Mechanical Engineering Theses and Dissertations

Permanent URI for this collection

http://hdl.handle.net/1903/2795

Browse

Now showing 1 - 20 of 1098

Second Wave Mechanics
(2024) Fabbri, Anthony; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The COVID-19 pandemic experienced very well-documented "waves" of the virus's progression, which can be analyzed to predict future wave behavior. This thesis describes a data analysis algorithm for analyzing pandemic behavior and other, similar problems. This involves splitting the linear and sinusoidal elements of a pandemic in order to predict the behavior of future "waves" of infection from previous "waves" of infection, creating a very long-term prediction of a pandemic. Common wave shape patterns can also be identified, to predict the pattern of mutations that have recently occurred, but have not become popularly known as yet, to predict the remaining future outcome of the wave. By only considering the patterns in the data that could possibly have acted in tandem to generate the observed results, many false patterns can be eliminated, and, therefore, hidden variables can be estimated to a very high degree of probability. Similar mathematical relationships can reveal hidden variables in other underlying differential equations.
A Framework for Remaining Useful Life Prediction and Optimization for Complex Engineering Systems
(2024) Weiner, Matthew Joesph; Azarm, Shapour; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Remaining useful life (RUL) prediction plays a crucial role in maintaining the operational efficiency, reliability, and performance of complex engineering systems. Recent efforts have primarily focused on individual components or subsystems, neglecting the intricate relationships between components and their impact on system-level RUL (SRUL). The existing gap in predictive methodologies has prompted the need for an integrated approach to address the complex nature of these systems, while optimizing the performance with respect to these predictive indicators. This thesis introduces a novel methodology for predicting and optimizing SRUL, and demonstrates how the predicted SRUL can be used to optimize system operation. The approach incorporates various types of data, including condition monitoring sensor data and component reliability data. The methodology leverages probabilistic deep learning (PDL) techniques to predict component RUL distributions based on sensor data and component reliability data when sensor data is not available. Furthermore, an equation node-based Bayesian network (BN) is employed to capture the complex causal relationships between components and predict the SRUL. Finally, the system operation is optimized using a multi-objective genetic algorithm (MOGA), where SRUL is treated as a constraint and also as an objective function, and the other objective relates to mission completion time. The validation process includes a thorough examination of the component-level methodology using the C-MAPSS data set. The practical application of the proposed methodology in this thesis is through a case study involving an unmanned surface vessel (USV), which incorporates all aspects of the methodology, including system-level validation through qualitative metrics. Evaluation metrics are employed to quantify and qualify both component and system-level results, as well as the results from the optimizer, providing a comprehensive understanding of the proposed approach’s performance. There are several main contributions of this thesis. These include a new deep learning structure for component-level PHM, one that utilizes a hybrid-loss function for a multi-layer long short-term memory (LSTM) regression model to predict RUL with a given confidence interval while also considering the complex interactions among components. Another contribution is the development of a new framework for computing SRUL from these predicted component RULs, in which a Bayesian network is used to perform logic operations and determine the SRUL. These contributions advance the field of PHM, but also provide a practical application in engineering. The ability to accurately predict and manage the RUL of components within a system has profound implications for maintenance scheduling, cost reduction, and overall system reliability. The integration of the proposed method with an optimization algorithm closes the loop, offering a comprehensive solution for offline planning and SRUL prediction and optimization. The results of this research can be used to enhance the efficiency and reliability of engineering systems, leading to more informed decision-making.
TOWARDS AUTOMATION OF HEMORRHAGE DIAGNOSTICS AND THERAPEUTICS
(2024) Chalumuri, Yekanth Ram; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The main aim of the thesis is to advance the technology in the development ofalgorithms and methodologies that will advance the care in hemorrhage diagnostics and therapeutics in low resource settings. The first objective of this thesis is to develop algorithms to primarily detect internal hemorrhage using non-invasive multi-modal physiological sensing. We developed a machine learning algorithm that can classify various types of hypovolemia and is shown to be performing superior to the algorithms developed primarily based on vital signs. To address the limitations in the data-driven approaches, we explored physics-based approaches to detect internal hemorrhage. In silico analysis showed that our physics-based algorithms can not only detect hemorrhage but also can detect hemorrhage even when hemorrhage is being compensated by fluid resuscitation. The second objective is to advance the regulatory aspects of physiological closed-loopcontrol systems in maintaining blood pressure at a desired value during hemorrhage and resuscitation. Physiological closed-loop control systems offer an exciting opportunity to treat hemorrhage in low resource settings but often face regulatory challenges due to safety concerns. A physics-based model with rigorous validation can improve regulatory aspects of such systems but current validation techniques are very naive. We developed a physics-based model that can predict hemodynamics during hemorrhage and resuscitation and validated these factors using a validation framework that uses sampled digital twins. Then we utilized the validated model to evaluate its efficacy in predicting the performance capability of the model and virtual patient generator in predicting the closed-loop controller metrics of unseen experimental data. To summarize, we tried to improve the hemorrhage care using novel algorithmdevelopment and in silico validation and evaluation of computation models that can be used to treat hemorrhage.
