Defense_APC_20121115_Final

Renewable energies | Eco-friendly production | Innovative transport | Eco-efficient processes | Sustainable resources
IFPEnergiesnouvelles
HDR – A. Pires da Cruz, IFP Energies nouvelles, November 15, 2012
Habilitation à Diriger des Recherches
Modeling chemical kinetics and
turbulence interactions for internal
combustion
engine reactive flow simulations
António Pires da Cruz
Engine CFD and Simulation department
November 15, 2012

012-IFPEnergiesnouvelles
Habilitation à Diriger des Recherches
Institut National Polytechnique de Toulouse
Correspondant:
Dr. T. Poinsot, Directeur de Recherche, CNRS & Université de Toulouse, France
Rapporteurs:
Prof. A. M. Dean, William K. Coors Distinguished Professor and Dean, Colorado
School of Mines, USA
Prof. E. Mastorakos, Cambridge University, UK
Prof. L. Vervisch, Professeur à l'INSA de Rouen, France
Membres du jury:
Prof. S. Candel, Professeur à l'Ecole Centrale de Paris et membre de l'Académie
des Sciences
M. F. Ravet, Expert à Renault SA
M. S. Henriot, Directeur à IFP Energies nouvelles

Background and resume
Education:
1988-1993: Mechanical Engineering, IST Lisboa, Portugal
1993-1994: Masters degree (DEA), Univ. Paris VI, France
1994-1997: PhD Thesis, Univ. Paris VI, France and IFPEN (December 9, 1997)
Professional experience:
1991-1993: IST, Portugal – Finite elements thermo structural modeling (part time)
1997-1998: IFPEN, France – Research engineer (4 months)
1998-2000: ExxonMobil Research and Engineering, USA – Post-doctoral position
Since 2000: IFPEN, France
2000-2004: IFP School professor and research engineer
2004-2011: Project manager – CFD modeling and simulation and engine combustion
Since 2008: Department head – Engine CFD and Simulation department

 PhD students:
 G. Subramanian with L. Vervisch, INSA Rouen and O. Colin, IFPEN,
2002-2005
 J. Anderlohr with F. Battin-Leclerc and R. Bounaceur, ENSIC
Nancy, 2006-2009
 Teaching (fundamental combustion, thermodynamics,
programming...):
 2000-2012, 639 hours, 60 to 90 students per year
 Projects
 IFPEN internal, GSM, ADEME, consortia, scientific collaborations,
contribution to numerous research proposals
 Member of the Combustion Institute (French section board
2006-2010)
 Publications:
 Papers with reading committee (WOS): 11
 Editorials: 1
Tuition, scientific activity and production

Overview
 Emissions from transportation: A global issue
 Engine CFD modeling
 Turbulent combustion modeling
 Chemical kinetic representation of surrogate
fuels
 Coupling turbulence and chemical kinetics
 Conclusions
 Future trends on engine combustion modeling

Green house gases: Estimated
human impact on surface temperature
Olivié et al., Atm. Chem. Phys., 2012
Total
Shipping
Aviation
CO2 –
Road transport
Non CO2 –
Road transport

CO2 sources from transport
Olivié et al., Atm. Chem. Phys., 2012
l'auto-journal (1er
nov 2012)
Shipping AviationRoad transport
Year 2000
Major CO2 reduction efforts made
by European (95 gCO2/km in 2020)
and Japanese auto OEMs must be
reinforced and globalized to all
countries and transport modes

Overview
fuels
 Conclusions

Modeling and Simulation1
 For the growing number of problems where experiments
are impossible, dangerous, or inordinately costly, (...)
computing will enable the solution of vastly more accurate
predictive models and the analysis of massive quantities
of data, producing (...) advances in areas of science and
technology that are essential (...). For example, (...)
computing will push the frontiers of:
 Reduction of the carbon footprint of the transportation sector
 Innovative designs for cost‐effective renewable energy
resources such as batteries, catalysts, and biofuels
 Design, control and manufacture of advanced technology
devices
 (...)
1
The opportunities and challenges of
exascale computing, US DOE ASCAC
Comitee, 2010

km
mm
µm
nm
Turbulent
flame
Combustion
chamber
Engine
m
Vehicle
Environmental
impact
Spatial scales
related to transport and emissions
Flame/Vortex
interaction
Molecular
reactions
Laminar
flame

Overview
fuels
 Conclusions

 Species i mass fraction Favre average
transport equation:
 Average and instantaneous reaction rate
(Arrhenius type):
Average flows: RANS turbulent
combustion modeling fundamentals

