ACCELERATING CO-OPTIMIZATION OF ENGINES AND FUELS WITH HIGH-FIDELITY SIMULATIONS
erhtjhtyhy
Sibendu Som Manager - Computational Multi-Physics Section
Argonne National Laboratory
LES4ICE CONFERENCE, December 11th 2018
Sponsors: U.S. Department of Energy; Gurpreet Singh, Michael Weismiller, Kevin Stork, Alicia Lindauer, Leo Breton (retd.), Bob Gemmer, David Forrest
Aramco Services Company: Yuanjiang Pei, Michael Traver, David Cleary
Convergent Science Inc. for licensing support & discussions: P. K. Senecal, E. Pomraning, K. Richards, & rest of the team
Prof. Michele Battistoni @ University of Perugia & Prof. Kaushik Saha @ IIT - Delhi
Travel support from IFP energies nourvelles
Contributions from my team: Riccardo Scarcelli, Muhsin Ameen, Roberto Torelli, Noah Van Dam, Prithwish Kundu, Zongyu Yue, Pinaki Pal, Gina Magnotti, Ceyuan Chen
X-ray measurements: Chris Powell, Brandon Sforzo, Katie Matusik
Computing resources: 1) ALCF resources: ASCR Leadership Computing Challenge (ALCC) award of 60 million core-hours on Mira
supercomputer 2) LCRC computing cluster @ Argonne National Laboratory
ACKNOWLEDGEMENTS
OVERVIEW
Introduction: Co-Optima project of US-DOE
Light-duty research – Validation of LES approach with Optical engine data – Prediction of cyclic variability – Effect of manufacturing tolerances on cyclic variability
Heavy-duty research – In-nozzle flows
• Fuel effects on cavitation and erosion • Real vs. nominal geometries
– Turbulent Combustion modeling with ANN
Future outlook – Exascale computing – Machine learning workflows
Goal: better
fuels and better
vehicles
sooner Fuel and Engine Co-Optimization
o What fuel properties maximize engine performance?
o How do engine parameters affect efficiency?
o What fuel and engine combinations are sustainable, affordable, and scalable?
HIGH-LEVEL GOALS AND OUTCOMES
9 National Laboratories, 17 Universities, multiple stakeholders
OBJECTIVES
Develop high-fidelity predictive tools that can assist in the co-optimization of fuels & engines
In-nozzle flow and fuel air-mixing ML for design optimization
Cyclic variability Knock tendency Reduction of time-to-science
Boosted SI/ACI Mixing Controlled
BACKGROUND: BOOSTED SI/ACI Cycle-to-cycle variability (CCV) – detrimental to IC engine
operation, and results in partial burn, misfire and knock
Dilute operation and LTC modes can lead to higher CCV
Large CCV can limit the thermal efficiency, increase the pollutant emissions and lead to unstable operation
Main reasons for CCV in SI engines: – Variability in the flowfield near the spark plug – Variability in the fuel distribution near the spark plug – Variation of turbulence intensity, spray structure – Variation of spark discharge characteristics
Co
urtesy: G
M R
&D
Numerically predicting CCV is challenging for 2 key reasons: • High-fidelity methods required to capture the turbulent flowfield • CCV is experienced over long timescales (1000s of engine cycles)
PARALLEL PERTURBATION MODEL (PPM) FOR CAPTURING CCV* * Ameen, Kuo, Yang, Som, “Parallel Methodology to Capture Cyclic
Variability in Motored Engines”, IJER 2017
Developed PPM technique that allows running 10s-100s of cycles concurrently • Isotropic random turbulence field perturbations • Turnaround time for the PPM approach is only limited by the number of available computing
cores and is significantly shorter than the consecutive cycle approach
Automotive-sized 4-valve pent-roof engine
PI: Magnus Sjoberg @ Sandia National Lab
Optical access to the combustion chamber is provided through three windows. Two mounted in the head and one in the piston bowl
PIV data with motored engine and with spray injection: 35 skip-fires, resulting in 35 flow fields
FLOW-FIELD VALIDATION WITH LES
Case #0 Case #1
Engine Speed 1000 RPM 1000 RPM
Load 345.4 kPa 374.5 kPa
Mode Stratified Well-mixed
Fuel Gasoline E30
Skip-fired Yes Yes
Throttled vs. Un-throttled Operation Simulated with multi-cycle LES using Converge code
– Performed by Noah Van Dam (currently faculty at University of Massachusetts)
– LES dynamic structure turbulence model – Base mesh size: 2mm, Min. mesh size: 0.125mm – PPM approach for 35 cycles – 72 hours/cycle on 48 processors
Piston Window
Pent-roof WindowSpark Plug
Fuel Injector
PIV Laser Sheet
9
* Experiments [Blessinger et al. IJER 2015] 8-hole VCO GDI Injector
Inner hole diameter: 140µm; length: 370µm
Outer bore diameter: 360µm; depth: 230µm
Non-combusting data with Iso-octane
SPRAY VALIDATION* WITH CONSTANT VOLUME DATA**
Temperature 700 K
Density 6 kg/m3
Pressure 12 bar
Liquid Penetration
Vapor Penetration
Spreading Angle
** Simulations [Van Dam et al. SAE 2018] 50 LES realizations per condition
Dynamics structure LES
0.125 mm min. mesh size, ~20 million cells, ~10 hours on 32 procs
26
Φ Distribution Probability of Φ>0.5
90% 10%
Exp. Sim. Exp. Sim.
