HDR_APC_INPT_dec2012

Habilitation à Diriger des Recherches
présentée par
António PIRES da CRUZ
Chef de Département à IFP Energies nouvelles
Le 15 novembre 2012 à IFPEN, Rueil-Malmaison
Modélisation du couplage entre cinétique
chimique et turbulence pour la simulation des
écoulements réactifs dans les moteurs à
combustion interne
Correspondant
Dr. Thierry Poinsot, Directeur de Recherche, CNRS & Université de Toulouse, France
Rapporteurs
Prof. Anthony M. Dean, William K. Coors Distinguished Professor and College Dean,
Colorado School of Mines, USA
Prof. Epaminondas Mastorakos, Cambridge University, UK
Prof. Luc Vervisch, Professeur à l'INSA de Rouen et CNRS, CORIA
Membres du Jury
Prof. Sébastien Candel, Professeur à l'Ecole Centrale de Paris et membre de l'Académie
des Sciences
M. Frédéric Ravet, Expert à Renault SA
M. Stéphane Henriot, Directeur à IFP Energies nouvelles
62873

Modélisation du couplage entre cinétique chimique et turbulence pour la sim-
ulation des écoulements réactifs dans les moteurs à combustion interne
Abstract: Dans un contexte social et économique qui impose à l’industrie du transport de
fortes contraintes de consommation en carburant, d’émissions, de performances et de coûts,
la simulation numérique joue un rôle clé. A IFPEN, j’ai pu développer une compétence en
analyse et en études de simulation de la combustion, essentiellement dans le domaine des moteurs
à combustion interne. Mon activité de recherche a été motivée par la simulation numérique
du couplage fort entre l’écoulement turbulent et la cinétique chimique dans le domaine de la
simulation tridimensionnelle moyennée des écoulements réactifs présents dans les chambres de
combustion des moteurs à allumage commandé et Diesel équipant des véhicules automobiles.
Ce rapport, construit sur les axes de la recherche, de la gestion de projets de recherche et
de l’encadrement et de l’enseignement, illustre mon activité dans ce domaine. Les principaux
résultats y sont décrits et des suggestions de recherches futures sont proposées.
Mots Clés: Moteur à Combustion Interne, Turbulence, Cinétique Chimique, Auto-inflammation,
Modélisation, Simulation
Modeling Chemical kinetics and turbulence interactions for internal combus-
tion engines reactive flow simulations
Abstract: Amid the current social and economic context imposing the transportation indus-
try strong fuel consumption, pollutant emissions, performance and cost constraints, numerical
simulation plays a key role. At IFPEN, I was able to develop skills in the area of combustion
modeling and simulation, mainly in the framework of internal combustion engine studies. My
research activity has been driven by the necessity of building 3D averaged models to simulating
the chemical kinetics and turbulent flow interactions inside the combustion chamber of diesel
and spark ignition engines. This document, oriented towards the axis of scientific research, re-
search project management and teaching and student advising, illustrates my activity in that
area. Main results are described and suggestions for future research are proposed.
Keywords: Internal Combustion Engine, Turbulence, Chemical kinetics, Auto-ignition, Mod-
eling, Simulation

Contents
1 Introduction 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Scientific activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Teaching and student supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Curriculum Vitae 5
3 Research Work 7
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Turbulent combustion modeling in diesel engines . . . . . . . . . . . . . . . . . . 7
3.2.1 Diesel engines combustion . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2.2 Diesel combustion modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.3 First steps: The PDFA/CHI model for diesel AI and combustion . . . . . 10
3.2.4 Fuel/Air mixing models towards combustion simulation . . . . . . . . . . 13
3.2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3 Modeling the mixture fraction variance and its evaporation source term . . . . . 16
3.3.1 Tested models for ρ ˙Sv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3.2 Experimental results and 3D RANS simulations . . . . . . . . . . . . . . . 17
3.3.3 Simulation results analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.4 Conclusions and recommendations . . . . . . . . . . . . . . . . . . . . . . 21
3.4 Kinetic modeling of surrogate fuels . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4.1 Development of a gasoline surrogate kinetic mechanism . . . . . . . . . . 24
3.4.2 Kinetic studies on an engine context: Influence of burned gases . . . . . . 27
3.4.3 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4.4 Future developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.5 Coupling turbulence and chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.5.1 Preliminary kinetic tabulation approaches for reactive flows . . . . . . . . 34
3.5.2 Tabulation of AI delays . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.5.3 Tabulation of a progress variable reaction rate . . . . . . . . . . . . . . . 37
3.5.4 The TKI model: AI delays and progress variable reaction rate . . . . . . 39
3.5.5 New turbulence/kinetics modeling approaches . . . . . . . . . . . . . . . . 40
3.6 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
i

ii CONTENTS
4 Project Management and Collaborative Research 45
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Combustion modeling and experimental databases . . . . . . . . . . . . . . . . . 46
4.2.1 BIOKIN: Diesel/biodiesel database . . . . . . . . . . . . . . . . . . . . . . 46
4.2.2 IDETHANOL: Analysis and modeling of direct injection spark ignition
engines fuelled by ﬂexible ethanol/gasoline mixtures . . . . . . . . . . . . 47
4.2.3 Reaction Design Model Fuels Consortium . . . . . . . . . . . . . . . . . . 50
4.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5 Teaching and student advising 55
5.1 Teaching activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.2 Research supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.2.1 Trainees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.2.2 PhD students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3 Future supervision of research work . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Bibliography 63
A Published work 75
A.1 Papers in journals with reading committee . . . . . . . . . . . . . . . . . . . . . . 75
A.2 Patent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
A.3 Editorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
A.4 Conferences and communications . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
A.5 Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
B Selected Articles 79
C Journal printout: ISI WoK report 181
D H Index printout: ISI WoK report 187
E Summary of tuition activities 189

Chapter 1
Introduction
1.1 Overview
My first contact with scientific research was in the early 1990’s during my faculty years at
IST, Lisbon, Portugal. The IST Mechanical Engineering department had then been sponsored
to help industrial partners building precision tools. I was engaged as a research student, and
became part of the team writing a finite element code to be used in optimizing tools submitted
to mechanical and thermal efforts.
That first experience led me to a scientific research career. My growing interest in the
disciplines of fluid mechanics and internal combustion engines made me look for a related PhD
thesis subject.
In 1994, I started my PhD work at IFP Energies nouvelles under the guidance of Doctors
Thierry Baritaud from IFP Energies nouvelles1 and Thierry Poinsot from IMFT/CNRS and
CERFACS in the field of turbulent combustion modeling applied to internal combustion engines.
After getting the PhD degree, I had a two years post-doctoral experience at ExxonMobil where
I had the opportunity to learn about hydrocarbon chemical kinetics. I was then engaged at IFP
Energies nouvelles. There, as an IFP-School associate professor, I was in charge of a master’s
degree in internal combustion engines and I built a turbulent combustion course which I still
teach. As a research engineer and project manager, I worked on the coupling between turbulence
and chemistry to model internal combustion engines reactive flows.
This report is organized as follows: After a brief introduction, Chapter 2 presents my resume;
Chapter 3 summarizes my research work and includes a list of publications and reports; Chapter 4
enumerates a selection of research projects I managed; Chapter 5 details my teaching and
research supervision activity; Appendix A contains an extensive list of all my published work,
including articles, conferences and IFP Energies nouvelles internal reports; Appendices B, C
and D present respectively a selection of scientific articles illustrating my research work, my ISI
Web of Knowledge publication list to date, and my current H-index with most cited articles
calculated by ISI Web of Knowledge; finally, Appendix E presents different tables summarizing
my teaching and research supervision activity.
1.2 Context
For several decades, IFP Energies nouvelles has been developing skills in the fields of combus-
tion analysis, modeling and simulation, essentially for internal combustion engine applications.
1
Presently at Ferrari S.p.A.
1

2 CHAPTER 1. INTRODUCTION
These systems are characterized by a strong coupling between a relatively intense turbulent flow
generated by the air or fuel/air mixture intake and the compression strokes and the velocity of
chemical reactions inherent to fuel oxidation.
In a social and economic context imposing very strong constraints on the automotive industry
in terms of emissions, performances and costs, numerical simulation plays a key role. It allows
the understanding and thorough analysis of flows inside combustion chambers and at the same
time, may contribute to cost reduction in the development of new devices.
The necessary condition for such a scenario is the availability of highly performing computer
codes where reliable physical models are implemented. This is typically the case when simu-
lating the effects of complex hydrocarbon chemical kinetics. Such effects are fundamental in
order to predict pollutant emissions and the use of alternate fuels. But they are also at the
center in understanding existing systems like Diesel engines. The revolution brought by those
engines in Europe during the last fifteen years needed and still asks for fundamental studies
where chemical kinetics plays a fundamental role since it controls fuel auto-ignition (AI). After
my PhD, I have since contributed to improving AI and combustion models applicable to internal
combustion engines. For such purpose, I have used several available tools: Direct numerical sim-
ulations (DNS), complex chemical kinetic simulations (Chemkin), 3D CFD Reynolds Averaged
Navier-Stokes (RANS) simulations (KIVA MB and then, IFP-C3D) as well as advanced optical
diagnostics in an engine environment.
1.3 Scientific activity
My research activity, mostly at IFP Energies nouvelles, addresses the numerical simulation
of the strong coupling between turbulent flows and combustion chemical kinetics. Its field
of application is the averaged three-dimensional simulation (RANS) of reactive flows inside
combustion chambers of Diesel and spark ignition engines.
During my PhD [1], I developed a combustion model with the goal of simulating AI and
high temperature oxidation of hydrocarbon fuels used in Diesel engines. The model coupled
chemistry and turbulence using a presumed probability density function (PDF) approach during
the ignition period and assumed the presence of a thin flame (flamelet) to model the high
temperature combustion [1–3]. This model was the first step at IFP Energies nouvelles towards
engine combustion modeling integrating turbulence effects by using presumed pdf’s. The concept
has been used since then in RANS as well as in Large Eddy Simulation (LES), both in 3D models
and in system simulation (0D modeling). It has also been applied to modeling air-fuel mixture
phenomena.
Having acquired a strong background in turbulent combustion modeling, I joined ExxonMobil
where during a two year period, supervised by Professor Anthony M. Dean2, I had the possibility
of broadening my combustion modeling skills towards the area of chemical kinetic oxidation of
automotive fuels [4, 5]. This allowed me to propose models coupling turbulence and chemical
kinetics, well adapted to simulating reactive flows inside internal combustion engines. This step
was fundamental in order to address new combustion modes based on AI phenomena as well as
to take into account the oxidation of alternative fuels.
Very quickly, it became obvious that integrating complex chemical kinetic mechanisms in-
cluding hundreds of species and thousands of reactions in a CFD simulation was hardly feasible.
Mechanism reduction was then necessary. One of the most promising techniques is a priori
2
At present, Distinguished Professor at Colorado School of Mines, Boulder, CO, USA

