Modeling internal combustion engines using the OpenFOAM® library

Modeling internal combustion engines using the OpenFOAM® library

T. Lucchini, A. Onorati, G. D’Errico, M. Fiocco G. Montenegro, A. Della Torre, L. Nocivelli

Politecnico di Milano, Department of Energy

GROUPPOLITECNICO DI MILANO

www.engines.polimi.it


Background

Internal combustion engines development will never stop

Efficiency increase Pollutant control

Heat

recovery

Downsizing After-

treatment

Alternative

fuels

Direct

injection

Combustion

systems

2



3

Background

Internal combustion engines are complex

Advanced tools are necessary • Experiments – optical measurements

• Models – computational fluid dynamics



4

Requirements

1) Support for complex geometries and automatic mesh generation

2) Fast and reliable for engine development

3) Accurate and advanced for diagnostic purposes

4) Customizable to include new features

Which CFD code for IC engines?



• Ready to use CFD code

Applications and model state of the art available for CFD

heat transfer, aerodynamics, compressible/incompressible flows, multi-phase…

Automatic polyhedral mesh generation

• Open-source

Research in collaborative environment

Code customization

• Object-oriented C++

Generic programming

Easy development of new models (classes) and applications (solvers)

5

Which CFD code for IC engines? OpenFOAM®



• Ready to use CFD code

Applications and model state of the art available for CFD

heat transfer, aerodynamics, compressible/incompressible flows, multi-phase…

Automatic polyhedral mesh generation

• Open-source

Research in collaborative environment

Code customization

• Object-oriented C++

Generic programming

Easy development of new models (classes) and applications (solvers)

void kEpsilon::correct()

{

solve

(

fvm::ddt(k)

+ fvm::div(phi, k)

- fvm::laplacian(nu() + nut, k)

==

nut*magSqr(symm(fvc::grad(U)))

- fvm::Sp(epsilon/k, k)

);

}

class kEpsilon

:

public turbulencModel

{

volScalarField k_;

void correct();

kk

kkt

k T

tt

0

0

2

2

1

uuu

class turbulenceModel

{

volScalarField mut_;

virtual void correct() = 0;

6

Which CFD code for IC engines? OpenFOAM®



Pre-Processing In cylinder flows & combustion After-treatment

Automatic mesh generation Gas exchange, fuel air mixing SI, CI, PCCI, HCCI combustion

After-treatment modeling SCR, DPF TWC, DOC

OpenFOAM at Polimi (Lib-ICE)

7



8

MESH 1

XXX CA YYY CA

MESH 2

YYY CA ZZZ CA

02 u

toldnew uxx

FV motion solver and/or topological changes

Mesh management for engine simulations

pistonLayer

{

type layerAdditionRemoval;

faceZoneName pistonLayerFaces;

minLayerThickness 0.0001;

maxLayerThickness 0.0012;

thicknessFromVolume 1;

oldLayerThickness 0.00102205;

active true;

}



9

1) Generation of multiple meshes2) Definition of a template case

3) engineCaseSetUp utilitycase

constant

init

system

case0300

case0320

……

case0720

case1440

……

dataOutput

4) runMultiCycleCasesolverName simpleColdSpeciesEngineDyMFoam;

startTimes

(/* list of startTime for each mesh */);

endTimes

(/* list of endTimes for each mesh */);

writeInterval

(/* list of writeInterval for each mesh */);

parallelRun on;

Bottleneck is mesh generation 1) CAD-based grid generators

• High amount of user time and experience, possibility to introduce errors

2) Automatic mesh generators: what is the best strategy?