Simulation vortex shifted relative to experiments. PIV optimized for near TDC, has lower accuracy at
early CAs. Largest differences are found in the intake jet
region. Indicates slightly wider intake jet width in the experiments than the simulations.
FLOW-FIELDS POST INJECTIONS @ -250 ATDC
Experiment* Simulation Difference
Fuel E30
Global Φ 1.0
Nominal net IMEP 375 kPa
Injection timings -298 , -283 , -268 aTDC
Injection duration 560 μs (3.36 CAD)
Injected mass (exp.) 17.814 mg (5.938 mg/inj.)
Injected mass (sim.) 18.6 mg (6.2 mg/inj.)
Injected parcels 14,000 (per inj.)
* M. Sjoberg from Sandia
FLOW-FIELDS POST INJECTIONS @ -12 ATDC Experiment* Simulation Difference
Fuel E30
Global Φ 1.0
Nominal net IMEP 375 kPa
Injection timings -298 , -283 , -268 aTDC
Injection duration 560 μs (3.36 CAD)
Injected mass (exp.) 17.814 mg (5.938 mg/inj.)
Injected mass (sim.) 18.6 mg (6.2 mg/inj.)
Injected parcels 14,000 (per inj.)
* M. Sjoberg from Sandia Flow patterns match relative well, although shift in
vortex persists. Flow-field perturbations and spray random number
seed perturbations result in similar outcomes in terms of “Differences”.
Consecutive 35 cycles compared with PPM 35 cycles
CAN PPM BE APPLIED FOR FIRED ENGINES? Experimental Evidence*
No apparent correlation between cycles. Similar behavior observed for peak pressure.
Four-cylinder SI engine with port fuel injection. In-cylinder pressure is measured for 1000 cycles.
* We acknowledge Prof. Federico Millo and Mohsen Mirzaeian from Politecnico di Torino for sharing the experimental data
FIRED METAL ENGINE: CONSECUTIVE LES VS. PPM LES*
* Ameen, Som, et al., “Numerical prediction of CCV in a PFI engine using a parallel LES approach,” Journal of Energy Resources Technology 2018
Case A Case B
r/min 2500 4000
IMEP (bar) 17 12
Fueling (mg/cycle/cylinder) 34.1 26.2
Spark timing (degrees) 711 694.5
Injection timing (degrees) 340 340
Code CONVERGE Computational Grid Size 0.7 mm in cylinder; Min. cell size: 0.175 mm
Peak Cell count 7 million
Subgrid-scale Model Dynamic Structure LES
Combustion Model G-Equation
Simulation Time 36 hours per cycle on 96 cores 50 consecutive cycles and 100 parallel cycles
Consecutive LES (50 cycles)
PPM LES (98 cycles)
Cas
e A
EFFECT OF OPERATING CONDITION CAPTURE WITH PPM
Experiment PPM-LES
Case A 7.64% 9.17%
Case B 4.14% 5.99%
Experiment
PPM LES
• The PPM LES is able to accurately predict the trends in COV with changing operating conditions
• Experiments stabilize after about 200 cycles • Simulations stabilize after about 60 cycles; reasonably
stabilizes after 20 cycles
We acknowledge Prof. Federico Millo and Mohsen Mirzaeian, Politecnico di Torino for sharing the experimental data
Case B
Intake port tolerances: • ±1 mm was imposed on the port • Intake port shape is largely
responsible for creating the tumble flow motion used to improve mixing, burn rate, and combustion stability
Spark plug orientation: • ±90 degrees variation was imposed • Flow-field and turbulence in the vicinity of the
