Other Engineering Science and Materials Commons^™

A Study On High-Frequency Bending Fatigue, Microhardness, Tensile Strength, And Microstructure Of Parts Made Using Atomic Diffusion Additive Manufacturing (Adam) And Additive Friction Stir Deposition (Afsd), Hamed Ghadimi

LSU Doctoral Dissertations

This dissertation reports the findings of several studies on the mechanical and microstructural properties of parts made using atomic diffusion additive manufacturing (ADAM) and additive friction stir deposition (AFSD). The design of a small-sized bending-fatigue test specimen for an ultrasonic fatigue testing system is reported in Chapter 1. The design was optimized based on the finite element analysis and analytical solution. The stress–life (S–N) curve is obtained for Inconel alloy 718. Chapter 2 presents the findings of ultrasonic bending-fatigue and tensile tests carried out on the ADAM test specimens. The S-N curves were created in the very high-cycle fatigue regime. …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

Degumming Of Hemp Fibers Using Combined Microwave Energy And Deep Eutectic Solvent, Bulbul Ahmed

LSU Master's Theses

Hemp is considered as one of the sustainable agricultural fiber materials. Degumming or surface modification of hemp bast is needed to produce single fibers for ensuing textile and industrial applications. The traditional degumming process necessitates a high amount of alkali, which causes detrimental environmental pollution. This study offers a new method to degum hemp fibers with reduced use of harmful alkali and precious water resources. In this work, hemp bast fibers were degummed by using combined microwave energy and deep eutectic solvent (DES). The properties of hemp fibers manufactured by this method were investigated and compared with the traditional alkali …

An Improved Earned Value Management Method Integrating Quality And Safety, Brian Briggs

LSU Doctoral Dissertations

The construction industry invests significant time and money to improve quality and safety while reducing cost and schedule impacts. The industry has a sincere desire to improve construction project management methods to improve efficiency. Historically, quality and safety underperformances result from undermanaged quality control and safety activities. The cost and schedule impacts associated with poor quality work have always had an impact on construction operations. The unprecedented challenges and uncertainties of COVID-19 highlighted the need to improve the Earned Value Management (EVM) method within construction to reflect these quality and safety activities. The central goal of this dissertation is to …

Atomistic Thermo-Mechanical Description Of The Deformation Behavior, Scaling Laws, And Constitutive Modeling Of Nanoporous Gold, Mohammed Hassan Yousef Saffarini

LSU Doctoral Dissertations

Metallic foams, or nanoporous (NP) metals as it is widely referred to in literature, with ligament sizes up to a few tens of nm show exceptional mechanical properties such as high strength and stiffness per weight ratio under different loading scenarios due to their high surface area to solid volume ratio. Therefore, they can be utilized in a wide range of applications making them of great interest to researchers. While their elasticity and yield strength have been the subject of several studies, very limited attention was given to the effect of size, strain rate, and temperature on the material plastic …

Development Of Reduced Order Models Using Reservoir Simulation And Physics Informed Machine Learning Techniques, Mark V. Behl Jr

LSU Master's Theses

Reservoir simulation is the industry standard for prediction and characterization of processes in the subsurface. However, simulation is computationally expensive and time consuming. This study explores reduced order models (ROMs) as an appropriate alternative. ROMs that use neural networks effectively capture nonlinear dependencies, and only require available operational data as inputs. Neural networks are a black box and difficult to interpret, however. Physics informed neural networks (PINNs) provide a potential solution to these shortcomings, but have not yet been applied extensively in petroleum engineering.

A mature black-oil simulation model from Volve public data release was used to generate training data …

Centrifugal Microfluidic Platform For Solid-Phase-Extraction (Spe) And Fluorescence Detection Applications, Yong Zhang

LSU Doctoral Dissertations

Solid phase extraction (SPE) is a widely used method to separate and concentrate the target molecules in liquid mixture. Traditional SPE has to be conducted in the laboratory with professional equipment and skilled operators. The microfluidic and 3D printing technology have opened up the opportunity in developing miniaturized automatic instruments. The main contribution of this research is to integrate the SPE process on a novel centrifugal platform. Various valves are applied on the platform to help control the aqueous sample and reagents in the cartridge.

First, a centrifugal microfluidic platform was built for automatically detecting trace oil pollution in water. …

A Modelling Study For Smart Pigging Technique For Pipeline Leak Detection, Caitlyn Judith Thiberville

LSU Master's Theses

Although leak incidents continue, a pipeline remains the most reliable mode of transportation within the oil and gas industry. It becomes even more important today because the projection for new pipelines is expected to increase by 1 billion BOE through 2035. In addition, increasing number and length of subsea tiebacks face new challenges in term of data acquisition, monitoring, analysis, and remedial actions. Passive leak-detection methods commonly used in the industry have been successful with some limitations in that they often cannot detect small leaks and seeps. In addition to a thorough review of related topics, this study investigates how …

Relating Individual Characteristics And Task Complexity To Performance Effectiveness In Individual And Collaborative Problem Solving, Kaveh Sheikhrezaei

LSU Doctoral Dissertations

The objective of this research is to examine the variables that influence performance effectiveness on individual and collaborative problem solving. The last few years have seen renewed interest in how team member personal characteristics and team composition characteristics impact team effectiveness.

Even with a growing quantity of organizations performing jobs by using groups, little is understood how people included in a team impact intragroup interaction and results. Most research investigates group’s performance based on a single characteristic which causes much confusion and contradictory results of the variables that impact overall group performance. Most research typically does not analyze the composition …

Artificial Intelligence Based Wrist Fracture Classification, Dineep Thomas

LSU Master's Theses

The problem of predicting wrist fractures from X-rays using Artificial Intelligence (AI) methods is addressed. Wrist fractures are the most commonly misdiagnosed fractures because of the complex anatomical structure of the wrist bone which includes several different bones. This research provides a predictive solution to automate the process of wrist fracture classifications and outlines a visualization technique to identify the probable location of the fractured region on the X-rays. This thesis describes a deep learning based approach for wrist fracture classification. Deep convolutional neural network (CNN) based models have been used for wrist fracture classification by combining different optimization techniques. …

Costs And Benefits Of Flood Mitigation In Louisiana, Arash Taghi Nezhad Bilandi

LSU Doctoral Dissertations

Assessing the costs and benefits of hazard mitigation efforts is an essential component of disaster management, planning, and resilience assessment. These calculations are particularly important in locations vulnerable to multiple hazards with high frequencies, such as coastal Louisiana. This study aims to provide an improved understanding of the costs and benefits of flood mitigation efforts in Louisiana funded by federal government grants between 2005 and 2015. Project data provided by the Governor’s Office of Homeland Security and Emergency Preparedness (GOHSEP) were summarized and missing values were imputed using robust statistical approaches. Elevation project cost was investigated for prediction by statistical …

Development Of Self-Healing Mechanisms For Asphalt Pavements, Max Abelardo Aguirre Deras

LSU Doctoral Dissertations

Self-healing mechanisms, such as microcapsules or hollow-fibers, filled with an asphalt rejuvenator present an emerging technology that would enhance an asphalt mixture’s resistance to cracking damage caused by vehicular and environmental loading. The objectives of this study were to: (a) Evaluate the effects of asphalt rejuvenators on hot-mix asphalt mixtures in order to test its effects on the fundamental engineering properties of the mixtures at high and intermediate temperatures; (b) Develop a synthesis procedure for production of microcapsules and hollow-fibers containing an asphalt rejuvenator; (c) Evaluate the self-healing efficiency of double-walled microcapsules and hollow-fibers filled with an asphalt rejuvenator, through …