Lecture 4

Discrete Phase Modeling

14. 5 Release

Advanced Combustion Modeling

© 2012 ANSYS, Inc. March 12, 2013 1 Release 14.5

 Outline
Introduction
Discrete Phase Model in FLUENT
• Physical Processes and Coupling
• Injections/particle Types
• Incorporating turbulence
Combusting Particle
• Devolatilization and char combustion
• Setting up particle reaction model
Best Practices for DPM Reactive Flows
Appendix
• Primary/secondary atomization
• Breakup/coalescence models
• Case studies/Validations

© 2012 ANSYS, Inc. March 12, 2013 2 Release 14.5

 Introduction
Many engineering flows involve interaction
between a gas phase and lightly loaded
particles/droplets, such as:
Boiler

• Cyclone Separator
ICE
• Pulverized coal/oil fired boilers
• Internal combustion engine Cyclones

• Scrubbers, etc.

Scrubber
This interaction is computed by the Discrete
Phase Model (DPM) in FLUENT

Courtesy of Lurgi

© 2012 ANSYS, Inc. March 12, 2013 3 Release 14.5

 DPM in FLUENT
Trajectories of particles/droplets are computed in a
Lagrangian frame
• Exchange energy, mass, and momentum with Continuous phase flow
field calculation
Eulerian gas phase (2-way coupled system)
Discrete phase volume fraction be less than 10%
• Mass loading can be large particle trajectory
calculation
• No particle-particle interaction*
Turbulent dispersion modeled by
• Stochastic tracking Update continuous
• Particle cloud model phase source terms
Ideally suited for situations where particles enter
and leave computational domain
• No settling inside the domain

* except when models for collision/ coalescence are employed

© 2012 ANSYS, Inc. March 12, 2013 4 Release 14.5
 Physical Processes of Coupled Systems
• Particle exchanges mass, momentum and energy with continuum phase
• Particle trajectory calculation dup
dt
  
 FD u  up  gx  p     p  Fx
• Heat transfer
dTp dm p
mp c p  hAp T  Tp   h fg  S x
dt dt
• Mass transfer
– Evaporation/boiling, reactions (homogeneous, heterogeneous)

Particle Type Description

Massless No drag; used for Residence Time Distribution studies
Inert Inert heating/cooling
Droplet heating/evaporation/boiling
Multicomponent Multi component evaporation
Combusting heating/devolatilization/heterogeneous surface reaction

© 2012 ANSYS, Inc. March 12, 2013 5 Release 14.5

 Coupling Between Phases
• One-way coupling : Particle tracking done as a post processing step
• Two-way coupling : Both gas and particulate phases affect each other
• In combustion systems, 2-way coupling of discrete and continuous phases
– Fluid phase influences particulate phase via drag, heat transfer and turbulence
transfer
– Particulate phase influences fluid phase via source terms for mass, momentum,
and energy equations
– Examples include
• Inert particle heating and cooling
• Droplet evaporation/boiling control volume
• Devolatilization
• Char combustion particle trajectory

Mass,
momentum,
energy

Two-way coupling
© 2012 ANSYS, Inc. March 12, 2013 6 Release 14.5
 Particle states definitions
DPM track particles in Lagrangian frame
Tracked particle enters a cell
• The Entry State properties get updated to the values at the exit from the
Previous Cell
The particle is tracked through the Current Cell
• The Current Properties are updated every particle time step
Intermediate values are lost
If the particle crosses a Cell Boundary, the particle is brought back to a position
on the boundary
• Particles
Previous – Move
Cell – Heat up and cool down
– Loose mass
Entry Particle State – Change composition
Current
Initial Particle State Cell Current Particle State
© 2012 ANSYS, Inc. March 12, 2013 7 Release 14.5
 Possible particle tracking options

• Steady particle tracking

with steady state solution
• Unsteady particle tracking
with steady flow
• Unsteady particle tracking
with unsteady flow
– Same time step size for both
– Different time step size for
particles and continuous phase

