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The ball screw is a critical device for precision linear motion control that has widespread applications in industrial robots, computer numerical control (CNC) machines, and high-precision leveling systems, among others. Because high-precision positioning is ensured by the addition of a preload to the ball screw system, it is crucial to detect and monitor the loss of preload at the earliest possible stage of degradation. The degradation process of the ball screw can be characterized in two stages: the initial reduction of preload without backlash, followed by a loss of preload with an emergence of backlash. To explore the change of the ball screw dynamics caused by degradation, a novel fixed cycle feature test (FCFT) is implemented in combination with multi-level mass experiments and a run-to-failure (RTF) test. The relationships of the ball screw dynamics with preload, worktable mass, and axis position are investigated, with a focus on the axial natural frequency as an indicator of preload loss. Experimental results validate the axial natural frequency's role as a reliable early detector of preload loss for interventive use in a prognostic system.

Prognostics and Health Management (PHM) has evolved into a pivotal technique, integral for enhancing the efficiency and availability of industries. The proliferation of real-time data, facilitated by advancements in sensor and communication technology, has led to innovative applications in various industries. However, this surge in data production poses new challenges for the PHM domain, necessitating solutions that encompass cloud technology and sophisticated modeling proficiency. Furthermore, the inherent variance and uncertainty present in collected data exacerbate the complexity of PHM modeling.This study introduces a methodical approach to developing an efficient model for PHM, particularly when dealing with limited data. A method to select samples accordingly forms the feature of this methodology, aiming to overcome the challenges posed by the substantial volume of data. The strategy involves the development of a sample generation and selection mechanism, ensuring the identification of relevant and usable samples that significantly enhance the overall model performance.To tackle the high volume and dimensionality of the data, a topological model is crafted, providing a robust framework for effective data representation. This model proves invaluable in navigating the intricacies of large datasets, contributing to the creation of a more resilient PHM model. Simultaneously, distinct evaluation strategies are devised to address the unique challenges presented by different areas within the PHM domain.The proposed methodology undergoes validation using several industrial cases, encompassing a simulated dataset of plastic injection molding, as well as two public datasets from the semiconductor and additive manufacturing sectors. Comparing the proposed method to established ones helps prove its effectiveness and superiority.In essence, this research fills a critical gap in the PHM landscape by presenting a methodical and adaptive approach to modeling with limited data. By integrating sample selection strategies and topological modeling, the proposed methodology stands as a testament to its efficacy. The validation through diverse industrial cases cements its applicability across various sectors, emphasizing its potential as a superior solution in comparison to existing methods. This research not only contributes to the advancement of PHM techniques but also reveals the importance of adaptive strategies in addressing the evolving challenges posed by burgeoning data in industrial applications.

This dissertation advances the field of structural damage identification by introducing innovative methodologies based on continuous wavelet transform (CWT) and physics-informed neural networks (PINNs). These methods significantly contribute to the theoretical framework of baseline-free damage identification, laying the foundation for accurate, real-time detection and assessment of damage, thereby enhancing the safety and longevity of engineering structures.A baseline-free damage identification method based on two-dimensional CWT (2D-CWT) is proposed for isotropic and homogeneous plates. An adaptive vanishing moments algorithm is proposed to adaptively determine the number of vanishing moments in Gaussian wavelets. The Teager energy operator (TEO) is applied to the modulus of the 2D-CWT, deriving an accumulative damage index based on the Teager energy. This method enhances the ability to identify and localize damage by suppressing global trends and intensifying local anomalies within flexural wavefields. Extensive numerical and experimental investigations on aluminum plates under various damage scenarios validate the effectiveness and robustness of this method.Further, a CWT-based damage identification method using proper orthogonal modes (POMs) of flexural wavefields is explored. This exploration includes the deployment of an adaptive truncation technique to select significant POMs for damage identification. By focusing on these significant POMs rather than entire flexural wavefields, the method enhances the efficiency of the adaptive vanishing moments algorithm. Numerical and experimental investigations of the proposed method are conducted on beams. Their results verified that the proposed method is accurate and noise-robust for identifying the location and extent of damage.A novel baseline-free damage identification method utilizing PINNs is introduced. The method involves training a PINN with a measured flexural wavefield from damaged plates to create a pseudo-pristine model, which can approximate the wavefield of a pristine plate. By comparing the measured wavefield with the prediction from this model and applying TEO, an accumulative damage index is generated to identify and localize damage. The effectiveness and noise-robustness of this method have been validated through numerical and experimental investigations, with additional parameter studies confirming its robustness across varying hyperparameters.Lastly, a data-guided physics-informed neural network (DG-PINN) framework is proposed to address the inefficiency of PINNs in solving inverse problems, including the PINN-based damage identification which is an inverse problem. The DG-PINN is divided the training into a pre-training phase focused on minimizing a data loss and a fine-tuning phase that optimizes a composite loss function. This innovative framework accelerates the training process while maintaining accuracy, as evidenced by extensive numerical investigations on various partial differential equations and an experimental investigation for damage identification from measured wavefields.In conclusion, these methodologies utilizing CWT and PINNs advance the field of structural damage identification by significantly enhancing the accuracy and efficiency of baseline-free structural damage identification, marking a substantial contribution to both the theoretical understanding and practical application of structural health monitoring.

