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2008-01-0358

Coupling of a 1-D Injection Model with a 3-D Combustion Codefor Direct Injection Diesel Engine Simulations

Julien Bohbot, Christos Chryssakis, Pierre Pacaud, Adlène BenkenidaIFP Powertrain Engineering

Copyright © 2008 SAE International

ABSTRACT

Modern diesel engines operate under injectionpressures varying from 30 to 200 MPa and employcombinations of very early and conventional injectiontimings to achieve partially homogeneous mixtures. Thevariety of injection and cylinder pressures, as well asinjector dynamics, result in different injection rates,depending on the conditions. These variations can becaptured by 1-D injection models that take into accountthe dynamics of the injector, the cylinder and injectionpressures, and the internal geometry of the nozzle. The

information obtained by these models can be used toprovide initial and boundary conditions for the spraymodeling in a 3-D combustion code. In this paper, amethodology for coupling a 1-D injection model with a 3-D combustion code for direct-injected diesel engines ispresented. A single-cylinder diesel engine has beenused to demonstrate the capabilities of the model under varying injection conditions. Moreover, this couplingstrategy opens a new methodology for 3-D calculationsthat do not need to fit initial conditions but use directly a0-D model for intake/exhaust conditions and injectionconditions. Using coupling strategy makes easier to run3-D engine simulations, reduce engineering time and

allows to investigate a large range of interestingphenomena.

INTRODUCTION

Stringent emissions regulations for diesel enginesrequire employing innovative technologies in order toreduce in-cylinder nitrogen oxide (NOX) and particulatematter (PM) emissions. Advanced fuel injectionstrategies, including multiple pilot and post injections canbe used to reach the limits of emissions reduction, while

maintaining the advantage of high thermal efficiency of adiesel engine [1-4].

The development of the Common Rail (CR) fuel injectionsystem allows more flexibility and accuracy in controllingthe injection parameters and the combustion process inorder to meet performance and emissions goals. TheCR system applies a constant fuel pressure in the railthat is common for all cylinders. However, manyresearchers report a drop in the injection pressure andinjection-to-injection variations when multiple injectionstrategies are employed [5-10]. These variations areattributed to injector dynamics, due to the needle motionand pressure waves occurring in the system. That leadsto varying injection rates, which can affect the

combustion and emissions formation processes,particularly NOX and soot. Typical Computational FluidDynamics (CFD) models for the in-cylinder processes inan engine are not able to capture these variations, sincethe exact injection rates are not known.

In order to improve the predictability of CFD models, acoupling between the injection system and the in-cylinder processes has to be performed. Previous workon 1-D/3-D coupling focuses on the gas dynamics of theintake system. Sinclair et al. [11] have developed adirect 1-D/3-D coupling model to compute the gasexchange system of a turbo-charged DI-diesel engine.

They have treated the intake manifold and ports as asingle 3-D model while the rest of the engine wasmodelled by a system of 1-D components. Theydeveloped a 1-D/3-D coupling interface and haveobtained encouraging results. Their work shows that 3-Dmodelling of a single component can be used in a full 1-D system using an appropriate 1-D/3-D interfacetreatment. Similar coupling approaches have beenfollowed by Borgh et al. [12], Riegler and Bargende [13]and Wehr et al. [14], who have developed 3-D CFDmodels for the intake manifold, coupled with 1-D enginemodels.

In this work a novel coupling approach between a 1-Dinjection model and a 3-D combustion code ispresented. The model is based on previous work by

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Bohbot et al.[15], who developed a coupling approachbetween a 1-D engine simulation software and a 3-Dcombustion code, for Port Fuel Injected (PFI) gasolineengines. This model is extended here by adding a directinjection model. The injection model has beendeveloped for a typical Common Rail system used in

diesel engines. Subsequently, the model is coupled withIFP's 3-D combustion code to demonstrate its behaviour in a single cylinder diesel engine, when multipleinjections are used, including pilot- and post-injectionevents.

In the following, the 1-D simulation code is brieflydescribed, as well as IFP's 3-D combustion code. Then,the injector model development and validation arepresented as well as its coupling with the 3-Dcombustion code. Finally, a single-cylinder diesel engineis used to demonstrate the capabilities of the model.

