Engineering Commons^™

Experimental Evaluation Of Membranes In The Recovery Of Hydrogen From Serpentinization, Rafael Dos Santos

LSU Master's Theses

Natural hydrogen generated through serpentinization reactions has emerged as a promising low-carbon energy resource. However, serpentinization-derived gas streams commonly contain methane (CH₄) and carbon dioxide (CO₂), requiring purification before hydrogen can be utilized in industrial applications. Membrane-based gas separation technologies offer a potentially cost-effective solution for hydrogen recovery; however, to the best of the author's knowledge, no previous studies have specifically investigated their application to serpentinization-derived gas streams.

This work evaluates the performance of two commercial polymeric membrane modules (Evonik and Generon) and one palladium-copper (Pd-Cu) membrane module (Okaya) for hydrogen recovery from gas compositions representative of serpentinization environments. An …

Impact Of Asphalt Binder Chemical Composition On The Performance Of Asphalt Mixture., Sagar Parajuli

LSU Master's Theses

Asphalt binder is composed of four chemical fractions, namely, saturates, aromatics, resins, and asphaltenes (SARA). Binders with the same performance grade (PG) can exhibit different chemical compositions due to variability in crude oil sources and refining processes. The objective of this study was to evaluate how such chemical variations influence the mechanical performance of asphalt mixtures at high, intermediate and low temperatures.

Eighteen 12.5-mm nominal Maximum Aggregate Size mixtures were designed using two aggregate types (limestone and gravel), and three binder grades (PG 67-22, PG 70-22m and PG 76-22m), each from three different sources with varying chemical fractions. A suite …

A Reduced-Order Framework For Stochastic Criticality Estimation In Granular Energetic Materials, Philip T. Melton

LSU Master's Theses

Granular energetic materials (EMs) exhibit stochastic shock initiation because pore collapse, frictional dissipation, and localized thermal activation depend on microstructural descriptors that vary between nominally identical samples. Fully resolved mesoscale and atomistic calculations can represent these mechanisms, but their computational cost limits direct ensemble evaluation of microstructure-conditioned criticality thresholds. This thesis introduces a reduced-order framework for stochastic criticality estimation in granular EMs by coupling five explicitly defined model components: first, a one-dimensional steady compaction-shock model that maps initial solid volume fraction and shock pressure to a bulk mass-specific dissipated-work budget; second, an SEM/synthetic-image segmentation workflow that extracts pore area, perimeter, …

Direct Measurement Of Stem Position For Gas-Lift Valves While Flowing At High Pressure, Paulo E. Reitz

LSU Master's Theses

The present work challenges the idea of estimating the gas lift valves true dynamic stem position by providing an alternative capable of tracking the real stem position during flow tests. This is of interest for the improvement of performance determination tests and procedures that involve critical safety concerns, complex training and considerable investments, both in time and money. This scenario, together with the strong interest of industry for valve optimization and the scarcity of works available in literature, encouraged this study. An apparatus named Vision System was developed for tracking and measuring the stem position during flow tests. The developed …

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 …

Public Understanding Of Ai-Enabled Cyber Threats And Its Impact On Cybersecurity Governance In The United States, Charlotte M. Barbrick

LSU Master's Theses

Artificial intelligence is spreading quickly in workplaces and everyday life, and that fast growth is creating cybersecurity and privacy risks that current laws and organizational practices do not fully address. This thesis asks whether AI-specific knowledge shapes public support for cybersecurity governance of AI-enabled systems in the United States. Using nationally representative survey data from the Pew Research Center’s American Trends Panel (ATP) Wave 119, fielded December 12--18, 2022, I estimate weighted regression models to test how objective AI knowledge shapes support for cybersecurity governance. I also test whether concern about data misuse helps explain that relationship and whether AI …

Implantable, Sensor-Embedded Vascular Graft Towards Wireless Monitoring Of Stenosis, Nnamdi Dike

LSU Master's Theses

Arteriovenous (AV) grafts are commonly used to provide vascular access for hemodialysis in patients with end-stage renal disease. Despite their widespread use, AV grafts are prone to complications such as stenosis and thrombosis. Early detection of these conditions remains challenging with current monitoring methods too costly or insufficient. This work presents the design, fabrication, and validation of an LC pressure sensor embedded within a model AV graft to enable real-time monitoring. The proposed system integrates a parallel-plate capacitive pressure sensor with a spiral inductor to form an LC circuit embedded within an elastomeric graft wall. The Ecoflex 00-30 dielectric layer …

Movable Bed Physical Model Investigation Of Bed Level Changes Caused By River Sediment Diversions, Hayden Cole Franklin

