Engineering Commons^™

Rapid Prediction Of Buoyancy-Driven Exchange Flows At The Great Salt Lake: Ml Models And A 1d Shallow Water Approach, Eric M. Larsen

All Graduate Theses and Dissertations, Fall 2023 to Present

The Great Salt Lake in Utah, USA, is a hypersaline terminal lake divided in to northern and southern arms by the Union Pacific Railroad causeway since the 1950's. This separation has caused a difference in density and water surface elevation between lake arms. These differences result in a buoyancy-driven exchange flow occurring through an engineered breach in the causeway. Traditionally, modeling the flow through the breach has been done by numerically solving the 1D steady shallow water equations, and using computational fluid dynamics (CFD). The CFD models yield high accuracy results, but require substantial computing resources. This research proposes the …

Hybrid Physics-Infused Machine Learning Framework For Fault Diagnostics And Prognostics In Cyber-Physical System Of Diesel Engine, Shubhendu Kumar Singh

All Dissertations

Fault diagnosis is required to ensure the safe operation of various equipment and enables real-time monitoring of associated components. As a result, the demand for new cognitive fault diagnosis algorithms is the need of the hour. Existing deep learning algorithms can detect, classify, and isolate faults. Still, most depend solely on data availability and do not incorporate the system's underlying physics into their prediction. Therefore, the results generated by these fault-detecting algorithms sometimes need to make more sense and deliver when tested in actual operating conditions.

Similar to diagnosis, the fault prognosis of diesel engines is paramount in numerous industries. …

Machine Learning-Guided Design Of Nanolubricants For Minimizing Energy Loss In Mechanical Systems, Kollol Sarker Jogesh, Md. Aliahsan Bappy

Mechanical Engineering Faculty Publications and Presentations

This study explores the significant potential of machine learningguided design in optimizing nanolubricants, focusing on their application in reducing friction and wear in mechanical systems. Utilizing neural networks and genetic algorithms, the research demonstrates how advanced computational techniques can accurately predict and enhance the tribological properties of nanolubricants. The findings reveal that nanolubricants, particularly those containing graphene and carbon nanotubes, exhibit marked improvements in reducing friction coefficients and wear rates compared to traditional mineral oil-based lubricants. Additionally, the enhanced thermal stability and load-carrying capacity of these nanolubricants contribute to substantial energy savings and increased operational efficiency. The study underscores the …

Studying The Performance Of Object Recognition With Fusion Of Visible Light And Infrared Images With Neural Networks, Plamen Petkov

Doctoral Dissertations and Master's Theses

Neural networks have been used for object detection and recognition in both color and intensity camera images. As the use of infrared cameras, colloquially termed thermal cameras, has increased and costs have decreased, object detection and recognition in infrared camera images have been increasingly studied. An infrared image is treated as an intensity image, just like a grayscale camera image, except the intensity corresponds to infrared radiation instead of visible light. The information provided by these two types of images are different, especially in different lighting and environmental situations, and some types of objects are more easily recognized in visible …

Implementation Of Explainable Ai For Bearing Fault Classification, Mohammad Mundiwala

Honors Scholar Theses

It is difficult to overstate the impact of artificial intelligence (AI) over the past decade. The rapid expansion of machine learning has stimulated a race to deploy AI in all facets of life, one such domain being machine health monitoring. There is no doubt that machine learning excels in prediction accuracy, but oftentimes, these models are cryptic and fail to provide valuable insight into their decisions. This paper presents an overview of a neural network and what it means to learn. Next, two distinct Explainable AI (XAI) techniques will be presented: Gradient Class Activation Mapping and SimplEx . Finally, these …

Communication Modality And Activity Recognition In Smart Manufacturing, Haodong Chen

Doctoral Dissertations

"Advancements in sensors, computational intelligence, and big data have been rapidly transforming and revolutionizing the manufacturing sector, leading to robot-rich and digitally connected factories. To facilitate such a transformative process, workforce training and coordination between the human workforce and robots have remained a major challenge. This study aims to create intelligent workforce training systems for human-computer interaction and human-robot collaboration. It proposes the use of dynamic gestures and speech commands for seamless communication between a worker and a robot, leveraging Convolutional Neural Networks (CNN) and multiple threading for recognition and integration. Additionally, a fine-grained activity recognition model has been crafted …

Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa

Dissertations, Master's Theses and Master's Reports

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …

Wave Energy Converter Wave Force Prediction Using A Neural Network, Morgan Kline

Dissertations, Master's Theses and Master's Reports

Due to the unpredictable nature of large bodies of water, wave energy can be a difficult renewable resource to rely on. One way to make Wave Energy Converters (WECs) more efficient is to apply a control strategy. In many control solutions, it is assumed that the wave excitation force is known into the future. In many instances, especially with complex waveforms, this is simply not the case. Simulation studies have shown the promise of wave force prediction using neural networks. This study demonstrates this experimentally and aims to characterize the important factors when designing such a network. Several wave elevation …