ON DATA-BASED MAPPING AND NAVIGATION OF UNMANNED GROUND VEHICLES
(2024) Herr, Gurtajbir Singh; Chopra, Nikhil; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Unmanned ground vehicles (UGVs) have seen tremendous advancement in their capabilities and applications in the past two decades. With several key algorithmic and hardware breakthroughs and advancements in deep learning, UGVs are quickly becoming ubiquitous (finding applications as self-driving cars, for remote site inspections, in hospitals and shopping malls, among several others). Motivated by their large-scale adoption, this dissertation aims to enable the navigation of UGVs in complex environments. In this dissertation, a supervised learning-based navigation algorithm that utilizes model predictive control (MPC) for providing training data is developed. Improving MPC performance by data-based modelling of complex vehicle dynamics is then addressed. Finally, this dissertation deals with detecting and registering transparent objects that may deteriorate navigation performance. Navigation in dynamic environments poses unique challenges, particularly due to the limited knowledge of the decisions made by other agents and their objectives. In this dissertation, a solution that utilizes an MPC-based planner as an \textit{expert} to generate high-quality motion commands for a car-like robot operating in a simulated dynamic environment is proposed. These commands are then used to train a deep neural network, which learns to navigate. The deep learning-based planner is further enhanced with safety margins to improve its effectiveness in collision avoidance. The performance of the proposed method through simulations and real-world experiments, demonstrating its superiority in terms of obstacle avoidance and successful mission completion is showcased. This research has practical implications for the development of safer and more efficient autonomous vehicles. Many real-world applications rely on MPC to control UGVs due to its safety guarantees and constraint satisfaction properties. However, the performance of such MPC-based solutions is heavily reliant on the accuracy of the motion model. This dissertation addresses this challenge by exploring a data-based approach to discovering vehicle dynamics. Unlike existing physics-based models that require extensive testing setups and manual tuning for new platforms and driving surfaces, our approach leverages the universal differential equations (UDEs) framework to identify unknown dynamics from vehicle data. This innovative approach, which does not make assumptions about the unknown dynamics terms and directly models the vector field, is then deployed to showcase its efficacy. This research opens up new possibilities for more accurate and adaptable motion models for UGVs. With the increasing adoption of glass and other transparent materials, UGVS must be able to detect and register them for reliable navigation. Unfortunately, such objects are not easily detected by LiDARs and cameras. In this dissertation, algorithms for detecting and including glass objects in a Graph SLAM framework were studied. A simple and computationally inexpensive glass detection scheme to detect glass objects is utilized. The methodology to incorporate the identified objects into the occupancy grid maintained by such a framework is the presented. The issue of \textit{drift accumulation} that can affect mapping performance when operating in large environments is also addressed.
De-conflicting management of fluid resuscitation and intravenous medication infusion
(2024) Yin, Weidi; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The treatment of combat casualties frequently involves infusion of multiple drugs (e.g. sedatives, opioids and vasopressors) in addition to fluid resuscitation. Usually, fluid resuscitation is performed first to restore the patient’s volume state, followed by the infusion of drugs that can optimize the hemodynamics and/or relief the pain. In some circumstances, however, fluid and drugs must be infused simultaneously. Simultaneous administration of fluid and intravenous drugs presents a practical challenge related to the interactions between them. On one hand, fluid infused dilutes the drugs by lowering its plasma concentration, thereby weakening the drugs’ intended clinical effects. On the other hand, the clinical effects of the intravenously administered drugs on the hemodynamics can interfere with the therapeutic goal of fluid resuscitation. Yet, the vast majority of existing work on closed-loop control of fluid resuscitation and intravenous drug infusion has focused on either fluid resuscitation or intravenous drug infusion alone, while methodologies and algorithms applicable to simultaneous administration of fluid and intravenous drugs have not been rigorously investigated. In the context of control engineering, this problem might be simply considered as a multivariable control problem. Nevertheless, the intricacy and nonlinearity in the system dynamics, in conjunction with limited sensor measurements makes this problem highly challenging. Hence, our work to analyze the conflicts between multiple treatments and to develop algorithmic framework to overcome such conflicts can represent a major leap toward the realization of complex automated medical care in the future, which can make a significant impact on human wellbeing. The main objective of this thesis is to investigate on de-conflicting management of fluid resuscitation and medication infusion, which is in twofold: first and foremost, to develop a mechanistic understanding of the interactions and interferences between the two treatments and second, to come up with novel solutions to address the challenges. To achieve the first goal of this project, we developed an integrated mathematical model of cardiovascular system and pharmacokinetics-pharmacodynamics(PK-PD) model of drugs. This study involves constructing the model based on current knowledge of physiology, isolated and interactive drug effects, parameter identification using real-world data to verify and validate the model, rigorously analyzing the results to demonstrate that multiple medical treatments can endanger the safety of patient care unless the treatments are properly controlled. To accomplish the second goal, we designed a strategy that realizes a safety assurance control of multiple treatments. This study involves model-based hemodynamic monitoring, robust nonlinear dynamic feedback control, safety assurance control design and treatment target mediation. In terms of controller design, we used a 2-degree of freedom PID controller for fluid loop, and an absolute stability guaranteed PID controller based on circle criterion and linear matrix inequalities(LMI) for drug loops. This dissertation considers a 2-input 2-output model(fluid resuscitation and propofol sedation), as well as a more sophisticated 3-input 2-output model(fluid resuscitation and propofol sedation with PHP vasopressor treatment) for case study. It turned out that the proposed methods worked well on both models. In addition, having more inputs provides more flexibility in terms of controller design.
