<p>  </p> <p>In this thesis, two optimizations of squealer tip geometries are completed for first stage turbine blades for use in a supercritical carbon dioxide turbine. First, an optimization is performed on a baseline trapezoidal turbine blade and a set of solution geometries is chosen from along the Pareto front. Next, a second optimization is completed on an advanced blade design and the geometries are grouped by performance characteristics and geometric features. The success of similar geometries across these two optimizations is also analyzed and demonstrates consistency of performance increases from tip geometries over the baseline geometry. An analysis of a flat tip geometry in a stationary condition is also performed to begin validation of annular cascades as a method for testing squealer tip geometries. </p>

<p>Recycling valuable cathode material from end-of-life (EOL) Li-ion batteries (LIBs) is essential to preserve raw material depletion and environmental sustainability. Direct recycling reclaims the cathode material without jeopardizing its original functional structures and maximizing return values from spent LIBs compared to other regeneration processes. This work employed two chemically delithiated lithium cobalt oxide (LCO) cathodes at different states of health (SOH), which are analogous to the spent cathodes but free of any impurities, to investigate the effectiveness of cathode regeneration. The material and electrochemical properties of both delithiated SOHs were systematically examined and compared to pristine LCO cathode. Further, those model materials were regenerated by a hydrothermal-based approach. The direct cathode regeneration of both low and high SOH cathode samples restored their reversible capacity and cycle performance comparable to pristine LCO cathode. However, the inferior performance observed in higher current density (2C) rate was not comparable to pristine LCO. In addition, the higher resistance of regenerated cathodes is attributed to lower high-rate performance, which was pointed out as the key challenge of the cathode recycling process. This study provides valuable knowledge about the effectiveness of cathode regeneration by investigating how the disordered, lithium-deficient cathode at different SOH from spent EOL batteries are rejuvenated without changing any material and electrochemical functional properties.</p>

<p>This thesis proposes the design and documents analysis for a Modular River Current Energy Converter (MRCEC) to improve the efficiency of hydrokinetic turbine power systems. The MRCEC can produce electricity from low-velocity river flow with increased energy affordability and availability. The MRCEC, for the scope of this thesis, consists of the hydroturbine and maintenance systems. The turbine in the MRCEC system is a cross-flow cycloidal turbine that yields a high power coefficient (0.515) through a novel pitch variance mechanism involving a 3D cam that adapts to varying river flow conditions to maximize operational efficiencies. The cycloidal turbine is a four-section three-blade turbine that uses a unique hydrofoil profile designed for the MRCEC. The cycloidal turbine is housed in a frame supported by a flotation system to harness energy from near-surface currents. The flotation system, in turn, is connected to the service dock which houses the mooring, debris blockage, and maintenance systems. The mooring system allows the MRCEC to be fixed at the working site while allowing for self-adjustment with varying river depths. The debris blockage system prevents debris carried by the river from interfering with an operational hydroturbine. The maintenance system enables the installation, operation, and maintenance functions by integrating a flipping mechanism to invert the turbine for transportation and maintenance purposes. Mechanisms of these systems are designed to appropriate standards, then simulated to validate functionality at all stages of installation, operation, and maintenance.</p>

<p dir="ltr">Current mission plans for harvesting lunar resources require further investigation of technological and energy requirements to do so. This paper presents an analysis of the thermodynamics involved in hydrogen (H₂) evolution and liquefaction within this scope. It highlights the use of solar-powered systems for electrolysis and membrane separation as efficient means to produce H₂ on the lunar surface. The study compares energy requirements and logistical considerations of in-situ resource utilization (ISRU) against transporting precursors from Earth, where the energy penalty stands at 540 MJ/kg. It is argued that an ISRU solution stands to present the most energy efficient option, particularly with the use of an active magnetic regenerative refrigeration (AMRR) system for liquefaction. Furthermore, an AMRR system also makes the currently proposed plan of shipping methane (CH₄) from the Earth for H₂ production more favorable than implementing ISRU with the state-of-the-art (SOA) reverse turbo-Brayton cryocoolers (RTBC). This emphasizes the significance of an AMRR system for H₂ production and the need for further research in its development. Additionally, this study underscores the significance of regenerative technologies and advanced life support systems for sustainable off-planet human habitation, particularly in the context of lunar and Martian missions.</p>

