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Particle collision rate and small-scale structure of particle concentration in turbulent flows

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Particle collision rate and small-scale structure of particle concentration in turbulent flows
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Hu, Kevin Cunkuo, 1963-
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xi, 146 leaves : ill. ; 29 cm.

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Fluid shear ( jstor )
Inertia ( jstor )
Isotropic turbulence ( jstor )
Particle collisions ( jstor )
Particle size classes ( jstor )
Quantum statistics ( jstor )
Shear flow ( jstor )
Simulations ( jstor )
Turbulence ( jstor )
Velocity ( jstor )
Aerospace Engineering, Mechanics and Engineering Science thesis, Ph.D ( lcsh )
Dissertations, Academic -- Aerospace Engineering, Mechanics and Engineering Science -- UF ( lcsh )
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Thesis:
Thesis (Ph. D.)--University of Florida, 1998.
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Includes bibliographical references (leaves 140-145).
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Typescript.
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Vita.
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by Kevin Cunkuo Hu.

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PARTICLE COLLISION RATE AND SMALL-SCALE STRUCTURE OF
PARTICLE CONCENTRATION IN TURBULENT FLOWS







by

Kevin Cunkuo Hu


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF
THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF
PHILOSOPHY

UNIVERSITY OF FLORIDA


1998














ACKNOWLEDGMENTS


I would like to take this opportunity to express my sincere thanks to my advisor,
Professor Renwei Mei, for his patience, encouragement, and help. This work would be
impossible without his guidance and insights. I appreciate my advisory committee
members, Professor James F. Klausner, Professor Corin Segal, Professor Ulrich H.
Kurzweg, and Professor Roger Tran-Son-Tay, for their suggestions and help. I also
express gratitude to Professor Michael W. Reeks (in England), Professor Ibrahim K.
Ebcioglu, Professor Otis Walton and Professor Nicolae D. Cristescu for their help.
Thanks to Dr. Guobao Guo and my fellow graduate students Xueliang Zhang,
Hong Shang, and others for their helpful discussions and friendship.
I acknowledge the financial support of the Engineering Research Center (ERC)
for Particle Science & Technology at the University of Florida, the National Science
Foundation (EEC-9402989), industrial partners of the ERC, and the ALCOA Foundation
Award.
I am deeply indebted to my wife, Rui, for her love, patience, and encouragement
during my thesis work, which were more than I could ever have asked.














TABLE OF CONTENTS

Page
ACKNOW LEDGM ENTS ........................................................................................ ii

LIST OF TABLES...................................................................................................vi

LIST OF FIGURES ................................................................................................ vii

ABSTRACT............................................................................................................ xii

CHAPTER 1 INTRODUCTION ........................................................................... 1
1.1 Smoluchowski's Prediction in Lam inar Shear Flow .......................................... 4
1.2 Various Theories for Collision Rate in Turbulence........................................... 5
1.3 Recent Development in Computer Simulation on Particle Collision Rate..... 7
1.4 Recent Development in Particle Concentration Non-uniformity ..................... 8
1.5 Scope of Present Study ........................................................................................ 11

CHAPTER 2 TURBULENCE AND PARTICLE COLLISION
DETECTION ......................................................................................................... 13
2.1 Turbulence Representation ................................................................................. 13
2.1.1 Isotropic, Gaussian Turbulence........................................................................ 13
2.1.2 Rapidly Sheared Homogeneous Turbulence .................................................... 16
2.1.3 Isotropic Turbulence Generation in a Periodic Box......................................... 17
2.2 Numerical Integration of Particle Motion and Error Analysis ....................... 19
2.3 Particle Collision Treatm ent ............................................................................... 21
2.3.1 Collision Detection Scheme ............................................................................. 21
2.3.2 Post-Collision Treatment.................................................................................. 24
2.4 Computer Simulation........................................................................................... 25
2.4.1 Time Step ......................................................................................................... 25
2.4.2 Turbulence Realization..................................................................................... 27
2.4.3 Validation of the Collision Detection Scheme ................................................. 27






iii








CHAPTER 3 A NEW THEORETICAL FRAMEWORK FOR PREDICTING
COLLISION RATE OF SMALL PARTICLES IN GENERAL TURBULENT
F L O W S .............................................................................................. ............................... 33
3.1 Introduction........................................................................................................... 33
3.2 Analysis................................................................................................................. 37
3.3 Numerical Simulation.......................................................................................... 43
3.4 Results and Discussions ...................................................................................... 44
3.4.1 Comparison with Simulation Results in a Laminar Shear Flow ...................... 44
3.4.2 Collision Rate and Velocity in a Gaussian, Isotropic Turbulence.................... 47
3.4.3 Collision in a Rapidly Sheared Homogeneous Turbulence.............................. 52
3.5 Summary and Conclusions.................................................................................. 55

CHAPTER 4 EFFECT OF INERTIA ON THE PARTICLE COLLISION
VELOCITY AND COLLISION ANGLE IN GAUSSIAN TURBULENCE .........59
4.1 Introduction .................................................................................................. 59
4.2 Saffnan & Turner's Analysis....................................................................... 62
4.3 Present Analysis ................................................................................................... 65
4.3.1 Asymptotic Analysis......................................................................................... 65
4.3.2 PDF-Based Method..........................................................................................68
4.4 Results and Discussions................................................................................ 69
4.5 Summary and Conclusions.................................................................................. 76

CHAPTER 5 QUANTIFICATION OF PARTICLE CONCENTRATION NON-
UNIFORMITY IN TURBULENCE............................................................................. 77
5.1 Introduction .................................................................................................. 78
5.2 Quantification Method.................................................................................. 79
5.2.1 Inherent Noise of the Concentration Non-uniformity...................................... 79
5.2.2 Concentration Non-uniformity Parameter........................................................ 82
5.3 Results and Discussions................................................................................ 89
5.4 Summary and Conclusions.................................................................................. 96

CHAPTER 6 COLLISION RATE AT FINITE PARTICLE INERTIA AND
SIZE IN GAUSSIAN TURBULENCE ........................................................................ 97
6.1 Introduction .................................................................................................. 98
6.2 Correction of Smoluchowski's Theory....................................................... 100








6.3 An Analysis on the Effect of Particle Inertia on the Collision Rate............ 106
6.3.1 Saffinan & Turner's Analysis ....................................................................... 106
6.3.2 Effect of Small Inertia on the Collision Kernel.............................................. 107
6.3.3 A Proposed Expression on Collision Kernel.................................................. 110
6.4 Comparison of the Analysis with Simulation Results.................................. 111
6.4.1 Effect of Particle Inertia................................................................................. 111
6.4.2 Effect of Particle Size..................................................................................... 113
6.4.3 Effect of Particle Mean Concentration........................................................... 114
6.4.4 Effect of Particle Gravitational Settling......................................................... 114
6.5 Collision Rate in Polydisperse Suspension..................................................... 115
6.5.1 Particle Size Spectrum.................................................................................... 122
6.5.2 Effect of Particle Inertia ................................................................................. 124
6.5.3 Effect of Particle Gravitational Settling ......................................................... 125
6.6 Collision Rate in Hard Sphere Suspension and Assessment of Collision
M odels ................................................................................................................. 127
6.7 Summary and Conclusions................................................................................ 129


CHAPTER 7 SUMM ARY AND CONCLUSIONS .............................................. 137



REFERENCES...................................................................................................... 140

BIOGRAPHICAL SKETCH ................................................................................ 146














LIST OF TABLES


Table 3.1 Comparison of the collision velocity among monosized
particles between prediction and direct numerical simulation
in lam inar shear flow................................................................................. 45
Table 3.2 Comparison of the collision velocity among monosized
particles between prediction and direct numerical simulation
in Gaussian, isotropic turbulence.............................................................. 50














LIST OF FIGURES


Figure 2.1 The effect of time step on the cumulative average of the normalized
collision kernel a', for inertialess particles in turbulence. D=0.05.............................. 26

Figure 2.2 Turbulence dissipation rate e against turbulence realization. (a) Variation of
ensemble average. (b) Cumulative average .................................................................. 29

Figure 2.3 Turbulence small eddy shear rate (e / v) 1/2 against turbulence realization.
(a) Variation of ensemble average. (b) Cumulative average......................................... 30

Figure 2.4 Inverse of total number of monodisperse particles against time in laminar
shear flow F=1.0, D=0.02............................................ ............................................... 31

Figure 2.5 Multiple collisions of HARD SPHERE particles in laminar shear flow...... 32

Figure 3.1 Sketch of particle collision velocity. The separation
distance is x2-xI = Rij= (ri+rj) ................................................................................... 35

Figure 3.2 Variations of J(), (Q), 74() with i where

fl= ), f2= G( ) and f3= ............................................................. ....... 36

Figure 3.3 Local coordinates for collision in laminar shear flow. 42

Figure 3.4 Comparison of the pdfs for the collision angles 9c between
the theory and the simulation in laminar shear flow ...................................................... 47

Figure 3.5 Convergence of a*2 as Nk -*oo ....................................................................... 50

Figure 3.6 Particle collision kernel a*, from direct numerical simulation in isotropic
turbulence. (a) Variation of the ensemble average. (b) Cumulative time average....... 51








Figure 3.7 (a) Normalized particle collision kernel in a highly sheared homogeneous
turbulence without contribution from the mean shear rate.............................................. 56

Figure 3.7 (b) Collision kernel in a rapidly sheared homogeneous turbulence. 6 denotes
the dissipation rate of the isotropic state at t=0 ............................................................... 57

Figure 3.8 Collision velocity in a rapidly sheared homogeneous turbulence.
so denotes the dissipation rate of the isotropic state at t=0 ............................................ 58

Figure 4.1 Sketch of particle collision scheme ............................................................... 63

Figure 4.2 Sketch of particle collision velocity and collision angles (012 & Oc ).
The separation distance is x2 x1 = R = (r, + r2)..................................................... 63

Figure 4.3 Comparison for the increased collision velocity due to the particle inertia
between numerical simulation and theoretical prediction, D/TI=0.957 .......................... 70

Figure 4.4 Effect of particle inertia on the probability density function of the particle
collision velocity wr. (a) D/I=0.957. (b) D/rh=2.522.................................................. 71

Figure 4.5 Effect of particle inertia on the probability density function of the particle
collision angle 012. (a) D/rl=0.957. (b) D/r9=2.522...................................................... 73

Figure 4.5 (c) Effect of particle inertia on the probability density function of the particle
collision angle 0 D/ri=0.957 ...................................................................................... 74

Figure 4.6 Effect of particle inertia on the mean collision angle 012 in isotropic,
G aussian turbulence ....................................................................................................... 74

Figure 4.7 Effect of particle inertia on the standard deviation of collision angle 012 in
isotropic, Gaussian turbulence ....................................................................................... 75

Figure 4.8 Effects of particle inertia and size on the probability density function of the
particle collision angle 012, pTk=1.023 ....................................................................... 75

Figure 5.1 The normalized variance of the concentration as function of cell size for
inertialess particles in isotropic turbulence. PTtk =1.023 .............................................. 83

Figure 5.2 The expressions (5.17) & (5.18) of the concentration non-uniformity as
function of cell size for inertialess particles in uniform shear flow. /F=oo ................. 84








Figure 5.3 The expressions (5.17) & (5.18) of the concentration non-uniformity as
function of cell size for inertialess particles in isotropic turbulence. PT k = ............... 86

Figure 5.4 The concentration non-uniformity parameter AP as function of cell size in
uniform shear flow. r=1.0, 03/F=l.0 ............................................................................ 87

Figure 5.5 The concentration non-uniformity as function of cell size for small inertia
particles in isotropic turbulence ..................................................................................... 88

Figure 5.6 The concentration non-uniformity as function of cell size for various
particle number densities at a given inertia in isotropic turbulence. p3T k =1.023........ 89

Figure 5.7 The concentration non-uniformity of sizeless particles as function of cell
size for various inertia in isotropic turbulence ............................................................... 91

Figure 5.8 The concentration non-uniformity parameter AD of finite-size particles as
function of cell size for various inertia in isotropic turbulence. (a) D/rn=0.957.
(b) D /il=2.52................................................................................................................... 93

Figure 5.9 The maximum concentration non-uniformity parameter AP of finite-size
particles as function of particle inertia in isotropic turbulence ...................................... 94

Figure 5.10 The concentration non-uniformity parameter AO as function of Ax/h for
small-size and zero-size particles in isotropic turbulence. P3 k =1.023 ......................... 94

Figure 5.11 The concentration non-uniformity of particles at a given inertia as function
of Ax/D cell size for various sizes in isotropic turbulence. (a) Log scale in x-axis.
(b) in norm al scale in x-axis ........................................................................................... 95

Figure 6.1 Collision kernel a ii against time in laminar shear flow. F=1.0, D=0.04.
(a) Variation of ensemble average. (b) Cumulative average...................................... 105

Figure 6.2 The effect of particle inertia on the increased collision kernel Aa 1 for
monodisperse particles for (a) D/rl=0.957. (b) D/rj=2.52 ........................................ 116

Figure 6.3 The effect of particle size on the collision kernel aot i for monodisperse
particles at a very dilute system. Ou k =oo................................................................... 117

Figure 6.4 The effect of particle size on the collision kernel a 1 for monodisperse
particles at 'not' very dilute system. p3- k = oo............................................................. 117








Figure 6.5 Variation of the collision kernel at 1 against particle number concentration
for monodisperse particles. 13PTk = oo, D/TI=0.435...................................................... 118

Figure 6.6 The effect of gravitational settling on the collision kernel a*i for
monodisperse particles. 3rk=1.023, D/rTI=2.52.......................................................... 118

Figure 6.7 The effect of gravitational settling for monodisperse particles. D/ri=2.52,
PT k = 1.023. (a) on the collision velocity. (b) on the concentration non-uniformity... 119

Figure 6.8 Collision kernel a41 of polydisperse particles against time. Prtk = O,
D/TI=0.957. (a) Variation of the collision kernel. (b) Cumulative average................. 120

Figure 6.9 Collision kernel cta of polydisperse particles with various inertia against
turbulence realization. D/rp=0.957. (a) Variation of the collision kernel.
(b) Cumulative average of the collision kernel............................................................ 121

Figure 6.10 Inverse of total number of particles against time for polydisperse particles.
D/TI=0.957. (a) primary size (v0) particles. (b) secondary size (2 v0) particles......... 122

Figure 6.11 The evolution of particle size distribution at various instants for
polydisperse particles. D/1=2.17, r k =1.023............................................................. 123

Figure 6.12 The effect of particle inertia on the increased collision kernel Aoal for
polydisperse particles for (a) D/il=0.957. (b) D/I=2.52 ........................................... 126

Figure 6.13 The effect of gravitational settling on the collision kernel a*, for
polydisperse particles. PTk=1.023, D/r1=2.52............................................................ 127

Figure 6.14 The effect of gravitational settling for polydisperse particles. D/rl=2.52,
PT k=1.023. (a) on the collision velocity. (b) on the concentration non-uniformity.... 128

Figure 6.15 Collision kernel a 1 of hard sphere particles with various inertia against
time in laminar shear flow. D=0.04. (a) Variation of the collision kernel.
(b) Cum ulative average................................................................................................. 130

Figure 6.16 The effects of the collision schemes on the collision kernel a I1 against time
in laminar shear flow. F=1.0, D=0.04. (a) Variation of the collision kernel. (b)
Cumulative average of the collision kernel................................................................... 131








*
Figure 6.17 The effects of the inertia on the collision kernel a11 against time for hard
sphere particles in Gaussian turbulence. D/Ti=0.957. (a) Variation of the collision
kernel. (b) Cumulative average of the collision kernel............................................... 132

Figure 6.18 The effects of the collision schemes on the collision kernel a* against time
in Gaussian turbulence. D/Ti=2.174, Prk= 1.023. (a) Variation of the collision kernel.
(b) Cumulative average of the collision kernel............................................................ 133

Figure 6.19 The effects of the collision schemes on the collision kernel a*,I of hard
sphere particles in Gaussian turbulence. D/il=0.957, Pt k =0.292. (a) Variation of the
collision kernel. (b) Cumulative average of the collision kernel................................ 134














Abstract of Dissertation Presented to the Graduate School of the University
of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor
of Philosophy


PARTICLE COLLISION RATE AND SMALL-SCALE STRUCTURE OF
PARTICLE CONCENTRATION IN TURBULENT FLOWS

by

Kevin Cunkuo Hu

May, 1998


Chairman: Renwei Mei
Major Department: Aerospace Engineering, Mechanics and Engineering Science

A study of particle collision and concentration non-uniformity in turbulent flows
is carried out. A correction to the classical result of Smoluchowski for the collision rate
of monodisperse particles in a laminar shear flow has been made. A theoretical
framework has been developed to evaluate the collision rate and collision velocity of very
small particles in general turbulent flows. The present approach differs significantly
from the classical approach of Saffinan & Turner in that the ensemble average is taken
after the collision rate for a given flow realization is calculated. This avoids the
assumption of isotropy, as needed in the classical approach, and allows for the evaluation
of the collision rate in general turbulent flows.
Direct numerical simulations and analytical studies on particle collisions in an
isotropic, Gaussian turbulence have been carried out. Several different collision models
(or post-collision treatments) are implemented in the simulations and the difference
among various collision models is examined. The effects of particle inertia, size, gravity,
and mean-concentration on the collision rate, collision velocity, and collision angle are








investigated. Asymptotic analyses have been developed to predict the effects of small
inertia on the increases in collision rate and collision velocity. Asymptotic prediction for
the increased collision velocity agrees well with the results of both numerical simulation
and PDF-based method. The increased collision velocity is found to contain terms of

both O((Ot k )-1) and O( (Tk )[2 ), where P1 is the particle response time and tk is the
kolmogorov time scale. The increases in particle size, gravitational acceleration, and
mean-concentration in general reduce the normalized particle collision rate in isotropic
turbulence for monosized particles as opposed to very small particles in the very dilute
limit.
A new method has been developed to quantify the concentration non-uniformity
of particles in turbulent flows. The effects of particle size and inertia on the concentration
non-uniformity are examined and elucidated. The concentration non-uniformity can be
greatly overestimated if the effect of finite particle size is neglected.














CHAPTER 1
INTRODUCTION



Particles and turbulence are around us everywhere and greatly impact our daily
life. Dusts, sand, rain drops, fine powders, glass beads, viruses and bacteria are all
'particles.' Most of the fluid flows occurring in nature such as air current and water flows

in rivers and seas are turbulent flows. The particles in the atmosphere remain airborne for
long periods, lowering visibility. The brown haze that is often seen over large cities in
autumn, winter and spring is due mainly to the interactions between particles and the air.
The improvement of particulate-fluid systems requires a better understanding of
the fundamental physics of the particle-particle and particle-fluid interactions. The
behavior of particulate-fluid mixtures may vary widely depending upon the conditions
under which the particle-fluid system functions. Particle-particle interactions involve the
collisions among particles which depend on inter-particle forces, particle size distribution,
and the concentration distribution. Particle-fluid interactions include the hydrodynamic
forces acting on particles by fluid and the feedback to fluid from particles, etc. Particle
collision, coagulation, and aggregation are the examples of most important scenarios of
those interactions that are determined by particle properties and flow structures. The fact
that collisions do occur among the particles of size ranging from a few Angstroms to a
few millimeters explains the vast amount of scientific and industrial applications of the
collision process. For example, molecular collisions help explain the physical properties
such as viscosity, diffusivity, and conductivity in gases and liquids. Numerous industrial
applications can be cited: design of fine spray combustion nozzles, control of industrial
emissions (Presser & Gupta 1993, Tu et al. 1996) and the understanding of the collision








process among suspended particles in pneumatic transport systems or in gas cleaning
chambers (Abrahamson 1975, William & Crane 1983), and in sewage disposal devices
(Hunt 1982), where particle size distribution and concentration non-uniformity are
important characteristics that depend on particle collision and coagulation. Most of
previous studies on particulate removal have focused on the filters, with little attention to
the particle-particle interactions and particle-fluid interactions. Despite many years of
research and development in particulate removal technologies, the collection of small
particles with size less than 1 un is still a rather difficult task. It is extremely difficult to
remove fine organic active particles (<0.03 mrn) from a waste stream because the particles
are much smaller than the pores in existing filters. Typical collection efficiencies of the
filtration devices decrease rapidly with the decrease of particle size in the range 0.2-30pm
(Davies 1953). A good alternative is to aggregate fine particles which can be removed
using conventional technologies. For the coagulation and aggregation to occur, particles
have to collide with each other and adhere. It is very desirable that one can predict how
the small particles are driven to collide with each other so they aggregate or grow in size
and become large enough to be captured by the filters. Obviously, the efficiency of
particle removal mainly depends on the control of particle collisions and concentration
non-uniformity in filtration systems (Fathikalajahi et al. 1996). It is evident that particle
collision is one of the most important interactions for understanding the fundamentals of
particle-fluid systems.
Particle collision kernel and collision rate are critical quantities in the
development of collision theories. The rate of flow induced particle collisions affects
directly the particle size distribution in particle production. Examples of turbulence
induced particle collision can be seen in the formation of rain drops in clouds, pulverized
coal combustion, agglomeration of fine powders in gas flows, air filtration equipment,
sewage disposal devices, fast fluidized beds, dust and spray burners, and so on. Particle
collisions can take place through a variety of collision mechanisms. Particle collisions
may be induced by centrifugal and gravitational forces, thermal forces in Brownian
motion, and van der Waal forces. In an actual system, the scenario after every contact
between particles may be different from one to another, depending on the underlying








physics of the system. The collisions among raindrops enhance the growth of droplets in
the air, since the contact between droplets may most likely result in the coalescence of
two droplets. The growth of bubbles in a liquid flow is another example of particle
coagulation upon collisions. The contacts between two solid particles may behave in
various different ways. When two solid particles are brought together the colliding pair
may stick or separate after collision upon contact. Many theoretical efforts were made to
predict the collision kernel and rate. To validate a theoretical analysis, direct numerical
simulations were carried out to obtain statistical average of the collision rate in a particle-
fluid system. Different interpretations of the immediate consequence after the contact
between a pair of particles have resulted in different collision models or so-called "post-
collision treatment" schemes. To quantify the post-collision treatment, a statistical
parameter called collision efficiency has been introduced. The collision efficiency is one
if particles coagulate to form a larger one. Usually, it is less than one since some of the
colliding particles may bounce back. Although many theoretical and experimental studies
have been devoted to estimates of the collision efficiency for water droplets settling
through still air (Klett & Davis 1973, Lin & Lee 1975, Beard & Ochs 1983) and droplets
settling down through laminar shear flow (Jonas & Goldsmith 1972), even an
approximate estimate of the collision efficiency in a complicated situation can be very
difficult to obtain. At this stage, it is still not possible to obtain an accurate solution for
the evaluation of collision efficiency in complex systems. To better focus on the issues of
our interests, the collision rate, the collision efficiency is assumed to be either one or
zero in this study. Although a unity collision efficiency will overestimate the size growth,
however, theoretical and numerical exploration on the governing physics may still hold
its significance for a better understanding of the interactions in particle-fluid systems.
Population balance equations are often used for predicting the evolution of
particle size distribution in particle production systems or in studying particle
coagulation. They are typically given in the form of








dn1
d t -anll1n1 a12nln2 a13nln3 -

dn2
dt 2 al jlnn a12nln2 CC22n2n2- 232n3 .- (1.1)

dn3
dt = a12nln2 a13nln3 X23n2n3 c33n3n3 -

dn4 1
dt a13n1n3 + 2 22n2n2 "a14n1ln4 24n2n4 -



In the above, ni is the number concentration of the ith particle size group. For primary

particles, i=l, with a volume v1, the particle in the ith group has a volume of iv1. The

quantity aijninj, denoted as ncij in this thesis,


ncij= aijninj (1.2)

is called collision rate which represents the number of collisions among ith particle with
jth particle per unit volume and per unit time. The coefficient aj is called the collision

kernel or collision function, it depends on particle size, particle inertia and flow structure,
and must be evaluated separately. The collision rate or collision kernel is a measure of
the ability of particles to collide, coagulate, and aggregate. It is essential to determine the
instantaneous particle size distribution and momentum transfer among particles in a
particulate system. Over past 80 years, many efforts have been devoted to developing the
particle collision theories for predicting the particle collision rate in fluid flows. A brief
review is given below.




1.1 Smoluchowski's Prediction in Laminar Shear Flow


A geometrical collision occurs when two particles reach a separation distance less
than R-ri+rj, in which ri and rj are the respective radii of two particle groups and R is the
radius of the colliding surface. Theoretical analysis of geometrical collisions in a uniform
shear flow was first carried out by Smoluchowski (1917). At a given instant the particle








number concentrations per unit volume are n, and nj for each size group. Taking any
particle of the ith-group as a target particle, the volume flow rate of thejth-group particles
reaching the colliding surface of radius R of the given target particle is nj f ,, wrdS in
which w is the relative velocity between ith-group and jth-group particles and Wr is the
radial component of w. Thus the number of collisions occurring among ith-group and
jth-group particles per unit time per unit volume is given by

ncij = ninj L, owdS= ninj F(ri+rj)3 = a, nin (1.3)

where F is the shear rate and

aij= F(ri+rj)3 (1.4)

is often called the collision kernel. For like particles, the number of collisions per unit
time and per unit volume was often simply taken as
1 4 3
ncii= nini (2r,)3. (1.5)
2 3
The factor was introduced to avoid double counting of the collisions among the like
2
particles. When the above results are used in the population balance equations, for

example, the rate of the "destruction" of ith-group particles due to collisions is -2 n ci +

4 4 4
Y n = nini F(2ri)3 ninj r(ri+rj)3 since each collision among the like particles

results in the loss of two particles of that size group. Equation (1.5) has been extensively
used in the literature in calculating the particle collision rate and particle size evolution.
In addition to Smoluchowski's work, Hocking & Jonas (1970) also calculated the
collision rate for the sedimenting particles in a uniform shear flow using Stokesian
theory. They studied the collision efficiency by which the particle coagulation was
assumed if the surface distance between two neighboring particles was less than a
prescribed fraction of particle diameter.


1.2 Various Theories for Collision Rate in Turbulence


When particles collide in complex flows, such as turbulent flows, or when more
than one collision mechanism are involved in the particulate system, it is quite difficult to








derive an exact expression for collision rate. Numerous efforts have been made to
estimate the collision rate by means of evaluating the collision velocity in turbulence
during past decades. Camp and Stein (1943) employed the concept of mean velocity
gradient related to the turbulent energy dissipation rate E and kinematics viscosity v and
obtained the collision kernel for turbulence shear-induced collision rate based on
Smoluchowski work (1917) in laminar shear flow.
4 R(__1/2
a = R' 2. (1.6)
3 v
Saffman and Turner (1956) gave two predictions of particle collision kernel in an
isotropic, Gaussian turbulence. In the first prediction, it was assumed that particles are
inertialess so that they follow the fluid completely and particle size is small compared
with the Kolmogorov length scale of the turbulence. The collision kernel was given

ca = 1294 R3 (E)1/2. (1.7)
IJ V
Their second prediction was for the particles with small inertia and gravity.

ai = 2(2n)12 R2 [(1 -)2 -t)2 (-)2
PO Dt

+(I )2i (T-2)2g2 +R2 ]1/2. (1.8)
3 po 9 v
However, collision kernel for like particles or the particles with same inertia is reduced to

a, =1.67 R3 ( ) 1/2 (1.9)
v
Thus the effect of particle inertia and gravity disappeared in the monodisperse case,
which is not physically sound. In addition, the result of their second analysis was
inconsistent with the first one, as easily seen from (1.7) and (1.9).
Abrahamson (1975) extended the concept of kinetic theory of gases for the
collisions among large particles in a high Reynolds number turbulence. It was assumed
that the velocities of colliding particles are uncorrelated, normally distributed in the
absence of gravitational settling. The variance of particle velocity v was estimated by








2 "2
= (1.10)
1+ 15 Tp 8/U2

where u2 is the mean-squared fluctuating velocity of the fluid and Tp is the particle

response time. The collision kernel, without gravitational settling, was given by

2 ( 2 > 2 > 1/2.
aij = 5.0 R ( + )12 (1.11)

Balachandar (1988) performed an analysis on the collision kernel by assuming
that the probability distribution function (PDF) of the velocity gradient in turbulence
follows a log-normal PDF distribution. He found a correction to Saffminan & Turner's
prediction for collision kernel in isotropic turbulence. Williams & Crane (1983) and
Kruis & Kusters (1996) performed stochastic analysis to obtain the collision kernel with
inclusion of the combined effects of local shear and particle inertia. Zhou et al. (1997)
carried out a similar analysis and modified the Kruis & Kusters' result in turbulence.
Those analyses on the collision kernels were obtained on the basis of two-point velocity
correlations without tracking the trajectories of individual particles in the turbulent flow
(hereinafter referred as Eulerian method). However, we have found that the collection of
the relative velocities (or collision velocities) upon collisions based on tracking the
trajectories of individual particles in turbulent flow is a biased average in favor of those
regions where the collision velocity is high. Hereinafter, this method based on particle
tracking is referred as Lagrangian method. The detailed discussions on the difference
between the results of Eulerian and Lagrangian methods on collision velocity will be
presented in chapter IV.




1.3 Recent Development in Computer Simulation on Particle Collision Rate


Recent developments of direct numerical simulations (DNS) in turbulence have
greatly enhanced the understanding of the turbulence-induced particle collisions.
Balachandar (1988) also carried out DNS of particle collision in an isotropic turbulence.
It was found that the probability distribution function of the velocity gradient follows a








log-normal distribution instead of Gaussian distribution. The collision kernel was
obtained in a frozen turbulence with a maximum of 500 particles in the flow field.
Artificially large particles were used in order to increase the collision events within a
reasonable computational time. It was concluded that the particle inertia does not affect
the average collision kernel for large, monosize particles. Large discrepancy in the
collision rate for monosize inertialess particles between his numerical results and
Saffinan & Turner's prediction was reported. However it is not clear whether this
discrepancy is due to the difference in the small scale turbulence structure or not.
Chen et al. (1995) carried out DNS results for the aerosols in a turbulent channel
flow. They mainly focused on the possible effects of collisions on the deposition rate of
the droplets on the channel walls. They found that the particle concentration in the wall
region can be significant while the bulk concentration is small. The collision kernel and
rate were found to peak in the wall region, and particle depositions can be enhanced by
the formation of larger particles.
Wang et al. (1997) also carried out DNS to investigate the collision kernel in
homogeneous turbulence. They allowed particles in the system to overlap in space which
is unphysical. The effects of finite inertia on collision kernel were also reported in their
DNS results (Zhou, Wexler & Wang 1997). Zhou et al. (1997) developed an analysis
showing that the increases in particle collision velocity, the non-uniformity of particle
concentration and the compressibility of particle velocity in the continuum sense are three
possible mechanisms to affect the overall collision kernel for particles with finite inertia.
However, they did not quantify these terms separately in their work. Thus how the
collision velocity and the concentration non-uniformity play in enhancing the overall
collision kernel remains unknown at this stage.




1.4 Recent Development in Particle Concentration Non-uniformity


Recent studies have shown that the particle concentration in turbulence may be
highly nonuniform. Heavy particles with finite inertia tend to accumulate in the regions








of high strain rate and to move away from the vortex cores. The occurrence of particle
accumulations is referred as concentration non-uniformity or preferential concentration.
The coherent vortical structures are the mechanisms that cause the nonuniform
distribution of particles. The coherent vortices and concentration non-uniformity have
been observed by many numerical simulations and experimental visualizations. The
numerical simulations for demonstration of the concentration non-uniformity were carried
out in plane mixing layers (Crowe et al. 1988, Chien & Chung 1987, Wen et al. 1992,
Tang et al. 1992, Wang 1992), in wake flows (Chien & Chung 1988, Tang et al. 1992),
in jet flows (Chung & Trout 1988, Hansell 1992), in wall-bounded flows (Kallio & Reeks
1989, McLaughlin 1989, Yonemura et al. 1993, Rouson & Eaton 1994, Tanaka et al.
1996), in homogeneous flows (Maxey & Corrsin 1986, Hunt et al. 1987, Fung & Perkins
1989, Squires & Eaton 1991, Wang & Maxey 1993, Fessler et al. 1993). The non-uniform
distribution of particles has also been found by the experiments in plane mixing layers
(Kobayashi et al. 1988, Kamalu et al. 1988, Lazaro & Lasheras 1992, Ishima et al. 1993),
in wake flows (Tang et al. 1992, Yang et al. 1993, Bachalo 1993), in jet flows (Longmire
& Eaton 1992), in wall-bounded flows (Rashidi et al. 1990, Young & Hanratty 1991).
Wang & Maxey (1993) have, through direct numerical simulations (DNS) in isotropic
turbulence, shown that the preferential concentration achieves maximum at P3k =1 in

which P3 is the reciprocal particle response time and Tk is the Kolmogorov time scale of

the turbulence. One of the consequences of the preferential concentration is the
enhancement of the settling velocity of heavy particles in the Stokes drag range (Maxey
1987, Mei 1994). One of the related industrial applications is that the particulate
removal efficiency of the venturi type scrubber was affected by the nonuniform
concentration distribution of droplets (Fathikalajahi et al. 1996).
Particle trajectories, therefore individual particle positions, can be obtained
accurately only in Lagrangian frame by solving the equations of particle motions.
Particles have finite response times in general but they do not have an intrinsic diffusivity
or viscosity associated with the motion of individual particles. Turbulence particle
diffusivity is the consequence of ensemble averaging over many realizations and it is used
for predicting the variation of average concentration field on a large scale. It cannot be








used to describe the small-scale spatial variation of the particle concentration. From the

computational point of view, a DNS using 1283 grid resolution for an isotropic

turbulence becomes routine and DNS using 2563 or even 5123 have been carried out.
For a reasonable contour representation of particle concentration, it is desirable to have at
least, say, 10 particles in each cell so that the statistical noise does not become

overwhelming. This requires 1.7x108 particles with a 2563 grid resolution. Due to the
preferential concentration, there will be regions and cells that do not contain any particle
or only one or two particles at a given instant. Thus, an accurate, three-dimensional
contour representation of instantaneous particle concentration with such high resolution
will still be heavily contaminated by the inherent statistical noise. No such three-
dimensional concentration contour has been reported to date. Due to the computer power
limitation, previous numerical simulations involved anywhere from 104 to 106 particles,
which is far below the level of reaching a reasonable statistics.
It has been difficult to quantify the extent/degree of particle concentration non-
uniformity induced by the small-scale turbulence structure. Squires & Eaton (1991) and
Wang & Maxey (1993) showed in their DNS the similarity between the instantaneous
particle concentration contours with the contours of the vorticity magnitude (or
enstrophy) in the same plane at the same instant. To quantify the deviation of the particle
concentration field from a statistically uniform distribution, Wang & Maxey (1993) used

a statistical quantity (P(k) Pb(k))2 averaged over space, where P(k) is the

probability of finding k particles in a given cell and Pb (k) follows the Bernoulli
distribution for a random distribution of the particles. While this quantity shows that the
preferential concentration maximizes near P3tk =1 (Wang & Maxey 1993), it varies with

total number of particles in each of their simulations. The particle-particle interactions
are neglected in their simulation and no systematic investigation on its dependence on the
cell size was carried out. Hence, its usefulness is limited. Although numerous
experimental studies have clearly shown the non-uniform particle distribution in turbulent
flows, it is quite difficult to measure the 3D non-uniformity so as to isolate the effects of
flow shear, particle inertia and gravity.








Obviously, the local accumulation will affect the encounters among the particles
and thus the overall collision rate is expected to be modified accordingly. However, the
effect of the preferential concentration on the collision kernel has never been estimated or
separated from other factors such as the increased particle collision velocity due to small
particle inertia.




1.5 Scope of Present Study


Particle collisions are extremely difficult to observe experimentally, even with
today's sophisticated laser instrumentations. Direct numerical simulation, however, has
its advantages of providing detailed information on particle trajectories, flow structures
and is ideally suited for parametrical investigations of particle and fluid flow properties.
Numerical implementation of particle interactions in a simulation, while posing a
considerable numerical challenge, introduces no fundamental difficulties. In this work,
extensive data obtained by numerical simulations on the collision velocity, collision
angle, collision kernel and collision rate are vital to understanding of the fundamental
physics in the particle-fluid interactions. One of the major objectives of this thesis is to
develop analytical and numerical methods for predicting the particle collision rate and
concentration non-uniformity with respect to the effects of turbulent shear, particle inertia
and gravity in particle-fluid systems.
As mentioned in 1.2, previous theories on the collision rate were restricted on
assumed turbulent flow. In Chapter III, we have developed a new theoretical framework
for prediction of the collision rate and collision velocity of small particles at zero inertia
in general turbulent flows. The collision velocity is a critical variable related to the
collision rate. However, the effects of particle inertia on the collision velocity and
collision angle have never been predicted using Lagrangian tracking method. In Chapter
IV, we carried out an asymptotic analysis to predict the effects of small particle inertia on
the collision velocity and presented the effect of the inertia on collision angle in isotropic
Gaussian turbulence. As pointed out in 1.4, no reliable method exists for quantifying








the concentration non-uniformity and the effect of the preferential concentration on the
collision rate has never been estimated. In Chapter V, a new quantification method has
been developed to reliably quantify the concentration non-uniformity of particles in
turbulent flows, and the effect of the preferential concentration on the collision rate is
predicted in isotropic Gaussian turbulence. The first result of Chapter VI is our
correction to the classical result of Smoluchowski for the collision rate of monodisperse
particles in a laminar shear flow. The combined effects of both the increased collision
velocity and increased concentration non-uniformity on the collision rate due to the
inertia are then examined. The effects of particle size, gravity and mean-concentration on
the collision rate are investigated through direct numerical simulations in Chapter VI.
The effects of the collision models on the collision rate are also presented in Chapter VI.















CHAPTER 2
TURBULENCE AND PARTICLE COLLISION DETECTION



2.1 Turbulence Representation

2.1.1 Isotropic. Gaussian Turbulence

The following model for the energy spectrum developed in Mei & Adrian (1995)
is assumed for turbulence,

3 2 k' 1 22
E(k) =2 uo Vk [l+(k/ko)217/6 exp(-To k2) (2.1)

where uo is the root-mean-squared turbulent velocity, k0 is a typical wave number and the

dimensionless parameter ro ---oko is related to the turbulent Reynolds number Re,.

Large Re. correspond to small rio and vice versa. The normalizing coefficient W in E(k)

is determined from (2.1) by satisfying the total energy requirement,
0 -4
k 2 (-1k 2'd
7 /6 2 exp2(-71 dk (2.2)
0 (1+k )

For small r10, E(k)~k4 when k = k/ko l and E(k)~k53, which is the scaling law in the

inertial subrange, when 1 k /1"qo. For large 10o, the above energy spectrum recovers

that of Kraichnan (1970). The relationship between the Eulerian integral length L, ko

and ri (or Re.) is given in Mei & Adrian (1994).

A turbulent eddy loses its identity as it is convected and dissipated; this behavior
is described by the eddy-self decay function D(x) which has an integral time scale TO.










The Fourier transformation of D(T) gives the power spectrum D(o)). A composite form

for the power spectrum D(o)) was constructed in Mei & Adrian (1995) as
2
exp(-ri Io)2)
() =A +(To(2.3)

where TI, is intended for the viscous dissipation time scale if the turbulent Reynolds

number is high, and T is close to To if ii I/To 1. The coefficient A is determined as

T exp(-rl /T2)
(2.4)
nt l-erf(rTl/T)

by satisfying J D(o) do) =1. The integral time scale To is related to T as To = 7tD(0) =
-oo

7lA .
The relationship between L I and To is not known in general; it can be taken to

have the form

To = cE(Re)LI 1/UO, (2.5)

where cE(Rex) was determined approximately in Mei & Adrian (1995) based on the

experimental data of Sato & Yamamotto (1988) for the fluid dispersion and the value of
high Reynolds number turbulent Prandtl number.
Using random Fourier modes representation, an isotropic, Gaussian, pseudo
turbulence
N
Ui (X, t) E bm) cos k(m) +(,(m) t
u ( ,t = [bi (k -x + .t )
m=!