THE IFP ENGINE SIMULATION CODE

The IFP engine simulation tools are developed under theLMS Imagine.Lab AMESim platform and consists of a 1-D library, IFP-ENGINE allowing the simulation of acomplete virtual engine, and of a 3-dimensionalcombustion code IFP-C3D that can be used for in-cylinder spray and combustion simulations. These twoparts are briefly described in this section.

THE IFP-ENGINE LIBRARY

The IFP-ENGINE library allows the simulation of acomplete virtual engine using a characteristic time-scaleof the order of the crankshaft angle. A variety of elements is available to build representative models for engine components, such as a twin scroll turbocharger,gasoline or diesel injectors, etc. Figure 1 shows theseIFP-ENGINE components. Moreover, the library uses anadvanced modeling approach to take accurately intoaccount the relevant physical phenomena taking place inthe engine[16]. The combustion process can bemodeled with efficient phenomenological models likeCFM1-D [ 26], but also using a mapped combustion,such as Wiebe's law for gasoline engine, and with theBarba [ 27] based model for Diesel engine.

THE IFP-C3D COMBUSTION CODE

In order to take full advantage of modern parallel super-scalar machines (SMP machines), IFP has developedIFP-C3D, a solver designed for hexaedrical unstructuredand parallel formalism.

The IFP-C3D code [17-20] solves the unsteadyequations of motion of a turbulent, chemically reactivemixture of gases, coupled to the equations for a multi-component vaporizing fuel spray. The Navier-Stokesequations are solved using a finite volume methodextended with an ALE (Arbitrary Lagrangian-Eulerian)

scheme. IFP-C3D uses a time-splitting integration andthe temporal integration scheme is largely implicit. Fuelevaporation, breakup, and spray/wall interactions are

modeled to simulate the injection. For turbulentcombustion the 3-Zone Extended Coherent FlameModel (ECFM3Z) , developed at IFP, is used [21-22].For Diesel combustion, a full database of auto-ignitiondelays and reaction rates allows us to compute cold andhot flame ignition [23]. The RNG k-ε turbulence model

[24] with either the Diwakar or Kays and Crawford walllaws are employed, depending on the fuel used(gasoline or diesel).

Species (and tracers), energy and RNG k-ε turbulentdiffusion terms are all implicitly solved by a genericdiffusion routine. Parallel optimized linear algebraiclibraries developed is used to reverse matrices,rendering parallelization very efficient. The SIMPLEmethod is applied for solving the coupled pressure-velocity system. All gradient terms used for pressure andthe Reynolds stress tensor are parallely computed.Moreover, preconditioning efficiently the pressure matrix[19] drastically reduces the simulation time. Theconvection terms are explicitly sub-cycled. A secondorder upwind scheme for scalars and momentumconvection is used.

With the development of parallel computers theexploitation of parallelism in order to reduce thecomputational time has become a major goal. The dataparallelism model selected, based primarily on the ratiobetween performance and development cost, is theOPEN-MP paradigm [25], which is easy to implement,standardized, portable, and scalable. After a profiling of the sequential version, it has been noticed that most of the CPU time is spent in the pressure solver and

diffusion terms computation. Implementing OPEN-MPdirectives into them accounts for a parallelization of approximately 50% of the IFP-C3D code. Finally, a verygood and scalable speed-up of around 3, when 4processors are used, is reachable for the benchmarkcases. Details and extensive validation on the code canbe found in [18-20].

An automatic mesh generator is available in IFP-C3D tocreate 2-D and 3-D unstructured wedge meshes withperiodic boundary conditions. The code is fullyintegrated in the LMS Imagine.Lab AMESim platformwith a friendly graphical user interface (GUI) and

automatic post-processing.

1-D INJECTION MODEL

MODEL DESCRIPTION

A 1-D model has been developed to simulate the injector dynamics and provide predictions for the fuel flow rateinto the cylinder. The model development has beenbased on the operation and geometrical characteristicsof a Bosch CRI2.1 used on a Diesel engine. The nozzlemodeled is of the Valve Covered Orifice (VCO) type.