© 2012 ANSYS, Inc. March 12, 2013 8 Release 14.5

 Steady particle tracking with steady flow
• DPM calculation is done after each Nth
continuous phase iteration
• Each particle track is calculated from N
the injection point till the final state
(also called fate)
• Tracking parameters
– Max. number of steps and
– Length scale or step length factor
• Integration time step is calculated as
– If length scale is specified
L
t 
Up  Uc
– If step length factor is specified
t *
t 
λ
• Calculations for a given particle will
continue till it escape from the
domain/reaches other fates/max no t*  Estimated time required for
of steps are reached particle to traverse the current cell
  Step length factor
© 2012 ANSYS, Inc. March 12, 2013 9 Release 14.5
 Unsteady particle tracking with steady
flow
• DPM calculation is done after
each Nth continuous phase N
iteration tp

• Each particle is ADVANCED J

from it's last position in the
previous DPM calculation
– For specified particle time step
size (tp )
• With the integration time step
calculated from tracking
parameters
– For J number of time steps

© 2012 ANSYS, Inc. March 12, 2013 10 Release 14.5

 Unsteady particle tracking with Unsteady
flow: Different time step size for particles and continuous phase
• DPM calculation is done
– At the beginning of each flow
time step N
tp
– And also after each Nth
continuous phase iteration
within the same time step
• During each DPM calculation,
particles are ADVANCED from
it's last position in the previous
flow time step
– Till the position at the end of
current flow time step
– With specified particle time
step size (tp )
– Therefore, number of dpm
time steps in a flow time step =
tflow / tp
© 2012 ANSYS, Inc. March 12, 2013 11 Release 14.5
 Unsteady particle tracking with Unsteady
flow: Different time step size for particles and continuous phase

Particle injection
• Particle Time Step
– Injecting particles in each
particle time step
– Integration time step is the
specified particle time step
• Fluid Time Step
– Injecting particle in each flow
time step
– Integration time step is the
specified particle time step

© 2012 ANSYS, Inc. March 12, 2013 12 Release 14.5

 Unsteady particle tracking with Unsteady
flow: Same time step size for particles and continuous phase

• DPM calculation is done

– At the beginning of each flow time N
step
– And also after each Nth continuous
phase iteration within the same
time step
• During each DPM calculation,
particles are ADVANCED from it's
last position in the previous flow
time step
– Till the position at the end of
current flow time step
– With flow time step size (tflow )
– Therefore, number of dpm time
steps in a flow time step = 1
© 2012 ANSYS, Inc. March 12, 2013 13 Release 14.5
 Source calculations
Effect of under-relaxation factor (URF)
• DPM source terms updated at every
particle iteration
– No of particle iterations required for
achieving full source term increases with
decrease in URF
– Must use URF of 1 if only one particle
iteration is done in a time step
• Calculations may not be stable in some cases
Effect of update DPM Sources Every Flow
Iteration
• Useful for unsteady calculations
• Particle source terms are calculated every
DPM iteration and applied gradually
during every continuous phase iteration

© 2012 ANSYS, Inc. March 12, 2013 14 Release 14.5

 Injections single

• Injection panel provides initial information about

– Location – Diameter group
– Velocity – Composition
– Temperature – Flow rate
– Start time – Stop time cone

• Several types available:

– Direct specification of initial conditions
• Single, group, surface, cone, etc surface

– Automated computation of initial conditions based on the

injector geometry
• These are called Atomizer Models
• Specifically to characterize liquid sprays
• More details in the Appendix

© 2012 ANSYS, Inc. March 12, 2013 15 Release 14.5

 Turbulent Dispersion Models
When particles enter a turbulent eddy, they try to follow it for
the time they are crossing the eddy.
This effect leads to lateral dispersion which has to be considered
in modeling
Two approaches are available:
• Discrete random walk model (Stochastic Tracking)

• Particle cloud model (Cloud Tracking)

