Engineering Commons^™

Lifecycle Reactive Transport Modeling For Assessing Integrity In Offshore Energy Systems, Temitope Ayokunle Ajayi

LSU Doctoral Dissertations

Subsurface leaks and spills are a matter of significant concern to the oil and gas industry because of their impact on health, safety and environment. It is thus important to study and assess the integrity of our energy systems and the physico-chemical processes that affect them.

To avoid leakage of fluids from wellbores, barrier materials are used for sealing formation fluids and creating a single flow path through the well. These barrier materials are constantly subjected to subsurface fluid fluxes and temperature, pressure and chemical conditions over the extended periods the wells remain underground. This makes it imperative to assess …

Effect Of Automation Level On Cognitive Workload When Collaborating With A Robotic Assistant, Mitchell A. Champagne

LSU Master's Theses

Manufacturing robotics have been used for decades to perform repetitive tasks, or tasks that require increased levels of speed, strength, or precision to meet production and specification requirements. Determining the appropriate degree of automation for both the human and robot collaborative team members is critical to optimize production as well as user experience. If the degree of automation is too high, the human will be out of the loop which can result in the loss of situational awareness and be detrimental to intervention time and accuracy. If the degree of automation is too low, then the human may experience greater …

Evaluating And Incorporating Site Variability In Different Geotechnical Engineering Applications, Md Habibur Rahman

LSU Doctoral Dissertations

Site investigation and characterization of subsurface soil conditions are crucial for geotechnical engineering design and analysis. Geotechnical properties vary inherently from one point to another within the same site. This study focused on evaluating the site variability from cone penetration tests (CPT) and soil boring data, and its implementation into the Load and Resistance Factor Design (LRFD) formulation. The total Coefficient of Variation (COV_R,total) for each site was estimated from COV_R,spatial (Spatial Coefficient of Variation) and COV_R,method (Method Coefficient of Variation) which were then used to calibrate the resistance factors (ϕ_spatial, ϕ_total) …

A Drift-Flux Model For Upward Two-Phase In Pipes With High Velocity Flows, Woochan Lee

LSU Doctoral Dissertations

This study proposes the evaluation and development of a drift-flux model for upward two-phase high-velocity flow in large diameter pipes. A case where the proposed model is applicable is WCD (Worst-Case-Discharge) calculations for offshore wells. WCD assumes relatively larger pipe diameters and higher flow rates than the flow experiments at laboratory conditions utilized to validate and develop most of the flow models available in the literature.

Most of the two-phase flow models describe flow regime transitions as discrete processes by assigning the required void fraction or velocity for each flow regime transition. Therefore, for each flow regime, flow-regime-dependent correlations or …

A Data-Based Framework For Monitoring And Controlling Particulate Systems, Vidhyadhar Manee

LSU Doctoral Dissertations

One of the limitations of conventional monitoring tools in crystallization is the inability to deal with high solids concentration. Image-based monitoring, in which RGB images of the solution are captured and analyzed by an object detection software, has been a promising alternative. The software used in these tools primarily depends on hand-coded heuristics to distinguish between the signal and the noise. With the recent success of supervised deep learning, a newer paradigm has emerged in which the heuristics can be learnt from labeled images. This approach is founded on the idea that it is easier to develop labels for data …

Evaluation Of Triple Negative Breast Cancer Tumor Matrix, Alejandra Ham

Honors Capstones

No abstract provided.

Machine Learning-Based Bridge Load Posting Prediction, Sai Naga Sasi Aditya Bandaru

LSU Master's Theses

1600 (12%) of Louisiana's 13,000 bridges that allow the passage of people, goods, and services are load posted, meaning they are deemed incapable of securely carrying all legal loads. A machine learning framework for estimating the number of load-posted bridges within a regional bridge portfolio would be extremely valuable in the future for allocating essential resources during long-term planning. A framework of this type is necessary since a significant proportion of the bridges are load posted. Furthermore, deterioration due to aging and future increases in legal loads can compound this situation. In this setting, there is a lack of data-driven …

Machine Learning-Based Bridge Load Posting Prediction, Sai Naga Sasi Aditya Bandaru

LSU Master's Theses

1600 (12%) of Louisiana's 13,000 bridges that allow the passage of people, goods, and services are load posted, meaning they are deemed incapable of securely carrying all legal loads. A machine learning framework for estimating the number of load-posted bridges within a regional bridge portfolio would be extremely valuable in the future for allocating essential resources during long-term planning. A framework of this type is necessary since a significant proportion of the bridges are load posted. Furthermore, deterioration due to aging and future increases in legal loads can compound this situation. In this setting, there is a lack of data-driven …

A Case Study Of Protecting Bridges Against Overheight Vehicles, Aly Mousaad Aly, Marc Hoffmann

Faculty Publications

Most transportation departments have recognized and developed procedures to address the ever-increasing weights of trucks traveling on bridges in service today. Transportation agencies also recognize the issues with overheight vehicles’ collisions with bridges, but few stakeholders have definitive countermeasures. Bridges are becoming more vulnerable to collisions from overheight vehicles. The exact response under lateral impact force is difficult to predict. In this paper, nonlinear impact analysis shows that the degree of deformation recorded through the modeling of the unprotected vehicle-girder model provides realistic results compared to the observation from the US-61 bridge overheight vehicle impact. The predicted displacements are 0.229 …

