Theses (Mechanical Engineering)

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    An integrated fault-tolerant model predictive control framework for UAV systems
    (2024) Xu, Binyan; Shi, Yang; Suleman, Afzal
    The application of unmanned aerial vehicles (UAVs) has considerably expanded over the past few decades, driven by their flexibility, efficiency, cost-effectiveness, and distinct advantages in executing tasks within dangerous and inaccessible environments. As the demand for UAVs grows, so does the expectation for their autonomy and reliability. Therefore, there is a need to enhance the efficiency and safety of UAV control systems. This dissertation proposes the development of innovative control strategies applicable to both individual and multi-agent systems, aiming to effectively address control challenges in UAV applications, such as complex dynamics, inherent constraints, unexpected faults, and resource limitations. To achieve this objective, a unified framework to effectively integrate model predictive control (MPC) with fault-tolerant control (FTC) is proposed, with the primary focus on identifying and addressing theoretical and practical challenges associated with this integration. The dissertation starts by providing a comprehensive introduction and systematic literature review, highlighting unresolved issues and gaps in fault-tolerant model predictive control (FTMPC). Essential mathematical preliminaries, including models and necessary theorems, are also discussed. Next, a novel adaptive fault-tolerant MPC method for fault-tolerant tracking control of constrained nonlinear systems is presented. This design integrates an adaptive fault estimator into the Lyapunov-based MPC framework, thereby ensuring closed-loop control performance and system stability in the presence of actuator faults with reduced computational complexity. The FTMPC framework is further extended by applying it to the trajectory tracking control problem of UAVs with input constraints and actuator faults. To tackle the unique UAV control challenges, it presents the design and stability analysis of a dual-loop, dual-rate hierarchical UAV control system. By implementing MPC only to the outer-loop at a slower sampling rate, it significantly reduces the computational demands of solving the MPC problem while maintaining the rapid response capabilities of the inner loop. Furthermore, the dual-sampling-rate issue is rigorously evaluated in the closed-loop analysis using singular perturbation theory, providing important guidelines for selecting control parameters based on the sampling frequency. Furthermore, the fault-tolerant formation control problem of a multi-UAV system interconnected through a directed communication graph is investigated. With the developed adaptive distributed Lyapunov-based MPC method, the formation tracking control objective is achieved with partially known leader information and unexpected actuator faults. This design also significantly reduces communication and computational burdens by requiring only a single round of calculation and communication per control update. Finally, unknown communication faults between agents in a nonlinear multi-agent system are addressed, instead of only considering the actuator faults that only affect individual local agents. To this end, a novel adaptive distributed observer-based DMPC method is developed, enhancing the resilience of distributed formation tracking in the presence of communication faults. This strategy is able to simplify the complexity of local MPC design by decomposing the original formation tracking control problem into several fully localized tracking control problems.
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    Ambient Air Pressure Effects on Droplet Descent and Dry Surface Impact Dynamics
    (2024) Evans, Curtis; Oshkai, Peter
    Droplet impact and the affect of ambient pressure on the droplet impact dynamics are of interest in many applications. These include applications in the industrial, biomedical, environmental and academic fields. When a droplet descends, prior to impact, the ambient pressure, air density and drag forces affect how the droplet behaves in this multi-phase flow. It has been shown that, by reducing ambient pressure, droplet splash can be removed completely [1]. The aim of this study is to perform experiments to further the understanding of that multi-phase flow and gain clarity on how ambient pressure may influence the droplet shape and in-turn, potentially influence the onset of prompt splash upon droplet impact on a dry surface. Instead of reducing ambient pressure to remove splash, this study attempts to increase ambient pressure to induce splash and investigate and compare the droplet dynamics in the pressurized and non-pressurized scenarios using high speed imaging. Conventionally, studies have investigated the droplet upon impact, mostly ignoring the droplet’s descent dynamics prior to contacting the dry surface [1] [2] [3] [4]. This study focuses on those pre-impact droplet dynamics by investigating how droplet shapes differ between the pressurized and non-pressurized scenarios. By imaging the droplet descent in a pressurized chamber, a significant difference in the droplet aspect ratio (width-to-height) could be witnessed in the pressurized scenario, compared to the non-pressurized one. The average aspect ratio for droplets descending in an environment with 4atm pressure tended to be greater than 1, deforming droplets to an oblate or elliptical shape. Under standard 1atm pressure, however, droplets under the same velocity conditions tended towards an aspect ratio of 1 (or a spherical shape). Additionally, the amplitude of droplet aspect ratio changes (from oblate to prolate) was higher in the 4atm condition, compared to the 1atm one. As such, it was concluded that when ambient pressure is increased to the point where prompt splash is witnessed, droplet aspect ratio is affected. This may be something to consider when developing a full understanding of the dynamics that affect droplet splash on impact with a dry surface.
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    Model Parameter Identification for Feed Drive Dynamics using Kernel-Based Methods
    (2024) McPherson, J.D.; Ahmadi, Keivan
    This thesis presents an application of kernel-based methods for identification of the feed drive's dynamics model parameters and prediction of the disturbances affecting feed drives during operation from the cutting forces. To this purpose, the Partially Linear - Least Squares Support Vector Machine (PL-LSSVM) and Kernel Recursive Least Squares - Tracker (KRLS-T) algorithms were utilised for batch identification and online prediction of disturbances. Experimental case studies were performed with two ball screw feed drives under simulated and real cutting conditions to verify the identification and in-operation prediction of cutting forces.