1.4. TEACHING AND STUDENT SUPERVISION 3
tabulation. Thousands of complex chemistry a priori simulations can be handled in a sim-
ple geometry reactor and only pertinent information, depending on the required complexity of
the combustion model, is retrieved for the CFD simulations. A priori tables covering a set of
thermodynamic conditions which bounds depend on the problem to be addressed are built and
integrated in the CFD solver where the necessary data is extracted using interpolation algo-
rithms. At IFP Energies nouvelles, I was the first to propose such methods which, since then,
have been thoroughly used and have led to a large number of research programs, internal or in
collaboration with research laboratories and industrial partners (cf. Fig. 1.1):
• I have developed the first Diesel AI model based on the tabulation of AI delays coupled with
a 3D CFD code. That model was then extended to a double AI delay tabulation, which
behavior is typical of hydrocarbons, present in the composition of Diesel fuels [6].
• The success of the first tabulation method motivated several new projects leading to the
development of the AI Tabulated Kinetics of Ignition (TKI) model, where ignition delays
and reaction rates are tabulated [7]. That model is still widely used at IFP Energies
nouvelles [8–10], where it is implemented in its engine 3D CFD code IFP-C3D, as well as
in other commercial CFD tools such as ADAPCO STAR-CD and AVL FIRE. The model
can receive inputs from any complex chemistry mechanism able to simulate the AI process
of gasoline or Diesel fuel surrogates as well as alternate fuels (NGV, bio-fuels...).
• I have proposed research subjects and supervised the work of several training and PhD
students described below.
Other than the coupling of complex kinetics and CFD codes, I also studied the possibility
of including global reduced kinetics (less then ten reactions) directly into CFD codes [9,10].
1.4 Teaching and student supervision
At IFP Energies nouvelles, as an IFP-School assistant professor, I built and lectured numerous
courses for high level master students in the fields of fundamental combustion, thermodynamics
and computer science. I have also lectured at Université Paris VI, Ecole des Mines de Nancy
and in a Brazilian University. At IFP-School, during a four year period between years 2000 and
2004, I was in charge of a master’s degree in internal combustion engines, in collaboration with
Université Paris VI and other French universities.
Along with my scientific research work, I have proposed and guided the PhD theses of G.
Subramanian [11] and J. Anderlohr [12] in the framework of coupling tabulated chemistry and
3D RANS simulation codes. I have also supervised five trainees, for periods from three to six
months, doing their final undergraduate work at IFP energies nouvelles on different subjects
related to combustion modeling and simulation studies.
A detailed list and brief description of all my teaching and research guidance activity is
presented in Chapter 5.
1.5 Summary
My scientific activity has allowed me throughout the years to publish eleven articles in high
ranking journals. A summary of my key work is presented in chapter 3. I have also proposed

4 CHAPTER 1. INTRODUCTION
Figure 1.1: Summary of combustion modeling activities at IFPEN based on a priori chemical
kinetic tabulation methods.
and managed diﬀerent research projects (chapter 4), I have built and given lectures at a master’s
degree level and I have guided PhD thesis studies (chapter 5).
Today, at IFP Energies nouvelles, I am in charge of the ”Engine CFD and Simulation”
department. This research unit has very strong links to a number of other units at IFP Energies
nouvelles which activities go way beyond the area of automotive engines. Collaborations with
academic and industrial research laboratories in France and abroad have also been established.
The main goal of the team is to improve the understanding of engine and vehicle systems based
on simulation tools. For such purpose, codes oriented towards 3D CFD modeling and system
simulation of engines and vehicles are developed, requiring intense collaborative work. My
research work and the contacts I have been able to establish are very important to my activity
as a team leader.

Chapter 2
Curriculum Vitae
Ant´onio PIRES da CRUZ
Born January 2nd
1971 in Estoril-Cascais (Portugal)
Nationality: Portuguese
Professional address
IFP Energies nouvelles
1 et 4, av. Bois Pr´eau
92852 RUEIL MALMAISON Cedex
France
e-mail: antonio.pires-da-cruz@ifpen.fr
Tel: +33 1 47 52 65 02
Present Occupation
Department Head of the Engine CFD and Simulation Department at the Energy Appli-
cations Techniques Division of IFP Energies nouvelles
Project manager at the IFP Energies nouvelles Transport Business Center
Assistant professor at IFP-School
Professional Activity
• Since 2008 IFP Energies nouvelles: Department Head of the Engine CFD and Simu-
lation Department
• Since 2005 IFP Energies nouvelles: Project Manager in the area of internal combus-
tion engines CFD modeling, simulation and advanced diagnostics
• Since 2000 IFP Energies nouvelles: In charge of research and development at the
Energy Applications Techniques Division
• 2000-2004 IFP Energies nouvelles: Assistant professor at IFP-School
• 1998-2000 ExxonMobil: Post-doctoral at ExxonMobil Research and Engineering De-
partment, Annandale, New Jersey, USA in the area of chemical kinetic modeling and
gaseous hydrocarbon combustion
5

6 CHAPTER 2. CURRICULUM VITAE
• 1997-1998 IFP Energies nouvelles: In charge of research and development at the
Energy Applications Techniques Division (4 months)
• 1991-1993 IST, Lisboa, Portugal: Research student at the Applied Mechanics De-
partment in the area of finite elements thermo-structural modeling
Education
• 1994-1997 PhD at Université Pierre et Marie Curie, Paris VI and at IFP Energies nou-
velles. PhD advisors: Doctor T. Poinsot of INPT, Toulouse and Doctor T. Baritaud
of IFP Energies nouvelles: ”Development of a self-ignition and combustion model
for Diesel engines”, Discipline: Mechanics; Specialty: Mechanics and Energy; PhD
obtained on December 9, 1997
• 1993-1994 Masters degree in ”Energy Conversion”, option ”Engines and Environ-
ment” at Université Pierre et Marie Curie, Paris VI and IFP-School
• 1988-1993 Mechanical Engineer degree, option ”Fluid Mechanics and Thermodynam-
ics” at Instituto Superior Técnico (IST), Lisboa, Portugal

Chapter 3
Research Work
3.1 Introduction
My research activity has been focused on three main topics: RANS (Reynolds Averaged
Navier-Stokes) turbulent combustion modeling, chemical kinetics modeling and modeling
the coupling between turbulence and chemical kinetics.
The present chapter highlights my contribution towards increasing the knowledge and
understanding of the items listed above. Most of the work was performed in the framework
of PhD and training studies (presented in chapter 5) and collaborative research projects
(presented in chapter 4). The first section in this chapter briefly recalls the need for tur-
bulent combustion modeling in internal combustion engines. The following four sections
summarize my main research topics. The last presents recent related work performed at
IFPEN as well as an overview on future trends of research in related fields.
Several research projects to which I have contributed are not addressed here even if
their volume was often important. Their link to my main research area is not straight
forward and most of them are unpublished. These include kinetic modeling of laminar
flame speeds, numerical work (development of a full ALE - Arbitrary Eulerian Lagrangian
- two phase flow code and implementation of characteristic boundary conditions in a one-
dimensional non steady-state laminar flame code), two phase flow simulation of gasoline
direct injection sprays and experimental work related to fuel effects on different engine
combustion modes.
3.2 Turbulent combustion modeling in diesel engines
3.2.1 Diesel engines combustion
Diesel engines are widely used in industrial applications wherever a good energetic ef-
ficiency is sought. This is the case of ground, rail and sea transportation, heavy duty
machinery or production of electricity. Ground transportation (light vehicles and heavy
duty) is by far the largest application for diesel engines. According to the International
Energy Agency (IEA), in 2008, diesel represented more than 50% of the total transport
fuel consumption in European OECD countries [13]. The same source predicts that for
the same region, in 2050, diesel fuel should still represent between 15% and 50% of the
total transport fuel demand, depending on the assumed modeling scenario (respectively,
7

8 CHAPTER 3. RESEARCH WORK
Blue Map for low CO2 emissions and Baseline for ”Business as Usual”). Such figures
largely justify a continuous effort in understanding and modeling diesel combustion in
order to build more efficient and less pollutant diesel engines.
Figure 3.1: Combustion in a 4-stroke diesel engine.
The principle of diesel engine combustion is illustrated in Fig. 3.1 representing a 4-
stroke diesel engine (intake, compression, combustion/expansion and exhaust). A detailed
explanation is given by Heywood [14]. After intake valve opening near the piston Top
Dead Center (TDC), air is introduced inside the chamber, aspirated by the piston down-
wards movement (intake stroke). Following intake valve closing near the Bottom Dead
Center (BDC), the piston moves up and compresses the air trapped inside the chamber
(compression stroke). Pressure and temperature rise accordingly. Close to TDC, fuel is
injected by a high pressure injection system (up to 2500 bar in modern diesel engines).
Diesel fuel is usually in the liquid state, but gaseous fuels, heavy oils or even pulverized
coal can also be used in diesel engines. Liquid fuel evaporates and mixes with air. Due to
high temperature and pressure, the air/fuel mixture ignites and is burned. The fuel injec-
tion/ignition phasing allows different combustion modes. In conventional diesel engines,
AI takes place during fuel injection and a diffusion flame between vaporizing fuel and air
is established. In diesel Low Temperature Combustion (LTC) engines, AI occurs after the
end of injection and combustion proceeds mostly by AI of the fuel/air mixture. The rise
in pressure and temperature due to combustion causes the piston to move downwards pro-
ducing torque (combustion/expansion or power stroke). Finally, near BDC, the exhaust
valve opens letting burned gases escape from the combustion chamber. The piston moves
up again pushing the remaining burned gases outside the chamber (exhaust stroke). In a
2-stroke diesel engine, there is a power stroke and a compression stroke with exhaust and
intake in between. The combustion process however is strictly equivalent to the former
4-stroke cycle.
Combustion in a diesel engine is therefore summarized in the following steps: Auto-
ignition (AI), diffusion flames (DF) and transition from one process to the other. Between
AI and DF, a multitude of possibilities occurs including the presence of highly stratified
premixed flames (PM) ignited by AI and propagating towards the flammable mixture
formed between the start of injection and the AI delay. The mixing, AI and combustion