MACHINE LEARNING IN SCARCE DATA REGIME FOR DESIGN AND DISCOVERY OF MATERIALS
(2024) BALAKRISHNAN, SANGEETH; Chung, Peter; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In recent years, data-driven approaches based on machine learning have emerged as a promising method for rapid and efficient estimation of the structure-property-performance relationships, leading to the discovery of advanced materials. However, the cost and time required to obtain relevant data have limited application of these methods to only a few classes of materials where extensive property data are available. Moreover, the material property prediction poses its own unique set of challenges, in part, due to: 1) the complex non-linear response of materials in different space and time domains, 2) inherent variability in material in terms of composition and processing conditions from the atomic to the macroscopic scales, and 3) the need for accurate, rapid and less expensive predictive models for accelerated material discovery. This dissertation aims to develop three novel machine learning frameworks for constructing targeted learning frameworks and discovering novel materials when dealing with limited available data. The dissertation also highlights the future directions and challenges of such approaches. In the first approach, we develop data-driven methods to estimate the material properties under shock compression. A novel featurization approach combining synthetic and physical features was developed showing substantial improvements in the machine learning model performance. The effects of feature engineering, model choices, and uncertainty in the experimental data were investigated. In the second approach, we develop a novel joint embedding framework that enables transfer learning, with the target of locally optimizing the shock wave properties of nitrogen-rich molecules. This work is motivated by a need to overcome challenges associated with the translation of machine learning approaches to domains where there is a relative lack of domain-specific data. However, the properties studied in the second approach do not consider factors needed to assemble a complete material system. Therefore, in the third and final approach, we investigate material systems whose properties at system level are determined by various upstream design factors, such as the composition of raw materials, manufacturing variability, and considerations involved while assembling the system. We propose a stacked ensemble learning framework to make statistical inferences about the system properties.
Automated Simulation and the Discovery of Mechanical Devices
(2024) Chiu, Kevin; Fuge, Mark; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Automatically designing or finding novel devices that accomplish new or existing functions remains one of the greatest unsolved problems in Design Automation. In part, this is due to 1) the interplay of physical form and usage, 2) the emergence of complex behaviors from combinations of simple geometries, and 3) the sparsity and instability of “interesting” physical phenomena with small changes in the design space, which have historically stymied past efforts, since most approaches required 1) human intuition and creativity, 2) infeasibly large amounts of computational power, or 3) a priori targeted desired behavior. In contrast, this dissertation takes a data-driven approach to addressing the general question “What device functionality emerges organically from knowledge of various physical laws?” To make this high-level question more precise, this dissertation tackles three interrelated sub-questions that address challenges that arise when attempting to deploy data-driven methods on function discovery tasks. First, to generate diverse and high-quality datasets from which an algorithm might find novel behavior, this dissertation asks, “How do we enumerate possible boundary conditions for a given physical law that can lead to well-defined solutions to a given partial differential equation?” Chapter 3 proposes a type-based indexing scheme and two properties of that scheme that can generate valid Finite Element Method (FEM) formulations, resulting in a three-fold increase in the number of simulations we generated from our limited set of boundary conditions. Chapter 4 proposes a regression formulation for predicting physical realizability in Stokes flow simulations as estimated with the magnitude of the pressure field. Second, this dissertation asks, “How do we encapsulate the emergence of complex behaviors from interactions between different components?” Chapter 5 proposes reframing this question as an error regression, using graph neural networks to adjust for the “error” — i.e., emergent behavior — incurred by composing multiple basis Navier-Stokes simulations into one large simulation. Lastly, given solution field data, this dissertation asks, “Under what conditions can we detect novel device behaviors through computer-driven sim-ulation and exploration?” Chapter 6 proposes a boundary representation method and modified a hierarchical clustering approach, called Silhouette-optimized Hierarchical Density-Based Spatial Clustering of Applications with Noise (SHDBSCAN), to identify clusters of fluidic devices with similar behaviors. This chapter shows that the solution field representation has a significantly stronger impact on detecting novel device behaviors than the clustering algorithm used, but that a significant challenge lies in capturing “interesting” behavior in the design space in the first place. Overall, this dissertation illuminates promising simulation methods for automating functional discovery and initial work on using data-driven methods to analyze such data. It also highlights several challenges, including the curse of dimensionality, that plague such approaches.
