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George W. Woodruff School of Mechanical Engineering

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https://hdl.handle.net/1853/70728

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College of Engineering

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Advanced Mechanical Bipedal Experimental Robotics Lab

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Computational Reactor and Medical Physics Laboratory

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Cryo Lab

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Environmentally Conscious Design and Manufacturing

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Fusion Research Center

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https://finding-aids.library.gatech.edu/agents/corporate_entities/120

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Publication Search Results

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Magnetic Steering to Save Sight: Trabecular Meshwork Cell Therapy as a Treatment for Primary Open Angle Glaucoma

(Georgia Institute of Technology, 2023-12-12) Bahrani Fard, Mohammad Reza

Glaucoma, which affects almost 80 million people worldwide, is the main cause of irreversible blindness. The most common type, primary open angle glaucoma (POAG), causes gradual loss of vision by damaging retinal ganglion cells. The major risk factor for POAG is high intraocular pressure (IOP). Current clinical treatments for POAG aim to reduce IOP, but they often have low success rates. The trabecular meshwork (TM) is a key regulator of IOP and has been shown to undergo significant changes in POAG including a loss of cells. This motivates the regeneration or restoration of the TM as a potential treatment for POAG. While TM cell therapy has shown promise in reversal of POAG pathology, previously-developed cell delivery techniques have resulted in poor cell delivery efficiency which elevates the risk of tumorigenicity and immunogenicity and undermines therapeutic potential. In addition, a lack of comprehensive characterization of the treatment effects in an appropriate POAG model is a roadblock to clinical translation. We here tackled these shortcomings by: 1) using an optimized magnetic delivery method to significantly improve the specificity and efficiency of delivery of cells to the mouse TM, in turn reducing the risk of unwanted side-effects, and 2) employing this optimized method to test the therapeutic capabilities of two types of cells in a mutant myocilin mouse model of ocular hypertension, characterizing the morphological and functional benefits of the treatment. The central hypothesis of this work is that an optimized magnetically-driven TM cell therapy can lead to long-term clinically significant levels of IOP reduction while minimizing the risks associated with unwanted off-target cell-delivery. This work resulted in the development of a novel magnetic TM cell therapy technique which outperformed those used previously. Employing this technique proved adipose-derived mesenchymal stem cells (hAMSC) and induced pluripotent stem cells differentiated towards a TM phenotype (iPSC-TM) to be effective in IOP lowering. Mesenchymal stem cells showed superior efficacy by stably lowering the IOP by 27% for 9 months, accompanied by increased cellularity in the conventional outflow pathway. These findings, bring magnetic TM cell therapy one step closer to clinical translation.
Categorization of additive manufacturing techniques for nuclear nonproliferation threat analysis

(Georgia Institute of Technology, 2023-12-08) Cannon, Natalie L.

Additive manufacturing, or AM, is a rapidly developing technology that simplifies and automates the production of intricate objects. Recently, AM methods have been imple- mented in the domains of nuclear weapons and nuclear enrichment technologies. However, there are presently limited international or domestic regulations for AM’s involvement in the nuclear sector, leading to unregulated proliferation pathways. Existing export regu- lations are broad in scope and do not account for the particular nuances of different AM techniques. It is crucial to scrutinize and assess the nuclear applications of AM methods to establish effective regulations and limitations for monitoring proliferation routes. This project involves identifying and assessing 31 of the most commonly employed AM meth- ods based on their potential impact on the nuclear fuel cycle. Using this identification and classification system, export controls can be directed at nuclear proliferation threats posed by AM, without disrupting the entire industry and fuel cycle. Additionally, this compre- hensive approach to regulating and monitoring proliferation channels would expose gaps in export regulations.
Calibration and Deployment of an Inertial Acoustic Vector Sensor for Autonomous Underwater Vehicles

