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Amyloid nanofibers are abundant in microorganisms and are integral components of many biofilms, serving various purposes, from virulent to structural. Nonetheless, the precise characterization of bacterial amyloid nanofibers has been elusive, with incomplete and contradicting results. The present work focuses on the molecular details and characteristics of PSMa1-derived functional amyloids present in Staphylococcus aureus biofilms, using a combination of computational and experimental techniques, to develop a model that can aid the design of compounds to control amyloid formation. Results from molecular dynamics simulations, guided and supported by spectroscopy and microscopy, show that PSMa1 amyloid nanofibers present a helical structure formed by two protofilaments, have an average diameter of about 12 nm, and adopt a left-handed helicity with a periodicity of approximately 72 nm. The chirality of the self-assembled nanofibers, an intrinsic geometric property of its constituent peptides, is central to determining the fibers' lateral growth.

A novel formulation of the weak form quadrature element method, referred to as the locally adaptive weak quadrature element method, is proposed to develop elements for nonlinear graded strain gradient Timoshenko and Euler–Bernoulli nanobeams. The equations of motion are obtained based on Hamilton principle while accounting for the position of the physical neutral axis. The proposed elements use Gauss quadrature points to ensure full integration of the variational statement. The proposed formulation develops matrices based on the differential quadrature method which employs Lagrange-based polynomials. These matrices can be modified to accommodate any number of extra derivative degrees of freedom including third-order beams and higher-order strain gradient beams without requiring an entirely new formulation. The performance of the proposed method is evaluated based on the free vibration response of the linear and nonlinear strain gradient Timoshenko and Euler–Bernoulli nanobeams. Both linear and nonlinear frequencies are evaluated for a large number of configurations and boundary conditions. It is shown that the proposed formulation results in good accuracy and an improved convergence speed as compared to the locally adaptive quadrature element method and other weak quadrature element methods available in the literature.Other Information Published in: Acta Mechanica License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s00707-022-03321-4

Many space new missions are on calendar this decade for Moon, Mars and other planets. Some of these missions are depending on simple reliable chemical thrusters but others are depending on the nonchemical advanced propulsion techniques. Although these methods may save time and produce faster journeys, we are still in need for faster methods to travel in space. What about reaching Mars in hours instead of months? Electron beam can do, and so do we; by replacing the electrons with other charged particles using the same electron beam principles. This is a fundamental research on enabling the electron beam to attract objects via its electromagnetic field and to transmit them with an approximate speed reaching at least 0.01 of light speed depending on the dual nature of the electron as a wave and particle, then applying the same principles on larger particles with better characters. The main idea lies in making the electron beam the basic structure in the new process and to build a whole new technology for spacecrafts to travel faster in space with much less cost. The process is based on the cathode ray tube design with consideration of deflection, acceleration and other properties of the electron beam.

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The project of the conceptual design of a hybrid-electric regional turboprop airliner seeks to address the fundamental issue of aviation emissions effect on climate change. Specifically, the aviation industry, through its extensive use of hydrocarbon fuel, is responsible for a large amount of CO2 and NOx greenhouse gas emissions that negatively affect the environment. This is done through the creation of the design of a new generation of regional airliner through a conceptual design challenger that has reduced emissions using a hybrid-electric powertrain. However, it is important to consider the human aspect of this design (who is going to use it? And how it may it be used?), because if the human factors relating to the design are not sufficiently addressed, then the design will not be able to be implemented in the real world, negating any potential benefits the design brings on paper. Therefore, it is crucial to perform an STS analysis on technologies like the one being studied in the conceptual design to see if they properly address the need of the human groups they are to serve, to see if they are truly practical in their current form. This can be done through the Social Construction of Technology socio-technical framework, where the relationship between the human social groups and new climate change mitigation can be modeled. The data required to create these models can be gathered through a literature review of existing material. Through this research, it is expected that which climate change mitigation measures can be most practically implemented in the short term will be able to be determined, along with what social relationship factors are currently negatively affecting the development of certain mitigation measures. Ultimately, the conceptual design project, with the STS research project will be able to provide an in-depth exploration of the possibilities of climate change mitigation measures in the aviation industry, with the conceptual design project providing a close technical overview of one measure (hybrid-electric powertrains), and the STS research providing a review of other measures through a socio-technical lens.

As a mechanical engineer, I have taken multiple courses that have helped my design, development, and engineering skills. One of these courses has been the most influential in my young engineering career, this course being mechatronics. Mechatronics engineering is the combination of mechanical, electrical, and computer science engineering to breathe life into systems, allowing them to operate such that they can function without the need for human interaction. This also acts as the link connecting my research paper and my technical capstone completed during my final year in college. Mechatronics is a fascinating field such that it can be applied to almost every scenario imaginable. In the case of my technical project, mechatronics was used to create a form of kinetic art. This art piece is an orrery that is designed to be hung from the ceiling in the mechanical building, allowing human interaction. As mechatronics is used to create art forms, it is also used in common machinery and appliances. Automatic sinks, dishwashers, and A/C units can all be considered mechatronic systems. Often, mechatronic systems are used in manufacturing design to allow less user interface and more precision in quantity manufacturing. This became the focus in my investigative thesis paper as it is noted that there have been negative mental health effects seen from workers using these systems. My thesis then questions how these devices have been designed that have caused these mental health problems. The technical capstone that was completed was the creation of a mechatronic orrery. The purpose of creating a mechatronic orrery is to demonstrate in a unique and fun way, the positions of the earth and moon, relative to the sun. This project creates a more aesthetically pleasing and accurate model of the earth, moon, and sun while educating people on the seasons, moon phases, eclipses and so on. The orrery is rapidly prototyped using laser cutting acrylic material as well as 3D-printing ABS plastic. This system consists of multiple motors and induction proximity sensors, as well as two microcontrollers to help process the data of the positions and run the motors. The final design consists of a sun and an earth and moon that rotates around the sun. The sun itself is not a traditional sun in that it will be more aesthetically pleasing rather than an accurate representation. However, the sun will contain a lighting fixture that will shine light on the earth as well as the moon. As the earth rotates, the light will show how the seasons are made relative to the earth’s atmosphere. As the light shines on the moon, it will show a rough estimation of how the phases of the moon are created. There will also be an electronic display that will show the phase of the moon as well as the date and the position of the earth. The orrery will have two modes. One mode is where the orrery will rotate on its own at a constant space, showing how the earth and moon are revolving around each other and the sun. The second mode will involve the user input in which a user can put in a date, and the orrery will automatically rotate to show the earth and moon’s position at that time. It will also be able to indicate the next eclipse, as well as other events regarding the lunar cycle. Note that this is still an ongoing project, and it is hoped that after an explicit technical report it will be able to be completed with these visions in mind. The thesis completed focused on how the design of certain manufacturing machinery has led to the decrease in mental health that is seen in the blue-collar workforce. To investigate this issue, I researched a specific case in manufacturing: CNC machine integration. By analyzing the CNC machine, it could be seen how this device was developed and introduced into manufacturing. By using Woolgar’s configuring the user framework, I presented that this technology could be analyzed to determine how it was written to interact with the user specifically. It could then be determined if there is evidence to determine if there were any design characteristics that would affect the mental health of these blue-collar workers in manufacturing. I used UVA’s digital library to gather sources regarding the history of CNC machines as well as their implementation into the manufacturing field. It was also used to find studies, statistics, and other methods of supporting evidence to use in the paper. This knowledge was combined with the technical STS framework used to analyze the case. The conclusion highlights that the lack of consideration in the initial implementation of CNC machines into manufacturing is a cause of the increase mental health dealt with in the manufacturing industry. It is the lack of consideration that is causing these mental stresses, and if more consideration is added to the implementation process there could be a positive increase seen in the mental health of these workers. Being able to complete both projects at the same time proved to be important as a young engineer and designer. As I was researching these negative mental health effects experienced by workers, it made me question my own designs for the capstone as well as anything I design going forward. During the design process, I found myself questioning the motives of each design making sure that they remained pure and true to their task. By completing these projects side-by-side, I found the goal is to be able to design an object that will positively leave an impact on its surroundings as well as the actors that it will be using the technology. The object should be able to go through changes with the social climate as well as be able to be redesigned to adapt the relevance of its task.

Time-of-flight mass spectrometry allows for the study of elemental, isotopic, and molecular composition from a material sample. The mass sensitivity and resolution of time-of-flight mass spectrometers increases with the length of the instrument, where increased sensitivity indicates increased ability to distinguish between ions of similar masses. This indicates an inherent limit of traditional time-of-flight mass spectrometers designed as monolithic instruments for integration onboard a spacecraft: the length of the spectrometer, and therefore its sensitivity, is constrained by the form factor of the spacecraft. This paper motivates and presents a novel architecture for in-space mass spectrometry through the use of distinct, free-flying spacecraft that will be the subject of future research. The Advanced Mass Spectrometry in Gravity-Free Architectures (AMIGAS) concept aims to enable ultra-long baseline (10-100 m) time-of-flight mass spectrometry that could dramatically improve the sensitivity and resolution of in-space time-of-flight mass spectrometers to the point of detecting millidalton-level differences in atom or molecule mass. This paper presents the scientific motivation for why such a high sensitivity would be beneficial and explores the associated engineering constraints. A high-level overview of the AMIGAS concept is also provided along with anticipated challenges for practical implementation.