<p dir="ltr">Investigating clearance flows and blockage generation in axial compressors represents a longstanding area of research for enhancing aerodynamic performance and operational stability in turbomachinery. With advancements in computational fluid dynamics (CFD), opportunities to explore these phenomena have expanded, allowing a deeper understanding of the turbomachine's inherently complex and highly unsteady flow fields. This work delves into these topics, focusing on the Purdue 3-Stage (P3S) compressor, an engine-representative, multistage, high-speed compressor.</p><p dir="ltr">The primary objective of this research is to compare the performance and stability characteristics of two distinct stator configurations: a shrouded baseline configuration and a cantilevered stator configuration. This comparison reveals the impacts of clearance flows and blockage generation on compressor operation. Through a series of experimental investigations, this study aims to identify the differences in performance and stability traits between these configurations and the flow structures responsible.</p><p dir="ltr">Experimental characterization has a central role in this study, involving the analysis of leakage flow structures, corner separations, wake structures, and resulting endwall blockage generation. This research seeks to provide detailed insights into the flow phenomena within the compressor by utilizing detailed measurement techniques, such as circumferential interrogation of the flow field using 7-element Kiel-head rakes. Pressure deficits associated with leakage flows, corner separations, and wakes are quantified to assess their impact on compressor performance.</p><p dir="ltr">In conjunction with experimental investigations, this work outlines the development and validation of the supporting high-fidelity CFD models. These models, employing scale-adaptive turbulence model simulations, aim to simulate the flow field within the compressor with accuracy and reliability. Validation of these models against experimental data ensures their fidelity in capturing the complex flow phenomena observed experimentally. Furthermore, a detailed exploration of convergence aspects, including iterative convergence, grid convergence, and periodic-unsteady signals, lays the foundations for building confidence in the model predictions.</p><p dir="ltr">The computational models complement experimental findings, allowing for a comprehensive flow field analysis focusing on endwall flow structures. Visualization of vortex core and three-dimensional blockage regions provides valuable insights into the flow physics governing compressor performance. Moreover, the comparative nature of computational simulations facilitates systematic exploration of geometric changes and their effects on compressor operation. This study leverages complementary methodologies of experimental measurements and high-fidelity computational models to advance the understanding of clearance flows and blockage generation in axial compressors.</p><p dir="ltr">The experimental analysis concludes that the cantilevered configuration achieves better performance and stability than the shrouded stator configuration. However, this conclusion is not apparent when the machine is considered holistically. The cantilevered stages show significant performance improvements, with increases in total pressure ratio of up to 1% and an increase in isentropic efficiency of as much as 2%. However, the common Stage 3 shrouded Stator 3 shows a corresponding deficit of as much as 2% loss in efficiency relative to the fully shrouded stator configuration baseline. These contrasting benefits in the cantilevered stator compressor show that Stage 3 seems to cancel the overall benefits gained by the cantilevered stator. Similar studies have been done on low-speed multistage compressors, but this shows the value of the study in a high-speed research compressor with appreciable stagewise temperature and density increase.</p>

