A Thesis
entitled
Ice Prevention and Weather Monitoring on Cable-Stayed Bridges
by
Nutthavit Likitkumchorn
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Master of Science Degree in Mechanical Engineering
______Dr. Terry Ng, Committee Chair
______Dr. Douglas K. Nims, Committee Member
______Dr. Victor J. Hunt, Committee Member
______Dr. Patricia R. Komuniecki, Dean College of Graduate Studies
The University of Toledo
August 2014
Copyright 2014, Nutthavit Likitkumchorn
This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of
Ice Prevention and Weather Monitoring on Cable-Stayed Bridges
by
Nutthavit Likitkumchorn
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Mechanical Engineering
The University of Toledo
August 2014
The Veteran’s Glass City Skyway (VGCS) is a large cable-stayed bridge with a single pylon. Since the bridge has gone into service in 2007, five major icing events have occurred causing a high risk to traveling public and lanes and/or bridge closures.
Several studies have been conducted to help the Ohio Department of Transportation
(ODOT) in developing ice hazard mitigation strategies. This thesis addresses four aspects of the strategies including the development of (1) an ice presence-and-state sensor, (2) a suitable thickness measurement device for this project, (3) anti and de-icing strategies using internal heating, and (4) anti-icing strategy using superhydrophobic coatings.
An “ice presence-and-state sensor” (UT icing sensor) based on electric impendence was successfully developed. The sensor is rugged and compact, and can be mounted directly on the stay. Once mounted, it has the ability to identify the presence of, ice, slush
(wet snow), or water. Currently the sensor is ready for deployment for actual application on the VGCS bridge. Additionally, an “UT laser thickness sensor” was found to be suitable for detecting a full profile of ice or snow thickness on a stay.
None of the anti/deicing strategies tested was found to be a solution for VGCS icing problems. The internal heating method failed to prevent ice from accumulating on the
iii specimen in windy condition. Though it can, in theory, melt the accumulated ice to avoid shedding, the cost of implementing the method will likely to be very high. The super- hydrophobic coatings tested also failed to prevent ice accumulation. Contrary to initially assumed, the ice accumulation rate is actually higher on a coated specimen than one that is uncoated.
iv
Acknowledgements
First of all, I would like to express deepest gratitude and appreciation to both my advisors Dr. Douglas K. Nims, a faculty member in The Department of Civil Engineering and Dr. Terry Ng, a faculty member in The Department of Mechanical Engineering for their full support, expert guidance, and encouragement throughout my study and research.
I also would like to thank Dr. Hunt for having served on my committee. His thoughtful questions and comments were valued greatly.
Thanks also go to my colleagues from the University of Toledo Mr. Ahmed
Abdelaal, Mr. Clinton Mirto, and Mr. Ali Arbabzadegan for helping me performing the experiments and guided me with their experience. I really appreciated it.
I would like to thank the University of Cincinnati graduate students and professors for their efforts in development of dashboard and installation of bridge sensors.
This project was sponsored by the Ohio Department of Transportation. The author gratefully acknowledges their financial support this study.
Finally, I wish to express my sincere thanks to my family for their unconditional love and support for the past years and giving me the opportunities to study abroad. I would not have been able to complete this thesis without their love and encouragement.
v
Table of Contents
Abstract ...... iii
Acknowledgements ...... v
Table of Contents ...... vi
List of Tables ...... x
List of Figures ...... xi
List of Abbreviations ...... xix
List of Symbols ...... xx
1 Introduction … ...... 1
1.1 Statement of Problem ...... 1
1.2 General Bridge Information ...... 3
1.3 Objective…...... 5
1.4 Organization ...... 6
2 Literature Review…...... 7
2.1 Introduction to Icing ...... 7
2.1.1 In-Cloud Icing ...... 7
2.1.2 Precipitation ...... 9
2.1.3 Frost ...... 11
2.2 History of Icing Events on VGCS ...... 12
2.3 Anti-Icing and De-Icing ...... 15
vi 2.3.1 Heating ...... 15
2.3.2 Superhydrophobic Coating ...... 16
2.4 Available Sensors...... 17
2.4.1 Dielectric Leaf Wetness Sensor ...... 18
2.4.2 Ultrasonic Ranging Sensor ...... 20
2.4.2.1 SR50AT ...... 21
2.4.2.2 AGKU1500GI ...... 23
3 Ice Presence and State Sensor Development ...... 27
3.1 Introduction ...... 27
3.2 Ice Presence and State Sensor Laboratory Test ...... 28
3.2.1 Sensors and Data Acquisition System ...... 29
3.2.2 Design of Experiment ...... 32
3.2.3 Laboratory Test Results ...... 37
3.2.3.1 UT Icing Sensor with 1-mm Electrode Spacing ...... 37
3.2.3.2 UT Icing Sensor with 7-mm Electrode Spacing ...... 44
3.3 UT Icing Sensor on Full Scale Experiments ...... 48
3.3.1 Specimens and Data Acquisition System Setup ...... 50
3.3.2 Full Scale Outdoor Test ...... 56
3.3.3 Full Scale Experimental Result ...... 58
3.3.3.1 Icing Experiments ...... 59
3.3.3.2 Wet Snow Experiments...... 62
3.3.3.2.1 Dry Snow Event ...... 63
3.3.3.2.2 Wet Snow on Top of Specimen ...... 67
vii 3.3.3.2.3 Wet Snow on East Side of Specimen ...... 72
3.4 Conclusion and Next Steps ...... 75
4 Thickness Measurement...... 76
4.1 Devices Setup and Initial Test ...... 77
4.1.1 AGKU1500GI ...... 77
4.1.2 SR50AT ...... 82
4.1.3 UT Laser Thickness Sensor ...... 85
4.2 Full Scale Experiment and Result ...... 88
4.3 Sensor Selection ...... 92
5 Thermal Experiments ...... 94
5.1 Experiment and Data Acquisition System Setup ...... 94
5.2 Resultant Data and Discussion...... 96
5.2.1 Anti-Icing Experiment ...... 96
5.2.2 De-Icing Experiment ...... 99
5.3 Thermal Experiment Conclusion ...... 100
6 Hydrophobic Coating Experiments ...... 102
6.1 UT Icing Tunnel Background ...... 102
6.2 Testing Section...... 103
6.2.1 Misting System ...... 104
6.2.2 Camera System ...... 105
6.2.3 Mounting System ...... 106
6.3 Coating Experiments in UT Icing Tunnel ...... 107
6.3.1 Testing Procedure ...... 107
viii 6.3.2 Experiments – Icing Progression ...... 108
6.3.2.1 Uncoated Specimen ...... 108
6.3.2.2 Hydrobead ...... 114
6.3.2.3 PhaseBreak TP ...... 120
6.3.2.4 Boyd WeatherTITE ...... 126
6.3.3 Result Summery of Icing Tunnel Coating Tests ...... 132
6.4 Full Scale Coating Test ...... 133
7 Conclusion and Future Work ...... 136
7.1 Conclusion ...... 137
7.2 Future Work ...... 138
7.3 Archiving Data ...... 139
References ...... 140
A Snow Density Calculations ...... 143
ix
List of Tables
2.1 Physical Properties of Atmospheric Ice ...... 11
2.2 Summary of Ice Accumulation and Ice Shedding Event ...... 14
4.1 Thickness Sensors Comparison ...... 92
6.1 Approximated Ice Thickness Comparison ...... 132
A.1 Dry Snow Density Calculation ...... 143
A.2 Wet Snow Density Calculation ...... 143
x
List of Figures
1-1 Ice Forming on Stay Sheath ...... 1
1-2 Ice on the Pylon and Stay Sheaths ...... 2
1-3 Ice Shattered on Bridge Deck ...... 2
1-4 Ice Sheets Almost Hit a Vehicle in the Far Lane ...... 3
1-5 Aerial View of the Veteran’s Glass City Skyway ...... 4
1-6 The Veteran’s Glass City Skyway at Night ...... 4
2-1 Rime Ice and Glaze Ice ...... 8
2-2 Wet Snow Accretion on Power Lines ...... 10
2-3 Image Sequence of Drop Impact on Uncoated Surface ...... 16
2-4 Image sequence of drop impact on a superhydrophobic surface ...... 17
2-5 Shape of the Dielectric Leaf Wetness Sensor ...... 19
2-6 Sample Raw Output from the LWS ...... 20
2-7 SR50AT Ultrasonic Sensor and Temperature Sensor ...... 22
2-8 Screenshot of Hyperterminal and Data Output ...... 23
2-9 AGKU 1500 GI ...... 24
2-10 AGKU 1500 GI Output Current VS Distance ...... 24
2-11 AGKU 1500 GI Circuit ...... 25
2-12 Screenshot of LabView Program for AGKU 1500 GI...... 26
3-1 UT Icing Sensor Circuit ...... 29
xi 3-2 Electrode Spacing Area of the UT Icing Sensor ...... 30
3-3 UT Icing Sensor Connected to Data Acquisition System ...... 31
3-4 Dashboard of UT Icing Sensor ...... 31
3-5 1-mm Electrode Spacing UT Icing Sensor ...... 32
3-6 7-mm Electrode Spacing UT Icing Sensor ...... 33
3-7 Water Measurement ...... 34
3-8 Ice Measurement ...... 34
3-9 75% Slush Measurement...... 35
3-10 50% Slush Measurement...... 35
3-11 25% Slush Measurement...... 36
3-12 Ice Measurement at 6 mm thickness ...... 36
3-13 Ice Measurement at 13 mm thickness ...... 37
3-14 Ice Measurement at 19 mm thickness ...... 37
3-15 Resistance of Ice for 1-mm Electrode Spacing Sensor ...... 38
3-16 Dashboard Screenshot of Ice Measurement ...... 39
3-17 Resistance of 75% Slush for 1-mm Electrode Spacing Sensor...... 39
3-18 Dashboard Screenshot of 75% Slush Measurement ...... 40
3-19 Resistance of 50% Slush for 1-mm Electrode Spacing Sensor...... 41
3-20 Dashboard Screenshot of 50% Slush Measurement ...... 42
3-21 Resistance of 25% Slush for 1-mm Electrode Spacing Sensor...... 42
3-22 Dashboard Screenshot of 25% Slush Measurement ...... 43
3-23 Resistance of Water for 1-mm Electrode Spacing Sensor ...... 43
3-24 Dashboard Screenshot of Water Measurement ...... 44
xii 3-25 Resistance of Ice for 7-mm Electrode Spacing Sensor ...... 45
3-26 Resistance of 75% Slush for 7-mm Electrode Spacing Sensor...... 45
3-27 Resistance of 50% Slush for 7-mm Electrode Spacing Sensor...... 46
3-28 Resistance of 25% Slush for 7-mm Electrode Spacing Sensor...... 46
3-29 Resistance of Water for 7-mm Electrode Spacing Sensor ...... 47
3-30 Resistances for 6-mm Thickness and 7-mm Electro Spacing Sensor ...... 48
3-31 VGCS Stainless Steel Specimens ...... 49
3-32 HDPE Specimen and Frame Structure ...... 49
3-33 North Facing Specimen with 120 Strands Inside ...... 50
3-34 Sensors Setup on VGCS Specimen...... 51
3-35 Sensors Setup on HDPE Specimen ...... 52
3-36 Cross Section and Sensors Setup Orientation of both Specimens ...... 52
3-37 UT Icing Sensor on HDPE Specimen ...... 52
3-38 MicroStrain V-Link ...... 53
3-39 MicroStrain TC-Link ...... 54
3-40 MicroStrain WSDA-Base (Signal Receiver) ...... 55
3-41 V-Link and UT Icing Sensor...... 56
3-42 Ice Testing ...... 57
3-43 Slush Testing ...... 57
3-44 Water Testing ...... 57
3-45 UT Icing Sensor Initial Test ...... 58
3-46 Misting Water on VGCS Specimen ...... 59
3-47 Ice Accumulation on VGCS Specimen...... 60
xiii 3-48 Stay Behavior in Icing Experiment ...... 61
3-49 Snow Gun...... 63
3-50 Snow in Known Volume Containers ...... 64
3-51 Snow Collected on top of HDPE Specimen ...... 64
3-52 UT Icing Sensors VS. LWS during Dry Snow Event ...... 65
3-53 Thermocouples during Dry Snow Event ...... 66
3-54 Top UT Icing Sensor VS Thermocouples during Dry Snow Event...... 67
3-55 Wet Snow Accretion on Top of HDPE Specimen ...... 68
3-56 UT Icing Sensors VS LWS during Wet Snow on Top Exp ...... 68
3-57 Snow Shed during Wet Snow on Top Experiment ...... 70
3-58 Thermocouples during Wet Snow on Top Side Experiment ...... 70
3-59 Top UT Icing Sensor VS Thermocouples during Wet Snow on Top Exp...... 71
3-60 Wet Snow Spraying from East ...... 72
3-61 Wet Snow Accretion on the East Side ...... 73
3-62 East UT Icing Sensor VS Thermocouples during Wet Snow on East Exp ...... 73
3-63 Wet Snow Shed on the East side...... 74
4-1 Data Acquisition Setup for the Sensors ...... 77
4-2 AGKU 1500 GI Current Output Testing (1) ...... 78
4-3 AGKU 1500 GI Current Output Testing (2) ...... 78
4-4 Result of AGKU 1500 GI Current Output Testing ...... 79
4-5 AGKU 1500 GI Manual Detection Range Plot ...... 79
4-6 AGKU 1500 GI Experimental Detection Range Plot ...... 80
4-7 Ultrasonic Sensors and UT Laser Thickness Sensor Setup (Side View) ...... 81
xiv 4-8 Ultrasonic Sensors and UT Laser Thickness Sensor Setup (Top View) ...... 81
4-9 Result of AGKU 1500 GI Temperature Compensation Test ...... 82
4-10 SR50AT Known Thickness Detection Test ...... 83
4-11 Result of SR50AT Known Thickness Test ...... 83
4-12 Result of SR50AT Temperature Compensation Test ...... 84
4-13 Base Picture for UT Laser Thickness Sensor ...... 85
4-14 Known Thickness Picture for UT Laser Thickness Sensor ...... 86
4-15 Unknown Thickness Picture for UT Laser Thickness Sensor ...... 86
4-16 Calibration Process for UT Laser Thickness Sensor ...... 87
4-17 Calculation Process for UT Laser Thickness Sensor ...... 87
4-18 HDPE Specimen before Snow Gun Operation ...... 88
4-19 HDPE Specimen after Snow Gun Operation ...... 89
4-20 SR50AT Output during the Full Scale Experiment ...... 89
4-21 AGKU 1500 GI Output during the Full Scale Experiment...... 90
4-22 Calculation Thickness of UT Laser Thickness Sensor on Full Scale Exp ...... 91
4-23 Snow Profile Output of UT Laser Thickness on Full Scale Experiment ...... 91
5-1 Locations of Collected Temperature Data ...... 95
5-2 Thermal Experiment Setup ...... 96
5-3 Anti-Icing Temperature Profile at 30 cm Location ...... 97
5-4 Anti-Icing Temperature Profile at 150 cm Location ...... 98
5-5 Anti-Icing Temperature Profile at 270 cm Location ...... 98
5-6 De-Icing Temperature Profile at 30 cm Location ...... 99
5-7 Melting Pattern during De-Icing ...... 100
xv 6-1 SolidWorks Design for the UT Icing Tunnel ...... 103
6-2 UT Icing Tunnel ...... 103
6-3 Testing Section of the UT Icing Tunnel ...... 104
6-4 Misting System in the Testing Section ...... 105
6-5 Panasonic HX_A100D Camera ...... 106
6-6 Mounting System of Testing Section ...... 106
6-7 Uncoated - 40 Micron – 0 sec ...... 108
6-8 Uncoated - 40 Micron - 15 sec ...... 109
6-9 Uncoated - 40 Micron - 30 sec ...... 109
6-10 Uncoated - 40 Micron - 45 sec ...... 109
6-11 Uncoated - 40 Micron – 1 min ...... 110
6-12 Uncoated - 40 Micron – 1:30 min ...... 110
6-13 Uncoated - 40 Micron – 2 min ...... 111
6-14 Uncoated - 40 Micron – 4 min ...... 111
6-15 Uncoated - 40 Micron – 6 min ...... 112
6-16 Uncoated - 40 Micron – 8 min ...... 112
6-17 Uncoated - 40 Micron – 10 min ...... 112
6-18 Uncoated - 40 Micron – After Test ...... 113
6-19 None Coating - 40 Micron – Shed Ice Sheet ...... 113
6-20 Hydrobead-Coated Specimen ...... 114
6-21 Hydrobead – 40 Micron – 0 sec ...... 115
6-22 Hydrobead – 40 Micron – 15 sec ...... 115
6-23 Hydrobead – 40 Micron – 30 sec ...... 116
xvi 6-24 Hydrobead – 40 Micron – 45 sec ...... 116
6-25 Hydrobead – 40 Micron – 1 min ...... 116
6-26 Hydrobead – 40 Micron – 1:30 min ...... 117
6-27 Hydrobead – 40 Micron – 2 min ...... 117
6-28 Hydrobead – 40 Micron – 4 min ...... 117
6-29 Hydrobead – 40 Micron – 6 min ...... 118
6-30 Hydrobead – 40 Micron – 8 min ...... 118
6-31 Hydrobead – 40 Micron – 10 min ...... 119
6-32 Hydrobead – 40 Micron – After Test ...... 119
6-33 Hydrobead – 40 Micron – Shed Ice Sheet ...... 120
6-34 PhaseBreak TP – 40 Micron – 0 sec ...... 121
6-35 PhaseBreak TP – 40 Micron – 15 sec ...... 121
6-36 PhaseBreak TP – 40 Micron – 30 sec ...... 121
6-37 PhaseBreak TP – 40 Micron – 45 sec ...... 122
6-38 PhaseBreak TP – 40 Micron – 1 min ...... 122
6-39 PhaseBreak TP – 40 Micron – 1:30 min ...... 123
6-40 PhaseBreak TP – 40 Micron – 2 min ...... 123
6-41 PhaseBreak TP – 40 Micron – 4 min ...... 123
6-42 PhaseBreak TP – 40 Micron – 6 min ...... 124
6-43 PhaseBreak TP – 40 Micron – 8 min ...... 124
6-44 PhaseBreak TP – 40 Micron – 10 min ...... 124
6-45 PhaseBreak TP – 40 Micron – After Test ...... 125
6-46 PhaseBreak TP – 40 Micron – Shed Ice Sheet ...... 125
xvii 6-47 WeatherTITE – 40 Micron – 0 sec ...... 126
6-48 WeatherTITE – 40 Micron – 15 sec ...... 127
6-49 WeatherTITE – 40 Micron – 30 sec ...... 127
6-50 WeatherTITE – 40 Micron – 45 sec ...... 127
6-51 WeatherTITE – 40 Micron – 1 min ...... 128
6-52 WeatherTITE – 40 Micron – 1:30 min ...... 128
6-53 WeatherTITE – 40 Micron – 2 min ...... 129
6-54 WeatherTITE – 40 Micron – 3 min ...... 129
6-55 WeatherTITE – 40 Micron – 4 min ...... 130
6-56 WeatherTITE – 40 Micron – 6 min ...... 130
6-57 WeatherTITE – 40 Micron – 8 min ...... 130
6-58 WeatherTITE – 40 Micron – 10 min ...... 131
6-59 WeatherTITE – 40 Micron – After Test ...... 131
6-60 WeatherTITE – 40 Micron – Shed Ice Sheet ...... 132
6-61 Hydrobead Sprayed on the half of VGCS Specimen ...... 133
6-62 Ice Droplets on the Hydrobead Surface ...... 134
6-63 Even Ice Layer on the Uncoated Surface ...... 134
xviii
List of Abbreviations
CRREL ...... Cold Region Research Laboratory
DAQ ...... Data Acquisition
HDPE ...... High Density Polyethylene in ...... inches kOhm...... kilohms
LWC ...... liquid water content LWS ...... Leaf Wetness Sensor mA...... milliamps micron ...... micrometers ml ...... milliliters mm ...... millimeters MOhm ...... megaohms MVD ...... median volume diameter m/s ...... meters per second m3/s ...... cubic meters per second
ODOT ...... Ohio Department of Transportation
UT ...... The University of Toledo
VGCS ...... Veteran’s Glass City Skyway
xix
List of Symbols
° ...... Degrees
A ...... Amps C ...... Celsius Ei ...... Input Voltage Eo ...... Output Voltage g...... gram k...... kilo m ...... meter R1...... Fixed Resistor R2...... Variable Resistor V ...... Volts
xx Chapter 1
Introduction
1.1 Statement of Problem
The Veteran’s Glass City Skyway (VGCS) is a large cable-stayed with a single pylon bridge and owned by the Ohio Department of Transportation (ODOT). Since the
VGCS went into service in 2007, there have been five major icing incidents in seven winters. In these incidents, ice formed on the cable-stays and built up to approximately 1.9 centimeters thick. Ice was formed to the cylindrical shape of the cable-stay sheath as seen in figure 1-1 and 1-2.