A growing body of work has linked key biological activities to the mechanical properties of cellular membranes, and as a means of identification. Here, we present a computational approach to simulate and compare the vibrational spectra in the low-THz region for mammalian and bacterial membranes, investigating the effect of membrane asymmetry and composition, as well as the conserved frequencies of a specific cell. We find that asymmetry does not impact the vibrational spectra, and the impact of sterols depends on the mobility of the components of the membrane. We demonstrate that vibrational spectra can be used to distinguish between membranes and, therefore, could be used in identification of different organisms. The method presented, here, can be immediately extended to other biological structures (e.g., amyloid fibers, polysaccharides, and protein-ligand structures) in order to fingerprint and understand vibrations of numerous biologically-relevant nanoscale structures.

A significant amount of energy is being used by ventilation and air conditioning systems to maintain the indoor environmental condition in a satisfactory and comfortable level. Many buildings, either new or existing (throughout their renovation process) are subjected to energy efficiency requirements but these must not be in the expenses of indoor environmental conditions. For instance, indoor air quality (IAQ) has to be considered while improving energy efficiency, otherwise occupants might be exposed to inappropriate indoor environment.Demand-controlled ventilation (DCV) is a method that provides comfortable IAQ level with lowest energy use. In this paper, the main objective is developing a new CO2-based DCV strategy and simulating it using EnergyPlus. The IAQ and energy consumption associated to this strategy have been compared with the results of CO2-based DCV strategies previously developed by the same authors in another article. The comparison shows that the new strategy performs better, both in energy use and IAQ. The recorded energy savings ranged between 6-14% comparing with the previously developed strategies while IAQ slightly improved.

Active noise control (ANC) is often optimised for the energetic reduction of total sound pressure, which does not always translate to increased acoustic comfort in terms of perceived annoyance and loudness reduction. Nevertheless, ANC has been shown to significantly improve acoustic comfort when frequencies below about 1000 Hz have been reduced roughly uniformly across all frequencies by about 10 dB(A). Hence, it is thus worthwhile to examine if a non-uniform reduction across the frequency bandwidth yields could yield greater overall acoustic comfort. Moreover, the importance of context in sound perception also necessitates an investigation into the effect of noise type and its interaction with the non-uniform attenuation `filter' on acoustic comfort.

A rapid review of the testing environments and methods for technologies implemented in automotive interiors (human-machine interfaces) followed by a guiding scheme to make an informed choice of the most feasible method to make these technologies experienceable to users before actual implementation.

To combat the rising threats of climate change, current combustion technologies must evolve to become cleaner and more efficient. This requires a better understanding of the fundamental properties of combustion. One way to gain this is through microgravity experiments, where the lack of buoyancy reduces flames to their most basic components, simplifying modeling efforts. The low-temperature combustion of warm and cool flames, which has applications in advanced engine technologies and implications in terrestrial and spacecraft fire safety, is favored in microgravity. In this work, microgravity spherical diffusion flames are generated aboard the International Space Station using a spherical porous burner. A transient numerical model with detailed chemistry, transport, and radiation is used to simulate the flames. This incorporates the UCSD mechanism with 57 species and 270 reactions. Hot, warm, and cool diffusion flames are all studied. Experimental flame temperature was measured using thin-filament pyrometry, which was calibrated using a blackbody furnace. The measured temperatures agreed reasonably well with numerical simulations for a wide range of conditions, and were in the range of 950-1600 K, with an estimated uncertainty of ± 100 K. The temperatures of the porous spherical burner were measured by a thermocouple embedded in its surface. These measured temperatures, combined with numerical simulations of the gas phase, yield insight into the complex heat transfer processes that occur in and near the porous sphere. Previous work has found that ethylene microgravity spherical diffusion flames extinguish near 1130 K at atmospheric pressure, regardless of the level of reactant dilution. The chemical kinetics associated with this consistent extinction temperature are explored using the transient numerical model. Species concentrations, reaction rates, and heat release rates are examined. Upon ignition, the peak temperature is above 2000 K, but this decreases until extinction due to radiative losses. This allows the kinetics to be studied over a wide range of temperatures for the same fuel and oxidizer. At high temperature, the dominant kinetics are similar to those reported for typical normal-gravity hydrocarbon diffusion flames. There are well defined zones of pyrolysis and oxidation, and negligible reactant leakage through the reaction zone. As the flame cools, there is increased reactant leakage leading to higher O, OH, and HO2 concentrations in the fuel-rich regions. The pyrolysis and oxidation zones overlap, and most reactions occur in a narrow region near the peak temperature. Reactions involving HO2 become more significant and warm flame chemistry appears. At atmospheric pressure, this low-temperature chemistry delays extinction, but does not produce enough heat to prevent it. As ambient pressure is increased, low-temperature chemistry is enhanced, allowing the flame to extend into the warm flame and cool flame regimes. Experimental results show that increasing the pressure from 1 atm to 3 atm decreased the ethylene extinction temperature by almost 60 K. Numerical simulations showed similar behavior, as well as the emergence of cool flame behavior when the pressure was increased to 50 atm. This allows the kinetics of spherical warm and cool diffusion flames and the role of increased HO2 participation to be examined. There are few options for studying cool diffusion flames experimentally that do not require expensive facilities that are unavailable to the average researcher. A method is presented for observing cool diffusion flames inexpensively using a pool of liquid n-heptane and parallel plates heated to produce a stably stratified stagnation flow. The flames were imaged with a color camera and an intensified camera. Measurements included gas phase temperatures, fuel evaporation rates, and formaldehyde yields. These are the first observations of cool flames burning near the surfaces of fuel pools. The measured peak temperatures were between 705 – 760 K and were 70 K above the temperature of the surrounding air. Autoignition first occurred at 550 K.

Airfoil optimization is critical to the design of turbine blades and aerial vehicle wings, among other aerodynamic applications. This design process is often constrained by the computational time required to perform CFD simulations on different design options, or the availability of adjoint solvers. A common method to mitigate some of this computational expense in nongradient optimization is to perform dimensionality reduction on the data and optimize the design within this smaller subspace. Although learning these low-dimensional airfoil manifolds often facilitates aerodynamic optimization, these subspaces are often still computationally expensive to explore. Moreover, the complex data organization of many current nonlinear models make it difficult to reduce dimensionality without model retraining. Inducing orderings of latent components restructures the data, reduces dimensionality reduction information loss, and shows promise in providing near-optimal representations in various dimensions while only requiring the model to be trained once. Exploring the response of airfoil manifolds to data and model selection and inducing latent component orderings have potential to expedite airfoil design and optimization processes. This thesis first investigates airfoil manifolds by testing the performance of linear and nonlinear dimensionality reduction models, examining if optimized geometries occupy lower dimensional manifolds than non-optimized geometries, and by testing if the learned representation can be improved by using target optimization conditions as data set features. We find that autoencoders, although often suffering from stability issues, have increased performance over linear methods such as PCA in low dimensional representations of airfoil databases. We also find that the use of optimized geometry and the addition of performance parameters have little effect on the intrinsic dimensionality of the data. This thesis then explores a recently proposed approach for inducing latent space orderings called Rotation Augmented Gradient (RAG) [1]. We extend their algorithm to nonlinear models to evaluate its efficacy at creating easily-navigable latent spaces with reduced training, increased stability, and improved design space preconditioning. Our extension of the RAG algorithm to nonlinear models has potential to expedite dimensional analyses in cases with near-zero gradients and long training times by eliminating the need to retrain the model for different dimensional subspaces

Level 5 advanced autonomous vehicles could be available for use as soon as 2030, allowing vehicles to drive anywhere without drivers. In this impending future, driverless vehicles may exclude people who use manual wheelchairs, of which there are currently 2.7 million in the U.S. alone. Current forms of wheelchair transportation often require aid from the driver to help the individual into the vehicle and be secured. Our team has developed a system to allow people in wheelchairs to enter an autonomous vehicle, secure themselves, and exit the vehicle without additional assistance. Our prototype to facilitate entry and exit into the vehicle is a deployable ramp which uses a modified four-bar system to allow for concealment within the floor of the vehicle as well as full deployment to the ground. Our prototype to achieve securement uses two outward-facing spring-loaded teeth to secure the wheelchair by the vertical components of its frame, using only the motion of the wheelchair for actuation. After the ramp deploys, the user will be able to wheel themselves up it and into the vehicle, where they can then back into the securement mechanism and lock into place. Then, a pull handle allows for mechanical release from the securement. The ramp is currently able to be moved manually along its intended motion path, however the eventual system would actuate automatically via motors on each arm. Upon testing the deflection of the ramp, it was able to withstand 900 pounds of weight as required by the ADA, and keep the maximum deflection of the ramp under 1.5mm. In order to test the securement mechanism, we scaled down the required forces and applied them to a 3D-printed version of the mechanism. Using the ratio of the yield strengths of PLA and aluminum 6061, we calculated these forces to be 264 N of lateral force and 160 N of axial force, which were numbers calculated from accelerations and turns associated with strenuous driving conditions. Upon testing we found that our mechanism successfully withstood these forces without yielding. Using the full-scale securement prototype, we were able to allow for 8 first-time users to secure themselves in under 40 seconds with times ranging from 14 to 38 seconds. Actuating the release for the securement mechanism took 10 pounds of force, exceeding our requirement of 5 pounds or less, and is currently inaccessible by the user while secured. Our tests concluded that with the exception of the securement release actuation, both mechanisms meet ADA requirements and are capable of allowing a user to enter, secure within, and exit a vehicle. Future steps include connecting the ramp to motors, extending the securement release mechanism to be accessible by the user, and reducing the force needed to release the securement.

