Engineering Commons^™

From Frames To Strains: Analytically Modeling Inelastic Deformation Under Mapped Single-Site Impacts From High-Speed Footage To Internal State Variable Codes, Joby Milo Anthony

Doctoral Dissertations and Projects

This work adds insight to the physical phenomena of microstructural and stress strengthening of metal components by the inelastic deformation from Surface Mechanical Attrition Treatment (SMAT). Impact behaviors observed by high-speed footage of a Crank-Slider Mechanism (CSM) are examined in the context of analytically moving rigid bodies in spacetime and resolving kinematics upon impact until restitution via Finite Element Analysis (FEA). A Coupled Discrete-Finite Element Model (CDFEM) leverages Bammann plasticity, Horstemeyer damage and void nucleation, growth, and coalescence and Cho recrystallization Internal State Variable (ISV) models to show the localization of plastic strain and onset of recrystallization under any single …

The Impact Of Dispatch Weight Restrictions On Derivative Aircraft Propulsion Technology Evaluation, Timothy T. Takahashi

Faculty Publications

This paper arises from an ARPA-E-sponsored project seeking design opportunities to retrofit existing aircraft with hybrid electric propulsion systems. Engineers typically configure aircraft to fly a given payload over a long range, which is subject to field performance constraints. In practice, operators fly transport aircraft (civilian and military) in a manner where dispatch consciously trades payload and/or range to enable safe operations to and from short runways. This work describes a simple yet novel analytical process suitable for inclusion in conceptual design or technology portfolio trade study evaluations to assess the impacts of weight-restricted dispatch upon usable payloads. We find …

Characteristics And Implementation Paths Of Goal For Building Energy Powerhouse, Liye Xiao, Jiaofeng Pan, Xiaojiong Wang, Deqiang Sun, Mingliang Qi, Jianlei Mo, Huimin Li, Ting Wang, Jie Yang, Jie Lin, Yuchao Wang, Haiting Chen

Bulletin of Chinese Academy of Sciences (Chinese Version)

China is a major energy-consuming country, and ensuring effective energy supply is one of the core tasks for promoting national development and national rejuvenation. To guarantee national energy supply and energy security, and to vigorously develop and utilize clean and low-carbon energy, the Outline of the 15th Five-Year Plan for National Economic and Social Development of the People’s Republic of China clearly states: “We will thoroughly implement the new energy security strategy, accelerate the construction of a clean, low-carbon, safe, and efficient new energy system, and build a strong energy nation. We will promote the safe, reliable, and orderly replacement …

A Predictive-Correlational Study Investigating Flight Students' Perceptions Of The Use Of An Artificial Intelligence Instructor In The Simulated Flight Training Environment, Zachary L. Lamothe

Doctoral Dissertations and Projects

The purpose of this quantitative predictive-correlational study is to investigate how perceived ease of use, perceived usefulness, performance expectancy, and perceived enjoyment impact attitude towards use and the behavioral intention to use an artificial intelligence flight instructor in simulated flight training among aviation students earning an aeronautical degree at a collegiate flight training school in the Mid-Atlantic region. Aviation has benefited from different technological advances, and using artificial intelligence technology for flight training could be another improvement as it becomes increasingly sophisticated. The study adds to the literature by addressing the problem of limited information on the perception of artificial …

Seizing Strategic High Ground Of Space Computing Power: Global Competition Landscape And China’S Path, Yan Chen, Wenbin Song, Ping Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The deep integration of artificial intelligence and commercial aerospace is accelerating the transformation of space computing power from conceptual exploration to engineering verification, becoming a key direction for building an integrated space-air-ground information infrastructure. This study delves into its strategic value, global landscape, industrial chain bottlenecks, and advancement paths. The research reveals that the core value of space computing power does not lie in replacing ground data centers, but rather in focusing on network coverage blind spots, data transmission limitations, and high-timeliness scenarios, providing a new supply model of “in-orbit computing + space-ground collaboration”. Currently, the world has entered a …

Assessment Of Buildings' Energy-Saving Strategies Resilience To Climate Change: The Case Of Mediterranean Climate, Aya S. Mohamed, Bakr M. Gomaa, Alaa Eldin N. Sarhan

