Engineering Commons^™

Advanced Machine Learning For Data-Driven Disease Prediction, Zekai Wang

Doctoral Dissertations

The rapid advancement in sensing and information technology has ushered us into an era of data explosion, where a large amount of data is now easily available and accessible in the clinical environment. This wealth of healthcare data offers new avenues for developing automated data-driven methods for disease diagnosis. Electronic Health Records (EHRs), serving as digital repositories of a patient's medical information, present unique opportunities to analyze and decipher clinical events and patterns within large populations. Given the rich information about a patient's health trajectory, leveraging EHRs through data-driven methodologies can significantly enhance clinical decision support systems.

However, utilizing real-world …

Multiscale Modeling Of Morphology And Proton/Ion Transport In Electrolytes, Zhenghao Zhu

Doctoral Dissertations

Understanding structure-function relationships in electrolytes is essential for advancing energy conversion and storage. This dissertation employs multiscale modeling and simulations to study the morphology and proton/ion transport in various electrolytes for electrochemical systems, including anion exchange membranes (AEMs), protic ionic liquids (PILs), pure phosphoric acid (PA) and aqueous acid solutions, ionic liquids (ILs), and polymerized ionic liquids (polyILs).

Mesoscale dissipative particle dynamics (DPD) simulations were employed to study the hydrated morphology of polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (SEBS)-based AEMs. The results indicate that the choice of the functional group moderately affects the water distribution and has little influence on the …

Reconfigurable, Reliable And Online Learning Enabled Memristive Neuromorphic Core Design For Brain-Inspired Computing, Nishith Nirjhar Chakraborty

Doctoral Dissertations

For several years, the von Neumann architecture has served as the foundation of contemporary computers due to its straightforward, cost-effective architecture for processing units and memory. However, this design encounters a significant impediment in meeting the growing demand for increased parallelism in complex computations. Additionally, the post-Moore's law era emphasizes the need for energy-efficient computing with fewer resources and reduced space. As a response to these challenges, researchers are actively seeking alternatives to the von Neumann architecture, with neuromorphic computing emerging as a promising candidate. Neuromorphic computing garnered attention initially for its ability to harness the parallelism inherent in bio-inspired …

Investigation Of Transport Phenomena In High-Performance Pem Water Electrolyzers, Anirban Roy

Doctoral Dissertations

The increasing demand for renewable energy sources to replace fossil fuels, requires a growing need for a steady energy supply from source to power distribution. Renewable energy often relies on intermittent natural sources or is geographically bound, necessitating energy storage solutions. Polymer Electrolyte Water Electrolyzers (PEWEs) offer a promising solution but require performance enhancements for economic viability. This study focuses on applying distributed diagnostic techniques for PEWEs to gain insights into improving their performance.

The research aims to investigate transport phenomena in high-performance PEWEs, utilizing a segmented cell approach to probe different aspects of transport within a single cell. This …

Development Of Digital Twin Applications In Power Systems To Improve Operation Of Emerging Grid Technology, Jeremy Till

Doctoral Dissertations

Power systems are continuously increasing in penetration of renewable energy resources and new technologies. Carbon-neutral requirements across the United States act as a catalyst forcing wide spread acceleration of these implementations. However, grid advancements exacerbate existing problems and potentially create new issues. New technologies for system planning and operation must be developed alongside the advancement of power systems. One such new technology is digital twins. Digital twins are a relatively new research topic in power systems. The main concept of a digital twin is a model of a real-world system that can run online with the physical system utilizing measurement …

Sand Aggregation By Methanotrophic Bacteria And Marine Microbial Hydrocarbon Degradation Under Differing Biogeochemistries, Ann-Marie G. Harik

Doctoral Dissertations

Methanotrophic bacteria, bacteria that can metabolize methane, are ubiquitous, EPS-producing, and capable of degrading hundreds of contaminants (Hazen, 2010). This dissertation looks at the possibility of using methanotrophic bacteria in sand surface aggregation and oil spill responses. Sand aggregation practices are widely used from infrastructure improvements to reversing desertification. In chapter 2, two selected methanotrophic cultures had their growth curves measured and their extracellular polymeric substances (EPS) production estimated. This second chapter tested and rejected two null hypotheses; the first null hypothesis was that there is no difference in EPS production rates over a culture’s growth curve, showing that the …

Leveraging Millimiter Wave Communications For Effective Remote Sensing, Zhiyang Zhang

Doctoral Dissertations

Millimeter wave (mmWave) is a promising technique in the 5th generation of cellular communications due to its large bandwidth and thus unprecedentedly high data transmission rate. To study the characteristics of mmWave and evaluate various algorithms, we build a high directional mmWave communication testbed.