• Fit to geometry boundary, oriented to main flow features

Solution: dedicated algorithms for automatic mesh generation of IC engines

Engine simulation workflow



10

Automatic mesh generation

Combustion chambers for Diesel Engines

Input data: • Piston bowl points • Injection angle

A pyhton program automatically recognizes the points on the STL and does the morphing of a template blockMesh to the geometry

Fully-hexaedral spray-oriented mesh

Spray-combustion simulations in a spray-oriented grid



11


snappyHexMesh Geometry-oriented BlockMesh STL + data

Full-cycle for Direct-injection engines

• A python program automatically recognizes the direction of ducts, cylinder and valves and writes the blockMeshDict

• snappyHexMesh is then run using the geometry-oriented background mesh

• The user provides combustion chamber geometry and data (bore, stroke, valve lifts)

0

2.5

5

7.5

10

0 180 360 540 720

Lift

[mm

]

Crank Angle [deg]

Exhaust

Intake

Bore 80 mm

Stroke 60 mm

Conrod 160 mm



12


Automatic generation of a geometry-oriented blockMesh



13

In-cylinder flows: TCC Engine

Computed flow field during the intake stroke

Acknowledgments: ECN, Prof. D. Reuss and Prof. V. Sick (University of Michigan)

0

50

100

150

200

250

360 420 480 540 600 660 720

Spe

cifi

c ki

ne

tic

en

erg

y [k

J/kg

]

Crank Angle [°]

Experimental

Coarse mesh

Fine mesh (1st ord.)

Fine mesh (2nd ord.)

0

10

20

30

40

50

60

70

80

90

100

360 420 480 540 600 660 720

RM

S o

f k

ine

tic

en

erg

y [k

J/k

g]

Crank Angle [°]

Experimental

Coarse mesh

Fine mesh (1st ord.)

Fine mesh (2nd ord.)

(a)

(b)

Flow at 450 CAD Exp. Calc.

Mesh details Validation

420 CAD

460 CAD

490 CAD

510 CAD

540 CAD



14

In-cylinder flows: GDI engine

Acknowledgments: Dr. Vaglieco, Ing. Sementa (CNR-IM)

180 CAD BTDC

65 CAD BTDC

13 CAD BTDC

Velocity CO2 (range: 0-0.03)



15

In-cylinder flows: GDI engine

460 CAD 500 CAD 540 CAD 580 CAD 600 CAD 640 CAD

Flow

Residual CO2 (EGR)



16

Spray modeling

Fuel-air mixing simulation in direct-injection engines

New sub-models for multi-hole nozzles

• Injection: Huh, Reitz-Bracco, Nurick • Atomization : Huh-Gosman, Bianchi • Breakup: KHRT • Wall-interaction: Bai and Gosman,

Stanton and Rutland • Evaporation: based on Spalding mass

number

We > 2600・La-0.18We > 5.0We < 5.0

Wall-film model (finite-area)

• Mass, momentum and energy equations for the liquid film solved on mesh boundary.



17

Spray modeling: validation

Evaporating spray in constant-volume vessel ECN Spray-A, non-reacting conditions. Fuel: n-dodecane

ramb = 22.8 kg/m3; Tamb = 900 K; pinj = 150 MPa

Acknowledgments: Sandia Engine Combustion Nework (ECN) 2d axy symmetric mesh

20 25 30 35 40 45 50

Mixture fraction

0 0.2

Exp.

Distance from injector [mm]

Calc.

10

5

0

-5

-10



18

Spray modeling: validation

GDI spray from multi-hole nozzle

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1 1.2

Pe

ne

trat

ion

[m

m]

Time ASOI [ms]

Exp. 100 bar Calc. 100 bar



0

20

40

60

80

100

120

140

160

0 50 100 150 200 250

SMD

[mm

]

Injection pressure [bar]

Data Set 1, Min

Data Set 1, Max

Data Set 2

Computed

pinj [bar]

mfuel [mg/shot]

tinj [ms]

100 48.1 3.70

150 50.5 3.00

200 50.4 2.66 Acknowledgments: Dr. Allocca, Ing. Montanaro (CNR-IM)

Calc.

