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 ssom@anl.gov

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