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    Long-term Functionality of Vacuum Insulation Panels (VIP) Subjected to Simulated Random Vibrations
    (2024) Zhan, Peng; Valeo, Caterina; Mukhopadhyaya, Phalguni
    Due to globalization and post-pandemic developments, the shipping industry's search for energy-efficient containers in the cold chain sector has increased significantly. In this scenario, the vacuum insulation panel (VIP), a thermal superinsulation, shows significant potential to be used in the construction of highly energy-efficient shipping containers. However, a concern is a lack of information about the long-term performance of VIPs due to truck vibrations during transit. This paper investigates this gap by presenting results from a laboratory test program involving unaged and aged VIP specimens (300 mm x 300 mm x 25 mm) with two types of core materials, fumed silica and fiberglass. The VIP specimens were subjected to random vibrations, reflecting actual in-service vibrations of a truck. Vibration tests were conducted using a Long Stroke Vibration Exciter (ball bearing type) for the low-frequency range following the procedure outlined in the ASTM Standards D4169 and D4827. Aged specimens underwent ’heat’ (70 ±1°C and 5 ±3 % RH) aging in an oven and ‘heat & vapour’ (70 ±1°C and 95 ±3 % RH) aging in a specialized environment chamber for up to 120 days before going through random vibration tests. The change of thermal conductivity of each specimen was tracked during the random vibration test at regular intervals using a heat-flow-meter, following the procedure in the ASTM Standard C518. The results from the thermal conductivity test indicate that, in general, fumed silica VIPs have a lower degradation rate than fiberglass VIPs when subjected to simulated random vibration tests. The findings of this study will have meaningful implications for the use of VIPs in energy-efficient refrigerated shipping containers. By identifying the impact of truck vibrations on the performance of VIPs, researchers and engineers can develop more effective methods to mitigate these effects and improve the overall integrity of the VIP-insulated highly energy-efficient refrigerated shipping containers. Overall, this study provides insights into challenges and opportunities associated with using VIPs in the cold chain industry and opens new avenues for further research and development in this field.
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    Impact of Mooring Systems on Wave Energy Converter Control and Useful Power Capture
    (2024) Funk, Spencer; Buckham, Bradley Jason
    With the effects of climate change becoming more extreme each year there is a dire need to reduce carbon emissions – especially in the energy sector. An energy source currently not in use by any sector is the energy stored in ocean waves. Wave energy can be captured by wave energy converters (WECs) which transform said energy into electricity using a power take-off device. However, these devices are currently too expensive to build and operate for the power they produce. The common approach to designing a WEC is to optimize its design with modelling and testing and then deploy it in the ocean with a mooring system. Notably, the mooring system is typically neglected at the modelling and testing stages. But the significance of the mooring dynamics to the system response is not fully understood. This work demonstrates that neglecting the mooring system can lead to up to a 50% power loss by modelling the power production of a controlled self-reacting point absorber (SRPA) with and without knowledge of the mooring system. To address these outstanding questions, four mooring designs were characterized using a unique approach and incorporated into a mechanical circuit model of the SRPA. The characterization approach was used to fit high-fidelity mooring simulation data to linear transfer function models with a high degree of accuracy. Such models significantly reduce simulation time of the SRPA by reducing the mooring dynamics to just the force at its connection to the SRPA. This new model was then used to determine impacts on dynamics and power production. The circuit model was also used to demonstrate the effect of the mooring on three control types. The effects amounted to up to a 40% reduction of the control variable, suggesting that current controllers may be overdesigned and more expensive as a result. Finally, the annual energy production of three control types were compared by modelling and simulating a moored SRPA in a realistic sea. The simulation results indicated that the performance gains previously seen for a new control type are not eroded by the mooring system or by the realistic sea, with the new control type resulting in four times more annual energy production than the others.
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    Mitigating variability of energy demand and supply in highly decarbonized energy systems
    (2024) Knittel, Tamara; Rowe, Andrew; Wild, Peter
    Transitioning towards sustainable energy systems requires elimination of greenhouse gas emissions associated with end-use energy demands and electricity generation. Electrification of end-use demands that traditionally use fossil fuels and expanding variable renewable energy generation are considered key strategies in achieving emission reduction targets. This dissertation investigates end-use electrification and renewable supply impacts on future electricity system infrastructure. Three studies analyze demand- and supply-side transition options for the province of British Columbia, Canada. The first study investigates building heat electrification impacts on capacity and flexibility requirements of the electricity grid in British Columbia. Energy demands are projected for 2050 considering variations in building stock evolution, building code implementation strategies, and building envelope efficiency improvements. Varying shares of heat pump penetration rates can be applied using hourly temperature-dependent COP profiles. This study examines the shape and magnitude of peak electricity demands and ramping requirements for electrified heating by computing regional end-use energy demand profiles for space and water heat in British Columbia’s residential and commercial building sectors. Results show that with an emphasis on building stock improvements, 90% heat pump penetration leads to an increase in electrical energy of 6% despite an annual population growth rate of 1.1%. The impact of high heat pump penetration rates on capacity requirements is significantly larger, with an increase of 37% in peak electricity demand for a 90% heat pump penetration rate. However, results of this study show that building energy codes and retrofit rates contribute relatively little to achieving net-zero emissions in the building sector. The second study provides a more comprehensive analysis of end-use electrification by combining the electrification of building heat with electrification of space cooling and road transportation to examine changes in capacity and flexibility requirements for British Columbia’s electricity grid. Two high-resolution demand simulation models are introduced that project electricity load curves for electrified end-uses in the building and road transportation sectors. Electric vehicle charging and space heating control are introduced as demand-side management strategies to examine the effect of load reduction and load shifting on capacity and flexibility requirements. In this work, building demands are modelled in hourly resolution and transportation demands are modelled in 15-minute time-steps. The shape and magnitude of peak electricity demands, range of electric loads, and ramping requirements are examined for a simultaneous electrification of 10 end-uses in varying temporal resolution. Results show that capacity and flexibility requirements increase by up to 93% and 320%, respectively, where future ramping requirements are largely driven by electrification of road transportation. Utilizing electric vehicle charging and space heating control reduces the increase in capacity and flexibility requirements by 19% and 238%, respectively, while shifting the timing of the peak event to early morning hours. Temporal resolution of demand models is an important determinant of flexibility requirements, leading to an increase of 520% when changing from an hourly to a 15-minute resolution. The third study assesses the duration and magnitude of periods with excess energy generation and energy deficiency in British Columbia’s energy system by 2050 where building heating, cooling, and road transportation is electrified using a portfolio of renewable energy sources. Future net load which is defined as the difference between electricity demand and variable renewable energy generation is determined to investigate dispatchable capacity requirements. The study examines net load for installed wind and solar capacities up to 50 GW for three wind penetrations. Three water supply scenarios are tested to identify changes in net load due to drought conditions and increased precipitation impacting hydroelectric power generation, an important consideration in hydro-dominant electricity systems. A one-year production cost model is used to quantify surplus energy and energy deficiency for a range of variable renewable supply scenarios. Results show that peak net load in 2050 will exceed present-day peak electricity demand in British Columbia, when building and road transportation end-uses are electrified, thereby necessitating built-out of variable renewable energy generation capacity. Individual hours of energy deficiency can be avoided with demand-side management or import of electricity from neighbouring systems. For an installed capacity of 30 GW, energy storage with a duration of 5 hours would enable system operators to manage most deficiency periods for all supply scenarios. A combination of large-scale built-out of variable renewable energy generation and short-duration energy storage can increase operational flexibility to meet growing electricity demand in 2050 after end-use electrification of building heating, cooling, and road transportation.