(Georgia Institute of Technology, 2023-12-08) Lawrence, Andrew James

For underwater acoustic directionality experiments below 1 kHz, it can be challenging to deploy a sufficiently sized array of hydrophones. The required mounting system could be too large to deploy feasibly in many situations. An inertial vector sensor can act as a solution to this problem. A Wilcoxon VS-301 is an inertial vector sensor, and it contains a pressure sensor and a 3-axis accelerometer that measures the particle acceleration of the water near the sensor, which is a directional quantity. This allows for a much smaller mounting system, but it has its own limitations. The vector sensor is sensitive to vibrations from movement and needs extensive calibration to ensure accuracy, as the directionality measurements from the vector sensor are calculated by comparing the amplitude measured on multiple accelerometers. A mounting system with minimal material in the near field was chosen for its ability to minimize acoustic interference, and it was thoroughly tested to ensure it was insulated from non-acoustic vibrations. Two calibration methods were investigated to characterize the VS-301’s sensitivities: an acoustic shaker and a standing wave tube device. Both were investigated due to the difference in medium, as one experiment is conducted in air while the other is conducted in water. With the final mounting system design and confidence in the sensor’s calibration, directionality measurements were taken in a large water tank to ensure that an acoustic source could be located accurately within 5 degrees within a frequency band of 350-1300 Hz.
The Interaction of Passive and Active Ankle Exoskeletons with Age-Related Physiological Changes to Improve Metabolic Cost

(Georgia Institute of Technology, 2023-12-08) Trejo, Lindsey H.

Difficulties with mobility were the most reported disability for those age 65 and over. It is well known that older adults are slower and less economical while walking compared to young. This is thought to be brought on by reduced ankle push off power and a redistribution of positive power generation to more proximal joints (e.g., hip). We believe muscle and tendon level changes create a structural bottleneck leading to these functional changes. Ankle exoskeletons have been shown to increase ankle push off, modify muscle-tendon dynamics, and reduce metabolic cost in young adults for a near immediate improvement in walking performance. There is a critical gap in understanding whether beneficial exoskeleton assistance strategies for younger adults will also benefit older adults and if so, what the underlying mechanism is that enables exoskeletons to reduce metabolic cost across age. My aims yielded a greater understanding of how people interact with ankle exoskeletons to modify metabolic cost. In Chapter 2, we characterize the muscle and tendon properties of young and older adults. This work is in prep to be submitted to the Journal of Biomechanics. In Chapter 3, we demonstrate how young and older adults respond to spring and motorlike exoskeletons. This work is in prep to be submitted to the Institute of Electrical and Electronics Engineers Transaction on Neural Systems and Rehabilitation Engineering. In Chapter 4, we demonstrate how spring and motorlike exoskeletons interact with the underlying calf muscle and tendon properties. These outcomes can improve the design and control of ankle exoskeletons to improve the cost of walking across age, leading to greater mobility and improved quality of life.
Learning 3D Robotics Perception using Inductive Priors

(Georgia Institute of Technology, 2023-12-07) Irshad, Muhammad Zubair

Recent advances in deep learning have led to a data-centric intelligence in the last decade, i.e. artificially intelligent models unlocking the potential to ingest a large amount of data and be really good at performing digital tasks such as text-to-image generation, machine-human conversation, and image recognition. This thesis covers the topic of learning with structured inductive bias and priors to design approaches and algorithms unlocking the potential of principle-centric intelligence for the real-world. Prior knowledge (priors for short), often available in terms of past experience as well as assumptions of how the world works, helps the autonomous agent generalize better and adapt their behavior based on past experience. In this thesis, I demonstrate the use of prior knowledge in three different robotics perception problems. 1. object-centric 3D reconstruction, 2. vision and language for decision-making, and 3. 3D scene understanding. To solve these challenging problems, I propose various sources of prior knowledge including 1. geometry and appearance priors from synthetic data, 2. modularity and semantic map priors and 3. semantic, structural, and contextual priors. I study these priors for solving robotics 3D perception tasks and propose ways to efficiently encode them in deep learning models. Some priors are used to warm-start the network for transfer learning, others are used as hard constraints to restrict the action space of robotics agents. While classical techniques are brittle and fail to generalize to unseen scenarios and data-centric approaches require a large amount of labeled data, this thesis aims to build intelligent agents which require very-less real-world data or data acquired only from simulation to generalize to highly dynamic and cluttered environments in novel simulations (i.e. sim2sim) or real-world unseen environments (i.e. sim2real) for a holistic scene understanding of the 3D world.
Investigation Of Critical Attributes For Transparency And Operator Performance In Human Autonomy Teaming (TOPHAT) For Intelligent Mission Planning