+cIm) sin(k(m) x + co( m t ) ] (2.6)

is constructed to simulate the turbulent flow with a specified energy spectrum. In the
above Nk is the number of the random Fourier modes in one fluid realization,

k(m)and o)(m) are the wavenumber and frequency of the m-th mode. The random

coefficients b(m) and c(m) are chosen as follows,










b(m) (m)(6 (m) k(m)/k (M) f(k, (nM) (2.7)
i j qJ i j
=b ((Si)-ki W k ) fk^m ca (2.7)

where b. follows a normal distribution with

= 0 & = 8". (2.8)

The factor (ij-k(M) k(m) /k (M) in Eq (2.26) ensures the incompressibility V-u=-0 for

every Fourier mode. The scale factor f(k, o) depends on the energy spectrum and the
probability density functions (pdf), pI(k) and p2(00), of k and co,

f2(k, ) = E(k) D(o) (2.9)
2~,o = 5 (2.9)
47N2kpl(k)p2((O)
Since E(k) decays as k-5/3 in the inertia subrange, a Gaussian distribution for k would lead
to a very slow statistical convergence for ui(x, t). To achieve convergence of the statistics

involving the derivatives such as &', the following algebraic pdf
axj

p- Ii(ki)= (1 + Iki)" fori=l, 2 & 3 (2.10Oa)
2
and P1(k) = P11(k1)P12(k2)Pi3(k3) (2.10Ob)

is used to sample k, for ko= 1. The frequency om) is generated with the following pdf,
1 1
P2() = t +(/uko)2 (2.11)

The scale factor is finally set to be

E(k)D(co)
f(k, (.) = E-- (2.12)
47rtNkk P l(kl)Pi2(k2)p13(k3)p2((O)
It is noted that in the present studies on particle collisions, the collision rate is dictated by
the spatial structure and the eddy-self decay has no effect on the collision rate. In the
direct numerical simulation of particle collision in isotropic turbulence, however, the









eddy self-decay can be easily incorporated. Hence, the dependence of b(m) and c(m) on (o

is still included.



2.1.2 Rapidly Sheared Homogeneous Turbulence


For an initially isotropic turbulence under rapid shear, F=a, the variation of

b(m) with time was given in Townsend (1976) based on rapid distortion theory (RDT).

Hunt & Carruthers (1990) gave an overview on the use of RDT for representing various
types of homogeneous turbulence. Denoting the total shear S as
S= Ft (2.13)
the new wavenumber vector x(m) after the shearing is given by
(M) = k(m)
I I
() = k(m) -Sk(m) (2.14)
x2 2 1
(M k(m)
x k 3 3
where k(m) is the wavenumber vector of the m-th mode before the mean shear is applied.
The amplitude of each Fourier mode becomes

b(f)(S) = b^m)(0) + alb(2)(0)

b()(S) = a2 b(2m)(O) (2.15)

b(m)(S) = b(3)(0) + a3b(m(0)

where b(m)(0) is the corresponding amplitude of the m-th mode before the rapid

distortion. Dropping the superscript m for convenience, the coefficient ai for each mode

m is given as
Sk k2-2k2+Sklk2
al= 2 2
a-k22 x









k2k2 k2 k2-Ski
2 23/2 [tan-it ()tan-] ( )]
2k23/ 2, 22
(k +k )3 k, l2i- A;- f
1 33k1k
a2 = k2/x2 (2.16)

Sklk3k2-2k2+Skjk2
a3 = k2 2 X2
1 ~3

k2k3 1 k2 k2-Sk.
,2 ,.23/2 [tan- (- ) tan(- )]
2 2 32 2k22

where k= VkT+k2+k and x= 2 +X 2+ The coefficient c(m) is similarly obtained. It
1 2 3 2 3*
is clear from the above that the amplitude of each mode is entirely determined by the total
shear S=Ft. The dissipation rate s(S) can be evaluated as
1 N 2
e(S)/v= I < N {(Xm)2 3 [b()2 (S) +C(M)2(S)] } >" (2.17)
m=l i=l
Finally, the total velocity field is

uj(x, t) = [ b(m)(S) cos((m) -x + )(m) t)
m=l

+ c( (S) sin((m) x +)(o Mt)]

+ Fx2 8ii. (2.18)

Since the shear rate is high, the term o(m)t in the above acts merely as a random phase.




2.1.3 Isotropic Turbulence Generation in a Periodic Box


For the direct numerical simulations of particle collisions in this work, the
velocity field of an isotropic and Gaussian turbulence is generated by a random Fourier
modes representation (2.6). To effectively conduct a computational study on particle
collision, a region of finite size in which a large number of collisions occur should be
used with periodic boundary conditions. To render the turbulence represented by random









Fourier modes periodic in a box of volume R3, the random wave numbers, k( m), are

rounded to the nearest even integers while all other quantities are held fixed so that the
flow is periodic within the box of volume irt3. The periodic box of volume n3 yields
virtually the same collision statistics when the volume is increased to (27t)3. Hence it3 is
used throughout this study. The corresponding statistics (turbulence rmns velocity u0,

Reynolds number Rex, Taylor micro length scale X, Kolmogorove length scale T, and

Kolmogorov time scale Tk) are computed based on the modified wavenumbers. As will

be demonstrated using the present analysis outlined in 2.1.1, the collision statistics,

when normalized using R3( /v)1/2 for anI and <-Wr>, the results are the same using the

turbulence generated in an unbounded domain and in a periodic box.
Unless specifically mentioned, most of the isotropic turbulence used in the
numerical simulations throughout this study is within the periodic box of length nt and
have the following characteristics: root-mean-squared turbulence velocity u0=l.O,

integral length scale L, 11 =--0.594, Taylor micro length scale X=0.277, Kolmogorov length

scale r=O.023, Kolmogorov time scale Tk = (v / )I/2 =0.073, and Rex =40.1. The

turbulence is imposed on a box it3 with periodic boundary conditions in three
perpendicular directions. The periodic treatment of boundary conditions allows the
simulation results by a finite volume to be extended to an infinite homogeneous system.
Initially, a given number of monosize particles are randomly introduced in the
flow field of volume it3. The periodic boundary conditions are also applied to the

motions of the particles. The particles are advanced using ui(x, t) given by (2.6) with a

time step At. A semi-implicit finite difference scheme is implemented to integrate the
equation of particle motion and to detect collision. The scheme has an accuracy of second
order. The detailed error analysis of the semi-implicit finite difference scheme will be
given in next section.








2.2 Numerical Integration of Particle Motion and Error Analysis


Major concerns in numerical simulation are the accuracy and robustness of the
numerical scheme. Since the collision detection in the present simulation is based on the
calculation of particle trajectories, the collision kernel/rate/velocity/angle are affected by
the accuracy of the finite difference scheme. In this study, both tracking particles and
detecting collision are carried out in a Lagrangian frame. A semi-implicit finite
difference scheme is implemented to integrate the equation of particle motion and to
detect collision. This scheme is unconditionally stable. With consistent treatment for
both particle velocity and turbulence, The truncation error in the semi-implicit scheme is
0((At)2) and independent of 3. The accuracy analysis of the scheme is given as follows.
The particle dynamic equation in the absence of (or weak) gravity is
dv
dt = P(u v). (2.19)

The semi-implicit finite difference scheme for the particle dynamic equation (2.19) is
Vn+i -- n 1
--- n I (U.V)n+l +(U-V)n]
At 2
= p[(un+l + u-(vn++v)]. (2.20)
2

Taylor series expansions for u"n+1 and vn+1 give
~~'=~ +AtdVn +1 2d2vn dv
vn+ vn + dAt d + (At)2d + (At)3 3vn+ (2.21)
dt 2 2 dt2 6 dt

un+ =u AdU 1 Ad2u( 1 3d3un
dt 2 dt62 d-"
Each side of equation (2.20) can be evaluated as
dvn I d2vn 1 2d3Vn
LHS= ++ -(At) + -(At + O(At)3 (2.23)
dt 2 ( t dt2 6 dt3
I Atdun dvn 1 2 d~u dvn3
RHS=p(un-vn)+-PAt( dt )+-IP(At)( d 2u- 2 )+O(At)3. (2.24)
2 dt dt 4 dt dt








Then equation (2.20) can be rewritten as
Vn+l --V 1
0 = n-- [(u-v) n+' + (U-v) ]
At 2
dv" 1 d2vn 2 d3vn
+-(At) -+-(At)+d
dt 2 dt2 6 dt3 "


- pi3(u v"


I PAt( du- dv" ) P(At)2 (d2un
2 dt dt 4 dt


Thus, equation (2.20) is equivalent to

dvn 1n d2vn
dt 2)- t dt2


- (At)2 d3vn
6 dt3


I dun dvn I d2un d2vn
+- PAt( )+ (At)2( dt2)
2 dt dt 4 dt dt


I dAt[2vn
2 dt2


dun dvnA
dt -dt


6 d3v
6At)2 dt3


1 2 d2un d2vn
+ -I(At) (-2 )
4 dt2 dt2
Taking derivative with t repeatedly, we get


d2vn dut
dOt dt


dvn
dt


1 d3vn
- At[ d n
2 dtT


d2u
-d(


d2vn)]
dt2 A


- (At)2 dt-
6 dt4


+ 4 dt3


d3vn
dt3


d2un


d2v)
dt2


I d 4v
-At[ --
2 dt4


d3un d3
1 _dO -_ __d _


1 2d n + I p 2d4vn d4vn
-6 dt- 4 dt4 dt- )t


Repeatedly substituting above equations into the RHS of (2.26) results in
dv n (A)2 d3vn 1(2 d3vn
_+ (At)2_, + O((At)3)
dt 6 d 4 dt 3


d2vT
-dt2


(2.25)


(2.26)


d 3vn
dt3)
dtO


(2.27)


(2.28)









1(At)2 d3vn (2.29)
= M)2d--+ O((At)3). (2.29)
12 dt3
It is noted that the truncation error in equation (2.29) is independent of P3. Thus
motions of particles with very large P can be simulated without loss of accuracy.




2.3 Particle Collision Treatment


2.3.1 Collision Detection Scheme

Collision detection between any pairing particles is given as follows (Chen et al.
1995). At nth time step to, these two particles are initially located at

r1o =(x0, y1O, z10) and r20=(x20, Y20, z20) After a small time advancement At,

at (n+J)th time step ti, new positions of these two particles are r11 =(x11, y11, z11)

and r21 = (x21, Y21, z21 ). The particle position vectors at time t, which is between to
and t I, can be expressed as

r1(t) = {x1o0 + v1(t-to), yio0 + Vy(t to), Zio0 + vz(t to)} (2.30)

r2(t)= {X20 +v,2(t-t0), Y20 +Vy2(t-t0), z20 + Vz2(t t0)} (2.31)

The distance between two particles, s(t), is
s(t)= I r2 (t) ri (t)|. (2.32)
These two particles will collide if the condition s(t) relative distance s(t) varies with time t, and it is not a monotonic function of t. The
minimum relative distance occurring at time tm is determined by

9s,
ast=t =0. (2.33)
a~t ttm

Combining (2.30)-(2.33) gives
D(
tm =to -- (2.34)
D2









where

DI = (xiO X2o)(Vxl Vx2) + (Yo y2o)(Vyi Vy2)

+(ZIo -Z2o)(vi Vz2) (2.35)

and
D2 =(Vxi -v,2 )2 +(Vyi Vy2)2 + (vz -v)2.) (2.36)

Equation (2.33) does not guarantee that the moment, when the above mentioned

minimum distance occurs, has to be within the time interval to and to + At. Three cases

need to be considered for calculating the minimum distance within a time step.
(i) if tm < to, then the minimum distance is at the initial time to.

Smin = s(to) (2.37)

i.e. Smin = {(X1io -X20o)2 +(yio -Y2o)2 +(Z10 Z20o)2}1/2. (2.38)

(ii) if tm > to + At, the minimum distance must occur at the time to + At.

Smin = s(to + At) (2.39)

i.e. Smin = (s2 +Sy +s2)12 (2.40)

where
Sx= xl0 -x20 +At(vxl Vx2)
Sy = 0 Y20 +At(vyl -Vy2)

sz= zl0 -z20 +At(vzl Vz2).
(iii) if to < tm to + At, the minimum distance occurs during the time interval

to and to + At. In this case, we have

Smin= S(tm) (2.41)
2 2 2 .1/2
i.e. Smin = (sx + Sy + S )1/2 (2.42)

where
Smx = XlO X20 + (tm to)(Vxl Vx2)
Smy = YI0 Y20 + (tm to)(Vyl Vy2)

Smz ZIO Z20 + (tm to)(Vzl Vz2 ) .





23


After Smin is evaluated within the time step, the examination of collision events is
still needed because Smin can be either larger or smaller than R. Collision occurs if

Smin < R (2.43)
At the collision instant tc,

s(t) = R (2.44)
and s(tc)=I r2(t0)- r(t.)1 (2.45)
Hence the collision instant tc can be obtained solving (2.44) and (2.45)
-b- ]b2 -4(s R2)w2
tc =2to+ (2.46)
2w2
where

b=2[(xio -X20)(vx, -vx2)+(YIO -y20)(VyI -Vy2)
(ZIo z20)(vzl vz2)
2 22
SO {(x10-x20) +(yIo-y20)2 +(ZI0-Z20)2}12
W = {(Vxi Vx2)2 + (Vy! Vy2)2 + (Vzl Vz2)2}/2 .

With perfect sticking collision model, any collision can produce a new born particle
(daughter particle). The daughter particle will inherit all possessions of parent particles,
i.e., conservation of mass and momentum. Let the daughter particle be numbered 3, then
the conservation law gives the size of daughter particle as

d3 =(d + d32)/3 (2.47)
where di (i=l 1, 2, 3) are particle radii. The velocity components of daughter particle are

Vx3 d + 2d2 (2.48)
d3

v = d3+ Vd3
Vy3- y 3y2 2 (2.49)
d3
vzld3 + V z2d 3
vz3 1 2d (2.50)
3and the position components at the end of time step
and the position components at the end of time step








3 3
X3 = {[xl0 + vxl(tc t0)]d1 + [x20 + Vx2(tc to)]d3} / d3 + v3(to t + At) (2.51)


Y3 = {[Y0io +Vy(tc -t0)]d + [Y20 + Vy2(tc to)]d} / d + Vy3(to tc + At) (2.52)


Z3 = {[z 0 + Vzi(tc to)]d3 + [z20 + vz2(tc t0)]d} / d3 + vz3(to tc + At) (2.53)


In general, the collision detection takes a lot of computer time since a large
number of particles are considered. The primitive collision detection scheme searches the
collision candidates by going through all particles N, so that the computing time is

proportional to N2. The collision detection method in Chen et al. (1995) is an efficient
scheme, and it is also implemented in this thesis. By dividing the computational domain
into a number of small cells, the potential collision partners for a given particle in one cell
are then searched within this cell and neighboring twenty-six cells during one time step.
This search only involves a small number of particles. The total computing time with this
scheme can be reduced dramatically. Only binary collision is considered by assuming a
negligible probability of multiple collisions in a small time step in a dilute condition. For
any particle moving out of the computational box, it is re-introduced into the box by
invoking periodicity. The collision rate or kernel can be determined, in principle, by
counting the number of collisions per time step. Initially, a given number of particles are
randomly distributed into the computational domain. The particles are evolved with
turbulence for a period of time before collision detection turned on. The collision events
are not counted until all particles with finite inertia have reached dynamical equilibrium
in turbulence.



2.3.2 Post-Collision Treatment


The collision event is counted when two particles are brought into contact, as
stated above. The collision among particles and its subsequent treatment are expected to
disturb the original system and permanently impact the subconsequent collision
evaluation. For the droplets in gas turbulence, the collision may, most likely, result in











coalesce of two droplets and a larger droplet is then formed by the colliding droplets.
Similar phenomena may be found in a system consisting of bubbles and fluids. The
collision between two solid particles may behave in different ways. Two solid particles
may stick together upon contact or the colliding solid particles will separate after
collision. In summary, the issue dealing with immediate consequence of those contacts is
referred as post-collision treatment. Several models with regard to post-collision
treatment have been developed during past decades.
In Chen et al. (1995) every collision results in the collision partners disappearing
from their respective size groups to produce a larger particle. Mass and momentum
conservation laws were applied for the birth of the new particle. This scheme is
considered to be a physically realistic model assuming a unity collision efficiency. It is
hereinafter referred as 'KEEP ALL' model. Balachandar (1988) used a different post-
collision scheme. Starting with monosize particles in system, after each collision, the
resulting larger particle was discarded in the computation so that it does not contribute to
future collisions. This post-collision treatment is hereinafter referred as the 'THROW
AWAY' scheme. Sundaram & Collins (1997) employed a 'HARD SPHERE' collision
model in which the particles were assumed to be rigid spheres and forced to bounce back
after the contact. Momentum conservation is applied to the particles after collision. In
contrast to the 'KEEP ALL' and 'THROW AWAY' schemes, the 'HARD SPHERE'
model allows a particle to have multiple collisions with other particles in a system. Wang
et al. (1997) tested three different schemes and presented one of their own. In their
scheme, collision does not disturb the system. The colliding particles are allowed to
overlap in space (hereinafter referred as 'OVERLAPPING' scheme), but no larger
particles are formed after the collision. Two colliding particles can separate after their
trajectories satisfy Ir2 (t)- r (t)l>R. Thus, a particle can have multiple collisions with

other particles. These four different post-collision treatments will, in principle, result in
different collision rate/kernel.









a(11

1.6



-1..... Dt=0.005, simulation
Dt=0.01, simulation

1.2


1.0. -- -



0.8



0 .6 -------- '
0.0 0.5 1.0 1.5 2.0 2.5 3.0

T



Figure 2.1 The effect of time step on the cumulative average of the normalized
collision kernel ca I for inertialess particles in turbulence. Particle size D=0.05.



2.4 Computer Simulation



2.4.1 Time Step


Three collision models: "THROW AWAY," "KEEP ALL" and "HARD

SPHERE" are employed in this work. For the following simulations with '"THROW

AWAY" and "KEEP ALL" collision models, the time step At=0.01O is used to advance the

motions of particles. Figure 2.1 shows the effect of time step on the collision kernel a *,

using "KEEP ALL" collision model. It can be seen that there is almost no difference

between the results of At=0.01 and At=0.005. Similar results are obtained by '"THROW

AWAY" scheme. The volume concentration of suspended particles is usually quite low

in many natural systems such as spray burners and air scrubbers, which is within the









interest of this work. In such dilute systems, the multiple collisions during a small time
step may be negligible, thus binary collision mechanism is considered solely in this
study.



2.4.2 Turbulence Realization


The averages of statistical quantities are evaluated in two directions: average over
all time steps and turbulence ensemble average over all turbulence realizations. The total
time steps mainly depend on the particle response time and the number of collision pairs
during whole time period. We start counting the time steps for collision detection after
the motions of particles have been evolved for three times the particle response time.
Ideally, to form the turbulence ensemble average, the numerical simulation should be
repeated an infinite number of realizations with different initial particle distributions. An
infinite number of turbulence realizations is impractical due to limited computer capacity
and may be unnecessary from the viewpoint of the accuracy of numerical scheme.
Figures 2.2 (a) & (b) show the turbulence dissipation rate against turbulence realizations.
While the ensemble averaged turbulence dissipation rate varies with the realization by a
large amplitude shown in figure 2.2 (a), the cumulative average of turbulence dissipation
rate approaches a constant for the number of turbulence realizations greater than 30, as
seen from figure 2.2 (b). Figures 2.3 (a) & (b) show the small eddy shear rate against
turbulence realizations. It can also be seen that, although the ensemble averaged small
eddy shear rate randomly varies with the turbulence realization in figure 2.3 (a), the
cumulative average of small eddy shear rate is close to a constant by the 40th realization
seen in figure 2.3 (b). Therefore, in this study forty turbulence realizations are used for
turbulence ensemble average.


2.4.3 Validation of the Collision Detection Scheme


Starting with monosize particles in system and using the "THROW AWAY"
collision model for post-collision treatment. The evolution of the particle number n1 (t)









follows the simple population balance equation:

dn1/dt=-a, In2 with n1(t = 0)=n0. (2.54)

The solution is
no / n1(t) = l +a n0t. (2.55)

It should be mentioned that the collision rate of monosize particles (2.54) is only accurate
for large number of particles in a system. For small number of particles, however, the
collision rate (2.54) must be corrected. The detailed discussions on this issue will be
given in 6.2. Since the statistics for the number of the remaining particles n1(t) is much

more accurate, the average slope of the curve 1 / n1(t) gives the collision kernel, so it can

be used to check the accuracy of the collision kernel obtained by simulation in laminar
shear flow.
To validate the collision detection, particle collision in a uniform shear flow of
velocity gradient [= 1.0 is first considered. For large number of particles the theoretical
prediction of the collision kernel aoij of Smoluchowski is accurate and is given by

ac = 4 R ir- / 3 For a small number of monosized particles the theoretical prediction

of the collision rate of Smoluchowski has to be corrected, as mentioned above. Ten
thousand particles of radius 0.075 are introduced into a box of volume 2x2x2. The time
step is Dt=0.01 and a total of 800 time steps is used. The number of shear flow
realizations is 80. Since no periodic condition may be imposed along vertical direction in
uniform shear flow, periodic boundary conditions are employed in the streamwise and
spanwise directions only. In such a case, a boundary correction of the collision rate has
to be imposed (Wang et al. 1997). Another correction on the collision rate is also applied
due to the inaccurate expression of the collision rate (6.2) Figure 2.4 shows the
particle number evolution. The collision kernel a 11 can be obtained from the average

slope of the curve. Very good agreement can be observed between simulation result and
Smoluchowski's prediction. Hence, the collision detection scheme and the numerical
implementation are validated.















9

8
(a)
7

c 6
o
L 5
S
S4




0
U
C
1e 3 .


**
p 2

1 *. *.
1 .* n I .
** *

0 5 10 15 20 25 30 35 40
Turbulence realizations

C
2.0


(b)
1.6


i ** **.** .** .
I 2 ** *12

U
C
S
0.8
I-


0.4 --- ---- --- ---- --- ---- --- ---
0 5 10 15 20 25 30 35 40

Turbulence realizations


Figure 2.2 Turbulence dissipation rate e against turbulence realization. (a) Variation of
ensemble average. (b) Cumulative average.













(S / v)1/2
25

(a) **
20 -

*o
15 .

.*** 4 *
10 *


5


0 I I "; ...; .. .. .. .......... ; .....I. .. .
0 5 10 15 20 25 30 35 40
Turbulence realizations


(s / v)"2

14.2
*
14.0

13. (b) *
13.8 6 *
13.6 \ ^


13.4

13.2

13.0

12.8

12.6 ----
0 5 10 15 20 25 30 35 40
Turbulence realizations


Figure 2.3 Turbulence small eddy shear rate (& / v) 1/2 against turbulence realization.
(a) Variation of ensemble average. (b) Cumulative average.










no / n

1.06


1.05

Smoluchow ski
1.04 ... Present Siulation


1.03-


1.02


1.01


1.00 I --I
0 1 2 3 4 5 6 7 8
T


Figure 2.4 Inverse of total number of monodisperse particles against time in laminar
shear flow. F=1.0, D=0.015.




For "HARD SPHERE" particles, the time step for advancing the motions of

particles is much less than 0.01. It is found that At=--0.001 may generate almost same

result as At=0.0005 for large inertia particles. For small inertia or zero inertia particles,

the collision kernel strongly depends on time step due to multiple collision mechanism,

which is shown in figures 2.5 for uniform shear flow. A pair of particles can have tens or

hundreds of collisions depending on the size of time step before they completely separate.

Similar multiple collisions have been found for particles in turbulence. Due to this

technical difficulty, the "HARD SPHERE" collision model is limited to particles with

large inertia.




































V2I




V2


Figure 2.5 Multiple collisions of HARD SPHERE particles in laminar shear flow.














CHAPTER 3
A NEW THEORETICAL FRAMEWORK FOR PREDICTING COLLISION
RATE OF SMALL PARTICLES IN GENERAL TURBULENT FLOWS




A new theoretical framework is developed to predict the collision rate and collision
velocity of small, inertialess particles in general turbulent flows. The present approach
evaluates the collision rate for small particles in a given instantaneous flow field based on
the local eigen-values of the rate of strain tensor. An ensemble average is applied to the
instantaneous collision rate to obtain average collision rate. The collision kernels
predicted by Smoluchowski (1917) for laminar shear flow and by Saffman & Turner
(1956) for isotropic turbulence are recovered. The collision velocities presently predicted
in both laminar shear flow and isotropic turbulence agree well with the results form direct
numerical simulation for particle collision in both flows. The present theory for
evaluating the collision rate and collision velocity is also applied to a rapidly sheared

homogeneous turbulence. Using the mean turbulent shear rate (6/v)1/2 as the
characteristic shear rate to normalize the collision rate, the effect of the turbulence
structure on the collision rate and velocity can be reasonably described. The combined
effects of the mean flow shear and the turbulence shear on the collision rate and collision
velocity are elucidated.




3.1 Introduction


Smoluchowski (1917) considered collision rate among spherical particles in a
laminar shear flow, (ux, uy, u.) = (Fy, 0, 0) with a constant gradient F. For a target








particle of radius ri centered at an arbitrary position xi, any particle with a radius rj
moving toward the target particle will cause a collision if
Ixi xj| < Rj = ri +rj (3.1)

is satisfied. The collision rate can be evaluated as
= ninj fwr "cr ^ ^o(3.2)

where wr is the radial component of the relative fluid velocity between xi and xj, wr<0

indicates an impending collision, -nln2wr is the particle flux moving inward to the target

particle, and A is a spherical surface with radius Rj-ri +rj. Using standard spherical

coordinates (R, 0, 4)) centered at particle i (see figure 3.1), wr on the spherical surface of
R=Rij can be written as Wr = FycosO = Rij sinO cosO cos). With dA= Rj sinO dOdo, it is

easily seen that
4li ~~~F R3 (3.3)
43 i
ncij nnj 3 r Rij (3.3)

or

Fij RR. (3.4)
3 i

Saffman & Turner (1956, hereinafter referred as ST) presented a classical theory
for the collision rate of small droplets in Gaussian, isotropic turbulence. The collision
rate among particle size group i and j is

c= ninj fWr. (3-5)

The ensemble average, denoted by < >, is necessary since turbulence is random. Because
of the continuity of the fluid flow, the volume influx entering the spherical surface of
radius Rj is equal to the efflux so that
nc1= ninj < entire Iwrl dA>. (3.6)
cij 2 n n entire sphere r

ST further interchanged the integration with the ensemble average so that ncij can be

evaluated in isotropic turbulence,








ncij 2 ninj entire sphere dA. (3.7)


-Wr y
0
I,
~C



u = Fy x
/ 4
,

z


Figure 3.1 Local coordinates for collision rate calculation in laminar shear flow.


For isotropic turbulence, the above becomes

ncij = ninj 27tRR (3.8)
where x is the local coordinate parallel to the line connecting the centers of the colliding
particles as shown in figure 3.2. For particle size smaller than Kolmogorove length scale
il, the relative velocity upon the collision was evaluated using the standard results of
small-scale turbulence theory as

= Rij< 01 > = Rij i 12 (3.9)

where s is the dissipation rate of turbulence and v is fluid kinematic viscosity. This gives
3 8n s ]/2 3 6 1/2
ncij = ninjRij [ ]v =.2944nnR () (3.10)

or aij=1.2944R (S 1/2 (3.11)

or C,3 =1.2944 (3.12)
oij (3-!)








*
where aii is normalized collision kernel.


V2


x



VIX V 2x = Wx



Figure 3.2 Sketch of particle collision velocity. The separation

distance is x2-xI = Rij= (ri+rj).


It needs pointed out that from equation (3.6) to equation (3.7), the interchange
between the integration with the ensemble average is only an approximation even in a
Gaussian, isotropic turbulence. Furthermore, the interchange between these two
operations severely limits the extension of ST's result and approach to other turbulent
flows such as the near wall region where the mean flow gradient contributes to . Of

course, in the theory of Smoluchowski for laminar shear flow, this issue does not arise
since the ensemble average is not needed. In a typical industrial facility where particle
collision leads to the desirable growth of particles, the inhomogeneity of the turbulent
flow structure is common. Due to the effects such as turbopheresis, particles tend to be
driven toward wall regions to cause a further inhomogeneity in the particle concentration
distribution. It was not clear how one can predict the collision rate of small particles in
the presence of both strong mean flow shear and high turbulence intensity (Kontamaris,
1995).
In this chapter, a new theoretical framework is presented for the collision rate of
inertialess particles, whose sizes are smaller than the Kolmogorove length scale, in
general turbulent flows. The present analysis starts from equation (3.5). Using the
leading order term, (xi-xj)-Vu, in the Taylor series expansion to approximate the relative









fluid velocity, w, at the collision instant, the collision rate for an arbitrarily given Vu is
evaluated first. The ensemble average is then carried out in a Gaussian, isotropic
turbulence to obtain the collision rate and collision velocity. Direct numerical
simulations are also performed to obtain the collision rate and collision velocity in a
Gaussian, isotropic turbulence. Good agreement is obtained between the present
*
prediction and the simulation for the collision kernel cai Excellent agreement is

obtained for the average collision velocity between the prediction and the simulation.
While the present result for the collision rate agrees with the prediction of ST, the present
theory predicts an average collision velocity <-Wr> that is 1.58 times the value given by

ST. The difference is caused by the bias of the collision toward higher values of collision
velocity which was not considered in previous studies on flow induced particle collisions.
Using the rapid distortion theory (RDT) for a rapidly sheared homogeneous turbulence
and the random Fourier modes representation, the dependence of particle collision kernel
aij on the mean flow shear rate and the turbulence structure in homogeneous shear flow is

studied under the present theoretical framework. It is found that the collision kernel, after
normalized by particle volume and the turbulence mean shear rate characterized by the
dissipation rate, mainly depends on the ratio of the mean flow shear rate to the turbulence
mean shear rate.




3.2 Analysis

The relative fluid velocity, w = u1 -u2, between target particle centered at x, and

the colliding particle centered at x2 in space with Ix, x21 = Irl2 = R less than the

Kolmogorove length scale -q can be expressed using Taylor series expansion as
w = U2-U1 ; r12.Vu +... (3.13)

r12 = X2- Xl (3.14)

to the leading order. The collision rate, ncl for a given Vu at a given instant is thus









nc12 =nn2 Wr< -w-ndA (3.15)

where n is the outward normal of the surface given by Ix2 X|i = Rj = R and w= w-n.

For a spherical surface, n= r12/R so that
J 1
-w-ndA = f -wJrl2 dA
wr;
SR Ow, 12 r1- r2 2dA. (3.16)

Representing Vu as the sum of a symmetrical part (rate-of-strain tensor) and an anti-
symmetrical part (vorticity tensor), it can be easily seen that the antisymmetrical part of
Vu does not contribute to wr*. Physically, it is simply because a rigid body rotation does
1
not contribute to normal velocity. For a symmetric tensor E = I (Vu +(Vu)T) or a

symmetric matrix Eij, a linear transformation, which is a rotation of the coordinate system

from (x, y, z) to (x-, z ), can be easily found to reduce Eij to a diagonal form as

=[eR 0 0 -
E = 0 ey 0 (3.17)
0 0 e2_
where (ek ey eZ ) are the eigen-values of E and are the principal values of the rate-of-
strain tensor. Due to incompressibility, ez + ey + ez = 0 so that there are only two

independent parameters (say ez and ey ) in E in order to evaluate the integral in (3.16).
Arranging the eigenvalues in the order of
ez =-(ex + ey) _ and defining
_y
-ewith -0.5<4<1 (3.19)

the integrand in (3.16) can be expressed as

r12 -Vu-rl2 = (ez R 2 + ey y 2 + e- j 2)


=- eR [-X 2 + Cy 2 -(1+g j 2]








= ex R2[cos2O+ sin20cos2-( l+)sin20sin2o] (3.20)

where a local spherical coordinate system (x =RcosO, Y =Rsin0cos+, z =Rsin0sino) has
been introduced. Hence
2itr
J Wr0-w-ndA= -R3ez ff <0[cos20+ 4sin20cos20-(1+-)sin2Osin2O] sinO dO do
W <0 JjWj.<0
fr W
0 0
=R3e e (9). (3.21)
Again, Wr<0 in the integration limit implies that only the non-positive values inside the

square bracket are evaluated in the integration. While an analytical expression for J9() is
difficult to obtain due to the dependence of the integration limits on arbitrary 4, accurate
numerical integration for Y(Q) can be easily carried out (using Simpson's rule). The
following interpolation is then constructed for (4) based on numerical integration
results,
8
8( [0.90725 + 0.2875(0.5+4)2 + 0.333(0.5+4)4] <0
8
,j [I + 0.554 + 0.7240712 0.9687443

+ 0.739244 0.230355]. >0 (3.22)
F 8
Consider the laminar shear flow for example. In this case, 4=0, eR = 2, H(0) = 3, so that
1 33
nc2 = n1n2 FR = nln2 FR3 which is the same as given by Smoluchowski (1917).

Equation (3.22) is valid for -1_<4-0.5 as well since it is based on (3.21). The restriction
for 4>-0.5 results only from equation (3.18) when the eigenvalues are arranged in such a
manner. For -1<_< 0, JY() is symmetrical with respect to C =-0.5. For example, 4=0

corresponds to a 2-D stagnation flow in (Rx z ) plane and C=-1 corresponds to a 2-D

stagnation flow in (R Y ) plane so that the collision rates are completely equivalent. It is
also interesting to note that c=-0.5 is equivalent to 4=1 since both correspond to an
axisymmetric stagnation flow with 4=-0.5 for an axisymmetric contraction and C=l for an
axisymmetric expansion; the only difference is the scale ek With these examples in








mind, a simple analysis for the flow field quickly shows that M1(_<0) can be obtained from
1;2f0) as

1--y )= I(1-- -1)-1+y :() for 4>0. (3.23)

Finally, the collision rate for small inertialess particles in a turbulent flow is simply

nC12 = nn2R3 < ej 9(4) > (3.24)
where the ensemble average is taken over all possible values of the rate-of-strain tensor.
It is emphasized that this result is applicable to general turbulent flow. Needless to say,
the isotropy assumption is not needed in the present framework. When the detailed
knowledge of Vu is available, such as from DNS or random Fourier modes
representation (Kraichnan, 1970) of turbulence, the ensemble average in (3.24) can be
carried out.
The foregoing analysis can also be extended to evaluate the average of the normal
(or radial) component of the collision velocity <-Wr>. For a given position with arbitrary
eR and 4 at any given instant, the instantaneous average of the normal collision velocity
can be calculated by integrating over the spherical surface of radius R as
0wr = Wr< (-wr)dA (3.25)

In the above, the superscript 4. on the left hand side denotes the area-average, (-Wr)dA in
the numerator can be interpreted as proportional to the probability of a particle striking
the target surface at a position (0, i) over an area dA and wr in the numerator is the

relative radial velocity of the collision at (0, 0) on the target surface. The denominator is
the normalizing factor for the probability. The product, wr(-wr), clearly indicates that

A is a biased average in favor of these regions in (0, 0) where (-wr) is high.
Suppose that NR realizations of turbulence be taken in a turbulent flow, the total number
of collisions will be
Nc = NRnIn2< .r< (-wr)dA>








per unit time using equation (3.5). The chance of a particle striking the target surface

over an area dA at (0, 4) relative to the axis of the principal direction R of the
instantaneous rate-of-strain tensor in a given realization is then nin2(-wr)dA|w<0/Nc.

Integrating over the target surface and summing over NR realizations, the ensemble
average, , is obtained,

Snin2(-Wr) Wr
W> =R < Wr Nc d> = (3.26)

The above can be simplified as

<2 9(e ) >
=-R < > (3.27)
r

where
2n
( = f f [cos20+ 4sin20cos20-(l+)sin2Osin2w]2r<0 sinO dO do. (3.28)
0 0
The integration can again be evaluated numerically and represented in the following
piecewise interpolations

"G =0.4+1.0777(0.5+4)2 0.64767(0.5+4)4 for 4<0

=0.62831 + 0.68495 0.0910832

+0.09030143 0.0346584 for C>O. (3.29)
2
Similarly, the mean square value of the radial component velocity < wr > can be
evaluated as

< e3 Wf) >
= R (3.30)

with

=0.17114+l1.2643(0.5+)2-0.48123(0.5+;)4 for 4<0

=0.45714 + 0.976554 + 0.396062








+ 0.072430(3 0.0291C4 for _>0. (3.31)
The functions 4), q(4)/I(), and 7{)/4( ) are shown in figure 3.3. Take a laminar
shear flow for example: ez =172 and 4=0 so that G/H= 0.62832 (=-d5) and G/H=-16/35.
It is easily seen that
F2 n8 18 7t
=-R-jj/[|]=-0 FR, (3.32)

2 2F3 168 18 4 ,
=R 835 3/[3]=-(FR)R2 (3.33)

The results of these two moments of wr can be used to validate the present theoretical
formulation by comparing with direct numerical simulation for particle collisions.


0.04-
-0.5


0.0 0.5


Figure 3.3 Variations of i(, G(), K-(Q. ) with where

fl-38 ](),f2= ) andf3= .








To apply the above results to turbulent flows, we consider the following two
cases: i) Gaussian isotropic turbulence; and ii) rapidly sheared homogeneous turbulence
under a mean flow gradient F. In both cases, the turbulence can be represented using
random Fourier modes and ensemble averages can be obtained without resorting to the
direct numerical solutions to the Navier Stokes equations for the fluid flows. Pertinent
comparison can be made with ST's prediction in the first case. Insights on the effect of
mean flow shear can be gained by examining the collision rate in the rapidly sheared
turbulence.




3.3 Numerical Simulation


The detailed representation of the isotropic turbulence and rapidly sheared
homogeneous turbulence are given in Chapter 2. In short, the isotropic turbulence is
represented in the form of
N
Ui(X, t)= Y[b m) cos(k(m) .x +tom) ( t )
m=!

+clm) sin(k(m). x + o(m) .t ) ] (2.6)

where k(m)and ((m) are the wavenumber and frequency of the m-th mode. The random

coefficients b1m) and cm) are their corresponding amplitudes. For sheared

homogeneous turbulence, the flow field is given by
N
Ui(X, t)= E[bim)(S)cos( (m) -x+to)(m)t )
m=!

+ cm)(S)sin(X(m) -x +o(m) -t)

+ Fx2 8i (2.18)

where X(m) is the new wavenumber after the distortion by the mean flow shear, S = Ft is

the total shear by the mean flow, and b7m)(S) is the amplitude of the m-th random mode


after the rapid shearing.









The scheme developed in Chen et al. (1995) is employed here to search for
particle collisions. Detailed description on the collision algorithm is given in Chapter 2.
Periodic boundary conditions are employed for both turbulent flow and particle motion. A
number of particles (Np) are randomly introduced in the flow field of volume n3. The

average collision velocity is obtained based on all the collision events for whole time
period and all turbulence realizations.
As discussed in Chapter 2, four different post-collision treatments will give
different collision rate and it is not clear which treatment is the best. In the study
throughout this chapter, the 'THROW AWAY' scheme is implemented. However, an
extremely low particle volume concentration is used. The initial particle volume
concentration is chosen carefully so that the average number of collisions in each
turbulence realization is less than 1 The post-collision treatment thus has little practical
effect on the collision statistics. A large number of turbulence realizations are used to
obtain sufficient ensemble average. A subsequent cumulative time average smoothes out
statistical noise in the collision rate.




3.4 Results and Discussions


3.4.1 Comparison with Simulation Results in a Laminar Shear Flow


In an earlier paper dealing with particle collision a laminar shear flow (Hu & Mei,
4 .
1998), comparison for the collision kernel, 1 FR3, based on Smoluchowski's prediction
'3
and the direct numerical simulation was presented; excellent agreement was obtained. To
confirm the present analyses in the laminar shear flow, we compare the collision velocity
given by equations (3.32-3.33) with that based on the direct numerical simulations. Zero
inertial particles of radius 0.009, 0.0125 and 0.02, respectively, are introduced into a
cubic box with length Lx=2, Ly=2.0, and Lx=2.0, with the restriction 0.1
uniform shear flow with a shear rate r=I.0 is imposed and the flow velocity field is given









by v=(u, v, w) = (Fy, 0, 0). Periodic boundary conditions are employed in the streamwise
and spanwise directions. Particles near y=0.1 and y=1.9 have less collisions because
there are no particles in regions of y<0.1 and y>l .9 in the simulation. A correction due to
this boundary effect (Wang et al. 1997) was needed for the collision rate (Hu & Mei,
1998). However, this boundary correction has little effect on the collision velocity since
the decrease in the collision kernel affects both the numerator and denominator in
equation (3.25). Three hundred time steps with a step size At=0.01 are used to advance
particles and a total of 40 realizations were employed to further increase the statistical
accuracy. Ten thousand particles are used for r=0.009, 0.0125 while four thousand
particles are used for larger size with r=0.02. Table 3.1 shows the comparisons for both

<-Wr> and . Since is the second order moment of Wr, excellent agreement

for these two moments indicates that the basis of the present formulation is sound. It is
worth noting that a naive, Eulerian-based estimation of the average collision velocity
4
based on the incoming particle flux, i FR3ni, and the available collision area, 2nR ,


would lead an erroneous value of FR instead of n7 FR for <-wr>.