Figure 1-1: Ice Forming on Stay Sheath
1
Figure 1-2: Ice on the Pylon and Stay Sheaths
As cable-stays warmed, the ice sheets shed and fell up to 76 meters to the roadway on a bridge deck area. Figure 1-3 shows shattered ice sheet on the bridge deck area.
Figure 1-3: Ice Shattered on Bridge Deck
In high wind condition, the ice sheets were blown across several lanes of traffic.
These ice-fall incidents caused a high risk to the traveling public and resulted in lane and bridge closures. Figure 1-4 shows an image from a video taken in icing incident in February
2011 (Belknap, 2011). Large sheets of ice circled in red were blown across the traffic. It almost hit a vehicle driving in the far lane.
2
Figure 1-4: Ice Sheets Almost Hit a Vehicle in the Far Lane (Belknap, 2011)
Since the VGCS is one of the main bridges across Maumee River, there are thousands of vehicles crossing the bridge daily. Lane closures are inconvenient to traveling public. Falling ice can cause severe damage to vehicles traveling on the bridge and put lives of motorist in danger. While ice is accumulating, ODOT personnel have to determine ice presence manually. This means they have to be on the bridge deck in live traffic during an icing event. This endangers ODOT personnel.
The ODOT has contracted the University of Toledo to develop a practical anti- icing/de-icing or thermal solution. In addition, the solution must preserve the aesthetics of the VGCS sheaths and be as environment friendly as practical.
1.2 General Bridge Information
The VGCS as known as the Maumee River Crossing is located in Toledo, Ohio. It represents one of most expensive projects undertaken in the history of the ODOT (ODOT,
2014). The cost of the construction of this project is approximately 237 million US dollars.
The project started in 1999 and the Skyway was officially opened on June 24, 2007. It carries three lanes of traffic in each direction on I-280 over Maumee River. The VGCS has
3 a span of approximately 400 meters. It also has 40-meter vertical clearance above Maumee
River and its pylon is approximately 76 meters above the bridge deck.
Figure 1-5: Aerial View of the Veteran’s Glass City Skyway (ODOT, 2014)
The VGCS is consisted of 20 cable-stays as seen in figure 1-5. Inside the cable- stays, there are 82 to 156 strands depending on their locations. The strands are cover by brushed stainless steel sheaths which are 50.8 centimeters in diameter and 0.625 centimeters in thickness. The stainless steel sheaths were chosen over other materials because of its aesthetic qualities shown in figure 1-6 and low life cycle cost. These unique characteristics of the VGCS sheaths, such as being made of stainless steel and having a large diameter, may contribute to the ice formation and shedding issue.
Figure 1-6: The Veteran’s Glass City Skyway at Night (Nims, 2010)
4 1.3 Objective
The objectives are to identify the most practical de-icing/anti-icing approach for solving the VGCS icing problem and to be able to detect ice formation and potential of ice shedding remotely to reduce risk of ODOT personnel being on a bridge desk while an icing event is occurring.
The Goodrich ice detector which was tested in the past failed to sense ice persistence. The Leaf Wetness Sensor (LWS), which is a commercial sensor designed to detect the presence of liquid water content, cannot be installed directly on the surface of the stays. The recognition of the presence of liquid water between the ice sheet and stay’s surface is expected to be a powerful predictor of imminent ice shedding. Such knowledge would help ODOT personnel respond properly toward the icing event. Therefore, an effort to develop an ice presence and state sensor is undertaken. By knowing the difference between water’s and ice’s properties, a simple resistance based sensor can be developed.
In addition, ice/snow thickness plays a significant role in the shedding event. The thicker ice/snow accumulation results in higher chance of and more severe shedding event.
Therefore, using a suitable thickness sensor on the bridge is very necessary. Different types of thick measuring devices are tested on a full scale experiment and compared for the best performance.
There is not yet a solution for preventing the ice accumulation on the VGCS. Anti and de-icing strategies should be tested on specimen which have the same properties as
VGCS stay. Since, there is a large empty space in the stay, the internal heating could be a potential solution. Both anti and de-icing strategies can be applied to this heating method.
5 The experiment should not be conducted at the bridge due to safety and more controlled condition. To do so, the full scale experimental station needs to be set up.
The superhydrophobic coating is another potential solution. The coating does not allow water to remain on the stay’s surface due to its special features. According to this theory, ice could not accumulate because there is no water presence on the surface. The initial experiment can be done in the UT icing tunnel. If the performance of the coating is promising, the test would be continued on the full scale experiment.
1.4 Organization
Chapter 1 introduces the problem statement and objective to help ODOT in implementing the most practical and efficient approach to manage icing events. Chapter 2 introduces icing properties, history of VGCS icing events, and looks into available anti- icing/de-icing chemicals, sensors, and technology that can be helpful to solve the icing problems. Chapter 3 introduces the UT developed state and presence sensor including data collected in a laboratory and outdoor experiments. Chapter 4 presents three thickness sensors and compares each sensor’s performance. Chapter 5 introduces anti/de-icing internal heating strategy including full scale experimental result and efficiency of the method. Chapter 6 presents anti-icing chemicals experiment in the UT icing tunnel and on full scale experiment, and determine the efficiency of each product. Chapter 7 provides a conclusion and recommendations for future work.
6 Chapter 2
Literature Review
2.1 Introduction to Icing
There are three categories of atmospheric ice formation that possibly occur to structures at different atmospheric conditions. These categories are classified by various form of water in atmosphere freezing and adhering to objects exposed to the air which are in-cloud icing, precipitation, and hoar frost. (Farzaneh, 2008)
2.1.1 In-Cloud Icing
In-cloud icing happens when extremely cool water droplets in a cloud or fog hit an object’s surface below 0 °C and freeze upon impact. The temperature of the water droplets can be as low as -30 °C and they do not freeze in the air due to their size. Accretions in this case have different sizes, shapes, and properties depending on the number of droplets in the air (liquid water content – LWC), their size (median volume diameter – MVD), the ambient temperature, the wind speed, the duration, the surface area of a structure, and the collection efficiency (Parent, 2011). There is a continuum of ice accretion appearance from rime at the coldest temperatures to glaze at warmest temperatures. Figure 2-1 is a side by side comparison of rime and glaze ice.
7 Rime ice has a density of 300 – 900 kg/m3 and classified as soft rime and hard rime.
Soft rime is white ice with a lot of air bubble and usually grows in pennant shape pointed into the wind direction. It appears when temperature is a significantly below 0 °C and the
LWC and MVD are low. This type of accretion has low density which is less than 600 kg/m3 and little adhesion.
Hard rime has higher LWC and MVD than soft rime ice. It usually grows in a layered structure consisting of clear ice and air bubbles and has a density of 600 to 900 kg/m3 which results in higher adhesion and difficulty in removal.
Glaze which has a density ranging from 900 to 920 kg/m3 occurs when a large LWC and MVD freezing rain does not freeze upon impact, but runs back on the surface of a structure and freezes later. Its structure is smooth and clear without any air bubbles. Due to very high density, its adhesion is very strong. This type of accretion is associated with precipitation.
Figure 2-1: Rime Ice and Glaze Ice (Whitacre, 2013)
8 2.1.2 Precipitation
Precipitation icing has a much higher accretion rate than that of in-cloud ice. This category of icing can be caused by two different varieties. The first variety is freezing rain.
The freezing rain accretion happens when rain falls and freezes on a surface whose temperature is below 0 °C. This often during an inversion. The ice density and adhesion are high due to this phenomenon.
The second variety is wet snow which has a density of 300-800 kg/m3. This type of icing is a complex phenomenon that involves several variables, such as air temperature, amount of precipitation, wind intensity and its direction, hydrometeor phase and size distributions (Bonelli, 2011). These parameters have a significant effect on the rate of wet snow accretion and the possible shedding.
Wet snow precipitation typically occurs when the ambient temperature is close to freezing point. There is not an exact temperature range for wet snow conditions, but according to many observations in different countries, the temperature is within range of 0 to 2 °C. Due to the above freezing temperature, the snowflakes can partially melt and their typical LWC and air bubble are between 15 to 40 % and less than 40 % of total volume respectively (Bonelli, 2011), (Admirat, 2008). Under these conditions, the snowflakes is sticky enough and tends to adhere on top and windward surface of structure. At this point, the wet snow is easy to remove at first. Although, if the temperature falls below 0 °C after the wet snow accumulation, it then freezes in a dense hard layer with a strong adhesion
(Farzaneh,2008).
9 To have a better understand regarding wet snow, the microclimate is needed to be studied. The vertical temperature gradient of the atmosphere is approximately 6 °C k/m
(Fikke et al., 2007). Wet snow is generally occurred when snowflakes fall from a colder air layer to a warmer layer near the surface. At this warmer air layer, the snowflakes increase their LWC. The study of this layer is very important because the longer the snowflakes are exposed in this layer, the more LWC they will have, which also means it will become more adhesive. Therefore, the most critical parameters is the accuracy of the forecasted air temperature, not only on the surface, but also along the vertical air profile, in which the physical process takes place. The wet snow events typically last between 18 to 24 hours, which is long enough to produce large layers of snow on structures. Figure 2-
2 shows an image of wet snow accretion on power lines. This is a wide spread problem throughout Europe. This can cause a severe damage when the shedding event occurs.
Figure 2-2: Wet Snow Accretion on Power Lines (INMR, 2014)
10 Dry snow accumulates at below 0 °C air temperature. Its density is very low and not exceed 100 kg/m3. The accumulation only happens in very low wind speed condition, generally below 2 m/s. In this case, LWC and air bubbles are less than 5 % and more than
70% of total volume respectively (Admirat, 2011).
2.1.3 Hoar Frost
The Hoar Frost has a density of less than 300 kg/m3. It is usually occurred when water vapor solidifies directly on a cool surface. This type of icing is featherlike and often accumulate in very low temperature and wind speed condition. The hoar frost typically does not cause significant icing problems. Although, it can possibly become soft rime which has large volume and weight in the right condition.
The typical physical properties of atmospheric ice are presented in Table 2.1 to compare the density, adhesion, appearance, and cohesion.
Table 2.1: Physical Properties of Atmospheric Ice (Farzaneh, 2008)
11 2.2 History of Icing Events on the VGCS
Since the VGCS has been opened to public transportation, there have been five winter weather events that caused the lanes closures due to concerns about ice falling from the stays and damaging vehicles or injuring people in the vehicle. Kathleen Jones, the Cold
Region Research Laboratory (CRREL) expert, and research team members have prepared a Toledo weather conditions associated with ice accumulation on the VGCS report (Jones,
2010), (Arbabzadegan, 2013). The information regarding the five icing events were provided by ODOT and UT graduate students and presented in table 2.2.
December 2007
On 9th and 10th December 2007, the Toledo Express Airport and Metcalf Field weather stations reported freezing rain and fog weather. Due to this condition, ice started to accumulate on the VGCS stay. The above freezing ambient temperature with rainfall triggered the ice shedding from the stays.
March 2008
On 27th March 2008, the same weather stations showed a snow and rain event with temperatures falling below freezing which caused ice accumulation on the VGCS stays. It was also reported to be foggy and overcast on that day. The next day, 28th March 2008, the sky was clear and solar radiation was very strong which resulted in ice shedding event in the afternoon.
December 2008
The ice was observed after a mix of freezing rain and snow with foggy and windy weather occurred on 17th December 2008. There were approximately 8 and 11 cm snow depth were reported at Toledo Express Airport and downtown Toledo respectively. The ice
12 formation lasted 7 days before shedding on 24th December 2008 due to rain with ambient temperature above freezing and windy conditions.
More freezing rain fell in the more of 26th December 2008, however, warmer air was moving through the area at night which did not result in ice accumulation on the stay.
January 2009
On 3rd January 2009, freezing rain with fog and temperature below freezing caused ice formation on VGCS stay, however, the ice was not as thick as the accumulation in
December 2008. This condition continued for another day before it became sunny on 5th
January 2009. Although, the temperature was too cold to trigger ice shedding. The ice formation lasted 10 days before it shed on 13th January 2009 due to warmer temperature with high wind.
The following week, additional snow and fog events mixed with clear sky and temperature below freezing were reported. However, there was no ice accumulation because the ice was mostly sublimated.
February 2011
The weather station at the VGCS Bridge was reported freezing rain, then a drop in ambient temperature with east wind on 20th February 2011. The glaze ice accumulation was observed due to this condition. In the next morning, the ice was measured to be approximately 0.6 and 1.2 cm on the top and east sides of the stay respectively. There were small icicles accumulated on the bottom side. On 22rd and 23th February 2011, the ambient temperature was below freezing, however, it was very sunny. The solar radiation was strong and heated up the stainless steel stays causing water layer between ice and stay sheath. Although, the ice did not shed because the temperature dropped and the layer of
13 water refroze at the end of the day. The next day, the weather condition was ambient temperature above freezing with overcast and light west wind. The ice shedding event started from 8:30 to 11:00 on 24th February 2011. During this period, 80 % of ice fell of the stays.
Table 2.2: Summary of ice accumulation and ice shedding events
Precipitation event causing Ice incident Ice shedding weather ice accumulation
Rain with temperature above December 2007 Freezing rain and fog freezing
Sunny with temperature above March 2008 Snow, rain, and fog freezing
Snow and fog; freezing rain Rain, gusty winds and December 2008 and fog temperatures above freezing
Gusty winds, temperature above January 2009 Freezing rain and fog freezing
Light wind, overcast, and February 2011 Freezing rain and clear sky temperature above freezing
From these five icing event, the conditions that cause ice to form on the stays and conditions that cause it to shed off the stays have been learned. Ice accumulation occurred in freezing rain and/or snow, which shows that they are the most probable causes for the icing events. In addition, they are usually accompanied with ambient temperature below freezing and fog.