Exp. t = 0.3 ms t = 0.5 ms t = 0.7 ms



19

GDI engine simulations Full-cycle simulation of GDI engines: spray and wall film evolution



20

GDI engine simulations

• Spray targeting optimization • Identification of sources of soot formation

(rich mixture pockets or wall-film)

Acknowledgments: Ing. Di Gioia, Dr. Bonandrini, Ing. Venturoli (Magneti Marelli)

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

450 495 540 585 630 675 720

Ho

mo

gen

eit

y in

dex H

. I.

Crank angle

Injector 1

Injector 2

Operating point 1, 1500 rpm

0.0

0.1

0.2

0.3

0.4

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2

Den

sit

y f

un

cti

on

Relative air/fuel ratio l

Injector 1

Injector 2

This work was sponsored and carried out in collaboration with

Full-cycle simulation of GDI engines



Spark-ignition combustion

Comprehensive combustion model

Understanding the complex interplay between ignition-system, local flow, turbulence, air/fuel ratio…

Design and development of more efficient combustion systems

21



22


Lagrangian particles for the spark-channel

Flame kernel equations

dt

dp

pdt

dT

TA

VSS

dt

dr k

kk

kplasmat

k

uk 11

r

r

dt

dp

ccm

QTT

m

m

dt

dT

pbpp

effspk

bp

p

pp

r

1

Rp Rs

Lp LsSpark

gap

ri

Ti (>10000 K) Tu (300-600 K)

Qel

Comprehensive combustion model Electrical circuit model

Initial thermal transient

cell

N

i

ii

spkV

Sf

1

Flame surface density

k

it

t

ii

i PDPxScScxx

u

t

mmrr

Coherent flamelet model (CFM) for combustion



23


0

0.4

0.8

1.2

1.6

2

0 0.2 0.4 0.6 0.8 1

En

fla

mm

ed

vo

lum

e [

cm

3]

Time after spark [ms]

Calc., 1000 rpm, l = 1.0

Calc., 1000 rpm, l = 1.0

Exp., 1000 rpm, l = 1.0

Exp., 1000 rpm, l = 1.0

l

l

l

l

Peripheral

Central

Validation: Herweg and Maly engine

0

0.4

0.8

1.2

1.6

2

0 0.2 0.4 0.6 0.8 1

En

fla

mm

ed

vo

lum

e [

cm

3]

Time after spark [ms]

Calc., 1000 rpm, l = 1.0

Calc., 300 rpm, l = 1.0

Calc., 1250 rpm, l = 1.0

Exp., 1000 rpm, l = 1.0

Exp., 300 rpm, l = 1.0

Exp., 1250 rpm, l = 1.0

l

l

l

l

l

l

Flow field, flame propagation and plasma temperature details



24

Diesel combustion

Objectives

• Provide a complete library of combustion models for Diesel engines using OpenFOAM technology:

Simplified models with reduced chemistry for engine design

Detailed models for advanced combustion modes and diagnostic purposes

Reduced chemistry Detailed chemistry

Characteristic-Time scale (CTC) Well stirred reactor model

Multiple Representative Interactive Flamelet Eulerian Transported PDF



25

Diesel combustion: CTC

Characteristic time-scale model

• Shell model (auto-ignition) + Eddy Dissipation Concept (mixing-controlled combustion). Emission sub-models: soot and NOx.

Bore 85 mm

Stroke 88 mm

CR ~15

0.5

0.6

0.7

0.8

0.9

1

0 1 3 5 7 9

No

n d

ime

nsi

on

al s

oo

t

Mass of fuel in the post-injection [mg/stroke]

Effect of post injection on soot-emissions Measured

Computed

Full-load conditions: soot emissions control



26

Diesel combustion: CTC

Characteristic time-scale model

• Shell model (auto-ignition) + Eddy Dissipation Concept (mixing-controlled combustion). Emission sub-models: soot and NOx.

Part-load conditions: improving soot-NOx trade-off

0

0.2

0.4

0.6

0.8

1

3.5 19.5 23.5 33.5 (std.)

