DI-Pul se Combustion Model Calibration in GT- Power...MSc Automotive Engineering Chalmers University...
Transcript of DI-Pul se Combustion Model Calibration in GT- Power...MSc Automotive Engineering Chalmers University...
DI-Pulse Combustion Model Calibration in GT-Power
26th October, 2015 DI-PULSE COMBUSTION MODEL CALIBRATION IN GT-POWER. MASOOM & VIJAY. 1
Vijayakrishnan Venkateshmohan
Technical Consultant
Volvo Car Corporation
Email: [email protected]
[Bosch]
Masoom Kumar
MSc Automotive Engineering
Chalmers University of Technology
Email: [email protected]
To calibrate and evaluate DI Pulse Diesel combustion model in GT-Power
26th October, 2015 2
Aim & Agenda
DI-PULSE COMBUSTION MODEL CALIBRATION IN GT-POWER. MASOOM & VIJAY.
What is DI Pulse?
Calibration of DI Pulse combustion model 1. Test data collection 2. Data validation 3. Calibration Validation of combustion model
DI Pulse is a phenomenological combustion model.
DI Pulse Combustion Model
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Injection Related Spray Penetration and breakup Entrainment Droplet evaporation
Combustion Related
Ignition Delay Premixed Combustion Diffusion Combustion
[Heywood,1988] [Georgia tech, 2015 ]
Methodology
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GT-Power
Cylinder pressure measured vs predicted
Data validation
Model calibration
[Wikipedia, 2015]
Engine test cell
[Nett Technologies, n.d]
Methodology
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GT-Power
Cylinder pressure measured vs predicted
Data validatio
n
Model calibration
[Wikipedia, 2015]
Engine test cell
[Nett Technologies, 2015]
[GT-Suite, 2015]
Fuel injection profile
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Fuel injection profile
Predictive injector
model in GT-Suite
Injector rate map
Data from
injector flow rig
[ASNU, 2015]
[Wang, 2015]
Injector rate map
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Populated from detailed injector model
Lesser simulation time compared to the detailed injector model
No error in fuel mass
Good reproduction of timing
Scalable
Does not account for pulse to pulse interaction
Methodology
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Engine test cell
GT-Power
Cylinder pressure measured vs predicted
Data validation
Model calibration
[Wikipedia, 2015]
[Nett Technologies, n.d]
Test cell data validation
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Obtain data from test cell
•Intake, cylinder & exhaust pressure.
•Emissions (CO, HC, NOx)
Perform initial data quality
check in AVL Concerto
•Encoder error.
•Pegging error.
•Error due to thermal shock.
Validate the data using
Three Pressure Analysis(TPA)
Additional validation using
Cylinder Pressure Only
Analysis(CPOA).
•Validate the trapped qty. obtained from TPA.
• Validate the data.
• Obtain trapped qty.
Test cell data validation
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Obtain data from test cell.
•Intake, cylinder & exhaust pressure.
•Emissions (CO, HC, NO)
Perform initial data quality check in AVL
Concerto
•Encoder error.
•Pegging error.
•Error due to thermal shock.
Validate the data using Three Pressure Analysis(TPA)
Additional validation using Cylinder Pressure Only
Analysis(CPOA).
•Validate the trapped qty. obtained from TPA.
• Validate the data.
• Obtain trapped qty.
Test was carried on 28 points in the RDE (Real drive emissions) cycle. Outputs Dynamic Intake, Exhaust and Incylinder Pressure Injection Strategy Residuals Swirl Emission (CO, HC and NOx)
Test cell data
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Test cell data validation
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Obtain data from test cell.
•Intake, cylinder & exhaust pressure.
•Emissions (CO, HC, NO)
Perform initial data quality check in AVL
Concerto
•Encoder error.
•Pegging error.
•Error due to thermal shock.
Validate the data using Three Pressure Analysis(TPA)
Additional validation using Cylinder Pressure Only
Analysis(CPOA).
•Validate the trapped qty. obatined from TPA.
• Validate the data.
• Obtain trapped qty.
Test cell data validation
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Obtain data from test cell.
•Intake, cylinder & exhaust pressure.
•Emissions (CO, HC, NO)
Perform initial data quality check in AVL
concerto
•Encoder error.
•Pegging error.
•Error due to thermal shock.
Validate the data using Three Pressure Analysis(TPA)
Additional validation using Cylinder Pressure Only
Analysis(CPOA).
•Validate the trapped qty. obtained from TPA.
• Validate the data.
• Obtain trapped qty.
Three Pressure Anaylsis (TPA)
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Trapped qty. at IVC
Volumetric efficiency
Trapping ratio
Fuel mass trapped at IVC
Fuel vapour fraction at IVC
Residual fraction at IVC
Cylinder pressure measured vs simulated
Intake,
cylinder & exhaust pressure
Injection profile
TPA
Cylinder pressure
data validation
Estimated trapped quantities
at IVC
Test cell data validation
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Obtain data from test cell.
•Intake, cylinder & exhaust pressure.
