Advanced Gasoline Engine Development
using Optical Diagnostics and Numerical Modeling
Dr. Michael Drake
General Motors R&D
Prof. Daniel Haworth
The Pennsylvania State University
Drake - Haworth
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Purposes of Paper & Talk
1. Provide a bridge between
Industrial
Optical experimentalist
Applied engineering
and
and
and
Academic
Numerical modeler
Combustion science
2. Demonstrate by examples that optical diagnostics and numerical modeling
are development tools important for advanced gasoline engines
3. Suggest future directions for optical diagnostics and numerical modeling
Mike Drake
Dan Haworth
Optical diagnostics
Numerical modeling
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Automobiles: The Best Looking Applied Combustion Devices
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Automobiles
American Society of Mechanical Engineers
“The automobile is the single
most important engineering
achievement of the 20th century.”
~ 60 million cars and trucks built annually
~ 12 % of world’s 6 billion people own automobiles
~ 27% of total energy consumption in US is for transportation
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Changes in the Automotive Industry
• Fuels Issues
• cost, availability
• renewable fuels, E85
• H2
• Lower Emissions
• Better Fuel Economy
(CO2 concerns)
• Market Expansion
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Gasoline Exhaust Emissions –
“March towards zero HC, NOx emissions”
1975
Euro 3 (2000)
@80K km ~ 50K mi
0.3
HC
g/mi
HC ~ 10 g/mi
NOx ~ 10 g/mi
0.2
@100K km ~ 62K mi
Euro 4 (2005)
0.1
Japan 2005
50% Reduction
US LEV2
0.050
Japan 2000
US National LEV & LEV 1
US ULEV2
0 0
0.050
US ULEV 1
0.1
0.2
NOx g/mi
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Global Warming Concerns
Fuel Economy / CO2 Improvements
European Union
25% to 35%
Canada
25%
CAN
US-Federal
Trucks 5% to 16%
EU
CA
US-F
JPN
CHN
Japan
23%
MEX
California
40%
BRZ
China
15%
AUS
XX% indicates magnitude of
potential FE/CO2 improvements
(time period varies by region)
Drake – Haworth
Australia
17%
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7
Vehicle Growth
Vehicles / 1000 people
U.S.
France
China
Drake - Haworth
744
660
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Advanced Technology Strategy
Improved
Vehicle
Fuel
Economy
and
Emissions
Reduced
Petroleum
Consumption
Hydrogen
Fuel Cell
Hybrid Electric
Vehicles
IC Engine
Improvements
Near-Term
Fuel
Infrastructure
Mid-Term
Long
-Term
Long-Term
Petroleum (Conventional and Alternative Sources)
Bio and Synthetic Fuels
Hydrogen
• Automotive companies are investing in all of these technologies.
• Advances in gasoline IC engines important for the foreseeable future.
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Advanced Gasoline Engine Development
using Optical Diagnostics and Numerical Modeling
Outline
1. Changes in automotive industry
2. Optical diagnostics and numerical modeling for gasoline engine
development
• SI (spark ignition)
• WG-SIDI (Wall-Guided Spark-Ignition Direct-Injection)
• SG-SIDI (Spray-Guided Spark-Ignition Direct-Injection)
• HCCI (Homogeneous-Charge Compression-Ignition)
3. Future needs and directions
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Optical Diagnostics in
Homogeneous SI engines
Laser Imaging – 1980’s
Flowfield - PIV
High speed film of combustion – 1930’s
(Rassweiler & Withrow, GM )
Flame wrinkling – Mie scattering
• turbulent premixed flame propagation
• large cycle-to-cycle variability
Flamefront Quenching – OH LIF
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Numerical Modeling in Homogeneous SI Engines
Intake flow in piston-cylinder
Gosman et al., 1980
Drake – Haworth
4-valve homogeneous SI engine
Khalighi, El Tahry, Haworth, 1995
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Role of Diagnostics and Modeling
• Research tools for Homogeneous SI engines
• Development tools for Stratified-charge gasoline engines
~1990 GM integrated optical diagnostics and CFD modeling
into stratified-charge engine development programs
All-metal
single-cyl.