The fuel pump and the regulator of the rail pressure arerepresented by very simple models assuming a constantflow rate and an ideal regulator. The electromagnetic

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part has been simplified, as a first approach, byproviding tables with the intensity and the voltage of thesignal to the solenoid valve. The fuel return outlet hasbeen modeled by assuming that the fuel returns to thefuel tank, where the pressure is maintained constant.

The control chamber guides hydraulically the plunger and needle lift by regulating the fuel pressure inside theinjector. When the solenoid valve opens, fuel leaves thecontrol chamber from the A-hole and the pressure drops.The pressure difference on the two sides of the plunger-needle assembly moves the needle upwards, thusopening the injection orifice of the nozzle. The inlet andthe outlet restrictors of the control chamber (Z-hole and

A-hole, respectively) have been modeled by utilizingorifice models available in the LMS Imagine.Lab

AMESim software. The part of the plunger that closesthe A-hole when the needle lifts is represented with a"flapper valve" model and a piston with a crown sectionis utilized in order to model accurately the upper surfaceof the plunge and calculate the volume displacementwhen the plunger moves.

The mechanical behavior of the plunger and the needleassembly have been modeled by assuming that theplunger, the pressure shoulder of the nozzle, and theneedle, react as one piece. In order to model the motionof this assembly, the compressibility of its elements hasto be considered. A simple and computationally efficientsolution is to distribute the masses in a way thatconserves the first oscillation mode of the system. Thishas been achieved by dividing the total mass in threeparts, as shown in Figure 1.

Figure 1: Mechanical model of the plunger-pressureshoulder-needle system

The two conic surfaces of the needle have beenmodeled using two piston elements and two disks thatare used to calculate the contact points of the needleinside the nozzle.

The high-pressure fuel lines have been modeled using

hydraulic elements in LMS-AMESim. They include therail, the line linking the fuel pump with the rail, the rail-

injector line, and the high-pressure circuit inside theinjector.

The model initialization is based on the assumption thatthe fuel pressure in the high-pressure circuit is equal tothe rail pressure. The initial position of the needle and

the plunger depends on the fuel pressure, due tocompressibility effects.

Figure 2: 1-D Injector model

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MODEL CALIBRATION

It is necessary to calibrate the injector model to fit thedifferent sub-model thanks to a large number of experimental points. A large part of the parameter of thesub-model are defined with geometrical data measured

on the injector. For the other parameters like thediameter of the hole A, hole Z and the also for thecavitation number of the injector hole, these parametersare fitted with single and multi-injection events.

A first calibration of the model has been performed byusing experimental measurements for relatively longinjection events (2200 µs) at a rail pressure of 160MPa.The comparison between model predictions andexperimental measurements for the needle lift and theinjection flow rate is presented in Figures 4 and 5respectively.

Figure 3: Comparison of needle lift predictions (inmeter) and experimental measurements

Figure 4: Comparison of injection rate predictions and

experimental measurements

A second calibration of the model has been performedfor multi-injection measurement with short injection

events (260µs, 330µs, 430µs) at 90MPa. For this multiinjection events, the dynamic of the system is veryimportant to predict the injection delay and the fuel massfor each injection. Rail pressure calibration have been

optimized to predict multi-injection events and pressureoscillation in the rail and in the pipe of the injectionsystem (see Figure 7). For multi injection events, aspilot and main spray injections, the electric commandhas been optimized to represent accurately the electricdelay (see Figure 5). The dynamic of the needle motionis also accurately predicted (see Figure 6). The injector 1-D model has been tested on various multi-injectionevents and the Figure 8 plots the injection rate obtainedwith this model. Time injection delays and durations arewell predicted. However, the pilot injection mass (shortinjection) given by experimental data have a strongvariability, Figure 9 plots the experimental injection rate

given for a pilot injection (260 µs) for different multiinjection events. The mass measured for this pilot havean average value equal to 3.97mg with a lower value of 3.13mg and an upper value of 4.50 mg. The parametersof the injector model have been fitted on this averagevalue for the pilot mass. For the main injection, theinjector model has been calibrated using operatingcondition listed in Table 1. However, the injector modeldo not predict a dynamic phenomenon that occurs in themain injection. This wave in the injection rate is notpredicted by the injector model and more precisely in thedynamic line. The signal pressure in the pipe shows thatwe do not predict the high frequency of the pressurevariation just before the main injection events. The line

of the injection system have to be improved but our attempts to increase the accuracy of this model havefailed.