HBRC Journal

The Earth's climate is changing, and projections indicate that global warming will continue throughout this century, leading to increased occurrences of extreme temperatures. This raises critical questions about the performance and resilience of different energy-saving design strategies in buildings under future climatic conditions. To address this, the present study investigates the impact of passive design strategies, including building orientation, window-to-wall ratio, south and east/west shading devices, and thermal insulation on a prototype building's energy performance across four timeframes: 2002, 2020, 2050, and 2080, using validated computer-based thermal simulations. The results indicate that individual strategies vary significantly in their effectiveness, with …

Optimizing Wind Turbine Blades Using Fiber-Reinforced Composites, Rayan A. Akeel

Discovery Day - Daytona Beach

This project explores how fiber-reinforced materials can improve wind turbine blade performance by making them lighter and more durable. It will examine different fiber types, their fabrication methods, and their impact on efficiency and strength.

Explainable Tree-Based Ensemble Models For Diabetes Prediction Using Shap, Maan Y Anad Alsaleem, Omar Shakir Hasan, Yahya Albugg

AUIQ Technical Engineering Science

Due to the generally unqualified nature of prediction data and the difficulty of interpreting predictions, predicting diabetes remains a significant hurdle in the adoption of machine learning within the medical domain. In this study, several tree-based machine learning techniques (LightGBM, XGBoost, CatBoost, and Gradient Boosting) were applied to predict diabetes using the 2015 BRFSS dataset, while two ensemble methods (soft voting and stacking) were employed to improve predictive accuracy. The performance analysis of the individual models and ensemble approaches indicates that CatBoost achieved the highest accuracy among the single classifiers (0.871), with an F1-score of 0.871 and a ROC–AUC of …

Visualization And Marker-Less Tracking Of User-Defined Pre-Processed Mri Articulator Data Using Deep Learning, Michael De George

Student Theses

This thesis presents a comprehensive framework for the automated tracking and visualization of articulatory movements based on magnetic resonance imaging (MRI) data. A well-known data analysis tool for markerless pose estimation, known as DeepLabCut, is investigated for this purpose. The performance of this tool is enhanced through the design and implementation of a pre-processor. DeepLabCut is a markerless pose estimation toolbox based on deep learning, which overcomes the issue of making manual annotations frame-by-frame. Limitations from manually marking the MRI images are addressed by implementing transfer learning with convolutional neural networks to achieve accurate, user-defined articulator tracking without markers. Current …

Towards A Methodology For Form Creativity Of Building Envelope To Enhance Thermal Performance (Biomimicry As A Tool For Form Creativity), Amal Ebrahim Ahmed Hassanin, Marwa Atef Abd-Elhady

Mansoura Engineering Journal

It has become necessary to achieve thermal comfort for users in buildings, which enhances human ability to work, create, or rest and enjoy. Since the building envelope serves as the link between the inside and outside of the building, forming the building envelope creatively improves and enhances thermal performance of buildings, thus achieving thermal comfort for users. Nature is the primary teacher and source of creativity for humans. Therefore, Biomimicry was chosen as a tool for creative formation, and the descriptive analytical approach was used by analyzing an example where nature was simulated in the formation of the external envelope …

An Open-Source Linear Actuated-Quartz Tube Furnace With Programmable Ceramic Heater Movement For Laboratory-Scale Studies Of Combustion And Emission, Casey Coffland, Ryan Bixler, Elliott T. Gall

Mechanical and Materials Engineering Faculty Publications and Presentations

The Linear Actuated Quartz Tube Furnace (LA-QTF) is an instrument engineered to heat and combust materials under controlled conditions, capable of achieving flaming and smoldering states. A ceramic ring furnace is linearly actuated parallel to the length of a quartz tube. The mode of combustion depends on temperature, fuel composition, and oxygen availability; the LA-QTF regulates combustion by controlling the temperature and position of the ring furnace, and airflow within the tube. The LA-QTF can maintain temperatures between 23 °C and 530 °C for extended time periods, with stable temperatures over long-duration (∼120 min) experiments. Flow rate is dependent on …