© 2012 ANSYS, Inc. March 12, 2013 16 Release 14.5

 Stochastic vs. Cloud Tracking
• Stochastic tracking:
– Accounts for local variations in flow properties such as temperature,
velocity, and species concentrations
– Sufficient number of tries required for smooth distribution of the
source terms
– Recommended for use in complex geometry
• Cloud tracking:
– Local variations in flow properties (e.g. temperature) get averaged out
inside the particle cloud
– Smooth distributions of particle concentrations and coupling source
terms
– Each diameter size requires its own cloud trajectory calculation

© 2012 ANSYS, Inc. March 12, 2013 17 Release 14.5

 Combusting Particles

© 2012 ANSYS, Inc. March 12, 2013 18 Release 14.5

 Combusting Particle Models

Four devolatilization models

• Constant rate model (default)
• Single kinetic rate model
• Two competing rates model (Kobayashi model)
• CPD model
Five char burnout models
• Diffusion-limited rate model (default)
• Kinetics/diffusion-limited rate model
• Intrinsic model
• CBK model
• Multiple surface reactions model

© 2012 ANSYS, Inc. March 12, 2013 19 Release 14.5

 Modeling Particle Reaction

For coal and other solid or liquid fuels, often only the fuel analysis
is known
• Proximate analysis (volatile, char, ash and moisture mass fraction)
• Ultimate analysis (elemental mass fraction C, H, O, S, N)
Based on this data we can set up the case in Fluent using the
species transport model or the mixture fraction method (one
or two mixture fractions)
Both approaches are valid and have been widely used
• The species transport model is more general and flexible if any
additional stream (different fuels or oxidizer) or specific reaction
(gasification) has to be considered

© 2012 ANSYS, Inc. March 12, 2013 20 Release 14.5

 Modeling Particle Reaction
• From the coal data the user will calculate the
following input:
– Enthalpy of formation and molecular weight of the
volatile
– Stoichiometry of the gas phase reactions
• Coal calculator
– Recommended option to set up the case
– Not recommended if the mixture/injection have
already been set up
• Coal moisture - Wet Combustion option

© 2012 ANSYS, Inc. March 12, 2013 21 Release 14.5

 Modeling Coal Combustion with Non-Premixed
Combustion Model

• When coal is the only fuel in the system, we can model

the coal using a single mixture fraction
– The fuel composition includes both volatiles and char
– The fuel stream composition is generally defined by using the empirical
method (input of atom fractions)
• Less accurate compared to two mixture fractions
• Runs and converges faster

© 2012 ANSYS, Inc. March 12, 2013 22 Release 14.5

 Modeling Coal Combustion with Non-Premixed
Combustion Model

• Coal can also be modeled using two mixture fractions

– One fraction represents char and the other, volatiles
– Char stream represented as solid Carbon, C(s)
– Volatile stream generally defined by empirical method
– More accurate than single mixture fraction for both volatile and char
• When coal is used with another fuel (gas or liquid)
– One mixture fraction for both coal volatiles and char
– Second mixture fraction for the second (gas or liquid) fuel
– Limit is 2 mixture fractions
• The two-mixture-fraction model is computationally
expensive
– Full tabulation option can reduce the run time but requires more disk
space
© 2012 ANSYS, Inc. March 12, 2013 23 Release 14.5
 Best Practice for DPM reactive flows

© 2012 ANSYS, Inc. March 12, 2013 24 Release 14.5

 Best Practice Guidelines

• Fuel Injection
– Cone type for liquid fuel with enough number of streams to define the
spray
– Surface with Rosin Rammler distribution for solids (coal, biomass, etc.)
– User can also introduce fuel using an external file (File injection)
• Selecting Devolatilization/Char Combustion models*
– Use 1 or 2 step Kobayashi devol model with default rates
– Use kinetic/diffusion char combustion model with default rates
– Multiple char model can be used for more than one surface reactions
such as needed in modeling solid fuel gasification
• Solid fuel reaction
– Use Species transport with eddy dissipation/finite rate model
• Coal Calculator will setup most of the case based on fuel analysis

*If rate data is available for your specific fuel, then it is better to enter that instead of using the defaults.