3.2. TURBULENT COMBUSTION MODELING IN DIESEL ENGINES 9
are all highly turbulent processes since high velocity turbulent flows are generated by
fuel injection and partly by the air movement resulting from the intake and compression
strokes. Chemistry plays a key role during AI and throughout the whole diesel combustion
event. Turbulence and chemical kinetics are strongly related.
3.2.2 Diesel combustion modeling
Reynolds Averaged Navier-Stokes (RANS) techniques were developed to allow the ap-
plication of Computational Fluid Dynamics (CFD) to solving industrial problems such
as diesel combustion, where a turbulent reactive flow has to be simulated. The numeri-
cal solution of instantaneous turbulent flow fields requires high order numerical schemes
(temporal and spatial) which coupled to the need of a detailed description of the entire
turbulent flow structure and fuel chemical kinetics lead to computational times incompat-
ible with industrial applications. The objective of RANS is therefore to find an averaged
solution for all flow variables by solving the average formulation of the transport equations.
Since averaging operations imply loss of information (the average is the first moment of
a n-moment problem), unclosed or non resolved terms appear in the averaged equations.
Closure rules or models are then necessary to solve the equations. The reactive version of
the averaged transport equations requires a turbulence model to represent flow dynamics
and a turbulent combustion model.
The oxidation of fuel/air mixtures due to combustion is a highly non linear chemical
kinetics multi-step process. Air/fuel combustion kinetics can be represented by sets of
tenths to hundreds of species and tenths to thousands of reactions depending on the com-
plexity of the fuel molecule. Rates of decomposition and formation for each individual
reaction have an exponential dependence on temperature and are proportional to reactive
species concentrations. Average reaction rates, necessary to solving the reactive RANS
equations, thus include chemical and turbulent contributions directly linked to turbulence
induced randomness. The major difficulty of turbulent combustion modeling in averaged
flows then becomes the identification of relevant phenomena to be taken into account and
phenomena that might be neglected. Assumptions to do so are based on an analysis pro-
cess that can go from physical intuition to thorough theoretical analysis of the combustion
event. Dimensional analysis and evaluation of characteristic time scales are useful tools.
When using the latter, modeling very fast processes is often neglected whereas slow and
limiting ones have to be taken into account.
In a diesel engine with all its variants and operating modes, it is difficult to identify
a priori which phenomena might be neglected and which have to be considered. As a
rule of thumb, it can be assumed that AI is a chemically driven process whereas high
temperature combustion is controlled by turbulent mixing (fresh and burned gases in a
premixed flame, fuel and air in a diffusion flame). Such a view is however quite restrictive
since the combustible mixture formed before AI is due to intense turbulent mixing. Also,
during transition from AI to a fully developed flame, no process can be considered of less
importance compared to the other. Poinsot and Veynante in their book on theoretical
and numerical combustion [15] present an overview of models that have been used in CFD
codes able to address some of the issues described above. Modeling such complex events
has driven my research career throughout the years.

3.2.3 First steps: The PDFA/CHI model for diesel AI and combustion
The objective of my PhD work was to use DNS data obtained at the EM2C laboratory
by Kalmthout and Veynante [16] and at IFPEN by Mastorakos et al. [17], as well as
theory, to propose a RANS model for diesel AI and combustion by taking turbulent
effects into account during the whole process. The final result of my PhD was a RANS
model, baptized PDFA/CHI [3], based on a combination of two different approaches: A
presumed density function (pdf) model for the first stages of combustion including the
AI period and subsequent premixed combustion period (my work); a flamelet model for
the high temperature diffusion flame based on a formulation proposed by Bilger [18] and
Williams [19]. The average fuel reaction rate ¯˙ωF is determined by Eq. 3.1 decomposed
into two terms, the PDFA contribution (Eq. 3.2) and the CHI contribution (Eq. 3.3).
Equation 3.1 was then applied to each computational cell of the RANS code.
¯˙ωF,PDFA/CHI = [1 − f (˜c)] ¯˙ωF,PDFA + f (˜c) ¯˙ωF,CHI (3.1)
¯˙ωF,PDFA =
+∞
−∞
˙ωF (X) p(X) dX (3.2)
¯˙ωF,CHI =
1
2
¯ρst ˜χst
YF,0
1 − Zst
p (Zst) (3.3)
In Eq. 3.1, f (˜c) is a coupling function based on the Favre average of a reaction progress
variable c, which definition is based on available unburned fuel compared to the total fuel
present at the beginning of the combustion process (a passive scalar). This function was
fit with the help of DNS results from Van Kalmthout and Veynante [16] and Mastorakos
et al. [17]. In Eq. 3.2, X represents the set of variables on which ˙ωF depends on (reactive
species concentration, temperature, etc.). Function p(X) is the joint probability density
function of the X variable set. In Eq. 3.3, ¯ρ is the average density, ˜χ is the Favre
averaged scalar dissipation rate, Z is the fuel/air mixture fraction and YF,0 is the fuel
mass fraction far from the flame. The subscript st refers to values where the air/fuel
mixture is stoichiometric.
Concerning the instantaneous fuel reaction rate (term ˙ωF (X) in Eq. 3.2), the assump-
tion was made that fuel combustion was a single step process where fuel is directly oxidized
into CO2 and water. A single step Arrhenius type reaction rate was then considered:
˙ωF = ρAY nF
F Y nF
O exp −
Ea
RT
(3.4)
where A, nF , nO and Ea are model parameters fit to match diesel fuel AI delays. The X
set in Eq. 3.2 has been reduced to four variables: The density ρ, the fuel mass concentra-
tion YF , the oxidizer mass concentration YO and the temperature T. Bilger [18] showed
that the AI and flame stabilization problem, characteristic of initially non premixed sys-
tems, can be solved as a function of only two variables: A mixture fraction, describing the
mixing between fuel and oxidizer; a progress variable tracking the evolution of the chem-
ical reactions inside the mixing layer. Therefore, the density, the reactive species mass
fractions and the temperature have to be expressed as a function of these two variables.

As shown by Poinsot and Veynante [15], during AI and diffusion flame establishment,
the flow variables can be expressed as a function of the mixture fraction. Algebraic for-
mulae or flamelet libraries can be used for such purpose. In the context of the PDFA/CHI
model, diffusion flame structures were determined for irreversible reaction, non equilib-
rium finite rate chemistry using the Cuenot and Poinsot algebraic formulation [20] based
on a Damköler number analysis. During the AI period, it was assumed that combustion
progress takes place at constant mixture fraction values. The instantaneous value of any
fluid variable y is therefore a linear combination between its pure mixing value ymix and
its established diffusion flame value ydif such that:
y(Z, c) = (1 − c) ymix(Z) + c ydif (Z) (3.5)
The joint pdf of the four variables in Eq. 3.4, representative of the influence of turbulence
in the system, has to be determined. There are several ways to address the problem.
These include the experimental measurement of the pdf [18,21], its calculation with the
help of a transport equation [22, 23] or by making the assumption that the pdf has a
presumed known behavior which depends on moments of the statistical variables being
described. The presumed pdf approach was chosen.
Since ρ, YF , YO and T are a function of Z and c, a joint pdf p(Z, c) has to be determined.
To do so, it is assumed that the pdf’s of Z and c are not correlated. This means that they
can be determined independently of one another such that p(Z, c) = p(Z).p(c). It is then
assumed that the mixture fraction has a β distribution (cf. [24]), a well suited function
for reactive flow calculations between segregated species. Finally, a constant value of the
progress variable c in the computational cell is assumed so that the pdf of c is a Dirac
function with its peak at c = ˜c.
The PDFA/CHI model was implemented in the KIVA-II MB 3D CFD code [25], an
IFPEN version of the Los Alamos National Laboratory KIVA-II code [26]. The model was
first tested against the DNS results of Van Kalmthout and Veynante [16] and Mastorakos
et al. [17] and then against methane AI experimental results obtained at IFPEN during
the PhD in a high pressure constant volume combustion cell capable of reproducing diesel
combustion thermodynamic conditions [2].
Figures 3.2 and 3.3 represent the computational domain, including vorticity fields
and mixture fraction iso-levels of the DSN simulations of Van Kalmthout and Veynante
and Mastorakos et al. Both configurations simulate the ignition and establishment of
a fuel/air diffusion flame. The former simulates an average steady state hydrogen/air
diffusion flame in constant intensity turbulence with ignition happening after a mixing
period due to the presence of hot air. The latter simulates the ignition and establishment
of a cold methane/hot air diffusion flame in time decaying turbulence.
Figures 3.4 and 3.5 illustrate the RANS simulation of the Van Kalmthout and Vey-
nante [16] DNS of a reactive mixing layer simulation. Fig. 3.4 represents the average re-
action rate calculated along the longitudinal 2D DNS space coordinate (along the mixing
layer). On the left hand side, the average DNS reaction rate is compared with respec-
tively, the PDFA and CHI contributions of the PDFA/CHI model. On the right hand
side, the PDFA and CHI contributions are weighted by the transition function f (˜c) in
Eq. 3.1. Several formulations of the transition function have been tested based on DNS
results. The best compromise was found to be f (˜c) = ˜c3
. The transition function reduces

Figure 3.2: Van Kalmthout and Veynante [16] steady state simulation of hydrogen/hot air
ignition and diffusion flame in constant intensity turbulence. Vorticity field and mixture fraction
iso-levels.
Figure 3.3: Mastorakos et al. [17] transient simulation of methane/hot air ignition and diffusion
flame in decaying turbulence. Vorticity field and mixture fraction iso-levels.
the weight of the high temperature CHI contribution during the ignition period and the
weight of the PDFA contribution during high temperature combustion. Fig. 3.5 shows
on the left hand side the DNS spatial average reaction rate and on the right hand side
the PDFA/CHI model spatial reaction rate distribution. A good overall agreement is
observed.
Developments proposed in my PhD work set the basis for more recent combustion
models proposed and developed at IFPEN. Notions such as the progress variable based
on fuel consumption, variable conditioning to specific zones of the computational cell,
transport of non reactive, passive scalars (e.g., the total fuel mass fraction) or the idea of
subgrid mixture distribution based on presumed pdf’s with a transport equation for the
second moment (variance) of fuel concentration have been largely used in the ECFM3Z
combustion model proposed by Colin and Benkenida [27]. Other ideas such as a dynamic
computation of boundary values of fuel and oxygen mass fractions used in a mixture
fraction formulation have been proposed and tested in my PhD and adopted afterwards