<p dir="ltr">The demand for renewable energy and electric vehicles (EVs) is increasing, necessitating efficient energy conversion and management solutions. The thesis addresses the critical challenge of dynamically converting multiple Direct Current (DC) inputs to multiple DC outputs while maintaining efficiency and scalability. The primary objective is to design and test a Multi Input Multi Output (MIMO) DC converter, focusing on verifying its scalability and load efficiency. The research investigates hardware requirements, the implementation of multiphase circuits, and the balancing of power between various inputs through multistage cycling. The study hypothesizes that multistage cycling balances the output power between inputs, and multiphase configurations can scale the converter without affecting efficiency. Methods include examining existing converters, simulating multistage circuits, and fabricating a prototype. Key deliverables include a working prototype demonstrating scalability and efficiency. Results indicate that the MIMO DC converter performs efficiently with multiple inputs and outputs, achieving over 90% efficiency. The use of Gallium Nitride (GaN) transistors and synchronous buck converter topology proves effective in minimizing losses and enhancing stability. The research holds significant value in advancing renewable energy and DC converter technology, promoting sustainability and efficient energy management. Future work should explore advanced filtration circuits, higher voltage testing, and more complex configurations to further enhance the converter's capabilities.</p>

The point absorber, one of the most popular types of ocean wave energy converter (WEC), usually consists of a rigid body buoy that can be efficiently modeled using existing WEC simulation tools. However, new wave energy technologies have looked to utilize flexible buoy structures to decrease costs, improve power generation, and increase portability. In addition to replacing rigid body designs, the combination of multiple renewable energy sources is another area that shows promising potential for increasing WEC power generation. With these concepts in mind, this work considers a new WEC design that features a flexible-inflatable buoy, an ocean current harvesting turbine, and a buoy shape that has been optimized for simultaneous wave and current energy harvesting. For this device, conventional modeling techniques cannot be used due to the highly nonlinear hydrodynamic interactions that result between the flexible buoy and the ocean waves. As a result, a Fluid-Structure Interaction (FSI) model must be used to determine how the flexibility of the buoy will influence the device's power generation. Currently, high-fidelity FSI modeling approaches are computationally expensive and unsuitable for early design decisions. As a result, this thesis utilizes a mid-fidelity method, the generalized modes modeling approach, to accurately and efficiently model the FSI of a WEC's flexible buoy. The resulting flexible buoy model was then compared to a rigid design to determine the performance differences between a rigid and flexible buoy, with a complex, optimized shape. / Master of Science / The ocean is a vast potential energy resource with a variety of different sources of renewable energy. Of these sources, ocean waves and ocean currents are two potentially massive power reserves present in many coastal areas. To capture energy from these sources, this work discusses the development of a device that can harvest energy from ocean waves and ocean currents simultaneously. In addition to harvesting energy from multiple sources, this device also features a flexible-inflatable buoy, with a shape that has been optimized for this unique application. However, since this device utilizes flexible materials, traditional modeling techniques used for rigid body designs would not be applicable. As a result, this work looks to model the interaction between the flexible buoy and the ocean waves to accurately predict the power generation of this device's wave energy converter.