Figure 5: Signal Intensity of the electric port for a pilot –

main injection (T1=260µs , T2=430 µs , delay

period=1777,8µs / 16deg.).

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Figure 6: Needle displacement of the injector for a

pilot/main injection (T1=260µs , T2=430 µs , delay

period=1777,8µs / 16deg.)

Figure 7: Signal of pressure in the pipe for a pilot –

main injection (T1=260µs , T2=430 µs , delay

period=1777,8µs / 16deg.).

Figure 8: Injection rate for a pilot/main injection

(T1=260µs , T2=430 µs , delay period=1777,8µs /

16deg.)

Figure 9: Discrepancies of the injection rate

experimental data for the pilot injection (260µs).

Pointnumber

Injection 1

duration (µs)Dwell(deg.)

Injection 2

duration (µs)

4 260 20 330

5 260 20 380

6 260 20 430

7 380

8 260 12 430

9 260 16 430

10 260 24 430

11 260 28 430

12 33013 430

21 380 20 380

Table 1: Experimental database used for injector calibration (1500 rpm, 90MPa)

MODEL VALIDATION

The injector model has been validated by comparing theinjected fuel quantity predictions with experimentalmeasurements under various rail pressures and injectiondurations. The injection chamber pressure for all caseswas maintained constant at 50 bar.

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0

20

40

60

80

100

120

140

160

2200 1800 1400 1000 800 600 500 400 300 200

Duration [µs]

Q u a n t i t y [ m m

3 ]

1200 bar - Exp

1200 bar - Model

1600 bar - Exp.

1600 bar - Model

Figure 10: Comparison of model predictions with experi-mental measurements for Pinj=1200, 1600 bar for singleinjection events.

In Figure 10, the comparison of model predictions withexperimental measurements for injection pressures of 1200 and 1600 bar shows very good agreement, for awide range of injection durations (from 200 to 2200 µs).For injection durations shorter than 200 µs the injectedquantities are very small and cannot be measuredwithout a significant error.

A second validation has been carried out for multiinjection events listed in Table 1. Mass injected andinjection rate calculated by the injector model have beencompared with experimental data in Figure 11. Results

show a good agreement for events with a pilot injectionduration equal to 260µs and a main injection equal to

430µs or 380µs. For the injection duration equal to

330µs (point 12), the injected volume quantities is notvery well predicted and have a relative error equal to20%. Because of a too small number of experiment point

with 330µs injection duration, we do not succeed to fitthe injector model for this injection duration that canexplained the lack of precision.

Injected volume

0,00

5,00

10,00

15,00

20,00

25,00

4 5 6 7 8 9 10 11 12 13 21

Point

Q u a n t i t y ( m m 3

Expe

Model

Figure 11: Comparison of model predictions with

experimental measurements for Pinj=900 bar for multiinjection events

DESCRIPTION OF THE 1-D/3-D COUPLED

APPROACH

The development of a direct coupling approach between

two different tools requires the definition of the type of data that have to be transferred and the time scale of thesynchronisation enforced by the physical system.

The main objective of this development is to model with3-D modelling only the injection and combustionprocesses (not the intake and exhaust phases). Animportant assumption of this coupling approach is theinitially homogeneous distribution of species in thecombustion chamber, after the closing of the intakevalves. The time scale of the synchronisation is thecombustion cycle duration (when both intake andexhaust valves are closed). This process was described

in detail in [15] for a PFI gasoline engine. The newelement here is the addition of the 1-D injection model,which provides information for the direct injection andcan be used for a Direct Injection (DI) diesel enginesimulation. This is achieved by sending the cylinder pressure information to the 1-D injection model, wherethe fuel flow rate is calculated as a function of theinjection and cylinder pressure, and the signal sent tothe solenoid valve. Subsequently, the calculated fuelflow rate by the 1-D injector model is returned to the 3-Dcombustion code to define the parameter of the 3-D fuelinjection.

