using les and system simulation to address non-cyclic engine

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Renewable energies | Eco-friendly production | Innovative transport | Eco-efficient processes | Sustainable resources

Using Large-Eddy & System Simulation to Address Non-cyclic Engine Combustion

Christian AngelbergerEngine CFD & System Simulation Dept.

ERC Symposium, 5 June 2013

1500 2000 3000 4000 55003

5

7

10

15

18

Engine speed (rpm)

IME

P (

bar)

9-10

8-9

7-8

6-7

5-6

4-5

3-4

2-3

IMEP standard deviation (%)

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� IFP Energies nouvelles

� Non-cyclic engine combustion and its modeling

� Principles & potentials of LES

� LES studies of CCV in SIE

� A system simulation CCV model based on knowledge from LES

� Conclusions & outlook

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IFP Energies nouvelles

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IFPEN: A public sector research, innovation & training center

� Mission: provide industry and public players with eff icient, economical, clean & sustainable technologies

� 5 research priorities� Renewable Energies, Eco-friendly Production, Innovative Transport, Eco-

efficient Processes Sustainable Resources

� Some key figures� 1,700 employees: 1,100 researchers, 150 PhD students� 2 locations in France

� 2011 budget: 300M€: 244M€ for research

� 12,600 active patents, 230 publications (2011 figures)

� Transport research is one key activity� Transport is one of the 3 IFPEN BUs� Dedicated training programs at IFP-School

� R&D in the Energy Applications Technique Division� 180 employees, 15 PhD students

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R101 R102 R103 R104 R105 R106➠Engine/Fuel matching (regular and alternative fuels)➠Vehicle testing (Roll test bench)➠Standard engine tests

➠Assembling and support to experimental test benches➠Technical assistance to operating test benches

➠Engine and vehicle conception, development, integration and calibration➠From CAD design to vehicle calibration

➠Heavy duty engines➠NGV engines

➠After-treatment (Catalysis, DeNOx, DPF,...➠Synthetic gas test bench

➠Modeling and software conception 0D / 3D (RANS, LES)

➠Engine CFD and engine and vehicle system simulation

➠Experimental analysis of combustion systems

➠ Advanced diagnostics (optical,...)➠ Electric machines characterization

The Energy Application Techniques Research Division

Director: S. HENRIOTAssistant : S. HENRY

Quality (ISO 9001)

M. PASQUEREAU

SecurityEnvironment (ISO 14001)

D. FERET – L. DAYDE – B. ALGOURDIN

Scientific Advisor

C. ANGELBERGER

Facilities and Technical Support

Department

A. RANINI

Engine CFD and Simulation Department

A. PIRES DA CRUZ

Engine TechnologyDepartment

P. PACAUD

Fuels -Emissions -Lubricants -Department

N. JEULAND

Engines LaboratoryDepartment, IFP -

LyonR. TILAGONE

Engine Syste m Analysis Department

B. THIROUARD

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A Department dedicated to R&D in engine and vehicle modelling & simulation

� Staff: 30 permanent, 20 PhD students & non-permanen t� 3D CFD of ICE combustion chambers

� 25+ years expertise in RANS spray and combustion model development

� capitalized in the IFP.C3D engine code commercialized by LMS� implemented in major CFD codes (Star-CD, FIRE, Converge): e.g.

ECFM3Z, ECFM, AKTIM & ISSIM spark-ignition models� numerous application studies (internal and industrial clients)

� System simulation (0D/1D) of engines, powertrains & vehicles� 10 years of development of IFP-Engine, IFP-Drive & IFP-Exhaust libraries,

commercialized by LMS in AMESim� combustion chambers: model development (Injection, combustion,

pollutants), many resulting from reduction of 3D models (e.g.. CFM1D)� powertrain componetsn: analytic description or tabulation

� complete vehicles and powertrains

� electric motors & powertrains, batteries� advanced application studies for internal users and industrial clients

� Since 2001: Development of a LES research around AVBP

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Non-cyclic engine combustionand its modeling

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Cyclic Combustion Variability in a SIE

� Evaluated via σσσσIMEP or σσσσPmax

1500 2000 3000 4000 55003

5

7

10

15

18

Engine speed (rpm)

IME

P (

bar)

9-10

8-9

7-8

6-7

5-6

4-5

3-4

2-3


High CCV at low load & idle� due to high EGR rates

High CCV at high load� due to enrichment� impacts knock limit

Many CCV factors are known�large scale motion & turbulence�reduction of flame speed�variation of trapped mass�variations of IGR/EGR�...

