Diesel Engine Experimental Design and Advanced Analysis Techniques

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By

Jonathan M. Davis

Graduate Program in Mechanical Engineering

The Ohio State University

2011

Master‘s Examination Committee: Shawn Midlam-Mohler, Advisor Dr. Giorgio Rizzoni Dr. Yann Guezzenec

Abstract

A new diesel engine control strategy has been developed. In order to successfully validate and implement this control strategy experimental design techniques were used which optimize the data collection process. This included the setup of cylinder pressure measurements as well as the creation of an automating testing program. This program automates engine testing, interfacing with key components such as the dynamometer and engine controller (ECU). Both steady state and transient testing algorithms were developed. Several advanced analysis techniques have been developed for the project. An in depth study on pegging cylinder pressure was completed, utilizing GT Power©. GT

Power© was also used to analyze an experimental design to simulate altitude at sea level in the test cell.

Acknowledgements

I would like to thank The Ohio State University and CAR for the facilities and opportunities provided. I would also like to thank Dr. Shawn Midlam-Mohler for the opportunity to participate in this research project as well as the guidance along the way. I would like to also thank Fabio Chiara and Jason Meyer, who were also on the project.

Vita

May 2005 ...... Dublin Scioto High School

June 2010 ...... B.S. Mechanical Engineering, The Ohio

State University

June 2010 to present ...... Graduate Research Associate, Center for

Automotive Research, The Ohio State

University

Table of Contents

Abstract ...... i

Acknowledgements ...... ii

Vita ...... i

List of Tables ...... i

List of Figures ...... i

Chapter 1 ...... 2

1.1 Introduction ...... 2

1.2 Literature Review...... 2

1.2.1 Cylinder Pressure Pegging Methods ...... 2

1.2.2 Combustion Noise ...... 11

1.2.3 Altitude Testing ...... 14

1.3 Motivation ...... 16

1.4 Project Objective ...... 16

Chapter 2 : Experimental Setup ...... 18

2.1 Engine Instrumentation ...... 23

2.1.1 Factory sensor locations ...... 23 ii

2.1.2 Test cell specific sensor locations and descriptions ...... 25

2.1.3 Cylinder Pressure ...... 34

2.2 Experimental Devices/Automation ...... 38

2.2.1 Steady state automation ...... 39

2.2.2 Transient automation ...... 52

Chapter 3 : Advanced Analysis Techniques ...... 59

3.1 CO2 Distribution inside Intake Manifold ...... 59

3.1.1 CO2 experimental setup ...... 60

3.1.2 CO2 experiment results ...... 65

3.1.3 CO2 experiment future recommendations ...... 67

3.2 Combustion Noise ...... 68

Chapter 4 : Simulation Enhancing Experiments ...... 74

4.1 GT Power© model accuracy ...... 74

4.2 Cylinder Pressure Pegging ...... 82

4.2.2 GT Power© pegging analysis ...... 85

4.2.3 Experimental Pegging Results ...... 89

4.2.4 Impact of Pegging Method on Heat Release...... 102

iii

4.2.5 Pegging Methods Conclusions ...... 124

4.3 Altitude Simulations ...... 125

4.3.1 Altitude simulation – 85 kPa...... 127

4.3.2 Altitude simulation – 65 kPa...... 131

Chapter 5 : Future Work and Conclusions ...... 135

Bibliography ...... 137

Appendix A: CO2 Experiment Test Plan ...... 141

List of Tables

Table Page

Table 1: Sensor labels corresponding to Figure 9 ...... 26

Table 2: Sensor labels corresponding to Figure 10 ...... 27

Table 3: Sensor location table corresponding to Figure 11 ...... 28

Table 4: Labels corresponding to Figure 12 ...... 29

Table 5: Analysis of variance for baseline case ...... 66

Table 6: Analysis of variance for CO2 injected into EGR pipe ...... 66

Table 7: Mean absolute pegging offset error from GT-Power© analysis...... 89

Table 8: Average experimental pegging offset for each cylinder (rounded) ...... 101

Table 9: Mean absolute pegging methods errors for ignition delay ...... 109

Table 10: Mean absolute pegging methods errors for total cumulative heat release ...... 113

Table 11: CA50 and CA90 difference compared to true GT-Power© result ...... 118

Table 12: Average difference between pegging methods ignition delay ...... 120

Table 13: Average absolute difference between pegging methods total heat release ..... 121

Table 14: Average absolute difference between pegging methods for CA50 and CA90

(Intake pegging method is baseline) ...... 122

Table 15: Well mixed case - CO2 injected after filter box ...... 141

Table 16: EGR entering manifold through EGR pipe ...... 142

List of Figures

Figure Page

Figure 1: J plotted verses k (Tunestal) ...... 10

Figure 2: Lucas Industries structure response (Russell, Palmer and Young) ...... 13

Figure 3: Exhaust aspirator system (Culshaw and McClure) ...... 16

Figure 4: Engine encoder ...... 19

Figure 5: DAQ system diagram ...... 22

Figure 6: Factory engine sensors: 1 - Charge pressure and temperature 2 - Fuel rail pressure 3 – Intake air throttle ...... 24

Figure 7: Factory engine sensors: 1 - Fresh air MAF sensor 2 - Compressor inlet 3 - Filter box...... 24

Figure 8: Factory engine sensors: 1 - EGR valve 2 - Turbocharger 3 - EGR cooler 4 -

EGR piping ...... 25

Figure 9: Test cell specific sensors ...... 26

Figure 10: Test cell specific sensors 2 ...... 27

Figure 11: Test cell specific locations exhaust side ...... 28

Figure 12: Test cell specific sensors exhaust side 2 ...... 29

Figure 13: AVL smoke meter in test cell ...... 30

Figure 14: LabVIEW program for AVL smoke meter ...... 32

Figure 15: EGR venturi flow meter ...... 33

Figure 16: Cylinder pressure cooling and actuation schematic ...... 35

Figure 17: Cylinder pressure water cooling manifolds ...... 36

Figure 18: Cylinder pressure cooling lines through valve cover ...... 36

Figure 19: Cylinder pressure sensors with ear clamps ...... 37

Figure 20: Charge amplifier serial connection computer program ...... 38

Figure 21: Steady state automation screen...... 45

Figure 22: Example of automation counter ...... 46

Figure 23: LabVIEW automation program ECU interaction ...... 49

Figure 24: Transient testing LabVIEW program ...... 55

Figure 25: FTP highway drive cycle engine dynamometer speed data ...... 57

Figure 26: FTP highway drive cycle engine dynamometer torque data ...... 58

Figure 27: CO2 uncertainty experimental setup ...... 61

Figure 28: EGR pipe flow path, 1:3 – CO2 sampling ports; left, middle, right respectively,

4 – 1000 L/min flow meter ...... 62

Figure 29: 1:3 – CO2 sampling ports; left, middle, right respectively, 4 - Horizontal ..... 62

Figure 30: 1 – CO2 flow meters, 2 – Point of entry for CO2, 3 – Heated CO2 value and bottle ...... 63 ii

Figure 31: CO2 well-mixed case test plan...... 64

Figure 32: CO2 EGR pipe injection case test plan ...... 64

Figure 33: Main effects plot for EGR pipe case ...... 67

Figure 34: Sound pressure level for 1 cycle of 1800 RPM, 125 ft-lb (step 5) ...... 70

Figure 35: Sound pressure level - structure for 1 cycle of 1800 RPM, 125 ft-lb (step 6) 70

Figure 36: A-weighting filter applied for 1 cycle of 1800 RPM, 125 ft-lb (step 7) ...... 71

Figure 37: Combustion noise algorithm validation ...... 73

Figure 38: Main injection SOI sweep ...... 73

Figure 39: Total fueling comparison...... 76

Figure 40: Peak cylinder temperature ...... 77

Figure 41: MAF comparison ...... 77

Figure 42: Intake manifold pressure comparison...... 78

Figure 43: Intake manifold temperature ...... 79

Figure 44: Exhaust runner 1 ...... 79

Figure 45: Compressor temperature ...... 80

Figure 46: Turbine inlet temperature ...... 80

Figure 47: Turbine inlet temperature ...... 81

Figure 48: NOx emissions ...... 81

Figure 49: CO emissions ...... 82

Figure 50: GT Power test matrix for pegging methods analysis ...... 86 iii

Figure 51: Pegging methods result using GT Power© ...... 87

Figure 52: Pegging offset comparison (small bubbles represent more accurate pegging) 87

Figure 53: Pegging comparison (smaller bubbles represent more accurate pegging) ...... 88

Figure 54: Experimental test matrix and GT Power test matrix run for pegging analysis 90

Figure 55: Example of intake pegging method at high boost, high speed ...... 91

Figure 56: Intake Pegging Method example, low speed, high boost ...... 91

Figure 57: Intake Pegging Method example, low speed, low boost, medium torque ...... 92

Figure 58: Intake Pegging Method example, low speed, no boost, low torque ...... 92

Figure 59: Exhaust pegging method example high speed, high backpressure ...... 93

Figure 60: Exhaust pegging method example low speed high torque ...... 93

Figure 61: Exhaust pegging method example low speed low torque ...... 94

Figure 62: Exhaust pegging method example low intake pressure...... 95

Figure 63: Overlap method example...... 96

Figure 64: Least Squares Method example high speed high torque ...... 97

Figure 65: Least Squares method example of high speed low torque ...... 97

Figure 66: Least Squares method low speed low torque ...... 98

Figure 67: Variable k two-point method high speed high torque ...... 99

Figure 68: Variable two-point method offset errors ...... 100

Figure 69: Variable k two-point method low speed low torque ...... 100

Figure 70: Experimental pegging offset average for five pegging methods ...... 102 iv

Figure 71: Ignition delay sensitivity to pegging errors ...... 107

Figure 72: Ignition delay pegging error sensitivity...... 108

Figure 73: Ignition delay errors for different pegging methods ...... 109

Figure 74: Normalized total heat release sensitivity to pegging errors ...... 110

Figure 75: Total heat release sensitivity to positive pegging errors ...... 111

Figure 76: Total heat release sensitivity to negative pegging errors ...... 111

Figure 77: Total heat release error due to pegging inaccuracy ...... 112

Figure 78: Total heat release errors for 5 different pegging methods ...... 113

Figure 79: CA50 sensitivity to pegging errors ...... 114

Figure 80: CA90 sensitivity to pegging errors ...... 115

Figure 81: CA50 sensitivity to positive pegging offset errors ...... 115

Figure 82: CA50 sensitivity to negative pegging offset errors ...... 116

Figure 83: CA90 sensitivity to positive pegging offset errors ...... 116

Figure 84: CA90 sensitivity to negative pegging offset errors ...... 117

Figure 85: CA50 errors for five different pegging methods ...... 117

Figure 86: CA90 errors for five different pegging methods ...... 118

Figure 87: Experimental ignition delay errors (intake pressure pegging method is the benchmark) ...... 119

Figure 88: Experimental total heat release errors (intake pressure pegging method is the benchmark) ...... 121 v

Figure 89: CA50 for different pegging methods compared to intake pegging method .. 123

Figure 90: CA90 for different pegging methods compared to intake pegging method .. 123

Figure 91: Compressor speed map at 85 kPa ...... 128

Figure 92: Compressor reduced mass flow at 85 kPa ...... 128

Figure 93: Compressor pressure ratio at 85 kPa ...... 129

Figure 94: Engine torque comparison at 85 kPa ...... 129

Figure 95: Fresh air flow at 85 kPa ...... 130

Figure 96: Residual fraction at 85 kPa ...... 130

Figure 97: Compressor speed map at 65 kPa ...... 131

Figure 98: Compressor pressure ratio at 65 kPa ...... 132

Figure 99: Compressor reduced mass flow at 65 kPa ...... 132

Figure 100: Engine torque at 65 kPa...... 133

Figure 101: Fresh air flow at 65 kPa ...... 133

Figure 102: Residual fraction at 65 kPa ...... 134

Chapter 1

1.1 Introduction

This project is in conjunction with diesel engine control development research at the

Center for Automotive Research (CAR). The project was intended to provide support in order to validate control strategies and provide physical data to accompany simulation data from GT Power©. A small four-cylinder diesel engine was instrumented in an engine dynamometer to provide this support. Due to the large amount of data that must be collected in conventional engines engineers are looking toward new methods of accelerating the process. Automating engine tests can greatly reduce the amount of testing time as well as provide robustness that manually changing setpoints cannot offer.

Computer simulations and analysis are also critical to the process, to reduce testing time.

With this in mind this research project implemented automation software, as well as utilized computer simulations where necessary to provide data that is difficult to obtain during engine testing.

1.2 Literature Review

1.2.1 Cylinder Pressure Pegging Methods

Cylinder pressure is the measurement of the crank angle resolved pressure profile inside the engine cylinder. Cylinder pressure measurements give valuable insight into the

2 engines combustion characteristics such as burn angles and heat release mass burn profiles. Piezoelectric transducers work by responding to pressure differences. This response is in the form of a charge referenced to an arbitrary ground. This charge is converted into a voltage by a charge amplifier. Since piezoelectric transducers are not absolute sensors they must be referenced to either a known phenomenon such as polytropic compression or to a signal such as intake pressure using a method known as pegging. There are various methods of pegging and no one method is an ideal solution for every situation. For example tuned intake systems or high engine speeds limit the ability to set the cylinder pressure at intake bottom dead center (IBDC) equal to the intake manifold pressure (Lee, Yoon and Sunwoo). The method chosen to peg cylinder pressure is important. Incorrect pegging methods can greatly affect heat release analysis.

Multiple papers have explored methods for pegging cylinder pressure. Each method attempts to find the offset, or bias, to apply to the original pressure signal.

This offset is expressed as

(1) ( ) ( )

where ( ) is the real cylinder pressure, ( ) is the measured cylinder pressure and

is the constant measurement error.

One of the most commonly referenced methods is inlet pressure referencing (Brunt and

Pond, Evaluation of Techniques for Absolute Cylinder Pressure Correction), (Randolph,

Methods of Processing Cylinder-Pressure Transducer Signals to Maximize Data

Accuracy). This method sets the cylinder pressure signal at IBDC to the intake manifold pressure. IBDC is normally used because it is assumed that the pressure drop across the ports is smallest at this point (Brunt and Pond, Evaluation of Techniques for Absolute

Cylinder Pressure Correction). Brunt and Pond examined the difference between intake and cylinder pressure in the IBDC region at a range of speeds discovering that a flat portion existed 10 to 15 degrees ABDC. It was suggested that this would make a better referencing region; however they emphasized the engine specific nature of these characteristics. Another paper determined that this method is accurate, but only in an untuned intake system or at very low speeds in a tuned system (Lee, Yoon and Sunwoo).

