modeling and optimization of trajectory-based combustion ... · • trajectory based combustion...

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Abhinav Tripathi, Prof Zongxuan Sun Abhinav Tripathi Professor Zongxuan Sun Department of Mechanical Engineering University of Minnesota Modeling and Optimization of Trajectory - based Combustion Control 1

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Page 1: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Abhinav Tripathi

Professor Zongxuan Sun

Department of Mechanical Engineering

University of Minnesota

Modeling and Optimization of Trajectory-based

Combustion Control

1

Page 2: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Presentation Outline

• Motivation, Background and Objective

• Trajectory based Combustion Control

– Previous Work: Modeling and Optimization Framework

• Approach for experimental validation

– A novel instrument Controlled Trajectory Rapid Compression and Expansion Machine (CT-RCEM)

– Unique capabilities of CT-RCEM

– Experimental characterization of CT-RCEM

• Conclusions

2

Page 3: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Low Temperature Combustion (LTC) modes are kinetically

modulated

Extremely fuel-lean operation leading to high fuel efficiency

High CR operation and faster heat release leading to higher

fuel efficiency

Low peak temperature leading to lower NOx

Background – Advanced Combustion Modes

Fuel efficiency

Em

issio

n p

erf

orm

ance Spark Ignition

Engine

Diesel

Engine

LTC

Engine

3

Global trend - tighter regulations for fuel efficiency and emissions

Page 4: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Background – HCCI Combustion

Existing control methods include

• Exhaust gas recirculation (EGR)

• Varying valve timing and

• Charge stratification

Control input is provided at a single time

instant during the cycle

HCCI Combustion Process

Chemical Kinetics of fuel

In-cylinder Gas Dynamics

Thermal heat releaseReaction productionReaction rate

PressureTemperatureSpecies concentration

Challenge - Combustion phasing

Control of ignition timing to achieve continuous operation is extremely

difficult in conventional ICE

4

Page 5: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Opposed Piston Opposed Cylinder Engine

No mechanical crankshaft

Direct Fuel Injection

Background – FPE at UMN

Variable compression ratio

•Advanced combustion strategy

•Multi-fuel operation

Reduced frictional losses

Faster response time

5

Exhaust Ports

Intake Ports

IntakePorts

ExhaustPorts

Check Valves

Servo Valve

On-off Valve

On-off Valve

LP

HPOuter Piston Pair

Inner Piston Pair

Hydraulic Chambers

Page 6: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Background – FPE at UMN

6

Page 7: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Presentation Outline

• Motivation, Background and Objective

• Trajectory-based HCCI Combustion Control

– Previous Work: Modeling and Optimization Framework

• Approach for experimental validation

– A novel instrument Controlled Trajectory Rapid Compression and Expansion Machine (CT-RCEM)

– Unique capabilities of CT-RCEM

– Experimental characterization of CT-RCEM

• Conclusions

7

Page 8: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Chemical Kinetics of Fuel

In-cylinder Gas Dynamics

Thermal heat releaseReaction productReaction rate

PressureTemperatureSpecies concentration

Variable Piston

Trajectories

Virtual Crankshaft

Mechanism

Volume

What is Trajectory-based Combustion Control

Use complete piston trajectory

as control input to modulate

in-cylinder gas dynamics

8

Free Piston

Engine

Page 9: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Optimal Trajectories

Control Signals

Piston displacement

In-cylinder gas volume, pressure, temperature and major chemical spices concentration

Model Based Trajectory

Optimization

Active Motion Control (Virtual

Crankshaft)

Inner Loop: piston motion control - virtual crankshaft

Outer Loop: Trajectory-based combustion control

Basic Framework - Trajectory-based Combustion Control

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Page 10: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Presentation Outline

• Motivation, Background and Objective

• Trajectory-based HCCI Combustion Control

– Previous Work: Modeling and Optimization Framework

• Approach for experimental validation

– A novel instrument Controlled Trajectory Rapid Compression and Expansion Machine (CT-RCEM)

– Unique capabilities of CT-RCEM

– Experimental characterization of CT-RCEM

• Conclusions

10

Page 11: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Controlled Trajectory Rapid Compression and Expansion Machine

- Developed at University of Minnesota

under 3 year NSF-MRI Grant

- High throughput and extreme operational

flexibility

- Ability to control the piston trajectory

inside the combustion chamber

CT-RCEM is new instrument that uses fluid power to enable unique experimental

capabilities for fundamental and applied combustion research

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Page 12: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

CT-RCEM – What’s unique?

CT-RCEM uses an

electro-hydraulic actuator

with feedback control to

drive the piston

Reference trajectory is fed

electronically to the controller

Same CR

Different

compression times

Different CR

Same compression

time

reference

trajectory

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Page 13: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Setup and Specifications Maximum Combustion Pressure 250 bar

Minimum Compression Time 20 ms

Maximum Compression Ratio 25

Combustion Chamber Bore 50.8 mm

Maximum piston travel 192 mm

TDC clearance 8 mm

Hydraulic Working Pressure 350 bar

Hydraulic Piston Bore 40 mm

Mass of Piston Assembly 1.7 kg

13

Key requirement:

High-force and high-speed

actuation

Page 14: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Repeatability of CT-RCEM

Four repetitions

for CR: 16.7,

compression

time 20 ms.