LSU Master's Theses

Since 1932, due to human and natural processes, over 2,000 square miles of Louisiana’s coast have been lost. River sediment diversions have been proposed as sustainable options to combat land loss. These projects, like the proposed Mid-Barataria Sediment Diversion, are designed to deliver sediment-rich Mississippi River water into nearby bays and estuaries, helping build and maintain land. However, river sediment diversions may alter river hydraulics and sediment transport, potentially inducing upstream degradation and downstream aggradation. Using the Lower Mississippi River Physical Model (LMRPM), this study quantitatively analyzed bed level changes as well as hydraulic conditions associated with the proposed Mid-Barataria …

Gas Lift Valve Operation Under Elevated Pressure And Temperature Conditions, Pedro O. De Melo Flores

LSU Master's Theses

Gas lift valves are critical components in artificial lift systems, and their performance directly influences well unloading efficiency and long-term production. Current industry models used to correct valve opening and closing pressure for temperature rely on assumptions that have not been extensively validated against experimental data. This study experimentally investigates the opening and closing behavior of injection pressure-operated (IPO) gas lift valves under controlled elevated-temperature conditions. A load rate test bench was modified to include a heating system capable of maintaining valve temperatures up to 220°F. Five gas lift valve designs from multiple manufacturers were tested over a range of …

A Pretraining-Based Framework For On-Device Training Of Imu-Based Locomotion Mode Detection For Wearable Active Exoskeletons, Muhammad Tahir Khan

LSU Master's Theses

Active exoskeletons are being developed to support human movement in physically demanding industries such as construction. For these systems to work effectively, they must be able to correctly identify the user’s current activity. This process is known as locomotion mode detection and plays an important role in selecting the appropriate control parameters for exoskeletons. Many existing approaches use inertial measurement units (IMUs) to recognize these activities and have shown strong performance. However, most of these methods depend on large amounts of labeled data collected under specific conditions. As a result, they often do not perform well when applied to new …

Wearable Sensor System Used To Measure Linear And Angular Acceleration Of The Head Of American Football Players, David A. Emerson

LSU Master's Theses

American football players are repeatedly subject to high-velocity impacts; collisions can exceed 100 g in the NFL [1]. Rotational accelerations of the head caused by high-velocity collisions have been proven to be extremely damaging to the highly organized brain fibers at the brain-skull interface [2]. The sensor system (SS) measures the nonplanar acceleration at four locations on a chin strap using ADXL314 accelerometers and utilizes rigid-body kinematics to estimate the head rotational state from the measured acceleration of the SS [3]. The SS was validated through progressive testing, including steady-state, linear impact, and rotational impact testing. The ADLX314 sensor exhibited …

Marine Vehicle Dynamics Using Koopman Operator Theory With Hybrid Observables, Mikhalib A L Green

LSU Master's Theses

Accurate modeling of marine vehicle dynamics remains challenging due to strong nonlinear hydrodynamic effects, environmental disturbances, and sensitivity to configuration changes, particularly for small-scale platforms. Classical physics-based models require extensive parameter identification and often exhibit degraded performance outside narrow operating regimes, while purely data-driven approaches may lack structure or impose high computational cost. This thesis presents a data-driven Koopman operator framework with hybrid observables for modeling the dynamics of unmanned marine vehicles. The proposed approach combines structured monomial observables with a learned neural network embedding to construct a lifted state representation in which the nonlinear vehicle dynamics are approximated by …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks, Donovan Gegg

LSU Master's Theses

Autonomous Underwater Vehicles (AUVs) are untethered robotic platforms used for tasks such as seafloor mapping, infrastructure inspection, and environmental monitoring. Recent technological advances have produced smaller, more affordable platforms, broadening access to research teams and small companies alike. This miniaturization comes at the cost of them handling drawbacks associated with a more compact machine such as reduced battery capacity as well as limited processing and sensing capabilities. These constraints make small-sized marine vehicle’s reliability critical as they can cause malfunctions, making the loss of a vehicle more likely. Actuator faults are particularly consequential as unintended and unstable control in an …

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Multimodal Thermal And Mechanical Characterization Of Cryopreservation Effects In Biological Cells, Subhrajyoti Sourav Kumar Kundu

LSU Master's Theses

Cryopreservation is critical for long-term storage of cells, tissues, organs, and reproductive cells in medicine, biotechnology, and conservation. However, its success is limited by ice formation and cryoinjury, prompting extensive research into analytical tools for understanding and improving cryopreservation outcomes. We outline the principles of each technique and how they are used to detect key thermal and physical events such as ice nucleation, vitrification, devitrification, and cryoinjury. DSC enables quantitative thermal characterization including critical cooling/warming rates (ranging from 1-200°C/min for various systems) and glass transition temperatures. Cryomicroscopy provides direct real time visualization of ice crystal dynamics, distinguishing intracellular versus extracellular …