This study proposes a novel approach to intracranial pressure (ICP) analysis. ICP is the pressure exerted by fluids and tissue inside of the brain and reveals crucial insights regarding a patient’s physiological state after undergoing traumatic brain injury (TBI). ICP waveform morphological analysis can provide clinicians with information regarding a patient’s health and facilitate proactive intervention and inhibit the development of secondary pathologies such as cerebral edema (swelling), ischemia (lack of blood to the brain), vascular injuries, neurological dysfunction, and cognitive/behavioral changes. By integrating arterial blood pressure (ABP) and electrocardiogram (ECG) data from patients who have undergone TBI this method aims to enhance the analysis of not just ICP waveform morphology but cross-signal morphological features. The proposed methodology was evaluated on ten patients and their respective ICP, ABP, and ECG data; it involved three key steps: 1) multimodal signal pre-processing alongside manual labeling of ICP waveform morphologies to train a base support vector machine (SVM) morphological classifier. 2) The use of semi-supervised learning leveraging a subject matter expert (SME) to further train the SVM ICP waveform morphological classifier and augment its training data set on all ten patients to assign incoming pre-processed ICP waveforms with a morphology label. A SME used the posterior probability of the SVM machine learning model to aid the algorithm in adapting to new and unseen ICP waveform morphologies that were not present in the initial manually labeled SVM training data set. 3) The utilization of dynamic time warping barycenter averaging (DBA) to produce representative averages (centroids) of ICP waveforms present in the SVM training data set and derivative dynamic time warping (DDTW)-driven subpeak identification to map subpeaks from DBA generated centroid templates with SME assigned ground truth subpeak(s) to incoming SVM classified ICP waveforms. This comprehensive proposed approach was extensively validated on 30,000 ICP waveforms and the results were evaluated by three reviewers. This validation volume drastically surpassed existing methods in literature and achieved an overall 98.2% accuracy score in identifying ICP waveform subpeaks across all ten patients and all three reviewers. By taking advantage of a SME, incorporating ICP, ABP, and ECG data simultaneously, and simplifying the approach as opposed to previous mathematically and computationally complex methodologies, this study was able to yield a substantial increase in ICP waveform subpeak identification performance enabling robust cross-signal morphological feature analysis. This investigation generated a framework capable of providing significant clinical insights to physicians treating patients with severe TBI, facilitating proactive intervention which could improve patient outcomes.Future work could involve accounting for additional multimodal biomedical signals to provide an even more comprehensive view of a patient’s physiological state. Additionally, change point detection could be performed on these various biomedical signals available to visualize when changes in the time series data are occurring, alerting physicians of a potential need for intervention. Lastly, taking advantage of the ICP waveform morphology labels generated in this investigation and assigning them a health index correlating to ICP waveform subpeak locations could facilitate the training/ real-time deployment of a deep neural network.