Table 3.1 Comparison of the collision velocity among monosized particles
between prediction and direct numerical simulation in laminar
shear flow.

Particle <-Wr> <-Wr>
r r
radius simulation prediction simulation prediction

0.009 5.6421x10-3 5.6549x10-3 3.7018xl0-5 3.7029x10-5
0.0125 7.8638x10-3 7.8540x10-3 7.1529x10-5 7.1429x10-5
0.02 1.2425x10-2 1.2566x10-3 1.8012x10-4 1.8286x10-4








In a laminar shear flow, Wr=-UxCOS0 and the collision angle Oc between the

collision velocity w and the axis connecting the center of colliding particle pair,
n/2_
easily evaluated so that the pdf for 0c is readily obtained. Since (-Wr)wr< sin0dOdo is

proportional to the probability of the particle striking the target surface at (0, )) with a
solid angle do)=dOd), it is readily seen that


(-wr)dA = -FR3sin2OcosO cos4) d0d4) for (0, 4)E Q,
= 0 elsewhere (3.35)

where Q is the region defined by (2 <0_
Wr<0. Interpreting sin20cos0sino as proportional to the probability density function of

incoming particles striking the target particle near (0, )), further integrating over 0 from 0
to 27t, treating 0 normalization, one obtains the pdf for the collision angle Oc


p(Oc) = -B sin2 ccos0c for in<_O
=0 for 0<09 where B is a normalizing factor which is 3 for 0 in radian and t/60 for 0 in degree. The

average angle is =f Ocpdf(0c)dec = 2.237 radian or 128.17. The numerical
7t2
simulation gives = 128.13. Figure 3.4 compares the pdfs based on the above
prediction and the direct numerical simulation using ten thousand particles of radius
0.009 over 40 realizations of initial particle positions. Excellent agreement is observed
for the whole region it/2<0c< The present theoretical framework is thus validated in the
laminar shear flow.









0.025 ....-,------



0.020



0.015



0.010

Analytical prediction
0.005 Simulation



0.000 # -,--
90 100 110 120 130 140 150 160 170 180
Collision angle 0 c

Figure 3.4 Comparison of the pdfs for the collision angles 0c between
the theory and the simulation in a laminar shear flow.




3.4.2 Collision Rate and Velocity in a Gaussian, Isotropic Turbulence


For particle collision in isotropic turbulence which is represented using equation
(2.6), the dimensionless collision kernel

aIl
S- R3(e/v)12 (s/v)1/2 (3.37)

is evaluated using NR=500 realizations. The dissipation rate E is based on the ensemble

average of the same NR realizations. In each realization, ez W/(4) and E/v are computed in

the flow field with 654 statistically independent data. It is found that a1I varies slightly

with the number of the Fourier modes, Nk, used in the turbulence representation.

Furthermore, it weakly depends on the power g in equation (2.10) with which the random









*
wavenumber is chosen. Figure 3.5 shows the variation of an as Nk increases from 100

to 4096 for t=1.2 and 1.4 for ri 0= 0.05 (Rex=53.8). The result seems to converge after
*
Nk=2048; a, 1(P.=1.2) converges to 1.2935 while a, 1(t=1.4) converges to 1.2945 with a

difference less than 0.1%. They are both very close to 1.2944, the value predicted by ST.

It is worth commenting that due to the rapid decrease of the energy spectrum E(k) in the

high k range, not all wavenumbers that are randomly generated need to be used in

equation (2.10). For those high wavenumber modes that correspond to very small

amplitudes, they can be simply neglected without compromising the accuracy. Thus the

effective number of the random modes is smaller than the specified Nk. From the

simulations, the effective number of the modes for g=1.4 is roughly three times that for

g=1.2 for the same Nk. Hence the fact that it takes larger values of Nk for p.=1.2 than for

ji=1.4 to get a converged result is not surprising. It is also interesting to note that, using

Nk=1024 and g=1.2, a higher value of Rex (=124.4 using fi 0=0.01) gives a,1 =1.2899 as
*
opposed to al =1.2912 at Rex=53.8. This clearly shows that shape of the energy
*
spectrum E(k) in isotropic turbulence has little effect on a, With Nk=200 and .=1.2,
*
a1 =1.2756 which is only 1.45% less than the converged result. Thus in the following

direct numerical simulation for particle collision, Nk=200 and p=1.2 will be used.

To validate the present analyses, direct numerical simulations of particle collision

in isotropic turbulence are performed. The turbulence in the periodic box of length n has

the following characteristics: root-mean-squared turbulence velocity uo=l1.0, integral

length scale L1 =0.594, Taylor micro length scale X=0.277, Kolmogorov length scale

71=0.023, Kolmogorov time scale Tk=(v/)l/2=0.073, and Re,=40.1. With the modified

Fourier modes in the periodic box, the dimensionless collision kernel based on the
*
prediction is ac =1.2711 as opposed to a11 =1.2756 without the modification of the

wavenumbers. Initially, five hundred particles of radius r=0.005 (r/ril=0.217) are

randomly distributed in the box of volume n3 with a particle volume concentration of









8.44xl0O6. These inertialess particles are advanced using local fluid velocity. A total
time period of T=6 (i.e. T/Tk=82.2) is simulated with time step At=0.01. The average

number of collisions within the entire period of the simulation 0 realization is about 0.4271<1 based on our new expression of the collision rate in 6.2.
To obtain a reliable statistics, a total of 10,000 realizations is used to generate an
*
estimated collision pairs Nc=4271. Figure 3.6 (a) shows the simulation result for ctaI in

0
numerical simulation data due to the small number of collisions within each time step
despite the fact that 10,000 realizations have been used. A cumulative average is applied

to reduce the noise. Figure 3.6 (b) shows the cumulative time average of cc1 from the

simulation. The direct numerical simulation is less than the predicted value (1.2711) by
about 2.5%. This difference may be caused by a small effect of particle size, since the
theoretical predictions are expected to be accurate only in the small size limit, i.e. r/r< while the particle size in numerical simulation is r/rl=0.217. The effect of finite particle

size on the collision rate will be explored in Chapter 6. The agreement for o 1 between

the simulation and the prediction is quite good.
Table 3.2 shows the results of the prediction and the numerical simulation for the

two moments of particle collision velocity, <-Wr> and , in isotropic turbulence. The

predicted values are based on =1.2 and Nk=200 in the periodic box with NR=400

realizations. The particle sizes are r/rI = 0.2175 and 0.4875, respectively. Satisfactory
agreements are obtained. On the other hand, ST (1956) gave an expression, equation
(3.9), for the Eulerian-based average particle radial collision velocity. Using that
<-Wr>
expression, one would obtain -j
expression, one would obtain /v)2 0.206. The present prediction for the periodic

domain with NR=400, after ensemble average, gives a value of 0.328 for this
dimensionless radial collision velocity which agrees rather well with the direct numerical
simulation show in Table 2. Since the direct numerical simulation is based on the
tracking of particle trajectories, the probability-based prediction for <-w,> is consistent









with the Lagrangian approach. The Eulerian-based approach for <-Wr> does not take into

account the bias of the collision probability toward the high values of-wr; hence it gives a

lower value for <-Wr>.



cci


1.30


1.29


1.28



1.27


1.26


1.25 --
1.OE+2


1.0E+3


1.OE+4


Figure 3.5 Convergence ofacq as Nk --+8.


Table 3.2 Comparison of the collision velocity among monosized particles
between prediction and direct numerical simulation in Gaussian,
isotropic turbulence.

Particle <-Wr> <-Wr>
r r
radius (r/rhi) simulation prediction simulation prediction

0.217 4.5430x10-2 4.5611x10-2 2.7356x10-3 2.7296x10-3

0.4875 1.0093x 10- 1 1.0040x10-1 1.3405x10-2 1.3224x10-2












oa11


2.5

(a) -- siulation
....... Prediction
2.0



1.5




1.0-



0.5
0 10 20 30 40 50 60 70 80 90

t( / v)1/2

*
a11

1.8


1.7 (b) ------- Prediction


1.6 Cumulative average of
simtlation result

1.5


1.4


1.3


1.2
0 10 20 30 40 50 60 70 80 90


t(e / v)/2


Figure 3.6 Particle collision kernel ax from direct numerical simulation in Isotropic

turbulence. (a) Variation of the ensemble average. (b) Cumulative time average.












3.4.3 Collision in a Rapidly Sheared Homogeneous Turbulence


The statistics of the turbulence, such as four non-zero components of the
Reynolds stress tensor, generated using RDT and the random Fourier modes was first

checked against the analytical values at short times (Ftnl) given by RDT (Townsend,
1976, p.84) to ensure the correct implementations. It was found through simulation that
the development of the sheared turbulence with time does not depend on the Reynolds
number, Rex, of the initial state or the energy spectrum at t=0. The turbulence part of the

flow in the RDT is determined by the total shear S=Ft. The complete flow field, sheared

turbulence plus the mean shear flow, thus depends on two parameters: F and S=Ft. Since
the collision rate depends on the spatial structure of the turbulence while the turbulence
structure becomes increasingly anisotropic as the total shear S increases, finding an
appropriate representation of the collision rate in the homogeneous turbulent shear flow is

not trivial.
To develop a better understanding on the dependence of the collision rate on the
turbulent flow, it is instructive to examine the collision rate due to the sheared turbulence

but without the contribution from the mean shear F; this part of the collision kernel will

be denoted as ai1. Another word, a jj is evaluated using the flow field given by

equation (2.18) but without the last term [x25il which is deterministic. Following the
*
definition for all in the isotropic turbulence case, we use the dissipation rate (s/v)1/2,

which increases with S, as the turbulence characteristic shear rate to normalize t I I,

-* an!
c" = R3(/V) 1/2 (without contribution from mean shear F). (3.38)

*
It is easily seen that a,1 depends on the total shear S=Ft only. Figure 3.7 (a) shows the
-*
variation of &1 on S. In the high S limit, the simulation results suggest that a11

approach a constant of 1.1676 in the form of









a1 -1.1676+0.659/S forS>10. (3.39)

In this rage of 0
turbulence intensity becomes dominated by the longitudinal component in the x1-

direction. Based on RDT prediction which neglects the nonlinear interaction among
different scales, the longitudinal component of the turbulence makes up 92.7% of the

total kinetic energy at S=Ft=20. The anisotropy invariant IlIb reaches a value of -0.25 at

S=20. At such large values of total shear, S, the RDT results may become a little
inaccurate in comparison with DNS result. However, for the present work, since the
RDT-based turbulence is strongly anisotropic, it serves our purpose to evaluate the effect
of such anisotropy in the turbulence on particle collision rate. When the collision kernel

is normalized using (s/v)"2, the maximum difference in an is only about 15% at the


*
extreme limit of S-+co. If the isotropic result is used, &a, ~-1.2944, only a maximum

error of 10% will result from the limiting value of aE at very large total shear. This
*
encouraging result will be used to develop an approximation for a1 in the presence of

strong mean shear.
Now consideration is given to the particle collision kernel with contribution from
both the turbulence part and the mean flow shear part as the flow field is given by the
*
entire right-hand-side of equation (2.18). Figure 3.7 (b) shows aI as a function of the

ratio of the mean shear rate to the turbulence shear rate, F7/(- )/2, for a range of shear
V

rates. For a given F, (s/v)1/2 increases as t or S increases so that the abscissa mainly

reflect the effect of t or S. To see the effect of mean flow shear rate, F is made

dimensionless by the turbulence mean shear rate at t=0 in the isotropic state (eo/v)1/2.

Four values of F/(eo/v)1/2 are used: 0.87, 2.18,4.36, and 8.72; they correspond to the solid

symbols in figure 3.7 (b). The open symbols specifically correspond to the cases when

S=0 (or t--=0) so that the turbulence is still isotropic. The dimensionless values of aoI









seem to collapse reasonably well. At large values of r/FI( )1/2, the curve becomes linear
V
as the collision rate is increasingly dominated by the mean shear so that aI scales

linearly with F as Smoluchowski's theory predicts. A small degree of scattering exists for

F/( )/2<2. This is caused by the 15% maximum variation of a11I over the whole range

of total shear S, as already demonstrated in figure 3.7 (a). There are two possibilities for

F/(& )1/2 1: i) both F and S=Ft are very small even comparing with (So/v)1/2 of the initial

isotropic turbulence; ii) F is finite but S=Ft is large so that (E/v)1/2 become large

comparing with (so/v)/2. In the first case, the turbulence is closer to the isotropic state
_* *
so that a11 -1.2944. In the second case, the turbulence is highly anisotropic and a&,

-+ 1.1676 for very large values of S. No attempt is made to correlate d& with the total

shear S since S is not relevant in practical applications such as in turbulent pipe flow.
_*
Using ,11 =1.2944 in the isotropic state to represent the turbulence part of the collision

kernel, a simple interpolation is proposed to represent the effect of mean shear and
turbulence on the collision rate,

2.2 22 1.3333 22 1/2.2
ax 1 R3 (/v)1/2 1 {1.2944 + [ (/v)1/2 I 2 (3.40)

This interpolation is shown by the solid line in figure 3.7 (b). A satisfactory agreement

can be observed. It is also worth commenting that when F is normalized using the eddy
2
turn over time, 3u0 /&o, of the initial isotropic turbulence, the shear-rate parameter

F* F3u/o0, (3.41)

corresponds to F*=36.98 and 92.45 for F/(Eo/v)1/2 =0.87 and 2.18. In a low Reynolds

number turbulent channel flow, the maximum value of F* is around 35 at a location of 10
wall units away from the wall (Kim et al. 1987). Hence, the parameters used in figure 3.7

(b) are relevant to practical situations. Although a 10% error exists for a11 given by

(3.40), the advantage of this simple approximation is that the total shear S=Ft is absent








and it depends only on the spatial structure of the turbulence characteristics by the scalar

quantity (e/v)"12.

Figure 3.8 shows the radial collision velocity, <-Wr>, as a function of F/( )1/2, for
(V

different values of F/(' )1/2. Similarly, the data seem to collapse approximately into one
V
curve in the range studied. The following interpolation fits the radial collision velocity
well,
<-Wr> 1.7 1 r F 17 1/17 (.1
Reiv)/ {0.32451 + 1 ./v)12 } 41)
(eiv'/ 6v~l2](.

For the root-mean squared radial collision velocity, , the behavior is very similar to
r
that of <-Wr>. While the data is not shown here, the following interpolation, which fits
the data quite well, is provided,
1/2
r____ 1 7 0.3381 F 17 1/1.7 3
R(e/v)"2 {0.3681 + (e/v) } (3.42)

The application of the above expressions to turbulent channel flow, which is
different from the homogeneous, rapidly sheared turbulence, is yet to be tested. However,
since the rapidly sheared turbulence at large total shear S considered in this work is
already drastically different from the isotropic turbulence, there are reasons to expect a

similar dependence ofoa 1, <-Wr>, and on F and (e/v)1/2 in a turbulent channel flow

which is of significant industrial importance.




3.5 Summary and Conclusions


A theoretical framework has been developed to evaluate the collision rate and
collision velocity of small particles in general turbulent flows. The classical results for
the collision kernels by Smoluchowski (1917) for laminar shear flow and by Saffman &
Turner (1956) for isotropic turbulence are recovered. The present approach differs









significantly from that of Saffmnan & Turner in that the ensemble average is taken after

the collision rate for a given flow realization is calculated. This avoids the assumption of

isotropy, as needed in Saffmnan & Turner, and allows for the evaluation of the collision

rate in general turbulent flows. The collision velocity predicted in both laminar shear

flow and isotropic turbulence agrees well with the direct numerical simulation for particle

collisions. The present theory is used to evaluate the collision rate and velocity of small

particles in the rapidly sheared homogeneous turbulence. It is found that the effect of the

turbulence structure on the collision rate and velocity can be mainly captured by using the

mean turbulent shear rate (F/v)112. The combined effects of the mean shear and the

sheared turbulence on the collisions are elucidated. Simple interpolations are developed

to evaluate the collision rate and velocity for arbitrary mean flow shear rate and

turbulence mean shear rate.


1.4






1.3 -


1.2 -


I ........ ........ I.


10 1


100


102


S= Ft


Figure 3.7 (a) Normalized particle collision kernel in a highly sheared homogeneous
turbulence without contribution from the mean shear rate.


*
0
*
0*

0*
*


*



1.1676









6-


5-


4
4 x

("" /v) 1/2
(S )
3 0
-= 0.87
elOx 2.18
2-
"X x 4.36
8.72
1 0 S=O =0or t=0
Interpolation


0 1 2 3 4

(d/v) 1/2


Figure 7 (b) Collision kernel in a rapidly sheared homogeneous turbulence.
cO denotes the dissipation rate of the isotropic state at t=0.











1.5 -***---






S1.0

~F
^ >^ r
71/2
S 0C /V~

0.5 0.87
2.18
X^5 x 4.36
8.72
0 S=0 or t=0
--- Interpolatior
0.0 ,-,,- i
0 1 2 3 4
F
(E/V) 1/2

Figure 3.8 Collision velocity in a rapidly sheared homogeneous turbulence.
c0 denotes the dissipation rate of the isotropic state at t=0.














CHAPTER 4
EFFECT OF INERTIA ON THE PARTICLE COLLISION VELOCITY AND
COLLISION ANGLE IN GAUSSIAN TURBULENCE



Numerical simulation of particle collisions in isotropic, Gaussian turbulence is carried out
to study the effects of particle inertia on the turbulence induced collision velocity and
collision angle. An asymptotic analysis is developed to predict the effect of the small
particle inertia on the collision velocity. A PDF-based method is also implemented to
predict the effect of the small particle inertia on the collision velocity. The analysis

predicts that the increase in the collision velocity is proportional to the terms (P3Tk)-I

and (0Ck )-2, in which p-1 is the particle response time and Tk is the Komogorov time
scale. Good agreement is obtained among three different methods: the numerical
simulation, asymptotic prediction, and the PDF-based method, for the collision velocity
of particles with small inertia. The effects of both particle inertia and size on the collision
angle have also been investigated. It is found that collision angle can be significantly
affected by both inertia and size. Average collision angle and the variation of collision
angle increase as the inertia increases, while the decrease in the size will also reduce both
the average collision angle and the variation of collision angle.




4.1 Introduction


Particle collisions can take place through a variety of collision mechanisms.
Particles may be brought together by inertial centrifugal and gravitational forces. The
velocity gradient is responsible for collisions among particles in laminar shear flow and is
a dominant factor for particle collisions in turbulence. Brownian motion and Van der








Waal forces can also affect the collisions. One of the common characteristics of these
collision mechanisms is the ability to generate a negative relative velocity between two
particles along the line connected with two particle centers. Of significance are the
statistics of the relative velocity of two particles at contact (hereinafter referred as
collision velocity) and the relative angle between the velocities of two particles at contact
(hereinafter referred as collision angle). For solid particles, collision causes momentum
exchange among particles and may influence the local and global particulate flow
conditions. The suspended solid particles are produced during the manufacturing process
of materials, the combustion of heavy fuel and pulverized coal. The collisions among
suspended particles will greatly affect the properties of particulate flow and thus influence
the performance of the systems. Adhesion may occur when two colliding solid particles
have small collision velocity, while large collision velocity will produce large restitutive
rebounce velocities for the colliding particles and may overcome the bonding force
determined by cohesive surface energy. The collision also affects the angular momentum
of particles. Tangential velocity components will be imposed to the colliding particles if
the collision angle 012 is not equal to zero. The particle rotation will be either enhanced

or reduced depending on the collision angle 012, which determines the orientation of the

particle tangential velocities upon collision. Gunaraj et al. (1997) performed
experiments measuring the collision angle 012 between heavy particles (silica sand

particles with 1000-1500 gm in diameter) in turbulent channel flow.
The collision velocity is also a critical quantity in determining the collision kernel
and collision rate. In uniform shear flow, Smolochowski (1917) integrated the volume
flow rate using collision velocity along the spherical surface of collision radius R
(= ri + rj, where ri and rj are the radii of collision pair respectively) and obtained

collision kernel for uniform shear flow. In turbulent flow, particle collisions are affected
by local turbulence fluctuations. In the absence of any other force creating relative
motion between particles, these local fluctuations entirely control relative velocity
distribution which determines the collision kernel. In most of the previous theories, the
collision kernel in a particle-laden system is linearly related to the collision velocity, the








first moment of the relative velocity distribution of colliding particles. Thus the scale and
form of these particle relative velocity distributions directly determine the collision rate.
The fact that the collision rate and collision kernel in previous studies are all proportional
to the collision velocity explains the critical importance of the prediction of collision
velocity (Saffinan & Turner 1956, Williams & Crane 1983, Kruis & Kusters 1996, et al).
The particle collision has been studied in two different ways. One is based on
Eulerian method, which is favorable to theoretical prediction and was followed by
previous studies on the theoretical evaluation of both collision velocity and collision
kernel. The other is to use Lagrangian tracking in direct numerical simulation of particle
collisions. For inertialess particles in both uniform laminar shear flow and isotropic
turbulence, the collection of collision velocities using Lagrangian method shows a biased
average in favor of those regions where wr is high, as shown clearly in Chapter 3.

Saffman & Turner (1956) obtained the collision velocity using average integration on the
spherical collision surface by Eulerian method without considering the influence of the
biased average. Although their formulation correctly predicts the velocity correlation
between two points for turbulent flow, it has been incorrectly interpreted as the collision
velocity upon collisions. The effect of particle inertia on the collision velocity was
recently studied also by Sundaram and Collins (1997). They used two statistical
variables: g(R), the particle radial distribution function at contact, and P(w/R), the
conditional relative velocity probability density function at contact, where w is the
relative velocity of two particles at contact. It is noted that in Sundaram and Collins'
work, in additional to the assumption of the exponential form for P(w/R), the g(R) was
evaluated based on their numerical extrapolation form for distances smaller than the first
numerically evaluated point. However, the usefullness of g(R) and P(w/R) is rather
questionable.
In this work, we study the effect of particle inertia on increasing particle collision
velocity and collision angle by carrying out numerical simulation of particle collisions in
an isotropic, Gaussian turbulence, and developing an asymptotic analysis to predict the
effect of small particle inertia on the collision velocity based on Lagrangian description.
An integration method is also implemented to predict the effect of the small particle








inertia on the collision velocity. The increase in the particle collision velocity due to
inertia predicted by both the asymptotic analysis and integration method is in agreement
with the numerical simulation results. The analysis predicts that the increase in the

collision velocity contains terms that are proportional to (PT k)- and (pxrk )-2. Good
agreement is obtained by the numerical simulation, asymptotic prediction and the
integration method for the collision velocity of particles with small inertia. The effects of
particle inertia and size on the collision angle have also been investigated. It is found that
collision angle are significantly affected by both inertia and size. Average collision angle
and the variation of collision angle increase as the inertia increases, while the decease in
the size will reduce both the average collision angle and the variation of collision angle.



4.2 Saffman & Turner's Analysis


In Saffman & Turner's (1956) prediction of the collision rate among inertialess
particles, the volume flow rate of particles towards a target particle was evaluated by an

integration, , where wr is the radial component of the relative velocity.

The integration was carried out over the entire spherical surface of radius R, as shown in
figure 4.1. Because of the continuity of fluid flow,

=

= Entire sphere <|wrI> dS. (4.1)
For isotropic turbulence, which is true at small scales for anisotropic turbulence, the
above becomes

= 27iR2 (4.2)
where x is the local coordinate parallel to the line connecting the centers of the colliding
particles as shown in figure 4.2 and < > denotes ensemble averaging.






63


A y
SCollision area
Sfor the target
F particle



x








Figure 4.1 Sketch of particle collision scheme


W = V -V2
wx = VIx V2x

Figure 4.2 Sketch of particle collision velocity and collision angles (012 & 0 J).
The separation distance is x2 xi = R = (r, + r2).










In the limit of zero particle inertia, particle velocity v follows the fluid velocity u
very well so that the radial component of relative velocity w = vi v2 upon collision is

Iwxl-lulx U2xl-R-l = R( --2 )/2 (4.3)
Ox 157t v
and =5 R2 <(U)2 > IR2E (4.4)
Sax 15 v
where wx is the collision velocity.
For particles with inertia, the volume flow rate of particles to a target particle was

expressed as Jffentire sphere Wl P(w)dw, where P(w) is the probability density function

(PDF) of w and contains the effects of the spatial variations of velocity in the fluid, the
turbulent accelerations due to the relative motion with the turbulence, and gravity. The
volume flow rate can be integrated assuming P(w) to be a normal distribution function.
For monodisperse particles,

JJentire sphere wl P(w)dw = 2 R3 2 12 (4.5)
3 v
Compared with the equation (4.2), the equivalent collision velocity based on equation
(4.5) can be evaluated

iWxl1 2R 3 ( 2rt)12/(2R2) = 2R(6_)l12 (4.6)
3 v 3 27tv

and - 1-R2- (4.7)
9 v

It is noted that equation (4.7) is independent of the inertia parameter 13. For the small
inertia limit 13--+oo, comparing equations (4.6) & (4.7) with equations (4.3) & (4.4), an
inconsistency that (4.6) & (4.7) can not recover (4.3) & (4.4) occurs, which is physically
incorrect.










4.3 Present Analysis


4.3.1 Asymptotic Analysis


To understand and predict the effect of inertia on the collision velocity wx, it is

assumed that the particle size is much less than the Taylor micro-length scale, and the
gravitational force is negligible. For simplicity it is assumed that turbulence is isotropic
and Gaussian. The particle dynamic equation in the absence of(or weak ) gravity is
dv
dt =3(u-v). (4.8)

The above can be written as
I dv
v-au (4.9)
13 dt

For large values of P, repeatedly substituting the left hand side into the right hand side
gives
I du I d2V I Du I( ) Vu+ I d2v
v-u ---+-- u---+-(u-v)UVu+---
P3dt D2 dt2 P3Dt 13 1P2 dt2
I Du I
-- +()(4.10)
f3Dt p3

The variance of w = vi V2x in which particle 1 and particle 2 are separated by a

distance of I x2 x, I= R is
1 Duix 1 Du2x
var(wx) var(Ulx U2x Du- )
Pi Dt P2 Dt

=<(Ulx U2x)2>1x2-x=R +<(I Dux 1- Du2x )2> -x=R
P13 Dt P32 Dt x2-xI=-R

1 Dux 1 Du2x
2 <(Ulx U2x)( Dt ^ Dt)>lx2-xl=R
01 Dt f02 Dt

= term I + term II + term III. (4.11)
The first term can be expanded as








term I=<(ul U2x)2 >lx2-x=R = +-2Ix2-x,=R

=2 2f(r=R) = 2u2 [1-f(R)] (4.12)
where u2 is the mean square intensity of turbulence and f(r) is the standard longitudinal
velocity correlation coefficient. If the particle size is much less than the Taylor micro-
length scale X, the above can be simplified to
term I= <(ul -U)2 >x2-x=R 2 (2 = IXR2v
U~,1~1,R 2U "2X 15 v

for R< Term II in equation (4.11) can be similarly handled,
I u1D,~ Di _!I(Dux)2>~(X

term II-- <( -L 2 )2 =R 2 <(DUI)> + 1 <(Du2t> D
'P1 Dt P2 Dt I x 1-R p Dt / 2 vDt

-2 1- (4.14)
13P2 Dt Dt x2-x=+R44)


where DIwhere < D U2X >1 X _X =R is the spatial correlation of ux- in the x-direction with a
Dt Dt x2 x iDt-
separation distance of R. Noting that var( Du--) = var( DU2 ) and expanding the
Dt Dt
correlation for small R, we obtain
[+
DUIDU 2-xI=R <(-x )2>[l+R2 a2fD(o) + ] (4.15)
Dt Dt 2 ax2 "'"

where fD is the correlation coefficient of Dux. Defining the micro length scale of the
Dt

fluid acceleration Du" as
Dt
I I a2fD() (4.16)
?! 2 x2(
term II can be expressed as

term 1-<(DUx)2>( l + 1 2 <(DUx)2>(1R2
Dt t F2 0P12 Dt 7 )








_<(DU)2>( I_ 1)2+ 2<(DU)2>R for R< (4.17)
Dt 1 0i ) + 0 Dt)T f D

Finally, equations (4.13) & (4.17) lead to
var(wx)- IlR2 +<(Dux)2>( 1 1 2 <(DU)2 R2
15 v Dt 01 02 P1P2 Dt TD

-2<( -u, )(- DUX )>. (4.18)
1 Dt P2 Dt
For the like particles, 31 = 02 = 1,

I 2c 2 /Dux Du2 2 (Dux 2 R2
var(w.)- 15R21 <(Ux-U2x)(- )> <(__ >
DDt Dt
(i) (ii) (iii)
(4.19)
where term (i) is the mean-squared collision velocity for inertialess particles, terms (ii) &
(iii) are contributed by the inertia. The net increase of the mean-squared collision
velocity due to the inertia can be expressed as

2 AWD U lx Du2x 2 Dux 2 ( 2
< 2 (u )-uX p<)+ R )2> (4.20)
SDt Dt

(ii) (iii)


It is noted that term (ii) represents the first-order effect of the inertia and term (iii)
contains the second-order effect of the inertia. The present prediction, equations (4.19) &
(4.20), differs from the equation (4.7) in two aspects. First, the collision velocity in the
limit p3-+oo is consistent with the equation (4.4). Second, the present analysis includes
two additional terms in the effect of inertia, as seen from equations (4.19) & (4.20) when
P1 = P2. The effect of inertia does not vanish when P1 = 02 while it does in Saffman &
Turner's analysis. It should be mentioned that in (4.20) is computed based on
Lagrangian tracking of all colliding particles.








4.3.2 PDF-Based Method


For inertialess particles, the collision velocity can be evaluated by the direct
integration (Mei & Hu 1998) in both uniform shear flow and turbulent flows.

Lwr = Jwr fwr and
2 w < Jw 2(_WrdS
= Jwr
where (-wr)dS in the numerator can be interpreted as proportional to the probability of a
particle striking the target surface over an area dS. The denominator is the normalizing
factor for the probability. The product, wr(-Wr), clearly indicates that is a biased
average in favor of these regions where (-wr) is high.
Equations (4.21) & (4.22) may be extended for particles with inertia. For small
inertia, as shown in 4.3.1, the particle velocity to the first order P is

v = u- (4.23)

The collision velocity may be expressed as
1 Du1x 1 Du2(
wr = wx = Vix V2x = Uix U2x- +-- (4.24)
P3I Dt P2 Dt

Jw<1 DuIx I Du2x )dS. (4.25)
r<0 wrdS w<0 f(U2 31 Dt 02 Dt

For monodisperse particles, PI = P3 = P3, equation (4.25) may be rewritten as

fWr From the equations (4.21), (4.24) and (4.26), the collision velocity is then found
From the equations (4.21), (4.24) and (4.26), the collision velocity is then found










w u Dt Dt

S2 Jwr

< r= (DtlX- --- (4.28)
Jw




The integration in right hand side of (4.28) are evaluated using Eulerian method.



4.4 Results and Discussions


The direct numerical simulations of the particle collisions in Gaussian turbulence
is performed to assess present asymptotic analysis and to predict collision angle. Initially,

a given number of particles are randomly distributed in the box of volume it3. The
inertial parameter PT|k varies from 0.022 to oo. The particle sizes D/irl=0.435, 0.957,
1.739, 2.522, 4.348 are considered, and the corresponding initial number of particles are
24000, 10000, 5000, 3000 and 1000 respectively. The particle collision detection is
turned on until all particles have reached dynamic equilibrium. The overlapping particles
are eliminated to avoid the false counting of the collisions before the collision detection
turned on. The particles are advanced by time step At=0.01. The total time steps, varying
from 200 to 600 after the equilibrium state, depend on the inertia. A total of 40
realizations is used for smooth statistics.
Figure 4.3 shows the comparisons of direct numerical simulations, asymptotic
analysis and PDF-based method for D/n=0.957. Very good agreement is observed among
the three methods for small inertia pT) k >3.6, so equation (4.20) is validated for prediction
of small inertia effect on the collision velocity. However, large deviation is observed for
pT k <3.6 since the asymptotic analysis cannot be applied for particles with large inertia.






70


The reason is that the first order approximation (4.23) is no longer valid when the 1

becomes small for particles with large inertia. The PDF results of collision velocity can

be found in figures 4.4 (a) & (b). The collision velocities are all negative, because

collision occurs only if the pair of particles are approaching each other. For not very

large inertia, say PT[ k> I, due to the strong correlation between particle velocity and fluid

velocity, the PDF curves are sharp and narrowed with a small variance and peak at almost

the same location, while zero inertia curve is at highest level. The PDFs are sharper for

small particle size (D/rT=0.957) at pTk>1.023, shown in figure 4.4 (a), compared with

figure 4.4 (b). However, the PDF curves become flatter and the location of the peak

shifts to larger IwrI for Pxk <0.073 due to little correlation between particle velocity and

fluid velocity at large inertia.




1.0E+O




1.OE-I
S---0-.-- prediction, Eq (420)
-... simulation
-*- integration, Eq (4.28)

1.0E-2




1.OE-3 -.




1.O0E-4 .. ..
0 5 10 15 20 25

1rk

Figure 4.3 Comparison for the increased collision velocity due to the particle inertia
between numerical simulation and theoretical prediction, D/r7=0.957.










PDF


0.0 4-*
-2.0


-1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0

collision velocity wr


PDF


-2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0

collision velocity wr


Figure 4.4 Effect of particle inertia on the probability density function of the
particle collision velocity wr. (a) D/A=0.957. (b) D/rl=2.522.









The effect of particle inertia on collision angle among monodisperse particles can
be found in figures 4.5 (a-c), 4.6 & 4.7. It is seen that the collision angles 012 & 0c

strongly depend on the inertia. For not very large inertia, fT > 1.023, the mean collision

angle 012 is less than 20 0 at D/A=0.957, as shown in figure 4.5 (a) & 4.6, and it is less

than 30 0 at D/r9=2.522, as shown in figures 4.5 (b) & 4.6. However, for very large

inertia, P3XTk <0.073, the mean collision angle 012 is greater than 80 0, which reflects little
biased orientation of collisions due to very weak correlation between the particles and
turbulent flow. It is noted that the PDF curves for PTk> 1.023 are much sharper and

narrower than those for 13Pk__0.073 and the variation of collision angle increases as the
particle inertia increases, as shown in figure 4.7. Figure 4.5 (c) shows the effect of inertia
on 0C. It is seen that the increase in inertia results in the collision angle 0c approaching

to symmetrical distribution with the mean 45 0.
The effect of particle size on the collision angle 012 can be seen in figure 4.8,
which clearly shows that the particle size has a significant effect on the collision angle.
The average collision angle 012 increases as the size increases. For D/T<11.739, the

maximum 012 is located at somewhere between 0and 10, and the average 012 is

around 10. However, for larger particles with large inertia, shown in figure 4.8, the

collision angle 012 is around 20 0, which is within the range of 14 0 32 0 obtained from
the experiments for heavy particles in a turbulent channel flow (Gunaraj et al. 1997).
Since little information is reported on the turbulent channel flow, the comparison is only
qualitative.









PDF

0.120
*(a)
0.100
P3 k
I k
0.080 -----7.308
S---A--- 3.654
|----1.023
0.060 ---0.292
I ... 0.073

0.040 ----..- 0.022
!.. -- infinite

0.020


0.000 % ^ --. "-' -- i -, _,,..
0 20 40 60 80 100 120 140 160

Collision angle 012

PDF

0.050

0.045 ,
(b)
0.040 r
fPTk
0.035 -___ 7.308

0.030 -- --- 3.654
0 .2 -a-1.023
0.025 -\ 0.292
a.
0.020 ^ -- --0.073
I o--0-- 0.022
0015 -0- infinite

0.010 .,AAAAAWAQ%

0.005 saw~x:3^ ^
AA AAAAAON LAA

0.000 m e
0 20 40 60 80 100 120 140 160

Collision angle 012

Figure 4.5 Effect of particle inertia on the probability density function of the
particle collision angle 012. (a) D/11=0.957. (b) D/r|=2.522.


180


180









PDF

o,020
0018 (C) r"n--u -
0.016- 7 \
0.014 ,

0.012 m m
0010 -7.308
,,,',/= /'y y -- 7.308 .^^A
/
0.008 *-/L -- 3.654 "
mM} -1.023
0*006 -*-O.9/ 0.292
0.004 ---0 073
/' -a- 0022 ?
0.002 -o-- infinite
0.000 "
0 10 20 30 40 50 60 70 80 90
Collision angle 0,
Figure 4.5 (c) Effect of particle inertia on the probability density function of the particle
collision angle 0c, D/I=0.957.

612


0 1 2 3 4 5 6 7
PTk

Figure 4.6 Effect of particle inertia on the mean collision angle 012 in isotropic,
Gaussian turbulence.













45

40

35

30

25

20

15

10

5

0


1 2 3 4 5 6 7


Figure 4.7 Effect of particle inertia on the standard deviation of collision angle 012 in
isotropic, Gaussian turbulence.

PDF


0.10

0.09 -

0.08

0.07 D / T1

0.06 .-..-.- 4.348
----- 2.522
0.05 --,--1.739

0.04 ". -----0.957
0.03 ,, -0.435
0.03 -/ *';

0.02 a .

0.01 -
...- ../ .--u
0.01 '/ "" -- "*-. -" -... -----

0.00 I I I m U.
0 10 20 30 40 50 60 70 80 90 100

Collision angle 012


Figure 4.8 Effects of particle inertia and size on the probability density function of
the particle collision angle 012, P1x k =1.023.











4.5 Summary and Conclusions


Numerical and analytical studies on particle collision velocity and angle in
isotropic, Gaussian turbulence have been carried out. The effects of particle inertia and
particle size on the collision velocity and collision angle are examined.
An asymptotic analysis has been developed to predict the effect of small particle
inertia on the particle collision velocity. The asymptotic prediction for the increased
collision velocity agrees well with the results of numerical simulation and PDF-based

method. The collision velocity is found to contain terms that are proportional to (IPxk )1

and (PTk)-2.
Particle collision angle strongly depends on particle inertia and size. The average
collision angle and the variation of the collision angle increase as the inertia increases.
Both the average collision angle and the variation of the collision angle decrease as the
particle size decreases.














CHAPTER 5
QUANTIFICATION OF PARTICLE CONCENTRATION NON-UNIFORMITY
IN TURBULENCE


For heavy particles with finite inertia, small scale structure of turbulence induces particle
preferential concentration. Particles tend to collect in the regions of high strain rate and
low vorticity, as has been observed in many Lagrangian simulations. Quantification of
the extent of the non-uniform particle concentration distribution has been difficult
because the particle concentration based on the Lagrangian statistics is highly noisy. A

method is developed to calculate reliably the variant of the particle concentration, < n'2 >,
in which n is the instantaneous particle number concentration and the prime is the

fluctuation in a small cube of volume (Ax)3, from the Lagrangian simulations of the

particle motions in an isotropic turbulence. The concentration non-uniformity is
measured using a new parameter A0 based on the first two lower order moments of n. It

is observed that the non-uniformity varies with Ax, suggesting a fractal nature of the
particle concentration distribution. For particles with zero size, the non-uniformity scales

with Ax as An (Ax) -b (b around 1) for small values of Ax. However, for finite-size

particles, the non-uniformity reaches a maximum value (around order one or less) at Ax/D
= 2.0-3.5, in which D is the particle diameter; it drops to zero as Ax/D gets close to one.

It is noted that the maximum value of the /2 is attained when the ratio of
particle response time to the Kolmogorov time scale is close to one, which has been
previously observed for the zero-size case based on other physical quantities.










5.1 Introduction


In many statistical problems, the lower order moments are often of interest. For

large number of monodisperse particles, the collision rate is nc=tXn1n2. The first

moment of particle concentration is clearly the average concentration n, i.e., the number

of particles per unit volume. The second order moment can then be expressed as

=n2+=n2(1+ -2 .1
n

where < > denotes ensemble average. The first moment n does not tell how the number
of particles varies with the volumes at different locations and gives no reflection of

turbulence structure at small scale levels. However, the second order moment, ,
-2 2
consists of two terms: n and < n' >. The variance of particle concentration, < n' >,
measures the variation of particle number density in the cells, i.e. the non-uniformity of
the particle distribution. Thus it is fundamentally important to the particle collision rate.