The first topic of this thesis consists of two parts. The first one seeks to track a single object by using blockage phenomenon. Algorithms to capture the moving object are proposed. Detailed experiments and numerical simulations are compared with actual GPS trajectory to demonstrate the accuracy of the proposed solutions. In the second part, we formulate …

Converter-Based Microgrid Testing Platform Development And Fault Detection Method For Inverter-Based Resources By Utilizing Instantaneous Power Theory, Nattapat Praisuwanna

Doctoral Dissertations

Renewable energy sources (RESs) are rapidly being installed into electric systems. Most RESs are inverter-based distributed generations (IDGs). To better manage energy from IDGs, microgrids will play a crucial role. However, microgrids that have high penetration of IDG will have modification in fault current levels and bidirectional power flow causing protective devices to misoperate, so the traditional protection system does not correctly function in the microgrids. Therefore, the integration of IDGs into the microgrids establishes some significant consequences on protection systems.

Overcurrent protection is widely utilized in the electric distribution systems today; however, it is sometimes difficult to distinguish between …

Feasibility And Uncertainty Evaluation Of Sequential Hybrid Manufacturing Using Optical Coordinate Metrology, Jake Dvorak

Doctoral Dissertations

Hybrid manufacturing has been suggested as a solution to global manufacturing challenges including stock availability, manufacturing costs, and difficulty in production of complex parts. However, feasibility and validation of hybrid manufacturing remain open research opportunities.

This research evaluates measurement uncertainty for optical coordinate metrology (OCM) within a sequential hybrid manufacturing (SHM) framework where a primary manufacturing process, a part geometry measurement, and a secondary manufacturing process are performed sequentially on optionally separate machine tools. In this work, an additively manufactured preform is produced, the part geometry is measured using structured light optical coordinate metrology, and the final geometry is obtained …

Functional Properties Of Bulk And Meta-Material High Entropy Alloys, Cameron Jorgensen

Doctoral Dissertations

High Entropy Alloys (HEAs) are a class of material which is well-known for their high-temperature mechanical strength and corrosion resistance. In these materials, the entropy of mixing is used to encourage alloying between elements which are typically immiscible, sometimes described as alloying beyond the Hume-Rothery Rules. Specifically, the entropy of mixing is increased by including multiple elements on a single lattice site, increasing the free energy cost associated with phase separation. In the HEA community, this ‘high entropy effect’ is typically associated with alloy containing five or more elements. Achieving alloying between immiscible elements generally means the atomic size and/or …

Studies On The Design And Behavior Of Strongback Braced Frames, Peter Talley

Doctoral Dissertations

When subjected to strong earthquake ground motions, conventional steel braced frames are vulnerable to soft-story mechanisms, whereby the weakest story accumulates more damage relative to the rest of the structure. This reduces the overall strength of the structure and increases the cost of repairs.

One method for mitigating this behavior is the use of a stiff vertical “spine” with a more ductile, energy-dissipating system. The spine typically spans the height of the structure and is designed to remain elastic, distributing earthquake demands across the height of the structure and bridging weak stories. One proposed frame is the “strongback” braced frame …

Prompt Vs Local Redeposition: Model Refinement And Experimental Design For Understanding High-Z Net Erosion In Magnetic Confinement Fusion, Davis C. Easley

Doctoral Dissertations

The economic and engineering success of magnetic confinement fusion reactors significantly depends upon the optimization of plasma facing component (PFC) design. For high-Z PFCs, the critical engineering condition is minimal net erosion (i.e. gross erosion – redeposition). Here, we present a high-Z net erosion model discriminating three primary redeposition mechanisms: prompt (geometric-driven), local (sheath-driven), and far (scrape-off-layer-driven). Using these distinctions, we show modeling for high-Z net erosion in magnetic-confinement fusion over a matrix of key plasma parameters. With Sobol’ methods we assess the sensitivity of each mechanism and show that prompt-vs-local trade-off critically explains underprediction in redeposition losses of up …