On The Application Of Machine Learning Techniques For Quality Assurance In An Automobile Paint Shop, Anis Fatima, Shakeel Ahmed, Muhammad Akif Shan

Michigan Tech Publications

The automotive industry depends on high-quality paint coatings for both aesthetic appeal and functional performance. However, surface imperfections such as scratches, paint runs, and orange peel can arise from process and environmental variations. This study employs machine learning (ML) and exploratory data analysis (EDA) to identify key factors that influence surface defect formation in automotive painting. Using historical production data from Lucky Motor Corporation Limited, models based on linear regression, support vector machines (SVM), and random forests were developed and validated under various process conditions. The best-performing model achieved an R² of 0.94, with a mean absolute error (MAE) of …

The Impact Of Optimization Approximation Algorithms On The Performance Of The Bht-Qaoa, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean …

Optimization And Energy Efficiency Analysis Of An Automatic Feed Mixer With A Rotating Drum Mechanism, Kris Witono, Talifatim Machfuroh, Nurlia Pramita Sari, Lisa Agustriyana, Aini Lostari

Journal of Mechanical Engineering Science and Technology (JMEST)

Energy-efficient feed mixer machines are important for improving the productivity and sustainability of small and medium-scale livestock farms. Previous studies primarily focused on either structural performance or mixing efficiency, with limited studies integrating both aspects. Therefore, this study evaluated an automatic rotating-drum feed mixer by combining Finite Element Method (FEM) analysis and energy modeling. The study used FEM simulations for different materials, namely A36 steel alloy, stainless steel 304, aluminium 6061, and galvanized steel, with thicknesses of 3 mm and 4 mm, as well as different drum systems. The FEM results showed that all evaluated materials met the minimum safety …

Techno-Economic Assessment And Life Cycle Analysis Of Electrocatalytic Reduction Of Co2 To Ethanol., Omotolani Elizabeth Oduyebo

LSU Master's Theses

This study presents a techno-economic analysis (TEA) and life cycle assessment (LCA) of the electrocatalytic reduction of CO₂ to ethanol, a multi-carbon (C₂) product with significant market value. Prior TEA studies have relied on simplified lump-sum separation cost estimates, and prior LCA studies have rarely examined the combined effect of CO₂ source and electricity supply on carbon intensity gaps that this work addresses through process-simulation-grounded analysis. An Aspen Plus process simulation was developed for an anion-exchange membrane (AEM) electrolyzer system coupled with an extractive distillation separation train using ethylene glycol as the entrainer, achieving 99.9 wt.% ethanol purity …

The Heterogeneous Response Of Astrocytes In Different Brain Regions Under Metabolic Stress, Pawan Kumar Thapaliya

USF Tampa Graduate Theses and Dissertations

Astrocytes play a central role in maintaining ionic homeostasis, neurotransmitter clearance, and brain en-ergy metabolism. Their excitability, reflected in activity-evoked intracellular sodium (Na+, [Na+]i) and calcium (Ca2+,[Ca+]i) dynamics, is increasingly recognized as a critical determinant of neuronal function and vulnerability, particularly under metabolic stress. Despite their importance, the mechanisms underlying astrocytic ionic signaling and its regional heterogeneity remain incompletely understood. In this thesis, I developed detailed computational and biophysical models to investigate astrocytic Na+ and Ca2+ dynamics, their bioenergetic consequences, and the roles of key ion transporters under physiological and metabolically stressed conditions, validating the models against experi- mental data. …

Influence Of Gas Atmosphere And Deposition Time On Si/Mno₂ Thin Films Properties Deposited By Magnetron Sputtering For Supercapacitor Application, Tansya Trisnatika Dewi, Boon Tong Goh, Reza Akbar Pahlevi, Ishmah Luthfiyah, Markus Diantoro

Journal of Mechanical Engineering Science and Technology (JMEST)

Thin films of MnO₂ were successfully deposited onto Si substrates using the magnetron sputtering technique in various gas atmospheres and deposition times to study their influence on the structural, morphological, electrical, and electrochemical properties for application as supercapacitor electrodes. Structural analysis using XRD showed that deposition under an Ar+O₂ gas atmosphere increased the crystallite size and crystallinity of the Si/MnO₂ films, while shorter deposition times reduced the crystallite size and lowered the crystallinity. Surface morphology observation using SEM showed that the film deposited in an Ar+O₂ gas atmosphere had smaller, more uniform, evenly distributed, and closely …