Envis: A User-Centered Web-Based Tool For Interactive Visualization Of Environmental Geospatial Data, Saima Sanjida Shila

LSU Master's Theses

Data visualization is an essential part of analyzing environmental geospatial data. Despite having the availability of large environmental datasets, there remains a lack of easily accessible, user-friendly, and interactive visualization tools in this field. Therefore, this study aims to develop a user-friendly and easily available web-based visualization tool. Before developing the tool, we conducted a survey of researchers at the LSU Coastal Studies Institute to collect their opinions on currently available visualization tools. In the survey, 55% of participants responded between somewhat satisfied to dissatisfied with their current visualization tools. Most of the participants mentioned two major limitations of existing …

Lithological Modeling And Aquifer Characterization Of The Coastal Lowlands Aquifer System In Texas, Emmanuel Egyin Hayford

LSU Master's Theses

The Texas Coastal Lowlands Aquifer System (CLAS) is a crucial water resource for the Gulf Coast, serving millions through municipal, agricultural, and industrial supplies. However, its sustainability is increasingly threatened by factors such as population growth, saltwater intrusion, land subsidence, and climate change. A significant limitation is the absence of a comprehensive, data-rich, three-dimensional lithologic model that accurately represents the intricate structure and geometry of the aquifer. Existing models are overly simplistic, treating the hydrogeologic unit as laterally uniform and failing to capture its true hydrogeologic complexity. This study addresses this gap by developing the first high-resolution 3D lithologic model …

Seeing What Matters: Safety-Critical Semantic Segmentation Via Transfer Learning On Construction Sites, Obiora J. Odugu

LSU Master's Theses

Autonomous robots are increasingly deployed on construction sites for tasks such as progress monitoring, inspection, and safety assessment. For these robots to operate effectively, they must perceive and interpret complex, dynamic environments populated by workers, machinery, and unstructured terrain. Achieving reliable perception depends on high performing semantic segmentation models trained on large volumes of annotated data—an expensive and logistically challenging requirement in construction due to privacy restrictions, variable site access, and slow digitalization. This research addresses the challenge of limited labeled data by investigating transfer learning as a label-efficient approach for construction-site segmentation. Specifically, it explores whether road construction imagery—abundant …

Effects Of Hydrological Connectivity On Created Marsh Sustainability, Katie Swann

LSU Master's Theses

While efforts are being made to curb the high rate of coastal land loss in Louisiana, and even to restore ecosystem services and functions, it is generally accepted that restoration of these areas and functions is not a one-to-one value. It may take decadal time scales to reach efficient functional processing of nutrients and carbon storage rates, which may never reach true capacity comparable to similar naturally-organized systems. Although it is imperative to preserve these areas, restoration of ecosystem services and biogeochemical processes of coastal wetlands are preferable to future without action scenarios. Effectively directing the use of the limited …

Sustainable Robotic Construction: A Computational Particle Packing Approach For The Development Of Sustainable, Low-Cement 3d Printable Materials, Aranya Aumit Paul

LSU Master's Theses

Construction 3D Printing (C3DP) holds great potential for automated construction but faces key challenges, including the high Portland cement content of its printing materials. The high cement content raises material costs, causes shrinkage and thermal cracking, and increases its carbon footprint. This study introduces a novel hybrid computational pipeline that combines image based automated gradation analysis with 2D stochastic particle packing simulation, to design low-cement printable materials with minimal experimental testing. The image-based gradation system achieved a mean absolute error under 3.64% across six fine and coarse aggregate types. The particle packing simulation demonstrated an average error of less than …

Chplx: The Hpx Solution For Bridging Chapel And C++, Shreyas Swanand Atre

LSU Master's Theses

Historically, domain scientists faced steep learning curves due to low-level programming models and fragmented tooling. Between 2003 and 2008, Cray, now part of HPE, introduced the Chapel language as part of DARPA’s High Productivity Computing Systems (HPCS) program. Today, Chapel remains under active development and is used across research and production projects. In parallel, the STE||AR Group has advanced C++-based parallel programming through HPX, a standards-conforming runtime that provides lightweight tasking, futures, and distributed execution while abstracting much of the algorithmic “heavy lifting.” Yet for many domain scientists, C++ presents a steeper learning curve than Chapel. To close this complexity …

Ai-Based Object Detection And Risk Identification For Enhanced Construction Site Safety, Mahdi Bonyaniakbarabadi

LSU Master's Theses

Recent advancements in computer vision for construction site safety have encountered significant hurdles, especially in the nuanced tasks of object detection and the identification of unsafe worker behaviors. These challenges are often exacerbated by complex and cluttered backgrounds, wide variations in object scale, and inconsistent image quality. While existing methodologies have utilized attention mechanisms to analyze spatial and temporal features, they frequently neglect the benefits of adaptive sampling and channel-wise feature adjustments, thereby failing to exploit potential spatiotemporal redundancies. This thesis introduces a two-pronged approach to address these limitations. First, we propose the Optimized-Position Network (OP-Net), a novel architecture for …

