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Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model
using
Hoon Lee
Center for Transportation Research
Argonne National Laboratory
Orlando, Florida, USA March 19, 2013
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Objective
Background
Diesel Engine & DPF
Two Approaches in Filtration Modeling
Theoretical Analysis
Pressure Drop Model
Soot Filtration Model
Experiment
Model Setup
Domain Setup & Meshing
Physical Assumptions
& Boundary Conditions
Filtration Algorithm
Cell Value Localization
Built-in Function Utilization
Recursive Operation
Computing Environment
Model Results
Channel-flow Profiles & Pressure Drop
Wall-flow Rearrangements
Local Soot Mass Deposited
Local Collection Efficiency
Soot Cake Layer Properties
Summary
Future Work
Acknowledgement
Outline
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Objective
To study quantitative analysis of soot filtration processes in DPF
(diesel particulate filter) systems by developing a three dimensional
model using a commercial CFD package, STAR-CCM+.
To analyze the time evolution and spatial distributions of local
filtration parameters – e.g. porosity, soot mass, collection efficiency, soot
cake profile - for each filtration period, along with evaluations of flow
properties and pressure drop characteristics across the DPF.
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Filtration
Algorithm
Model
Results Summary
Future
Work Acknowledgement
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+ 4
Background (1/2)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Diesel Particulate Filter (DPF)
Highway diesel vehicles are required to meet
the stringent PM emission standards.
Physically trap (Filtration), and chemically
oxidize PM (Regeneration) periodically.
Uncontrolled regeneration may occur which
causes system failure due to highly exothermic
reaction.
Prediction of particulate deposition
in the porous filter wall is important.
Plug
Porous
wall
Diesel Engine
Pros: High Thermal Efficiency, Fuel Economy,
Torque, Low Emission (CO, UHC)
Cons: Noise, Vibration, $, Emission (PM, NOX)
NOX reduction by SCR and/or EGR
PM needs to be reduced in both mass and
number... ☞ Arising issue for GDI engines, too!
PM and NOX are the major emissions
regulated. (USA: EPA Tier 4, EU: Euro 5 / 6)
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Two Approaches in Filtration Modeling
H. Lee et al. 2012 DEER Conference