Figure 3.4: Van Kalmthout and Veynante [16] spatial simulation. Average reaction rates along
the computational domain. PDFA/CHI with a coupling function f (˜c) = ˜c3. Ret = 104.6.
Figure 3.5: Van Kalmthout and Veynante [16] spatial simulation: mean reaction rate DNS
measurements compared with the PDFA-CHI model. Lines represent iso-contours of the mixture
fraction.
in the ECFM3Z formulation.
In spite of the promising results obtained at the end of the PhD study, the PDFA/CHI
model presented major drawbacks which prevented it from being used in more realistic
industrial configurations. The main difficulties were the use of a one-step chemistry that
had to be fit for different fuels, the inability to handle different combustion modes such
as premixed and diffusion flames (necessary in the majority of modern engine combustion
solutions) and long CPU times, not compatible with industrial needs required in RANS
simulations. This was due to the numerical heaviness of the pdf integration algorithm in
equation 3.2. Most of these drawbacks have been solved through time and new formula-
tions and models were since developed and widely used although the PDFA/CHI model
never found an industrial application of its own.
3.2.4 Fuel/Air mixing models towards combustion simulation
In my PhD work, partly described in the previous section, a mixing model based on a
presumed pdf approach was proposed. However, the high computing cost of the model
resolution made it prohibitive for practical problems. In the years that followed, at

IFPEN, Duclos proposed a much simpler approach based on a subgrid conditioning of the
average fuel/air distribution inside each computational cell. The work of Duclos remained
unpublished until Colin and Benkenida later formalized the ECFM3Z model for Extended
Coherent Flame Model - 3 Zones [27].
ECFM3Z is a modular turbulent mixing and combustion model. It is based on a Favre
averaged combined combustion progress variable (˜c) and mixture fraction ( ˜Z) description.
This formulation allows a simultaneous representation of premixed turbulent flames, auto-
ignition, diffusion flames and pollutant formation. The combination of these combustion
sub-models led to the ECFM3Z(Z, c) structure formalized by Colin and Benkenida [27].
Figure 3.6 presents the conditioning and mixing processes described by the ECFM3Z
model. Each computational cell is divided into three bins in the mixture fraction (Z)
space: The unmixed fuel zone F (injected gaseous fuel and fuel from liquid evaporation
feed this zone), the mixture zone M containing fuel, air and dilutant gases and the
unmixed air + dilutant A zone. Dilutant gases may be issued from trapped burned
gases from previous engine cycles or from Exhaust Gas Recirculation (EGR). These zones
are delimited by mixture fraction bounds Zm, ZM and Zs (m=min, M=max and s =
saturation from evaporation). Bounds m and M are estimated from flammability limits
and AI propensity and the s bound is the maximum mixture fraction a cell can contain,
based on fuel vapor saturation. Each zone is in turn divided into an unburned gases zone
(u) and a burned gases zone (b) equivalent to a bi-modal distribution following a thin
flame or flamelet approach. In the original mixing model implemented in ECFM3Z [27],
the mass flux from zones (A) and (F) towards (M) is assumed proportional to the inverse
of large eddies turbulent time-scale k/ε where k is the turbulent kinetic energy and ε its
dissipation rate.
Figure 3.6: Subgrid conditioning in ECFM3Z, mixing process and combustion progress. A:
Unmixed air plus EGR; F: Unmixed fuel; M: Fuel/air/EGR mixture zone; u: Unburned gases;
b: Burned gases.
Subramanian, in his PhD work which I directed with Prof. Luc Vervisch, in collab-
oration with O. Colin from IFPEN, proposed an improved mixing formulation based on
a presumed β-pdf to control the mass flux between unmixed and mixed zones as well as
the bounds of the mixed zone (M) [28]. The new mixing and combustion model named

ECFM3Z-PDF is illustrated in Fig. 3.7. Here, the average cell species mass fractions
contained in the air plus EGR and fuel zones are determined by integrating respectively
the pdf between 0 and Zm and ZM and Zs. The average mass fractions of species in the
mixed zone are determined from the difference between cell average values and average
values conditioned to the A and F zones. The mass flux between unmixed and mixed
zones is therefore implicitly calculated.
Figure 3.7: ECFM3Z-PDF mixing model. On each of the three mixing zones, the equivalence
ratio or mixture fraction is distributed according to the β-pdf. A, F and M zones are bounded
by Zm, ZM and Zs.
The ECFM3Z-PDF formalism allows computing mean reaction rates during AI taking
into account the local mixture fraction stratification. This is similar to the PDFA-CHI
model described in the previous section. This work was the starting point for an improved
version of the ECFM3Z-PDF mixture and combustion model [29] implemented today in
the IFP-C3D 3D CFD code [30].
3.2.5 Conclusions
Poinsot and Veynante in their book on theoretical and numerical combustion [15] recall
that neither ”no-model” or Arrhenius type models nor eddy break-up type models are
well fit for simulating turbulent combustion. The first consider that the average reaction
rate, calculated by strongly non linear expressions, can be simply computed from average
quantities (species concentrations, temperature...) by taking only chemical kinetics into
account. The latter assume that turbulent combustion is only driven by turbulent effects
(large scale eddy turnover times).
My PhD subject was oriented towards taking turbulent effects into account during
diesel engine AI processes. This was accomplished with the help of a presumed pdf ap-
proach for simulating the computing cell sub-grid fuel/oxidizer mixing. In Subramanian’s
PhD, which I proposed and helped orienting, a new mixing model was developed in the
framework of the IFPEN ECFM3Z turbulent combustion model. This mixing model was
later used to improve the description of the average cell mixing mechanisms.

3.3 Modeling the mixture fraction variance and its evaporation
source term
During his PhD, Subramanian also addressed the closure of the mixture fraction variance
ρυ = Z 2
transport equation:
∂ρυ
∂t
+
∂ρuiυ
∂xi
=
∂
∂xi
µ
Sc
∂υ
∂xi
−
∂ρui υ
∂xi
(I)
− 2ρui Z
∂Z
∂xi
(II)
− 2
µ
Sc
∂Z
∂xi
2
(III)
+ ρ ˙Sv
(IV )
(3.6)
where µ is the fluid molecular viscosity and Sc is the Schmidt number. Appropriate
models for the unclosed terms I, II, III, IV are necessary. Turbulent transport (I) and
production terms (II) are modeled with the classical gradient transport assumption. The
Scalar dissipation rate χ term (III) is closed algebraically following a linear relaxation
assumption [24].
Subramanian studied in particular the modeling of the mixture fraction variance equa-
tion source term due to evaporation (ρ ˙Sv) [11]. He tested three models proposed in the
literature against experiments in diesel engine conditions performed by Bruneaux [31]
at IFPEN in a constant volume, high pressure combustion vessel equipped with a high
pressure diesel injector. The liquid fuel evaporation acts as a source term in the average
mixture fraction transport equation and also in the transport equation of its second order
moment. While the fuel evaporation rate is directly related to the average or first order
moment, it is unclear how its fluctuations are affected by evaporation.
The combustion chamber, developed and built at IFPEN [32], allows full optical access
into the combustion area. The Laser Induced Exciplex Fluorescence (LIEF) technique [31]
was applied for quantitative imaging of the gas phase fuel mass density.
3.3.1 Tested models for ρ ˙Sv
The three tested models for the ρ ˙Sv term were the Demoulin and Borghi (DB) model [33],
the Hollman and Gutheil (HG) model [34] and the SDM model by Réveillon and Vervisch
(RV) [35]. Different assumptions on how fuel evaporation generates mixture fraction
variance are considered for each model:
• The DB model which assumes that the fuel source mass ˙S is only relevant around the
droplet surface;
• The HG model which assumes that the mixture fraction Z, proportional to unburned
fuel mass fraction, and the fuel evaporation rate ˙S are correlated, a reasonable hy-
pothesis on an evaporation problem. Therefore, Z = α ˙S and Z = α ˙S ;
• The RV model, based on DNS calculations of evaporating droplets of a dilute spray in
a turbulent flow field, where the conditional source term ( ˙S | Z) can be approximated
as a monotonic function of Z, which depends on local spray properties.

3.3. MODELING THE MIXTURE FRACTION VARIANCE AND ITS
EVAPORATION SOURCE TERM 17
3.3.2 Experimental results and 3D RANS simulations
The Bruneaux [31] quantitative experimental results of vapor fuel mass concentration (ρFu
in kg/m3
), obtained using the Laser Induced Exciplex Fluorescence (LIEF) technique,
were also used here for model testing. Between five and fifty individual images were
recorded for each time instant during the experiment. They were then post-processed
in order to extract average values and fluctuations of vapor fuel mass concentrations.
Uncertainty analysis including all measurements showed that a 80% level of confidence
could be anticipated.
Simulations were performed on IFP-C3D [30], a hexahedral unstructured parallel solver
for reactive compressible gas flows with sprays dedicated to multi physics three dimen-
sional simulations. The code is developed at IFP Energies nouvelles to compute reactive
flows in internal combustion engines. IFP-C3D solves the Reynolds Averaged Navier-
Stokes (RANS) equations using an unstructured formalism, the finite volume method
on staggered grids, time splitting, SIMPLE loop, sub-cycled advection, turbulent and
lagrangian spray and a liquid film model and integrates highly sophisticated spark igni-
tion, compression ignition and turbulent combustion models coupled with multiple fuel
chemical kinetics. It is able to deal with complex moving geometries with large volume
deformation induced by moving geometrical parts (intake/exhaust valve, piston...). To
reduce simulation elapsed times, the MPI parallelism was implemented in IFP-C3D.
Liquid fuel injection and evaporation were simulated using the lagrangian spray atom-
ization and break-up WAVE-FIPA model [36]. Model parameters were adjusted to match
the experimental results of liquid and vapor fuel penetration.
Each experimental LIEF image represents an instantaneous 2D fuel density field. Av-
erage fuel mass density was then obtained from the 5 to 50 images recorded for each time
step. For a given time step, Eq. 3.7 holds:
ρFu(x, y) =
i=n
i=1
ρFu(x, y)/n (3.7)
where n is the number of images recorded at a given time. Variable ρFu is available from
the RANS simulation code and can its values can therefore be easily compared to the
experimental ones.
The comparison of experimental and simulated fuel mass fraction variance VFu is
however less trivial. Its definition is given by the following relation:
VFu(x, y) =
i=n
i=1
(ρFu(x, y) − ρFu(x, y))2
/n (3.8)
VFu is not directly obtained from experiments since due to the experimental difficulty
of determining local temperatures, the measured quantity is ρFu(x, y) and not the total
density ρ(x, y). In order to compare experimental and simulated results, an assumption
regarding the total density ρ is necessary. Variance obtained from the RANS code is:
υC3D = Z 2
⇒
i=n
i=1
ρYFu − ρYFu
2
/n (3.9)

