Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Shah, Aaryaman V.
Master of Applied Science - MASc
University of British Columbia
Ice accretion on aviation surfaces imposes operational and safety challenges, severely impacting critical flight structures and equipment. For optimized ‘smart’ de-icing systems of the future, microwave resonant-based planar sensors are proposed for their high sensitivity to icing and versatility in integration. This thesis presents five microwave resonant sensors for ice sensing under simulated real-world icing conditions. Three planar ice sensors are designed for harsh roadway environments. A Split-Ring Resonator (SRR) sensor (1.2 GHz) is tested for sensitivity to icing with protective epoxy coatings. A complementary split ring resonator (CSRR) sensor with a robust epoxy-coated 1mm-thick aluminum build (1.18 GHz) monitors snow accretion. Asymmetric stripline CSRR sensors with increased shielding are implemented for exploratory time-domain-reflectometry measurements, for wired large-area sensor networks. A conformal single-port dual-frequency SRR sensor (1.9-2.2 GHz) was integrated onto a NACA 0018 airfoil with electrothermal de-icing capabilities to sense in-flight ice crystal icing (ICI). Tested in the National Research Council’s (NRC’s) Research Altitude Test Facility (RATFac) at 19,000 ft, 100 m/s, +3°C and melt ratios of 11%, 13%, 16% and 25%, the sensor differentiates %melt while monitoring de-icing and anti-icing processes. Icing on smaller Unmanned Aerial Vehicles (UAVs), drones, and electric vertical take-off and landing (eVTOL) crafts impact aerodynamics and weight, severely limiting operation. A novel multi-frequency SRR sensor (2-4 GHz) is integrated with a zone-based electrothermal de-icing system and a low interfacial-toughness coating on a NACA 0012 airfoil for location-specific accretion monitoring and zone-based ‘smart’ de-icing. Tested at the Anti-Icing Materials International Laboratory’s (AMIL) icing wind tunnel at varying LWC (0.3, 0.5 and 0.8 g/m3), airspeed (20 and 40 m/s) and temperature (-5°C: glaze, -10°C: mixed, and -20°C: rime) conditions, the sensor monitors ice accretion, tracking the real-time resonant frequency response with a maximum sensitivity of 179 MHz/mm. A fitted time-constant is found to be inversely proportional to increasing ice thickness, facilitating icing rate measurement and ice thickness prediction using real-time microwave sensors. This work represents a significant advancement in the field of microwave SRR ice sensing technology, bringing it one step closer to practical implementation as integrated location-specific ice sensors for ‘smart’ ice protection systems (IPSs).
Towards Integrated Microwave Ice Sensors for On-ground, In-flight and Unmanned Aerial Vehicle (UAV) Applications by Aaryaman V. Shah B.A.Sc. The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Electrical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) February 2024 © Aaryaman V. Shah, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Towards Integrated Microwave Ice Sensors for On-ground, In-flight and Unmanned Aerial Vehicles (UAV) Applications submitted by Aaryaman V. Shah in partial fulfillment of the requirements of the degree of Master of Applied Science . Dr. Mohammad H. Zarifi, School of Engineering Supervisor Dr. Kevin Golovin, University of Toronto Supervisory Committee Member Dr. Ian Foulds, School of Engineering Supervisory Committee Member Dr. Kasun Hewage, School of Engineering University Examiner iii Abstract Ice accretion on aviation surfaces imposes operational and safety challenges, severely impacting critical flight structures and equipment. For optimized ‘smart’ de-icing systems of the future, microwave resonant-based planar sensors are proposed for their high sensitivity to icing and versatility in integration. This thesis presents five microwave resonant sensors for ice sensing under simulated real-world icing conditions. Three planar ice sensors are designed for harsh roadway environments. A Split-Ring Resonator (SRR) sensor (1.2 GHz) is tested for sensitivity to icing with protective epoxy coatings. A complementary split ring resonator (CSRR) sensor with a robust epoxy-coated 1mm-thick aluminum build (1.18 GHz) monitors snow accretion. Asymmetric stripline CSRR sensors with increased shielding are implemented for exploratory time-domain-reflectometry measurements, for wired large-area sensor networks. A conformal single-port dual-frequency SRR sensor (1.9-2.2 GHz) was integrated onto a NACA 0018 airfoil with electrothermal de-icing capabilities to sense in-flight ice crystal icing (ICI). Tested in the National Research Council’s (NRC’s) Research Altitude Test Facility (RATFac) at 19,000 ft, 100 m/s, +3°C and melt ratios of 11%, 13%, 16% and 25%, the sensor differentiates %melt while monitoring de-icing and anti-icing processes. Icing on smaller Unmanned Aerial Vehicles (UAVs), drones, and electric vertical take-off and landing (eVTOL) crafts impact aerodynamics and weight, severely limiting operation. A novel multi-frequency SRR sensor (2-4 GHz) is integrated with a zone-based electrothermal de-icing system and a low interfacial-toughness coating on a NACA 0012 airfoil for location-specific accretion monitoring and zone-based ‘smart’ de-icing. Tested at the Anti-Icing Materials International Laboratory’s (AMIL) icing wind tunnel at varying LWC (0.3, 0.5 and 0.8 g/m3), airspeed (20 and 40 m/s) and temperature (-5°C: glaze, -10°C: mixed, and -20°C: rime) conditions, the sensor monitors ice accretion, tracking the real-time resonant frequency response with a maximum sensitivity of 179 MHz/mm. A fitted time-constant is found to be inversely proportional to increasing ice thickness, facilitating icing rate measurement and ice thickness prediction using real-time microwave sensors. This work represents a significant advancement in the field of microwave SRR ice sensing technology, bringing it one step closer to practical implementation as integrated location-specific ice sensors for ‘smart’ ice protection systems (IPSs). iv Lay Summary Ice and snow accretion are a reality that impact most of the global population affecting a wide range of industries from roadways and renewable energy generation to airplanes, drones and flying cars. To ensure safe operation, ice protection systems are imperative and reliable ice sensors are an indispensable part, monitoring accretion and facilitating timely preventive measures for continued operation. This thesis investigates microwave resonant ice sensors, developing and testing them for real-world implementation in various industrial use cases. This robust, low power means of highly accurate sensing shows promising results as roadway ice sensors. The technology is further enhanced for application on curved aviation surfaces to sense the complex in-flight icing process in real-time and is tested in icing wind tunnels. An integrated ice protection system is further developed for optimized ‘smart’ de-icing in the context of advanced urban air travel, bringing this technology one step closer to real-world implementation. v Preface This thesis was conducted under the supervision of Prof. Mohammad H. Zarifi at the Okanagan Microelectronics and Gigahertz Applications (OMEGA) Laboratory at UBC. A section of Chapter 1 and a version of Chapter 2 is published as an invited review on microwave ice sensors to the IEEE Microwave Magazine. In this work, I was the lead author responsible for the preparation of the manuscript, editing and revising. All sections included in this thesis were contributed by myself with the exception of section 2.3 which was contributed by Niksan. Sections not included in this thesis were contributed by Wagih, Jain and Colegrave. Wagih and Zarifi were supervisory authors and assisted with conceptualization and review. • A. Shah, O. Niksan, M. C. Jain, K. Colegrave, M. Wagih and M. H. Zarifi, "Microwaves See Thin Ice: A Review of Ice and Snow Sensing Using Microwave Techniques," in IEEE Microwave Magazine, vol. 24, no. 10, pp. 24-39, Oct. 2023, doi: 10.1109/MMM.2023.3293617. A version of Chapter 3.1 is published by Kozak et.al at the IEEE Sensors 2021 Conference held in Sydney. I conducted the design and optimization of the SRR sensor under the guidance of Jain and Kozak and assisted with experimentation and manuscript preparation. Experimentation was conducted by Jain and the results, manuscript preparation and read-out circuitry designed were prepared by Kozak. Zarifi was the supervisory author who conceptualized the work, with McClelland assisting with reviewing and manuscript preparation. • R. Kozak, M. C. Jain, J. McClelland, A. Shah and M. Zarifi, "Durable Ice Sensors utilizing Microwave SRRs Coated with Protective Epoxy for De-Icing Control," 2021 IEEE Sensors, Sydney, Australia, 2021, pp. 1-4, doi: 10.1109/SENSORS47087.2021.9639610. A version of Chapter 3.2 is published at the SAE International Conference on Icing of Aircraft, Engines, and Structures (2023) held in Vienna. I was the primary researcher and was responsible for the sensor design, simulation, experimentation, data analysis, and manuscript preparation. Niksan assisted with data analysis on the TDR sensing section, with Zarifi as the supervisory author who assisted with conceptualization and reviewing of the manuscript. • A. Shah, O. Niksan, and M. H. Zarifi, "Planar Microwave Sensor for Localized Ice and Snow Sensing," SAE Technical Paper 2023-01-1432, 2023, https://doi.org/10.4271/2023-01-1432. A version of Chapter 4 was published in the IEEE Transactions on Aerospace and Electronic Systems. I was the lead researcher and was responsible for the fabrication of the airfoil test article, the ice accretion simulations, data analysis and manuscript preparation. Niksan contributed with the sensor simulation and microwave modelling and was assisted by Niknahad. Jain supported test article assembly and prepared testing equipment and instrumentation software. Fuleki and Rasimarzabadi were from the National vi Research Council Aerospace Group and conducted wind tunnel testing, set ice crystal icing test conditions and reviewed the manuscript. Zarifi was the supervisory author who assisted with conceptualization, on-site testing, editing and reviewing of the manuscript. • A. Shah et al., "Unlocking Conformal Microwave Split Ring Resonant Sensors for In-flight Ice Sensing," in IEEE Transactions on Aerospace and Electronic Systems, doi: 10.1109/TAES.2023.3328580. A version of Chapter 5 is in the process of being submitted to Nature Communications. I am the primary author of this work, conducting microwave sensor simulation, ice accretion simulations, airfoil test article fabrication, de-icing electronics, on-site testing, data acquisition, analysis and manuscript preparation. Zarasvand and Azimi coated the test article and assisted in selecting wind tunnel test conditions. Harvey calibrated the icing wind tunnel and assisted in test article setup. Momen organized wind tunnel testing. Golovin and Zarifi were supervisory authors who assisted with conceptualization and reviewing of the manuscript. • A. Shah, K. Zarasvand, Z. Azimi, D. Harvey, G. Momen, K. Golovin and M. H. Zarifi, " Multi-Location Microwave SRR Ice Sensor for Integrated, Zone-Based ‘Smart’ De-icing: UAV Flight Conditions," in Nature Communications [In process of submission] In addition to the work presented in the thesis, I supported multiple research projects at OMEGA Lab and contributed to the following publications which are not included in this thesis. • O. Niksan, J. D. Fowler, V. Balasubramanian, A. Shah, S. Pakpour and M. H. Zarifi, "A Waveguide Resonator Sensor for Bacterial Growth Monitoring: Towards Antibiotic Susceptibility Testing," 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023, San Diego, CA, USA, 2023, pp. 847-850, doi: 10.1109/IMS37964.2023.10188101. • O. Niksan, A. Shah and M. H. Zarifi, "Waveguide Iris Sensor with Thermal Modulation for Non-Intrusive Flow Rate Measurements," 2022 IEEE/MTT-S International Microwave Symposium - IMS 2022, Denver, CO, USA, 2022, pp. 1048-1051, doi: 10.1109/IMS37962.2022.9865459. • O. Niksan, M. C. Jain, A. Shah and M. H. Zarifi, "A Nonintrusive Flow Rate Sensor Based on Microwave Split-Ring Resonators and Thermal Modulation," in IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 3, pp. 1954-1963, March 2022, doi: 10.1109/TMTT.2022.3142038. • D. Kilani, A. Shah, F. Niknahad, and M.H. Zarifi, “A WiFi-based System for Ice Monitoring in Harsh Environment Using 2.7 GHz Microwave Sensor” in 2024 18th European Conference on Antennas and Propagation (EuCAP), Glasgow, Scotland, 2024 [Submitted] • D. Kilani, A. Shah, F. Niknahad, S. Olsen, M. Meaker and M.H. Zarifi, “Wireless Microwave Sensor Network Using Split Ring Resonator for Ice Monitoring Applications” in IEEE Transactions on Microwave Theory and Techniques [In process of submission] vii Table of Contents Abstract ................................................................................................................................................. iii Lay Summary ........................................................................................................................................ iv Preface .................................................................................................................................................... v Table of Contents ................................................................................................................................. vii List of Tables ......................................................................................................................................... xi List of Figures ...................................................................................................................................... xii Acknowledgements ........................................................................................................................... xviii Dedication ............................................................................................................................................ xix Chapter 1: Introduction ........................................................................................................................ 1 1.1 Motivation for Ice Sensing: An Ongoing Challenge ................................................................. 1 1.2 Objectives .................................................................................................................................. 3 1.3 Thesis Outline............................................................................................................................ 5 Chapter 2: Background ......................................................................................................................... 6 2.1 Microwave Resonant Sensor Fundamentals .............................................................................. 6 2.2 Electromagnetic properties of Ice and Snow ............................................................................. 8 2.3 Microwave Ice Sensors ........................................................................................................... 10 2.4 Integrated Ice-Sensing Solutions ............................................................................................. 13 2.4.1 Integrated Electrothermal De-icing .......................................................................... 13 2.4.2 Coatings for De-icing ............................................................................................... 14 2.4.3 Integrated System ..................................................................................................... 14 viii Chapter 3: On-Ground Ice Sensors ................................................................................................... 16 3.1 Epoxy-Coated Split Ring Resonator (SRR) Sensor ................................................................ 16 3.1.1 Sensor Design .......................................................................................................... 16 3.1.2 Experimental Procedure and Test Setup .................................................................. 17 3.1.3 Results and Discussion ............................................................................................. 18 3.1.4 Ice Sensing with Epoxy Coating Layer .................................................................... 18 3.2 Complementary Split Ring Resonator (CSRR) Snow Sensor ................................................. 20 3.2.1 Introduction .............................................................................................................. 21 3.2.2 Resonant-Based Microwave Detection of Ice and Snow ......................................... 22 3.2.3 Robust Epoxy-Coated Aluminum-Plate CSRR Sensor ............................................ 23 3.2.4 Experimental Setup and Data Acquisition ............................................................... 24 3.2.5 Icing Tests ................................................................................................................ 25 3.2.6 Snow Accretion Tests .............................................................................................. 26 3.2.7 Radar-Inspired Time Domain Measurements .......................................................... 28 3.2.8 Conclusions .............................................................................................................. 30 Chapter 4: In-flight Ice Sensor ............................................................... Error! Bookmark not defined. 4.1 Introduction ............................................................................................................................. 32 4.2 Theory, Design, Simulation and Fabrication ........................................................................... 35 4.2.1 Mixed Phase Ice Crystal Icing Conditions and Wind Tunnel .................................. 35 4.2.2 Airfoil Selection ....................................................................................................... 36 4.2.3 FENSAP-Ice Simulation .......................................................................................... 36 ix 4.2.4 Microwave Sensor Modelling and Simulations ....................................................... 37 4.2.5 Fabrication of Test Article ....................................................................................... 41 4.3 Test Setup and Conditions ....................................................................................................... 42 4.3.1 Test Conditions ........................................................................................................ 42 4.3.2 Test Article Setup in Wind Tunnel Section ............................................................. 42 4.4 Results and Discussion ............................................................................................................ 44 4.4.1 Microwave SRR Sensor Response ........................................................................... 44 4.4.2 Test Case Results for Varying Twb ........................................................................... 45 4.5 Conclusion ............................................................................................................................... 50 Chapter 5: Integrated Multi-Location Ice Protection System ......................................................... 52 5.1 Introduction ............................................................................................................................. 53 5.2 Theory, Design and Simulations ............................................................................................. 56 5.2.1 Ice Protection System Design .................................................................................. 56 5.2.2 Test Conditions and Icing Simulation ...................................................................... 57 5.2.3 Microwave SRR Ice Sensor Simulations ................................................................. 68 5.3 Fabrication and Testing ........................................................................................................... 71 5.3.1 Integrated IPS System .............................................................................................. 71 5.3.2 Wind Tunnel and Testing Methodology .................................................................. 72 5.4 Results and Discussion ............................................................................................................ 73 5.4.1 Data Transformation and Modelling ........................................................................ 73 5.4.2 Microwave SRR Icing Rate Measurement ............................................................... 75 x 5.5 Conclusion ............................................................................................................................... 81 Chapter 6: Conclusion and Future Work .......................................................................................... 82 6.1 Conclusion ............................................................................................................................... 82 6.2 On-going and Future Work ..................................................................................................... 84 Bibliography ......................................................................................................................................... 87 xi List of Tables Table 3.1. The change in resonance from bare due to water and ice extracted from Fig. 3.4 .................... 19 Table 4.1. Test Cases conducted in NRC Wind Tunnel at a speed of 100 m/s, altitude of 19000 ft and a static air temperature of +3°C ..................................................................................................................... 42 Table 5.1 Testing Conditions in an Icing Wind Tunnel ............................................................................. 57 Table 5.2. Measured Linear Proportionality Constant kx Relating Maximum Ice Thickness with Accretion Time for Icing Rate Monitoring ................................................................................................. 78 Table 5.3. Extracted constants τ and α from exponential association fitting of the |ΔFR| sensor response for each test condition ................................................................................................................................. 79 xii List of Figures Figure 1.1. Snow Cover in February 2022 obtained for NASA's Terra Satellite [1] ................................... 1 Figure 2.1. Split Ring Resonator (SRR) (a) Depicted Circuit Model, (b) Observed transmission gain (S21 (dB) Response and Parameters to Monitor ................................................................................................... 7 Figure 2.2. Split Ring Resonator (SRR) with (a) material placed on its sensitive gap (b) Change in transmission gain or S21 response of a sensor with application of material. ................................................. 8 Figure 2.3. Real permittivity and dielectric loss of ice and water plotted against frequency. ..................... 9 Figure 2.4. Frost formation on an SRR sensor. (a) time based S21 response of the freezing and thawing process, (b) resonant amplitude versus time, (c) resonant frequency versus time, and (d) quality factor versus time [38] ........................................................................................................................................... 12 Figure 2.5. Salty ice detection based on modified coupling of SRR structures [40] ................................. 13 Figure 2.6. Superhydrophobic coated modified microwave sensor with a patterned ground heater for detection and prevention of ice accumulation. [39] Reprinted with permission from R. Kozak, B. D. Wiltshire, Md. A. R. Khandoker, K. Golovin, and M. H. Zarifi, “Modified Microwave Sensor with a Patterned Ground Heater for Detection and Prevention of Ice Accumulation,” ACS Applied Materials & Interfaces, vol. 12, no. 49, pp. 55483–55492, Nov. 2020, doi: https://doi.org/10.1021/acsami.0c17173.. Copyright 2020 American Chemical Society. ............................................................................................ 14 Figure 2.7. (a) Prototype panel on a Peltier stage with a 3D printed mold for ice testing. (b) ice with embedded thermocouple frozen on the prototype panel. Infrared images showing the heat map of the prototype panel (c) before switching on the heater and (d) after switching on the heater. (e) Recorded resonant amplitude and (f) resonant frequency vs. time depicting the water being frozen to the LIT coating covering the sensor, heating the surface locally from -21℃ to -5℃, detaching the ice at -5℃ with a shear force, and then thawing the system. [65] ........................................................................................ 15 Figure 3.1. Microwave SRR sensor design and dimensions. ..................................................................... 16 Figure 3.2. The experimental test setup ..................................................................................................... 17 Figure 3.3. Simulated and measured sensor response with bare, 0.5 mm, and 1 mm of epoxy. ................ 18 xiii Figure 3.4. Measured bare, ice, and water response for the (a) uncoated, (b) 0.5 mm epoxy, and (c) 1 mm epoxy coated sensors. The red dotted circles indicate the placement of the material under test for each experiment................................................................................................................................................... 