Theses (Mechanical Engineering)

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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.
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    Towards An Intelligent Data Acquisition System for UAV-based 3D Reconstruction
    (2023-08-29) Hatami Gazani, Sara; Najjaran, Homayoun
    Uncrewed Aerial Vehicles (UAVs) have become a pivotal platform for acquiring data in various applications, particularly for inspection, monitoring, and modeling purposes. However, the limited flight time and energy consumption of UAVs have necessitated the development of intelligent data acquisition systems for these platforms. In cases where there is no geometric proxy of the target and its location is unknown, it becomes crucial to establish a model that enables the detection of the target through visual data. Subsequently, the UAV incrementally acquires data as it navigates around the target, utilizing onboard sensors to complete its interpretation of the target or the scene. To accomplish this, the UAV adheres to a strategy known as view planning, which plays a critical role in three dimensional (3D) reconstruction of infrastructure using UAV-based imaging and significantly influences the quality of the reconstruction results. The selected views to be captured must essentially reveal the most unknown information about the target to ensure efficiency as well as utility. In this work, we propose three essential components of an intelligent data acquisition system: i) identification of the optimal views to prioritize, ii) multi-task sensor fusion for depth completion and object detection, and iii) reinforcement learning of appearance-based next-best-view (NBV) planning. The main focus of this study is to establish a relationship between the visual features observed in 2D images and the corresponding 3D model of the target, aiming to avoid the computational cost of handling 3D data.
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    Molecularly Imprinted Polymers (MIP) Combined with Raman Spectroscopy for Selective Detection of Δ⁹-tetrahydrocannabinol (THC)
    (2023-08-10) Yeganegi, Arian; Hoorfar, Mina
    This thesis presents the development of a proof-of-concept sensor for the sensitive and selective detection of Trans-Δ⁹-tetrahydrocannabinol (THC) using a molecularly imprinted polymer (MIP) synthesized with a THC template. The sensor combines MIP technology with Raman spectroscopy to achieve label-free monitoring of THC based on a single identifying Raman peak. The MIP sensor exhibits a prominent peak at 1614 cm-1 in the Raman spectrum, attributed to the THC target molecule, enabling the selective quantification of bound THC with a low detection limit of 250 ppm. Comparative studies with a non-imprinted polymer (NIP) control demonstrate higher sensitivity of the MIP to the THC target molecule (67% higher average intensity), confirming the presence of THC-specific recognition sites within the synthesized MIP material. Additionally, the selectivity of the MIP-based sensor is demonstrated by analyzing the Raman spectrum of MIP exposed to Cannabidiol (CBD), ethanol, and acetone.
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    Ocean surface drift modelling with uncertainty assessment using fuzzy number theory
    (2023-07-20) Blanken, Hauke; Valeo, Caterina
    Predicting the drift of objects floating at the ocean surface is a challenging task, with significant implications on marine emergency operations such as oil spill response and search and rescue efforts. Object drift is governed by a combination of currents, winds, and waves at the ocean surface, and depends strongly on object geometry. This introduces considerable uncertainty into drift prediction, as the model uncertainty from hydrodynamic models for these governing forces is aggregated and combined with systemic uncertainty about their respective influence on the drift of objects with various geometries. The first portion of this thesis examines the observed drift and dispersion of 206 GPS-tracked drifting buoys deployed in a fjord system on the west coast of Canada, which is subject to proposed expansion of shipping for oil and gas resource extraction. The observed mean drift patterns are found to be best explained by considering a combination of observed near-surface currents and winds, but the trajectories of individual buoys are characterized by significant dispersion around the mean. This dispersion results in buoys grounding against the shoreline on timescales of 12 15 hours, and corresponds with observed changes in the dispersion regime from near-field to far-field. Drift tracks exhibit fractal characteristics, and dispersion is found to be well modelled using fractional Brownian motion, rather than a traditional random- walk. Based on this, a statistical model for surface drift in the region is proposed, and shown to skillfully reproduce historical observations of sheening from an oil spill in the region. In the second portion of the thesis, uncertainty assessment in drift prediction us- ing fuzzy numbers is introduced and the relationship between forcing from currents, wind, and waves is examined in detail. Forcing data is taken from an observing platform in the northeast Pacific (Ocean Station Papa). Uncertainty in the forcing data is characterized from reported instrument uncertainty and spectral estimates of energy at unresolved time scales. This uncertainty is expressed and aggregated as fuzzy numbers, and propagated through seven day simulations of the trajectories of a set of surface drifting buoys with five different geometries using the transformation method. For generality, the effect of buoy geometry is accounted for deterministically, thereby avoiding traditionally used empirical coefficients. All possible linear combi- nations of forcing terms are explored, and the optimal combination of forcing terms for each drifter type is identified through the use of a novel skill metric. With the optimal forcing combination, the model is found to correctly identify the area where a buoy is expected to be found for the duration of the simulation. Comparison with the commonly used nine parameter leeway method shows similar performance, but without the need for object-specific empirical coefficients. Finally, the fuzzy-based method for propagating uncertainty in drift trajectory models is extended to spatiotemporally variable velocity fields. For this, a 4th-order Runge-Kutta solver for the integration of velocities, with inverse-distance weighting interpolation, is expanded to include possibility as an additional dimension. Perfor- mance of the method is demonstrated by tracking particles through one revolution of a monopole vortex, the strength of which varies sinusoidally with distance from the center. Both steady and unsteady maximum amplitudes are considered, and a steady uncertainty is prescribed as a fuzzy number. The model is shown to produce adequate results with reasonable computational effort, and the free parameters of the simulation are optimized through a sensitivity analysis.