Since nfi is a simple average of particle concentration in a turbulent flow while < n'2>
depends on the turbulence structure and particle inertia, etc., our attention is given to the

behavior of < n'2 >.
2 -2
In this study, we use the normalized variance of particle concentration, / n
to quantify the concentration non-uniformity in turbulent flows. The normalized variance
is obtained via direct numerical simulation. As will demonstrated in 5.2, the inherent

statistical noise is eliminated from the /n By this new method no assumption
regarding the particle-particle interactions is needed. The effects of particle size and
inertia on the concentration non-uniformity can be quantified separately. It is found that
the concentration non-uniformity can be greatly overestimated if the particle size is
ignored. For particles with zero size, the non-uniformity scales with Ax as

/2- (Ax) -b (b around 1) for small values of Ax for particles whose response









time is comparable with turbulence Kolmogorov time scale, where Ax is the length of a

small cube of volume (Ax)3 used in the Lagrangian simulations of the particle motions in

turbulence. However, for finite-size particles, the non-uniformity reaches a maximum
value (around order one or less) at Ax/D = 2.0-3.5, in which D is the particle diameter
and rf is Kolmogorov length scale; it drops to zero as Ax/D gets close to one. The

maximum value of the /< n > 2 is obtained when the ratio of particle response time
to the Kolmogorov time scale is around one.




5.2 Quantification Method


5.2.1 Inherent Noise of the Concentration Non-uniformity


Suppose the computational domain L3 is divided into N. equal-volume cells each

with length Ax so that N, = (L/Ax)3. Obviously n = N/Ne is the first moment of n.

For zero-size, inertialess particles, the probability of a particle residing in a
particular cell is p = 1/Nc,. With N particles in the domain, the probability of k particles

residing in a specific cell of volume (Ax)3 obeys the Bernoulli distribution,
N! N
P(k)_k(Nk! Pk(1- P)N-k. (5.2)

The second moment of n is, by definition,
2 N
= Zk'p(k). (5.3)
k=0
Thus the variance of the particle concentration for a given cell is

=-n = k2P(k)- n 2. (5.4)
k=0
For Np(1- p) >>1, P(k) given by (5.2) can be expressed as

P(k) L2N1 exp[-(k-Np)2/2Np(l- p)], (5.5)
-\27tNp(l -p)









for k in the neighborhood of Np, which is known as DeMoivre-Laplace theorem. In the

limit of large Np(l1- p), which is very close to n for small p, an asymptotic integration

gives
N -2
=j k2P(k) dk n +n(1-p). (5.6)
0

Denoting as for zero-size, inertialess particles (P-.oo), we obtain

=- n 2=n(-p). (5.7)
2 -2
Denoting r as the ratio of < n'2 > to n ,

r= -2 (5.8)
n
it follows for zero-size, inertialess particles that

r.- (5.9)
n n

For n =0(1) or smaller, the DeMoivre-Laplace theorem does not hold anymore. In DNS
the number of particles used is relatively small from the point view of smooth statistics,

as mentioned in Section I. In DNS, n may be much less than one as cell size becomes
small. With finite-size particles, the ultimate number distribution of particles is reduced
to either zero or one per cell as the cell size is smaller than 0.5773D, where D is the

particle diameter. For n <<0(1), particle number concentration is more likely to obey
Poisson distribution
,ke-k
P(k)=-- -, (5.10)
k!
where X,=Np. The second moment of n is then
SN N 2 ke-k
= YkPk2) Yk (5.11)
k=0 k=0 k
and the variance of the particle concentration for a given cell is








-2 Nk2 ,'e-k -X 2
=- n = Yk ne (5.12)
k=O k!
ForN >>l,
N 2 Xke- k 2k e- oo ek -.
Y_ k-- k2 Y -_k- 1:[k(k- 1)+k]+--
k=O k! k=O k! k=O k!
00 Xke^ Ck Xk Ck
= Yk(k-1) --+ ke
k=O k! k=0 k!

X2 O k-2 0 xk-1
=Xe E +k e- Y
k=O (k 2)! k=O (k 1)!

X e~eX+Xe~eX
= X2 e-k ek.+ke-k ek

= X2 +X. (5.13)

Since X=Np= n, the variance of n can be expressed as
2 N k2 Cke- -2 2 -
= Yk2 n 2X X-n
k=O k!
-2 -2 -
=n + n-n = n. (5.14)
It follows for zero-size, inertialess particles that
< n'2 > I
-2I
r-4--2 (5.15)
n n


2 -2
For n >>1, seen from both equations (5.9) and (5.15), r. =< n'2 > /n -+0 so that

the variance of the particle concentration distribution is very small when it is normalized
by the square of the first moment of n. However, if N is fixed while N, takes a large

value in the limit, we have n << 1 and r. /n >> 1. In a practical simulation of particle

motions in turbulence, N is always finite and fixed. Since we are interested in
quantifying the small-scale variation of the particle concentration, it is necessary to

reduce successively the size of the cell, (Ax)3, on which n and are computed. It is

noted that the relatively large value of < n', > for inertialess particles is entirely due to the









fact that there is not enough particles in each cell as the size is reduced and r, -1/n

contains no contribution from the preferential concentration. As far as the inertial effect

is concerned, r-1/n is purely the inherent statistical noise which can be improved by

using large values of n in principle. As the inertia of the particles increases, < n'2> will

contain an increasing effect of the inertia bias. The fact that r -1/n >>1 for inertialess

particles when n << 1 implies, however, that r= < n'2 >/ n is dominated completely by

the inherent statistical noise r. -1/n. The contribution from the inertial effect to

or r thus must be isolated from the statistical noise. Figure 5.1 shows the comparison

between the DNS results r, for inertialess particles and the inherent noise 1/n, where
2 -2
/ n at every instant was recorded and a total of 300 time steps and 40 turbulence
realizations are used in our DNS. The details regarding turbulence representation, the
characteristics of the turbulence and particle collision detection scheme have been given

2 -2
in 2.1.3. It clearly shows that /n is indeed dominated by 1/n which has to be
subtracted.




5.2.2 Concentration Non-uniformity Parameter


For particles with finite inertia, the variance of the particle concentration can be
computed in direct numerical simulations

1 Nc

=-- (n-n)2. (5.16)
NC j=l










1.0E+5

1.0E+4

1.OE.3
1.0E+34

1.0E+2


_.E O- D=0.01, W =12,000 "
1.0E- a zero size, N=12,000

-1 zero size, N=500 -
1.0E-1 -- "-,i,
o N=500, 1/n
1.0E-2 N=12,000, 1 / n

1.0E-3

1.0E-4 i i :-: : : : :
0.1 1 10 100
Ax/ i

Figure 5.1 The normalized variance of the concentration as function of cell size for
inertialess particles in isotropic turbulence. Pf k =1.023.



The above variance of the concentration non-uniformity is dominated by the statistical

noise, as shown in figure 5.1. For zero-size, inertialess particles, the above should

approach to = n(1- p) for n >0(1), and =n for n 0(1). Defining the

normalized difference between above and as

n'2 n'2 () n'2
<-13 >= <-__->.<-*-o>
-2 -2 -2
n n n

n' (J3) 1 -p
> p for n > 0(1), (5.17)
2
n n

and
'2 n'2
<_!_ >= <_!->.<-p-4>
-2 -2 -2
n n n










n'2 ( ) I
=<--->- -
-2
n n


-1.20 F


-1.60


for n << 0(1).


1 10 100
Ax/ r
Figure 5.2 The expressions (5.17) & (5.18) of the concentration non-uniformity as
function of cell size for inertialess particles in isotropic turbulence. PT k =00.



The effect of inertia on the variance of n can be identified. Both equations (5.17)

and (5.18) represent the contribution to from the inertial effect for n > 0(1) and

n << 0(1), respectively. Figure 5.2 shows the results of equations (5.17) and (5.18) for

isotropic turbulent flow. Equation (5.17) obviously gives very small value for large Ax/D

for n >>0(1), while equation (5.18) on other hand gives small values for small Ax/D for
2 .-2
n << 0(1). For inertialess particles, / n from both expressions has 0(1) variation

2over cell size. Equation (5.17) gives over cell size. Equation (5.17) gives /n --I- as cell size Ax approaches to zero


(5.18)


1- Q ----a


- 3-- =0.058, Eq (5.18)
- D=0.032, Eq (5.18)
-o-- D=0.022, Eq (5.18)
zero size, Eq (5.18)
--- D=0.058, Eq (5.17)
-0-D=0.032, Eq (5.17)
-- D=0.022, Eq (5.17)
- -zerosize, Eq (5.17)


-0.40


-0.80








,2 .-_2
while equation (5.18) gives /n -*1 as Ax increases. This is physically unsound

because the preferential concentration is an inertial phenomenon and should not appear in

turbulent flows with inertialess particles. Figure 5.3 shows the DNS results of

using equations (5.17) & (5.18) in a uniform shear flow for inertialess particles. The
uniform shear flow is represented by u=(Fy, 0, 0) with the velocity gradient F=1.0.
2 .-2
Comparing with figure 5.2, very similar behavior for < n' >/n is obtained even for a

uniform shear flow. The results are unphysical because there is no mechanism to drive
inertialess particles to form non-uniform concentration in a uniform shear flow. Thus,
one should not expect to apply the equation (5.17) or (5.18) to quantify the concentration
non-uniformity. However, the above results gave us a valuable hint. That is, the results
obtained by both equations (5.17) & (5.18) can be interpreted as an additional statistical
noise for both inertialess and inertial particles. It is hence suggested that the actual

contribution of the inertia to < n'2 > for an arbitrary 13 may be expressed as

= [-n(l- p)] [-n(- p) ] 0=, (5.19)

or

= [ n] [ n ] (5.20)

Apparently, zero concentration non-uniformity is obtained for inertialess particles in both
turbulence and uniform shear flows based on equations (5.19) & (5.20). It is noted that

is a function of 13 and n in equation (5.20), while is mainly determined

by 13 and n in (5.19). The influence ofp on in (5.19) may be negligible as far as

a small p is concerned, i.e.
= f(13, n ), since p appears in both first and second

terms in right side of equation (5.19). A large p, however, means the large cell size (the
box size is the limit), which usually gives approximately uniform particle distribution. It

is also noted that
--0 as n -+oo (or N-+oo). Thus, it is expected that the effect of

inertia on the concentration non-uniformity can be captured using equations (5.19) &
(5.20). To quantify the extent of the concentration non-uniformity using the second









moment of n, a dimensionless quantity AP called concentration non-uniformity

parameter, is defined as
<"n2> it, ) - (-P)
A --2 -- 1 -- (5.21)
n n n n

or


2 2
1 1
An [ -2 -1- J -I -2-- J=oo.
n n n n


0.00 I


-0.50 I


(5.22)


1 .5 0 I i l i- I I I I-* I I :4: : *:
0 0.2 0.4 0.6 0.8 1
Ax
Figure 5.3 The expressions (5.17) & (5.18) of the concentration non-uniformity as
function of cell size for inertialess particles in uniform shear flow. 13/=co.


Figure 5.4 shows the concentration non-uniformity parameter for the uniform shear

flow using equations (5.21) & (5.22). It is seen that AO is very small for all cases in a

uniform shear flow. Figure 5.5 shows AO in turbulent flows for a large range of sizes for

particles with zero inertia. It is also shown that, for zero-size particles, the concentration

non-uniformity at small inertia (Prk =7.3) still increases with the decreasing cell size.


Sn


--.-9. a___.._ .._____.._____ _____ -------__


---- D=0.04, Eq (5.18)
-a- D=0.02, Eq (5.18)
zero size, Eq (5.18)
- -D=0.04, Eq (5.17)
---- D=0.02, Eq (5.17)
-a- zero size, Eq (5.17)









The detailed discussions about the size effect on the collision rate will be given in 5.3.

The above results obtained by (5.21) & (5.22) are reasonable and hence strongly suggest
that equations (5.21) & (5.22) can be used to quantify and predict the particle

concentration non-uniformity induced by particle inertia in general turbulence. It should

also be noticed that both equations (5.21) and (5.22) always yield the same shape differed

by a constant (-=1). To facilitate the subsequent discussions, equation (5.22), which does

not contain p and is thus in a simpler form, is used hereinafter.



AP
004


0.03


0.02


0.00 -


Ax
Figure 5.4 The concentration non-uniformity parameter An as function of cell size in
uniform shear flow. F=1.0, /F=1.0.


Figure 5.6 shows the effect of total particle number N on the concentration non-

uniformity parameter AP. It is seen that An remains almost on the same curve when N

increases from 25,000 to 50,000 and 100,000 for all values of N, (from 23 to 2783).


o zero size, Eq (5.22)
o D=0.04, Eq (5.22)
o D=0.02, Eq (5.22)
S.- zero size, Eq (5.21)

&- ------ D=0.04, Eq (5.21)
^ l V- D=0.022, EEq (55.221)


eo0oo 0 o- -0 -. 10.0 r-TT. .-* .. .- .-




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PARTICLE COLLISION RATE AND SMALL-SCALE STRUCTURE OF
PARTICLE CONCENTRATION IN TURBULENT FLOWS
by
Kevin Cunkuo Hu
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF
THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF
PHILOSOPHY
UNIVERSITY OF FLORIDA
1998

ACKNOWLEDGMENTS
I would like to take this opportunity to express my sincere thanks to my advisor,
Professor Renwei Mei, for his patience, encouragement, and help. This work would be
impossible without his guidance and insights. I appreciate my advisory committee
members, Professor James F. Klausner, Professor Corin Segal, Professor Ulrich H.
Kurzweg, and Professor Roger Tran-Son-Tay, for their suggestions and help. I also
express gratitude to Professor Michael W. Reeks (in England), Professor Ibrahim K.
Ebcioglu, Professor Otis Walton and Professor Nicolae D. Cristescu for their help.
Thanks to Dr. Guobao Guo and my fellow graduate students Xueliang Zhang,
Hong Shang , and others for their helpful discussions and friendship.
I acknowledge the financial support of the Engineering Research Center (ERC)
for Particle Science & Technology at the University of Florida, the National Science
Foundation (EEC-9402989), industrial partners of the ERC, and the ALCOA Foundation
Award.
I am deeply indebted to my wife, Rui, for her love, patience, and encouragement
during my thesis work, which were more than I could ever have asked.
u

TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS ii
LIST OF TABLES vi
LIST OF FIGURES vii
ABSTRACT xii
CHAPTER 1 INTRODUCTION 1
1.1 Smoluchowski's Prediction in Laminar Shear Flow 4
1.2 Various Theories for Collision Rate in Turbulence 5
1.3 Recent Development in Computer Simulation on Particle Collision Rate 7
1.4 Recent Development in Particle Concentration Non-uniformity 8
1.5 Scope of Present Study 11
CHAPTER 2 TURBULENCE AND PARTICLE COLLISION
DETECTION 13
2.1 Turbulence Representation 13
2.1.1 Isotropic, Gaussian Turbulence 13
2.1.2 Rapidly Sheared Homogeneous Turbulence 16
2.1.3 Isotropic Turbulence Generation in a Periodic Box 17
2.2 Numerical Integration of Particle Motion and Error Analysis 19
2.3 Particle Collision Treatment 21
2.3.1 Collision Detection Scheme 21
2.3.2 Post-Collision Treatment 24
2.4 Computer Simulation 25
2.4.1 Time Step 25
2.4.2 Turbulence Realization 27
2.4.3 Validation of the Collision Detection Scheme 27
in

CHAPTER 3 A NEW THEORETICAL FRAMEWORK FOR PREDICTING
COLLISION RATE OF SMALL PARTICLES IN GENERAL TURBULENT
FLOWS 33
3.1 Introduction 33
3.2 Analysis 37
3.3 Numerical Simulation 43
3.4 Results and Discussions 44
3.4.1 Comparison with Simulation Results in a Laminar Shear Flow 44
3.4.2 Collision Rate and Velocity in a Gaussian, Isotropic Turbulence 47
3.4.3 Collision in a Rapidly Sheared Homogeneous Turbulence 52
3.5 Summary and Conclusions 55
CHAPTER 4 EFFECT OF INERTIA ON THE PARTICLE COLLISION
VELOCITY AND COLLISION ANGLE IN GAUSSIAN TURBULENCE 59
4.1 Introduction 59
4.2 Saffman & Turner’s Analysis 62
4.3 Present Analysis 65
4.3.1 Asymptotic Analysis 65
4.3.2 PDF-Based Method 68
4.4 Results and Discussions 69
4.5 Summary and Conclusions 76
CHAPTER 5 QUANTIFICATION OF PARTICLE CONCENTRATION NON¬
UNIFORMITY IN TURBULENCE 77
5.1 Introduction 78
5.2 Quantification Method 79
5.2.1 Inherent Noise of the Concentration Non-uniformity 79
5.2.2 Concentration Non-uniformity Parameter 82
5.3 Results and Discussions 89
5.4 Summary and Conclusions 96
CHAPTER 6 COLLISION RATE AT FINITE PARTICLE INERTIA AND
SIZE IN GAUSSIAN TURBULENCE 97
6.1 Introduction 98
6.2 Correction of Smoluchowski’s Theory 100
IV

6.3 An Analysis on the Effect of Particle Inertia on the Collision Rate 106
6.3.1 Saffinan & Turner's Analysis 106
6.3.2 Effect of Small Inertia on the Collision Kernel 107
6.3.3 A Proposed Expression on Collision Kernel 110
6.4 Comparison of the Analysis with Simulation Results 111
6.4.1 Effect of Particle Inertia Ill
6.4.2 Effect of Particle Size 113
6.4.3 Effect of Particle Mean Concentration 114
6.4.4 Effect of Particle Gravitational Settling 114
6.5 Collision Rate in Polydisperse Suspension 115
6.5.1 Particle Size Spectrum 122
6.5.2 Effect of Particle Inertia 124
6.5.3 Effect of Particle Gravitational Settling 125
6.6 Collision Rate in Hard Sphere Suspension and Assessment of Collision
Models 127
6.7 Summary and Conclusions 129
CHAPTER 7 SUMMARY AND CONCLUSIONS 137
REFERENCES 140
BIOGRAPHICAL SKETCH 146
v

LIST OF TABLES
Table 3.1 Comparison of the collision velocity among monosized
particles between prediction and direct numerical simulation
in laminar shear flow 45
Table 3.2 Comparison of the collision velocity among monosized
particles between prediction and direct numerical simulation
in Gaussian, isotropic turbulence 50
vi

LIST OF FIGURES
Figure 2.1 The effect of time step on the cumulative average of the normalized
collision kernel for inertialess particles in turbulence. D=0.05 26
Figure 2.2 Turbulence dissipation rate s against turbulence realization, (a) Variation of
ensemble average, (b) Cumulative average 29
1/2
Figure 2.3 Turbulence small eddy shear rate (s / v) against turbulence realization.
(a) Variation of ensemble average, (b) Cumulative average 30
Figure 2.4 Inverse of total number of monodisperse particles against time in laminar
shear flow. T= 1.0, D=0.02 31
Figure 2.5 Multiple collisions of HARD SPHERE particles in laminar shear flow 32
Figure 3.1 Sketch of particle collision velocity. The separation
distance is X2-X1 = ± Ry= ±(r,+rj) 35
Figure 3.2 Variations of #(Q, (jQ,HQ with where
r 3 , fl 8 f2 HQ md f3 HQ 36
Figure 3.3 Local coordinates for collision in laminar shear flow. 42
Figure 3.4 Comparison of the pdfs for the collision angles 0C between
the theory and the simulation in laminar shear flow 47
Figure 3.5 Convergence of a,2 as Nk ->oo 50
Figure 3.6 Particle collision kernel a(l from direct numerical simulation in isotropic
turbulence, (a) Variation of the ensemble average, (b) Cumulative time average 51
Vll

Figure 3.7 (a) Normalized particle collision kernel in a highly sheared homogeneous
turbulence without contribution from the mean shear rate 56
Figure 3.7 (b) Collision kernel in a rapidly sheared homogeneous turbulence. So denotes
the dissipation rate of the isotropic state at t=0 57
Figure 3.8 Collision velocity in a rapidly sheared homogeneous turbulence.
So denotes the dissipation rate of the isotropic state at t=0 58
Figure 4.1 Sketch of particle collision scheme 63
Figure 4.2 Sketch of particle collision velocity and collision angles (012 & 0C).
The separation distance is x2 - x, = ±R = ±(rj + r2) 63
Figure 4.3 Comparison for the increased collision velocity due to the particle inertia
between numerical simulation and theoretical prediction, D/ri=0.957 70
Figure 4.4 Effect of particle inertia on the probability density function of the particle
collision velocity wr. (a) D/r|=0.957. (b) D/r|=2.522 71
Figure 4.5 Effect of particle inertia on the probability density function of the particle
collision angle 012. (a) D/r|=0.957. (b) D/r|=2.522 73
Figure 4.5 (c) Effect of particle inertia on the probability density function of the particle
collision angle 0 c, D/r|=0.957 74
Figure 4.6 Effect of particle inertia on the mean collision angle 0i2 in isotropic,
Gaussian turbulence 74
Figure 4.7 Effect of particle inertia on the standard deviation of collision angle 012 in
isotropic, Gaussian turbulence 75
Figure 4.8 Effects of particle inertia and size on the probability density function of the
particle collision angle 012, p-ck=1.023 75
Figure 5.1 The normalized variance of the concentration as function of cell size for
inertialess particles in isotropic turbulence. Pt k=1.023 83
Figure 5.2 The expressions (5.17) & (5.18) of the concentration non-uniformity as
function of cell size for inertialess particles in uniform shear flow. p/T=oo 84
Vlll

Figure 5.3 The expressions (5.17) & (5.18) of the concentration non-uniformity as
function of cell size for inertialess particles in isotropic turbulence. Pxk =oo 86
Figure 5.4 The concentration non-uniformity parameter Ap as function of cell size in
uniform shear flow. T=1.0, p/T=1.0 87
Figure 5.5 The concentration non-uniformity as function of cell size for small inertia
particles in isotropic turbulence 88
Figure 5.6 The concentration non-uniformity as function of cell size for various
particle number densities at a given inertia in isotropic turbulence. pxk=1.023 89
Figure 5.7 The concentration non-uniformity of sizeless particles as function of cell
size for various inertia in isotropic turbulence 91
Figure 5.8 The concentration non-uniformity parameter Ap of finite-size particles as
function of cell size for various inertia in isotropic turbulence, (a) D/ri=0.957.
(b) D/ti=2.52 93
Figure 5.9 The maximum concentration non-uniformity parameter Ap of finite-size
particles as function of particle inertia in isotropic turbulence 94
Figure 5.10 The concentration non-uniformity parameter Ap as function of Ax/r| for
small-size and zero-size particles in isotropic turbulence. Px k=1.023 94
Figure 5.11 The concentration non-uniformity of particles at a given inertia as function
of Ax/D cell size for various sizes in isotropic turbulence, (a) Log scale in x-axis.
(b) in normal scale in x-axis 95
Figure 6.1 Collision kernel a ü against time in laminar shear flow. T=1.0, D=0.04.
(a) Variation of ensemble average, (b) Cumulative average 105
Figure 6.2 The effect of particle inertia on the increased collision kernel Aa¡, for
monodisperse particles for (a) D/r)=0.957. (b) D/r|=2.52 116
Figure 6.3 The effect of particle size on the collision kernel a|j for monodisperse
particles at a very dilute system. Px k = oo 117
Figure 6.4 The effect of particle size on the collision kernel a|j for monodisperse
particles at ‘not’ very dilute system. Px k = qo 117
IX

Figure 6.5 Variation of the collision kernel against particle number concentration
for monodisperse particles. Px k = qo , D/r|=0.435 118
Figure 6.6 The effect of gravitational settling on the collision kernel for
monodisperse particles. Pxk=1.023, D/t^=2.52 118
Figure 6.7 The effect of gravitational settling for monodisperse particles. D/r|=2.52,
Pxk=1.023. (a) on the collision velocity, (b) on the concentration non-uniformity... 119
Figure 6.8 Collision kernel a*] of polydisperse particles against time. 0xk =<»,
D/r|=0.957. (a) Variation of the collision kernel, (b) Cumulative average 120
Figure 6.9 Collision kernel a¡, of polydisperse particles with various inertia against
turbulence realization. D/r|=0.957. (a) Variation of the collision kernel.
(b) Cumulative average of the collision kernel 121
Figure 6.10 Inverse of total number of particles against time for polydisperse particles.
D/rpO.957. (a) primary size (v0) particles, (b) secondary size (2 v0) particles 122
Figure 6.11 The evolution of particle size distribution at various instants for
polydisperse particles. D/r|=2.17, pxk=1.023 123
Figure 6.12 The effect of particle inertia on the increased collision kernel Aa ^ for
polydisperse particles for (a) D/r|=0.957. (b) D/r]=2.52 126
Figure 6.13 The effect of gravitational settling on the collision kernel a*, for
polydisperse particles. Pxk=1.023, D/r|=2.52 127
Figure 6.14 The effect of gravitational settling for polydisperse particles. D/ri=2.52,
Pxk=1.023. (a) on the collision velocity, (b) on the concentration non-uniformity.... 128
Figure 6.15 Collision kernel of hard sphere particles with various inertia against
time in laminar shear flow. D=0.04. (a) Variation of the collision kernel.
(b) Cumulative average 130
Figure 6.16 The effects of the collision schemes on the collision kernel a*! against time
in laminar shear flow. T=1.0, D=0.04. (a) Variation of the collision kernel, (b)
Cumulative average of the collision kernel 131
x

Figure 6.17 The effects of the inertia on the collision kernel against time for hard
sphere particles in Gaussian turbulence. D/r| =0.957. (a) Variation of the collision
kernel, (b) Cumulative average of the collision kernel 132
Figure 6.18 The effects of the collision schemes on the collision kernel a¡, against time
in Gaussian turbulence. D/r|=2.174, Pxk=1.023. (a) Variation of the collision kernel,
(b) Cumulative average of the collision kernel 133
Figure 6.19 The effects of the collision schemes on the collision kernel a|, of hard
sphere particles in Gaussian turbulence. D/ti=0.957, pxk =0.292. (a) Variation of the
collision kernel, (b) Cumulative average of the collision kernel 134
xi

Abstract of Dissertation Presented to the Graduate School of the University
of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor
of Philosophy
PARTICLE COLLISION RATE AND SMALL-SCALE STRUCTURE OF
PARTICLE CONCENTRATION IN TURBULENT FLOWS
by
Kevin Cunkuo Hu
May, 1998
Chairman: Renwei Mei
Major Department: Aerospace Engineering, Mechanics and Engineering Science
A study of particle collision and concentration non-uniformity in turbulent flows
is carried out. A correction to the classical result of Smoluchowski for the collision rate
of monodisperse particles in a laminar shear flow has been made. A theoretical
framework has been developed to evaluate the collision rate and collision velocity of very
small particles in general turbulent flows. The present approach differs significantly
from the classical approach of Saffinan & Turner in that the ensemble average is taken
after the collision rate for a given flow realization is calculated. This avoids the
assumption of isotropy, as needed in the classical approach, and allows for the evaluation
of the collision rate in general turbulent flows.
Direct numerical simulations and analytical studies on particle collisions in an
isotropic, Gaussian turbulence have been carried out. Several different collision models
(or post-collision treatments) are implemented in the simulations and the difference
among various collision models is examined. The effects of particle inertia, size, gravity,
and mean-concentration on the collision rate, collision velocity, and collision angle are
Xll

investigated. Asymptotic analyses have been developed to predict the effects of small
inertia on the increases in collision rate and collision velocity. Asymptotic prediction for
the increased collision velocity agrees well with the results of both numerical simulation
and PDF-based method. The increased collision velocity is found to contain terms of
both 0((pTk) ) and 0(((ÍTk) ), where P is the particle response time and xk is the
kolmogorov time scale. The increases in particle size, gravitational acceleration, and
mean-concentration in general reduce the normalized particle collision rate in isotropic
turbulence for monosized particles as opposed to very small particles in the very dilute
limit.
A new method has been developed to quantify the concentration non-uniformity
of particles in turbulent flows. The effects of particle size and inertia on the concentration
non-uniformity are examined and elucidated. The concentration non-uniformity can be
greatly overestimated if the effect of finite particle size is neglected.
Xlll

CHAPTER 1
INTRODUCTION
Particles and turbulence are around us everywhere and greatly impact our daily
life. Dusts, sand, rain drops, fine powders, glass beads, viruses and bacteria are all
‘particles.’ Most of the fluid flows occurring in nature such as air current and water flows
in rivers and seas are turbulent flows. The particles in the atmosphere remain airborne for
long periods, lowering visibility. The brown haze that is often seen over large cities in
autumn, winter and spring is due mainly to the interactions between particles and the air.
The improvement of particulate-fluid systems requires a better understanding of
the fundamental physics of the particle-particle and particle-fluid interactions. The
behavior of particulate-fluid mixtures may vary widely depending upon the conditions
under which the particle-fluid system functions. Particle-particle interactions involve the
collisions among particles which depend on inter-particle forces, particle size distribution,
and the concentration distribution. Particle-fluid interactions include the hydrodynamic
forces acting on particles by fluid and the feedback to fluid from particles, etc. Particle
collision, coagulation, and aggregation are the examples of most important scenarios of
those interactions that are determined by particle properties and flow structures. The fact
that collisions do occur among the particles of size ranging from a few Angstroms to a
few millimeters explains the vast amount of scientific and industrial applications of the
collision process. For example, molecular collisions help explain the physical properties
such as viscosity, diffusivity, and conductivity in gases and liquids. Numerous industrial
applications can be cited: design of fine spray combustion nozzles, control of industrial
emissions (Presser & Gupta 1993, Tu et al. 1996) and the understanding of the collision
1

2
process among suspended particles in pneumatic transport systems or in gas cleaning
chambers (Abrahamson 1975, William & Crane 1983), and in sewage disposal devices
(Hunt 1982), where particle size distribution and concentration non-uniformity are
important characteristics that depend on particle collision and coagulation. Most of
previous studies on particulate removal have focused on the filters, with little attention to
the particle-particle interactions and particle-fluid interactions. Despite many years of
research and development in particulate removal technologies, the collection of small
particles with size less than 1 pm is still a rather difficult task. It is extremely difficult to
remove fine organic active particles (<0.03 pm) from a waste stream because the particles
are much smaller than the pores in existing filters. Typical collection efficiencies of the
filtration devices decrease rapidly with the decrease of particle size in the range 0.2-30pm
(Davies 1953). A good alternative is to aggregate fine particles which can be removed
using conventional technologies. For the coagulation and aggregation to occur, particles
have to collide with each other and adhere. It is very desirable that one can predict how
the small particles are driven to collide with each other so they aggregate or grow in size
and become large enough to be captured by the filters. Obviously, the efficiency of
particle removal mainly depends on the control of particle collisions and concentration
non-uniformity in filtration systems (Fathikalajahi et al. 1996). It is evident that particle
collision is one of the most important interactions for understanding the fundamentals of
particle-fluid systems.
Particle collision kernel and collision rate are critical quantities in the
development of collision theories. The rate of flow induced particle collisions affects
directly the particle size distribution in particle production. Examples of turbulence
induced particle collision can be seen in the formation of rain drops in clouds, pulverized
coal combustion, agglomeration of fine powders in gas flows, air filtration equipment,
sewage disposal devices, fast fluidized beds, dust and spray burners, and so on. Particle
collisions can take place through a variety of collision mechanisms. Particle collisions
may be induced by centrifugal and gravitational forces, thermal forces in Brownian
motion, and van der Waal forces. In an actual system, the scenario after every contact
between particles may be different from one to another, depending on the underlying

3
physics of the system. The collisions among raindrops enhance the growth of droplets in
the air, since the contact between droplets may most likely result in the coalescence of
two droplets. The growth of bubbles in a liquid flow is another example of particle
coagulation upon collisions. The contacts between two solid particles may behave in
various different ways. When two solid particles are brought together the colliding pair
may stick or separate after collision upon contact. Many theoretical efforts were made to
predict the collision kernel and rate. To validate a theoretical analysis, direct numerical
simulations were carried out to obtain statistical average of the collision rate in a particle-
fluid system. Different interpretations of the immediate consequence after the contact
between a pair of particles have resulted in different collision models or so-called “post¬
collision treatment” schemes. To quantify the post-collision treatment, a statistical
parameter called collision efficiency has been introduced. The collision efficiency is one
if particles coagulate to form a larger one. Usually, it is less than one since some of the
colliding particles may bounce back. Although many theoretical and experimental studies
have been devoted to estimates of the collision efficiency for water droplets settling
through still air (Klett & Davis 1973, Lin & Lee 1975, Beard & Ochs 1983) and droplets
settling down through laminar shear flow (Jonas & Goldsmith 1972), even an
approximate estimate of the collision efficiency in a complicated situation can be very
difficult to obtain. At this stage, it is still not possible to obtain an accurate solution for
the evaluation of collision efficiency in complex systems. To better focus on the issues of
our interests, the collision rate, the collision efficiency is assumed to be either one or
zero in this study. Although a unity collision efficiency will overestimate the size growth,
however, theoretical and numerical exploration on the governing physics may still hold
its significance for a better understanding of the interactions in particle-fluid systems.
Population balance equations are often used for predicting the evolution of
particle size distribution in particle production systems or in studying particle
coagulation. They are typically given in the form of

4
dii!
~fa~ = 'al lnlnl ‘ a12nln2 ‘ a13nln3 ‘ —
dn2 1
-fa =2 allnlnl - a12nln2 - a22n2n2' a23n2n3 " - (L1)
dn3
—= a12n1n2 - oti3n,n3 - a23n2n3 - a33n3n3 -...
dn4 l
dt - a13nln3 + 2 a22n2n2 ' a14nln4 " a24n2n4 ' —
In the above, n¡ is the number concentration of the /th particle size group. For primary
particles, i=l, with a volume v¡, the particle in the /th group has a volume of ¿v¡. The
quantity a¡jn¡nj, denoted as nCjj in this thesis,
ncij = aijninj (1.2)
is called collision rate which represents the number of collisions among /th particle with
yth particle per unit volume and per unit time. The coefficient a¡j is called the collision
kernel or collision function, it depends on particle size, particle inertia and flow structure,
and must be evaluated separately. The collision rate or collision kernel is a measure of
the ability of particles to collide, coagulate, and aggregate. It is essential to determine the
instantaneous particle size distribution and momentum transfer among particles in a
particulate system. Over past 80 years, many efforts have been devoted to developing the
particle collision theories for predicting the particle collision rate in fluid flows. A brief
review is given below.
1.1 Smoluchowski's Prediction in Laminar Shear Flow
A geometrical collision occurs when two particles reach a separation distance less
than R=Tj+rj, in which r¡ and rj are the respective radii of two particle groups and R is the
radius of the colliding surface. Theoretical analysis of geometrical collisions in a uniform
shear flow was first carried out by Smoluchowski (1917). At a given instant the particle

5
number concentrations per unit volume are n¡ and nj for each size group. Taking any
particle of the /th-group as a target particle, the volume flow rate of the y'th-group particles
reaching the colliding surface of radius R of the given target particle is nj iWr£0wrdS in
which w is the relative velocity between /th-group and y'th-group particles and wr is the
radial component of w. Thus the number of collisions occurring among /th-group and
yth-group particles per unit time per unit volume is given by
n Cij = n¡nj íwr so wrds = n¡nj \ r(ri+fj)3 = ct¡j n,nj (1.3)
where T is the shear rate and
ciij= jnn+rj)5 (1.4)
is often called the collision kernel. For like particles, the number of collisions per unit
time and per unit volume was often simply taken as
nc¡i= ^nini jT(2r¡)3 . (1.5)
The factor — was introduced to avoid double counting of the collisions among the like
particles. When the above results are used in the population balance equations, for
example, the rate of the "destruction" of /th-group particles due to collisions is -2 n Cjj +
Inc = - n¡n¡ - T(2r¡)3 - I n¡nj - r(r¡+rj)3 since each collision among the like particles
j*i ,J 3 j*i 3
results in the loss of two particles of that size group. Equation (1.5) has been extensively
used in the literature in calculating the particle collision rate and particle size evolution.
In addition to Smoluchowski's work, Hocking & Jonas (1970) also calculated the
collision rate for the sedimenting particles in a uniform shear flow using Stokesian
theory. They studied the collision efficiency by which the particle coagulation was
assumed if the surface distance between two neighboring particles was less than a
prescribed fraction of particle diameter.
1.2 Various Theories for Collision Rate in Turbulence
When particles collide in complex flows, such as turbulent flows, or when more
than one collision mechanism are involved in the particulate system, it is quite difficult to

6
derive an exact expression for collision rate. Numerous efforts have been made to
estimate the collision rate by means of evaluating the collision velocity in turbulence
during past decades. Camp and Stein (1943) employed the concept of mean velocity
gradient related to the turbulent energy dissipation rate e and kinematics viscosity v and
obtained the collision kernel for turbulence shear-induced collision rate based on
Smoluchowski work (1917) in laminar shear flow.
a.-W)1'2. 0.6)
1J 3 v
Saffinan and Turner (1956) gave two predictions of particle collision kernel in an
isotropic, Gaussian turbulence. In the first prediction, it was assumed that particles are
inertialess so that they follow the fluid completely and particle size is small compared
with the Kolmogorov length scale of the turbulence. The collision kernel was given
a =1294 R3(-)1/2. (1.7)
u v
Their second prediction was for the particles with small inertia and gravity.
a = 2(2*)1,2 R! [(1 - -£-)2 (X, - t2 )2 A2
u Po Dt
2o-—)2(Xi-x2)2g2+ÍR2-j1'2.
3 po 9 v
(1.8)
However, collision kernel for like particles or the particles with same inertia is reduced to
a =1.67 R’(-)ln. (1.9)
11 V
Thus the effect of particle inertia and gravity disappeared in the monodisperse case,
which is not physically sound. In addition, the result of their second analysis was
inconsistent with the first one, as easily seen from (1.7) and (1.9).
Abrahamson (1975) extended the concept of kinetic theory of gases for the
collisions among large particles in a high Reynolds number turbulence. It was assumed
that the velocities of colliding particles are uncorrelated, normally distributed in the
absence of gravitational settling. The variance of particle velocity v was estimated by

7
= (1.10)
1+ li Tp e/u‘
where u is the mean-squared fluctuating velocity of the fluid and tp is the particle
response time. The collision kernel, without gravitational settling, was given by
a¡j = 5.0 R2 ( < v2 > + < v2 > )1/2. (1.11)
Balachandar (1988) performed an analysis on the collision kernel by assuming
that the probability distribution function (PDF) of the velocity gradient in turbulence
follows a log-normal PDF distribution. He found a correction to Saffman & Turner’s
prediction for collision kernel in isotropic turbulence. Williams & Crane (1983) and
Kruis & Kusters (1996) performed stochastic analysis to obtain the collision kernel with
inclusion of the combined effects of local shear and particle inertia. Zhou et al. (1997)
carried out a similar analysis and modified the Kruis & Kusters’ result in turbulence.
Those analyses on the collision kernels were obtained on the basis of two-point velocity
correlations without tracking the trajectories of individual particles in the turbulent flow
(hereinafter referred as Eulerian method). However, we have found that the collection of
the relative velocities (or collision velocities) upon collisions based on tracking the
trajectories of individual particles in turbulent flow is a biased average in favor of those
regions where the collision velocity is high. Hereinafter, this method based on particle
tracking is referred as Lagrangian method. The detailed discussions on the difference
between the results of Eulerian and Lagrangian methods on collision velocity will be
presented in chapter IV.
1.3 Recent Development in Computer Simulation on Particle Collision Rate
Recent developments of direct numerical simulations (DNS) in turbulence have
greatly enhanced the understanding of the turbulence-induced particle collisions.
Balachandar (1988) also carried out DNS of particle collision in an isotropic turbulence.
It was found that the probability distribution function of the velocity gradient follows a

8
log-normal distribution instead of Gaussian distribution. The collision kernel was
obtained in a frozen turbulence with a maximum of 500 particles in the flow field.
Artificially large particles were used in order to increase the collision events within a
reasonable computational time. It was concluded that the particle inertia does not affect
the average collision kernel for large, monosize particles. Large discrepancy in the
collision rate for monosize inertialess particles between his numerical results and
Saffrnan & Turner’s prediction was reported. However it is not clear whether this
discrepancy is due to the difference in the small scale turbulence structure or not.
Chen et al. (1995) carried out DNS results for the aerosols in a turbulent channel
flow. They mainly focused on the possible effects of collisions on the deposition rate of
the droplets on the channel walls. They found that the particle concentration in the wall
region can be significant while the bulk concentration is small. The collision kernel and
rate were found to peak in the wall region, and particle depositions can be enhanced by
the formation of larger particles.
Wang et al. (1997) also carried out DNS to investigate the collision kernel in
homogeneous turbulence. They allowed particles in the system to overlap in space which
is unphysical. The effects of finite inertia on collision kernel were also reported in their
DNS results (Zhou, Wexler & Wang 1997). Zhou et al. (1997) developed an analysis
showing that the increases in particle collision velocity, the non-uniformity of particle
concentration and the compressibility of particle velocity in the continuum sense are three
possible mechanisms to affect the overall collision kernel for particles with finite inertia.
However, they did not quantify these terms separately in their work. Thus how the
collision velocity and the concentration non-uniformity play in enhancing the overall
collision kernel remains unknown at this stage.
1.4 Recent Development in Particle Concentration Non-uniformitv
Recent studies have shown that the particle concentration in turbulence may be
highly nonuniform. Heavy particles with finite inertia tend to accumulate in the regions

9
of high strain rate and to move away from the vortex cores. The occurrence of particle
accumulations is referred as concentration non-uniformity or preferential concentration.
The coherent vortical structures are the mechanisms that cause the nonuniform
distribution of particles. The coherent vortices and concentration non-uniformity have
been observed by many numerical simulations and experimental visualizations. The
numerical simulations for demonstration of the concentration non-uniformity were carried
out in plane mixing layers (Crowe et al. 1988, Chien & Chung 1987, Wen et al. 1992,
Tang et al. 1992, Wang 1992), in wake flows (Chien & Chung 1988, Tang et al. 1992),
in jet flows (Chung & Trout 1988, Hansell 1992), in wall-bounded flows (Kallio & Reeks
1989, McLaughlin 1989, Yonemura et al. 1993, Rouson & Eaton 1994, Tanaka et al.
1996), in homogeneous flows (Maxey & Corrsin 1986, Hunt et al. 1987, Fung & Perkins
1989, Squires & Eaton 1991, Wang & Maxey 1993, Fessler et al. 1993). The non-uniform
distribution of particles has also been found by the experiments in plane mixing layers
(Kobayashi et al. 1988, Kamalu et al. 1988, Lazaro & Lasheras 1992, Ishima et al. 1993),
in wake flows (Tang et al. 1992, Yang et al. 1993, Bachalo 1993), in jet flows (Longmire
& Eaton 1992), in wall-bounded flows (Rashidi et al. 1990, Young & Hanratty 1991).
Wang & Maxey (1993) have, through direct numerical simulations (DNS) in isotropic
turbulence, shown that the preferential concentration achieves maximum at pxk =1 in
which p is the reciprocal particle response time and xk is the Kolmogorov time scale of
the turbulence. One of the consequences of the preferential concentration is the
enhancement of the settling velocity of heavy particles in the Stokes drag range (Maxey
1987, Mei 1994). One of the related industrial applications is that the particulate
removal efficiency of the venturi type scrubber was affected by the nonuniform
concentration distribution of droplets (Fathikalajahi et al. 1996).
Particle trajectories, therefore individual particle positions, can be obtained
accurately only in Lagrangian frame by solving the equations of particle motions.
Particles have finite response times in general but they do not have an intrinsic diffusivity
or viscosity associated with the motion of individual particles. Turbulence particle
diffusivity is the consequence of ensemble averaging over many realizations and it is used
for predicting the variation of average concentration field on a large scale. It cannot be

10
used to describe the small-scale spatial variation of the particle concentration. From the
computational point of view, a DNS using 1283 grid resolution for an isotropic
O T
turbulence becomes routine and DNS using 256 or even 512 have been carried out.
For a reasonable contour representation of particle concentration, it is desirable to have at
least, say, 10 particles in each cell so that the statistical noise does not become
o o
overwhelming. This requires 1.7x10 particles with a 256 grid resolution. Due to the
preferential concentration, there will be regions and cells that do not contain any particle
or only one or two particles at a given instant. Thus, an accurate, three-dimensional
contour representation of instantaneous particle concentration with such high resolution
will still be heavily contaminated by the inherent statistical noise. No such three-
dimensional concentration contour has been reported to date. Due to the computer power
limitation, previous numerical simulations involved anywhere from 104 to 106 particles,
which is far below the level of reaching a reasonable statistics.
It has been difficult to quantify the extent/degree of particle concentration non¬
uniformity induced by the small-scale turbulence structure. Squires & Eaton (1991) and
Wang & Maxey (1993) showed in their DNS the similarity between the instantaneous
particle concentration contours with the contours of the vorticity magnitude (or
enstrophy) in the same plane at the same instant. To quantify the deviation of the particle
concentration field from a statistically uniform distribution, Wang & Maxey (1993) used
a statistical quantity X (P(k) - Pb (k)) averaged over space, where P(k) is the
probability of finding k particles in a given cell and Pb(k) follows the Bernoulli
distribution for a random distribution of the particles. While this quantity shows that the
preferential concentration maximizes near Pxk=l (Wang & Maxey 1993), it varies with
total number of particles in each of their simulations. The particle-particle interactions
are neglected in their simulation and no systematic investigation on its dependence on the
cell size was carried out. Hence, its usefulness is limited. Although numerous
experimental studies have clearly shown the non-uniform particle distribution in turbulent
flows, it is quite difficult to measure the 3D non-uniformity so as to isolate the effects of
flow shear, particle inertia and gravity.