19 Figure 3.5. Microwave Sensors as highly reliable, integrated, localized icing rate measurement sensors as envisioned on aircraft structures ............................................................................................................. 21 Figure 3.6. Schematic of proposed robust, modified aluminum-plate CSRR resonant structure, a 3D CAD model and a stack up of the of the fabricated resonator with a microstrip transmission line with a width of 2.45 mm ...................................................................................................................................................... 23 Figure 3.7. Simulated and Measured S11 response (reflectance) for the modified ground plane epoxy coated CSRR sensor in a bare and iced configuration. Plotted electric-field density, indicating the sensitive region on the sensor’s surface. ..................................................................................................... 24 Figure 3.8. Snow and Ice testing experimental setup consisting of a Cold Chamber Freezer with hanging rope nucleation sites and a Humidifier. The sensor is monitored using a Vector Network Analyzer (VNA). .................................................................................................................................................................... 25 Figure 3.9. Time based measurement of icing on the proposed sensor. (a) Resonant Frequency and (b) Resonant Amplitude response monitored for a freezing-icing-thawing cycle. The measured resonant frequency and resonant amplitude change between the bare sensor and the iced sensor are 60 MHz and 1.19 dB, respectively. .................................................................................................................................. 26 Figure 3.10. Time-based measurements of Snow accretion on the sensor surface. (a) Resonant Frequency (RF) and (b) Resonant Amplitude (RA) for a layer of snow with increasing thickness. The measured RF and RA change between the bare sensor and the sensor with 1 mm thickness of snow are 35 MHz and 4.9 dB, respectively. (c) Depicts the resonator covered in 1mm of snow. ........................................................ 27 Figure 3.11. Modified asymmetric stripline CSRR structure. Three identical sensors fabricated for implementation in a Time Domain Reflectometry based cascaded sensor network ................................... 29 Figure 3.12. Location-based sensing of water on a wired 3-sensor array across a span of 10 meters; (a) the measured reflection is correlated with a bare measurement of the sensor network and the resultant plots indecate the presence of water, based the sensor location with the most significant variation from bare; (b) the experimental setup and, (c) the envisioned application of the proposed system on an aircraft wing ............................................................................................................................................................ 30 xiv Figure 4.1. Microwave Ice Sensor as envisioned on critical exterior aircraft structures for accurate local measurement of accretion. .......................................................................................................................... 33 Figure 4.2. NRC RATFac facility for ice testing at different altitudes and variant environmental parameters such as temperature and humidity. ........................................................................................... 35 Figure 4.3. Ice accretion profile after 100 seconds on a NACA 0018 with a chord of 250 mm is simulated using FENSAP Ice, with 5.4 g/m3 ice crystal particles with a % melt ratio of 11%, 13%, 16%, 25% and 30%. The simulation was conducted at 19,000 ft, a speed of 100 m/s and a total air temperature of +3°C. 3D view of ice accretion after 100 seconds of icing for 11% melt ratio. .................................................... 37 Figure 4.4. (a) The schematic of the edge-coupled, single-port, dual frequency SRR sensor for location specific detection of accretion (b) The designed 3D model of the conformed SRR-based sensor strategically placed on a NACA 0018 airfoil (in ANSYS HFSS), and the CAD model of accreted ice shape imported from ANSYS FENSAP ICE. ............................................................................................. 38 Figure 4.5. Electric Field displayed on the sensor’s surface (a) at 2.01 GHz for the leading-edge SRR (b) at 2.16 GHz for the second (upper) SRR. (c) The simulated scattering parameter (S11) of the sensor for varying melt ratios and thickness of the 3D accretion model. .................................................................... 40 Figure 4.6. Fabricated NACA 0018 test article with installed microwave sensors, heater, and temperature sensors, inset is thermal camera image of the heaters in action. ................................................................. 41 Figure 4.7. (a) fabricated microwave sensor and its integration over a NACA0018 airfoil, (b) ice cloud entering the wind tunnel, (c), (d), (e) various views of the installed sensor system and test article inside the wind tunnel, (f) side view of ice hitting the sensor on the leading edge of the airfoil, (g) ice accretion on the sensor on the leading edge with de-icing heaters “OFF”. ................................................................ 43 Figure 4.8. Measure S11 (dB) versus Frequency (GHz) of the two-notch single port microwave sensor during Test #7 at four separate instances .................................................................................................... 44 Figure 4.9. Test #1, #2 and #3 with a %melt = 25% (refer to Table 4.1), Icing for 20 s, 10 s and 10 s respectively, with natural accretion shedding and no heating. .................................................................... 45 Figure 4.10. Test #4, #5 and #6 with a %melt = 16%; Icing for 50, 30 and 30 seconds respectively, with electrothermal de-icing applied after Test #5 and an active icing and de-icing test case observed for Test #6. ............................................................................................................................................................... 46 xv Figure 4.11. Test #7 with a %melt = 16%; Icing for 480 seconds with electrothermal de-icing applied 380 seconds into icing to observe a post-icing active de-icing event. ............................................................... 47 Figure 4.12. Test #8, #9 and #10 with a %melt = 11%; Icing for 270, 780 and 480 seconds respectively, with electrothermal de-icing applied after Test #9 and kept on for active icing and de-icing during Test #10 .............................................................................................................................................................. 48 Figure 4.13. Test #11 and #12 with a %melt = 13%; Icing for 420 and 540 seconds respectively, with electrothermal de-icing applied for both tests. ............................................................................................ 49 Figure 4.14. Extracted resonant frequency ΔFr after 10 sec of icing for all test cases against the %melt ratio. ............................................................................................................................................................ 50 Figure 5.1. Envisioned implementation of location-specific integrated microwave SRR ice sensors, zone-based electrothermal de-icing and a de-icing coating for ‘smart’ ice protection for use on urban air mobility crafts like eVTOLs, UAVs and drones. ........................................................................................ 54 Figure 5.2. 3D CAD Model of the Microwave SRR sensor integrated with conformal heaters onto a NACA 0012 airfoil for location-specific ice sensing and zone-based electrothermal de-icing. ................. 56 Figure 5.3 (a) Calculated ice density for the temperature and airspeed conditions outlined in Table I using Equation 5.3. (b) Calculated ice thickness for the temperature, LWC and airspeed conditions outlined in Table 5.1 using Equation 5.2. (c) 2D accretion profiles extracted at 300 seconds for a -5°C [glaze], -10°C [mixed] and -20°C [rime] ice accretion simulation conducted on FENSAP Ice, indicating the variation in profile and accretion thickness. The location of the three SRR tags are highlighted for reference. (d) Corresponding ice thickness solution for the profiles in Figure 5.3(c) are extracted and plotted against the y-axis of the airfoil to obtain the ice thickness along the airfoil profile. Inset shows a 3D profile of the horn-shape glaze ice formation. (e) Ice thickness solution for -20°C [rime] ice conditions with variations in LWC (0.3 g/m3, 0.5 g/m3 and 0.8 g/m3 at 20 m/s) and in airspeed (20 and 40 m/s at 0.5g/m3) plotted at 480 seconds of icing. Inset shows a 3D profile of the rime ice formation at 0.5 g/m3 LWC. (f) Maximum Ice thickness solution extracted from each shot of the multi-shot FENSAP Ice simulations for every simulated condition highlighted in Table 5.1, showing that the ice thickness linearly increases with LWC, temperature and airspeed. ......................................................................................... 67 Figure 5.4. (a) The designed two-port (S21) multi-frequency microwave split ring resonator (SRR) sensor integrated with individual heated zones for location-specific accretion measurement and zone-based smart de-icing control. (b) The simulated transmission coefficient (S21 dB) response of the multi-frequency xvi microwave SRR sensor from Ansys HFSS. The sensor’s response to loading the leading-edge (LE) sensor (SRR 1) with a 16 mm x 16 mm material box with varying ice thickness, 𝛿𝑖𝑐𝑒 and a constant permittivity εice = 3.15 (pure, solid ice) is plotted and the change in the resonant frequency (ΔFR1) is indicated. (c) The absolute change in resonant frequency |ΔFR| extracted from the simulated sensor response and plotted against increasing ice accretion thickness from 0.5 mm to 8 mm (modelling time-based accretion) for material permittivity values between 2 and 3.15 (modelling accretion with different density). The corresponding ice densities for the simulated permittivity values are calculated using Equation 5.6 and are presented alongside each response. ....................................................................................................... 70 Figure 5.5. Fabricated NACA 0012 Test Article with Integrated Microwave SRR sensor for location-specific sensing and individually actuated electrothermal ice protection instrumentation for guided de-icing control. Inset: video snapshot of installed test article in AMIL wind tunnel. .................................... 71 Figure 5.6. (a) Measured S21 (dB) frequency response (transmission coefficient) of the multi-frequency microwave SRR sensor plotted every 10 seconds for 600 seconds at the -20°C, 20 m/s and 0.8 g/m3 icing wind tunnel testing condition. (b) Zoomed-in view of the S21 (dB) frequency response of SRR tag 1 for 600 seconds of ice accretion at -20°C, 20 m/s and 0.8 g/m3. (c) Time-based ice accretion results for a set of tests conducted at -20°C, 20 m/s and an LWC of 0.8 g/m3. Extracted and normalized absolute resonant frequency shift |ΔF| for SRR 1 (black), SRR 2 (blue) and SRR 3 (red) are presented. The first test depicts an 8 min long active de-icing test followed by a sequence of 1 min, 3 min, 5 min and 10 min tests with ice accretion thickness measurements. LE = leading edge. .............................................................................. 74 Figure 5.7. Measured ice thickness (mm) plotted as a function of time for 560 seconds of accretion at (a) -5°C glaze icing conditions, (b) -10°C mixed icing conditions, and (c) -20°C rime icing conditions. Extracted resonant frequency shift |ΔF| for tests conducted at (d) -5°C glaze icing conditions, (e) -10°C mixed icing conditions, and (f) -20°C rime icing conditions. Pictures of ice accretion at (g), (h) -5°C glaze icing conditions, (i) -10°C mixed icing conditions, and (j) -20°C rime icing conditions. .......................... 77 Figure 5.8. (a) The extracted rate of saturation of the microwave frequency response, 𝜏, plotted against the icing rate indicator, 𝑘𝑥, for all tested conditions outlined in Table 5.1 and fitted using a power law trend. (b) Measured ice thickness (values labeled) plotted alongside the predicted ice thickness using Equation 5.9, for the conditions outlined in Table 5.1. ............................................................................. 80 Figure 6.1. Wireless Microwave Ice Sensor Node for application as a real-time wind turbine ice monitoring solution ..................................................................................................................................... 84 xvii Figure 6.2. Waveguide Microwave Ice Sensor for ice monitoring on metallic surfaces ........................... 85 xviii Acknowledgements First and foremost, I would like sincerely to thank my supervisor Prof. Mohammad H. Zarifi for his constant guidance, support and mentorship throughout my journey. His passion, excitement and outlook towards the research undertaken truly encouraged me to put my best foot forward and go the extra mile. I am ever grateful to him for his commitment towards my growth, clearly identifying my challenges while helping me hone my strengths. I would also like to acknowledge the Professor for the community of strong, bright research engineers he has strived to build at OMEGA Labs, around the values of collaboration, support and teamwork. It is this community that has allowed me to learn, grow and succeed at showcasing the work presented in this thesis. I would like to thank Omid Niksan, my friend, co-author and mentor for all the conversations, explanations and discussions. His patience in taking the time to answer my million questions and support my study as a microwave engineer is something I must recognise. I would also like to thank Mandeep Jain and Ryan Kozak who trained me as an undergraduate research assistant and set me on this research path. Additionally, I would like to thank my colleagues Jack McClelland and Vishal Balasubramaniam for their camaraderie during our time at OMEGA Labs. I would like to thank Raymond Seida, Durwin Bossy, Jiyee Yoon and Praveen Rajan of UBC Okanagan’s Machine Shop and Research Labs for their support and guidance on the various mechanical and fabrication projects I undertook. I would like to thank my committee member Prof. Kevin Golovin and his team from the DREAM Lab for their collaboration on the DND ice sensing projects, which allowed for the testing and use of an icing wind tunnel. I would like to also acknowledge my committee member Prof. Ian Foulds for his MEMS course that helped me bridge my mechanical background with my electrical education. I would like to acknowledge the Syilx Okanagan Nation for the use of their unceded territory, the land on which this research was conducted. Lastly, my deepest gratitude goes to my family for their sacrifices and their unconditional love and support, without whom I would not be. xix Dedication I dedicate this to my family. Chapter 1: Introduction 1 Chapter 1: Introduction The aim of the introduction is to illustrate the motivation behind this project and describe the objectives that will be achieved through this thesis. An overview of the chapters to come and an outline of the thesis’ structure will be presented. 1.1 Motivation for Ice Sensing: An Ongoing Challenge Figure 1.1. Snow Cover in February 2022 obtained for NASA's Terra Satellite [1] Ice and Snow are a reality a large percent of the global population experience on a regular basis with over 31% of landmass [1] experiencing seasonal snow and ice accretion as observed in Figure 1.1, a satellite image of the global snow cover for February 2022 [2]. In the US alone, ice and snow impact 70% of its population resulting in over 1300 deaths from icing related roadway accidents and causing an estimated 2.3 billion dollars annually spent on road snow and ice control operations [3]. Infrastructure in regions that receive ice and snow, must be specially designed to reliably operate, with specific considerations for power grids [4], antenna communication structures and cable bridges [5]. Expanding marine shipping and industrial operations in arctic regions have increased the need for safe and reliable operation of equipment and ships in atmospheric accretion and salty-icing conditions [6]. Wind turbines that rotate at high speeds and heights require through considerations for atmospheric ice formation to prevent damage, where unconsidered icing results in substantial power reduction or complete outage [7]. Similarly, ice accretion on flying objects, such as aircraft wings or turbo-propellors, are highly critical Chapter 1: Introduction 2 challenges and have been the focus for sensing and de-icing for decades [8] due to the fatal effects of icing on airplanes [9]. For continued operation in icy or snowy conditions, accretion must be accurately sensed in time for stoppage or de-icing. Various commercial technologies [5], [6], [10], [11] and industrial standards [12], [13] for ice detectors exist. Theses application-specific sensors each apply different technologies, including magnetostrictive vibration [14], capacitive [15], optical [16], [17] and ultrasonic [18], [19] methods for detection of ice or snow. Magnetostrictive sensors used as primary in-flight ice detectors for airplanes [14], offer ‘global’ air condition measurements and result in total airplane de-icing in place of optimized local ice-removal [15]. Commercial wind turbine sensors are also ‘global’ sensors that can be insensitive to the actual onset of icing on the blades and result in late turbine stoppage [11]. Current optical methods for ice detection suffer from real-world noise and are expensive and challenging to integrate [16], [20], [21]. Capacitive and resistive roadway ice sensors have limited accuracy, are high power solutions that require challenging, wired installation [22]. Ultrasonic methods are relatively new [18] and require testing in real-world, high-vibration and noise environments. As we move towards greener, more efficient systems, current ice detection and de-icing methods must be improved, challenged, and substituted in favor of localized ice sensing and ‘guided’ optimal de-icing. This would require through understanding of the type and contents of the locally accreted ice and mechanisms to efficiently de-ice the application specific surface for reliable operation. Microwave-based sensing methods have recently shown significant advancements, exhibiting excellent performance as highly-accurate real-time sensors for industrial [23], biomedical [24], [25], [26], [27], and chemical applications [28], [29], [30], [31]. Microwave devices have been successfully implemented for characterization of materials in solid, liquid, and gaseous phase [32]. These types of microwave sensors are compatible with wireless communication protocols and allow for communication and implementation of wireless sensing mechanisms [33]. These sensors are capable of reliable operation in harsh environments and are easily implemented due to their low cost of fabrication. Numerous industrial implementations of microwave sensors exist. Most prominently, these types of sensors are used for moisture and humidity measurements in the pulp and wood industry [34]. Various microwave flowrate sensors for oil, gas and water measurements are commercially available [35]. Microwave sensors see great use in wireless downhole monitoring, corrosion monitoring and subsea oil and gas applications [36]. As biomedical sensors, microwave devices see wide application in non-invasive sensing, diagnostics, and therapy due to the relatively innocuous nature of microwave radiation and its ability to penetrate through biological media [37]. Specifically, resonant microwave structures have demonstrated extreme sensitivity, accuracy, repeatability, and high quality-factors [33]. These various attributes make microwave sensors Chapter 1: Introduction 3 great candidates for further investigation as wireless, low-cost, low-power sensors for highly accurate, local ice and snow detection. Detection of ice and frost formation on planar microwave split ring resonator (SRR) sensors was initially demonstrated by Wiltshire et al. [38]. Kozak et.al. implemented a unique SRR design with a built-in heater to monitor the formation of ice and conduct de-icing on super hydrophobic coatings and through epoxy [38], [39]. Luckasavitch et.al. implemented magnetically coupled planar SRR sensor configurations to detect the presence of contaminated ice and salty water for marine ice detection applications [40]. These previous works successfully prove the concept of microwave resonant sensor technology for ice sensing through controlled laboratory cold-plate testing. This thesis aims to take this technology a step further by integrating and applying the technology in specific industrial use-cases and testing it in simulated real-world icing conditions. Industrial interest in the implementation of this technology has led to the initiation of this project. 1.2 Objectives With the goal of taking microwave resonant ice sensing technology a step further, the primary objective of this thesis is to design sensors for explicit industrial applications and test them in simulated real-world icing conditions. Specifically, this work will concentrate on planar, on-ground sensors for roadways, and conformal sensors for curved aerodynamic structures for in-flight and aviation applications. To achieve this goal, the research is divided into the following sub-objectives. 1. Investigate planar microwave resonant sensors for application as on-ground ice and snow sensors. Split-Ring Resonator (SRR) and Complementary Split Ring Resonator (CSRR) type microwave sensors operating in the 1-2 GHz range will be investigated for integration with at least 1mm of protective epoxy coating for implementation as robust roadway ice and snow sensors. The sensor must accurately sense at least 100 µl of ice and at least 1 mm of snow accretion in real-time at temperatures down to at least -20°C. Hypothesis: Resonant-based microwave sensors can successfully sense and differentiate ice and snow accretions. The planar sensors will also operate with wear-resistant epoxy coatings in a non-contact manner, as required for application in high-wear locations like roadways and driveways for the sensing of accretion. Task: Microwave resonant sensors will be designed in the specified frequency span and fabricated with protective epoxy coatings for testing as on-ground ice sensors. The sensitivity of Chapter 1: Introduction 4 the coated sensor to ice will be investigated. The fabricated sensor will be tested in simulated snow accretion conditions in a snow-chamber at the mentioned conditions, investigating the sensor response to snow. 2. Investigate conformal microwave SRR sensors in the 2 GHz range in aeronautical conditions for ‘impact’ ice accretion measurement. Test the sensor in icing wind tunnel simulated mixed-phase ice-crystal icing conditions at an altitude of 19,000 ft, airspeed of 100 m/s and total air temperature of +3°C for varying melt ratio conditions (11%, 13%, 16% and 25%). Additionally, monitor electrothermal de-icing and anti-icing on the airfoil surface. Hypothesis: Microwave SRR sensors will successfully determine the presence of ice under simulated real-world aeronautical ‘impact’ icing conditions. The sensor will accurately determine the onset of icing and further differentiate the %melt conditions outlined. Task: A NACA 0018 airfoil test article with electrothermal de-icing capabilities will be fabricated specifically for installation in the icing wind-tunnel test section at the National Research Council’s (NRC’s) Research Altitude Test Facility (RATFac). A dual-frequency conformal SRR sensor in the 2 GHz range will be affixed to the test article and its performance will be tested in mixed-phase aeronautical ice-crystal accretion conditions as outlined. 3. Investigate conformal microwave SRR sensors in the 2-4 GHz range under low-Reynolds number icing conditions (Re~ 105-106) expected for unmanned aerial vehicles (UAVs), drones and electric-vertical take-off and landing crafts (eVTOLs). Design an integrated microwave-sensor and zone-based electrothermal ice-protection system for conformal application on an airfoil surface. Test the sensor in -5°C, -10°C and -20°C conditions for measurement of glaze, mixed and rime type of ‘impact’ icing respectively. Test the sensor in varying speed (20 m/s and 40 m/s) and Liquid Water Content (LWC) conditions for measurement of the rate of ice accretion. Hypothesis: The multi-frequency microwave SRR ice sensor will successfully sense zone-specific icing along the chord of the airfoil structure under the outlined conditions. The sensor response will accurately differentiate the icing rate and successfully sense glaze, mixed and rime type ‘impact’ icing. The sensor will be integrated with an electrothermal de-icing system for guided de-icing. Task: An aluminium NACA0012 airfoil test article will be fabricated with a conformal zone-based electrothermal de-icing system. A multi-frequency microwave SRR ice sensor with 3 sensing locations located with-in specific electro-thermal zones will be designed in the 2-4 GHz range for the sensing of ‘impact’ ice accretion at the outlined conditions for glaze, mixed and Chapter 1: Introduction 5 rime- type ice sensing. The measurement of variations in the icing rate will be achieved through the real-time monitoring of the microwave frequency response. 