RANS Turbulent versus kinetic modeling
Turbulent mixing layer with 1-step chemistry
x
time [µs]
Turbulent
combustion model
"Arrhenius" or
"order 0 model" approach
time [µs]
TF = 300 K
Tox = 1000 K
YF,0 (nC7H16) = 1
Yox,0 = 0.233
Zst,nC7 = 0.062
Zmr ~ Zst

Main observations and highlights
 Different ignition delays:
 Kinetic: Total mixing (~0.05 ms)
 Turbulent: AI delay depends on
mixing time (~0.3 ms)
 Different ignition locations:
 Kin: Z most reactive1
 Turb: Highest probability of
finding Z most reactive
 Reaction rate distribution:
 Kin: High reaction rate over all
the mixing zone
 Turb: Reaction progress
towards lean and rich zones
 After AI:
 Kin: Reaction at Zst
 Turb: Mixing layer too diluted
Probability of finding Zst
Turb "no model"
1
E. Mastorakos et al., C&F, 1997
Zmr ~ Zst = 0.062

Conclusions:
RANS turbulent combustion modeling
 Turbulence and kinetics are intrinsically coupled:
 Non linear chemical kinetics (exponential behavior with
temperature)
 Large turbulent fluctuations (temperature fluctuations
several 100 K)
 In RANS, turbulent structures are handled statistically
 Molecular mixing (at the smallest scales) and turbulent
fluctuations must be taken into account (eg. Mastorakos
et al., CMC auto-ignition, CST, 1997): Models for numerical
mixing are required
 Kinetic reaction rates must be integrated over the
mixing distribution (eg. Pires da Cruz et al., C&F, 1999)

Overview
fuels
 Conclusions

Choice of surrogate fuels
 From historical ICE surrogate fuels:
 n-Heptane: Diesel auto-ignition
 iso-Octane: Gasoline laminar flame speed
 PRF (nC7 / iC8): Gasoline auto-ignition (knock)
 To more complex surrogate fuels:
 IDEA-EFFECT diesel: 70% nC7 / 30% amn (volume)
 Biodiesel: 70/30 + methyl oleate (BIOKIN)
 XTL + diesel: 70/30 + iC8 / nC16 (BIOKIN)
 Toluene Reference Fuel (TRF): European gasoline
(literature + GSM 2006 proportions)
 TRF/ethanol: EXX for gasoline / ethanol mixtures
 MFC2 extensive surrogate fuels database

TRF fuel for gasoline ICE
SI engine experimental results1
suggested the validity
of the TRF2
as a gasoline surrogate
Heat release rate
CO2 emissions
NOx emissions
1
P. Anselmi et al., Rapport IFPEN 60419, GSM E2.3, 2007
2
C. Pera and V. Knop, Fuel, 2012
iC8H18
Standard gasoline
TRF mixture

 Primary
Mechanism
 Secondary
Mechanism
Mech generator EXGAS LRGP
 C0-C2
Reactions
Base
Bounaceur
et al.2
(LRGP)
n-Heptane / iso-Octane (PRF)3Toluene
Kinetic
Model
THERGAS
KINGAS
ReactiveSpecies C0-C2 Reactions
Base
Primary
Mechanism
Secondary
Mechanism
Building a TRF/NOx kinetic mechanism1
NOx
GRI-MECH
3.0
Coupling: PRF-
toluene
Coupling: NOx-PRF
Coupling:
NOx-toluene
1
J. Anderlohr et al., C&F, 2009
2
R. Bounaceur et al., Int. J. Chem. Kin., 2005
3
F. Buda et al., C&F, 2005

Mechanism validation (PSR)
n-Heptane
Temperature [K]
MolarFraction[-]
￭▲
￭
￭
￭
￭
￭
￭
￭
￭
￭￭￭￭￭￭
▲￭▲
▲
▲
▲
▲
▲
▲ ▲ ▲ ▲ ▲ ▲ ▲
▲
￭
￭
￭￭￭￭￭
￭
▲
▲ ▲ ▲ ▲ ▲ ▲
Temperature [K]MolarFraction[-]
▲
￭
￭
▲
▲
toluene
▲
Pressure: 10 atm, Ф: 1
Dilution: 98 mol %
Moréac et al., C&F, 2006
Sim
Exp
Fuel + 500 ppmv NO
Fuel (no NO)
▲
●
Fuel + 50 ppmv NO ∎Good agreement between simulation and experience on the impact of
NO over alkanes and toluene oxidation

Final kinetic mechanism: TRF + NOx1
 Validated over a wide range of experimental
conditions
 Model size:
 Number of species: ~ 500
 Number of reactions: ~ 3000
 Number of reactions NOx – HC: ~ 400
1
J. Anderlohr et al., C&F, 2009