14 Ice shedding events occurred when ambient temperature warms above freezing, which may be accompanied sunshine, rain, or strong winds. According to the historical data, it has not been found that the ice sheds before these shedding conditions occur. Therefore, icing event duration depends on how long ice accumulation condition lasts before the shedding conditions takeover.
2.3 Anti-Icing and De-icing
Anti-icing prevents ice from accumulating on an object, on the other hand, de-icing is a removal of ice from an object. Both methods are widely used in the aerospace industry which can be transferred to this project. Although, some scaling need to be done to adjust parameters. For the purposes of this research and literature review, the main concentration is on heating systems and super-hydrophobic coatings.
2.3.1 Heating
Heating is considered to be an active method meaning that it uses external systems and require an energy supply. The first strategy, “anti-icing heating system”, can be done by either using heating resistance or warm air. In this method, the surface of the object should be kept above 0 °C to prevent icing. Additional temperature sensors are installed to protect the object from permanent damage induced by overheating. The advantage to heating for anti-icing purposes that it could prevent ice from building up on the object completely if it is kept at a warm enough temperature. On the other hand, it requires significant amount of energy to keep the object at the required temperature which result in increasing energy cost. Furthermore, the overheating could permanently damage the object’s integrity. More detail and testing result will be given about the heating method in the chapter 5.
15 The second strategy, “de-icing”, causes the melting of ice in order to force its shedding. The same methods used in anti-icing strategy can be considered de-icing methods. The only difference is that less energy is required for anti-icing than for de-icing.
2.3.2 Superhydrophobic Coating
The use of superhydrophobic coatings is considered to be a passive anti-icing method because it takes advantage of the physical properties of the object surface to prevent ice formation. A superhydrophobic coating does not allow water to remain on the surface because of its special features. These coating are designed to create a very high contact angle (≥ 150°) between the water droplet and the surface of the object (Wang, 2007). As the contact angle increases, the surface contact becomes less which results in water running of the object surface. Figure 2-3 and 2-4 shows the comparison of water droplet impact on non-superhydrophobic and superhydrophobic surfaces respectively (Antonini, 2011). A non-superhydrophobic surface tends to allow the drop to spread over a relatively wide area of the surface. For the surface with super-hydrophobic, the drop tends to retain a spherical shape and it can usually shed from the surface with a slight disturbance, for example surface tilting or vibration.
Figure 2-3: Image Sequence of Drop Impact on Uncoated Surface (Antonini, 2011)
16
Figure 2-4: Image sequence of drop impact on a superhydrophobic surface (Antonini, 2011)
The advantage of the superhydrophobic coatings are low cost, a protection of the whole surface, and easy maintenance. This method can be combined with a heating system for a lower energy consumption. However, ice prevention on an object by coating alone is not realistic. Several studies prove that ice still occurs even on coated surfaces, regardless of temperature (Kimura, 2003). None of the coatings is found to completely prevent ice formation. During this research, three superhydrophobic coatings are tested. More details and testing results will be discussed in the follow chapter.
2.4 Available Sensors
There are three primary motivations for developing the ice present and state sensor;
(1) the limited utility in revealing ice persistence of the Goodrich ice detector (Ryerson,
2008), (2) the LWS which is a commercial sensor for detecting the presence liquid water content cannot be installed directly on the stay’s surface, and (3) the presence of liquid water beneath the ice is found to be a precursor to ice shedding. Recognizing the presence of water beneath the stays is expected to be a powerful predictor of imminent shedding.
17 Therefore, an effort to develop an ice presence and state sensor was undertaken. The sensor is presented and discussed in chapter 3.
2.4.1 Dielectric Leaf Wetness Sensor
The LWS measures the leaf surface wetness by measuring the dielectric constant of the sensor’s upper surface. It requires very low power to operate (2.5-5 VDC), which has an advantage to make many measurements over a long period of time with minimal battery usage. This sensor also has very high resolution, which has an ability to detect very small amounts of water or ice on the sensor surface. The operating environment of the sensor is between -20 to 60 °C.
The principle of LWS measurement is that different mediums have different dielectric constants. The dielectric constant of water (80) and ice (5) are much higher than that of air (1), so the measurement is strongly dependent on the presence of moisture or frost on the sensor surface (Decagon Devices, 2014). The sensor can measure the dielectric constant of a zone approximately 1 cm above its surface. The sensor outputs a mV signal proportional to the dielectric of the measurement zone, and therefore proportional to the amount of water or ice on the sensor surface.
The LWS is designed to have a similar properties as a real leaf. The sensor has similar shape as a typical leaf as seen in figure 2-5. Not only they have similar shapes, but also they have similar thermodynamic properties. The heat capacity of the LWS and a real leaf is 1480 and 1425 J m-2 K-1 respectively. The LWS is made of a 0.65 mm fiberglass which is very close to 0.4 mm thickness of a typical leaf. The sensor is also made to closely
18 match the radiative properties of real leaves. Finally, the surface coating of the LWS is hydrophobic which also is similar to a leaf with a hydrophobic cuticle.
Figure 2-5: Shape of the Dielectric Leaf Wetness Sensor (Decagon Devices, 2014)
Most leaf wetness applications do not require knowledge of the amount of water on the surface, it just detects whether there is any water on the surface. To make this determination, a reference of minimum wetness (wetness threshold) must be identified.
Figure 2-6 shows the sample raw output from the LWS. The LWS outputs 445 raw data counts when the surface is dry. When the surface of the sensor is totally wet, for example in a heavy rain, the output goes up to around 1100 counts. This is vary depending on amount of water on the surface of the sensor, the higher amount of water will result in higher raw data count. And because ice (frost) has a much lower dielectric constant than that of liquid water, the sensor output from frost is much lower than that from rain (water).
19
Figure 2-6: Sample Raw Output from the LWS (Decagon Devices, 2014)
2.4.2 Ultrasonic Ranging Sensor
Ultrasonic sensors are devices that use electrical-mechanical energy transformation to measure distance from the sensor to the target object. A piezoelectric transducer in the ultrasonic sensor is used to send and detect sound waves. The transducer generates high frequency sound waves and evaluates the echo by the detector which is received back after the wave reflecting off the target. The sensor then calculates the time interval between sending the wave signal and receiving the echo to determine the distance to the target. The time for an ultrasonic sensor’s wave to strike the target and return is directly proportional to the distance to the object.
There are many advantages of using an ultrasonic ranging sensor. The biggest advantage is there is no physical contact with the object to be detected, therefore, there is no friction and wear. It has unlimited operating cycles since there is no mechanical contact with the object. Also, an ultrasonic sensor’s response is not dependent upon the surface color or optical reflectivity of the object. For example in this project, the sensing of a clear
20 sheet of ice, white snow, and a shiny stainless steel sheath is the same. And finally, the detected targets can be in the solid, liquid, granular or power state.
On the other hand, an ultrasonic ranging sensor has some disadvantages. Changes in the environment, such as temperature, pressure, humidity, and airborne particle affect ultrasonic response. It also has a minimum sensing distance. An ultrasonic is most suitable for sensing a smooth surface object because smooth surfaces reflect sound energy more efficiently than rough surfaces. Using an ultrasonic sensor to sense a rough surface could result in false reading, especially using in precise operation. And finally, it has problem sensing targets with low density, for example foam and cloth, because these materials tend to absorb sound energy. Therefore, it may be difficult to sense these materials at long range.
There are two ultrasonic ranging sensors tested during this project. One is SR50AT
Sonic Ranging Sensor form Campbell Scientific, Inc. and another is AGKU1500GI
Ultrasonic Sensor from EGE-Elektronik Spezial-Sensoren GmbH.
2.4.2.1 SR50AT Sonic Ranging Sensor
The SR50AT Sonic Ranging Sensor as shown in figure 2-7 measures the distance from the sensor to a target. The most common applications are measuring snow depths and water levels. The sensor determines the distance to a target by sending out the 50 kHz ultrasonic wave and receive the returning echo that is reflected from the target (Campbell
Scientific, 2014). The time interval taken between sending out and receiving is used to calculate the distance between the sensor and a target.
21
Figure 2-7: SR50AT Ultrasonic Sensor and Temperature Sensor (Campbell Scientific, 2014)
The SR50A Sonic Ranging Sensor requires 9-18 VDC power to operate. Its measurement range is from 0.5 to 10 meters with a resolution of 0.25 millimeters. And it can be operated in the temperature range between -45 and +50 °C.
The advantage of SR50AT is that it includes a temperature sensor to compensate for the speed-of-sound variation in air temperature. The temperature compensation is automatically applied to the sensor output for an accurate reading. The sensor is also capable of picking up small targets that are highly absorptive to sound, such as low density snow. However, SR50AT is not a perfect device. Too rough of a target surface can cause a bad reading. In addition, a poor reflector of sound such as extremely low density of snow will have an effect on the reading also.
The SR50AT is recommended to be operated with a terminal program such as
Hyperterminal. This program can be used to change factory default or existing settings.
The interval of data collection and method of thickness measurement can also be changed using the program. Figure 2-8 shows the screenshot of Hyperterminal and data output. In
22 the second and fourth columns of the data output shows the thickness of snow in
millimeters and temperature in degree Celsius respectively.
Thickness (mm) Temperature (°C)
Figure 2-8: Screenshot of Hyperterminal and Data Output
2.4.2.2 AGKU 1500 GI Ultrasonic Sensor
Similar to other ultrasonic sensors, AGKU 1500 GI Ultrasonic Sensor measures the
distance from a sensor to a target using ultrasonic wave. The transmitting and receiving
signal components are built into one probe as seen in figure 2-9.
23
Figure 2-9: AGKU 1500 GI (EGE-Elektronik, 2014)
The AGKU 1500 GI Ultrasonic Sensor requires 18-30 VDC power to operate. Its measurement range is from 200 to 1500 millimeters which is corresponding to the current output between from 4 to 20 mA as seen in figure 2-10. It can be operated in the temperature range between -15 and +70 °C. This sensor does not have a temperature sensor to compensate with changes in ambient temperature. The sensor requires a circuit to be able to operate. The circuit is shown in figure 2-11. The load resistance (RL) used in the circuit is between 0 to 500 ohms (EGE-Elektronik, 2014).
24
20
16
12
8
Output Current (mA) 4
0 200 400 600 800 1000 1200 1400 1600 1800 2000 Distance (mm)
Figure 2-10: AGKU 1500 GI Output Current VS Distance
24
Figure 2-11: AGKU 1500 GI Circuit (EGE-Elektronik, 2014)
Since data output of AGKU 1500 GI is current, National Instrument device and
LabView program are used to collect the output. The screenshot of the LabView program written for this sensor is shown in figure 2-12. The collected data (current output) is then calculated to be the thickness of ice or snow using a calibration.
25
Figure 2-12: Screenshot of LabView Program for AGKU 1500 GI
26 Chapter 3
Ice Presence and State Sensor Development
3.1 Introduction
There are three primary motivations for developing the ice presence and state sensor.
1) The Goodrich ice detector cannot sense ice persistence. The ice detector can detect
the accretion of ice and by using an integration strategy to adjust for the ice detector
heating cycles can estimate the initial thickness of the ice (Ryerson, 2008).
However, the ice sensing tip of the ice detector is not similar to a stay and the
electronics are a heat source. Therefore, the ice may sublimate or fall off the sensor
tip before it releases from the stay. Thus, the ice detector has limited utility in
revealing ice persistence.
2) The Leaf Wetness Sensor (LWS) is the only commercial sensor that is designed to
detect the presence of liquid water on a leaf. It is made of fiberglass to simulate the
thermal behavior of a leaf and is coated to simulate the hydrological characteristics
of a leaf and it cannot be installed directly on the surface of the stay. Even if it was
27 force fit to the surface of the stay the fiberglass substrate would prevent it from
replicating the stay behavior fully.
3) Liquid water beneath the ice is a precursor to ice shedding. Coupled with stay
surface temperature from the stay thermistors, recognizing the presence of water
beneath the stays is expected to be a powerful predictor of imminent shedding.
Such knowledge would give ODOT time before a shedding event to change the
traffic patterns.
Therefore, an effort to develop an ice presence and state sensor was undertaken.
The goal is to develop a small sensor that adheres directly to the sheath surface, accurate acquires the temperature of the surface or water above the surface, and detects the presence of water beneath the ice. A simple resistance based sensor was developed. Laboratory and outdoor tests were successful in differentiating the state of water on the sensor as it changed leading up to an experimental ice shedding event. The sensor cannot reliably provide an indication of the thickness of the ice. The sensor is ready for deployment.
3.2 Ice Presence and State Sensor Laboratory Testing
The Ice Presence and State Sensor (hereafter referred to as the “UT icing sensor”) was successfully developed using the differences in the electrical resistance properties of water, ice, and slush. Pure water will not conduct electricity to any measurable degree.
However in naturally occurring water, the impurities in the water permit some conduction.
Ice can also conduct electric current like water. But the conductivity is much reduced because the ion motion through the solid is thousands of times smaller than the motion of the same ions in the liquid water. Therefore, water is a much better conductor than ice.
28 Finally, slush, which is the mixture of water and ice, is also conductive. It does not allow conduction as well as water, but permits much more conduction than ice. By using this basic property (conductivity), this sensor can detect the state of a medium whether it is water, ice, or slush.
3.2.1 Sensors and Data Acquisition System
Conductivity is the reciprocal of resistivity: conductivity = 1/resistivity. Therefore, it is inversely proportional to resistance. UT icing sensor is developed to detect the resistance of a medium present on the surface of the sensor. Figure 3-1 shows the schematic circuit of the UT icing sensor. R1, R2, Eo, and Ei represent the fixed resistor, variable resistor, output data, and input voltage, respectively. R2, also called electrode spacing, is the area where sensor detects the state of a medium as seen in Figure 3-2. Eo is connected to data acquisition system which transfers data to a computer. Ei is connected to a power supply which provides a direct current of 5 V and 500 mA.
Electrode Spacing
Figure 3-1: UT Icing Sensor Circuit
29 Thermocouple
Variable Resistor (R2)
Figure 3-2: Electrode Spacing Area of the UT Icing Sensor
As seen in figure 3-2, a K-type thermocouple is attached to the UT icing sensor. This thermocouple plays a very important role in determining the state of a medium. The criteria has been created as if the temperature of the medium is below -2 °C, it is considered to be ice. If the temperature of the medium is above 2 °C, it is considered to be water. And finally, if the temperature of the medium is between -2 and 2 °C, it is considered to be slush.
National Instrument devices were chosen for the data acquisition system for the UT icing sensor as shown in Figure 3-3. The temperature data and resistance data are transferred through NI USB-TC01 Thermocouple Measurement and NI USB-6210 devices respectively. Both devices are connected to a LabView program which interprets and shows the data on its dashboard as shown in figure 3-4. In the dashboard, the high temperature limit for ice and low temperature limit for water can be set according to the rule. It also shows the possible state of the medium whether it is water, slush, or ice. The rules are mentioned before, if the temperature of the medium is below -2 °C, above 2 °C, and between
2 and -2°C, it is considered to be ice, water and slush respectively. In addition, the dashboard presents the real time plots of resistance (kOhm) and temperature (°C) according to time (s).
30 The dashboard is also automatically saved the data into an excel files once the user clicks on the stop button.
Direct Current Power Supply NI USB-6210
UT Icing Sensor
NI USB-TC01
Figure 3-3: UT Icing Sensor Connected to Data Acquisition System
Figure 3-4: Dashboard of UT Icing Sensor
31 3.2.2 Design of Experiments
The purpose of the initial experiment was to measure resistances for three mediums; water, slush and ice. All measurements are done in a controlled room temperature at 22 °C.
The resistance of ice is known to be the highest of all, on the other hand, water is known to have lowest resistance. The experiment is done using two UT icing sensors. Figure 3-5 and
3-6 shows UT icing sensor with 1-mm and 7-mm electrode spacing respectively.
1 mm
Figure 3-5: 1-mm Electrode Spacing UT Icing Sensor
32 7 mm
Figure 3-6: 7-mm Electrode Spacing UT Icing Sensor
The UT icing sensor and thermocouple were glued onto the bottom of a plastic container. Then, ice, slush, and water are filled in the container to measure the resistance of each medium. Figure 3-7 and 3-8 shows water and ice measurement respectively. In addition to measuring the resistance of water and ice, slush’s resistance is also to be measured. Water and ice are blended in the curtain ratio to make slush. This ratio is corresponding to weights of ice and water. In this experiment three types of slush are tested;
75% slush, 50% slush, and 25% slush. Figure 3-9 shows 75% slush which is the mixture of
75% ice and 25% water. This type of slush has the least liquid water content (LWC) of all the slushes. Figure 3-10 shows 50% slush which is the mixture of 50% ice and 50% water.
This type of slush has medium LWC. And finally, figure 3-11 shows 25% slush which is the mixture of 25% ice and 75% water. This type of slush has the most LWC of all the slushes.