1.3 Thesis Outline This thesis is divided into six chapters to successfully meet the goal of testing microwave ice sensors in simulated real-world icing conditions and further investigate them for specific industrial icing applications. Chapter 1: The Introduction presents a motivation for this research and outlines three specific objectives that will be the focus of this work. Chapter 2: The Background provides a theoretical review on microwave resonant sensors, the electromagnetic properties of ice and snow as a dielectric mixture and a comprehensive literature review of microwave resonant-based ice sensors. Chapter 3: The On-ground ice sensor chapter will investigate planar microwave resonant structures for harsh applications with protective coatings for the sensing of ice and snow on roadways. An additional experimental study into new sensing mechanisms is investigated. Chapter 4: The In-flight ice sensor chapter investigates microwave SRRs in experimental mixed-phase ice-crystal icing for the sensing of icing in conformal aeronautical applications. Chapter 5: The Integrated Ice Protection System presents a multi-frequency sensor for location specific ice sensing and zone-based ‘smart’ de-icing in the context of advance urban air travel. Chapter 6: The Conclusions and Future Works concludes the findings and results of this research work and presents recommendation for future work towards Ice Protection Technologies for ice sensing and optimal de-icing using microwave sensors. Chapter 2: Background 6 Chapter 2: Background The background section explores microwave resonant sensors for application in ice sensing. This chapter begins with a section on microwave resonant sensor fundamentals, discussing the sensing mechanism and principle of operation. Following this, a background on electromagnetic properties of ice and snow as a material in the microwave regime is presented. The final section presents a literature review of previously published microwave sensors for ice sensing. A version of this chapter was published in the IEEE Microwave Magazine with the title “Microwaves See Thin Ice: A Review of Ice and Snow Sensing Using Microwave Technique”. 2.1 Microwave Resonant Sensor Fundamentals Microwaves are defined as electromagnetic waves with frequencies between 300 MHz and 300 GHz, corresponding to a wavelength of 1m to 1mm. A material’s interaction with electromagnetic waves can be characterized by two distinct properties, its degree of polarization or real permittivity, and its dielectric loss or imaginary permittivity [41]. Materials can undergo molecular displacements when exposed to time-harmonic (alternating) electromagnetic fields. Exploiting this phenomenon, the field of microwave sensing was born out of the ability to differentiate materials based on their interaction with electromagnetic fields [42]. A microwave sensor can be devised as a ‘resonator’ which displays a predictable, characteristic electromagnetic resonance as a function of the structure’s geometry and vicinity. These measurable characteristics are highly accurate for optimized structures [43]. When materials are introduced into the sensor’s local environment, its resonant characteristics are perturbed [44]. Exploiting this phenomenon, microwave sensor designers detect materials in the vicinity of these resonant structures by characterizing the change in resonant frequency, resonant amplitude, and general frequency response [45]. A throughly instigated microwave resonator for sensing applications is the split ring resonator (SRR). An SRR is a conductive ring structure with a gap cut out. These microwave structures have been applied in extensively in the field of microwave sensing and have shown robust and highly accurate results for material characterization and sensing. The resonant mechanism of an SRR can be explained by (2.1) where its characteristic resonant frequency, 𝑓𝑟𝑒𝑠, is determined as a function of its length, 𝐿, wave velocity in vacuum, 𝑉𝑝, and the effective permittivity of the medium, 𝜀𝑒𝑓𝑓 [46]. For a first-order mode resonance, (2.1) requires the resonator length to be equal to half the wavelength (𝐿 = 𝜆/2) [47]. Chapter 2: Background 7 𝑓𝑟𝑒𝑠 = 𝑉𝑝2𝐿√𝜀𝑒𝑓𝑓 [𝐻𝑧] (2.1) SRRs implemented as microstrip structures can be excited with microstrip transmission lines that conventionally support Quasi TEM mode propagation. For ease of interpretation and design, such implementations require a model that can accurately predict the sensors behavior. The operation of such a microwave sensor can be modeled using a lumped-element equivalent circuit model. A rough circuit model of the presented sensor and its expected transmittance are observed in Figure 2.1. The resonant behavior of such a microstrip line fed SRR can be monitored with respect to three variables: resonant frequency, 𝑓𝑟𝑒𝑠, resonant amplitude, 𝐴𝑟𝑒𝑠, and a -3dB or half-power quality factor, 𝑄𝐹−3𝑑𝐵 = 𝑓𝑟𝑒𝑠 𝑓ℎ − 𝑓𝐿⁄ . Figure 2.1. Split Ring Resonator (SRR) (a) Depicted Circuit Model, (b) Observed transmission gain (S21 (dB) Response and Parameters to Monitor The split region of a microwave resonator can be modeled as a capacitive region due to charge accumulations on the edges of the split. This capacitive section will result in a relatively high concentration of E-fields along the gap. Perturbation of the electric fields in this capacitive region can be regarded as a change in the effective permittivity (See (2.1)), 𝜀𝑒𝑓𝑓, and results in a shift in the resonant characteristics as seen in Figure 2.2. The perturbation of field distributions can be modelled by changes in the capacitive element of an LC resonant circuit. Not only can different materials be characterized based on this change in resonant features, but also real-time changes in properties and the evolution of processes can be monitored in a non-contact method. It is this unique property among various others that make such microwave sensors ideal for low power, conformal, real-time monitoring of ice-snow accretion and frost formation. Chapter 2: Background 8 Figure 2.2. Split Ring Resonator (SRR) with (a) material placed on its sensitive gap (b) Change in transmission gain or S21 response of a sensor with application of material. 2.2 Electromagnetic properties of Ice and Snow Microwave measurements and characterization of ice, water and snow have been thoroughly studied in the last century. Various methods of characterizing ice and snow as materials, defining parameters that describe its behavior and extracting working models to predict them were applied. A comprehensive document, the “Engineering Properties of Ice and Snow”, was published by Malcom Mellor in 1977 [48]. With respect to the microwave regime, ice and water each have their own distinct electromagnetic properties. Considering these materials separately, we can obtain a dielectric permittivity and loss tangent value for each. Water has a real relative permittivity of 90 (at 0°C and 5GHz), and a relatively high loss tangent due to the water O-H bond polarization at microwave frequencies [49] in a liquid water medium where molecules can slide over each other allowing the movement of polar molecules. Ice has a dielectric permittivity of 3.2 (at 0°C and 5GHz) [49], [50] and a far lower dielectric loss of ~0.001 (at -20°C and 1 GHz) [50] which is attributed to the inability of the O-H bonds to break out of the strong hexagonal crystalline structure formed due to the solidification of liquid water. This large difference in permittivity, as indicated in Figure 2.3, is very useful in clearly differentiating between ice and water. Ice accretion characteristics are far more complex and are determined based on local temperature, pressure, relative humidity, and water-ice mixtures. This results in ice formations with different densities, structures and mixtures of ice, water, and air. Snow is a very good example of a mixture of ice, water and air where different environmental conditions affect its type and accretion characteristics. This makes ice and snow detection challenging, requiring thorough consideration for each variation of the material. Chapter 2: Background 9 Figure 2.3. Real permittivity and dielectric loss of ice and water plotted against frequency. As microwave sensors detect material based on effective permittivity change, they can detect the difference in accreted ice and snow based on densities and liquid water contents [51], [52]. These relations can be theoretically modelled using various microwave dielectric mixing theories [53]. In the field, empirical models can be used to obtain various characteristics of the snow depending on the frequency of operation and required accuracy. For frequencies between 10 MHz and 1 GHz, a first order approximation can be used to model the dielectric permittivity of accreted snow as a linear function of density, 𝜌, and a quadratic function of volumetric liquid water content 𝑊 [52]. 𝜀′ = 1 + 𝑎𝜌 + 𝑏𝑊 + 𝑐 𝑊2 Such simple models allow for easy characterization through the constants 𝑎, 𝑏 and 𝑐, for different types and conditions of ice and snow depending on the sensing application. Using such empirical models, different snowing and icing environments like roadways, bridge cables, wind turbines and airplanes can be modelled and characterized. Chapter 2: Background 10 2.3 Microwave Ice Sensors Microwave sensing systems consists of a large range of technologies that use different techniques to sense materials using electromagnetic radiation in the microwave spectrum [54]. Ice detection using microwave technologies has been historically performed using dielectric waveguide [55], [56], resonant cavity [57], and resonant fork techniques [58]. These historical methods pose various challenges for conformal aircraft application as ice sensors due to their bulky design, large size, low special resolution, and relatively low sensitivity. The mechanical challenges with conformal implementation for aeronautical ice sensing are addressed by the implementation of microwave devices using planar microstrip technology. Arndt, G. D., et al. [59] presents a microstrip patch antenna for the detection of ice as also seen in [60]. These ice detectors distinguish the presence of ice based on the change in the measured reflectance or transmittance due to the near-field effect of the material under test on a radiating microwave antenna. These sensors exhibit low sensitivity as seen by the effective resonant frequency shift of under 20 MHz for over 10 mm of ice thickness in the work by Arndt, G. D., et al. This can be attributed to the limited near-field confinement of electromagnetic energy by the implemented resonant structure. Confining electromagnetic energy in a smaller volume result in a more sensitive device due to the large measurable change for small changes in the environment [42]. Kozak et al. presented a microwave antenna sensor for detection of ice [61], in an attempt to increase the sensitivity through increased electric field concentration. Additionally, antenna-based sensors are radiating elements that pose challenges for implementation in airplanes where interference is a vital concern. Bianchi, M., et al. [62] presents a microstrip sensor for measurement in the change to the amplitude at a specific matched frequency due to the mismatch caused by a layer of ice. These sensors are suitable as ‘detectors’, allowing for limited characterization of ice, water, or air presence but are unable to sense the complex microscale characteristics of ice accretions that include varying densities and mixtures as presented in the mixed-phase ice crystal icing conditions tested in this work. Bianchi, M., et al. also presents a sensor that is a resonant microstrip line capacitively coupled to a transmission line. This sensor exploits the characteristic resonance of the line resonator and the coupling fields between the transmission line to sense the presence of ice layers, however, it still experiences lower spatial sensitivity and radiation concerns. This microstrip resonant microwave method is not further developed or presented in literature for real-time sensing of ice accretion in airplane applications. Chapter 2: Background 11 The microwave resonant technology asserts multiple advantages over other microwave methods discussed. Split ring resonators (SRRs) are chosen for their simplistic design, robust operating characteristics and low-cost fabrication. These devices have low radiative loss and display a characteristic frequency response, resistant to the effects of electromagnetic interference and noise [63]. Furthermore, this technology allows for the design of electromagnetic structures with dimensions that are small in comparison to their resonant wavelength. If we consider the length of a the microstrip line resonator, it is half of the wavelength at its resonant frequency. In comparison, the ring resonator has a characteristic half-wavelength resonance determined by the length of the ring’s circumference, which is further decreased due to the inclusion of the highly capacitive split in the resonant ring, effectively decreasing the total area within which the electromagnetic energy is stored while increasing the sensitivity of the resonant device. Microwave resonators can be geometrically tailored to function as a sensing element. These resonators can be implemented in a planar structure to provide a flat platform for ice buildup/accumulation. Microwave resonators can be incorporated into various topologies. Wiltshire et al. investigated SRRs for detecting ice formation on the split section and frost buildup on the entire surface of a passive sensor [38]. The sensor consisted of a double SRR structure that was electromagnetically coupled to two open-ended transmission lines. This component topology resulted in a band-pass frequency response of the passive sensor. This resonant structure had a resonant frequency of 4.85 GHz, a 3 dB quality factor of 250, and a peak S21 (dB) value of -14.5 dB. The sensitivity of the device’s resonant response to frost formation was investigated by placing it on a Peltier device and cooling it to reach sub-zero temperatures (≈−10 °C) on the sensor’s surface. The Peltier’s temperature was controlled using a PID controller. The results of this investigation indicated that within only 15 secs of frost build-up, the entire resonant profile, including resonant frequency, resonant amplitude, and quality factor, changed with a noticeable trend as observed in Figure 2.4. The formation of frost on the sensor changed the effective permittivity of the SRR’s capacitive regions, ultimately changing the characteristics of the band-pass response. During the thawing process, with water (εr=80) on the resonator, the resonant quality factor was significantly degraded in the monitored frequency range since water’s high permittivity substantially changed the effective permittivity in the resonator’s nearby region. The split ring resonator’s sensitivity to the effective permittivity of its nearby region was further confirmed by drop-casting a water droplet (εr=80) on the rings. Further icing of the droplet on the resonator was detected by monitoring the resonant profile over time as the sensor cooled. For droplet freezing, the sensor’s resonant frequency had a of 220 MHz shift in the resonant frequency. The work presented by Wiltshire et al. showed the capability of microwave SRR sensors for monitoring ice, and frost formation, with the further ability of detecting Chapter 2: Background 12 transition states (i.e., ice to water and water to ice) in cold climatic conditions. Applying this technique, modified resonant structures have also been published for optimized ice thickness sensing [64]. Figure 2.4. Frost formation on an SRR sensor. (a) time based S21 response of the freezing and thawing process, (b) resonant amplitude versus time, (c) resonant frequency versus time, and (d) quality factor versus time [38] As mentioned earlier, the presence of water on the gap region of an SRR-based sensor can significantly decrease both the loaded and the unloaded quality factor, which are monitorable in the bandpass response. This degradation in the quality factor was more pronounced when the dielectric loss factor of the water droplets increased. Such an increase can be attributed to the inclusion of conductive materials, most often salt and water mixture. The inclusion will remain after freezing (saline ice), and the loading effect of the salt-ice mixture will continue to degrade the bandpass resonant response. Consequently, a capacitance-dominant, split section of SRRs cannot be employed to monitor saline ice formation from salt water. This issue was solved by Luckasavitch et al. [40], when they utilized the coupling mechanism between two SRRs to detect saline ice. By configuring the placement of the two SRRs, with their maximum Chapter 2: Background 13 current densities adjacent to each other, the dominant coupling mechanism occurred through magnetic fields. This configuration with the coupled SRR elements is shown in Figure 2.5. This topology slightly reduced the sensitivity to permittivity variations in the coupled region, while introducing the capability of detecting conduction current variations for saline ice and water. For 80 µL of a 5% salt-water mixture, the coupled resonator sensor demonstrated a 119 MHz shift in resonant frequency upon ice formation. The results suggested that the “magnetically” coupled SRRs maintained the band-pass resonant profile necessary to distinguish saltwater and saline ice. This work, therefore, indicates that the coupling mechanism between adjacent resonators can be utilized to broaden the range of detectable materials, including but not limited to saline ice and water. Figure 2.5. Salty ice detection based on modified coupling of SRR structures [40] 2.4 Integrated Ice-Sensing Solutions 2.4.1 Integrated Electrothermal De-icing Sensing ice is only part of the challenge with ice and snow. After ice is detected, a system must begin applying de-icing measures. Electrothermal de-icing is a modern method of de-icing applied in various applications like airplanes, wind turbines, and driveways. Kozak et al. presented a planar SRR resonator sensor with embedded resistive heating within the microstrip ground plane in [39]. The planar structure of the SRR sensor further allows the additional functionality of “heating” for the prevention of ice accumulation. Compared to conventional SRR structures, this work replaced the intact ground plane with patterned traces to increase the DC resistance of the ground plane to effectively utilize it as a heater as depicted in Figure 2.6. Chapter 2: Background 14 Figure 2.6. Superhydrophobic coated modified microwave sensor with a patterned ground heater for detection and prevention of ice accumulation. [39] Reprinted with permission from R. Kozak, B. D. Wiltshire, Md. A. R. Khandoker, K. Golovin, and M. H. Zarifi, “Modified Microwave Sensor with a Patterned Ground Heater for Detection and Prevention of Ice Accumulation,” ACS Applied Materials & Interfaces, vol. 12, no. 49, pp. 55483–55492, Nov. 2020, doi: https://doi.org/10.1021/acsami.0c17173.. Copyright 2020 American Chemical Society. 2.4.2 Coatings for De-icing Furthermore, the planar topology of the SRR sensor allowed the application of an ice-phobic coating on its surface to investigate the effectiveness of such engineered materials. Kozak et al. investigated the freezing rates of droplets on superhydrophobic treated and untreated SRRs. The treated surface was able to delay freezing time by 250s, which is equivalent to a decrease in the freezing rate by a factor of three. Moreover, by fitting the melting curves and extracting the time constants of heated and unheated sensors, it was found that the heating decreased the normal melting time by a factor of two. This work demonstrated the overall multi-functionality of an SRR-based sensing structure and the ability to incorporate de-icing and anti-icing capabilities into a single sensor device. 2.4.3 Integrated System Azimi Dijvejin et al. demonstrate an integrated smart, hybrid (passive and active) de-icing system through the combination of low interfacial toughness (LIT) coatings, printed circuit board heaters, and an ice-detecting microwave sensor in [65]. Upon sensing ice and snow with the installed microwave sensor, the integrated system applies minimal heating to induce crack propagation and subsequent de-icing at low temperatures and forces without melting the ice (shown in Error! Reference source not found.). This low-power integrated system, which applies materials technology and integrated sensing and heating capabilities for de-icing, is a clear step toward the next phase of localized, optimized ice sensing and de-icing. Chapter 2: Background 15 Figure 2.7. (a) Prototype panel on a Peltier stage with a 3D printed mold for ice testing. (b) ice with embedded thermocouple frozen on the prototype panel. Infrared images showing the heat map of the prototype panel (c) before switching on the heater and (d) after switching on the heater. (e) Recorded resonant amplitude and (f) resonant frequency vs. time depicting the water being frozen to the LIT coating covering the sensor, heating the surface locally from -21℃ to -5℃, detaching the ice at -5℃ with a shear force, and then thawing the system. [65] Water added to sensor Water freezing into ice Ice adhered to the surface Ice detached from the surface Ice thawed Heater on and lateral force applied Heater on and lateral force applied (a) (b) (c) (d) (e) (f) 3D printed mold 30. 22.0 Heater on Heater off Heater on Water added Water added Chapter 3: On-ground Microwave Resonant Sensors 16 Chapter 3: On-Ground Ice Sensors In this chapter, three sensors are designed for planar ice and snow sensing in harsh roadway or runway environments. Split Ring Resonator (SRR) and Complementary Split Ring Resonator (CSRR) type sensors are presented for real-time non-contact ice and snow sensing through protective coatings. In addition, exploratory time-domain-reflectometry (TDR) measurements for wired large-area sensor networks are presented. 3.1 Epoxy-Coated Split Ring Resonator (SRR) Sensor The following section presents a two-port bandpass SRR sensor for ice sensing at 1-1.5 GHz. 3.1.1 Sensor Design A circular planar microstrip split-ring resonator was designed and simulated in ANSYS High Frequency Structure Simulator (HFSS) software to operate in the frequency range of 1.2 - 1.3 GHz with -3 dB quality factor higher than 175. This operational frequency range was selected to ensure a maximum difference in the relative permittivity between water (εr = 90) and ice (εr = 3.2) [24], [25]. A circular ring design was selected to minimize the capacitance of the sensor. The sensor was fabricated on RT/Duroid 5880 substrate with a relative permittivity of 2.2, dielectric thickness of 0.79 mm, and copper thickness of 35 μm. By selecting a substrate with low relative permittivity, the fringing field strength is maximized, resulting in a more sensitive device. The sensor design and dimensions are optimized for a high-quality resonance using the chosen substrate configuration and are presented in Figure 3.1. The SRR sensor is coupled with two feed transmission lines with increase annular coupling and an optimally mitered bend for decreased reflection. The two-port system is designed in a bandpass configuration allowing for the monitoring of the peak response in the sensor’s transmittance response (S21). This design offers a large Figure 3.1. Microwave SRR sensor design and dimensions. Chapter 3: On-ground Microwave Resonant Sensors 17 sensing area and is designed in the 1-1.5 GHz frequency range with the aim to sense a greater thickness of material due to the lower frequency of operation compared to other SRR configurations previously designed for ice sensing. 3.1.1.1 Protective Epoxy Coating For the purpose of this study, two sensors have been coated in a layer of protective epoxy with thicknesses of 0.5 mm and 1 mm respectively. By studying multiple protective coating thicknesses, the respective influence on the sensor’s response can be extracted. The relative permittivity and loss tangent of the epoxy were measured using a N1501A dielectric probe kit and determined to be 2.6 and 0.003 respectively at 1 GHz and 24 ℃. 3.1.2 Experimental Procedure and Test Setup The transmission coefficient, S21, of the sensor was measured using a N9918 Keysight Vector Network Analyzer (VNA) over a frequency range of 0.9 to 1.5 GHz with an output power of 0 dBm, bandwidth of 1 kHz, and resolution of 1601 points. The sensor was placed on a custom-made experimental setup consisting of a Peltier cooling stage for temperature variation and a thermocouple for temperature measurements as seen in Figure 3.2. The resonant profile of the sensor was recorded using LabVIEW software. During the experiments a 50 µL droplet of water was placed on the gap of the SRR at room temperature (22.3 °C). To freeze the water droplet, the surface temperature of the sensor was reduced to -10 ℃ using the Peltier cooling stage. This procedure will investigate the change in the response of the sensor to water and ice as the protective epoxy coating is made thicker. Figure 3.2. The experimental test setup Chapter 3: On-ground Microwave Resonant Sensors 18 3.1.3 Results and Discussion 3.1.3.1 Sensor Validation The simulated and measured response of the sensor with different epoxy coating thicknesses are provided in Figure 3.3. The variation between simulated and measured responses are due to the HFSS simulation not accounting for SMA connectors or soldering effects as well as fabrication errors such as over etching of the resonant structure and uneven epoxy coating thickness. The fabricated sensor featured a resonant frequency of 1.226 GHz, resonant amplitude of -13.019 dB, and quality factor of 187. After applying 0.5 mm of epoxy to the substrate, the resonant frequency changed to 1.187 GHz and the resonant amplitude changed to -8.313 dB. For 1 mm of epoxy the resonant frequency changed to 1.168 GHz and the resonant amplitude changed to -7.539 dB. The quality factor of the 0.5 mm and 1 mm epoxy coated sensors are 88 and 80 respectively. As the thickness of epoxy coating increased, the resonant amplitude of the signal also increased. This is due to the relative permittivity of the epoxy matching the relative permittivity of the substrate, resulting in an increase in coupling between the transmission lines and resonator. 