where YFu is the fuel mass fraction, ρ and ρ are the local and average densities inside
the cell. The total local density is hence assumed to be similar to the chamber average
ρ = ρ0, a reasonable assumption in a non reactive mixing problem where the density does
not vary significantly. Equation 3.9 then becomes:
υC3D = ρ2
0 YFu − YFu
2
⇒ ρ2
0 × υ (3.10)
Figure 3.8 shows the comparison between the experiments and the simulations of
average fuel mass density at time t = 0.8 ms, time t = 0 referring to the start of injection.
Figures 3.8(a) and 3.8(b) represent respectively the experimental and simulated average
fuel density fields. The color scale is the same and its maximum value is 5.0 kg/m3
.
Figure 3.9 shows the fuel density profiles at various cross-sections xi with i = [1, 2, 3]
along the injector axis x. Close to the injector, at cross section x1 −x1 (20 mm away from
the injector), simulation over-predicts the fuel density. This can be attributed to well
known difficulties inherent to lagrangian spray models. However, further downstream, at
cross-sections x2 −x2 and x3 −x3 (respectively 40 mm and 50 mm away from the injector),
experimental and simulated fuel concentration profiles show good agreement.
Figure 3.8: Average fuel concentrations at t = 0.8 ms: Experiments and simulations are repre-
sented with the same color scale. (a) LIEF - ρFu. (b) IFP-C3D - ρFu.
The influence of constant C in the scalar dissipation rate model (linear relaxation
model for term III in equation 3.6) was first studied. Fuel mass fluctuations between
LIEF experiments and model results showed that in this particular case, the scalar fuel
fluctuation levels are in good agreement with LIEF experiments when C is set to 2, a
value commonly used in the literature [24].
Then, the relative importance of the ˙Sv term relative to production and dissipation
contributions in equation 3.6 was studied (respectively, terms IV, II and III). It was
observed that all three terms have the same order of magnitude, independently of the
˙Sv model. This means that in liquid spray evaporation, scalar fluctuations induced by
spray evaporation cannot be neglected. This is particularly important in the liquid-vapor

Figure 3.9: Average radial fuel concentration profiles along axis x in Fig. 3.8(a) at t = 0.8 ms.
Experiments and simulations. ρFu, x = 2, 4 and 5 cm
interface zone where the ˙Sv term has an important contribution to the global fluctuation
level.
The DB [33], HG [34] and RV [35] models for ˙Sv were finally evaluated and compared.
Figure 3.10 shows, at time t = 1.0 ms after the start of injection (SOI), the comparison of
fuel mass fluctuations from LIEF experiments (Fig. 3.10(b)), with those obtained with the
three tested models (Fig. 3.10(c), Fig. 3.10(d) and Fig. 3.10(e)). All units are (kg/m3
)2
.
The color scales of the experiments and the model results are shown in Fig. 3.10(a) and (f)
respectively. It can be seen from Fig. 3.10(c), 3.10(d) and 3.10(e) that qualitatively, all
three models correctly reproduce the experimental scalar fuel fluctuation field. However,
quantitatively, model DB predicts higher levels of fluctuations than models HG, and RV .
This has been observed consistently at all other timings as well. The LIEF experiments
show that fuel mass fluctuations vary from 0 to a maximum of 1.5 (kg/m3
)2
. Simulations
show that with model DB, the maximum value lies around 9.0 (kg/m3
)2
, with model
HG, 3.0 (kg/m3
)2
and with model RV , 8.0 (kg/m3
)2
. The maximum number of images
(50 images) was used for averaging at this instant.
Figure 3.11 presents fluctuations along the x axis as indicated in Fig. 3.8(a) at t =
0.8 ms and t = 1.2 ms after SOI. Figure 3.12(a) and Fig. 3.12(b) present fluctuation
cross-section radial profiles (x1 − x1 and x2 − x2 profiles in Fig. 3.8(a) respectively) at
t = 0.8 ms after SOI. Up to 35 mm away from the injector (Fig. 3.11), fluctuation levels
are overestimated by all models by a maximum factor of 10 for model DB, 3 for model
HG and around 8 for model RV when compared to LIEF results (see cross section x1 −x1
in Fig. 3.12(a)). Profiles obtained without spray evaporation closure ( ˙Sv = 0) are also
shown. They are close to those obtained with model HG indicating that model HG

Figure 3.10: Fuel mass fluctuations ρ2
0υ at t = 1.0 ms. LIEF color scale; (a) LIEF; (b) DB; (c)
HG; (d) RV ; simulated color scale.
source term is globally small when compared to the balance between source terms (II)
and (III). However, as it has been mentioned before, the relative importance of the three
source terms is similar in the evaporation zone (around x = 20 mm). Since even for case
(Sv = 0), the fluctuations are overestimated close to the injector, it is assumed that this
is mainly due to the over-prediction of the mean fuel concentration in this zone as shown
in Fig. 3.9 at cross section x1 − x1 corresponding to x = 20 mm.
Figure 3.11: Fuel mass fluctuation profiles along injector axis x in Fig. 3.12(a) from LIEF,
models DB, HG and RV and for ˙Sv = 0. ρ2
0υ at t = 0.8 and t = 1.2 ms.

Figure 3.12: Fuel mass fluctuation cross-section radial profiles (x1 −x1 and x2 −x2 in Fig. 3.8(a))
from LIEF, models DB, HG and RV and for ˙Sv = 0. ρ2
0υ at (a) t = 0.8 and (b) t = 1.2 ms.
3.3.3 Simulation results analysis
The differences between models DB and HG are related to the fact that the former
assumes a production source term equivalent to the maximum possible variance due to
evaporation. The fuel evaporates at saturation conditions Z∗
and variance is only pro-
duced by a fuel concentration near saturation values. Therefore, the term Z ˙S which
represents one of the major contributions to υ is in this case Z ˙S
DB
= (Z∗
−Z) ˙S. On the
other hand, in model HG, Z ˙S
HG
= 0.5 Z 2 ˙S/Z. Since by definition of mixture fraction
variance, Z 2
≤ Z(Z∗
− Z), it is concluded that Z ˙S
HG
≤ CZ ˙S
DB
. This explains the
much higher fuel fluctuation values when using the DB model.
Model RV should be the most accurate since it considers the joint statistics of the
mixture fraction Z and the evaporation source term ˙Sv. However, it was developed and
validated for dilute spray cases like gasoline injection applications. Therefore, based on
the simulation results performed by Subramanian to simulate the specific case presented
here, it does not seem adequate for dense diesel sprays.
In the downstream part of the evaporated jet (x > 40 mm), all three models lead
to similar results, in agreement with experiments. This indicates that the influence of
the evaporation source term (IV) in the variance transport equation 3.6 is limited to
the liquid/vapor interface region. Downstream, fluctuations are essentially controlled by
terms (II) and (III).
3.3.4 Conclusions and recommendations
Subramanian, under my supervision, addressed the issue of scalar variance and dissipation
modeling in the presence of evaporating liquid sprays. Such conditions are important for
direct injection diesel and gasoline engines simulation where the evaluation of the fuel/air
mixing has first order importance. He showed that:
• The evaporation source term in the mixture fraction variance equation cannot be
neglected. Inside the core of the spray, its order of magnitude is close to the variance

production and dissipation terms due respectively to average mixture fraction gradient
and scalar dissipation rate.
• Three models for the evaporation source term in the variance equation have been
tested. In the experimental conditions used here, the HG model performed better
than the models DB and RV which both over-predict variance due to evaporation.
• This evaporation source term influences the fluctuation levels close to the liquid
spray. Further downstream, fluctuations are controlled by production/destruction
from mean gradients and scalar dissipation.
The DB and the HG models are implemented in the IFP-C3D code where they are
used for RANS 3D engine combustion modeling. The HG model is recommended and
has also proven to be more accurate and important on stratified gasoline direct injection
operating conditions [37].
Several modern RANS combustion models like PCM (Presumed Conditional Mo-
ment) [38–40] or CMC (Conditional Moment Closure) [41–43] rely on the computation of
average reaction rates by integrating kinetic data and a presumed pdf over the mixture
fraction and progress variable space. Since the presumed pdf information is often retrieved
from the first and second moments (average and variance respectively) of a probabilistic
distribution, accurate information for the latter is necessary. The work described in this
section contributed to improving mixture fraction variance computations by testing the
performance of three variance equation source term models due to evaporation.
In the future, intensive analysis of engine conditions injection experimental databases
such as the ones produced by Bruneaux et al. [44–46] can be performed for better model
validation. In such context, M. Causse, a training student I helped guiding in collaboration
with G. Bruneaux [47,48] (cf. section 5.2.1), put together several analytical techniques to
push such an evaluation further. However, in this field, there is still a large room for new
studies. Extending the analytical analysis of liquid injection databases could be the object
of future work (PhD or others), especially if new ones could be built using sophisticated
and ever growing experimental techniques mostly relying on advanced optical diagnostics.
As mentioned above in this section, another feature necessary for modeling the mixture
fraction variance is the evaluation of the scalar dissipation rate (term III in Eq. 3.6). A
linear relaxation assumption is often used but the resolution of transport equations both
for premixed combustion (dissipation of the progress variable) [49,50] and diffusion flames
(several modeled transport equations for the dissipation of the mixture fraction in Eq. 3.6
can be found in [51]) has been tested. However, the complexity of those equations and
the modeling efforts necessary to represent their unclosed terms have jeopardized the
efforts granted so far. Evaluation of mixture fraction and progress variable variances
would largely benefit from better estimations of their respective dissipation terms. This
could be the object of new studies, again relying on modern analytical and experimental
techniques.
3.4 Kinetic modeling of surrogate fuels
The previous section focused on a description of turbulence effects on the combustion
process. Several models were presented but the overall assumption was the simplifica-