Maximum deviation of individual

pressure profiles from ensemble

average of repetitions ≈ 0.2 bar

Repeatability Analysis for

Compression ratio 16.7

Stroke 131 mm

Compression time 20 ms

Peak velocity 12.5 m/s

Peak tracking error 0.6 mm

Average velocity 7 m/s

Peak flow rate 920 l/min

Peak instantaneous power 0.4 MW

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Page 15: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Mimicking Engine Operation

in CT-RCEM

• Investigating partial ignition of DME for

engine-like sinusoidal trajectory

• Fuel mixture – DME:O2:N2 = 1:4:40 (lean

and diluted with extra nitrogen)

• Stroke: 121 mm, CR: 15.5, RPM: 1500

• Creviced piston to trap boundary layer

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Page 16: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

HCCI Combustion for an FPE

Trajectory

10 15 20 25 30 35 40 45 50

Time (ms)

0

20

40

60

80

100

120

Po

sitio

n (

mm

)

Piston Trajectory

-180 -120 -60 0 60 120 180

Crank Angle

• Autoignition test for DME

• Fuel-air equivalence ratio (ɸ) 0.75

• Initial temperature 50°C• Initial pressure 15 psi

• Ambient pressure 14.15 psi

• Stroke: 121 mm, flat piston

• CR: 11.3, 1500 rpm

10 15 20 25 30 35 40 45 50

Time (ms)

0

200

400

600

800

1000

1200

Pre

ssu

re (

psi)

Gas Pressure

-180 -120 -60 0 60 120 180

Crank Angle

25 26 27 28 29 30 31 32 33 34 35

Time (ms)

0

200

400

600

800

1000

1200

Pre

ssu

re (

psi

)

Gas Pressure

-45 -30 -15 0 15 30 45

Crank Angle

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Page 17: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

10 15 20 25 30 35 40 45 50

Time (ms)

0

20

40

60

80

100

120

Po

sitio

n (

mm

)

Piston Trajectory

Same speed same CR different shape

w: 1.2

w: 1.0

w: 0.8

-180 -120 -60 0 60 120 180

Crank Angle

10 15 20 25 30 35 40 45 50

Time (ms)

0

20

40

60

80

100

120

Po

sitio

n (

mm

)Piston Trajectory

Same speed different CR

CR: 9.5

CR: 10.4

-180 -120 -60 0 60 120 180

Crank Angle

0 10 20 30 40 50 60

Time (ms)

0

20

40

60

80

100

120

Po

sitio

n (

mm

)

Piston Trajectory

Same CR different speed

1500 rpm

1000 rpm

-180 -120 -60 0 60 120 180

Crank Angle

Producing different FPE

Trajectories

10 15 20 25 30 35 40 45 50

Time (ms)

0

20

40

60

80

100

120

Posi

tion

(m

m)

Piston Trajectory

Asymmetric operation

w: 1.2

wc:1.2 - we: 0.8

-180 -120 -60 0 60 120 180

Crank Angle

Unique ability to electronically change CR, speed and shape of piston

trajectory allows reproduction of any FPE trajectory

Further development is

underway to demonstrate the

intake and exhaust stroke, and

spark ignition

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Page 18: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

CT-RCEM for Engine Applications – FPE and Conventional ICE

• Scope to incorporate multi-cycle testing

• Flexibility to accurately pre-set initial conditions (charge pressure and temperature)

• Flexibility to accommodate in-situ or ex-situ species diagnostics through optical

(chemiluminescence, LIF) or gas sampling methods (GCMS)

• Replaceable head design to incorporate direct injection, spark plug, or intake

manifold as required

• Uniquely suited for investigating turbocharged combustion modes

• Replaceable piston crown to enable evaluation of piston crown geometries

• Flow field and turbulence studies by using various piston designs, changing piston

trajectories or even including an intake stroke

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Page 19: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Optical Diagnostics – Accessing Chemical Kinetics Information

30 ms compression 20 ms compression

30 ms 20 ms

• Optical measurement work

by capturing the

fluorescence of

intermediate species

• Chemiluminescence or

Laser induced fluorescence

• Measurement of

intermediate species such

as OH*, CH*, CH2*

• Comparing image

intensities gives a

quantitative estimate of

species concentration

Camera

Combustion

chamber

Piston

19

Page 20: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Conclusions

• A controlled trajectory rapid compression and expansion machine has been developed at

UMN for addressing the research needs of fundamental and applied combustion

investigation

• The exceptional flexibility and the novel capabilities have been demonstrated

• CT-RCEM offers a unique opportunity to experimentally validate the concept and

framework of trajectory based combustion control

• We are looking for opportunities to collaborate to further develop and demonstrate the

capabilities of the CT-RCEM.

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Page 21: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun21

Backup Slides

Page 22: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Operational Flexibility of CT-RCEM

Inert mixture testing

Ensemble average for four repetitions

of compression of Air-CO2 mixture

• Compression ratio: 12.4, 14.2,

15.5, 16.7

• Compression time: 20 ms & 30 ms

Enables calibration of heat transfer

models using experimental data over

a wide range of operating conditions

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Uniquely suited for experimental

validation of trajectory based

combustion control

Page 23: Modeling and Optimization of Trajectory-based Combustion ... · • Trajectory based Combustion Control –Previous Work: Modeling and Optimization Framework • Approach for experimental

Abhinav Tripathi, Prof Zongxuan Sun

Simulating FPE Operation

• Piston position and actual pressure

shown here is measured data

• Adiabatic pressure, isothermal

pressure, and temperature traces

are estimated

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