3.1.4 Ice Sensing with Epoxy Coating Layer The resultant S21 plots for the uncoated, 0.5 mm epoxy, and 1 mm epoxy coated sensors are presented in Figure 3.4 (a), (b), and (c) respectively. Table 3.1 contains the change in resonant frequency and resonant amplitude due to the presence of water and ice on the gap of the SRR sensor with respect to the bare resonant profile. The uncoated sensor displayed the greatest shift in resonant frequency, 0.0341 GHz or 2.78%, when ice was on the sensor. As seen in Figure 3.4(a), the sensor had no resonant profile when Figure 3.3. Simulated and measured sensor response with bare, 0.5 mm, and 1 mm of epoxy. Chapter 3: On-ground Microwave Resonant Sensors 19 water was present. This was due to high dielectric loss of water disrupting the sensor’s resonant behavior. In contrast, the coated sensors both retained resonant profiles when water is present on the sensor. Observed in the results of Table 3.1, as the thickness of the protective epoxy coating increases, the change in resonance decreases which can be attributed to less fringing field interaction with the water or ice on the sensor. Resonant amplitude and resonant frequency were both determined to be excellent detection parameters as both showed measurable and consistent change in all three experiments. Table 3.1. The change in resonance from bare due to water and ice extracted from Fig. 3.4 Coating Thickness ∆ Resonant Amplitude (GHz) ∆ Resonant Frequency (dB) Ice Water Ice Water Uncoated -0.821 N/A -0.034 N/A 0.5 mm Epoxy 0.536 -4.662 -0.012 -0.084 1 mm Epoxy 0.702 -3.379 -0.006 -0.057 Figure 3.4. Measured bare, ice, and water response for the (a) uncoated, (b) 0.5 mm epoxy, and (c) 1 mm epoxy coated sensors. The red dotted circles indicate the placement of the material under test for each experiment. Chapter 3: On-ground Microwave Resonant Sensors 20 3.2 Complementary Split Ring Resonator (CSRR) Snow Sensor The following section considers a Complementary Split Ring Resonator (CSRR) sensing structure for greater robustness in measurement through the increased shielding of the excitation transmission line. SRRs, designed as conductive rings with a split-gap, behave as LC resonators and can be realized as resonant magnetic dipoles that can be excited by an axial magnetic field or by properly polarized time-varying external electric fields. In an SRR structure excited by a microstrip line, an electric field concentration is observed at the split-gap with a magnetic field concentration observed on the side opposite to the split at the first resonance. In contrast, a CSRR which also behaves as an LC resonator is the complementary negative of an SRR where the split-ring shape is removed from a conductive ground plane. The CSRR structure acts like a resonant electric dipole which can be excited by an axial electric field or alternately by a time varying external magnetic field. In a CSRR structure excited by a microstrip line, a magnetic field concentration is observed at the split-gap with an electric field concentration observed on the side opposite to the split at the first resonance [63]. With a greater E-field concentration at the sensing split-gap, SRR sensors show great sensitivity to changes in the permittivity of the effective surrounding medium while CSRR sensors with H-field concentrations at the split-gap are known for greater sensitivity to changes in dielectric loss, showing greater promise for on-ground ice sensor applications where the presence of impurities and de-icing salt is expected. The section applies the CSRR topology in an attempt to shield the exposed transmission-lines from the previously presented bandpass SRR design to prevent error in the frequency response due to loading of the transmission lines and coupling to the split-ring sensor. The defect-ground structure sufficiently protects the transmission line and further offers the ability to integrate the sensor-design with-in an aluminum plate design for future integration within the aerostructure itself. This results in the design of a band-stop sensor with a single through transmission line used to excite the two-port CSRR based resonant sensor for the sensing of ice and snow with an additional design presented for two-sided transmission shielding for on-ground sensor application. Planar Microwave Sensor for Localized Ice and Snow Sensing Abstract~ Ice and snow accretion on aircraft surfaces imposes operational and safety challenges, severely impacting aerodynamic performance of critical aircraft structures and equipment. For optimized location-based ice sensing and integrated ‘smart’ de-icing systems of the future, microwave resonant-based planar sensors are presented for their high sensitivity and versatility in implementation and integration. Here, a conformal, planar complementary split ring resonator (CSRR) based microwave sensor is presented for robust detection of localized ice and snow accretion. The sensor has a modified thick aluminum-plate design and is coated with epoxy for greater durability. The fabricated sensor operates at a resonant frequency of 1.18 GHz and a resonant amplitude of -33 dB. Monitoring the Chapter 3: On-ground Microwave Resonant Sensors 21 resonant frequency response of the sensor, the freezing and thawing process of a 0.1 ml droplet of water is monitored, and a 60 MHz downshift is observed for the frozen droplet. Using an artificial snow chamber to create falling snow, a 1 mm thick accretion of snow shows a 35 MHz downshift in resonant frequency. The proposed sensor system can be extended using a novel radar-inspired method of Time-Domain Reflectometry (TDR). TDR based ice/snow sensors can be implemented in an array or network structure for reliable, local and distributed ice and snow accretion monitoring on aircraft structures. Applying Time-Domain Reflectometry (TDR) methods, three identical sensors with the same resonant frequency are monitored over an approximate length of 10 m and localized sensing of water is presented. This novel method offers a pathway towards implementation of large network-based resonant-microwave sensors for future reliable integrated localized icing and snow accretion rate-measurement sensors. 3.2.1 Introduction Aircraft icing is a complex multi-physics phenomenon that poses a serious threat to aviation safety [66], accounting for over 800 fatalities between 1982 and 2000 [9]. From on-ground aircraft snow and ice accretion to structural, atmospheric in-flight icing, and icing on propellers, blades and engine-inlets, detection and mitigation strategies for continued operation are essential for uninterrupted, safe air travel [67]. Current ice detection systems are generally controlled by ‘global’ ice detectors [8], [14] that apply total de-icing measures upon detection. As the industry moves towards more efficient aerospace systems, the need for local ice detection and optimized de-icing systems is apparent [15]. Microwave-based sensing methods have shown considerable advances as highly accurate real-time sensors for industrial, biomedical and environmental applications [23], [28], [37], [68], [69], [70], [71], [72], [73]. These sensors operate based on the interaction of electromagnetic waves with material in the device’s local environment. Material media under the influence of an alternating electromagnetic wave can be characterized based on its intrinsic properties, defined as the real permittivity or degree of polarization and the imaginary permittivity or dielectric loss [74]. Exploiting these material properties, microwave sensors have been implemented for characterization of materials in solid, liquid, and gaseous Figure 3.5. Microwave Sensors as highly reliable, integrated, localized icing rate measurement sensors as envisioned on aircraft structures Chapter 3: On-ground Microwave Resonant Sensors 22 forms. These real-time sensors allow for monitoring of active processes and offer insight into complex chemical processes and phase transitions. These devices have been successfully implemented in harsh, real-world environments for various sensing challenges including defect detection, ambient monitoring, chemical analysis, coating health monitoring, process control, microfluidics and biological sensing. As planar structures, microwave devices can be implemented as conformal, low-profile sensors using various substrate materials including Teflon, composites, ceramic, plastics, fiberglass and more, and can further be integrated with wired and wireless read-out systems. These low-cost devices are highly versatile, easy-to-implement, and show great promise for application in various industries. [30], [33] Recently, planar microwave Split Ring Resonator (SRR) sensors have demonstrated accurate and reliable measurements of ice and frost [38]. These sensors are capable of sensing very thin layers of icing, detecting their presence immediately upon onset of icing with a measurable resolution of 0.1mm. These sensors have been proposed for detection of icing in a variety of locations, including roadways, wind turbines, unmanned air vehicles and more. These resonant-based sensors can detect icing in the presence of contaminants like salt [40] and through protective coatings like epoxy [75] and superhydrophobic coatings [39]. These sensors have also been integrated with ground-plane heaters to actively monitor ice accretion and apply de-icing measures [39]. Furthermore, low-power wireless read-out circuitry has been designed and presented for real-world implementation [75]. Wireless antenna based and chip-less tag-based ice sensors have also been demonstrated [76], [77]. These sensors have also been integrated with Low-Interfacial Toughness (LIT) hydro-phobic coatings for highly accurate local ice detection and low-powered de-icing [65]. These various characteristics of planar microwave sensors make them great candidates for integration on aircrafts as local ice detection sensors for smart de-icing on aircraft structures. In this work, a modified sensor design incorporating a complementary split-ring resonator (CSRR) structures is designed for robust and reliable sensing of ice and snow on similar aluminum surfaces. An experimental investigation of ice and snow accretion on this planar microwave sensor is presented. Finally, a novel, radar-inspired time-domain-reflectometry (TDR) method is introduced for local ice sensing along a large surface area as envisioned in Figure 3.5. 3.2.2 Resonant-Based Microwave Detection of Ice and Snow Microwave SRR sensors operate by monitoring the characteristics of an electromagnetic resonance, generated within a specifically engineered structure. These characteristics, which include resonant frequency, resonant amplitude and quality factor, are a function of the structure’s geometry and the Chapter 3: On-ground Microwave Resonant Sensors 23 material present in the sensor’s vicinity. A sample material’s interaction with electromagnetic waves can be characterized into two distinct properties, namely the degree of polarization (i.e., real part of the permittivity) and loss factor [47]. In the microwave regime, ice and water are highly distinguishable with ice having a real permittivity of 𝜀𝑟 = 3.2 and water with an 𝜀𝑟 = 80 and high dielectric loss at 5GHz and 0°C [38], [49]. Snow is an example of an accretion which is a mixture of ice, air and water [48]. Different types of snow can be modelled using various microwave dielectric mixing theories [53] and further, empirically characterized based on their density and liquid water content [78], [79]. Permittivity (𝜀𝑟) of dry snow, a mixture of ice and air, is a proportional function of snow density which is between 1.2 and 3.2 [50]. Similarly, wet snow, a mixture of ice, water and air, is a function of density and wetness, and has a higher permittivity (𝜀𝑟) between 3 and 80 [80]. 3.2.3 Robust Epoxy-Coated Aluminum-Plate CSRR Sensor In this work, a modified Complementary Split Ring Resonator (CSRR) sensor is designed. This resonant microwave structure is proposed to electrically shield the transmission line interrogating the resonant structure from the accreting surface. This allows for reliable sensing of accretion at the specific location of the sensor and prevents de-tuning of the transmission line due to accretion. This sensor is further modified by substituting the generally used 35μm copper cladding for a 1mm thick aluminum plate for increased mechanical robustness and future integration onto aluminum aerospace structures. This also demonstrates the surface transmission characteristics of microwaves, which only require a small skin depth of the conductor’s surface for transmission. The sensor is further Figure 3.6. Schematic of proposed robust, modified aluminum-plate CSRR resonant structure, a 3D CAD model and a stack up of the of the fabricated resonator with a microstrip transmission line with a width of 2.45 mm Chapter 3: On-ground Microwave Resonant Sensors 24 coated with a 1mm thick layer of epoxy to increase the wear resistance of the sensor and demonstrate such microwave sensors’ ability to detect material through protective and special coatings as required by the application. The sensing structure is optimized using ANSYS high frequency structure simulator (HFSS), a finite element method based commercial 3D electromagnetic structure simulator. The optimized geometry and layout of the modified aluminum plate CSRR structure is depicted in Figure 3.6. The sensor was designed with a resonant frequency around 1.25 GHz and the optimized design was waterjet-cut out of a 0.040” (~1mm) thick aluminum sheet. The measured response of the fabricated sensor demonstrated a good match with the simulated band-stop response, with a minimum amplitude of -33.66 dB at 1.18 GHz, as observed in the amplitude of the reflection coefficient (S11) plotted against the monitored frequency presented in Figure 3.7Error! Reference source not found.. Placing a 0.5 mm thick box with the permittivity of ice (𝜀𝑟 = 3.2) on the sensor surface, a simulated frequency response is obtained. The measured change in the reflection response observed for a ~100 𝜇𝑙 of water frozen into ice is also presented. The measured change in resonant frequency is ~60 MHz which correlates with a simulated change of ~45MHz. 3.2.4 Experimental Setup and Data Acquisition Figure 3.7. Simulated and Measured S11 response (reflectance) for the modified ground plane epoxy coated CSRR sensor in a bare and iced configuration. Plotted electric-field density, indicating the sensitive region on the sensor’s surface. Chapter 3: On-ground Microwave Resonant Sensors 25 The reflection coefficient of the sensor was measured every 10 seconds using an S5065 Copper Mountain Vector Network Analyzer (VNA) over a frequency range of 0.8 to 1.5 GHz, with an output power of 0 dBm, a bandwidth of 1 kHz and a resolution of 2001 points. These tests were conducted in a controlled cold chamber and a k-type thermocouple was used to monitor the surface temperature of the epoxy-coated CSRR sensor. This chamber is further modified with nucleation sites (ropes) and a humidifier to produce artificial snow. The experimental set-up for snow-accretion is presented in Figure 3.8. 3.2.5 Icing Tests Icing tests were conducted by placing the sensor in a controlled cold chamber at a temperature of -21.6℃. A 100 𝜇𝑙 drop of water was placed on the sensors surface and the phase change process was monitored as the droplet froze. The resonant frequency and resonant amplitude are monitored through the process and the time-based sensor results are presented in Figure 3.9. A clear trend was observed as the sensor accurately senses the presence of water (𝜀𝑟 = 80) as seen by the large shift in resonant frequency. The freezing process was monitored, and a constant frequency shift of 60 MHz and an amplitude drop of 1.19 dB from bare is observed when the water completely freezes to ice. The sensor was then brought to room temperature and the thawing process is monitored. Once the ice melted, the surface of the sensor was wiped clean and a clear return to bare resonant frequency response was observed. Figure 3.8. Snow and Ice testing experimental setup consisting of a Cold Chamber Freezer with hanging rope nucleation sites and a Humidifier. The sensor is monitored using a Vector Network Analyzer (VNA). Chapter 3: On-ground Microwave Resonant Sensors 26 3.2.6 Snow Accretion Tests Figure 3.9. Time based measurement of icing on the proposed sensor. (a) Resonant Frequency and (b) Resonant Amplitude response monitored for a freezing-icing-thawing cycle. The measured resonant frequency and resonant amplitude change between the bare sensor and the iced sensor are 60 MHz and 1.19 dB, respectively. Chapter 3: On-ground Microwave Resonant Sensors 27 Snow accretion tests were conducted in the cold chamber presented in Figure 3.10. At a measured temperature of -21℃ and a high relative humidity around 57%, the chamber produced artificial snow that nucleated on the installed ropes. This process results in the formation of snow with a relatively constant liquid water content (LWC) of ~0.9%, measured using a commercial Snow and Avalanche Research snow sensor from the WSL Institute SLF. This instrument is also used to measure the density of the produced snow (ice-water-air mixture) which was found to be around 210 kg/m3 as compared to the density of solid ice at 917 kg/m3. Tracking the resonant frequency and resonant amplitude, a time-based measurement of snow accretion over 13.7 hours is presented in Figure 3.9. The sensor is instantaneously sensitive to the onset of snow accretion. The sensor displays a maximum resonant frequency drop of 35 MHz and a Figure 3.10. Time-based measurements of Snow accretion on the sensor surface. (a) Resonant Frequency (RF) and (b) Resonant Amplitude (RA) for a layer of snow with increasing thickness. The measured RF and RA change between the bare sensor and the sensor with 1 mm thickness of snow are 35 MHz and 4.9 dB, respectively. (c) Depicts the resonator covered in 1mm of snow. Chapter 3: On-ground Microwave Resonant Sensors 28 resonant amplitude drop of 4.9 dB for 1 mm of snow accreted on the sensor surface at the end of the 13.7-hour period. 3.2.7 Radar-Inspired Time Domain Measurements 3.2.7.1 TDR-based Microwave sensing The proposed CSRR sensor has successfully demonstrated detection of ice and snow on its sensitive region. However, to determine location specific icing, a large network of sensors each with their own resonant frequency are required as proposed in [81]. Such an implementation is impractical for commercialization as it would require wide-band frequency allocation and complex electronics. In addition, practically designing such frequency-based microwave devices with more than 4-5 sensing locations pose great challenges due to harmonic frequency overlap. To enable multi-location sensing of ice and snow over large areas of significance on aircraft structures, an array of wired sensors operating at nearly the same frequency, using a novel radar-inspired time domain reflectometry (TDR) method is proposed. This method, currently used to test wiring for fault detection and, to observe time-based movements of objects on Radar, has been thoroughly studied and commercially implemented for industrial and military use [82]. TDR methods allow users to observe reflections produced on a transmission line or in free space as a function of the time it takes for the electromagnetic wave to travel through the line and back. At specific impedance-based discontinuities in the transmission line, characteristics reflections are observed. By measuring the time after which this reflection is observed, a distance on the line can be calculated based on the following equation, 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = (𝑉𝑃 ∙ 𝑐) × (𝑡 2⁄ ), where 𝑉𝑃 is the velocity factor of the transmission cable, 𝑐 is the speed of light in vacuum and 𝑡 is the time taken for the wave to travel to the discontinuity and back on the transmission line [83]. 3.2.7.2 Experimental Investigation Three identical CSRR structures fed with modified asymmetric stripline structures are optimized and fabricated as observed in Figure 3.11. These sensors offer improved transmission line shielding and increased selectivity to sensing material on the sensor face. The sensors are cascaded with three flexible coaxial cables, each measuring 3 meters in length, mimicking a wired multi-sensor array, and is terminated Chapter 3: On-ground Microwave Resonant Sensors 29 with a 50-ohm load as seen in Figure 3.12 (b). The S5065 Copper Mountain VNA was used to measure the S11 Bandpass response in the time domain. In three individual experimental test cases, water was placed on each one of the three sensing faces and the reflection coefficient of the sensor network was recorded. To differentiate the location of sensor loading, a correlation factor [84] is used to compare the measured reflection coefficient at each of the three sensors’ locations, ~3 m apart, at each test case against a bare measurement with no water on any sensor face. The observed correlation factors for each sensor location are presented for the three test cases in Figure 3.12 (a). Based on the results of Figure 3.12 (a), the sensor loaded with water droplets showed the lowest correlation with a bare sensor network response. This observation was selective and observed repeatedly for each of the three cases. It was found that by solely monitoring the bandpass reflection of the sensor array, the presence of a material on the sensor surface is distinguishable. The presence of non-zero dB change in unloaded sensor locations is attributed to the large time-intervals considered for each sensor’s reflection signals for correlation. Furthermore, the reflections generated by the multiple resonant sensors modify the time-based response observed at sensor location closer to the reading port and require mechanisms to filter. Future use of such methods require accurate time-interval demarcation and altered response tracking strategies to improve sensitivity and obtain greater accuracy using such methods. Figure 3.11. Modified asymmetric stripline CSRR structure. Three identical sensors fabricated for implementation in a Time Domain Reflectometry based cascaded sensor network Chapter 3: On-ground Microwave Resonant Sensors 30 3.2.8 Conclusions Microwave CSRR based resonant structures have demonstrated great promise as highly reliable ice and snow sensors for integration onto aircraft structures for location-based sensing. The aluminum-plate epoxy-coated sensor is sensitive to thin accretions of ice, showing a resonant frequency shift of 60 MHz for a 0.1 ml frozen droplet. The sensor also demonstrated a ~35 MHz shift for a 1mm thick accretion of relatively dry snow with an LWC of 0.9 % and a density of 215 kg/m3. Although sufficient to determine icing conditions, the CSRR based sensor shows relatively lower frequency shift in comparison to similar SRR structure. Further investigation into the comparison of the two structures (CSRR and SRR) as well as the benefits of each in harsh, contaminated on-ground sensing must be considered. Applying radar-inspired time-domain reflectometry-based measurement techniques, the bandstop response of a three-sensor network spanning 10 meters is experimentally investigated. On correlating the measured reflection of the three-sensor network for each individual test case, a clear trend is observed with the greatest deviation from bare network measurement at the location of sensor with water on its sensing surface. The results indicated that the TDR measurement method is capable of distinguishing between identical microwave sensing structures in a cascaded sensor environment. This presented novel method would allow for real-world regulatory restricted design feasibility and possible commercial implementation of Figure 3.12. Location-based sensing of water on a wired 3-sensor array across a span of 10 meters; (a) the measured reflection is correlated with a bare measurement of the sensor network and the resultant plots indecate the presence of water, based the sensor location with the most significant variation from bare; (b) the experimental setup and, (c) the envisioned application of the proposed system on an aircraft wing Chapter 3: On-ground Microwave Resonant Sensors 31 such sensor networks over large sensing regions of interest.Chapter 4: In-flight Ice Sensor 32 Chapter 4: In-flight Ice Sensor This chapter presents the design and testing of a dual frequency microstrip resonant SRR sensor under aeronautical ice crystal icing conditions. The sensor is intended for used as an in-flight ice sensor for the location specific sensing of accretion. This work presents the first icing wind-tunnel simulated real-world in-flight ‘impact’ ice accretion measurements, testing the SRR sensor at the National Research Council’s Research Altitude Test Facility (RATFac), at an altitude of 19,000 ft and airspeed of 100 m/s. The sensor designed in the following section is a dual-frequency sensor design with two SRR sensors resonating at distinct arbitrary frequencies between 1.75-2.25 GHz coupled in series to a single open-ended transmission line. The sensor is designed on a single substrate layer for conformal application along the chord of a curved airfoil surface and limited to one-port to simplify installation with-in the airfoil test article. An SRR design is used due to the relatively pure nature of the ice accretion expected in-flight or with-in the icing wind-tunnel as well as the greater sensitivity to permittivity expected with an SRR structure as compared to a CSRR design. Unlocking Conformal Microwave Split Ring Resonant Sensors for In-flight Ice Sensing Abstract— In-flight ice accretion is an ongoing challenge for airplane safety as it disrupts the aerodynamics of wings, increasing drag and decreasing lift. This work proposes a conformal ice sensing method based on microwave split ring resonator (SRR) sensors for early ice detection on critical aircraft surfaces. A single-port dual-frequency sensor with SRRs operating at ~1.95 GHz and ~2.13 GHz was implemented and integrated on a NACA 0018 airfoil with chord of 0.25 m. The sensor’s performance was experimentally investigated in ice crystal icing (ICI) conditions at the National Research Council’s (NRC) Research Altitude Test Facility (RATFac), at a simulated altitude of 19,000 ft, a velocity of 100 m/s and, a total air temperature of +3˚C. The performance of the sensor was evaluated with the resonant frequency parameter to extract correlations in different icing conditions. The sensor was tested in different melt ratio conditions (11%, 13%, 16% and, 25%) at varying accretion time periods, accurately determining the onset of icing, the increase in accretion volume and its eventual shedding, while successfully differentiating the %melt conditions tested. Additionally, the sensor’s response to electrothermal de-icing procedures is presented, demonstrating its capability for monitoring the efficiency of de-icing procedures and sensing the formation of ice during anti-icing operations. 