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    In-vitro In-silico Modeling of Glioblastoma Tumor Growth and Invasion
    (2024) Amerehbozchalouee, Meitham; Akbari, Mohsen; Nadler, Ben
    In this thesis, we investigate various aspects of tumor progression through formation, growth, and invasion, by a multidisciplinary approach involving mathematical modeling and experimental validation. We begin this study by modeling the transient formation of tumors by a system of reaction-diffusion partial differential equations (PDEs) that considers adhesion forces, cell proliferation, and pressure-induced growth. The process of tumor formation includes a preliminary contraction phase where adhesion forces densify cell aggregation. This phase proceeds until the cell concentration reaches a threshold, the so-called “relaxed concentration” at equilibrium. Afterwards, cell proliferation raises concentration and produces pressure which breaks the equilibrium. Providing analytical and numerical solutions, the model’s reliability is confirmed through experiments with tumor-cultured human glioblastoma (hGB) cancer cell lines. We expand the model to analyze the instability of radially symmetric growth in response to asymmetric perturbations. By improving the model to incorporate additional variables such as nutrient concentration, consumption rates, and surface tension, we focus on the asymmetric modes of growth, which grow in time and change the spherical configuration of the tumor. We show that a high nutrient source concentration allows for a large tumor size, which increases the number of unstable excited asymmetric modes. However, high rates of nutrient consumption and surface tension can lead to a smaller size of the tumor and a smaller number of growing asymmetric modes. This analysis, indicating the natural instability of the spherical configuration of tumor was confirmed by a comparison between the shapes of in-vitro hGB tumors and the configuration of the first few asymmetric modes predicted by the model. To further understand the effect of tumor microenvironment (TME) on tumor configuration, we study biomechanical stimulus-induced remodeling of tumors in response to gradients of external biochemical stimuli, considering the tumor as an evolving material. We develop an evolution law for the remodeling-associated deformation which correlates the remodeling to a characteristic tensor of external biochemical stimuli. The asymmetric remodeling and the induced mechanical stresses are analyzed for different types of biochemical distributions. Using a tumor-on-a-chip platform, the degree of remodeling is estimated for the ellipsoidal tumors over time. Additionally, we explore invasion as one of the key hallmarks of tumors by introducing a continuum model that integrates various factors to predict a distinctive shell-type invasion pattern in which cells at the outer layer of the tumor collectively move away from the core and form a shell-type shape. We adopt a non-convex free energy that allows for phase separation to model the motion of the invasive shell. To develop a more realistic model, we extend our mathematical framework to include heterogeneities within a tumor as they play a crucial role in cancer diagnosis, treatment, and prognosis. We present a hybrid discrete-continuum (HDC) model incorporating experimental measurements and in-vitro tumor-on-a-chip platforms to study tumor growth, invasion, and their dependency on matrix stiffness. The model integrates the continuum field of variables with a discrete approach and incorporates the random walk method for individual cell migration. Moreover, we study the influence of matrix stiffness on tumor growth and invasion using a PEGDA-printed tumor-on-a-chip platform. The presented framework is capable of distinguishing the growth and invasion of non-resistant versus chemo-resistant tumors, as well as the inhibitory effect of a chemotherapeutic drug. We also show that U251 non-resistant tumors grow faster compared to the temozolomide (TMZ)-resistant tumors, whereas the TMZ-resistant tumors have the longest invasion length. We utilize a stochastic approach that is consistent with observed biological behavior and provides a more realistic representation of the invasion process. This hybrid model, validated against an in-vitro co-culturing of non-resistant and TMZ-resistant hGB tumors with healthy neurons all embedded within a hydrogel matrix, shows promise in quantitative predictions on volumetric growth, invasion length, and invasion patterns of tumors. Our study concludes by highlighting the comprehensive understanding achieved through analytical modeling, experimental validation, and hybrid modeling techniques. The findings lay the groundwork for future investigations into therapeutic interventions, considering the intricate interplay between biological and mechanical factors in the TME.