Unmanned Aerial Vehicles (UAVs) have seen a rise in applications to various fields. With plenty of algorithms to support automation in UAV flights, Global Positioning System (GPS) is still the major source of position estimation. This has limited the application of UAVs to areas where GPS signal is available and strong. Thus, some other method of position estimation for the UAV is required to expand the UAV application to GPS-denied areas. Moreover, when an operator is piloting a UAV from a remote location, the operator is solely relying on the camera feed coming from the UAV to move the UAV. This camera feed gives a limited field of view of the environment, and the human operator may accidentally run the UAV into an obstacle. In this research, a method of using Hector SLAM for performing position estimation of the UAV in a GPS-denied indoor environment is presented. The Hector SLAM uses a 2D LiDAR mounted on top of the quadcopter to scan the unknown environment. Furthermore, to empower the UAV to autonomously avoid obstacles, an algorithm using Artificial Potential Field method is developed in this thesis which maneuvers the UAV away from obstacles while being piloted by a human operator. The system is developed using Robot Operating System (ROS) and PX4 autopilot. Two different ways, setpoints and attitude commands, of operating the UAV using a keyboard are implemented and compared. The algorithm has been tested in Gazebo Classic simulator and its performance is evaluated.

This thesis investigates the development of a sophisticated strategy for capturing a ground target moving unpredictably through a road network in a secure area. The approach assumes that the target, which does not perform evasive maneuvers, can be tracked using strategically deployed unattended ground sensors (UGS) that log and timestamp its movements. A high-altitude Unmanned Aerial Vehicle (UAV) acts as the pursuer in this critical search and capture mission, relying solely on intelligence from the UGS it comes into contact with. We introduce a state-of-the-art deep reinforcement learning method aimed at enabling the UAV to effectively intercept the target by utilizing the fragmented information from the sensors. This novel strategy trains the UAV to autonomously decide its course of action, driven by a reward system rooted in a pursuit-evasion game, despite having only partial information. Our results demonstrate a perfect success rate in capturing targets that move at a constant speed across the secure area under perfect sensor conditions, and a notable 97% success rate even when facing a 10% chance of sensor data inaccuracies.

Movement is essential to human function and life. Wearable neuromotor sensing enables accessible and non-invasive study of the systems that govern human movement. In this thesis, I present three approaches in materials science, in electronics, and in multimodal sensor fusion for biomechanics, to enhance wearable neuromotor sensing. This thesis begins by improving signal-to-noise of surface electromyography (EMG). I develop a novel preclinical ex-vivo model to experimentally isolate the bioelectrochemical features of single skin-electrode contact. In this model, soft conductive polymer hydrogels made of PEDOT: PSS present nearly an order of magnitude decrease in the skin- electrode contact impedance (88%, 82%, and 77% at 10Hz, 100Hz, and 1kHz, respectively) when compared to clinical electrodes. Integrating these soft conductive polymer blocks into an adhesive wearable sensor increases the EMG signal-to-noise ratio (average 2.1dB increase, max 3.4dB increase) when compared to clinical electrodes across all human subjects. I demonstrate the utility of this higher fidelity EMG in a neural interface system: EMG-based velocity-control of a robotic arm to complete a pick and place task. This thesis then expands what can be sensed with EMG electrodes. Changes in electrode-skin conditions due to contact pressure variation, sweat, and dehydration lead to variation in bioimpedance across skin locations and thus variation in the EMG voltages measured at skin electrodes. By combining analog circuits, digital signal processing, and analytic calculations using bioimpedance principles, a novel system enables the decoupling of EMG and bioimpedance signals by simultaneously measuring both signals with the same electrodes already used for EMG. Design rationales for the system are explicitly defined and benchtop characterizations show accurate bioimpedance measurements (R^2 ~ 0.96) under carefully controlled EMG-like signals from a function generator. I demonstrate system utility in vivo during controlled force generation tasks where controlled alteration to subjects’ skin-electrode conditions produce changes in both EMG and bioimpedance. Finally, this thesis leverages multimodal sensor fusion machine learning to fuse EMG and muscle ultrasound imaging for a critical movement application: balance. Elderly non-fatal falls from balance loss cost American society $50 billion in direct healthcare costs. Ultrasound enables muscle state tracking, especially of deep musculature not accessible to surface EMG. I design a novel multi-stage machine learning architecture with kinematics, ultrasound, and EMG sensing to forecast and to estimate the ankle torques subjects generate in single leg balance when perturbed anteriorly, medially, and laterally. The pipeline results in 6% normalized RMSE in both the sagittal and the frontal plane torque estimation. Furthermore, the pipeline forecasts torques 74 milliseconds in the future even under the influence of perturbations, opening new avenues for balance assistance and diagnosis where future human intent may be useful or essential. I present preliminary work for a stretchable adhesive EMG array embedded with a flexible ultrasound probe. Such a device would benefit from the bioelectronic performance of PEDOT:PSS. Data from the device could be directly leveraged by the multimodal machine learning pipeline for balance control or other applications in human intent. Ultimately, my thesis aims to build accessible tools for researchers interested in neuromotor sensing. I build from basic preclinical characterization to applied machine learning forecasting on human subject data. I hope this thesis helps future researchers in their works and encourages readers to mix fields to improve their science.