engines
Optical
single-cyl.
engines
Multicylinder
Engines
Vehicle
Test fleet
Production
CFD
modeling
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GM Optical Spray-Guided SIDI Engine
• Optimum optical access
from sides and bottom
Fuel
Injector
• Structurally similar to
all-metal engines
• Operationally similar to
all-metal engines
(few % lower IMEP,
hotter piston)
Piston Window
Head Window
Quartz Cylinder
Piston with Window
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Optical SG SIDI Engine and Cameras
High speed (4K – 60K f / s) digital camera systems
Fuel
Injector
Filters
& lens
Intensifier
CMOS
Camera
Piston Window
High-speed camera
Lens
Image intensifier
Filter
CMOS
W or w/o Intensifier
12 bit resolution
8 Gbyte storage
3 NMOS Intensified
Camera System
8 bit resolution
1.6 Gbyte storage
Lasers
Copper vapor
Dichroic
High rep Nd:YAG
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Stratified-Charge Engines
Stratified-charge engine
(Ricardo - circa 1914)
Fuel-Efficient but
Sensitive to Small Perturbations
Production WG SIDI Engine
(Mitsubishi 1996)
The breakthrough came when, by using
laser technology and high-speed cameras,
engineers were able to study exactly what
goes on at the moment of ignition.
– Mitsubishi executive, 1997
Key enablers
• Use of laser diagnostics in development
• High pressure fuel injectors
• Better computer controls
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Why were Optical Diagnostics Required?
b) Stratified Wall -Guided SIDI
Ignition
Air
+EGR
Fuel
Fuel
Inj.
Φ >1
Wall
Φ = 1 Wall
Stratified
Stratified
film
Wetting
<
Φ
1
Flame
Propagation
Fuel Distribution
Optimum Fuel Distribution
• Average Ф ~ 0.3, requires strat.
• Ф > 0.6 for Ignition
• Ф ~ 1 for flame propagation
• Minimize lean fringes for HC
• Minimize wall film
• Misfires result from
non-optimum fuel distribution
Control of Fuel Distribution
• In-cylinder motion
• Fuel spray shape & momentum
• Contoured piston surface
Difficult to optimize Fuel Distribution without being able to
“ look inside the box with optical diagnostics”
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Types of Stratified Charge Direct Injection
Gasoline Engines
Wall-Guided
Spray-Guided
( This animation is only an Artist’s Rendition )
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Smoke Formation in Wall-Guided SIDI Engine
Side view
Liquid fuel film
Bottom view
3
Soot Emission (mg / cycle)
Pool fire on piston
Swirl fuel spray
10. mg fuel injected
0.1 mg wall film mass
0.01 mg smoke
0.03
0.02
0.01
0
0
µm
0.05
0.1
0.15
0.2
Maximum wall film mass (mg / cycle)
0
Refractive index matching
Fansler, Drake - GM
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High Speed Imaging of Soot Formation and Oxidation
With Multihole Fuel Injector
Multihole fuel spray
1.0
<Total>
Lens
Liquid fuel film
Soot (KLintegrated)
0.8
0.6
High-speed camera
Premixed
Premixed
flame
<Pool Fire>
Image intensifier
Filter (OH*)
<Distributed premixed> Pool fire
Pool
fire
0.4
Dichroic
0.2
(soot)
650 nm
750 nm
0.0
3
-30
0
30
60
1.0
9000 images /Crank
s Angle (°ATDC)
1.0
90
120
Soot
Soot
(KL)
(KL)
µm
0
• Diagnostics identified source of smoke
• Diagnostics and CFD aided in minimizing smoke
0.2
0.0
Fansler,
Stojkovic, Drake - GM
-30
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Summary: Wall-Guided SIDI Engines
Spray-Guided
+ good ignition stability
- small fuel economy gain
- requires lean NOx catalyst & low S fuel
- pool fires and smoke
Status:
• In Production
• Limited regional markets
• Not expanding
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Multihole Fuel Spray in SG SIDI Engine
Liquid Mie
Liquid
Φ =1 vapor
contour
CFD Model
Reynolds Average
(3D, time-dependent)
Adaptive gridding
(180 K cells)
Lagrangian spray
parcels
Spray chamber
characterization
Multicomponent
vaporization model
Spray-wall model
Drake, view
Fansler, Lippert - GM
Bottom
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Conditions at spark for SG SIDI Engine
5
Vapor Equivalence Ratio
CFD
Expt.
