integrating filtration mechanism with a 3d diesel...

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


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



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


3D Diesel Particulate Filter (DPF) Model using STAR-CCM+ 4

Background (1/2)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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)



Two Approaches in Filtration Modeling

H. Lee et al. 2012 DEER Conference

H. Lee et al. SAE 2013-01-1583

Background (2/2)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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.



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


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


Filtration

Algorithm

Darcy-Forchheimer’s Law



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)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


Filtration

Algorithm



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


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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)



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


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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



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


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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



Cell Value Localization

Filtration Algorithm (1/3)


Analysis Experiment

Computing

Environment

Model

Setup

Filtration

Algorithm

Model

Results Summary

Future


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



Built-in Function Utilization



Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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 . . . . . .





Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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



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)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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



Model Results (1/5)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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



Model Results (2/5)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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

]


0.035

0.038

0.041

0.044

0.047

0.050

0 0.2 0.4 0.6 0.8 1


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



Model Results (3/5)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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

]


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

]


Inlet

Mid

Outlet

90s: Depth filtration 600s: Transition 1000s: Cake filtration

Filter

wall

Soot

cake



Model Results (4/5)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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



Model Results (5/5)


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


Filtration

Algorithm

Soot Cake Layer Properties

Porosity (ρs,cake=120 kg/m3)

Thickness (@ 1000 s)

Maintain 0.99↑ during first

1000 seconds of filtration



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.


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


Filtration

Algorithm



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.


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


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



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


Analysis Experiment

Computing

Environment

Model

Setup

Model

Results Summary

Future


CD-adapco

Scott Wilensky [email protected]

East Region Technical Support Team Lead

Filtration

Algorithm



For more information,

please contact Hoon Lee at

[email protected]

integrating filtration mechanism with a 3d diesel...

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