33
Figure 3-7: Water Measurement
Figure 3-8: Ice Measurement
34
Figure 3-9: 75% Slush Measurement
Figure3-10: 50% Slush Measurement
35
Figure 3-11: 25% Slush Measurement
Furthermore, the measurements were made for three different thicknesses in each medium: 6 mm, 13 mm, and 19 mm. Figure 3-12 to 3-14 shows examples of measuring resistance of ice for three thicknesses. The blue lines represent the thicknesses and the medium needs to be filled up to the line to get an accurate measurement. The similar fashion measurements were also done for water and slush.
Figure 3-12: Ice Measurement at 6 mm thickness
36
Figure 3-13: Ice Measurement at 13 mm thickness
Figure 3-14: Ice Measurement at 19 mm thickness 3.2.3 Laboratory Test Results
3.2.3.1 UT Icing Sensor with 1-mm Electrode Spacing
The first set of resistance measurements was done using UT icing sensor with 1-mm electrode spacing, the sensor can be seen in Figure 3-5. The results are shown in figure 3-
15.
37 4000000
3500000
3000000
2500000
2000000 6 mm Thickness 13 mm Thickness 1500000
Resistance Resistance (Ohm) 19 mm Thickness 1000000
500000 ~200000 표ℎ푚 0 -10 -8 -6 -4 -2 Temperature ( C)
Figure 3-15: Resistance of Ice for 1-mm Electrode Spacing Sensor
The figure above shows comparison of resistances of ice in three different thicknesses corresponding to temperatures. Three measurements were done while letting the ice melt at room temperature. The ice which has a temperature of -2 °C has resistance as high as approximately 3,500 kOhm and as low as approximately 200 kOhm. Figure 3-16 shows the screenshot of the monitor while measuring the resistance of ice. The “possible state” section shows that ice is detected (as indicated by the red arrow).
38
Figure 3-16: Dashboard Screenshot of Ice Measurement
80000
70000
60000
50000
40000 75% Slush 6 mm Thickness Resistance Drop 30000 75% Slush 13 mm Thickness
Resistance Resistance (Ohm) 75% Slush 19 mm Thickness 20000
10000
0 0 20 40 60 80 100 120 Time (s)
Figure 3-17: Resistance of 75% Slush for 1-mm Electrode Spacing Sensor
Figure 3-17 shows comparison of resistances of 75% slush in three different thicknesses corresponding to time. Three measurements were done while letting slush melt at room temperature for 120 seconds. The slush which was at a temperature range of -2 and
39 2 °C has resistance as high as approximately 70 kOhm and as low as approximately 10 kOhm. The resistance drop seen in the graph shows that the sensor detects part of slush that has a higher liquid water content (LWC). Figure 3-18 shows the screenshot of the dashboard while measuring the resistance of 75% slush. The possible state section shows that slush is detected.
Figure 3-18: Dashboard Screenshot of 75% Slush Measurement
40 1400
1200
1000
800 Resistance Drop 50% Slush 6 mm Thickness 600 50% Slush 13 mm Thickness
Resistance Resistance (Ohm) 400 50% Slush 19 mm Thickness
200
0 0 20 40 60 80 100 120 Time (s)
Figure 3-19: Resistance of 50% Slush for 1-mm Electrode Spacing Sensor
Figure 3-19 shows comparison of resistances of 50% slush in three different thicknesses corresponding to time. Similar to the previous experiment, three measurements were done while letting slush melt at room temperature for 120 seconds. The slush which was in a temperature range of -2 and 2 °C has resistance as high as approximately 1,300
Ohm and as low as approximately 250 Ohm. It also shows resistance drop which means the sensor detects the part of slush that had higher LWC at that time. The 50% slush of 13 mm thickness shows low resistance because the sensor exposes to a very high LWC. Figure 3-
20 shows the screenshot of the dashboard while measuring the resistance of 50% slush. The possible state section shows that slush is detected.
41
Figure 3-20: Dashboard Screenshot of 50% Slush Measurement
300
250
200
150 Resistance Drop 25% Slush 6 mm Thickness 25% Slush 13 mm Thickness 100 Resistance Resistance (Ohm) 25% Slush 19 mm Thickness
50
0 0 20 40 60 80 100 120 Time (s)
Figure 3-21: Resistance of 25% Slush for 1-mm Electrode Spacing Sensor
Figure 3-21 shows comparison of resistances of 25% slush in three different thicknesses corresponding to time. Three measurements were done while letting slush melted in a room temperature for 120 seconds. The slush which is in a temperature range of
-2 and 2 °C has resistance as high as approximately 270 Ohm and as low as approximately
42 250 Ohm. The resistance drop seen in the graph shows that the sensor detects part of slush that has higher LWC. The 25% slush at all thicknesses shows low resistance because this type of slush has very high LWC. Figure 3-22 shows the screenshot of the dashboard while measuring the resistance of 25% slush. The possible state section shows that slush is detected.
Figure 3-22: Dashboard Screenshot of 25% Slush Measurement
250
200
150 Water 6 mm Thickness 100 Water 13 mm Thickness
Resistance Resistance (Ohm) Water 19 mm Thickness 50
0 0 20 40 60 80 100 120 Time (s)
Figure 3-23: Resistance of Water for 1-mm Electrode Spacing Sensor
43 Figure 3-23 shows comparison of resistances of water in three different thicknesses corresponding to time. Three measurements were done in a room temperature for 120 seconds. The water which is in a temperature range above 2 °C has resistance as high as approximately 207 Ohm and as low as approximately 188 Ohm. In this case, there is no presence of resistance drop because water has uniform property throughout its body. Figure
3-24 shows the screenshot of the dashboard while measuring the resistance of water. The possible state section shows that water is detected.
Figure 3-24: Dashboard Screenshot of Water Measurement 3.2.3.2 UT Icing Sensor with 7-mm Electrode Spacing.
The second set of resistance measurements was done using UT icing sensor with 7- mm electrode spacing, the sensor can be shown in Figure 3-6. The same procedure was performed as 1-mm electrode spacing sensor. The results are shown in the following figures.
44 6000000
5000000
4000000
3000000 6 mm Thickness 13 mm Thickness 2000000 Resistance Resistance (Ohm) 19 mm Thickness
1000000
0 -8 -7 -6 -5 -4 -3 -2 Temperature ( C)
Figure 3-25: Resistance of Ice for 7-mm Electrode Spacing Sensor
200000
180000
160000
140000
120000
100000 75% Slush 6 mm Thickness
80000 75% Slush 13 mm Thickness
Resistance Resistance (Ohm) 60000 75% Slush 19 mm Thickness
40000
20000
0 Resistance Drop 0 20 40 60 80 100 120 Time (s)
Figure 3-26: Resistance of 75% Slush for 7-mm Electrode Spacing Sensor
45 700
600
500
400 50% Slush 6 mm Thickness 300 50% Slush 13 mm Thickness
Resistance Resistance (Ohm) 200 50% Slush 19 mm Thickness
100
0 0 20 40 60 80 100 120 Time (s)
Figure 3-27: Resistance of 50% Slush for 7-mm Electrode Spacing Sensor
350
300
250
200 25% Slush 6 mm Thickness 150 25% Slush 13 mm Thickness
Resistance Resistance (Ohm) 25% Slush 19 mm Thickness 100
50
0 0 20 40 60 80 100 120 Time (s)
Figure 3-28: Resistance of 25% Slush for 7-mm Electrode Spacing Sensor
46 250
245
240
235
230
225 Water 6 mm Thickness 220 Water 13 mm Thickness
Resistance Resistance (Ohm) 215 Water 19 mm Thickness
210
205
200 0 20 40 60 80 100 120 Time (s)
Figure 3-29: Resistance of Water for 7-mm Electrode Spacing Sensor
The result of 7-mm electro spacing sensor shows very similar trend compared to the previous experiment. The resistance of ice is the highest of all, above 400 kOhm. On the other hand, the resistance of water is the lowest and that of slush is in between. The slush’s resistance depends on its LWC. The result indicates that the higher LWC, the lower the resistance as seen in the result of 25% slush having lower resistance than 50% and 75% slush respectively. Figure 3-30 is provided to present resistances of all mediums at the 6 mm thickness and 7 mm electro spacing. It clearly shows that each medium has its own resistance range and this trend is similar for other cases.
47 10000000
1000000
100000 Ice 75% Slush 10000 50% Slush
Resistance Resistance (Ohm) 25% Slush 1000 Water
100 0 20 40 60 80 100 120 Time (s)
Figure 3-30: Resistances for 6-mm Thickness and 7-mm Electro Spacing Sensor
Previous figures appear that the thickness does not play a constant role in the medium’s resistance. Overall, the result from this experiment is very valuable and was applied to the full scale experiment which will be discussed in the next section.
3.3 UT Icing Sensor on Full Scale Experiments
Since the performance of the UT icing sensor in the laboratory was promising, the sensor was installed on a full scale specimen. The goal of experiments was to simulate the ice and wet snow formulation on the specimens and track the behavior of those mediums in the more realistic environment. The full scale experiment was initially designed in winter
2012-2013. For the efficiency and safety issues, the experiment station was located at the
Scott Park campus of the University of Toledo instead of at the VGCS. Three 10 ft-long cable stay sheath with the same diameter and material as the VGCS stays have been set up as shown in figure 3-31. In winter 2013-2014, a high density polyethylene (HDPE)
48 specimen from another bridge was added to the experiment station as seen in figure 3-32.
Ice and wet snow experiments were performed at the experiment station.
Figure 3-31: VGCS Stainless Steel Specimens
Frame Structure HDPE Specimen
Figure 3-32: HDPE Specimen and Frame Structure
49 3.3.1 Specimens and Data Acquisition System Setup
At the experiment station, the three VGCS specimens have been set up on a concrete pad. All three specimens are aligned North - South and two of them are supported by concrete blocks to have approximately 30 degree angle to the ground as seen in figure 3-31 representing slope of the shallowest stay-cables at VGCS bridge. These two supported specimens are facing in different direction, one facing North and the other facing South, as same as the orientation of the VGCS bridge. The solar radiation has an effect on this orientation which will be discuss in the result of this chapter. Figure 3-33 shows 120 un- tensioned strands placed inside the North facing specimen. This experimental setup is to simulate the bridge conditions as realistically as possible.
Strands
Figure 3-33: North Facing Specimen with 120 Strands Inside
The HDPE specimen had a diameter of 20 centimeters. The HDPE specimen was set up in a similar orientation as the South facing VGCS specimen. Figure 3-32 shows the
HDPE specimen supported by concrete blocks so it makes approximately 30 degree angle
50 to the ground. The frame structure was built to be able to install sensors, such as ultrasonic and thickness sensors, for more data collection during the experiments.
The UT icing sensors developed in a laboratory were installed on both VGCS and
HDPE specimens. The sensors were installed approximately at the mid-span of both specimens as shown in Figures 3-34 and 3-35. Figure 3-35 also shows that the dielectric leaf wetness sensor is installed on the top part of the HDPE specimen. Four sets of the UT icing sensors are placed on East, Top, West, and Bottom sides of each specimen, Figure 3-
36 represents the cross section of a specimen and its sensors setup orientation. The reason the sensors were set up as shown is because the thermal properties are need to be discovered along the circumference of the specimens while ice or wet snow occurs. This information will be very helpful to determine ice or snow accretion and shedding. Figure 3-37 shows the
UT icing sensor attached to the surface of HDPE specimen.
UT Icing Sensors
Figure 3-34: Sensors Setup on VGCS Specimen
51 LWS UT Icing Sensors
Figure 3-35: Sensors Setup on HDPE Specimen
Top
West East
Bottom Figure 3-36: Cross Section and Sensors Setup Orientation of both Specimens
UT Icing Sensor
Solar Radiation Sensor
Figure 3-37: UT Icing Sensor on HDPE Specimen
The UT icing sensors tested in the full scale experiments were the same design as
the ones used in the laboratory. However, a different the data acquisition system was used.
52 The Scott Park data acquisition system recorded voltage rather than resistance. The new
MicroStrain data acquisition system were selected because it is wireless. The wireless feature is very convenient for this type of experiment. Figure 3-38 and 3-39 shows
MicroStrain V-Link and TC-Link respectively. The same UT icing sensor circuit built for the laboratory test is connected to MicroStrain V-Link directly. This data acquisition system provides 7 mV and was set to read the voltage output from the UT icing sensor circuit as opposed to resistance in the laboratory experiments. The commercial LWS which has similar electrical mechanism as the UT icing sensor is also connected to the MicroStrain V-
Link to be able to compare the result data of both sensors. The MicroStrain TC-Link is a wireless thermocouple data acquisition system. It works with any type of thermocouple in which K-type thermocouples are chosen for this experiment.
Figure 3-38: MicroStrain V-Link
53
Figure 3-39: MicroStrain TC-Link
According to the schematic circuit of the UT icing sensor shown in figure 3-1, R1,
R2, Eo, and Ei represents fixed resistor, variable resistor, output data, and input voltage respectively. The equation of the sensor circuit is shown below.
푅1 퐸표 = × 퐸푖 (푅2 + 푅1)
R1 and Ei are designed to be fixed which in this case, there are 1 kOhm and 7 mV respectively. The output voltage (Eo) is depended on what detected on the sensor (R2). The equation before can be further simplified as:
퐸 푅 표 = 1 퐸푖 (푅2 + 푅1)
퐸 (푅 + 푅 ) 푖 = 2 1 퐸표 푅1
퐸 푅 푖 = 2 + 1 퐸표 푅1
The final equation above shows that when Ei and R1 are fixed, Eo and R2 have an inverse relationship. According to the circuit, there are four cases can occur:
1) If the sensor detects nothing (air) which has resistance close to infinity, the output
voltage will be the lowest.
54 2.) If the sensor detects water which has resistance as low as 200 Ohm, the output
voltage will be the highest.
3.) If the sensor detects ice which has resistance is as high as 2 MOhm, the output voltage
will be low but not as low as that of air.
4.) If the sensor detects slush (wet snow) which has been proven in the previous section
that its resistance range is between that of ice and water, the output voltage will be
in a range between ice and water as well.
The output voltage indicated what was present on the surface of the UT icing sensor.
That is the output voltage reflected the state of the water in the space between the electrodes.
Because of this significant difference in voltage ranges, the state of the medium can be easily determined whether it is ice, water, slush (wet snow) or air. For all UT icing sensor plots in this thesis, the unit is MicroStrain’s arbitrary counts. The relationship between the output voltage of the icing sensor and the output of the MicroStrain DAQ is linear, but the calibration constant was not reset to correspond to voltage. Thus, the report output is in raw counts.
Figure 3-40: MicroStrain WSDA-Base (Signal Receiver)
55 Both V-Link and TC-Link send radio signals to a base station called WSDA-Base as seen in figure 3-40. This base is connected to a computer via USB port and transfers the data collected to Node Commander Software. This software is consisted by a dashboard that presents real time data and configures the data acquisition system when needed. The collected data can be exported as an Excel file for further analysis.
3.3.2 Full Scale Outdoor Tests
The UT icing sensor connected to MicroStrain data acquisition system was testing in a laboratory once more before deploying to a full scale experiment. The sensor was attached to a small container, shown in figure 3-41, which later was filled with three different states of medium (ice, slush, and water) to determine an output voltage of each state.
Figure 3-41: V-Link and UT Icing Sensor
Figure 3-42 to 3-44 shows ice, slush, and water on the surface of the UT icing sensor while collecting sensor output data.
56
Figure 3-42: Ice Testing
Figure 3-43: Slush Testing
Figure 3-44: Water Testing
The resultant data from this testing is presented in the figure 3-45. Each medium shows a characteristic range of output. The range of water is very high, on the other hand, ice shows a very low range, but not as low as a dry surface (air between the electrodes). The
57 output range of slush was between water and ice. Since it is a mixture of ice and water, it has a non-uniform property which results in inconstant output as shown in the figure. As resulted, all four states clearly have different ranges of data output. Therefore, the presence of medium on the surface of the sensor can be easily determined by the ranges.
Where T is temperature in °C
T > 2 -2 < T < 2 T > 2 T < -2
Figure 3-45: UT Icing Sensor Initial Test 3.3.3 Full Scale Experimental Result
During winter of 2012 - 2014, multiple full scale experiments were done, including icing and wet snowing. Icing experiments on VGCS specimen, which was done during the winter of 2012-2013, overall results have been presented and discussed in Arbabzadegan
58 2013. This thesis focuses on the UT icing sensor data from the icing experiment. In addition, wet snow experiments on HDPE in the winter of 2013-2014 are discussed here.
3.3.3.1 Icing Experiments
The experiments were performed at the Scott Park site on cold nights, the ambient temperature was -2 to -15 °C. Water was misted slowly on the VGCS specimen as seen in figure 3-46. This caused the latent heat of transformation not to raise the specimen temperature above 0°C (Arbabzadegan, 2013). Figure 3-47 shows the ice pattern accumulated on the specimen after misting water for total time of 8 to 10 hours. The ice thickness was measured to be approximately 13 mm.
Figure 3-46: Misting Water on VGCS Specimen
59
Figure 3-47: Ice Accumulation on VGCS Specimen
During this experiment, the data acquisition system monitored the behavior of the ice. Since the slush state was not taken in account, a higher fixed resistor (R1) was placed into the sensor circuit to expand the voltage range that could be sensed. Increasing the range permitted a more sensitive indication of the ice state. The range of ice state is wider than that found in the initial laboratory test and figure 3-45.