Effective Drag Coefficient Prediction On Single-View 2d Images Of Snowflakes, Cameron Hudson

Graduate College Dissertations and Theses

The drag coefficient of snowflakes is an crucial particle descriptor that can quantify the relationships with the mass, shape, size, and fall speed of snowflake particles. Previous studies has relied on estimating and improving empirical correlations for the drag coefficient of particles, utilizing 3D images from the Multi-Angled Snowflake Camera Database (MASCDB) to estimate snowflake properties such as mass, geometry, shape classification, and rimming degree. However, predictions of the drag coefficient with single-view 2D images of snowflakes has proven to be a challenging problem, primarily due to the lack of data and time-consuming, expensive methods used to estimate snowflake shape …

The Generation Of A Physics Informed Machine Learning Model To Predict Defect Evolution In Materials & On The Thermally Activated Regime Of Dislocation Motion: A Simulation Driven Study On The Mechanical Behavior Of Crystals, Liam Myhill

All Theses

Line defects in crystals, known as dislocations, govern the mechanisms of plastic deformation at the micro-meso scale. The study of dislocations has proliferated the field of materials science and engineering for since the 1950’s, and modern studies show increasing utilization of computational methods to model the evolution of line defects in material systems. In keeping with modern research practice, the studies herewith demonstrate the use of advanced computing to generate models which can be used to better understand the behaviors of dislocations within crystal matrices. An advanced high-throughput model for a physically informed machine learning graph neural network (PIML-GNN) is …

Elastic Sensing Skin For Monitoring Of Concrete Structures, Emmanuel Abiodun Ogunniyi

Theses and Dissertations

Soft elastomeric capacitors (SECs) are emerging as potential low-cost solutions for monitoring cracks and strains in concrete infrastructure, a crucial aspect of structural health monitoring. Effective long-term monitoring of civil infrastructure can reduce the risk of structural failures and potentially reduce the cost and frequency of inspections. However, deploying structural health monitoring (SHM) technologies for bridge monitoring is expensive, especially long-term, due to the density of sensors required to detect, localize, and quantify cracks. Previous research on soft elastomeric capacitors (SEC) has shown their viability for low-cost monitoring of cracks in transportation infrastructure. However, when deployed on concrete for strain …

Vibration-Based Machine Learning Models For Condition Monitoring Of Railroad Rolling Stock, Sergio M. Martinez

Theses and Dissertations

One of the primary causes of rail rolling stock derailments is attributed to bearing and wheel axle failures. The health of train bearings is primarily monitored at target locations through wayside detection systems. This practice is susceptible to bearing failure and potential derailments at points in between these wayside systems. To remedy this, the University Transportation Center for Railway Safety (UTCRS) has developed a wireless onboard monitoring system that can continuously monitor the vibration response, which directly correlates to the health of bearings. This data is used to train regression-based machine learning algorithms and long-term prediction neural networks to predict …

Investigation Of Fatigue Response With Analytical And Machine Learning Models And Hygroscopic Analysis Of Asymmetric Bistable Cfrp Composites, Shoab Ahmed Chowdhury

All Dissertations

Asymmetric bistable carbon fibre reinforced plastic (CFRP) composites enable a broad range of applications as they can sustain multiple stable configurations and have small snap-through load requirements. These unique features, coupled with their light strength-to-weight and stiffness-to-weight ratios, have made them preferred options for multifunctional systems. This study investigates the fatigue and hygroscopic response of 2-ply, [0/90] bistable CFRP laminates and proposes predictive modeling approaches for improved performance.

While previous studies widely researched and documented the fatigue of general composites in axial loading, fatigue analysis of asymmetric bistable composites in the out-of-plane snap-through direction is inadequate. This study performs fatigue …

In Situ Process Monitoring And Machine Learning Based Modeling Of Defects And Anomalies In Wire-Arc Additive Manufacturing, Eduardo Miramontes

Masters Theses

Wire Arc Additive Manufacturing (WAAM) has made great strides in recent years however, there remain numerous persistent challenges still hindering more widespread adoption. Defects in the parts produced degrade their mechanical performance. Inconsistency in the geometry of the weld beads or undesirable anomalies such as waviness, or humps can lead to loss of geometric accuracy and in extreme cases, when anomalies propagate to subsequent layers, build failure. Such defects can be mitigated by a controls framework, which would require a model that maps undesirable outcomes to information about the process that can be obtained in real time. This thesis explores …

Development Of Atomistic Machine Learning Approaches For Thermal Properties Of Multi-Component Solids And Liquids, Alejandro David Rodriguez

Theses and Dissertations

Currently, heat transfer in many industries is the limiting factor for innovation, especially in the energy sector. For example, maximizing thermal conductivity of ceramic coatings in power plant devices improves the overall electrical to thermal energy ratio, whereas minimizing thermal conductivity is required for desirable heat-to-electricity conversion in thermoelectric devices. As such, rapid discovery of new materials with extreme thermal conductivity values is quintessential for the near-future deployment of current and developing energy applications.