© 2012 ANSYS, Inc. March 12, 2013 25 Release 14.5

 Best Practice Guidelines

• Combusting particle properties

– Make sure that the volatile and char fraction are specified on dry basis
– If char is oxidized to CO, make sure that you change the burnout
stoichiometry ratio and Heat of reaction for burnout, since the default
values for these are for char oxidizing to CO2
– Wet combustion Liquid fraction is on volume basis
– For the multiple char reaction model, the solid species mass fractions
are defined in the injection panel
• Evaporating particle properties
– Make sure that the following properties are properly prescribed:
• Saturation vapor pressure
• Binary Diffusivity
• Latent Heat
• Boiling Point
• Evaporation Temperature (start of evaporation law)
© 2012 ANSYS, Inc. March 12, 2013 26 Release 14.5
 Best Practice Guidelines
•Solution Controls
– The default under relaxation are fine for simple cases
– For more complex cases the default URF can be too aggressive and the
solution can become unstable
– The effect of under relaxation is highly non-linear
• Underrelax density when using the mixture-fraction PDF model (0.7)
• Underrelax velocity for high buoyancy flows
• Underrelax species to start up the solution (0.9)
• Once solution is stable, attempt to increase species, energy, mixture and
radiation URF’s to 1

© 2012 ANSYS, Inc. March 12, 2013 27 Release 14.5

 Best Practice Guidelines
•Solution Strategy
– First, converge the non reacting flow using first order discretization
– Activate particle tracking; run with 30+ flow iterations every dpm tracking
• Use DPM URF = 0.1 – 0.2
– Patching of high temperature/species may be needed to start reactions
– Enable radiation when temperature field and flame shape have been
established
– Solve until mass/energy balance is obtained and solution monitors stabilize
– If particle radiation interaction is to be included, use the DO model
– Activate this interaction after flow and thermal solution is converged
• To improve convergence in cases with particle-radiation interactions:
• Lower the DPM URF (DPM URF 0.1 or lower)
• Increase the number of flow iterations per DPM to 50 – 60

© 2012 ANSYS, Inc. March 12, 2013 28 Release 14.5

 Best Practice Guidelines
• Some more general notes on convergence
– Often the problem in converging a coal combustion simulation is related to
the high source term generated in certain cells
• To distribute these sources more evenly:
• Increase the number of DPM stochastic tries
• Note that this will increase the CPU time
• Increase the number of gas phase iterations per DPM iteration
– Residuals should be less than 10-3 except for Energy, radiation and mixture
fraction, which should be less than 10-6
– The mass and energy flux reports must balance
– Monitor variables of interest (e.g. mean temperature at the outlet)
– Solution is stable and not changing if the case is run further
– Ensure contour plots of field variables are smooth, realistic and steady
– Ash tracking may increase the dpm tracking time; ash can be removed via a
udf

© 2012 ANSYS, Inc. March 12, 2013 29 Release 14.5

 Best Practice Guidelines
• Some new features in 14.5
– Node based smoothing
– Node-based averaging shares a
DPM parcel’s effects between
several cells, accounting for
parcels ability to partially occupy
several cells simultaneously

Volume fraction
Volume fraction node based
standard average average

© 2012 ANSYS, Inc. March 12, 2013 30 Release 14.5

 Appendix
Atomizer models
Breakup and Coalescence models
Multiple char reaction model
Examples & validations

© 2012 ANSYS, Inc. March 12, 2013 31 Release 14.5

 Evaporating Liquid Fuel Droplets

© 2012 ANSYS, Inc. March 12, 2013 32 Release 14.5

 Evaporating Particle Models

• Diffusion Controlled
– For low evaporation rates
• Convection/Diffusion Controlled
(default)
– For higher evaporation rates
• Both models require accurate
specification of the saturation
pressure and diffusion
coefficients.