To reduce error, it was recommended that several points be used near IBDC.

Another method references the cylinder pressure during the exhaust stroke to the exhaust pressure. This method may solve some of the potential problems with the intake pressure pegging method since exhaust back pressure fluctuations are generally smaller because of reduced tuning effects. A large window average is generally used to help eliminate some of the high frequency variations. In one study 40 degrees ABDC to 40 degrees BTDC was used. This method still is limited to low engine speed (Lee, Yoon and Sunwoo) and was found to be inferior to the more widely used intake pegging method in Randolph‘s study (Randolph, Methods of Processing Cylinder-Pressure Transducer Signals to

Maximize Data Accuracy).

Other methods are explored, which target the elimination of the additional crank-angle resolved intake or exhaust sensors. Each of these methods assumes that the engine 4 behaves as a polytropic process during the compression phase before combustion. This assumption is not true when mass loss or excessive heat losses occur during the compression stroke (Lee, Yoon and Sunwoo).

The first method is a two-point referencing method which uses two points and .

The relationship can be written as

(2) ( ) ( ) ( ) ( )

Combining Equation(1)‎ and Equation (2)‎

( ) ( ) ( ) (3) * + ( ) ( ) ( )

Rearranging Equation(3)‎

( ) ( ) ( ) [ ] ( ) (4)

( ) [ ] ( )

This method should be referenced on a cycle to cycle basis to avoid errors. One disadvantage of this method is that it uses a fixed polytropic coefficient which can be slightly inaccurate (Lee, Yoon and Sunwoo). Multiple papers reference 1.27 as an appropriate value for diesel engines (Randolph, Methods of Processing Cylinder-Pressure

Transducer Signals to Maximize Data Accuracy), (Lee, Yoon and Sunwoo).

Measurement errors can propagate themselves in coefficient estimation leading some to opt for the fixed coefficient method (Brunt and Pond, Evaluation of Techniques for

Absolute Cylinder Pressure Correction). The choices of and is also of importance.

should always be located after the intake valve closes, while should be before ignition, which will differ between CI and SI engines. It is recommended that several values are averaged, for instance a +/- 5 degree window, in order to reduce the effect of measurement noise. It was found that this method was more sensitive to measurement noise than the intake pressure pegging method. Even so, it was found to be the better method for combustion analysis (Brunt and Pond, Evaluation of Techniques for Absolute

Cylinder Pressure Correction). Randolph concluded in his study that the intake pressure pegging method produced better results, but noted that this was because intake/exhaust design and part-load conditions did not generate the pressure fluctuations which exist in many manifolds. He notes that in an engine which has these effects, the fixed coefficient polytropic method would be preferred (Randolph, Methods of Processing Cylinder-

Pressure Transducer Signals to Maximize Data Accuracy).

The second method attempts to eliminate some of the issues using a fixed polytropic coefficient by replacing it with a variable one, which was found in multiple papers

(Randolph, Methods of Processing Cylinder-Pressure Transducer Signals to Maximize

Data Accuracy), (Lee, Yoon and Sunwoo). Modifying Equations (1)‎ and (2)‎ to use three data points Equation (5)‎ was developed.

( ) [ ] ( ) ( ) ( ) (5)

( ) ( ) ( ) [ ] ( )

Since the polytropic coefficient cannot be calculated analytically Equation (5)‎ is expanded in a Taylor series expansion about an initial value . As long as k is close to

the higher order terms will not significantly change the result. Using should produce good results (Randolph, Methods of Processing Cylinder-Pressure Transducer

Signals to Maximize Data Accuracy).

( ) ( ) * + ( ) ( )

( ) ( )

(6)

( )

Once k is calculated Equation (4)‎ is used to calculate the offset. It was warned that this method is very sensitive to noise in the measurement system, which leads to inaccurate polytropic coefficients, leading to incorrect cylinder pressure offset values. It was

7 recommended to use an average cylinder pressure signal to calculate k and then use a moving average to determine the sensor offset (Lee, Yoon and Sunwoo).

The fixed polytropic compression method, which Pond and Brunt concluded was the best for combustion analysis, has issues when the polytropic coefficient is not fixed. The three-point method attempts to fix this issue by including a variable coefficient, but the method will produce significant errors if the correct polytropic coefficient is far away from the value of which the Taylor series was expanded about (Lee, Yoon and

Sunwoo). The third and potentially most accurate polytropic method is a least-squares method with a variable polytropic coefficient. It attempts to solve some of the problems found in the other polytropic methods. The least squares method combines Equations (1)‎ and (2)‎ , where C is a constant

(7)

This can be posed as

(8)

( ) ( )

Applying this to a range of data points in the compression stroke the following equations can be developed.

( ) ( ) (9)

( ) ( ) ( ) (10) ( )

(11)

One of the issues with Equation (11)‎ is that the number of cylinder pressure measurements, n, is greater than the number of unknown parameters within , which in this case is two. This means that the equation cannot be directly solved. To fix this

Tunestal proposes the use of a least squares solution as seen in Equation (13)‎ (Tunestal).

̂ ( ) (12)

While it is possible to use a fixed polytropic exponent, as mentioned earlier it is not ideal due to cycle to cycle variations. Tunestal proposes that the residuals resulting from the least squares analysis (Equation (13)‎ ) can be minimized by altering the k value used.

There is a k value which minimizes the residuals. This value is taken as the true polytropic coefficient for the compression stroke (Tunestal).

(13)

A loss function can be defined for the residuals seen below.

(14)

Figure 1: J plotted verses k (Tunestal)

By finding the minimum of J, k is found. This can be found using the Newton method.

The derivation can be viewed elsewhere (Tunestal).

( ) (15)

In order to use the Newton method, the second derivative can be found. For computational simplicity the finite difference Newton method is used.

( ) ( ) (16) ( )

1.2.2 Combustion Noise

Combustion noise is an inherent and important parameter in diesel engines. When fuel is injected in a diesel engine, it does not ignite right away. There is a delay period where the fuel must mix with air to form a near stoichiometric mixture, termed the ignition delay.

Given the correct temperature and pressure the fuel will then ignite. Once the portion of the fuel that is premixed ignites, it burns very rapidly which produces a steep rise in pressure within the cylinder. The steep-fronted pulse physically deflects the engine structure, which causes it to ‗ring‘, which is termed as combustion noise (Russell and

Young).

The combustion process is the main source of noise for a direct injection naturally aspirated diesel engine. However for turbocharged diesel engines, combustion noise is not always the predominant source when at steady state. It can be for low loads and at transient operations though. Noise is caused by the vibration of the surfaces of the structure, the accessories attached to the engine and the oil pan and valve covers. There are two forces which cause these to vibrate; combustion and the mechanical motion of the engine (Jenkins). The mechanical motion can be divided into several sources: piston slap, fuel injection equipment, valve trains, gearing and accessories. For those interested in an 11 overall number for the noise levels of the engine, complicated strategies must be implemented, such as an anechoic or semi-anechoic noise test cell, with a considerable amount of post-processing required (Reinhart). Lucas Industries Noise Centre introduced a combustion noise meter, which uses the signal from the cylinder pressure sensors to calculate a value for the noise level of combustion alone, without relying on extensive acoustic instrumentation. The key to their invention is a structural response curve shown in Figure 2, which allows one to find the contribution of the combustion process to the overall noise level. The structure response is found by measuring noise levels 1 meter away from the engine in a semi-anechoic acoustic environment. The engine is placed in an operating region where combustion noise is dominant, either by changing fueling parameters or the fuel‘s cetane number. The sound pressure level inside the cylinder, measured from cylinder pressure sensors, is compared with the 1 meter microphone reading. The difference between the readings is termed the structure attenuation of the engine. This procedure was performed on several engines and the averaged and smoothed out curve is the structure response curve that is used.

The proposed system records the cylinder pressure signal and weights it using the structural response curve and A-weighting filter. Since this curve is not for specific engines, there is an inherent error in the noise calculation; the only way to know that the noise level measurement is accurate is to have the structural response curve for the engine of use. Despite this limitation, this method has still found use in industry using the average structural response curves.

Figure 2: Lucas Industries structure response (Russell, Palmer and Young)

The general procedure for their measurement system is as follows (Haworth and

Russell):

a. Capture data for 20 individual engine cycles

b. Perform Fourier analysis on the time derivative of each cycle

c. Calculate the modulus (cn)

d. Calculate over 20 or more engine cycles

e. Correct for 6 dB/octave pre-whitening and scales

f. Apply structure response function

g. Apply A-weighting

h. Calculate overall level of combustion noise in db(A) 13

Once the Fourier analysis and modulus are calculated, a mean square cylinder pressure spectrum is calculated for the moduli and scaled by the reference pressure which is 20 micropascals, resulting in a reading in decibels. The structure response and A-weighting is then subtracted from the cylinder pressure spectrum. Finally the mean square is calculated in order to produce the overall A-weighted combustion noise level.

Another method explored is using the time derivative of the cylinder pressure signal and attempting to correlate this to the combustion meter reading. While there is most definitely a trend, the data scatter makes it less than ideal (Reinhart) (Russell and Young).

Peak rate of heat release also appears to have a linear correlation to combustion noise across a wide variety of engines (Haworth and Russell).

1.2.3 Altitude Testing

Generally if engine manufactures want to calibrate at altitude conditions they must drive vehicles into altitude conditions. For some manufactures very expensive test cells are used which can simulate a wide range of temperatures and pressures, allowing for accurate simulated altitude testing. It would be ideal to do neither if an affordable and accurate alternative method is available. Literature is limited, but several sources are available to explore these ideas. Galster and Garner explored the idea of only throttling the intake, leaving the exhaust pressure unchanged. This allowed them to test altitude conditions while near sea level. The study from 1967 is dated and features a carbureted SI engine. The experimenters were mainly interested in spark plug operating temperatures

(Galster and Garner). This paper recognized that not changing the engine back pressure

14 resulted in inaccuracies in the experimental results. To correct for this they made a horsepower correction factor.

Southwest Research also provides a way to simulate altitude conditions up to approximately 7500 feet (78 kPa) at sea level through a High Altitude CV. Their design consists of a dilution tunnel, which feeds air past a throttle using a pump in order to reduce the pressure of the tunnel. The intake, exhaust and crankcase are all connected to this tunnel during testing. It was verified that no EGR occurred from the exhaust region back into the intake through the tunnel, although in their second paper they mention a special baffle to isolate the two. The test engine in their first paper was a turbocharged

11.1 L inline 6-cylinder. The second paper used two engines, a naturally aspirated 10.4 L and a Cummins turbocharged 14.0 L (Chaffin and Ullman) (Human, Ullman and Baines)

A more complicated system was created by the U.S Bureau of Mines to test oxidation catalysts at different altitudes. Their test engine was a naturally aspirated Caterpillar diesel engine. Their system uses a valve to reduce the inlet pressure to simulate altitudes above sea level and a vortex blower to test below sea level conditions. The exhaust system was unique as it used an exhaust aspirator system which can reduce back pressure by 30 kPa.

Figure 3: Exhaust aspirator system (Culshaw and McClure)

1.3 Motivation

In order to accelerate control algorithm and calibration development and control costs and development time, advanced experimental techniques are needed. Coupling of computer simulation and real world engine data is required in order to reduce actual test cell experimental time which is costly both in time and money. Computer simulations can be used to simulate situations not readily available using engine testing. When physical engine data is needed it is advantageous to quickly record data, allowing for more data to be collected in less time.

1.4 Project Objective

The goal of this project was to instrument a diesel engine to record information such as emissions, flow rates, pressures and temperatures. An advanced cylinder pressure system was to be installed and implemented, allowing for important information to be gained about the engines operating conditions. Data acquisition and processing techniques were

16 also to be developed. Computer simulations were to be used where appropriate to aid in the design of the testing instruments and to validate data processing algorithms.

Automation testing programs were to be developed allowing for quick acquisition of data in both steady state and transient operation modes. All this work was to be done in conjunction with control algorithm development, allowing for the calibration and validation on a physical test engine.

Chapter 2 : Experimental Setup

Experimental testing is a key part of the engine development cycle. Much of the engine calibration process is performed using engine dynamometer tests. Control strategies also are validated using experimental engine tests. In this research project small sections of the engine operating space will be calibrated. Engine control strategies will also be validated. Significant time can be spent developing engine test cell equipment. Several details of the test cell equipment are described in the next section, outlining sensor locations, instrumentation of cylinder pressure and the creation of an automating testing program.

The research described was performed at the Center for Automotive Research at The

Ohio State University. A engine test cell was used which was equipped with a four- quadrant 300 HP AC dynamometer which was used to control engine speed. Near the end of the project the engine was moved to a 200 HP DC dynamometer test cell in order to make room for another project. The test cell is also equipped with a mobile laminar flow element which is used for precise engine air flow measurements. The laminar flow element is attached to a 55 gallon drum, which serves as an accumulator to dampen out high frequency oscillations. The test cell is also equipped with emissions analyzer equipment. This consists of a Horiba emissions analyzer (Horiba MEXA 7500) with the capability to measure dry CO, NOx, O2, wet THC and CO2. The Horiba also has two 18 individual analyzers allowing for the simultaneous measurement of two sample lines.

Two sample lines can be attached to each analyzer, with the choice of which sample line to measure from. Only one sample line per analyzer can be measured from.

There are two acquisition systems for this project. These are termed ―slow-speed‖ and

―high-speed‖ referring to 100 Hz and crank angle resolved measurements, respectively.

The high-speed and slow-speed data acquisition systems are run on two separate computers. The high-speed data acquisition system was developed for this project so that there was a separate data acquisition system for the 300 HP AC and 200 HP DC dynamometer test cells. A diagram is shown in Figure 5, outlining the different connections to the two computers.

The first piece of the high-speed DAQ system is the encoder. A half-degree resolution optical encoder was installed on the engine‘s crankshaft as seen in Figure 4.

Figure 4: Engine encoder

The encoder is hooked up to a National Instruments SCB-68 I/O connector block. The block is used to accept the Z and B pulse from the encoder. LabVIEW then takes this signal and outputs a clean square pulse to be used in the LabVIEW measurements. The

SCB-68 is connected to the computer using a National Instruments PCI-6602 counter/timer card.