spark plug is known to influence the initial flame kernel development which in turn affects the combustion stability of SI engines
EFFECT OF MANUFACTURING TOLERANCES ON CCV* Ford Engine 4 cylinder, 4-stroke
Bore×Stroke 87.5× 94 mm
Sweep volume 0.46 L
Compression ratio 11.3:1
Injector DI
SOI 340 bTDC
Spark Timing ~45.8 bTDC
Engine speed 1500 rpm
Exhaust runner
Intake runner
Intake portsExhaust ports
Spark plug
Piston
Liner
Inflow boundary
Outflow boundary
* Chen, C., Ameen, M., Wei, H., Iyer, C., Ting, F., Vanderwege, B., Som, S. LES analysis on cycle-to-cycle variation of combustion process in a DISI engine (SAE No. 2019-01-0006)
• Average of 20 cycles shown; @ 1 CA before spark timing. • Combustion phasing and its CCV not significantly affected, since mean Phi is not affected. • CCV of IMEP is strongly affected, especially for +90 deg. • +90 degree (rotation towards the intake port):
• causes the flame development to be more sensitive to local flow field. • lowest mean and highest RMS velocity in the spark plug gap (possibly due to vortex shedding by
the ground strap which opposes the tumble flow).
SPARK PLUG ORIENTATION EFFECT
Baseline Spark plug rotation: -90 deg Spark plug rotation: +90 deg
Me
an v
elo
city
(m
/s)
RM
S ve
loci
ty
(m/s
)
• Average of 20 cycles shown; @ 1 CA before spark timing • Strong effect on combustion phasing (~1.5 on CA10 and ~2.5 on CA50) for J geometry, which has
the most reactive mixture in the spark plug gap. • CCV of IMEP affected by up to 50% for L geometry. • Apply ML tools developed in our previous study (Kodavasal et al., ASME 2017) and corelate
high/low cycles with flow fields near spark plug – Collaboration with David Hung & co-workers.
INTAKE PORT TOLERANCE EFFECT
Baseline “J” intake geometry
(shifted 1 mm lower)
“L” intake geometry (shifted 1 mm higher)
Me
an v
elo
city
(m
/s)
Me
an P
hi
BACKGROUND: HEAVY-DUTY RESEARCH
Computational Design Tool
Predict lifetime and
performance of injector
Quantify impact on spray, combustion,
and emissions
Capabilities
Cavitation-induced
erosion
Influence of manufacturing tolerance and surface finish
Sources of hole-to-hole
variability
Scientific Insight
Cavitation erosion has been reported in fuel injectors on orifice, needle and needle seat surfaces
Hours – Months
CAVITATION EROSION DUE TO FLUID-SOLID INTERACTION
How should repeated impacts from cloud implosion events be linked to the progressive material erosion process?
10-100 ns
10 15 12.5
Maximum Pressure [MPa]
[1]
[1] Magnotti, Saha, Battistoni, Som. Evaluation of a new cavitation erosion metric based on fluid-solid energy transfer in channel flow simulations. ICLASS 2018.
Representative Timescales for…
Cloud Implosion Event
Material Damage
REALISTIC GEOMETRY RESULTS IN IMPROVED CAVITATION PREDICTION
ΔP* = 205 bar ΔP = 183 bar
Nominal Geometry
Experimental Observation Simulation Predictions
ΔP = 183 bar
Informed Geometry
[1] Skoda et al., WIMRC 3rd Int. Cavitation Forum, 2011.