However predicting a stability map by simulation is difficult:

�non-linear interactions of many complex phenomena

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Technology evolution of SIE and link to CCV

� Downsizing, high dilution and stratified/lean burn are today major trends to the reach ambitious European CO2 and pollut ant emission targets� Increased importance of CCV and their link to abnormal combustion

higher downsizing& dilution

� Crucial to better understand and predict CCV� to support design improvements

� to facilitate efficient engine control strategies and calibration

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0 5 10 15 20 25 30 35 40 45 50 55Angle vilebrequin [deg]

dQ [

1/°V

]

0.0

0.1

0.2

0.3

0.4

-40 -20 0 20 40 60 80 100 120Angle vilebrequin [deg]

Pcy

l [ba

r]

20

40

60

80

100

120

0 100 200 300 400 500 600 700 800

Pm

ax

[ba

r]

406080

100120140

0 5 10 15 20 25 30 35 40 45 50 55Angle v ilebrequin [deg]

FMB

[-]

0.00.20.40.6

0.81.0

Other non-cyclic engine phenomena

� Fast transients under cold operation of GDI-SIE� hardly accessible to standard

optimization based on steady operating points

� “Soliton” cycles in SIE (GDI)� GDI with stratified charge

� superknock in downsized SIE

� @ very low cyclic frequency (1/1,000-1/10,000)

PM emissions essentially produced during cold transients

� and...� Variability of engine-out

emissions (soot in CIE)

� combustion mode switching (SI-CAI,CI-LTC, ...)

� cold starts ...

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� Results from complex interactions between non-linear p henomena� Requires a 3D CFD approach

� that predicts individual engine cycles� and gives access to detailed, instantaneous & local flow phenomena

Accessible to 1D CFD

Combustion chamber�trapped mass�global composition

(EGR, IGR, F/A eq. ratio)�large scale flow patterns

(swirl, tumble)�small scale turbulence�injection strategy�walls: thermal, liquid films, ...�ignition system & interaction

with gas flow

Exhaust system�turbulence

�flow dynamics�acoustics

Intake system�turbulence


Modeling non-cyclic engine combustion

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Principles & potentials of Large-Eddy Simulation

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LES: A CFD addressing instantaneous local, spatially filtered, flow

E(k) E(k) E(k)

kkk kc kc

resolved modelledinstantaneous spectrum

implicit filtering by the numerical approach (schemes + mesh)

E(k) E(k) E(k)

kkk

resolved modelledmean spectrum RANS

LES

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Examples of LES of engine flows

LES of engine aerodynamics LES of & 6hole GDI spray

LES of combustion in a PFI SIE

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solved with a 100 µµµµm resolution

What can LES resolve on a practical mesh?

10-1

10-210-3

10-410-5

10-6

droplet size

premixed flame thickness

vortex size

integral length scale

m

Kolmogorv scale

� Reynolds numbers of ICE flows are moderate

� Practical meshes can resolve up to 80% of the flow energy� meshes with 10-100Mcells

� Yet many 2phase, mixing & combustion phenomena cannot be resolved� and have to be modelled

� LES does not automatically solve all problems of CF D� However it yields an improved description of the tu rbulent flow in an ICE

� an important part of convection is resolved

� yielding a spatially filtered physical realisation of the flow

� This improves predictions of mixing, combustion and sprays� as flow and turbulence are major inputs to related models

� modeling is facilitated (filtering more easy to grasp than ensemble averaging)

� and increasingly so as mesh resolution can be increased

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IFPEN’s approach to LES for ICE flows

� "engineering" LES = use CFD codes developed for RANS, replacing k- εεεε by a sub-grid turbulence model� appealing owing to its simplicity� doesn't allow accurately resolving large scales

� only a large simulation, without eddies?...