In Situ Direct-Write Materials Processing Methods In Electron Microscopes, John Lasseter

Doctoral Dissertations

Focused beam induced processing holds great promise for advanced nanoscale device design and prototyping but often has severe limitations in material quality, purity and compatibility. In particular focused electron beam induced deposition (FEBID) can create 3D nanostructures of extremely complex geometries, but the deposited material purity is often very low (< 10% metal). Ex situ functionalization processes, such as sputter coating, do not conformally coat the nanostructures but instead apply pure material from the top down. Here, a laser based photothermal coating method leveraging the geometry-dependent thermal transport properties is used to apply high-quality pure material conformally coat the exposed nanostructures and …

Enhancing Insights Into Traffic Flows And Activities: Evaluating And Exploiting Machine Learning Algorithms In Real-World Scenarios, Diyi Liu

Doctoral Dissertations

Understanding truck activities has become increasingly crucial in traffic research, considering the increase in electric vehicles, the potential failure of critical infrastructure, etc. There are many different data sources to monitor the traffic flow. In this study, four different data sets generated from different approaches are used to extract traffic information. An innovative approach is devised and implemented for each data set to get valuable insights. Chapter I improves a recent Linear Programming method to tackle the truck identification problem based on the results of the radar detector or its equivalents (e.g., single loop detector). Tested under different contexts, the …

Process-Property-Structure Relationships In Advanced Rare Earth Magnet Manufacturing: Towards Enhanced Performance And Developing Application, Kaustubh Vidyadhar Mungale

Doctoral Dissertations

This research aims to study advanced rare earth magnet manufacturing, focusing on the structure-process-property relationships that govern their performance and applications. Rare earth minerals are classified as critical materials because they are essential in manufacturing products across numerous cutting-edge technologies including electric vehicles, renewable energy systems, and high-performance electronics.

Bonded magnets are composites with permanent magnet powder embedded in a polymer matrix. Finely powdered (3-300 microns) rare earth based intermetallics such as neodymium iron boron (NdFeB) and samarium iron nitride (SmFeN) are blended with engineering polymers such as epoxy, polyamides (PA6/PA12) and polyphenylene sulfide (PPS), followed by molding the compound …

Cruising Towards Durability: Investigating Degradation In Polymer Electrolyte Fuel Cells (Pefcs) For Sustainable Vehicle Power, Preetam Sharma

Doctoral Dissertations

Energy production is central to the climate challenge, as a significant portion of greenhouse gases responsible for trapping heat in the Earth's atmosphere arises from burning fossil fuels to generate electricity and heat. To mitigate the adverse effects of climate change, emissions must be reduced by almost half by 2030 and achieve net-zero emissions by 2050. Polymer electrolyte fuel cells (PEFCs) offer numerous advantages compared to traditional internal combustion engines in vehicles. Fuel cell electric vehicles are known for their exceptional operating efficiency (over 60%), impressive driving range (more than 400 miles), and quick refueling times (under 5 minutes).

Automotive …

An Analysis To Improve Interim Final Project Cost Predictions, William C. Smolter

Doctoral Dissertations

When managing a project, a project manager is often faced with the decision to act if a project appears to be over budget. While this decision seems straightforward, for some projects this can be a costly decision that causes delays and missed deadlines or spending even more resources on analyzing individual expenses.

Currently, project management research has assumed that the project manager knows the correlation between the completed work and work remaining deterministically or it assumes that a general gaussian distribution holds and proceeds with cost projecting from there.

This research challenges this assumption by forcing varying correlation distribution curves …

A Convex Approach To Advanced Air Mobility Trajectory Optimization, Yufei Wu

Doctoral Dissertations

This dissertation addresses the challenge of real-time trajectory optimization for electric Vertical Take-Off and Landing (eVTOL) vehicles within the framework of Advanced Air Mobility (AAM). With urban airspaces becoming increasingly crowded, ensuring the safety, efficiency, and feasibility of eVTOL operations is crucial. This research primarily focuses on the development and application of convex optimization techniques to solve trajectory optimization problems that not only enhance operational capabilities but also ensure adherence to stringent safety and efficiency standards.