The purpose of this study is to assess the workload impact of the proposed system in comparison with industry-standard drone methods and to assess pilots' situational awareness with respect to key events in the mission. Participants' feedback on the usability of the system and suggestions for a user-centric design is also collected in this study.

While there are over 600,000 lower-limb amputees in the US, commercially available prostheses remain limited to mostly passive devices. People that walk with a passive prosthesis experience an increase in energy expenditure, a decrease in comfortable walking speed, and gait asymmetry which leads to degenerative conditions. To address these limitations, researchers have developed powered prostheses with the aim of replicating the net positive energy biological limbs supply to humans in walking. These active devices have been shown to decrease users' metabolic cost and increase their comfortable walking speed. However, the control methods to achieve these results typically require hours of heuristic tuning for every user and every behavior. This motivates developing more formal prosthesis control methods that generalize between users. Formal nonlinear control methods have been developed to realize energy efficient, human-like walking on bipedal robots. These model-based approaches provide a systematic approach to generate and realize provably stable walking gaits. However, these methods cannot be directly applied to prostheses since they depend on a dynamic model of the entire system, and in the case of the prosthesis, the human dynamics are unknown. To address this challenge, we develop a theoretical framework to translate model-based bipedal control methods to prostheses with the aim of realizing a generalizable prosthesis control method. We separate the prosthesis subsystem from the remaining human portion of the system and model the human's impact on the prosthesis dynamics with a measure of the interaction forces between the human and the prosthesis. We theoretically prove that a model-based controller developed in this separable subsystem framework is equivalent to one developed with knowledge of the full-order human-prosthesis system. With control Lyapunov functions, we develop a wider class of subsystem controllers that solely depend on local information but provide full-order system guarantees, even in the presence of force estimate errors. This work bridges the gap between bipedal control methods and prostheses, allowing us to leverage the benefits of model-based approaches on prostheses. We demonstrated a controller of this class through an online optimization-based approach on a powered knee-ankle prosthesis, realizing the first model-dependent lower-limb prosthesis controller that accounts for the interaction force between the human and the prosthesis. For a first pass, a force-estimation method was used that yields improved tracking of the desired trajectories over model-independent prosthesis control methods. Then, we incorporated a load cell into the prosthesis platform at the human-prosthesis attachment point to measure the interaction forces, and an inertial measurement to measure the rotation and velocity of the human's thigh. These two sensors completed the prosthesis dynamics model. A pressure sensor incorporated into the prosthesis' shoe measured the ground reaction forces, enabling the prosthesis to respond to its real-world environment, proving robust to 4 different terrains. We extended this controller to a multi-domain hybrid system approach to model the changing contact points occurring in human heel-toe roll. By allowing the prosthesis to sense the human's large varying dynamic load and respond accordingly, this model-based prosthesis controller emulated subject-specific human kinematic trends on a knee-ankle prosthesis for two subjects with no tuning in between, suggesting this approach could yield a method that generalizes between users.

Intelligent autonomous systems, such as those in healthcare, transportation, and manufacturing, require thorough safety evaluations before deployment. However, traditional evaluation methods struggle to handle the high-dimensional input spaces and rare failures that occur in safe autonomous systems. This presents a significant challenge, as the extreme rarity and high dimensionality make it difficult to generate sufficient data to accurately estimate rare-event probabilities. In previous attempts to tackle this issue, techniques like naive sampling or accelerated sampling algorithms have yielded incorrect and overconfident results, posing a risk to public safety. To address this challenge, this thesis proposes three novel safety evaluation algorithms that incorporate machine learning methods: Deep Importance Sampling (Deep IS), Deep Probabilistic Accelerated Evaluation (Deep-PrAE), and Computationally Efficient and Robust Evaluation of Safety (CERTIFY). These algorithms are scalable, reliable, and efficient, allowing for evaluating intelligent autonomy prototypes in digital-twin platforms like high-fidelity driving simulators. The use of machine learning enables these algorithms to handle high-dimensional input spaces and extreme rarity, making them suitable for evaluating modern safe intelligent autonomy algorithms. Deep IS is a highly efficient evaluation algorithm that estimates rare-event probabilities associated with the safety of autonomous systems by combining a deep neural network, mixed-integer programming, and importance sampling. Benchmarking studies have shown that Deep IS can identify and quantify safety risks associated with perception algorithms in autonomous vehicles with superior efficiency and accuracy compared to traditional evaluation algorithms such as Naive Monte Carlo, Multilevel Splitting, or Cross Entropy. Deep-PrAE builds upon Deep IS by providing theoretical guarantees for efficient estimation of the upper or lower bounds of rare-event probabilities. This allows Deep-PrAE to prevent underestimation of the target value in black-box problems, achieving valid bounds efficiently for various numerical experiments while balancing efficiency and correctness compared to other evaluation algorithms. CERTIFY combines Deep-PrAE with a neural network verification algorithm to efficiently compute an upper bound for the failure rate of autonomous systems. In benchmarking studies, CERTIFY demonstrated up to two orders of magnitude efficiency improvement in safety certification tasks, ensuring that the true performance of an algorithm is below a certain threshold. Despite maintaining a valid upper bound similar to Deep-PrAE, CERTIFY results in a much faster evaluation procedure, even when the underlying problem dimension is high. The proposed algorithms were benchmarked in a common driving scenario simulator, highlighting their relevance to assessing complex autonomous vehicle algorithms like perception systems under various noise models. The findings suggest that traditional evaluation algorithms may prematurely conclude safety due to a lack of sample size, making them unreliable for certifying safe autonomous systems. On the other hand, the proposed algorithms offer a scalable, reliable, efficient alternative for certifying safe autonomous systems by computing a meaningful upper bound that can be used to certify the autonomous agent's performance. The study's findings serve to empower developers, researchers, and policymakers in selecting and deploying suitable evaluation algorithms to effectively identify and mitigate safety risks. This study contributes to the ongoing efforts to develop safer and more reliable intelligent autonomy in various application domains dealing with rare failure cases and high-dimensional spaces.

Hydrogels, crosslinked polymeric networks mostly filled with water, have unique mechanical properties, including extreme compliance, stretchability, and conformability. However, their intrinsic ionic conductivity (10−5 to 10−1 S cm−1) limits their use in soft electronics, wearable technologies, or bioelectronics. One way to improve their electrical properties is to introduce conductive fillers into a soft hydrogel matrix. In turn, the high concentrations of metallic fillers required to achieve adequate conductivity for digital circuit applications degrade hydrogel’s desirable mechanical properties. To overcome this limitation, we need a new material architecture that maintains the soft (< 102 kPa) and stretchable (> 50% strain) properties of hydrogels while also enabling high electrical conductivity (> 100 S cm−1). This dissertation introduces various soft conductive composites that achieve low electrical resistance through percolating networks of embedded silver particles. Most of this work focuses on a novel material architecture and a processing method in which silver flakes are suspended with a tough hydrogel. By controlling the water content inside, the composite functions as either a conductor or an insulator. Moreover, this property is reversible so that it can reconfigure the conductive pathways within the composite. Overall, this dissertation contributes to the diversity of hydrogel-based conductors for soft and stretchable electronics.

Since the establishment of the Agile Manifesto in the early 2000s, new software development methodologies have emerged under the heading of 'Agile', differentiating themselves from so-called traditional methods and gaining an unprecedented reputation far beyond the boundaries of software development. Although there are now a number of empirical studies demonstrating the effectiveness of agile software development methods in relation to their goal of "developing better ways to develop software" (Beck et al., 2001), even after two decades the term agile remains an abstract concept whose definition, operationalization and even conclusions about the triggers for its success are disputed among researchers. For the authors of the agile manifesto, the novelty of the agile method lies in the "recognition of people as the primary drivers of project success...". (Highsmith and Cockburn, 2001). In practice, according to some authors, this is reflected in the creation of a work environment in which people are highly motivated (Asproni, 2004). A glance at the literature on the relationship between agility and motivation shows a mixed picture. While Kakar (2017) was able to demonstrate higher motivation among employees in agile projects compared to those in non-agile projects, Arndt (2020) found no difference between the two groups. Memeti et al. (2021), who, unlike Kakar (2017) and Arndt (2020), did not compare groups, could not demonstrate a direct relationship between the use of agile practices and motivation. However, these results contradict the work of Gunawardena (2009), that found a positive correlation. Even though the result of the above mentioned work cannot be compared without reservation as the authors partly use different constructs of motivation and agility in their work and possibly therefore arrive at different results, these give cause for a new investigation of the relationship between agility and motivation.