4.1 Introduction Aircraft icing poses a major threat to aviation safety [66]. As the cause of several fatal accidents [9][67], strict regulations on icing detection, protection and prevention have been implemented, with aircrafts requiring specific certification for safe operation in icing conditions [85]. Aircrafts currently Chapter 4: In-flight Ice Sensor 33 require primary in-flight ice detector systems (PFIDS) to reliably determine the accretion of ice [86], characterize the icing conditions encountered and, in a timely manner, alert the flight systems and crew to implement ice protection systems (IPS) [87]. Aircraft icing is a complex physical phenomenon which is dependent on local flow parameters such as airspeed, temperature, liquid water content (LWC), droplet size, etc.[67], which in turn depends on the surface curvature, angle of attack and airplane design. At present, the majority of the PFIDS are probe-style ice accretion detectors based on magneto strictive vibration. These highly reliable sensors protrude into the airflow and provide a ‘global’ measurement of the general icing environment [14], at locations remote to aerodynamically critical accreting surfaces. Thereby, PFIDs positioning is crucial, and the amount of accretion on the critical surfaces is indirectly estimated [86]. This indirect method of sensing further results in higher fuel consumption due to the pre-emptive activation of the de-icing IPS [15]. To offer a ‘direct’ method of sensing ice accretion on critical surfaces, conformal surface-mount sensors are considered for accurate, local measurements of ice build-up, including run-back icing. This local sensing of accretion opens the possibility for area-based sensing of the amount and type of accretion for eventual integrated ‘smart’ de-icing. Currently, different on-wing ice detection methods are being tested and are in various stages of development and commercialization [8]. Various capacitive [15], [88], optical [89], [90] and ultrasound [18], [19] methods are being implemented to detect ice accretion on airplane wings. To tackle current Figure 4.1. Microwave Ice Sensor as envisioned on critical exterior aircraft structures for accurate local measurement of accretion. Chapter 4: In-flight Ice Sensor 34 challenges with aircraft integration [90], placement in harsh environments, reliability, and general sensitivity to thin-ice build-up, planar microwave-resonant based sensing techniques are proposed for local in-flight icing rate sensing as envisioned in Figure 4.1. Historically, microwave sensing techniques using dielectric waveguides [91], [92], resonant cavity [93], and resonant forks [58] have been used for accurate detection of ice in aerospace applications. These methods pose various challenges for conformal aircraft application as ice sensors due to their bulky design, large size, low special resolution, and relatively low sensitivity. For conformal implementation on aircraft structures, microstrip technology was applied [91] and impedance-based sensing methods [94] are implemented for accurate detection. Radiating resonant antenna-based sensors are also presented for wireless detection of icing [76], [95] with temperature compensation [96]. Microwave Split Ring Resonator (SRR) topology has recently been applied for a variety of material and process sensing challenges, and are chosen for their high spatial resolution, relatively simple design and low-cost fabrication on thin, conformal substrates [33], [97]. These devices have low radiative loss and display a characteristic frequency response, with relatively high-quality factors [46]. Furthermore, this technology allows for the design of electromagnetic structures with dimensions that are small in comparison to their resonant wavelength for greater spatial confinement of electromagnetic energy and enhanced sensitivity [42]. Microwave resonant sensing methods have recently shown significant advancements, exhibiting excellent performance as real time sensors for industrial, biomedical, and environmental applications [23], [98]. These sensor devices operate based on the interaction of electromagnetic waves and the material in the sensor’s local environment, which allows for non-contact characterization of a material under test [41], [99], [100]. These sensors have been successfully implemented for differentiating solid, liquid, and gaseous materials, in real time processes [101], [102]. Furthermore, these sensors can be easily integrated for application in harsh environment [103] and further enhanced with machine learning techniques for accurate sensing in challenging environments [104]. Sensing of ice and frost formation on planar microwave split ring resonator (SRR) sensors was initially demonstrated by Wiltshire et al. [38]. The SRR sensor was highly effective in differentiating between fresh water, ice and frost based on the measured resonant frequency, resonant amplitude, and quality factor variation in the transmission coefficient, S21, by exploiting the difference in relative permittivity between ice and water (εr = 3.2 and εr = 90 respectively at 0 °C and 5 GHz). Following this, Kozak et.al. implemented SRR designs with a built-in heater to monitor the formation of ice on super hydrophobic coatings and through epoxy [39], [75]. Luckasavitch et.al. implemented magnetically coupled planar SRR sensor configurations to detect the presence of contaminated ice and salty water for Chapter 4: In-flight Ice Sensor 35 marine ice detection applications [40]. Niksan et al. introduced a microwave system consisting of a passive resonant surface, wireless readout, and an AI-assisted classification of water and ice in laboratory settings [77]. This work presents the first implementation of an SRR sensor under simulated real-world ‘impact’ icing conditions generated in an icing wind tunnel. The multi-location SRR sensor presented is designed for location-specific sensing of highly lossy mixed-phase ice crystal icing accretions on an airfoil under aeronautical conditions. Time-based measurements of the aeronautical ‘impact’ ice-crystal icing tests and implemented electro-thermal de-icing procedures are presented and discussed. 4.2 Theory, Design, Simulation and Fabrication 4.2.1 Mixed Phase Ice Crystal Icing Conditions and Wind Tunnel Ice accretion is a phenomenon dictated by naturally occurring temperature, altitude, humidity, and precipitation conditions. Years of studying these conditions allow for governing bodies like the Federal Aviation Authority (FAA) to outline specific expected icing conditions for certified air travel. Exploring ice accretion monitoring using the unique method of planar microwave structures was subjected to a month-long testing campaign in the National Research Council’s (NRC) Research Altitude Test Facility (RATFac) under a range of atmospheric parameters such as temperature, altitude, humidity and icing conditions. The sensor was bombarded with ice and water particles during the test and was cyclically heated to de-ice the surface. The schematic of the test facility of the NRC’s RATFac is presented in Figure 4.2, which is an icing wind tunnel capable of simulating ice crystal icing (ICI) conditions and was used to simulate real-world aerospace icing conditions. This world-class testing facility is unique in its small size and high-speed capabilities such that it allows for efficient Figure 4.2. NRC RATFac facility for ice testing at different altitudes and variant environmental parameters such as temperature and humidity. Chapter 4: In-flight Ice Sensor 36 instrumentation testing and viewing of ice accretion at a microphysical scale. This tunnel simulates flight altitudes of up to 51,000 ft with temperatures between -50°C and +40°C and airspeeds up to 275 m/s. It allows for testing liquid water contents (LWC) ranging from 0.25 to 5 g/m3 and ice crystal icing system for ice crystal contents (ICC) between 0.2 to 20 g/m3. The microwave sensor was monitored for simulated conditions at airspeeds of ~100 m/s, an altitude of 19000 ft (5.8 km), particle size having the Dv50 = 40 microns and at a total air temperature (TAT) of +3 °C. A total water content (TWC) of 5.4 g/m3 was tested at different melt ratios (%melt) (11%, 13%, 16% and 25%) defined as liquid water content (LWC) to total water content (TWC), resulting in a proportionally changing wet-bulb temperature (Twb) between -3 °C and +1 °C. The LWC was from natural melt on the particles as seen inside an aircraft engine. These test points were unique in that at Twb below 0°C, the LWC was supercooled and affected the accretion characteristics. Previous studies conducted at the NRC facility showed a strong relationship between the %melt, wet-bulb temperature (Twb), and the type and amount of accretion [105], [106]. This variable was also highly dependent on the altitude as evaporative cooling increases with decreasing pressure [107]. In addition to ice accretion monitoring, the process of electro-thermal de-icing was monitored during the experiments. 4.2.2 Airfoil Selection For successful implementation of the sensor into the NRC’s RATFac facility, a test article of specific dimensions was designed and fabricated. A symmetric NACA 0018 profile with a chord length of 0.25m was chosen due to its comparability to historical ice accretion testing experiments and its large thickness to chord ratio allowed the installation of other equipment within the airfoil. 4.2.3 FENSAP-Ice Simulation A commercial 3D in-flight ice accretion simulation tool, ANSYS FENSAP-Ice (2022R2), was utilized to model the test conditions to understand the type and location of ice accretion to optimize sensor design and placement. The effect of varying %melt was simulated to understand the rate of accretion. A multi-shot accretion simulation with ten 10-second shots for a total icing time of 100 seconds was conducted for computationally acceptable and accurate simulation of the time-based accretion characteristics [108], resulting in the formation of distinct ice accretion. Chapter 4: In-flight Ice Sensor 37 The NACA 0018 model was simulated at an airspeed of 100 m/s, an altitude of 19000 ft, and a total air temperature of +3 °C. The model assumes that the injected ice crystal content (ICC) of 5.4 g/m3 is at 0°C and a %melt value is chosen to simulate different LWC/TWC ratios. The resulting ice accretion after 100 seconds of icing for %melt of 11%, 13%, 16%, 25% and 30% are presented in Figure 4.3. The observed trend is in-line with findings from Currie et al. in such mixed-phase ice crystal icing conditions, where a maximum, relatively plateaued icing severity is observed at %melt ratios between 10% and 20% with relatively sharp drop-off in severity at %melt values lower or higher [109]. The greatest accretions are observed at 13% and 16% melt ratio conditions which have relatively identical accretion shapes after 100 seconds of icing. The 11% case shows thinner ice accretion than the 13% and 16% cases but far thicker than the 25% and 30% cases, which show a decreasing trend in icing severity with the increase in melt ration beyond 20%. Though idealized, these simulations still offer an understanding of the expected ice shapes and the location of maximum accretion. From these limited %melt FENSAP Ice simulation results, a trend with respect to %melt ratio is observed. It can be hypothesized that at higher %melt values like 30%, the sensor would experience more water and less ice particles in its proximity. Consequently, at %melt values like 11%, 13% and 16%, the sensor would experience less water and greater amount of ice particles in its vicinity. 4.2.4 Microwave Sensor Modelling and Simulations The designed microwave sensor for ice accretion operated based on a split ring resonator topology. The split ring resonator, in particular, enabled a highly confined distribution of electromagnetic fields (E-Figure 4.3. Ice accretion profile after 100 seconds on a NACA 0018 with a chord of 250 mm is simulated using FENSAP Ice, with 5.4 g/m3 ice crystal particles with a % melt ratio of 11%, 13%, 16%, 25% and 30%. The simulation was conducted at 19,000 ft, a speed of 100 m/s and a total air temperature of +3°C. 3D view of ice accretion after 100 seconds of icing for 11% melt ratio. Chapter 4: In-flight Ice Sensor 38 fields) near the ring gap region [31], [46], increasing the spatial resolution. The high E-field intensity was exploited for detecting variations in the electromagnetic properties of the nearby environment through monitoring dielectric properties such as permittivity and electromagnetic loss [110]. The dimensions of the fabricated single-port, dual-frequency, and two-location split-ring resonator (SRR) sensor is presented in Figure 4.4 (a). Two SRR sensing structures were edge-coupled to a microstrip transmission line (TL). The sensor is designed for reflectance measurements as it is considered more suited for highly lossy media by [97], [111], as expected with mixed-phase ice and lossy water mixtures under ice crystal icing conditions. The single port sensor designed with an open-ended transmission line offered additional benefits for relatively easier integration into the test article. The sensors were designed for location-specific ice sensing. In this regard, the FENSAP Ice simulation results from Figure 4.3 were used to inform the placement of each resonant sensing SRR location. Figure 4.4 (b) shows the implemented 3D model of the sensor in the ANSYS HFSS design environment with the imported 2D FENSAP Ice accretion profile. The maximum ice accretion was observed on the leading edge and resulted in the placement of the first ring, with a lower frequency, on the leading edge of the NACA 0018 airfoil. The second SRR was placed further along the chord of the airfoil to monitor possible runback ice accretion on the upper surface airfoil profile, ~56mm from the leading edge. The electrical length of the transmission line was optimized to ensure a good impedance matching at the resonant frequency of the two SRRs. Figure 4.4. (a) The schematic of the edge-coupled, single-port, dual frequency SRR sensor for location specific detection of accretion (b) The designed 3D model of the conformed SRR-based sensor strategically placed on a NACA 0018 airfoil (in ANSYS HFSS), and the CAD model of accreted ice shape imported from ANSYS FENSAP ICE. Chapter 4: In-flight Ice Sensor 39 In an event of ice accretion on the airfoil, the SRRs were loaded with ice accretion of a certain liquid water content, where the complex permittivity of the wet ice altered the resonant characteristics of the SRRs (resonant frequency, and quality factor). As the split ring resonators were in continuous interaction with the transmission line, the changes in their resonant characteristics were mapped in the reflection coefficient, measured from one side of the transmission line. The resonant frequency of an SRR (𝜔𝑟) is related to the effective permittivity of its surrounding medium, 𝜀𝑒𝑓𝑓, through (4.1), 𝜔𝑟 = 𝑣𝑜2𝑙√𝜀𝑒𝑓𝑓(4.1) where 𝑣𝑜 is the velocity of electromagnetic wave propagation in free space, and 𝑙 represents the physical length of the SRR. For a binary mixture of materials (e.g., ice and water), the effective permittivity of the surrounding (mixed) medium can be derived using the Maxwell-Garnette mixing formula [112], 𝜀𝑒𝑓𝑓 = 𝜀𝑖 1 +1 + 2𝑚3(𝜀𝑤/ 𝜀𝑖 − 1)1 +1 − 𝑚3(𝜀𝑤/ 𝜀𝑖 − 1)(4.2) where 𝑚, 𝜀𝑖, and 𝜀𝑤 denote the mixing (melt) ratio, ice’s permittivity (as the host medium), and water’s permittivity, respectively. It is evident that with increasing 𝑚 (melt ratio) in (4.2), the nominator to denominator ratio will increase, resulting in a higher 𝜀𝑒𝑓𝑓for the medium surrounding the SRRs. Thereby, (4.1) and (4.2) imply that for a higher melt ratio in the accreted body, a larger frequency shift is expected. To analyze the changes in resonant characteristics (e.g., resonant frequency) during an icing event, a parametric, full-wave electromagnetic simulation was performed in ANSYS HFSS. In the modeling environment, the leading edge SRR element was loaded with block of accreted ice, 4mm× 4mm with varying thickness. From FENSAP Ice simulation (Figure 4.3), accretions with varying thickness over time are expected. The electromagnetic wave simulations were performed for a bare sensor, an ice body with an 11% melt ratio, and an ice body with a 16 % melt ratio. The melt ratios were substituted in (2) and the resultant effective values were set as the dielectric constant of the modeled bodies in Ansys HFSS. Assuming εr values of 3.15 and 0.00 for ice and water, the resultant dielectric constant of the 11% and 16% water mixtures (in ice) were 4.17 and 4.72, respectively. The scattering parameter (S11) of the sensor and the electric field distribution on the sensor were simulated and presented in Figure 4.5, with a clear trend observed between the melt percentage of the Chapter 4: In-flight Ice Sensor 40 modeled accretion and the associated microwave response [Figure 4.5 (c)]. The leading-edge sensor with no accretion demonstrated resonance at 2.01 GHz. When the leading-edge element was loaded with a modeled accretion of 11% melt ratio (i.e., 11% volume fraction of water in ice) and a thickness (t) of 1 mm, a ~110 MHz of shift in the resonant frequency was observed. Maintaining the melt ratio at 11% and increasing the thickness of accretions to 2 mm and 3 mm, resulted in 119 MHz and 121 MHz of changes from the initial (bare) resonant frequency, respectively. It can be seen that the sensing element showed higher sensitivity (characterized by 𝛥𝐹) for changing thickness in 1-2 mm range, compared to 2-3mm range. To better demonstrate the effect of increasing melt ratios on the sensor’s resonant frequency, the modeled accretion was also simulated with dielectric properties corresponding to 16% melt ratio, and a frequency change of ~125 MHz was observed in the simulated results [Figure 4.5 (c)]. The observed higher shift, compared to the changes of 11% melt ratio was aligned with the indications of (4.1) and (4.2), confirming that the resonant frequency is correlated with the melt ratio and dielectric constant of an accreted body. The parametric sweep of thickness values for the modeled accretions was similarly repeated for the case of 16% melt ratio. The results indicated 139 MHz and 141 MHz change in the resonant frequency, for the 16% wet accretion with a thickness of 2mm and 3 mm, respectively. It was seen that for both 11% and 16% melt ratios, the sensor had higher sensitivity (measured by ΔF) for f1=2.01 GHz f2=2.16 GHz (a) (b) (c) Figure 4.5. Electric Field displayed on the sensor’s surface (a) at 2.01 GHz for the leading-edge SRR (b) at 2.16 GHz for the second (upper) SRR. (c) The simulated scattering parameter (S11) of the sensor for varying melt ratios and thickness of the 3D accretion model. Chapter 4: In-flight Ice Sensor 41 thickness values in the range of 1-2 mm. This is due to the decrease in field intensity with elevation from the gap of SRRs (vertical dimension), and perturbations near to the sensor [113] have higher impact on the resonant characteristics. The trends seen in Figure 4.5 (c) indicated that the wetness of the accretion had a greater impact on the effective permittivity and resonant frequency shift, in comparison with changes in volume of accretion. Furthermore, thicker accretions (larger volumes of ice) on the leading-edge element affected the resonant frequency, due to the increase in material filling surrounding an SRR. 4.2.5 Fabrication of Test Article The NACA 0018 test article with a chord of 0.25 m was fabricated in two sections. A leading-edge profile was cut out of a block of aluminum using Wire Electro-Discharge Machining for high accuracy and superior finish. The trailing edge and main body of the structure were 3D printed using Nylon 12 plastic, to house instrumentation and mount to the sidewall of the test section. The integrated microwave sensor was fabricated on a 0.79 mm thick Rogers 5880 substrate and mounted offset from the center of the article, as seen in Figure 4.6. The resonator was mounted such that the sensing gap of the first resonator was placed on the tip of the leading edge and the second SRR was placed ~56 mm further along the profile. For de-icing purposes, 2 miniature cartridge heaters and 2 flexible silicone heating pads were also installed into the leading edge inside of the airfoil structure. Figure 4.6. Fabricated NACA 0018 test article with installed microwave sensors, heater, and temperature sensors, inset is thermal camera image of the heaters in action. Chapter 4: In-flight Ice Sensor 42 4.3 Test Setup and Conditions 4.3.1 Test Conditions The test conditions inside the wind tunnel are summarized in Table 4.1. All tests were conducted at an airspeed of ~100m/s, an altitude of ~19,000 ft (7 psi) and a total air temperature (dry bulb) of +3 °C. The wet bulb temperature was varied between -2°C to +1°C for different melt ratios (LWC/TWC) ranging from 0.11 to 0.25. 4.3.2 Test Article Setup in Wind Tunnel Section The test article was installed in the rear end of the test section in the NRC RATFac as seen in Figure 4.7. The test article was mounted onto a plexiglass viewing window to allow for easy viewing during ice accretion and camera placement outside the harsh testing environment. The test article was installed at an angle of attack (AoA) of 0° and kept constant for the duration of the testing period. Table 4.1. Test Cases conducted in NRC Wind Tunnel at a speed of 100 m/s, altitude of 19000 ft and a static air temperature of +3°C Chapter 4: In-flight Ice Sensor 43 An Anritsu MS46322B Vector Network Analyzer (VNA) was used to measure the microwave response of the single port sensor. The VNA was calibrated in the frequency span of 1 GHz to 2.5 GHz, with 1501 sweeping points for the measurements. Measurements were recorded every ~8 seconds using an automated setup consisting of a custom LabView program. Only cartridge heaters were used as de-icing heater elements due to power supply constraints. The two leading edge cartridge heaters were powered at a constant voltage of 30 V and 0.5 A when turned on. (a) (b) (c) (d) (e) (f) (g) Ice accretion Figure 4.7. (a) fabricated microwave sensor and its integration over a NACA0018 airfoil, (b) ice cloud entering the wind tunnel, (c), (d), (e) various views of the installed sensor system and test article inside the wind tunnel, (f) side view of ice hitting the sensor on the leading edge of the airfoil, (g) ice accretion on the sensor on the leading edge with de-icing heaters “OFF”. Chapter 4: In-flight Ice Sensor 44 4.4 Results and Discussion 4.4.1 Microwave SRR Sensor Response The measured S11 (dB) response of the dual-frequency single port sensor from Test #7 is shown in Figure 4.8. The SRR resonator located on the leading edge of the airfoil demonstrated a resonant notch at 1.946 GHz with an amplitude of -40 dB. The SRR placed further along the chord had a resonant notch at 2.129 GHz with a notch amplitude of -60 dB. The reflection coefficients, S11 (dB), plotted in Figure 4.8 present the frequency response at four time-instances during the icing Test #7. The frequency response 5 seconds before icing, showing the bare-sensor’s dual-frequency response is plotted in black. 