Nominal Geometry
Informed Geometry
Minimum Diameter -- 293
Maximum Diameter -- 331
Mean Diameter 303 µm 313
K-factor 0 (straight) -3.0 (diverging) Includes surface finish effects
𝑬𝒊𝒎𝒑𝒂𝒄𝒕,𝒊 =𝓐
𝝆𝒄 𝒑𝟐 𝒕 𝒅𝒕𝝉
𝟎
NEW METRIC* BASED ON FLUID-SOLID ENERGY TRANSFER HELPS CHARACTERIZE IMPACT EVENTS
𝑬𝒔𝒕𝒐𝒓𝒆𝒅 𝑵 = 𝑬𝒊𝒎𝒑𝒂𝒄𝒕,𝒊
𝑵
𝒊=𝟏
SOLID
FLUID
Eimpact Ereflected
Eabsorbed
t, N
Estored
TIncubation
𝑬𝒓𝒆𝒇𝒍𝒆𝒄𝒕𝒆𝒅 = 𝑬𝒊𝒎𝒑𝒂𝒄𝒕 (𝑷 < 𝝈𝒀)
𝑬𝒓𝒆𝒇𝒍𝒆𝒄𝒕𝒆𝒅 = 𝟎 (𝑷 > 𝝈𝒀)
Energy threshold is dependent upon material properties and induced strain rate from hydrodynamic impacts
* Magnotti, Saha, Battistoni, Som. Evaluation of a new cavitation erosion metric based on fluid-solid energy transfer in channel flow simulations. ICLASS 2018.
STORED ENERGY AND EROSION POTENTIAL
Measured* Cavitation
Erosion
ΔP = 183 bar
Nominal Geometry ΔP = 205 bar
Informed Geometry ΔP = 183 bar
Higher erosive potential is predicted by the informed geometry relative to the nominal geometry, consistent with experimental observations.
RANS cannot capture the pressure fluctuations and erosion. LES is necessary!
*Skoda et al., WIMRC 3rd Int. Cavitation Forum, 2011
• Micron-level high resolution real geometry of ECN Spray G injector, by X-Ray CT imaging* at 7-BM beamline of APS at Argonne by Chris Powell and team;
• Geometric features show considerable differences between measured values and nominal values;
GDI INJECTOR: REAL GEOMETRY V.S. NOMINAL GEOMETRY
Geometric feature Real Nominal
Hole diameter D1 [µm] 175 165
Hole length L1 [µm] 150 170
Hole length/diameter ratio L1/D1 0.86 1.03
Hole inlet corner radius R1 [µm] 4.93 0
Counterbore diameter D2 [µm] 394 388
Counterbore length L2 [µm] 402 470
R1
* Matusik et al., ILASS-Europe, 2017
Bump at nozzle exit
Void in counter-bore
Surface roughness:~5 μm
135 sector domain
Software Converge
Turbulence LES, Dynamic structure
Two-phase flow
Volume of Fluid (VOF) Piecewise-Linear Interface Calculation
Mesh spacing 2.5 μm embedding on nozzle surface; 5 μm (Min.) in the chamber with AMR Total cell count: ~35 millions