Electronic Design Automation (EDA) software automates the IC design process and increases the design efficiency and capabilities. With the scaling of semiconductor technologies, the importance of EDA has rapidly increased. At the heart of most EDA design problems are combinatorial optimization problems, which are usually solved with heuristics, or analytical method in classical methods. Owing to the increasing difficulties of the EDA problems and demand for EDA tools designing increasingly complicated electronic systems, classical EDA algorithms are struggling to keep up with the ever more challenging EDA design tasks. Thus, better and more efficient EDA solutions are urgently needed to address the challenge. In recent years, there have been some major development in the field of machine learning, especially in (deep) reinforcement learning. Recent success of reinforcement learning has been demonstrated in fields including robotics, games as well as combinatorial optimization problems. This motivates us to explore the possibility of applying machine learning specifically reinforcement learning to come up with better EDA algorithms.In this research, we propose new algorithms to solve combinatorial problems in EDA based on reinforcement learning and supervised learning. To achieve this, we identified two research objectives. The first one is to developing reinforcement learning algorithms for solving EDA routing problems that performs better and more efficient than the current EDA routing solutions. Under this research objective, we propose three technical goals: (1) DQN Global Routing that solves global routing with Deep Q-learning; (2) Attention Routing that solves detailed routing with attention-based reinforcement learning; (3) Spectral DQN Routing that solves generic EDA routing and obstacle avoiding routing with combination of DQN and spectral method. Our proposed RL-based routing solutions provide better and more efficient alternatives compared with existing routing solutions. The second research objective is to develop machine-learning-based algorithm for automatic 2D space partitioning generation with island minimization objective. To achieve this, we propose the GOMLP algorithm that solves the power plane generation with supervised learning. The power plane generation, as an instance of space partitioning generation with island minimization objective has no readily available algorithms to solve. Our proposed method is the first successful attempt in developing automatic power plane generation algorithms by leveraging supervised learning. We also extend the GOMLP to solve multilayer power plane generation problems with the H-GOMLP that combines GOMLP with hierarchical clustering method.

In this study, the entropy generation resulting from heat and mass transfer of water-based nanofluid through an annulus within two concentric vertical pipes filled with a porous medium is investigated. This study considers the effects of thermal radiation, viscous dissipation, thermal buoyancy, and axial pressure gradient in addition to heat and mass transfer. Brownian motion and thermophoresis have been introduced through the Buongiorno model. The similarity solution was used to solve nonlinear ordinary differential equations. The Runge-Kutta-Fehlberg method is used to solve these equations with the related boundary conditions. The effects of pertinent parameters such as pressure gradient, thermal radiation, viscosity parameter, thermophoretic parameter, Brownian motion parameter, and Eckert number are investigated numerically. This study found that the Bejan number increases as the viscosity parameter increases and decreases as the other active parameters increase. As the radiation parameter, thermophoretic parameter, Brownian parameter, and Eckert number increase, the Nusselt number decreases. The total entropy generation rate is found to increase with the fluid viscosity rate, Grashof number, thermal Biot number, and variable pressure gradient. However, the Bejan number is found to decrease with these parameters, as well as the Prandtl number.

The history of software development is directly linked to the constant emergence of new methods and processes. Only a few of the newly developed methods are able to establish themselves for the first time and sustain themselves in the long term without becoming obsolete. In the early 2000s, however, a group of software practitioners introduced a new term to characterise new software development methods that were distinct from the state of the art, henceforth called traditional methods. These were not only well received by practitioners, but were also widely adopted, and for a short time were regarded as "... the best thing to happen to software development in recent years" (Cohen et al., 2004). Even today, more than two decades later, the concept of agility has not lost its prestige and continues to dominate the discourse and its influence far beyond the boundaries of software development. With the aim of understanding the success factor of agile software development in terms of the socio-psychological effects it has on employees, this study aims to investigate the relationship between agile software development and work characteristics.

This study presents two approaches to investigate the surface-layer structure during the morning transition using uncrewed aircraft systems. The first approach employs three uncrewed aircraft systems- each equipped with a single multi-hole probe- simultaneously measuring horizontal transects were partnered with a fourth measuring vertical profiles during two consecutive mornings as part of the 2017 Collaboration Leading Operational Unmanned Aerial System Development for Meteorology and Atmospheric Physics (CLOUDMAP) measurement campaign near Stillwater, Oklahoma, U.S.A. Data were analyzed to extract time-dependent single-point statistics of kinematic and thermodynamic variables from the uncrewed aircraft systems. In addition, an approach is presented by which multi-point spatial statistics in the form of auto- and cross-correlations could be calculated from the measurements. The second approach employs two fixed-wing uncrewed aircraft systems simultaneously flying horizontal transects with a third rotorcraft uncrewed aircraft system for vertical profiling during a limited deployment at the University of Kentucky North Research Farm (UKNRF), Lexington, U.S.A. The first fixed-wing aircraft is equipped with a custom-built multi-hole-probe-based vorticity probe. The configuration of the vorticity probe allows the estimation of the velocity and small-scale velocity gradients. These gradients are employed to estimate the dissipation rate and vorticity fields which can be used for identifying and characterizing the atmospheric boundary layer structure. The second fixed-wing aircraft is equipped with a single multi-hole probe used to resolve the advection velocity of coherent structures which can be used to approximate the streamwise spatial flow field using frozen Taylor’s hypothesis. The results from the first approach during CLOUDMAP campaign reflect differences in the evolution of spatial statistics with altitude for each of the two days at scales smaller than 500 m, despite very similar synoptic conditions. Conditional averaging was also applied to identify the structure of sweep and ejection motions and results revealed similarities to observations from canonical wall-bounded flow. Whereas, the results from the UKNRF campaign reveal the ability of the vorticity probe to estimate the dissipation rate flow field even though the spatial separation between the probes is much larger than Kolmogorov length scale. The mean dissipation rate estimated from the vorticity probe showed good agreement with the dissipation rate estimated based on Kolmogorov theory from the streamwise power spectrum. Additionally, the instantaneous dissipation rate flow field had signatures, represented by intermittent intense regions, associated with coherent structures which align with similar signatures in the flow fields of the vorticity and virtual temperature fluctuations. The observed structures have spatial length scales in the order of 200 m. Additionally, the spatial extent of the detected coherent structures, as well as the results from the conditional averaging of sweep and ejection events, imply that these regions could correspond to either low-momentum streaks, that evolve to form the legs of hairpin vortices, or the hairpin vortices legs. Finally, the observed average flow structure of different atmospheric quantities during the three flights has characteristics associated with coherent structures similar to what was observed previously in the atmospheric surface layer and in canonical wall-bounded flows., © 2023 Loiy Al-Ghussain

Ceramic materials, commonly defined as metallic oxides, nitrides or carbides, exhibit outstanding mechanical, chemical, and thermal stability. Their favorable properties have resulted in a global market of more than $400 Billion and are commonly used in the electronics, automotive, tooling, defense, and refractory industries. Despite their advantages and popularity, ceramic materials are lagging in additive manufacturing (AM) technologies and comprise only 1% of the global market for additively manufactured materials. Therefore, ceramic materials have not realized the numerous advantageous associated with additive manufacturing: cheaper/faster rapid prototyping, unprecedent design freedoms, cheaper small-batch fabrication, shorter supply chains, and less material waste. This thesis evaluates the feasibility of additively manufacturing oxide and carbide-based ceramics using the binder jetting and laser powder bed fusion AM processes. This involves developing print parameters, evaluating post processing methods, understanding the effects of process parameters on part quality, and comparing printed parts to those manufactured using conventional methods. This thesis presents a body of knowledge, methodologies, and results that can be used as the basis to fabricate ceramic-based materials in powder bed processes. First, a comprehensive design of experiments coupled with statistical analyses determined which binder jet parameters influence the printed densities of oxide preforms. Regression models accurately predict printed densities and can assist in selecting process parameters that will print a desired density. Then, a study investigated the densification behavior of postprocessed binder jet preforms composed of three distinct oxide powders: (1) large grained, highly faceted alumina powder, (2) large grained, spherical silica powder, and (3) small grained, polycrystalline zirconia powder. The heating profiles were parametrized to understand the effect of sintering parameters on densification behavior, microstructure, and phase composition. In another study, carbides were binder jetted with a metallic binder introduced by spherical, sintered-agglomerated composite powder and post processed via sintering and hot isostatic pressing (HIP) steps. The effects of sinterHIP parameters on porosity, microstructure, hardness and oxidation behavior of these cemented carbides were evaluated. In a final study, the laser powder bed fusion process was used to additively manufacture dense carbides with a metallic binder phase. A range of processing parameters yielding highly dense (99%) specimens were discovered using a sequential series of experiments comprised of single bead, multi-layer, and cylindrical builds. The methodologies and results presented in this thesis suggest numerous exciting avenues for future research topics, many of which have the potential to significantly impact ceramic manufacturing for years to come by realizing the advantageous of additive manufacturing.