•Emissions (CO, HC, NO)
Perform initial data quality check in AVL
concerto
•Encoder error.
•Pegging error.
•Error due to thermal shock.
Validate the data using Three Pressure Analysis(TPA)
Additional validation using Cylinder Pressure Only
Analysis(CPOA).
•Validate the trapped qty. obtained from TPA.
• Validate the data.
• Obtain trapped qty.
Cylinder pressure only analysis (cpoa)
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Cylinder pressure
Trapped
quantities
CPOA
Validation
of trapped
quantities
Cylinder pressure measured vs simulated
Methodology
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Engine test cell
GT-Power
Cylinder pressure measured vs predicted
Data validation
Model calibration
[Wikipedia, 2015]
[Nett Technologies, n.d]
Run a DOE to obtain the 4 multipliers. Optimum multipliers chosen to minimize the average error.
DI-Pulse Calibration
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Multipliers
Entrainment rate
Ignition delay
Premix combustion rate
Diffusion combustion rate
Parameter Error limit
Burn rate (%) 0.0054
IMEP (%) 2.2
CA50 (deg) 0.9
Max. Pressure (bar) 2.8
NOx (%) 20
Di-pulse calibration result
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Parameter Unit Average Error Limit
Average Error
Calculated Burn rate % 0.0054 0.0045
IMEP % 2.2 1.6
CA50 Degree 0.9 .8
Max. Pressure
Bar 2.8 2.6
NOx % 20 20.8
The average error for all 25 points were calculated and tabulated below.
DI-Pulse model can only predict NO. Here the NO from the model is calibrated against NOx from measurement data.
0
20
40
60
80
100
120
900 1400 1900 2400 2900 3400 3900
Loa
d p
erc
en
tage
Speed (RPM)
Within error limits
Outside error limits
DI-Pulse Calibration Result
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Error in IMEP (+/- 5%)
Error in Max pressure (+/- 5bar)
0
20
40
60
80
100
120
900 1400 1900 2400 2900 3400 3900
Loa
d p
erc
en
tage
Speed (RPM)
Within error Limts
Outside error limits
-11
-9
-7
-5
-3
-1
1
3
5
7
9
11
0 5 10 15 20 25
% E
rro
r in
Ma
x P
Case Number
-6-5-4-3-2-10123456
0 5 10 15 20 25
% E
rro
r in
IM
EP
Case Number
DI-Pulse Calibration Result
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Error in CA 50 (+/- 2 deg)
Error in NOx (+/- 20%)
0
20
40
60
80
100
120
900 1400 1900 2400 2900 3400 3900
Loa
d p
erc
en
tage
Speed (RPM)
Outside Error limits
Within Error limits
-3,5-3
-2,5-2
-1,5-1
-0,50
0,51
1,52
2,53
3,5
0 5 10 15 20 25
Err
or
in C
A 5
0
Case Number
-70-60-50-40-30-20-10
010203040506070
0 5 10 15 20 25
% E
rro
r in
NO
x
Case Number
0
20
40
60
80
100
120
900 1400 1900 2400 2900 3400 3900
Loa
d p
erc
en
tage
Speed (RPM)
Outside error Limits
Within error Limits
DI-Pulse Calibration Result
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Error in NOx (+/- 20%)
Error in NOx (absolute error)
0
200
400
600
800
1000
1200
1 5 9 13 17 21 25N
Ox
Con
cent
ratio
n (p
pm)
Case Number
Nox MeasuredNox Predicted
-70-60-50-40-30-20-10
010203040506070
0 5 10 15 20 25
% E
rro
r in
NO
x
Case Number
Although the absolute NOx prediction is not very good, the model predicts the trend quite well.
DI-Pulse Prediction
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0
20
40
60
80
100
120
1000 1500 2000 2500 3000 3500 4000
Load
(%)
Speed (RPM)
Points used for model prediction
Full load curve Points for prediction
The DI-Pulse model was validated against 3 points as shown below.
DI-Pulse Prediction Result
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Parameter
Unit Error Limit
Point 1 (low load)
Point 2 (mid load)
Point 3 (High load)
Burn rate error
RMS .0052 .0011 .0020 .0026
IMEP % +/-5 1.75 -0.45 -0.69
CA50 Degree +/-2 .1 -1 1
Max. Pressure
Bar +/-5 -0.9 3.4 -6.7
NOx (ppm)
% +/-20 -34.3 5.7 -17.1
The average error for the 3 point were calculated and tabulated below.
DI-Pulse model was successfully calibrated for 25 points and validated against 3 points.
All the key performance metrics were within the suggested error limits, except NOx at low-mid loads. However the NOx trend prediction is quite good.
It is recommended to Gamma Technologies to include NO2 prediction capability in the DI Pulse model, and further investigate the NOx model.
An updated injector model may provide even better results
Additional points are recommended in the high speed – high load region to improve the results
The DI-Pulse model in general is good and can be used for future work
Conclusion
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acknowledgment
Björn Jonsson at VCC Thank
you
Robert Wang and Shawn Harnish at GT
Sven B Andersson at Chalmers Jian Zhu at VCC