PHYSICS-INFORMED DEEP LEARNING FRAMEWORK FOR PROBABILISTIC MODELING OF ENVIRONMENTALLY INDUCED DEGRADATION
(2024) Habibollahi Najaf Abadi, Hamidreza; Modarres, Mohammad; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Evaluating the degradation behavior and estimating the lifetime of engineering systems and structures is crucial to ensure their safe and reliable operation. Deep learning (DL) models, which are in the form of multi-layer neural networks (NN), have been widely used for the prognostics of such systems and structures, primarily by estimating their degradation intensity and remaining useful life (RUL). Although DL prognostic models have shown promising performance, there are limitations with such models that need to be considered. Firstly, they only learn the data patterns without consideration of the governing physics of degradation. Excluding physics, accompanied by the lack of interpretability in DL models, makes them prone to violating physical laws while showing a good fit to the training data. This issue may lead to weak generalization, mainly for predicting situations outside the training dataset. Secondly, they require significant data for sufficient training, which may not always be available. To estimate degradation and lifetime, NNs are typically trained in a supervised setting using labeled data that ideally have been collected at different levels of degradation up to the failure points. However, collecting that data is usually expensive and time-consuming, particularly for durable systems with long lifetimes, as material degradation (e.g., corrosion, fatigue, wear, or creep) is often slow. Therefore, there is a need for a model that possesses interpretability and follows the underlying physics of degradation that occurs in real-world conditions. Additionally, this model should be trainable with limited data.This dissertation proposes a novel data-driven framework to address the abovementioned limitations, including disregarding physics, lack of interpretability, and the need for big data in DL prognostics models. The framework comprises two NNs: a physics discovery NN and a predictive NN. The former models the underlying physics of degradation, while the latter makes probabilistic predictions for degradation intensity. The physics discovery NN guides the predictive NN and forces it to follow the underlying physics of degradation, which results in better life estimations. In this way, less data is required for sufficient training as the physics discovery model acts as a constraint and limits the search space for the parameters in the training of the predictive model. Additionally, integrating the state-of-the-art feature importance measurement methods into the physics discovery model makes it possible to identify the primary environmental factors that significantly impact the degradation process. This work enhances the interpretability by shedding light on the dominant factors influencing the system's degradation. The application of the proposed approach is demonstrated through two case studies based on publicly available datasets for degradation phenomena. The outcome of this research study can be used to develop a prognostics and health management system that can facilitate a low-cost and high-performance predictive maintenance strategy for systems experiencing environmentally induced degradation. Also, the proposed method can guide data collection from the field by revealing the influential factors that play crucial roles in the degradation of systems. Moreover, the proposed approach offers valuable benefits to designers, enabling them to incorporate appropriate preventive and mitigation strategies into their designs.
ENERGY ANALYSIS OF A METRO TRANSIT SYSTEM FOR SUSTAINABILITY AND EFFICIENCY IMPROVEMENT
(2023) Higgins, Jordan Andrew; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The industrial sector in the US accounted for 33% of the overall energy consumption and 23% of total GHG Emissions in 2022, necessitating the need for energy efficiency and decarbonization of this sector. This study identifies common opportunities and challenges while performing energy audits for the State of Maryland public transportation maintenance complex and proposes site-specific energy efficiency measures. Utilizing performance indices such as Energy Use Intensity (EUI) and load factor from end-use energy data, as well as walkthrough observations from energy audits, energy efficiency measures specific to each facility were formulated to augment the overall energy performance. Additionally, energy modeling helped pinpoint the additional scope of energy efficiency improvements that could have potential significant energy performance improvements and reduce on-site GHG emissions. Among the energy conservation measures considered, the re-sizing and decarbonization of HVAC equipment has the greatest contribution to energy and GHG savings, with a 100% decrease in natural gas, a 37% decrease in electricity use annually, and net decrease of 272 Mton CO2. This study aims to highlight the similarities and differences in existing transportation and maintenance facilities and the applicable technology(ies) that could streamline and serve as a guide for energy audits for transportation maintenance facilities by demonstrating the most common energy efficiency measures and subsequent achievable savings for these facilities.
TOPOLOGICAL ANALYSIS OF DISTANCE WEIGHTED NORTH AMERICAN RAILROAD NETWORK: EFFICIENCY, ECCENTRICITY, AND RELATED ATTRIBUTES
(2023) Elsibaie, Sherief; Ayyub, Bilal M.; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The North American railroad system can be well represented by a network with 302,943 links (track segments) and 250,388 nodes (stations, junctions, and waypoints), and other points of interest based on publicly accessible geographical information obtained from the Bureau of Transportation Statistics (BTS) and the Federal Railroad Administration (FRA). From this large network a slightly more consolidated subnetwork representing the major freight railroads and Amtrak was selected for analysis. Recent improvements in network and graph theory and improvements in all-pairs shortest path algorithms make it more feasible to process certain characteristics on large networks with reduced computation time and resources. The characteristics of networks at issue to support network-level risk and resilience studies include node efficiency, node eccentricity, and other attributes derived from those measures, such as network arithmetic efficiency, network geometric central node, radius, and diameter, and some distribution measures of the node characteristics. Rail distance weighting factors, representing the length of each rail line derived from BTS data, are mapped to corresponding links, and are used as link weights for the purpose of computing all pair shortest paths and subsequent characteristics. This study also compares the characteristics of North American railroad infrastructure subnetworks divided by Class I carriers, which are the largest railroad carriers classified by the Surface Transportation Board (STB) by annual operating revenue, and which together comprise most of the North American railroad network. These network characteristics can be used to inform placement of resources and plan for natural hazard and disaster scenarios. They relate to many practical applications such as network efficiency to distribute traffic and a network’s ability to recover from disruptions. The primary contribution of this thesis is the novel characterization of a detailed network representation of the North American railroad network and Class I carrier subnetworks, with established as well novel network characteristics.