A Multi-Scale Numerical Analysis Of Wave Propagation Through Coastal Vegetation Using Proteus & Xbeach, Cole M. Statler

LSU Master's Theses

Coastal vegetation attenuates waves in the nearshore zone and protects shorelines from storm surge and wave action. It also traps nutrients and sediment, and participates in the formation of land over longer time scales. These features of vegetation make it a common component of nature-based engineering projects. Interaction of water waves and flow with vegetation, however, can involve unsteady and turbulent flow, non-hyrdostatic pressure effects, and air entrainment that can hinder the use of simple parametric representations of vegetation drag. XBeach, a one- and two-dimensional production model applies reduced-order methods, prioritizing computational efficiency. Vegetation is represented in XBeach as a …

The Operational Performance Of Buildings In A Humid Subtropical Climate: Insights From Empirical Data And Assessment Models, Oluwafemi Awolesi

LSU Master's Theses

This thesis evaluates the operational performance of residential and non-residential buildings in a humid subtropical climate, emphasizing Indoor Environmental Quality (IEQ) and energy performance through empirical data and assessment models. Motivated by the increasing need for integrated sustainability evaluations, this study combines a systematic review with multi-method field investigations to advance understanding in this domain. The systematic review critically analyzed 99 case study articles, identifying dominant IEQ assessment methodologies and highlighting methodological inconsistencies that hinder cross-study comparability. Emerging computational approaches, although promising, remain underutilized. The review advocates for standardized, climate-responsive, and feedback-oriented evaluation frameworks. Field investigations assessed IEQ and energy …

Adaptive Multi-Agent Reinforcement Learning For Electric Vehicle Charging Optimization Under Dynamic Traffic Conditions, Shaghayegh Rabbanian

LSU Master's Theses

Electric vehicle (EV) charging optimization is a critical challenge in sustainable transportation. This study focuses on three fundamental questions: (1) when is the best time to charge an EV, (2) where is the optimal charging location, and (3) how should charging be planned considering navigation and routing decisions. Our primary objective is to determine the optimal time and location for EV charging while accounting for key factors such as real-time traffic conditions, spatial distribution of charging stations, and EV-specific attributes such as state of charge (SOC), driving range, and efficiency. To develop a robust and adaptive EV charging recommendation system, …

Efficient Small Tool Detection In Construction Via Lightweight Deep Neural Networks, Maryam Soleymani

LSU Master's Theses

Construction sites are dynamic and inherently hazardous environments, where small hand tools—although essential—pose serious safety risks due to their frequent use, portability, and tendency to be misplaced or dropped. This study introduces a novel and lightweight deep learning-based architecture, Lightweight Small Tool Detection (LSTD), specifically designed for fast detection of small tools in unstructured and challenging construction environments. Recognizing that small object detection remains a persistent limitation in existing computer vision models, particularly under poor lighting or cluttered backgrounds, LSTD integrates advanced modules for enhanced feature extraction, fusion, and classification. It achieves notable improvements in accuracy, recall, and computational efficiency …

From Devices To The Cloud: Digital Forensics In The Changing Social Media Landscape, Joseph Brown

LSU Master's Theses

This thesis presents a comprehensive digital forensic analysis of emerging and alternative social media platforms, including Truth Social, Threads, Bluesky, Nextdoor, and Neighbors. These platforms, which range from politically aligned alt-tech networks to hyperlocal neighborhood apps, present unique forensic challenges and security vulnerabilities. Across all case studies, established forensic techniques were applied using a hybrid methodology combining mobile device analysis, network traffic monitoring, and API interrogation. Findings include the discovery of plaintext credentials, session tokens, and other sensitive artifacts, particularly in platforms with weaker security postures such as Truth Social, Bluesky, Nextdoor, and Neighbors. Threads, by contrast, demonstrated greater resilience …

Empirical Analysis Of Incentivized Energy Assessment Recommendations And Iiot Cost-Effectiveness In U.S. Smes, Fatemeh Ghafari

LSU Master's Theses

The industrial sector in the United States accounted for 33% of total energy consumption in 2022. Small and medium-sized enterprises (SMEs), which comprise 99.9% of U.S. businesses present a critical opportunity for energy efficiency improvements. Although energy audits provide actionable Assessment Recommendations (ARs) to reduce consumption, high implementation costs remain a key barrier to adoption.

While prior studies have explored rebate programs, most are limited to residential and commercial sectors or focus narrowly on specific technologies or geographic areas for the industrial sector. The objective of this study is to empirically investigate the “Incentivized Assessment Recommendation” (IAR) trends and to …