11
Obviously, the local accumulation will affect the encounters among the particles
and thus the overall collision rate is expected to be modified accordingly. However, the
effect of the preferential concentration on the collision kernel has never been estimated or
separated from other factors such as the increased particle collision velocity due to small
particle inertia.
1.5 Scope of Present Study
Particle collisions are extremely difficult to observe experimentally, even with
today’s sophisticated laser instrumentations. Direct numerical simulation, however, has
its advantages of providing detailed information on particle trajectories, flow structures
and is ideally suited for parametrical investigations of particle and fluid flow properties.
Numerical implementation of particle interactions in a simulation, while posing a
considerable numerical challenge, introduces no fundamental difficulties. In this work,
extensive data obtained by numerical simulations on the collision velocity, collision
angle, collision kernel and collision rate are vital to understanding of the fundamental
physics in the particle-fluid interactions. One of the major objectives of this thesis is to
develop analytical and numerical methods for predicting the particle collision rate and
concentration non-uniformity with respect to the effects of turbulent shear, particle inertia
and gravity in particle-fluid systems.
As mentioned in §1.2, previous theories on the collision rate were restricted on
assumed turbulent flow. In Chapter III, we have developed a new theoretical framework
for prediction of the collision rate and collision velocity of small particles at zero inertia
in general turbulent flows. The collision velocity is a critical variable related to the
collision rate. However, the effects of particle inertia on the collision velocity and
collision angle have never been predicted using Lagrangian tracking method. In Chapter
IV, we carried out an asymptotic analysis to predict the effects of small particle inertia on
the collision velocity and presented the effect of the inertia on collision angle in isotropic
Gaussian turbulence. As pointed out in §1.4, no realiable method exists for quantifying

12
the concentration non-uniformity and the effect of the preferential concentration on the
collision rate has never been estimated. In Chapter V, a new quantification method has
been developed to reliably quantify the concentration non-uniformity of particles in
turbulent flows, and the effect of the preferential concentration on the collision rate is
predicted in isotropic Gaussian turbulence. The first result of Chapter VI is our
correction to the classical result of Smoluchowski for the collision rate of monodisperse
particles in a laminar shear flow. The combined effects of both the increased collision
velocity and increased concentration non-uniformity on the collision rate due to the
inertia are then examined. The effects of particle size, gravity and mean-concentration on
the collision rate are investigated through direct numerical simulations in Chapter VI.
The effects of the collision models on the collision rate are also presented in Chapter VI.

CHAPTER 2
TURBULENCE AND PARTICLE COLLISION DETECTION
2.1 Turbulence Representation
2.1.1 Isotropic. Gaussian Turbulence
The following model for the energy spectrum developed in Mei & Adrian (1995)
is assumed for turbulence,
3 2 ^ | 2 2
E(k) = 2 uo V7?" .. , ,2,17/6 exP(-Tlo k) (2-1)
z Icq [l+(k/ko) ]
where u0 is the root-mean-squared turbulent velocity, is a typical wave number and the
dimensionless parameter rio^o^o is related to the turbulent Reynolds number Re^.
Large Re^ correspond to small r|0 and vice versa. The normalizing coefficient \\i in E(k)
is determined from (2.1) by satisfying the total energy requirement,
1 00 k4 _2 - 2 -
V = Í -2 17/, exP(- Tl0 k 2) die . (2.2)
o (1 + k )17/6
For small q0, E(k)~k4 when k= k/k0«l and E(k)~k'5/3, which is the scaling law in the
inertial subrange, when l«k«l/q0. For large q0, the above energy spectrum recovers
that of Kraichnan (1970). The relationship between the Eulerian integral length Ln^
and r|0 (or Rex) is given in Mei & Adrian (1994).
A turbulent eddy loses its identity as it is convected and dissipated; this behavior
is described by the eddy-self decay function D(x) which has an integral time scale T0.
13

14
The Fourier transformation of D(x) gives the power spectrum D(co) . A composite form
for the power spectrum D(g>) was constructed in Mei & Adrian (1995) as
_ exp(-qJco2)
D(0))
(2.3)
where r|, is intended for the viscous dissipation time scale if the turbulent Reynolds
number is high, and T is close to T0 if t|,/T«1. The coefficient A is determined as
T exp(-T]|fr2)
n l-erf^/T)
(2.4)
00 —
by satisfying \ D(co)dco =1. The integral time scale T0 is related to T as T0 = 7iD(0) =
-00
71Á .
The relationship between Ln and T0 is not known in general; it can be taken to
have the form
T0 = c£(Rex)Ln/u0, (2.5)
where c£(Re^) was determined approximately in Mei & Adrian (1995) based on the
experimental data of Sato & Yamamotto (1988) for the fluid dispersion and the value of
high Reynolds number turbulent Prandtl number.
Using random Fourier modes representation, an isotropic, Gaussian, pseudo
turbulence
u¡(x, t)= I[b|m)cos(k(m)-x +o>(m)-t )
m=l
+c[m) sin(k(m) • x +(D(m)-t ) ] (2.6)
is constructed to simulate the turbulent flow with a specified energy spectrum. In the
above Nk is the number of the random Fourier modes in one fluid realization,
k(m)and co(m) are the wavenumber and frequency of the m-th mode. The random
coefficients b^m) andc(¡m) are chosen as follows,

15
b(m) =b('n>(8irk[m)k(.m)/k(m) ) f(k (2.7)
where b(m) follows a normal distribution with
j
= 0 & = Sjj.
J
J
(2.8)
(rn) fm') (in)
The factor (Sy-k^ k\; /k ) in Eq (2.26) ensures the incompressibility V-u=0 for
every Fourier mode. The scale factor f(k, to) depends on the energy spectrum and the
probability density functions (pdf), pj (k) and p2(co), of k and go,
r2„. E(k) D(co)
i (k, oo) = 2
(2.9)
47iNkp1(k)p2(co)
Since E(k) decays as k'5/3 in the inertia subrange, a Gaussian distribution for k would lead
to a very slow statistical convergence for u¡(x, t). To achieve convergence of the statistics
C?U:
involving the derivatives such as , the following algebraic pdf
3xj
Pii(ki)= ^(l+lkj)-*1 for i=l, 2 & 3
and
P1(k) = PI1(k1)PI2(k2)P13(k3)
(2.10a)
(2.10b)
(m) .
is used to sample k¡ for 1^=1. The frequency co is generated with the following pdf,
1 1
P^®) n l+(io/UolCo)2
The scale factor is finally set to be
(2.11)
f2(k, co) = j - (2.12)
4;rNkk Pii(k1)p12(k2)p13(k3)p2(cD)
It is noted that in the present studies on particle collisions, the collision rate is dictated by
the spatial structure and the eddy-self decay has no effect on the collision rate. In the
direct numerical simulation of particle collision in isotropic turbulence, however, the

16
eddy self-decay can be easily incorporated. Hence, the dependence of b1.™’ and c(.m) on co
is still included.
2.1.2 Rapidly Sheared Homogeneous Turbulence
5U
For an initially isotropic turbulence under rapid shear, T=—, the variation of
dy
b*."1’ with time was given in Townsend (1976) based on rapid distortion theory (RDT).
Hunt & Carruthers (1990) gave an overview on the use of RDT for representing various
types of homogeneous turbulence. Denoting the total shear S as
S = H (2.13)
the new wavenumber vector x(m) after the shearing is given by
(m) _.(m)
X i - K |
x(2m) = k(2m) -Skâ„¢
x(m) = k(m)
(2.14)
3 3
where k(m^ is the wavenumber vector of the m-th mode before the mean shear is applied.
The amplitude of each Fourier mode becomes
b(Jn)(S) = b(Jn)(0) + a{b(â„¢\0)
b(^(S) = a2b(â„¢)(0)
b(3m)(S) =b(3m)(0) +a3b(^(0)
(2.15)
where b^^O) is the corresponding amplitude of the m-th mode before the rapid
distortion. Dropping the superscript m for convenience, the coefficient a¡ for each mode
m is given as
Sk2. k2-2k^+Skik2
a'“ k2+k2'
K1 K3

17
k2k'
(k^)3/2k,
1 k2
[tan '('
i k2-Ski
)-tan-\-¡===)]
Vk?+k3 v
kí+k3
&2 = k2/x2
(2.16)
Sk,k3 k2-2kfok,k2
a-’"k2,k2
k.j+K-3
k2k3 , k2
¿T53H ltan‘ <
(k;*k3‘)
V
) - tan''('
k|*k3
k2-Ski
4
ki+k3
)]
V 2 2 2 /2 2 2 (m)
kj+k2+k3 and X= A/X1+X2+X3 • The coefficient c ¡ is similarly obtained. It
is clear from the above that the amplitude of each mode is entirely determined by the total
shear S=ft. The dissipation rate s(S) can be evaluated as
e(S)/v = ^ < £ {x(m)2 ¿ [b m= 1 i=l
>.
(2.17)
Finally, the total velocity field is
N
u¡(x, t) = £ [ b(.m,(S) cos(x(m) -x + (o',,,;t)
m=l
+ c^m)(S) sin(x(m) -x + io + Tx2 8ji.
(mi
(2.18)
(mV .
Since the shear rate is high, the term co t in the above acts merely as a random phase.
2.1.3 Isotropic Turbulence Generation in a Periodic Box
For the direct numerical simulations of particle collisions in this work, the
velocity field of an isotropic and Gaussian turbulence is generated by a random Fourier
modes representation (2.6). To effectively conduct a computational study on particle
collision, a region of finite size in which a large number of collisions occur should be
used with periodic boundary conditions. To render the turbulence represented by random

18
Fourier modes periodic in a box of volume 7i3, the random wave numbers, k(.m) , are
rounded to the nearest even integers while all other quantities are held fixed so that the
flow is periodic within the box of volume 7t3. The periodic box of volume 7i3 yields
virtually the same collision statistics when the volume is increased to (27t)3. Hence 7t3 is
used throughout this study. The corresponding statistics (turbulence rms velocity uo,
Reynolds number Re^., Taylor micro length scale X, Kolmogorove length scale q, and
Kolmogorov time scale xk) are computed based on the modified wavenumbers. As will
be demonstrated using the present analysis outlined in §2.1.1, the collision statistics,
when normalized using R3(s/v),/2 for an and <-wr>, the results are the same using the
turbulence generated in an unbounded domain and in a periodic box.
Unless specifically mentioned, most of the isotropic turbulence used in the
numerical simulations throughout this study is within the periodic box of length n and
have the following characteristics: root-mean-squared turbulence velocity u0 = 1.0,
integral length scale Ln =0.594, Taylor micro length scale X=0.277, Kolmogorov length
scale q=0.023, Kolmogorov time scale xk = (v/e)12 =0.073, and Re^=40.1. The
turbulence is imposed on a box 7t3 with periodic boundary conditions in three
perpendicular directions. The periodic treatment of boundary conditions allows the
simulation results by a finite volume to be extended to an infinite homogeneous system.
Initially, a given number of monosize particles are randomly introduced in the
flow field of volume 7t3. The periodic boundary conditions are also applied to the
motions of the particles. The particles are advanced using u¡(x, t) given by (2.6) with a
time step At. A semi-implicit finite difference scheme is implemented to integrate the
equation of particle motion and to detect collision. The scheme has an accuracy of second
order. The detailed error analysis of the semi-implicit finite difference scheme will be
given in next section.

19
2.2 Numerical Integration of Particle Motion and Error Analysis
Major concerns in numerical simulation are the accuracy and robustness of the
numerical scheme. Since the collision detection in the present simulation is based on the
calculation of particle trajectories, the collision kemel/rate/velocity/angle are affected by
the accuracy of the finite difference scheme. In this study, both tracking particles and
detecting collision are carried out in a Lagrangian frame. A semi-implicit finite
difference scheme is implemented to integrate the equation of particle motion and to
detect collision. This scheme is unconditionally stable. With consistent treatment for
both particle velocity and turbulence, The truncation error in the semi-implicit scheme is
0( (At)2) and independent of (5. The accuracy analysis of the scheme is given as follows.
The particle dynamic equation in the absence of (or weak ) gravity is
dv
d¡-=P(«-v). (2.19)
The semi-implicit finite difference scheme for the particle dynamic equation (2.19) is
,n+l
Vn+l-vn 1
= -p[(U-v)n*'+(u-v)"]
At 2
1
= -p[(un+1 +un -(vn+1 +vn)].
(2.20)
,n+l
dt
u
. n+1
= un +At-
dt
n+1 and vn+1 give
+ ^(At)2^- + l(At)3^+ ...
2 dt2 6 dt3
(2.21)
1 2 d2un 1 .. .3 d3un
â– +o(At) j 2 +<(At) -7T-+ -
2 dt2 6 dt3
(2.22)
Each side of equation (2.20) can be evaluated as
dv11 1
LHS= + —(At)
dt 2
^4(At)2TT+ 0(At)3
dt 6 dt
RHS= P(u1" - v1» ) +1PAt(^ + jP(At)2 ^
2 dt dt 4 dt2 dt2
) + 0(At)J
(2.23)
(2.24)

20
Then equation (2.20) can be rewritten as
vn+1-vn 1
0= —— -p[(u-v)n+1 +(u-v)n]
At 2
dvn 1 d2vn 1 2 d3vn
+ T (At) —7-7- + — (At)2 -+...
dt
dV
dt
1 dun dvn 1 , d2un d2vn
- p(u" - v " ) - - PAt(—- - —- P( At)2 (— —) +
dt dt
dt"
dt"
Thus, equation (2.20) is equivalent to
dvn n nx 1 d2vn 1 A - dV
— P(un - Vn) = --(At)—y---(At)2
dt 2 dt 6
1 dun dvn 1 7 dzur
+ - p At( ) + - p( At)2 (^-4-
2H dt dt r dt2
dt3
dV
dt"
-)+...
1 A rd2vn _ ,dun dv" 1 ,d3v"
= _ - 7^ -rr-
2 dt dt dt 6 dt
1
+ ¿P(At)2(^-
4 dt
d2vn
dt2
)•
Taking derivative with t repeatedly, we get
dV „,du" dvn 1 *.rd3vn „,d2u" d2v\,
2At[^-p(—•-^-)]
dtz
d3un
-l(At)2^ + |p(At)^(
6 dt4 4 dt3
dt"
d3vn
dt
and
3_, n
d v
dt-
-P(
d2un d2vn
dt"
1 d4vn
,2 ) = --Ai[—¡—P(- 3
dt 2 dt4 dt3
d3un d3vn
- 7(At)2“7T- + 7p(At)2( 4
6 dt5 4 dt
dt3
d4vn d4vn
)]
dt4
)â– 
Repeatedly substituting above equations into the RHS of (2.26) results in
i3_.n
dv n
—— P(u"-v")=--(At)
dt 6 dt
2 dV +|(At) 3
4 dt3
2 d V' + 0((At)3)
(2.25)
(2.26)
(2.27)
(2.28)

21
(2.29)
It is noted that the truncation error in equation (2.29) is independent of p. Thus
motions of particles with very large p can be simulated without loss of accuracy.
2.3 Particle Collision Treatment
2.3.1 Collision Detection Scheme
Collision detection between any pairing particles is given as follows (Chen et al.
1995). At nth time step t0, these two particles are initially located at
rio=(xiO’ y to ’ zio) and r20 =(x20, y20’ z2o)- After a small time advancement At,
at (n+/)th time step tj, new positions of these two particles are rn =(xn, yn, z,,)
and r21 = (x21, y21, z21). The particle position vectors at time t, which is between t0
and 11, can be expressed as
ri(t)={x10 + vxl(t-t0), y10 +vyl(t-t0), z10+vzl(t-t0)} (2.30)
r2(t)={x20+vx2(t-t0), y20+vy2(t-t0), z20 +vz2(t-t0)}. (2.31)
The distance between two particles, s(t), is
s(t)= |r2(t) - r, (t)|.
(2.32)
These two particles will collide if the condition s(t) relative distance s(t) varies with time t, and it is not a monotonic function of t. The
minimum relative distance occuring at time tm is determined by
= (233)
Combining (2.30)-(2.33) gives
(2.34)

22
where
D, =(x10 -x20)(vxl -vx2) + (y10 -y20)(vyl -vy2)
+ (z10-z2o)(vz,-vz2) (2.35)
and
D2 =(Vxl - Vx2)2 + (Vyl - vy2)2 + (Vzl “ Vz2>2- (2-36)
Equation (2.33) does not guarantee that the moment, when the above mentioned
minimum distance occurs, has to be within the time interval t0 and t0 + At. Three cases
need to be considered for calculating the minimum distance within a time step.
(i) if tm < t0, then the minimum distance is at the initial time t0.
smi„=s(t0) (2.37)
Le- smin = {(x10 "x20)2 +(yiO ~y20)2 +(z10 ~Z20)2}I/2- (2-38)
(ii) if tm > t0 + At, the minimum distance must occur at the time t0 + At.
smin = s(t0 + At) (2.39)
i.e. smin =(sj +s2y + s*)1/2 (2.40)
where
sx =xI0 -x20 +At(vxl -vx2)
Sy =yi0 -y20 +At(Vyi -vy2)
Sz=z10-Z20+At(vzl-Vz2).
(iii) if t0 t0 and t0 + At. In this case, we have
Smi„=s(tm) (2.41)
ie- smin =(sL +s^y +s^)1/2 (2.42)
where
**mx — x10 — x20 (^m — ^oX^xl — ^*2)
^my y 10 — y20 "*" (^ m —^oX^yl ~ ^y2)
Smz =z10 ~ z20 +(*m -t()XVzl _Vz2)‘

23
After smin is evaluated within the time step, the examination of collision events is
still needed because smin can be either larger or smaller than R. Collision occurs if
«min - R (2-43)
At the collision instant tc,
s(tc) = R
and s(tc) =|r2(tc) - r,(tc)|.
Hence the collision instant tc can be obtained solving (2.44) and (2.45)
-b - -Jb2 - 4(Sq - R2)w2
lc=t0+ —d2
(2.44)
(2.45)
(2.46)
where
b=2[(x10 -x20)(vxl - vx2) + (y10 -y20)(vyl -vy2)
(Z10 ~z2o)(Vzl — Vz2) 1
s0 = Ux10 -x20)2 +(yiO-y20)2 +(z10 -z20)2}'/2
w = ttvxl - vx2)2 + (vyl - vy2>2 + Kl “ Vz2)2}1/2 •
With perfect sticking collision model, any collision can produce a new bom particle
(daughter particle). The daughter particle will inherit all possessions of parent particles,
i.e., conservation of mass and momentum. Let the daughter particle be numbered 3, then
the conservation law gives the size of daughter particle as
d3=(d3 + d3)1/3 (2.47)
where d¡ (i=l, 2, 3) are particle radii. The velocity components of daughter particle are
vxidi +vx2d3
Vx3 =
vy3 =
¿3
Vy|d| +Vy2d2
¿3
(2.48)
(2.49)
Vz3 ='
__ Vzldl +vz2d2
^3
and the position components at the end of time step
(2.50)

24
x3 = {[*10 + vxl(lc - to)]dl + [x20 + vx2(tc - lo)]d2} / d3 + ~ lc + At) (2-51)
Y3 = {frío + Vyli'c ~ lo)]dl + ^20 + vy2(tc “ t0)]d2> ' d3 + Vy3(t0 - tc + At) (2.52)
z3 = {[Zio + vzi(tc - t0)]df + [z20 + vz2(tc - t0)]d2} / d3 + vz3(t0 - tc + At) (2.53)
In general, the collision detection takes a lot of computer time since a large
number of particles are considered. The primitive collision detection scheme searches the
collision candidates by going through all particles N, so that the computing time is
'y
proportional to N . The collision detection method in Chen et al. (1995) is an efficient
scheme, and it is also implemented in this thesis. By dividing the computational domain
into a number of small cells, the potential collision partners for a given particle in one cell
are then searched within this cell and neighboring twenty-six cells during one time step.
This search only involves a small number of particles. The total computing time with this
scheme can be reduced dramatically. Only binary collision is considered by assuming a
negligible probability of multiple collisions in a small time step in a dilute condition. For
any particle moving out of the computational box, it is re-introduced into the box by
invoking periodicity. The collision rate or kernel can be determined, in principle, by
counting the number of collisions per time step. Initially, a given number of particles are
randomly distributed into the computational domain. The particles are evolved with
turbulence for a period of time before collision detection turned on. The collision events
are not counted until all particles with finite inertia have reached dynamical equilibrium
in turbulence.
2.3.2 Post-Collision Treatment
The collision event is counted when two particles are brought into contact, as
stated above. The collision among particles and its subsequent treatment are expected to
disturb the original system and permanently impact the subconsequent collision
evaluation. For the droplets in gas turbulence, the collision may, most likely, result in

25
coalesce of two droplets and a larger droplet is then formed by the colliding droplets.
Similar phenomena may be found in a system consisting of bubbles and fluids. The
collision between two solid particles may behave in different ways. Two solid particles
may stick together upon contact or the colliding solid particles will separate after
collision. In summary, the issue dealing with immediate consequence of those contacts is
referred as post-collision treatment. Several models with regard to post-collision
treatment have been developed during past decades.
In Chen et al. (1995) every collision results in the collision partners disappearing
from their respective size groups to produce a larger particle. Mass and momentum
conservation laws were applied for the birth of the new particle. This scheme is
considered to be a physically realistic model assuming a unity collision efficiency. It is
hereinafter referred as ‘KEEP ALL’ model. Balachandar (1988) used a different post¬
collision scheme. Starting with monosize particles in system, after each collision, the
resulting larger particle was discarded in the computation so that it does not contribute to
future collisions. This post-collision treatment is hereinafter referred as the ‘THROW
AWAY’ scheme. Sundaram & Collins (1997) employed a ‘HARD SPHERE’ collision
model in which the particles were assumed to be rigid spheres and forced to bounce back
after the contact. Momentum conservation is applied to the particles after collision. In
contrast to the ‘KEEP ALL’ and ‘THROW AWAY’ schemes, the ‘HARD SPHERE’
model allows a particle to have multiple collisions with other particles in a system. Wang
et al. (1997) tested three different schemes and presented one of their own. In their
scheme, collision does not disturb the system. The colliding particles are allowed to
overlap in space (hereinafter referred as ‘OVERLAPPING’ scheme), but no larger
particles are formed after the collision. Two colliding particles can separate after their
trajectories satisfy |r2 (t) — i-](t)|>R. Thus, a particle can have multiple collisions with
other particles. These four different post-collision treatments will, in principle, result in
different collision rate/kemel.

26
<*ii
Figure 2.1 The effect of time step on the cumulative average of the normalized
collision kernel a|t for inertialess particles in turbulence. Particle size D=0.05.
2.4 Computer Simulation
2.4,1 Time Step
Three collision models: “THROW AWAY,” “KEEP ALL” and “HARD
SPHERE” are employed in this work. For the following simulations with “THROW
AWAY” and “KEEP ALL” collision models, the time step At=0.01 is used to advance the
motions of particles. Figure 2.1 shows the effect of time step on the collision kernel a|j
using “KEEP ALL” collision model. It can be seen that there is almost no difference
between the results of At=0.01 and At=0.005. Similar results are obtained by “THROW
AWAY” scheme. The volume concentration of suspended particles is usually quite low
in many natural systems such as spray burners and air scrubbers, which is within the

27
interest of this work. In such dilute systems, the multiple collisions during a small time
step may be negligible, thus binary collision mechanism is considered solely in this
study.
2.4.2 Turbulence Realization
The averages of statistical quantities are evaluated in two directions: average over
all time steps and turbulence ensemble average over all turbulence realizations. The total
time steps mainly depend on the particle response time and the number of collision pairs
during whole time period. We start counting the time steps for collision detection after
the motions of particles have been evolved for three times the particle response time.
Ideally, to form the turbulence ensemble average, the numerical simulation should be
repeated an infinite number of realizations with different initial particle distributions. An
infinite number of turbulence realizations is impractical due to limited computer capacity
and may be unnecessary from the viewpoint of the accuracy of numerical scheme.
Figures 2.2 (a) & (b) show the turbulence dissipation rate against turbulence realizations.
While the ensemble averaged turbulence dissipation rate varies with the realization by a
large amplitude shown in figure 2.2 (a), the cumulative average of turbulence dissipation
rate approaches a constant for the number of turbulence realizations greater than 30, as
seen from figure 2.2 (b). Figures 2.3 (a) & (b) show the small eddy shear rate against
turbulence realizations. It can also be seen that, although the ensemble averaged small
eddy shear rate randomly varies with the turbulence realization in figure 2.3 (a), the
cumulative average of small eddy shear rate is close to a constant by the 40th realization
seen in figure 2.3 (b). Therefore, in this study forty turbulence realizations are used for
turbulence ensemble average.
2.4,3 Validation of the Collision Detection Scheme
Starting with monosize particles in system and using the “THROW AWAY”
collision model for post-collision treatment. The evolution of the particle number nj (t)

28
follows the simple population balance equation:
dn,/dt =-ann^ with n,(t = 0) = n0. (2.54)
The solution is
no / n,(t) = l + a,,not. (2.55)
It should be mentioned that the collision rate of monosize particles (2.54) is only accurate
for large number of particles in a system. For small number of particles, however, the
collision rate (2.54) must be corrected. The detailed discussions on this issue will be
given in §6.2. Since the statistics for the number of the remaining particles n^t) is much
more accurate, the average slope of the curve 1/ n((t) gives the collision kernel, so it can
be used to check the accuracy of the collision kernel obtained by simulation in laminar
shear flow.
To validate the collision detection, particle collision in a uniform shear flow of
velocity gradient T=1.0 is first considered. For large number of particles the theoretical
prediction of the collision kernel ay of Smoluchowski is accurate and is given by
a = 4 R - T / 3 . For a small number of monosized particles the theoretical prediction
of the collision rate of Smoluchowski has to be corrected, as mentioned above. Ten
thousand particles of radius 0.075 are introduced into a box of volume 2x2x2. The time
step is Dt=0.01 and a total of 800 time steps is used. The number of shear flow
realizations is 80. Since no periodic condition may be imposed along vertical direction in
uniform shear flow, periodic boundary conditions are employed in the streamwise and
spanwise directions only. In such a case, a boundary correction of the collision rate has
to be imposed (Wang et al. 1997). Another correction on the collision rate is also applied
due to the inaccurate expression of the collision rate (§6.2) Figure 2.4 shows the
particle number evolution. The collision kernel a! ¡ can be obtained from the average
slope of the curve. Very good agreement can be observed between simulation result and
Smoluchowski’s prediction. Hence, the collision detection scheme and the numerical
implementation are validated.

29
e
s
Turbulence realizations
Figure 2.2 Turbulence dissipation rate s against turbulence realization, (a) Variation of
ensemble average, (b) Cumulative average.

30
(s/v),/2
(s / v)1/2
1 /?
Figure 2.3 Turbulence small eddy shear rate (s / v) against turbulence realization,
(a) Variation of ensemble average, (b) Cumulative average.

31
no / ni
T
Figure 2.4 Inverse of total number of monodisperse particles against time in laminar
shear flow. f=1.0, D=0.015.
For “HARD SPHERE” particles, the time step for advancing the motions of
particles is much less than 0.01. It is found that At=0.001 may generate almost same
result as At=0.0005 for large inertia particles. For small inertia or zero inertia particles,
the collision kernel strongly depends on time step due to multiple collision mechanism,
which is shown in figures 2.5 for uniform shear flow. A pair of particles can have tens or
hundreds of collisions depending on the size of time step before they completely separate.
Similar multiple collisions have been found for particles in turbulence. Due to this
technical difficulty, the “HARD SPHERE” collision model is limited to particles with
large inertia.

32
x
Figure 2.5 Multiple collisions of HARD SPHERE particles in laminar shear flow.

CHAPTER 3
A NEW THEORETICAL FRAMEWORK FOR PREDICTING COLLISION
RATE OF SMALL PARTICLES IN GENERAL TURBULENT FLOWS
A new theoretical framework is developed to predict the collision rate and collision
velocity of small, inertialess particles in general turbulent flows. The present approach
evaluates the collision rate for small particles in a given instantaneous flow field based on
the local eigen-values of the rate of strain tensor. An ensemble average is applied to the
instantaneous collision rate to obtain average collision rate. The collision kernels
predicted by Smoluchowski (1917) for laminar shear flow and by Saffman & Turner
(1956) for isotropic turbulence are recovered. The collision velocities presently predicted
in both laminar shear flow and isotropic turbulence agree well with the results form direct
numerical simulation for particle collision in both flows. The present theory for
evaluating the collision rate and collision velocity is also applied to a rapidly sheared
homogeneous turbulence. Using the mean turbulent shear rate (s/v) as the
characteristic shear rate to normalize the collision rate, the effect of the turbulence
structure on the collision rate and velocity can be reasonably described. The combined
effects of the mean flow shear and the turbulence shear on the collision rate and collision
velocity are elucidated.
3.1 Introduction
Smoluchowski (1917) considered collision rate among spherical particles in a
laminar shear flow, (ux, uy, uz) = (Ty, 0, 0) with a constant gradient f. For a target
33

34
particle of radius r¡ centered at an arbitrary position x¡, any particle with a radius rj
moving toward the target particle will cause a collision if
|x¡ - Xj| < Rij = r¡+r¡ (3.1)
is satisfied. The collision rate can be evaluated as
"‘ij“nini I„,<0-WrdA
(3.2)
where wr is the radial component of the relative fluid velocity between x¡ and Xj, wr<0
indicates an impending collision, -n,n2wr is the particle flux moving inward to the target
particle, and A is a spherical surface with radius R¡j=rj +r¡. Using standard spherical
coordinates (R, 0, <)>) centered at particle i (see figure 3.1), wr on the spherical surface of
. . 2 .
R=Rjj can be written as wr = TycosG = R^ sin0 cos0 cos. With dA= Ry sin0 d0dc(>, it is
easily seen that
nCij = ninj 3 r (3.3)
or
aij= 3 r Ry • (3-4)
Saffman & Turner (1956, hereinafter referred as ST) presented a classical theory
for the collision rate of small droplets in Gaussian, isotropic turbulence. The collision
rate among particle size group i and j is
»c¡j = n¡ni' (3-5)
The ensemble average, denoted by < >, is necessary since turbulence is random. Because
of the continuity of the fluid flow, the volume influx entering the spherical surface of
radius Ry is equal to the efflux so that
nc--= Tnini. (3.6)
cij 2 1 J entire sphere r* v '
ST further interchanged the integration with the ensemble average so that nCjj can be
evaluated in isotropic turbulence,

35
Figure 3.1 Local coordinates for collision rate calculation in laminar shear flow.
For isotropic turbulence, the above becomes
nCjj = n¡nj 2iiRy <|wx|> (3.8)
where x is the local coordinate parallel to the line connecting the centers of the colliding
particles as shown in figure 3.2. For particle size smaller than Kolmogorove length scale
r|, the relative velocity upon the collision was evaluated using the standard results of
small-scale turbulence theory as
-«R||<^>= RijC^f)1'2 (3.9)
where e is the dissipation rate of turbulence and v is fluid kinematic viscosity. This gives
ncij-niiijR2 [ff Í I’'2 =1 2944n¡njRy (1/2 (3.10)
or «y -l.2944Ry (J)1/2 (3.11)
*
«1|
R.^1'2
-1.2944
or
(3.12)

36
*
where ay is normalized collision kernel.
â–º x
Figure 3.2 Sketch of particle collision velocity. The separation
distance is X2-X1 = ± Ry- ±(r;+rj).
It needs pointed out that from equation (3.6) to equation (3.7), the interchange
between the integration with the ensemble average is only an approximation even in a
Gaussian, isotropic turbulence. Furthermore, the interchange between these two
operations severely limits the extension of ST's result and approach to other turbulent
flows such as the near wall region where the mean flow gradient contributes to <|w |>. Of
course, in the theory of Smoluchowski for laminar shear flow, this issue does not arise
since the ensemble average is not needed. In a typical industrial facility where particle
collision leads to the desirable growth of particles, the inhomogeneity of the turbulent
flow structure is common. Due to the effects such as turbopheresis, particles tend to be
driven toward wall regions to cause a further inhomogeneity in the particle concentration
distribution. It was not clear how one can predict the collision rate of small particles in
the presence of both strong mean flow shear and high turbulence intensity (Kontamaris,
1995).
In this chapter, a new theoretical framework is presented for the collision rate of
inertialess particles, whose sizes are smaller than the Kolmogorove length scale, in
general turbulent flows. The present analysis starts from equation (3.5). Using the
leading order term, (x¡-Xj)-Vu, in the Taylor series expansion to approximate the relative

37
fluid velocity, w, at the collision instant, the collision rate for an arbitrarily given Vu is
evaluated first. The ensemble average is then carried out in a Gaussian, isotropic
turbulence to obtain the collision rate and collision velocity. Direct numerical
simulations are also performed to obtain the collision rate and collision velocity in a
Gaussian, isotropic turbulence. Good agreement is obtained between the present
*
prediction and the simulation for the collision kernel ay . Excellent agreement is
obtained for the average collision velocity between the prediction and the simulation.
While the present result for the collision rate agrees with the prediction of ST, the present
theory predicts an average collision velocity <-w,> that is 1.58 times the value given by
ST. The difference is caused by the bias of the collision toward higher values of collision
velocity which was not considered in previous studies on flow induced particle collisions.
Using the rapid distortion theory (RDT) for a rapidly sheared homogeneous turbulence
and the random Fourier modes representation, the dependence of particle collision kernel
ay on the mean flow shear rate and the turbulence structure in homogeneous shear flow is
studied under the present theoretical framework. It is found that the collision kernel, after
normalized by particle volume and the turbulence mean shear rate characterized by the
dissipation rate, mainly depends on the ratio of the mean flow shear rate to the turbulence
mean shear rate.
3.2 Analysis
The relative fluid velocity, w = u, -u2, between target particle centered at X] and
the colliding particle centered at in space with |xj - x2| = |r12| = R less than the
Kolmogorove length scale r| can be expressed using Taylor series expansion as
w = u2 -U! « r12 Vu +... (3-13)
**12 = X2-*1 (3.14)
to the leading order. The collision rate, nq2 , for a given Vu at a given instant is thus

