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IIMPROVINGMPROVING SPRAYSPRAY MODELSMODELS FORFORIIMPROVINGMPROVING SPRAYSPRAY MODELSMODELS FORFORADVANCEDADVANCED COMBUSTIONCOMBUSTION STRATEGIESSTRATEGIES
Federico Perinia, Paul C. Milesb, Rolf D. Reitza
aUniversity of Wisconsin–MadisonUniversity of Wisconsin MadisonbSandia National Laboratories
IEA 36th Task Leaders Meeting 2014
Acknowledgements
g“Energy Conservation and Emission Reduction in Combustion”
Stavanger (Norway), June 9-13, 2014
AcknowledgementsD.O.E. Office of Vehicle Technologies, P.M. Gupreet Singh
Sandia National Laboratories
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
1
TransitionTransition toto largelarge--scalescale computationscomputationsU d t d th ‘l lit ff t ’ f fl iti l d th l- Understand the ‘locality effects’ of flow, compositional and thermal
non-uniformities on combustion- Prepare the path towards comprehensive flow and transport modelling(LES DNS) and future engine studies with full engine geometry
First KIVA4 implementation (Torres, 2006) as the base code
(LES, DNS) and future engine studies with full engine geometry
p ( , ) Need for a framework that is tailored to internal combustion engine simulations Buggy but enabled support for unstructured geometries
Large-scale combustion chemistry Large scale combustion chemistry Sparse Analytical Jacobian chemistry solver (‘SpeedCHEM’) High-Dimensional cell Clustering
Extended and improved spray modelling
Parallelization of the flow field and spray solution
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
2
PPC PPC mixturemixture preparationpreparation experimentsexperimentsExperiments carried out at Sandia National pLaboratories by P.C. Miles, D. Sahoo, S. Busch
- Optically-accessible Sandia-GM 1.9L engineBosch CRI2 2 7 hole injector- Bosch CRI2.2 7-hole injector
- Variable swirl ratio intake: Rs = 1.5 to 4.5- Fuel for mixture preparation studies: PRF25
PLIF equivalence ratio measurements- PLIF equivalence ratio measurements
CA = -15 deg ATDC0 5
CA = -15 deg ATDC0 5
SAE 2013-24-0061
30
35PPC injection rates, 8.8 mg, CRIP 2.2
pinj = 500 bar
b
e fr
om fi
rede
ck [c
m]
-1.0
-0.5
0.0
0.5
P1P2
P3
e fr
om fi
rede
ck [c
m]
-1.0
-0.5
0.0
0.5
P1P2
P3 15
20
25
30
rate
[g/s
]
pinj = 860 bar
pinj = 1220 bar
r axis [cm]
dist
ance
0 1 2 3 4
-2.0
-1.5
0 0.5 1 1.5 2 2.5 3 3.5 4
r axis [cm]
dist
ance
0 1 2 3 4
-2.0
-1.5
0 0.5 1 1.5 2 2.5 3 3.5 40 0 2 0 4 0 6 0 8 1
0
5
10inj.