<p dir="ltr">The energy consumption in the building sector is responsible for over 36% of the total energy consumption across the globe. Of all the energy-consumer devices within a building, heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy consumed. This makes HVAC systems a source of preventable and unexplored energy waste that can be tackled by incorporating intelligent operations. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. In this regard, the opening chapter delves into the evolving landscape of the HVAC industry. It explores how rapid advancements in technology, growing concerns about climate change, and the ever-present need for energy efficiency are driving innovation. The chapter highlights the shift from static to dynamic HVAC systems, where buildings become sensor-rich networks enabling advanced control strategies like Model Predictive Control (MPC) and Fault Detection and Diagnosis (FDD). we first provide a comprehensive review of the literature concerning the application of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, time step, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. Next, a thorough, real-world case study for the design and implementation of a generalized data-collection and control architecture for HVAC systems in an educational building is proposed. The proposed MPC method adds a supervisory control layer on top of the current BMS by delivering temperature setpoints to the legacy controller. This means that the technique may be used to a variety of current HVAC systems in different commercial buildings. In addition, the utilization of remote web services to host the cloud-based architecture significantly minimizes the amount of technical expertise generally necessary to create such systems. In addition, we provide significant lessons learned from the installation process and we list indicative prices, therefore minimizing uncertainty for other researchers and promoting the use of comparable solutions. Chapter two focuses on Fault Detection and Diagnosis (FDD), a critical component of maintaining optimal HVAC performance and minimizing energy waste. HVAC systems are susceptible to malfunctions over time, leading to increased energy consumption and higher maintenance costs. FDD techniques play a vital role in identifying and diagnosing these faults early on, allowing for timely repairs and preventing further deterioration. This chapter introduces a novel bi-level machine learning framework for diagnosing faults in air handling units. This framework addresses key challenges associated with FDD. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). By proposing this framework, we address three persistent challenges in this field: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment. It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. Chapter three tackles the practical implementation of Model Predictive Control (MPC) in a real-world commercial building setting. It details the development, implementation, and cost analysis of a universally applicable cloud-based MPC framework for HVAC control systems. This chapter offers valuable insights into the feasibility and effectiveness of MPC in achieving energy efficiency goals while maintaining occupant comfort. The chapter delves into the hardware and software components used for data acquisition and MPC implementation. It emphasizes the use of cloud-based microservices to ensure seamless integration with existing building management systems, promoting wider adoption of this advanced control strategy. Three innovative control strategies are presented and evaluated in this chapter. The chapter presents compelling evidence for the effectiveness of these strategies, showcasing significant energy savings of up to 19.21%. Chapter four focuses on Occupancy-based Demand Controlled Ventilation (DCV) as a means to optimize indoor air quality (IAQ) while minimizing energy consumption. This chapter highlights the growing importance of IAQ in the wake of the COVID-19 pandemic and its impact on occupant health and well-being. Current ventilation standards often rely on static occupancy assumptions, which can lead to over-ventilation during unoccupied pe riods and wasted energy. This chapter proposes a dynamic occupant behavior model using machine learning algorithms to predict CO2 concentrations within buildings. The chapter investigates the performance of various machine learning algorithms, ultimately identify ing a Multilayer Perceptron (MLP) as the most effective in predicting CO2 levels under dynamic occupancy conditions. This model allows for real-time modulation of ventilation rates, ensuring adequate IAQ while minimizing energy consumption. The concluding chapter presents experimental findings on the effectiveness of adaptive Variable Frequency Drive (VFD) control strategies in optimizing HVAC energy consump tion. Variable Frequency Drives allow for adjusting the speed of electric motors, including those powering HVAC fans. This chapter explores the potential of using real-time occu pancy predictions to optimize VFD operation. The proposed control strategy demonstrates impressive energy savings, achieving a 51.4% reduction in HVAC fan energy consumption while adhering to ASHRAE IAQ standards. This chapter paves the way for occupant-centric ventilation strategies that prioritize both human health and energy efficiency. These results underscore the potential of predictive control systems to transform building operations to ward greater sustainability and efficiency. The chapter acknowledges the need for further validation through extended monitoring and analysis. In summary, this thesis contributes significantly to the advancement of smart building technologies by proposing practical frameworks for implementing advanced control strategies in HVAC systems. The findings presented here offer valuable insights for building designers, engineers, facility managers, and policymakers interested in creating sustainable, energy efficient, and occupant-centric buildings. The developed frameworks have the potential to be applied across a wide range of building types and climatic conditions, promoting broader adoption of smart building technologies and contributing to a more sustainable built environment.</p>

