Engineering Commons^™

Physics-Based Machine Learning Methods For Control And Sensing In Fish-Like Robots, Colin Rodwell

All Dissertations

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots.

Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to …

A Digital Triplet For Utilizing Offline Environments To Train Condition Monitoring Systems For Rolling Element Bearings, Ethan Wescoat

All Dissertations

Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves …

Deep Reinforcement Learning For The Design Of Structural Topologies, Nathan Brown

All Dissertations

Advances in machine learning algorithms and increased computational efficiencies have given engineers new capabilities and tools for engineering design. The presented work investigates using deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to design 2D structural topologies. Three unique structural topology design problems are investigated to validate DRL as a practical design automation tool to produce high-performing designs in structural topology domains.

The first design problem attempts to find a gradient-free alternative to solving the compliance minimization topology optimization problem. In the proposed …

Improving Sizing Resolution Of Particle Impactors In The Nanoparticle Range, Shivuday Kala

All Dissertations

The application of particle size measurement extends across many fields: air quality measurement, pharmaceutical studies, paint and coating production, and nanoparticle formulation to name a few. Therefore, accurate measurement of nanoparticles is critical to aerosol science. While devices currently exist that can size and count nanoparticles such as electrical mobility spectrometers, dynamic light scattering devices, and small angle X-ray scattering devices, their high costs, complex operation, and lack of outdoor usability, restrict their use in practical applications. Among the devices that can size aerosols down to the nanoscale, cascade impactors stand out because of their robustness, relatively simple design, low …

Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon

All Dissertations

The development of composite materials for structural components necessitates methods for evaluating and characterizing their damage states after encountering loading conditions. Laminates fabricated from carbon fiber reinforced polymers (CFRPs) are lightweight alternatives to metallic plates; thus, their usage has increased in performance industries such as aerospace and automotive. Additive manufacturing (AM) has experienced a similar growth as composite material inclusion because of its advantages over traditional manufacturing methods. Fabrication with composite laminates and additive manufacturing, specifically fused filament fabrication (fused deposition modeling), requires material to be placed layer-by-layer. If adjacent plies/layers lose adhesion during fabrication or operational usage, the strength …

Improving Hexapod Platform Pose Accuracy - A Photogrammetry-Based Approach, Sourabh Karmakar

All Dissertations

The aim of this research is to make a newly constructed Stewart-Gough Platform-based test frame Tiger 66.1 operational by developing control software and estimating the error in its pose accuracy. The accuracy of the platform is affected by one source or multiple sources. The typical error sources are kinematic and structural, some of them originate from manufacturing imperfections, assembly deviations, elastic deformations, thermal deformations, and joint clearances which change the expected kinematic behavior of the manipulator. Also, some non-mechanical errors like transmission error, sensor accuracy, algorithm error, and truncation error in calculation contribute significantly in some cases. Using pose deviations …

Impacts Of Connected And Automated Vehicles On Energy And Traffic Flow: Optimal Control Design And Verification Through Field Testing, Tyler Ard

All Dissertations

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven …

Cfrp Delamination Density Propagation Analysis By Magnetostriction Theory, Brandon Eugene Williams

All Dissertations

While Carbon Fiber Reinforced Polymers (CFRPs) have exceptional mechanical properties concerning their overall weight, their failure profile in demanding high-stress environments raises reliability concerns in structural applications. Two crucial limiting factors in CFRP reliability are low-strain material degradation and low fracture toughness. Due to CFRP’s low strain degradation characteristics, a wide variety of interlaminar damage can be sustained without any appreciable change to the physical structure itself. This damage suffered by the energy transfer from high- stress levels appears in the form of microporosity, crazes, microcracks, and delamination in the matrix material before any severe laminate damage is observed. This …

Controlled Manipulation And Transport By Microswimmers In Stokes Flows, Jake Buzhardt