� thus very limited in terms of predictivity

� "brute force" LES = put all efforts on increasing mes h resolution, neglecting sub-grid modelling� will be the solution on the VERY long term

� yet accurate sub-grid modelling will remain essential on the way

� IFPEN’s approach: A pragmatic compromise� use a CFD code specifically developed for LES: AVBP

� exploit the potential of parallel machines to achieve return times compatible with practical ICE applications

� develop accurate sub-grid models for combustion and sprays

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Main characteristics of AVBP

� Jointly developed & owned by CERFACS and IFP� for GT and ICE

� Numerics adapted for LES� 2nd and 3rd order centred convective schemes� explicit time advancement

� Unstructured moving meshes� any element in FV, linear and bilinear elements in FE

� LES models for turbulence, combustion and liquid inje ction� Smagorinsky, WALE, SIGMA

� E/E and E/L dilute spray models� premixed and non-premixed combustion, auto-ignition, spark ignition

� Adapted boundary conditions� NSCBC to impose wave amplitudes and avoid reflections

� Development of an adapted GUI� used by SAFRAN group for internal work on LES for GT� developed in the GSM (PSA, Renault & IFPEN) for LES of ICE

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A key feature of AVBP: Parallel Eficiency

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LES for ICE: A collaborative research programme around AVBP, driven by IFPEN

AVBP development & maitenanceCERFACS & IFPEN

PhDs: 15 since 2001GIS SUCCESS + Renault, PSA Peugeot-Citorën,

numerics, physical models, methodologies

EC FP5 LESSCO2Feasibility of CCV by LES

ANR SGEmacLES method for CCV

ANR CamPaSmulticyl. LES

ANR SIGLELES of engine sprays

EC FP7 LESSCCVExploiting LES for proposing

CCV models for 1D CFD ANR ICAMDACLES & 1D-CFD of CCV & abnormal combustion

in downsized DI-SIE

EC FP6 & FP7LES of aero GT ignitionBilateral LES application to

engine aerodynamics

ANR ASTRIDELES & 1D-CFD of transients in DI-SIE

Champ de vitesse à l’admission

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LES studies of Cyclic Combustion Variability

in SIE

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SGEmac: An engine database dedicated for validating LES methodologies

� Numerous crank-resolved p and T measurements

� Optical diagnostics for local flow and combustion� PIV

� OH* LIF� Chemiluminescence

Journée 3D RANS - LES, IFP, Pera & Lacour, 05/09/200821

Pin_pl Pin_man Pin_A

Pcyl

Pex_man

Pex_pl

Pin_B

Pm, Tm

Pm, Tm Tm Tm

TmTm

Tm

Pm, Tm

Pm, Tm

IntakePlenum

ExhaustPlenum

MixingPlenum

Cylinder with optical access

Lacour & Pera (2011)

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The operating points in the SGEmac databaseSparkadv.

Rot. speed

IMEP σIMEP

[-] [RPM] [bar] [%]

Stab-ref Optim 1200 3.0 1.1

Stab-sa- < Optim 1200 2.9 1.9

Stab-sa+ > Optim 1200 2.9 1.3

Stab-PMI Optim 1200 4.6 0.9

Stab-RPM Optim 1800 2.7 1.2

Instab-Dil Optim 1200 3.2 7.6

Instab-ϕ Optim 1200 3.3 8.9

Instab-for Optim + 1/10

desact.