Overview
fuels
 Conclusions

10 years and more ago...
 New and growing vehicle emissions
regulations
 Fuel economy and low CO2 emissions
imposed
 New and alternate fuels: Biodiesel, ethanol,
NGV...
 Technological breakthroughs, like DI diesel
and gasoline, leading to chamber geometrical
constraints
 Raising experimental security regulations and
costs
 Benefits of CFD had been already highlighted

Engine CFD
Turbulent combustion modeling issues
 Except for DNS, in LES and RANS, flames
mostly have subgrid dimensions
 Subgrid turbulent combustion models are
needed
 Due to combustion reaction rate non linearity,
turbulent effects have to be taken into
10-4
10-5
10-6
10-3
Thermallaminar
flamethickness10-2
Turbulentscales
DNScellsize
LEScellsize
RANScellsize
m

Turbulence / Chemistry interactions
 Necessary ingredients:
 Turbulence model
 Description of fuel/air or fresh/burned gas mixing
 Chemical kinetics – Complexity depends on
required goals (heat release only, major species,
regulated or non regulated pollutants...)
 Turbulent combustion models:
 Flamelet assumptions (two state description)1
 Flamelets with presumed PDFs2
 Thickened flame description3
 Transported PDFs4
 CMC5
1
S. Candel and T. Poinsot, CST, 1990
2
L. Vervisch et al., J. of Turb., 2004, Michel et al. C&F, 2008
3
O. Colin et al., Phys. Fluids, 2000
4
S. Pope, PECS, 1985
5
R. Bilger, Phys. Fluids, 1993 & E. Mastorakos et al., 1997

Taking chemical kinetics into account
 ICE fuels: Complex mixtures of tenths to
hundreds of pure component fuels
 Each fuel: Hundreds to thousands of chemical
species and reversible reactions
 1st
Step: Choose convenient surrogate fuels
 2nd
Step: Adapt or build kinetic mechanisms for the
surrogates (depending on required goals)
 3rd
Step: Include chemical kinetic information into the CFD
code (complex, reduced, tabulated kinetics)

Levelofinformation
Simulationtime  Simple chemistry (1 global
reaction): Reaction rate
 Reduced chemistry
(~ 10 reactions): Heat
release, major species
 Tabulated chemistry: HR,
tabulated species
 Direct chemistry / CFD
coupling: HR, all mechanism
species
27
Complex kinetics and CFD: How to?
 

?? Stiffness
??HPC

Kinetic tabulation techniques
 Major benefits:
 Reproduction of fuel diversity, if kinetic mechanisms
available
 Major species (heat release) available
 Minor species – regulated and NR pollutants – available, if
tabulated
 Fuel complex kinetics reproduced: eg. Double stage AI
 Reduced implementation efforts and computing time
 Coupling with reduced or detailed kinetics remains possible
 Drawbacks:
 Assumption of a system invariant, a priori, chemical
behavior
 Which system to tabulate? Auto-ignition? Diffusion or
premixed flames?
 Tabulation needs non dimensional time: Progress variable

TKI1
: A "simple" tabulation and
interpolation approach
Ti,pj,φl
Ti+1,pj,φl
Ti,pj+1,φl
1
, , , resc
T p Xω φ 
 ÷
 
&
, , ,
k
c resT p Xω φ 
 ÷
 
&
1
, , ,
k
c resT p Xω φ+
 
 ÷
 
&
c%
, , , resT p Xφ
1
( , )
k k
c c cfctω ω ω
+
=& & &
1
d
τ C1
Ti,pj+1,φl+1
Ti+1,pj,φl+1
No tunable
AI parameter
in the code
AI constant
pressure simulation
Input
Output
1
O. Colin et al., 30th
Int. Symp. Comb., 2004

TKI and reduced kinetics coupling: Diesel
injection timing1
CORK2
: Heat release
Zeldovich: NO
PSK: Soot
1
V. Knop et al., OGST, 2008
2
S. Jay et al., SAE, 2007
TKI: AI delay

Integration of
kinetic tabulations and turbulence1
1
G. Subramanian et al., SAE, 2007
 Subramanian (PhD)
proposed the first TKI-
PDF model version at
IFPEN, including:
 Tabulated kinetics
(fully tabulated
reaction rates)
 PDF fuel/air mixing
 A priori integration of
turbulence effects
 His model set the
grounds for further
tabulated approaches
at IFPEN:
 PhD Galpin (LES)
 PhD Michel (ADF-