38
nq2 = nin2 i - -w ndA
wr<0
(3.15)
where n is the outward normal of the surface given by |x2 - X|| = Ry = R and wr= w n.
For a spherical surface, n= r12/R so that
Í „ -w-ndA = 77 1 -w r12dA
w.<0 R w,<0
Í
R Jwr<0-r12-V"-r12dA-
(3.16)
Representing Vu as the sum of a symmetrical part (rate-of-strain tensor) and an anti-
symmetrical part (vorticity tensor), it can be easily seen that the anti symmetrical part of
Vu does not contribute to wr. Physically, it is simply because a rigid body rotation does
1 T
not contribute to normal velocity. For a symmetric tensor E = ^ (Vu +(Vu) ) , or a
symmetric matrix Ey, a linear transformation, which is a rotation of the coordinate system
from (x, y, z) to (x , y , z ), can be easily found to reduce Ey to a diagonal form as
E =
ex 0 0
0 ey 0
0 0 e
Z -J
(3.17)
where (ex , e^ , e2 ) are the eigen-values of E and are the principal values of the rate-of-
strain tensor. Due to incompressibility, ex + ey + e2 = 0 so that there are only two
independent parameters (say ex and ey ) in É in order to evaluate the integral in (3.16).
Arranging the eigenvalues in the order of
ez = -(ex + ey) % and defining
ey
S ex
with -0.5<<;<1
the integrand in (3.16) can be expressed as
- r12 Vu r12 = - (ex x 2 + ey y 2 + e2 z 2)
= 'ex [x2 + ^y 2-(l+Q Z2]
(3.19)

39
= -ex R2[cos20+ <^sin20cos2-( 1-H^)sin20sin24>] (3.20)
where a local spherical coordinate system (x =Rcos0, y =Rsin0cos, z =Rsin0sin been introduced. Hence
<0-w-ndA= -R3ex J J*w ^q[cos20+ <^sin20cos24>-( 1-H^)sin20sin2
0 0
=r (3.2i)
Again, wr<0 in the integration limit implies that only the non-positive values inside the
square bracket are evaluated in the integration. While an analytical expression for HQ is
difficult to obtain due to the dependence of the integration limits on arbitrary ¿¡, accurate
numerical integration for HQ can be easily carried out (using Simpson's rule). The
following interpolation is then constructed for HQ based on numerical integration
results,
HQ ~ | [0.90725 + 0.2875(0.5+¿¡)2 + 0.333(0.5+¿¡)4] ¿¡<0
[1 + 0.55¿¡ +0.724071¿¡2-0.96874¿¡3
+ 0.73924¿¡4 - 0.23035¿¡5]. ¿¡>0 (3.22)
r 8
Consider the laminar shear flow for example. In this case, ¿¡=0, ex = ~, 7/(0) = j, so that
18 4
nc,2 = njn2 ^ TR3 3 = nin2 3 ^R3 which is the same as given by Smoluchowski (1917).
Equation (3.22) is valid for -l<¿¡<-0.5 as well since it is based on (3.21). The restriction
for ¿¡>-0.5 results only from equation (3.18) when the eigenvalues are arranged in such a
manner. For -1<¿¡<0, HQ is symmetrical with respect to ¿¡ =-0.5. For example, ¿¡=0
corresponds to a 2-D stagnation flow in (x , z ) plane and ¿¡=-1 corresponds to a 2-D
stagnation flow in (x , y ) plane so that the collision rates are completely equivalent. It is
also interesting to note that ¿¡=-0.5 is equivalent to ¿¡=1 since both correspond to an
axisymmetric stagnation flow with ¿¡=-0.5 for an axisymmetric contraction and ¿¡=1 for an
axisymmetric expansion; the only difference is the scale ex . With these examples in

40
mind, a simple analysis for the flow field quickly shows that 7/( 7^>0) as
^'Tk; } = -1) = ^ m for ;>0. (3.23)
Finally, the collision rate for small inertialess particles in a turbulent flow is simply
nq2 = n^R3 < ex HQ > (3.24)
where the ensemble average is taken over all possible values of the rate-of-strain tensor.
It is emphasized that this result is applicable to general turbulent flow. Needless to say,
the isotropy assumption is not needed in the present framework. When the detailed
knowledge of Vu is available, such as from DNS or random Fourier modes
representation (Kraichnan, 1970) of turbulence, the ensemble average in (3.24) can be
carried out.
The foregoing analysis can also be extended to evaluate the average of the normal
(or radial) component of the collision velocity <-Wj>. For a given position with arbitrary
ex and C, at any given instant, the instantaneous average of the normal collision velocity
can be calculated by integrating over the spherical surface of radius R as
Í „ wr(-wr)dA
w.<0 r
*= f .
Wr<0v r
(3.25)
In the above, the superscript J3 on the left hand side denotes the area-average, (-wr)dA in
the numerator can be interpreted as proportional to the probability of a particle striking
the target surface at a position (0, 4>) over an area dA and wr in the numerator is the
relative radial velocity of the collision at (0, ) on the target surface. The denominator is
the normalizing factor for the probability. The product, wr(-wr), clearly indicates that
) where (-wr) is high.
Suppose that NR realizations of turbulence be taken in a turbulent flow, the total number
of collisions will be
Nc = NRn,n2

41
per unit time using equation (3.5). The chance of a particle striking the target surface
over an area dA at (0, ) relative to the axis of the principal direction x of the
instantaneous rate-of-strain tensor in a given realization is then nin2(-wr)dA|^ Integrating over the target surface and summing over Nr realizations, the ensemble
average, , is obtained,
, n^-Wr)
< - Nr =
w'dA>

wr (3.26)
The above can be simplified as
< w > = - R
r
<\m>

(3.27)
where
£(Q = J* J [cos20+ (¡¡sin20cos2 0 0
The integration can again be evaluated numerically and represented in the following
piecewise interpolations
^ =0.4+1.0777(0.5+Q2 - 0.64767(0.5+Q4 for £<0
=0.62831 + 0.68495<; - 0.091083¿¡2
+ 0.090301¿;3 - 0.034658C4 for ¿¡>0. (3.29)
Similarly, the mean square value of the radial component velocity < wr > can be
evaluated as
< w.
> = R
<<4*10 >
< es MQ >
(3.30)
with
nQ ,
=0.17114+1.2643(0.5+Q2-0.48123(0.5+Q4 for ;<0
=0.45714 + 0.97655¿; + 0.39606¿;2

42
+ 0.072430C3 - 0.0291£4 for ¿¡>0. (3.31)
The functions HQ, QiQlHQ, and HQ^HQ are shown in figure 3.3. Take a laminar
shear flow for example: ex =T/2 and <^=0 so that 0.62832 (=ti/5) and 16/35 .
It is easily seen that
_ T2 n 8 T 8 n __
--R q 5 3 ^2 3 1 ~ " 10 (3.32)
2 ,T316 8 T8 4 ,
= R^T353/[I31 = 3i(rR)l (3.33)
The results of these two moments of wr can be used to validate the present theoretical
formulation by comparing with direct numerical simulation for particle collisions.
Figure 3.3 Variations of HQ, g(Q, HQ with C, where
f| 8 HQ, h and f3 ^g •

43
To apply the above results to turbulent flows, we consider the following two
cases: i) Gaussian isotropic turbulence; and ii) rapidly sheared homogeneous turbulence
under a mean flow gradient T. In both cases, the turbulence can be represented using
random Fourier modes and ensemble averages can be obtained without resorting to the
direct numerical solutions to the Navier Stokes equations for the fluid flows. Pertinent
comparison can be made with ST's prediction in the first case. Insights on the effect of
mean flow shear can be gained by examining the collision rate in the rapidly sheared
turbulence.
3.3 Numerical Simulation
The detailed representation of the isotropic turbulence and rapidly sheared
homogeneous turbulence are given in Chapter 2. In short, the isotropic turbulence is
represented in the form of
+c-m) sin(k(m) • x +co(m)-t ) ]
(2.6)
where k(m,and co coefficients b-m| and c-m| are their corresponding amplitudes. For sheared
homogeneous turbulence, the flow field is given by
u¡(x, t)= I[bSm)(S)cos(X(m)-x+co(m)-t )
m=l
+ Cjm)(S)sin(x(m) • x +co(m) • t )
+ Tx2 5¡i
(2.18)
where x the total shear by the mean flow, and b(¡m>(S) is the amplitude of the m-th random mode
after the rapid shearing.

44
The scheme developed in Chen et al. (1995) is employed here to search for
particle collisions. Detailed description on the collision algorithm is given in Chapter 2.
Periodic boundary conditions are employed for both turbulent flow and particle motion. A
number of particles (Np) are randomly introduced in the flow field of volume n3. The
average collision velocity is obtained based on all the collision events for whole time
period and all turbulence realizations.
As discussed in Chapter 2, four different post-collision treatments will give
different collision rate and it is not clear which treatment is the best. In the study
throughut this chapter, the ‘THROW AWAY’ scheme is implemented. However, an
extremely low particle volume concentration is used. The initial particle volume
concentration is chosen carefully so that the average number of collisions in each
turbulence realization is less than 1 . The post-collision treatment thus has little practical
effect on the collision statistics. A large number of turbulence realizations are used to
obtain sufficient ensemble average. A subsequent cumulative time average smoothes out
statistical noise in the collision rate.
3.4 Results and Discussions
3.4.1 Comparison with Simulation Results in a Laminar Shear Flow
In an earlier paper dealing with particle collision a laminar shear flow (Hu & Mei,
1998), comparison for the collision kernel, j TR , based on Smoluchowski's prediction
and the direct numerical simulation was presented; excellent agreement was obtained. To
confirm the present analyses in the laminar shear flow, we compare the collision velocity
given by equations (3.32-3.33) with that based on the direct numerical simulations. Zero
inertial particles of radius 0.009, 0.0125 and 0.02, respectively, are introduced into a
cubic box with length Lx=2, Ly=2.0, and Lx=2.0, with the restriction 0.1 uniform shear flow with a shear rate T=1.0 is imposed and the flow velocity field is given

45
by v=(u, v, w) = (ry, 0, 0). Periodic boundary conditions are employed in the streamwise
and spanwise directions. Particles near y=0.1 and y=1.9 have less collisions because
there are no particles in regions of y<0.1 and y>1.9 in the simulation. A correction due to
this boundary effect (Wang et al. 1997) was needed for the collision rate (Hu & Mei,
1998). However, this boundary correction has little effect on the collision velocity since
the decrease in the collision kernel affects both the numerator and denominator in
equation (3.25). Three hundred time steps with a step size At=0.01 are used to advance
particles and a total of 40 realizations were employed to further increase the statistical
accuracy. Ten thousand particles are used for r=0.009, 0.0125 while four thousand
particles are used for larger size with r=0.02. Table 3.1 shows the comparisons for both
<-W[> and . Since is the second order moment of wr, excellent agreement
for these two moments indicates that the basis of the present formulation is sound. It is
worth noting that a naive, Eulerian-based estimation of the average collision velocity
4 , ,
based on the incoming particle flux, j TR n¡, and the available collision area, 27tR",
2 . 71
would lead an erroneous value of ~ TR instead of Jq TR for <- Wr>-
Table 3.1 Comparison of the collision velocity among monosized particles
between prediction and direct numerical simulation in laminar
shear flow.
Particle
radius
<-wr>
simulation
<-wr>
prediction

r
simulation

r
prediction
0.009
5.6421X10-3
5.6549x10-3
3.7018X10-5
3.7029x10-5
0.0125
7.8638x10-3
7.8540x10-3
7.1529X10-5
7.1429x10-5
0.02
1.2425X10-2
1.2566x10-3
1.8012X10-4
1.8286xl0-4

46
In a laminar shear flow, wr=-uxcos0 and the collision angle 9C between the
collision velocity w and the axis connecting the center of colliding particle pair,
7i/2<0c<7t, happens to equal to 9 as shown in figure 3.1. In the simulation, 9C can be
easily evaluated so that the pdf for 9C is readily obtained. Since (*wr)w <0 sin0d0d is
proportional to the probability of the particle striking the target surface at (0, ) with a
solid angle da>=d9d<|). it is readily seen that
(-wr)dA = -rR3sin20cos0 cos)eQ,
= 0 elsewhere (3.35)
. . 71 7171 7T 71 -37C
where Q is the region defined by <0<7i, -y <(p<2 ) and (O<0<2 , ^ <(p<~) in which
wr<0. Interpreting sin20cos0sin<(> as proportional to the probability density function of
incoming particles striking the target particle near (0, ), further integrating over from 0
to 27i, treating O<0 normalization, one obtains the pdf for the collision angle 9c
p(0c) = -B sin20ccos0c for 7r<0<7t/2
= 0 for O<0<7i/2 (3.36)
where B is a normalizing factor which is 3 for 0C in radian and n/60 for 9C in degree. The
average angle is <0c> = f 0cpdf(0c)d9c = 2.237 radian or 128.17°. The numerical
nJ 2
simulation gives <0c> = 128.13°. Figure 3.4 compares the pdfs based on the above
prediction and the direct numerical simulation using ten thousand particles of radius
0.009 over 40 realizations of initial particle positions. Excellent agreement is observed
for the whole region n/2 laminar shear flow.

47
Figure 3.4 Comparison of the pdfs for the collision angles 0C between
the theory and the simulation in a laminar shear flow.
3.4,2 Collision Rate and Velocity in a Gaussian, Isotropic Turbulence
For particle collision in isotropic turbulence which is represented using equation
(2.6), the dimensionless collision kernel
• dll
“"■rW”" (137)
is evaluated using Nr=500 realizations. The dissipation rate 8 is based on the ensemble
average of the same Nr realizations. In each realization, ex #(Q and e/v are computed in
the flow field with 654 statistically independent data. It is found that an varies slightly
with the number of the Fourier modes, Nk, used in the turbulence representation.
Furthermore, it weakly depends on the power p in equation (2.10) with which the random

48
wavenumber is chosen. Figure 3.5 shows the variation of au as Nk increases from 100
to 4096 for |i=1.2 and 1.4 for f) 0= 0.05 (Re>.=53.8). The result seems to converge after
* *
Nk=2048; an(p=1.2) converges to 1.2935 while an(p=1.4) converges to 1.2945 with a
difference less than 0.1%. They are both very close to 1.2944, the value predicted by ST.
It is worth commenting that due to the rapid decrease of the energy spectrum E(k) in the
high k range, not all wavenumbers that are randomly generated need to be used in
equation (2.10). For those high wavenumber modes that correspond to very small
amplitudes, they can be simply neglected without compromising the accuracy. Thus the
effective number of the random modes is smaller than the specified Nk. From the
simulations, the effective number of the modes for p=1.4 is roughly three times that for
p=1.2 for the same Nk. Hence the fact that it takes larger values of Nk for p=1.2 than for
p=1.4 to get a converged result is not surprising. It is also interesting to note that, using
Nk=1024 and p=1.2, a higher value of Re^ (=124.4 using f| 0=0.01) gives an =1.2899 as
♦
opposed to an =1.2912 at Re^=53.8. This clearly shows that shape of the energy
*
spectrum E(k) in isotropic turbulence has little effect on an . With Nk=200 and p=1.2,
♦
an =1.2756 which is only 1.45% less than the converged result. Thus in the following
direct numerical simulation for particle collision, Nk=200 and p=1.2 will be used.
To validate the present analyses, direct numerical simulations of particle collision
in isotropic turbulence are performed. The turbulence in the periodic box of length n has
the following characteristics: root-mean-squared turbulence velocity uo=1.0, integral
length scale Ln=0.594, Taylor micro length scale A.=0.277, Kolmogorov length scale
ti=0.023, Kolmogorov time scale xic=(v/s)1/2=0.073, and Rex=40.1. With the modified
Fourier modes in the periodic box, the dimensionless collision kernel based on the
* *
prediction is a,, =1.2711 as opposed to an =1.2756 without the modification of the
wavenumbers. Initially, five hundred particles of radius r=0.005 (r/r|=0.217) are
randomly distributed in the box of volume 7t3 with a particle volume concentration of

49
8.44x1 O'6. These inertialess particles are advanced using local fluid velocity. A total
time period of T=6 (i.e. T/xk=82.2) is simulated with time step At=0.01. The average
number of collisions within the entire period of the simulation 0 realization is about 0.4271 <1 based on our new expression of the collision rate in §6.2.
To obtain a reliable statistics, a total of 10,000 realizations is used to generate an
*
estimated collision pairs Nc=4271. Figure 3.6 (a) shows the simulation result for an in
0 numerical simulation data due to the small number of collisions within each time step
despite the fact that 10,000 realizations have been used. A cumulative average is applied
*
to reduce the noise. Figure 3.6 (b) shows the cumulative time average of an from the
simulation. The direct numerical simulation is less than the predicted value (1.2711) by
about 2.5%. This difference may be caused by a small effect of particle size, since the
theoretical predictions are expected to be accurate only in the small size limit, i.e. r/r|«1,
while the particle size in numerical simulation is r/r|=0.217. The effect of finite particle
*
size on the collision rate will be explored in Chapter 6. The agreement for a! j between
the simulation and the prediction is quite good.
Table 3.2 shows the results of the prediction and the numerical simulation for the
2
two moments of particle collision velocity, <-Wj> and , in isotropic turbulence. The
predicted values are based on p=1.2 and Nk=200 in the periodic box with Nr=400
realizations. The particle sizes are r/t| = 0.2175 and 0.4875, respectively. Satisfactory
agreements are obtained. On the other hand, ST (1956) gave an expression, equation
(3.9), for the Eulerian-based average particle radial collision velocity. Using that
expression, one would obtain
<-Wj>
R(e/v)1/2
= 0.206. The present prediction for the periodic
domain with NR=400, after ensemble average, gives a value of 0.328 for this
dimensionless radial collision velocity which agrees rather well with the direct numerical
simulation show in Table 2. Since the direct numerical simulation is based on the
tracking of particle trajectories, the probability-based prediction for <-Wp> is consistent

50
with the Lagrangian approach. The Eulerian-based approach for <-w,> does not take into
account the bias of the collision probability toward the high values of -wr; hence it gives a
lower value for <-w,>.
*
Nk
Figure 3.5 Convergence of a,, as Nk -»8.
Table 3.2 Comparison of the collision velocity among monosized particles
between prediction and direct numerical simulation in Gaussian,
isotropic turbulence.
Particle
radius (r/r|)
<-wr>
simulation
<-wr>
prediction

r
simulation

r
prediction
0.217
4.5430X10-2
4.5611x10-2
2.7356x10-3
2.7296X10-3
0.4875
1.0093X10-1
1.0040x10-'
1.3405x10-2
1.3224x10-2

51
an
t(e/v)1/2
90
Figure 3.6 Particle collision kernel a,, from direct numerical simulation in Isotropic
turbulence, (a) Variation of the ensemble average, (b) Cumulative time average.

52
3.4.3 Collision in a Rapidly Sheared Homogeneous Turbulence
The statistics of the turbulence, such as four non-zero components of the
Reynolds stress tensor, generated using RDT and the random Fourier modes was first
checked against the analytical values at short times (Tt«l) given by RDT (Townsend,
1976, p.84) to ensure the correct implementations. It was found through simulation that
the development of the sheared turbulence with time does not depend on the Reynolds
number, Re> , of the initial state or the energy spectrum at t=0. The turbulence part of the
flow in the RDT is determined by the total shear S=Tt. The complete flow field, sheared
turbulence plus the mean shear flow, thus depends on two parameters: T and S=Tt. Since
the collision rate depends on the spatial structure of the turbulence while the turbulence
structure becomes increasingly anisotropic as the total shear S increases, finding an
appropriate representation of the collision rate in the homogeneous turbulent shear flow is
not trivial.
To develop a better understanding on the dependence of the collision rate on the
turbulent flow, it is instructive to examine the collision rate due to the sheared turbulence
but without the contribution from the mean shear T; this part of the collision kernel will
be denoted as an. Another word, an is evaluated using the flow field given by
equation (2.18) but without the last term Tx25¡i which is deterministic. Following the
definition for an in the isotropic turbulence case, we use the dissipation rate (s/v)1/2,
which increases with S, as the turbulence characteristic shear rate to normalize an,
-* an
a" "rW'2
{without contribution from mean shear F).
(3.38)
It is easily seen that ex,, depends on the total shear S=Tt only. Figure 3.7 (a) shows the
. . - * *
variation of an on S. In the high S limit, the simulation results suggest that approach a constant of 1.1676 in the form of

53
¿,, -1.1676 + 0.659/S forS>10. (3.39)
In this rage of 0 turbulence intensity becomes dominated by the longitudinal component in the \\-
direction. Based on RDT prediction which neglects the nonlinear interaction among
different scales, the longitudinal component of the turbulence makes up 92.7% of the
total kinetic energy at S=Tt=20. The anisotropy invariant lib reaches a value of -0.25 at
S=20. At such large values of total shear, S, the RDT results may become a little
inaccurate in comparison with DNS result. However, for the present work, since the
RDT-based turbulence is strongly anisotropic, it serves our purpose to evaluate the effect
of such anisotropy in the turbulence on particle collision rate. When the collision kernel
is normalized using (e/v) , the maximum difference in au is only about 15% at the
_ *
extreme limit of S-»oo. If the isotropic result is used, -1.2944, only a maximum
_ *
error of 10% will result from the limiting value of ¿n at very large total shear. This
*
encouraging result will be used to develop an approximation for an in the presence of
strong mean shear.
Now consideration is given to the particle collision kernel with contribution from
both the turbulence part and the mean flow shear part as the flow field is given by the
*
entire right-hand-side of equation (2.18). Figure 3.7 (b) shows an as a function of the
ratio of the mean shear rate to the turbulence shear rate, T/(~ )l/2, for a range of shear
rates. For a given T, (e/v)1 2 increases as t or S increases so that the abscissa mainly
reflect the effect of t or S. To see the effect of mean flow shear rate, T is made
dimensionless by the turbulence mean shear rate at t=0 in the isotropic state (so/v)1/2.
Four values of T/(eo/v)I/2 are used: 0.87, 2.18, 4.36, and 8.72; they correspond to the solid
symbols in figure 3.7 (b). The open symbols specifically correspond to the cases when
S=0 (or t=0) so that the turbulence is still isotropic. The dimensionless values of an

54
seem to collapse reasonably well.
g
At large values of T/(~ )1/2, the curve becomes linear
as the collision rate is increasingly dominated by the mean shear so that a,, scales
linearly with f as Smoluchowski's theory predicts. A small degree of scattering exists for
r/(- ) /2<2. This is caused by the 15% maximum variation of a,, over the whole range
of total shear S, as already demonstrated in figure 3.7 (a). There are two possibilities for
T/(~ )1/2 «1: i) both T and S=Tt are very small even comparing with (so/v)1'2 of the initial
isotropic turbulence; ii) T is finite but S=Tt is large so that (s/v) become large
comparing with (sq/v)12. In the first case, the turbulence is closer to the isotropic state
_ * _ *
so that ¿U -1.2944. In the second case, the turbulence is highly anisotropic and ¿,,
_ *
—>1.1676 for very large values of S. No attempt is made to correlate ¿n with the total
shear S since S is not relevant in practical applications such as in turbulent pipe flow.
Using ¿n =1.2944 in the isotropic state to represent the turbulence part of the collision
kernel, a simple interpolation is proposed to represent the effect of mean shear and
turbulence on the collision rate,
* «11
„ (, oq442.2 r 1 -3333 T 2.2,1/2.2
~ R3(8/v),i2 "! 1 (e/v)1'2 1 ! '
(3.40)
This interpolation is shown by the solid line in figure 3.7 (b). A satisfactory agreement
can be observed. It is also worth commenting that when T is normalized using the eddy
2
turn over time, 3uQ /eq, of the initial isotropic turbulence, the shear-rate parameter
T* = T3uq/£o, (3.41)
corresponds to T*=36.98 and 92.45 for T/(eo/v)i/2 =0.87 and 2.18. In a low Reynolds
number turbulent channel flow, the maximum value of T* is around 35 at a location of 10
wall units away from the wall (Kim et al. 1987). Hence, the parameters used in figure 3.7
*
(b) are relevant to practical situations. Although a 10% error exists for an given by
(3.40), the advantage of this simple approximation is that the total shear S=Tt is absent

55
and it depends only on the spatial structure of the turbulence characteristics by the scalar
quantity (e/v)1/2.
Figure 3.8 shows the radial collision velocity, <-w,>, as a function of T/(~ )1/2, for
BQ i i*y #
different values of T/(~ ) . Similarly, the data seem to collapse approximately into one
curve in the range studied. The following interpolation fits the radial collision velocity
well,
T
<-wr> , 7 n
7772 * {0.3245 *7 + [—
-,1.7,1/1.7
.1/2 1 /
(3.41)
R(s/v),/z " L10 (E/V)‘
. . .2
For the root-mean squared radial collision velocity, , the behavior is very similar to
that of <-Wf>. While the data is not shown here, the following interpolation, which fits
the data quite well, is provided,
2 i/2
- in 16011-7 ■ r °‘3381 r 11.7,1/1.7
R(s/v)1/2 _ {0-3681 +[(s/v)./2] > •
(3.42)
The application of the above expressions to turbulent channel flow, which is
different from the homogeneous, rapidly sheared turbulence, is yet to be tested. However,
since the rapidly sheared turbulence at large total shear S considered in this work is
already drastically different from the isotropic turbulence, there are reasons to expect a
similar dependence of aj i, <-Wj>, and on T and (e/v) in a turbulent channel flow
which is of significant industrial importance.
3.5 Summary and Conclusions
A theoretical framework has been developed to evaluate the collision rate and
collision velocity of small particles in general turbulent flows. The classical results for
the collision kernels by Smoluchowski (1917) for laminar shear flow and by Saffman &
Turner (1956) for isotropic turbulence are recovered. The present approach differs

56
significantly from that of Saffman & Turner in that the ensemble average is taken after
the collision rate for a given flow realization is calculated. This avoids the assumption of
isotropy, as needed in Saffman & Turner, and allows for the evaluation of the collision
rate in general turbulent flows. The collision velocity predicted in both laminar shear
flow and isotropic turbulence agrees well with the direct numerical simulation for particle
collisions. The present theory is used to evaluate the collision rate and velocity of small
particles in the rapidly sheared homogeneous turbulence. It is found that the effect of the
turbulence structure on the collision rate and velocity can be mainly captured by using the
mean turbulent shear rate (s/v) . The combined effects of the mean shear and the
sheared turbulence on the collisions are elucidated. Simple interpolations are developed
to evaluate the collision rate and velocity for arbitrary mean flow shear rate and
turbulence mean shear rate.
Figure 3.7 (a) Normalized particle collision kernel in a highly sheared homogeneous
turbulence without contribution from the mean shear rate.

57
Figure 7 (b) Collision kernel in a rapidly sheared homogeneous turbulence.
eq denotes the dissipation rate of the isotropic state at t=0.

58
Figure 3.8 Collision velocity in a rapidly sheared homogeneous turbulence.
e0 denotes the dissipation rate of the isotropic state at t=0.

CHAPTER 4
EFFECT OF INERTIA ON THE PARTICLE COLLISION VELOCITY AND
COLLISION ANGLE IN GAUSSIAN TURBULENCE
Numerical simulation of particle collisions in isotropic, Gaussian turbulence is carried out
to study the effects of particle inertia on the turbulence induced collision velocity and
collision angle. An asymptotic analysis is developed to predict the effect of the small
particle inertia on the collision velocity. A PDF-based method is also implemented to
predict the effect of the small particle inertia on the collision velocity. The analysis
predicts that the increase in the collision velocity is proportional to the terms ((3xk )_l
and (Pxk) , in which P is the particle response time and xk is the Komogorov time
scale. Good agreement is obtained among three different methods: the numerical
simulation, asymptotic prediction, and the PDF-based method, for the collision velocity
of particles with small inertia. The effects of both particle inertia and size on the collision
angle have also been investigated. It is found that collision angle can be significantly
affected by both inertia and size. Average collision angle and the variation of collision
angle increase as the inertia increases, while the decrease in the size will also reduce both
the average collision angle and the variation of collision angle.
4.1 Introduction
Particle collisions can take place through a variety of collision mechanisms.
Particles may be brought together by inertial centrifugal and gravitational forces. The
velocity gradient is responsible for collisions among particles in laminar shear flow and is
a dominant factor for particle collisions in turbulence. Brownian motion and Van der
59

60
Waal forces can also affect the collisions. One of the common characteristics of these
collision mechanisms is the ability to generate a negative relative velocity between two
particles along the line connected with two particle centers. Of significance are the
statistics of the relative velocity of two particles at contact (hereinafter referred as
collision velocity) and the relative angle between the velocities of two particles at contact
(hereinafter referred as collision angle). For solid particles, collision causes momentum
exchange among particles and may influence the local and global particulate flow
conditions. The suspended solid particles are produced during the manufacturing process
of materials, the combustion of heavy fuel and pulverized coal. The collisions among
suspended particles will greatly affect the properties of particulate flow and thus influence
the performance of the systems. Adhesion may occur when two colliding solid particles
have small collision velocity, while large collision velocity will produce large restitutive
rebounce velocities for the colliding particles and may overcome the bonding force
determined by cohesive surface energy. The collision also affects the angular momentum
of particles. Tangential velocity components will be imposed to the colliding particles if
the collision angle 012 is not equal to zero. The particle rotation will be either enhanced
or reduced depending on the collision angle 012, which determines the orientation of the
particle tangential velocities upon collision. Gunaraj et al. (1997) peroformed
experiments measuring the collision angle 012 between heavy particles (silica sand
particles with 1000-1500 pm in diameter) in turbulent channel flow.
The collision velocity is also a critical quantity in determining the collision kernel
and collision rate. In uniform shear flow, Smolochowski (1917) integrated the volume
flow rate using collision velocity along the spherical surface of collision radius R
(= r¡ + rj, where r¡ and r¡ are the radii of collision pair respectively) and obtained
collision kernel for uniform shear flow. In turbulent flow, particle collisions are affected
by local turbulence fluctuations. In the absence of any other force creating relative
motion between particles, these local fluctuations entirely control relative velocity
distribution which determines the collision kernel. In most of the previous theories, the
collision kernel in a particle-laden system is linearly related to the collision velocity, the

61
first moment of the relative velocity distribution of colliding particles. Thus the scale and
form of these particle relative velocity distributions directly determine the collision rate.
The fact that the collision rate and collision kernel in previous studies are all proportional
to the collision velocity explains the critical importance of the prediction of collision
velocity (Saffman & Turner 1956, Williams & Crane 1983, Kruis & Kusters 1996, et al).
The particle collision has been studied in two different ways. One is based on
Eulerian method, which is favorable to theoretical prediction and was followed by
previous studies on the theoretical evaluation of both collision velocity and collision
kernel. The other is to use Lagrangian tracking in direct numerical simulation of particle
collisions. For inertialess particles in both uniform laminar shear flow and isotropic
turbulence, the collection of collision velocities using Lagrangian method shows a biased
average in favor of those regions where wr is high, as shown clearly in Chapter 3.
Saffman & Turner (1956) obtained the collision velocity using average integration on the
spherical collision surface by Eulerian method without considering the influence of the
biased average. Although their formulation correctly predicts the velocity correlation
between two points for turbulent flow, it has been incorrectly interpreted as the collision
velocity upon collisions. The effect of particle inertia on the collision velocity was
recently studied also by Sundaram and Collins (1997). They used two statistical
variables: g(R), the particle radial distribution function at contact, and P(w/R), the
conditional relative velocity probability density function at contact, where w is the
relative velocity of two particles at contact. It is noted that in Sundaram and Collins’
work, in additional to the assumption of the exponential form for P(w/R), the g(R) was
evaluated based on their numerical extrapolation form for distances smaller than the first
numerically evaluated point. However, the usefulness of g(R) and P(w/R) is rather
questionable.
In this work, we study the effect of particle inertia on increasing particle collision
velocity and collision angle by carrying out numerical simulation of particle collisions in
an isotropic, Gaussian turbulence, and developing an asymptotic analysis to predict the
effect of small particle inertia on the collision velocity based on Lagrangian description.
An integration method is also implemented to predict the effect of the small particle

62
inertia on the collision velocity. The increase in the particle collision velocity due to
inertia predicted by both the asymptotic analysis and integration method is in agreement
with the numerical simulation results. The analysis predicts that the increase in the
collision velocity contains terms that are proportional to (Pxk)-1 and (pxk)~2. Good
agreement is obtained by the numerical simulation, asymptotic prediction and the
integration method for the collision velocity of particles with small inertia. The effects of
particle inertia and size on the collision angle have also been investigated. It is found that
collision angle are significantly affected by both inertia and size. Average collision angle
and the variation of collision angle increase as the inertia increases, while the decease in
the size will reduce both the average collision angle and the variation of collision angle.
4.2 Saffman & Turner’s Analysis
In Saffman & Turner’s (1956) prediction of the collision rate among inertialess
particles, the volume flow rate of particles towards a target particle was evaluated by an
integration, <}w <0 -wrdS>, where wr is the radial component of the relative velocity.
The integration was carried out over the entire spherical surface of radius R, as shown in
figure 4.1. Because of the continuity of fluid flow,
=
= lentire sphere dS. (4.1)
For isotropic turbulence, which is true at small scales for anisotropic turbulence, the
above becomes
== 2tiR2 <|wx|> (4.2)
where x is the local coordinate parallel to the line connecting the centers of the colliding
particles as shown in figure 4.2 and < > denotes ensemble averaging.

63
Figure 4.1 Sketch of particle collision scheme
W = V, — V 2
wx = vlx — V2x
Figure 4.2 Sketch of particle collision velocity and collision angles (012 & 0C).
The separation distance is x2 - x( = ±R = ±(rj + r2).

64
In the limit of zero particle inertia, particle velocity v follows the fluid velocity u
very well so that the radial component of relative velocity w = \l - v2 upon collision is
1 wxl~lu,x- u2xl~R| l= Mt—)1'2
OX 1571 V
(4.3)
and
= 5 R2 <(—)2 > = -R2-
x dx 15 v
(4.4)
where wx is the collision velocity.
For particles with inertia, the volume flow rate of particles to a target particle was
expressed as JJ/entjre sphere I wl P(w)dw, where P(w) is the probability density function
(PDF) of w and contains the effects of the spatial variations of velocity in the fluid, the
turbulent accelerations due to the relative motion with the turbulence, and gravity. The
volume flow rate can be integrated assuming P(w) to be a normal distribution function.
For monodisperse particles,
Entire sphere^ P(W)dw = |R3 C^)"2- (4-5)
Compared with the equation (4.2), the equivalent collision velocity based on equation
(4.5) can be evaluated
|wxI~|r3(—),,2/(2tiR2)= ^R(-^—)l/2 (4.6)
3 v 3 2nv
and ~ — R2 —. (4.7)
9 v
It is noted that equation (4.7) is independent of the inertia parameter (3. For the small
inertia limit p—»co, comparing equations (4.6) & (4.7) with equations (4.3) & (4.4), an
inconsistency that (4.6) & (4.7) can not recover (4.3) & (4.4) occurs, which is physically
incorrect.

65
4.3 Present Analysis
4.3.1 Asymptotic Analysis
To understand and predict the effect of inertia on the collision velocity wx, it is
assumed that the particle size is much less than the Taylor micro-length scale, and the
gravitational force is negligible. For simplicity it is assumed that turbulence is isotropic
and Gaussian. The particle dynamic equation in the absence of (or weak ) gravity is
dv
dt
= P(u-v).
The above can be written as
1 dv
v = u
(3 dt
(4.8)
(4.9)
For large values of p, repeatedly substituting the left hand side into the right hand side
gives
1 du 1 d2v 1 Du \ , „ ^ , 1 d2v
v = u --— +~t—-r-= u —+—(u -v) Vu+
P dt p2 dt2 P Dt p
1 Du _, 1 ,
~ U + O(-r-) .
P Dt p2
P2 dt2
(4.10)
The variance of wx = vlx - v2x in which particle 1 and particle 2 are separated by a
distance of | x2 - Xj| = R is
, . . 1 Dulx 1 Du2x
var(wx) ~ var(ulx - u2x - ^ + ^ )
M
Dt
Dt
x2 , .1 Du
= <(Uix-U2x) >| X2-X1=±R +<(
ix _ 1 Du2x 2>l
> I x2-X]=±R
Pi Dt P2 Dt
" 2 <(ulx - u2x)(
1 Du
lx 1 Du2x WI
x2-xi=±R
p! Dt p2 Dt y X2_X1
= term I + term II + term III.
The first term can be expanded as
(4.11)

66
term I = <(uIx -u2x)2>|Xz_x, =±R = + - 2|X2_Xj =±R
= 2< u2 > - 2< u2 >f(r=R) = 2 u2 [ 1 -f(R)] (4.12)
where uq is the mean square intensity of turbulence and f(r) is the standard longitudinal
velocity correlation coefficient. If the particle size is much less than the Taylor micro¬
length scale X, the above can be simplified to
terml = <(ulx -u2x)2>|x x ±R ~ 2uj; -(—)2 = 77R2-
21 2 X 15 v
for R«X. (4.13)
Term II in equation (4.11) can be similarly handled,
term II = <(
J_Du^__LDu^2 J_<(Duu)2>+J_<(Du2;L)2>
P, Dt P2 Dt 1x2 X|_±R p2 Dt ' p2 ^ Dt '
- 2—|x x_
a a r^. IX2-X,-
PlP
Dt Dt
=±R
(4.14)
where < Du,x *')U2x
Duv .
Dt Dt
>l x2 — x 1 =±r is the spatial correlation of in the x-direction with a
separation distance of R. Noting that var(^^-) = var( D^x ) and expanding the
correlation for small R, we obtain
Dt Dt 1x2 X|_±R V Dt 1 2 dx2 1
Duv
<
(4.15)
where fD is the correlation coefficient of . Defining the micro length scale of the
fluid acceleration as
Dt
1 = l^fplO)
A.2D 2 dx2
term II can be expressed as
5ibL)2>
Dt 'P? P2' PiP2 ' Dt
term II ~ <(^-)2>(-l- + —^-<(-^)2> (1- —)
v n. 2 V r,2 a2 > R R V r>t ' V A.2D 2
(4.16)

67
Du 2. ,1 1 .7 . 2 _Dux,2^ R
Dt
— < —^-)2 >( — -)2 +
Dt V(3, P2 P,P2
Finally, equations (4.13) & (4.17) lead to
v 1 ,Dux .2.1 1 .2 , 2
var(wx)~ —R“ —+ <(——) >(———) + —— x
15 v Dt Pi P2 P1P2 Dt
<(^2L)2>JL. for R«a.d . (4.17)
Dux 2 R
A-d
• 2 <(ulx -u2x)(
1 Dulx 1 Du2x
P, Dt p2 Dt
For the like particles, Pj = P2 = P>
1 2 e 2
var(wx)~ — R - - - <(ulx-u2x)(
)>•
(4.18)
Du
R
15 v p
(i)
Du,x Du2x . 2 .umXx2 —
~)> + TT<(~77JL)2>72_’
a.d
Dt
Dt
(ii)
Dt
(iii)
(4.19)
where term (i) is the mean-squared collision velocity for inertialess particles, terms (ii) &
(iii) are contributed by the inertia. The net increase of the mean-squared collision
velocity due to the inertia can be expressed as
2 2 . X/Du,x Du2x . 2 .DuXx2 R‘
< Awx> <( u,x — u2x)(—XjL —)>+ -T<(—-)2>^-.
x p ot Dt p2 Dt X2d
(4.20)
(ii)
(iii)
It is noted that term (ii) represents the first-order effect of the inertia and term (iii)
contains the second-order effect of the inertia. The present prediction, equations (4.19) &
(4.20), differs from the equation (4.7) in two aspects. First, the collision velocity in the
limit p—>00 is consistent with the equation (4.4). Second, the present analysis includes
two additional terms in the effect of inertia, as seen from equations (4.19) & (4.20) when
P] = P2. The effect of inertia does not vanish when Pi = P2 while it does in Saffman &
Turner’s analysis. It should be mentioned that in (4.20) is computed based on
Lagrangian tracking of all colliding particles.