All Dissertations

Remotely actuated microscale swimming robots have the potential to revolutionize many aspects of biomedicine. However, for the longterm goals of this field of research to be achievable, it is necessary to develop modelling, simulation, and control strategies which effectively and efficiently account for not only the motion of individual swimmers, but also the complex interactions of such swimmers with their environment including other nearby swimmers, boundaries, other cargo and passive particles, and the fluid medium itself. The aim of this thesis is to study these problems in simulation from the perspective of controls and dynamical systems, with a particular focus …

Vibration-Based Fault Diagnostics In Wind Turbine Gearboxes Using Machine Learning, Abdelrahman Amin

All Dissertations

A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal …

Extensional Flows Of Polymer Solutions In Planar Microchannels, Mahmud Kamal Raihan

All Dissertations

Non-Newtonian fluids such as polymer solutions often flow under microscale extensional conditions in many natural and engineering flow fields such as in microfluidic chips, porous rocks, biological membranes and filters, printheads in additive manufacturing, etc. The changing cross sectional areas of the internal flow passages therein exert additional extension on the flow along with the shearing. Numerous studies have been dedicated to understanding the extensional flows of polymer solutions over the years. However, most of these studies only focused on flexible polymers exhibiting elasticity in their macroscopic rheology, whereas rigid polymers that portray shear-thinning but often elude elasticity in the …

Multiscale Modeling And Gaussian Process Regression For Applications In Composite Materials, Joshua Arp

All Dissertations

An ongoing challenge in advanced materials design is the development of accurate multiscale models that consider uncertainty while establishing a link between knowledge or information about constituent materials to overall composite properties. Successful models can accurately predict composite properties, reducing the high financial and labor costs associated with experimental determination and accelerating material innovation. Whereas early pioneers in micromechanics developed simplistic theoretical models to map these relationships, modern advances in computer technology have enabled detailed simulators capable of accurately predicting complex and multiscale phenomena.

This work advances domain knowledge via two means: firstly, through the development of high-fidelity, physics-based finite …

Applications Of Large Eddy Simulations To Novel Internal Combustion Concepts, Patrick O'Donnell

All Dissertations

Computational fluid dynamics (CFD) simulations of internal combustion engines (ICEs) are becoming an increasingly popular tool in the automotive industry to either explain experimentally observed trends or perform lower cost design iterations. The convenience of commercially available CFD software and advancements made in computing hardware have been the impetus behind this growing popularity. However, obtaining accurate results using these software packages is not a trivial process and requires an in-depth understanding of the underlying numerical methodology and sub models for various physical phenomena. Specific to the ICEs, CFD simulation often entails the use of models for detailed chemistry and combustion, …

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 …

Mesoscale Modeling And Machine Learning Studies Of Grain Boundary Segregation In Metallic Alloys, Malek Alkayyali

All Dissertations

Nearly all structural and functional materials are polycrystalline alloys; they are composed of differently oriented crystalline grains that are joined at internal interfaces termed grain boundaries (GBs). It is well accepted that GB dynamics play a critical role in many phenomena during materials processing or under operating environments. Of particular interest are GB migration and grain growth processes, as they influence many crystal-size dependent properties, such as mechanical strength and electrical conductivity.

In metallic alloys, GBs offer a plethora of preferential atomic sites for alloying elements to occupy. Indeed, recent experimental studies employing in-situ microscopy revealed strong GB solute segregation …

Classification Of Electrical Current Used In Electroplastic Forming, Tyler Grimm

All Dissertations

Electrically assisted manufacturing (EAM) is the direct application of an electric current to a workpiece during manufacturing. This advanced manufacturing process has been shown to produce anomalous effects which extend beyond the current state of modeling of thermal influences. These purported non-thermal effects have collectively been termed electroplastic effects (EPEs).