This dissertation develops new mobility and manipulation capabilities for planetary exploration rovers through comprehensive wheel-soil interaction modeling and then demonstrates these new techniques on rovers. While wheeled rovers are the current paradigm for robotic space exploration, wheeled mobile robot performance is only well understood for normal driving conditions. A better understanding of wheel-soil interaction lets us do more with rovers in two key ways: 1) add manipulation capabilities through the use of nonprehensile manipulation, e.g. by using wheels to dig, and 2) retain mobility when experiencing degradation of the mobility system, e.g. loss of a wheel motor. Both of these areas require knowledge of wheel-soil interaction forces beyond the scope of existing methods. First, we present the concept of Nonprehensile Terrain Manipulation (NPTM) and illustrate the scope of potential applications for planetary exploration rovers. We then select one NPTM action, wheel-based trench excavation in soft soil, as a candidate action that is achievable on current rovers with no hardware addition but that requires new modeling. Next, we present a closed-form model of soil flow around a wheel driving in regolith that can be used both to model trenches dug by rover wheels and to improve terramechanics models. We then detail a new terramechanics model that covers all slip angles and all ranges of slip and skid and validate it with tests on two wheels operating over a wide range of states. Then, we demonstrate the feasibility of NPTM, the need to account for mobility system failure, and the viability of recovering from failure on full-scale rovers operating in a variety of environments. This is done through a series of demonstrations on NASA rover prototypes in lunar simulant and a Martian analog environment. Finally, we implement an optimization framework to automatically generate driving strategies for both digging trenches in soil and recovering from multiple types of mobility system failure. We demonstrate the generated driving strategies on a miniature rover in soft soil and use the optimization to predict overall rover mobility in these modes.

The main goal of this study is to present the effects of spraying parameters on the numerical evaluations of the fundamental behaviors of an air-assisted spray gun during the formation of child droplets in the spray flow field and material deposition on the target surface. For this purpose, first of all, the air-assisted spray gun geometry was created using the Solidworks software. Then, a computational domain with a 3D, unstructured grid structure was generated using the ANSYS-Workbench meshing tool. Numerical calculations were conducted using ANSYS-Fluent 2020-R2 commercial software. Different breakup models and their effects on the child droplet size were investigated. By coupling the Taylor analogy breakup (TAB) model and discrete phase model (DPM), the droplet size, trajectory, and coating thickness calculations were made under different atomizing air pressures. Also, the effects of spraying distance and droplet size on coating thickness and the critical Weber (We) number on the atomized particle diameter and particle speed were investigated. The results show that with the increase in atomizing air pressure, droplet sizes decrease and the film thickness on the center of the target surface and droplet speeds increases. Also, increasing the critical Weber number makes it more difficult to atomize the droplets. Keywords: air-assisted spray gun; film thickness; discrete phase model (DPM); breakup

Tightly-coupled integrated energy systems of the future represent a class of technologies that promise to play a crucial role in improving modern power systems across a wide range of criteria towards our shared goal of decarbonization. Differential-algebraic equation (DAE) models represent a large class of process models resulting from phenomenological description of dynamic processes in energy systems. Model predictive control (MPC) is an optimization-based form of control that is capable of naturally handling multi-input multi-output systems and inequality constraints. It exploits fully nonlinear process models, usually DAEs, to provide high accuracy and reliability. The performance of MPC is contingent on the accuracy and reliability of the embedded DAE model itself. This thesis proposes DAE modeling and reformulationtechniques and improves performance of optimization and MPC for large-scale energy systems via fundamental developments in tools and frameworks for modeling and optimization. First, we develop an index reduction reformulation for ill-posed, high-index DAEs and apply it to a multiscale dynamic model for a flue gas desulfurization process. This reformulation enables tractable numerical solution as well as using various advanced control strategies. An economic MPC framework is developed to demonstrate optimal operational strategies compared to ad-hoc operating policies. Next, we consider a reversible solid-oxide fuel cell system (rSOFC) that can flexibly operate between fuel production and power generation modes. We present a comprehensive analysis of careful control of such systems using Nonlinear MPC (NMPC) and compare it to sophisticated classical control strategies. Reversible SOFCs are highly complex systems and can play a crucial role in decarbonization and grid stabilization, especially in the context of increasing complexity of modern power systems due to increasing integration of renewable energy sources. We develop a control framework that enables the system to quickly switch between setpoints while ensuring safe operation by avoiding large thermal gradients that are detrimental to the physical health of the fuel cell stack. Finally, we consider the phenomenon of vapor-liquid equilibrium (VLE) that has wide-ranging applications including, and especially, in energy systems in which power generation processes or supporting equipment involve phase changes. We propose an improved formulation for VLE that enables application of VLE in large-scale flowsheet optimization for virtually any operating conditions via complementarity formulations that deal seamlessly with phase transitions and even supercritical excursions. Moreover, the proposed approach is efficient, non-intrusive and can be extended to incorporate a wider range of thermodynamic models. The approach is demonstrated on the optimization of an air separation column with non-ideal flash calculations and disappearing phases.

Electrical signals form the basis for information transfer in several biological systems and devices that capture these signals are of significant interest to the healthcare industry. Such devices are currently made from Si based components and metal interconnects and typically have an elastic modulus about 3-4 orders of magnitude higher than that of biological tissue. This gap has spurred the development of stretchable electronic devices where a soft platform ‘hosts’ the mechanically stiff electrical elements such as chips and interconnects, while the low elastic modulus substrate achieves a seamless interface with the biological tissue of interest. Such systems are playing an increasingly important role in emerging areas such as soft robotics and smart clothing. In this thesis, aerosol jet nanoparticle 3D printing is used to create a stretchable electronic system consisting of three-dimensional interconnects enabling multilayered connections embedded within a thin (

Level 3 (or conditionally) automated driving involves the continuous performance of an automated driving system of the entire driving task within a specific operational design domain, or the exact environmental conditions with which it can successfully navigate. Level 3 automation requires the driver to remain ready to resume control of the vehicle in response to takeover requests, which are issued when the system encounters a scenario outside of its operational design domain. With increasing vehicle automation, drivers are given greater freedom to engage in non-driving-related tasks (NDRTs), which can have a potentially detrimental effect on the driver's ability to fulfil takeover requests. Exploration of psychological and cognitive factors relevant to L3 automation, such as increasing cognitive load due to NDRT engagement, is critical to the development of driver monitoring systems which can track driver state changes and intervene when necessary. Physiological data collection offers a promising means of realising this goal, however much remains uncertain with respect to the effect of NDRT performance on drivers' physiological responses. Furthermore, even less is known regarding how the type of NDRT influences performance. Previous work has indicated that NDRTs which overlap with the driving task (i.e., visual and manual tasks) have a greater impact on performance, but this hypothesis has yet to be tested on physiological outcome measures.

Literature has shown that 3D printed composites may have highly anisotropic mechanical properties due to variation in microstructure as a result of filament deposition process. Laminate composite theory, which is already used for composite products, has been proposed as an effective method for quantifying these mechanical characteristics. Starting with the analysis of comparing the printing orientation of premanufactured carbon fiber reinforced filament, the mechanical properties of 3D printed objects were examined. The mechanical properties changed not only as a result of machine choice, but how the sample is oriented along the printing bed. The analysis continued with looking at the dynamic properties of 3D printed composites. Results showed that the direction of the extruded strands altered the modal frequencies even for a sample with the same geometry. With the direction of the extruded strands affecting the mechanical properties, the composition of these strands is also shown to affect the mechanical properties of 3D printed composites. For commercially available fiber reinforced filaments, it was found through microscopic analysis that the fiber content stated by the manufacturer is inaccurate. In order to apply Classical Laminate Composite Theory (CLCT) the fiber volume fraction and aspect ratio need to be known. This lead to the creation of custom filament with the desired fiber content and geometry. The Halpin-Tsai model was used to predict the mechanical behavior of short fiber reinforced composites. Finally, the mechanical properties of continuous fiber composites were examined. The continuous fiber samples showed that fiber orientation had an vast effect on mechanical properties. A well oriented composite notably outperformed other fiber orientations with a drastic drop in Young's modulus even with slight misalignment in fiber direction, but also resulted in brittle responses which may not be preferable. CLCT is applied using the simulation software ansys workbench. The results showed considerable correlation for each orientation and can be an accurate predictor of mechanical characteristics for 3D printed continuous fiber composites., © 2023 Jordan Garcia

The world requires an upgrade in battery performance before the ubiquitous electrification of transportation is feasible. But the novel materials that could unlock safer, more energy-dense batteries are difficult to discover. The battery material design process suffers from too much choice and frequent trade-offs, resulting in decades of research yielding only a handful of winning materials. New forms of laboratory automation hold promise for reducing the time and capital spent in research and development. A fully automated experiment can be coupled to a learning model to rapidly iterate on material designs without human involvement - “autonomous experimentation”. This thesis is the first attempt at automating the discovery of functional liquid electrolytes for batteries. Two instances of automated electrolyte experiments are presented - both featuring new hardware and software packaged into functioning test-stands. Three instances of automated optimization of liquid electrolytes for batteries are presented - one in the aqueous design space, and two in the non-aqueous space