DEVELOPMENT AND IN-SITU CHARACTERIZATION OF BI-LAYERED LAMINATED COMPOSITES FOR ENHANCED MOISTURE BARRIER PERFORMANCE
(2023) Gandikota, Ibaad; McCluskey, Francis Patrick; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Silicone encapsulations are widely used in high-temperature electronic applications, providing excellent properties like thermal stability, high purity, and chemical resistance. However, silicone is susceptible to moisture-induced failures due to high moisture permeability. This study mainly focuses on improving the moisture ingression characteristics of the silicone encapsulation by adding a polyurethane moisture barrier layer. This study focuses on the effects of moisture ingression by adding polyurethane and testing with embedded relative humidity sensors at different environmental conditions. The diffusivity of both the bi-layered composites and the pure encapsulation materials was assessed using two distinct experimental methods for the calculation of diffusivity based on the principles of 1-dimensional Fick's law of diffusion. The diffusivities were statistically analyzed to determine significant differences between the samples, and the experiment yielded a minimum of 65% reduction in diffusivity across the samples. Furthermore, a thermomechanical analysis was performed on two different GaN power MOSFETs by the application of different underfill and potting encapsulations to determine stresses and strains on the solder bumps.
DISSECTION AND MODELING OF AEDC WIND TUNNEL 9 CONTROL LAW AND FACILITY DURING BLOW PHASE
(2023) Gigioli, Samuel George; Gupta, Ashwani K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This work presents the progress towards a mathematical modeling of the Arnold Engineering Development Complex (AEDC) Wind Tunnel 9 control law during the blow phase of a given tunnel run, composing of electrical analog physics, ideal gas control volume physics, incompressible fluid mechanics, and force balance kinematics. This work is unique to Tunnel 9 and unique in respect to other works, as no other existing models of the current control law exist. The primary goal of this work is to provide enhanced support to the Tunnel 9 engineers with the ability to model different run conditions. Key facility measurements can be estimated, aiding in the determination if proposed non-standard run conditions will meet or maintain the facility capabilities, and if the facility can be operated under safe operating limits. The secondary goal of this model is to progress toward a digitally controlled valve system to replace the current analog system. Such will help provide advantages in the facility (1) performance, (2) health monitoring, (3) maintainability, and (4) sustainment.
EXPLORING AN ALTERNATIVE TECHNOLOGY FOR MANUFACTURING ELECTRONICS FOR EXTREME TEMPERATURES
(2023) Patel, Mital; McCluskey, Patrick; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Within our increasingly digital world, there is a demand to integrate electronics into every industry to take advantage of applications in communication, optimization, and artificial intelligence. Relatively untapped areas for electronics implementation are the extreme environments where high temperatures (>300°C) are present. These environments are common within energy, automotive, and aerospace industry es. Current high temperature technologies limit reliable use of electronics to ~200°C. Emerging technologies, such as transient liquid phase (TLP) bonding, copper sintering, and thick films, have not yet demonstrated resilient operation above 300°C. Possessing various remarkable properties, diamond is a promising material that can be used in manufacturing electronic devices operable well above 500°C. Graphene and graphite additionally can serve as conductive material for circuitry or other electronic elements. The compatibility and versatility of these three materials demonstrate the potential for robust, all-carbon electronics for high temperature applications. Chemical vapor deposition (CVD), the predominant method of synthesizing diamond for electronics, involves very costly, long processes at extreme temperatures. A relatively underdeveloped, alternative method utilizes the pyrolysis of polymer precursors into diamond. This study aims to further explore this method using Poly(naphthalene-co-hydridocarbyne) (PNHC). The polymer synthesis, processing, and pyrolysis have been performed here, and the process parameters and outcomes at each step have been documented. Native graphite and graphene growth on diamond surfaces allows for the integration of conductive material on insulating diamond. Four known methods of diamond graphitization, assisted with the metal catalysts nickel, copper, and iron, have also been applied to support the fabrication of carbon-based electronics. Ultimately in this study, the synthesis of diamond has been unsuccessful, but multi-layer graphene has been grown on polycrystalline diamond with high sheet carrier concentration and mobility values of 1.0*1015 cm-2 and 629.1 cm2 Vs-1, respectively.