The study is structured into several critical analyses and methodological developments across multiple chapters. In the first chapter, I introduce a multi-phase trajectory optimization …

Hardware-Based Solutions For Improving Situational Awareness In Power Systems With High Renewable Integration, Yuru Wu

Doctoral Dissertations

This doctoral dissertation addresses the evolving challenges in power system frequency dynamics as the penetration of renewable energy sources increases, leading to a decrease in system inertia. The integration of these renewable sources, particularly those involving electronic inverters, introduces high-frequency harmonics and oscillations, necessitating enhanced accuracy and wideband measurement capabilities for effective monitoring and situation awareness in power systems.

The dissertation makes several significant contributions to this field. In Chapter 2, the dissertation introduces a review of the related literature, detailing the development and application of monitoring devices in modern power systems. Chapter 3 proposed a variety of hardware devices …

Microstructure-Based High Temperature Processing And Failure Analyses Of Engineering Alloys Under Complex Conditions, Dong Han

Doctoral Dissertations

From traditional petrochemical applications to contemporary manufacturing techniques, advanced structural materials are subjected to intricate thermomechanical and environmental conditions. In these engineering domains, high-temperature deformation plays a pivotal role, profoundly influencing the ultimate outcomes of industrial applications. Nevertheless, the underlying mechanisms of this deformation remain elusive, largely due to the absence of a microstructure-based mechanistic understanding of high-temperature processing and failure of materials.

This dissertation endeavors to comprehensively examine these mechanisms through computational methodologies, focusing on three quintessential topics: grain boundary cavitation failures (2 examples: stress relaxation cracking (SRC) and grain size dependence), high-temperature hydrogen attack (HTHA), and additive friction …

Impact Of Student Beliefs And Self-Efficacy On Performance In Higher Education Stem Courses, Lauren Nicole Fogg

Doctoral Dissertations

In engineering education, students often face feelings of inadequacy, leading to academic struggles and potential dropout. This dissertation investigates the impact of interactive course materials on students' confidence and self-efficacy in problem-solving, focusing on an Engineering Materials class at Louisiana Tech University. Over four quarters, involving seven sections and 218 students, a 13-question Likert scale survey was administered repeatedly, alongside demographic data and textbook usage surveys. The study aims to compare students’ attitudes and beliefs when not using a textbook versus when using an interactive web-native book. Hypotheses suggest that the interactive book will enhance problem-solving beliefs, confidence, and grades. …

Phase Interface Dynamics And Heat Transfer Mechanisms In Evaporating Droplet And Pool Boiling Processes, Md Tanbin Hasan Mondal

Doctoral Dissertations

Despite the significant importance and widespread use of phase-change cooling techniques, there are still fundamental questions about the microscopic processes that govern the heat transfer mechanisms. In order to gain a better understanding of the underlying physics involved, it is essential to have information at the microscale regarding the surface temperature distribution with time as well as the location and speed of the moving contact line (MCL). A comprehensive understanding of heat transfer mechanisms and phase-interface behavior during phase-change cooling is crucial for improving heat transfer models, optimizing surface engineering, and maximizing overall effectiveness. Firstly, this dissertation presents a capacitance-based …

Milling Stability Map Identification And Machining Parameter Optimization Using Bayesian Inference, Aaron William Cornelius

Doctoral Dissertations

This dissertation describes a physics-guided Bayesian learning approach for statistically modelling and optimizing machining processes under a state of uncertainty. This approach uses a series of automatically-selected cutting tests to refine uncertainties about the machining system's dynamics and cutting force and identify higher productivity cutting parameters. The algorithm is evaluated experimentally and compared to the cutting tool manufacturer’s recommendations, both in laboratory conditions and in an industrial setting to optimize the machining process for a large aluminum component. These results show that the proposed Bayesian model can quickly identify both highly-productive machining parameters and accurate information about the underlying system …

Power System Electromagnetic Transient Simulation Using A Semi-Analytical Approach, Min Xiong

Doctoral Dissertations

This dissertation investigates efficient power system electromagnetic transient (EMT) simulations using a semi-analytical approach.