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    A Laboratory Study on the Influence of Guided Drop Tower Carriage Mass and Kinematic Differences to Full-Surrogate Free Falls Toward Enhanced Helmet Certification Methods
    (2024) Brice, Aaron; Dennison, Christopher
    Falling from height presents a significant risk for military personnel due to the frequency at which they perform high exposure maneuvers, such as walking along unstable structures, repelling from buildings or aircrafts, and low altitude egressing. Traumatic brain injury (TBI) resulting from falls from height (FFH) account for approximately 20% of TBIs with a reported cause in the military, despite the presence of protective head gear. This is likely because current certification testing performed on military helmets emphasize protection against ballistic threats over blunt impacts, such as falls. Military personnel have identified the need for the next generation of helmets to provide better protection against blunt impacts. To develop such helmets, a method for helmet evaluation in scenarios that are representative of real-life falls must be established as the new standard for helmet impact testing. Guided vertical drop towers are a test device commonly used to evaluate the impact attenuating properties of protective headgear in headfirst falls during certification testing. These devices provide a simple, low cost, repeatable means for conducting certification tests over using full-body surrogates to replicate a person experiencing a headfirst fall. However, there are some limitations to the guided drop tower that may limit their ability to properly replicate a fall from height. The most notable limitations are that guided drop towers are constrained to only a single degree of freedom and the impact mass of a drop tower assembly typically only includes the mass of a human head and neck rather than the mass of a full-body. At present there is little work on how these limitations may yield a differing kinematic response between a guided drop tower and that of an actual fall. The objectives of this thesis was to determine if kinematic differences exist between a guided drop tower and a free-falling person, in unhelmeted and helmeted scenarios. The outcomes of this thesis will contribute toward the development of enhanced test standards that evaluate protective headgear in scenarios that are more representative of real-life falls. iii A custom guided drop tower equipped with a Hybrid III head/neck and adjustable weight drop carriage along with a full-body Hybrid III 50th percentile male surrogate, to represent a falling person, were subjected to two experimental series 1) unhelmeted impacts at four angles between 30° and 75° and four impact velocities between 1.50 m/s and 3.00 m/s and, 2) helmeted impacts at 30° and 75° with impact velocities of 3.00 m/s and 4.50m/s. Impacts in both series were conducted onto a rigid impact surface and kinematic measures of head center of gravity linear acceleration, angular acceleration, and angular velocity were measured. Results of the unhelmeted impact series identified that the drop tower can provide an acceptable approximation of the linear acceleration but not the angular velocity that is likely to be experienced by a person in a headfirst frontal impact. This is due to the angular velocity differing in either the magnitude of the peak angular velocity or direction and time instance of peak measures. Changes to the mass of the drop carriage, to be closer to that of a full dummy, did not bring angular velocity closer to that measured for the full dummy. The helmeted impact study identified that a drop tower is likely to yield an underestimate of peak kinematics in shallow angle impacts and an overestimate of peak kinematics in steep angle impacts. This suggests that the drop tower, in its current form, provides a varying estimate of the resultant peak kinematics in helmeted impacts which is dependent on impact angle. These differences in response are primarily attributable to variances in helmet liner engagement when comparing the drop tower and a person falling. The results of this research found that in their current form guided drop towers do not provide a true representation of the kinematic response that is likely to result in a headfirst fall, either unhelmeted or helmeted. Further the addition of mass to the drop carriage in either scenario did not alter the drop tower’s response to a point where it matched the measured response of the falling surrogate .These differences in kinematic responses between the drop tower and what is likely to be experienced by a falling person, specifically in the case of underestimated responses in shallow angle helmeted falls emphasizes the need to further develop testing methods to ensure that future helmets are evaluated in a way that effectively tests the helmet’s impact-attenuating abilities in an actual fall.
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    Flow Analysis of Non-Spherical Granular Materials in a Two-Dimensional Hopper
    (2024) Mortezapour, Abdolreza; Nadler, Ben
    Non-spherical granular materials have been of an interest for the various research communities and industries due to their widespread presence in natural and engineered systems. These materials, which include substances like soil, powders, dry sludges, and grains, exhibit complex behaviors influenced by factors such as grains interactions and boundary conditions. Under sufficient conditions, these materials can flow, ranking second only to water as the most handled materials in diverse industries. Therefore, understanding how these materials flow is important in different domains, from wastewater treatment and mining to food and pharmaceutical industries. Granular flow within hoppers, driven by gravity, provides cost-effective transportation and is widely used in material handling and storage systems. This research aims to investigate the behavior of non-spherical grains in flow within a hopper through implementing a Finite Element Analysis (FEA) suite and using a previously developed model for non-spherical granular flow. A simulation similar to an available experiment is conducted by implementing the developed model for both spherical and non-spherical grains. The results from the simulation consistently align with those of the experiment, demonstrating the validity and accuracy of the simulation. Moving forward, more complex conditions in a practical application are examined to showcase the capability of the model and the implementation approach. The simulation results reveal the effect of boundary conditions and model parameters on grains orientation and flow within the hopper. The main motivation behind this research lies in establishing a foundation for utilizing the capabilities of a FEA suite to facilitate further investigations spanning a broad range of geometries and conditions, addressing challenges in numerical modeling of complex non-spherical granular flows. The outcome of this research in successfully integrating the developed model into the suite and simulating granular flow in different conditions and geometries, can be employed for further studies with practical significance for industries dealing with granular materials. It lays the groundwork for implementing a versatile FEA suite to simulate complex behaviors of granular materials. This foundation is viable for further studies addressing potential issues related to grain flow in hoppers, aiming to optimize industrial processes and improve material handling and storage techniques.
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    Monitoring Earth Using the Software Defined Radio (SDR) Earth Imager
    (2024-02-23) Sharif, Radwan N. K.; Herring, Rodney A.