Efficient Low Dimensional Representation Of Vector Gaussian Distributions, Md Mahmudul Hasan

LSU Doctoral Dissertations

This dissertation seeks to find optimal graphical tree model for low dimensional representation of vector Gaussian distributions. For a special case we assumed that the population co-variance matrix $\Sigma_x$ has an additional latent graphical constraint, namely, a latent star topology. We have found the Constrained Minimum Determinant Factor Analysis (CMDFA) and Constrained Minimum Trace Factor Analysis (CMTFA) decompositions of this special $\Sigma_x$ in connection with the operational meanings of the respective solutions. Characterizing the CMDFA solution of special $\Sigma_x$, according to the second interpretation of Wyner's common information, is equivalent to solving the source coding problem of finding the minimum …

Progression Of Marsh Edge Erosion: Quantification And Conceptualization Of Shoreline Mechanisms In Terrebonne Bay, La, Amina Meselhe

Honors Capstones

No abstract provided.

Optimizing The Self-Healing Efficiency Of Hydrogel Encapsulated Bacteria In Concrete, Ricardo Hungria

LSU Master's Theses

Calcium carbonate precipitation through microbial means is a promising pathway for concrete self-healing technologies, mainly for its microcrack sealing attributes. The main goal of this project was to study and optimize the crack healing efficiency of hydrogel encapsulated bacteria in concrete. To achieve this purpose, Bacillus pseudiformus was implemented as the bacteria strain at a concentration of 10⁸ cells/ml. This bacterium strain along with yeast extract was combined along with three different mineral precursors corresponding to magnesium acetate, calcium lactate, and sodium lactate. All these three precursors were evaluated at two concentrations (67.76 mM/l and 75 mM/l). For each …

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 …

Assembly Of Nanostructures At Solid-Liquid And Liquid-Air Interfaces, Yingzhen Ma

LSU Doctoral Dissertations

Molecular and nanoscale colloids such as surfactant, fatty acid and metallic nanoparticles are widely used in numerous applications such as detergents, biomedicine, and catalyst. The assemblies of these colloids show different morphological behavior in aqueous solution due to the wide range of intermolecular interactions such as hydrogen bonding, van der Waals, and electrostatics. The morphology of these assemblies can be changed by environmental factors including temperature, ionic strength, and salinity. However, the guidance to direct assembled state of colloidal assemblies at heterogenous interfaces under various external stimuli remains poorly understand. In this Ph.D. dissertation, we show the adsorption and reconfiguration …

Distributed Control And Learning Of Connected And Autonomous Vehicles Approaching And Departing Signalized Intersections, Joshua Onyeka Ogbebor

LSU Master's Theses

This thesis outlines methods for achieving energy-optimal control policies for autonomous vehicles approaching and departing a signalized traffic intersection. Connected and autonomous vehicle technology has gained wide interest from both research institutions and government agencies because it offers immense promise in advancing efficient energy usage and abating hazards that beset the current transportation system. Energy minimization is itself crucial in reducing the greenhouse emissions from fossil-fuel-powered vehicles and extending the battery life of electric vehicles which are presently the major alternative to fossil-fuel-powered vehicles. Two major forms of fuel minimization are studied. First, the eco-driving problem is solved for a …

Practical Considerations And Applications For Autonomous Robot Swarms, Rory Alan Hector

LSU Doctoral Dissertations

In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical …

Numerical Modeling Of Bulk Sediment Deposition And Scour In The Lower Mississippie River Physical Model, Ronald Joseph Rodi

LSU Doctoral Dissertations

Land loss restoration along the southeast Louisiana coast relies on the replenishment of sand from the sediment of the Mississippi River. To further the understanding of sediment transport and hydraulic characteristics of the lowermost segment of the river, the Louisiana Coastal Protection and Restoration Authority (CPRA) funded construction of the Lower Mississippi River Physical Model (LMRPM). This distorted-scale, movable bed model encompasses the lowermost 193-mile reach of the river, including from Donaldsonville, Louisiana to the Gulf of Mexico the Bonnet Carre Spillway and planned river sediment diversions.

Designed to replicate the prototypical river hydraulics and bulk bedload (sand) transport, scaled …

Performing Memory Forensics For Object Recovery From Android Application Memory, Sneha Sudhakaran

LSU Doctoral Dissertations

The analysis of application-specific behavior has become an increasingly important technique in cyber forensics and incident response. The ability to determine the precise actions taken by a user can be the difference between a successful analysis and one that fails to meet its goals. The precise actions includes URLs visited, files downloaded, messages sent and received, images viewed, and data accessed. Evidence extraction from application memory at runtime is an effective solution to successfully extract valuable objects allocated by each application, and it is evident that there is a need for more Android forensics analysis tools that support recovering evidence …

Novel Platforms For Large-Scale Adherent Culture Of Mammalian Cells, Ashkan Yekrangsafakar

LSU Doctoral Dissertations

With recent advances in biotechnology, there is a strong and urgent need for robust platforms to culture mammalian cells on a large scale to produce biopharmaceuticals. To this end, various bioreactors have been developed over the past decades, but their capacity and efficiency are often limited by insufficient mass transfer rate and excessive shear stress. In this work, multiple novel bioreactors for the large-scale adherent culture of anchorage-dependent cells were developed.