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    Multi-Regional Simulation of Energy Demand for Road Transport: Electrification of Passenger, Light-, Medium-, and Heavy-duty Commercial Vehicles
    (2023-06-30) Lowry, Colton Amos; Wild, Peter Martin; Rowe, Andrew Michael
    Electrification is among the most effective strategies to decrease greenhouse gas emissions from road transportation. However, as more vehicles shift away from conventional drivetrains, demands on the electricity system must, necessarily, increase. In this study, a Road Transportation Energy Simulator (RTES) is developed that simulates hydrogen demand for Fuel Cell Electric Vehicles (FCEV) and electricity demand profiles for Plug-in Electric Vehicles (PEV). The RTES is open source, can simulate four scalable regions concurrently, and supports the entire road transport sector, including passenger/light-, medium-, and heavy-duty commercial vehicles. In the RTES, passenger vehicle demand profiles are generated with the Electric Vehicle Infrastructure Projection Lite Tool from National Renewable Energy Lab (NREL). Demand profiles for light, medium and heavy-duty commercial vehicles and all FCEVs are generated using the NREL Fleet DNA dataset along with the Heavy-Duty Electric Vehicle Depot Charging Tool, also developed by NREL. In British Columbia, 25 and 100 kW charging rates are sufficient for over 90% of medium- and heavy-duty vehicles, respectively. Electricity demand profiles are simulated for individual commercial vehicle weight classes and act as a reference for other modelers. The RTES is then used to simulate four transportation scenarios for the province of BC in 2050. In the four scenarios, 100% of passenger and light-duty vehicles are zero emission vehicles (ZEV) while 77% of medium- and heavy-duty vehicles are ZEVs. Depending on the drivetrain proportions used, the annual energy demand varies between 33.9 and 45.54 TWh. In addition, the peak electrical demand and maximum ramping rate are dictated by the charging strategies and rates that PEVs use. Utilizing an immediate high power charging rate leads to a peak demand of 22.98 GW and a maximum ramping rate of 8.54 GW/15-minutes. On the other hand, a load leveling charging strategy reduces the peak demand and maximum ramping rate to 8.78 GW and 0.57 GW/15-minutes, respectively.
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    Robust model predictive control for switched dynamic systems: asynchronous switching, stability and feasibility, and fault-tolerance
    (2023-06-19) Tan, Tianyu; Shi, Yang
    Switched systems, as a superior modeling tool, are generally composed of a finite number of subsystems and a logical principle interpreted by switching signals that regulate the mode transitions between subsystems. Throughout the past few decades, switched systems have achieved phenomenal success in a wide range of engineering industries, such as the chemical process industry, robotics field, power electronics industry, generic engineering, smart automotive industry, waste treatment industry, etc. In addition to the prevalent industrial applications, switching dynamics has also stimulated broad interest in academia since the transient responses induced by mode transitions may introduce instability factors into the control synthesis and analysis even with all subsystems operating stably. Considering the exposure to the challenging and volatile industry environment, switching control systems may also face threats to numerous inevitable failures, e.g., asynchronous switching, unconstrained switching, and controller failures, and many switched systems may also encounter physical limitations owing to spatial and system constraints as well as external disturbances. To this end, the majority of previous studies have concentrated on switching control design against an individual fault, but few results are devoted to investigating the attenuation of the combined effect of multiple faults which happen simultaneously while fulfilling system constraints. Switched model predictive control (MPC), as an optimal control methodology of switched systems, can effectively incorporate system constraints into the optimization problem while providing optimal control actions with a certain degree of inherent robustness. However, how to ensure the closed-loop stability and recursive feasibility of the switched MPC algorithm is still an open problem nowadays. Therefore, to achieve the goal of reliable and executable switching controller design, this dissertation studies three problems in switched MPC and one switching stabilization control problem for a class of constrained switched systems from a theoretical context. Effective switched MPC algorithms are designed with guaranteed closed-loop stability and recursive feasibility. Additionally, a novel robust stability criterion for switched systems is explored subject to the aforementioned faults. In Chapter 1, we present a comprehensive literature review of state-of-the-art switching control techniques, fault-tolerant switching control design, and switched MPC synthesis and