(Georgia Institute of Technology, 2023-12-07) Srivastava, Divya

AI-advised Decision Making is a form of human-autonomy teaming in which an AI recommender system suggests a solution to a human operator, who is responsible for the final decision. This dissertation work seeks to empower and most effectively utilize these human decision makers by supporting their cognitive process of judgement. We propose doing so by providing the decision maker with relevant information that the AI uses to generate possible courses of action, as an alternative solution to explaining or interpreting complex AI models. Findings indicate that this technique of supporting the human’s judgement process is effective in (1) boosting the human decision maker’s situation awareness and task performance, (2) calibrating their trust in AI teammates, and (3) reducing overreliance on an AI partner. Additionally, participants were able to determine the AI’s error boundary, which enabled them to know when and when not to rely on the AI’s advice. These and associated findings are then summarized as design guidance for increasing non-algorithmic transparency in human-autonomy teams, so that the guidance can be applied to other domains.
Automated Exploration of High-Mix, Low Volume Direct Write Design Spaces Through Artificial Intelligence

(Georgia Institute of Technology, 2023-12-06) Johnson, Marshall

Additive manufacturing has experienced considerable growth over the recent decades from technological infancy to functional use across a range of industries and applications. This growth has come with increased challenges as optimal function requires the marriage of both the proper material and geometry. This challenging optimization problem is currently solved through the intervention of AM experts to ensure proper process parameters are selected. However, the need for such interventions obstructs AM from reaching its full potential to deliver customized solutions in a high-mix, low-volume (HMLV) circumstance. Thus, there is a crucial need for solutions that accommodate frequent changeovers that are innate to HMLV without sacrificing the ability to analyze the complex properties of interest. Artificial Intelligence (AI) and its specific success with image analysis represents an opportunity assist in AM for HMLV manufacturing and ease the burden from AM experts. The proposed research will utilize direct ink write (DIW) to demonstrate the capabilities of AI to impact HMLV manufacturing in the following case studies: The first study will develop a generalizable image analysis and AI tool to classify DIW printed linear self-supporting filaments and demonstrate the tool’s multiple value to automated experimentation. The second study will apply further AI techniques to build upon the image analysis and AI tool using data-driven exploration to discover sufficient parameters to DIW print complex non-linear self-supporting geometries. The third study will utilize similar image analysis and AI methodology on the DIW printing of quantum dot (QD) ink drops, a vastly different AM application, to classify the spatial ink distribution of the printed drops. The tools constructed will lay the groundwork for AI-driven HMLV analysis on the fundamental print element scale which can be extended in future work to full functional parts.
Mesoscale Computational Analysis on the Reactivity of Heterogeneous Energetic Materials with Electromechanical Properties

(Georgia Institute of Technology, 2023-12-06) Shin, Ju Hwan

Extensive research was performed in the past to investigate how energetic materials (EM) ignite via thermomechanical dissipation produced by viscoplastic deformation, friction, and fracture. Yet, little is known regarding the other types of excitation leading to ignition. Electromechanically induced dissipation is one notable example of such mechanisms that have become increasingly vital to understand in the recent development of a novel, multifunctional stimulus capable of sensitizing and triggering ignition. To quantify and better understand how certain electromechanical properties of EMs can alter their ignition behavior, a two-step, multiphysics framework spanning multiple timescales is developed. The numerical simulations first track the development of electric field (E-field) in the material under external mechanical load over the microsecond timescale. The model uses a coupled mechanical-electrostatic framework for computing the stress, strain gradient, and E-field distributions of P(VDF-TrFE)/nAl (i.e., a composite film consisting of nanoaluminum particles embedded within PVDF-TrFE binder) possessing flexoelectric and piezoelectric properties. The attainment of sufficient E-field intensity within the material is then used as part of the input for the subsequent analysis, wherein dielectric breakdown and exothermic reaction processes are simultaneously resolved over the nanosecond timescale based on an electrodynamic-chemical-thermal framework. Here, the breakdown process is explicitly modeled as the irreversible transition of the material from its initially dielectric phase into conductive phase wherever the local E-field exceeds the dielectric breakdown strength. The transient current flow leads to resistive dissipation and temperature rise in these highly localized regions of breakdown, resulting in the formation of thermal hotspots that eventually serve as critical sites for the activation and the progression of exothermic reactions. The chemical reaction is modeled as a single-stage, forward kinetic process involving the catalyzed decomposition and direct pyrolysis of the PVDF-TrFE binder, followed by the exothermic fluorination of the Al particles. The reaction rates are characterized using a temperature-dependent Arrhenius equation. The species transport (i.e., Al, PVDF-TrFE, and products) is modeled as diffusion and advection driven by the pressure gradient. The analyses focus on the effects of load intensity and microstructural attributes, such as Al particle size, particle volume fraction, void size, and porosity level, on the mechanical-electrical-chemical-thermal processes with the determination of conditions for ignition being of particular interest. Further, the individual contributions of the electromechanical properties to the overall ignition behavior are delineated using poled (flexoelectric and piezoelectric) and unpoled (flexoelectric only) specimens. The ignition times (i.e., minimum time required for ignition to occur) of poled specimens are found to be ~10% shorter than those of unpoled specimens, consistent with the accompanying experiment. Smaller particle and void sizes also promote a more rapid ignition. Similarly, higher particle volume fraction is shown to lead to a quicker ignition event, owing to the intensified flexoelectric behavior at the macroscopic level and higher chemical energy stored in the aluminum. The aforementioned framework enables a systematic establishment of the material behavioral trends and the microstructure-reaction relations.
An investigation of programmable spatiotemporal defect and exceptional points in piezoelectric metamaterials