    The ionosphere, which is the highest region of Earth's atmosphere, contains waves created from both space and Earth disturbances. The ionosphere is considered the largest sensor on Earth and has been the subject of study since the 1920s, primarily through the use of ionosondes. A Software Defined Radio (SDR) Earth Imager has been devised to obtain information about these Earth disturbances. This research is divided into four stages: 1) engineering of the SDR Earth Atmosphere Imager, 2) imaging of waves that exist within the ionosphere, 3) determining the location of the earth disturbance that created the waves, and 4) measuring the power of the ionospheric waves. The Earth Imager device functions similarly to a camera by utilizing an antenna array to create images of the ionosphere and its waves. The radio wave, i.e., the carrier wave of the ionosphere information, is transmitted up through the atmosphere at a near-vertical incidence from the Earth's surface. It reflects off the ionosphere back down to the Earth's surface, where it is detected by an antenna array to produce a phase image of the ionosphere. The proof of concept of the SDR Earth Imager occurred at the University of Victoria, Victoria, BC, Canada, and was initially constructed at the Dominion Radio Astrophysical Observatory (DRAO), Penticton, BC, Canada. From the DRAO data analysis, two types of waves were found: one with a constant frequency, possibly originating from power losses in transmission lines, and another with a single sharp spike, potentially caused by earthquakes or lightning. Further experiments at the University of New Mexico, utilizing Long Wavelength Array (LWA-1 and LWA-SV) antennae arrays, served as a high-resolution radio wave camera. The datasets from the LWA-1 and LWA-SV sites provided results showing the wavevector directions of one set of ionospheric waves, i.e., the strongest sets of waves, which have a spatial frequency of 0.06 cycle/m. The wavevectors were used to identify the location of the generation of the ionospheric waves and, thus, the likely source of the disturbance. This Ph.D. research thesis shows a correlation between the waves in the ionization layer and Earth's disturbing events, including man-made disturbances such as the electromagnetic radiation emitted by power lines and electrical grids, which generate waves within the ionosphere. Further, this research illustrated how the phase image, not the amplitude image, determined from Fourier analysis, is critical to characterizing these waves. The phase image enables the characterization of these waves by providing information about their phase shifts, frequencies, and wave vectors. This research demonstrates a clear relationship between waves within the ionosphere and disturbing events occurring on Earth. One significant finding of this dissertation is the deduction that all power generated and consumed by humans is not completely dissipated but rather transformed and captured by the Earth's ionosphere. This fact may assist climate modelers in gaining a better understanding of climate change.
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    Data-driven Surrogate Models for Wind Turbine Design and Maintenance Applications
    (2024-01-29) Haghi, Rad; Crawford, Curran
    There is a gap between the current contribution of wind energy to the global electricity generation mix and its potential capacity. This discrepancy underscores the necessity for addressing social, economic, and technical hurdles that are impeding the broader integration and acceptance of wind energy. The research focuses on tackling the modelling challenges in wind energy by employing Surrogate Model (SM) techniques, combining probabilistic methods, machine learning, and simulation technologies. This dissertation aims to develop SMs capable of mapping wind time series to the power output as well as extreme and fatigue loads on wind turbines. In this dissertation, I try to answer a number of crucial questions: determining the most effective type of SM for this mapping, identifying the optimal sampling method for building these SMs, extending the applicability of the developed SMs with minimal effort, and leveraging publicly available simulation tools and wind turbine models for turbine health assessment. These objectives are essential for improving wind turbine design, operation, and maintenance, enhancing their efficiency and reliability. Throughout the dissertation, there is an effort to bridge the gap between theoretical research and practical application. The surrogate models developed are presented as a contribution to the integration of theoretical concepts with practical applications in the field of wind turbine design and maintenance. Central to this research is the development of SMs for effectively mapping wind time series to the extreme and fatigue loads experienced by wind turbines. The aim is to find the optimal SM type that balances accuracy with computational feasibility. As the wind turbine faces diverse conditions, I propose adaptable methodologies to optimize the SM performance across various settings. Additionally, I investigate the potential of combining publicly available wind turbine models with probabilistic data-driven models to assess turbine health. First, a non-intrusive Polynomial Chaos Expansion (PCE) is constructed based on the outputs from the NREL 5MW Blade Element Momentum (BEM) model, demonstrating the convergence of sectional statistics in the results. Subsequently, I utilize the SM to estimate thrust and torque on the rotor and perform a sensitivity analysis of the extreme loads to the number of Monte Carlo Simulations (MCS) in the SM. Transitioning from the PCE realm, I adopt a sequential Machine Learning (ML) method to map wind time series to the Damage Equivalent Load (DEL) of wind turbine loads. I demonstrate that the developed SM, based on a Temporal Convolutional Network (TCN)-Fully Connected Neural Network (FCNN) architecture, is capable of capturing the wind turbine structural dynamics. It demonstrates adaptability in digesting the upstream wakes and accurately estimating the DEL utilizing Transfer Learning (TL). Moving beyond purely synthetic data, I propose the development of a probabilistic data-driven model, integrating limited wind turbine measurements with synthetic data for wind turbine health assessment purposes. I illustrate that an Approximate Gaussian Process Regression (AGPR) trained on a year’s worth of Supervisory Control and Data Acquisition (SCADA) data, combined with simulation outputs from a publicly available wind turbine model, emerges as a promising probabilistic tool for wind turbine health assessment.
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    Effect of Au Nanoparticles on Mitigating Negative Effect of Humidity on ZnO-Based Gas Sensors
    (2024-01-22) Alaghmandfard, Amirhossein; Hoorfar, Mina
    This thesis presents ZnO-based gas sensors for the detection of analytes, using Au nanoparticles to reduce the destructive effects of humidity on gas detection. The ZnO nanostructures are fabricated using the thermal decomposition method for different lengths of time and at varying temperatures. These structures are characterized by the X-ray diffraction technique, revealing the wurtzite hexagonal close-packed ZnO structures. In addition, scanning electron microscopy is employed to characterize the morphology of the synthesized ZnO structures. The results show that the length of ZnO nanostructures increases by raising the calcination temperature for 12 hours. The changes in the electrical current of the sensor are studied to determine the presence of target gases at various concentrations. The results show that the ZnO nanostructures prepared at 380 oC revealed the best response toward different humidity levels due to a higher number of oxygen vacancies, which are perfect sites to react with the target gas molecules. After selecting the best ZnO-based sensor, Au nanoparticles are sputtered onto the ZnO nanostructures with different thicknesses. Based on the results, the 0.1-nm-thick Au layer creates the best sensors to reduce the effect of humidity while demonstrating a constant response toward the target gas at different humidity levels. The sensor also shows good sensitivity and selectivity toward the triethylamine gas target with a response of 17.57, which is 62.75, 60.59, 4.81, 8.29, 4.30, 42.85, 70.28, and 292.83 times higher than the response toward Acetone, Methanol, Diethyleneamine, Benzene, Toluene, Ethanol, 1-propanol, and H2, respectively. This sensor revealed fast response and recovery times of 9.8 s and 4.4 s, respectively and promising stability over 24 days.