The IFP-ENGINE library uses an assumption of threeperfect gases (air, fuel, and burnt gases) whereas IFP-C3D utilises a mixture containing 12 gases (fuel, O2, N2,CO2, H2O, CO, H2, NO, OH, O, H, N). To ensure thegases compatibility between the two codes, IFP-C3Dcomputes the mass of the 3 gases needed by IFP-ENGINE using the fuel mass, the O2 mass, and themass of the species produced by the combustion asdescribed below :

ENGINE

air fresh

i

DC

ispecie

ENGINE

gasesburnt

DC

o

ENGINE

air fresh

DC

fuel

ENGINE

fuel

mmm

mm

mm

∑=

−=

=

=

11,1

3

)(

3

2

3

233,0

1

To complete the direct coupling method, we havedefined a temporal staggered algorithm to manage thetime lag between the two codes. As shown in Figure 12,the staggered algorithm can be split in three steps.

First, the engine gas exchange system is calculated byIFP-ENGINE. When IFP-ENGINE reaches the intake

valve closure (IVC) angle, the calculation is stopped andIFP-C3D starts. Then the 1-D library sends the IVC

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angle, the trapped mass and the mixture composition tothe 3-D code.

Figure 12: Staggered coupling algorithm

The second step is the 3-D combustion calculation usingthe 0-D/1-D information as boundary and initialconditions. The 3-D calculation is stopped at the exhaustvalve opening (EVO) crank angle. Then, IFP-C3D sendsto the 1-D library the heat release histories (combustion,wall transfer) and the species mass consumptionhistories.

Finally, IFP-ENGINE restarts the calculation from theIVC to the next engine cycle. IFP-ENGINE uses duringthe combustion cycle the 3-D data to compute at eachtime step the heat release and the mass consumption.

To manage the coupled calculation, the MPI 2 (MessagePassing Interface) paradigm is used to create the linkbetween the two codes. The MPI library is used with amaster/slave mode. The master LMS-AMESim/IFP-ENGINE creates an intra-communicator with the IFP-C3D (slave) and can command and communicate withIFP-C3D at any time step. The master/slave mode

allows the possibility to manage many slaves to extendthe calculation for more than one cylinder but also to use3-D modelling to other single components (such as air box, 3-D pipe, etc.). Moreover, the CPU time need by 3-D calculation and 1-D simulation are largely different andgenerally 3-D calculations have to be performed using aparallel architecture computer with large memory.Homogeneous grid of computer can be used with MPIparadigm and LMS-AMESim calculation can beperformed on a laptop and 3-D calculations on a PCCluster (see Figure 13). This functionnality allows toreduce the calculation return time and makes thecoupled calculation time return compatible with industrial

requirement.

Figure 13: MPI link between laptop computer runningLMS-AMESim and a Cluster running IFP-C3D.

VALIDATION OF THE COUPLING ALGORITHM

A first validation of the coupled algorithm is done in abasic engine system. This basic system contains asingle cylinder model of a direct fuel injection dieselengine. The engine model diagram is presented inFigure 14. The 1-D/3-D coupling algorithm described

previously is used for the modelling of the fuel injectionand combustion processes. A sector mesh is generatedwith the internal mesher of IFP-C3D and contains 5000cells. This coarse grid was selected as a first step inorder to reduce the computational time and demonstratethe new methodology.

Figure 14: Diagram of single cylinder model

The initial mass fraction of fuel is set to null in the

combustion chamber. The in-cylinder pressure is drawnin Figure 15. As shown in this figure, the engine system

LMS Imagine Lab

AMESIM

MPI link

PC Cluster running

IFP-C3D inparallelmode

IFP-ENGINE

IFP-ENGINE

IFP-C3D

IFP-C3D

IFP-ENGINEcycle n cycle n+1

Heat release,Mass consumptionTrapped mass

Gases massfraction

1

2

3

1

2

3

Time

Intake valveclosure

Exhaust valveopening

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is stabilising after few engine cycles when using the 1-D/3-D combustion modelling that assesses the stabilityof the staggered coupling algorithm.