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
3
0 0.2 0.4 0.6 0.8 1x 10-3time [s]
PPC PPC mixturemixture preparationpreparation validationvalidation30
/s]
pinj=860bar RS=2.2 SOI=-23.3
t hP1 (squish)CA 10 0 0
10
20
inje
ctio
n ra
te [c
msector meshSquish mixing = slightly
underestimated
1.5
CA = -10.0experiment simulation
-20 -15 -10 -50
crank angle [deg aTDC]
11
0.5
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
4 0phi [-]
PPC PPC mixturemixture preparationpreparation validationvalidation30
/s]
pinj=860bar RS=2.2 SOI=-23.3
t hP2 (rim)
CA 10 0 0
10
20
inje
ctio
n ra
te [c
msector meshA few millimeters close tothe bowl rim not availablein the experimental image
1.5
CA = -10.0experiment simulation
-20 -15 -10 -50
crank angle [deg aTDC]
11
0.5
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
5 0phi [-]
PPC PPC mixturemixture preparationpreparation validationvalidation30
/s]
pinj=860bar RS=2.2 SOI=-23.3
t hP3 (bowl)CA 10 0 0
10
20
inje
ctio
n ra
te [c
msector meshSlight underprediction of jet penetration reachingback up to the centerline
1.5
CA = -10.0experiment simulation
-20 -15 -10 -50
crank angle [deg aTDC]
11
0.5
Reasonably accurate equivalence ratio
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
6 0phi [-]
equivalence ratio prediction where
measured data available
Spray Spray modellingmodelling improvementimprovement efforteffort
Current spray model validation(unstructured code, sector mesh same to SAE2013-01-1105) Current spray model validation(unstructured code, sector mesh same to SAE2013-01-1105)
Flow prediction validation(unstructured full engine model generalized RNG k-ɛ closure)
Flow prediction validation(unstructured full engine model generalized RNG k-ɛ closure)(unstructured full engine model, generalized RNG k-ɛ closure)
(CaF 2013, submitted; THIESEL 2014, submitted) (unstructured full engine model, generalized RNG k-ɛ closure)
(CaF 2013, submitted; THIESEL 2014, submitted)
Spray model improvement and calibration(Sandia ECN spray experiments) Spray model improvement and calibration(Sandia ECN spray experiments)
Full engine cycle validation, Sandia 1.9L light duty engine(unstructured, full mesh and full cycle engine calculation)
Spray A
Injection-induced turbulenceeffects on near-nozzle flow
Model study to capture near-SOI transient
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
7
effects on near nozzle flow SOI transient
SandiaSandia Spray A Spray A modelingmodeling900K, 60 bar, tinj = 1.5 ms
• [contour] fuel vapor mass fraction, in the range [0, 0.05]• [green dots] liquid phase distribution projectionj [g ] q p p j
50 us 100 us 300 us
IFP
Sandia
ERC
500 us 800 us 1500 us
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
8
Spray Spray constantsconstants GA GA studystudyM lti l i t ti d l h d t i l t th ff t f h d l• Multiple interacting spray models hard to isolate the effects of each model
• Hard to validate each of these isolated phenomena against experiments• May be aided by future DNS simulations
A GA optimization to answer these questions:
What parameter regions should we move in? When we used to calibrate the spray constants, how muchwere we tweaking the gas-phase prediction too?were we tweaking the gas-phase prediction too? Is there an optimal calibration set, and, does thisinclude “historically used” values or does it suggest new ones, which better fit the newest and highly confident Sandiaexperiments?
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
9
Spray Spray constantsconstants GA GA studystudy6 model variables6 model variables
Variable name std value range toRT time constant CRT 1.0 0.05 50.0RT wavelength cnst C 0 1 0 01 10 0RT wavelength cnst. CΛRT 0.1 0.01 10.0KH decay timescale cnst. B1 40.0 10.0 100.0Gas-jet Stokes number St 3.0 0.1 5.0Gas-jet entrainment cnst K 0 7 (ideal=0 45) 0 3 3 0Gas-jet entrainment cnst. Kentr 0.7 (ideal=0.45) 0.3 3.0Max gas-jet velocity frac. γ 0.6 0.2 0.9
5 Spray A objectives5 Spray A objectives
Phenomenon merit1) Vapor penetration integral mean squared error (MSE)2) Vapor dispersion mean integral MSE (future addition)2) Vapor dispersion mean integral MSE (future addition)3) Liquid ramp integral MSE4) Steady liquid region mean peneration error5) penetration stdev error
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
10
5) penetration stdev error
Spray A Spray A objectivesobjectivesSpray A, C12H26, 900K, O2=0.0, tinj=1.5ms
5
6
7m
]j
Experimentsimulation
1) MSE on predicted vapor penetration
t sim d
vvf
2
exp1
2
3
4
pene