5 seconds after the onset of the icing event, the green plot is added, showing a change in the resonant frequency due to the impingement of ice crystals. The notch response of the leading-edge sensor at 1.9 GHz disappears instantly due to the wetness and lossy nature of the water film and ice crystal accretion. As the water film and ice accretion increased on the suction side of the airfoil, a gradually increasing shift in resonant frequency and amplitude was observed in the second SRR frequency response at 2.1 GHz. A plot (blue) 150 seconds into the ice-water accretion, by which time a significant accretion of ice at the 16% melt ratio condition, results in a significant frequency and amplitude shift from bare response. A de-icing procedure was implemented at 380 seconds to dislodge the accretion. A final plot (red), 5 seconds after the ending of the icing event, shows the return of resonant response to its bare profile similar to an ice-free and clean airfoil. This demonstrates the Figure 4.8. Measure S11 (dB) versus Frequency (GHz) of the two-notch single port microwave sensor during Test #7 at four separate instances Chapter 4: In-flight Ice Sensor 45 successful performance of the proposed sensor, in terms of its frequency response, namely the |S11| (dB) in the monitored spectra, for ice sensing and de-icing performed in the wind tunnel. The tests conducted were differentiated by time-period, LWC/TWC ratio (melt ratio) and the wet bulb temperature (Twb). Melt ratio had a significant impact on the shape and amount of ice accreted on airfoils in mixed-phase conditions. Simply tracking the change in the characteristic resonant frequency (ΔFr) of the second SRR at 2.1 GHz, a time-based response is extracted for sensing and plotted for the outlined test conditions reported in Table 4.1. 4.4.2 Test Case Results for Varying Twb Test Cases #1, #2 and #3 were conducted at a Twb of 1°C and had short ice-on time periods of 20 seconds, 10 seconds, and 10 seconds respectively. These test cases were conducted at a speed of 100 m/s, a test temperature of 3°C and an altitude of 19,000 ft. For these tests, no heating or de-icing was applied. Figure 4.9 shows the measured shift in the resonant frequency versus time in these test cases. The measured frequency shift (ΔFr) responded quickly to the icing conditions, showing a significant sensitivity toward ice accretion on the sensor’s surface. A maximum ΔFr of 30 MHz was observed in Test #1 as compared to a maximum ΔFr of 27 MHz in Test #2 and ΔFr of 17 MHz in Test #3. This difference in ΔFr for similar test conditions could be related to the decrease in icing time between Test #1 and Tests #2, #3. Additionally, due to the 8 second data acquisition rate, data could have been lost for icing Test #2 and #3 due to their short 10 second ice-on time. #1 #2 #3 Figure 4.9. Test #1, #2 and #3 with a %melt = 25% (refer to Table 4.1), Icing for 20 s, 10 s and 10 s respectively, with natural accretion shedding and no heating. Chapter 4: In-flight Ice Sensor 46 As shown in by Figure 4.9, the decaying response of the microwave sensor when the input ice crystals, depicted by the blue dash-line, was turned “OFF” could be related to natural ice shedding and melting, occurring at an approximately similar rate for the three tests. Due to the positive wet bulb temperature of +1°C, and a %melt of 25%, the sensor is expected to see more water in its vicinity, with a lower icing severity, as expected from simulation. The sensor’s response returned to its baseline after natural ice shedding and water film dispersion. Test Cases #4, #5 and #6 were conducted at a %melt of 16%, a Twb of -1°C and had an ice-ON time of 50 seconds, 30 seconds, and 30 seconds, respectively. The resonant frequency versus time for these tests are presented in Figure 4.10. A higher resonant frequency variation (ΔFr) of 53 MHz was observed for Test #4 in comparison to a 49 MHz shift for Test #5 which can be attributed to the longer accretion time and greater accretion thickness expected at Test #4. A linear frequency shift with increase in icing time was not observed. However, the trends are in line with simulations, due to the predictable saturation of the sensor’s resonant response with increase in accretion thickness [Figure 4.5 (c)]. This is notably observed in the longer the Test Case #7 in Figure 4.11 (480 seconds). Due to the larger accretion expected at 16% melt ratio (Twb = -1°C), the accreted ice takes longer to naturally melt and shed in the air flow [Figure 4.10]. The sensor demonstrated a plateau response in Test Case #4 before abruptly returning to base line as expected for a natural ice shedding event. To accelerate the de-icing of the accreted ice, embedded cartridge heaters in the leading edge were turned on at 1000 sec, after Test #5. The heating of the accreted ice shape resulted in a drop in the resonant frequency due to the melting of the interface between the sensor and accreted ice. Similar to Test #4, the results showed a sudden return to baseline due to the abrupt shedding of ice and dispersion of water film as a result of electrothermal de-icing. Test Case #6 was performed during active electrothermal icing as depicted by the red dash-line in Figure 4.10. A larger maximum resonant frequency shift of 61 MHz was observed for Test #6. This can be attributed to the presence of melted ice particles on the heated sensor surface (~ #6 #5 #4 Figure 4.10. Test #4, #5 and #6 with a %melt = 16%; Icing for 50, 30 and 30 seconds respectively, with electrothermal de-icing applied after Test #5 and an active icing and de-icing test case observed for Test #6. Chapter 4: In-flight Ice Sensor 47 +5°C) which result in a greater effective permittivity due to the significant contribution of water molecules with a permittivity of ~80 in comparison to ~3.15 for ice. The complex multi-physics phenomenon of an icing event with active de-icing is monitored and the resultant gradual return to baseline is observed as the heated surface melts and disperses the wet ice-water mixture. An ice-free surface was confirmed from the sensor’s response at ~1500 sec (~ 250 sec after ice-OFF) when the sensor response returned to its baseline. Test Case #7, as depicted in Figure 4.11, was conducted at a Twb of -1°C with a far longer ice-ON period of 480 seconds as compared to the cases #1-#6. The response of the sensor could be evaluated in three time spans. Up until ~200 sec, the sensor demonstrated a sharp change in ΔFr due to the direct accumulation of ice over the sensor’s surface. This trend was in-line with the simulations, in which, for only 1 mm of ice a large change (~110 MHz) in resonant frequency was observed. Between 200 sec and 500 sec, ice continued to accrete and resulted in a gradual increase in ΔFr until it plateaued to ~60 MHz due to the saturation of sensors response as expected with increasing accretion thickness. At 530 sec, the electro-thermal de-icing was activated. The sharp downshift of 40 MHz in the sensor’s resonant frequency can be attributed to the melting of the interface layer between the ice (𝜀𝑟~3.15) and the sensor, to form a film of water (𝜀𝑟~80) on the SRRs. The active de-icing event was monitored as the water film resulted in the weakening of ice adhesion and shedding of the accreted ice due to the air flow, as observed by the sensor’s return to base line. #7 Figure 4.11. Test #7 with a %melt = 16%; Icing for 480 seconds with electrothermal de-icing applied 380 seconds into icing to observe a post-icing active de-icing event. Chapter 4: In-flight Ice Sensor 48 In Test Cases, #4, #5, #6 and #7, a melt ratio of 16% and a Twb of -1°C resulted in pronounced accretions. The plateaued resonant frequency shift of ~60 MHz is relatively large and can be attributed to the higher effective liquid water content in the accreted ice. Test Cases #8, #9 and #10, as presented in Figure 4.12 were conducted at a %melt of 11% and a negative wet-bulb temperature (Twb) of -3°C with a long ice-ON time of 270 sec, 780 sec, and 480 sec respectively. The test cases were conducted at the same airspeed, temperature, and altitude of 100 m/s, +3°C and 19,000 ft. Electrothermal de-icing was applied after Test # 9 and the heater remained “ON” for the duration of Test #10 to monitor a longer active icing and de-icing event. A maximum ΔFr of 13 MHz was observed in Test as compared to a maximum ΔFr of 16 MHz in Test 9 and maximum ΔFr of 51 MHz in Test #10. Considering similar tests, Test #8 and #9 at 270 sec after ice-ON, for assumed identical accretion thickness, an equal ΔFr of 13 MHz is observed. The 3 MHz difference in maximum ΔFr between Test #8 and #9 can be attributed to the additional ~80 sec of icing for Test #9 before the sensor’s output is saturated. The natural melting and shedding response was observed after Test #8, where as an electrothermal de-icing and shedding response was seen after Test #9. Test #10 showed the greatest maximum ΔFr of 51 MHz due to the melting of ice particles and higher volume of water in the sensor’s vicinity. Additionally, the formation, melting and subsequent shedding of an accreted shape of ice is hypothesized based on the observed results during the active icing and anti-icing test case. Comparing the saturated resonant frequency variations of the 11% and 16% %melt cases (Test #4-#7 vs Test #8-#10) a lower ΔFr of 16 MHz was observed for the 11% case in comparison to the ΔFr of 60 #8 #9 #10 Figure 4.12. Test #8, #9 and #10 with a %melt = 11%; Icing for 270, 780 and 480 seconds respectively, with electrothermal de-icing applied after Test #9 and kept on for active icing and de-icing during Test #10 Chapter 4: In-flight Ice Sensor 49 MHz observed in the 16% case. This is due to the higher permittivity of the wetter-accretions at a higher %melt, as predicted by the simulations in Figure 4.5 (c). Test Case #11 and #12, as depicted in Figure 4.13, were conducted at a %melt of 13% and a Twb of -2°C and had a long ice-ON time of 420 seconds and 540 seconds, respectively. The de-icing was continuously active throughout the tests, where the heaters were “ON” for the duration of the testing, emulating an anti-icing condition. Approximately 70 sec after ice-ON, a maximum ΔFr of 40 MHz was observed in #11 as compared to a maximum ΔFr of 38 MHz in #12. It is hypothesized that the power of the heaters was insufficient to prevent ice accretion in the test tunnel resulting in the formation of a glaze-like ice accretion. A gradual decrease in ΔFr is observed before a saturated ΔFr of ~15 MHz is reached in both tests. To evaluate the sensor’s ability to differentiate melt ratio, the instantaneous change in ΔFr after 10 seconds of ice accretion is extracted and a trend with respect to the melt ratio is observed as shown in Figure 4.14. The observed ΔFr increased with higher %melt as expected for environments with a greater presence of water in the sensor’s vicinity. This observation is in accordance with the predicted indications of Equation 4.1 and Equation 4.2, and the simulated response of the sensor in Figure 4.5 (c). This campaign successfully tests the microwave resonant SRR technology in an icing wind tunnel at a simulated altitude of 19,000 ft, airspeed of 100 m/s and total air temperature of +3°C for accurate, time-based sensing of ice crystal icing conditions. The sensor was tested in different melt ratio conditions (11%, 13%, 16% and, 25%) at accretion time periods from 10 to 780 seconds. A strong correlation with #11 #12 Figure 4.13. Test #11 and #12 with a %melt = 13%; Icing for 420 and 540 seconds respectively, with electrothermal de-icing applied for both tests. Chapter 4: In-flight Ice Sensor 50 the sensor’s resonant frequency response was noted with a maximum shift of 60 MHz observed for 3 0 seconds of accretion at 16% melt ratio, on the second SRR located further along the profile. The sensor accurately detected the onset of icing, the increase in accretion volume and the eventual shedding of the accreted ice and water film. The sensor was further successful in differentiating the accreted ice at the various %melt conditions tested. Electrothermal de-icing and anti-icing procedures were also monitored, demonstrating the sensor’s capability to evaluate the efficiency of de-icing procedures and sense the undesirable formation of ice during anti-icing operations. Based on the results observed, the sensor is sensitive to different types of accretion as a result of the minute variations in the permittivity of the accretion. Further testing of icing conditions with monitoring of the actual accretion volume, contents, shape and density would allow for characterization of the icing type and severity, for location specific sensing of in-flight accretion. 4.5 Conclusion Here in this work, for the first time, a split ring resonator sensor was tested at a simulated altitude of ~19,000 ft and exposed to ice-particles bombardment with a velocity of up to 100 m/s for ‘impact’ ice sensing within an icing wind tunnel. The sensor demonstrated a robust performance for over 180 hours of exposure to a range of temperatures, pressures, and wind speeds. The sensor was a single port device where microwave signals were obtained from a transmission line edge-coupled to two sensing SRRs for dual-frequency operation. The sensor was designed to operate at 1.9~2.2 GHz with a standard power level of -10 dBm to avoid radiation and interference with other aviation and navigation systems. The sensor was capable of monitoring ice accretion, melting and shedding during the de-icing and anti-icing processes initiated by Figure 4.14. Extracted resonant frequency ΔFr after 10 sec of icing for all test cases against the %melt ratio. Chapter 4: In-flight Ice Sensor 51 the embedded heaters in the NACA001 test article. The sensor’s response demonstrated a strong correlation between the resonant frequency variation and the wetness of the accreted ice (greater change with increasing water content) and the duration of the ice formation over the leading edge of the airfoil. The sensitivity of the sensor to accretion thickness cannot be determined due to lack of information on the ice accretion thickness and contents although the sensor displays a sharp and instant response to icing conditions for as little as 10-second-long icing tests. The effect of de-icing and heating on the sensor’s frequency response is negligible and a maximum of ~60 MHz change in resonant frequency is observed. The proposed microwave based SRR sensor demonstrated a great potential in identifying ice with relative permittivity of ~3 from water with relative permittivity of ~80 whilst still being flexible to be deployed over curved aerodynamic structures. These sensors could potentially contribute to the future of Ice Protection Systems (IPS) systems to optimize de-icing and anti-icing efficiency for lower fuel consumption in aerospace applications. Chapter 5: Integrated Multi-Location Ice Protection System 52 Chapter 5: Integrated Multi-Location Ice Protection System This chapter presents the design and testing of a multi-frequency microstrip resonant SRR sensor under UAV and eVTOL flight conditions for the sensing of ‘impact’ ice accretion. The sensor is integrated with a zone-based electrothermal de-icing system and a superhydrophobic, low-interfacial toughness coating and, is conformally installed on a fabricated NACA 0012 airfoil. The integrated system is tested extensively in an icing wind tunnel and intended for used as an in-flight icing rate sensor for the chord-wise location specific sensing of accretion and zone-based low-power de-icing. The sensor in this work consists of three resonant sensors implemented for zone-specific ice sensing with SRR topology implemented for greater sensitivity to permittivity change. The SRRs are designed with extended split-gaps to increase the sensing area and capacitive sensing region. The three sensors are designed on a separate thin-substrate as resonant tags and coupled in series to a two-port transmission line for transmittance measurement. The two-port design offered greater robustness and prevented noise and charge build-up observed in the previous open-line single port SRR design presented in Chapter 4. Furthermore, the use of an additional substrate to couple the SRR sensors with the transmission line by placing the resonator directly above the microstrip line as apposed to placing it next-to the line offers greater coupling to the resonant rings and prevent errors and loss of sensing functionality due to loading or shorting as observed in the design presented in Chapter 4. Multi-Location Microwave Ice Sensor for Integrated, Zone-Based ‘Smart’ De-icing Abstract— Atmospheric icing during flight significantly risks smaller unmanned aerial vehicles, drones, and electric vertical take-off and landing crafts. In-flight icing negatively affects their aerodynamics and increases weight, thus restricting their operational envelopes and reducing seasonal reliability. Here a smart ice detection and removal system utilizing multi-frequency microwave split-ring-resonator sensors for location-specific impact ice sensing is introduced. The sensor is integrated onto an airfoil with a multi-zone electrothermal de-icing system and a de-icing coating for a zone-based ice protection system. Experimental testing is conducted within a refrigerated icing wind tunnel under low-Reynolds number conditions (Re~105) at airspeeds (20 and 40 m/s) relevant to urban air mobility. The sensor’s response to distinct accretion characteristics at -5°C, -10°C and -20°C, and at varying liquid water contents (0.3, 0.5, and 0.8 g/m3), is investigated. The sensor exhibits a high sensitivity of 179 MHz/mm and data post-processing enables precise monitoring of icing rates under various conditions and with different types of ice accretions for prediction of ice thickness. This proof-of-concept system shows significant potential for use in green energy and aviation sectors, offering prompt and efficient ice protection to maintain the safety and effectiveness of aerospace vehicles. Chapter 5: Integrated Multi-Location Ice Protection System 53 5.1 Introduction Ice accretion on aerial vehicles in flight is an unavoidable and potentially fatal hazard [67] that requires timely detection and protective countermeasures for safe operation [86]. The accumulation of ice on critical flight surfaces like wings and propellers severely impacts the aircraft’s aerodynamics, increasing drag and the chances of catastrophic failure [87]. For smaller aircrafts like unmanned aerial vehicles (UAVs), drones, and newer passenger electrical vertical take-off and landing (eVTOL) type crafts, the threat of ice is even greater due to the increased aerodynamic impact of icing on thinner, smaller airfoil surfaces [114]. Moreover, the increase in weight due to ice severely hampers payload capabilities and operational ranges for UAVs, drones and eVTOL crafts that have power limitations and are generally designed to be lightweight [115]. This emerging field of smaller, more versatile aircrafts has given rise to the Urban Air Mobility (UAM) industry which has a mission to create reliable, time-efficient means of transportation that utilize airspace [116]. To operate reliably in mixed weather conditions, this industry must overcome several obstacles, particularly in the area of atmospheric in-flight ice protection. [117], [118]. Traditional Ice Protection Systems (IPS) for larger airplanes used in commercial aviation employ ‘global’ ice detectors [14], [86] that monitor the general icing environment. Such ‘global’ detectors require the maximum and total activation of conventional de-icing measures like mechanical (pneumatic boots), chemical (propylene glycol), or thermal (jet engine exhaust or electric heaters) systems for safe operation [119]. These conventional IPS measures are, therefore, impractical for power-limited UAVs, drones and eVTOL aircrafts that require lightweight, optimized, and autonomous [120] IPS solutions with selective and controlled activation for extended range and greater payload capabilities. Chapter 5: Integrated Multi-Location Ice Protection System 54 For UAV and eVTOL type smaller crafts, lightweight de-icing solutions exist and include piezoelectric systems that use mechanical resonances to break and shed accreted ice [121], [122], de-icing fluid systems that use chemicals to cause accreted ice to melt [123] and, electro-thermal systems that use parting strips and flexible heaters for optimized de-icing through strategic activation for ice-shedding [124], as opposed to conventional de-icing or power-hungry evaporative anti-icing. The effectiveness of each of these de-icing solutions is dependent on reliable, location-specific ice sensors for the accurate activation of the ice protection mechanisms. In this regard, the piezoelectric systems offer some detection capabilities through resonant profile variations at low MHz [121] and kHz [125] frequencies but suffer from errors and sensitivity to vibrations and noise [126]. Current chemical de-icing methods [123] require supplementary sensors and pilot actuation, operating constantly to offer ice protection limited by de-icing fluid reservoirs. Electro-thermal ice shedding systems in development [124] which require actuation at specific ice thickness, monitor surface temperature to detect icing but are incapable of determining the accretion thickness for optimal actuation. Each of the above-mentioned UAV/eVTOL de-icing solutions would benefit from integrated location-specific, real-time ice sensors that can determine the icing conditions and selectively actuate the de-icing mechanisms under in-flight icing conditions. Microwave resonant sensing methods have shown great promise as sensitive, location-specific, real-time sensors for various industrial [34], [54], [127], biomedical [37], [128], [129], and chemical [24], [35], [71], [130] sensing applications. These sensors differentiate materials based on their electromagnetic properties, namely the relative permittivity (𝜀𝑟), and loss tangent. Monitoring the sensor’s microwave Figure 5.1. Envisioned implementation of location-specific integrated microwave SRR ice sensors, zone-based electrothermal de-icing and a de-icing coating for ‘smart’ ice protection for use on urban air mobility crafts like eVTOLs, UAVs and drones. Chapter 5: Integrated Multi-Location Ice Protection System 55 response facilitates the non-contact characterization of a material in the sensor’s local environment through its interaction with electromagnetic waves. Microwave sensing mechanisms have historically proven to be highly accurate and reliable for atmospheric in-flight ice detection [8], [91] due to the large difference in relative permittivity between ice (εice = 3.15), water (εwater = 0) and air (εair = 1). Recently, split ring resonator (SRR) based microwave sensors have particularly shown great promise as highly sensitive, location-specific sensors for real-time ice and frost detection [38], [131]. SRRs have successfully been tested for saline ice sensing [40], snow sensing [103], ice-phobic coating evaluation [65], electrothermal heater integration [39] and harsh environment applications [75]. In these research works, SRR sensors were tested in controlled laboratory environments using ice that had formed on cold plates or in cold chambers. In real-world wind turbine and aerospace applications, so-called impact ice accretion occurs under dynamic conditions with different accretion characteristics governed by local airspeed, liquid water content (LWC), surface temperature, altitude, geometry, and other location-specific parameters. For use as in-flight ice sensors for IPS actuation, testing under impact icing conditions is imperative to determine the real-time characteristics of icing at varying conditions. In a first, Shah et al., tested a single port, dual-frequency microwave SRR sensor under impact ice crystal icing (ICI) in an icing wind tunnel [132]. The sensor was tested at conditions specific to in-flight icing in jet engines at an altitude of ~5800 m, airspeed of 100 m/s and total air temperature of +3°C. This work successfully proved the capability of SRR technology to detect the onset of in-flight icing and differentiate ice crystal melt ratio (%melt) whilst also monitoring de-icing and anti-icing operations. However, this work failed to monitor the amount of ice accretion and did not characterize the microwave sensor’s time-based response to measure the location, volume, thickness, or type of ice accretion for ice-thickness prediction and icing rate measurement. In this work, a two-port (S21), multi-frequency microwave SRR ice sensor is developed to monitor location-specific accretion characteristics for icing rate measurement and real-time ice thickness prediction. The sensor is integrated with individually controlled zone-based electrothermal heaters and coated with a de-icing coating for a complete integrated smart ice protection system (IPS) as envisioned in Figure 5.1. The integrated system is tested in an icing wind tunnel at sea level under low-airspeed and low-Reynolds number flight conditions characteristic to urban UAV or eVTOL in-flight icing conditions. The SRR sensor monitors time-based glaze, rime and mixed type icing conditions at varying LWC and airspeed conditions to determine the microwave sensor response for accurate icing rate measurement. Chapter 5: Integrated Multi-Location Ice Protection System 56 5.2 Theory, Design and Simulations 5.2.1 Ice Protection System Design The multi-frequency location-specific microwave SRR sensor was specifically designed for integration with a zone-based electrothermal de-icing system and a de-icing coating for a smart IPS. To test the system in an icing wind tunnel, a NACA 0012 airfoil profile was chosen for comparability to historical ice accretion tests and the IPS was installed on an airfoil with a chord (𝑐) of 203.2 mm (8 in) and a span of 304.8 mm (12 in) as seen in Figure 5.2. The zone-based electrothermal de-icing system was comprised of seven individually controlled Kapton heaters operating at a maximum power of 120 W and power density of 15500 W/m2 (10W/in2). Each heater was 25.4 mm (1 in) wide and 304.8 mm (12 in) long and pasted along the airfoil span on the outer surface of the aluminum profile, to create 4 symmetric heating zones as visualized in Figure 5.2. The multi-zone electrothermal heater configuration was selected to mimic the latest electrothermal multi-zone de-icing heater mats employed by Boeing on its 787 Dreamliner [133]. Placing the SRR tags directly in the center of the 25.4 mm heater-zone allowed for location-specific sensing of ice accretion and the eventual implementation of smart integrated ice sensing and guided de-icing. The IPS includes a 127 μm thick ultra-high molecular weight polyethylene (UHMWPE) coating that was draped over the entire airfoil, electrothermal de-icing system and microwave SRR sensor. This coating has been shown to exhibit low-interfacial toughness (LIT) with ice, facilitating ice detachment with low applied forces for ice shedding without melting on structures like wind turbines [65] or on rotor blades found on eVTOL and drone type crafts. With respect to the impact icing conditions, the LIT coating is intrinsically a hydrophobic material and will affect the type and shape of ice profiles in Figure 5.2. 