paregen.eu€¦ · Frapolli, N., et al. Large Eddy Simulations and Tracer -LIF Diagnostics of Wall Film Dynamics in an Optically Accessible GDI Research Engine. SAE Technical Paper

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Flow Channel – Liquid Film Validation

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Reference conditionTw=300KNon-reactingU=0m/stinj= 0.5 ms

Simulation Experimentsingle shot

Experimentaverage




Reference conditionTw=300KNon-reactingU=0m/stinj= 0.5 ms Simulation

Exp. Mean




Reference conditionTw=300KNon-reactingU=0m/stinj= 0.5 ms

Simulation Experiment


Flow Channel – sensitivity to variations


Influence ofWall temperatureInjection timeReacting/Non-reactingBulk flow velocity

Trends well captured for most variations

Simulation Experiment Simulation ExperimentWall temperature

Bulk flow velocity

Injection duration

Reacting/Non-reacting

300 K352 K

0.5 ms1.0 ms

1.83 m/s10.0 m/s

Non-reactivereactive


Flow Channel – combustion validation injection duration 0.5 ms


Flame and soot evolution

Paper in preparation


Optical engine – Liquid Film Validation


Reference condition:RPM = 1200 rpmSOI = -330° CAPinj = 200 barPamb = 1 bar

Snapshots of film thickness and extentEvolutions of total mass, area and median of film thickness

Sim. Exp.

Reference:Frapolli, N., et al. Large Eddy Simulations and Tracer-LIF Diagnostics of Wall Film Dynamics in an Optically Accessible GDI Research Engine. SAE Technical Paper No. 2019-24-0131, 2019.

SimulationExperiment


Optical engine – sensitivity to variations


Influence ofStart of InjectionInjection pressureEngine speedEngine load

Fair agreement withexperimental trends

Simulation Experiment

Simulation Experiment Simulation Experiment

Simulation ExperimentStart of Injection

Engine speed

Injection pressure

Engine load

Reference:Frapolli, N., et al. Large Eddy Simulations and Tracer-LIF Diagnostics of Wall Film Dynamics in an Optically Accessible GDI Research Engine. No. 2019-24-0131. SAE Technical Paper, 2019.

-360 CA-330 CA-300 CA

35 MPa20 MPa15 MPa

600 RPM1200 RPM2000 RPM

0.5 bar1.0 bar


Optical and thermodynamic engine Combustion validation


Optical engineFlame and sootevolutionsFurther evidence thatsoot is formed in regions in the vicinityof wall films

Thermodynamic engine:

PressureHeat Release Rate

Flame and soot optical engine Pressure thermodynamic engine

HRR thermodynamic engine

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Virtual Gasoline Particle Sensor (vGPS)

LHVnenginemfuelTintakepintake

PM/PN

0 10 20 30 40 50 60

axial distance [mm]

0

1

2

3

4

5

6

7

8

spra

y [-

]

Transient axial spray,D

distribution by Musculus & Kattke

Steady tip spray,D

by Naber & Siebers

Impingement modelWall film evaporation model

Modified Musculus-Kattke / Naber Siebers Spray model

Vibe model for combustionHTR Model for cylinder pressure

Soot model

pintakepinjectionρairρliquid

Stip(t)vtip (t)ϕtip (t)

Tburned, pO2

mcritical

0 50 100 150 200 250 300 350

CA after SOI

-0.5

0

0.5

1

1.5

2

2.5

3

fuel

mas

s [k

g]

10 -5

fuel mass sticking to wall (simulation)

fuel mass reaching wall (simulation)

fuel mass injected

wall film mass, VGPS

model parameter

model calibration

Model inputs(from ECU)

Meas.

Requirements:Applicable on an ECU in real-time

Development inputs(from 3D CFD)

Measurement inputs(from Testbench)


vGPS: Engine Results


single cylinder engine multi-cylinder engine

Computation time: ~5 ms per cylce

Stoichiometric operationLean operationRich operation

• Calibration on Bosch single cylinder naturally aspirated research engine

• Good Agreement with experiment (except first OC) r2 = 0.97

• Calibration only minorly different to Bosch Engine. No calibration algorithm executed

• Data compared with PN>23 nm

• Good agreement with experiment r2 = 0.8


Integration of new emission modelsin a system approach


In-cylinder prediction(Physical/accuracy)

Mean Value Engine Model(Empirical/fast)

Driveline / vehicle / driver model

Fast RDE evaluation(Real time)

1

Development of a model reduction method to extrapolate OD/1D combustion/emissions prediction capabilities at the system level (in-vehicle). Tool coupling for a complementary approach for emissions prediction including sootsFast evaluation of powertrains including emissions over real driving cycles

2 Map-based engine model(Map)

vGPS

Air path


Fast models for emissions evaluation


Collaboration with ETH for vGPS integration and validation on a virtual vehicle

Integration of the vGPS sensor as a C-code in Simcenter AmesimCoupling with a vehicle model based on a Mean Value Engine ModelValidation of the tool coupling on a RDE cycle (RWC by IDIADA)

cpu performance / RT compliance validated : real time ratio ~ 50

C-code integration validated

Fixed-time step Variable-time step Simulation time [s] 103.6 24 Ratio with real-time 11.4 49.2

Vehicle speed

Particle number


Conclusions


Numerical Large Eddy Simulation (LES) platform developed for GDI engines Systematic validation with increasing complexity (spray vessel, flow channel, engines)Good predictive capabilities with respect to

Spray evolutionFuel film evolutionCombustion and soot formation

Insights from CFD complement experimental findings and aid interpretationCFD results have been exploited for reduced order modelling

vGPS developed with good predictive capabilitiesSuccessful integration in AMESIM vehicle MVEM with RDE PN emission prediction capability

Combination of experiments and various levels of simulation complexity are essential toward development of predictive tools suitable for GDI engine optimization

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paregen.eu€¦ · Frapolli, N., et al. Large Eddy Simulations and Tracer -LIF Diagnostics of Wall Film Dynamics in an Optically Accessible GDI Research Engine. SAE Technical Paper