analysis as well as the motivations and objectives of this dissertation. Chapter 2 provides some notations and preliminaries which are useful in succeeding chapters. Chapter 3 studies the switched MPC problem without using terminal constraints with known switching sequences. Chapter 4 concerns the asynchronously switched MPC problem with mode-dependent dwell-time (MDT) constraints. Two stability criteria are claimed by driving state trajectories into the devised common terminal set. In Chapter 5, the stabilization problem for a class of constrained switched linear systems is investigated subject to multiple faults. A contractive set for initial states is established with MDT restriction and a non-conservative uniformly asymptotic stability condition is developed regarding the contractive set. In Chapter 6, we investigate the robust MPC (RMPC) for asynchronously switched linear systems in the presence of joint effects of controller failures and additive disturbances. Chapter 7 concludes this dissertation and provides some promising future research directions.
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    Impact of Cascaded Hydro Operational Constraints on Power System Flexibility Requirements for Variable Renewable Energy Integration
    (2023-05-26) Mauel, Jennifer; Wild, Peter Martin; Rowe, Andrew Michael
    Variable renewable energy resources will play an increasingly prominent role in electricity systems as global economies pursue ambitious decarbonization and electrification targets. Power system flexibility will become a valued asset as more variable renewable energy resources are integrated into energy systems. The extent to which power systems can provide flexibility services to accommodate increased net load variability depends considerably upon the constraints of the existing energy resources in the generation mix. Jurisdictions whose electricity supply is predominantly hydroelectric operate within a unique set of operational, environmental, and regulatory constraints specific to large storage hydro systems. The constraints associated with large hydro systems may limit the extent to which hydro-dominant electricity systems can accommodate VRE resources. This thesis presents a model for cascaded hydro generation resources that is compatible with the SILVER production cost model and presents a case study of future VRE integration into the British Columbia electricity system. Modelling results indicate that a fully decarbonized BC electricity system is feasible assuming that hydro generation assets have a high degree of operational flexibility and adequate transmission capabilities.
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    Electric vehicle implications of disaster induced power outages
    (2023-05-18) Churchill, Mike; Crawford, Curran; Bristow, David
    The increasing electrification of the transport sector will create an increased vulnerability to power outages caused by disasters. This thesis provides two contributions in this area by offering suggestions for increasing earthquake grid resilience and modeling the use of electric vehicles (EVs) providing aid during a disaster induced outage. In British Columbia, Canada, the Lower Mainland and the Greater Victoria area on Vancouver Island have seen the largest adoption of EVs in the province and are located in an area of high seismic hazard, so it is crucial for the region to understand and plan for the impact of a large earthquake on the power system. This thesis compiles lessons learned from past large earthquakes in Chile, Japan, and New Zealand and applies them to increasing the power system resilience of the Lower Mainland and Vancouver Island. These suggestions are also compared with how fuel infrastructure resilience could be increased in the region of study. When used in conjunction with microgrids, EVs can potentially remain functional for the duration of a power outage. This thesis uses an agent-based model to study the behaviour of a fleet of EVs providing disaster relief during a power outage. EVs are tasked with donating energy to a shelter (Task 1), delivering critical supplies (Task 2), and providing transport for personnel or performing inspections (Task 3). Using a six EV fleet with two of each EV type, it was found that the 250, 350, and 450 kWh storage sizes could provide for outages of 0.5 to 1 day, 1 to 1.5 days, and 2 to 4 days, respectively. The rate of energy donated to the shelter was found to be 350 kWh/day, while the Type 1, 2, and 3 EVs, used energy at the microgrid at a rate of about 200 kWh/day, 100 kWh/day, and 50 kWh/day, respectively. Increasing battery storage size reduced the variation in the average daily energy use of the EVs and creating a six EV population with only Type 2 and 3 EVs was found to reduce variation even further and substantially increased the length of outage that the various microgrid storage sizes could provide for with 250 kWh storage now providing for outages of 2 to 4 days, while 350 and 450 kWh storage sizes routinely accommodated the EVs operating for a full two weeks (the time horizon of the model).