While there is a distinct difference in results between steady-state (ideal DC) testing and pulsed current testing, the very definition of these two EAM methods has not been well established. A "long" pulse may be considered DC current; a "short" pulse may produce electroplastic effects; and even "steady-state" current shapes …

Deep Reinforcement Learning And Game Theoretic Monte Carlo Decision Process For Safe And Efficient Lane Change Maneuver And Speed Management, Shahab Karimi

All Dissertations

Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communication and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. This dissertation presents a decision process model for real-time automated lane change and speed management in highway and urban traffic. In lane change and merge maneuvers, it is important to know how neighboring vehicles will act in the imminent future. Human driver …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

All Dissertations

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Mechanics Modeling Of Non-Rigid Origami: From Qualitative To Quantitative Accuracy, Jiayue Tao

All Dissertations

Origami, the ancient art of paper folding, has recently evolved into a design and fabrication framework for various engineering systems at vastly different scales: from large-scale deployable airframes to mesoscale biomedical devices to small-scale DNA machines. The increasingly diverse applications of origami require a better understanding of the fundamental mechanics and dynamics induced by folding. Therefore, formulating a high-fidelity simulation model for origami is crucial, especially when large amplitude deformation/rotation exists during folding.

The currently available origami simulation models can be categorized into three branches: rigid-facet models, bar-hinge models, and finite element models. The first branch of models assumes that …

Modeling, Control And Estimation Of Reconfigurable Cable Driven Parallel Robots, Adhiti Raman Thothathri

All Dissertations

The motivation for this thesis was to develop a cable-driven parallel robot (CDPR) as part of a two-part robotic device for concrete 3D printing. This research addresses specific research questions in this domain, chiefly, to present advantages offered by the addition of kinematic redundancies to CDPRs. Due to the natural actuation redundancy present in a fully constrained CDPR, the addition of internal mobility offers complex challenges in modeling and control that are not often encountered in literature.

This work presents a systematic analysis of modeling such kinematic redundancies through the application of reciprocal screw theory (RST) and Lie algebra while …

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 …

Multiple Heat Exchanger Cooling System For Automotive Applications – Design, Mathematical Modeling, And Experimental Observations, Zaker Syed

All Dissertations

The design of the automotive cooling systems has slowly evolved from engine-driven mechanical to computer-controlled electro-mechanical components. With the addition of computer-controlled variable speed actuators, cooling system architectures have been updated to maximize performance and efficiency. By switching from one large radiator to multiple smaller radiators with individual flow control valves, the heat rejection requirements may be precisely adjusted. The combination of computer regulated thermal management system should reduce power consumption while satisfying temperature control objectives. This research focuses on developing and analyzing a multi-radiator system architecture for implementation in ground transportation applications. The premise is to use a single …

Infusing Kirigami Principles Into Design Of Mechanical Properties, Hesameddin Khosravi

All Dissertations

The emergence of mechanical metamaterials — which derive their properties primarily from the underlying architecture rather than the constituent material — has unleashed a new era of material design and functionalities. To fully materialize the promising potentials of metamaterials, it is crucial to develop versatile, scalable, and easy-to-fabricate methods that can both generate and tailor the underlying periodic architecture. To this end, we propose the use of kirigami — a popular recreational art of cutting and manipulating paper — as a platform to create periodicity and super-stretchability. Kirigami has become a design and fabrication framework for constructing metamaterials, robotic tools, …

Multi-Robot Symbolic Task And Motion Planning Leveraging Human Trust Models: Theory And Applications, Huanfei Zheng

All Dissertations

Multi-robot systems (MRS) can accomplish more complex tasks with two or more robots and have produced a broad set of applications. The presence of a human operator in an MRS can guarantee the safety of the task performing, but the human operators can be subject to heavier stress and cognitive workload in collaboration with the MRS than the single robot. It is significant for the MRS to have the provable correct task and motion planning solution for a complex task. That can reduce the human workload during supervising the task and improve the reliability of human-MRS collaboration. This dissertation relies …

Direct Numerical Simulation Of Supercritical Co2 Mixing And Combustion, Syed Mohammad Ovais