The analog measurements for the high-speed system are also recorded using the SCB-68

I/O connector block. 4 cylinder pressure signals, one intake pressure signal and one exhaust pressure signal are input to the block from the Kistler signal conditioner. The connector block is connected to the computer via a National Instruments PCI-6123 16-bit card with 8 simultaneous sampled analog inputs available.

All the inputs to the high speed computer are recorded using LabVIEW software. The

LabVIEW program displays cylinder pressure data, as well as other information such as heat release, PV-diagrams and encoder information.

The slow-speed computer also has data acquisition PCI cards which are connected to two

BNC racks and one thermocouple module. Most test cell specific measurements are recorded using the modules. Engine speed, torque and power as recorded by the dynamometer are sampled at 10 Hz via serial connection for the 300 HP AC dynamometer test cell. The DC dynamometer test cell uses BNC outputs to record engine speed and torque. The difference in measurement between test cells is simply convenience, since the two dynamometer control systems are different. The smoke meter is also connected via a serial connection and the FSN number as well as other data is

20 recorded. Factory engine sensors are recorded using a calibration interface program provided by the engine manufacturer. This program is run on a separate computer, with the desired signals sent over an Ethernet connection to the slow-speed computer at a sample rate of 10 Hz.

Figure 5: DAQ system diagram 22

2.1 Engine Instrumentation

At the start of the project, an engine had been installed in the test cell described above and had been equipped with factory and test cell specific sensors. Several new systems were installed during the current project, such as a smoke meter and cylinder pressure measurements. Other measurements were refined, such as the turbocharger speed sensor,

EGR venturi sensor and a second mass air flow sensor.

2.1.1 Factory sensor locations

The factory sensors are used by the engine controller. All stock sensors that were necessary to the engine controller‘s proper functioning were kept. The below diagrams show their locations. Figure 6 shows the front of the engine sensors including charge temperature and pressure, fuel rail pressure and the intake air throttle.

Figure 7 shows the MAF sensor location and the fresh air inlet path. Figure 8 shows the

EGR system and turbocharger location.

Figure 6: Factory engine sensors: 1 - Charge pressure and temperature 2 - Fuel rail

pressure 3 – Intake air throttle

Figure 7: Factory engine sensors: 1 - Fresh air MAF sensor 2 - Compressor inlet 3 - Filter

box

Figure 8: Factory engine sensors: 1 - EGR valve 2 - Turbocharger 3 - EGR cooler 4 -

EGR piping

2.1.2 Test cell specific sensor locations and descriptions

The engine has been instrumented with many sensors which the factory engine does not have. Figure 9, Figure 10, Figure 11 and Figure 12 show these sensor locations.

Figure 9: Test cell specific sensors

Table 1: Sensor labels corresponding to Figure 9

1 Post intercooler thermocouple 2 Post intake air throttle thermocouple 3 Bottom of intake manifold thermocouple 4 Post-venturi EGR thermocouple 5 2 oil pan thermocouples 6 Coolant inlet thermocouple 7 Post intercooler MAF sensor (MAF IAT sensor)

Figure 10: Test cell specific sensors 2

Table 2: Sensor labels corresponding to Figure 10

1 Left intake UEGO sensor 2 Right intake UEGO sensor

3 CO2 intake sampling point 4 High-speed intake pressure sensor

Figure 11: Test cell specific locations exhaust side

Table 3: Sensor location table corresponding to Figure 11

1 Exhaust runner cylinder 1 thermocouple 2 Exhaust runner cylinder 2 thermocouple 3 Compressor outlet thermocouple 4 Turbine outlet thermocouple 5 Exhaust UEGO sensor 6 Turbocharger Speed sensor (Red/White twisted wire)

Figure 12: Test cell specific sensors exhaust side 2

Table 4: Labels corresponding to Figure 12

1 Exhaust runner 4 thermocouple 2 High speed exhaust pressure sensor 3 Exhaust runner 3 thermocouple 4 Engine coolant outlet thermocouple 5 EGR coolant outlet thermocouple 6 EGR coolant inlet thermocouple

2.1.2.1 Smoke meter

An AVL smoke meter was instrumented for this project. This is especially important during the calibration stage of the research project, because fueling parameters may need to be selected in order to reduce smoke. The smoke meter is a filter-type smoke meter, which has been equipped with the high pressure measuring option. The smoke meter was installed after the Horiba emissions analyzer line on the exhaust pipe.

Figure 13: AVL smoke meter in test cell

While the smoke meter comes with a control module, a LabVIEW program was created in order to automate the process. The smoke meter is not a continuous measurement, but instead samples only when commanded to do so and gives readings of soot over a defined sample period. The program screen can be seen in Figure 14. The smoke meter program

30 is interfaced with the slow-speed data acquisition LabVIEW program. The user can manually measure and purge if desired. Several settings are available including the option to purge before measuring, the choice of sampling a fixed volume or fixed time, the desired volume or time to sample and the number of samples to make. If any commands are not present in the LabVIEW program, users can manually send commands through the serial connection using commands found within the manual. Once the measurement is complete, the system automatically purges and displays a variety of results such as FSN, paper blackening and the resulted measurement length and volume. The program also automatically communicates with the smoke meter signaling errors, actual recorded samples and several other important pieces of information. The use of the LabVIEW program makes it much more user friendly verses the control module. It is also much more powerful since it includes the ability to access many parameters and commands not available with the control module. Most importantly it allows for the automation of the testing procedure which is described in full in section 2.2.

Figure 14: LabVIEW program for AVL smoke meter

2.1.2.2 EGR Venturi Sensor

The measurement of EGR flow rates can be relatively difficult, especially during transient operations. Several methods are available. During steady state operation an accurate method is to use the below equation. The CO2 concentration values can be found using an emissions analyzer, which samples from both the intake and exhaust manifolds.

(17)

Due to the lag within the emissions measurement system it isn‘t possible to record transient data using this method. Another possible technique is subtracting the mass air 32 flow found using the speed density equation from the fresh air flow sensor measurement.

This has the potential for inaccuracies during transients because the fresh air sensor is located before the turbocharger and requires an accurate volumetric efficiency model.

Previous work had been done which designed a measurement device which attempts to solve these problems. A venturi, seen in Figure 15, was placed in the middle of the EGR pipe, which allows for actual EGR flow rate measurements.

Figure 15: EGR venturi flow meter

Several sensors are used, including a delta pressure sensor, absolute pressure sensor and temperature sensors. This allows for an accurate measurement of EGR flow, especially critical during transients.

2.1.3 Cylinder Pressure

Cylinder pressure measurements were required for this project for the calculation of combustion noise, heat release and MEP, as well as allowing for diagnostics such as misfire and low compression. The engine was instrumented with Kistler ThermoComp quartz pressure probes that are rated for a range up to 250 bar. These sensors are mounted through the engine head and cooling jacket. The engine was also equipped with

Piezoresistive absolute pressure sensors with PiezoSmart technologies. These sensors were placed both in the intake and exhaust manifolds. The intake pressure sensor was rated at 5 bar and the exhaust pressure sensor was rated at 10 bar. The PiezoSmart sensors store their calibration information within the sensor which can be automatically loaded into the Kistler signal conditioner using provided software. The sensors are also air pressure controlled, which allows the sensors to only be exposed to the elements when needed.

All the cylinder pressure sensors require cooling. A cooling system was designed for this project which utilizes readily available city water. The schematic is shown in Figure 16.

Figure 16: Cylinder pressure cooling and actuation schematic

City water enters a manifold which distributes water to the six different sensors via high temperature Viton hoses, which is seen in Figure 17. After the water exits the sensors it enters into another manifold which then empties into a drain. In order to cool the cylinder pressure sensors the cooling lines and sensor signal go through the valve cover. Holes were drilled in the valve cover and small grommets placed inside the hole to reduce oil leakage and prevent the cooling lines from rubbing against the plastic valve cover. Due to oil leakage at high speeds high temperature RTV sealant was applied around all the holes.

This reduced leakage to acceptable levels. The valve cover is shown in Figure 18.

Figure 17: Cylinder pressure water cooling manifolds

Figure 18: Cylinder pressure cooling lines through valve cover

The Viton tubing is attached to the cylinder pressure sensors via a barbed connection.

This design proved fatal, with the lines popping off during engine operation, resulting in

36 a substantial amount of water in the oil. To attempt to remedy this problem ear clamps were placed over the tubing. This worked fairly well, although after some period of time several lines developed a slow drip. Although not an ideal solution, the leakage rate was acceptable since the water evaporates from the oil once the engine is at operating temperature. This new design is seen in Figure 19.

Figure 19: Cylinder pressure sensors with ear clamps

All the cylinder pressure, as well as intake and exhaust pressure sensors are connected to a signal conditioner which amplifies the sensors charge output. The charge amplifier is connected to a National Instruments SCB-68 I/O module via the amplifier‘s analog outputs, but it is also connected to the high-speed computer via a serial connection. This allows users to set parameters such as calibration data, range and filter. The software program is shown in Figure 20, with the calibration screen on the left and the modules and measurement control on the right. 37

Figure 20: Charge amplifier serial connection computer program

2.2 Experimental Devices/Automation

Typically in a low budget test cell, when engine testing is needed an operator must manually set parameters such as engine torque and speed. While this works well for small amounts of data, if large amounts of testing is needed this can significantly slow the process, limiting the amount of data that can be collected. If more parameters need to be changed such as injection timings, fuel quantities and EGR valve position, the time to move data point to data point will significantly increase. Besides the time it will take, manual testing is also prone to user errors when such a large amount of parameters have to be changed. As an example if a full factorial DOE was to be completed manually the operator would have to set the engine speed, pedal position, pilot injection SOI, pilot injection quantity, main injection SOI, EGR position and intake air throttle (IAT) position. Then the operator must determine whether the engine is in steady state or not, which means tediously observing trends in engine parameters such as exhaust

38 temperature. The other option would be to wait a fixed amount of time, but if the engine is in steady state after 30 seconds and one is waiting 3-5 minutes for steady state 2.5-4.5 minutes could be wasted, which adds up over a lot of data points. Once the operator determines the engine is in steady state, the save button on the high and slow-speed

LabVIEW programs must be pressed, as well as the sampling button for the smoke meter.

This tedious process then must be repeated over hundreds of data points. One can see that this takes an exceedingly large amount of time just to set operating conditions, not to mention the potential for user error. This is why an automating testing program was created using LabVIEW.

2.2.1 Steady state automation

The first automation program that was created was a steady state program built into the existent slow-speed LabVIEW program. This was meant to handle a large range of possibilities. The main steady state automation screen can be found in Figure 21. The user interface will be explained first, along with the basic features of the program. The actual inner workings of the program will be described last. The interface allows the user to load a .CSV file in the format seen on the user interface in Figure 21. Each row of the

.CSV file is a different operating condition. Each column is a different commanded value, which is described next.

1. Column 1, enter 1 to have LabVIEW send the speed commands, 0 if the

user wants to manually enter speed commands on the dynamometer

controller interface

2. Column 2, Speed setpoint in RPM, only commanded if column 1 is set to

3. Column 3, Enter 1 to have LabVIEW control the engine based on torque,

0 if the user wants to control on a different method

4. Column 4, torque value in Ft-lb, only commanded if column 3 is set to 1

5. Column 5, enter 1 to have LabVIEW control the engine based on pedal

position, 0 if the user wants to control on a different method (i.e. torque)

6. Column 6, pedal position in % to be commanded, only read if column 5 is

set to 1

7. Column 7, Enter 1 to have LabVIEW control IAT position, 0 to have the

engine controller control it

8. Column 8, IAT position in % (0% is completely open, 100% is

completely closed) command, only commanded if column 7 is set to 1

9. Column 9, Enter 1 to have LabVIEW control EGR position, 0 to have the

engine controller control it

10. Column 10, EGR position command in % (100% is completely open, 0%

is completely closed), only commanded if column 9 is set to 1

11. Column 11, Enter 1 to have LabVIEW control pilot injection quantity, 0 to

have the engine controller control it

12. Column 12, Pilot fuel quantity in mg/stroke, only command if column11 is

set to 1

13. Column 13, Enter 1 to have LabVIEW control Main injection SOI, 0 to

have the engine controller control it

14. Column 14, Main injection SOI (degrees BTDC), only commanded if

column 13 is set to 1

15. Column 15, Enter 1 to have LabVIEW control pilot injection SOI, 0 to

have the engine controller control it

16. Column 16, Pilot injection SOI (degrees BTDC), only commanded if

column 15 is set to 1

An easy method to creating the .CSV file is to use Matlab, which created the 738 data point DOE, which was loaded into the program seen in Figure 21.

Once the test matrix is loaded there are several parameters that must be set before running a test. The first is the choice of whether to save data or not. If saving the save length can be set in the bottom right hand corner of the screen. The seconds to wait before saving must be set whether the user actually wants to save or not. The program stays at each operating condition for this amount of time and then either saves or goes onto the next point, depending on what was chosen for saving. If the user chose to save data the program will move onto the next point after the save if complete. The last choice is whether the user wants to use the steady state control algorithm or not. This algorithm determines if the engine is in steady state before proceeding to save. If using the steady state algorithm there is a choice of sensors that the user can select from. This can be an infinite number of possibilities but currently exhaust temperature and NOx emissions are employed. Multiple sensors are selected by using the Ctrl button on the keyboard and 41 selecting the desired sensors. The slope threshold length must be set, which is in seconds.

This is the amount of time that the steady state algorithm will collect data before making a determination if the engine is in steady state or not. The longer the time the better chance the engine is actually in steady state, but this also can greatly increase testing time. The slope threshold must be set next for each sensor. The threshold is entered left to right in the row. For example if exhaust temperature and NOx emissions are selected, exhaust temperature‘s slope threshold goes in the first column and the NOx emissions slope threshold goes in the second column. This parameter is critical to the functionality of the steady state algorithm. The threshold is in unit/length. For exhaust temperature with a slope threshold length of 30 seconds this number would be °C / 30 seconds. This means that after 30 seconds the steady state algorithm will look at exhaust temperature and see if it has changed 3 °C for example, in 30 seconds. If the engine‘s exhaust temperature has changed less than this, then the engine is deemed to be in steady state.