Hollow MicroCarriers (HMC) was developed as an alternative solution for the microcarrier-based culture system in a stirred-tank bioreactor. In the conventional microcarrier technique, cells are exposed to the harmful …

Nanoparticle Quantification And Distribution On Fluorescently Coated Angioplasty Balloons, Allison Dobson Zieschang

Honors Capstones

No abstract provided.

A New Generation Of Open-Graded Friction Course For Enhanced Durability And Functionality, Hossam Abohamer

LSU Doctoral Dissertations

This study aims at (1) enhancing Open Graded Friction Course (OGFC) mixes durability using additives and other by-products; (2) investigating the impacts of selected factors on OGFC pavements seepage characteristics; (3) developing a quantitative tool to model the deterioration in OGFC pavements functional performance; and (4) developing new guidelines of Air Void (AV) content for OGFC for optimum functionality and durability. For the durability objective, eight mixes were prepared with a PG 76-22 binder and two sources of aggregate (i.e., # 78 limestone and # 67 sandstone). Three Warm Mix Additives (WMA), one by-product (i.e., crumb rubber [CR]), and two …

Passive Air Samplers For Community Monitoring Of Air Toxics Following Hurricane Ida In Terrebonne Parish, Sarah Besson

Honors Capstones

No abstract provided.

Machine Learning Assisted Discovery Of Shape Memory Polymers And Their Thermomechanical Modeling, Cheng Yan

LSU Doctoral Dissertations

As a new class of smart materials, shape memory polymer (SMP) is gaining great attention in both academia and industry. One challenge is that the chemical space is huge, while the human intelligence is limited, so that discovery of new SMPs becomes more and more difficult. In this dissertation, by adopting a series of machine learning (ML) methods, two frameworks are established for discovering new thermoset shape memory polymers (TSMPs). Specifically, one of them is performed by a combination of four methods, i.e., the most recently proposed linear notation BigSMILES, supplementing existing dataset by reasonable approximation, a mixed dimension (1D …

Investigation Of Groundwater Depletion And Leveel Underseepage With Unstructured-Grid Modeling Approach, Ye-Hong Chen

LSU Doctoral Dissertations

Unstructured grid is a tessellation of geometric shapes in irregular patterns that provides flexibility in grid design for groundwater modeling. However, groundwater modeling is mostly developed with uniform grid tessellation and layer, which could simplify model structure or cause expensive computational costs in high-resolution simulations. Unstructured grid incorporates non-uniform horizontal and non-uniform vertical discretizations providing the capability to replicate complex hydrostratigraphy, capture geologic features that are crucial for groundwater flow simulation, and reduce computational costs while maintaining a high resolution for areas of interest. This study contains three parts to investigate unstructured-grid approach on constructing high-fidelity groundwater models, comparisons with …

Model-Guided Design Of Rna-Based Synthetic Circuits For The Dynamic Regulation Of Gene Expression, Jordan R. Ryan

LSU Master's Theses

A longstanding goal in synthetic biology has been to build synthetic gene circuits with the ability to harness nature’s capability of precise gene expression regulation. Advancements in RNA technology have established RNA-based regulators with distinct advantages over traditional protein-based regulators such as faster signal propagation, versatile programmability, and low cellular burden, which has created an interest in the field to construct innovative synthetic gene circuits using de novo RNA- based regulators. However, our understanding of the behavior and kinetics of RNA-RNA interactions for the construction of gene circuits is incomplete. This thesis proposes a model-guided design framework that integrates mechanistic …

Image And Video Segmentation Of Appearance-Volatile Objects, Yongqing Liang

LSU Master's Theses

Segmentation is a process of partitioning a digital image or frame into multiple regions or objects. The goal of segmentation is to identify and locate the objects of interest with their boundaries. Recent segmentation approaches often follow such a pipeline: they first train the model on a collected dataset and then evaluate the trained model on a given image or video. They assume that the appearance of object is consistent in training and testing sets. However, the appearance of object may change in different photography conditions. How to effectively segment the objects with volatile appearance remains under-explored. In this work, …

Design Report Of The 2022 High Voltage Battery Pack And System For Fsae Tigerracing Electric Vehicle, Clay Knight

Honors Capstones

No abstract provided.

Led Enhancements To The Mimir Animatronic Figure, Collin J. Devillier

Honors Capstones

No abstract provided.

Designing For The Future: Sensor And Gauge Assembly Work Cell Design, Kaitryana Leinbach

Honors Capstones

No abstract provided.

Thermal Simulation Of Additive Manufacturing From G-Code, Noah Foster

Honors Capstones

No abstract provided.