The vibrational properties of crystalline materials are essential for their ability to conduct heat. Fundamentally, the restorative atomic forces of displaced atoms are sufficient to represent …

Utilization Of Machine Learning To Investigate Material State, Ana B. Abadie

Honors College Theses

The ability to predict material behavior that undergoes various loading conditions is critical to the development of reliable and safe components. Thermal and mechanical fatigue loading can cause significant damage to materials, leading to failure and potential safety hazards. Machine learning algorithms have emerged as a promising tool for improving accuracy and efficiency of predicting material behavior under such loading conditions. This research provides a comprehensive overview of a machine learning algorithm that is able to analyze and predict material state independently of the loading sequence. Unidirectional carbon fiber reinforced polymer (UD CFRP) composite which has undergone two different loading …

Computational Analysis Of Water Braking Phenomena For High-Speed Sled And Its Machine Learning Framework, Jose A. Terrazas

Open Access Theses & Dissertations

Specializing in high-speed testing, Holloman High-Speed Test Track (HHSTT) uses a process called "water braking" as a method to bring vehicles at the test track to a stop. This method takes advantage of the higher density of water, compared to air, to increase braking capability through momentum exchange. By studying water braking using Computational Fluid Dynamics (CFD), forces acting on track vehicles can be approximated and prepared for prior to the actual test. In this study, focus will be made on the brake component of the track sled that is responsible for interacting with the water for braking. By discretizing …

Probabilistic Machine Learning For Battery State Of Health Prognostics, Charli Zaretsky

Honors Scholar Theses

The ability to understand and predict the state of health (SOH) of lithium-ion batteries is an integral component of their widespread commercial use. There are various methods through which SOH can be analyzed and predicted, and this paper discusses these different methods, and the strengths and weaknesses of each. This paper also details an analysis of lithium-ion battery SOH through two data-driven machine learning methods: XGBoost and Gaussian process regression. A comparison is made between each method’s accuracy in predicting next-cycle discharge capacity using electrochemical impedance spectroscopy (EIS) readings and battery charge and discharge rates, from a dataset given in …

Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi

Graduate Theses, Dissertations, and Problem Reports

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …

Structural Health Monitoring Using Machine Learning And Synthetic Data, Michail Tzimas

Graduate Theses, Dissertations, and Problem Reports

Structural health monitoring spans many decades of research across multiple engineering fields. However, typical monitoring processes for damage detection of complex structures usually prohibit real-time or fast detection of debilitating damage to the structure. One of the major issues of real-time detection of damage is the enormity of data that needs to be processed, which is worsened by the relative inability of fast relaying of data to structural engineers. With the rapid advancement of Machine Learning, both issues can be overcome, and detection of failure is achieved with non-invasive techniques. This dissertation explores the applicability of Machine Learning as a …

Imitation Learning For Swarm Control Using Variational Inference, Hafeez Olafisayo Jimoh

Graduate Theses, Dissertations, and Problem Reports

Swarms are groups of robots that can coordinate, cooperate, and communicate to achieve tasks that may be impossible for a single robot. These systems exhibit complex dynamical behavior, similar to those observed in physics, neuroscience, finance, biology, social and communication networks, etc. For instance, in Biology, schools of fish, swarm of bacteria, colony of termites exhibit flocking behavior to achieve simple and complex tasks. Modeling the dynamics of flocking in animals is challenging as we usually do not have full knowledge of the dynamics of the system and how individual agent interact. The environment of swarms is also very noisy …

Multiscale Topology Optimization With A Strong Dependence On Complementary Energy, Dustin Dean Bielecki

All Dissertations

A discrete approach introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. This framework relies on utilizing parameters such as density and nodal deflections to predict optimized topologies. A three-stage NN framework is employed for the discrete approach to reduce computational runtime while maintaining physics constraints.