The same thing is done with all other steady state sensors chosen. Once the seconds to wait before saving value is passed, the program starts making judgments on if the engine is in steady state. If the steady state algorithm determines the engine is in steady state, then the program begins saving the data and then moves onto the next data point (next row in the .CSV file). If the algorithm determines the engine is not in steady state (one of the thresholds has been surpassed) the program waits the slope threshold length, for instance 30 seconds, and checks again. This process repeats itself until the engine is deemed in steady state. These parameters must be calibrated based on the engine and operating conditions. Setting the threshold to 0.5 °C may be unrealistic for some engines,

42 especially if fueling is changing at all (controlling torque). The graph on the automation screen shows the slope of the different sensors over the threshold time length. The slope in the graph represents the amount the sensor has changed over the amount of time since the start of the steady state window. For instance if the threshold length is set to 30 seconds, the graph will continuously show how much the exhaust temperature has risen

(in degrees per second) since the beginning of the 30 seconds. Once the 30 seconds is up the algorithm picks the last slope number and makes a judgment on steady state based on the slope threshold that was set.

The automation screen also displays several helpful parameters. The setpoints and the actual values from the engine are shown on the screen to make sure the parameters are set correctly. Two error boxes in the bottom right corner of the screen display any errors occurred with interactions with the dynamometer. The bottom left hand corner of the screen shows three parameters. The first is the row, which is the data point the program is currently on. The ―x+1‖ indicator is the time in seconds on the current data point. The box next to that shows the amount of time until the next row. This value was set in the seconds to wait before saving box.

The final piece to the program is actually running tests. Two options are available. The first is to run the tests but to manually cycle through the rows (data points). This is done by pressing the run test button. The ―Automate?‖ button should be off. Rows are set using the ―Row (auto and manual)‖ button. The counter ―x+1‖ button counts to 7 seconds and starts over. The next data point then must be manually selected. The second choice is to automate the process. This is done by simply pressing the ―Run Test?‖ button and 43 flipping the ―Automate?‖ switch. This will start the test at row 0. If another data point is desired the row is entered in ―Row (auto and manual)‖ and the ―Set row (auto only)‖ button is to be pressed. From there the automation program takes over and will cycle through all data points until the end. Once the last point is reached the program patiently waits at the last point until the user shuts off the test.

The actual inner workings of the program are described next. The user must select which test cell the program is being used in via a drop down menu in the LabVIEW program.

The difference between the two test cells is that one test cell uses an InterLoc-V controller and the other uses a Dyn-Loc IV controller from Dyne Systems to control the dynamometer. The automation process required interaction with many different processes, which will be also described.

Figure 21: Steady state automation screen 45

2.2.1.1 Counter

The most fundamental part of the program is the clock. The automation algorithm is contained within a Case Structure within LabVIEW, which are run at 5 Hz. The clock increments in steps of 0.2; this adds to one second due to the 5 Hz timing.

Figure 22: Example of automation counter

An example of the clock structure is outlined in Figure 22. The clock increments until the clock value (x+1) passes the seconds to wait value. In order to avoid the complexity of the code several pieces were left out of the diagram in Figure 22. The block restarts when the automation button if flipped. Once the seconds to wait value is passed, the code also requires that the slow-speed and high-speed systems are done saving and that the smoke

46 meter is not sampling. Once all criteria are met the counter is simply started over by multiplying the value by zero.

2.2.1.2 Dynamometer automation

The component which changes between the two test cells is the dynamometer control code. Both codes are based on the sample principle, which is a serial connection. While there is a possibility for a wide range of controls to be sent, the automation program only uses a few of them. The code for both test cells opens the serial connection. The

InterLoc-V dynamometer (300 HP AC dyno) reads data including speed, torque and power from the serial connection. The Dyn-Loc-IV dynamometer (200 HP DC dyno) records data from BNC connections. These are recorded along with the other data

(temperatures, pressures, etc.) but also used for the rest of the control code. The torque and speed are used in the torque control sections of the program. The ramp rate or LAC is set using the program. It also selects the correct shaft rotation direction for the 300 HP

AC dyno test cell, since the dynamometer services two engine bays. While the code is very simple, the initial upfront work to interact with the dynamometers is more complicated and time consuming.

2.2.1.3 Engine interactions

The engine interaction section of the code is the main part that will not be compatible between engines. However the framework will still work, it is just a matter of changing commands. The basic principle is that commands are sent over an Ethernet connection to a separate computer which is connected to the engine‘s ECU via a USB cable. Other setups could easily modify the code to use CAN or other communication protocols. 47

Figure 23 shows an example of this framework. If the automating system is enabled, the for-loop is enabled. String commands are sent to the Ethernet connection, which in this example is called ―WRITE ECU COMMAND + Parameter Value + Enter‖. This command is only activated if the preceding column is a 1. Earlier this framework was described, where column 1 is a 1 or 0 to signal whether to command engine speed, and column 2 is the actual speed command value. The for-loop loops through every column in the current row of the DOE (current operating condition). However the critical part to making this specific application work is only activating the command in a specific time window. Due to the limited amount of commands that can be send at one time, each command is send at a specific time period. In this example the command is sent when the

―x+1‖ counter is between 0 and 0.2. A command is sent every 0.4 seconds. If this wasn‘t done there would be at least 16 commands sent at least every second, plus pedal position commands. The system simply cannot handle this amount of commands. Other systems may be different, but the framework can be easily used for other applications.

Figure 23: LabVIEW automation program ECU interaction

2.2.1.4 Torque control

There are two choices for controlling torque. The first is commanding pedal position.

This can be done either in the automatic or manual modes. The second choice for controlling torque uses a simple feedforward PI controller, which can also be used in either automatic or manual modes. The feedforward component uses a modified torque to pedal position map taken from the engine ECU. The map uses requested torque and current speed and outputs the feedforward pedal position. The PI controller then corrects for the inaccuracies in the map. The controller saturates the commanded value at 0 and

100 %.

2.2.1.5 High-speed and slow-speed computer interaction

Another important part of the program is the command of cylinder pressure recording.

There is a separate high-speed computer which records cylinder pressure using

LabVIEW. This is described in more detail in Section 2.1.3‎ . The slow-speed and high- speed computers are connected via a serial connection. When the slow-speed system is saving it sends several commands for the high-speed system to save data. Since the high- speed computer records data in packets of cycles, there are places especially when starting the cycle number over where commands are not read. This makes it necessary to send the save command three different times, the last two only activated if the high-speed system is not saving by a specific time. The high and slow-speed computers both send a continuous command saying whether they are saving or not. The slow-speed computer uses this information to wait before proceeding to the next data point and the high-speed computer uses the information during the transient automation stage of the program.

Another key piece of information that is sent is the file number. To easily sync files, the slow-speed and high-speed computers save data using the same file number.

When the slow-speed computer starts to save it sends a command to open the intake and exhaust pressure sensors via air actuation. This is done using a digital signal command out to a National Instruments BNC-2110 extension system. This output goes to a circuit board that turns the power on and off to the air control solenoid.

2.2.1.6 Smoke meter interaction

The smoke meter interaction is a rather tricky and complicated interface. The slow-speed computer interacts with the smoke meter via a serial connection. The code is located 50 outside the 10 Hz framework of the main LabVIEW code, which is necessary for successful operation. Commands are sent, such as SMES to measure. The key to the interaction though is that each command must be preceded by ―\02\s‖ and followed by

―\03‖. Several sub-VI programs were built to simplify the main program. These are broken into a status VI, measure VI and purge VI. The status VI is active at all times, alerting the user of a variety of information, described more in depth in Section 2.1.2.1‎ .

The basic framework is that the program sends a command for information and the smoke meter sends information back in a particular format, which is picked apart into usable information. The measuring VI is the most complicated. This is because commands can only be sent once, but must have time to process. If a measure command is sent more than once the smoke meter system will start to measure and restart right after receiving multiple commands and never actually sample. The VI will also command the system to purge if needed and will record results after the system is done measuring. This works for both the automation system and manual user operation. The purge VI is the simplest, purging for manual operation and alerting the user that the system is purging.

2.2.1.7 Steady-state algorithm

For simplicity of the main LabVIEW code the steady-state algorithm was created as a sub-VI. The first part of the code creates the matrix of data to examine for steady state, based on the sensors selected for the user interface. This matrix is built continuously via a feedback loop until the slope threshold length value is reached. Once the preset amount of time is reached the matrix is cleared and started over. As the matrix is being built the data continuously enters a for-loop which calculates the slope of the data for each sensor

51 via a least squares regression. The for-loop then outputs an array which contains the slope for each sensor up to that time period. For instance if NOx emissions and exhaust temperature are selected the array will include two numbers, updating 10 times a second to make up 10 Hz. The for-loop creates the x-value that is used in the regression, which simply labels each data point 1, 2, etc. The output slope from the linear regression is then converted to a per-second number and compared to the slope threshold which was set by the user. If the slope is less than the slope threshold an output of ―true‖ is set which tells the automation program it is okay to save if it is ready.

2.2.2 Transient automation

One of the reasons for the creation of a steady state testing automation program was to hopefully have created a framework in which a transient automation program could be created. The steady-state automation program was simply modified, mainly eliminating unnecessary code. The user interface can be seen in Figure 24. Like the steady state program the user inputs the test data file as a .CSV file. The file is divided into several simple sections. Each row represents a time interval of the transient cycle. Currently there is a minimum spacing of 0.2 seconds allowed. This means that cycles such as an FTP cycle must be broken down into discrete increments, which can easily be done in Matlab.

Below describes the column layout.

1. Time value (can be any multiple of 0.2 seconds)

2. Engine torque setpoint in Ft-lb

3. Engine speed setpoint in RPM

The user also has the choice whether to save on the slow-speed and high-speed machines.

The program displays the current speed and torque profiles, which are graphed against the commanded values. The program also displays the time since starting the cycle and the length of the cycle in seconds. The cycle simply starts with the press of the ―Run

Transient Cycle?‖ button.

The coding for the program is very similar to the steady state automation

2.2.2.1 Counter

The counter remains exactly the same, but eliminates the choice of how many seconds to wait before continuing to another data point and any steady state criteria.

2.2.2.2 Dynamometer interactions

Engine speed is commanded the same was that the steady state program is. Care is needed to ensure the ramp rate or LAC is set high enough to achieve transient operations.

The ramp rate stays constant throughout the test, so the highest rate needed should be the one set for the duration of the test.

2.2.2.3 Engine interactions

Most of the engine ECU commands are eliminated. There is no longer a choice to change any parameters except torque. This was done for several reasons. First there wasn‘t a need for changing these parameters, and second the ECU interaction system cannot handle sending so many commands at the rate that would be required.

2.2.2.4 Torque control

The engine torque control was changed to eliminate the PI controller. The feedforward component is all that is left. It would not be possible to control torque reliably at the required speed.

2.2.2.5 High-speed and slow-speed computer interaction

The interaction between the computers must change due to the nature of how the high- speed computer originally saved. In order to save during the steady state tests the high- speed system saves a preset amount of cycles. If cylinder pressure data is required during the transient operation a set amount of cycles is not possible. The transient program system sends a command letting the high-speed computer know it is saving. The high speed computer does not stop saving until it receives the command that the slow-speed computer is done saving.

Figure 24: Transient testing LabVIEW program 55

2.2.2.6 Running drive cycles using automation software

A simple model was created which takes drive cycle data, which is in m/s, and translates it in the engine speed and torque. This is a backwards model, which back calculates the engine speed and torque based on gear selection.

(18)

(19)

These are then used to calculate the output torque of the vehicle.

( )

(20)

In the equations is the vehicle acceleration, is the diameter of tire, and is the mass of the vehicle. The 10% mass addition is to account for the inertia of the unsprung mass.

This output torque is then translated to the engine based on the selected gear ratio. The selection of the gear is rather complicated. A simple strategy was used, which attempted to achieve low engine RPM‘s, while not skipping gears and not shifting excessively. The goal of the model was to provide a realistic means of testing control strategies on the

56 engine in use for this research project. While more sophisticated models are available, this level of fidelity would not add much to the overall conclusions of control strategy comparisons. It is meant to validate the proposed control strategy, not make real world vehicle to engine test cell comparisons.

This simple model allows for the conversion of any drive cycle to be used in the test cell on the dynamometer. One of such examples is displays below. Figure 25 and Figure 26 are speed and torque data from the FTP highway drive cycle which was run in the test cell, using the transient automation program. The engine speed and torque were found by taking the FTP highway vehicle cycle and using the model to convert it. Figure 25 and

Figure 26 show that the transient automation program does a very good job of tracking both engine speed and torque.

Figure 25: FTP highway drive cycle engine dynamometer speed data

Figure 26: FTP highway drive cycle engine dynamometer torque data

Chapter 3 : Advanced Analysis Techniques

Two new advanced analysis techniques were developed specifically for this project. The first technique is an experimental design which allows for the determination of the level of mixing within the intake manifold. The second method which was developed, utilizes engine cylinder pressure data to calculate combustion noise, a critical parameter in user acceptability in diesel engines.

3.1 CO2 Distribution inside Intake Manifold

EGR mixing within the intake manifold is a relative uncertainty, which can cause a variety of problems. Ideally EGR would be well mixed with the incoming fresh air so that the distribution across cylinders is even. If the EGR distribution is uneven emissions and performance could suffer. This research uses several EGR measurement techniques which rely on uniform mixing. The first is a venturi EGR flow meter which is placed in line with the EGR pipe. The second measurement is two different UEGO sensors in the intake manifold. Lastly CO2 is sampled out of the intake manifold to provide a precise reading on the EGR flow rate.

In order to quantify this mixing a new method of determining mixing of CO2 was developed. Previous studies showed large amounts of uncertainty in CO2 fraction, but

59 suffered from large measurement uncertainties due to low CO2 fractions and too much variability in the system such as EGR and fresh flow rates. The goal of this study was to eliminate outside variability and concentrate purely on CO2 mixing within the intake manifold.

3.1.1 CO2 experimental setup

Compressed CO2 in K size bottles was used to simulate the EGR gases. CO2 entered the engine based on two different flow paths. The first flow path brings CO2 into the intake manifold by injecting it just after the air filter (Figure 30), which is before the turbocharger. This represents the well-mixed case since it has plenty of time to mix with the fresh air. The second flow path injects CO2 into the EGR pipe at the exhaust side of the EGR venturi flow meter (Figure 28). CO2 is mixed with compressed shop air before entering the flow meter in order to make up a total EGR flow rate. Total EGR flow rate into the EGR pipe is measured using a 1000 L/min flow meter which contains an electronic control value which regulates flow (Figure 28). CO2 is measured using two 200

L/min flow meters (Figure 30), combining to make a maximum possible flow rate of 400

L/min. One meter had an electronic control valve, whereas the other was controlled manually.