60 40 20 UT Icing 35 15 Thermocouple 30 10
25 5
20 0
15 -5 Temperature ( C) 10 -10
5 -15 UT Icing Icing UT SensorOutput (Raw Counts) A B C D 0 -20
Time
02/16/2013 09:00 02/16/2013 02/16/2013 02/16/2013 03:00 02/16/2013 06:00 02/16/2013 12:00 02/16/2013 15:00 02/16/2013 18:00 02/16/2013 21:00 02/17/2013 00:00 02/16/2013 02/16/2013 00:00
Figure 3-48: Stay Behavior in Icing Experiment
The UT icing sensor and thermocouple data for icing experiment on February 16th,
2013 are presented in figure 3-48. Part A in the figure shows the sensor output before starting the experiment. The icing sensor output was very low because it did not detect anything but air. The thermocouple showed the temperature of the stay which is as same as the ambient temperature.
Part B focuses ice accumulation scenario. The cool water was misted starting around
5:00 AM, the UT icing sensor output shows suddenly increase due to the presence of water.
The thermocouple shows that the temperature rises up to zero degrees because of latent heat during the ice transformation. The UT icing sensor output and temperature then gradually dropped because the surface of the specimen was covered with ice. Even though the water was kept misting on the specimen and more ice built up, the sensors were not able to sense
61 that because they can only detect what happen at the surface of the specimen. The misting was stopped before sunrise which was around 7:30 AM.
Part C illustrates ice melting due to a solar radiation. The sun rose around 7:45 AM.
The sun radiation had significant impact on the specimen temperature. As sun rose, the specimen and increased as well. However, the ambient temperature was constant throughout that day at -2 °C. Around 10:00AM, ice sheet on the specimen started to melt due to the rise of specimen temperature. This created small water layer between ice sheet and the surface of the specimen. Again the thermocouple shows the latent heat during ice transformation.
But this time, the ice turned to water. The UT icing sensor also clearly showed that it detected the presence of water. The change in specimen temperature caused the presence of ice and water throughout the day. However, the specimen temperature was not high enough for shedding event. The ice sheet was still on the surface of the specimen at the end of the day.
After sunset, the ambient temperature sudden decreased as shown in part D. This caused the specimen temperature to drop as well as. The output of the UT icing sensor indicated the presence of ice without water at the surface.
Comparison of the ice accumulation monitoring and UT icing sensor data shows that the sensor reliably reports the presence of ice on the specimen surface. When no natural icing events occurred and the results appeared to be very similar.
3.3.3.2 Wet Snow Experiments
Multiple wet snow experiments were performed at Scott Park site on cold nights during winter of 2013 - 2014. For more controlled experiments, a snow gun was developed.
62 The mechanism of the gun is mixing compressed air and water inside the gun, it then
streams the mixed liquid through a small nozzle into an ambient temperature of -2 °C or
lower. The 33-gollon air compressor was selected to be able to supply compressed air
continuously. The LWC of the snow can be adjusted depending on amount of water mixing
with the compressed air; more amount of water supplied, higher LWC snow. Figure 3-49
shows the setup of the snow gun. Detailed assembly and principle are presented by Clinton
(Mirto, to be published). This snow gun was used to create wet snow on the HDPE
specimen to be able to study its thermal properties during accretion and shedding events.
Snow
Air Compressor
Figure3-49: Snow Gun 3.3.3.2.1 Dry Snow Event of 17th February 2014
On 17th February 2014, natural dry snow started to fall down in the evening. It
snowed until midnight and the ambient temperature stayed cold below 0 °C throughout the
night. During the event, the density of the snow was measured. To do so, three known
volume containers were placed at the site open ground to collect snow during the event as
shown in figure 3-50. The containers of snow were weighted and calculated for an average
density (Appendix A). The average density was calculated to be 81.2 kg/m3 which is
considered dry snow according to section 2.1.2.
63
Figure 3-50: Snow in Known Volume Containers
The next day, 18th February 2014, dry snow was still appeared on top of the specimen as seen in figure 3-51. Later in the day, the weather became very sunny and the ambient temperature rose up to approximately 15 °C. Figure 3-52 and 3-53 show the output from UT icing sensors and thermocouples respectively.
Figure 3-51: Snow Collected on top of HDPE Specimen
64 40 12 Icing East 38 10 Icing Top Dry Snow 36 Melting 8 Icing West 34 6
Icing 32 4 Bottom Dry Snow Falling
at 19:55 OUTPUT COUNTS) LWS (RAW LWS
30 2 UT ICING SENSOR OUTPUT (RAW COUNTS) (RAW OUTPUT SENSOR ICING UT
28 0
TIME
2/17/2010 06:00 2/17/2010 2/16/2010 19:30 2/16/2010 21:00 2/16/2010 22:30 2/16/2010 00:00 2/17/2010 01:30 2/17/2010 03:00 2/17/2010 04:30 2/17/2010 07:30 2/17/2010 09:00 2/17/2010 10:30 2/17/2010 12:00 2/17/2010 13:30 2/17/2010 15:00 2/17/2010
Figure 3-52: UT Icing Sensors VS. LWS during Dry Snow Event
Figure 3-52 shows that when the dry snow fell on the specimen, both Top UT icing sensors and LWS could not detect anything because of very low LWC in the snow. In this case, East, West, and Bottom sensors were not taken into consideration because snow did not accrete on those areas. Throughout the night, the ambient temperature stayed at approximately -4 °C, therefore the LWC of snow stayed very low as well. However, it was very sunny in the next morning and the ambient temperature rose up quickly. Both sensors started to sense higher LWC around 10:00 meaning that the dry snow started to melt due to the sun radiation and ambient temperature. Between 10:00-14:30, both sensors showed dry snow was in the process of melting. Since LWS was painted by a hydrophobic coating, the melted snow was easily shed of the surface of the sensor, unlike the UT icing sensors.
65 This event can be easily seen between 13:30–15:00 in the graph, the LWS detected lower
LWC than the Top UT icing sensor.
20
15
10
5 East Top 0
West TEMPERATURE ( ( C)TEMPERATURE
-5 Bottom Dry Snow Falling at 19:55 Dry Snow Melting -10
TIME
2/16/2010 19:30 2/16/2010 21:00 2/16/2010 22:30 2/16/2010 00:00 2/17/2010 01:30 2/17/2010 03:00 2/17/2010 04:30 2/17/2010 06:00 2/17/2010 07:30 2/17/2010 09:00 2/17/2010 10:30 2/17/2010 12:00 2/17/2010 13:30 2/17/2010 15:00 2/17/2010
Figure 3-53: Thermocouples during Dry Snow Event
The temperature of the specimen dropped when the dry snow started to fall at 19:55.
Then it stayed cool throughout the night as seen in figure 3-53. Around 10:00 in the next day, East, West, and Bottom temperature started to rise, except the Top temperature because of the coverage of snow. During 10:00-12:00, the Top temperature stayed around
0 °C during the process of dry snow melting. The temperature then suddenly rose up from
0 to 8 °C at 12:15 because the coverage of snow was melt away and thermocouple was exposed directly to the sun.
66 40 20
38 15
UT Icing 36 10 Thermal Lag 34 5 Thermocouple
32 0 Temperature ( C)
30 -5 UT Icing Icing UT SensorOutput (Raw Counts) 28 -10
Time
2/17/2010 01:30 2/17/2010 2/16/2010 19:30 2/16/2010 21:00 2/16/2010 22:30 2/16/2010 00:00 2/17/2010 03:00 2/17/2010 04:30 2/17/2010 06:00 2/17/2010 07:30 2/17/2010 09:00 2/17/2010 10:30 2/17/2010 12:00 2/17/2010 13:30 2/17/2010 15:00 2/17/2010
Figure 3-54: Top UT Icing Sensor VS Thermocouples during Dry Snow Event
Figure 3-54 shows a comparison of Top UT icing sensor and thermocouple during
this event. “Thermal Lag”, which represented by steady specimen temperature at 0°C, was
clearly shown when the dry snow started to melt at 10:00 and it continued until 12:00. This
thermal lag phenomenon occurs due to latent heat during a phase transition. In this case,
the dry snow was making a phase transition to water.
3.3.3.2.2 Wet Snow on the Top of the Specimen Experiment on 13th March 2014
This experiment simulated wet snow falling on top of the specimen. To do so, the
snow gun was spraying on top of the HDPE specimen until snow accumulated up to 20 cm
above the specimen surface as seen in figure 3-55. This process took approximately two
hours which was between 21:36 and 23:45. The average snow density generated by a snow
67 gun was also calculated (Appendix B). It was calculated to be 208.7 kg/m3 which is considered wet snow since it was more than 100 kg/m3 (Farzaneh, 2008). Similar to the dry snow event, the sky was really sunny in the next morning. The high solar radiation and ambient temperature resulted in wet snow shedding.
20 cm thickness
Figure 3-55: Wet Snow Accretion on Top of HDPE Specimen
29 9 Starting Snow 28 8 Icing East
27 Snow 7 Icing Top 26 Shedding 6 at 11:36 25 5 Icing West 24 4
23 3 Icing Bottom Stopping 22 Snow 2
LWS LWS Output(Raw Counts) LWS 21 1
UT Icing Icing UT SensorOutput (Raw Counts) 20 0
Time
3/14/2014 03:00 3/14/2014 12:00 3/14/2014 3/13/2014 19:30 3/13/2014 21:00 3/13/2014 22:30 3/13/2014 00:00 3/14/2014 01:30 3/14/2014 04:30 3/14/2014 06:00 3/14/2014 07:30 3/14/2014 09:00 3/14/2014 10:30 3/14/2014 3/13/2014 18:00 3/13/2014
Figure 3-56: UT Icing Sensors VS LWS during Wet Snow on the Top Side Experiment
From the figure 3-56, both Top UT icing sensor and LWS sensed high LWC immediately when the snow gun was started to spray wet snow at 21:46. The first layer of
68 the wet snow showed the most LWC because it accumulated on the surface of the sensor directly. As more layers of wet snow built up, the first layer which did not exposed to the snow gun anymore started to cool down and presented lower LWC. This is shown between
21:45 to 23:45 as both UT icing sensor and LWS sensed lower LWC in of the snow of the sensor surfaces. After stopped spraying, the LWC became even lower due the cool temperature (approximately -8 °C) throughout the night. The next morning around 9:00, both UT icing sensor and LWS started to detect higher LWC again because the snow started to melt due the solar radiation and high ambient temperature. As seen in the graph 3-4, the
UT icing sensor detected more LWC earlier than the LWS because it was installed on the surface of the specimen. On the other hand, LWS was installed above the surface of the specimen, therefore it did not show what actually occurred on the surface. However, both sensors had similar plots showing that the snow had very high LWC before it shed from the specimen. At 10:15, the UT icing sensor sensed very high LWC meaning that much of snow had melted. The sensor then showed the reading of air state at 11:15. This sent a warning that the wet snow was not attach to the surface of the specimen anymore and it was ready to shed very soon. At 11:36, the wet snow shed from the specimen as seen in figure 3-57. The wet snow shed and fell on the West side of the specimen. The reason behind this is because the sun had melted part of the snow on the East side, therefore the center of gravity of the snow shifted to the West side and it shed off to the West.
69 Snow shed to the west side of the specimen
Figure 3-57: Snow Shed during Wet Snow on Top Experiment
20
15
10 Starting Snow East 5 Stopping Snow Top West
Temperature ( C) 0 Bottom Ambient -5 Snow Shedding at 11:36 -10
Time
3/14/2014 06:00 3/14/2014 3/13/2014 19:30 3/13/2014 21:00 3/13/2014 22:30 3/13/2014 00:00 3/14/2014 01:30 3/14/2014 03:00 3/14/2014 04:30 3/14/2014 07:30 3/14/2014 09:00 3/14/2014 10:30 3/14/2014 12:00 3/14/2014 3/13/2014 18:00 3/13/2014
Figure 3-58: Thermocouples during Wet Snow on the Top Side Experiment
According to figure 3-58, the temperature of the top side of the specimen increased to 0 °C immediately as wet snow started accumulating. Similar to dry snow event, the
70 temperature of the specimen dropped during the night due to cool ambient temperature and rose very high in the next morning. During 9:30-11:15, the Top temperature stayed around
0 °C during the process of wet snow melting. The temperature then suddenly rose up from
0 to 16 °C at 11:36 because the snow shed and thermocouple was exposed directly to the sun. At this moment, the ambient temperature was 8.8 °C.
29 20 UT Icing 28 15 27 Thermocouple Shedding at 26 Thermal Lag 10 11:36 25 5 24
23 0 Temperature ( C) 22 -5 21
UT Icing Icing UT SensorOutput (Raw Counts) Warning Time 20 -10
Time
3/14/2014 00:00 3/14/2014 3/13/2014 19:30 3/13/2014 21:00 3/13/2014 22:30 3/13/2014 01:30 3/14/2014 03:00 3/14/2014 04:30 3/14/2014 06:00 3/14/2014 07:30 3/14/2014 09:00 3/14/2014 10:30 3/14/2014 12:00 3/14/2014 3/13/2014 18:00 3/13/2014
Figure 3-59: Top UT Icing Sensor VS Thermocouples during Wet Snow on the Top Side Experiment
Figure 3-59 shows a comparison of Top UT icing sensor and thermocouple during this experiment. Similar to the dry snow event, the plot clearly shows “Thermal Lag” when the wet snow started to accumulate and freeze at 21:55 and when it started to melt at 9:30.
These phenomenon occurred first while the wet snow making wet snow (high LWC) to dry snow (low LWC) transition and second when it started to melt making snow to water transition on the surface of the specimen. The “Warning Time” is an alert time before the
71 shedding event occurred starting from the end of the last peak of the UT icing sensor. It is very useful for snow shedding prediction. In this experiment, the warning time was 45 minutes before shedding.
3.3.3.2.3 Wet Snow on East side of the Specimen Experiment on 18th March 2014
This experiment was performed using the similar procedure as the previous wet snow experiment. But instead of spraying the snow on the top of the specimen, it was sprayed to the east side of the specimen as shown in figure 3-60 and 3-61. Wet snow accumulated up to 10 cm and was shed due to solar radiation and above freezing temperature in the next morning.
Figure 3-60: Wet Snow Spraying from East
72 10 cm thickness
Figure 3-61: Wet Snow Accretion on the East Side
The data collected from this experiment was also very similar to the previous wet snow experiment. The only difference was the East UT icing sensor detected the LWC instead of the Top sensor. Since the data from both experiment are very similar, only the comparison of East UT icing sensor and thermocouple is presented.
27 16 UT Icing Starting Snow Shedding at 26 10:55AM 12
Thermocouple 25 8 Thermal Lag 24 4
23 0
22 -4 Temperature ( C)
21 -8
UT Icing Icing UT SensorOutput (Raw Counts) Warning Time 20 -12
Time
3/18/2014 00:45 3/18/2014 01:30 3/18/2014 02:15 3/18/2014 03:00 3/18/2014 03:45 3/18/2014 04:30 3/18/2014 05:15 3/18/2014 06:00 3/18/2014 06:45 3/18/2014 07:30 3/18/2014 08:15 3/18/2014 09:00 3/18/2014 09:45 3/18/2014 10:30 3/18/2014 11:15 3/18/2014 12:00 3/18/2014 3/18/2014 00:00 3/18/2014
Figure 3-62: East UT Icing Sensor VS Thermocouples during Wet Snow on the East Side Experiment
73 At 1:05, the snow gun was started to spray wet snow and the East UT icing sensor detected high LWC suddenly as seen in figure 3-62. The East temperature also increased to approximately 0 °C and it stayed in that range for a little while showing the “Thermal
Lag” similar to the previous experiment. After the snow gun was stopped, both temperature and LWC started to decrease throughout the night. Next morning, the radiation of the sun increased the ambient temperature and started to melt the snow. The East UT icing sensor then detected higher LWC after 9:15 and the thermocouple showed “Thermal Lag” again meaning that the snow is making snow to water transition. The sensor then later showed the reading of air state at 10:30. At this moment, it was sending a warning that the wet snow was not attach to the surface of the specimen anymore and it was ready to shed very soon. At 10:55, the wet snow shed to the East side of the specimen at the ambient temperature at 6.8 °C as seen in figure 3-63. The UT icing sensor was given a warning time of 30 minutes before the shedding occurred.
Snow shed from the specimen.
Figure 3-63: Wet Snow Shed on the East side
Several more wet snow experiments were performed during winter of 2013 - 2014.
The results of the UT icing sensor were very similar in all cases. The warning time before shedding was recorded to between 30 minutes to 2 hours. It varies depending on the current
74 ambient temperature, solar radiation, and location of the snow accretion. The location appears to make the most impact on shedding event of all factors. According to multiple experiments, the snow accretion on the side gave shorter warning time of 30 to 45 minutes.
On the other hand, the warning time for the top accretion was between 45 minutes to 2 hours. The observation and comparison of data during these experiments show that the UT icing sensor has shown its reliability of reporting the snow accumulation and giving a warning before snow shedding.