Run time ~80,000 core hours, 100 µs simulation
SPRAY G* SIMULATIONS WITH REAL GEOMETRY
ECN Spray G Injector 28 Hole #5
Initial Needle Lift: 5 μm
Needle Lift Profile
Ambient temperature 298 K
Density 3.5 Kg/m3
Injection Pressure 190 bar
Fuel temperature 298 K
Fuel Iso-octane * https://ecn.sandia.gov/
Liquid core spreads into a sheet, rather than cylindrical column
INFLUENCE OF GEOMETRY AND SURFACE FINISH
Nominal Real
Z. Yue, M. Battistoni, S. Som. Spray characterization at start of injection for a gasoline direct injector. ICLASS 2018.
Liquid deposit on injector tip
Nominal Realistic
20 μs 40 µs 60 µs 80 µs 100 µs
Unstable structure leading to detachment
30
35
40
45
50
55
60
65
70
75
20 40 60 80 100
SMD
[u
m]
Time [us ASI]
Real
Nominal
11% difference in SMD
Manufacturing tolerances and surface finish details are observed to
affect the spray structure
X-RAYS* TO SIMULATIONS
Heavy-duty production XPI injector from Cummins
Radial motion disabled
* Courtesy C. Powell & B. Sforzo @ ANL
ONE-WAY COUPLING 1D ROI
FUEL EFFECTS ON NEEDLE MOTION*
* R. Torelli et al. CAV2018, May 2018
Experiments by Powell et al. at APS
Different fluid-structure interactions occurring between the needle and the two fuels
For Diesel the flow is characterized by a larger momentum due to the higher fuel density. Likely causes the needle to vibrate with amplitudes that are larger than Gasoline
Die
sel
Gas
olin
e
CAPTURING FUEL EFFECTS WITH ONE-WAY COUPLING*
Noticeable differences in fuel sprays due to their respective internal nozzle flow
Plume-to-plume differences in liquid and vapor penetration, as well as wider spreading angles at SOI & EOI
Diesel Gasoline
* Torelli, Som, et al., SAE Paper No. 2018-01-0303
CAVITATION EROSION ASSESSMENT IN REAL INJECTOR
Time: 0.135 ms
Example of cavitation clouds
Some of the pressure peaks on the injector surface
Orifice 3
Orifice 4
Magnotti et al. “Evaluation of a new cavitation erosion metric based on fluid-solid energy transfer in channel flow simulations” ICLASS 2018
Simulations able to predict cavitation formation at the bottom of the orifice inlets for diesel injector running on gasoline
A new metric* for the identification of cavitation erosion potential was employed to highlight areas where strong cloud collapse occurs
The regions where the highest pressure peaks were recorded are also in agreement with the information obtained through the X-ray scans
COMBUSTION RESEARCH – 5 YEARS & BEYOND …
Predictive Capability
Needs
Next-generation Hardware
Workflows Software & Tools
Research @
National labs
HIGH-FIDELITY MODELS ACROSS THE DESIGN SPACE
(Capability Computing)
HIGH THROUGHPUT SIMULATIONS TO EXPLORE THE FULL DESIGN SPACE
(Capacity Computing)
Few
larg
e h
igh
-fid
elit
y si
mu
lati
on
s o
n
10
00
s o
f p
roce
sso
rs r
un
fo
r 2
-3 w
eeks
10
00
s of m
ediu
m-fid
elity simu
lation
s o
n ~1
0k o
f pro
cessors ru
n fo
r 2-3
weeks
hole # 1
hole # 2 hole # 3
“String type” cavitation
hole # 1
Developing Nek5000 (spectral element based) higher-order, highly-scalable code for engine simulations using hybrid LES/DNS approach
SCALING CONVERGE ON NEXT-GENERATION HARDWARE
Clusters Super-Computer Next-gen Super-Computer in 2021
50 racks 1st Exascale Machine
Thousand petaflops or a quintillion, 1018, floating
point operations per second 1,000,000,000,000,000,000
2013-16 2017-20
Mira Cluster (best case)
Cluster (worst case)
cases per ensemble 12000 16 3
cores per ensemble 768000 1024 192
time to science 4 days 6 months 3 years
COST BENEFIT ANALYSIS on MIRA: 12,000 cases, 64 cores/case
Moiz, Pal, Som, et al., “A Machine Learning - Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing,” SAE Paper No. 2018-01-0190
MACHINE LEARNING FOR DESIGN OPTIMIZATION (ML-GA)*
Challenge: Traditional CFD based optimization can be time-consuming Goal: Reduce the time to design by using ML
Run time on a cluster
12 hrs/sim → 800 sims w/ 8 per batch→
2 months
Run time on a
SuperComputer
250 sims in one batch→
A day
Run time on a cluster
12 hrs/sim → 250 sims w/ 8 per batch→
2 weeks
CFD-GA ML-GA
ML-GA
is faster
and
scalable
ML model → best fit the complicated surface GA model → find optimum over the surface
Notation Input Parameter min max units
nNoz Number of Nozzle holes 8 10 -
TNA Total Nozzle Area 1 1.3 -
Pinj Injection Pressure 1400 1800 bar
SOI Start of injection timing -11 -7 dATDC
Nang Nozzle Inclusion Angle 72.7 82.7 deg
EGR EGR fraction 0.35 0.5 -
Tivc IVC temperature 323 373 K
Pivc IVC pressure 2.0 2.3 bar
SR Swirl Ratio -2.4 -1 -
INCORPORATING ML WORKFLOW INTO CONVERGE
80
85
90
95
100
105
110
0
0.2
0.4
0.6
0.8
1
nNoz NozArea NozAng EGR Pivc Tivc Swirl Pinj SOI MERIT
Merit
valu
e
Norm
aliz
ed p
are
mete
rs
Design optimization
CFD-GAMLGA
• ML training data provided by Aramco Research Center (Detroit) for Gasoline Compression Ignition conditions
• For 9 parameters, ~250 simulations sufficient to train ML • ML challenges are training data and uncertainty quantification and
error estimation to know when our ML is a good model
FIVE TO TEN YEARS OUT*
Aurora (installed in 2021) is running well and support a broad suite of large-scale applications in simulation, data analysis and machine learning
We have well functioning testbeds for Quantum Computing and Neuromorphic Computing used by many users
Capacity + Capability Computing!