First, based on state-space equations of system EMT models, a semi-analytical solution (SAS) is acquired using the Differential Transformation Method (DTM). The DTM can efficiently derive the SAS of any linear or nonlinear system as a power series in time in a recursive manner using well-developed transformation rules. A high-order SAS allows a large time step to speed up the simulation while maintaining the same level of accuracy. Also, a variable time step approach is proposed to further improve its efficiency. Case studies on multiple systems …

A Pattern Matching Algorithm For Self-Adjusting Basal Rates In Insulin Pump Systems, Lauren Smith

Doctoral Dissertations

In a Type 1 Diabetic, Insulin can be administered in a pump system. There are two types of insulin that must be given: basal and bolus. Basal insulin is a long-acting form of insulin that works in the background while fasting, while Bolus insulin is rapid/short acting given in response to food to immediately begin working to lower blood sugar.

Modeling in Diabetes can be represented by algorithmic approaches ranging from simple autoregressive models of the Continuous Glucose Monitor time series to multivariate nonlinear regression techniques of machine learning. Other examples of modeling in Diabetes include prediction models of hypoglycemia …

Characterization Of Ceramic Powders Through Powder Rheology, Samuel Weimer

Doctoral Dissertations

Frequently in the literature, it has been shown that any single characterization technique is incapable of sufficiently describing the overall rheology properties of a powder. Consequently, much work has gone into exploring multivariate relationships between common rheological tests. However, such efforts have been primarily focused on powders used in pharmaceutical and food industries. Much less rheology work has been conducted for powders of other industries, such as ceramic powders used in making grinding media and crucibles. Yet, it has been shown that supplementing particle size distribution and chemical composition measurements of ceramic powders with powder rheology techniques can greatly increase …

Design And Develop Lignin Based Recyclable Copolymers For Hydrophobic Coatings, Di Xie

Doctoral Dissertations

Due to the abundance, renewability, biodegradability, overall hydrophobicity, good compatibility with cellulose, and anti-UV/oxidant abilities, lignin has great application potentials in hydrophobic coatings on cellulose-based substrates. However, lignin's structural heterogeneity and rigidity challenge its value-added utilization. Herein, Kraft lignin (KL), from paper mills, is fractionated into more homogeneous fractions (FL), nanosized into lignin micro-nanospheres (LMNS), chemically modified and copolymerized with other constituents to fabricate hydrophobic coating materials with improved coating performances.

To investigate structure-property relationships of lignin-based copolymers, solvent fractionation is conducted to obtain FLs with different molecular weight (MW) and hydroxyl (OH) contents to prepare copolymers by integrating with …

Development And Feasibility Studies Of Ai-Powered Socially Assistive Robotics To Promote Wellbeing Of Persons With Alzheimer’S Disease And Related Dementias, Fengpei Yuan

Doctoral Dissertations

The number of persons living with Alzheimer's Disease and Related Dementias (PLWDs) has been keeping growing. In 2024, it is estimated that there will be approximately 6.7 million individuals living with Alzheimer's Dementia. This number will increase to about 14 million in 2060. Due to the damage in neurons, the capabilities of memory, thinking, and language will decline as the disease progress. As a result, persons with dementia will gradually withdraw from their social activities and become more dependent on others during their activities of daily living. Making it worse, our society is not ready for the increasing requirements of …

Feature Interaction Selection For High-Dimensional Experimental Data, Di Bo

Doctoral Dissertations

In a material development process, discerning the effect of material properties and their interactions on material behaviors is critical to achieving the desired functionality of a material. This causal analysis often involves a small experimental dataset arranged in a high dimension and is challenged by the curse of dimensionality. Feature selection can alleviate such a challenge by producing a short list of features that are significant, but identifying significant feature interactions is very challenging. In this proposal, we propose a couple of approaches that can evaluate and determine important interactions, including a randomized subspace-based model (RSM), feature subspace selection (FSS), …

Cmos-Memristive Neuromorphic Architecture For Nonlinear Signal Processing, Manu Rathore

Doctoral Dissertations

Neuromorphic computing mimics the functional components and structure of the human brain to achieve highly efficient computing with minimal resources and power consumption. Creating neuromorphic systems in Complementary Metal-Oxide-Semiconductor (CMOS) technology offers an alternative computing paradigm to Von neumann computing. However, implementing these systems on an CMOS Integrated Circuit (IC) poses major challenges. These challenges include implementing synaptic weight multiplication and weight tuning operation that conserves energy and occupies minimal area. Additionally, designing a network-on-chip architecture that is reconfigurable and offers a full-connectivity design space is crucial. Furthermore, implementing a complete architecture for nonlinear data processing and, specifically, online learning …