simulating integrated engine-emissions-doc-dpf performance · soot emissions model zphysically...

Simulating IntegratedEngine-Emissions-DOC-DPF

Performance

Acknowledgements

David FosterDave Kapparos, Indranil Brahma, Kushal Narayanawamy,

Stephen England, Andrea StrzelecERC University of Wisconsin-Madison

Yongsheng HeGeneral Motors Research & Development

Gurpreet Singh and Kevin StorkDepartment of Energy: FreedomCAR and Vehicle Technologies Program

Christopher RutlandEngine Research Center

University of Wisconsin-Madison

2

System Models: Current Literature

He, GM R&D (SAE 2007-01-1138)

– Integrated in SimulinkPischlinger, et al., FEV (SAE 2007-01-1128)

– In-house integration environmentGuthenke, et al., DaimlerChrysler (SAE 2007-01-1117)

– GUI based in-house integration environmentPiscaglia, et al., Polt. Di Milano (SAE 2007-01-1133)

– Simulink– DPF model to test ECU model

Aftertreatment Devices

Engine and Emissions

Exhaust flowNumerical parameters

• Detailed physics basedAT models

• Primary use: evaluate AT configurations

• No engine or emissionsmodels- Driven by laboratory data

3

Simulink

ERC Work: Integrated System Level Model

Aftertreatment Devices

Engine and Emissions

Exhaust flowNumerical parametersControl connection

Combustion Models

Emission Models

Engine Flow Models

LNT, etc. Models

DOCModel

DPFModel

4

System Modeling Approach

Develop an integrated system level computer model– Capture interactions:

• Device – Device• Device - Engine

– Transient– Conventional and LTC (HCCI, PCCI, etc.) diesel combustion

Develop new component models as needed– Examples: LTC heat release, emissions, heat transfer

Incorporate existing models/modules– Examples: DPF, DOC, LNT, …

Integrate components as modules in an overall system level environment– Environment: Matlab-Simulink

• Also allows controllers– Develop modular approach for efficiency and ease-of-use

Validation– Component level: experimental data– System level: experimental data (when available) and “sense checks”

5

Objectives and Results

Explore steady state and transient scenarios– Examine and help explain device interactions– Suggest ‘guidelines’ of operation

• Based on model results and analysis

Recent results– LTC combustion under load transients– Effect of DPF loading and regeneration on engine

operation– Compare different DPF regeneration techniques– Model prevention and control of runaway DPF

regenerations

6

Diesel Engine Model

WAVE or GT-PowerIndustry standardIncludes:– Heat transfer models– Flow models– Turbocharger model (VGT)– Simple Combustion model

• Heat release rates from experimental pressure

• Could also use built in heat release models

• Calibration requiredCommunicate with Simulink using sensors and actuators

Example of a WAVE engine model with sensors and actuators

Exhaust Fuel Injector (Ahead of DOC)

exhaust

Intake Throttle

7

Traditional Diesel Engine Model - Disadvantages

Simple combustion (heat release) model– Prescribed function, calibrated with engine data– Simple spray models

Simple emissions models

Sufficient for conventional diesel operation but not for new LTC type combustion technologies

Need improved combustion / heat-release models and emissions models for current application

8

User Defined Cylinder Models

Fuel Injection

Energy, SpeciesConservation

CombustionVaporization

IVC EVO

Heat Transfer

Emissions In-Cylinder

Models

GT-Power Engine Model

9

Multi-Zone Combustion Model

Combustion chamber initialized with multiple zonesThe zones include sub-models for vaporization, chemical kinetics (CHEMKIN), heat transfer, energy/species conservation

Schematic representation of zones

IVC

EVO

2 31 54

10

-40 -30 -20 -10 0 10 20 30 40400

600

800

1000

1200

1400

1600

1800

2000

22005

4

3

2

1

Zone 1 Zone 2 Zone 3 Zone 4 Zone 5

Tem

pera

ture

[K]

