Modeling and Optimization of Trajectory-based Combustion ... · Abhinav Tripathi, Prof Zongxuan Sun...

Abhinav Tripathi, Prof Zongxuan Sun

Abhinav TripathiProfessor Zongxuan Sun

Department of Mechanical EngineeringUniversity of Minnesota

Modeling and Optimization of Trajectory-based Combustion Control

1


Presentation Outline

• Motivation, Background and Objective

• Trajectory based Combustion Control

– Previous Work: Modeling and Optimization Framework

• Approach for experimental validation– Simplified Problem: Combustion Phasing Control

– Hardware-in-loop Framework using CT-RCEM

– Experimental Results

• Conclusions

2


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

Emis

sion

per

form

ance

Spark Ignition Engine

DieselEngine

LTCEngine

3

Global trend - tighter regulations for fuel efficiency and emissions


Background – HCCI Combustion

• HCCI is a kinetically modulated combustion mode

• Relies on thermodynamic conditions (P,T) for autoignition instead of external input unlike SI and CI

• 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

4

Highly non-linear and coupled dynamics

Is there a better way to modulate the in-cylinder dynamics?


No mechanical crankshaft

Opposed Piston Opposed Cylinder Engine

Direct Fuel Injection

Background – FPE at UMN

Piston trajectory not constrained by mechanical crankshaft

Variable compression ratio and variable piston trajectory shape

•Advanced combustion strategies

•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

Virtual crankshaft mechanism enables the

FPE pistons to precisely track the

prescribed piston trajectory









• Conclusions

6


Chemical Kinetics of Fuel

In-cylinder Gas Dynamics

Thermal heat releaseReaction productReaction ratePressureTemperatureSpecies 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

7

Free Piston Engine


Inner Loop: piston motion control - virtual crankshaft

Outer Loop: Trajectory-based combustion control

Basic Framework - Trajectory-based Combustion Control

8

Find piston trajectory which minimizes emissions & maximizes work output

Control

signalsSupervisory Control Virtual

Crankshaft

In-cylinder pressure

Optimal piston

trajectory

Piston position

Inner-loop: Active Motion Control

Outer-loop: Trajectory Optimization

Operating

point

Output

power

Combustion Analyzer









• Conclusions

9


Experimental Validation – Simplified Problem

10

Control Objective: Attain optimal combustion phasingby changing the piston trajectory

• Combustion phasing determined in terms of Location of Peak Pressure (LPP)

• Piston trajectory characterized in terms of shape parameter (Ω)

• Desired LPP (Λ𝑑𝑑𝑑𝑑𝑑𝑑) is the optimal LPP determined offline through simulation

• Supervisory control determines the trajectory input based on the error in combustion phasing

• Active motion control ensures that the FPE operates on the trajectory determined by supervisory control

Supervisory Control

Combustion Analysis

Λ𝑑𝑑𝑑𝑑𝑑𝑑 +

-

Λ𝑖𝑖

𝑒𝑒𝑖𝑖 Ω𝑖𝑖+1

𝑥𝑥(𝑡𝑡)

𝑃𝑃(𝑡𝑡)

Virtual Crankshaft FPE


Hardware-in-loop Setup Using CT-RCEM

11

Control Objective: Attain optimal combustion phasing by changing the piston trajectory • CT-RCEM used to obtain combustion data

• Extremely precise control over initial and boundary conditions

• Ability to perform cycle based chemical kinetics analysis for NOx, HC, CO studies as required

• For this study, offline iterative learning control used as supervisory control

Iterative Learning Control

Combustion Analysis

Λ𝑑𝑑𝑑𝑑𝑑𝑑 +

-Λ𝑖𝑖

𝑒𝑒𝑖𝑖 Ω𝑖𝑖+1

𝑥𝑥(𝑡𝑡)

𝑃𝑃(𝑡𝑡)

Active Motion Control

CT-RCEM

Ω𝑖𝑖+1 = Ω𝑖𝑖 + 𝑘𝑘 × 𝑒𝑒𝑖𝑖;𝑒𝑒𝑖𝑖 = Λ𝑑𝑑𝑒𝑒𝑑𝑑 − Λ𝑖𝑖

Control Law


Experimental Validation –Simplified Problem

12

• Reduced computational burden for supervisory control

• True sinusoid represented by Ω = 1

• Piston spends more time at high P-T conditions (near TDC)

for trajectories with higher omega

• Effective means for controlling combustion phasing by

modulating the duration of time spent by fuel-air mixture at

high pressure and temperature conditions near TDC

Parameterize piston trajectory in terms of shape parameter 𝜴𝜴

0 5 10 15 20 25 30 35 40

Time (ms)

0

25

50

75

100

125

Posi

tion

(mm

)

Piston trajectories characterizedby shape parameter

=0.6

=1.0

=1.4

0 5 10 15 20 25 30 35 40

Time (ms)

-15

-10

-5

0

5

10

15

Velo

city

(m/s

)

-180 -120 -60 0 60 120 180

Crank Angle (deg)


Controlled Trajectory Rapid Compression and Expansion Machine

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CT-RCEM uses an electro-hydraulic actuator with feedback control to drive the piston

- 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

Same CRDifferent compression times

Different CRSame compression time

reference trajectory


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

14

Key requirement:High-force and high-speed

actuation


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

15

Uniquely suited for experimental validation of trajectory based

combustion control


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

16


Mimicking Engine Operation in CT-RCEM

0 10 20 30 40 50 60

Time (ms)