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    Enabling DC Field-Directed Chaining of Nanowires for Microelectronic Applications
    (2024-01-03) Arafat, Md Yeashir; Bhiladvala, Rustom
    Solar cells, light-emitting diodes, small-scale sensors, and large-area displays are examples of devices that benefit from the use of transparent conductive electrodes (TCEs). Indium tin oxide (ITO) is the most widely used transparent electrode material, exhibiting both high transparency and conductivity. However, the low concentration of indium in its ores makes it an expensive material to process. Indium price fluctuations lead to unsteadiness in manufacturing costs. Moreover, the fragile nature of ITO limits its usefulness in the fabrication of flexible electrodes. To address these issues, transparent conductive oxides and polymers, carbon nanotubes, graphene, and metal nanowires are being explored as potential candidates to replace ITO as the primary transparent conductor. Nanowire (NW) networks offer several advantages over ITO in terms of low cost, ease of fabrication, and flexibility. Large area coverage with ordered NW chains is challenging as it is difficult to control an electric field and its gradient in large electrode gaps. Electric field-directed chaining in a nanowire (NW) suspension was previously demonstrated as a simple and cost-effective process for large area coverage, with high conductivity and transparency. However, generating an effective dielectrophoretic (DEP) force for the desired NW assembly requires a high frequency to overcome the charge screening effect due to the polarity of water or alcohol, commonly used as suspension media. This requirement is a major limitation. High frequency can also generate harmful electromagnetic radiation as well as power loss in wiring. Moreover, the magnitude of the electric field and DEP force decreases sharply in the region away from the electrodes. Therefore, more NWs are bunched in the vicinity of electrodes, while at distant locations NWs are observed to form curls and branches, producing poorly aligned chains. Here we present the use of squalane (C30H62), a non-polar, non-toxic, unreactive, viscous organic liquid, for the suspension of NWs in an electric field-directed assembly. Our theoretical analysis suggested that squalane could reduce voltage drop at the electrode, enabling adequate DEP force for chaining. Moreover, this could be done at a lower frequency because of the low electrical conductivity and dielectric constant of squalane. Additionally, we may expect that the high viscosity of squalane will suppress the electroosmotic flow of the medium and Brownian motion of NWs, thereby facilitating the chaining process. Experiments have been performed with both polar and non-polar suspension media to observe their effectiveness in DEP-assisted NW chaining. Our experiments confirmed that squalane does generate NW chains at low-frequency AC (and down to DC) fields, whereas conventional polar suspension media require substantially higher frequency. Finally, a magneto-electro-kinetic model has been developed to explore how combining an external magnetic field with the electric field may enable better control of the NW alignment far from the electrodes.
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    Exosome Isolation: A Microfluidic TiO2-Based Approach with Liposome Modeling
    (2023-12-20) Motamedi, Seyedeh Zahra; Hoorfar, Mina
    Exosome isolation is the first challenge for any exosome research, often limited by extended processing times, high costs, and potential impurities. In exosome isolations, preserving particle integrity, recovery, and size distribution is paramount for clinical applications. This study aims to overcome the limitations of conventional techniques by taking advantage of the specific affinity between titanium dioxide (TiO_2) particles and exosomal lipid bilayers. To emulate exosomes, liposomes with a size of 100 nm, composed of a mixture of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and cholesterol, were employed as exosome surrogates. These synthetic lipid vesicles closely replicate exosome attributes, rendering them suitable models for studying isolation methodologies due to their analogous size, density, and phospholipid bilayer composition. Using liposomes, which are more available and easier to work with, enables to explore the potential impact of our isolation method on exosome characteristics, offering insights into the adaptability of the developed approach for medical applications. We utilized TiO_2-based isolation for the attainment of efficient mixing and effective incubation with target particles to optimize their interaction. In assessing this methodology, we embraced two approaches: a conventional manual method and a microfluidic technique. We studied the effect of the incubation time and the amount of TiO_2 particles and the design and flowrate for the bulk and microfluidic approach respectively. A comprehensive evaluation incorporating dynamic light scattering (DLS) and zeta Potential Measurements, in conjunction with Fluorescence and Brightfield Imaging techniques, was conducted to carefully develop and evaluate the microfluidic TiO_2-based exosome isolation methodology using a liposome modeling. The analysis encompassed their effectiveness, recovery rates, and post-processed vesicle size distribution, affirming the method's reliability achieving a capturing efficiency of 94.49% and a recovery rate of 84.53%.
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    Optimizing Thermal Performance of Building Envelopes by Mitigation of Thermal Bridging – Experimental and Numerical Investigation
    (2023-12-13) Alhawari, Abdalhadi; Mukhopadhyaya, Phalguni
    Due to regulatory requirements and a growing environmental consciousness, improving building energy performance is crucial in today's construction industry. Thermal bridge, which compromises buildings’ energy efficiency, durability, and indoor air quality is a pressing concern for building design and performance. This research aims to explore and address the phenomenon of thermal bridges in building envelope construction by offering valuable insights and innovative solutions. Two analysis methodologies have been incorporated in this research. The first method is a finite element simulation tool (HEAT3), which was used to predict the efficacy of the proposed ideas. The second method is a laboratory investigation that was performed using the hot box apparatus. A crucial aspect of this research initiative involved designing, constructing, and calibrating a unique hot box apparatus. This apparatus was constructed using vacuum insulation panels (VIPs) as core materials for its envelope. The choice of materials and construction details ensured exceptional temperature stability with minimal fluctuations within the chambers, a crucial factor for the performance of the hot box apparatus. Owning such a test facility provides a substantial advantage such as the ability to conduct multiple tests for each sample in significantly shorter timeframes, unlike commercial laboratories. Laboratory assessment is an important method to evaluate the real-world performance of building components. Besides numerical analysis, this dissertation stands as the first to experimentally assess the efficacy of an available thermal break product, which was highlighted in the literature as the most effective technique to mitigate the impacts of balcony thermal bridges. This dissertation also investigates two novel techniques aimed at reducing heat loss through balcony thermal bridges. Another key focus of this research was to investigate the impact of a generic aluminum cladding attachment system on the thermal performance of lightweight steel-framed wall systems. Overall, the outcomes of this research initiative demonstrate a high degree of consistency between results obtained through numerical simulations and experimental measurements. This work serves as a valuable resource for architects, engineers, and policymakers, facilitating the promotion of sustainable and energy-efficient building practices. It not only addresses critical issues related to thermal bridges but also proposes innovative solutions and provides a robust experimental platform to advance our understanding of building performance and energy efficiency.