Crank Angle [deg ATDC]

Tem

pera

ture

[K]

SOI = -128 ATDC; Injection Duration = 32.5CADStart of combustion predicted accuratelyCapture variations in combustion phasing due to thermal and charge stratifications: Useful for parametric LTC studiesLimitations: Requires specified fuel distribution (e.g. no spray)

No mixing between zones

Multi- Zone Model Validation

Temperature Distribution

-40 -30 -20 -10 0 10 20 30 400

10

20

30

40

50

60

70

80

Experiment Simulation

Cyl

inde

r Pre

ssur

e [b

ar]


0

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

Hea

t Rel

ease

Rat

e [J

/deg

]

Cyl

inde

r Pre

ssur

e[ba

r]

HR

R [J

/deg

]

CAT 3401 SCOTE,821 rpm, 25% Load

11

CFD In-Cylinder Model

Accurate modeling of fuel injection, spray dynamics, mixing, vaporization, chemistry and emissions calculationsFine – coarse mapped grid

Detailed in-cylinder CFD model

(KIVA-Chemkin)

IVC

EVO

12

CFD In-Cylinder Model

Accurate modeling of fuel injection, spray dynamics, mixing, vaporization, chemistry and emissions calculationsFine – coarse mapped grid

IVC

EVO

13

-30 -20 -10 0 10 20 30 40 50 600.0

1.5

3.0

4.5

6.0

7.5

9.0

10.5

12.0

13.5

15.0

0

100

200

300

400

500

600

Model Validation

Simulation results agree very well for lower loadsCaptures cool flame ignition and main combustion for early injection operation

30% load, EGR = 72 %

-30 -20 -10 0 10 20 30 40 50 600.0

1.5

3.0

4.5

6.0

7.5

9.0

10.5

12.0

13.5

15.0

0

100

200

300

400

500

600

700

800

Cyl

inde

r Pre

ssur

e [M

P a]


Experiment

Simulation

20% load, EGR = 71 %


Cyl

inde

r Pre

ssur

e [M

P a]

HR

R [J

/ deg

]

Experiment

Simulation

SOI = -50 ATDC SOI = -50 ATDC

14

Run Times for Different Approaches

The modeling approaches can be used in multiple cycle, transient simulations

Single Zone user combustion model

4 minutes

5 zone external cylinder model 17 minutes

GT-Power with mapped grid approach in 2-D

~70 minutes

GT-Power with refined grid in 2-D (1052 cells at BDC)

~ 9 hours

GT-Power with mapped grid using 3-D sector grids

~90 minutes

GT-Power with refined 3-D sector grids (5460 cells at BDC)

~ 1 day

15

CA50 Controlled by Actuating IVC

Step transient induced in CA50Controller forces a delay in intake valve closure

IVCactuated

CA50 Controlled

Target CA50

Sensed CA50

Actuated IVC

CA

50 (

CA

D) IVC

(CA

D)

Time (s)

16

Dual Control – Control CA50 During Load Increase

Step change in load

IVC actuated to maintain combustion phasing

Target CA50

Sensed CA50

Actuated IVC

CA50 set to –6.6 deg

CA

50 (

CA

D)

IVC (C

AD

)

IVCactuated

Target IMEP

Sensed IMEP

Fuel Injected

IMEP

(bar

)

Time (s)

Fuel Injected (m

g/cycle)

IMEP Controlled

Fuelactuated

17

Diesel-PCCI Mode Change: Cooled EGR and IVC

Model transition: conventional – PCCI –conventionalConventional SOI:

–12 deg ATDCPCCI mode SOI:

IVC + 5CAD

IVC response for PCCI under different EGR conditions– IVC actuation during

the mode transition decreases with increase in EGR

IVC

[CA

D]

0 1 2 3 4-150

-145

-140

-135

-130

-125

-120

Time [s]