H. Lee et al. SAE 2013-01-1583
Background (2/2)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Lagrangian: Qualitative analysis by tracking
particle trajectories with appropriate B.C.s.
S. Bensaid et al. Chem. Eng. J., 2009.
Chem. Eng. Sci., 2010.
P. Tandon et al. Chem. Eng. Sci., 2010.
H. Kato et al. Int. J. Engine. Res., 2011.
Eulerian: Quantitative analysis of soot filtration
process by coupling specific filtration algorithms.
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Pressure Drop Model
Δ𝑃 = Δ𝑃𝑝𝑜𝑟𝑜𝑢𝑠 𝑤𝑎𝑙𝑙 + Δ𝑃𝑠𝑜𝑜𝑡 𝑐𝑎𝑘𝑒 + Δ𝑃𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛 + Δ𝑃𝑐𝑜𝑛𝑡/𝑒𝑥𝑝𝑎𝑛𝑠
=𝜇
𝑘𝑜𝑢𝑤𝑤𝑠 + 𝛽𝜌𝑢𝑤
2𝑤𝑠 +𝜇
𝑘𝑠𝑜𝑜𝑡 𝑢(𝑥)𝑤
0
𝑑𝑥 +𝜇𝐹
3𝑎2𝑈𝑜,𝑖𝑛𝐿𝜉 +
𝜇𝐹
3𝑎2𝑈𝑜,𝑜𝑢𝑡𝐿𝜉 + ζ𝑐𝑜𝑛𝑡
𝜌𝑢2
2+ ζ𝑒𝑥𝑝
𝜌𝑢2
2
Each velocity term is defined as,
𝑢𝑤 =𝑄𝑜𝐴𝑓𝑖𝑙𝑡
=𝑈𝑜𝐴𝑜4𝑎𝐿
=𝑈𝑜𝑎
2
4𝑎𝐿=𝑈𝑜𝑎
4𝐿
𝑢(𝑥)𝑑𝑥𝑤
0
= 𝑄𝑜
𝐴𝑓𝑖𝑙𝑡(𝑥)
𝑤
0
𝑑𝑥 = 𝑄𝑜
4( 𝑎 − 2(𝑤 − 𝑥 )𝐿
𝑤
0
𝑑𝑥 =𝑄𝑜8𝐿
ln𝑎
𝑎 − 2𝑤
𝑈𝑜,𝑖𝑛 =𝑄
𝐴𝑜𝑖𝑛𝑙𝑒𝑡
=𝑄
𝜋𝐷2
4 12 12 (𝑎 − 2𝑤)2
(𝑎 + 𝑤𝑠)2
=16𝑄
𝜋𝐷2σ(𝑎 − 2𝑤)2
𝑄𝑜 =𝑄𝐴𝑜
𝐴𝑜𝑖𝑛𝑙𝑒𝑡
=𝑄(𝑎 − 2𝑤)2
𝜋𝐷2
4 12 12(𝑎 − 2𝑤)2
(𝑎 + 𝑤𝑠)2
=16𝑄(𝑎 + 𝑤𝑠)
2
𝜋𝐷2
𝑢 =𝑄
𝑁𝑎2
: Clean filter condition for Ao
𝑄𝑜, 𝑈𝑜, 𝑢𝑤 , 𝑢(𝑥) 𝑄,𝑈 Half-cut
sample for
experiment
a
w
ws
a - 2w
Theoretical Analysis (1/2)
: Soot cake
thickness (w) for Ao
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Darcy-Forchheimer’s Law
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Soot Filtration Model
Unit Collector Mechanism
Collection Efficiency
𝒅𝒄𝟎 =𝟑(𝟏 − 𝜺𝟎)
𝟐𝜺𝟎𝒅𝒑𝒐𝒓𝒆
𝒅𝒄𝟎𝟑
𝒃𝟑= 𝟏 − 𝜺𝟎
Diffusional
Deposition (𝜼𝑫)
Flow-line
Interception (𝜼𝑹)
𝑑𝑐(𝑖, 𝑡) = 23
4𝜋
𝑚𝑙𝑜𝑐𝑎𝑙(𝑖, 𝑡)
𝜌𝑠𝑜𝑜𝑡,𝑤𝑎𝑙𝑙+
𝑑𝑐02
313
𝜀 𝑖, 𝑡 = 1 −𝑑𝑐(𝑖, 𝑡)
𝑑𝑐0
3
(1 − 𝜀0)
𝑘 𝑖, 𝑡 = 𝑘0𝑑𝑐(𝑖, 𝑡)
𝑑𝑐0
2𝑓(𝜀 𝑖, 𝑡 )
𝑓(𝜀0)
𝛷 𝑡 =𝑑𝑐(𝑖, 𝑡)
2 − 𝑑𝑐02
𝛹 𝑏 2 − 𝑑𝑐02
𝜂𝐷 = 3.5 𝑔 𝜀 𝑃𝑒−23= 3.5 𝑔 𝜀
𝑈𝑖𝑑𝑐𝐷
−23
𝜂𝑅 = 1.5 𝑁𝑅2 𝑔 𝜀
3
1 + 𝑁𝑅3−2𝜀3𝜀
𝜂𝐷𝑅 = 𝜂𝐷 + 𝜂𝑅 − 𝜂𝐷𝜂𝑅
𝐸 𝑖, 𝑡 = 1 − 𝑒𝑥𝑝 −3𝜂𝐷𝑅 1 − 𝜀 𝑖, 𝑡 Δ𝑦
2𝜀(𝑖, 𝑡) 𝑑𝑐(𝑖, 𝑡)
Key parameters for CFD code (UDF) to specify region properties
collector
collector
Particulates
Key parameters for User Code to obtain local soot mass (mw)
Theoretical Analysis (2/2)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Clean Filter Test
Soot Loading Test
2” x 6” cordierite DPF Test Results
ko=2.30E-13 ko=1.77E-13
PM Size Distribution PM Mass Concentration
SMPS TEOM
Experiment
Clean filter permeability (ko) and particle-laden flow properties are directly measured. Soot cake permeability (ks,cake), particle density (ρs), and soot cake porosity (εs,cake) can be estimated. Packing densities (ρs,wall, ρs,cake) are assumed.