3.4. KINETIC MODELING OF SURROGATE FUELS 23
tion of chemical kinetics. They all rely on infinitely or very fast chemistry assumptions.
However, they can be coupled in one way or the other with more realistic fuel oxidation
chemistry.
For CFD to be used today in realistic industrial problems, chemical kinetics can no
longer be neglected. In the automobile industry, in the aeronautic industry but also
in industrial combustion, exhaust pollutant emissions standards for CO, NOx and soot
get more stringent over the years and benefits in fuel consumption and therefore CO2
emissions reductions are sought. Other species emissions which are classified today as
Non Regulated Pollutants (NRP) may become the object of tighter regulations in the
near future. Finally, fuel diversity is another very important issue that transport industry
has to take into account.
Combustion exhaust product speciation and fuel effects can only be tackled by a
CFD code if the combustion chemistry is conveniently represented. Unfortunately, the
decomposition of fuel and its oxidation follow complex paths where thousands of elemen-
tary steps involving hundreds of chemical species are present. Also, as fuel complexity
increases (larger species and mixtures of a large number of species), the size of such
mechanisms increases exponentially. Finally, industrial combustion problems, especially
in internal combustion engines, are characterized by very broad variations in thermody-
namic conditions. The chemical mechanisms must therefore be able to model the whole
combustion event throughout the entire set of ambient conditions. It is then necessary to
develop such mechanisms.
Additionally, real fuels (gasoline or diesel for example) are in general mixtures of
hundreds of chemical species, their composition varying as a function of the refining
procedures used to produce them. Most of those species are difficult or very expensive
to identify. Chemical mechanisms are therefore confronted with two major difficulties:
Chosen fuel species must represent sets of a large number of real fuel components and once
the representative species to be modeled are chosen, the associated kinetic mechanisms
have to be available or ultimately, have to be developed. The reduced set of fuel species
chosen to represent the real fuel is called the surrogate set of fuels or more generally, the
surrogate fuel.
Surrogate fuels can be chosen based on different criteria. For instance, if the goal is to
simulate diesel combustion, then AI properties are first order. AI delays of the surrogate
fuel have to be similar to those of the real fuel, for all possible engine thermodynamic
conditions. If the objective is also to be able to reproduce soot emissions, then the nature
of the surrogate has to be consistent with that choice. Very large mechanisms, difficult
to handle numerically, hence have to be used. If on the other hand premixed flame
propagation is the only important issue, smaller mechanisms taking into account only
high temperature chemistry can be used.
The importance of chemical kinetics to represent fuel effects and pollutant emissions
has driven an important part of my research. Some of the projects I have managed,
described in chapter 4 of this report, were dedicated to generating kinetic mechanisms
well adapted to representing fuel kinetics on internal combustion engines. Here, I will
focus on the PhD thesis work of J. Anderlöhr, performed under my supervision between
2006 and 2009. During his PhD, J. Anderlöhr developed a new kinetic mechanism for
gasoline and diesel surrogates. Part of his work is described in references [12,52].

3.4.1 Development of a gasoline surrogate kinetic mechanism
Gasoline is mostly composed of hydrocarbons with four to ten carbon atoms (cf. Fig. 3.13)
produced in the early stages of oil distillation. As shown in Fig. 3.14, all hydrocarbon
families are present in different proportions (saturated and branched alkanes, olefins,
aromatics...). Since combustion in a spark ignition (SI) engine is mostly premixed, the
laminar flame speed of the surrogate should be representative of the gasoline laminar
flame speed. Prediction of knock and super knock (uncontrolled AI before or around
spark advance timing) behavior is also fundamental on SI engines. The surrogate AI
delays should therefore be representative of gasoline AI.
Figure 3.13: Number of carbon atoms distribution for a common European commercial gasoline.
Source: IFPEN TAE 7000.
Figure 3.14: Hydrocarbon families distribution for a common European commercial gasoline.
Source: IFPEN TAE 7000.
1,2,4-trimethylpentane or more commonly, iso-octane (iC8H18), a highly branched
alkane, has been widely used as a gasoline surrogate because of its high octane num-
ber (100) and gasoline like laminar flame speed. Since iso-octane is a pure component
and a primary reference fuel (the octane number of a fuel is the volume of iso-octane in

an iso-octane/n-heptane mixture with the same AI delay as the tested fuel in the same
thermodynamic conditions), it does not exhibit any octane sensitivity (its octane num-
ber does not depend on thermodynamic conditions). In a gasoline spark ignition engine,
where thermodynamic conditions vary widely, knock behavior might therefore be difficult
to capture with iso-octane alone as a surrogate fuel.
In order to better represent AI of commercial gasoline, which Research Octane Num-
ber (RON) lies between 95 and 98, iso-octane/n-heptane mixtures are good candidates.
However, as stated before, since these two hydrocarbons are primary reference fuels, their
mixtures do not have any octane sensitivity either. This means that their octane number
does not change as a function of thermodynamic conditions. Several studies have however
shown that the fuel octane number may change significantly depending on the local values
of temperature, pressure and equivalence ratio inside the combustion chamber as well as
engine speed and load. It is for example the case of vehicle studies performed in the late
nineteen seventies at IFPEN by Douaud and Eyzat [53] as well as engine and vehicle tests
by Kalghatgi [54,55]. The conclusion is that a single octane number surrogate fuel is not
fit to represent commercial gasoline AI (therefore, knock behavior) on engine conditions.
Octane sensitivity can be added by introducing a third fuel component to the PRF
surrogate mixture. Several authors have proposed toluene (C7H8), an aromatic hydro-
carbon for such purpose [56–60]. Toluene has several advantages: It has high octane
sensitivity, it can represent the aromatic hydrocarbons family in the surrogate and it has
a low C/H ratio. It thus allows simultaneously a good representation of gasoline octane
ratings (Research - RON and Motored MON), a better kinetic representation in terms of
pollutant species linked to the presence of aromatics and be representative of the gasoline
carbon/hydrogen atomic ratio (C/H). A gasoline surrogate called TRF (Toluene Refer-
ence Fuel) with fixed proportions for the three components might therefore be able to
represent AI in different thermodynamic conditions and represent at the same time the
reference fuel energy and mass contents.
Since 2002, IFPEN has studied the implementation of complex kinetics on its engine
CFD code IFP-C3D. From there on, it became necessary to represent real fuels by their
corresponding surrogates with which kinetic simulations are possible. For diesel engines,
where AI properties are first order, n-heptane has been widely used by a number of
authors. For gasoline spark ignition engines, since 2006, IFPEN has been using a TRF
mixture to simulate knock behavior. The composition has been fixed to match gasoline
RON and MON values (95 and 85 respectively) as well as its C/H ratio. The result
is a mixture composed in volume percentages of 43% iso-octane, 43% toluene and 14%
n-heptane [61]. This gasoline surrogate was tested in a single cylinder IFPEN engine
where it showed the appropriate AI properties (similar knock behavior) and also good
trends in terms of combustion velocity and pollutant formation [62]. I was in charge
of the management of the different projects where these studies were held (Chapter 4).
More recently, experiments performed at LRGP in Nancy [63] showed that for atmospheric
pressure at 358 K, gasoline and its TRF surrogate described above have very close laminar
flame speeds for all equivalence ratios inside the flammability limits. The authors have
also performed experiments with 15% addition of ethanol to gasoline and its surrogate
and verified that a good matching was also found (cf. section 4.2.2).
Once the surrogate fuel has been defined, kinetic models must be developed in or-
der to simulate its AI behavior and if necessary, its laminar flame speed. Under my

supervision, Anderlohr et al. [64] worked on the generation of a TRF mechanism able to
capture the kinetic behavior of TRF mixtures in a very broad range of thermodynamic
conditions. Additionally, the authors have coupled their kinetic mechanism with a NOx
sub-mechanism aimed at reproducing the influence of NOx on hydrocarbon AI. A detailed
description and results can be found in reference [64]. Only a short overview is given here.
The Anderlohr et al. TRF mechanism was developed in collaboration with the LRGP
laboratory in Nancy, France and follows the methodology and mechanism generation rules
implemented there. It is based on the PRF AI model from Buda et al. [65] coupled with
the model for the oxidation of toluene provided by Bounaceur et al. [66]. Additionally
reactions of NOx with PRF and toluene compounds were written. Pressure dependent
rate constants were defined by the formalism proposed by Troe [67]. Thermochemical
data for molecules and radicals were calculated by the THERGAS software [68], which is
based on additivity methods proposed by Benson [69]. In the case of nitrogen containing
compounds, thermochemical data proposed by Marinov [70] and Burcat [71] has been
used. The resulting mechanism contains 3000 reactions and 536 species and is available
on request. It can be divided into four main blocks, which validation is included in their
respective publications:
• The oxidation of n-heptane and iso-octane sub-mechanism was generated by the soft-
ware EXGAS-ALKANES according to the principles described by Buda et al. [65].
According to the EXGAS methodology, the mechanism is composed of three blocks: A
C0-C2 reaction base involving species with up to two carbon atoms; a primary mech-
anism involving initial organic compounds and oxygen as reactants and including low
and intermediate temperature reactions of alkanes; a lumped secondary mechanism
where molecules produced in the primary mechanism, with the same molecular for-
mula and the same functional groups, are lumped into one unique species without
distinction between different isomers. The secondary mechanism includes global reac-
tions producing, in the smallest number of steps, molecules or radicals which reactions
are included in the C0-C2 reaction base.
• The oxidation of toluene consisting in a collection of reactions including aromatics
and aromatic derived species such as benzene, benzyl and their by products as well as
unsaturated C0-C4 species. Upon redundancy verifications with the previous alkane
C0-C2 base, the latter is kept.
• Cross reactions between alkanes and toluene.
• Reactions involving NOx species derived from the modeling work of Glaude et al. [72]
on the effect of the addition of NO on the oxidation of n-butane and n-pentane.
Reactions for NOx interacting with alkanes and aromatic compounds were written in
this work.
The kinetic model for the oxidation of TRF blends has been successfully validated
against different experimental applications over a wide range of thermochemical condi-
tions. AI delays obtained in Rapid Compression Machines (RCM), Shock Tubes (ST)
and Homogeneous Combustion Compression Ignition (HCCI) engine experiments and
concentration profiles measured in Jet Stirred Reactor (JSR) experiments have been sim-
ulated. The TRF/NOx model was validated against HCCI and JSR experiments for a