EGR 10%

EGR 27%

EGR 40%

Intake valve closes early with increase in cooled EGR

CA50 set to - 5.7 ATDCIMEP set to 5.3 bar

18

Emissions Models

Need accurate, fast emissions models to drive the aftertreatment device models

Focus: Simpler, faster engine combustion models (for now)Long run times for DPF filling

General approach:– Use good physically based phenomenological model as

starting point– Improve model using neural networks to replace model

coefficients

Models required– Soot– NOx– CO2– HC

completed

under development

19

Soot Emissions Model

Physically based neural network modelPhysical model: Bayer and Foster,SAE 2003-01-1070Phenomenological models for– Injection spray

• Predicts spray angle, liquid penetration, liftoff length, local equivalence ratio, temperatures, etc.

– Particulate formation and oxidation

Inputs are obtained from the engine model– Profiles of in-cylinder pressure, in-cylinder mean temperature,

mass flow rate of fuel through injector, heat release rate– Engine speed, percent EGR, global equivalence ratio

0.5 1.8sosf EE

RTs RTsf f so s

dm C m P e C m P edt

φ−−

= −

Small soot particles formedfrom rich initial combustion

Large soot particles in vortex head, mixing until eventually oxidized by diffusion flame.

Entrained Hot Air

20

Neural Net Soot Emissions Model

Brahma et. at., SAE 2005-01-1122

193 4( )ww wΦ+

50.5wP

6 7sf

f

Ew w

RTe−

⋅ +

20wfm

141.8wP

15 16so

o

E w wRTe−

⋅ +

Φ

Csf

mf

P

-Esf/RTf

CSO

ms

P

-Eso/RTo

YO2

6 72019 5

3 4

( )0.5( )

sf

f

Ew w

RTww wsf fw wx C m P e

−⋅ +

Φ+= ⋅ ⋅ ⋅ ⋅

15 1622 141.8

2

( )2

10 11( )So

os O

E w wRTw w

so sm Y Py C w w m e−

⋅ +

⋅ ⋅ ⋅= ⋅ +

23212 217 1 2 8 9 18( ) ( )s wwdm

w wx w x w y w y wdt

= + − + +⎡ ⎤⎣ ⎦ 2 2210 11( )s s

ww m wm+

Neural network weights added to the physical model

Neural network weights trained using experimental engine data for 8 modes of operation – Approximately 57% reduction in

error of predicted soot

Converted into an M-file– M-file version runs in 1/3 the

time of the original Simulinkversion

– Small time steps are no longer propagated to other component models

21

Soot Model and Time Steps

Each component / module in integrated system requires a certain time step, Δt

Overall system runs at ~2.5ms Δt (primarily set by engine model)

Soot requires 0.5 CA data ( ~ 0.05 ms Δt)– Model requires crank-angle resolved spray and heat release

profiles

Simulink manages time stepping– Probes models to determine required times– Adjusts overall simulation to smallest Δt in system

Soot model Δt requirement can severely limit overall system speed

22

Solutions for Managing Disparate Time Scales

Hide inner workings of model from Simulink– Use self-contained Matlab or Fortran/C routines– Use sub-cycling time integration inside routines– Use level 2 S-functions

• These can specify their own hit time

Use external file fordata transfer– Pass small Δt data

outside of Simulink

Implement periodic ‘triggering’– Skip calculation in model except every ‘n’ engine cycles– Use most recent solution until next calculation

Combustion Models

Emission Models

Engine Models

LNT, etc. Models

DOCModel

DPFModel

23

NOx Emissions Model

Physically based neural network model (Brahma and appendix of England et. al., SAE 2006-01-0263)Implemented as an M-fileInputs are obtained from the engine and soot modelsNOx prediction is based on maximum rate of formation