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    Characterization of an Encapsulation Platform for pH-sensitive Delivery to the Colon
    (2023-10-24) Miller, Madison; Hoorfar, Mina
    Targeted delivery of bioactive molecules to specific locations within the GI tract allows for better orally delivered therapies, as the molecules will only be released upon reaching the desired absorption or delivery location. Targeted delivery aids in protecting the bioactivity of sensitive cargo as it traverses the GI tract, allows for smaller dosages to be administered and, in some cases, can reduce side effects. In this study, a microfluidic droplet generation platform is designed for production of pH-sensitive microcapsules for targeted delivery of bioactive molecules to the colon. Optical microscopy is used to compare the size distributions of microcapsules generated on-chip and those generated through a simple bulk double emulsion. Scanning electron microscopy is used to characterize the microcapsule morphology. To test the pH-sensitive nature of the microcapsules, they are loaded with dyed microparticles to mimic micron-sized bioactive cargo being interlocked in the polymeric capsule matrix. Their release in acidic and neutral solution is then analyzed, to simulate exposure to the stomach and colon. A preliminary study is then completed using E. coli DH5 alpha as the capsule payload. Results show that a maximum of 7.8 ± 2.0% of the encapsulated microparticles are released in acidic medium, while a maximum of 70.7 ± 3.7% are released in neutral solution after 6 hours of exposure, thus confirming the pH-sensitive characteristics of the microcapsules (based on results across 3 trials). Finally, capsules were loaded with E. coli and exposed to both neutral and acidic solution. After 6 hours, 0 viable CFU/ml were recorded, and in neutralsolution5.56x106 ±0.9x106,2.6x107 ±1.8x106 and2.2x108 ±2.4x107 CFU/ml were released across 3 trials, corresponding to a maximum viability of 1.37 ± 0.15%. These results were supported by a zeta potential study, which also showed targeted release in neutral solution, and minimal change in zeta potential for capsules in acidic solution. Although these results support the pH-sensitive properties of the microcapsules, they indicate incompatibility of the platform with live cargo. Future work for this study includes testing the capsules with other bioactive cargo, such as vitamins, minerals, and pharmaceuticals, and altering the capsule structure for better compatibility with live bacteria.
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    Model Predictive Control for Linear Systems with Random Packet Dropouts with Application to Quadrotors
    (2023-09-26) Song, Yue; Shi, Yang
    In recent years, quadrotors have garnered significant attention in both industry and academia due to their excellent maneuverability and hovering ability. This results from onboard sensors with high accuracy, remote controllers with high performance, and network communication among sensors, controllers, and plants with high efficiency. A quadrotor control system of this type can be regarded as a networked control system (NCS) enjoying remarkable scalability, resource efficiency, and ease of maintenance. However, new challenges in controller design arise from network-induced issues. Model predictive control (MPC), as an optimization-based control method, is able to provide not only the optimal control input for the current time instant but also the predicted state and input sequences, which provide a promising solution to handle network-induced issues. Moreover, the state and input constraints that exist in many applications can be effectively dealt with by MPC. These appealing features have motivated the development of many MPC schemes for quadrotors and NCSs. However, how to effectively solve network-related problems by MPC, and how different factors in MPC implementation affect the control performance are still open problems. We propose a robust output feedback MPC framework for constrained networked quadrotor control systems subject to packet dropouts and external disturbances. The packet dropouts randomly happen in both sensor-controller (S-C) and controller-actuator (C-A) channels. The proposed output feedback MPC scheme consists of a state observer that accommodates the random measurement loss and a state feedback MPC that stabilizes the perturbed system. The proposed observer enables the estimation error dynamics to be represented by a switched system. By developing a generalized robust positive invariant (GRPI) set under the switched system formulation, the estimation error can be confined to this invariant set, which serves as the explicit error bound of state estimation. Similarly, an extended robust positive invariant (ERPI) set is developed to describe all possible realizations of the deviation between the predicted and actual state. Then, the GRPI and ERPI sets are utilized to tighten the state and input constraints to alleviate the effects of random packet dropouts and disturbances. By imposing tightened constraints on the predicted states and inputs in the optimal control problem, the system can be stabilized by the proposed output feedback MPC scheme with guaranteed constraint satisfaction. Simulation results are provided to validate the effectiveness of the proposed method. Three MPC schemes are adopted and compared for quadrotor control, including conventional MPC, tube-based MPC, and Lyapunov MPC. Moreover, different factors that may affect the control performance are considered in a dual-loop control framework. Firstly, since the disturbances usually appear in practical implementations, the robustness of three MPC schemes against different levels of disturbances is evaluated and compared. Then, to simulate the real control processes and validate the effectiveness of three MPC frameworks, the control inputs generated by three controllers with different prediction models are applied to the same nonlinear quadrotor system. Moreover, since the sampling rates of inner and outer control loops in the dual-loop control framework are usually assumed to be the same, we explore how different dual-loop sampling ratios affect the control performance, which facilitates the controller design for quadrotors and provides a direction for theoretical studies. Finally, after concluding the obtained results, future study directions in quadrotor control are provided at the end of this thesis.