CFD
4
3
Cyclic
fluctuations
2
1
Injection
0
-60
Optical Experiments
-50
-40
-30
Crank Angle (deg AT DC)
-20
16
Dichroic
(431 nm)
(C2* 516 nm)
Expt.
CFD
14
Liquid Mass (mg)
Image intensifier
Filter (CN* 388 nm)
12
10
8
6
4
2
Injection
0
Spark luminosity
CN* - fuel vapor
C2* - relative liquid fuel
-60
-50
-40
-30
Crank Angle (deg ATDC)
-20
Drake, Fansler, Lippert - GM
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Two Stage Combustion in SG SIDI Engines
Premixed combustion
• laminar flame tables
Diffusion flame combustion
• eddy-dissipation model (EDM)
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High swirl
Two-Stage Combustion in SG SIDI Engines
Soot
OH*
Optical
Expt.
Rich products
Premixed reac.
rate
CFD
Low swirl
b.
Optical
Expt.
CFD
a.
• Agreement for a wide range of engine operating conditions.
Drake, Fansler, Lippert - GM
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Cycle-to-cycle Fluctuations in
Spray, Spark, and Rich Combustion
Bottom view, 60 K frames/s
Normal cycle
Misfire cycle
Drake, Fansler - GM
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Normal Cycle
Misfire Cycle
Misfires in this SG-SIDI Engine
Normal cycle
4 misfires in 2000 cycles
CFD
Flame
kernel
Fuel (Φ=1)
Drake – Haworth
Misfire cycles caused by local flow
in the wrong direction, away from
fuel in bottom of piston bowl.
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Normal Cycle
SG SIDI Engine
(piezoelectric hollow cone spray)
Imaging of Double-pulsed Spray
Schlieren imaging (30 K f/s)
CFD of spray and vaporization
Altenschmidt et al., DaimlerChrysler AG,
7th Int. Symp. on Internal Combustion
Diagnostics, Baden-Baden, 2006.
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SG SIDI Engine
(piezoelectric hollow cone spray)
LIF and CFD of spray
Better Fuel Economy
Low HC Emissions
Fischer et al., BMW, 7th Int. Symp. on Internal
Combustion Diagnostics, Baden-Baden, 2006.
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Summary: Spray-Guided SIDI Engines
- random misfires
- spark plug or injector fouling
- lean NOx catalyst (low S fuel)
+ lower soot and hydrocarbon emissions
+ wider stratified-charge operating range
+ 10 – 15% better fuel economy
Status:
Research with Multihole Fuel Sprays
Near Production with Piezoelectric Sprays
Drake – Haworth
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SG SIDI
Ignition Fuel
Fuel
Inj.
Traditional HCCI
Air
+EGR
Φ >1
Stratified
Φ =1
Flame Propagation
Φ <1
• stratified
• spark ignition
• flame propagation (~10 m/s)
• long combustion duration
(~40 CA)
Fuel
Inj.
Fuel
Air
+EGR
~Homogenous
Autoignition
• homogeneous (very dilute)
• compression ignition – T controlled
• homogeneous autoignition
• short combustion duration
(~10 CA)
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HCCI - Control of Emissions
• Control in-cylinder temperature by varying residual and EGR
• T = 1500 - 1800 K for low NOx, and CO
• CO and UHC emissions come from cool walls and piston ring crevices
• Conventional catalyst effective for stoichiometric operation
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Thermochemistry is Important in HCCI. . .