AI for science is now the driving application, and simulations are nearly all managed by AI
Zetta FLOP systems are being designed though its not clear how to account for the quantum and neuromorphic compute
AI is used to manage systems, augment programming environments, drive performance optimization and passively analyze all datasets
* Rick Stevens, Associate Laboratory Director, Argonne
THANK YOU [email protected]
LES of Rotating Detonation Engine from AFRL (Dr. Brent Rankin) using Converge
John Deur, Director of Engine Research at Cummins
Scalability
Un
cert
ain
ty Q
uan
tifi
cati
on
[10x] 360 degree cylinder geometry
[10x] multiple cycle variations
[10x] more accurate turbulence model (LES)
[10x] accurate spray dynamics
[50x] detailed chemical kinetics & plasma chemistryfor real transportation fuels
TOTAL Speedup needs are 500,000 times TODAY’S standard (today’s industry standard is 64 cores with 24 hour turnaround)
Using a RANS code for LES
Good LES code
Thierry Poinsot, Research Director at CERFACS
Higher – order Schemes
TWO PARALLEL R&D PROJECTS
Light-Duty Medium/Heavy-Duty
Boosted SI Mixing Controlled Kinetically
Controlled Multi-mode SI/ACI
Near-term Near-term Mid-term Longer-term
Higher efficiency
via downsizing
Even higher efficiency
over drive cycle
Improved engine
emissions
Highest efficiency and
emissions performance
ALL PERTURBATION STRATEGIES SHOW ALMOST THE SAME MEAN FLOW
Flow Spray Both
DIFFERENCES ARE SMALL AND SIMILAR TO STATISTICAL UNCERTAINTY
Flow-Both Spray-Both Spray-Flow
*Differences listed as first perturbation minus the second
PREVERO Channel “K” geometry modeled to study cavitation shedding and cloud collapse events
Model Set-up
Software Converge
Turbulence LES, Dynamic Structure Model
Two-phase flow Homogeneous Relaxation Model (HRM)
Liquid: compressible, barotropic fluid
Mesh spacing
40 μm base grid size
5 μm min grid size in embedded regions
Peak cell count: 1.9 million cells
Run time ~24 hours on 36 cores per 100 µs simulated time
994 μm
40 μm
303 μm
Liquid
Fuel
Fuel
Temperature
[K]
Upstream
Reservoir Pressure
[bar]
Pressure Drop
Across Throttle
[bar]
Nitrogen
Mass
Fraction1
n-heptane* 327 300 150 - 265 2.0e-05
[1] Battistoni et al., “Effects of noncondensable gas on cavitating nozzles,” Atomization and Sprays, 2015.
Extract Red Channel
from RGB
Image
NOMINAL VS. REALISTIC GEOMETRY
Extract Boundaries
Nominal Geometry Realistic Geometry
Minimum Diameter -- 293
Maximum Diameter -- 331
Mean Diameter 303 µm 313
K-factor 0 (straight) -3.0 (diverging) 𝐾 = 𝑑𝑖𝑛𝑙𝑒𝑡 − 𝑑𝑜𝑢𝑡𝑙𝑒𝑡
10
Protrusion ~ 11 µm
Surface features can have significant influence on flow development and cavitation inception
Experimental Image1
[1] Skoda et al., WIMRC 3rd Int. Cavitation Forum, 2011.
• Micron-level high resolution real geometry of ECN Spray G injector, by X-Ray CT imaging at 7-BM beamline of APS at Argonne;
• Geometric features show considerable differences between measured values and nominal values;
• Manufacturing defects and surface roughness potentially perturb in-nozzle flow and spray development.