Figure 27: CO2 uncertainty experimental setup

Figure 28: EGR pipe flow path, 1:3 – CO2 sampling ports; left, middle, right respectively,

4 – 1000 L/min flow meter

Figure 29: 1:3 – CO2 sampling ports; left, middle, right respectively, 4 - Horizontal

Figure 30: 1 – CO2 flow meters, 2 – Point of entry for CO2, 3 – Heated CO2 value and

bottle

The test matrix was created based on experimental data from the engine of interest. First the experimental data available was separated into cases where EGR was used and where it was not. Out of the cases where EGR was used, a test matrix was created which attempted to sweep 14 different combinations of speed, torque and EGR flow rate. Table

15 and Table 16 in Appendix A display the full test plan. Figure 31 and Figure 32 show the visual location of the test plans as well as the relevant flow rates

CO2 Flow Rate [L/min] 160 140

120 163 lb] - 100 80 173 217

60 49 63 CO2 Flow Rate [L/min] 40 86 173 54

Engine Engine Torque[ft 20 0 43 62 127 162 -20 0 500 1000 1500 2000 2500 3000 Engine Speed [RPM]

Figure 31: CO2 well-mixed case test plan

Total EGR Flow Rate [L/min] 160 140

120 343 lb] - 100 80 263 208 237

60 100132 Total EGR Flow Rate [L/min] 40 279 353 192

Engine Engine Torque[ft 20 0 309 488 550 -20 0 500 1000 1500 2000 2500 3000 Engine Speed [RPM]

Figure 32: CO2 EGR pipe injection case test plan

For the well mixed case the CO2 flow was first estimated using the fresh air flow and desired CO2 fraction, which was 5%. This represents the CO2 flow column. The actual

CO2 flow rate that was used varied a little, since 5% was dialed in using the emissions analyzer. The flow controller also reads for air, not CO2 and a correction factor of .7382 is made to measurements of CO2.

For the case where CO2 is injected into the EGR pipe, the flow rate of CO2 found in the well mixed case is used. The total EGR flow rate is calculated based on the engine data available. If the CO2 flow rate is not high enough to make up the total EGR flow rate, compressed air is used to make up the difference. Again the flow rate read on the controller is different than that calculated from the engine data because of the correction factor for CO2.

3.1.2 CO2 experiment results

Steady state data was collected first at the well mixed case at each different sampling point in the manifold, left, middle, right and all open. Next the flow path of the CO2 injection was changed to the EGR pipe and the experiment repeated, sampling out of the left, middle, right valves as well as the valves open. If the mixing of CO2 in the intake manifold was poor, one would expect large differences between the different sampling points

An analysis of variance (ANOVA) was performed in order to statistically determine the mixing within the intake manifold.

Based on the ANOVA results in Table 5 there is no statistical difference between valves since the P value is much higher than the level of significance of 0.05. This is expected since the baseline case should represent a well-mixed charge flow. The speed/torque combination is statistically significant, which means that the CO2 fraction changed with speed/torque case, which is expected.

Table 5: Analysis of variance for baseline case

Source DF Seq SS Adj SS Adj MS F P Valve 3 0.0000432 0.0000432 0.0000144 0.05 0.984 Speed/Torque Combination 13 0.0819708 0.0819708 0.0063054 22.64 0.000 Error 59 0.0164314 0.0164314 0.0002785 Total 75 0.0984454

For the case of interest, where CO2 is injected into the EGR pipe, the analysis of variance results are shown in Table 6. Based on this analysis there is not a statistical significance between the different valve configurations, since the P value is much greater than the

0.05 level of significance. Like the baseline case, the speed/torque combination has statistically different CO2 fractions, which is expected.

Table 6: Analysis of variance for CO2 injected into EGR pipe

Source DF Seq SS Adj SS Adj MS F P Valve 3 0.00178 0.00178 0.00059 0.43 0.730 Speed/Torque Combination 13 3.54512 3.54512 0.27270 198.99 0.000 Error 59 0.08085 0.08085 0.00137 Total 75 3.62775 66

Figure 33 shows these results visually. The overall conclusion is that there is good mixing inside the intake manifold and there should not be statically significant differences between EGR in each cylinder.

Figure 33: Main effects plot for EGR pipe case

3.1.3 CO2 experiment future recommendations

The CO2 and total EGR flow rate is achieved using a flow controller. A good controller is critical to obtaining useful measurements. Even slight drift in the flow rate can dramatically alter results. Since CO2 is in limited supply, letting the system settle for large time periods is not an option, therefore a good flow controller is needed. It is

67 recommended that a flexible pipe be used to connect the total EGR flow rate meter/controller to the EGR pipe due to the potential for large amount of vibrations.

Care should be taken to ensure that the compressed air system can handle the flow rates required to achieve proper EGR flow rates. Air and CO2 pressures are bounded by limits set by the flow meter/controller, which means that multiple shop airlines may be needed to achieve desired flow rates. In addition the source of CO2 should be large enough to ensure constant operation. K size bottles will provide sufficient time intervals between bottle changes at low flow rates, but as rates increase bottles deplete quickly, requiring frequent changes. If K size bottles are the only option multiple bottles should be located in close proximity. Another option is a manifold to connect multiple bottles. Care should also be taken with the CO2 bottles to ensure the lines do not freeze due to the large expansion ratio. A rope heater can be used to heat the pressure regulator. It is recommended to use some type of heat exchanger for the CO2 gases. A simple water bath in conjunction with coiled tubing works fairly well. This will ensure that the CO2 is at room temperature when entering the flow path.

3.2 Combustion Noise

Combustion noise is an inherent and important parameter in diesel engines. Based on a review of literature on diesel engine noise, it was found that combustion noise was not the primary source of noise for turbocharged engines. This means that in order to quantify combustion noise, noise measurements outside the engine would be inappropriate. Combustion noise is of interest to the project, since the proposed control strategy adjusts fueling parameters in order to improve fuel economy and reduce NOx 68 emissions. The engine must be able to meet consumer acceptability requirements for noise, therefore it was deemed necessary to be able to quantify noise levels as fueling parameters were changed from the originally calibrated points. In order for the combustion noise algorithm to be useful it must be relatively simple, must be able to be automated using Matlab and can only require a reasonable amount of computation time.

It must also be able to differentiate between variations in fueling parameters and ideally would provide consistent results with data available from the engine‘s manufacturer.

Out of the various methods to quantify combustion noise the Lucas combustion noise method was selected because it meets the criteria listed above in terms of a simple straightforward method. Although the technique is old (~1985), newer methods have increased complexity requiring more computational power as well as a large increase in time to correctly code the algorithms.

The method used for this project does not change much from the originally proposed method from Lucas, mainly improvements in data acquisition and computers allows for some of the parts to be left off.

The general method is as follows:

1. Capture a large amount of cycles (in our case 200) 2. Perform Fourier Analysis on the differential ( ) of each cycle 3. Calculate absolute value of Matlab‘s FFT signal and divide by N/2 4. Calculate power of first half of FFT

5. Calculate ( )

6. Subtract structure response and A-weighting filter 7. Take RMS value of value resulting from 6 – This is the overall sound level

Figure 34: Sound pressure level for 1 cycle of 1800 RPM, 125 ft-lb (step 5)

Figure 35: Sound pressure level - structure for 1 cycle of 1800 RPM, 125 ft-lb (step 6)

Figure 36: A-weighting filter applied for 1 cycle of 1800 RPM, 125 ft-lb (step 7)

The general method was described in the literature review, including the use of the structure response function. The end result, the combustion noise, is first averaged over each cycle and then over each cylinder. An example of the process is shown in Figure 34,

Figure 35 and Figure 36. The figures show one operating condition (1800 RPM, 125 ft- lb) and one cycle out of the 200 collected for that data point. In order to verify that the algorithm successfully predicts combustion noise, it is compared against experimental data at the engine manufacturer. The proposed results are termed Center for Automotive

Research (CAR) in the figures.

Figure 37 shows that the proposed method consistently under predicts the combustion noise. This doesn‘t necessarily mean that the algorithm will not be useful for the current project. There are several possible reasons for the differences. First, the calibration on the experimental engine at CAR and at the calibration used to obtain the engine 71 manufacturer‘s data has been proven to be different. Differences in calibrated fueling parameters can have a significant difference in combustion noise, although noting that the proposed algorithm always under predicts noise, this most likely is not the full answer.

Another explanation is that the algorithm used at the engine‘s manufacturer and the algorithm used at CAR is different. Since the current algorithm uses a structure response, even if the same method is used, if a different response is used at the engine‘s manufacturer it would affect the results. Another difference could be how combustion noise is averaged between cylinders. If only one cylinder is used to quantify the engine‘s combustion noise and this current algorithm uses an average of all four cylinders there could be major differences.

One option is to create a constant shift in the algorithm at CAR, which should allow for comparisons between the experimental data from the engine‘s manufacturer and the experimental data from CAR can be made. Before this is done however, a full sweep of the engine should be completed using the same fueling parameters between the engine manufacturer and CAR.

Another criterion was that there needed to be enough resolution in the results that changing different fueling parameters would result in noticeable differences in combustion noise values. This was validated by sweeping the pilot injection timing at

2000 RPM and 50 ft-lb three different times throughout the day.

Figure 38 shows the results, which have been normalized. The combustion noise calculation allows for sufficient resolution, showings significant differences between

72 different main injection timings. It also shows that there is good consistency with the measurements, since three measurements at different points in the day all are close to one another.

Figure 37: Combustion noise algorithm validation

Figure 38: Main injection SOI sweep 73

Chapter 4 : Simulation Enhancing Experiments

4.1 GT Power© model accuracy

Engine models are quickly becoming one of the more important parts of engine development. It allows engine designers the ability to drastically reduce calibration time as well as explore more engine design possibilities. A previously created GT Power© model of the engine of interest in this research project been used for various research projects. The model has been used to create a residual gas model, investigate cylinder pressure pegging methods, investigate simulating altitude in a dynamometer test cell at sea level, as well as others. All of these simulations are used to predict results in the actual experimental engine which this model was created from. The accuracy of the results of the model is important in certain situations. If the model is too inaccurate, it could lead to incorrect results when used on the physical engine, which can lead to elevated emissions or reduced fuel consumption. However for certain projects the accuracy of the model is not as important. This is true for the way the model is used in this research project.

The GT Power© engine model includes all the major components of the actual engine related to breathing and combustion. However there are differences and approximations

74 that were made. To start, the model uses a DI-jet combustion model with a nominal calibration. The model has the ability to either control on power output, peak cylinder pressure or commanded fuel injection. Power output and peak cylinder pressure is controlled by adjusting the fuel injection quantity with a PID controller. The actual engine uses a pilot and a main injection, however this model lumps these into one injection. The total fuel injected is used at the main injection timing within the model.

This section of the model was modified because most of the parts had parameters which were overridden, some of which were invalid. Overriding parameters within a part makes it very confusing and leads to the possibility of errors in the model.

The model is also equipped with a waste-gated turbocharger. The maximum turbo shaft speed is controlled by limiting fueling. The waste gate is activated based on a set 2.9 bar boost pressure. This is done based on equations only, in order to avoid the use of a PID controller. PID controllers can drastically increase simulation time, in this software package because of convergence issues.

EGR flow rate is controlled by a feedforward PID controller with the setpoint set from experimental data. This PID controller is highly dependent on the accuracy of the feedforward model, so time has been spent calibrating these data points. The heat transfer from the EGR cooler is modeled from simple heat exchanger formulas.

The charge air cooler is modeled based on a Dodge Durango. The output temperature of the charge air cooler is set based on experimental data, although simplified equations can be used to calculate the heat transfer if desired.

An intake air throttle is used. Based on experimental data the intake air throttle is set to a fixed valve position, which is translated using a CdA lookup table.

The GT Power© model was executed in order to make a comparison between simulated and experimental results. Previous data has been collected on the physical engine and this data is used in validating the model. 90 total cases were run, which represent a complete sweep of the engine, from idle to the engines maximum engine speed, and from 0 Nm to the maximum torque of the engine.

Fueling/Engine Torque - The first comparison is the required fueling to produce the same torque as the experimental results. The accuracy is very good, with a mean error of 1.49% and a standard deviation of 5.43%.

Figure 39: Total fueling comparison

Peak Cylinder Pressure – Peak cylinder pressure is compared below. The simulated results seem to always give higher peak pressures than the experimental results with a -

14.88% mean error and 12.63% standard deviation. Since the Di-jet model is not fully calibrated this is not surprising.

Figure 40: Peak cylinder temperature

MAF – The next comparison is fresh air flow (MAF). At higher flow rates, the model does a good job but it over estimates MAF at quite a few of the lower flow rate cases.

Due to the large errors at low flow rates, the model will need to be improved for some uses. For the current research project, the flow errors will not invalidate the conclusions drawn since the model is compared against itself. Overall the mean error is -13.78% with a standard deviation of 14.15%.

Figure 41: MAF comparison

Intake Manifold Pressure – The third comparison is intake manifold pressure. The model does a good job overall at predicting intake manifold pressure. The overall mean error is

0.35% with a standard deviation of 5.94%. This means that the manifold pressure most likely is not the cause of the MAF errors.

Figure 42: Intake manifold pressure comparison

Charge Cooler Outlet Temperature – This model has the choice of either using heat exchanger equations for the heat transfer or setting the outlet temperature of the charge cooler. The latter was run for this validation, since it was used in previous research projects. This method is fine when linked with experimental data, but is not very useful when the model is used for predictions in areas that have no experimental data available.

Intake Manifold Temperature – The next component is intake manifold temperature. The prediction of manifold temperature is very poor. The overall mean error is 6.78% and the standard deviation is 21.57%. This is somewhat surprising since the outlet of the charge cooler is fixed to the experimentally found values. The most probable reason for this bad prediction is because of the inlet temperature of the EGR gases. This would make sense because the exhaust gas temperature is found based on heat exchanger equations, not experimental data. Once more data is available it would be a prudent to recalibrate this part of the model. 78

Figure 43: Intake manifold temperature

Exhaust Temperature – Unfortunately since no EGR temperature data is available, the closet comparison that can be made is exhaust temperature. Experimental data was available for each individual runner and Figure 44 shows the results of this comparison.

Overall the data matches pretty well and should not be the root cause of the intake manifold temperature errors. However this doesn‘t mean that the heat transfer through the

EGR cooler or EGR pipe is correct, but instead that the starting temperature of the EGR gases should be predicted fairly well. The overall mean error is 1.57% with a standard deviation of 11.21%.

Figure 44: Exhaust runner 1

Compressor Temperature – The compressor out temperature does a fairly good job at lower temperatures, but around the 100 to 150 C region, there are errors of about 20 degrees. The overall mean error is 14.0% with a standard deviation of 11.21%.