33.5 (adv.)

No

n-d

ime

nsi

on

al s

oo

t

Measured

Computed

0

100

200

300

400

500

600

700

3.5 19.5 23.5 33.5 (std.)

33.5 (adv.)

NO

x[p

pm

] Measured

Computed

External EGR [%] External EGR [%]

EGR = 0% EGR = 30%



27

Diesel combustion: detailed kinetics

Well-mixed model

Each cell is a homogeneous reactor: assumed d-pdf for temperature and composition

mRIF model

Eulerian-Field PDF

Chemistry acceleration • TDAC: combination of on-line tabulation and

mechanism reduction algorithms • Multi-Zone CCM: on-line tabulation based on

specified parameters (T, Z, …)

xH~

pjst ,~,

xT~ xYhxH ii

~~CFD

domain

xZxZ 2'',

~

Flamelets tZY ij ,

~,

dpYMYZ

ij

N

j

ji

f

1

0

,

1

~~

jM

Diesel spray combustion can be represented by a set of unsteady diffusion flames

1

2

3

Nf

……

……

……

Y11, Y2

1,…, YM1, T1

Y12, Y2

2,…, YM2, T2

Y1N, Y2

N, …,YMN, TN

Y13, Y2

3,…, YM3, T3

p(Yi)

……

……

……

Acknowledgments: Dr. Contino (VUB Bruxelles), Dr. Jangi (Lund University of Technology)



28

Diesel combustion: detailed kinetics

1 2 3 Model comparison on constant-volume diesel combustion experiments:

1) Well-stirred reactor

2) Eulerian-Field PDF

3) Multiple Representative Interactive Flamelets

Identification of the best approach for the combustion problem to be studied



Diesel combustion: well-mixed

PCCI combustion

347 mm, exp 12 mm, exp 18 mm, exp

7 mm, calc 12 mm, calc 18 mm, calc

Air-fuel mixing model validation

HCCI combustion

Heavy-duty optical engine (SANDIA) operating with PRF30 fuel.

Medium-duty engine operating at low-load with iso-octane fuel.

29 Acknowledgments: Dr. Contino (VUB Bruxelles)

5 CAD 8 CAD

11 CAD 13 CAD

Fuel-air mixing and PCCI flame propagation process



30

Diesel combustion: mRIF

0

1

500 2300

bm

ep

/bm

ep

ma

x

Engine speed [rpm]

O.P. 1

O.P. 2

O.P. 3

Cyl. 6

B x S 128 x 144 mm

CR 16.5

SR 1.4

Max pinj 1600 bar

Heavy-duty Diesel engine (collaboration with )

-20 0 20 40

Crank Angle [ ]

-20 0 20 40

Crank Angle [ ]

0

30

60

90

120

150

180

210

-50 -25 0 25 50

Cyl.

Pre

ssu

re [

bar]

Crank Angle [ ]

#1, Exp.

#1, Calc.

#2, Exp.

#2, Calc.

#3, Exp.

#3, Calc.

0

100

200

300

400

-20 0 20 40

Heat

rele

ase r

ate

[J/

]

Crank Angle [ ]

• Validation on three relevant points including pilot injections.

• 30 flamelets for the main injection event.

• N-dodecane kinetics used to represent diesel fuel.



31

After-treatment

Contributions

1) Full-scale simulation of after-treatment devices 2) Modeling of surface chemistry in washcoats 3) Spray, wall-film and wall cooling for SCR systems



32

SCR modeling: injection of AdBlue

*T

K

Thermal

Breakup

Deposition Splash

Rebound

• Multicomponent liquid mixture customized properties for Urea along with multi component liquid film

• Temperature dependence of the spray-wall interaction and wall cooling effect

Cold wall

Hot wall



33

Reacting flows in porous media

From micro-scale to full scale simulation of after-treatment systems

Reconstruction of micro-scale geometry

Fluid-structure simulation of the microporous structure and generation of look-up tables

From micro-scale to macro-scale approach



34

After-treatment: DPF

EX80-

100/17 EX80-

200/14

DPF steady-state simulation DPF thermal transient

t = 1.5 s

t = 10 s



35

After-treatment: surface chemistry 773 K

573 K

473 K

Figure 11: Foam-type reactor, sample E: CO field inside the channel for an inlet CO mass fraction YCO = 4.6% and a feed flow rate Q =

3000Ncc/ min.