As explained in the previous section, the IFP-ENGINElibrary uses an assumption of 3 homogeneous gases

while IFP-C3D uses at least 12 gases. The gascompatibility is ensured with the assumption that IFP-C3D burnt gases mass is equal to the IFP-ENGINEburnt gases mass. However, the IFP-ENGINE burnt gasmixture is obtained from a stoichiometric combustionand is quiet different from the burnt gases mixtureobtained with the 3-D combustion modelling. Thisdifference leads to a small lack of accuracy.Nevertheless, the differences between pressureobtained with IFP-C3D and the pressure obtained withIFP-ENGINE using the 3-D combustion modelling dataare negligible.

Figure 15: Cylinder pressure evolution and stabilisation

APPLICATION ON A SINGLE-CYLINDER DIESEL

ENGINE

The coupled model has been applied on a Diesel single-cylinder direct injection diesel engine with a BoschCommon Rail injection system in order to demonstrateits capabilities. The main characteristics of the single-cylinder diesel engine are given in Table 2. A 72° wedge

mesh is used to describe the combustion chamber inIFP-C3D which is linked with the 1-D LMS-AMESimsoftware that includes the fuel injector and the enginesimulator (cf. Figure 17). The total CPU time for onecomputation on two AMD 2 GHz processors isapproximately 12 hours to simulate 8 engine cycles.Mainly, the CPU time is spent by the 3-D calculations.First, the computational grid has been generated usingthe internal mesher of the code. This sector meshcontains 10.000 cells and is plotted in Figure 16. Themesh size is adapted during the cycle to optimize thenode clustering during the motion of the geometry. For all operating points simulated, a first coupled calculation

has been performed without combustion and injection tofit the wall temperature and the air loop. Secondly, theinjection system is activated and the ECFM3Z[ 21 22]

combustion model is used in IFP-C3D. The in-cylinder pressure predicted by the calculation without injection isplotted in Figure 19 and is in good agreement with theexperimental data. This numerical result assess thatthermal effect and trapped mass in the cylinder arecorrectly computed with IFP-ENGINE coupled with IFP-

C3D.

Figure 16: Computational grid for the 72° wedge

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Figure 17: 1-D injector and engine simulator

Engine typeDiesel Single cylinder

Bowl IFP

Number of cylinders 1

Bore [mm] 87

Stroke [mm] 92

Compression ratio [ - ] 14.7

Connecting rod length [mm] 149.9

Engine displacement [cm3] 546.9

IVO/IVC 42° BTDC/124° BTDC

EVO/EVC 146° ATDC/43° ATDC

Table 2: Engine set-up characteristics

COUPLED 1-D INJECTION / 3-D COMBUSTIONCALCULATION

The operating point selected for comparison of themodel results with experimental measurements of thecombustion in the cylinder is given in Table 3.

Engine Speed [RPM] 1500

IMEP [bar] 3.44

Injection Pressure [bar] 900

Inj. Timing [° CA ATDC]Pilot: -17°Main: -5°

Fuel volume injected[mm

3]

3.9 mm3- 12,80mm3

Table 3: Engine operating conditions

In the Figure 20 injection flow rate is shown, asmeasured experimentally for the case of a pilot injectionat -17° with a main injection at -5°. For these injectiontiming, the injection system predict with accuracy the

mass of fuel injected and the injection rate. However, themodel cannot predict a wave phenomena in the maininjection that explain the error in the prediction of theinjected mass. This wave phenomenon is observed in allinjection events and we did not succeed to understandthis physical process to avoid this lack of accuracy.

As described before, a first calculation has been done tofit the engine system parameter. The system isstabilizing after approximately four engine cycles. Theintake pressure is set to 0.1108MPa and the exhaustpressure to 0.1208MPa (experimental plenum pressure).The wall temperature are set to 450K for the piston and

the cylinder wall and to 370K for the head cylinder. InFigure 18, the in-cylinder pressure and total masshistories are presented and attest that the dynamic

system has converged. The in-cylinder predicted by thecalculation without injection is plotted in Figure 19 and isin good agreement with experimental data. This resultassess that thermal effect and mass in the cylinder arecorrectly computed with IFP-ENGINE coupled with IFP-C3D. The next step is now to inject fuel in the system

and let IFP-C3D combustion code simulate the 3-Dcombustion process.