trat
ion
[cm
dvt
f0
exp
p1
Spray A, C12H26, 900K, O2=0.0, tinj=1.5ms
0 0.5 1 1.5 210
-3
0
1
time [s]
1
1.2
1.4
[cm
]
p y , 12 26, , 2 , inj
l
x 10time [s]
0 4
0.6
0.8
uid
pene
trat
ion
4,5: error on mean and stdevof steady liquid length
l
0 0.5 1 1.5 23
0
0.2
0.4
ti [ ]liq
u
;;
exp
exp5
exp
exp4 l
llf
lll
f simsim
l
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
11
x 10-3time [s]
Spray A Spray A objectivesobjectives
3) liquid penetration on the ramp up
0.8
1
tion
[cm
]
rampt sim d
lll
tf
0
2
exp3
1
0.4
0.6
liqui
d pe
netr
at
ramp lt 0
exp
2) Mean integral vapor dispersion MSE (under development)
1
0 1 2x 10
-4
0
0.2
time [s]
axiszzzz
MSEMSEMSEMSEMSEf 504030202 5
1
0.16
i tr
20 30 40 50mm axial
0.08
0.12
e fr
actio
n [-]
experimentsimulation
z = 30 mm
z
0
0.04
mix
ture
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
12
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80
distance [cm]
ResultsResults: breakup : breakup modelmodel constantsconstants
KH decay time scale constant (B1)- affects the liquid ramp phase ( RT breakup not occurring yet), not the steady-stateq p p ( p g y ), y- Vapor penetration: B1 > 50 we do not want breakup to compensate for turbulence!-Liquid ramp: B1 ϵ [35 – 44] converging to the widely validated B1 = 40
RT model constants (wavelength CλRT, timescale CτRT)
Coordinates the variablesColors merit: the bluer, the betterRT model constants (wavelength CλRT, timescale CτRT)
- Crucial to liquid length prediction, which is “a tiny bit” after RT breakup happens- the RT timescale seems to have optimal values ~ CτRT = 3.7; or 2<CτRT<4
,
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
13
ResultsResults: breakup : breakup modelmodel constantsconstants
KH decay time scale constant (B1)- affects the liquid ramp phase ( RT breakup not occurring yet), not the steady-stateq p p ( p g y ), y- Vapor penetration: B1 > 50 we do not want breakup to compensate for turbulence!-Liquid ramp: B1 ϵ [35 – 44] converging to the widely validated B1 = 40
RT model constants (wavelength CλRT, timescale CτRT)RT model constants (wavelength CλRT, timescale CτRT)- Crucial to liquid length prediction, which is “a tiny bit” after RT breakup happens- the RT timescale seems to have optimal values ~ CτRT = 3.7; or 2<CτRT<4
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
14
ResultsResults: gas: gas--jet jet modelmodel constantsconstants
Very definite behavior for vapor penetration and liquid rampy p p q pγ ϵ [0.7 – 0.9] Better to apply most of the effective gas-jet velocityKentr ϵ [0.6 – 0.9] for vapor penetration,
[0.8 – 1.5] for liquid ramp slightly higher than currently used
Stokes ϵ [0.8 – 1.0] Significantly smaller than the value St = 3.0 suggested in (Abani and Reitz, 2008) for steady gas-jet modelling Study of
Stokes number effects
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
15
Stokes number effects
GA GA optimizationoptimization summarysummary
Confirms complex interactions among the models Suggests that the Stokes number calibration used for steadygas jet modelling is overestimated needs a deeper studygas-jet modelling is overestimated needs a deeper study Confirms standard “historically used” and well validated values(e.g., B1 = 40)
Currently setting up a more comprehensive optimization study:- Large number of individuals and generations- GRNG turbulence validated for vapor penetration- Inclusion of jet dispersion merit as an objectivej p j
Final validated calibration set will be used for grid resolutionstudy
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
16
s udy
GasGas--jetjet modelmodel StokesStokes numbernumber studystudy
Eddy generation frequency in the modeled gas-jet equal tog j qparticle responsiveness in the
spray jet drops
IV StokesStrouhalxf Assumed to be a model
pflow
StokesStrouhalU
• A noticeably smaller range [0 8 1 0] suggested by the GA optimization
calibration parameter St = 3
• A noticeably smaller range [0.8 – 1.0] suggested by the GA optimization
• The constant value can be replaced by a local estimation, exploiting injection-induced turbulence effects at the nozzle
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
17
GasGas--jetjet modelmodel StokesStokes numbernumber studystudy• Stokes calculation from RNG k-ɛ predicted integral length scale at the nozzlep g g
104near-nozzle Stokes number, Spray A Initial slope jet-induced turbulence
not yet developed in the nozzle cell
102
St [-
]
stable St = (0.75 0.03)
Reasonable – the CFD model isunderresolved at the nozzle!