This thesis presents a new parallelized real-time path planning process. This process is an extension of the Real-Time Rapidly Exploring Random Trees* (RT-RRT*) algorithm developed by Naderi et al in 2015 [1]. The RT-RRT* algorithm was demonstrated on a simulated two-dimensional dynamic environment while finding paths to a varying target state. We demonstrate that the original algorithm is incapable of running at a sufficient rate for control of a 7-degree-of-freedom (7-DoF) robotic arm while maintaining a path planning tree in 7 dimensions. This limitation is due to the complexity of maintaining a tree in a high-dimensional space and the network frequency requirements of the control signal for a real robotic system. We develop and implement a parallelized version of RT-RRT*, dubbed Parallel RT-RRT* (PRT-RRT*), that can update motion plans in a dynamic environment while sending control signals at a high frequency. To achieve this, PRT-RRT* establishes a method of efficient communication between separate collision detection, path planning, and control nodes. We show that PRT-RRT* is capable of solving the dynamic path-planning problem on the 7D Franka Emika Panda robotic arm., © 2023 David Yackzan

The field of high-pressure materials research has grown steadily over the last seven decades, with many remarkable discoveries having been made. This work is part II of a three-part series summarising recent progress in laser material processing within diamond anvil cells (L-DACs); this article focuses on the practice of laser-driven dynamic compression within diamond anvil cells (i.e., LDC–DAC experimentation). In this case, materials are initially pre-compressed within diamond anvil cells, then further dynamically compressed through the use of a high-power pulsed laser, often with the intent to isentropically compress, rather than to heat samples. The LDC–DAC approach provides a novel route to much higher dynamic pressures (approaching 1 TPa), as compared to conventional static compression within a single-stage DAC (

The bonding of dissimilar metals is a powerful and important part of modern manufacturing. Being able to combine the capabilities of many different alloys in one coherent structure is of great interest to designers, engineers, and manufacturers. However, many alloy combinations are deemed “un-weldable” for a variety of reasons, such as large differences in physical properties or the formation of deleterious material phases at the compositional gradient between the materials. Additive manufacturing presents an array of unique advantages over other multi-material fabrication techniques that may allow for the development of successful bonds in alloys that have in the past failed. Assessment of a material junction using a wide range of tools such as thermal and thermodynamic simulation – accompanied by thorough experimental analysis – is the key to overcoming these challenges. In the current work, a variety of computational tools are employed alongside experimental methods to assess whether past fabrication failures can be made into successes. Out-of-the-box solutions include CALPHAD (Calculation of phase diagrams) and material kinetics simulations to assess the range of material properties and phase formations that can occur and nearly and alloy composition. A thermal simulation program specifically developed for additive manufacturing process development is also detailed, including its comparison to conventional computational tools and the range of its possible applications. Several case studies are detailed involving the development of multi-material bonds with additive manufacturing. The first involves the avoidance of brittle carbide formation at the interface between 304 stainless steel and Inconel 625 nickel superalloy. In the second study, AL-6XN super-austenitic stainless steel is bonded with Invar 36 to assess whether this steel has potential for use as a highly corrosion-resistant option for additive manufacturing. The last study details the bonding of Inconel 718 nickel superalloy with GRCop-84, a high-strength copper alloy; the possibility of using a Ni interface layer is posited as a method to avoid severe intermetallic formation at the interface between these two alloys. In each study, simulation techniques are combined with experimental validation to assess the efficacy of additive manufacturing as a tool for the development of these multi-metal combinations. The principles behind the avoidance of bond failure in each study may have applications for the further identification of alloys and alloy combinations for multi-material fabrication, even when said alloys have previously been discounted by traditional manufacturing techniques.

Unsteady MHD mixed convection of nanofluid heat transfer in a permeable microchannel with temperature-dependent fluid properties is studied under the influence of a first-order chemical reaction and thermal radiation. The viscosity and thermal conductivity are assumed to be related to temperature exponentially. Using suitable dimensionless variables and parameters, the governing partial differential equations (PDEs) are transformed to their corresponding dimensionless forms solved numerically by a semi-discretization finite difference scheme along with the Runge-Kutta-Fehlberg integration technique. The effects of model parameters on the profiles of velocity, temperature, concentration, skin friction, the Nusselt number, and the Sherwood number are discussed qualitatively with the aid of graph.

There are approximately 2.1 million people currently living with limb loss in the US. This number is projected to double by 2050 due to the prevalence of cardiovascular diseases and diabetes. Among the individuals who have undergone amputation, 80-90% are lower leg amputees. For many of them, prostheses are crucial in enabling them to carry out regular activities and perform their jobs. However, current prostheses on the market often have issues with comfort and stability, which reduces the efficacy of the device. A socket that is too loose causes instability; one that is too tight causes skin irritation and pressure sores. Our project sought to address this issue by validating the feasibility of a socket design that can adjust its fit to match the load required at each stage of the walking gait cycle. To do this, we designed a custom socket that can tighten and loosen via a lacing mechanism on two sides of the socket. The lacing mechanism uses steel cable and four wheel bearing hardpoints. When tension is applied to the cable, the walls of the socket tighten. We then tested the effect of this socket on the user’s residual limb. The objective was to produce pressure map that showed how forces on the residual limb varied over various socket tensions and residual limb axial loads. Results showed that as the cable tension increased, the pressure in different locations on the socket walls increased similarly and without respect to axial load applied to the residual limb. The pressure at the distal tip of the residual limb also decreased as cable tension increased, but it increased with increasing axial load. We concluded that the relatively even wall pressure distribution created by the lacing tightening mechanism makes it a promising choice for further work, because evenly distributed pressure helps to prevent pressure points that can cause skin breakdown, sores, and discomfort. Future work would center around integrating a motor to create a socket that senses the gait cycle and adjusts the fit automatically. Our project highlights the potential of a lacing tightening mechanism to address the issues of socket comfort and stability faced by lower-limb amputees with prostheses. As further research is conducted, this design could lead to more effective and efficient prosthetic devices for lower limb amputees, ultimately improving their quality of life.

Experimental mechanics traditionally uses tensile tests for material behavior characterization, the procedures for execution and the instruments for measuring are well documented and established, although this, image-based approaches grown in acceptance as alternative methodologies to retrieve measurements from this kind of tests, because image-based systems have the capability of global regions evaluation preserving tests’ space-temporal details for further analysis. The main problem of this kind of procedures is the computational cost together with complex configurations and higher costs for necessary hardware, despite the fact of require trained personnel to operate the systems.

In this project we use a wearable robotic device containing gyroscopic actuators to influence balance and posture during gait and balance tasks. We aim to improve balance of people with pure degenerative ataxia.

Phase diagrams have been studied over centuries and applied to explain a variety of phenomena. In metallurgy, a phase diagram is an encyclopedia of an alloy which includes information about structure transformation, liquid-liquid phase separation, solid-solid phase separation, peritectic transition, eutectic transition, order-disorder transition, magnetic-paramagnetic phase transition and so on. Calculating a phase diagram is challenging which requires the knowledge of the Gibbs free energy of all phases at all conditions. From a macro point of view, the Gibbs free energy is related to enthalpy and entropy. From a micro point of view, these thermodynamic functions are reflections of micro particles or quasi-particles like molecules, atoms, ions, electrons, phonons etc. In solids, atoms are glued to a crystalline lattice. For an alloy, the way that chemical species are organized in the lattice determines the free energy of this phase which is called the configurational free energy. At finite temperature, atoms move away and vibrate around their site. These motions involve a collection of atoms and become intense at higher temperature. In liquids, atoms are free from the crystalline lattice, diffuse over time, and after reach equilibrium, form a uniform and isotropic system. It is also seen that atoms in a liquid have a non-uniform local structure and favor a certain type of species in the present of their neighborhood. All these contribute to the Gibbs free energy and it would be interesting to solve real world problems from these underlying fundamental physics. In this thesis, a systematic framework is presented to calculate the Gibbs free energy using first-principles methods and explain phase behaviors from a micro perspective. We will 1) predict Al-Co, Al-Ni binary phase diagrams and Al-Co-Cu ternary phase diagram; 2) calculate vibrational free energy from covariance matrices, compare with experimental measurements and explain BCC-HCP structure transformation; 3) study liquid-liquid phase separation in Li-Na and eutectic transition in K-Na; 4) examine information entropy model in LJ liquids and seek for a empirical rule for general liquid systems; 5) study the tiling pattern and tile decoration of decagonal quasicrystal and explain the stability at finite temperature.