Trained using experimental engine data for 8 modes of operation 1 2 3 4 5 6 7 8

0.2

0.4

0.6

0.8

1

Operating Mode Number

Nor

mal

ized

ppm

NO

x

Model PredictionExperimental

Operating Mode Number

Nor

mal

ized

NO

x

Experimental NOx Compared to Model Prediction

1/ 222 2

_X 1 1/ 2

_

exp [O ] [N ]NO max Diffusion Flame

Diffusion Flame

KT

KT

⎡ ⎤⎛ ⎞−⎢ ⎥⎜ ⎟⎜ ⎟⎢ ⎥⎝ ⎠= ⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

24

DOC Model

Proprietary GM model (Bissett)– Includes kinetics for oxidation of CO, HC, and NO

to NO2

Implemented as a Fortran version– Faster than Simulink version

Fortran allows decoupling of integration time– DOC model can operate at large time steps (10

ms or longer)

25

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200 250 300 350

ExperimentalModel

Modified version of the MTU 1-D model (based on work of Konstandopoulos and Johnson)

Fortran code linked to Simulink using an S-function (level 2)

Input and output modified for dynamic use in Simulink and time integration improved

Calibrated against experimental data

Darcy law pressure drop for the wall and soot layer

Device sub models describe:– Particle deposition inside wall

and on wall surface– Flow and temperature fields– Catalytic and thermal oxidation

of soot

DPF Model

Time [min]

Pre

ssur

e D

rop

[kP

a]

Comparison of Experimental DPF Pressure Drop and DPF Model Prediction

Deep Bed Filtration

Cake Filtration

Tran

sitio

n

26

Combined DPF-LNT

DPF

LNT

+

SNT

=

PlugExhaust gases in

Exhaust gases out(higher CO, CO2

levels)Soot ‘cake’ deposit

Leanstorage

NOx storage

Exhaust gases in

Exhaust gases out

Soot depositFlow chronology

NOx

NO

x

27

Controllers and Validation

Controllers implementedin Simulink for:– Engine load (fueling)– Engine speed– EGR– Turbo boost– DPF regeneration

The component models tested and validated individually

The integrated model is tested to verify proper component interaction– DPF loading and regeneration simulations to test the model

and component interaction– The simulation results are consistent with experimental

results presented by Singh et. al. (SAE 2006-01-0879)

Combustion Models

Emission Models

Engine Models

LNT, etc. Models

DOCModel

DPFModel

28

DPF Loading and RegenerationSimulate a light duty, turbo charged, CIDI engine– Common rail direct fuel injection– Variable geometry turbine

Regeneration: modes 3 and 5– DPF initially loaded to 3 [g/l] of soot and regeneration starts at 3.2 [g/l]– Mode 5: Fuel injected ahead of the DOC– Mode 3: Fuel injected ahead of the DOC with intake throttling assistance

Regeneration tests were done with mode switching (mode 5 to 2) during regeneration to investigate prevention/control of regeneration runaway

ModeNumber Load

EngineSpeed[RPM]

ApproximateFueling Rate

[kg/hr]

EGR [mass %]

2 Very Low 970 1.42 62.143 Low 1120 4.035 37.965 Moderate-Low 1520 9.365 19.158 Moderate 2320 18.85 10.50

29

DPF Regeneration (Mode 5)

Fuel injected in exhaust before DOCIncreases reactions in DOC and exhaust TControlled to achieve a desired DPF inlet temperature of 600 °C

0 10 20 30 40250

300

350

400

450

500

550

600

650

Engine OutDPF OutDOC Out

Time [min]

Exha

ust T

empe

ratu

re [

°C]

Exhaust Temperatures

DPF Regeneration

0 10 20 30 40250

260

270

280

290

300

Total BSFCEngine BSFC

Time [min]

BSF

C [

g/kW

-h]

Fuel Consumption

DPF Regeneration

30

DPF Regeneration (Mode 5)

Simulation results are consistent with the experimental results given by Singh et. al. (SAE 2006-01-0879)

0 10 20 30 400

5

10

15

20

25

0 10 20 30 40

2

4

6

8

10

12

14

Time [min] Time [min]

Mass of Soot Trapped in the DPF

Trap

ped

Soot

[g]