single fuel component as well as various fuel blends. Validations show that the model
accurately captures the complex impact of NO on HC oxidation. The impact of pressure
and temperature for various concentrations of NO is well retrieved for all fuels. Fig. 3.15
illustrates the mechanism behavior in simulating the impact of NO addition (0, 50 and
500 ppm volume) on the oxidation of n-heptane, iso-octane and toluene at 10 atm in a
JSR. Simulation results are compared to experimental data by Moréac et al. [73].
The sub-mechanism containing nitrogen species was analyzed by sensitivity and flow
analysis and the important reaction channels have been identified allowing a deeper un-
derstanding of the complex interactions between NO and HC oxidation. This is illustrated
in Fig. 3.16 presenting the main channels of n-heptane oxidation in the presence of various
amounts of NO at pressure 10 atm and temperature 665 K. The agreement between sim-
ulated and experimental results obtained in this study for different experimental setups
over a wide range of thermochemical conditions shows that the proposed mechanism can
be used for IC engine applications. Today, this mechanism is the basis of IFPEN kinetic
studies on gasoline and diesel surrogate kinetics. It is also the reference mechanism for
kinetic based tabulation methods (Section 3.5).
3.4.2 Kinetic studies on an engine context: Influence of burned gases
Even if the main purpose of hydrocarbon oxidation kinetic mechanisms development at
IFPEN is their application on engine combustion simulation, a validated mechanism can
also be used to perform different kinetic studies. On an internal combustion engine con-
text, one very interesting aspect is the influence of combustion products and intermediates
on hydrocarbon AI. On conventional diesel engines, and especially, on HCCI and Con-
trolled AI (CAI) engines, Exhaust Gas Recirculation (EGR) is widely used in order to
control NOx production and combustion initiation, phasing and velocity. Also, engine
OEM’s are regaining interest on two-stroke engines where fuel, air and large amounts of
burned gases mix before ignition.
On a common IC engine, the burned gases main component is molecular nitrogen
(N2), which volumetric proportion in air is approximately 79%. However, they also con-
tain major combustion products (CO2 and H2O) as well as minor combustion products
and frozen combustion intermediates (CO, H2, OH, unburned hydrocarbons...). Other
than their thermodynamic effect (each species has its own heat capacity which varies
with temperature), combustion products and intermediates also play a kinetic role, either
directly on elementary reactions or by their third body efficiency. Subramanian et al. [74]
and Anderlöhr et al. [75] have performed such studies under my supervision.
Anderlöhr et al. have used the TRF mechanism described on Section 3.4 in order to
test the influence of EGR containing species on hydrocarbon AI. Simulations were run at
atmospheric pressure for a large range of equivalence ratios and initial temperatures. The
fuel was a mixture of n-heptane, iso-octane and toluene in respective molar percentages
13.7, 42.6 and 43.7%. The following definitions apply: a positive impact is defined when
AI is accelerated compared to a reference case where EGR only contains N2, whereas
a negative impact implies retarded AI relative to that reference case. The authors have
shown that CO2 and H2O have a negative thermal heat capacity effect in competition with
an accelerating kinetic impact due to collision induced third-body effects. The influence of
CO on hydrocarbon oxidation is restricted to its direct participation on oxidizing reactions
and its thermal impact is negligible compared to N2. Main combustion products CO2 and

Figure 3.15: Experimental (symbols) and simulated (lines) concentration proﬁles of (left from top
to bottom) n-heptane (Fuel Moreac 1), iso-octane (Fuel Moreac 2) and toluene (Fuel Moreac 3)
mole fractions and (right) corresponding CO mole fractions obtained in a JSR [73] for the
stoichiometric oxidation of 1500 ppm of n-heptane, 1250 ppm of iso-octane and 1500 ppm of
toluene at 10 atm for an addition of 0 ppm NO (black circles • and thick full line), 50 ppm NO
(white squares and thin full line ) and 500 ppm NO (grey shaded triangles and dotted
line . . .).

Figure 3.16: Flow analysis for the stoichiometric oxidation of 1500 ppm of n-heptane in a JSR
at a pressure of 10 atm and a temperature of 665 K with addition of (up left) 0 ppm of NO, (up
right) 50 ppm of NO and (bottom) 500 ppm of NO.
H2O have a negative thermal impact compared to N2, resulting from their increased heat
capacities. Kinetically, they interact as collision partners with hydrocarbon oxidation
through H2O2 dissociation. Globally, the authors have shown that the composition of a
dilutant strongly impacts hydrocarbon oxidation and that the presence of CO2 and mainly
H2O may lead to an acceleration of the oxidation of the reactive species, in spite of their
unfavorable thermal properties compared to N2.
Fig. 3.17 illustrates some of the results obtained. Other than the above mentioned
eﬀects of dilutant nature on AI delays, strong variations can also be observed in the max-
imum temperatures reached close to reaction equilibrium. Generally, the lowest maximum
temperatures are obtained with CO2 dilution and the highest with CO. H2O dilution re-
sults in temperatures ranging between those of N2 and CO2. This is related to respective
values of speciﬁc heat capacities for each species (Cp[CO < N2 < H2O < CO2]). Under
lean conditions (φ = 0.4), maximum temperatures reached for CO addition considerably
overcome those obtained for pure N2. This is due to the heat generated by CO oxida-
tion in the presence of high amounts of oxygen. Under stoichiometric conditions, higher

temperatures are also observed with CO addition. This is due to the competition for
oxygen consumption between the hydrocarbon fuel and the added CO. Direct CO oxida-
tion generates more heat than the competing incomplete fuel combustion, which produces
partially oxidized hydrocarbons. In rich conditions (φ = 4.0), the temperature for CO
addition is still increasing by the end of the simulation. In spite of the lack of oxygen,
the same competition effect also seems to be happening.
Figure 3.17: Temperature evolution as a function of non dimensional time τ during hydro-
carbon/air oxidation diluted by pure N2, CO, CO2 and H2O at various equivalence ratios,
temperature of 750 K, 97% molar dilution and atmospheric pressure. τ = 1 corresponds to the
AI delay of the reference case (dilution by pure N2).
As also shown in Fig. 3.17, CO might have a very strong impact on AI delays. However,
on typical engine operating conditions, the amount of CO in the burned gases is quite
small compared to other combustion products contained in EGR. Although its absolute
effect is strong, its importance is reduced by its small proportion.
A similar approach was followed by Subramanian et al. [74] who showed the complex
mechanisms involved in hydrocarbon oxidation in the presence of CO. They used the
LRGP n-Heptane mechanism from Buda et al. [65] to perform AI simulations in closed
reactors and observed a non monotonic AI delay as a function of temperature upon CO
addition. Results are summarized in Fig. 3.18.
At low temperatures (T ∼ 600 K), CO lengthens the AI delay by 5 to 10% due to the
increase of the CO+OH −→ CO2+H reaction velocity in the presence of CO removing OH
radicals from the system. Indeed, in a hydrocarbon oxidation chain mechanism, hydroxyl
radicals (OH) are the main chain carriers. At low temperature, OH shortage leads to
less alkyl radicals R· (RH is the original hydrocarbon) and therefore to less peroxyalkyl
radicals (ROO·) from O2 addition and ultimately, a decreased reactivity.
At higher temperatures (T ∼ 1000 K), CO shortens the AI delay in the order of
15 to 20%. As above, CO addition accelerates reactions CO + OH −→ CO2 + H but
reaction HO2 + CO −→ CO2 + OH is also promoted. A competition between respectively
inhibiting effects (OH consumption) and accelerating effects (OH production) is therefore
introduced. At 1000 K, H radicals produced by the former reaction react again via
H + O2 −→ OH + O providing more OH radicals. As a result, OH molar concentration
increases. Hence, fuel metatheses is enhanced (RH + OH −→ R· + H2O) leading to a
higher alkyl radical concentration. Consequently, O2 addition now increases, producing

Figure 3.18: Variation of AI delays as a function of the added CO (in %), calculated by the
LRGP n-Heptane mechanism from Buda et al. [65] at T = 600 K and 1000 K, p = 20 × 105 Pa
and φ = 0.7, 1.0 and 3.0. The reference condition corresponds to 0 CO addition.
more peroxyalkyl radicals (ROO·) which in turn leads to auto-acceleration of reactant
decomposition and hence to the decrease of the AI delay.
3.4.3 Concluding remarks
Reliable kinetic mechanisms for hydrocarbon oxidation are required for simulating com-
bustion and understanding detailed combustion phenomena where experimental or the-
oretical results are difficult or impossible to obtain. The need for mechanisms able to
represent very different real and surrogate fuels used in internal combustion engines is
rapidly increasing. This is due to the diversification of fuel sources, to the need for ob-
taining more reliable results in terms of major and minor combustion products and to
the increasing possibility of using those mechanisms coupled with 3D turbulent flow CFD
codes. A brief survey of techniques for coupling chemistry and CFD to which development
I have contributed is given in section 3.5.
IFPEN expertise in building kinetic mechanisms is limited. To develop them, we
have put together collaborations with external laboratories which expertise in the area is
recognized worldwide. It was the case of the LRGP laboratory in Nancy, France, with
whom I helped building an almost ten years partnership. Relationships were also built
with other entities: For the last seven years, IFPEN has been part of an international
consortium (Reaction Design Model Fuels Consortium) where I have been representing
IFPEN. Several projects which I helped building and have managed are addressed in
Chapter 4.
The PhD of Anderlohr, which I directed in collaboration with F. Battin-Leclerc and
R. Bounaceur from LRGP, Nancy, was an attempt to gather some in-house expertise
on kinetic mechanism development. The success of his work has been demonstrated
by the day-to-day use of the resulting mechanism which has been improved since then.
The kinetic modeling work at IFPEN has continued and other PhD subjects as well as
engineering projects are under way.