3D CAD Model of the Microwave SRR sensor integrated with conformal heaters onto a NACA 0012 airfoil for location-specific ice sensing and zone-based electrothermal de-icing. Chapter 5: Integrated Multi-Location Ice Protection System 57 comparison to conventional bare aluminum profiles. This property is challenging to model and expected to be most evident for glaze icing conditions with temperatures just below freezing, where the impinging water droplets might not freeze upon impact and may flow along the chord before freezing, resulting in greater chord-wise spreading of the accreted profile. 5.2.2 Test Conditions and Icing Simulation 5.2.2.1 Low-Reynolds Icing Ice accretion is a complex process that is dependent on several conditions including surface temperature, air speed, altitude, LWC, accreting profile, and orientation. The accretion process on current airplanes used for commercial aviation is a well-established field and considers accretion on larger aircrafts at high cruise altitudes (>8000 m) and speeds (>200 m/s) with Reynolds numbers in the 107 to 109 range. In contrast, icing for smaller crafts like UAVs, eVTOLs and drones occur on smaller airfoil profiles at lower altitudes down to sea level (0 - 1200 m) and lower cruise speeds (10 - 90 m/s), with Reynolds numbers in the 105 to 107 range [134]. The low-Reynold icing conditions experimentally tested in this work are presented in Table I. This corresponds to a Reynolds number of ~3×105 for the 20 m/s cases and ~6×105 for the 40 m/s cases and are within the range of airspeeds expected during UAV and eVTOL operation [135]. The impingement of water droplets at conditions below 0°C results in ice accretions that can be broadly classified into (1) glaze ice, a clear, transparent accretion with a higher density that forms at temperatures just below freezing; (2) rime ice, an opaque, white accretion with lower density that forms at temperatures far below freezing; and (3) mixed ice, an accretion with intermediate properties resulting in translucent ice profiles. To test the integrated microwave SRR sensor’s sensitivity to different types of accretion, three temperature (𝑇) conditions, -5°C, -10°C and -20°C, were explored, as seen in Table 5.1, corresponding to glaze, mixed and rime ice conditions, respectively. Table 5.1 Testing Conditions in an Icing Wind Tunnel Liquid Water Content (g/m3) Airspeed (m/s): 20 40 Temperature (°C) -5 - 0.5 0.8 0.5 -10 0.3 0.5 0.8 0.5 -20 0.3 0.5 0.8 0.5 Highlighted conditions are simulated on Ansys FENSAP Ice Chapter 5: Integrated Multi-Location Ice Protection System 58 To investigate the real-time microwave SRR ice sensor’s ability to differentiate the rate of accretion, the LWC was varied between 0.3 g/m3, 0.5 g/m3, and 0.8 g/m3 at the 20 m/s airspeed (𝑉) condition. The effect of increasing the airspeed on accretion characteristics and accretion rate was also considered with testing conditions at 40 m/s and a constant LWC of 0.5 g/m3 at each selected temperature. For simplicity, this work only considered a 0° angle of attack (AoA). 5.2.2.2 Icing Physics Determining the ice accretion thickness, 𝛿𝑖𝑐𝑒, is imperative to optimize ice protection systems for safe and reliable operation. Anderson and Tsao [136] evaluated the ice accretion characteristics on a NACA 0012 airfoil and offer the following relation to determine ice thickness, 𝛿𝑖𝑐𝑒 = 𝑛𝑓 𝐴𝑐 𝛽𝑜 𝑑 (5.1) and 𝑛𝑓 is the freezing fraction, 𝐴𝑐 is the accumulation parameter, 𝛽𝑜 is the water droplet collection efficiency at the stagnation line (leading edge for 0° AoA), and 𝑑 is the leading-edge diameter of the airfoil. The collection efficiency, 𝛽𝑜, is a function of the geometry, water droplet median volume diameter (MVD), and the droplet Reynolds number (𝑅𝑒𝛿) and was found to be ~1. The freezing fraction, 𝑛𝑓, may be extracted from [136] and was simplified to a linear relation as an inverse function of total air temperature (𝑇) in °C. The accumulation parameter, 𝐴𝑐, is a function of the LWC, airspeed (𝑉), the icing time (𝑡) and the density of the accretion (𝜌𝑖𝑐𝑒). Thus, Equation 5.1 can be expanded to, 𝛿𝑖𝑐𝑒 = [(0.0395 × −𝑇𝑥) (𝐿𝑊𝐶𝑥 × 𝑉𝑥𝑑 × 𝜌𝑖𝑐𝑒) (𝛽𝑜) (𝑑) ] × 𝑡 (5.2) Accordingly, the ice thickness is proportional to the icing time (𝛿𝑖𝑐𝑒 ∝ 𝑡), LWC (𝛿𝑖𝑐𝑒 ∝ 𝐿𝑊𝐶), and airspeed (𝛿𝑖𝑐𝑒 ∝ 𝑉) and inversely proportional to the ice density (𝛿𝑖𝑐𝑒 ∝ 1 𝜌𝑖𝑐𝑒⁄ ) and air temperature (𝛿𝑖𝑐𝑒 ∝ 1 𝑇⁄ ). To model the ice density, 𝜌𝑖𝑐𝑒, the Macklin ice density model [137] can be used, 𝜌𝑖𝑐𝑒 = 378 + 425 log10 𝑅𝑀 − 82.3(log10 𝑅𝑀)2 (5.3) where 𝑅𝑀 = − (𝑑𝑖𝑎. ‖𝑉𝑑‖) (2𝑇𝑤𝑎𝑙𝑙)⁄ , and 𝑑𝑖𝑎 is the droplet diameter in micron, 𝑉𝑑 is the droplet impact velocity in m/s, and 𝑇𝑤𝑎𝑙𝑙 is the temperature of the accreting surface in °C [138]. The ice density, 𝜌𝑖𝑐𝑒, was calculated using Equation 5.3, assuming a droplet MVD diameter (𝑑) of 20 µm, a droplet impact velocity (𝑉𝑑) equal to the airspeed (𝑉) and a wall temperature (𝑇𝑤𝑎𝑙𝑙) equal to the static air temperature (𝑇). The calculated 𝜌𝑖𝑐𝑒 for each temperature and airspeed condition outlined in Table 5.1 is plotted in Figure 5.3(a). The density is directly proportional to air temperature (𝜌𝑖𝑐𝑒 ∝ 𝑇), indicting that lower temperature resulted in ice formation with low density and, proportional to airspeed (𝜌𝑖𝑐𝑒 ∝ 𝑉) with Chapter 5: Integrated Multi-Location Ice Protection System 59 an increase in ice density for higher airspeeds. The calculated density ranged between 721 kg/m3 [-20°C and 20 m/s] and 889 kg/m3 [-5°C and 40 m/s]. Visually, this variation in density would result in the distinct appearance of the accretion, differentiating clear, glaze ice at the -5°C condition, cloudy mixed ice at the -10°C condition, and opaque white rime ice at the -20°C condition. Using Equation 2, the ice thickness, 𝛿𝑖𝑐𝑒, was calculated assuming 10 minutes of icing for each outlined testing condition and was plotted against the accretion temperature in Figure 5.3(b). The effect of temperature is evident, with the lowest temperature condition (-20°C, rime) displaying the greatest thicknesses. Additionally, increasing LWC and airspeed increases the expected ice thickness. A linear relationship between 𝛿𝑖𝑐𝑒, and accretion time, (𝑡), was observed from Equation 5.2 where, 𝛿𝑖𝑐𝑒 = 𝑘𝑥 × 𝑡 (5.4) and 𝑘𝑥 can be defined as the icing rate indicator for each testing condition. The icing rate indicator, 𝑘𝑥, will be considered below for comparison with the microwave sensor parameters to facilitate accurate measurement of icing rate and ice accretion thickness. For greater accuracy in ice accretion modelling, computational tools can be considered for superior modelling of the complex phenomena and will be used to determine the accretion rate expected. 5.2.2.3 FENSAP-Ice Simulations A commercial 3D in-flight ice accretion simulation tool, Ansys FENSAP-Ice (2022R2), was utilized to model the ice accretion characteristics on an identical aluminum NACA 0012 profile to identify the expected accretion rates and profiles for the highlighted test conditions in Table 5.1. Multishot accretion simulations with ten 60-second shots for a total icing time of 600 seconds were conducted for computationally acceptable and accurate simulations of the time-based icing phenomena [108] for the -10°C and -20°C conditions using a ‘Rime’ ice-water model. For the glaze icing condition at -5°C, a ‘Glaze-Advanced’ ice-water model was used with ‘Beading’ for greater accuracy in modelling the roughness and accretions at glaze icing conditions, where accreting supercooled water droplets may not freeze on impact and flow along the profile before freezing, resulting in characteristic horn-like clear accretion. The glaze-ice simulations required sixty 10-second shots to model the formation of horn-like glaze ice and posed computational remeshing challenges due to the complex nature of the accreted profile as seen in the inset of Figure 5.3(d), resulting in shorter simulated glaze-icing periods. It is important to note that in-flight accretion tools are still under review and development for the modelling of low-Reynold icing conditions with greater accuracy [139]. Accretion profiles for rime conditions are considered more accurate and present reliable results in comparison to simulations of icing at glaze conditions. Chapter 5: Integrated Multi-Location Ice Protection System 60 To model the unsteady, real-time icing phenomenon multi-shot quasi-unsteady simulations using multiple timed steady-state ‘shots’ were conducted using Ansys FENSAP Ice. To compare the ice shapes at -5°C (glaze), -10°C (mixed) and -20°C (rime) icing conditions, a chord-wise slice of the accreted profile from simulations conducted at an airspeed of 20 m/s and an LWC of 0.5 g/m3 after 300 seconds of icing are plotted in Figure 5.3(c). The position of the three SRR sensor tags are highlighted and allow for the prediction of accretion location with respect to each sensor tag. The corresponding ice thickness (𝛿𝑖𝑐𝑒) solution extracted at 300 seconds for each temperature condition is plotted against the y-axis of the airfoil in Figure 5.3(d), with the location of the SRR tags also highlighted. The inverse effect of temperate on the icing rate (𝑘𝑥 ∝ 1 𝑇⁄ ) was observed where, with the lowest temperature, the -20°C rime-icing case showed the greatest accretion volume, followed by the -10°C mixed-icing case and then the -5°C glaze-icing case with the lowest accretion volume. Additionally, the ice shape from the -5°C glaze-icing case presented characteristic horn-like accretion profiles as observed in the inset of Figure 5.3(d). In comparison, accretions at -20°C and -10°C, rime and mixed icing conditions, presented more rounded, smooth profile as observed in the inset of Figure 5.3(e). Chapter 5: Integrated Multi-Location Ice Protection System 61 A similar plot of the extracted ice thickness after 480 seconds of ice accretion at a constant -20°C Chapter 5: Integrated Multi-Location Ice Protection System 62 rime-icing condition is presented in Figure 5.3(e) to study the effect of varying LWC (0.3, 0.5 and 0.8 Chapter 5: Integrated Multi-Location Ice Protection System 63 g/m3) and airspeed (20 and 40 m/s). At a constant speed of 20 m/s, the accretion volume is directly Chapter 5: Integrated Multi-Location Ice Protection System 64 proportional to the LWC (𝑘𝑥 ∝ 𝐿𝑊𝐶), with the 0.8 g/m3 condition showing the most accretion in Chapter 5: Integrated Multi-Location Ice Protection System 65 comparison to the 0.5 g/m3 and the 0.3 g/m3 cases. At an increased speed of 40 m/s and an LWC of 0.5 Chapter 5: Integrated Multi-Location Ice Protection System 66 g/m3 the greatest accretion volume was observed, as expected for the greater airspeed conditions (𝑘𝑥 ∝Chapter 5: Integrated Multi-Location Ice Protection System 67 𝑉). The simulated trends are in-line with theoretical expectations and offer insight into the rate of (a) (b) (c) (d) (f) (e) Tag 2 Tag 1 Tag 3 Tag 2 Tag 1 Tag 3 Tag 2 Tag 1 Tag 3 Horn-shape Glaze Ice Rime Ice Figure 5.3 (a) Calculated ice density for the temperature and airspeed conditions outlined in Table I using Equation 5.3. (b) Calculated ice thickness for the temperature, LWC and airspeed conditions outlined in Table 5.1 using Equation 5.2. (c) 2D accretion profiles extracted at 300 seconds for a -5°C [glaze], -10°C [mixed] and -20°C [rime] ice accretion simulation conducted on FENSAP Ice, indicating the variation in profile and accretion thickness. The location of the three SRR tags are highlighted for reference. (d) Corresponding ice thickness solution for the profiles in Figure 5.3(c) are extracted and plotted against the y-axis of the airfoil to obtain the ice thickness along the airfoil profile. Inset shows a 3D profile of the horn-shape glaze ice formation. (e) Ice thickness solution for -20°C [rime] ice conditions with variations in LWC (0.3 g/m3, 0.5 g/m3 and 0.8 g/m3 at 20 m/s) and in airspeed (20 and 40 m/s at 0.5g/m3) plotted at 480 seconds of icing. Inset shows a 3D profile of the rime ice formation at 0.5 g/m3 LWC. (f) Maximum Ice thickness solution extracted from each shot of the multi-shot FENSAP Ice simulations for every simulated condition highlighted in Table 5.1, showing that the ice thickness linearly increases with LWC, temperature and airspeed. Chapter 5: Integrated Multi-Location Ice Protection System 68 accretion at these different testing conditions. The maximum thickness of ice at every shot was extracted from the multi-shot Ansys FENSAP Ice simulations and is plotted in Figure 5.3(f). The extracted rate of ice accretion indicator from Ansys FENSAP Ice simulations, 𝑘𝑠𝑖𝑚, is presented in Table 5.2 alongside the experimentally measured results, 𝑘𝑚𝑒𝑎𝑠, for comparison. 5.2.3 Microwave SRR Ice Sensor Simulations 5.2.3.1 Principle of Operation Microwave split ring resonator (SRR) sensors are microstrip structures that display a characteristic frequency response with high quality-factors and low radiative losses, allowing for the design of electromagnetic structures with enhanced sensitivity [46]. Material sensing using SRR sensors is performed based on the real-time monitoring of the sensor’s characteristic resonant response as it changes due to the electromagnetic properties of media in its vicinity. The resonant frequency of the SRR (FR) depends on the ring length (𝑙) and the effective relative permittivity (𝜀𝑒𝑓𝑓) of the surrounding dielectric medium, 𝐹𝑅 = 𝑣𝑜2𝑙√𝜀𝑒𝑓𝑓(5.5) where 𝑣𝑜 is the speed of electromagnetic wave propagation in free space. Tracking this frequency response in real-time offers a robust means of monitoring changes in the environment of the sensor through variations in 𝜀𝑒𝑓𝑓 and can be used to determine ice thickness and accretion rate. For a bare sensor in air, 𝜀𝑚𝑎𝑡 = 𝜀𝑎𝑖𝑟 = 1. From Equation 5.5, the increase in 𝜀𝑒𝑓𝑓 due to the introduction of material with permittivity greater than 1 results in a down-shift in resonant frequency that can be monitored to determine the icing condition. The relative permittivity of pure solid ice is 𝜀𝑖𝑐𝑒 = 3.15 and this difference between 𝜀𝑎𝑖𝑟 and 𝜀𝑖𝑐𝑒 enables for the accurate sensing of thin layers of ice at the onset of icing using microwave sensors. The electromagnetic fields propagating in the media above the sensor’s surface have a frequency dependent maximum thickness (𝛿𝑚𝑎𝑥), beyond which the effect of material on the resonant frequency is negligible. Analytical and empirical models exist to determine 𝜀𝑒𝑓𝑓 for a microstrip line loaded with materials of varying thickness, from 0 to 𝛿𝑚𝑎𝑥 [140]. For greater accuracy, full wave 3D electromagnetic simulation tools were used here to simulate the resonant characteristics expected for incremental ice thickness (𝛿𝑖𝑐𝑒) loading conditions. From FENSAP-Ice accretion simulations, the density of the ice is expected to vary with temperature and airspeed and can be attributed to variations in the amount of trapped air bubbles within the ice. The electromagnetic properties of such ice accretions can be modelled as a mixture of ice and air with an 𝜀𝑟 Chapter 5: Integrated Multi-Location Ice Protection System 69 between 1 < 𝜀𝑖𝑐𝑒 < 3.15. Various comparable analytical [141] and empirical [142] models exist to determine 𝜀𝑟 as a function of its density, 𝜌𝑖𝑐𝑒 (g/cm3). Using the following empirical relation by Kovacs et.al [142] 𝜀𝑖𝑐𝑒 = (1 + 0.845 𝜌𝑖𝑐𝑒)2 (5.6) Solid, clear ice with a density of 917 kg/m3 exhibits 𝜀𝑖𝑐𝑒 = 3.15 and firn (powdered snow) with a density of 500 kg/m3 shows 𝜀𝑖𝑐𝑒 = 2 [143] offering a range of permittivity values expected for real-world ice and snow. Using Equation 5.6, the corresponding 𝜀𝑟 for the calculated 𝜌𝑖𝑐𝑒 are marked on the right axis of Figure 5.3(a). For the same thickness of ice accretion, greater changes in resonant frequency ∆𝐹𝑅 are expected for denser glaze ice as compared to more porous rime ice. 5.2.3.2 SRR Sensor Design A two-port (S21) microwave SRR sensor was designed as a multi-frequency sensor for monitoring ice accretion on multiple discrete zones as shown in Figure 5.4(a). Designed on low-loss, flexible Rogers 5880 substrates, the sensor was devised in two parts, with a 50-ohm transmission line on a copper-backed 0.51 mm thick substrate and three SRR tags on a 0.13 mm thick substrate (white tag in Figure 5.4(a)). The SRRs were placed on top of the transmission line in series to simplify implementation, avoiding the complex power splitting required for parallel implementation. The three SRR structures were optimized for a sharp, high quality (high Q) resonant band-stop response as seen in Figure 5.4(b), which presents the simulated transmission coefficient (S21 (dB)) of the sensor from Ansys High Frequency Structures Simulator (HFSS). The sensor was specifically designed in the 2-4 GHz range due to the large difference in permittivity of water and ice at these frequencies. The SRRs were designed to resonate at frequencies at least 500 MHz apart to prevent resonant profile overlap during loading. The specific frequencies chosen were arbitrary and a result of the physical form factor of the SRR tags. The location of the optimized SRRs along the transmission line was determined by the configuration of the discrete zone-based electrothermal de-icing system with the SRR tag placed directly in the center of the zone to be monitored, as depicted in Figure 5.4(a). The largest SRR with the lowest frequency was placed on the leading-edge zone as it was expected to experience the greatest volume of ice, and to prevent interference through resonant profile overlap during accretion. 5.2.3.3 Time-based Ice Accretion Modelling To model the time-based accretion of ice, a 16 mm x 16 mm material box with increasing ice thickness (0 mm to 10 mm) and the permittivity of solid ice (εice = 3.15) was placed on the SRR located on the leading edge (SRR Tag 1) as depicted in Figure 5.4(a). The resultant transmission coefficient response (S21 (dB)) is presented Figure 5.4(b). These simulated responses indicate the sensor’s selectivity to ice Chapter 5: Integrated Multi-Location Ice Protection System 70 accretion location, with no changes observed in the resonant profiles of unloaded SRR tags 2 and 3. The bare, unloaded resonant frequency of SRR tag 1 (𝐹𝑅1) is marked and the change in resonant frequency (Δ𝐹𝑅1) is indicated. In the remainder of this work, the Δ𝐹𝑅1 parameter is used to determine ice accretion characteristics and monitor the increase in ice thickness and rate of ice accretion. The resonant frequency was chosen for its strong correlation to ice accretion permittivity and for its robust response in harsh, noisy environments. A parametric simulation of the loading of SRR tag 1 (as performed in Figure 5.4(b)), was conducted with varying ice thickness, 𝛿𝑖𝑐𝑒 (0 to 8 mm) and permittivity, 𝜀𝑖𝑐𝑒 (2 to 3.15), modelling incremental ice accretion with different densities. The absolute change in resonant frequency, |Δ𝐹𝑅|, was extracted and is Figure 5.4. (a) The designed two-port (S21) multi-frequency microwave split ring resonator (SRR) sensor integrated with individual heated zones for location-specific accretion measurement and zone-based smart de-icing control. (b) The simulated transmission coefficient (S21 dB) response of the multi-frequency microwave SRR sensor from Ansys HFSS. The sensor’s response to loading the leading-edge (LE) sensor (SRR 1) with a 16 mm x 16 mm material box with varying ice thickness, 𝛿𝑖𝑐𝑒 and a constant permittivity εice = 3.15 (pure, solid ice) is plotted and the change in the resonant frequency (ΔFR1) is indicated. (c) The absolute change in resonant frequency |ΔFR| extracted from the simulated sensor response and plotted against increasing ice accretion thickness from 0.5 mm to 8 mm (modelling time-based accretion) for material permittivity values between 2 and 3.15 (modelling accretion with different density). The corresponding ice densities for the simulated permittivity values are calculated using Equation 5.6 and are presented alongside each response. Chapter 5: Integrated Multi-Location Ice Protection System 71 plotted against increasing ice accretion thickness in Figure 5.4(c). Using Equation 5.6, the corresponding 𝜌𝑖𝑐𝑒 value for each simulated 𝜀𝑖𝑐𝑒 value is indicated and offers insight into the effect of ice accretion density on the microwave sensor response. A greater |Δ𝐹𝑅| occurred for denser glaze accretions as compared to rime ice for the same accretion thickness, indicating the sensors’ ability to differentiate ice accretions based on density. With respect to 𝛿𝑖𝑐𝑒, thin layers of ice resulted in large changes in |Δ𝐹𝑅|, indicating a high sensitivity to the onset of icing. The |Δ𝐹𝑅| response saturated as the accretion tended towards the maximum thickness (0 → 𝛿𝑚𝑎𝑥 ~6mm) indicating an exponentially decreasing rate with increasing ice thickness. Monitoring the real-time resonant profile and tracking |Δ𝐹𝑅| offers insight into the rate of ice accretion and enables ice thickness prediction. 5.3 Fabrication and Testing 5.3.1 Integrated IPS System 5.3.1.1 Test Article Fabrication The integrated ice protection system (IPS) consisting of a specifically designed multi-location microwave SRR ice sensor and a zone-based electrothermal Kapton heater configuration was installed on an aluminum NACA 0012 airfoil as shown in Figure 5.5. The two-part microwave SRR sensor was patterned using an in-house PCB etching fabrication method on two Rogers 5880 substrates as outlined in the SRR Sensor Design section and pasted together using low-loss glue. The Kapton heaters were pasted onto the outer surface of the aluminum profile before the fabricated microwave SRR sensor was mounted conformally on the profile with the center of SRR tag 1 placed directly on the leading edge. Two flexible coaxial cables were fed through the airfoil profile and were connected into readout circuitry for microwave measurement. The test article was then completely covered by the 127 μm thick LIT de-icing Figure 5.5. Fabricated NACA 0012 Test Article with Integrated Microwave SRR sensor for location-specific sensing and individually actuated electrothermal ice protection instrumentation for guided de-icing control. Inset: video snapshot of installed test article in AMIL wind tunnel. Chapter 5: Integrated Multi-Location Ice Protection System 72 coating and mounted in the icing wind tunnel as seen from a video snapshot of the installed test article inset in Figure 5.5. 5.3.1.2 Microwave Electronics An Anritsu MS46322B Vector Network Analyzer (VNA) was used to measure the microwave response of the dual-port SRR ice sensor. The VNA was calibrated in the frequency span of 1.8 GHz to 4 GHz, with 2001 sweeping points for the measurements which were recorded every ~10 seconds using an automated setup consisting of a custom LabView program. The output power was set to 0 dBm (1mW) for low-power operation. 5.3.1.3 Electrothermal Heating Electronics To individually control the Kapton heater zones during testing, a switch board was employed to selectively activate the heaters. An AC transformer was utilized to vary the power output (maximum 120 W per heater) through the control of voltage between 0-120 V. Additionally, to monitor the temperature of the interface region at each zone, 3 K-type thermocouples were internally installed within the aluminum profile, along Zones 1-3 (see Figure 5.4(a)). A fourth thermocouple was installed under the conformal microwave sensor at the leading edge to monitor the temperature under the sensor and a fifth was placed on the sensor’s surface along the leading edge. 5.3.2 Wind Tunnel and Testing Methodology Impact ice accretion tests were conducted at the Anti-icing Materials International Laboratory’s (AMIL) icing wind tunnel (IWT) located at the University of Quebec at Chicoutimi (UQAC), Canada. The closed-loop, low-speed refrigerated wind tunnel operates at speeds up to 70 m/s and temperatures between -30°C to +25°C at sea level pressure. Icing conditions were generated using an oscillating spray-nozzle assembly upstream of a 1.5 m long, 0.6 m high, and 0.5 m wide test section. The LWC was calibrated using an LWC-200 liquid water content measuring device with a standard deviation of 0.1 g/m3. The median volumetric diameter (MVD) of the distilled-water droplets produced for testing was 20-30 μm. The 203 mm chord NACA 0012 test article was installed at a 0˚ AoA. The conditions tested in the IWT are presented in Table 5.1. For each testing condition, a sequenced time-based test was conducted with 60, 180, 300, and 600 seconds of ice accretion to measure the accretion rate as a function of accretion time. The 2D profile of the accreted ice shape could not be recorded but the maximum ice thickness (𝛿𝑖𝑐𝑒) measurements were conducted after every test by averaging three thickness Chapter 5: Integrated Multi-Location Ice Protection System 73 measurements across the 50.8 mm span of the sensor surface along with recorded video and pictures of the profile. 