68
4.3.2 PDF-Based Method
For inertialess particles, the collision velocity can be evaluated by the direct
integration (Mei & Hu 1998) in both uniform shear flow and turbulent flows.
lWr = —r
!wr (4.21)
and
< wp=
Jwr<0W?(~Wr)dS
(4.22)
iwr where (-wr)dS in the numerator can be interpreted as proportional to the probability of a
particle striking the target surface over an area dS. The denominator is the normalizing
factor for the probability. The product, wr(-wr), clearly indicates that is a biased
average in favor of these regions where (-wr) is high.
Equations (4.21) & (4.22) may be extended for particles with inertia. For small
inertia, as shown in §4.3.1, the particle velocity to the first order (3 is
1 Du
v = u -
P Dt
The collision velocity may be expressed as
(4-23)
Wr=Wx = Vix-V2x= Ulx -U
*2x ‘
1 Dulx + 1 Du2x
p, Dt p2 Dt
Í,
_w jc_[ 1 Du.x , ( ^u2x
wr<0 wrd^ Jwr<0 "(ulx u2x" n t+ r, t~v. )d$-
P | Dt P 2 Dt
For monodisperse particles, P, = P2 = p, equation (4.25) may be rewritten as
íwr<0 -WrdS = íwr<0 [U2x-u.x+¿(^-^)]dS-
From the equations (4.21), (4.24) and (4.26), the collision velocity is then found
(4.24)
(4.25)
(4.26)

69
< Wf> =
1 Du,x Du2 2
Dt
Dt
)r dS>
1 Dulx Du2x
Jwr
    Similarly, the mean-squared collision velocity is
    )]dS>
    < w¡:> =
    < iwr
    p Dt
    Dt
    < L <0tu2x - u,x + l(%--^)]dS >
    (4.27)
    (4.28)
    Jwr<°1 “ p' Dt Dt
    The integrations in right hand side of (4.28) are evaluated using Eulerian method .
    4.4 Results and Discussions
    The direct numerical simulations of the particle collisions in Gaussian turbulence
    is performed to assess present asymptotic analysis and to predict collision angle. Initially,
    a given number of particles are randomly distributed in the box of volume 7t3. The
    inertial parameter Pxk varies from 0.022 to qo. The particle sizes D/q-0.435, 0.957,
    1.739, 2.522, 4.348 are considered, and the corresponding initial number of particles are
    24000, 10000, 5000, 3000 and 1000 respectively. The particle collision detection is
    turned on until all particles have reached dynamic equilibrium. The overlapping particles
    are eliminated to avoid the false counting of the collisions before the collision detection
    turned on. The particles are advanced by time step At=0.01. The total time steps, varying
    from 200 to 600 after the equilibrium state, depend on the inertia. A total of 40
    realizations is used for smooth statistics.
    Figure 4.3 shows the comparisons of direct numerical simulations, asymptotic
    analysis and PDF-based method for D/q=0.957. Very good agreement is observed among
    the three methods for small inertia fix k >3.6, so equation (4.20) is validated for prediction
    of small inertia effect on the collision velocity. However, large deviation is observed for
    Pxk<3.6 since the asymptotic analysis cannot be applied for particles with large inertia.

    70
    The reason is that the first order approximation (4.23) is no longer valid when the p
    becomes small for particles with large inertia. The PDF results of collision velocity can
    be found in figures 4.4 (a) & (b). The collision velocities are all negative, because
    collision occurs only if the pair of particles are approaching each other. For not very
    large inertia, say Pxk>l, due to the strong correlation between particle velocity and fluid
    velocity, the PDF curves are sharp and narrowed with a small variance and peak at almost
    the same location, while zero inertia curve is at highest level. The PDFs are sharper for
    small particle size (D/t] =0.957) at Pxk> 1.023, shown in figure 4.4 (a), compared with
    figure 4.4 (b). However, the PDF curves become flatter and the location of the peak
    shifts to larger |wr| for Pxk <0.073 due to little correlation between particle velocity and
    fluid velocity at large inertia.
    < Aw * >
    Figure 4.3 Comparison for the increased collision velocity due to the particle inertia
    between numerical simulation and theoretical prediction, D/r|=0.957.

    71
    PDF
    PDF
    Figure 4.4 Effect of particle inertia on the probability density function of the
    particle collision velocity wr. (a) D/r]=0.957. (b) D/ti=2.522.

    72
    The effect of particle inertia on collision angle among monodisperse particles can
    be found in figures 4.5 (a-c), 4.6 & 4.7. It is seen that the collision angles 012 & 0C
    strongly depend on the inertia. For not very large inertia, Pxk >1.023, the mean collision
    angle 0i2 is less than 20° at D/r|=0.957, as shown in figure 4.5 (a) & 4.6, and it is less
    than 30° at D/r|=2.522, as shown in figures 4.5 (b) & 4.6. However, for very large
    inertia, pxk <0.073, the mean collision angle 0i2 is greater than 80 0, which reflects little
    biased orientation of collisions due to very weak correlation between the particles and
    turbulent flow. It is noted that the PDF curves for Pxk >1.023 are much sharper and
    narrower than those for Pxk <0.073 and the variation of collision angle increases as the
    particle inertia increases, as shown in figure 4.7. Figure 4.5 (c) shows the effect of inertia
    on 0C. It is seen that the increase in inertia results in the collision angle 0C approaching
    to symmetrical distribution with the mean 45 0.
    The effect of particle size on the collision angle 012 can be seen in figure 4.8,
    which clearly shows that the particle size has a significant effect on the collision angle.
    The average collision angle 012 increases as the size increases. For D/r|<1.739, the
    maximum 012 is located at somewhere between 0°and 10°, and the average 012 is
    around 10°. However, for larger particles with large inertia, shown in figure 4.8, the
    collision angle 012 is around 20 0, which is within the range of 14 0 ~ 320 obtained from
    the experiments for heavy particles in a turbulent channel flow (Gunaraj et al. 1997).
    Since little information is reported on the turbulent channel flow, the comparison is only
    qualitative.

    73
    PDF
    PDF
    Collision angle 012
    Figure 4.5 Effect of particle inertia on the probability density function of the
    particle collision angle 012. (a) D/r|=0.957. (b) D/r|=2.522.

    74
    PDF
    Collision angle 0C
    Figure 4.5 (c) Effect of particle inertia on the probability density function of the particle
    collision angle 0C, D/r|=0.957.
    012
    Figure 4.6 Effect of particle inertia on the mean collision angle 0i2 in isotropic,
    Gaussian turbulence.

    75
    fU
    Figure 4.7 Effect of particle inertia on the standard deviation of collision angle 012 in
    isotropic, Gaussian turbulence.
    PDF
    Figure 4.8 Effects of particle inertia and size on the probability density function of
    the particle collision angle 012, ptk=1.023.

    76
    4.5 Summary and Conclusions
    Numerical and analytical studies on particle collision velocity and angle in
    isotropic, Gaussian turbulence have been carried out. The effects of particle inertia and
    particle size on the collision velocity and collision angle are examined.
    An asymptotic analysis has been developed to predict the effect of small particle
    inertia on the particle collision velocity. The asymptotic prediction for the increased
    collision velocity agrees well with the results of numerical simulation and PDF-based
    method. The collision velocity is found to contain terms that are proportional to (Pxk)~'
    and (pxk)~2.
    Particle collision angle strongly depends on particle inertia and size. The average
    collision angle and the variation of the collision angle increase as the inertia increases.
    Both the average collision angle and the variation of the collision angle decrease as the
    particle size decreases.

    CHAPTER 5
    QUANTIFICATION OF PARTICLE CONCENTRATION NON-UNIFORMITY
    IN TURBULENCE
    For heavy particles with finite inertia, small scale structure of turbulence induces particle
    preferential concentration. Particles tend to collect in the regions of high strain rate and
    low vorticity, as has been observed in many Lagrangian simulations. Quantification of
    the extent of the non-uniform particle concentration distribution has been difficult
    because the particle concentration based on the Lagrangian statistics is highly noisy. A
    method is developed to calculate reliably the variant of the particle concentration, < n' >,
    in which n is the instantaneous particle number concentration and the prime is the
    fluctuation in a small cube of volume (Ax)3, from the Lagrangian simulations of the
    particle motions in an isotropic turbulence. The concentration non-uniformity is
    measured using a new parameter Ap based on the first two lower order moments of n. It
    is observed that the non-uniformity varies with Ax, suggesting a fractal nature of the
    particle concentration distribution. For particles with zero size, the non-uniformity scales
    with Ax as Ap~ (Ax)_b (b around 1) for small values of Ax. However, for finite-size
    particles, the non-uniformity reaches a maximum value (around order one or less) at Ax/D
    = 2.0-3.5, in which D is the particle diameter; it drops to zero as Ax/D gets close to one.
    It is noted that the maximum value of the / is attained when the ratio of
    particle response time to the Kolmogorov time scale is close to one, which has been
    previously observed for the zero-size case based on other physical quantities.
    77

    78
    5.1 Introduction
    In many statistical problems, the lower order moments are often of interest. For
    large number of monodisperse particles, the collision rate is nc=ann . The first
    moment of particle concentration is clearly the average concentration n, i.e., the number
    of particles per unit volume. The second order moment can then be expressed as
    =n +=n (1+———) (5.1)
    n
    where < > denotes ensemble average. The first moment n does not tell how the number
    of particles varies with the volumes at different locations and gives no reflection of
    turbulence structure at small scale levels. However, the second order moment, ,
    consists of two terms: n and . The variance of particle concentration, ,
    measures the variation of particle number density in the cells, i.e. the non-uniformity of
    the particle distribution. Thus it is fundamentally important to the particle collision rate.
    Since n is a simple average of particle concentration in a turbulent flow while
    depends on the turbulence structure and particle inertia, etc., our attention is given to the
    behavior of < n' >.
    2 ~2
    In this study, we use the normalized variance of particle concentration, < n >/ n
    to quantify the concentration non-uniformity in turbulent flows. The normalized variance
    is obtained via direct numerical simulation. As will demonstrated in §5.2, the inherent
    2 -2
    statistical noise is eliminated from the /n . By this new method no assumption
    regarding the particle-particle interactions is needed. The effects of particle size and
    inertia on the concentration non-uniformity can be quantified separately. It is found that
    the concentration non-uniformity can be greatly overestimated if the particle size is
    ignored. For particles with zero size, the non-uniformity scales with Ax as
    / ~ (Ax) (b around 1) for small values of Ax for particles whose response

    79
    time is comparable with turbulence Kolmogorov time scale, where Ax is the length of a
    small cube of volume (Ax)3 used in the Lagrangian simulations of the particle motions in
    turbulence. However, for finite-size particles, the non-uniformity reaches a maximum
    value (around order one or less) at Ax/D = 2.0-3.5, in which D is the particle diameter
    and r| is Kolmogorov length scale; it drops to zero as Ax/D gets close to one. The
    maximum value of the < n' >/< n > is obtained when the ratio of particle response time
    to the Kolmogorov time scale is around one.
    5.2 Quantification Method
    5.2.1 Inherent Noise of the Concentration Non-uniformity
    Suppose the computational domain L3 is divided intoNcequal-volume cells each
    with length Ax so that Nc = (L/Ax)3. Obviously n = N/Nc is the first moment of n.
    For zero-size, inertialess particles, the probability of a particle residing in a
    particular cell is p - 1/NC. With N particles in the domain, the probability of k particles
    residing in a specific cell of volume (Ax)3 obeys the Bernoulli distribution,
    P = idO«0! /O-f)™. <5-2)
    The second moment of n is, by definition,
    = Zk2P(k). (5.3)
    k=0
    Thus the variance of the particle concentration for a given cell is
    = -n2= £k2P(k)-n2. (5 4)
    k=0
    For Np(l- p) »1, P(k) given by (5.2) can be expressed as
    (5.5)

    80
    for k in the neighborhood of Np, which is known as DeMoivre-Laplace theorem. In the
    limit of large Np(l- p), which is very close to n for small p, an asymptotic integration
    gives
    = f k2P(k)dk~ n2 + n(l-p). (5.6)
    0
    Denoting as for zero-size, inertialess particles (P-ko), we obtain
    =- n2 = n(l- p).
    2 ~2
    Denoting r as the ratio of < n' > to n ,
    _
    r_ .2 ’
    n
    it follows for zero-size, inertialess particles that
    __hE
    rao _2
    n n
    (5.7)
    (5.8)
    (5.9)
    For n =0(1) or smaller, the DeMoivre-Laplace theorem does not hold anymore. In DNS
    the number of particles used is relatively small from the point view of smooth statistics,
    as mentioned in Section I. In DNS, n may be much less than one as cell size becomes
    small. With finite-size particles, the ultimate number distribution of particles is reduced
    to either zero or one per cell as the cell size is smaller than 0.5773D, where D is the
    particle diameter. For n «0(1), particle number concentration is more likely to obey
    Poisson distribution
    A.ke-X
    P(k)=——, (5.10)
    k!
    where A.=Np. The second moment of n is then
    = Zk2P(k)= Zk2 , (5.11)
    k=0 k=o k!
    and the variance of the particle concentration for a given cell is

    81
    _2 N xke~X -2
    = -n = £k———n .
    k=0
    k!
    (5.12)
    ForN »1,
    k„—X of) ^ ^ X. x
    Ik2 ^r-= Zk2
    k=0
    k!
    k=0
    k!
    = Z[k(k-l) + k]
    k=0
    k!
    Xke~x 00 Xke x
    = Ik(k-l) _
    k=o k! k=o k!
    _ , OO ^ , 00 Xk 1
    =X2 e~x 2 — +Xe~x I
    kto (k — 2)!
    = X2 e~x ex+Xe~x ex
    kto (k-1)!
    = l2+X.
    (5.13)
    Since X,=Np= n, the variance of n can be expressed as
    =
    k=0
    k!
    -2 2 -
    = n + n-n = n.
    (5.14)
    It follows for zero-size, inertialess particles that
    < n'« > _ 1
    fn " _2
    n~
    n
    (5.15)
    2 ~2
    For n »1, seen from both equations (5.9) and (5.15), r^ =< n'^ > /n -»0 so that
    the variance of the particle concentration distribution is very small when it is normalized
    by the square of the first moment of n. However, if N is fixed while Nc takes a large
    value in the limit, we have n « 1 and r^ ~l/n »1. In a practical simulation of particle
    motions in turbulence, N is always finite and fixed. Since we are interested in
    quantifying the small-scale variation of the particle concentration, it is necessary to
    reduce successively the size of the cell, (Ax) , on which n and < n > are computed. It is
    noted that the relatively large value of for inertialess particles is entirely due to the

    82
    fact that there is not enough particles in each cell as the size is reduced and r^-l/n
    contains no contribution from the preferential concentration. As far as the inertial effect
    is concerned, r~l/n is purely the inherent statistical noise which can be improved by
    using large values of n in principle. As the inertia of the particles increases, < n' > will
    contain an increasing effect of the inertia bias. The fact that r^-l/n »1 for inertialess
    particles when n « 1 implies, however, that r= < n' >/ n is dominated completely by
    the inherent statistical noise r00~l/n . The contribution from the inertial effect to
    or r thus must be isolated from the statistical noise. Figure 5.1 shows the comparison
    between the DNS results r^ for inertialess particles and the inherent noise 1/n , where
    /n at every instant was recorded and a total of 300 time steps and 40 turbulence
    realizations are used in our DNS. The details regarding turbulence representation, the
    characteristics of the turbulence and particle collision detection scheme have been given
    in §2.1.3. It clearly shows that /n is indeed dominated by 1/n which has to be
    subtracted.
    5.2.2 Concentration Non-uniformity Parameter
    For particles with finite inertia, the variance of the particle concentration can be
    computed in direct numerical simulations
    < n'2 (p) > =—— Z(nj -n)2 .
    Nc j=i
    (5.16)

    83
    Figure 5.1 The normalized variance of the concentration as function of cell size for
    inertialess particles in isotropic turbulence. Pxk=1.023.
    The above variance of the concentration non-uniformity is dominated by the statistical
    noise, as shown in figure 5.1. For zero-size, inertialess particles, the above should
    approach to = n(l- p) for n >0(1), and =n for n « 0(1). Defining the
    9 9
    normalized difference between above and < n“>as
    n'Ü n'ü (p)
    < !_>= < ’ > - < — >
    -2 -2 -2
    n
    n
    n
    and
    1-P
    n
    > - <
    n'
    2
    00
    >
    for n>0(l),
    (5.17)
    -2
    n
    -2
    n

    84
    = <
    n'§ (P)
    -2
    n
    > -
    n
    for n«0(l).
    (5.18)
    1 10 100
    Ax / T|
    Figure 5.2 The expressions (5.17) & (5.18) of the concentration non-uniformity as
    function of cell size for inertialess particles in isotropic turbulence. ptk=oo.
    The effect of inertia on the variance of n can be identified. Both equations (5.17)
    2 • . -
    and (5.18) represent the contribution to from the inertial effect for n > 0(1) and
    n « 0(1), respectively. Figure 5.2 shows the results of equations (5.17) and (5.18) for
    isotropic turbulent flow. Equation (5.17) obviously gives very small value for large Ax/D
    for n »0(1), while equation (5.18) on other hand gives small values for small Ax/D for
    n « 0(1). For inertialess particles, / n from both expressions has 0(1) variation
    2 _2
    over cell size. Equation (5.17) gives < n'p >/n -> -1 as cell size Ax approaches to zero

    85
    2 “2
    while equation (5.18) gives /n —>1 as Ax increases. This is physically unsound
    because the preferential concentration is an inertial phenomenon and should not appear in
    turbulent flows with inertialess particles. Figure 5.3 shows the DNS results of
    using equations (5.17) & (5.18) in a uniform shear flow for inertialess particles. The
    uniform shear flow is represented by u=(Ty, 0, 0) with the velocity gradient T=1.0.
    2 “2
    Comparing with figure 5.2, very similar behavior for /n is obtained even for a
    uniform shear flow. The results are unphysical because there is no mechanism to drive
    inertialess particles to form non-uniform concentration in a uniform shear flow. Thus,
    one should not expect to apply the equation (5.17) or (5.18) to quantify the concentration
    non-uniformity. However, the above results gave us a valuable hint. That is, the results
    obtained by both equations (5.17) & (5.18) can be interpreted as an additional statistical
    noise for both inertialess and inertial particles. It is hence suggested that the actual
    contribution of the inertia to < n' > for an arbitrary (3 may be expressed as
    = [ - ñ(l- p) ]p - [ - ñ(l-p) ]p=00, (5.19)
    or
    = [ - n ] p - [ - n ] p=a0. (5.20)
    Apparently, zero concentration non-uniformity is obtained for inertialess particles in both
    turbulence and uniform shear flows based on equations (5.19) & (5.20). It is noted that
    is a function of P and n in equation (5.20), while is mainly determined
    by p and n in (5.19). The influence of p on in (5.19) may be negligible as far as
    a small p is concerned, i.e.
    = f(P, n ), since p appears in both first and second
    terms in right side of equation (5.19). A large p, however, means the large cell size (the
    box size is the limit), which usually gives approximately uniform particle distribution. It
    is also noted that
    ->0 as n ->qo (or N->oo). Thus, it is expected that the effect of
    inertia on the concentration non-uniformity can be captured using equations (5.19) &
    (5.20). To quantify the extent of the concentration non-uniformity using the second

    86
    moment of n, a dimensionless quantity Ap called concentration non-uniformity
    parameter, is defined as
    Ap “ [ _
    < n'p > (1-p)
    n n
    r (1-p)
    Ip — L 2 - 1 p=o
    n
    -2
    n
    (5.21)
    or
    I < n'2 (p = co) > 1
    AB = [-zf—= ]p-[
    L n
    n
    -2
    n
    ” — 1 P=QO '
    n
    (5.22)
    Figure 5.3 The expressions (5.17) & (5.18) of the concentration non-uniformity as
    function of cell size for inertialess particles in uniform shear flow. p/T=oo.
    Figure 5.4 shows the concentration non-uniformity parameter for the uniform shear
    flow using equations (5.21) & (5.22). It is seen that Ap is very small for all cases in a
    uniform shear flow. Figure 5.5 shows Ap in turbulent flows for a large range of sizes for
    particles with zero inertia. It is also shown that, for zero-size particles, the concentration
    non-uniformity at small inertia (Pxk=7.3) still increases with the decreasing cell size.

    87
    The detailed discussions about the size effect on the collision rate will be given in §5.3.
    The above results obtained by (5.21) & (5.22) are reasonable and hence strongly suggest
    that equations (5.21) & (5.22) can be used to quantify and predict the particle
    concentration non-uniformity induced by particle inertia in general turbulence. It should
    also be noticed that both equations (5.21) and (5.22) always yield the same shape differed
    by a constant (=1). To facilitate the subsequent discussions, equation (5.22), which does
    not contain p and is thus in a simpler form, is used hereinafter.
    AP
    Figure 5.4 The concentration non-uniformity parameter Ap as function of cell size in
    uniform shear flow. T=1.0, p/T=1.0.
    Figure 5.6 shows the effect of total particle number N on the concentration non¬
    uniformity parameter Ap. It is seen that Ap remains almost on the same curve when N
    increases from 25,000 to 50,000 and 100,000 for all values of Nc(from 23 to 2783).

    88
    Even when N decrease to 12,000, 5,000 and 2,000, Ap does not change much from those
    at N= 100,000. This suggests that Ap is only very weakly dependent on N and the results
    for Ap obtained at finite N (2,000-12,000) can be used to represent Ap of N-»co. Hence,
    Ap defined by (5.21) & (5.22) contains only the effect of inertia and can be used
    conveniently as a measure for small scale concentration non-uniformity. It should be also
    pointed out that while Ap has a minimal statistical error due to finite N, it still contains
    statistical errors due to finite number of turbulence realizations.
    AP
    Figure 5.5 The concentration non-uniformity as function of cell size for small inertia
    particles in isotropic turbulence.

    89
    AP
    Figure 5.6 The concentration non-uniformity as function of cell size for various
    particle number densities at a given inertia in isotropic turbulence, pxk=1.023.
    5.3 Results and Discussions
    Since this chapter is focused on quantifying the concentration non-uniformity in
    order to investigate the effects of particle inertia and finite size on the collision rate, the
    “THROW AWAY” scheme is employed for finite-size particles. Both the tracking of the
    motions of particles and the collision detection are carried out in a Lagrangian frame.
    The semi-implicit finite difference scheme with 2nd order accuracy is implemented to
    integrate the equation of particle motion and to detect collision.
    To examine the effects of the inertia and size on particle concentration non¬
    uniformity induced by the small-scale structure of turbulence, N particles are randomly
    seeded in a box of n3 in an isotropic turbulence. Initially, the velocities of the particles
    are set to be the same as that of the turbulence at corresponding locations. There are two
    categories of particles considered: zero-size and finite-size particles. For zero-size

    90
    particles, following previous studies on the preferential concentration, no particle
    collision detection is needed so that particles can overlap in space, which is equivalent to
    the use of the “OVERLAPPING” scheme. For finite-size particles, however, collision
    between particles has to be detected, and the collided pair will disappear from their
    current size groups as far as geometrical collision is concerned. Periodical boundary
    conditions are imposed on both the particles and turbulence. Instantaneous particle
    concentration fields are obtained by tracking 2000, 5000, 12000, 25,000 to 50,000 and
    100,000 particles respectively. The box is divided into Nc equal-volume cells each with
    length Ax so that Nc = (it/Ax)3. The isotropic turbulence in the periodic box has the
    following characteristics: integral length scale L=0.594, Taylor micro length scale k
    ~0.277, Kolmogorov length scale q=0.023, Kolmogorov time scale xk =(v/ s)1/2 =0.073,
    and Re ^ =40.1. The time step used in this study is dt=0.01 and the ratio of total time
    (after the particles have reached dynamic equilibrium) to Kolmogorov time is 82 for most
    of the cases in the following discussions. The instantaneous non-uniform concentration is
    calculated and recorded at every time step after particles have reached dynamically
    equilibrium. The average concentration non-uniformity is obtained based on the recorded
    concentration non-uniformity by all time steps and all turbulence realizations.
    Figure 5.7 shows the effects of inertia on the concentration non-uniformity for
    sizeless particles. It can be seen that the maximum value of Ap occurs around Pxk =1.0,
    which agrees with the results of Wang & Maxey (1993). It is noted that all curves for
    different inertia increase monotonically as the cell size decreases, and the slopes are all
    close to -1 in the small cell size limit except that the curve for pxk =0.511 has a slightly
    smaller slope at the same region. Evidently, the results in figure 5.7 show the divergence
    of Ap as cell size becomes small, so does figure 5.6. The singular behavior of Ap is
    unphysical for finite-size particles because the particle concentration becomes either 0 or
    1 per cell when the cell size falls below particle diameter. Analytically it can be easily
    shown from equations (5.21) & (5.22) that Ap is equal to zero if the 0-1 Poisson
    distribution is reached.

    91
    1.0E+01
    1
    1.0E+00
    1.0E-01
    1.0E-02
    1.0E-03
    ♦—0.511
    1.0E-04
    10
    100
    Ax / T|
    Figure 5.7 The concentration non-uniformity of sizeless particles as function of cell
    size for various inertia in isotropic turbulence.
    Figures 5.8 (a-b) show the non-uniformity parameter Ap for finite-size particles
    over the whole range of cell sizes. The collection of the maximum values of
    concentration non-uniformity parameter Ap for finite-size particles as function of particle
    inertia in isotropic turbulence is shown in figure 5.9. For Pxk> 0.5, the non-uniformity
    parameter Ap increases as the inertia increases. The maximum Ap is reached at
    Pxk=0.5. For very large inertia Pxk <0.5, however, the non-uniformity parameter Ap
    decreases as the inertia further increases. It is observed that Ap varies with inertia as
    —9
    (Pxk) for large values of Pxk. It is also noted from figure 5.9 that the non-uniformity
    Ap at D/ri=0.957 is larger than that at larger particle size D/r|=2.522. Detailed
    discussions on the relationship between the non-uniformity parameter Ap and the
    collision rate will be given in Chapter 6.

    92
    Figure 5.10 compares Ap of small finite-size particles with that of zero size for
    pxk ~i. It can be seen that there exists a maximum Ap for all finite-size particles for all
    different inertia. Clearly, the use of sizeless particles not only results in a singular
    concentration non-uniformity in the limit of small cell size but also overestimates the
    value Ap even at finite cell size. Figures 5.11 (a) & (b) show that the value of the
    maximum Ap increases as the particle size becomes small. A maximum value of Ap
    always exists for all finite-size particles. It is reasonable to expect that the maximum Ap
    will ultimately become infinite if one uses infinite small size of particles such as sizeless
    particles. It is seen that the maximum Ap is reached at normalized cell size Ax/D=2.0-2.5
    for all particle sizes (D/r|=0.957-2.522) except for the smallest particles in figures 5.11
    (a) & (b), where the maximum Ap is reached at Ax/D=3.5 for D/r|=0.435.
    The effects of particle size on the particle concentration non-uniformity may be
    explained as follows. A reason for particle size to play an important role in quantification
    of the concentration non-uniformity may be explained by the mechanism of binary
    collisions between particles. Since particles are pairing to form the non-uniformity before
    they collide each other as turbulent flow evolves in time, the most meaningful scaling
    quantity should be able to measure the degree of the number of particles closely paired at
    every instant. The dimensionless cell size Ax/D=2.0~3.5, with being able to contain more
    than a pair of particles, is a proper frame to capture this significant phenomenon.

    93
    Ax / D
    1 10 100
    Ax / D
    Figure 5.8 The concentration non-uniformity parameter Ap of finite-size particles as
    function of cell size for various inertia in isotropic turbulence, (a) D/r| =0.957.
    (b) D/ti=2.52.

    94
    Figure 5.9 The maximum concentration non-uniformity parameter Ap of finite-size
    particles as function of particle inertia in isotropic turbulence.
    0.1 1 10 100
    Ax / T|
    Figure 5.10 The concentration non-uniformity parameter Ap as function of Ax/p
    for small-size and zero-size particles in isotropic turbulence. k=1.023.

    95
    Ap
    Figure 5.11 The concentration non-uniformity of particles at a given inertia as function
    of Ax/D cell size for various sizes in isotropic turbulence, (a) Log scale in x-axis.
    (b) in normal scale in x-axis.

    96
    5.4 Summary and conclusions
    A new method is developed to calculate reliably the normalized concentration non-
    2 —2 ...
    uniformity parameter /n , in which n is the instantaneous particle number
    concentration and the prime is the fluctuation in a small cube of volume (Ax)3, from the
    Lagrangian simulations of the particle motions in turbulence, and n is the mean
    concentration of particles. The inherent statistical noise is eliminated from the parameter
    /n . The effects of particle size and inertia on the concentration non-uniformity
    can be quantitatively examined using present method. It is found that the concentration
    non-uniformity parameter will be greatly overestimated if the effect of particle size is
    neglected. For particles with zero size, the non-uniformity varies with the inverse of Ax,
    i.e. Ap ~ 1/Ax for small values of Ax when particle response time is comparable with
    turbulence Kolmogorov time scale. However, for finite-size particles, the concentration
    non-uniformity parameter reaches a maximum value instead of unbounded value for
    sizeless particles. For finite-size particles, the maximum non-uniformity is reached at
    Ax/D = 2.0-3.5 while it becomes zero when Ax/D approaches to one. Particle size is an
    important length scale in quantifying particle concentration non-uniformity. The
    maximum value of the concentration non-uniformity is reached when the ratio of particle
    response time to the Kolmogorov time scale is close to one.

    CHAPTER 6
    COLLISION RATE AT FINITE PARTICLE INERTIA AND SIZE
    IN GAUSSIAN TURBULENCE
    The effects of particle inertia, size, concentration and gravity on the collision kernel are
    investigated in an isotropic, Gaussian turbulence. An asymptotic analysis is developed to
    predict the effect of the increased collision velocity on the collision rate due to small
    particle inertia. A unified expression of the collision kernel including effects of the
    increases in both collision velocity and concentration non-uniformity is proposed for
    small inertia particles. Good agreement is obtained between the simulation results and
    the prediction by the unified expression for particles with small inertia. The theoretical
    prediction of the collision kernel for zero inertia particles is critically examined by
    carrying out the direct numerical simulations at both a very dilute concentration with
    small number of particles and a dilute concentration with large number of particles. It is
    found that the normalized collision kernel decreases as particle size or mean
    concentration increases. In contrast to Saffman & Turner's prediction for the effect of
    particle gravity on a^, the gravitational settling is found to reduce a j, for monodisperse
    particles. It is found that “HARD SPHERE” model suffers from multiple collision
    problem and the collision kernel predicted by the “HARD SPHERE” model is dependent
    on time step size for small inertia particles. The classical result of Smoluchowski for the
    collision rate of monodisperse particles in a laminar shear flow is shown to be inaccurate
    due to the inclusion of the self-collisions. The present work extends Smoluchowski’s
    result by excluding the self-collisions in the counting of collision pairs.
    97

    98
    6.1 Introduction
    As pointed out in Chapter 1, the particle inertia affects the particle collision rate
    through various mechanisms: increase in the collision velocity, particle concentration
    non-uniformity, and compressibility of the particulate phase in the continuum sense.
    Zhou et al. (1997) made an attempt to include all these mechanisms in a mechanistic
    analysis for particle collision rate. In particular, the effects of the concentration non¬
    uniformity were modeled with an adjustable parameter that is based on the simulation
    results of the collision using the “OVERLAPPING” scheme. It must be pointed out that
    the “OVERLAPPING” scheme for particle detection scheme is equivalent to having a
    zero particle size as far as the particle concentration non-uniformity is concerned. It has
    clearly been demonstrated in Chapter 5 that neglecting particle size leads to a significant
    over-estimation of the concentration non-uniformity. Hence, the model of Zhou et al.
    (1997) contains a serious over-estimation on the effect of particle inertia through an over¬
    estimation of the concentration non-uniformity.
    In addition to particle inertia measured by Pxk, the particle size also plays
    complicated roles on the collision rate. Consider the zero inertia limit for example. For
    particles with small size, D particle size D and (s/v)1/2. This is so because for |x¡ -Xj|~D expansion can be applied to obtain fluid velocity difference, Au, between two close points
    separated by a distance |x¡ - Xj |=R in space in the form of
    <|w|> ~ <|Au |> ~ | (x¡ - Xj)-Vu + ... | ~ R(e / v)I/2+.„. (6.1)
    On average the leading order term for w is thus proportional to (s / v)l/2 and D. For
    I Xj - Xj| ~ D»r|, the velocity difference Au between two points in space is given by the
    inertial subrange scaling law as
    |Au|~ (sR)1/3+ ...
    (6.2)

    99
    which is independent of viscosity. Note that the dependence of |Au| on R is only to the
    one-third power which is quite less than the linear behavior for R to reducing the particle concentration non-uniformity, an increase in particle size also
    reduce the rate of increase in the collision velocity. When this is coupled with particle
    inertia and finite particle volume concentration, the situation becomes much more
    complicated. A detailed numerical study on the effects of particle inertia, particle size,
    and particle concentration on the collision rate in turbulence is warranted.
    To that end, an asymptotic analysis is first developed to predict the effect of the
    small particle inertia on the collision kernel due to the increase in collision velocity. The
    effect of particle size on the collision kernel is critically examined by carrying out the
    direct numerical simulation at a very dilute concentration for zero inertia particles. It is
    found that the collision kernel decreases as particle size increases. The collision kernel is
    found to be affected by particle mean concentration of the system. A unified expression
    of the collision kernel including effects of the increase in collision velocity and
    concentration non-uniformity is proposed for small inertia particles. The unified
    expression is evaluated through direct numerical simulations. The effect of gravitational
    settling on the collision kernel is observed through DNS result and explained by the
    affected collision velocity and concentration non-uniformity. The results of three
    collision models: “THROW AWAY”, “KEEP ALL” and “HARD SPHERE” are
    presented and compared. It is found that good agreement between the “THROW
    AWAY” and “KEEP ALL” models are reached and verified by Smoluchowski’s result in
    laminar shear flow, while the “HARD SPHERE” model gives quite different result due to
    the multiple collision mechanism. It is observed through DNS that, even at a large inertia
    such as Pxk=l and with long time PT=52, the collision kernel using “HARD SPHERE”
    model appears continuously increasing with time, but the results of both the “THROW
    AWAY” and “KEEP ALL” models are quite steady from the beginning and also in a
    good agreement. The Smoluchowski’s collision rate for monodisperse particles (1.5) in a
    laminar shear flow is found to be inaccurate due to the inclusion of the self-collisions. A
    correction to Smoluchowski’s formulation has been made by excluding the self-collisions

    100
    in the counting of collision pairs. A numerical simulation for particle collisions in a
    laminar shear flow at very low concentration is carried out to validate the present result.
    6.2 Correction of Smoluchowski’s theory
    As mentioned in Chapter 2, the collision rate and kernel may vary with the
    collision models. In order to minimize the effects of the collision models on the collision
    rate and kernel, numerical simulation of particle collisions has to be carried out at a very
    dilute particle concentration in the system. Although equations (1.3) and (1.5) are used
    usually for systems involving a large number of particles so that the number
    concentration in the continuum sense is well defined, the calculation of the collision rate
    given by equation (1.3) is not necessarily limited to the systems with large number of
    particles. There has been recent development in the numerical studies of particle
    collision in fluids in which a finite number of particle trajectories, instead of continuous
    particle cloud, are studied. It has come to our attention that equation (1.5) involves the
    counting of self collisions among monodisperse particles, despite the factor of ^ which
    has already been introduced. This issue can be easily seen in the Lagrangian framework.
    Suppose there are N¡ particles of /th-size group and Nj particles of y'th-size group
    in the fluid of volume V at a given instant. The number of collisions among /th-size and
    yth-size particles is related to the number of possible combinations among these two
    groups. The number of possible combinations among /th-size and yth-size groups is N¡Nj
    so that the collision rate can be related to the number of possible combinations as
    nc¡j= aij NjNj /V2. (6.3)
    However, considering the collisions among the same size group, the number of possible
    combinations of particles is ^N¡(N¡-1), not ^ Nf. Take N¡=2 for example. There can
    be only one possible collision in the system. However, equation (1.5) implies that there

    101
    are two possible collisions. Based on the number of possible combinations, the collision
    rate among the same size group in the given volume should be
    He, = yN¡(N¡-l) jT(2r¡)3 V'2 (6.4)
    or
    nc,,= |ni(n¡-l/V) | r(2r,)3 forniX/V (6.5)
    where n¡ = N¡/V and nj = Nj/V are simply the average number of particles per unit
    volume. For a large value of N¡, or n¡V»l, equation (6.5) recovers equation (1.5).
    Using the number of possible combinations, equation (6.4) can also be
    consistently derived from equation (6.3) as a special case. Considering N¡=2M particles
    of radius r¡ in a given volume V and separating these particles into two groups each
    having 2M_1 particles, the number of collision candidates among these two groups is 22(M"
    However, there are collisions among the particles within each group. To find the
    number of collisions within the group, all the particles in the group can be further divided
    into two sub-groups, each with 2 particles on such second level sub-group. The total
    number of the collision candidates in the second level sub-groups is 2x22(M'2). This
    process can be continued until there is only one particle in each sub-group on the last
    level so that all possible combinations are counted. Summing up the collisions among all
    sub-groups of different levels yields
    nCjj = 1 T(2r¡)3 [22(M‘1) + 2x22(M'2) + 22x22(M'3) + + ... + 2M'2 22 + 2M'12° ]
    = ir(2r¡)32M(2M-l)/2= ÍNi(N¡-l)ir(2r¡)3. (6.6)
    Thus equation (6.4) is now consistent with equation (6.3) and is indeed a special case of
    equation (6.3).
    To confirm the validity of equation (6.4) in the context of particle collision in
    fluid flows, a direct numerical simulation is carried out to examine the collisions among
    the like particles. Fifty particles of radius r¡=0.02 are randomly introduced at t=0 into a
    cubical box with length Lx=2, Ly=1.8, and Lz=2.0 so that V=7.2, with the restriction

    102
    0.1 velocity field is given by v=(u, v, w) = (Ty, 0, 0). All particles move with the fluid along
    the x-direction. The motion of particles is updated with a time step At=0.03 and a total of
    three hundred time steps is used in each realization. Periodic boundary conditions are
    employed along x- and z-directions so that the particles that have left the box at the end
    of the nth time step are reintroduced, at (n+l)th time step, by image particles entering the
    box from the opposite side. In a uniform shear flow, no periodic condition can be
    imposed along the y-direction and particles near y=0.1 and y=1.9 have less collisions
    because there are no particles in regions of y<0.1 and y>1.9 in direct numerical
    simulation. A correction on the collision rate due to this boundary effect (Wang et al
    1997) is thus employed for the number of collisions
    37t 2r,
    nC::= nc¡i]. /(I L)
    '-â– i L Jsimu v 16 L 7
    (6.7)
    in the shear flow. There is another possible correction (Wang et al 1997) due to the finite
    volume fraction of the particles in the volume. However, the volume fraction in the
    present case is 0.0233% so that the maximum volume correction suggested is less than
    0.1% according to the correction scheme (Wang et al 1997); this correction is neglected
    here. An efficient scheme (Chen et al 1995) is used to search for particle collisions. The
    “THROW AWAY” collision model is employed. However, since the collision kernel is
    so low considered in this case that the probability of having two collisions in the entire
    300 time steps within one realization is very small so that the treatment of the collided
    particles is not a concern at all in the present case.
    Based on equation (6.4) and taking N¡=50, it is seen that the average number of
    collisions within 0 nc¡j V T = |Ní(N¡-1) j r(2r¡)3V' T = 0.13067. (6.8)
    In order to achieve a reliable statistics, 10,000 collisions are desirable so that a total of
    NR=100,000 realizations are used to generate an estimated collisions Nc=l3,067 based
    on equation (6.8).

    103
    From the direct numerical simulation of Nr=100,000 realizations, with a 1.31%
    boundary correction given by equation (6.7), the total number of collisions is Nc=13,093.
    This agrees with our estimate (Nc=l 3,067) within 0.2%. If equation (6.10) is used, the
    estimated number of collisions would be Nc=l3,333 which is nearly 2% larger than the
    simulation result.
    As seen from equation (6.4), if there is more than one collision in 0 the right hand side will not remain a constant, nor does nCjj. However, the ratio,
    nCii V /[-Nj(N¡-l)] which gives the collision kernel should remain constant. Based
    on the counting of the number of collisions in each time step from the simulation, the
    collision kernel can be calculated using
    nc V
    aii=-¡ 215 • (6.9)
    -N.iN.-DV'1
    On the other hand, if equation (6.10) is applied, one would obtain, numerically, a
    different collision kernel by
    nc V
    a¡¡ = • (6.10)
    —NNV'1
    2
    Figure 6.1 (a) shows the numerically obtained value of a¡¡ for 0 is a significant noise due to the small number of collisions within each time step despite
    the fact that 100,000 realizations have been used. In order to obtain reliable data, a time
    average is then employed for a¡¡. Figure 6.1 (b) shows the cumulative time average of a¡¡
    and a,, together with the prediction by Smolouchowski [a¡¡ =— T(2r¡) ]. It is clearly seen
    from the time averaged result that the present formulation using ^N¡(N¡-1) gives a
    collision kernel that is consistent with equation (6.9) while the original formulation using
    ^ N¡N¡ is consistently smaller by about 2% comparing with that given by equation (6.9).