REAL GEOMETRY V.S. NOMINAL GEOMETRY
ECN SprayG Injector 28 X-ray scan, 1.7 µm resolution
Bump at nozzle exit
Void in counter-bore
Surface roughness: ~5 μm
# 1 # 2 # 3Engine Operating Condition
TFMState of-the-art model in literature
FAST AND PREDICTIVE TURBULENT COMBUSTION MODEL*
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
800 900 1000 1100
Ign
itio
n d
ela
y (m
s)
Ambient temperature (K)
Experiment
TFM
HR-MZ
* P. Kundu, M. Ameen, S. Som, Combustion and Flame 2017
Progress Developed Tabulated Flamelet Model (TFM) that is more predictive and at least 50% faster than other models in literature
Uniqueness High-fidelity model captures the interactions between fuel chemistry and fluid dynamics in combustion chamber
Benefits More reliable engine simulations with a faster turn-around time than other combustion models in literature. Use of ANN enables us to use full reaction mechanisms without reduction
Availability Available through collaborative projects and can be integrated with an OEM’s workflow for simulations
Production GM 1.9 L diesel engine run on gasoline compression ignition mode (GCI)
Global Sensitivity Analysis (GSA) on fuel properties
400K cells
8000 cores
128 simulations
50 sp, 150 rxn (Liu)
5 fuel-related inputs perturbed
5 days on Mira
GLOBAL SENSITIVITY ANALYSIS WITH FUEL PROPERTIES
Define the Sensitivity Index (SI) as a ratio of variances
‒ 𝑆𝐼𝑖 ≡𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑖𝑛 𝑦 𝑑𝑢𝑒 𝑡𝑜 𝑖𝑛𝑝𝑢𝑡 𝑋𝑖
𝑇𝑜𝑡𝑎𝑙 𝑡𝑎𝑟𝑔𝑒𝑡 𝑦 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒=𝑉𝑎𝑟𝑋𝑖 𝐸𝑋~𝑖 𝑦 𝑋𝑖
𝑉𝑎𝑟 𝑦
Boosted SI
=> 7000
simulations
due to CCV
variable description baseline min max
T(f,crit) critical temperature 540 k 530 k 550 k
Density density 1.00 0.95 1.05
HOV heat of vaporization 1.0 0.9 1.1
VP vapor pressure 1.0 0.9 1.1
Viscosity viscosity 1.0 0.7 1.3
0 0.2 0.4 0.6 0.8
HOV
Critical Temp
Vapor Pressure
Viscosity
Density
Normalized Sensitivity Index
CA50
0 0.2 0.4 0.6
HOV
Viscosity
Critical Temp
Vapor Pressure
Density
Normalized Sensitivity Index
CO
0 0.2 0.4 0.6 0.8
HOV
Critical Temp
Viscosity
Vapor Pressure
Density
Normalized Sensitivity Index
NOx
0 0.2 0.4 0.6 0.8
HOV
Viscosity
Critical Temp
Density
Vapor Pressure
Normalized Sensitivity Index
HC
CA 50
• Fuel properties varied in Monte Carlo
fashion
• Fuel property variations (in this range)
have a significant influence on CA50
– Fuel HOV and critical temperature seem to
influence CA50
GSA RESULTS FOR GCI
0 0.2 0.4 0.6 0.8
HOV
Critical Temp
Vapor Pressure
Viscosity
Density
Normalized Sensitivity Index
CA50
0 0.2 0.4 0.6
HOV
Viscosity
Critical Temp
Vapor Pressure
Density
Normalized Sensitivity Index
CO
0 0.2 0.4 0.6 0.8
HOV
Critical Temp
Viscosity
Vapor Pressure
Density
Normalized Sensitivity Index
NOx
0 0.2 0.4 0.6 0.8
HOV
Viscosity
Critical Temp
Density
Vapor Pressure
Normalized Sensitivity Index
HC
CO
Data
Knowledge Tools
• Fuel-air mixing • Pressure dependent auto-ignition • Turbulence-chemistry interaction (TCI) • Soot formation • Cyclic Variability
• APS • RCM • Metal Engines
• Super-computing resources • ML workflows • Predictive sub-models • ‘Best practices’ for OEMs
RESEARCH VISION AT ARGONNE FOR NEXT 5-YEARS
Spectral Element code, Turbulent Combustion
Turbulent Combustion, Gas Turbines
Multi-phase flows, Wind-turbines
Turbulent Combustion, RDEs, ML
Fuel-Engine Interactions, Supercritical Injection
Multi-phase flows
Ignition modeling, Jet Ignition
Fast solvers Combustion
Novel engine architectures
Spray modeling for Piston Engines
MULTI-PHYSICS COMPUTATION SECTION
Ignition modeling, Fuel-engine interactions
Two-phase flows
Aerodynamics, Wind Turbines
Spectral codes, Turbulence Modeling
Flame Spray Synthesis Two-phase
flows Data science &
ML
Fuel-engine interactions
Fuel-engine optimization
Low-Temperature Plasma