Dynamic Control of Dexterous Soft Robotic Systems
(2023) Weerakoon, Weerakoon Mudiyanselage Lasitha Tharinda; Chopra, Nikhil; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Soft robotics has grown exponentially during the past two decades due to the possibility of expanded manipulation capabilities over existing rigid robots in complex, unstructured environments. Additionally, soft robots can mitigate current safety risks associated with rigid robots due to their softness. The inspiration for soft robotics has been mainly due to the many examples from nature, such as the agile environmental interactions of the elephant trunk and octopus tentacles. Over the past two decades, several applications ranging from underwater operations to minimally invasive surgeries to space operations have been identified for soft robots. Motivated by these, the overall objective of this dissertation is to study and develop control frameworks for high-fidelity motion control of soft robotic systems. This entails exploiting generalized dynamics models for robust/adaptive control strategies for achieving various operational tasks involved in non-ideal environments, utilizing integrated sensing technologies, and investigating control of underactuated soft robotic systems. This dissertation delve into passivity-based adaptive task space control for soft robots, mitigating uncertainty in the parameters as accurate parameter estimation is particularly hard in soft robotic systems. Further, this approach is extended to task space bilateral teleoperation of a soft follower-rigid leader system exploiting null space velocity tracking to achieve sub-task goals such as conforming to the degree of curvature limits in the soft robot. An enhanced dynamics model is also introduced tailored for planar soft robots and elaborate on passivity-based robust control methods for task space trajectory tracking within this context. This enhanced dynamics model is subsequently extended to encompass 3D spatial soft robots and a comprehensive framework for passivity-based robust task space bilateral teleoperation is discussed. Extensive numerical simulations and experiments are conducted to illustrate the efficacy of these proposed control frameworks. Moreover, to deploy soft robots in the real world, this dissertation study integrated sensing and control of soft robots and a stretchable soft-sensing skin for proprioception s introduced. The mapping from the strain signal to the curvature degree is estimated using a recurrent neural network. Further, an adaptive control framework for curvature tracking is proposed, leveraging the soft stretchable sensing skins and providing experimental evidence of its successful application. This dissertation also introduces a novel robotic system known as the hybrid rigid-soft robot, composed of serially attached rigid and soft links, offering a fusion of the dexterity inherent to soft robots with the precision and payload capacity associated with rigid counterparts. Notably, the study demonstrates that well-established passivity-based adaptive and robust control techniques can effectively apply to this unique class of robots. A soft inverted pendulum with a revolute base is also introduced, establishing a scientific foundation and a methodological approach for introducing innovative soft robots in various practical applications. An energy-based controller is discussed for the swing-up and stabilization of the soft inverted pendulum system, highlighting the efficacy of the controller through simulations. Further, a comprehensive control architecture is developed for the swing-up and stabilization of a class of underactuated mechanical systems, including the soft inverted pendulum, by applying output partial feedback linearization and linear control techniques that avoid switching between controllers. The utility of this control architecture is illustrated using numerical simulations on the soft inverted pendulum. These research endeavors collectively contribute to advancing the understanding of soft robotics and developing effective control strategies for various dexterous soft robotic systems.
DIRECT LASER WRITE PROCESSES FOR SPIDER INSPIRED MICROHYDRAULICS AND MULTI-SCALE LIQUID METAL DEVICES
(2023) Smith, Gabriel Lewis; Bergbreiter, Sarah; Sochol, Ryan; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Direct Laser Write (DLW) through two-photon polymerization (2PP) empowers us to delveinto the realm of genuine three-dimensional design complexity for microsystems, enabling features smaller than a single micrometer. This dissertation develops two novel fabrication processes that leverage DLW for functional fluidic microsystems. In the first process, we are inspired by arachnids that use internal hemolymph pressure to actuate extension in one or more of their leg joints. The inherent large foot displacement-to-body length ratio that arachnids can achieve through hydraulics relative to muscle-based actuators is both energy and volumetrically efficient. Until recent advances in nano/microscale 3-D printing with 2PP, the physical realization of synthetic complex ‘soft’ joints would have been impossible to replicate and fill with a hydraulic fluid into a sealed sub-millimeter system. This dissertation demonstrates the smallest scale 3D-printed hydraulic actuator 4.9 × 10^−4 mm^3 by more than an order of magnitude. The use of stiff 2PP polymers with micron-scale dimensions enable compliant membranes similar to exoskeletons seen in nature without the requirement for low-modulus materials. The bio-inspired system is designed to mimic similar hydraulic pressure-activated mechanisms in arachnid joints utilized for large displacement motions relative to body length. Using variations on this actuator design, we demonstrate the ability to transmit forces with relatively large magnitudes (milliNewtons) in 3D space, as well as the ability to direct motion that is useful towards microrobotics and medical applications. Microscale hydraulic actuation provides a promising approach to the transmission of large forces and 3D motions at small scales, previously unattainable in wafer-level 2D microelecromechanical systems (MEMS). The second fabrication process focuses on incorporating functionality through the use of liquid metals in 3D DLW structures. Room temperature eutectic Gallium Indium (eGaIn)- based liquid metal devices with stretchable, conductive, and reconfigurable behavior show great promise across many areas of technology, including robotics, communications, and medicine. Microfluidics provide one means of creating eGaIn devices and circuits, but these devices are typically limited to larger feature sizes. Developments in 3D printing via DLW have enabled sub-100 µm complex microfluidic devices, though interfacing microfluidic devices manufactured with DLW to larger millimeter-scale systems is difficult. The reduced channel diameter creates challenges for removing resist from the channels, filling microchannels with eGaIn, and electrically integrating them to larger channels or other circuitry. These challenges have prevented microscale liquid metal devices from being used more widely. In this dissertation, we demonstrate a facile, low-cost multiscale process for printing DLW microchannels and devices onto centimeter-scale custom fluidic channel substrates fabricated via stereolithography (SLA). This work demonstrates a robust interface between the two independently printed materials and greatly simplifies the filling of eGaIn microfluidic channels down to 50 µm in diameter, with the potential to achieve even smaller feature sizes of liquid metals. This work also demonstrates eGaIn coils with resistance of 43-770 mΩ and inductance of 2-4 nH. As a result, this process empowers us to manufacture interfaces that are not only low-temperature but also conductive and flexible. These interfaces find their application in connecting with sensors, actuators, and integrated circuits, thereby opening new avenues in the field of 3D electronics. Furthermore, our approach extends the lower limits of size-dependent properties for passive electronic components like resistors, capacitors, and inductors crafted from liquid metal, expanding the frontiers of possibilities in miniature electronic design.