5.4 Results and Discussion 5.4.1 Data Transformation and Modelling 5.4.1.1 Microwave Data The integrated microwave ice sensor’s transmission coefficient (S21 (dB)) response within AMIL’s IWT, with an interval of 10 seconds, was recorded during the testing conditions outlined in Table 5.1. Sixty measurements of the S21 (dB) response for a total of 600 seconds of ice accretion at -20°C, 20 m/s and 0.8 g/m3 were extracted at the frequency span of 1.8 GHz to 4 GHz, as shown in Figure 5.6(a). SRR tag 1 operated at the resonant frequency (𝐹𝑅) of 2.430 GHz with the resonant amplitude of -20.21 dB. SRR tag 2 operated at an 𝐹𝑅 of 2.956 GHz, with an amplitude of -33.77 dB, and SRR tag 3 operated at an 𝐹𝑅 of 3.458 GHz and an amplitude of -46.79 dB. The obtained frequency response of the SRR tags were ~500 MHz apart as designed in simulation. Figure 5.6(b) presents the variation in the resonant response of SRR tag 1 (leading edge sensor) with respect to time as ice accumulated on the leading edge of the airfoil. As the thickness of ice increased, a down-shift in the resonant profile was observed by tracking the change in resonant frequency, ∆𝐹𝑅, the chosen variable of interest. An absolute change in resonant frequency, |∆𝐹𝑅1| of 246.4 MHz was recorded in SRR tag 1 response for a measured ice thickness of 9.40 mm after 600 seconds of icing at -20°C, 20 m/s and 0.8 g/m3. The SRR sensor displayed a maximum measured sensitivity of 179 MHz/mm for 0.56 mm of ice. To accurately obtain the |∆𝐹R| for each SRR tag from the transmission coefficient (S21 (dB)), three local minima (𝐹R1 , 𝐹R2 , 𝐹R3) were computed and normalized by deducting 𝐹𝑅𝑥(𝑏𝑎𝑟𝑒), the bare resonant frequency of each SRR tag. 𝐹Rx(𝑏𝑎𝑟𝑒) was determined by averaging 5 measurements (50 seconds) before the start of an icing test, where the test article was free of ice accretion. A time-based plot of the |∆𝐹𝑅| for a set of 60-, 180-, 300-, and 600-second ice accretion tests at -20°C, 20 m/s and 0.8 g/m3 LWC is presented in Figure 5.6(c). A large shift in |∆𝐹R1| was observed, indicating that SRR tag 1 monitoring Zone 1 was heavily iced. SRR tag 2 showed a lesser shift in |∆𝐹R2|, indicating a very small amount of accreted ice in the vicinity of the second sensor in Zone 2. SRR tag 3 showed no change in the resonant frequency response, |∆𝐹R3| = 0, indicating no ice accretion on the third tag at Zone 3. The |∆𝐹R| response from each testing condition was used to determine the rate of accretion, the conditions encountered, location-specific accretion profile information, and further predict the ice accretion volume. Chapter 5: Integrated Multi-Location Ice Protection System 74 5.4.1.2 Ice Accretion Data The corresponding maximum ice thickness measurements were conducted at the end of each test and are reported in the inset of Figure 5.6(c), indicating a relatively linear increase in ice thickness with increased accretion time. Using Equation 5.4, a linear proportionality constant, 𝑘meas, was extracted to indicate the accretion rate and was found to be 𝑘𝑚𝑒𝑎𝑠 = 0.9461 𝑚𝑚𝑚𝑖𝑛 for the -20°C, 20 m/s and 0.8 g/m3 test condition. A similar linear fitting of the maximum ice thickness versus accretion time was performed for each testing condition, and the resultant icing rates are presented in Table 5.2. The extracted proportionality constants from Ansys FENSAP-Ice simulation, 𝑘sim, are also presented alongside the Figure 5.6. (a) Measured S21 (dB) frequency response (transmission coefficient) of the multi-frequency microwave SRR sensor plotted every 10 seconds for 600 seconds at the -20°C, 20 m/s and 0.8 g/m3 icing wind tunnel testing condition. (b) Zoomed-in view of the S21 (dB) frequency response of SRR tag 1 for 600 seconds of ice accretion at -20°C, 20 m/s and 0.8 g/m3. (c) Time-based ice accretion results for a set of tests conducted at -20°C, 20 m/s and an LWC of 0.8 g/m3. Extracted and normalized absolute resonant frequency shift |ΔF| for SRR 1 (black), SRR 2 (blue) and SRR 3 (red) are presented. The first test depicts an 8 min long active de-icing test followed by a sequence of 1 min, 3 min, 5 min and 10 min tests with ice accretion thickness measurements. LE = leading edge. Chapter 5: Integrated Multi-Location Ice Protection System 75 measured values. The differences between 𝑘meas and 𝑘sim could be attributed to the effect of the LIT coating, experimental error, and/or profile variation. A large 𝑘 value indicated a high rate of ice accretion and was proportional to LWC, airspeed and inversely proportional to temperature, in accordance with ice accretion simulations. This is visualized by the gradient shading in Table 5.2 where the condition with the greatest rate of accretion (40 m/s, -20°C and 0.5 g/m3) is shaded darkest. The 𝑘meas value exhibited a strong correlation with respect to accretion thickness and was used as an indicator for icing rate to compare with the microwave measurements. 5.4.1.3 Fitting Frequency Response A As observed from Figure 5.6(c), the presence of ice resulted in a large change in |∆𝐹𝑅| which increased until it reached to a saturated value beyond which additional ice accretion had a negligible effect on |∆𝐹𝑅|. The extracted |∆𝐹𝑅| had a characteristic response that could be modelled using a one-phase exponential association model, |∆𝐹𝑅| = 𝛼𝑥 (1 − exp (−𝑡 𝜏𝑥)) (5.7) where 𝛼 is the saturated frequency response for a sensor loaded with material of thickness > 𝛿𝑚𝑎𝑥 and 𝜏 is a time-constant indicating the rate of saturation. Curve fitting was applied to the |∆𝐹R| data for the condition outlined in Table 5.1 and the fit parameters 𝛼 and 𝜏 for each test condition in Table 5.3 were obtained. The fitting showed an R-squared value greater than 0.96 for all conditions, signifying a robust correlation. 5.4.2 Microwave SRR Icing Rate Measurement The experimented airspeed and LWC conditions were classified according to the air-temperature, along with the recorded ice thickness, where the absolute resonant frequency shift |𝛥𝐹R| over a 560-second accretion period was extracted for different types of in-flight icing as shown in Figure 5.7 (a, d, g, h), for glaze, Figure 5.7(b, e, i) for mixed, and Figure 5.7(c, f, j) for rime. Figure 5.7 (a), (b), and (c) show the ice thickness versus time, presenting the rate of ice accretion and the effective thickness of ice accumulated on the sensor for different air temperatures, LWCs and airspeed conditions. Consistent with the FENSAP Ice simulations, the results indicated an inverse relationship between temperature and accretion thickness. The -5°C conditions exhibited the least ice thickness, followed by the -10°C mixed icing condition. The -20°C rime icing condition showed the highest ice thickness and the fastest rate of accretion. The effect of varying the LWC between 0.3, 0.5 and 0.8 g/m3 Chapter 5: Integrated Multi-Location Ice Protection System 76 was noticeable, with larger LWC values leading to a substantial increase in ice accretion thickness and a faster icing rate. Additionally, raising the airspeed from 20 m/s to 40 m/s accelerated the accretion rate, as evidenced by the observed increases in ice thickness over the same time period. These results were aligned with the expected accretion outcomes for similar test conditions [144] and could be used to calibrate the frequency response for the prediction of ice thickness using the microwave SRR ice sensor. Figure 5.7(d) exhibits the microwave frequency response, |∆𝐹R|, for the wetter -5°C glaze icing condition with a distinct saw-tooth pattern. This is likely due to the presence of liquid water near the sensor. Conversely, the -10°C mixed (Figure 5.7(e)) and -20°C rime (Figure 5.7(f)) icing conditions showed more stable |𝛥𝐹R| frequency responses, typical of time-based ice accretions with minimal or no liquid water flow near the sensor. In the -5°C glaze icing condition, the incoming supercooled liquid water droplets (𝜀𝑤𝑎𝑡𝑒𝑟 = 80) didn't immediately freeze upon impact, leading to a sharp rise in |𝛥𝐹R|. This liquid water eventually froze along the accreted surface or flowed along the chord of the LIT-coated airfoil before accreting as ice (𝜀𝑖𝑐𝑒 = 3.15) downstream. This process resulted in a decrease in |𝛥𝐹𝑅1| of the leading-edge sensor. As seen in Figure 5.7(d), this saw-tooth phenomenon was repeatedly witnessed for all testing conditions at -5°C and water droplet flow was observed in all the recorded videos from the experiments (Supplementary Video 1). In addition to the |𝛥𝐹R1| response of SRR tag 1, the response of SRR tag 2, |𝛥𝐹R2| was also extracted for the -5°C condition, indicating the ice accretion further along the chord of the airfoil in heated Zone 2. As observed by the T2 response in Figure 5.7(d), the Zone 2 SRR sensor experienced ice accretion in all glaze conditions, with the higher airspeed test condition (40 m/s, 0.5 g/m3) showing the highest increase in |𝛥𝐹R2| and displaying the characteristic saw-tooth response as a result of liquid water droplets flowing across the sensor. The observed runback phenomenon was attributed to the hydrophobic LIT coating, and this led to relatively similar maximum ice accretion thicknesses across various LWC and airspeed cases, as illustrated in Figure 5.7(a). These results demonstrate the multi-zone microwave sensor's capability to precisely detect runback icing and continuously monitor the effectiveness of de-icing coatings in real-time. The sensor’s ability to identify both liquid water and ice also enables the monitoring of anti-icing and active de-icing procedures, facilitating the optimization of ice protection operations. Considering the microwave responses in Figure 5.7 (d), (e) and (f) a clear trend in |𝛥𝐹R| was observed for conditions with different LWC, airspeed and air temperature. The frequency response |𝛥𝐹R1| of SRR tag 1, located on the leading edge of the test article, was fitted with the exponential association model described in Equation 5.7 and the extracted constants 𝛼 and 𝜏 for each test condition are presented Chapter 5: Integrated Multi-Location Ice Protection System 77 in Table 5.3. The fitted curves are included in Figure 5.7(e) and (f) for the -10°C and -20°C conditions, which validated the effectiveness of the chosen model. The fitting parameter 𝛼 represents a steady state |𝛥𝐹R1| that would be expected for a fully loaded (𝛿𝑖𝑐𝑒 = 𝛿𝑚𝑎𝑥 ) microwave sensor response. This parameter provides insights into the ice accretion's permittivity, which can be utilized to determine the accretion's density using Equation 5.6. From Table 5.3, 𝛼 is proportional to density (𝛼 ∝ 𝜌𝑖𝑐𝑒) as expected from simulation, with the low-density opaque, white rime accretions (Figure 5.7(i)) at -20°C displaying the lowest 𝛼 values and the clear glaze accretions (Figure 5.7(g), (h)) at -5°C displaying the highest 𝛼 values. Consistent with the density calculations derived from Equation 5.3, and as shown in Figure 5.7(b), an increase in airspeed led to a higher density of ice accretion. This was reflected by an increase in the parameter 𝛼. Furthermore, for the accurate extraction of 𝛼, the accretion thickness must be at least greater than 6 mm, the 𝛿𝑚𝑎𝑥 determined from microwave sensor simulation in Figure 4(c). The ice accretion tests were confined to a maximum (a) (b) (c) (d) (e) (f) Figure 5.7. Measured ice thickness (mm) plotted as a function of time for 560 seconds of accretion at (a) -5°C glaze icing conditions, (b) -10°C mixed icing conditions, and (c) -20°C rime icing conditions. Extracted resonant frequency shift |ΔF| for tests conducted at (d) -5°C glaze icing conditions, (e) -10°C mixed icing conditions, and (f) -20°C rime icing conditions. Pictures of ice accretion at (g), (h) -5°C glaze icing conditions, (i) -10°C mixed icing conditions, and (j) -20°C rime icing conditions. (g) (h) (i) (j) -5°C, 40 m/s, 0.5 g/m3 -5°C, 40 m/s, 0.5 g/m3 -10°C, 40 m/s, 0.5 g/m3 -20°C, 20 m/s, 0.5 g/m3 Clear Glaze Ice Cloudy Mixed Ice Opaque Rime Ice Chapter 5: Integrated Multi-Location Ice Protection System 78 duration of 600 seconds, leading to a maximum ice thickness < 6 mm. This limitation hindered precise density calculations for conditions where the ice thickness was below 𝛿𝑚𝑎𝑥. Only three tests recorded ice accretion thicknesses greater than 6 mm. Therefore, further testing, which includes measuring ice density over extended accretion periods to achieve increased ice thicknesses, would be necessary for the accurate characterization of the microwave sensor's capability to measure ice density. Table 5.2. Measured Linear Proportionality Constant kx Relating Maximum Ice Thickness with Accretion Time for Icing Rate Monitoring Liquid Water Content (g/m3) Airspeed (m/s): 20 40 LWC (g/m3): 0.3 0.5 0.8 0.5 Temperature (°C) -5 - 0.39 0.45 0.53 [0.40] [0.65] -10 0.22 0.42 0.60 0.87 [0.52] -20 0.38 0.52 0.95 1.12 [0.3585] [0.58] [0.79] [1.07] Note: kmeas , Measured vs [ksim], Simulated Chapter 5: Integrated Multi-Location Ice Protection System 79 For appropriate activation of ice protection systems, it is imperative to determine the severity of the icing environment. The rate of |𝛥𝐹R| saturation, 𝜏, was regarded as a key parameter due to its inverse proportional relationship with the rate of icing. The fastest rate of |𝛥𝐹𝑅| saturation (smallest 𝜏) indicated the greatest rate of ice accretion and is highlighted with the darkest shade in Table 5.3 where the gradient shading represents the severity of the accretion rate. Increasing the LWC at constant airspeed (20 m/s) resulted in a greater rate of ice accretion, and this was reflected in the |𝛥𝐹R1| microwave frequency response, with the 0.8 g/m3 cases displaying the smallest 𝜏 values (fastest saturation) and the 0.3 g/m3 cases presenting the largest 𝜏 values (slowest saturation). Increasing the airspeed from 20 m/s at 0.5 g/m3 to 40 m/s and 0.5 g/m3 resulted in a sharp decrease in 𝜏, indicating the fastest rate of saturation for the 40 m/s condition among conditions at a constant temperature. The inverse effect of air temperature on the rate of ice accretion was also apparent in the microwave sensor response. The -20°C condition exhibited the lowest τ value, indicating a faster rate of accretion under constant airspeed and LWC conditions. By comparing the shaded gradient from Table 5.2 (icing rate indicator, 𝑘𝑥), and Table 5.3 (rate of |𝛥𝐹𝑅| saturation, 𝜏), the capability of the microwave sensors to track the rate of ice accretion in time-based impact ice conditions is effectively demonstrated. Figure 5.8(a) presents 𝑘 versus 𝜏, shows a clear trend that is consistent across various icing types. This observed pattern can be accurately represented using a power law model: 𝜏 = 𝑎 𝑘𝑥𝑏 (5.8) where, 𝑎 = 58.19 (min/mm) and 𝑏 = −0.92. Inserting Equation 5.8 into Equation 5.4, 𝛿𝑖𝑐𝑒 = (𝜏𝑎)1𝑏⁄× 𝑡 (5.9) Table 5.3. Extracted constants τ and α from exponential association fitting of the |ΔFR| sensor response for each test condition Temperature (°C) Airspeed (m/s): 20 40 LWC (g/m3): 0.3 0.5 0.8 0.5 -5 τ - 125 117 106 α - 229 241 242 -10 τ 241 120 102 68 α 218 227 240 246 -20 τ 149 111 65 51 α 215 221 239 244 Chapter 5: Integrated Multi-Location Ice Protection System 80 enables the prediction of ice thickness (𝛿𝑖𝑐𝑒) by real-time monitoring of the changes in microwave frequency response from the deployed SRR sensor. The predicted ice thickness values calculated using Equation 5.9 are plotted against the measured ice thickness for 600 seconds of icing at each testing condition in Figure 5.8(b). The predicted ice thickness is generally accurate with a maximum difference of 0.6 mm observed between predicted and measured ice thickness. These findings confirm that microwave SRR sensors are remarkably sensitive for real-time applications, making them ideal for use as location-specific ice sensors. 5.4.2.1 Future Work The current system is limited by the integration methods used for the designed microwave sensor due to the 0.64 mm thick conformal plastic sensor substrate. This resulted in a span-wise discrepancy in icing and de-icing characteristics of the airfoil. As presented in [39], [65], microwave sensors are compatible with industrial etching and flexible electronic fabrication techniques used for the fabrication of electrothermal heaters and can be custom-built and installed for span-wise conformal de-icing characteristics. Future integration efforts can also consider methods presented in [103] for aluminum profile-integrated microwave sensors. Additionally, by changing the frequency of operation, the maximum measured ice thickness, sensor size and form factor of the SRRs can be modified to meet the specific needs of the discrete sensing locations, whether on larger aircraft or smaller drones. Figure 5.8. (a) The extracted rate of saturation of the microwave frequency response, 𝜏, plotted against the icing rate indicator, 𝑘𝑥, for all tested conditions outlined in Table 5.1 and fitted using a power law trend. (b) Measured ice thickness (values labeled) plotted alongside the predicted ice thickness using Equation 5.9, for the conditions outlined in Table 5.1. (a) (b) Chapter 5: Integrated Multi-Location Ice Protection System 81 5.5 Conclusion This work pioneers a multi-location implementation of microwave Split Ring Resonator (SRR) type sensors for in-flight ice accretion as a part of an integrated zone-based Ice Protection System (IPS). The work addresses the critical need for optimized ice protection systems with integrated ice sensing for accurate measurement of location-specific accretion in the context of modern urban air mobility flight, particularly for UAVs, eVTOL crafts, and drones. The sensor was successfully incorporated with an electrothermal de-icing system and a LIT coating, evidencing the conformal microwave SRR’s integrability with current industry-practiced de-icing technologies. The SRR sensor's successful wind tunnel testing and alignment with simulations validates its accuracy and readiness for practical implementation. This positions microwave SRRs as a promising solution for smart de-icing systems with decreased power consumption and expanded operational envelopes with particular implications for use in IPS for UAVs, eVTOL crafts, and other urban air vehicles operating in atmospheric icing conditions. Chapter 6: Conclusion 82 Chapter 6: Conclusion and Future Work In this chapter, the research findings and overall contributions of this thesis work are summarized. This is followed by a section describing possible the future work which must be performed to take this technology from prototype to commercial solution. 6.1 Conclusion This thesis successfully implements microwave resonant sensors for use as highly sensitive real-time ice and snow sensors in both planar and conformal applications for harsh, real-world environments. Through this work, microwave SRR technology is tested for the first time in simulated real-world snow accretion conditions and under ‘impact’ ice accretion conditions generated in two icing wind tunnel test campaigns. The detailed conclusion for each sub-objective outlined in Chapter 1 is summarized below. 1. The first sub-objective is achieved through the fabrication and testing of two resonant microwave sensors. A Split Ring Resonator (SRR) operating at 1.2 GHz is designed and optimized for ice sensing, testing the sensors sensitivity to ice with increasing epoxy thickness. A second resonant sensor is designed for robust, harsh environment operation with an epoxy-coated, modified thick-plate aluminum Complementary Split Ring Resonator (CSRR) sensor design operating at 1.18 GHz. The sensor is tested in ice and snow accretion conditions, successfully monitoring the real-time accretion process down to -20°C. This method is further enhanced with the design of a third shielded asymmetric stripline CSRR sensors for experimental investigation of time-domain reflectometry (TDR) sensing mechanisms for single frequency, wired large-area sensor networks for integrated implementation on locations such as airplane wings. The outcome of this work proves the ability of microwave SRR and CSRR sensors to sense both ice and snow, a dielectric mixture of ice, water and air, for application as non-contact sensing solution through protective coatings for use in harsh environments like roads and runways. 2. The second sub-objective is achieved through the design of a conformal dual-frequency SRR sensor operating at ~1.95 GHz and ~2.13 GHz for integration on a NACA 0018 test article. The sensor is tested at the National Research Council’s (NRC’s) Research Altitude Test Facility (RATFac) under ice-crystal icing at 19,000 ft, 100 m/s, +3°C for varying melt ratios of 11%, 13%, 16% and 25%. The sensor successfully differentiates various %melt conditions, accurately sensing the onset and increase in the mixed-phase accretions under aeronautical ‘impact’ icing. Additionally, the sensor successfully monitors active icing and de-icing tests. The outcome of this Chapter 6: Conclusion 83 work proves microwave SRR sensors can be conformally integrated on aeronautical and aviation structures for accurate sensing of ‘impact’ ice accretion in simulated real-world ice crystal icing conditions generated in an icing wind tunnel, successfully detecting the difference in %melt conditions as a function of the time-based accretion characteristics. This work sets the stage for integrated ice sensors for use in location-specific optimized ice protection solutions. 3. The third sub-objective is achieved through the design and fabrication of a NACA 0012 test article with a conformal zone-based electrothermal de-icing system integrated with a multi-frequency conformal microwave SRR sensor operating at 2-4 GHz for location-specific accretion monitoring. The sensor is tested at -5°C, -10°C and -20°C for the sensing of glaze, mixed and rime-type ‘impact’ ice accretions at the Anti-Icing Materials International Laboratory’s (AMIL) icing wind tunnel. The sensor is tested at speeds of 20 m/s and 40 m/s, corresponding to low-Reynold number icing conditions (Re~ 105 to 106) expected at unmanned aerial vehicle (UAV), drone, and electric vertical take-off and landing (eVTOL) craft flight. Tested at varying Liquid Water Conditions of 0.3 g/m3, 0.5 g/m3 and 0.8 g/m3, the sensor successfully determine the onset of icing while facilitating icing rate measurement through the real-time tracking of the resonant frequency response with a maximum sensitivity of 179 MHz/mm of ice. The outcome of this work represents a significant advancement in the field of microwave SRR ice sensing technology, bringing it one step closer to practical implementation as integrated location-specific ice sensors for ‘smart’ ice protection systems (IPSs). This thesis on microwave ice sensors for planar harsh environment roadway applications and conformally integrated ice protection in aviation applications has led to the publication of one magazine review article, one conference paper, and one journal articles with me as the leading author along with a conference paper as a co-author and an additional journal paper in the process of submission. • A. Shah, O. Niksan, M. C. Jain, K. Colegrave, M. Wagih and M. H. Zarifi, "Microwaves See Thin Ice: A Review of Ice and Snow Sensing Using Microwave Techniques," in IEEE Microwave Magazine, vol. 24, no. 10, pp. 24-39, Oct. 2023, doi: 10.1109/MMM.2023.3293617. • R. Kozak, M. C. Jain, J. McClelland, A. Shah and M. Zarifi, "Durable Ice Sensors utilizing Microwave SRRs Coated with Protective Epoxy for De-Icing Control," 2021 IEEE Sensors, Sydney, Australia, 2021, pp. 1-4, doi: 10.1109/SENSORS47087.2021.9639610. • A. Shah, O. Niksan, and M. H. Zarifi, "Planar Microwave Sensor for Localized Ice and Snow Sensing," SAE Technical Paper 2023-01-1432, 2023, https://doi.org/10.4271/2023-01-1432. • A. Shah et al., "Unlocking Conformal Microwave Split Ring Resonant Sensors for In-flight Ice Sensing," in IEEE Transactions on Aerospace and Electronic Systems, doi: 10.1109/TAES.2023.3328580. • A. Shah, K. Zarasvand, Z. Azimi, D. Harvey, G. Momen, K. Golovin and M. H. Zarifi, " Multi-Location Microwave SRR Ice Sensor for Integrated, Zone-Based ‘Smart’ De-icing: UAV Flight Conditions," in Nature Communications [In process of submission] Chapter 6: Conclusion 84 6.2 On-going and Future Work The main goal of this thesis was to implement microwave SRR sensors and test them in simulated real-world icing conditions for implementation as ice sensors for the real-time measurement of ice accretion. Although this goal was achieved as discussed in the concluding remarks for each outlined sub-objective, there still remains a great deal of work in the field of microwave SRR ice sensors for practical, commercial implementation of this technology. Some of the current, on-going work and possible future works are listed below. ⎯ The design and implementation of a wireless SRR Ice sensor node with integrated read-out circuitry as part of a wireless de-icing control system for wind turbine applications as outlined Figure 6.1. Wireless Microwave Ice Sensor Node for application as a real-time wind turbine ice monitoring solution Chapter 6: Conclusion 85 in Figure 6.1. ⎯ The design and implementation of waveguide ice sensors for measurement of icing on metallic surfaces where sensors cannot be installed as depicted in Figure 6.2. ⎯ Investigation into the TDR ice sensing methods for multi-location sensing using resonant microwave sensors introduced in Section 3.2.7 of this thesis. ⎯ Design of microwave SRR sensors for integrated electrothermal Ice Protection Systems that can be fabricated with the same manufacturing process as etched-foil Kapton Heaters for airfoil-span conformal icing and de-icing characteristics. ⎯ Testing of the microwave SRR sensor in a variety of icing conditions encountered in aviation, with consideration for air temperature, air speed, humidity, surface temperature, liquid water content (LWC), ice crystal content, etc. ⎯ Implementing machine learning and artificial intelligence models to determine the effect of each of the above-mentioned icing condition variables on the microwave SRR response for greater prediction accuracy of ice accretion characteristics. 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Thesis/Dissertation
eng
Electrical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Zarifi, Mohammad Hossein
Towards integrated microwave ice sensors for on-ground, in-flight and unmanned aerial vehicle (UAV) applications
Text