Influence Of Fiber Loading And Cu/Sio₂ Fillers On Mechanical And Physical Properties Of Sugarcane Bagasse Epoxy Composites, Ansor Salim Siregar, Indra Indra, Agung Fauzi Hanafi, Sunny Ineza Putri

Journal of Mechanical Engineering Science and Technology (JMEST)

The rising worldwide need for lightweight, high-strength, and eco-friendly materials has driven considerable investigation into natural fiber composites. This research offers an extensive examination of the creation and characterization of a new hybrid composite designed for high-performance sports equipment. The composite structure consists of an epoxy matrix strengthened with sugarcane bagasse fibers and synergistic blend of copper and silicon dioxide hybrid particulate fillers. Three unique composite variations were created, with systematically adjusted weight fractions of sugarcane bagasse fiber (35%, 31%, 27%) and hybrid fillers. A thorough assessment of mechanical and physical properties was carried out following ASTM standards. The results …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Advanced Finite Element Modeling And Design Enhancement Of Slender Square Concrete-Filled Double-Skin Steel Tubular Columns, Mahmoud T. Nawar, Ayman El-Zohairy, Mohamed Emara, Raghda I. Halima

Faculty Publications

Limited research exists on the behavior of square CFDST slender columns, especially under the consideration of the relation global buckling and confinement effect. This study evaluates square concrete-filled double-skin steel tubular (CFDST) columns using nonlinear finite element analysis (FEA) to simulate structural behavior under axial and eccentric loads until failure. Parametric analyses of extensive specimens of square CFDST pinended columns evaluate various parameters, providing design insights for engineering applications. The study was conducted over a wide range of slenderness ratios. Four concrete varieties with compressive strengths were tested: normal concrete (NC), engineered cementitious composites (ECCs), high-strength concrete (HSC), and ultra-high-strength …

Post-Fire Flood Modeling In Ruidoso, New Mexico: Improving Burn Scar Runoff Predictions, Pramesh Bhaila

Civil Engineering ETDs

Recent large wildfires in New Mexico have demonstrated severe hydrological driven by vegetation loss and the development of hydrophobic soil surfaces. This thesis evaluates the performance of three infiltration models—Curve Number (CN), Linear Constant (LC), and Green–Ampt (GA)— for the watershed impacted by the 2024 South Fork and Salt Fires near Ruidoso, New Mexico. All three models performed comparably during post‑fire simulations, with strong performance in 2024 and notable degradation in 2025, particularly for the CN. Post-fire analyses indicate substantial reductions in infiltration capacity, with CN increasing by 18–24%, LC initial deficit decreasing by 65–77%, effective hydraulic conductivity reduced by …

Evaluation Of Intracavity Electromagnetic Field Probes And Sources: Implications For Shielding Effectiveness, Joseph Anthony Ferreri

Electrical and Computer Engineering ETDs

To measure the electric field in a reverberant cavity, a small, minimally invasive probe is required. Common solutions include electrically small surface mounted monopole antennas, B-dots, and D-dots. To obtain an accurate field measurement with a particular probe, it is necessary to characterize it to compensate for its ability to convert electric field into voltage which requires a gauge factor known as effective height. The characterization process is straight forward in open space on a ground plane but requires more insight when in situ in a reverberant cavity. This work adapts ground plane probe characterization methods for cavity measurements, facilitating …

Millimeter-Wave Antenna Gain Enhancement Through Stacked Planar Substrates, Zachary Bergstedt

Electrical and Computer Engineering ETDs

This work presents a new wideband millimeter-wave (mmWave) and sub-terahertz antenna design with flexible directivity through the integration of stepped horn antennas and transverse substrate integrated waveguide (SIW) slots for radar and communication applications. The work gives a theoretical and analytical basis for this filter-inspired approach to improving bandwidth and directivity, and presents design and results for a standalone stepped horn, a Ka-band antenna with a solid stepped horn and SIW feed, and W-band antennas with empty SIW feeds and stepped horns manufactured out of multiple planar layers. The realized antennas show bandwidth up to 40% and gain up to …

