Engineering Commons^™

Understanding And Modeling Drivers’ En-Route Diversion Behavior During Congestion, Mohammad Shapouri

LSU Doctoral Dissertations

In the field of transportation, traffic assignment models primarily have been used to forecast driver route preferences, translating their choices into traffic flow patterns across networks. These models are grounded in distinct behavioral theories and strive to explain how drivers navigate routes based on network features and personal tendencies. Using an aggregation approach, conventional traffic assignment models distribute demand among paths, considering utility and attractiveness. Despite their prevalent use in transportation planning and operations, the fundamental behavioral assumptions of these models have rarely been thoroughly explored. This gap is further compounded by their limited consideration of real-time adjustments and choices …

Understanding And Modeling Drivers’ En-Route Diversion Behavior During Congestion, Mohammad Shapouri

LSU Doctoral Dissertations

In the field of transportation, traffic assignment models primarily have been used to forecast driver route preferences, translating their choices into traffic flow patterns across networks. These models are grounded in distinct behavioral theories and strive to explain how drivers navigate routes based on network features and personal tendencies. Using an aggregation approach, conventional traffic assignment models distribute demand among paths, considering utility and attractiveness. Despite their prevalent use in transportation planning and operations, the fundamental behavioral assumptions of these models have rarely been thoroughly explored. This gap is further compounded by their limited consideration of real-time adjustments and choices …

An Investigation Into The Challenges Of Contemporary Additive Manufacturing: Insights Into The Metallurgical Response Of Materials And Relevant Solution, Huan Ding

LSU Doctoral Dissertations

Additive Manufacturing (AM) has gained attention in recent years due to its unique capabilities in the fabrication of complex parts. As with any new research, there is still a lack of sufficient understanding in the field of additive manufacturing, and further investigation is needed to solve existing problems. Ultimately, the aim is to enable the widespread use of AM components across various industries.

Chapter One provides a brief introduction to the background and current bottlenecks of additive manufacturing technology. Chapter two focuses on the development of high-strength 7075 aluminum alloy (Al7075) for Fused Deposition Modeling and Sintering (FDMS) technology. Al7075 …

Electrophertic Deposition And Characterization Of Molybdenum Disulfide On Silicon Substrates, Alex J. Young

LSU Doctoral Dissertations

The electrical characteristics of 2D materials such as high electron mobility and current density are of great interest to various fields from optoelectronics to renewable energy. Researchers have focused their efforts on transition metal dichalcogenides (TMDCs) due to their direct energy band gap. One such TMDC that has garnered much attention is molybdenum disulfide (MoS2). MoS2 has electrical properties comparable to graphene and is a TMDC with characteristics amenable to applications such as solar cells and sensors. Commonly deposited through time-consuming and complex deposition methods such as chemical vapor deposition (CVD), the viability of MoS2 as an electronic material will …

Structural Health Monitoring And Repair Of Welded Thermoplastic Composite Joints Using Embedded Multifunctional Films, Wencai Li

LSU Doctoral Dissertations

Thermoplastic composites (TPCs) have gained widespread use, particularly in large or integrated structural components, necessitating effective joining techniques. Fusion bonding (welding) has emerged as a suitable method for TPCs due to their ability to be reshaped through heating and cooling, eliminating the need for mechanical fasteners, long curing times, and extensive surface preparation. Among the welding techniques, ultrasonic welding (USW) stands out for its fast-cycling time and potential for automating large-scale structures, thereby reducing energy consumption. However, limited industrial applications of USW in this context require further knowledge to instill confidence in the process. Moreover, composite structures are susceptible to …

Cyberinet: Integrated Semi-Modular Sensors For The Computer-Augmented Clarinet, Matthew Bardin

LSU Doctoral Dissertations

The Cyberinet is a new Augmented instrument designed to easily and intuitively provide a method of computer-enhanced performance to the Clarinetist to allow for greater control and expressiveness in a performance. A performer utilizing the Cyberinet is able to seamlessly switch between a traditional performance setting and an augmented one. Towards this, the Cyberinet is a hardware replacement for a portion of a Clarinet containing a variety of sensors embedded within the unit. These sensors collect various real time data motion data of the performer and air fow within the instrument. Additional sensors can be connected to the Cyberinet to …

Residential Building Flood Risk Assessment And The Benefits Of Home Elevation, Ayat Al Assi

LSU Doctoral Dissertations

Evaluating flood risk is an essential component of understanding and increasing community resilience. A robust approach for quantifying flood risk in terms of average annual loss (AAL) in dollars is needed to provide valuable information for stakeholder decision-making inside and outside the special flood hazard area (SFHA, which corresponds to the 100-year floodplain). To further inform flood mitigation strategies, quantifying flood risk reduction with home elevation above an initial first-floor height (FFH₀) and the cost effectiveness of federal mitigation assistance for elevation are important steps to enhance awareness of the effect of elevation in reducing flood risk. …

Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan

LSU Doctoral Dissertations

Machine learning (ML) is a powerful tool that provides meaningful insights for operators to make fast and efficient decisions by analyzing data from power systems. ML techniques have great potential to assist in solving optimization problems within a shorter time frame and with less computational burden. AC optimal power flow (ACOPF), dynamic economic dispatch (D-ED), and security-constrained unit commitment (SCUC) are the three energy management optimization functions studied in this dissertation. ACOPF is solved every 5~15 minutes. Because of the nonconvex and complex nature of ACOPF, solving this problem for large systems is computationally expensive and time-consuming. Classification and regression …

Software-Defined Networking Security Techniques And The Digital Forensics Of The Sdn Control Plane, Abdullah Alshaya

LSU Doctoral Dissertations

Software-Defined Networking (SDN) is an efficient networking design that decouples the network's control plane from the data plane. When compared to the traditional network architecture, the SDN architecture shares many of the same security issues. The centralized SDN controller makes it easier to control, easier to program in real-time, and more flexible, but this comes at the cost of more security risks. An attack on the control plane layer of the SDN controller is a major security concern.

First, centralized design and the existence of a single point of failure in the control plane compromise the accessibility and availability of …

Regenerative Medicine For Tendon/Ligament Injuries: De Novo Equine Tendon/Ligament Neotissue Generation And Application, Takashi Taguchi

LSU Doctoral Dissertations

Tendon and ligament injuries are debilitating conditions across species. Poor regenerative capacities of these tissues limit restoration of original functions. The first study evaluated the effect of cellular administration on tendon/ligament injuries in horses using meta-analysis. The cellular administration was effective in restoring ultrasonographic echogenicity and increasing vascularity during early phase of healing. Additionally, it improved microstructural organization of healed tissue in terms of cellularity and fiber alignment. However, the study did not support its use for increasing rate of return to performance, expression/deposition of tendon-specific genes/proteins, or mechanical properties.

The findings led to the second study that engineered implantable …

Shape Memory Polymer-Based Multifunctional Syntactic Foams, Siavash Sarrafan

LSU Doctoral Dissertations

With the increase in popularity of shape memory polymers (SMPs), especially in applications such as aerospace, textile, biomedical engineering, and even structures, the weight of the material and the devices made with it has always been a crucial factor. Using the shape memory polymer as a matrix to make a syntactic foam is one of the best and most affordable approaches to creating a lighter material that still has the shape memory effect. The addition of particles of different stiffness, strength, and size, with variable fractions, creates a composite that enables engineering the mechanical, as well as other physical and …

Development Of Novel Electrodes And Electrolytes For Safer Aqueous Ammonium Ion Batteries With Enhanced Performance., Shelton Farai Kuchena

LSU Doctoral Dissertations

The Lithium-ion battery (LIBs) system has dominated the battery market because of its superior energy and power density. Problems related to LIBs such as safety, scarcity of cobalt and lithium have led researchers to explore alternative battery systems. NH₄⁺ ion is a nonmetal charge carrier with lower molar mass (18 mol g^-1) and smaller hydrated ionic size (3.31 Å) which results in excellent electrochemical properties. Furthermore, NH₄⁺ ion has a tetrahedral structure that has no preferred orientation as compared to spherical metal ions giving a different intercalation chemistry based on hydrogen bonding. These properties …

Domain Specific Analysis Of Privacy Practices And Concerns In The Mobile Application Market, Fahimeh Ebrahimi Meymand

LSU Doctoral Dissertations

Mobile applications (apps) constantly demand access to sensitive user information in exchange for more personalized services. These-mostly unjustified-data collection tactics have raised major privacy concerns among mobile app users. Existing research on mobile app privacy aims to identify these concerns, expose apps with malicious data collection practices, assess the quality of apps' privacy policies, and propose automated solutions for privacy leak detection and prevention. However, existing solutions are generic, frequently missing the contextual characteristics of different application domains. To address these limitations, in this dissertation, we study privacy in the app store at a domain level. Our objective is to …

Stabilizing Control Schemes For Grid-Connected Hybrid Pv-Energy Storage Systems, Indra Narayana Bhogaraju