Ready to model
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
0.0E+00
1.0E-13
2.0E-13
3.0E-13
4.0E-13
5.0E-13
6.0E-13
7.0E-13
8.0E-13
0.0E+00 2.0E-03 4.0E-03 6.0E-03
Perm
eab
ilit
y,
ko [
m2]
0.0
0.3
0.6
0.9
1.2
1.5
1.8
2.1
2.4
0.0E+00 2.0E-03 4.0E-03 6.0E-03
Pre
ssu
re D
rop
[kP
a]
Vol. Flow Rate [m3/s]
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
0 10 20 30 40 50 60
Pre
ssu
re D
rop
[kP
a]
Time [min.]
● Low flow rate (7.4 SCFM): 200CPSI
● High flow rate (9.0 SCFM): 200CPSI
● 100 CPSI (ws=17 mils)
● 200 CPSI (ws=12 mils)
◆ Analytical Solution
◆ 100 CPSI (ws=17 mils)
◆ 200 CPSI (ws=12 mils)
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Domain Setup
Geometry is based on a 200CPSI, lab-scaled
(2”x 6”) cordierite filter with regions of upstream
flow and soot cake formation.
Meshing
Volume meshes are generated by using
Trimmer for porous regions (filter wall, soot
cake), and Polyhedral for fluid and solid
regions (channels, plugs).
Model Setup (1/2)
a
w
ws
CFD
domain
Total 2,013,762 cells Upstream=L 0.78”, Plug= L 0.39”, ws= 12.0 mils
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
POROUS REGION FLUID REGION SOLID REGION
Filter wall Soot cake Upstream Plugs I/O Channels
10
9
8
7
6
5
4
3
2
1
Trimmer is exclusively used for filter wall, consisting of 10 separate porous regions, to represent soot filtration. (Growth rate = 1, Cell size = thickness of each region)
All cells in wall regions are regular hexahedrons
Filtration
Algorithm
z y
x
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Model Setup (2/2)
Physical Assumptions 1. Fluid: 3D, Ideal gas, Laminar, Incompressible
2. Implicit unsteady method (2nd order temporal discretization, Δ𝑡 = 0.05 sec)
3. Segregated flow & energy solver (2nd order convection scheme, URF= 0.5P, 0.2V)
4. Convective heat loss
5. No flow in the axial(z) direction in wall regions
6. Homogeneous distribution of particulates in the flow
7. Particle properties (dp=54.5 [nm], ρp=2.87 [g/cm3]) evaluated by experiments
Boundary Conditions
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Pressure Outlet
125.44 [kPa] (= 18.2 psi) Mass flow Inlet
3.05E-6 [kg/s] (= 7.4 SCFM)
Convection
Specify convection flux across
the boundary to environment
(ambient)
Adiabatic
Neglect heat loss for
thermal condition at
channel inlet
Slip
Define geometrically
symmetry planes/surfaces
ℎ 𝑐𝑜𝑛𝑣 =𝑄
𝐴𝑒𝑞𝛥𝑇=
𝑚 𝑐𝑝 𝑇𝑖 − 𝑇𝑜
𝐴𝑒𝑞 𝑇 − 𝑇𝑎𝑚𝑏
𝑅𝑡,𝑓 =1
𝜅𝑤𝑟
Neglected
Potential energy
Chemical reactions
Compression effect
Expansion effect
Plugging effect
Soot Cake Transport
Ash formation
Total Temperature
195.0 [C]
Filtration
Algorithm
z y
x
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Cell Value Localization
Filtration Algorithm (1/3)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Filtration
Algorithm
Model
Results Summary
Future
Work Acknowledgement
Problem
: To make each CFD cell acting as a unit collector,
cell index must be ordered, so that the cell
values can be transferred in certain direction.