    104
    An extended version is thus suggested for the formulation of the population
    balance equations in view of the foregoing analyses. The evolution equations of particle
    population are
    dn.
    = -ot,1n1[n1 -1/V]+ -al2n,n2 -aI3n1n3-...
    dt
    dn2 1
    "dt" 2
    dn
    = -aun,[n, — 1/V]+ -a12n,n2 -a22n2[n2 -1/V]+ -a23n2n3-
    dt
    = a12n,n2 -a13n,n3 -a23n2n3 -a33n3[n3 -1/V]+-...
    for n¡V>l, i=l,2, 3,... (6.11)
    where [ f ] denotes that this term is zero if f<0. Mathematically, n¡V can be a non¬
    integer in the population balance equations. The restriction on taking only the non¬
    negative value of a¡¡ n¡[n¡ - 1/V] ensures that there is no generation in the /th group due to
    the collisions among the like particles.
    It needs to be pointed out that the effect of the correction in equation (6.11) is
    relatively minor. For a system with large number of particles (N¡»1), the present
    correction carries no tangible impact on the evolution of the particle size distribution.
    However, the present analyses place the evaluation of particle collision rate, as given by
    equation (6.5), on a more consistent footing.

    105
    1.2E-4
    1.0E-4
    8.0E-5
    6.0E-5
    4.0E-5
    2.0E-5
    a¡
    T
    Figure 6.1 Collision kernel a¡¡ against time in laminar shear flow. T= 1.0, D=0.04.
    (a) Variation of ensemble average, (b) Cumulative average.

    106
    6.3 An Analysis on the Effect of Particle Inertia on the Collision Rate
    6.3.1 Saffman & Turner's Analysis
    Saffrnan & Turner presented the first theory for inertialess particles which follow
    fluid motion completely. The collision rate among particle groups 1 and 2 is evaluated as
    nc12= nin2 (6.12)
    where wr is the radial component of the relative velocity, -n^ wris the particle flux
    moving inward to the target particle, and the integration is carried over the entire
    spherical surface of radius R, as shown in figure 4.1. Because of the continuity of fluid
    flow,
    nCi2=|nln2 ^entire sphere lWrldS>
    = ^nln2 /entire sphere • (6-13)
    The integration in right side of equation (6.13) may be expressed by equation (4.2) for
    isotropic turbulence, then the collision rate yields
    nC)2= njn2 2rcR2 <|wx|>. (6.14)
    Recall that x is the local coordinate parallel to the line connecting the centers of the
    colliding particles and < > denotes ensemble averaging. It should be noted that for the
    particles with zero inertia, the ensemble averaged collision velocity <|wx|> is expressed
    by (4.3), so that
    nclJ=n,n2 R3(|jV2«1.294n,n2 R3(V2 = al2 n,n2 (6.15)
    where a12=1.294R3(—)I/2 . (6.16)
    v
    For particles with inertia and gravity, Saffman & Turner gave their second
    prediction for identical particles

    107
    ot,, =1.67 R3 (—)1/2 (6.17)
    v
    An inconsistency occurs that the constant 1.67 in equation (6.17) differs with the constant
    1.294 in equation (6.16) as particle inertia goes to zero.
    6.3.2 Effect of Small Inertia on the Collision Kernel
    To understand the effect of inertia on particle collisional rate, we consider an
    asymptotic theory for small inertia. It is assumed that the particle size is much less than
    the Taylor micro-length scale, and the gravitational force is negligible. As in Saffman &
    Turner (1956), we also assume that turbulence is isotropic and Gaussian.
    Since the collision velocity |wx| is more relevant to the collision kernel as seen
    from equation (6.13), we start with |wx| to examine the effect of small particle inertia.
    Based on the asymptotic analysis of collision velocity, the mean-squared collision
    velocity is expressed by equation (4.19) for small inertia, and the net increase of the
    mean-squared collision velocity due to the inertia is given by the equation (4.20). Notice
    that the collision velocity is the relative velocity of two particles upon contact and hence
    wx(= vix - v2x) is always a negative value by projecting the relative velocity (v, - v2)
    along the radial direction (xj - x2 X
    wx I X2-Xj=±r < 0 for all collisions, (6.18)
    and
    Ix2-xi=±R < °- (6-19)
    Although u and v are Gaussian, wx does not follow Gaussian distribution
    because of equations (6.18) & (6.19), and it is demonstrated in the figures 4.4 (a) & (b),
    the PDF of collision velocity for various inertia. Clearly, wx is not Gaussian mainly
    because the curves of the PDF distributions are not symmetrical to the y-axis (the vertical
    line wx=0). However, careful observation also tells that, for small inertia, the PDFs of
    the collision velocity are almost symmetrical to the vertical line at wx= and

    108
    appear close to be Gaussian distributions with respect to wx=< w *>• It is also noticed
    that the collision velocity PDFs of small inertia particles peak at the same wx. That is to
    say, if the mean collision velocity at zero inertia is taken out from the raw wx, the
    remained is Awx = wx((3)- , the net increase of the collision velocity due
    to the inertia, and it is quite close to symmetrical and may be approximated by Gaussian
    distribution. Thus we have
    <|A wx |> =J~ < Awx >,
    (6.20)
    where is the net increase of the mean-squared collision velocity due to the
    inertia as defined by equation (4.20) for identical particles. Substituting by
    equation (4.20) into (6.20),
    <|Awx|>=J- [- ^ <(ulx-u2x)(
    V n B
    Dulx Du2x . 2 .DuXx2 R~ i/2
    lx ^ )>+—<(—TLy> y'¿
    Dt
    Dt
    Dt
    "D
    (6.21)
    Denoting a,,(P) as the collision kernel at arbitrary inertia and an(p->oo) as
    the collision kernel at zero inertia, the net increase of the collision kernel for identical
    particles due to the inertia is defined as 8a, ,,
    8a,,= an(P)-an(P->oo)
    = 27iR2 <|Awx|>,
    and denoting the normalized collision kernel as
    «11
    «11 =
    R3(V2
    (6.22)
    (6.23)
    then the net increase of normalized collision kernel can be expressed as 8a, j,
    8a,,
    8«n =
    R3(V2
    v
    (6.24)
    Combining (6.21), (6.22) & (6.24), we have

    109
    5an-,
    87tV
    2 , w Du.x Du?x
    — [ T <( ulx ~ u2x X
    e pR2 |X Dt Dt
    y>+
    <(^)2>]’'2,
    P2X2d ' Dt
    (6.25)
    and the net increase of collision rate for the system of a large number of particles due to
    particle inertia may be written as
    Du tv Du-
    1 -> 8nv ir 2 . ,.uuix .
    5nc"_2n| rr ['p<'U2xX^; 5T^
    2 ,Duv 2^ R* 1/2
    +—- c (—111¿.
    ^ lDt; ^DJ
    P‘
    (6.26)
    For non-identical particles 1 and 2, the net increase of the normalized collision kernel is
    Sot|i=1
    VER- U\ 1
    2
    P” (-L<(^)2>(-!—!-)2-4<(Ul>-U2xX-^--^
    1 0 r2 Dt 'p, P2 R2 P, Dt p2 Dt
    +
    •<(^)2>r'2,
    p,p2x2D ' Dt
    and the net increase of collision rate due to particle inertia may be expressed as
    Dt
    (6-27)
    „ • 871V „ 2 r ,Duv ,2,1 1x2
    8„Cl2=n,„2l|— R-[<(—) >(---)
    - 2<( ulx - u2x)(^- -i- ^ )>+ ^4- < (%)2 >]1/2 •
    p, Dt p2 Dt
    P,P
    1P2
    Dt
    (6.28)
    ‘D
    The present prediction, equation (6.25), differs from the second theory of Saffman
    & Turner in two aspects. First, 6a|j ->0 in the limit p—»oo, which is consistent with the
    result of inertialess particles, while Saffman & Turner’s second prediction has
    =1.67 which is inconsistent with the first result. Second, the present analysis
    includes two additional terms for inclusion of the effect of the inertia, as seen from
    equation (6.25). The effect of inertia does not vanish in (6.25) while it does in Saffman &
    Turner’s prediction when p, = P2, shown in equation (6.17). These two terms would
    have disappeared if was assumed to be perfectly correlated or XD—>oo which was

    110
    essentially assumed by Saffman & Turner (1956, p.26) by stating that "the air velocities
    near two close particles are the same". These two additional terms dominate the effect of
    inertia on the collision kernel for particles of nearly equal inertia.
    6.3.3 A Proposed Expression on Collision Kernel
    As mentioned previously, in addition to the increased collision kernel due to the
    increased collision velocity, the collision kernel is also affected by the preferential
    concentration due to the inertia. In Chapter 5, a net increase of the concentration non¬
    uniformity can be quantified by the new quantity Ap, the equation (5.22). To include the
    effects of both collision velocity and the concentration non-uniformity on the collision
    kernel due to the inertia, the total collision rate for the system of a large number of
    identical particles due to the inertia may be expressed as
    nc(l =ñ2(l + Ap)[a;,(P-»®)+5a;,(P)] R1 (V2
    = S2o;,R'(£)''2 (6.29)
    v
    where n is the mean concentration, a^(P -> co) denotes the normalized collision kernel
    at zero inertia, 5a|((P) is the increased part of normalized collision kernel due to the
    finite inertia, and a *, represents the overall normalized collision kernel
    a;i=(l+Ap)[a;i(P->ao)+8a;i(P)]. (6.30)
    It should be mentioned that in previous theories a \ i was treated as a constant, the terms
    Ap and Saj^P) have not been included. The net increase of normalized collision kernel
    at any inertia is then
    Aai, = (l+Ap)[a|,(P->oo)+8a|1(P)] - aJ,(P->co)
    = Ap[a|1(p-»ao)+5a¡1(p)]+5a|1(p).
    (6.31)

    Ill
    For small inertia, 5a¡1(P)=5a|1, where is defined by equation (6.25). Combining
    the equations (5.22), (6.25) & (6.31) yields the final form of the total net increase of
    normalized collision kernel
    .2
    .,2
    +
    +
    [ —2
    -=]
    n
    n
    Í871V
    2
    L [-
    pR2
    ¡8nv
    2
    [-
    7
    < n (P = oo) > 1 *
    — = ] p=oo } {ot 11 (P °°)
    n
    -2
    n
    PR‘
    <(uix-u2x)(
    <(Ulx-U2xX
    Dulx Du2x . 2
    Dt Dt
    Dulx Du2x
    â–  )> +
    p2X2D 'Dt
    A2>i,,2>
    Dt
    Dt
    , 2 ,Du„.2 ,i/?
    )>+ 7279-<(^A) >] •
    P2»-d Dt
    (6.32)
    Equation (6.32) is referred as “unified expression” of the collision kernel for simplicity in
    the following discussions. Obviously, Aajj is a complicated function of the inertia
    instead of constant or zero-value predicted by previous theories.
    6.4 Comparison of the Analysis with Simulation Results
    In this section, only monodisperse particles in Gaussian turbulence are considered,
    and “THROW AWAY” collision model is employed. The variation of the collision
    kernel with particle inertia, size, number concentration and gravitational settling will be
    discussed in succession.
    6.4.1 Effect of Particle Inertia
    To isolate the effect of the inertia on the collision kernel in our simulation and
    compare with the unified expression (6.32), the inertialess part of collision kernel
    °c*i(P -> o°) must be taken out from the raw collision kernel a|j(p). The inertial part of
    collision kernel is then obtained as Aaij=a¡i(p) - a^ip —> oo). Figures 6.2 (a) & (b)

    112
    show the variation of the collision kernel Aa^ with the inertia. The contributions from
    the preferential concentration, Ap, and from the collision velocity increase, 5an, due to
    inertia are also plotted. To facilitate a comparison between the prediction and simulation,
    the unified prediction based on equation (6.32) is also plotted in figure 6.2 (a), where the
    particle size is less than Kolmogorov length scale, i.e. D/q=0.957. Figure 6.2 (b) shows
    similar results for larger particle size at D/r|=2.522. The inertial increase San due *° the
    increased collision velocity is calculated based on equation (6.25) using the multiplication
    factor obtained at zero inertia. The inertial part due to the preferential concentration is
    obtained by taking the maximum of the Ap curves over the whole range of the cell sizes
    discussed in Chapter 5. It is noted that the application of the multiplication factor
    obtained at zero inertia may be only meaningful for small inertia. The unified prediction
    is then computed by equation (6.32) based on Sa^ and Ap determined. The inertial part
    of normalized collision kernel Aa^ increases as the inertia increases. The inertial
    increase Aa*| is observed to reach a maximum at Pxk=0.146 and decreases thereafter
    for both D/r|=0.957 and D/ri=2.522. However, the maximum Ap due to inertia is
    reached at pxk=0.5 for both D/r|=0.957 and D/r|=2.522, as discussed in Chapter 5. The
    reason for the maximum Aa \ i is that increasing the inertia decorrelates particles from
    the turbulent flow, and both the collision velocity and concentration non-uniformity
    increase, so the collision kernel is enhanced. However, the inertial increase will also
    reduce the ability for particles to pick up energy from the turbulence. Further increase of
    the inertia will eventually disable particles to get energy from the neighboring fluid and
    particles tend to be randomly distributed so that the concentration non-uniformity will be
    reduced till disappeared. Comparison between figures 6.2 (a) & (b) also shows quite
    different magnitudes of the increased collision kernel by different particle sizes, that is,
    more enhancement of the collision kernel is observed as the size becomes smaller. It is
    noted that the predicted Aa i ¡ based on the unified expression appears to agree with the

    113
    simulation for small inertia particles. However, large deviation is seen for Pxk < 2. This
    is expected since the asymptotic analysis assumes small particle inertia.
    6.4,2 Effect of Particle Size
    In order to understand the effects of particle size and concentration, zero inertia is
    first considered. Figures 6.3 & 6.4 show the collision kernel as a function of the size at
    different range of particle concentration. Figure 6.3 is for very dilute particle
    concentration. Only 500 particles for the size D/r|=0.435, 300 particles for D/r|=0.609,
    200 particles for D/ri=0.87 and 50 particles for D/ri=0.957, 2.522, 3.478 are used in the
    simulation. The reason for using such dilute concentration is to avoid the dependence of
    the collision kernel on the post-collision treatment, as discussed in Chapter 3. Further
    discussions on the effects of the post-collision treatment will be given in §6.6. Since the
    particles have zero inertia in figures 6.3 & 6.4, the effect of particle size on the
    normalized collision kernel can be clearly observed. The normalized collision kernel
    obtained by simulation is expected to recover the theoretical prediction (an=1.271) as
    particle size approaches to the small limit (D/r|«l). However, increasing particle size
    will reduce the normalized collision kernel. It appears difficult to investigate the collision
    kernel for very small particles because of enormous computer capacity required, and it is
    also infeasible to investigate the collision kernel for large size D/r|>5 in a “very dilute
    regime”, because keeping “very dilute concentration” may not have enough particles to
    generate reliable statistics while a given enough number of particles may violate the
    condition “very dilute concentration”. It is seen from figure 6.3 that a|| approaches
    constant for large particles. Figure 6.4 shows the effect of particle size on the collision
    kernel for large number of particles, where the total particles are N=36000 - 500 and the
    corresponding sizes are D/r|=0.217 - 13.36. Compared with figure 6.3, the difference is
    that the magnitudes of the collision kernel are smaller and continue reducing as the size
    increases after D/r)>8. The dependence of the normalized collision kernel a|, on the

    114
    size, shown in figures 6.3 & 6.4, reveals that the simple cubic law (a,, ~ R3) can not
    accurately describe the effect of particle size on the collision kernel a t,.
    6.4.3 Effect of Particle Mean Concentration
    Based on the results of the effect of particle size on the collision kernel in §6.4.2,
    different magnitudes of the collision kernel observed between figure 6.3 and figure 6.4
    gave us a hint that the average particle concentration may also affect a¡j. This has been
    confirmed by figure 6.5, where the total number of particles N=500, 1,000, 5,000, 12,000
    and 24,000 with a diameter D/r|=0.435 are used for five sets of direct numerical
    simulations. It clearly shows that the increase in the particle concentration will reduce
    an.
    6.4.4 Effect of Particle Gravitational Settling
    As discussed in §1.3, Saffman & Turner's prediction of collision kernel for
    monodisperse particles reduced to a constant a n=1.67 in the presence of gravity. To
    examine their results, a set of numerical simulations is carried out with inclusion of
    particle gravity. In figure 6.6, the effects of gravitational settling on the collision kernel
    are plotted against the settling velocity for the particles of diameter D/r|=2.52 with
    inertia at ptk=1.023. As contrast with Saffman & Turner's prediction, increase in
    particle settling velocity will reduce the collision kernel. The collision kernel drops down
    quickly as settling velocity increases from 0 to 8, and further increase of settling velocity
    causes the collision kernel to be reduced slowly. The reason for above effect may be
    explained as follows. The turbulence is consisted of numerous eddies ranging from small
    eddies to large eddies. Large eddies are responsible for advecting both the particles and

    115
    small eddies. The relative velocity between nearby particles is mainly generated by the
    small eddies which are critical to the particle collisions. As the settling velocity
    increases, the time period for the particles moving through the small eddies decreases,
    thus the effects of the small eddies on the particles also decrease. Due to the decreasing
    effects of the small eddies on the particles, the relative velocity between nearby particles
    decreases. This has been confirmed by figure 6.7 (a), which shows that the collision
    velocity indeed decreases as the settling velocity increases. Decreasing effects of the
    small eddies will also reduce the forces driving particles to form preferential
    concentration seen in figure 6.7 (b). It clearly shows that the concentration non¬
    uniformity varies with settling velocity in a similar way as the collision kernel does.
    6.5 Collision Rate in Polydisperse Suspension
    In the following simulations, monosize particles are initially introduced into the
    system, and the “KEEP ALL” collision model is applied to consider the particle
    agglomeration and size evolution. The same turbulence as above is generated. The
    number of particles will decrease as the collision proceeds and large particles are formed.
    The particle size evolution and the variation of the collision kernel with particle inertia
    and gravitational settling will be discussed as follows.
    Figures 6.8 (a) & (b) show the turbulence ensemble average of the collision kernel
    against time for inertialess polydisperse particles. The collision kernel decreases at the
    beginning and becomes stable after T/xk>7.0. The difference between the simulation
    and prediction may be due to the effects of both the mean particle concentration and the
    size, as mentioned in §6.4.2 and §6.4.3. The collision kernel against turbulence
    realization is given in figures 6.9 (a) & (b). Very smooth collision kernels are obtained
    by forty turbulence realizations for polydisperse particles at the inertia pxk=oo, 1.023,
    0.512 & 0.292.

    116
    Aa¡,
    1.0E+01
    1.0E+00
    1.0E-01
    1.0E-02
    1.0E-03
    0.01 0.1 1 10 100
    P^k
    Aan
    1.0E+01 T
    1.0E+00
    1.0E-01
    1.0E-02
    1.0E-03 -I * i * |
    0.01 0.1 1 10 100
    P^k
    (b)
    H ' I I
    non-uniformity, Eq (5.22)
    Total inertial effect, Eq (6.32)
    i * —■ ■ ■ ■ ■ -i * *— i
    Figure 6.2 The effect of particle inertia on the increased collision kernel Aa|, for
    monodisperse particles for (a) D/r|=0.957. (b) D/r|=2.52.

    117
    1.60
    1.50
    1.40
    1.30
    1.20
    1.10
    1.00
    0.90
    0.80
    0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
    D / r)
    Figure 6.3 The effect of particle size on the collision kernel a|| for monodisperse
    particles at a very dilute system. Pt k = co.
    all
    1.60
    1.40
    1.20
    1.00
    0 80
    0.60
    0.40
    0 2 4 6 8 10 12 14
    D / T|
    Figure 6.4 The effect of particle size on the collision kernel a¡, for monodisperse
    particles at ‘not’ very dilute system. Ptk = oo.
    • •
    • •
    • simulation
    prediction
    • simulation
    prediction
    ••

    118
    a,,
    15
    1.4
    1.3
    1.2
    â– 
    â– 
    1.1
    1
    â–  simulation
    prediction
    â– 
    â– 
    â– 
    0.9
    0.8
    1.0E+2 1.0E+3 1.0E+4 1.0E+5
    N
    Figure 6.5 Variation of the collision kernel against particle number concentration
    for monodisperse particles. Pxk=oo, D/r|=0.435.
    « !1
    Settling velocity Vs
    Figure 6.6 The effect of gravitational settling on the collision kernel ajj for
    monodisperse particles. p-tk=1.023, D/r|=2.52.

    119
    <|wr|>
    Settling velocity Vs
    Figure 6.7 The effect of gravitational settling for monodisperse particles. D/r|=2.52,
    Pxk=1.023. (a) on the collision velocity, (b) on the concentration non-uniformity.

    120
    T/xk
    Figure 6.8 Collision kernel a|, of polydisperse particles against time. (3xk = oo,
    D/r|=0.957. (a) Variation of the collision kernel, (b) Cumulative average.

    121
    Turbulence realization
    *
    a,,
    Turbulence realization
    Figure 6.9 Collision kernel of polydisperse particles with various inertia against
    turbulence realization. D/r]=0.957. (a) Variation of the collision kernel, (b) Cumulative
    average of the collision kernel.

    122
    6.5.1 Particle Size Spectrum
    Figure 6.10 shows the size distribution at three time instants. The slope of the size
    evolution keeps changing as the collision proceeds with time. It appears that no dynamic
    equilibrium of particle size spectrum exists in present particle-laiden system, while a
    slope of-2 was reported by Pearson et al (1984) and Balachandar (1988) for the so-called
    dynamic equilibrium of particle size distribution. It is noted that a constant rate of
    primary particles was introduced into the system and large particles were removed from
    the systems in Monte Caro simulations of Pearson et al. and in Balachandar, while
    present simulation does not add any extra particles or remove any large particles. Figures
    6.11 (a) & (b) show the number evolution of both primary and secondary size particles,
    where 5,000 particles of diameter D/r]=0.957 are initially distributed. It is seen that the
    instant number of primary particles and the generation of secondary size particles are
    greatly affected by particle inertia. The population of new particles grows quickly as the
    inertia increases.
    Figure 6.10 The evolution of particle size distribution at various instants for
    polydisperse particles. D/r|=2.17, ptk=1.023.

    123
    no / ni
    T
    Figure 6.11 Inverse of total number of particles against time for polydisperse particles.
    D/ti=0.957. (a) primary size (v0) particles, (b) secondary size (2 v0) particles.

    124
    6.5.2 Effect of Particle Inertia
    To investigate the effects of the inertia on the collision kernel for polydisperse
    particles, the inertial part of collision kernel Aa^a^P) - a^iP -> <») is calculated
    first. Figures 6.12 (a) & (b) show the variation of the collision kernel Aa ^ with the
    inertia. The effects of the increase in inertia on Ap, 8a|| and the unified prediction
    (6.32) are also plotted in figures 6.12 (a) & (b) for comparison. The particle sizes are
    D/r|=0.957 and 2.52 in figures 6.12 (a) & (b) respectively. The number of particles used
    in simulation is 5,000 and 2,000 respectively. Similar to the monodisperse suspension,
    the 5a|j due to increased collision velocity is calculated based on the prediction (6.25)
    using the multiplication factor obtained by inertialess polydisperse particles, and the
    inertial part due to the preferential concentration is collected based on the maximum of
    the Ap curves. It should be pointed out that the Ap curves for polydisperse suspension
    are obtained by calculating the concentration non-uniformity among the primary size
    particles, so that it represents the pure contribution from the non-uniform distribution of
    primary particles. The unified prediction is obtained by 5a|j and Ap determined. It is
    seen that the inertial part of the collision kernel increases as the inertia increases. Similar
    to monodisperse particles, the inertial part Aa*[ is also observed to reach a maximum at
    Pxk=0.146 and decreases thereafter for both D/r|=0.957 and D/r|=2.522, and the
    maximum Ap due to inertia is also reached at Pxk-0.5 for both D/r)=0.957 and
    D/ri=2.522. The reason for the maximum Aa^ is similar to the monodisperse particles.
    Increasing the inertia decorrelates particles from the turbulent flow, and both the collision
    velocity and particle non-uniformity increase, so the collision kernel is enhanced.
    However, increasing inertia will also reduce the ability for particles to pick up energy
    from the turbulence. Further increase of the inertia will eventually disable particles to get
    energy from the neighboring fluid and particles tend to be randomly distributed so that the
    concentration non-uniformity will be disappeared. It is observed by comparison between

    125
    figures 6.12 (a) & (b) that larger collision kernel is obtained by smaller size D/rpO.957.
    Since all parameters used in figures 6.12 (a) & (b) are the same except the size of primary
    particles, it is evident that particle size has a significant effect on the enhancement of the
    inertial part Aa¡,. It is also noted that the unified prediction of collision kernel Aan
    seems to be in agreement with the simulation at small inertia for polydisperse particles.
    Similar to the monodisperse particles, large deviation is observed for (3xk < 2. This is
    also expected since the asymptotic analysis assumes small particle inertia. As seen from
    previous discussions, the collision kernel is complicated by the collision velocity and
    preferential concentration and possibly also by the compressibility of the particle velocity
    field. Based on the simulation results of both monodisperse particles and polydisperse
    particles, however, it may be concluded that the increases in both collision velocity and
    preferential concentration due to the inertia are the dominant factors enhancing the
    collision kernels for small inertial particles.
    6.5.3 Effect of Particle Gravitational Settling
    The effect of gravitational settling on the collision kernel a*, can be found in
    figure 6.13. The increase in particle settling velocity will reduce the collision kernel of
    polydisperse particles. The collision kernel varies rapidly in the region 0< Vs <8 and then
    slows down and seems to approach a constant at Vs>8. The similar mechanisms as
    discussed in previous section may be applied here to explain the similar physics for
    polydisperse particles. Because of the decreasing effects of the small eddies on the
    particles, the collision velocity is expected to decrease as the settling velocity increases,
    as shown in figure 6.14 (a). Similarly, the concentration non-uniformity of polydisperse
    particles is also expected to decrease as the gravitational settling is enhanced, which has
    been consistently shown by the figure 6.14 (b). It is also observed that small difference
    between the figures 6.14 (a) & (b) and figures 6.7 (a) & (b) is consistent with the similar
    comparison of the collision kernel between the figure 6.13 and figure 6.6.

    126
    Aan
    Figure 6.12 The effect of particle inertia on the increased collision kernel Aa¡, for
    polydisperse particles for (a) D/r|=0.957. (b) D/r|=2.52.

    127
    *
    a
    ii
    Settling velocity Vs
    Figure 6.13 The effect of gravitational settling on the collision kernel for
    polydisperseparticles. Pxk=1.023, D/ri=2.52.
    6.6 Collision Rate in Hard Sphere Suspension and Assessment of Collision Models
    As mentioned in chapter 2, the “HARD SPHERE” model generates multiple
    collisions and may not be used for particles with small inertia. In addition to the issue on
    multiple collisions, we have found that Smoluchowski’s result in laminar shear flow
    cannot be recovered by the “HARD SPHERE” collision model. Figures 6.15 (a) & (b)
    show the simulation results of hard sphere particles in laminar shear flow with various
    shear rates and inertia. Figures 6.16 (a) & (b) show the comparison of three collision
    models with Smoluchowski’s prediction. While the “KEEP ALL” and “THROW
    AWAY” models can give good agreement with the Smoluchowski’s result, the “HARD
    SPHERE” model, however, underpredicts the collision kernel by a large percentage.

    128
    <|wr|>
    Settling velocity Vs
    Figure 6.14 The effect of gravitational settling for polydisperse particles. D/t]=2.52,
    (kk=1.023. (a) on the collision velocity, (b) on the concentration non-uniformity.

    129
    Figures 6.17 (a) & (b) show the effect of particle inertia on the collision kernel
    using “HARD SPHERE” model in Gaussian turbulence. Maximum collision kernel is
    reached at Pxk=2.192 does not make sense, which is mainly due to multiple collision
    mechanism as discussed above. The fact that the collision kernel increases with time
    even at Pi k =1.023 may suggest that the “HARD SPHERE” model may be appropriate
    for simulating collision kernel only if Pt k «1. It is seen from figures 6.17 (a) & (b) that
    the collision kernel appears to reach steady value at Pxk=0.512 or 0.146. Figures 6.18 (a)
    & (b) show the comparison of three collision models for prediction of the collision kernel
    at p-rk=1.023. The collision kernels against time can be observed for the “KEEP
    ALL” and “THROW AWAY” models and good agreement is also obtained between these
    two models, while the “HARD SPHERE” model results in twice the collision kernel and
    seems still diverging as PT=51. The comparison among three collision models at a
    larger inertia, Pxk =0.292, is given in figures 6.19 (a) & (b). The good agreement
    between the “KEEP ALL” and “THROW AWAY” model is again observed. Although
    the collision kernel obtained by using “HARD SPHERE” model is steady at Pxk =0.292,
    it differs considerably from the results of other two models.
    6.7 Summary and Conclusions
    The classical result of Smoluchowski for the collision rate of monodisperse
    particles in a laminar shear flow is shown to be inaccurate due to the inclusion of the self-
    collisions. The present work extends Smoluchowski’s result by excluding the self¬
    collisions in the counting of collision pairs. A numerical simulation for particle collisions
    in a laminar shear flow at very low concentration is carried out to validate the extended
    result of Smoluchowski.

    130
    *
    an
    T
    a!i
    Figure 6.15 Collision kernel a|, of hard sphere particles with various inertia against
    time in laminar shear flow. D=0.04. (a) Variation of the collision kernel, (b) Cumulative
    average.

    131
    *
    2.8
    2.4 -
    2.0
    - HARD SPHERE
    â–  THROW AWAY
    KEEPALL
    Smoluchowski
    0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
    T
    *
    T
    Figure 6.16 The effects of the collision schemes on the collision kernel against time
    in laminar shear flow. T=1.0, D=0.04. (a) Variation of the collision kernel, (b)
    Cumulative average of the collision kernel.

    132
    *
    «ii
    *
    Figure 6.17 The effects of the inertia on the collision kernel a*, against time for hard
    sphere particles in Gaussian turbulence. D/t] =0.957. (a) Variation of the collision kernel,
    (b) Cumulative average of the collision kernel.

    133
    a,,
    Figure 6.18 The effects of the collision schemes on the collision kernel a|t against time
    in Gaussian turbulence. D/r|=2.174, Pxk=1.023. (a) Variation of the collision kernel.
    (b) Cumulative average of the collision kernel.

    134
    *
    T
    ♦
    «n
    T
    Figure 6.19 The effects of the collision schemes on the collision kernel of hard
    sphere particles in Gaussian turbulence. D/ri=0.957, pxk =0.292. (a) Variation of the
    collision kernel, (b) Cumulative average of the collision kernel.

    135
    The theoretical prediction of the collision kernel for zero inertia particles is
    critically examined by carrying out the direct numerical simulation at a very dilute
    concentration with small number of particles in isotropic, Gaussian turbulence. It is
    found that the simulation results of finite size particles (D/t] >0.435) are asymptotically
    approaching to the theoretical prediction. The normalized collision kernel a*] decreases
    as particle size increases, while is a constant based on all theoretical predictions.
    The direct numerical simulations at a dilute concentration with a large number of
    particles have also been carried out for examining the size effect on aj, in the
    turbulence. The normalized collision kernel a*, decreases as particle size increases.
    The effect of particle mean concentration on a*t is examined. It is found that
    decreases as the mean concentration increases, while previous theories showed no effect
    of the mean concentration on an-
    The effect of particle inertia on the collision kernel is investigated by both
    analysis and numerical simulations in an isotropic, Gaussian turbulence. A unified
    expression of the collision kernel including effects of the increased collision velocity and
    concentration non-uniformity is proposed for small inertia. Good agreement is obtained
    between the simulation results and the unified expression for particles with small inertia.
    In contrast to Safffnan & Turner's prediction for the effect of particle gravity on
    the collision kernel. The gravitational settling is found to reduce the collision kernel
    among monodisperse particles. Sharp decrease in the collision kernel is observed when
    the dimensionless settling velocity increases from 0 to 8, and further increase in the
    settling velocity will also result in the decrease of the collision kernel but a slow rate of
    decrease.
    The effect of various collision models on the collision kernel is also examined. It
    is found that the theoretical prediction of collision rate at small size and zero inertia limit
    can be recovered by both “THROW AWAY” and “KEEP ALL” models, while the
    “HARD SPHERE” model cannot be used for prediction of collision rate at small inertia

    136
    even for laminar shear flow due to the multiple collision mechanism. Reliable
    calculation for the collision kernel using the “HARD SPHERE” model requires extremely
    small time step for small inertia particles. Steady values of collision kernel can be
    reached only for very large inertia using “HARD SPHERE” model. In general, the
    collision kernel based on the “HARD SPHERE” model is larger than the results obtained
    by both the “THROW AWAY” and “KEEP ALL” models for particles with large inertia
    in Gaussian turbulence.

    CHAPTER 7
    SUMMARY AND CONCLUSIONS
    A new theoretical framework is developed to evaluate the collision rate and
    collision velocity of very small particles in general turbulent flows. The classical results
    for the collision kernel by Smoluchowski (1917) for laminar shear flow and by Saffman
    & Turner (1956) for isotropic turbulence are recovered. The present approach differs
    significantly from that of Saffman & Turner in that the ensemble average is taken after
    the collision rate for a given flow realization is calculated. This avoids the assumption of
    isotropy and allows for the evaluation of the collision rate in general turbulent flows. The
    collision velocity predicted in both laminar shear flow and isotropic turbulence agrees
    well with the results from direct numerical simulation of particle collisions. The present
    theory is used to evaluate the collision rate and velocity of small particles in the rapidly
    sheared homogeneous turbulence. It is found that the effect of the turbulence structure on
    the collision rate and velocity can be mainly captured by using the mean turbulent shear
    rate (e/v)1/2. The combined effects of the mean shear and the sheared turbulence on the
    collisions are elucidated. Simple interpolations are developed to evaluate the collision
    rate and velocity for arbitrary mean flow shear rate and turbulence mean shear rate.
    As particle inertia increases, the collision velocity increases and particle
    concentration becomes increasingly non-uniform. These factors significantly affect the
    particle collision rate in turbulence. The effect of inertia on the collision rate is examined
    by considering these two distinctive contributions: the effect of the increased collision
    velocity and the effect of the increasing concentration non-uniformity.
    Numerical and analytical studies on particle collisions in isotropic and Gaussian
    turbulence are carried out. The effect of particle inertia on the collision velocity is
    examined. An asymptotic analysis is developed to predict the effect of small particle
    137

    138
    inertia on the particle collision velocity. Asymptotic prediction for the increased collision
    velocity agrees well with the results of numerical simulation. The collision velocity is
    —1 —9
    found to contain terms that are proportional to (Pxk) and (Pxk) . The effects of both
    particle inertia and size on the collision angle are examined through numerical
    simulations. The collision angle depends strongly on the particle inertia and particle size.
    The average collision angle and the standard deviation of the collision angle increase with
    the increases of both inertia and particle size.
    In order to examine the effect of concentration non-uniformity on the collision
    rate quantitatively, a new method has been developed to quantify the particle
    concentration non-uniformity using a concentration non-uniformity parameter. The
    concentration non-uniformity parameter is the ratio of the variance of particle
    concentration to the square of average particle concentration with inherent statistical
    noise removed. The effects of both particle size and inertia on the concentration non¬
    uniformity are quantitatively examined. The concentration non-uniformity will be greatly
    overestimated if the effect of finite particle size is not included. For sizeless particles
    with particle response time comparable to the turbulence Kolmogorov time scale, the
    measure of concentration non-uniformity parameter is inversely proportional to the cell
    length Ax, in which the concentration non-uniformity is measured, for small values of Ax.
    However, for finite-size particles, the concentration non-uniformity parameter reaches a
    maximum value instead of unbounded for sizeless particles. For finite-size particles, the
    maximum non-uniformity is reached at Ax/D = 2.0-3.5 while it becomes zero when Ax/D
    is close to one. Particle size is an important quantity that affects particle concentration
    non-uniformity. The maximum value of the concentration non-uniformity is reached
    when particle response time is close to the Kolmogorov time scale.
    The effects of the inertia on the collision velocity and concentration non¬
    uniformity are combined to predict the effect of particle inertia on the collision kernel. A
    unified expression of the collision kernel including both of these is proposed for small
    inertia. Good agreement is obtained between the simulation results and the unified
    expression for particles with small inertia.

    139
    The effect of particle size on the collision kernel in turbulence is examined for
    particles with zero inertia at a very dilute concentration limit. The normalized collision
    kernel a|,= an /[Rf¡(e/v)l/2] decreases as particle size increases. The theoretically
    predicted value for is only asymptotically observed in the small size and very dilute
    limit. At higher concentration, a * j is also observed to decrease with increasing particle
    size. As the mean concentration increases, a¡, decreases which suggests that the
    theoretically predicted value for a \j is only valid in a very dilute limit.
    The classical result of Smoluchowski for the collision rate of monodisperse
    particles in a laminar shear flow is shown to be inaccurate due to the inclusion of the self¬
    collisions. The present work extends Smoluchowski’s result by excluding the self¬
    collisions in the counting of collision pairs. The extended result for the collision rate is
    validated by a numerical simulation for particle collisions in a laminar shear flow at very
    low concentration.
    Finally, the effect of various collision models on the collision kernel is
    investigated. Theoretical prediction for collision rate at small size and zero inertia limit
    can be recovered by both “THROW AWAY” and “KEEP ALL” models, while the
    “HARD SPHERE” model cannot be used for predicting the collision rate at small inertia
    even in laminar shear flow due to an unphysically large number of multi-collisions
    between the same pair. Calculation of the collision kernel using the “HARD SPHERE”
    model requires extremely small time step for small inertia particles. Steady values of
    collision kernel can be reached only for very large inertia using “HARD SPHERE”
    model. In general, the collision kernel based on the “HARD SPHERE” model is larger
    than the results obtained by both the “THROW AWAY” and “KEEP ALL” models for
    particles with large inertia in Gaussian turbulence.

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    BIOGRAPHICAL SKETCH
    Kevin Cunkuo Hu (his original Chinese name: Cunkuo Hu) was bom on February
    25, 1963 in Ruian, Zhejiang province, People’s Republic of China. He earned his
    bachelor’s degree and master’s degree in aerospace propulsion, Beijing University of
    Aeronautics and Astronautics, Beijing, People’s Republic of China, in 1983 and 1986,
    respectively. During the period of studying for his master’s degree, he specialized in the
    area of gas-liquid two-phase flow. From 1986 to 1993, he worked as an aerospace engineer
    and was involved in the areas of two-phase flow, fuel atomization and combustion in the
    31st Research Institute of the Ministry of Aviation and Space Industry, Beijing, P.R. China.
    Since fall 1993, he has been working in the fields of fluid mechanics and particle science as
    a Ph.D. candidate in the Department of Aerospace Engineering, Mechanics and Engineering
    Science at University of Florida, Gainesville, Florida, United States of America.
    146

    I certify that I have read this study and that in my opinion it conforms to acceptable
    standards of scholarly presentation and is fully adequate, in scope and quality, as a
    dissertation for the degree of Doctor of Philosophy.
    Renwei Mei, Chairman
    Associate Professor of Aerospace Engineering,
    Mechanics and Engineering Science
    I certify that I have read this study and that in my opinion it conforms to acceptable
    standards of scholarly presentation and is fully adequate, in scope and quality, as a
    dissertation for the degree of Doctor of Philosophy.
    Associate Professor of Aerospace Engineering,
    Mechanics and Engineering Science
    I certify that I have read this study and that in my opinion it conforms to acceptable
    standards of scholarly presentation and is fully adequate, in scope and quality, as a
    dissertation for the degree of Doctor of Philosophy.
    Ulrich H. Kurzweg
    Professor of Aerospace Engineering, Mechanics
    and Engineering Science

    I certify that I have read this study and that in my opinion it conforms to acceptable
    standards of scholarly presentation and is fully adequate, in scope and quality, as a
    dissertation for the degree of Doctor of Philosophy.
    Roger Tran-Son-Tay
    Associate Professor of Aerospace Engineering,
    Mechanics and Engineering Science
    I certify that I have read this study and that in my opinion it conforms to acceptable
    standards of scholarly presentation and is fully adequate, in scope and quality, as a
    dissertation for the degree of Doctor of Philosophy.
    F. Klausner
    ¿sociate Professor of Mechanical Engineering
    This dissertation was submitted to the Graduate Faculty of the College of
    Engineering and to the Graduate School and was accepted as partial fulfillment of the
    requirements for the degree of Doctor of Philosophy.
    Karen A. Holbrook
    Dean, Graduate School