All Dissertations

The supercritical CO₂ power cycle (sCO₂ ) is a relatively new technology, which promises to reduce CO₂ emissions with potentially higher efficiencies. However due to challenging conditions posed by supercritical pressures, the mixing and ignition phenomena in sCO₂ combustion is relatively less understood and studied. The primary objective of the current study is to investigate these fundamental processes using homogeneous ignition calculations (HMI) and direct numerical simulations (DNS). Broadly, the study is divided into two major parts. In the first part supercritical mixing in sCO₂ relevant conditions is investigated. To achieve this, DNS of temporally …

Development Of A Reverse Engineered, Parameterized, And Structurally Validated Computational Model To Identify Design Parameters That Influence American Football Faceguard Performance, William Ferriell

All Dissertations

Traumatic brain injury (TBI) continues to have the greatest incidence among athletes participating in American football. The headgear design research community has focused on developing accurate computational and experimental analysis techniques to better assess the ability of headgear technology to attenuate impacts and protect athletes from TBI. Despite efforts to innovate the headgear system, minimal progress has been made to innovate the faceguard. Although the faceguard is not the primary component of the headgear system that contributes to impact attenuation, faceguard performance metrics, such as weight, structural stiffness, and visual field occlusions, have been linked to athlete safety. To improve …

Investigating The Effects Of Topology On The Fracture And Failure Mechanisms Of Low Density Metamaterials, Kaitlynn Melissa Conway

All Dissertations

Advances in additive manufacturing have enabled the creation of low density metamaterials with fine features and complex topographies. These new metamaterial topologies and size scales not previously possible broaden the spectrum of lightweight materials with unique properties that are advantageous in a variety of applications. There however is a lack of understanding of metamaterial failure and fracture behaviors. Studies tend to report only a few material properties rather than a comprehensive description of behavior. Due to this, there is a hesitancy to incorporate metamaterials into engineering designs despite proven remarkable properties. This work seeks to investigate in three parts the …

Control, Decision-Making, And Learning Approaches For Connected And Autonomous Driving Systems With Humans-In-The-Loop, Fangjian Li

All Dissertations

By virtue of vehicular connectivity and automation, the vehicle becomes increasingly intelligent and self-driving capable. However, no matter what automation level the vehicle can achieve, humans will still be in the loop despite their roles. First, considering the manual driving car as a disturbance to the connected and autonomous vehicles (CAVs), a novel string stability is proposed for mixed traffic platoons consisting of both autonomous and manual driving cars to guarantee acceptable motion fluctuation and platoon safety. Furthermore, humans are naturally considered as the rider in the passenger vehicle. A human-centered cooperative adaptive cruise control (CACC) is designed to improve …

Development And Application Of 3d Kinematic Methodologies For Biomechanical Modelling In Adaptive Sports And Rehabilitation, Anne Marie Severyn

All Dissertations

Biomechanical analysis is widely used to assess human movement sciences, specifically using three-dimensional motion capture modelling. There are unprecedented opportunities to increase quantitative knowledge of rehabilitation and recreation for disadvantaged population groups. Specifically, 3D models and movement profiles for human gait analysis were generated with emphasis on post-stroke patients, with direct model translation to analyze equivalent measurements while horseback riding in use of the alternative form of rehabilitation, equine assisted activities and therapies (EAAT) or hippotherapy (HPOT). Significant improvements in gait symmetry and velocity were found within an inpatient rehabilitation setting for patients following a stroke, and the developed movement …

Characterization Of Friction Element Welding Using Finite Element Modeling, Ankit Varma

All Dissertations

Friction element welding (FEW) has been advocated as a solution to weld different materials together, with the ability to join high-strength materials for a range of thicknesses with low input energy and a short processing time. This work develops a coupled thermal-mechanical finite element model to better understand the physical mechanisms involved in the process and to predict temperature and material flow during the process. Furthermore, microstructural analysis is performed for the steel layer using a scanning electron microscope and Vickers microhardness tester to understand the variation in its grain structure and hardness. Results from the finite element model and …