0

20

40

60

80

100

120

140

Posi

tion

(mm

)

Piston Trajectory

rep1

rep2

0 10 20 30 40 50 60

Time (ms)

0

5

10

15

20

25

Pres

sure

(bar

)

Gas Pressure

rep1

rep2

0 10 20 30 40 50 60

Time (ms)

-1

-0.5

0

0.5

1

Erro

r (m

m)

Tracking Error

rep1

rep2

• 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

17


Experimental Results

18

0 10 20 30 400

20

40

60

80

100

120

140

Pos

ition

(m

m)

0

20

40

60

80

Pre

ssur

e (b

ar)

Effect of piston trajectory on combustion for T 0= 45C

0 10 20 30 40

Time (ms)

-1.5

-1

-0.5

0

0.5

1

1.5

Erro

r (m

m)

Tracking error for different

1=1.2

2=1.0

3=0.8

• HCCI combustion of premixed dimethyl-ether (DME)• CR 12.1, equivalent operating speed 1500 rpm• Fuel-air equivalence ratio (𝜙𝜙) 0.6• Initial conditions: 𝑇𝑇0 = 45°𝐶𝐶;𝑃𝑃0 = 1.03 𝑏𝑏𝑏𝑏𝑏𝑏• Desired LPP Λ𝑑𝑑𝑑𝑑𝑑𝑑 = 5° crank angle after TDC

Supervisory control adjusts piston trajectory until 𝜦𝜦𝒅𝒅𝒅𝒅𝒅𝒅 is attained


Experimental Results

19

Trajectory based combustion control is an

effective method for combustion phasing control

Iteration Ω𝑖𝑖 Λ𝑖𝑖 Req δΩ𝑖𝑖 Actual Ω𝑖𝑖+1

𝑖𝑖 = 1 1.2 0.4° 0.27 1.0

𝑖𝑖 = 2 1.0 3.1° 0.12 0.8

𝑖𝑖 = 3 0.8 5.4° - -

For implementation purpose, resolution of Ω has been limited to ±0.2 for this study

Heat release at TDC (Otto cycle)

Heat release at Λ ≈ Λ𝑑𝑑𝑑𝑑𝑑𝑑


Heat Release Analysis

20

19 19.5 20 20.5 21

Time (ms)

0

200

400

600

Hea

t Rel

ease

(J)

Heat release analysis for T0

= 45 0 C

0.8

1

1.2

Qf

𝑑𝑑𝑄𝑄𝑏𝑏𝑎𝑎𝑎𝑎 =𝛾𝛾

𝛾𝛾 − 1𝑃𝑃𝑑𝑑𝑃𝑃 +1

𝛾𝛾 − 1𝑃𝑃𝑑𝑑𝑃𝑃 −𝑃𝑃𝑃𝑃

𝛾𝛾 − 1 2 𝑑𝑑𝛾𝛾

Initial temperature 45°𝐶𝐶

Omega 1.2 1.0 0.8

LPP (°𝐶𝐶𝐶𝐶) 0.37° 3.24° 5.41°

Comb. efficiency (𝑄𝑄𝑏𝑏𝑎𝑎𝑎𝑎/𝑄𝑄𝑓𝑓) 90.9% 89.4% 90.6%

Indicated thermal efficiency 19.5% 21.4% 23.6%

• Piston spends less time near TDC for trajectories with lower omega leading to:

• lower heat loss

• less time for completion of combustion reactions

• Indicated thermal efficiency trend is dominated by heat loss effects for this operating point

• Significant dependence of indicated thermal efficiency on piston trajectory

• Excessive heat loss in the CT-RCEM due to large stroke to bore ratio

𝑑𝑑𝑄𝑄𝑏𝑏𝑎𝑎𝑎𝑎 = 𝑑𝑑𝑄𝑄𝑐𝑐𝑐𝑐𝑐𝑐𝑏𝑏 − 𝑑𝑑𝑄𝑄𝑙𝑙𝑐𝑐𝑑𝑑𝑑𝑑 = 𝑑𝑑𝑑𝑑 + 𝑃𝑃𝑑𝑑𝑃𝑃

𝑄𝑄𝑏𝑏𝑎𝑎𝑎𝑎 = �𝑡𝑡𝑑𝑑𝑐𝑐𝑐𝑐

𝑡𝑡𝑒𝑒𝑐𝑐𝑐𝑐𝑑𝑑𝑄𝑄𝑏𝑏𝑎𝑎𝑎𝑎 𝑄𝑄𝑓𝑓 = 𝑐𝑐𝑓𝑓𝐿𝐿𝐿𝐿𝑃𝑃𝑓𝑓


Conclusions

• Framework for implementation of trajectory based combustion control strategy for Free Piston Engine operation has been presented

• A controlled trajectory rapid compression and expansion machine has been developed at UMN for addressing the research needs of fundamental and applied combustion investigation

• Hardware-in-loop framework has been proposed for experimental validation of trajectory based combustion control

• Experimental results show that piston trajectory is an effective method for combustion phasing control

• Ongoing work includes extending the hardware-in-loop framework for efficiency analysis of a modular electrical free piston engine operating on trajectory based combustion control

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Modeling and Optimization of Trajectory-based Combustion ... · Abhinav Tripathi, Prof Zongxuan Sun...

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