Skiing has become an incredibly popular sport, with over 200 million skiers worldwide. As the sport has matured, so has safety technology. These improvements have dropped the overall injury rate for skiers, but anterior cruciate ligament (ACL) injuries continue to be a problem. A lack of technology addressing knee safety has caused ACL tears to dominate an increasing share of all major skiing injuries. An ACL tear causes serious issues, requiring expensive surgery and around a year of recovery. Over 55% of these tears in skiing stem from two mechanisms of injury (MOIs). One MOI involves an outward rotation of the knee while the skier falls forward; the other involves an inward rotation with the skis crossing while the skier leans backwards. The ski binding that we have designed combats these MOIs by detecting them before they can tear a skier’s ACL. Multiple sensors send data to a microcontroller, whose decision-making algorithm activates a release mechanism if a skier is at risk of tearing their ACL via one of the two identified MOIs. The sensors consist of one inertial measurement unit (IMU) and flex sensor on each leg. The IMUs measure relative angular position and relative angular speed of the skis, while the flex sensors measure knee flexion angles. If a release condition is detected, a solenoid is activated, triggering the mechanical system that releases the boot from the binding. We have identified and developed two prototypes for the mechanical release system that are paired with our higher fidelity sensor system. While testing is required to determine the exact release thresholds, our work proves the viability of a ski binding system that detects and responds to the two most common MOIs for an ACL tear before a skier suffers an ACL injury.

Results of iodine mass flow rate modeling in multidirectional plasma thruster

Robots have been used to automate many tasks, and yet designing robots remains largely a manual process. This thesis seeks to “automate automation” by developing methods to automate both design of the mechanism and control of the robot. To make these automation problems tractable, we use modular robots, as their components can be readily rearranged to form a customized robot for each new task. This work addresses two problems: control over many different modular designs, and selecting the best design for each task. First, we present a control framework geared toward the case where a large number of designs are made from a set of modules, such that it would be prohibitively expensive to create a new control policy from scratch for each robot design. We introduce modular policies that learn to control a range of designs in one training process. In the policy architecture, information about the dynamics and controls is shared among modules of the same type via shared neural network parameters. We use deep reinforcement learning algorithms to train the policies on multiple designs and environments simultaneously. We then show that modular policies can transfer to new designs and environments not seen in training. With a modular control framework in place, we apply it to select the best robot designs for specific tasks. We frame design optimization as a sequential decision making problem in which modular robots are incrementally constructed one module at a time. We then implement a deep reinforcement learning approach to train a design value function, an estimate of how each module added to the design will impact performance. The design value function serves as a search heuristic to efficiently identify the best design for each new task. This method is applied to two classes of robots: designing manipulators to reach locations in a workspace, and designing mobile robots to traverse a non-flat terrain. Through these contributions, this thesis shows that leveraging modularity in learning enables the creation and transfer of robot behaviors across tasks and designs.

Prediction of the linear amplification of disturbances in hypersonic boundary layers is challenging due to the presence and interactions of discrete modes (e.g. Tollmien-Schlichting and Mack) and continuous modes (entropic, vortical, and acoustic). While direct numerical simulations (DNS) and global analysis can be used, the large grids required make the stability calculations expensive, particularly when a large parameter space is required. At the same time, parabolized stability equations are non-convergent and unreliable for problems involving multi-modal and non-modal interactions. We therefore apply the One-Way Navier-Stokes (OWNS) Equations to study transitional hypersonic boundary layers. OWNS is based on a rigorous, approximate parabolization of the equations of motion that removes disturbances with upstream group velocity using a higher-order recursive filter. We extend the original algorithm by considering non-orthogonal curvilinear coordinates and incorporate full compressibility with temperature-dependent fluid properties. The generalized OWNS methodology is validated by comparing to DNS data for flat plates and a sharp cone, and to linear stability theory results for local disturbances on the centerline of the Mach 6 HIFiRE-5 elliptic cone. OWNS provides DNS-quality results for the former flows at a small fraction of the computational expense. We further demonstrate the capability of OWNS to track fully 3D instabilities by applying the algorithm to a complex Mach 6 finned-cone geometry as well as a 3D Mach 1.5 turbulent jet. It is often desirable, especially for design purposes, to compute worst-case disturbances, i.e. solving the inverse problem, otherwise known as resolvent or input-output analysis. While DNS and global analysis can be used to compute optimal forced responses, their large computational expense render these tools less practical for large design parameter spaces. We address this issue by modifying the original OWNS framework to find the optimal forcing and responses using Lagrangian multipliers via an iterative, adjoint-based, space-marching technique that appreciably reduces the computational burden compared to the global approach that uses singular value decomposition without sacrificing accuracy. The input-output OWNS model is validated against optimal forcings and responses of a Mach 4.5 flat-plate boundary layer from literature and a Mach 1.5 turbulent jet. We then apply these equations to study worst-case disturbances on the centerline of the Mach 6 HIFiRE-5 elliptic cone and on a highly cooled Mach 6 flat-plate boundary layer. Although the worst-case forcings are theoretically informative, they are not physically realizable. In natural receptivity analysis, disturbances are forced by matching local solutions within the boundary layer to outer solutions consisting of free-stream vortical, entropic, and acoustic disturbances. We pose a scattering formalism to restrict the input forcing to a set of realizable disturbances associated with plane-wave solutions of the outer problem. The formulation is validated by comparing with DNS of a Mach 4.5 flat-plate boundary layer. We show that the method provides insight into transition mechanisms by identifying those linear combinations of plane-wave disturbances that maximize energy amplification over a range of frequencies. We also discuss how the framework can be extended to accommodate scattering from shocks and in shock layers for supersonic flow.

Slender structures are mechanical components which have at least one spatial dimension much smaller than another. Some canonical examples are beams, rods, ribbons, plates, and shells. Although these systems have been studied for many centuries, the focus of development has generally been limited to small strains and the onset of buckling modes. Outside of this regime, both geometric and material non-linearities contribute significant complexity to the analytical and computational techniques which can be applied to these problems. Despite this, large deformations demonstrate tremendous potential in engineering applications, particularly with soft materials. This thesis examines various methods of modeling slender structures. We focus on large strain behaviors, often accentuated by spontaneous strains generated with active materials. These systems demonstrate a wide range of interesting and useful behaviors, such as bifurcations, snap-through, and cyclic deformations.

Practical robotic assistive devices have the potential to transform many aspects of our society, from enabling locomotive autonomy to facilitating rehabilitation. However, as is typically the case when having autonomous systems interact closely with humans, one must simultaneously solve multiple grand challenges. My work focuses specifically on 1) leveraging hybrid system theory to achieve stable and robust walking that generalizes well across various human models and environmental conditions, and 2) developing an online learning strategy to customize the experimental walking for individual user comfort. The presented methodology is grounded in realizing lower-body exoskeleton locomotion for subjects with motor complete paraplegia, with extensions to other robotic applications. The contributions are broken down as follows. First, by leveraging tools from nonlinear control theory, I propose techniques for systematically addressing locomotive robustness. These techniques include: using saltation matrices to generate robust gaits with experimental demonstrations on the Atalante lower-body exoskeleton; and developing an input-to-state stability perspective to certify robustness to uncertain impact events. Importantly, these methods aim to better understand the mathematical conditions underlying robust locomotion -- a necessary step towards realizing safe locomotion across varying human models and environmental conditions. Second, I develop a preference-based learning framework to explicitly optimize user comfort during exoskeleton locomotion (achieved using the aforementioned nonlinear control methodology) by learning directly from subjective feedback. This framework is implemented in real-world settings, including the clinical realization of user-preferred locomotion for two subjects with motor complete paraplegia.Third, the extensibility of this framework is demonstrated through three general robotic applications: tuning constraints of the gait generation optimization problem with demonstrations on a planar biped; tuning Lyapunov-based controller gains on a 3D biped; and tuning control barrier function parameters for performant yet safe exploration on a quadrupedal platform. Lastly, I discuss other relevant clinical considerations for lower-body assistive devices including how exoskeleton locomotion influences metabolic cost of transport, the study of latent factors underlying user-preferred walking, and embedding musculoskeletal models directly in the gait generation process.

Latent thermal energy storage use Phase Change Material (PCM) because of its ability to absorb or release a large amount of latent heat within a narrow temperature range. The low conductive heat transfer in PCMs can be improved with thermal enhancement techniques such as the addition of highly conductive metal foams and extended surfaces like fins or periodic metal structures within the PCM domain. High-order modeling tools like Computational Fluid Dynamics (CFD) are widely used for the simulation of different types of PCM heat exchangers (HXs). High computing costs are typically associated with CFD, particularly for the complex transient phase-change processes. This becomes restrictive in some applications such as PCMHX optimization where the conventional process is limited by the computational cost of the high-order physics models. A simulation tool with a faster turnaround is necessary for such cases, even if it comes with a small accuracy penalty. Resistance-capacitance based model (RCM) can be a suitable solution for this type of problem as the model is computationally inexpensive. RCM does not solve for the mass and momentum governing equations as in CFD, but can still predict the PCMHX characteristics with reasonable accuracy, especially for configurations where conduction is the dominant heat transfer mechanism. This work presents the development of RCMs for four types of PCMHXs which are a rectangular PCM enclosure enhanced with copper foam subject to constant heat flux, a geometry enhanced with 3D lattice structures subject to constant heat flux, a cylindrical PCMHX with annular fins and tube in conjugate heat transfer with single-phase heat transfer fluid and a cylindrical PCMHX with annular fin and tube enhanced with copper foam. In all these geometries the effect due to the flow of molten PCM can be considered negligible and the geometries are regarded as structured. The models were validated against experimental data and compared against CFD models for computational cost and prediction accuracy. Both of the models predicted the energy storage within 0.8% of the experimental data for the rectangular PCM enclosure enhanced with copper foam. For the cylindrical PCMHX with annular fins, the maximum RMSE for average PCM temperature prediction was found to be 0.62K for CFD and 0.7K for RCM. These results show that RCM can predict the average temperature profile and energy storage up to 5 orders of magnitude faster than CFD while having negligible prediction deviation. The validated model for annular finned PCMHX is used with a multi-objective genetic algorithm to optimize a PCMHX integrated with a domestic water heater. Additionally, thermal Ragone plots were generated to compare different designs at various operating conditions which can be used for optimal design selection.