© 2012 ANSYS, Inc. March 12, 2013 33 Release 14.5

 Evaporating Particle Models
• Many sub models are
available in the DPM
panel
– Temperature dependent
latent heat option
– Breakup of droplets

© 2012 ANSYS, Inc. March 12, 2013 34 Release 14.5

 Plain-Orifice Atomizer

Pipe with a round hole

Three regimes
• Single phase Liquid Jet
• Cavitating
• Flipped Orifice Walls Downstream Decreasing
Gas
cavitation
Inputs: parameter
• Atomizer location Vapor
p1  pv
• Axis (3D) Liquid Jet K
• Mass flow rate Vapor
p1  p2
• Start and stop times Orifice Walls
Downstream
Gas
• Vapor pressure
• Inner diameter
• Orifice length
• Inlet corner radius of curvature
Liquid Jet
• Spray angle Constant A
• Azimuthal start and stop angles (3D) Downstream
Orifice Walls
Gas

© 2012 ANSYS, Inc. March 12, 2013 35 Release 14.5

 Pressure Swirl Atomizer
Implemented Linearized Instability Sheet Atomization (LISA) model
of Schmidt et al. (1999)
Assumes that KH waves break the sheet up into ligaments which
then break up into droplets due to varicose instability

• User Inputs:
• Atomizer location
• Axis (3D)
• Mass flow rate
• Start and stop times
h0 • Inner diameter
Lb • Spray half angle
• Upstream pressure
• Sheet constant
h • Ligament constant
• Azimuthal start and
dL • stop angles (3D)

d0

© 2012 ANSYS, Inc. March 12, 2013 36 Release 14.5

 Air-Blast Atomizer
Additional air is directed through the nozzle, leading to smaller droplet
diameters
Modeled as a variation of pressure-swirl atomizer

•User Inputs:
• Atomizer location
Gas Flow Initial
Angle
• Axis (3D)
• Mass flow rate
Inner Diameter • Start and stop times
Outer
Diameter • Inner diameter
Liquid Flow
• Outer diameter
• Spray half angle
• Maximum relative velocity
•between central air and sheet
• Sheet constant
• Ligament constant
• Azimuthal start and stop
• angles (3D)

© 2012 ANSYS, Inc. March 12, 2013 37 Release 14.5

 Flat-Fan Atomizer
Liquid enters as a flat sheet
Sheet breakup is taken from pressure-swirl atomizer

normal vector • User Inputs:

• Atomizer location
2 • Axis (3D)
• Normal (3D)
virtual
origin
center point • Mass flow rate
• Start and stop times
• Spray half angle
• Orifice width
• Flat fan sheet constant

© 2012 ANSYS, Inc. March 12, 2013 38 Release 14.5

 Effervescent Atomizer
Super-heated or very hot liquid is discharged
Liquid is evaporating rapidly when leaving nozzle
A dense liquid core surrounded by a shroud of smaller droplets
•User Inputs:
• Atomizer location
• Axis (3D)
• Mass flow rate
• Start and stop times
• Inner diameter
• Vapor pressure
• Mixture quality
m •Mass fraction of
u superheated
 l Cct A •injected liquid that
vaporizes
d max  d Cct
• Saturation temperature
 
2
• Dispersion constant
 
d 0  d max e  S  • Maximum Half Angle
• Azimuthal start and stop angles
(3D)
© 2012 ANSYS, Inc. March 12, 2013 39 Release 14.5
 Secondary spray models
Several advanced secondary spray models are available:
• Collision and Coalescence Model (O’Rourke)
• Taylor Analogy Breakup (TAB) Model
• Kelvin-Helmholtz (Wave) Breakup Model
• KHRT Model
• SSD Model
• Dynamic Drag Model for Distorting Drops
– Since droplets do deform, it is important to use the right drag law
These models are fully compatible with the primary atomization
models.