Rapid
compression
facility
Experimental Data
OH ( ∆H 0f )298,OH ↓ 6%
Curran
Mechanism by Curran
et al. et al. 2002
1200
Ruscic et al. 2002
Herbon et al. 2002
1000
0
0
Mechanism by Curran et al. with revised (∆H f)298,OH
and reduced 2OH(+M)=H
(+M)
2OH +2OM
⇔rate
H coefficient
O +M
2
2
χOH [ppm]
Isooctane
Ф = 0.35
T = 971K
P = 14 atm
857
species,
Mechanism by
Curran
et al. with3606
revisedreactions
(∆H f)298,OH
2
A ↓ 74%
800
600
(Set up to add one
arrow and associated
text per click)
400
200
Experiment
He et al. 2006
pm]
0
80
8
9
10
11
12
13
Time [ms]
14
15
16
17
18
• Uncertainties in thermodynamics and kinetics remain.
• Measurements under engine-conditions are required
to test reliability of large chemical mechanisms.
He et al. – U. Michigan
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Real engines are not homogeneous nor isothermal
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15
363°CA
1
364°CA
368°CA
365°CA
4.4
1.4
1
370°CA
1
1
366°CA
8.5
372°CA
367°CA
12
374°CA
378°CA
High speed chemiluminescence imaging shows
• Ignition occurs at multiple sites; different locations each engine cycle
• Successive ignition throughout chamber
• Flame propagation too slow to be important
Dec, Hwang, Sjöberg - Sandia
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Hybrid CFD / Multizone Modeling of HCCI
Zone Mapping by T, ξ, Dilution
3D CFD
0D Detailed Chemistry Model
Calculated heat release
Hergart et al. SAE 2005-01-0218
Rask, Lippert, Smyth, FISITA 2006
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HCCI - Expanding the speed / load range
700
HomogeneousSpark
SI Ignition
Homogeneous
600
Transition
Domain
Engine Load
NMEP (kpa) .
500
400
Rate of pressure rise
or ringing noise limit
Ringing Intensity Noise Limit
Transition
Domain
Transition
Domain
Traditional
Traditional
Traditional
HCCI
Domain
HCCI
Domain
HCCI
Domain
300
200
Stratified Ignition
StratifiedDomain
Stratified Autoignition
Domain
SparkAided HCCI
100
Combustion Stability Limit
Combustion stability limit
0
0
500
1000
1500
2000
2500
3000
3500
Engine
Speed(rpm)
(rpm)
Engine
Speed
• Homogeneous spark ignition operation at high loads
• Stratification used to expand speed / load range in HCCI mode
Rask, Lippert, Smyth, FISITA 2006
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Stratified Spark - Aided HCCI
-3 5 b T D C
spark
-3 3 b T D C
-3 1 b T D C
-2 9 b T D C
Flame
propagation
-2 7 b T D C
-2 5 b T D C
-2 3 b T D C
-2 1 b T D C
--1 9 b T D C
-1 7 b T D C
-1 5 b T D C
-1 3 b T D C
Flame
propagation
--1 1 b T D C
HCCI
-9 b T D C
-7 b T D C
-5 b T D C
• Second fuel injection creates rich zone near spark plug
• Spark ignition causes heat release and earlier transition to HCCI
• Allows ignition control at low loads
Reuss - GM, Natarajan - U Michigan
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HCCI : Turbulence-Chemistry Effects
Detailed Isooctane & NOx Chemistry Model (105 species)
ISAT storage / retrieval
Compare three models
CFD w/PDF
Turbulent fluctuation affect
- ignition timing
- emissions
N2O significant pathway to NO ?
Kung, Haworth – Penn State
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Summary: HCCI Engines
+ Low NOx, soot emissions
+ Conventional catalyst
+ FE gains similar to SG-SIDI
- Difficult to control (T sensitive)
- Individual cylinder control
Status:
Research
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Advanced Gasoline Engine Development
using Optical Diagnostics and Numerical Modeling
Outline
1. Changes in automotive industry
2. Optical diagnostics and numerical modeling for IC engine
development
• SI (spark ignition)
• WG-SIDI (Wall-Guided Spark-Ignition Direct-Injection)
• SG-SIDI (Spray-Guided Spark-Ignition Direct-Injection)
• HCCI (Homogeneous-Charge Compression-Ignition)
3. Future needs and directions
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Future of Modeling in IC Engine Development
RAS
Solution to
govern. eqns.