Figure 45: Compressor temperature

Turbine Inlet Temperature – The GT Power© model is generally lower in temperature at the inlet to the turbine as compared to the experimental results. This means that the heat transfer from the exhaust manifold is not modeled completely accurately, with too much heat transfer. This is known because of the validation of the exhaust runner temperature.

The overall mean error is 19.57% and standard deviation is 15.15%.

Figure 46: Turbine inlet temperature

Turbocharger speed – The speed of the turbocharger correlates very well to the experimental data. Initial guesses are used from the experimental data in the model. The overall mean error is -6.35% and standard deviation is 15.99%.

Figure 47: Turbine inlet temperature

NOx Emissions – The NOx emissions predictions are in the ballpark, but are not very accurate. The model could be used to get a general sense of the magnitude of the NOx emissions, but could not be used where any accuracy is needed. The overall mean error is

-10.58% and standard deviation is 57.13%.

Figure 48: NOx emissions

CO Emissions – The GT Power© model does a very poor job of predicting CO emissions.

There is almost no correlation between the simulated and experimental results. This GT

Power© model should not be used to make inferences on CO emissions.

Figure 49: CO emissions

The GT Power© model does a good job at predicting torque response. The MAF prediction could be better. The manifold pressure correlated well with experimental data, so this most likely is not the root cause of the flow problems. The intake manifold temperature was poorly predicted by the model, which could be due to inaccurate EGR temperatures. Emissions were not well predicted by the model. CO emissions were not predicted with any accuracy, but the trends in NOx were predicted fairly well.

4.2 Cylinder Pressure Pegging

Cylinder pressure pegging is critical to analysis based on the cylinder pressure of an engine. The most common type of cylinder pressure sensors are relative in nature, which means that they must be pegged to a known signal or shifted based on known phenomenon such as polytropic compression. The use of cylinder pressure pegging methods is a relative unknown, with no real method to determine what method to use. 82

This analysis attempts to determine the most accurate pegging method for the engine of interest and provide a method for future engine research projects.

Five total methods were analyzed, including three methods that require additional sensors and two methods that do not. The first three methods peg cylinder pressure based on intake pressure, exhaust pressure and an average of the two during the valve overlap period. The last two methods are a least squares method and a variable polytropic coefficient two-point method. Each method has been evaluated to ensure pegged cylinder pressure values are realistic and that the heat release analysis is also realistic. The five different methods are explained in the following sections.

4.2.1.1 Intake pressure pegging method

The intake pressure pegging method uses a crank angle resolved intake manifold pressure sensor to peg the cylinder pressure. Based on the literature review this method was commonly used and produced good results when intake manifold tuning effects were not significant. A region from 10 degrees before IBDC to 10 degrees after IBDC was chosen as the averaging region. This is based on recommendations found during the literature review, but is later shown to be effective based on experimental data. The averaged intake pressure during the pegging region is compared with the average cylinder pressure during the same 20 degree region. The difference between the values represents the cylinder pressure offset. The cylinder pressure data is then shifted by this pressure offset, which results in the cylinder pressure being pegged.

4.2.1.2 Exhaust pressure pegging method

Similarly to the intake pressure pegging method, the exhaust pressure pegging method uses a crank angle resolved exhaust manifold pressure sensor to peg the cylinder pressure. Based on the literature review this method may be better because there are generally less tuning effects in the exhaust manifold than in the intake manifold. A region from 68 degrees ABDC to 20 degrees BTDC of the exhaust stroke was chosen as the pegging region. This area was chosen mainly based on experimental data, which showed the fewest pressure fluctuations in this region. Similarly to the intake pressure pegging method, the exhaust pressure pegging method compares the averaged exhaust and cylinder pressure during the pegging region. The difference between the two is the cylinder pressure offset, which is applied to the cylinder pressure signal, resulting in a pegged signal.

4.2.1.3 Valve overlap pressure pegging method

The valve overlap pressure pegging method uses the average of the intake and exhaust pressures to peg the cylinder pressure. The pegging region that is used is 5 degrees after

IVO to 5 degrees before EVC. Much like the other two methods, the average overlap pressure is compared to the average cylinder pressure during this region. The difference between the two represents the cylinder pressure offset. This offset is applied to the cylinder pressure signal, which results in the cylinder pressure being pegged.

4.2.1.4 Least-squares polytropic pegging method

The least-squares polytropic pegging method is one of two methods which do not use a physical intake or exhaust pressure sensor to peg the cylinder pressure signal. The key to 84 the method is the assumption of polytropic compression during the compression stroke.

The least-squares pegging method improves upon other methods which use only a few points, which tends to be very susceptible to errors. The method calculates the polytropic coefficient based on minimizing a loss function. The complete derivation was shown in the literature review; therefore it will not be shown again here. Based on the literature review a region from 90 to 49 degrees BTDC was chosen.

4.2.1.5 Variable polytropic coefficient two-point method

The second polytropic method that was analyzed was the variable polytropic coefficient two-point method. This method improves upon other methods by using three data points to calculate a variable polytropic coefficient, but uses only two data points to calculate the overall cylinder pressure offset. Using polytropic formulas, it is possible to derive the cylinder pressure offset. The derivation of the method was shown in the literature review and will not be rederived here.

4.2.2 GT Power© pegging analysis

The first step in analyzing which cylinder pressure pegging method is most accurate was a GT Power© study. The major advantage to using a tool such as GT Power© is that pegging methods can be compared with the known correct cylinder pressure signal, which GT Power© produces. This is not possible when using experimental data, since the correct cylinder pressure signal is never truly known. The best approximation of the true cylinder pressure signal is the one that has been pegged using the most accurate pegging method. All five methods were analyzed; overlap pressure, exhaust pressure, intake pressure, least squares and variable polytropic coefficient two-point method. Simulations 85 were completed over the operating region of the engine, sweeping 90 combinations of speed and torque, as seen in Figure 50.

Figure 50: GT Power test matrix for pegging methods analysis

At each data point, with a unique engine torque and speed value, all five methods were used to peg the cylinder pressure resulting from the GT Power© simulation. This resulting cylinder pressure from the simulation is the correct cylinder pressure signal, which does not need pegged. Therefore each cylinder pressure pegging method, if completely accurate, should produce a pegging shift of 0 kPa. With this known each method can be analyzed for accuracy. Results from the study are shown in Figure 51, Figure 52 and

Figure 53. In the bubble plots smaller bubbles represent more accurate pegging, while large bubbles represent less accurate, since offsets closer to a 0 kPa are more accurate. In general all five pegging methods are less accurate as engine speed increases. Least squares and variable polytropic two-point methods are the most consistent across the whole engine operating region, although they are less accurate than other methods at low 86 engine speeds. Since the two polytropic methods (two-point and least squares) are almost identical, only the least squares method is displayed in the bubble plots. Most methods are immune to engine torque increases, but the overlap method is less accurate at higher torques.

Figure 51: Pegging methods result using GT Power©

GT Power Pegging Offset Comparison 350.00

300.00

250.00 200.00 Overlap Method 150.00 Exhaust Method 100.00 Intake Method

50.00 Engine Engine Torque[Nm] 0.00 -50.00 500 1000 1500 2000 2500 3000 3500 4000 4500 Engine Speed [RPM]

Figure 52: Pegging offset comparison (small bubbles represent more accurate pegging)

GT Power Pegging Offset Comparison 350.00

300.00

250.00 200.00 150.00 Least Squares 100.00 Intake Method

50.00 Engine Engine Torque[Nm] 0.00 -50.00 500 1000 1500 2000 2500 3000 3500 4000 Engine Speed [RPM]

Figure 53: Pegging comparison (smaller bubbles represent more accurate pegging)

Overall the intake method is the most accurate at low engine speeds, while the polytropic methods are the most consistent across the operating region and the generally the most accurate at high engine speeds. The overlap method at times is more accurate at high engine speeds, but is inconsistent. Table 7 shows the absolute mean pegging offset error.

A completely accurate pegging method would have a value of 0 kPa. Overall the intake pressure pegging method is the most accurate, while the polytropic methods are second most accurate. It will be important to demonstrate the impact of the pegging errors as well as demonstrate these methods are successful when used on experimental data.

Table 7: Mean absolute pegging offset error from GT-Power© analysis

Pegging Method Mean Absolute Pegging Offset Error (kPa)

Valve Overlap 8.72

Exhaust Pressure 10.41

Intake Pressure 4.47

Least Squares 6.47

Two-Point 6.27

4.2.3 Experimental Pegging Results

While there is no way to truly validate cylinder pressure pegging using experimental data, it is possible to ensure the pegging methods produce realistic data. Observations can also be made from the agreement between the pegged cylinder pressure and the intake and exhaust pressure signals. 49 total experimental data points were used, which attempted to sweep the operating region of the engine. A comparison is shown between the test points used in GT-Power© and the points used for the experimental analysis. Several experimental test points were not available due to problems with the research engine.

Figure 54: Experimental test matrix and GT Power test matrix run for pegging analysis

4.2.3.1 Intake Pegging

Overall the intake pressure and cylinder pressure have good agreement during the pegging region, as well as much of the rest of the intake stroke. Figure 56 and Figure 57 represent the worst of the cases in terms of matching the cylinder pressure and intake pressure over a large region. These plots also show that the region prior to BDC is in a large transient, which may not be ideal for pegging.

Figure 58 shows that there is good agreement between cylinder and intake pressure for cases with low speed, boost and torque. Overall by examining the different cases, the location of BDC for the pegging region seems to be appropriate.

Figure 55: Example of intake pegging method at high boost, high speed

Figure 56: Intake Pegging Method example, low speed, high boost

Figure 57: Intake Pegging Method example, low speed, low boost, medium torque

Figure 58: Intake Pegging Method example, low speed, no boost, low torque

4.2.3.2 Exhaust Pegging

Based on the literature review pegging cylinder pressure to the exhaust pressure signal might be more accurate than the intake pressure pegging method because there are 92 generally less tuning effects in the exhaust manifold than in the intake manifold.

However this most likely is not a concern because looking at Figure 55, a high engine speed case, the intake pressure varies little over the intake stroke, which would suggest intake manifold tuning effects are minimal.

Figure 59: Exhaust pegging method example high speed, high backpressure

Figure 60: Exhaust pegging method example low speed high torque 93

Figure 61: Exhaust pegging method example low speed low torque

As can be seen from the above figures, it is indeed true that there are minimal tuning effects within the exhaust manifold since there are not many large pressure fluctuations.

The cylinder pressure signal and exhaust pressure signal fit together very well in all cases. The worst case is Figure 60, where the two signals deviate at maximum 5 kPa.

Using the exhaust pegging method does bring into question whether the cylinder pressure could practically fall as low as it does during some cases. Below in Figure 62 is an example where the exhaust pegging method produces a very low cylinder pressure around EVO.

Figure 62: Exhaust pegging method example low intake pressure

4.2.3.3 Valve Overlap Method

While at first it may seem that averaging the intake and exhaust pressure signals for the valve overlap pegging value would create an average of the intake and exhaust offsets, this is not true as seen in Figure 70. In general at high engine speeds while using the overlap method the exhaust and cylinder pressure line up, while the intake pressure generally is significantly different. At low engine speeds, the intake pressure matches up better with the cylinder pressure. While the method generally produced reasonable results, Figure 63 represents an example of an unreasonable cylinder pressure, which is below 0 kPa. This is a first indication that this method is not suitable for this engine.

Figure 63: Overlap method example

4.2.3.4 Least-Squares Polytropic Method

The least-squares polytropic method overall produces good results. Most of the pegged cylinder pressure results are realistic. One issue with the least squares method and the two-point method is that since all the cylinders are not referenced to a fixed value such as intake or exhaust pressure, it is more error prone.

Figure 65 shows this well since one of the cylinder pressure signals has been incorrectly pegged. As long as the analyses are based on an average cylinder pressure, these issues do not appear to present a problem. Furthermore for the least squares method this is the only case where this happened in all the cases analyzed.

Figure 64: Least Squares Method example high speed high torque

Figure 65: Least Squares method example of high speed low torque

Figure 66: Least Squares method low speed low torque

Figure 66 displays that the least squares method does have good agreement with the intake and exhaust pressure sensors. This is true over the range of operating speeds and torques.

4.2.3.5 Variable Polytropic coefficient two point method

The last method of pegging that was analyzed is the two-point variable polytropic method. This method is computationally simpler than the least squares method, but since it only uses two points to determine the offset it can be error prone. A least squares method should be more accurate, or at least less susceptible to errors. Figure 67 shows one of quite a few points where there is an unrealistic amount of spread between cylinders.

Figure 67: Variable k two-point method high speed high torque

Figure 67 shows that, much like the least squares method, since all the cylinders are not pegged to the same reference point there can be large variations and differences between engine cylinders. This doesn‘t mean necessarily that all cylinders need to have the exact same pressure, but a range of 50 to 175 kPa seems unlikely. In general the two-point method has more spread between cylinders than the least squares method.

Figure 68: Variable two-point method offset errors

Figure 68 shows the same errors as the least squares method, except worse.

Figure 69: Variable k two-point method low speed low torque

100

Like the least squares method at low engine speeds and torques the two-point method does a good job matching the sensors, but with a bit more spread between the cylinders.

4.2.3.6 Overall experimental pegging offset comparison

The five different methods were analyzed for how realistic the pegged cylinder pressure signals were. Both the exhaust pressure and valve overlap methods had several questionable results as the pegged cylinder was most likely unrealistically low. The least- squares and variable polytropic two-point methods had larger cylinder to cylinder variations, although the least-squares method was less susceptible to these errors.

To get some idea of the variation between the methods average cylinder pressure offset values for each method are compared. There is no way to calculate pegging offset errors since the true cylinder pressure values are not known.

Table 8: Average experimental pegging offset for each cylinder (rounded)

Cylinder 1 Cylinder 2 Cylinder 3 Cylinder 4 Average Method [kPa] [kPa] [kPa] [kPa] [kPa] Overlap 108 160 94 101 116 Exhaust 92 150 90 89 105 Intake 111 169 108 102 122 Least Squares 113 169 112 107 125 Two-point 111 158 108 97 119

101

Average Experimental Pegging Offset 180 160

140 120 Overlap 100 Exhaust 80 Intake 60 Least Squares

Average Average Offset [kPa] 40 Two-point 20 0 Cylinder 1 Cylinder 2 Cylinder 3 Cylinder 4 Average

Figure 70: Experimental pegging offset average for five pegging methods

4.2.4 Impact of Pegging Method on Heat Release

In addition to ensuring that results are realistic and that there is good agreement between the cylinder pressure signal and the pegging metric (i.e. intake pressure) during the sampling region for pegging, heat release analysis has proved to be useful in determining which method produces the most accurate results.