Reasonable – the CFD model isunderresolved at the nozzle!
0 0 2 0 4 0 6 0 8 1 1 210-2
100stable St (0.75 0.03)
A very stable region during steady-state injection
0 0.2 0.4 0.6 0.8 1 1.2x 10-3time [s]
1.5
cm]
Spray A, C12H26, 900K, O2=0.0, tinj=1.5ms
high St significantly delayed liquidlength development near SOI
0.5
1
uid
pene
trat
ion
[clength development near SOI
Stokes number crucial to modelling jet-
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
180 0.5 1 1.5
x 10-3
0
time [s]
liq
ExperimentSimulation, integral-length-based St
induced turbulence effects on droppenetration
GasGas--jetjet modelmodel StokesStokes numbernumber studystudy• Constant Stokes, St = 3.0 (gas-jet model), St = 0.75 (as in the steady part) , (g j ), ( y p )• Variable Stokes, St = (computed at nozzle local cell)
1.4Spray A, C12H26, 900K, O2=0.0, tinj=1.5ms
7Spray A, C12H26, 900K, O2=0.0, tinj=1.5ms
0.8
1
1.2
ratio
n [c
m]
4
5
6
ratio
n [c
m]
0.2
0.4
0.6
liqui
d pe
netr
ExperimentSt = 3.0St = 0.75St = (computed) 1
2
3
vapo
r pe
netr
ExperimentSt = 3.0St = 0.75St = (computed)
0 1 2 3 4x 10
-4
0
time [s]
St = (computed)
0 0.5 1 1.5 2x 10
-3
0
time [s]
St = (computed)
Limited effects over steady-state liquid and vapor penetration
Significantly affects the initial liquidcore development transient
E ll t t h f th i iti l d l t “b ” ith St 0 75
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
19
Excellent match of the initial development “bump” with St = 0.75
FurtherFurther researchresearch directionsdirectionsSpraySpray Extend GA optimization and carry the improved calibration over to engine simulations
Fluid solution Parallelization for large scale computations Parallelization for large-scale computations The KIVA lesson: a simple (Jacobi!) preconditioner can be very robust, and work very well
(30+ years) if tailored to the problem (= coarse but topology-changing mesh) The most used ILUTP+BiCGStab solver “just works well” possible to improve
d h h l l h f h l l l dpreconditioning the physical relationship of the pressure-velocity coupling is exploited
Chemistry Chemistry solver now scales linearly with problem size (sparse analytical Jacobian ) Need to move to scaling much less-than-linearly with the problem size
adjoint-sensitivity-aided partitioned (ASAP) clustering
Turbulence & Transport We are correctly modeling neither turbulent nor molecular transport Simple, linear isotropic turbulence models fail even over a simple gas jet case but widely
used for engines! Accurate transport modelling diffusion viscosity etc
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
20
Accurate transport modelling diffusion, viscosity, etc
Acknowledgementsg
D.O.E. Office of Vehicle Technologies, P.M. Gupreet SinghSandia National Laboratories
Engine Research Center, University of Wisconsin – MadisonIEA Combustion 2014 – Stavanger, Norway – June 9-13, 2014
21