Pressure Drop Across DPF

DPF RegenerationPr

essu

re D

rop

[kPa

]

DPF Regeneration

Note long regeneration

time

31

Throttle Assisted Regeneration (Mode 3)

Engine exhaust temperatures too low for fuel injection before DOC (> 300 C required) Throttle engine intake air flow– Increases equivalence ratio and engine exhaust T (ΔT = 44 °C)

Higher exhaust T allows fuel injection in exhaust before DOC

0 10 20 30 40

240

260

280

300

320

340


BSF

C [

g/kW

-h]

Time [min]

DPF Regeneration

Fuel Consumption

0 10 20 30 400

100

200

300

400

500

600

700


Exha

ust T

empe

ratu

re [

°C]

Time [min]

DPF Regeneration


32


0 10 20 30 400

1

2

3

4

5

6

7

0 10 20 30 40

5

10

15

20

Pres

sure

Dro

p [k

Pa]

Time [min]

Trap

ped

Soot

[g]

Time [min]

DPF Regeneration

DPF Regeneration



Note long regeneration

time

33


Engine out NOx increased significantly during DPF regenerationIntake throttling required reducing EGR to maintain sufficient O2 for given loadReduced EGR resulted in higher engine out NOx

0 10 20 30 4030

40

50

60

70

80

90

100

Time [min]

NO

x [p

pm]

Engine-Out NOx

DPF Regeneration

34

Good Device Gone Bad

35

Runaway Regeneration (Mode 5)

Regeneration by fuel injection before DOCTry to reduce regeneration time by injecting more fuelResults in runaway regeneration

0 1 2 3 40

200

400

600

800

1000

1200


0 1 2 3 4250

275

300

325

350


Time [min]

BSF

C [

g/kW

-h]

Fuel Consumption

Exha

ust T

empe

ratu

re [

°C]

Time [min]


DOC fuel injection stop

Temperature continues to rise after fuel shutoff

36

Runaway Regeneration (Mode 5)

0 1 2 3 40

5

10

15

20

0 1 2 3 40

2

4

6

8

10

12

Time [min] Time [min]

Pres

sure

Dro

p [k

Pa]

Trap

ped

Soot

[g]



Note “runaway”regeneration

time

37

Preventing Runaway Regenerations

Use ΔT predictive capability in the regeneration controller via simple energy balance:

Very simple energy balance will get the DPF inlet temperature within 10 [°C] of the target value

Use PI feedback control to adjust the predicted exhaust fuel injection rate to obtain the target DPF inlet temperature

Feedback PI controller alone (without predictive capability) is not recommended

exh exh exhfuel

fuel

m Cp TmLHV

Δ=&

&

38

Regeneration During Mode 5-2 TransientRunaway regeneration occurs when DPF is very hot and:

– Exhaust flow rate is suddenly lowered in mode transitions– Excess O2 is available in exhaust– Consistent with results from Koltsakis, et al. (SAE 2007-01-1127)

Prevent runaway regeneration by intake throttling to reduce available O2 in exhaust

0 200 400 600 800 10000

200

400

600

800

1000

1200

InletMidpointOutlet

0 200 400 600 800 10000

200

400

600

800

1000

1200

InletMidpointOutlet

Time [s] Time [s]

DPF

Wal

l Tem

pera

ture

[°C

]

Regeneration: Runaway after Mode Switch

DPF

Wal

l Tem

pera

ture

s [°

C]

Prevent Runaway with Intake Throttling

Mode 5 Mode 2

Mode 2Mode 5

DPF Wall Temperature DPF Wall Temperature

Regeneration

39

Regeneration: Runaway after Mode Switch

Prevent Runaway with Air Injection into DPF

0 200 400 600 800 10000

200

400

600

800

1000

1200

InletMidpointOutlet

Regeneration During Mode 5-2 Transient

Alternative method to prevent runaway:– Air injection into exhaust

Cool air removes heat from soot oxidation reactions

0 200 400 600 800 10000

200

400

600

800

1000

1200

InletMidpointOutlet

Time [s] Time [s]