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    Human-Robot Skill Transfer via Dynamic Movement Primitives based on Reinforcement Learning
    (2023-08-30) Hong, Jayden; Najjaran, Homayoun
    Efficient trajectory adaptation is crucial for improving overall robot performance. The use of Reinforcement Learning (RL), despite its promise in robot motion planning, suffers from long training times and limited generalizability. Learning from Demonstrations (LfD) offers an alternative solution by transferring human-like skills to robots. However, human demonstrations may not align optimally with robot dynamics due to biomechanical differences. To address these challenges, this thesis proposes novel frameworks that combine RL, LfD, and the Dynamic Movement Primitives (DMP) framework. The DMP framework overcomes LfD limitations but requires parameter tuning of second-order dynamics. In this work, a systematic approach is introduced to extract dynamic features from human demonstrations, enabling automatic parameter tuning within the DMP framework. These extracted features facilitate skill transfer to RL agents, leading to more efficient trajectory exploration and significantly improved robot compliance. Additionally, the thesis presents a framework that integrates Implicit Behavior Cloning (IBC) with DMP to leverage RL training speed through human demonstrations. The framework demonstrates faster training, higher scores, and increased stability in both simulation and real robot experiments. Comparative studies highlight the advantages of the proposed method over conventional RL agents. The findings of this thesis hold significant implications for enhancing performance and adaptability of robots in practical applications. By incorporating human expertise from demonstrations to leverage conventional RL methods, this research offers novel approaches to improving efficiency and generalizability in robot motion planning.
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    Improved Remaining Useful Life Prediction Models For Condition-based Maintenance Planners: A Data-Driven Approach Using Transformer-Based Architectures
    (2023-06-19) Ogunfowora, Oluwaseyi; Najjaran, Homayoun
    Systems and machines undergo various failure modes that result in their health degradation, so maintenance actions are required to restore them back to a state where they can perform their expected functions. Machine health degradation is inevitable, and so is the maintenance cost associated with it. However, with proper maintenance plans, maintenance costs can be reduced, machine life can be extended, and ultimately we can ensure workplace safety. The field of prognostics is vital to systems health management and proper maintenance planning. A reliable estimation of the remaining useful life (RUL) of machines holds the potential for substantial cost savings. Data-driven methods for predictive maintenance have been recognized as one of the most promising maintenance strategies because of their high efficiency and low cost compared to other strategies. This work uses a sequential approach through experimentation to investigate the two main machine-learning-based methods for remaining useful life prediction, the similarity-based and direct-approximation methods. Drawing insights from existing works in the literature, the two stages of development of a similarity-based model (SBM) were optimized resulting in the development of improved similarity-based models using supervised and unsupervised machine learning methods for the health index construction. Ultimately, this work proposes a novel remaining useful life estimation model that leverages the concept of Large Language Models (LLMs) for more efficient time series data representation learning and prediction applied to the remaining useful life prediction use case. The experimental results indicate that the proposed Encoder-Transformer architecture outperforms the existing state-of-the-art models. Other highlights of this work include the bottom-up experimental approach taken to select the best methods and make improvements. The benefits of this approach can be seen from the improved remaining useful life prediction models developed in this work compared to their other counterparts in the literature and the insights this work provides. In this work, ten separate machine-learning models were developed, trained, and tuned for experimentation purposes. To summarize, three improved RUL prediction models: an Encoder-Transformer direct-approximation-based model, an Improved Unsupervised Learning-based Similarity-based model with Principal Component Analysis (PCA), and a Transformer-Assisted Similarity-based models were developed in this work. These models rank first, second, and fourth best amongst the twelve state-of-the-art models they were compared in the literature.
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    Remediation of Heavy Metals with Poplar Trees
    (2023-08-30) Talebzadeh, Mahta; Valeo, Caterina
    The increase in population and car fleets has led to a sharp rise in the generation of carwash wastewater (CWW). CWW contains heavy metals, detergents, oil and grease, and suspended solids, and is considered one of the most polluting industries in terms of water consumption and wastewater production. In various parts of the world CWW is not treated but left to drain and impair receiving waters. Numerous jurisdictions are examining how simple green infrastructure like low impact development (LID) technologies, such as rain gardens that treat polluted urban stormwater may be used to help treat the pollutant loads in CWW. Given that many of these green technologies use trees, this thesis examines how poplar trees are impacted by and remediate heavy metals that exist in CWW. The study was conducted at the University of Victoria, BC, Canada, and involved both laboratory work and field work conducted from 2021 to 2022. The research focused on tree health and heavy metal uptake and evaluated the performance of the designed treatment field in removing contaminants from CWW. The proposed methodology is grounded in a low-impact development (LID) approach tailored for wastewater treatment, particularly suited to remote, rural, and underserved areas, including developing nations like India, Malaysia, and other countries in the Middle East. The results demonstrate a decreasing trend in the concentration of zinc, cadmium, nickel, iron, copper, and lead from the point of application (point 1) at the field site to the effluent point (point 4), indicating good performance for removing these heavy metals. The removal rates for zinc, nickel, lead, iron, copper, and cadmium were 78.4%, 61.9%, 82.4%, 86.4%, 78.1%, and 98.95% respectively. The study assessed the uptake of heavy metals by poplar trees by analyzing the concentrations in tree leaves. The data showed variations in heavy metal concentrations between different trees and leaf positions, with some metals exhibiting higher concentrations in the bottom leaves and others showing higher concentrations in the top leaves. The concentrations of heavy metals in the leaves were also influenced by seasonal variations and leaf turnover. Overall, the research findings indicate the impact to poplar trees in systems that use poplar trees for treating carwash wastewater, highlighting the importance of considering seasonal variations and leaf turnover when studying heavy metal uptake in trees. These findings carry significance for enhancing wastewater treatment procedures and encouraging responsible approaches across a range of industrial and environmental contexts.