3.4 Conclusion and Next Steps
A sensor that can differentiate between ice, slush, and water on the VGCS stays has been developed. The sensor is rugged and compact so it can be mounted directly on the stay.
In an icing and/or wet snow event, it will be covered by the medium and detect the presence of liquid water in the interstice between the ice/wet snow layer and the stay sheath. When there is water in this space, ice/wet snow fall is imminent. The sensor has been tested in the laboratory and on full scale mock-ups of the VGCS and HDPE sheath outdoors. The next step is to deploy the sensor on a bridge.
75 Chapter 4
Thickness Measurement
Ice and snow thickness plays a significant role in the shedding event. The thicker the ice and snow accumulation results in higher chance of and a more severe shedding event. In the previous shedding event at VGCS Bridge, the ice was accumulated up to 1.9 cm and fell down 76 m down to a roadway. Similar scenario also happened on another bridge in the North America as wet snow accumulation which has a high LWC was presented on the cables of the bridge. Its energy increased significantly when felling 100 m to the deck below. Both scenarios cause high risk in traveling public and it has been a major concern for structures in this region. By knowing an accurate thickness of ice and snow, it allows a better prediction of shedding and helps bridge personnel to properly respond to the situation such as proper time for road closure on instigating action control.
Due to limitation of outdoor experimental days, only wet snow thickness experiments were performed. In this chapter, three thickness measurement devices are presented and compared to determine which is the most accurate and efficient.
76 4.1 Devices Setup and Initial Test
For an optimum use, the devices needed to be set up as instructed in the chapter 2.
Some of the devices needed an extra circuit built into it for them to work properly.
4.1.1 AGKU 1500 GI Ultrasonic Sensor
An extra circuit as seen in the schematic drawing in figure 2-11 was built for this type of ultrasonic sensor. The sensor was set up as instructed in chapter 2.4.2.2. Power source supplied 25 V as the sensor is required the power 18 to 30 V. Since the output of this device is current, National Instrument device and LabView software was selected to be its data acquisition system. The setup of the data acquisition system is shown in figure
4-1.
Power Source
National Instrument Device
Figure 4-1: Data Acquisition Setup for the Sensors
To make sure that this sensor is working properly, three initial tests were done on current output, detection range, and temperature compensation. Manual of the AGKU 1500
GI claims that the sensor can detect at a range between 200 to 1500 mm given as a graph shown in figure 2-10. The current output test was done before the sensor was deployed to the full scale experiment. During the test, the sensor was set at fixed position and pointing
77 at the 6 cm diameter cylinder at different distance along a center line according to the sensor as seen in figure 4-2 and 4-3.
Figure 4-2: AGKU 1500 GI Current Output Testing (1)
Figure 4-3: AGKU 1500 GI Current Output Testing (2)
The figure 4-4 shows the graph of the current output versus distance. The experimental data does not line up with the manufacturer’s data. However, experimental the current output and distance still have a linear relationship similar to the manufacturer’s data. It also shows that the sensor actually is capable of detect up to 1900 mm instead of
1500 mm which is more than that described in its specification.
78 24
20
16
12 Manufacturer's Data
8 Experimental Current Current Output (mA) 4
0 0 500 1000 1500 2000 2500 Distance (mm)
Figure 4-4: Result of AGKU 1500 GI Current Output Testing
The second test was done to determine the detection range of the range when an object target is not on a center line of the sensor. In this test, similar procedure as current output test was performed. The same target (6 mm diameter cylinder) was used during the test and placed in different locations off center along the sensor detection line. Figure 4-5 and 4-6 represent the manual and experimental range detection data respectively. The results show that both range detection data have very similar trend. Similar to the previous test, the experiment data indicates that the sensor can detect the range longer than that described in it specification.
Figure 4-5: AGKU 1500 GI Manual Detection Range Plot (EGE-Elektronik, 2014)
79 mm 125 87.5 62.5 37.5 12.5 -12.5 -37.5 -62.5 -87.5 -125 mm
Figure 4-6: AGKU 1500 GI Experimental Detection Range Plot
The final test for AGKU 1500 GI was the temperature compensation test. If the sensor was to deploy during icing events, it must be able to operate properly in an extremely cold temperature. Therefore, testing if the sensor is capable of temperature compensation is very important. Without this function, the sensor could sent out a false alarm. To do the test, the sensor was mounted on a frame and pointed at the HDPE specimen throughout days exposed to variation of temperatures as seen in figure 4-7 and 4-8.
80 Thermistor for SR50AT Laser
AGKU 1500 GI Camera
SR50AT
Figure 4-7: Ultrasonic Sensors and UT Laser Thickness Sensor Setup (Side View)
Laser
AGKU 1500 GI
SR50AT
Figure 4-8: Ultrasonic Sensors and UT Laser Thickness Sensor Setup (Top View)
The graph in figure 4-9 shows the temperature compensation result of AGKU 1500
GI. It indicates that the sensor does not have this function as it results in the decrease of the thickness even though nothing was accumulate on the specimen. On the night of 27th
March 2014, the sensor read the thickness to be almost -5 mm which was incorrect and this could cause a wrong sensor reading in an actual application.
81 20 20
15 15 Thickness Temperature 10 10
5 5
0 0 Thickness (mm) Thickness
-5 -5 ( Temperature C)
-10 -10
-15 -15
3/25/2010 15:30 3/25/2010 3/25/2010 21:30 3/25/2010 00:30 3/26/2010 03:30 3/26/2010 06:30 3/26/2010 09:30 3/26/2010 3/25/2010 18:30 3/25/2010 Time
Figure 4-9: Result of AGKU 1500 GI Temperature Compensation Test
4.1.2 SR50AT Sonic Ranging Sensor
SR50A sonic ranging sensor was connected to the power source and directly to a computer shows in figure 4-1. Hyperterminal software was installed in the computer to be able to read and collect the output of the sensor. The screenshot of the software is shown in figure 2-8. The output of the sensor is digital, therefore the thickness of the snow shows directly on the dashboard of the software. The sensor is also attached to a thermistor which is used for the temperature compensation purpose. Temperature corrections is automatically applied to the readings. The thermistor is installed inside the white honeycomb-like housing as shown in figure 4-7.
To make sure this sensor operates properly, two initial tests were done; known thickness detection, and temperature compensation tests. The first test was done inside a laboratory, figure 4-10 shows the setup of the test. The sensor was set at fixed position and
82 pointed perpendicularly to the HDPE specimen. The known thickness object (12 mm) then was stuck on a surface of the specimen and the sensor readings were collected.
SR50AT 12 mm thickness object
Figure 4-10: SR50AT Known Thickness Detection Test
Figure 4-11 shows the reading of the sensor when the know thickness object was placed on the specimen at time step of 59 s. The sensor read 12 mm thickness which was same exact as the actual thickness. The result of this test showed that the sensor was accurate and capable of detecting small target.
14 Known thickness object was placed on the surface (12 mm) 12
10
8
6
Thickness Thickness (mm) 4
2
0 0 20 40 60 80 100 120 Time (s)
Figure 4-11: Result of SR50AT Known Thickness Test
83 According to SR50AT manual, this type of sensor is temperature compensated and the temperature correction is automatically accountable into the readings. However to check the proper operation, the sensor was tested for the temperature compensation using similar setup and procedures as AGKU 1500 GI. The setup of the experiment can be seen in figure 4-7. The result of the test is shown below in figure 4-12. It indicates that SR50AT is temperature compensated very well. Throughout 26th - 27th March 2014, the thickness stayed constant at 0 mm which was expected. However, there are some readings at 1 mm which is a very small error and still in an acceptable error range.
20 20
15 15 Thickness Temperature 10 10
5 5
0 0
Thickness (mm) Thickness Temperature ( Temperature C) -5 -5
-10 -10
-15 -15
3/26/2010 15:30 3/26/2010 18:30 3/26/2010 21:30 3/26/2010 00:30 3/27/2010 03:30 3/27/2010 06:30 3/27/2010 09:30 3/27/2010 Time
Figure 4-12: Result of SR50AT Temperature Compensation Test
The initial tests of the AGKU 1500 GI and SR50AT ultrasonic sensors affirmed the results of Fan Zhang (February, 2014), a graduate student at The University of Cincinnati.
The sensors gave high confidence in the basic procedure, the outdoor test then followed.
84 4.1.3 UT Laser Thickness Sensor
The UT laser thickness sensor is developed by UT advisor and students. There are two main components in this sensor which are a line laser and camera as shown in figure
4-7. The principle of the sensor is the usage of an expanded laser beam to map a surface profile and a camera to detect a position at different thicknesses. To do so, at least three pictures have to be taken; first is a picture of the laser beam on the stay (base picture) as seen in 4-13, second is a picture of a laser beam on a known thickness object on the stay
(known thickness picture) as seen in figure 4-14, and last is a picture of an unknown thickness object such as ice and snow as seen in figure 4-15. After pictures were taken, they will be inputted to image processing Matlab software which was developed by Ahmed
Abdelaal, PhD Student at The University of Toledo. The Matlab codes were written to output the thickness of any object on top of the stay. More detail about the UT laser thickness sensor is presented in Abdelaal, to be published.
Figure 4-13: Base Picture for UT Laser Thickness Sensor
85 Known Thickness Object
Figure 4-14: Known Thickness Picture for UT Laser Thickness Sensor
Snow
Figure 4-15: Unknown Thickness Picture for UT Laser Thickness Sensor
Figure 4-16 shows the base and known thickness pictures used for a calibration.
The known thickness is to be inputted to the calibration process to calculate numbers of pixels between the base line and the known thickness line. Then, the unknown thickness picture is inputted to the software to find the actually thickness of an object on top of the stay as shown in figure 4-17. In the next section, the first field trial for the UT laser thickness sensor result will be presented and discussed.
86 Base Known Picture Thickness Picture
Figure 4-16: Calibration Process for UT Laser Thickness Sensor (Abdelaal, to be published)
Unknown thickness picture
Calculated Thickness
Figure 4-17: Calculation Process for UT Laser Thickness Sensor (Abdelaal, to be published)
87 4.2 Full Scale Experiment and Result
On the night of 16th April 2014, a full scale wet snow experiment was performed to test all three thickness sensor devices. The setup of the sensors was kept the same as figure
4-7. The snow gun was used to stream wet snow on top of the HDPE specimen starting at
4:10 and stopping at 5:15. Figure 4-18 and 4-19 show pictures of before and after snow gun was operated. At the end of snow gun operation, wet snow accumulated between 50 to
85 mm thick along the specimen. The thickness was vary along the specimen as seen in figure 4-19. The wet snow then was left on top of the specimen and shed by ambient temperature and sun radiation at 11:42.
Figure 4-18: HDPE Specimen before Snow Gun Operation
88
Figure 4-19: HDPE Specimen after Snow Gun Operation
Since thickness of the wet snow was not uniform along the specimen, the
thicknesses at specific locations were manually measured using a Vernier caliper and
compared to the output of the sensors. The location where SR50AT detected had the
thickness of 83.7 mm. Figure 4-20 shows the data output from the sensor. The manually
measured thickness (83.7 mm) was compared to the sensor output of 83 mm thickness, it
demonstrated that the sensor was accurate in this case.
90 Manually Measure = 83.7 mm 80 70 60 Wet Snow Shed 50 40 30
Start Spraying (mm) Thickness 20 10 0 -10
Time
4/16/2014 05:00 4/16/2014 06:30 4/16/2014 08:00 4/16/2014 09:30 4/16/2014 11:00 4/16/2014 12:30 4/16/2014 4/16/2014 03:30 4/16/2014
Figure 4-20: SR50AT Output during the Full Scale Experiment
89 The thickness of wet snow at a location where AGKU 1500 GI detected was
manually measured to be 82 mm. Figure 4-21 shows the data output from the sensor. The
manually measured thickness was compared to the sensor output of 69 mm thickness, the
percent error was more than 15%. Therefore, this experiment revealed that the sensor is
not accurate in this freezing condition due to the lack of temperature compensation
function. Manually Measure = 82 mm
90 80 70 Wet Snow Shed 60 50 40 30
Thickness Thickness (mm) 20 Start Spraying 10 0 -10
Time
4/15/2010 03:30 4/15/2010 05:00 4/15/2010 06:30 4/15/2010 08:00 4/15/2010 09:30 4/15/2010 11:00 4/15/2010 12:30 4/15/2010
Figure 4-21: AGKU 1500 GI Output during the Full Scale Experiment
And finally, the thickness of wet snow at a location where the UT laser thickness
sensor detected was manually measured to be 60 mm. Figure 4-22 shows the calculated
thickness from the software of the sensor. The software was also developed to output a
cylindrical grid mapping snow profile along the stay as seen in figure 4-23. Since the snow
accumulation were not uniform, these grid lines were very useful to determine exact
thicknesses at locations other than the center. The thickness output of 57 mm was compared
90 to manually measured and resulted in 5% error which is acceptable. Therefore, this experiment demonstrated that the sensor was reasonably accurate in this experiment.
57 mm
Figure 4-22: Calculation Thickness of UT Laser Thickness Sensor during the Full Scale Experiment
Figure 4-23: Snow Profile Output of UT Laser Thickness Sensor during the Full Scale Experiment
91 4.3 Sensor Selection
After the specification and performance of each sensor were studied, the three sensors are to be compared. Table 4.1 shows the result of the advantage and disadvantage of sensors.
Table 4.1: Thickness Sensors Comparison
Sensors Advantage Disadvantage
Small and light weight (Easy Not temperature compensated AGKU1500GI to install) Measure thickness at one point
Heavy weight (More difficult to Temperature compensated install) SR50AT Accurate Measure thickness at one point The highest price among them all
Temperature independent Detect a full profile of snow UT Laser The daylight ‘sometimes’ is along curved surface Thickness affecting the mapped laser line (visualization) besides Sensor (Solution is in progress) (In progress) measurement. Accurate
Since the AGKU1500 GI ultrasonic sensor was proven to be inaccurate in the freezing condition, it is definitely not to be considered. On the other hand, the SR50AT sonic ranging sensor and UT laser thickness sensor outputted accurate results. However, the UT laser thickness sensor is more suitable for this project’s application because the surface of the stay is curved and the sensor allows users to see the whole profile of snow
92 accretion along the surface. In addition, this sensor is still in progress. Its performance can be optimized by the development of ability to work in daylight using infrared laser.
93 Chapter 5
Thermal Experiments
Heating system is one of the most widely used technologies for anti/de-icing method. Since there is a large empty space in the VGCS stay, the internal heating could be a practical solution. However, experimenting this on the bridge is inefficient and dangerous. The experiment was performed to establish a thermal model for a VGCS’s specimen located at Scott Park campus during the winter of 2012 - 2013. The model included convective heat flow through the inside of a specimen section. The data acquisition system was setup on the specimen to collect temperature at point of interest.
During the experiment, the data and icing monitoring were studied and examine the feasibility of heating.
5.1 Experiment and Data Acquisition System Setup
The VGCS specimen with un-tensioned strands inside was selected to simulate the thermal model. The thermocouples were installed at 30, 150, and 270 cm from the beginning of the specimen surface. Next to the each thermocouple, small hole was made to be able to put probe thermocouple through and collect the inside temperature of the stay at specific locations. The specimen cross-sectional drawing in figure 5-1 shown locations
94 where data were collected. Location A, B, C, and D represent data collected at surface of strands, 6 cm above the strands, 12 cm above the strands, and outside surface of the specimen respectively.
Figure 5-1: Locations of Collected Temperature Data
A 70,000 BTU forced air space heater was chosen to be a heat source as seen in figure 5-2. The convective heat flow provided by the heater was allowed to go inside and release at the end of specimen section while cool water was misting on the surface specimen. For the thermal experiment, anti-icing and de-icing methods were performed using the same setup. The resultant data are presented and discussed in the following section.
95 Heater
Hot Air
Figure 5-2: Thermal Experiment Setup
5.2 Resultant Data and Discussion
5.2.1 Anti-Icing Experiment
The anti-icing experiment was performed on a windy night of 21st February 2013.
The ambient temperature was constant throughout the experiment at -5.5 °C. The heater provided averaged 45 °C heat flow inside the specimen continuously starting at 22:00. The main goal was to keep the specimen’s surface temperature above freezing to prevent ice accumulation during misting. The experiment failed due to the surrounding wind speed increase. The resultant data is presented in the following figures.
96 Ice Accumulation
Figure 5-3: Anti-Icing Temperature Profile at 30 cm Location
Figure 5-3 shows the temperature profile at the location of 30 cm from the beginning of the specimen surface. The temperature was initially collected at 21:50 when the heater was not yet turned on. Once it was on, both inside and surface temperatures suddenly increased. Because the strands were insulated by epoxy, their temperatures (1A) increased slower than other locations inside the specimen. On the other hand, the top region of inside locations (1C) experienced higher temperatures due to the heat rise phenomenon.
The surrounding wind speed played a significant role in the surface temperature (1D). At
23:10, the wind speed increased from 3.4 to 6.8 m/s resulting in surface temperature immediately dropped close to freezing point. As a result, the ice started to accumulate on the specimen. Even though the wind speed decreased at 23:30, surface temperature was still close to freezing point due to the ice accumulation.