A continuous representation that uses complementary energy (CE) methods to solve a representative element's homogenized properties consists of an embedded structure that is …

Prediction Of Meltpool Depth In Laser Powder Bed Fusion Using In-Process Sensor Data, Part-Level Thermal Simulations, And Machine Learning, Grant King

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

The goal of this thesis is the prevention of flaw formation in laser powder bed fusion additive manufacturing process. As a step towards this goal, the objective of this work is to predict meltpool depth as a function of in-process sensor data, part-level thermal simulations, and machine learning. As motivated in NASA's Marshall Space Flight Center specification 3716, prediction of meltpool depth is important because: (1) it can serve as a surrogate to estimate process status without the need for expensive post-process characterization, and (2) the meltpool depth provides an avenue for rapid qualification of microstructure evolution. To achieve the …

A Machine Learning Approach To Robotic Additive Manufacturing Of Uv-Curable Polymers Using Direct Ink Writing, Luis A. Velazquez

LSU Master's Theses

This thesis presents the design and implementation of a robotic additive manufacturing system that uses ultraviolet (UV)-curable thermoset polymers. Its design considers future applications involving free-standing 3D printing by means of partial UV curing and the fabrication of samples that are reinforced with fillers or fibers to manufacture complex-shape objects.

The proposed setup integrates a custom-built extruder with a UR5e collaborative manipulator. The capabilities of the system were demonstrated using Anycubic resin formulations containing fumed silica (FS) at varying weight fractions from 2.8 to 8 wt%. To fully cure the specimens after fabrication, a UV chamber was used. Then, measurements …

Total Sky Imager Project, Ryan D. Maier, Benjamin Jack Forest, Kyle X. Mcgrath

Mechanical Engineering

Solar farms like the Gold Tree Solar Farm at Cal Poly San Luis Obispo have difficulty delivering a consistent level of power output. Cloudy days can trigger a significant drop in the utility of a farm’s solar panels, and an unexpected loss of power from the farm could potentially unbalance the electrical grid. Being able to predict these power output drops in advance could provide valuable time to prepare a grid and keep it stable. Furthermore, with modern data analysis methods such as machine learning, these predictions are becoming more and more accurate – given a sufficient data set. The …

Material Synthesis And Machine Learning For Additive Manufacturing, Jaime Eduardo Regis

Open Access Theses & Dissertations

The goal of this research was to address three key challenges in additive manufacturing (AM), the need for feedstock material, minimal end-use fabrication from lack of functionality in commercially available materials, and the need for qualification and property prediction in printed structures. The near ultraviolet-light assisted green reduction of graphene oxide through L-ascorbic acid was studied with to address the issue of low part strength in additively manufactured parts by providing a functional filler that can strengthen the polymer matrix. The synthesis of self-healing epoxy vitrimers was done to adapt high strength materials with recyclable properties for compatibility with AM …

Predicting The Progression Of Diabetes Mellitus Using Dynamic Plantar Pressure Parameters, Mathew Sunil Varre

UNLV Theses, Dissertations, Professional Papers, and Capstones

Introduction: Diabetic peripheral neuropathy is one of the common complications of type-2 diabetes mellitus (DM). Changes in the intrinsic plantar tissue coupled with repetitive mechanical loads and loss of sensation may lead to foot related complications (skin break down, ulcerations, and amputations) in persons with neuropathy if left untreated. The purpose of this dissertation was to stratify individuals with pre-diabetes, diabetes with and without neuropathy using dynamic plantar pressure parameters during walking, using machine learning algorithms.Methods: Plantar pressure data was collected from one hundred participants during walking with pressure measuring insoles fixed between the feet and thin socks. Simultaneously high-definition …

Generative Designs Of Lightweight Air-Cooled Heat Exchangers, Connor Miller

Mechanical Engineering Undergraduate Honors Theses

The development of high-performance air-cooled heat exchangers is required to permit the rapid growth of vehicle and aircraft electrification. In electric vehicles and airliners, the motors and power electronics are integrated into a compact space, leading to unprecedently high power density. To achieve higher overall thermal efficiency, the heat exchangers must be extremely light while maintaining their heat transfer performance and mechanical robustness. Recently advances in 3D metal printing, e.g., direct metal laser sintering, and selective laser melting, have enabled the manufacturing of high-performance robust heat exchangers by eliminating thermal boundary resistance and ensuring a uniform thermal expansion coefficient. Nonetheless, …

The Role Of Transient Vibration Of The Skull On Concussion, Rodrigo Dalvit Carvalho Da Silva

Electronic Thesis and Dissertation Repository

Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to …

Molecular Modeling Of High-Performance Thermoset Polymer Matrix Composites For Aerospace Applications, Prathamesh P. Deshpande

Dissertations, Master's Theses and Master's Reports

The global efforts from major space agencies to transport humans to Mars will require a novel lightweight and ultra-high strength material for the spacecraft structure. Three decades of research with the carbon nanotubes (CNTs) have proved that the material can be an ideal candidate for the composite reinforcement if certain shortcomings are overcome. Also, the rapid development of the polymer resin industry has introduced a wide range of high-performance resins that show high compatibility with the graphitic surface of the CNTs. This research explores the computational design of these materials and evaluates their efficacy as the next generation of aerospace …