MECHANICS AND THERMAL TRANSPORT MODELING IN NANOCELLULOSE AND CELLULOSE-BASED MATERIALS
(2023) RAY, UPAMANYU; Li, Teng; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Cellulose, the abundantly available natural biopolymer, has the potential to be a next generation wonder material. The motivation behind this thesis stems from the efforts to obtain mechanical properties of two novel cellulose-based materials, which were fabricated using top-down (densified engineered wood) and bottoms-up (graphite-cellulose composite) approaches. It was observed that the mechanical properties of both the engineered wood (strength~596 MPa; toughness ~3.9 MJ/m3) and cellulose-graphite composite (strength~715 MPa; toughness ~27.7 MJ/m3) surpassed the equivalent features of other conventional structural materials (e.g., stainless steel, Al alloys etc.). However, these appealing properties are still considerably inferior to individual cellulose fibrils whose diameters are in the order of nanometers. A significant research effort needs to be initiated to effectively transfer the mechanical properties of the hierarchical cellulose fibers from the atomistic level to the continuum. To achieve that, a detailed understanding of the interplay of cellulose molecular chains that affects the properties of the bulk cellulosic material, is needed. Modeling investigations can shed light on such underlying mechanisms that ultimately dictate multiple properties (e.g., mechanics, thermal transport) of these cellulosic materials. To that end, this thesis (1) applies molecular dynamics simulations to decipher why microfibers made of aligned nanocellulose and carbon nanotubes possess excellent mechanical strength, along with understanding the role of water in fully recovering elastic wood under compression; (2) delineates an atomistically informed multi-scale, scalable, coarse grained (CG) modeling scheme to study the effect of cellulose fibers under different representative loads (shearing and opening), and to demonstrate a qualitative guideline for cellulose nanopaper design by understanding its failure mechanism; (3) utilizes the developed multi-scale CG scheme to illustrate the reason why a hybrid biodegradable straw, experimentally fabricated using both nano- and micro-fibers, exhibits higher mechanical strength than individual straws that were built using only nano or microfibers; (4) investigates the individual role of nanocellulose and boron nitride nanotubes in increasing the mechanical properties (tensile strength, stiffness) of the derived nanocellulose/boron-nitride nanotube hybrid material; (5) employs reverse molecular dynamics approach to explore how the boron nitride nanotube based fillers can improve thermal conductivity (k) of a nanocellulose derived material. In addition, this thesis also intends to educate the readers on two perspectives. The common link connecting them is the method of engineering intermolecular bonds. The first discussion presents a few novel mechanical design strategies to fabricate high-performance, cellulose-based functional materials. All these strategies are categorized under a few broad themes (interface engineering, topology engineering, structural engineering etc.). Another discussion has been included by branching out to other materials that, like nanocellulose, can also be tuned by intermolecular bonds engineering to achieve unique applications. Avenues for future work have been suggested which, hopefully, can act as a knowledge base for future researchers and help them formulate their own research ideas. This thesis extends the fundamental knowledge of nanocellulose-based polymer sciences and aims to facilitate the design of sustainable and programmable nanomaterials.
ELECTRICAL AND STRUCTURAL FORMATION OF TRANSIENT LIQUID PHASE SINTER (TLPS) MATERIALS DURING EARLY PROCESSING STAGE
(2023) Nave, Gilad; McCluskey, Patrick; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The growing demands of electrification are driving research into new electronic materials. These electronic materials must have high electrical conductivity, withstand harsh environments and high temperatures and demonstrate reliable solutions as part of complete electronic packaging solutions. This dissertation focuses on characterizing the initial stage of the manufacturing process of Transient Liquid Phase Sinter (TLPS) alloys in a paste form as candidates for Pb-free high-temperature and high-power electronic materials.The main objective of this dissertation work is to investigate the factors and decouple the multiple cross effects occurring during the first stage of TLPS processing in order to improve the understanding of material evolution. The work proposes, develops, and conducts in-situ electrical resistivity tests to directly measure material properties and analyze the dynamics at different stages of the material's evolution. The research explores various factors, including alloying elements, organic binders, and heating rates, to understand their effects on the development of electrical performance in electronic materials. More specifically, the work examines the performance of Ag-In, Ag-Sn and Cu-Sn TLPS paste systems. Additionally, packing density and changes in cross-section are investigated using imaging techniques and image processing to gain insights into the early formation of the material's structural backbone. An Arrhenius relationship together with Linear Mixed Models (LMM) techniques are used to extract the activation energies involved with each of the processing stages. The study then develops procedures to model different states of the TLPS microstructures at different heating stages based on experimentally observed data. Using these models, the study uses Finite Element Method (FEM) analysis to verify the experimental results and gain a better understanding and visualization into the involved mechanisms. This investigation not only sheds light on the material's behavior but also has implications for robust additive manufacturing (AM) applications.