101

Q 105 ·m3/ s

10− 1

100

k m[m

/s]

foam A (YCO 3 %) - CFD

foam A (YCO 3 %) - measured

foam A (YCO 1 %) - CFD

foam A (YCO 1 %) - measured

foam D - CFD

foam D - measured

foam E - CFD

foam E - measured

Figure 10: Foam-type reactor: mass-transfer coefficients for different

foam samples.

surface, which are responsible for its mass transfer to-

wards the reaction region. It can be seen in Figures 12

and 13 that CFD simulations give a reasonable predic-

tion of the light-off curve, describing thetransition from

akinetic-controlled to a diffusion-controlled process.

The CO conversion at high temperature is correctly

predicted while an overestimation of the light-off tem-

perature with respect to the measurements can be ob-

served, in particular for the lower CO concentration.

Thiscan beexplained considering theassumption, made

in the simulations, of uniform temperature distribution

on the foam catalyst surface. Actually, this assumption

doesnot describeaccurately theexperimental condition

under which measurements were performed. As a mat-

ter of fact, on thecontrary of theplate-type reactor con-

400 500 600 700 800T [K]

0

20

40

60

80

100

CO

conver

sion

[%]

CFD (YCO 3 %)

measured (YCO 3 %)

CFD (YCO 5 %)

measured (YCO 5 %)

Figure 12: Foam-type reactor, sample E: CO conversion for an inlet

feed flow rate Q = 3000Ncc/ min.

figuration, the foam-typeonedoesnot allow an efficient

removal of the heat from the catalytic surface. There-

fore, the temperature of the catalyst is expected to be

higher than the temperature measured in the front of

the foam, where the thermocouple is located. For this

reason, the computed light-off curve is shift towards a

higher temperature, if compared to the measured one.

Moreover, it can be seen that, at the light-off tem-

perature, when the conversion is reaching its maxi-

mum value, the curve exhibits a smooth transition from

kinetic- to diffusion-controlled CO conversion. As a

matter of fact, the present model does not include a

pore-diffusion sub-model, therefore a sharp transition

should beexpected in this region. However, in this case

the explanation for the smoothness of the curve can be

8

• Mass transfer limit regimes allows to validate the model and extract information for design purposes

GAS

WASHCOAT

SUBSTRATE



36

Waste heat recovery

• Fully integrated real gas thermodynamics: Helmoholtz theory ad multi region CHT

• Customized classes to handle single and multi region mesh motion for volumetric machines

Simulation of volumetric machines

Volumetric expander Stirling engine

Scroll compressor



37

Conclusions

• OpenFOAM is the ideal platform for simulation of IC engines:

Implementation of detailed models to simulate thermal and fluid dynamic processes in all the relevant engine components (cylinder, intake, exhaust)

Development of CFD methodologies to be successfully applied for engine design and optimization.

• Continuous development of OpenFOAM technology (and Polimi libraries…) offers solutions which can support design of cleaner and more efficient Internal Combustion Engines.



2day meeting announcement

The ICEG of Politecnico di Milano is pleased to announce the Second Two‐day Meeting on Internal Combustion Engine Simulations Using OpenFOAM® Technology will be held in Milan November 2015.More information on: http://www.engines.polimi.it/2day/index.php

http://www.engines.polimi.it/2day/index.php

Modeling internal combustion engines using the OpenFOAM® library

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