1200 3.0 1.8

F/A eq. ratio

EGR %

[-] [%]

1.0 0

1.0 0

1.0 0

1.0 0

1.0 0

1.0 33

0.59 0

1.0 0

stable (low CCV)

unstable (high CCV)

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Numerical set-up of the SGEmac LES

� Tetrahedral meshes� 40 mesh phases to describe full cycle� between 2 and 12Mcells

� 0.1mm at valve seat, 0.2mm at spark,average ~0.4mm in cylinder

� Lax-Wendroff scheme� WALE sgs model� Combustion models

� ECFM-LES with ISSIM spark model

� TF-LES wit ADEL spark model

� Boundary conditions� full LES (cylinder + coarse ducts)

� LES of cylinder + unsteady BC from 1D CFD

� Return time per cycle� 0.8 day for motored

� 1.5 days for fired250-500 processors 1D CFD

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Possible approaches to LES of piston engines (IC = Initial Conditions)

SingleCycle

MultiplePerturbedCycles

IC ICIC IC

part

of

cycl

e

full

cycl

efu

ll cy

cle

full

cycl

e

...part

of

cycl

e

part

of

cycl

e

part

of

cycl

e... ...

+ perturbations

full

cycl

efu

ll cy

cle

...

full

cycl

efu

ll cy

cle

...

...

full

cycl

efu

ll cy

cle

...1

1

2

1

n

MultiplePerturbed Consecutive

Cycles

MultipleConsecutiveCycles

+ perturbations

1 2 n

nCelik (Virg. U),Rutland (ERC)

X-S. Bai (LTH)... Adomeit (FEV) Goryntsev (EKT) Motored: Haworth (GM)Hasse (BMW)

� quantification of CCV as combustion phase included � independence of initial conditions & perturbations� accounts for possible dependencies on preceding cyc les

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MCC LES of 25 cycles of the unstab_diloperating point

full 720°°°°CA cycle in 1,5 dayson an average of 350 processors

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-50-40-30-20-10

x10

-3

6420-2

LES instantanées LES moyenne PIV

-40x10-3

-35

-30

-25

-20

Z [m

]

-30 -20 -10 0 10 20 Ux [m/s]

-60x10-3

-50

-40

-30

-20

Z [m

]

20100-10-20 Ux [m/s]

-70x10-3

-60

-50

-40

-30

-20

Z [m

]

-20 -10 0 10 Ux [m/s]

280° BTDC 240° BTDC 220° BTDC

X

Y

Z

Adm.Echap.

Validation of intake aerodynamics predicted by a 25 cycle MCC LES - Intake

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-60x10-3

-50

-40

-30

-20

Z [m

]

1050-5 Ux [m/s]

-50-40-30-20-10

x10

-3

6420-2

LES instantanées LES moyenne PIV

-80x10-3

-70

-60

-50

-40

-30

-20

Z [m

]

100-10 Ux [m/s]

-70x10-3

-60

-50

-40

-30

-20

Z [m

]

840-4 Ux [m/s]

-50x10-3

-40

-30

-20

Z [m

]

840-4 Ux [m/s]

180° BTDC

140° BTDC120° BTDC

100° BTDC

Validation of intake aerodynamics predicted by a 25 cycle MCC LES - Compression

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Prediction of combustion for the stable reference point by LES

trapped mass

COV (%) trapped mass

COV (%) Pmax

COV (%) IMEP (~3 bar)

EXP 180 0.2 4.72 1.12

LES 179.5 0.1 3.1 1.04

cycle 3

cycle 5cycle 5

cycle 4

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Quantitative prediction of CCV by LES for the two unstable points

unstab_dil

unstab_lean

DIL CA5 (deg)

Cov(CA5)%

CA50(deg)

Cov(CA50)%

IMEP(bar)

Cov(IMEP)%

Pmax(bar)

Cov(Pmax) %

EXP -1.75 5.5 21.3 7.6 3.1 7.61 16.47 12.48

LES -2.7 6.4 22 11 2.9 12.5 16.2 12

LEAN CA5 (deg)

Cov(CA5)%

CA50(deg)

Cov(CA50)%

IMEP(bar)

Cov(IMEP)%

Pmax(bar)

Cov(Pmax) %

EXP -3 6 19 7.7 3.3 8.91 18.9 14.77

LES -4.8 7 16.5 11 3.1 10.5 19.2 13.2

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Exploring causes for CCV: Global effects