PERFORMANCE ENHANCEMENTS OF MICRO CORIOLIS VIBRATORY GYROSCOPES THROUGH LINEARIZED TRANSDUCTION AND TUNING MECHANISMS
(2023) Knight, Ryan; DeVoe, Don L; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
A quadruple mass Microelectromechanical System (MEMS) Coriolis vibratory gyroscope has been re-engineered with the singular focus of minimizing nonlinear transduction mechanisms, thereby allowing for angle random walk (ARW) noise reduction when operating at amplitudes higher than 2 μm. The redesign involved six primary steps: (i) the creation of an aspect-ratio independent deep reactive ion etch with minimal notching on 100 μm thick silicon-on-insulator device layer, (ii) the creation of micro-torr vacuum packaging capability, enabling operation at the thermoelastic dissipation limit of silicon, (iii) the redesign of Coriolis mass folded flexures and shuttle springs, (iv) the linearization of the antiphase coupler spring rate while maintaining parasitic modal separation, (v) the substitution of parallel plate transducers with linear combs, and (vi) the implementation of dedicated force-balanced electrostatic frequency tuners. Cross-axis stiffness is also reduced through folded-flexure moment balancing to further reduce ARW. By balancing positive and negative Duffing frequency contributions, net fractional frequency nonlinearity was reduced to -20 ppm. The gyroscope presented in this research has achieved, a first reported of its kind, an ARW of 0.0005 °/√hr, with an uncompensated bias instability of 0.08 °/hr. These advancements hold promise for enhancing navigation and North-finding applications. In tandem with gyroscope performance enhancements, vacuum packaging of ceramic chip carrier physics packages has achieved pressure levels below 1 micro-torr, a first in the field and remains state-of-the-art. Besides high-performance MEMS inertial sensors, ultrahigh vacuum packaging proves beneficial for chip scale atomic clocks, which require micro-torr vacuum levels to maintain fractional frequencies less than 10^-12. Finally, an approach to tuning the quality factor mismatch between degenerate modes in as-fabricated gyroscopes has demonstrated a reduction in gyroscope bias instability. This tuning can be achieved by incorporating lead zirconate titanate into regions where the trade-off between mechanical Q, tuning Q, and bias instability reduction is balanced. Both modeling and empirical frequency data justify this approach, suggesting, for typical MEMS foundry Q mismatch of 7%, a 70× reduction in bias instability.
Direct Laser Writing-Enabled Microstructures with Tailored Reflectivity for Optical Coherence Tomography Phantoms
(2023) Fitzgerald, Declan Morgan; Sochol, Ryan D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
As the continuous push to improve medical imaging techniques produces increasingly complex systems, so too must the phantoms critical to the accurate evaluation of these systems evolve. The inclusion of precise geometries is a well documented gap in optical coherence tomography (OCT) phantoms, a gap felt more severely as the technology improves. This thesis seeks to investigate the feasibility of utilizing new manufacturing techniques in the production of OCT phantoms with complex geometries while developing a phantom to determine the sensitivity of OCT systems. The new manufacturing methods include the replication of microstructures printed via direct laser writing into PMMA photoresist, the tailored smoothing of surface roughness inherent to direct laser writing, and the selective retention of surface roughness in certain regions. Each of these methods were implemented in the manufacture of an OCT sensitivity phantom and were found to be effective in each of their respective goals.The efficacy of the sensitivity phantom in evaluating the minimum reflectance still detectable by an OCT system shows promise. Effective reflectivity ranging from 0 to ~1 was accomplished within a single angled element and should provide a basis for determining the minimum reflectivity that results in a signal-to-noise ratio of 1. Further improvements must be made to the phantom footprint and manufacturing before the phantom’s reliability is certain.
THE EFFECTS OF TEMPERATURE AND SURFACTANTS ON SECONDARY DROPLETS GENERATED BY THE IMPACT OF RAINDROPS ON A WATER SURFACE
(2023) Zhang, Xiguang; Duncan, James; Liu, Xinan; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The effects of temperature and surfactant on secondary droplets produced by the impact of raindrops on water surface were experimentally studied in a rain facility that consists of a rain generator and a deep water pool. The rain generator is a 0.9 m × 0.6 m rectangular tank with 360 hypodermic needles mounted on its bottom. A constant water height is maintained in the tank to obtain a constant dripping rate of raindrops from the needles. The rain generator is placed 2.2 meters above the water pool that is 1.22 m long by 1.22 m wide with a water depth of 0.31 m. A circular motion of the rain generator varies the impact locations of the raindrops on the water surface.Both the raindrops and secondary droplets are measured with an in-line holographic technique that employs a collimated laser beam and a high-speed camera. The diameters and two-dimensional positions of the raindrops and secondary droplets were first reconstructed in each holographic image using a GPU-based holographic reconstruction algorithm. Then an in-house particle tracking code was implemented to compute their diameters, trajectories and instantaneous velocities. The measurement data shown in this thesis was taken at 9.5 cm above the water surface of the pool. In this study, the effects of temperature and surface tension on the production of the secondary droplets were examined separately. When studying the temperature effect, the temperature of the water in the rain generator varied from 7 degrees Celsius to 20 degrees Celsius (room temperature) while the water temperature in the pool was maintained at room temperature. When studying the surface tension effect, certain amounts of soluble surfactant (Triton X-100) was added into the water pool to vary the surface tension from 40 mN/m to 73 mN/m, while the rain water is kept clean with a surface tension of 73 mN/m. It is found that both the rain temperature and the surface tension of the water pool have an impact on the production of secondary droplets. The temperature of the rain could change the viscosity by more than 40%, therefore resulting in a significant difference in the number and the size distribution of the production of secondary droplets. On the other hand, while the surface tension of the water pool does not heavily influence the number of secondary droplets, it does contribute to a difference in size distributions of these droplets at around R = 120 μm.

Browse

Recent Submissions