<p dir="ltr">Lithium-ion (Li-ion) batteries are promising electrochemical energy storage and conversion systems to drive the rechargeable world toward a sustainable future. Following the breakthrough of material innovations, advanced Li-ion batteries have significantly mitigated the range and lifetime anxieties of electric vehicles (EVs) and consumer electronics. Nevertheless, state-of-the-art Li-ion chemistries still suffer from several defects, such as rapid degradations under abusive or fast-charge scenarios and unfavorable high thermal instabilities. Essentially, aging mechanisms and safety hazards of Li-ion cells are strongly coupled events. The cell safety factors are most likely to be deteriorated as degradation progresses, making the cell less safe after a long-term deployment. In this thesis, we comprehensively investigate thermo-electrochemical interactions on the safety of Li-ion batteries. Fundamental principles of Li-ion batteries, basic knowledge about material-level thermal instabilities at electrode-electrolyte interphases, thermal characterization approaches, and thermal runaway mechanisms under abusive scenarios are fully overviewed. Thermally unstable characteristics of key cell components, including inter-electrode crosstalk as a result of oxygen liberation from cathode lattice structures, significant electric energy release from massive internal short circuit due to separator collapse, anode-centric lithium-plating-induced early exotherm, and silicon-dopant-driven thermal risks of composite anodes, are specifically discussed to understand their critical role in accelerating cell-level thermal runaway catastrophes. Aging pathways of Li-ion cells under off-normal conditions, particularly overdischarge and fast charging, are thoroughly elucidated using a promising reference electrode architecture, which effectively deconvolutes the electrode behaviors from the complex full-cell performance for precise identification of the root causes in cell failure. Given the profound revelation of degradation-safety sophistication in various Li-ion chemistries, corresponding mitigation and prevention strategies are proposed to maximize cell lifetime and reliability. This thesis provides new insights into aging and safety diagnostics of cutting-edge Li-ion batteries, taking one step further in the online monitoring of battery state of health to develop adaptive battery management systems.</p>

<p dir="ltr">The demand for sustainable and renewable energy sources has prompted significant research and development efforts in the field of biomass gasification. Biomass gasification technology holds significant promise for sustainable energy production, offering a renewable alternative to fossil fuels while mitigating environmental impact. This thesis presents a detailed study on the design, development, and implementation of a Plug-Flow Reactor Biomass Gasifier integrated with an Automated Auger Jam Detection System and a Blower Algorithm to maintain constant reactor pressure by varying blower speed with respect to changes in reactor pressure. The system is based on indirectly- heated pyrolytic gasification technology and is developed using Simulink™.</p><p dir="ltr">The proposed gasification system use the principles of pyrolysis and gasification to convert biomass feedstock into syngas efficiently. An innovative plug-flow reactor configuration ensures uniform heat distribution and residence time, optimizing gasification performance and product quality. Additionally, the system incorporates an automated auger jam detection system, which utilizes sensor data to detect and mitigate auger jams in real-time, thereby enhancing operational reliability and efficiency. By monitoring these parameters, the system detects deviations from normal operating conditions indicative of auger jams and initiates corrective actions automatically. The detection algorithm is trained using test cases and validated through detailed testing to ensure accurate and reliable performance.</p><p dir="ltr">The MATLAB™-based implementation offers flexibility, scalability, and ease of integration with existing gasifier control systems. The graphical user interface (GUI) provides operators with real-time monitoring and visualization of system status, auger performance, and detected jam events. Additionally, the system generates alerts and notifications to inform operators of detected jams, enabling timely intervention and preventive maintenance. </p><p dir="ltr">To maintain consistent gasification conditions, a blower algorithm is developed to regulate airflow and maintain constant reactor pressure within the gasifier. The blower algorithm dynamically adjusts blower speed based on feedback from differential pressure sensors, ensuring optimal gasification performance under varying operating conditions. The integration of the blower algorithm into the gasification system contributes to stable syngas production and improved process control. The development of the Plug-Flow Reactor Biomass Gasifier, Automated Auger Jam Detection System, and Blower Algorithm is accompanied by rigorous simulation studies and experimental validation.</p><p dir="ltr">Overall, this thesis contributes to the advancement of biomass gasification technology by presenting a detailed study on a plug flow reactor biomass gasifier with indirectly- heated pyrolytic gasification technology with an Automated Auger Jam Detection System and Blower Algorithm. The findings offer valuable insights for researchers, engineers, policymakers, and industry stakeholders supporting the transition towards cleaner and more renewable energy systems.</p>