM(particle_data) /

2;

if (kinectic_energy_on_sigma >= criticalarea)

{

/* Message0("Particle was collected %f %f \n", collection_efficiency,r);*/

MARK_PARTICLE(particle_data, P_FL_REMOVED);

return PATH_ABORT;

}

else

{

return 0;

vn = NV_DOT(P_VEL(particle_data), normal);

NV_VS(P_VEL(particle_data), -=, normal, *, vn * 2.);

NV_V(P_VEL0(particle_data), =, P_VEL(particle_data));

}

/* Message0("Particle did not collide, collision probability was smaller than random

number %f %f",ChanceOfCollision,RandCollisionNumber);*/

return 0;

}

}

}


Form two of collection efficiency UDF tested for the Eulerian-Eulerian approach is given

below; the main body of the UDF is the same as that used for the Eulerian-Lagrangian approach

and the following lines of code can replace those for Lagrangian particles in the previous UDF.

for (x=1; x<4; x++)

/*x<4 is one less than the number of secondary phases in the simulation*/

/*find random number and chance of collision*/

{

TotalVolumeOfDropletsInCell=C_VOF(c,phase_water[x])*C_VOLUME(c,t);

NumberOfDroplets=TotalVolumeOfDropletsInCell/((M_PI*pow(C_PHASE_

DIAMETER(c,phase_water[x]),3))/6);

VELPHASEX=sqrt(C_U(c,phase_water[x])*C_U(c,phase_water[x])+C_V(c,p

hase_water[x])*C_V(c,phase_water[x])+C_W(c,phase_water[x])*C_W(c,phas

e_water[x]));

VolumeSweptByX=((M_PI*pow(C_PHASE_DIAMETER(c,phase_water[x]),2

))/4)*NumberOfDroplets*VELPHASEX*P_DT(particle_data);

ChanceOfCollision=VolumeSweptByX/C_VOLUME(c,t);

RandCollisionNumber=uniform_random();

if (ChanceOfCollision>=RandCollisionNumber)

{

/* Message0("Particle collided, check if it got collected %f %f \n",

ChanceOfCollision,RandCollisionNumber);*/

vmag=NV_MAG(P_VEL(particle_data));

stokes_number=(P_RHO(particle_data)*P_DIAM(particle_data)*

P_DIAM(particle_data)*vmag)/(18*C_MU_L(c,phase_water[x])*

droplet_sauter_mean);

impaction_power=0.759*pow(stokes_number,mohebbi_r);

stokestopower=stokes_number/(stokes_number+1);

collection_efficiency=pow(stokestopower,impaction_power);

}

}


UDF for particle collection on the wall:

DEFINE_DPM_BC(dust_collection_on_wall, particle_data, thread_face, f_index,

f_normal, dim)

{

real vmag=0;

real vn=0;

real normal[3];

real kinectic_energy_on_sigma;

real criticalarea;

vmag = NV_MAG(P_VEL(particle_data));

kinectic_energy_on_sigma= ((P_MASS(particle_data) * vmag*vmag)/ 2) /0.072;

criticalarea= M_PI * P_DIAM(particle_data)*P_DIAM(particle_data) / 2;

NV_V(normal, =, f_normal);

if (kinectic_energy_on_sigma >= criticalarea)

{

MARK_PARTICLE(particle_data, P_FL_REMOVED);

return PATH_ABORT;

}

else

{

Reflect_Particle(particle_data, f_normal, dim, f_index, thread_face,

thread_face, f_index);

return PATH_ACTIVE;

vn = NV_DOT(P_VEL(particle_data), normal);

NV_VS(P_VEL(particle_data), -=, normal, *, vn * 2.);

NV_V(P_VEL0(particle_data), =, P_VEL(particle_data));

}

return PATH_ACTIVE;

}


.

UDF to get volume fraction of discrete phase in a cell:

DEFINE_EXECUTE_AT_END(get_dpm_VF)

{

Injection *I;

Injection *dpm_injections = Get_dpm_injections();

Particle *p;

int counter = 0;

Domain *d = Get_Domain(1);

cell_t c;

Thread *t;

thread_loop_c(t, d)

{

begin_c_loop(c, t)

{

C_UDMI(c, t, 0) = 0;

}

end_c_loop(c, t)

}

loop(I,dpm_injections)

{

loop(p,I->p)

{

counter++;

c = P_CELL(p);

t = P_CELL_THREAD(p);

C_UDMI(c, t, 0) += ((M_PI * (P_DIAM(p) * P_DIAM(p) *

P_DIAM(p)) *P_N(p)) / 6)/C_VOLUME(c, t);

if (C_UDMI(c, t, 0) >= 1)

{

C_UDMI(c,t,0)=1;

}

else

{

C_UDMI(c,t,0)=C_UDMI(c,t,0);

}

}

}

}


UDF for Sartor Abbot drag law:

DEFINE_EXCHANGE_PROPERTY(s,cell,mixture_thread,primary_index,secondary_i

ndex)

{

/*declare variables*/

real air_velocity_x, air_velocity_y, air_velocity_z, water_velocity_x,

water_velocity_y, water_velocity_z, rel_vel_x, rel_vel_y, rel_vel_z,

rel_vel_mag, rey_num, co_drag;

/*Get phase threads*/

Thread *thread_a, *thread_w;

thread_a=THREAD_SUB_THREAD(mixture_thread, primary_index); /*air*/

thread_w=THREAD_SUB_THREAD(mixture_thread, secondary_index);

/*water*/

/* properties to calculate relative Reynolds number*/

air_velocity_x=C_U(cell, thread_a);

air_velocity_y=C_V(cell, thread_a);

air_velocity_z=C_W(cell, thread_a);

water_velocity_x=C_U(cell,thread_w);

water_velocity_y=C_V(cell,thread_w);

water_velocity_z=C_W(cell,thread_w);

rel_vel_x=air_velocity_x-water_velocity_x;

rel_vel_y=air_velocity_y-water_velocity_y;

rel_vel_z=air_velocity_z-water_velocity_z;

rel_vel_mag=sqrt(rel_vel_x*rel_vel_x+rel_vel_y+rel_vel_y*rel_vel_z*rel_vel_

z);

rey_num=C_R(cell,thread_a)*rel_vel_mag*C_PHASE_DIAMETER(cell,thread_

w)/C_MU_L(cell,thread_a);

if (rey_num<0.1)

co_drag= 24/rey_num;

else

if (rey_num>0.1 || rey_num<5)

co_drag=(24/rey_num)*(1+0.0916*rey_num);

else

if (rey_num>5 || rey_num<1000)

co_drag=(24/rey_num)*(1+(0.158*pow(rey_num,0.66667)));

else

rey_num=0.42;

return co_drag;

}

IMPROVEMENT OF CENTRIFUGAL WET SCRUBBER … Ali Thesis.pdf · scrubbers, European Fluid ... 3.3.2...

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