97 Ice Accumulation
Figure 5-4: Anti-Icing Temperature Profile at 150 cm Location
Ice Accumulation
Figure 5-5: Anti-Icing Temperature Profile at 270 cm Location
The temperature profiles at the location of 150 and 270 cm from the beginning of the specimen surface are shown in figure 5-4 and 5-5 respectively. The trend of the profiles appeared to be as same as ones for 30 cm location. Similarly, the wind speed had a large
98 impact on the surface temperature. As the wind speed increased, the surface temperature decreased. At these locations, the ice accumulated on the surface at 23:10 as well.
5.2.2 De-Icing Experiment
The de-icing experiment was performed in the morning of 21st February 2013. The wind was calm and ambient temperature varied between -5 to -4 °C throughout the experiment. The heater provided averaged 50°C heat flow inside the specimen continuously starting at 5:15 to melt 1 cm pre-accumulated ice. The main purpose was to monitor ice behavior and study heat distribution inside the specimen during the experiment.
The resultant data is presented in the following figure.
25
20 Ice Completely 15 Melt
10 5:00 6:00 5 7:00
Temperature Temperature C) ( 8:00 0 9:00 -5 10:00
-10 Ice on Surface 1A 1B 1C 1D Location
Figure 5-6: De-Icing Temperature Profile at 30 cm Location
Due to the similarity of temperature profiles along inside of the specimen, the temperature profile at the location of 30 cm shown in figure 5-6 was used to represent other locations as well. The initial temperature was collected at 5:00 before the heater was turned on. Similar to the anti-icing experiment, both inside and surface temperatures suddenly
99 increased after the heater was on as seen in data set of 6:00. The identical temperature profile also occurred in this experiment. The strand’s temperatures (1A) increased slower than other locations inside the specimen due to epoxy coating. Heat rising resulted in higher temperature in the top region (1C). As time passed, the ice started to melt slowly corresponding to gradually increased surface temperatures (1D). The melting pattern is shown in Figure 5-7. After approximately 5 hours, the ice was completely melted without any shedding event.
Figure 5-7: Melting Pattern during De-Icing (Arbabzadegan, 2013)
5.3 Thermal Experiments Conclusion
Because of the large empty space inside the VGCS stay, the internal heating method was selected to perform anti/de-icing experiments. The test was done at Scott Park site rather than the bridge due to safety and to provide a more controlled environment. The purpose of both experiments was to understand the stay’s response in the anti/de-icing while heat flowing inside the stay. During the test, the heat distribution inside the specimen
100 was also studied. The anti-icing result was found to be wanting because the surrounding wind speed was a significant factor in the change of specimen’s surface temperature. On the other hand, the de-icing strategy seemed to work well since it melted the pre- accumulated ice without shedding under no-wind condition. This strategy is recommended to be further tested during windy conditions. If it could melt the ice without shedding as well, this strategy could be a potentially practical solution for the VGCS icing problem.
However, its tradeoff is high energy and maintenance costs.
101 Chapter 6
Superhydrophobic Coating Experiments
6.1 UT Icing Tunnel Background
To test superhydrophobic coatings efficiently, an icing tunnel was configured to produce icing scenarios similar to a natural environment. The icing tunnel was designed and built by University of Toledo master student, David Whitacre. There are two main parts of the icing tunnel; which include a freezing room and a tunnel system, as seen in figure 6-1 (Whitacre, 2013). The square 2.4 meter freezing room was built to support the cooling unit which was installed on the roof, also seen in figure 6-2. All walls of the room were insulated to reduce temperature gains; considering that ice testing was performed in this system, gains or increases in temperature from the outside air, which is significantly warmer than the circulating air within the icing tunnel system, are not desired. The cooling unit has the capability to maintain a constant temperature as low as -20 °C. The freeze room has an outlet and inlet to allow the supercool air to travel through the tunnel system as a closed loop system. A multi-speed fan was installed inside the tunnel system for the user to be able to adjust wind speed during experiments. Additionally, temperature was adjustable, thus, allowing various conditions suitable for icing to be achieved (i.e. high winds and very cold temperature (-20 °C), light winds and very cold temperatures (-20 °C),
102 high winds and cold temperature (-10 °C), etc.).The tunnel system also included a test
section, which is the area where an object may be placed in order for experiments to be
performed.
Cooling Unit Test Section
Tunnel System Freezing Room
Figure 6-1: SolidWorks Design for the UT Icing Tunnel (Whitacre, 2013)
Cooling Unit Test Section
Tunnel System
Freezing Room
Figure 6-2: UT Icing Tunnel
6.2 Test Section
This section consists of a 30 cm diameter optically clear tube as shown in Figure 6-
3. This test section allows users to take pictures of a specimen during an experiment. It
103 also includes a misting system, camera, and specimen mounting systems, which will be presented in next sections.
Figure 6-3: Testing Section of the UT Icing Tunnel
6.2.1 Misting System
The icing tunnel can create both rime and glaze types of ice by the use of a misting system. This system uses nozzles to produce a fine mist, very small water droplets, which get blown onto the testing specimen at variable speeds. In order for the mist to reach the specimen at a proper distance to allow the water droplets to freeze in a manner that resembles field conditions, the misting system was installed in front of the testing section roughly 30 cm, as seen in figure 6-4. The setup consists of three misting nozzles that are hooked up to standard city water, which is adequate because it operates in low pressure.
As previously mentioned, the icing tunnel is designed to decrease in temperature to as low as -20 °C. This allows the testing object and water droplets to be supercooled, and thus, ice accretion is allowed to occur when the droplets contact the surface of the testing specimen.
The misting system allows the user to change the nozzle size to either a 0.30, 0.35, or 0.40 mm orifice. Having different nozzle sizes allows the user to perform experiments with
104 different droplet sizes and flow rates. The flow rate associated to each nozzle is between
4.41 x 10-7 and 6.93 x 10-7 m3/s (Whitacre, 2013). The nozzles were tested for droplet size using Particle Image Velocimetry (PIV) method. More detail regarding PIV is presented in
Jones 2014. The result shows that 0.30, 0.35, and 0.40 mm orifice nozzle distributes droplet size of around means of 50, 40, and 42 micrometers (micron), respectively.
Camera
Misting System
Figure 6-4: Misting System in the Testing Section
6.2.2 Camera System
A Panasonic HX-A100D camera was installed within the testing section, shown in figure 6-4, to allow the user to clearly document each experiment. The camera lens maintains roughly the same temperature as inside the testing section, which ultimately eliminates any difficulty in taking pictures due to the possible formation of condensation on the testing section tube from the temperature difference. The camera seen in figure 6-5 has a high definition 1080P video recorder and is waterproof up to 5 feet of water. The camera does not have a video screen, however, the Panasonic Image application can be installed on a smartphone or tablet. This application allows for a real time video to be streamed to the device it is installed on via Wi-Fi. The application also allows the user to control recording, take pictures, and change the settings of the camera. (Panasonic, 2013)
105
Figure 6-5: Panasonic HX_A100D Camera (Panasonic 2013)
6.2.3 Mounting System
Additionally, located in the test section is a mounting system. This system allows a testing specimen to be mounted across the testing section as seen in figure 6-6. The mounting system also includes a load cell which is used to detect lift and drag forces as well as a dial to change the angle of the object during the experiment. However, the experiments performed for this project do not concern the lift and drag forces and therefore, the force data were neglected for the experiments.
Figure 6-6: Mounting System of Testing Section
106 6.3 Coating Experiments in UT Icing Tunnel
6.3.1 Testing Procedure
In order to do the experiments in the icing tunnel, the cooling unit was turned on and provided adequate time to reach a temperature of -5.5 °C, which was found to be the typical temperature for a freezing rain storm. The test was started by mounting a coated specimen into the test section and then turning on the fan speed to be approximately 8.8 m/s, which was found to be the typical wind speed for a freezing rain storm. After allowing the system to stabilize, the camera was turned on and started recording while the misting system was installed into the test section. Once everything was installed, the misting system was turned on and allowed to run throughout the test. The camera recorded and saved the footage of the entire experiment on a memory card, thus, allowing the user to remove the memory card in order to replay the video This permitted both the user and the rest of the research team to have a better understanding of the effects the coating had on the relationship between ice formation and the test specimen. The length of each test was 10 minutes long. After the test was done, the misting system and camera were turned off, the coated specimen was removed from the test section, and the ice accumulation was measured. The next coated specimen was then placed into the test section and the same procedure was repeated. This test procedure was completed for an uncoated specimen as well as Hydrobead (www.hydrobead.com), PhaseBreak TP (www.microphasecoatings
.com/phasebreak.php), and Boyd WeatherTITE (http://www.boydcoatings.com
/CRC_6000_Series.pdf ) coatings.
107 6.3.2 Experiments – Icing Progression
6.3.2.1 Uncoated Specimen
The first test was done by misting 40 micron supercool droplets on an uncoated specimen. The purpose of this test was to compare the uncoated specimen to the hydrophobic coated specimens in order to determine whether the coatings would help reducing the rate of ice accumulation. As mention in the procedure section, the specimen experienced a temperature and wind speed of -5.5 °C and 8.8 m/s, respectively, which simulated a freezing rain storm. The figures below show the progression of the ice accumulation.
Figure 6-7: Uncoated - 40 Micron – 0 sec
As seen in figure 6-7, the uncoated specimen was installed into the test section. The dark line drawn across the specimen is representative of stagnation points along the specimen.
108
Figure 6-8: Uncoated - 40 Micron - 15 sec
After the mist system was turned on, droplets began to form on the surface of specimen as seen in figure 6-8.
Water Droplets
Water Puddle
Figure 6-9: Uncoated - 40 Micron - 30 sec
As seen in figure 6-9, droplets were pushed to the top and bottom of the specimen while water puddled along the stagnation line.
Water Droplets
Frozen Puddle
Figure 6-10: Uncoated - 40 Micron - 45 sec
109 As time passed, the puddle along the stagnation line begin to freeze while the droplets on the top and bottom of the specimen were still in liquid form.
Water Puddle
Frozen Puddle
Figure 6-11: Uncoated - 40 Micron – 1 min
Water Puddle
Frozen Puddle
Figure 6-12: Uncoated - 40 Micron – 1:30 min
The frozen puddle continued to accumulate uniformly along the stagnation line while the water droplets on the top and bottom of the specimen began to puddle as shown in figure 6-11 and 6-12.
110 Frozen Puddle
Water Droplets
Figure 6-13: Uncoated - 40 Micron – 2 min
The ice continued to accumulate uniformly on the first layer of ice at the stagnation line. The puddles formed on the top and bottom of the specimen began to freeze similarly to what happened at the stagnation line. However, there was still water droplets forming on the bottom of the specimen.
Icicles
Figure 6-14: Uncoated - 40 Micron – 4 min
Figure 6-14 shows that the whole specimen was covered with ice and the accumulation continued increasing as time passed. The ice at bottom of the specimen starts forming icicles due to the gravity.
111 Uneven Surface
Icicles
Figure 6-15: Uncoated - 40 Micron – 6 min
Uneven Surface
Icicles Figure 6-16: Uncoated - 40 Micron – 8 min
Droplets of water had frozen on the earlier layer of uniform ice, thus, causing uneven ice accumulation. This pattern started from the right side of the specimen, as see in figure 6-15 and 6-16. The thickness of ice accumulation continued to increase and the length of icicles increased as well.
Figure 6-17: Uncoated - 40 Micron – 10 min
112 Almost 50% of the surface of the specimen is covered with an uneven surface of ice, particularly the right side as seen in figure 6-17. Also, several icicles were present and became very long by the end of the test.
Figure 6-18: Uncoated - 40 Micron – After Test
At the completion of the test, the specimen was removed from the test section. The thickest part of the ice was measured to be approximately 6.5 mm. The specimen then was placed in room temperature (approximately 21 °C) and left to shed. After some time, the ice came off as one big sheet as seen in figure 6-19.
Figure 6-19: None Coating - 40 Micron – Shed Ice Sheet
113 The uncoated specimen then was tested with two other nozzle sizes using the same procedure as above. The droplet sizes of the nozzles were 42 and 50 micron. Both tests showed ice accretion the surface of the specimen. The thickest part of the ice was measured to be approximately 5.5 and 5 mm, respectively, for 42 and 50 micron droplet size nozzles.
6.3.2.2 Hydrobead
The first coating tested was Hydrobead, which came in a spray can. This is a hydrophobic coating that is clear in color as seen in figure 6-20. The coating was sprayed on the entire surface of the specimen. The specimen was allowed to dry for 20 minutes as instructed in the manual prior to installation in the test section.
Figure 6-20: Hydrobead-Coated Specimen
The test followed procedure described in section 6.3.1, the figures below illustrate the progression of the ice accumulation for Hydrobead coating test experiencing 40 micron supercool water droplet size.
114
Figure 6-21: Hydrobead – 40 Micron – 0 sec
As seen in figure 6-21, the Hydrobead-coated specimen was installed into the test section. Similar to the previous test, the dark line drawn across the specimen is representative of stagnation points along the specimen.
Figure 6-22: Hydrobead – 40 Micron – 15 sec
The water droplets began to appear on the surface of specimen once the misting system was turned on as shown in figure 6-22.
115
Figure 6-23: Hydrobead – 40 Micron – 30 sec
At 30 seconds, the small water droplets began to form into bigger droplets or puddles. The bigger droplets move to the top and bottom of the specimen while the smaller droplets remain near the stagnation line.
Water Droplet
Frozen Droplet
Figure 6-24: Hydrobead – 40 Micron – 45 sec
Water Droplet
Frozen Droplet
Figure 6-25: Hydrobead – 40 Micron – 1 min
116 Water Droplet
Frozen Droplet
Figure 6-26: Hydrobead – 40 Micron – 1:30 min
As time passed, the smaller droplets along the stagnation line began to freeze while the bigger droplets on top and bottom of the specimen were still in a liquid state, which can be seen in figure 6-24 to 6-26.
Frozen Droplet
Frozen Droplet
Figure 6-27: Hydrobead – 40 Micron – 2 min
The frozen droplets continued to accumulate along the stagnation line while the water droplets on top and bottom of the specimen began to freeze.
Icicles
Figure 6-28: Hydrobead – 40 Micron – 4 min
117 Figure 6-28 shows that most of the specimen’s surface is covered with frozen droplets causing an uneven ice accumulation. The ice at bottom started forming icicles due to the gravity.
Figure 6-29: Hydrobead – 40 Micron – 6 min
Figure 6-30: Hydrobead – 40 Micron – 8 min
More water droplets froze on the previous layer of ice, thus, the specimen was experiencing ice accretion as seen in figure 6-29 and 6-30.
118
Figure 6-31: Hydrobead – 40 Micron – 10 min
At the end of the experiment, the surface of the specimen was completely covered by an uneven ice layer and the icicles located at the bottom have become long and oriented downstream.
Figure 6-32: Hydrobead – 40 Micron – After Test
Similar to the previous test, the specimen was removed off from the test section once the test was completed. The thickest part of the ice was measured to be approximately
10 mm. The ice sheet, shown in figure 6-33, shed from the specimen when placing it in room temperature for a period of time.
119
Figure 6-33: Hydrobead – 40 Micron – Shed Ice Sheet
The Hydrobead-coated specimen was then tested with two other nozzle sizes using the same procedure. The droplet sizes of the nozzles were 42 and 50 micron, respectively.
Both tests showed ice accretion on the surface of the specimen. The thickest part of the ice was measured to be approximately 6.5 mm, for both 42 and 50 micron droplet size nozzles.
6.3.2.3 PhaseBreak TP
The next coating tested was PhaseBreak TP, a superhydrophobic coating with grey color as seen in figure 6-34. This coating consisted of 2 parts; a primer and TP. Both parts were applied on a specimen using a foam brush and were given time to cure between coats as instructed in the manual. After the coating was fully cured, the specimen was installed in the test section and the procedure described in section 6.3.1 was followed. The following figures portray the progression of the ice accumulation for PhaseBreak TP coating experiencing 40 micron supercool water droplets.
120
Figure 6-34: PhaseBreak TP – 40 Micron – 0 sec
The PhaseBreak TP-coated specimen was installed into the test section as seen in figure 6-34. Once again, the dark line drawn across the specimen is representative of stagnation points along the specimen.
Figure 6-35: PhaseBreak TP – 40 Micron – 15 sec
Figure 6-36: PhaseBreak TP – 40 Micron – 30 sec
121 After 30 seconds of misting, droplets of water appeared on the surface of the specimen. The droplets kept moving on the surface of the specimen due to wind passing through the test section.
Water Droplet
Frozen Droplet
Figure 6-37: PhaseBreak TP – 40 Micron – 45 sec
Water Droplet
Frozen Droplet
Figure 6-38: PhaseBreak TP – 40 Micron – 1 min
As time passed, the water droplets along the stagnation line began to freeze while the droplets on the top and bottom of the specimen were still in a liquid state, which can be seen in figure 6-37 and 6-38.
122 Water Droplet
Frozen Droplet Icicles Figure 6-39: PhaseBreak TP – 40 Micron – 1:30 min
Water Droplet Frozen Droplet
Icicles
Figure 6-40: PhaseBreak TP – 40 Micron – 2 min
The frozen droplets continued to accumulate along the stagnation line while the water droplets on the bottom of the specimen began to freeze, creating icicles. However, droplets of water still appear on the top of the specimen.