Heat release analysis can relatively easily be derived. Making assumptions of modeling the fluid as an ideal gas, making the assumption of no blow-by, thus fixing the mass inside the cylinder, and making assumptions that a closed volume is of interest, thus fixing the use of the equations to be between IVC and EVO, we can derive the following model.

102

̇ (21)

Knowing the definition of specific heat for an ideal gas (Moran and Shapiro) and performing the chain rule

( ) (22)

The equation becomes

( ) ̇ (23)

Work can be replaced by a well-known equation

∫ (24)

̇ (25)

103

( ) (26)

Ideally a different variable besides temperature would be used. Based on the idea gas equation

(27)

(28)

[ ]

(29)

Inserting this back into the equation

( ) [ ] (30)

Rearranging

( ) ( ) ( ) (31)

Knowing a relation between

(32)

104

(33)

( ) ( ) ( ) ( ) (34)

( ) can be estimated several ways. A simple equation approach was chosen due to the level of complexity when considering a two-zone model with the addition of EGR. A method proposed by Brunt and Platts uses a simple temperature dependent model for ( ) (Brunt and Platts, Calculation of Heat Release in Direct Injection Diesel

Engines)

( ) (35)

This analysis allows the comparison of different pegging methods. Heat release can significantly vary with pegging method. A common trend with incorrect pegging methods seems to be large heat additions or large heat removal in the compression stroke, which would not be expected. This can cause errors in temperature predictions which are used in predicting the ignition delay. Pegging errors can also affect the accuracy of the total heat release, which impacts combustion efficiency calculations. The accuracy of CA50 and CA90 can also be affected. How cylinder pressure pegging errors affect each of these parameters will be examined next, both in simulation and in experimental results.

105

4.2.4.1 Impact of Pegging Methods on Simulated Heat Release

A sensitivity analysis was been performed on four different heat release measures. This includes CA50, CA90, ignition delay and total heat release.

The first metric analyzed was ignition delay. Inaccurate pegging methods affect the ignition delay by producing incorrect cylinder pressure temperature predictions. The ignition delay formula which was used is listed below

( ) (36) ̃

where A, n and are calibration parameters; p and T are cylinder pressure and temperature respectively, located at start of injection. Constants were found in Heywood, using Spadaccini and TeVelde No. 2. The choice of parameters is irrelevant to this study, since the focus is on the ignition delay sensitivity to pegging errors instead of the raw ignition delay values. The cylinder pressure results for the 90 data points used in the GT

Power© simulation were shifted with an offset to simulate incorrect cylinder pressure pegging. An analysis from -60 kPa to 60 kPa was performed based on the errors seen in the pegging methods comparison using GT Power©. Figure 71 displays the results of the sensitivity analysis. Each line is a different operation condition, totaling 90 total engine speed and torque combinations.

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Figure 71: Ignition delay sensitivity to pegging errors

Figure 72 displays the location and relative magnitude of this sensitivity. Since the sensitivity plots in Figure 71 were relatively linear, a linear sensitivity was used. The highest sensitivity to pegging errors appears to be at low engine speeds and torques.

Errors become prevalent at an engine speed of 3600 RPM. The overall mean sensitivity is

0.0279 ms/kPa. The mean value may not be the best value to use however because the ignition delay pegging error sensitivity is highly dependent on engine speed and torque.

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Ignition Delay Pegging Error Sensitivity (ms/kPa) 300.00 0.02 0.01 0.01 250.00 0.02 0.24 0.01 0.02 0.01 200.00 0.02 0.10 0.03 0.02 150.00 0.03 0.02 0.02 0.05 0.04 0.05 0.03 0.02 0.02 100.00 0.05 0.02 0.06 0.08 0.07 0.09 0.06 0.04 0.03 0.02 0.01 0.02 0.05 50.00 0.11 0.12 Engine Engine Torque[RPM] 0.00 0.15 0.13 0.11 0.08 0.05 0.04 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.02 0.03 500 1000 1500 2000 2500 3000 3500 4000 -50.00 Engine Speed [RPM]

Figure 72: Ignition delay pegging error sensitivity

To show how the five different pegging methods affected ignition delay errors, the results from the GT Power© simulation were used.

Figure 73 shows the ignition delay errors against the 90 simulated data points. These errors are the absolute difference compared to the true ignition delay value calculated from the GT Power© simulation. One thing to note is that Figure 51 and Figure 73 look very similar. The high sensitivity at low speeds seen in Figure 72 does not affect the overlap, exhaust and intake methods because the pegging offset error is very close to zero. It does affect the least squares and two-point polytropic methods. To give some idea of which pegging method produces the fewer errors in predicting ignition delay Table 9 displays the absolute averaged data. The same conclusions are made as before, with the intake pressure pegging method as the most accurate method.

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Table 9: Mean absolute pegging methods errors for ignition delay

Pegging Method Mean Absolute Pegging Ignition Delay Error (%) Valve Overlap 3.01 Exhaust Pressure 3.53 Intake Pressure 1.39 Least Squares 2.15 Two-Point 2.07

Figure 73: Ignition delay errors for different pegging methods

The second metric that was analyzed was total cumulative heat release. Total cumulative heat release affects the calculation of engine efficiency, since this heat release value can be compared to the amount of energy stored within the fuel. The heat release analysis equations were derived earlier, showing the use of a variable specific heat ratio.

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Figure 74 shows the results of the analysis which shifted the cylinder pressure signal from GT Power© ±60 kPa, representing pegging errors. The sensitivity is divided into two separate regions, negative and positive offset errors. Negative pegging offset errors produce a greater sensitivity to offset errors, while positive offset errors have a smaller sensitivity. While the switch in slope is not exactly at a pegging offset error of 0, an analysis on the location was not determined to be beneficial since it is dependent on engine speed and torque.

Figure 74: Normalized total heat release sensitivity to pegging errors

The positive pegging offset error slope has a strong dependence on fueling, but a weak one with engine speed. The sensitivity plot and curve fit can be seen in Figure 75.

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Figure 75: Total heat release sensitivity to positive pegging errors

The negative pegging offset sensitivity has a weak dependence on both engine speed and fuel mass and thus an overall averaged value is more appropriate.

Figure 76: Total heat release sensitivity to negative pegging errors

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In order to determine how pegging inaccuracies using the five different proposed pegging methods affected total heat release, the pegging methods were compared against the GT-

Power© true value. Figure 77 displays the least accurate method, the exhaust pegging method, against the most accurate, the intake pegging method. The pegging methods appear to be most inaccurate at low engine torques and high engine speeds. Figure 78 display the results for each pegging method across the data points. Except for the low torque regions of operating region, the pegging errors do not affect the total cumulative heat release very much.

Total Heat Release Errors for Pegging Methods 350.00

300.00 250.00 200.00 150.00 Exhaust 100.00 Intake 50.00 Engine Engine Torque[Nm] 0.00 -50.00 0 1000 2000 3000 4000 5000 Engine Speed [RPM]

Figure 77: Total heat release error due to pegging inaccuracy

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Figure 78: Total heat release errors for 5 different pegging methods

Table 10 shows the overall mean absolute errors of the pegging methods on total cumulative heat release. The same order in the accuracies of the pegging methods exists, where the intake pressure pegging method is the best.

Table 10: Mean absolute pegging methods errors for total cumulative heat release

Pegging Method Mean Absolute Pegging Total Cumulative Heat Release Error (%) Valve Overlap 2.45 Exhaust Pressure 2.89 Intake Pressure 0.68 Least Squares 1.74 Two-Point 1.67

The final metric that was examined is CA50 and CA90 which are both commonly used combustion parameters. Unlike the ignition delay and total heat release sensitivity results, 113

CA50 and CA90 tend to exhibit non-linear behavior which makes it difficult to quantify the sensitivity. Figure 79 and Figure 80 shows the results for CA50 and CA90, respectively. CA90 exhibits non-linear behavior much more than CA50 does. CA90 generally is linear for negative pegging offset errors, only becoming non-linear for some cases in positive pegging offsets. CA50 exhibits similar behavior, except for the nonlinearities in the positive pegging offset error region. In order to quantify the sensitivity for both CA50 and CA90 linear values were used, broken into negative and positive pegging offset error regions. While the linear nature is not true for all data points, it was seen to be the best way to make overall statements about the sensitivity of the system.

Figure 79: CA50 sensitivity to pegging errors

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Figure 80: CA90 sensitivity to pegging errors

Figure 81 and Figure 82 show the results for CA50. The sensitivity is larger for positive pegging errors. The errors are dominant at low engine torques and higher engine speeds.

CA50 Sensitivity for Positive Pegging Offset Errors 350.00 300.00 250.00 200.00 150.00 CA50 Sensitivity [Deg/kPa] 100.00 50.00 Mean Sensitivity = 0.01936 0.00 -50.00 0 1000 2000 3000 4000 5000

Figure 81: CA50 sensitivity to positive pegging offset errors

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CA50 Sensitivity for Negative Pegging Offset Errors 350.00 300.00 250.00 200.00 150.00 CA50 Sensitivity [Deg/kPa] 100.00 Mean Sensitivity = 0.004365 50.00 0.00 -50.00 0 1000 2000 3000 4000 5000

Figure 82: CA50 sensitivity to negative pegging offset errors

CA90 is influenced more by the pegging offset errors as seen in Figure 83 and Figure 84.

Like the CA50 sensitivity positive pegging offset errors produce a must higher sensitivity, especially at low engine speeds. For negative pegging offset errors the smallest sensitivity values are at low engine speed and torque values.

CA90 Sensitivity for Positive Pegging Offset Errors 400.00

300.00

200.00 CA90 Sensitivity [Deg/kPa] 100.00 Mean Sensitivity = 0.4696 0.00 0 1000 2000 3000 4000 5000 -100.00

Figure 83: CA90 sensitivity to positive pegging offset errors 116

CA90 Sensitivity for Negative Pegging Offset Errors 400.00

300.00

200.00 CA90 Sensitivity [Deg/kPa] 100.00 Mean Sensitivity = 0.04914 0.00 0 1000 2000 3000 4000 5000 -100.00

Figure 84: CA90 sensitivity to negative pegging offset errors

The five different pegging methods were also compared to the true CA50 and CA90 values from the correct GT Power© pressure traces. Variations in CA50 predictions were very small when compared to the true GT Power© value. There is also no discernable difference between the five different pegging methods accuracies.

Figure 85: CA50 errors for five different pegging methods 117

CA90 errors, as seen in Figure 86, are larger than CA50 ones. This was seen in the sensitivity analysis, so it was expected. As with the CA50 analysis, there is no discernable difference between the different pegging methods as seen in Table 11.

Figure 86: CA90 errors for five different pegging methods

Pegging Method CA50 Difference (Deg) CA90 Difference (Deg) Valve Overlap 0.10 1.10 Exhaust 0.11 1.10 Intake 0.08 0.90 Least Squares 0.077 0.92 Two-Point 0.075 0.89

4.2.4.2 Impact of Pegging Methods on Experimental Heat Release

The same four heat release metrics were analyzed on experimental heat release data from a 49 data point test matrix shown in Figure 54. The first metric that was analyzed on the 118 experimental data was ignition delay. Since there is no correct benchmark as there was with the GT-Power© study, the intake pressure pegging method was used as the bench mark. The GT-Power© showed clear indication that the intake pressure pegging method was the most accurate. Figure 87 displays that there are significant differences between the other pegging methods as compared to the intake pressure pegging method. The averaged values are shown in Table 12. The exhaust pressure pegging method is the most different from the intake pressure pegging method when calculating the ignition delay.

Figure 87: Experimental ignition delay errors (intake pressure pegging method is the

benchmark)

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Table 12: Average difference between pegging methods ignition delay

Average Percent Difference Compared to Intake Pegging Pressure Pegging Method (%) Method Experimental GT Power Valve Overlap 5.84 3.60 Exhaust 9.13 4.98 Least Squares 4.51 1.65 Two-Point 7.96 1.62

Table 12 also displays a comparison to GT Power© simulation results. The difference between the pegging methods is smaller, but has similar conclusions. The biggest difference is that the experimental results show the short comings of the two-point method, since it is more susceptible to sensor noise.

The second metric that was analyzed was total heat release. Figure 88 displays the results of the analysis. The four other methods produce significantly different total heat release results. Averaged values for the four different methods are shown in Table 13. The exhaust method‘s total heat release is the most different from the intake pressure pegging methods. One thing also to note is that the two-point and least-squares methods are different. In the GT-Power© model the two methods were almost identical.

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Figure 88: Experimental total heat release errors (intake pressure pegging method is the

benchmark)

Table 13: Average absolute difference between pegging methods total heat release

Average Percent Difference Compared to Intake Pegging Pressure Pegging Method (%) Method Experimental GT Power© Valve Overlap 2.73 2.42 Exhaust 5.06 4.17 Least Squares 2.16 1.87 Two-Point 3.51 1.80

Table 13 also shows a comparison between the experimental and GT Power© simulation values. The comparison shows good agreement, except the two-point method has a higher experimental difference, due to its susceptibility to signal noise.

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The last metric is CA50 and CA90. The results seen in Figure 89 and Figure 90 follow very closely to those obtained in the GT-Power© analysis. The pegging methods do not have much effect on CA50, but can have a larger effect on CA90. Average results are shown in Table 14. There is no discernable difference between the different pegging methods for CA50 predictions. Differences in CA90 are not large, but does show that the polytropic methods may be slightly worse using experimental data, whereas GT-Power result has the two methods as the closet agreement to the intake pegging method.

Table 14: Average absolute difference between pegging methods for CA50 and CA90

(Intake pegging method is baseline)

CA50 Difference (Deg) CA90 Difference (Deg) Pegging Method Experimental GT-Power Experimental GT-Power Valve Overlap 0.07 0.14 0.60 1.74 Exhaust 0.12 0.19 0.91 2.11 Least Squares 0.10 0.05 1.17 0.73 Two-Point 0.14 0.05 1.48 0.73

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Figure 89: CA50 for different pegging methods compared to intake pegging method

Figure 90: CA90 for different pegging methods compared to intake pegging method

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4.2.5 Pegging Methods Conclusions

Five methods have been analyzed in order to determine which cylinder pressure pegging method is most appropriate. The intake pegging method has proved to be the most accurate method, based on several factors.