3.4.4 Future developments
Kinetic modeling for future automobile and other powertrain and energy efficiency appli-
cations still requires considerable efforts. Battin-Leclerc, in a recent review article [76],
thoroughly discusses the subject. In a non exhaustive way, the following items can be
cited:
• New, more detailed and more reliable mechanisms for hydrocarbon species and oxy-
genates must be developed. Important hydrocarbon families to address are large
alkanes (especially lightly ramified branched alkanes abundant in gas, bio or coal to
petroleum fuels), cyclic alkanes (naphtenes, which nature is close to aromatics yield-
ing soot), linear and branched alkenes (largely present in gasoline), aromatics (behind
soot formation processes and also present in all petroleum cuts), ethers, or methyl
and ethyl esters (biodiesel). A strong knowledge exists on small species (C0−C2)
reactivity and alkane kinetics (especially up to C10 linear alkanes). However, as men-
tioned before, requirements for surrogate fuels to better represent real fuels increase.
A large panel of surrogate choices is therefore essential.
• Considering the diversity of today’s fuels, reliable methods for choosing their surro-
gates in terms of number and nature of components are necessary. They should take
into account the outputs to be simulated (reaction velocity, energy release, major
species production, pollutant formation, etc. . . ).
• An improved knowledge of species thermodynamic data and elementary reaction ki-
netic rates is also needed. These can be obtained by advanced experimental techniques
or calculated based on quantum mechanics and master equation methods such as DFT
(Density Functional Theory). Only a complete set of elementary reactions where no
determining steps are omitted can give confidence on the ability of a mechanism
to handle multiple thermodynamic data, a characteristic of the internal combustion
engine environment where all variables vary widely.
• New experimental data is required to develop and validate kinetic mechanisms. Only a
large panel of laboratory data, obtained from different experimental devices and multi-
ple thermodynamic conditions, can guarantee the reliability of a chemical mechanism.
For example, low temperature AI, high pressure flame speeds, extreme equivalence
ratio AI and flame propagation, dilution by combustion products and intermediates
and of course, multiple fuels and their mixtures.
• Finally, mechanism reduction techniques have to be optimized and new reduction
tools have to be developed. The use of very large mechanisms issued from advanced
mechanism generation methods or involving a large number of surrogate components
is often limited by computational resources. Even if during the last decade, great im-
provements in computer hardware and software have been achieved, chemical solvers
for these large mechanisms and CFD solvers for industrial problems involving chemical
kinetics are still limited by computer power. New reduction techniques are therefore
necessary.
IFPEN is working on these areas in order to improve its expertise in combustion
modeling dedicated to propulsion systems. I, as a research manager, have the possibility

3.5. COUPLING TURBULENCE AND CHEMISTRY 33
of choosing priority directions and proposing research projects accordingly. From my
point of view, our main goal is to improve the use of complex chemical methods on
CFD simulations. Our major efforts and resources should be put there. Mechanism
generation and reduction skills as well as production of reliable experimental data to
validate models are areas that should be kept and supported by strong collaboration with
partner laboratories.
As to my own research in combustion chemical kinetic modeling, it should address
specific needs of modern simulation tools for propulsion systems. As mentioned before,
many recent kinetic studies exist and are on going, dedicated to new fuels, in the context
of a growing global fuel diversity. Resulting mechanisms are often available and efforts
to make them compatible among each other are also growing (e.g. the Reaction Design
Model Fuels Consortium). This is a necessary condition to mutualize research in the area.
Missing elements are often the capability of using such mechanisms to simulate realistic
industrial devices and the availability of kinetic mechanisms to simulating the oxidation
of exotic or less common complex fuels like for example heavy fuel oils (highly valuable
among the energy production industry or maritime transportation). The former can be
performed by using available mechanism reduction techniques and tools oriented towards
specific setups (including flame stabilization, flame propagation and pollutant emissions).
Compilation and use of such techniques will be the object future studies. The latter relies
on IFPEN knowledge and expertise on transport fuels formulation, fuel/engine adequacy
and fuel/additives blending and its ability to compile, merge and produce new dedicated
mechanisms for specific non standard applications. Future studies are also foreseen on
this area.
3.5 Coupling turbulence and chemistry
For a long time, turbulent combustion modeling and chemical kinetics modeling have fol-
lowed parallel and independent paths. Since the majority of industrial combustion flows
are turbulent, turbulent combustion was a discipline mostly addressed by researchers deal-
ing with practical issues where a fast solution was sought. To simulate fuel chemistry,
global reaction rates (mostly one and two step reaction mechanisms, never more than
ten steps) have been applied. These may allow fair estimations of flame temperatures or
pressure evolutions and flame locations, the usual calculated variables. In problems where
some chemical information was needed, the complexity of the kinetic mechanisms neces-
sary for the simulation of hydrocarbon flames is so huge that simplifying assumptions
are immediately needed. For a well established turbulent combustion system, because
of the high temperatures involved, chemical kinetics can be considered very fast (both
in premixed and diffusion flames) and therefore, air/fuel mixture or fresh gases/burned
gases mixture are rapidly the dominant effects. In parallel, the community of combustion
kinetics researchers has continued to increase its understanding on elementary reactions,
reaction paths and species thermodynamic properties that govern hydrocarbon decompo-
sition and oxidation.
In recent years, several factors contributed to an increasing number of interactions
between the two communities. First, the exponential growth of computer power and
electronics allowed using highly computer power demanding tools that associated to the
development of new experimental devices led to a rapid growth of both the understand-

ing of hydrocarbon kinetics and the chemical mechanisms that resulted. Second, emission
standards applied to combustion devices, especially in the automotive industry, brought
the need to simulating more than just flame temperature evolutions. Pollutant species
prediction became necessary and was not reachable without some chemical complexity.
Then, pressure on oil prices and availability imposed the need for fuel diversity. Only
mechanisms starting from the original fuel and taking into account its specificities can
help retrieving fuel effects. Finally, the constant search for fuel efficiency and reduced
pollutant emissions led to more complex combustion systems like flameless combustion,
oxygen enriched combustion, exploitation of waste industrial gas and hydrocarbons, com-
bustion systems based on auto-ignition phenomena or large use of recycled burned gases.
Again, the use of complex chemistry is mandatory to solving such problems. The issue
of turbulence/chemistry coupling for combustion modeling has then become of first order
importance.
The complexity of turbulence and combustion kinetics modeling comes from the strong
non linearity of both processes, making them extremely sensitive to initial and boundary
conditions. In the case of turbulence, the non linearity comes from the convection term
in the Navier Stokes equations where small perturbations in the velocity field can be
overly amplified. As to combustion kinetics, the generic term representing the reaction
rate of a chemical reaction is proportional to the product of species concentrations and
presents an exponential dependency on temperature. Both in average RANS simulations
of reactive flows and Large Eddy Simulation (LES) where flame thickness is comparable
in magnitude to the grid size, turbulence and chemical kinetics coupling has to be taken
into account.
3.5.1 Preliminary kinetic tabulation approaches for reactive flows
Upon my arrival at IFPEN, one of my tasks was precisely to set the ground for the inclu-
sion of detailed chemical kinetics on our RANS code for internal combustion engine flows
simulation. The necessity of getting reliable complex kinetic mechanisms for representa-
tive fuels of diesel and gasoline combustion was then identified. This was accomplished
at first by setting up collaborations with research laboratories capable of providing such
mechanisms (cf. Chapter 4). Later, I have supervised the training period of J. Galiez [77]
where some improvements were proposed to a mechanism describing the influence of NO
on n-Heptane ignition. This work was followed by the PhD of J. Anderlöhr [52] already
described in section 3.4.
In a RANS CFD code, chemical kinetics and turbulence can be coupled in a num-
ber of ways. For instance, the resolution of species transport equations for all species
present in a complex or reduced kinetic mechanism could be proposed. This is illustrated
for example by the work of Liang et al. [78] or Ge et al. [79]. Then, a large system of
transport equations would have to be solved with some chemical processes showing char-
acteristic time scales much lower than others and very different from flow time scales.
This might bring difficulties in terms of simulation time, not compatible with an indus-
trial applications, as well as computer memory issues where a single processor is unable
to load and process all the necessary information. Also, the formalism frequently used in
such problems where average reaction rates are written as a function of average species
concentration and temperature is mostly inappropriate and can lead to highly inaccurate
results [80] pp. 217.

3.5. COUPLING TURBULENCE AND CHEMISTRY 35
Another approach found in the literature is the resolution of reduced or complex
kinetics directly coupled with turbulent combustion models like the RIF model by Pe-
ters [24,81,82] or the kinetic/CMC approach [43,83] based on the CMC model developed
independently by Bilger [41] and Klimenko [42] and then unified by the two authors [84].
The idea of tabulating chemical kinetic information which would then be easily re-
trieved in a CFD code by table search and interpolation techniques was therefore investi-
gated. Tabulation techniques were first proposed by Maas and Pope [85] who developed
the on-the-fly Intrinsic Low Dimensional Manifold (ILDM) technique. A priori tabulation
methods such as ISAT [86] (In Situ Adaptive Tabulation), FPI [87] (Flame Prolongation
of ILDM) or FGM [88] (Flamelet Generated Manifolds) were also studied. As long as the
parametric variation of table entries is widely covered, results can then be quite accurate.
In the case of internal combustion engines, multiple parameters have to be taken into
account: Temperature, pressure, equivalence ratio and dilution are the most important.
A kinetic table can be as detailed as allowed by memory storage capacity and the abil-
ity to perform large amounts of kinetic simulations. For example, dilutant gas or fuel
composition could be extra parameters to take into account.
Tabulation methods are attractive but might also present important difficulties. First,
they are not valid outside the boundaries of the a priori table. Extrapolation should not
be allowed implying, for ICE applications, very wide ranges for each parameter (in an
ICE, fresh gas temperature might vary from 400 K up to 1500 K, pressure from 0.1 MPa
up to 20 MPa or more and so on). Second, tabulated points have to be close enough in
order to retrieve the non linearity intrinsic to combustion phenomena. For the tabulation
of an AI process or a flame front, time or flame coordinates respectively also have to
be tabulated. This is accomplished with the help of a combustion progress variable c
which has to be skillfully defined. There has been a lot of discussion in the literature
about this issue since a progress variable remains an artifact to retrieve a time or space
coordinate in a tabulated database. Several definitions applied to combustion modeling
have been carefully analyzed by Bray et al. [89]. Subramanian during his PhD [28] has
also addressed the issue by comparing several definitions from the perspective of engine
combustion modeling. Hence, there is no unique definition and no theoretical justification
to use one definition rather than another. Common sense prevails in the way that the
progress variable should fit the best interest of the problem being solved. The question of
the chemical simulation outputs to tabulate is a fundamental one. Are integral quantities
such as AI delays or laminar flame velocities sufficient or does one need more detailed
information on chemical species and reaction rates? Additionally, a kinetic database is
built from multiple simulations of a chosen simplified but representative kinetic system,
e.g., a closed constant volume homogeneous reactor, a laminar one-dimensional premixed
flame, etc. In an internal combustion engine, as shown by Jay and Colin [90], the strong
volume and pressure variations that occur during the combustion process lead to major
difficulties in choosing the right simplified system. Finally, tabulation methods per se
do not solve the turbulence/kinetic coupling. They only help simplifying the complex
chemistry resolution in a turbulent CFD code. A turbulent combustion model still has to
be applied in top of the chemical kinetic data.
To overcome this last issue, different solutions were proposed in the literature. The
most relevant is the PCM/FPI [91–93] methodology based on the independent FPI [87]
and PCM [38,39] models which couple respectively tabulated chemistry with a presumed

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