Figure 18: In-cylinder pressure and total mass historiesfor coupled calculation without injection.

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Figure 19: In-cylinder pressure (bar) calculated with thecoupled system without fuel injection.

Figure 20: Injection rate modeled vs. Experimental data

for dwell = 12°

In a second step, the full system has been simulated.For this calculation, all the initial parameters needed byIFP-C3D to compute the combustion are automaticallycomputed and received from the injector simulator andthe engine system simulator. Actually, the 1-D/3-Dcalculation is like a typical system simulation. Injectioncommand is filled as the experimental test in the systemand the calculation can be run. For engineers used tomake 3-D calculations, this coupled way allows to avoidthe difficult task to fit the initial parameters and is really a

great advantage. However, the coupled calculationneeds more CPU times to stabilize the dynamic system.

As seen in Figure 21 and Figure 22, the system hasconverged after five engine cycles. The Figure 23 showsthe comparison of the in-cylinder pressure predicted bythe coupled simulation. The coupled simulation result isin good agreement with the experimental pressure. Asseen in this graph, a small variation of the pressure canbe observed and is due to the fluctuation of the in-cylinder mass and of the mass fraction of the burntgases but also is due to the injected mass flow ratefluctuations. The Figure 24 shows the injected mass flowrate history using a crank angle modulo 720° abscissa

and we can notice that the pilot injection and the maininjection mass flow rate are influenced by the pressurein-cylinder history. The fuel mass predicted by theinjector simulator for each engine cycle is varyingbetween +/- 6% in comparison with the average value(see Figure 25). The injected volumes of the pilot and of the main injection have a cycle/cycle variation behavior.The experimental data gives for this operating conditiona rate of IMEP instability equal to 1.7 % that do not fitwith this large variation. In a first step, because of a lackof experimental data to validate this variation andeventually to fit the 1-D injector model, the coupledcalculation used a fixed mass of fuel for each enginecycle given by the average of injected mass values. IFP-C3D uses the instantaneous injection rate given by the1-D injector model to model the fuel spray with this

average mass. The Figure 26 plots the combustion heatreleased and we can notice the two local extremumrepresenting the pilot injection and the main injectioncombustions. Fluctuations in the heat mass released arelarger and attest that small fluctuations may have alarger impact on 3-D calculation prediction and in

particular for pollutant predictions. Obviously, thevariation of injected fuel mass may have a major effecton 3-D combustion calculation. The 1-D/3-D coupledsystem is able to predict the average experimental IMEPplotted on Figure 27. As seen in this figure, the dynamicsystem is converging after few cycles to theexperimental IMEP. Figure 28 and Figure 29 present 3-D view of the combustion chamber. For this multi-injection events, the spray particles impact the cylinder wall during the pilot injection. These pictures arecorresponding to the last engine cycle during the pilotinjection and the main injection.

Figure 21: In-cylinder pressure history

Figure 22: Burnt gases mass fraction history

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Figure 23: In-cylinder pressure history vs. Experimentdata for dwell = 12° (8 cycles plotted with a crank anglemodulo 720 deg.)

Figure 24: Injected mass flow rate history (8 cyclesplotted with a crank angle modulo 720 deg.)

Figure 25: Injected volume deviation history (%)

Figure 26: Combustion heat released time history (8cycles plotted with a crank angle modulo 720 deg.)

Figure 27: IMEP history vs. experiment

Figure 28: View liquid film thickness of the 3-Dcalculation during the pilot injection. It can be observed aliquid film formation during the pilot injection.

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Figure 29: View liquid film thickness of the 3-Dcalculation during the main injection. Liquid fuel droplet

are evaporating before reaching the cylinder wall.