Modern cities concentrate most of the world's population, energy demand, and human-induced greenhouse emissions. It is crucial to support a paradigm shift in which cities move beyond being energy consumers and strive to become sustainable producers of energy resources. As one of the most sustainable alternatives regarding environmental impact, cost-effectiveness, and social integration, solar energy is expected to become an ever more ubiquitous part of our intricate human world, playing a key role in this energy transition. Swarms of interconnected solar harvesting devices may one day coordinate to provide urban services. These technologies may harvest solar energy as a sustainable open-ended source (e.g., powering households with building-integrated photovoltaics), or as a means to extend range and functionality (e.g., managing heat and daylight, or powering public lights, pollution sensors, traffic signs, and drones for urban deliveries). Most cities have only begun to tap into their solar energy potential. But the densely-populated urban environment is particularly complex and involves various phenomena that are difficult to capture, including many fixed and moving shadow-casting objects, highly constrained 3D spaces that are shared for multiple purposes, and dynamic conditions in frequent and sometimes unpredictable change. Therefore, it can be challenging to make the most of the available resources to improve the cost-effectiveness and efficiency of these urban technologies. To support the design, optimization, and evaluation of such systems, this project focuses on developing and characterizing new modeling and simulation capabilities for solar harvesting in urban environments. The goal is for these capabilities to balance and navigate the trade-offs between modeling accuracy, precision, efficiency, flexibility, and practicality, while maintaining theoretical consistency with anisotropic conceptualizations of solar irradiance. This is important because many common assumptions that are reasonable in more traditional open spaces may lead to insufficient geometrical flexibility, theoretical inconsistencies, or non-negligible inaccuracies in complex environments, especially concerning diffuse irradiance phenomena. The methodology involves systematically building a modeling framework with modules related to view factors, transmittance, beam and diffuse shadows, heat transfer equilibrium, and a virtual environment that emulates urban infrastructure and receives weather and radiation data as inputs. Novel mathematical and statistical models, algorithms, and programs are developed. Each module and the framework as a whole are evaluated and fine-tuned based on tens of thousands of simulations. These capabilities are integrated to build simulation tools, which enable the exploration of urban solar harvesting applications across a wide range of virtual scenarios from cities around the world, with different latitudes, weather patterns, and infrastructure complexity. The results indicate that anisotropic solar radiation phenomena have complex interactions that are crucial for maximizing solar harvesting in urban environments, that modeling these phenomena involves decisive trade-offs, and that these trade-offs can be leveraged to capture most of the complex dynamics with lower computational cost. The contributions of this project seek to support a more diverse and widespread integration of solar harvesting technologies into urban environments, which will hopefully help cities to become more resilient, efficient, and sustainable.

In my dissertation, I will use atomistic simulations to study the behavior of water and salt solutions confined in sub-nanometer and nanometer wide boron nitride nanotubes (BNNTs). This study will provide fundamental insight into the effect of nanoconfinement on a) the structure of water-in-salt electrolytes (WiSEs) inside nanometer-wide BNNTs, b) structure, filling thermodynamics, and hydrogen bond (HB) kinetics and strength for water confined in BNNTs of various diameters. For the past few decades, nanoconfined fluids are being actively researched by scientists and engineers due to their interesting properties that are absent in their bulk counterparts. Such fluids could play a crucial role in developing novel nanofluidic devices for application in the fields of energy storage, membrane separation, and bioengineering. In the context of nanoconfined fluids, nanotubes are a very popular choice as structures used for confining these fluids. Among them, carbon nanotubes (CNTs) and boron nitride nanotubes (BNNTs) are two of the most well-studied nanotubes. They exhibit outstanding mechanical properties, chemical stability, and thermal conductivity. However, their electronic properties are dissimilar. CNTs are known to be semiconducting materials, while BNNTs are regarded as electrical insulators. These properties make each of these nanotube types quite unique. Also, due to the polar nature of the B-N bonds, the behavior of the fluid confined inside the BNNT is distinctly different compared to the fluid confined inside the CNT. In chapter 2, I use all-atom molecular dynamics (MD) simulations to report a novel non-covalent endohedral nanotube-salt-water hybrid formed by confining a 10 molal (m) LiTFSI aqueous solution inside a (7, 7) BNNT that has an internal diameter comparable to the size of the TFSI anion. The simulation reveals that TFSI- ions and hydrated Li+ ions form axially non-overlapping periodic blocks inside the (7, 7) BNNT. Also, the TFSI anions are immobile inside the hybrid, and as a result, traps the water encapsulated in between them. These circumstances lead to an interesting scenario where the entrapped water is confined in all directions resulting in zero-dimensional water blocks. Further, water exists in a liquid state in these zero-dimensional blocks. Finally, it is demonstrated (by detailed free energy curves) that this hybrid is stable against increasing temperature and the change of the surrounding chemical composition. In chapter 3, I use all-atom MD simulations to show that water free localization of a TFSI- ion at the negative electrode is possible for a moderately concentrated LiTFSI based WiSE confined inside a 1-nm diameter BNNT. I explain this possibility as a combined effect of the affinity of the TFSI- ion to enter an empty nanometer-wide BNNT prior to other electrolyte molecules, and the size of the TFSI- being comparable to the diameter of the BNNT. In chapter 4, I use ReaxFF MD simulations to study the water structure and thermodynamics inside BNNTs of various diameters. This study shows that water is thermodynamically more stable when confined inside BNNTs compared to bulk water. Also, entropic favorability is found to be the predominant factor in stabilizing BNNT-confined water despite the mild hydrophilicity of hexagonal boron nitride. Further, the factors dictating the entropic favorability varies with the BNNT diameter due to significant changes in water structure. In chapter 5, I explore the effect of BNNT based confinement on the water hydrogen bond kinetics and strength. First, appropriate water HB definitions were devised for BNNT-confined water systems (with varying degrees of confinement). Using these definitions, pairs of water molecules with HB configurations are identified and the corresponding HB kinetics and strengths are quantified.

This dissertation is primarily concerned with the integration of additive manufacturing (AM) techniques into planar magnetics to achieve more efficient designs for power modules, which are in high demand. The two main focuses of this dissertation are: (1) the use of a paste-based AM technique called syringe-printing to create planar transformer windings without the need for pressure, using silver-based paste. The dissertation will address manufacturing considerations such as trace width, gaps, and heights that are printable, as well as the impact of electrical resistivity on the sintering process for the syringe-printed silver-based windings; and (2) the evaluation of the reliability of the syringe-printed silver-based windings, which will involve assessing adhesion performance between the metal/ceramic interface, conducting accelerated life tests (including thermal aging and thermal cycling tests), and identifying failure modes, failure sites, failure mechanisms, and developing degradation/failure models.In order to achieve the desired printing geometry in terms of width and gaps between segments, printing settings were studied parametrically by fitting targeted values with actual values. A low-temperature sintering profile was optimized, with a dwell time of 8 hours at 350°C resulting in a resistivity as low as 4.39E-8 Ω∙m, which was approximately 2.5 times higher than bulk silver. To improve bonding prior to syringe-printing the silver-based windings, it was suggested that an adhesive layer consisting of titanium (Ti) and silver (Ag) be deposited onto the alumina substrate. A degradation model was developed for thermal aging tests. Two batches of single-layer 7-turn syringe-printed windings were subjected to thermal cycling tests, and the corresponding failure modes and mechanisms were investigated. The failure data was used to combine with the strain-energy density extracted from the finite element simulation to develop the fatigue model, with the Coffin-Mason model being fitted for future comparison. A more conservative model could be recommended for real-world applications. Finally, the silver-based paste was syringe-printed onto a cooler with a limited footprint area, which served as the primary and secondary planar transformer board and was used in a 10 kW DC-DC full-bridge power converter with 97% efficiency. Corresponding thermal and electrical performance were discussed.

The present invention provides methods for inhibiting the formation of ice on a surface, reducing contact line pinning at a water-solid interface, inhibiting the transition of water from a vapor state to a solid state (i.e., desublimation), and decreasing adhesion of a substance to a surface, which methods comprise, in various aspects, applying to a surface one or more phase change materials where the phase change materials have a melting point above a temperature at which ice formation occurs on the surface. Anti-icing compositions are further provided.