© 2012 ANSYS, Inc. March 12, 2013 40 Release 14.5

 Collision and Coalescence Model
Particles move around and may collide with each other
The mean expected number of collisions between one drop in a
parcel 1 with all droplets in parcel 2 is calculated from
(O’Rourke, 1981)
The probability distribution for the number of collisions of a drop in
parcel 1 with all the drops in parcel 2 is Poisson Distribution

r2

r1

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 Collision and Coalescence Model (Cont’d)
What happens after collision?
• Droplets may bounce or coalesce
Head-on collision leads to coalescence
Oblique collisions tend to bouncing depending on the Weber number and a
critical offset
r2

b > bcrit => bouncing r1

The properties of the coalesced drops are determined from conservation laws
while momentum conservation determines the velocity of grazing droplets

Note: Model is applicable only for We < 100  pu 2d p

We 
Only one collision per time step assumed 

© 2012 ANSYS, Inc. March 12, 2013 42 Release 14.5

 Taylor Analogy Breakup (TAB) Model
Raleigh-Taylor’s analogy between an oscillating, distorting droplet
and a spring mass system (O’Rourke, 1981):
• Surface tension  Spring restoring force
• Drag  External force
• Droplet viscosity  Damping force

C F  g u 2 Ck  Cd  l
y   y  y
Cb l r 2
l r 3
l r 2

Droplet breaks up if distortion exceeds some level, then, energy

balance is used to determine child drop size (number of drops
from mass conservation)
Child droplets have a velocity component normal to the parent
drop velocity

© 2012 ANSYS, Inc. March 12, 2013 43 Release 14.5

 TAB Model (Cont’d)
After breakup, the number of DPM parcels remains constant,
number of particles in a parcel increases and diameter
decreases
Valid for low Weber number sprays (We<100)
Validation done by comparing to the spray bomb experiments of
Hiroyasu

10

8
Penetration[cm]

6
0.1MPa
1.1MPa
4 3MPa
5MPa
TAB 0.1MPa
2 TAB 1.1MPa
TAB 3.0MPa
TAB 5.0MPa
0
0 1 2 3 4 5 6
Time[ms]
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 Wave Breakup Model

Aerodynamic shear causes

waves on droplet, unstable
Kelvin-Helmholtz waves
grow and small droplets
stripped off
Reitz (1987) derived from a jet
stability analysis the
maximum growth rate and
corresponding wavelength
The size of the child droplets is
proportional to the fastest
growing wavelength

© 2012 ANSYS, Inc. March 12, 2013 45 Release 14.5

 Wave Model (Cont’d)
When a prescribed mass of droplets has been shed, a new particle forms
Applicable for high Weber number sprays
Validation done by comparing to experimental spray bomb data

10

8
Penetration[cm]

0.1MPa
4 1.1MPa
3MPa
5MPa
2 Wave 0.1 MPa
Wave 1.1 MPa
Wave 3.0 MPa
0 Wave 5.0 MPa
0 1 2 3 4 5
Time[ms]

© 2012 ANSYS, Inc. March 12, 2013 46 Release 14.5

 Stochastic Secondary Droplet (SSD)
SSD breakup methodology provides a statistically realistic
model for simulating high Weber number sprays under
diesel conditions
• Parameters for the size distribution are based on local conditions
• Liquid injected into the domain is represented by blobs with a known
size (set by the user).
• The breakup model predicts the time at which breakup occurs, the
number and properties of the new drops.
• Drops larger than a critical radius, rc , are subject to breakup:
Wecr l
rc 
 g urel
2

• The breakup time is defined as:

l r
tbu  B
 g urel
Reference:
• Apte, et.al., “LES of atomizing spray with stochastic modeling of secondary breakup”, IJMF
29, 2003, pp 1503-1522
© 2012 ANSYS, Inc. March 12, 2013 47 Release 14.5
 Stochastic Secondary Droplet (SSD)