Tabulation of
AI delay Tabulation of
2 AI delays
FPI like tabulation models – progress variable
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Tabulation of delays and reaction rates TKI
PhD Subramanian Tabul.
of Reaction rates (RANS)
Laminar Fl
Speed (GSM)
PhD Galpin Tabul. of react.
rates and mass fract (LES)
PhD Michel Tabul diffusion
flames+PDF (RANS)
PhD Anderlohr Post-
oxidation (RANS)
PhD Mauviot (0D)
FPI + ECFM
(GSM)
FPI V cst (GSM)
FPI expansion (GSM)
FPI compression /
expansion (GSM)
FPI V variable +
fluctuations (GSM)
PhD Lecocq FPI + PDF +
ECFM (LES)
PhD Dulbecco (0D)
StrainLam Fl
Speed (ANR)
PhD Bouali FPI/DNS
PhD Bernard CO + NOx 0D
PhD Vervisch Soot + NOx
PhD Tillou Tabulated
diffusion flames+PDF (LES)
PhD Karkar Soot
100% IFPEN
IFPEN PhD Tab
Lam Fl Speed Tab
GSM Tab
Kinetic tabulated approaches at IFPEN

Overview
fuels
 Conclusions

Conclusions
 In the context of present and future severe
reduction of exhaust emissions and CO2 from
IC engines, simulation plays an important and
increasing role
 3D IC engines CFD can help:
 Studying more complex IC engines:
 Combustion chamber geometries and technological
solutions
 Complex operating conditions
 Set boundary conditions for increasingly complex
after-treatment systems
 Optimizing fuel diversity
 Taking vehicle hybridization into account:


Highlights on IC engine CFD modeling
 IC engines: Turbulent reactive flows
 In RANS CFD, the intimate coupling between
turbulence and chemical kinetics must be
modeled:
 Mixing and flame structures are subgrid
 Turbulent structures are not simulated
 Perfect stirred reactor kinetic assumptions are not
adapted
 Flamelet assumptions can be used: eg. ECFM
model for premixed or partially premixed
flame propagation
 Presumed PDF approaches are well fit for

My main contributions to IC engines CFD
 Among the first attempts to couple chemistry and
turbulence in RANS models for auto-ignition and
diffusion flames, using a presumed PDF approach
(PhD)
 Introduction of fuel complex chemistry into engine CFD
codes using tabulation techniques:
 AI delay for diesel combustion and knock in SI engines
(Pires da Cruz, 2004)
 Tabulation of progress variable reaction rates (Colin et
al., 2004)
 First coupling of tabulated chemistry and turbulence
using a presumed PDF approach (Subramanian, 2005)
 Modeling the complex kinetics of realistic diesel and
gasoline surrogate fuels (Anderlohr, 2009)
 Construction of extensive experimental databases for

Overview
fuels
 Conclusions

How about the future?
 HPC development during the last few years is
changing the engine CFD landscape:
 It allows resolution of more complex geometrical
problems
 It strongly reduces computing return time leading to use
of more discrete meshes and higher precision numerical
methods
 More complex and CPU time consuming models can be
proposed, including complex chemical kinetics and
turbulence
 LES and maybe DNS are gaining momentum:
 For industrialization, large research efforts are required
 In LES, turbulence/chemistry interactions are easier to
describe, but subgrid modeling is still necessary
 In LES, the modeling effort is probably lower: More fit

Future of RANS IC engine modeling
 RANS simulations are nevertheless still the most
industrial "friendly"
 A priori integrated [chemistry/presumed PDF] has proven
to be a fair approach but requires tabulated kinetics
 Tabulation techniques are becoming rather complex
when it comes to simulating IC engines (variable volume
and pressure, realistic flame configurations, multiple
object species, fuel diversity...)
 Complex kinetics are now available for a large number of
realistic components – Tabulation techniques do not tend
to facilitate the use of chemistry in the future
 Kinetic reduction techniques have also been largely
improved
 How about further exploring mixed tabulation / reduced

Acknowledgements
 T. Poinsot (PhD advisor, HDR correspondent) and all jury members
 IFPEN and all my colleagues (present and former) at IFPEN, with
whom I worked closely during all these years (CFD, optical
diagnostics, engines, thermodynamics...)
 My PhD students and their co-advisers (L. Vervisch, O. Colin, F.
Battin-Leclerc and R. Bounaceur)
 My colleagues at ExxonMobil who helped me getting rid of my
chemical complex!
 EM2C, CERFACS, CORIA, LRPG and all the other CNRS, university
and research laboratories colleagues with whom I have collaborated
 All my former hierarchy and S. Henriot who have always provided the
maximum to granting a high quality R&D environment
 Many thanks to those who have closely supervised my research work
through the years and who are absent today (P. Eyzat, T. Baritaud,
JM Duclos, A. Torres)
 GSM, industrial partners, Ademe, ANR, EU... for financing
 My family who put up with lack of evenings, weekends and holydays!

Renewable energies | Eco-friendly production | Innovative transport | Eco-efficient processes | Sustainable resources
www.ifpenergiesnouvelles.com

Defense_APC_20121115_Final

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