Structured meshing is NOT allowed in standard CFD tools
mlocal,1 (t) = min,1(t) E1(t)
mlocal,2 (t) = min,2(t) E2(t)
mlocal,i (t) = min,i(t) Ei(t)
Inlet
Outlet
mcake (t)= min (t) 𝜱(t)
min,1 = min (1-Ф)
min,2 = min,1 - mlocal,1
= min,1 (1-E1)
min,i = min,i-1 (1-Ei-1)
Cake layer
Layer 1
Layer 2
Layer i
.
.
.
min (t) = χs Qs 𝜟t
mout (t) = min,N(t) (1-EN(t)) z
y
x
0 ≤ ≤ 1 Soot Cake Mass Fraction
Solution
: Having the same cell indices in y direction by
meshing each wall layers, separately.
Localized parameters
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Built-in Function Utilization
Filtration Algorithm (2/3)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Problem
: Classic unit collector mechanism causes a
circulation error during initialization. Thus, Eq.(3)
needs to be modified to account 𝑬 𝒊, 𝒕 − 𝟏 .
☞
…but, time array can NOT be handled through UDFs.
𝒅𝒄(𝒊, 𝒕) 𝜺 𝒊, 𝒕
𝑬 𝒊, 𝒕 𝒎𝒍𝒐𝒄𝒂𝒍 𝒊, 𝒕
Recall
𝑑𝑐(𝑖, 𝑡) = 23
4𝜋
𝑚𝑙𝑜𝑐𝑎𝑙(𝑖, 𝑡)
𝜌𝑠𝑜𝑜𝑡,𝑤𝑎𝑙𝑙+
𝑑𝑐02
3 1/3
𝜀 𝑖, 𝑡 = 1 −𝑑𝑐(𝑖, 𝑡)
𝑑𝑐0
3
(1 − 𝜀0)
𝐸 𝑖, 𝑡 = 1 − 𝑒𝑥𝑝 −3𝜂𝐷𝑅 1 − 𝜀 𝑖, 𝑡 Δ𝑦
2ε(𝑖, 𝑡) 𝑑𝑐(𝑖, 𝑡)
𝑚𝑙𝑜𝑐𝑎𝑙 𝑖, 𝑡 = 𝑚𝑖𝑛 𝑖, 𝑡 𝐸(𝑖, 𝑡)
…𝐄𝐪. (𝟏)
…𝐄𝐪. (𝟐)
…𝐄𝐪. (𝟑)
…𝐄𝐪. (𝟒)
𝑚𝑙𝑜𝑐𝑎𝑙 𝑖, 𝑡 = 𝑚𝑖𝑛 𝑖, 𝑡 𝑬(𝒊, 𝒕 − 𝟏)
diameter void fraction
mass characteristic
Solution
: Store current (t) cell values using Table function,
then access the data by interpolating the table as
fields (UDF) at the next time step (t+1).
𝜺𝟎 𝒅𝒄𝟎 𝒎𝒍𝒐𝒄𝒂𝒍 = 𝟎
𝜺 𝒊, 𝟏 𝒅𝒄(𝒊, 𝟏) 𝒎𝒍𝒐𝒄𝒂𝒍 𝒊, 𝟏
𝑬 𝟎 Store
Table
t=0 (Initialize)
t=1
Access via UDF & User Code
Access via UDF & User Code
𝒎𝒍𝒐𝒄𝒂𝒍 𝒊, 𝟐
𝑬 𝒊, 𝟏 Store
t=2 . . . . . .
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Filtration Algorithm (3/3)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Recursive Operation
Problem
: Flow changes with engine operating condition.
Local soot mass must be accumulated through
time integral, considering collection efficiency.
…but standard CFD code do NOT have ability to allow mathematical recursiveness through UDFs.