Alternative Positioning, Navigation, And Timing In Global Navigation Satellite System Denied Environments, Joshua R. Atencio

Electrical and Computer Engineering ETDs

Global Navigation Satellite Systems (GNSS) provide the majority of critical positioning, navigation, and timing (PNT) services for civilian, commercial, and military applications. However, GNSS is vulnerable to service denial from spoofing and jamming from adversaries and environmental obstruction. These vulnerabilities highlight the need for resilient Alternative PNT (APNT) methods. This dissertation investigates APNT frameworks operating in GNSS denied environments. We develop coalition formation and matching theoretic models that allow users APNT services from anchor nodes under resource constraints and in adversarial or emergency conditions. The proposed frameworks optimize positioning accuracy, network utility, and system stability while accounting for geometric dilution …

Calibration Methodology, Diagnostic Performance, And Redesign Of The Current Monitors On The Z Machine At Sandia National Laboratories, Stacie Hernandez

Electrical and Computer Engineering ETDs

A proper evaluation of the current diagnostics fielded in the inner-MITL region of the Z facility in 3D simulation models had not been performed until now. The evaluation of the current monitors has brought insight to their performance in a new view that has led to discoveries. The B-dot probe was the current diagnostic-of-choice since before the refurbishment of the Z facility [1] and until the development of the Inductively Driven Transmission Line (IDTL) current diagnostic [2]. Experimental data has shown that the IDTL can produce cleaner signals and it is more robust than conventional B-dots. Simulation (modeled using COMSOL …

Applications And Comparisons Of Machine Learning Methods In Ultra-Fast Laser Control, Aasma Aslam

Electrical and Computer Engineering ETDs

This dissertation demonstrates the applications and comparative analyses of machine learning methods in ultrafast laser control. By learning the relationship between the system’s input parameters and output pulse characteristics, the performance of a laser can be significantly improved. In this work, the results are presented in two stages by utilizing data from the femtosecond laser system. The first stage concerns two neural networks, named NN1 (fitrnet) and NN2 (feedforwardnet). The second stage, which extended with five different models, namely the linear regression (fitlm), the support vector machine (SVM), the Gaussian process regression (GPR), the boosted tree (fitrensemble), and LASSO (fitrlinear), …

Kinetic Study Of Oil–Water Separation In A Dual Port Inlet Cyclone Separator, Ikhwanul Qiram, Agung Nugroho

Journal of Mechanical Engineering Science and Technology (JMEST)

In this study, a Computational Fluid Dynamics method is used to investigate the oil–water separation kinetics in a dual-port inlet cyclone separator. This was achieved using the Reynolds Stress Model coupled to an Eulerian multiphase framework. Three Reynolds numbers were studied (Re = 1.41×10⁵, 1.94×10⁵ and 2.52×10⁵) to analyse the flow; axial velocity distribution, vortex stability, radial migration velocity and separation efficiency were examined individually. Results indicate that both the radial migration velocity (vᵣ) and separation probability (p_remove) grow with Reynolds number, especially for larger oil droplets (10–100 µm). The best condition concerned is that …

Brain-Based Mechanisms Of Behavioral Impairment In Fetal Alcohol Spectrum Disorder (Fasd): The Neuroimaging Biomarkers Of Inhibitory Control, Zinia Pervin

Biomedical Engineering ETDs

The developing brain is highly susceptible to alcohol-induced toxicity, often resulting in long-term deficits in executive function and behavioral regulation. Inhibitory control impairments are among the most prominent deficits observed in Fetal Alcohol Spectrum Disorder (FASD). This study investigated the neural mechanisms of inhibitory dysfunction using a multimodal MEG–DTI approach in 67 children aged 6–8 years (34 with FASD, 33 controls) who performed a Go/No-Go task. Source-level MEG analyses revealed reduced stimulus-locked cortical activation in the anterior cingulate cortex and significant group-by-hemisphere interactions in the superior parietal cortex and cuneus. Time–frequency analyses showed diminished response-locked beta power in the sensory-motor …