DPF

Wal

l Tem

pera

ture

[°C

]

DPF

Wal

l Tem

pera

ture

s [°

C]

Mode 5 Mode 2

Mode 2Mode 5

DPF Wall Temperature DPF Wall Temperature

40

Controlling Runaway Regenerations

The integrated model has been used to simulate how a runaway regeneration can be detected and controlled– Reactive control

Can the integrated model be used to detect unfavorable conditions before a runaway regeneration starts to prevent runaway proactively?– To investigate this possibility phase diagrams

have been generated

41

DPF Phase Diagrams

Phase Diagrams:– Max DPF wall T– Function of:

• Equivalence ratio (e.g. available O2)

• Exhaust flow rate

Example:– Mode 8 to mode 2

transient during regeneration

Conditions:– Initial DPF wall

temperature = 600 °C– Soot loading = 4 g/l– Inlet temperature =

162 °C (after switch to mode 2) Global Equivalence Ratio, φ

Exh

aust

Flo

w R

ate

[kg/

s]

Maximum DPF Wall Temperature [°C]Experienced By Time = 10 [s]

mode 2

mode 8

0.2 0.4 0.6 0.8 1

0.02

0.04

0.06

0.08

0.1

0.12

600

620

640

660

680

700

720

Global Equivalence Ratio, φ

Exh

aust

Flo

w R

ate

[kg/

s]


mode 2

mode 8

0.2 0.4 0.6 0.8 1

0.02

0.04

0.06

0.08

0.1

0.12

600

620

640

660

680

700

720


Exh

aust

Flo

w R

ate

[kg/

s]


mode 2

mode 8

0.2 0.4 0.6 0.8 1

0.02

0.04

0.06

0.08

0.1

0.12

600

620

640

660

680

700

720


Exh

aust

Flo

w R

ate

[kg/

s]


mode 2

mode 8

0.2 0.4 0.6 0.8 1

0.02

0.04

0.06

0.08

0.1

0.12

600

620

640

660

680

700

720


Exh

aust

Flo

w R

ate

[kg/

s]


mode 2

mode 8

0.2 0.4 0.6 0.8 1

0.02

0.04

0.06

0.08

0.1

0.12

600

620

640

660

680

700

720


Exh

aust

Flo

w R

ate

[kg/

s]


mode 2

mode 8

0.2 0.4 0.6 0.8 1

0.02

0.04

0.06

0.08

0.1

0.12

600

620

640

660

680

700

720

safe region

unsafe region

42

Using the Phase Diagrams

Runaway regeneration occurs when DPF is very hot and:– Exhaust flow rate is low– Excess O2 is available

By using the phase diagram unsafe operating conditions can be identified and avoided by:– Intake throttling– Air injection into DPF inlet


Exh

aust

Flo

w R

ate

[kg/

s]


mode 2

mode 8

0.2 0.4 0.6 0.8 1

0.02

0.04

0.06

0.08

0.1

0.12

600

620

640

660

680

700

720

Intake Throttling

Regen

Mode Chan

geAir Injection

Mode 2

Mode 8

43

Summary

Integrated system model for diesel aftertreatment studies

Advanced engine combustion model for LTC studies

Accurate emissions models to drive the aftertreatment devices

Accurate, modular aftertreatment devices models

Implemented in Simulink for controls and numerical management

Integrated model used to explore DPF regeneration

– Phase maps to explore runaway regeneration recovery

44

Ongoing Activities

Development of additional submodels– CO and HC emissions models– Incorporation of an SCR device model

Continue improvements to integrated system– Improve computer run times– Improve modularity and ease-of-use

Continue to use the system model to study device interactions and appropriate operation for transient and regeneration scenarios– Develop ‘guidelines for operation’

45

Thank You

simulating integrated engine-emissions-doc-dpf performance · soot emissions model zphysically...

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