The exhaust pressure pegging method, while it has better agreement between cylinder pressure and exhaust pressure during the pegging region, suffers in several different analyses. The first is that pegging to exhaust pressure results in unrealistically low cylinder pressure during the intake phase in some cases. The second and more conclusive analysis was the GT-Power© analysis. The exhaust pressure pegging method was the worst method overall when compared to the true simulated cylinder pressure value. It was also the worst in the heat release analysis, with the largest errors in ignition delay and total heat release predictions.

The overlap method, similar to the exhaust method suffered from unrealistically low cylinder pressures during the intake stroke and wrong heat release results. The overlap method was the second worst method overall based on the GT-Power analysis for both cylinder pressure and heat release.

The least squares and variable polytropic two point method both are attractive due to the elimination of a sensor. The variable polytropic two-point method had issues with a large spread in pressure across the different cylinders. Some of the results produced cylinder pressures below 0, which is unrealistic. The least squares and two-point polytropic methods produced cylinder pressure offsets and heat release values which were very

124 similar. However this was not true when experimental data was used for the heat release analysis. The variable polytropic method was the second most different from the intake pressure pegging method in the experimental heat release analysis. This was somewhat expected, since the method is error prone with only using two data points.

The least squares method had some problems with pegging one cylinder far different than the others. This was relatively minor compared to the two-point method. Overall the least-squares method is the preferred polytropic method and would be the most suitable method if the intake pressure pegging method is not used.

The intake pegging method produced very good results and never created unrealistic cylinder pressure values. The intake method was the most accurate in the GT-Power© study, for both cylinder pressure pegging and heat release analysis. Overall the intake method was chosen for its robustness and accuracy. The engine was already equipped with an intake sensor so the added robustness of this method makes it the most favorable.

While these results are for a specific engine, the method of analysis has been proved to be successful and can be used on different engines. The analysis method showed that GT

Power© is a useful tool in determining pegging method accuracy for specific engines. The analysis also showed the importance of verifying the GT Power© results with experimental data.

4.3 Altitude Simulations

Due to the nature of the proposed control strategy, there is not a need for calibration tables at altitude conditions. In order to verify this control strategy and its ability to

125 handle altitude conditions, experimental testing is needed. Since there is no vehicle available, driving into altitude conditions is not feasible. There is also not an altitude chamber available for use. If a method was available which allowed for engine testing at sea level, without the need for a multi-million dollar altitude chamber, it would prove useful to our current control strategy validation. Based on the literature review there are two schools of thought. The first is that only the intake pressure is changed, leaving the backpressure and crankcase of the engine at sea level conditions. The second idea would be to reduce both the intake and exhaust back pressures. The first would be the easiest approach, requiring only a simple throttle in the intake, before the turbocharger. This approach does however bring about several concerns. The first and most important is the question of how the turbocharger will behave when it sees altitude like conditions at the intake and sea level conditions at the exhaust. If this strategy is going to work, the compressor on the turbocharger must remain within its operating regions, thus staying away from the surge line. The second question is how accurate it is to not change the exhaust pressure. The effect on fresh air flow and the presence of residuals within the combustion chamber are of interest. One method of validating this method is through the use of simulation. It is possible to compare two different models, one model which simulates altitude using the intake throttle and leaves the exhaust pressure at sea level, and the other model which simulates true altitude by changing both the intake and exhaust pressures.

GT Power© was used as the simulation tool. The only way to verify the accuracy of such a method without the use of simulation would be to compare actual altitude data with the

126 data collected at sea level using the intake air throttle. This is not an option for this project, since no such data is available. This is not to mention the cost and time that would be needed to verify the method works. If the method does not work there could be severe turbocharger or engine damage. With this being said simulations were run in GT

Power©, saving time, money and making accuracy conclusions possible. Two models were compared, one that throttled the intake upstream of the air filter, and one which changed the end conditions. Two altitudes were simulated, 85 and 65 kPa, which correspond to approximately 6.000 and 12,000 feet respectively.

Simulated results refer to throttling only the intake and actual results refers to changing both intake and exhaust pressures.

4.3.1 Altitude simulation – 85 kPa

Special interest was put into determining what would happen to the turbocharger at these simulated conditions. It could be possible that not changing the exhaust pressure would lead to surge within the compressor, which is very dangerous and can damage the engine.

Figure 91 shows that the turbocharger stays out of surge for most cases. The most important thing to note is that the operating regions of the engine which produce surge, or near surge conditions is similar to actual altitude case. This is good news for the validation of the throttled only case.

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Figure 91: Compressor speed map at 85 kPa

Figure 92 and Figure 93 show that while the simulated case is a little smaller than the actual case, there is very good agreement.

Figure 92: Compressor reduced mass flow at 85 kPa

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Figure 93: Compressor pressure ratio at 85 kPa

Even engine torque is comparable between the two cases, with only noticeable differences at maximum torque.

Figure 94: Engine torque comparison at 85 kPa

Fresh air flow also appears to correlate very well to the actual altitude case.

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Figure 95: Fresh air flow at 85 kPa

Overall residuals compare well between the simulated and actual case. There are a few points that do not match up, but overall it is very good.

Figure 96: Residual fraction at 85 kPa

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4.3.2 Altitude simulation – 65 kPa

It would be expected that if there would be differences between the actual and simulated cases that it would occur at higher altitudes. This case represents the highest altitude that would be experimented at in the test cell. As seen from Figure 97, the compressor stays out of stall for most points, only near or at surge when the actual case is there as well.

Figure 97: Compressor speed map at 65 kPa

Figure 97 and Figure 99 show that there are significant differences between the simulated and actual cases. Leaving the exhaust pressure unchanged and only throttling the intake results in much lower pressure rations and mass flows through the compressor. The good news is that the turbocharger still remains happy and does not fall into the surge region any more than the actual case does.

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Figure 98: Compressor pressure ratio at 65 kPa

Figure 99: Compressor reduced mass flow at 65 kPa

Engine torque correlates fairly well at quite a few points, but is very far off at others. This means that if studies on engine performance are of interest, only throttling the intake would not be a good idea.

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Figure 100: Engine torque at 65 kPa

At higher fresh air flows the simulated case is consistently less than the actual case. This makes sense because the actual case has less back pressure, which makes breathing of the engine easier.

Figure 101: Fresh air flow at 65 kPa

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The residual analysis shows that the two cases are not too different. More errors exist when compared to the 85 kPa case, but it isn‘t as bad as some of the other parameters.

Figure 102: Residual fraction at 65 kPa

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Chapter 5 : Future Work and Conclusions

Experimental setup of a diesel engine and the study of advanced analysis techniques have been performed for the current project. Data acquisition systems were setup to include the acquisition of crank angle resolved cylinder pressure data. This allowed for the calculation of combustion noise, which will be critical to future calibration studies.

Engine testing automation programs were created to increase testing accuracy and reduce testing time. Steady state and transient programs proved highly successful during testing.

CO2 mixing results showed that the intake manifold design allowed for sufficient mixing of EGR. New experimental techniques were created as a result of this study.

GT Power© was used in several studies. It was shown that the GT Power© model was sufficiently accurate for most studies. Improvements are needed in fresh air flow predictions as well as emissions if it is determined that emissions are needed for future studies. GT Power© was also used to determine the most suitable cylinder pressure pegging method for the research engine of interest. Intake pressure pegging was deemed the best method overall. Altitude studies were also completed with GT Power©, which showed that the use of an intake air throttle at sea level will allow for sufficient accuracy for control algorithm development.

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More experimental data and validation of the proposed control strategy will be needed once a new research engine is available. Work from this project will be used to run large calibration DOEs and run transient cycles. The proposed altitude testing design will be implemented in the near future as well.

My future plans upon graduation are to work for General Motors in the area of powertrain testing.

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Bibliography

AVL, et al. "The Application of a New Software Tool for Sepearting Engine Combustion

Noise and Mechanical Noise Excitation." SAE International (2007).

Brunt, Michael F. J. and Christopher R. Pond. "Evaluation of Techniques for Absolute

Cylinder Pressure Correction." SAE International (1997).

Brunt, Michael F. J. and Kieron C. Platts. "Calculation of Heat Release in Direct

Injection Diesel Engines." SAE International (1999).

Chaffin, Cynthia A. and Terry L. Ullman. "Effects of Increased Altitude on Heavy-Duty

Diesel Engine Emissions." SAE International (1994).

Collings, N. and G. H. Neo. "Pressure Data Analysis of Formula One Racing Engines."

SAE International (1997).

Culshaw, John R. and B. Thompson McClure. "Laboratory Evaluation of an Oxidation

Catalytic Converter at Various Simulated Altitudes." SAE International (1992).

Desantes, Jose M. and Alberto Broatch. "Wavelet Transform Applied to COmbustion

Noise Analysis in High-Speed DI Diesel Engines." SAE International (2001).

137

Galster, G. M. and D. A. Garner. "High Altitude Can Affect Automotive Ignition System

Performance." SAE International (1967).

Haworth, R. and M. F. Russell. "Combustion Noise from High Speed Direct Injection

Diesel Engines." SAE International (1985).

Heywood, John B. Internal Combustion Engine Fundamentals. Mcgraw-Hill Inc., 1988.

Homsy, Samuel C. and Arvind Atreya. "An Experimental Heat Release Rate Analysis of

a Diesel Engine Operating Under Steady State Conditions." (n.d.).

Human, David M., Terry L. Ullman and Thomas M. Baines. "Simulation of High

Altitude Effects on Heavy-Duty Diesel Emissions." SAE International (1990).

Jenkins, S. H. "Analysis and Treatment of Diesel-Engine Noise." Journal of Sound and

Vibration (1975): 293-304.

Lee, Kangyoon, Maru Yoon and Myoungho Sunwoo. "A Study on pegging methods for

noisy cylinder pressure signal." ScienceDirect (2007).

Lee, Moohyung, Stuart Bolton and Sanghoon Suh. "A Procedure for Estimating the

Combustion Noise Transfer Matrix of a Diesel Engine." International Congress on

Sound and Vibration (2008).

Moran, Michael J. and Howard N. Shapiro. Fundamentals of Engineering

Thermodynamics. John Wiley and Sons, 2004.

138

Priede, T. "Relation Between Form of Cylinder-Pressure Diagram and Noise in Diesel

Engines." IMechE Proceedings (1960).

Randolph, Andrew L. "Cylinder-Pressure-Based Combustion Analysis in Race Engines."

SAE International (1994).

—. "Methods of Processing Cylinder-Pressure Transducer Signals to Maximize Data

Accuracy." SAE International (1990).

Reinhart, Thomas E. "An Evaluation of the Lucas Combustion Noise Meter on Cummins

'B' Series Engines." SAE International (1987).

Russell, M F, D C Palmer and C D Young. "Measuring Diesel Noise at Source with a

View to its Control." Vehicle Noise and Vibration (1984): 97-105.

Russell, M. F. and C. D. Young. "Measurement of Diesel Combustion Noise." Autotech

(1985).

Scarpati, Jose, et al. "Prediction of Engine Noise using Parameterized Combustion

Pressure Curves." SAE International (2007).

Tunestal, Per Anders. "The Use of Cylinder Pressure for Estimation of the In-Cylinder

Air/Fuel Ratio of an Internal Combustion Engine." 1993.

Wei, Shu Ge-qun and Han Rui. "The Transfer Function of Combustion Noise in DI-

Diesel Engine." SAE International (2005).

139

Woomert, Dale E. "High Altitude Laboratory Studies of Compression Ignition Engines."

SAE International (1966).

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Appendix A: CO2 Experiment Test Plan

Table 15: Well mixed case - CO2 injected after filter box

Torque (lb- Temp (Deg Pressure Fresh flow Fresh flow CO2 flow CO2 flow- Speed Valve ft) C) (kPa) (kg/min) (L/min) (L/min) controller

1 2 800 55.8 34 101.325 1.12 973.38 48.67 65.93 3 All 1 2 800 0.9 30 101.325 1.01 867.13 43.36 71.00 3 All 1 2 1000 61.7 30 101.325 1.47 1262.52 63.13 91.00 3 All 1 2 1000 30.6 33 101.325 1.25 1083.61 54.18 91.00 3 All 1 2 1000 1.2 33 101.325 1.44 1248.32 62.42 92.00 3 All 1 2 1400 41 25 101.325 2.03 1713.8 85.7 116.0778 3 All 1 2 1600 82.7 33 101.325 2.33 2019.8 101.0 170.00 3 All 1 1800 124.2 2 33 101.325 3.76 3259.5 163.0 230.00 3 141

All 1 2 1800 83.1 33 101.325 3.3 2860.7 143.0 210.00 3 All 1 2 1800 1.6 33 101.325 2.93 2540.0 127.0 186.00 3 All 1 2 2000 83 33 101.325 4 3467.5 173.4 245.00 3 All 1 2 2200 41 33 101.325 4 3467.5 173.4 257.00 3 All 1 2 2200 1.2 33 101.325 3.73 3233.5 161.7 228.00 3 All 1 2 2400 82.5 33 101.325 5 4334.4 216.7 322.00 3 All Table 16: EGR entering manifold through EGR pipe

Torque CO2 - Total EGR flow Temp Pressure Total EGR Total EGR - Speed Valve (lb-ft) Controller (kg/min) (Deg C) (kPa) (L/min) Controller

1 2 800 55.8 70.50 0.116 34 102.0149 100.2052 118.66 3 All 1 2 800 0.9 71.00 0.21 35 99.25776 187.0523 205.64 3 All 1 2 1000 61.7 91.00 0.154 38 104.4274 131.651 155.47 3 All 1000 30.6 1 91.00 0.219 38 102.0149 191.6454 215.47 142

2 3 All 1 2 1000 1.2 92.00 0.351 38 101.3256 309.2472 333.33 3 All 1 2 1400 41 144.00 0.328 35 104.1 278.6 316.3 3 All 1 2 1600 82.7 170.00 0.311 35 104.1 264.2 308.7 3 All 1 2 1800 124.2 230.00 0.404 35 104.1 343.2 403.4 3 All 1 2 1800 83.1 210.00 0.31 35 104.1 263.3 318.3 3 All 1 2 1800 1.6 186.00 0.618 35 112.0 487.8 536.5 3 All 1 2 2000 83 245.00 0.263 35 112.0 207.6 271.7 3 All 1 2 2200 41 257.00 0.501 46 130.0 352.9 420.2 3 All 1 2 2200 1.2 228.00 0.703 43 116.0 549.7 609.4 3 All 1 2 2400 82.5 322.00 0.37 35 138.0 237.0 321.3 3 All

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