EFFECT OF INJECTION PARAMETERS ONCOMBUSTION

The coupled model has been tested by comparingmodel predictions with experimental measurements. Theinjection timing of the pilot injection in the single-cylinder engine has been varied, in order to study the effect on

combustion. The experimental injection flow rate for alloperating point are plotted in Figure 31. In Figure 32 theIndicated Mean Effective Pressure (IMEP) is shown for 5different injection timings that leads to different injectedmass (Figure 33). The main injection was held constantat –5° ATDC, while the pilot was varied from -17° to -33°

ATDC. The coupled model is able to predict the IMEPvariation when the pilot injection timing is varying. Theinjection rate for each operating condition isautomatically computed by the injector model and IFP-C3D computes the combustion using ENGINE results for the trapped mass and burnt gases mass fraction.

Actually, the IMEP is mainly dependent of the injectedmass and of the prediction of the injector model.However, spray impact have been noticed for late pilotinjection timing which can explain IMEP variation.Figure 30 presents the pressure computed for a delay of 16° between pilot and main injection. The 1-D injector model have to be tested more intensively withexperimental data to improve the numerical results andto understand the cycle/cycle dynamic behavior.

Figure 30: In-cylinder pressure (bar) for dwell=16°.

Figure 31: Experimental injection rate

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

12 16 20 24 28

dwell (deg°)

I M E P Experiment

Model

Figure 32: Calculation vs. experimental measurements

of IMEP and injected mass as a function of the dwellangle.

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0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

12 16 20 24 28

Injected Mass (kg/h)

Figure 33: Experimental fuel flow rate

CONCLUSIONS

This paper describes a new methodology that involved1-D injector simulation, 0-D air loop simulation and 3-Dcombustion calculation coupled to simulate the fullengine system with a predictive combustion modeling.This multi-scale simulation allows to compute 3-Ddetailed combustion using accurate and dynamicconditions given by the 0-D air loop model and 1-Dinjection model. This coupled algorithm can be used tomake easier 3-D calculations but also to avoid thedifficult task to define all initial conditions needed by the

3-D calculation. Indeed, more than one cycle is neededto converge to a stabilized solution and a coupledsimulation needs at least five engine cycles. However,these kind of calculation fill already the industrialrequirement of a calculation return time lower than 12hours on two PC's processors to simulate eight enginecycles. This return calculation time may decrease withmore than two processors for parallel calculation andwith new processor development (high speed multi-coreprocessor, high number of processor per node).Moreover, using heterogeneous parallel grid computer helps to manage multi-scale calculation and may alsoreduce the CPU cost.

This coupling algorithm has been used in a Diesel directinjection single cylinder engine on a variety of injectionconditions. Numerical results are in good agreementwith experimental results. An injector model has beengenerated and has been fitted on experimental data.Simulation results have shown that the mass flow ratepredicted by the 1-D model is influenced by the in-cylinder pressure histories. These fluctuations (+/- 6% of the injected mass) have to be investigated and moredetailed experimental data are needed to validate thisbehavior. However, coupled numerical simulations showthat dynamic behavior of the injector model is not

negligible and can have a major effect on 3-Dcombustion predictions. Actually, the injector model isfitted using experimental data in cell and these data are

not sufficient to fit the model for the dynamic behaviour when taking account a counter-pressure coming fromthe combustion chamber.

This coupling algorithm can be used to simulate 3-Dcycle/cycle variation due to fluctuation in the injection

system or in the air loop system. Moreover, 3-Dcalculations can compute pollutant emissions which canbe used by the 1-D engine system model to predict theengine pollutant emissions. In the future, the enginesystem will be modified to add EGR loop and to testcoupled calculation for operating conditions with EGR.

This paper presents a first attempt to make a coupledcalculation with a fine injection system and a basicengine system using a 3-D combustion code. This workshows that today 3-D simulations can be used in acomplex engine system to simulate complex dynamicphenomena due to the dynamic interactions between allthe different sub-models that can be found in anautomotive engine.

ACKNOWLEDGMENTS

We would like to acknowledge Mr. Juan Francisco Almarza for his contribution in the injection modeldevelopment and Mrs Maria Alejandra Thirouard for her contribution to provide the engine experimental data. Wewould like also to acknowledge Mr Christophe Hanauer for experimental injection data .

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