𝑚in = χs Qs 𝛥t
𝑚𝑖𝑛, 1= 𝑚𝑖𝑛 (1-Φ) 𝑚𝑐𝑎𝑘𝑒= 𝑚𝑖𝑛 Φ
𝑚𝑙𝑜𝑐𝑎𝑙, 1=
(𝑚𝑖𝑛,1𝐸1)
𝑁
𝑡=1
𝑚𝑐𝑎𝑘𝑒 < 𝑚𝑐𝑎𝑘𝑒, 𝑚𝑎𝑥 𝑚𝑙𝑜𝑐𝑎𝑙, 1
< 𝑚𝑙𝑜𝑐𝑎𝑙, 𝑚𝑎𝑥
Φ = 1
No
Yes
𝑚𝑙𝑜𝑐𝑎𝑙,𝑖 =
(𝑚𝑖𝑛,𝑖𝐸𝑖 (1− 𝐸𝑘)
𝑖−1
𝑘=1
)
𝑁
𝑡=1
𝑤 =
𝑚𝑐𝑎𝑘𝑒
1
𝐴𝑠𝜌𝑠,𝑐𝑎𝑘𝑒
𝑁
𝑡=1
Soot mass conc. Flow rate
Partition factor
(0 ≤ Φ ≤ 1)
𝝆𝒔,𝒘𝒂𝒍𝒍 𝜋 𝑁𝑑𝑐,𝐶𝐹𝐷 𝑑𝑐,𝑚𝑎𝑥3 − 𝑑𝑐0
3
6
𝜌𝑠,𝑐𝑎𝑘𝑒𝑉𝑐𝑎𝑘𝑒,𝐶𝐹𝐷
No
(𝒕 + 𝜟t)
Itera
tio
n
Get
𝜀, 𝐸, 𝑑𝑐
En
d o
f d
ep
th
filt
rati
on
𝑚𝑐𝑎𝑘𝑒= 0
En
d o
f c
ak
e
filt
rati
on
𝛹𝑏
Yes
Solution
: Couple User Code and Monitor function.
Link
Compile
Monitor
Run
User Code
Field
Sum
Object file (.obj, .so)
Library (.dll)
STAR-CCM+
Lin
ux
Win
Load
Load
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Computing Environment
Argonne TRACC (Transportation Research and Analysis Computing Center)
A national user facility to meet US DOT advanced computation needs.
A focal point for computational research for transportation applications.
Linked to federal and non-federal R&D facilities, regional, state and city
departments of transportation, and university research centers.
High Performance Clusters
Total 3,968 cores in 220 compute nodes
Zephyr : 16 AMD 6273 (cores/CPU) * 2 (CPUs/node) * 92 (nodes)
Phoenix : 4 AMD 2378 (cores/CPU) * 2 (CPUs/node) * 128 (nodes)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
To
tal
so
lve
r e
lap
se
d t
ime
[s]
0
100
200
300
400
500
1 2 3 4 5 6 7 8 9 10
Iteration [#]
10 Iterations Benchmark Test 1 core: Local (3.4GHz, 16GB)
2 cores: Local
4 cores: Local
8 cores: Cluster (2.3GHz, 32GB)
16 cores: Cluster
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Model Results (1/5)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Channel-flow Profiles
P
L
U
G
P
L
U
G
P
L
U
G
P
L
U
G
Pressure Drop Characteristics
Pressure
Velocity
300s 600s 1000s
Effect of percolation factor (ψ) [kPa]
[m/s]
125.0
126.0
127.0
128.0
129.0
130.0
131.0
0 0.2 0.4 0.6 0.8 1
0.0
10.0
20.0
30.0
0.0
10.0
20.0
30.0
0 0.2 0.4 0.6 0.8 1
Normalized Channel Length [-]
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
0 300 600 900 1200 1500 1800
Pre
ss
ure
Dro
p [
kP
a]
Time [sec]
Experiment
Model (ψ=0.9)
Model (ψ=0.86)
Depth
filtration
Cake
filtration
Transition
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Model Results (2/5)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Wall-flow Rearrangements
Pre-transition regime Post-transition regime [m/s] [m/s]
30s 90s
120s Depth filtration
300s: Depth filtration end 600s: Transition
1000s: Cake filtration
0.020
0.028
0.036
0.044
0.052
0.060
0 0.2 0.4 0.6 0.8 1
Wa
ll-t
hro
ug
h V
elo
cit
y [
m/s
]
Normalized Channel Length [-]
0.035
0.038
0.041
0.044
0.047
0.050
0 0.2 0.4 0.6 0.8 1
Normalized Channel Length [-]
0.000
0.020
0.040
0.060
30s 90s 120s
Ve
loc
ity A
vg
.