Value
Today
Future
Ensembleaveraged
Fast
All engine
development
work
All engine
development
work
Complex
geometries
Joint pdf’s for
fluctuations
Compares to
experiment
LES
Spatiallyfiltered
Instantaneous
DNS
Unaveraged
Unfiltered
Instantaneous
Captures large
scale
unsteadiness and
cyclic variability
Early efforts
Isolates physical
processes
Improved
Improved
submodels for submodels for
RAS and LES RAS and LES
Provides insights
Causes of
cyclic
variablility
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Future
Better understanding and control
over Engine Processes
Turbulent Air intake
Sprays
Ignition and early flame
Fuel vaporization & mixing
Stratified Combustion
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Cyclic Variability in In-cylinder Flows: PIV & LES Modeling
P. Adomeit, S. Pischinger, 7th Int. Symp. on Internal Combustion Diagnostics, 2006.
• Goal is to determine causes of cyclic variability
• Difficult to compare experiment with LES (requires statistical analysis of many cycles)
• Need to run LES model for many cycles to reach statistical steady-state
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Sprays, atomization, vaporization, spray-wall
Today: Spray Models require
Spray Lab calibrations
Exp
CFD
Future Measurement Needs
Inside nozzle: (cavitation)
fluorescence-shifted PIV
high- rep. Schlieren
Near nozzle: (optical measurements difficult)
Xray or ballistic imaging
Vaporization:
exciplex (liq., vapor, Tliq.)
CARS (T vapor)
vapor (Raman of dye-doped fuel)
Future Fuel Injector Needs
• rapid multipulsing
• control over small quantities
• robustness to coking
• rate shaping
Drop size:
liquid exciplex / Mie
Spray-wall and near wall:
Refractive index matching
T phosphor
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High-Speed PIV, Spray, and Spark Imaging
Fajardo, Sick - U. Michigan
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Spark Ignition
Important Processes
• time dependent spark energy
• spark stretching by local velocities
• restrikes after excessive stretching
• heat loss to electrodes
Examples of Modeling Approaches
• AKTIM (Arc Kernel Tracking Ignition Model)
Blokkeel
• DPIK (Discrete Particle Ignition Kernel)
Tan, Reitz
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Transition from Spark Kernel to Turbulent Flame
High repetition rate OH LIF imaging, white line indicates flame contour
4.6 ms
0.8 ms
4.8 ms
9.6 ms
14.2 ms
DNS flame kernel growth, surface corresponds to c = 0.5
Gashi et al. 2005
• Curvature, wrinkling, flame propagation effects important
• Current engine models arbitrarily switch from spark to combustion model,
except for modified G-eqn. model by Ewald and Peters.
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Multimode Combustion
SG SIDI engines:
DNS of nonhomogeneous
flame propagation
Haworth et al., 2000
HCCI:
DNS of HCCI A: spontaneous
ignition B,C: flamefront propagation
Chen et al. 2006
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Conclusions
1. Improved fuel economy and lower emissions are required for future engines.
• Minimize environmental impact
• Use fuels wisely
2. Hybrids and Fuel Cells important in mid- to long-term.
3. Gasoline direct injection engines important for forseeable future.
• DI homogeneous in production
• SG-SIDI near production
• HCCI in research phase
4. Optical diagnostics and CFD modeling are key engine development tools
• Optical diagnostics has led CFD modeling in WG SIDI engines
• CFD modeling rapidly becoming more important
5. Combustion engineers and scientists can have an impact on future engines.
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Acknowledgements
GM
Fansler, Lippert, Rask, Najt, Stojkovic, Reuss,
Kuo, Solomon, Grover, Zeng, Barths, Parrish, Szekely
VW
BMW
Toyota
Mitsubishi
Hentschel
Schuenemann
Shimizu, Furuno
Ando
U. Michigan
Penn State
Duisberg
Heidelberg
Sandia
Aachen
UC Berkeley
Natarajan, Fajardo, Sick, Wooldridge, He
Kung
Schulz, Zimmermann
Duewel
Dec
Adomeit, Pischinger, Wieske, Ewald, Peters, Felsch
Dibble
Thank you to Steve Pope and Marcus Aldén
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