Ignition

unst_dil

&

unstab_lean

Ignition

stab_ref

Strength of

the tumble

motion

The combustion phase of the unstable points is characterized by:� stronger large scale flow� smaller laminar flow velocity

cyclic variations of large flow scales and turbulence strongly amplified as

compared to the stable reference point

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Exploring causes for CCV: Local effectsunstab_lean case

cycl

e 21

(sl

ow)

cycl

e 18

(fa

st)

27degCA ASI 27degCA ASI37degCA ASI 47degCA ASI

cycle 21 (slow)

cycle 18 (fast)

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Exploring causes for CCV: Local effectsunstab_lean case

37degCA ASI

higher horizontal velocity favors propagation of the flame away

from the spark for cycle 18

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Two main approaches

LES� MCC

= mutiple consecutive cycles

� Perturbing model parameters� e.g. params of a Viebe function

� Easy & low CPU time� No predictive capability

� redo a LES for each case studied

Accessible to 1D CFD

Combustion chamber�trapped mass�global composition

(EGR, IGR, F/A eq. ratio)�large scale flow patterns

(swirl, tumble)�small scale turbulence�injection strategy�walls: thermal, liquid films, ...�ignition system & interaction

with gas flow

Exhaust system�turbulence


Intake system�turbulence


Modelling CCV in system simulation/1D CFD

1D CFD MCC +random perturbation of

parameters for combustion chamber model based on LES

findings

� Perturbing physical parameters� e.g. tumble ratio

� Based on physical modeling� predictive capability, not LES for each case

� Higher CPU time

-

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CFM1D: A physical model for SI combustionin the AMESim IFP.Engine platform

� Based on the formal reduction of the 3D CFM model� A 2-zone model (fresh/burned gases)

� Set of 0D equation for � mean and turbulent kinetic energies (K,k)� species and energy

� fresh and burned gas composition and enthalpy

� Turbulent flame surface calculation� accounts for the combustion chamber geometry

� 0D equation for the flame wrinkling

derived from the FSD 3D equation

� Ul and δl given by correlations� lt = f(piston position), u’=(2k/3)1/2

� Detailed 0D ignition model based on ISSIM 3D model� electrical circuit description and interaction with gas

� energy transferred to the gas� turbulent critical ignition energy to account for misfiring

t mS S= ΞSm

St

Sm

S

Sm

St

Sm

S

Sm

St

Sm

S

Sm

St

Sm

S

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Reproduction of ensemble averaged behavior by CFM1D

� Mean cycle results over the whole SGEmac database with CFM1D after calibration of the K,k model

stab_ref unstab_dil unstab_lean

stab_SA+ stab_SA- stab_rpm stab_load

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CCV modeling: Perturbing each cycle based on LES findings

� LES has allowed identifying key CCV mechanisms� importance of flow properties fluctuations on the flame development� no correlation between the tumble and spark-plug velocity variability

� strong impact of thermodynamic effects (naturally included in 1D CFD)

� Post-processing LES of the motored case allowed ident ifying three main physical parameters key to CCV

� They were chosen as the parameters to perturb in order to introduce CCV in MCC simulations with CFM1D� mean kinetic energy K deduced from the PDF of the

tumble ratio @ IVC taken from LES – Approximatedas a Gaussian, with σ=6,5% ⇒ direct impact on k and u’

� integral length scale using a log normal distribution with σ=10% estimated from LES

� velocity fluctuation at spark plug using a Gaussian PDF with σ=4/m/s from LES ⇒ computation of the electrical arc convection and turbulent ignition energy

� Mean values of the PDF are those predicted by CFM1D

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Reproduction of the SGEmac database punts in terms of CCV by CFM1D

stab_ref unstab_dil unstab_lean

stab_SA+ stab_SA- stab_rpm

stab_load

9: Pera, Richard and Angelberger, SAE 2012-01-0127

� MCC simulation of 100 cycles for stable 200 cycles for unstableconditions� same relative perturbations for all pooint