LSU Doctoral Dissertations

A nonlinear stabilizing control scheme based on Lyapunov theory is proposed for a grid- connected hybrid photovoltaic (PV)/ battery/supercapacitor (SC) system. The system dynamics is developed in the stationary reference frame, and the state-space model of the system is derived and used to formulate the Lyapunov function (LF) candidate. The global asymptotic stability of the LF-based controller is discussed in detail. The real-time implementation feasibility of the proposed control scheme is validated through hardware-in-the-loop (HIL) studies of a grid- connected hybrid system under solar energy generation and grid load variations. To address the issue of digital computational time that leads …

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

Physics-Based Crystal Plasticity Model For Predicting Microstructure Evolution And Dislocation Densities, Juyoung Jeong

LSU Doctoral Dissertations

This work presents three different studies investigating plastic deformation mechanisms in metals and alloys using crystal plasticity finite element (CPFE) modeling. The first study presents a new nonlocal crystal plasticity model for face-centered cubic single crystals under heterogeneous inelastic deformation. The model incorporates generalized constitutive relations that incorporate the thermally activated and drag mechanisms to cover different kinetics of viscoplastic flow in metals and describes the plastic flow and yielding of single-crystals using dislocation densities. The model is compared to micropillar compression experiments for copper single crystals and clarifies the complex microstructural evolution of dislocation densities in metals. The second …

The Impact Of Case Management Intervention For Insured Asthma Patients In Louisiana, An Empirical Study, Mohamed Mohamed Ohaiba

LSU Doctoral Dissertations

Asthma is a chronic condition whose symptoms are managed/prevented using medication and interventions. The overarching objective of this study was to evaluate the impact of patients' demographics on case management enrollment and healthcare utilization, as well as to develop machine learning models to predict high-cost patients.

To accomplish these goals, the Man-Whiteness test, the chi-squares test, logistic regression and odds ratios, and machine learning models were implemented. The average cost of the non-enrolled CM group was significantly higher than the enrolled group (p-value .0001). In addition, the non-enrolled groups had considerably more visits to the emergency department than the other …

Atomistic Simulation Studies Of Thin Film Growth And Plastic Deformation In Metals And Metal/Ceramic Nanostructures, Reza Namakian

LSU Doctoral Dissertations

Despite the significant improvements in manufacturing and synthesis processes of metals and ceramics in the past decades, there are still areas in which the procedure is still frequently more of an art or skill rather than a science. Therefore, systematic and combined experimental and computational studies are required to facilitate the development of techniques that offer thorough understanding of the events taking place during manufacturing and synthesis processes. With regard to these issues, it is paramount to address microscale characterizations and atomic scale understanding of the events during fabrication processes. One of the focuses of this study is unraveling fundamental …

Non-Equilibrium Colloidal Phenomena In Magnetic Fields And Photoillumination: From Controlling Living Microbots To Understanding Microplastics, Ahmed Al Harraq

LSU Doctoral Dissertations

Colloids are a ubiquitous class of materials composed of microscopic particles suspended in a continuous phase which are found in everyday products and in nature. Colloids are also useful models for studying the spontaneous arrangement of matter from individual building blocks to mesophases. Standard treatment of colloid science is based on the assumption of equilibrium conditions, as defined in traditional thermodynamics. However, novel assembly mechanisms and motility are unlocked by pushing colloids away from equilibrium using external energy. In addition, many colloids in nature and in industrial applications exchange energy and mass with the surrounding environment thus behaving in a …

Data-Driven Nonparametric Joint Chance-Constrained Programming For Power Systems Scheduling, Chutian Wu

LSU Doctoral Dissertations

This dissertation is dedicated to implementing data-driven nonparametric joint chance constraints (JCC) to power system optimization problems. Power generated by renewable sources, such as solar farms, is an uncertain parameter. Several approaches solve optimization under uncertainty, including stochastic programming, robust programming, and chance-constrained programming. Uncertain parameters may not belong to any parametric class of probability functions. Thus, methods that consider such uncertainty as a random variable that fits in a known probability density function (PDF) have limitations. This study focuses on chance-constrained programming under nonparametric or data-driven distributionally robust uncertainty settings.

Studies based on chance-constrained programming usually focus on individual …

Computational Study Of C-C Coupling Reactions On Heterogeneous Catalysts, Md Saeedur Rahman

LSU Doctoral Dissertations

The utilization of carbon dioxide (CO₂) in chemical production has attracted global research interest. Reacting CO₂ with methane (CH₄) removes these greenhouse gases from the atmosphere and turns both compounds into building blocks for organic compound synthesis. A commonly explored pathway involves dry reforming of methane (DRM), which reacts CH₄ and CO₂ to form syngas, a mixture of H₂ and CO. Syngas is a widely used feedstock for synthesizing chemicals ranging from methanol to fuels via the Fischer-Tropsch (FT) process. However, DRM has a large positive ΔGº, which requires the …