Wecr l
rc 
 g urel
2

l r
tbu  B
 g urel

Average NP for
daughter parcels

© 2012 ANSYS, Inc. March 12, 2013 48 Release 14.5

 Hiroyasu 2D case

Preliminary results

• Spherical drag law

• Large NP (1000)
• Medium mesh
• No Collision
• No Coalescence
• Data well predicted
• Penetration defined
as point where 95%
of the mass is
accounted for in the
axial direction

© 2012 ANSYS, Inc. March 12, 2013 49 Release 14.5

 Dynamic Drag Model
Droplets experience rapid deceleration in sprays; they are distorted
and flattened by the surrounding gas
Drag on a disk is much higher than on a sphere. In order to model
this effect, the coefficient of drag is interpolated between the drag
of a sphere and a disk, based on distortion

Cd  Cd ,sphere1  2.632 y 

This distortion, y , can be predicted using the TAB model, described

above.

Aerodynamic
forces, distortion

y=0 y=1
© 2012 ANSYS, Inc. March 12, 2013 50 Release 14.5
 Multiple Char Reactions Model
• Model multiple particle surface reactions
– Example
C(s) + O2 -> CO2
C(s) + H2O -> H2 + CO
• Theory
– At particle surface, diffusion = reaction

D' (C  Cs )  RCsN

where N is the apparent reaction order, subscript s denotes surface

• Diffusion

D'  D / d p (T  Tp ) / 2 
0.75

where dp is the particle diameter, T is temperature and D is the

diffusion rate constant

© 2012 ANSYS, Inc. March 12, 2013 51 Release 14.5

 Multiple Char Reactions Model
• Reaction

R  Ap h Y p R '
N
  E / RT  R' 
R'  T Ae  pn  
 D ' 

• Ap is the particle surface area

• Yp is the mass fraction of the surface species in the particle
• h is the effectiveness factor
• pn is the bulk partial pressure of the gas species
– Inputs
• For each reaction
– Diffusion rate constant, D
– Effectiveness factor, h
– For more that one gas phase reactant, the diffusion limited species (largest
concentration gradient)

© 2012 ANSYS, Inc. March 12, 2013 52 Release 14.5

 Case Study: Coal Combustion
• ROFA® Case Study
– 350 MW T-fired boiler
– Sub-bituminous coal
– NO reduction 50%
– Other improvements:
• Urea injection
• Limestone injection

*Courtesy of MobotecUSA

© 2012 ANSYS, Inc. March 12, 2013 53 Release 14.5

 Case Study: Coal Combustion
• ROFA® Case Study
• Maximum temp baseline
=3300F
• Same in ROFA but more
evenly distributed in the
lower furnace
• Flames are not attached
to the coal nozzle
• ROFA jets penetrate deep

*Courtesy of MobotecUSA

© 2012 ANSYS, Inc. March 12, 2013 54 Release 14.5

 Case Study: Coal Combustion
• ROFA® Case Study
• Oxygen distribution
• Sub-stoich in ROFA case
• Mixing is the key
• Good mixing helps in O2 Mass
burning the CO in the upper Fraction
furnace

*Courtesy of MobotecUSA

© 2012 ANSYS, Inc. March 12, 2013 55 Release 14.5

 Case Study: Coal Combustion
• ROFA® Case Study
• CO distribution
• Lots of CO in the lower
furnace with ROFA
• ROFA jets help in good
mixing of O2 and CO to
burn the CO completely CO (ppm)
• Typical CO levels are 20
ppm

*Courtesy of MobotecUSA

© 2012 ANSYS, Inc. March 12, 2013 56 Release 14.5

 Case Study: Coal Combustion
• ROFA® Case Study
• NO in ppm
• NO very low throughout the
furnace with ROFA
• Due to the sub-stoich
conditions NO (ppm)
• Even with more oxygen
from ROFA jets, the
increase in NO is not
substantial

*Courtesy of MobotecUSA

© 2012 ANSYS, Inc. March 12, 2013 57 Release 14.5

 Case Study: Coal Combustion

*Courtesy of MobotecUSA

© 2012 ANSYS, Inc. March 12, 2013 58 Release 14.5

 Case Study 2: Spray in a Port-Injection Engine
• Fluent dynamic mesh is used to model the moving valve
• DPM and spray model is used in conjunction with the dynamic mesh
model