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CFM1D: Matekunas curve and COV predictions compared to experiment

stab_ref unstab_dil

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10Experimental COV(IMEP) [%]

CF

M1D

CO

V(IM

EP

) [%

]

0

5

10

15

20

0 5 10 15 20Experimental COV(Pmax) [%]

CF

M1D

CO

V(P

max

) [%

]

stable conditions stable conditions

unstableconditions

unstableconditions

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Application of CFM1D_CCV to 4cylinder engines

� Two engine models were built using AMESim/IFP.Engin e� Renault F4RT: 4 cyl. 16v, 2L, turbocharged PFI (0D model)� Opel Z18XER: 4 cyl.,16v (VVT), nat. aspired, PFI (1D model)

� Calibration of CFM1D_CCV as no LES results available� 1st step : Standard calibration of the turbulence model to match mean

experimental values (pressure trace, IMEP, emissions...)

� 2nd step : Calibration of the CCV model based on multi-cycle simulations (101 cycles simulated - 1st cycle removed from the statistics)

� Unique set of relative deviation parameters (in % of the mean value) for the whole engine map)

� Optimization of standard deviation of the tumble and integral length scale PDF’s:

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Stability map predicted by CFM_CCv for the F4RT

1500 2000 3000 4000 55003

5

7

10

15

18

Engine speed (rpm)

IME

P (

bar)

9-10

8-9

7-8

6-7

5-6

4-5

3-4

2-3


1500 2000 3000 4000 55003

5

7

10

15

18

Engine speed (rpm)

IME

P (

bar)

9-10

8-9

7-8

6-7

5-6

4-5

3-4

2-3


Experimental map CFM1D σσσσTumble = 5% σσσσlt =12%

Instability linked to dilution & high enrichment

1500 2000 3000 4000 55003

5

7

10

15

18

Engine speed (rpm)

IME

P (

bar)

1,25-1,3

1,2-1,25

1,15-1,2

1,1-1,15

1,05-1,1

1-1,05

Equivalence ratio

1500 2000 3000 4000 55003

5

7

10

15

18

Engine speed (rpm)

IME

P (

bar)

20-24

16-20

12-16

8-12

4-8

Residual gases rate (%)

IGR rate (%) Equivalence ratio

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Conclusions

� Non-cyclic combustion gains in importance as a mastering of individual cycles becomes necessary fo r complying with stringent FC & emission constraints

� LES is a well suited tool for studying non-cyclic e ngine combustion phenomena

� LES is not a mere research tool, it can be applied for detailed studies of practical interest

� It was shown how LES can help advancing the understanding of CCV in SIE

� Capitalizing the knowledge on CCV from LES in the form of 1D CFD/system simulation models was shown to yield encouraging results that could be exploite d for calibration or control strategy development purpose s

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Outlook: On-going LES research @ IFPEN

� The ICAMDAC ANR project: Studying abnormal combustion in downsized GDI engines (IFPEN + Renault, PSA & others)� experimental and LES study of knock and super-knock� capitalize acquired knowledge for system simulations

� The ASTRIDE ANR project: Exploring fast transients und er cold conditions in GDI (IFPEN + Continental, Renault, PSA & others)� experimental and LES study of fast transients

� development of techniques to characterize specificities of such flows

� Collaborative projects: Detailed LES studies of downs ized GDI engines� spray/aerodynamics interactions (design robustness)

� cyclic combustion variability

� PhD research� coupling of 1D CFD and the AVBP code

� LES model for wall films in ICE

� LES of a Diesel engine

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Acknowledgements:� Financial support by the European Commission (LESSC O2 & LESSCCV projects)� Financial support from ANR (SGEmac, CamPas, SIGLE, ICAMDAC & ASTRIDE projects)� Financial support from Groupement Scientifique Moteur (Renault, PSA Peugeot-Citroën

& IFPEN)� Computing resources provided by GENCI (grant N. x2b 6139)

www.ifpen.com

powertrain-modeling-simulation.ifpen.com

using les and system simulation to address non-cyclic engine

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