(Georgia Institute of Technology, 2023-12-05) Lima Thomes, Renan

From Newton's conceptualization of air as an arrangement of lumped spring-masses in the 17th century to the present day, the evolution of elastic wave manipulation has spanned centuries. In parallel, recently, the realm of piezoelectric shunt damping has evolved to synthetic impedance circuits, which enables digital programmability. Inspired by these developments, this thesis offers an experimental framework for the realization of two wave phenomena in elastic media: space-time wave localization and exceptional points (EPs). First, the concept of space-time wave localization using programmable defects is experimentally demonstrated. The dynamic properties of the local resonators of an electromechanical metamaterial, comprising piezoelectric elements connected to synthetic impedance circuits, are digitally controlled to modulate a trivial point defect in space and time. The experimental results show that the vibration energy is gradually transferred and localized over subsequent unit cells according to the defect position. In another topic, this thesis introduces an experimentally validated framework for creating tunable exceptional points in electromechanical waveguides. EPs are non-Hermitian singularities typically found in parity-time (PT) symmetric systems with balanced gain and loss. Here, piezoelectric transducers are shunted through synthetic impedance circuits that emulate resistors (responsible for the gain and loss) and inductors (responsible for the tunability), and whose properties can be programmed via software. While the inherent structural damping of the waveguide has the effect of breaking PT symmetry, we show that EPs can still be created by using non-trivial gain and loss combinations. Ultimately, the results in this thesis pave the way for the practical realization of space-time wave localization and EPs in elastic media, opening avenues for their application in novel wave devices.
A Skin-Like Sternal Patch to Monitor Autonomic Tone During Cognitive Stress and Sympathetic Arousals in Disordered Sleep

(Georgia Institute of Technology, 2023-12-05) Zavanelli, Nathan G.

The central focus of this thesis is the development of skin-like wearable electronics and sensors that seamlessly integrate with the human body and provide hospital quality physiological monitoring and diagnostics in a simple, minimally obtrusive platform. One of the most poignant tragedies in modern medicine is that many pathologies with highly effective treatments remain undiagnosed, especially in marginalized communities. This suffering is fueled by a systemic failure in current diagnostics techniques: one the one hand, hospital grade in lab tests are expensive, low throughput, and ill-suited for continuous monitoring; on the other, wearable electronics are fundamentally limited by rigid mechanics and wired interfaces that prevent conformal skin contact, leading to poor signal quality and degraded long-term wearability. To address this critical shortcoming, I have conducted analytical, computational, empirical, and human subjects studies in soft materials and interfaces to enable a new class of wearable, wireless devices and sensors with mechanics finely tuned to transduce electrical, mechanical, and optical bio-signals from the human body. Whereas most medical research benefits only the most advantaged, my work is targeted specifically to the unique needs of marginalized communities, providing advanced diagnostic solutions to tackle some of the most pressing medical diagnostics challenges, both here in the United States and around the world. Specifically, this thesis introduces a wireless, fully integrated, soft patch with skin-like mechanics optimized through analytical and computational studies to capture seismocardiograms, electrocardiograms, and photoplethysmograms from the sternum, allowing clinicians to investigate the cardiovascular response to acute changes in autonomic state. This system demonstrated exceptional promise in detecting sleep apneas, classifying sleep stage, identifying system vasoconstriction, and quantifying cognitive stress in trials with human subjects. This thesis focuses on the fundamental studies in soft materials, flexible electronics, signal processing and machine learning that enables these potentially transformative healthcare outcomes.