Frozen Droplets
Figure 6-41: PhaseBreak TP – 40 Micron – 4 min
123 Frozen Droplets
Figure 6-42: PhaseBreak TP – 40 Micron – 6 min
While more ice accumulated along the stagnation line and the bottom of the specimen, the water droplets on top began to freeze; this can be seen in figure 6-41 and 6-
42.
Figure 6-43: PhaseBreak TP – 40 Micron – 8 min
Figure 6-44: PhaseBreak TP – 40 Micron – 10 min
124 More droplets of water froze on the initial layer of ice as time elapsed. Both the thickness of ice accumulation and the length of icicles located along the bottom of the specimen continued to increase as time passed. By the end of the test, the surface of the specimen is completely covered by an uneven ice layer and the icicles became really long and oriented downstream.
Figure 6-45: PhaseBreak TP – 40 Micron – After Test
The specimen was removed from the test section after the test was completed. The thickest of the ice was measured to be approximately 10 mm. The ice melted to a thin and small sheet, shown in figure 6-46, and shed from the specimen at room temperature.
Figure 6-46: PhaseBreak TP – 40 Micron – Shed Ice Sheet
125 The PhaseBreak TP-coated specimen was then tested with two other nozzle sizes using the same procedure. The droplet sizes of the nozzles are 42 and 50 micron, respectively. Both tests showed ice accretion on the surface of the specimen. The thickest part of the ice was measured to be approximately 6.5 and 5 mm, respectively, for 42 and
50 micron droplet size nozzles.
6.3.2.4 Boyd WeatherTITE
The last coating tested was provided by Boyd Coating Research Company,
Incorporated and is their WeatherTITE coating. It is a white superhydrophobic coating.
There were three parts to this coating, each part came in a separated container labeled part
A, B and C. The three parts were mixed and applied on a specimen using a foam brush. It was then given time to cure as instructed in the manual. After the coating was fully cured, the specimen was installed in the test section. The following figures show the progression of the ice accumulation for WeatherTITE experiencing 40 micron supercool water droplets.
Figure 6-47: WeatherTITE – 40 Micron – 0 sec
The WeatherTITE-coated specimen was installed into the test section as shown in figure 6-47. The dark line drawn across the specimen is representative of stagnation points along the specimen.
126 Water Droplet
Water Puddle
Figure 6-48: WeatherTITE – 40 Micron – 15 sec
After 15 seconds of misting, droplets of water appeared on the surface of the specimen as seen in figure 6-48. The droplets were pushed to the top and bottom of the specimen while water puddled along the stagnation line.
Water Droplets
Frozen Puddle
Figure 6-49: WeatherTITE – 40 Micron – 30 sec
Water Droplets
Frozen Puddle
Figure 6-50: WeatherTITE – 40 Micron – 45 sec
127 As time passed, the puddle along the stagnation line began to freeze while the droplets on top and bottom of the specimen were still in a liquid form as shown in figure
6-49 and 6-50.
Water Droplets
Ice
Figure 6-51: WeatherTITE – 40 Micron – 1 min
Water Droplets
Ice
Figure 6-52: WeatherTITE – 40 Micron – 1:30 min
After 1:30 minutes, the ice continued to accumulate uniformly along the stagnation line while the water droplets on top and bottom of the specimen began forming into bigger droplets or puddles as shown in figure 6-51 and 6-52.
128 Water Droplet Ice Droplet
Icicle
Figure 6-53: WeatherTITE – 40 Micron – 2 min
Water Droplet Ice Droplet
Icicle
Figure 6-54: WeatherTITE – 40 Micron – 3 min
The ice continued to accumulate uniformly on top of the first layer of ice at the stagnation line. Some of the big water droplets on top of the specimen began to freeze and the icicles started to build up on the bottom of the specimen, which can be seen in figure
6-53 and 6-54.
129 Ice Droplet
Icicle
Figure 6-55: WeatherTITE – 40 Micron – 4 min
Ice Droplet
Icicle
Figure6-56: WeatherTITE – 40 Micron – 6 min
As time passed, more layers of ice accreted at stagnation line. All big droplets of water on the top of the specimen froze as shown in figure 6-55 and 6-56. The icicles also become longer.
Uneven Ice Surface
Figure 6-57: WeatherTITE – 40 Micron – 8 min
130 Uneven Ice Surface
Figure 6-58: WeatherTITE – 40 Micron – 10 min
The thickness of ice accumulation continued increasing and so did the length of icicles. By the end of the test, the surface of the specimen is covered by an uneven ice layer due to water droplets freezing in the previous layer. Otherwise, the water droplets that freeze quickly rather than spreading out create an uneven surface for the ice accretion. The icicles also become long and oriented downstream.
Figure 6-59: WeatherTITE – 40 Micron – After Test
After the test, the specimen was removed from the test section. Figure 6-59 shows the ice accretion on the specimen after the test. The thickest part of the ice was measured
131 to be approximately 8 mm. The ice sheet, shown in figure 6-60, shed from the specimen while placing it at room temperature.
Figure 6-60: WeatherTITE – 40 Micron – Shed Ice Sheet
6.3.3 Result Summery of Icing Tunnel Coating Tests
After performing superhydrophobic coating tests, the table below was generated to compare the result of each coating and droplet size.
Table 6.1: Approximated ice thickness comparison of coatings and droplet sizes
Coating None Hydrobead PhaseBreak TP WeatherTITE Droplet Size
40 micron 6.5 mm 10.0 mm 10.0 mm 8.0 mm
42 micron 5.5 mm 6.5 mm 6.5 mm 9.5 mm
50 micron 5.0 mm 6.5 mm 5.5 mm 9.5 mm
The results show that none of the coatings can prevent the ice from accumulating on the specimen. It is noticeable that the coated specimens tend to have higher ice accumulation. This is due to ice droplets freezing quickly rather than spreading out during the tests causing an uneven surface which can trap more water. Once the water freezes, it
132 becomes a thicker layer of ice on the surface. On the other hand, the ice formed evenly on the surface of the uncoated specimen. Due to the uniform surface, the water tends to slide off the surface, instead of getting trapped. This causes lower ice accumulation on the specimen. In addition, the results of the experiments also show that smaller droplet sizes causes higher ice accumulation. This is true for most cases during the coating experiments.
The only coating that does not have this trend is the WeatherTITE coating. This could be due to a different chemical makeup of the coating.
6.4 Full Scale Coating Test
Since the result of the coatings from the icing tunnel was not promising, only one coating was selected to be used in experiments for the full scale test, which was Hydrobead.
This particular coating was selected due to its clear color and ability to keep the polished look of the specimen, thus, would be applicable to the VGCS. The test was perform during the winter of 2012, the ambient temperature was approximately -5 °C throughout the test.
The coating was sprayed on half of the surface of the VGCS specimen, and the other half was left untouched, as seen in figure 6-61. The specimen was allowed to dry for 20 minutes as instructed in the manual.
Uncoated Section
Hydrobead Section
Figure 6-61: Hydrobead Sprayed on the half of VGCS Specimen
133 After misting water on top of the entire speciman surface, therefore covering both the Hydrobead and uncoated sides of the sheath, it was observed that small droplets of water appeared on Hydrobead surface. The water droplets did not slide off the surface due to the larger than typical diameter of the VGCS stay sheaths. Instead, they stayed on the surface and within seconds became droplets of ice as shown in figure 6-62. Additionally, the ice formed on the uncoated surface, however, it formed an even ice layer as seen in figure 6-63.
Figure 6-62: Ice Droplets on the Hydrobead Surface
Figure 6-63: Even Ice Layer on the Uncoated Surface The result of Hydrobead full scale test had a similar result when compared to the results from the icing tunnel. The superhydrophobic coatings first caused water to become
134 small droplets on the specimen surface, then the droplets suddenly turned to ice. This coating also changes the icing formation and accumulation rate; which tends to increase both, thus resulting in a worse outcome than an uncoated surface. This test shows that the superhydrophobic coating is not a practical solution for the VGCS. Another flaw of
Hydrobead was that it got gummy and discolored after several months outdoors.
135 Chapter 7
Conclusion and Future Work
The Veteran’s Glass City Skyway (VGCS) is a large cable-stayed with a single pylon bridge and owned by the Ohio Department of Transportation (ODOT). It is one of the main bridges across Maumee River in Toledo, Ohio. The VGCS stays have unique features such as stainless steel, larger diameter, and brush finished surface. These characteristics contribute to icing problems. Five icing incidents occurred since the bridge has gone into service in 2007. Ice shedding caused severe damage to vehicles traveling on a bridge and put lives of motorist in danger. It also resulted in lane closures which caused inconvenient to traveling public and economic losses.
The objectives of this thesis are to identify the most practical de-icing/anti-icing approach for solving the VGCS icing problem and to be able to detect ice formation and potential of ice shedding remotely to reduce risk of ODOT personnel being on a bridge desk while an icing event is occurring. UT icing sensor was developed to detect and understand the behavior of ice and wet snow. The outdoor experiment station was designed to be able to perform full scale test. Icing and wet snow simulations were performed to monitor the mediums’ behavior on VGCS and HDPE specimens. During the simulations, thickness sensors were also tested and compared their performance.
136 Furthermore, the anti/de-icing internal heating test was performed on the specimen which had the same thermal properties as VGCS stays. During the test, the efficiency and heat distribution was studied. And finally, different types of superhydrophobic coatings were tested in the UT icing tunnel and on outdoor station. By doing so, the chemical’s efficiency was determined and decided whether it was a practical solution for VGCS icing problem or not.
7.1 Conclusion
Since there is no commercial ice presence and state sensor that can be installed directly on the stay’s surface, the UT icing sensor has been developed. Its unique feature is a capability of differentiating between ice, slush, and water. Also being rugged and compact, the sensor can be mounted directly on the stay. In an icing and wet snow events, the sensor will be covered by the medium and detect the presence of liquid water in the interstice between the medium layer and the stay sheath. When water presents in this space, the medium shedding is imminent. The sensor has been tested in the laboratory and on full scale mock-ups of the VGCS and HDPE stays outdoors. The experimental result was very promising and the sensor is ready for deployment.
Three thickness measurement devices, such as AGKU1500GI Ultrasonic, SR50AT
Sonic Ranging, and UT Laser Thickness Sensors, were tested on the full scale experiment.
This was the first field trial for the sensors. The AGKU1500GI was revealed to be inaccurate when operated in very cold condition due to a lack of temperature compensated feature. On the other hand, both SR50AT and UT laser thick sensor outputted thickness results accurately. However, UT laser thickness sensor is more suitable for this project because of
137 its ability of detecting the full profile of snow or ice on a stay rather than SR50AT which can detect only one spot on the stay.
The internal heating method is considered to be potentially practical solution for icing problem on the VGCS stay. The anti-icing strategy was found to be wanting due to the failure of preventing ice accumulation in windy condition. The surrounding wind had a significant effect on the stay’s surface temperature. However, the de-icing strategy could efficiently melt the accumulated ice without any shedding incident under no-wind condition.
However, to implement this strategy on the VGCS Bridge will result in energy and maintenance costs concerns.
Another anti-icing strategy was the use of superhydrophobic coatings. After the testing, the result showed that none of the coatings; Hydrobead, PhaseBreak TP, and
WeatherTITE, efficiently prevented ice from accumulating. Not only anti-icing inability, but coatings also caused higher accumulation rate than uncoated specimen. The reason was that water was beaded into droplets and froze suddenly on the coated surface becoming ice droplets. As water was continuously misted on the specimen, it was trapped between the ice droplets rather than slid off the surface. The water then froze and became a new layer of ice.
This cycle kept continuing until the water was stopped. By the end of the test, the accumulated ice was found to be very thick. Therefore, it has been revealed that superhydrophobic coatings are not a practical solution for this project.
7.2 Future Work
Based on the anti/de-icing experimental results, the de-icing using internal heating was the only strategy that could be used to de-ice bridge stays. This strategy is suggested to
138 be further tested. The experiment should be performed during windy condition and observed whether it can melt the pre-accumulated ice without any shedding or not.
Furthermore, this project should focus on the UT icing sensor. The principle of the
UT icing sensor has been proved in this thesis. The sensor should be tested in an ice shedding experiment. The author has been suggested to start with 1.3 cm ice accumulation and go until the ice sheds. The resultant data of ice shedding event should be reviewed and compared to the wet snow shedding result discussed in chapter 3. The sensor also should be developed to be more durable in a real environment and temperature compensated. It should be able to last a long time to reduce maintenance cost. The data acquisition system could be changed to have a longer signal range and battery life. The sensor then should be deployed on the bridge and integrated into the dashboard.
7.3 Archiving Data
Several experiments were conducted during the research. All data, pictures, and videos could not be included in this thesis. This information has been put in CDs and provided to Dr. Terry Ng and Dr. Douglas K. Nims, faculty members in the College of
Engineering at The University of Toledo.
139
References
Admirat, T. , Dolle, B. (2011). “Wet Snow accretion on overhead lines with French report
of experience”. Cold Region Science and Technology 65: 43-51
Antonini, M. I., T. Horn, M. Marengo, A. Amirfazli (2011). “Understanding the effect of
superhydrophobic coatings on energy reduction in anti-icing systems”. Cold
Regions Science and Technology 67: 58-67.
Arbabzadegan, A. (2013). “Ice Prevention or Removal of Veteran’s Glass City Skyway
Cables”. University of Toledo
Belknap, J.W. (2011). “Designing Ice Management System for the Veteran’s Glass City
Skyway”. University of Toledo.
Bonelli, P., Lacavalla, M. (2011). “Wet snow hazard for Power lines: a forecast and alert
system applied in Italy”. Nat. Hazards Earth Syst. Sci. 11: 2419-2431.
Campbell SR50AT (2014). SR50AT Sonic Ranging Sensor. Manuals,
www.campbellsci.com.
Decagon LWS (2014). Leaf Wetness Sensor. Manuals, www.decagon.com.
EGE AGKU1500GI (2014). AGKU1500GI Ultrasonic Sensor. Brochure, www.ege-
elektronik.com.
Farzaneh, M. (2008). “Atmospheric Icing of Power Networks”. Springer Science+Business
Media B.V.: 229-237, 328-333.
140 Fikke, S., Ronsten, G., Heimo, A., Kunz, S., Ostrozlik, M., Persson, P. E., Sabata, J.,
Wareing, B., Wichure, B., Chum, J., Laakso, T., Santti, K., and Makkonen, L.
(2007). COST 727: “Atmospheric Icing on structures, Measurements and data
collection on icing: State of the Art”. Publication of MeteoSwiss: 75, 110
Kimura, S., Sato, T., Kosugi, K. (2003). “The Effect of Anti-Icing Paint on the Adhension
Force of Ice Accretion on a Wind Turbine Blade”. BOREAS VI, FMI,
Pyhatunturi, Finland: 9.
Jones, K. F., (2010). “Toledo Weather Conditions Associated with Ice Accumulation on
the Skyway Stays (Draft)”. Cold Regions Research and Engineering Laboratory,
Hanover, NH 03755.
Jones, N. (2014). “A Parametric Study of Micro Atomizing Nozzles on a Rotary Fuel
Slinger”. University of Toledo.
Nims, D. K. (2010). “Ice Prevention or Removal on the Veteran’s Glass City Sky Cables
(Draft Interim Report)”. Ohio Department of Transportation Office of Research
and Development, State Job Number 134489.
Panasonic Camera (2013). HX-A100D High Definition Camera. Manuals,
www.panasonic.com.
Parent, O., A. I. (2011). “Anti-icing and de-icing techniques for wind turbines: Critical
review”. Cold Regions Science and Technology 65: 88-96.
Photograph of Wet Snow Accretion on Power Lines. Photograph. n.d.. Widespread
Blackout Triggers Study of Contaminated Wet Snow on Insulator Strings.
http://www.inmr.com/2014/03/widespread-blackout-triggers-study-
contaminated-wet-snow-insulator-strings/. www.inmr.com. 13th April 2014.
141 Ryerson, C. C. (2008). “Assessment of Superstructure Ice Protection as Applied to Offshore
Oil Operations Safety”. Cold Regions Research and Engineering Laboratory,
Hanover, New Hampshire 03755-1290.
Wang X., E. B., G. F. Naterer (2007). "Experimental correlation of forced convection heat
transfer from a NACA airfoil." Experimental Thermal and Fluid Science 31:
1073-1082.
Whitacre, D. L. (2013). “Development of an Icing Research Wind Tunnel at the University
of Toledo”. University of Toledo.
Zhang, F., Hunt, V. J., Helmicki, A. J. (February, 2014). Unpublished Presentation on
Laboratory Testing of Ultrasonic Sensors for Snow Thickness Measurement,
UCII.
142 Appendix A
Snow Density Calculations
Table A.1: Dry Snow during the Event of 17th February 2014
Container Container with Snow Volume Density Container WT (g) Snow WT (g) WT (g) (ml) (kg/m3) 1 68 140 72 970 74.2 2 8 20 12 125 96.0 3 8 34 26 355 73.2 Average Density 81.2
Table A.2: Wet Snow Generated by the Snow Gun
Container Container with Snow Volume Density Container WT (g) Snow WT (g) WT (g) (ml) (kg/m3) 1 68 240 172 970 177.3 2 8 38 30 125 240.0 Average Density 208.7
143