1.0E-03
2.0E-03
3.0E-03
4.0E-03
300s 600s 1000s
Std
. D
evia
tio
n
UNIFORMIZED ACCELERATED
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Model Results (3/5)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Local Soot Mass Deposited
y-z plane (@ x = 1/4a)
Deposited soot profiles
x-y plane (@ z = 1/2L)
0.0E+00
1.0E-14
2.0E-14
3.0E-14
4.0E-14
5.0E-14
6.0E-14
7.0E-14
8.0E-14
0 50 100 150 200 250 300
Lo
cal
So
ot
Mass [
kg
]
Wall Penetration [μm]
Inlet
Mid
Outlet
0.0E+00
1.0E-14
2.0E-14
3.0E-14
4.0E-14
5.0E-14
6.0E-14
7.0E-14
8.0E-14
0 50 100 150 200 250 300
Lo
cal
So
ot
Mass [
kg
]
Wall Penetration [μm]
Inlet
Mid
Outlet
0.0E+00
1.0E-14
2.0E-14
3.0E-14
4.0E-14
5.0E-14
6.0E-14
7.0E-14
8.0E-14
0 50 100 150 200 250 300
Lo
cal
So
ot
Mass [
kg
]
Wall Penetration [μm]
Inlet
Mid
Outlet
90s: Depth filtration 600s: Transition 1000s: Cake filtration
Filter
wall
Soot
cake
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Model Results (4/5)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Local Collection Efficiency (dP=54.5 nm)
y-z plane (@ x = 1/4a) x-y plane (@ z = 1/2L)
Streamlines
40%
50%
60%
70%
80%
90%
100%
0 200 400 600 800 1000
Lo
cal
Co
llecti
on
Eff
icie
ncy
Time [sec]
Wall layer 1
Wall layer 3
Wall layer 5
Wall layer 7
Wall layer 9
Filter
wall
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Model Results (5/5)
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Soot Cake Layer Properties
Porosity (ρs,cake=120 kg/m3)
Thickness (@ 1000 s)
Maintain 0.99↑ during first
1000 seconds of filtration
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Summary
A 3D CFD model was successfully developed for quantitative analysis
of transient soot filtration processes in a wall-flow type DPF.
The local value and rearrangement behaviors of each filtration
parameter are well predicted within isotropically discretized meshes
in the multi-layered porous wall regions.
Self-developed user subroutines, developed on basis of the unit
collector mechanism, are integrated with the CFD code.
Built-in functions – Table, Monitor, UDF – were combined and fully
coupled with algorithm to calculate the local value of soot mass and
collection efficiency in the wall layer at each time step.
Results were visually demonstrated at the channel length scale in 3D,
representing correlations among wall flow pattern, soot mass
distribution, and soot cake profile.
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Filtration
Algorithm
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Future Work
Modeling additional porous and fluid regions
Create additional soot cake regions near the surface of the plugs to take into account plugging effects.
Create the downstream region to consider flow-expansion effects.
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
Integrating PM oxidation reaction mechanisms
Utilize soot filtration simulation results (soot mass distribution and soot cake profile) for the
initial state of regeneration simulation.
Apply chemical kinetics of soot oxidation in consideration of the effects of O2, CO, NO2 and
HCs (additional user subroutines need to be developed).
Filtration
Algorithm
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
Acknowledgement
Financial Support by US DOE - Office of Vehicle Technologies
Argonne TRACC
Hubert Ley [email protected]
TRACC Director
Steven Lottes [email protected]
Simulation, Modeling, Analysis Leader
Cezary Bojanowski [email protected]
Computational Mechanics Engineer
Waldemar Nowakowski [email protected]
System Administrator
Objective Background Theoretical
Analysis Experiment
Computing
Environment
Model
Setup
Model
Results Summary
Future
Work Acknowledgement
CD-adapco
Scott Wilensky [email protected]
East Region Technical Support Team Lead
Filtration
Algorithm
Integrating Filtration Mechanism with a
3D Diesel Particulate Filter (DPF) Model using STAR-CCM+
For more information,
please contact Hoon Lee at