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 Spray Images

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 Wallfilm Images

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 Wallfilm Images

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 Case Study 3: Spray in a Diesel Engine
• A Caterpillar engine is used to demonstrate the spray in a direct-
injection diesel engine
– A 60 degree sector is used due to the symmetric geometry and injections
• Fluent dynamic mesh is used to model the moving piston
• DPM and spray model is used in conjunction with the dynamic mesh
model
• Particle and vapor are plotted together

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 Case Study 3: Spray Images

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 Case Study 3: Spray in a Diesel Engine

Contour of Fuel Mass-fraction Temperature contour

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 Case Study 1- Spray Modeling in a Diesel
injection air inlet
(T = 710 K,
p = 5 MPa)
quartz glass
Injector: window pressure
chamber
• proprietary common-rail
injector
– 7 holes
• Injection pressure = 1600 bar incident
beams

• Orifice diameter = 0.167 mm

liner
• Injection profile is given
100 mm outlet
Spray chamber:
• Air flow velocity = 0.05 m/s
• Air temperature = 710 K
• Air pressure = 50 bar
Fuel: EN 590 summer diesel fuel

• SAE 2006-01-0241, Adjustment and Verification of Model Parameters for Diesel Injection CFD Simulation
– Prof. Dr. Winfried Waidmann, Fachhochschule Aalen, Aalen, Germany
– Dr. Andreas Boemer, DEUTZ AG, Köln, Germany
– Dr. Markus Braun, Fluent Deutschland GmbH, Darmstadt, Germany

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 Modeling Setup by Authors

Models Parameters Comments

Solid cone injection 10 degree cone half angle Primary break-up, value metered from the
shadowgraphs
KH-RT breakup model B0 = 0.61, B1 = 18, C3 = 2.5, c Secondary break-up
= 30
Droplet collision Default Necessary in combination with the secondary
break-up model
Initial droplet diameter 0.167 mm Identical to nozzle diameter
Fuel injection temperature 330 K 50 K below measured nozzle temperature
Aerodynamic drag Dynamic drag coefficient Includes droplet deforming due to
aerodynamic forces
Injection velocity Variable, max. 430 m/s Calculated from measured time dependant
mass flux (Figure 2)
Turbulent droplet dispersion Default Turbulent tracking of the droplets
Number of injected particle streams 500 parcels per time step Distributes the discrete phase source terms
onto the flow
Time stepping 50 ms Corresponds to 0.5 degree of crank angle
Turbulence Standard k, e-model Turbulence model not varied
Fuel N-Heptane To represent the diesel fuel

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 Modeling Setup (Modifications)

Models Parameters Comments

Solid cone injection 10 degree cone half angle Primary break-up, value metered from the
shadowgraphs
KH-RT breakup model B0 = 0.61, B1 = 18, C3 = 2.5, Secondary break-up
c = 30
Droplet collision Default Necessary in combination with the
secondary break-up model
Initial droplet diameter Sqrt(C_D) * 0.167 mm The discharge coefficient needs to be included
Fuel injection temperature 330 K 50 K below measured nozzle temperature
Aerodynamic drag Dynamic drag coefficient Includes droplet deforming due to
aerodynamic forces
Injection velocity 430 / (C_D * A_nozzle * The discharge coefficient needs to be included
rho_liq)
Turbulent droplet dispersion Default Turbulent tracking of the droplets
Number of injected particle 500 parcels per time step Distributes the discrete phase source terms
streams onto the flow
Time stepping 50 ms Corresponds to 0.5 degree of crank angle
Turbulence Standard k, e-model Turbulence model not varied
Fuel C12H26 A better representation for spray modeling

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 Results: Shape of the Spray
Experimental

Simulation

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 Results: Penetration Length

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 Results: Drop Size Distribution

Measuring planes

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