Fracture often occurs near interfaces, whether the interface is within a material or between different materials. Interfaces exist across disparate length scales, from microscopic to macroscopic, which leads to a wide range of fracture phenomena. For example, within a single material, fracture near a microscopic interface can set its bulk fracture behavior. In a system of different materials, even when a crack is contained in one material, the mechanics near an interface can govern the driving force of the crack. This thesis studies fracture near interfaces in fiber-matrix composites, soft materials, and thin films. Applications include wind turbines, soft adhesives, and microchips. We show how the rate-dependent interactions at an interface affect the fracture behavior of a ceramic fiber and a ceramic matrix composite. We formulate a boundary value problem in which a crack runs through the matrix, with the fibers being intact and bridging the crack. The composite is subject to a tensile load normal to the crack. Both the fibers and the matrix are elastic, and the sliding stress between them is linear in their relative velocity. When the loading rate is low, the sliding stress is low, so that tension in each fiber is distributed over a long length. Breaking the fiber dissipates elastic energy over a long length of the fiber, leading to high toughness. When the rate of the applied load is high, the sliding stress is also high, so that tension in the fiber is concentrated in a short length. This concentration of stress leads to low toughness. We model this rate-sensitive toughness using a shear lag model. We demonstrate how the interactions near the interface dictate the fracture behavior of the composite at various loading rates. Next, we show how fracture of hydrogels relates to the interface between polymer chains and water. Because water has a low viscosity and lubricates polymer chains, we find that the stress-stretch curve of the hydrogel is rate-independent and has negligible hysteresis. However, we report that the toughness of the hydrogel is rate-dependent when the polymer chains are long. We invoke a shear lag model to explain how the interactions at the interface of a polymer chain dictate the length along which stress is transmitted and consequently, the toughness. We find that the scaling of our analytical model agrees well with our experimental data. We next consider the interface between a hydrogel adhesive and a rigid adherend. Experimentally, we find that fracture initiates near the corner between the adhesive and adherend interface. To investigate the conditions for fracture, we numerically compute the non-linear elastic field around the interface. We find that for a 90-degree corner the elastic field is finite. We simulate various material models and use the near-corner elastic field to correlate the conditions for fracture. We analyze fracture in a brittle thin film near a ductile layer and show that plastic deformation in the ductile material affects fracture in the brittle material. We use a model structure common in many semiconductor devices: a metal line encased by a silicon substrate and a brittle oxide layer. In the triaxially constrained metal, the stresses readily exceed the yield strength of the metal. We compute the stress in the oxide, as well as the energy release rate of a crack. We demonstrate how the proximity of the crack to the metal interface markedly affects cracking behavior. We discuss strategies to avert cracking in the oxide. Much of the work in this thesis was motivated by experimental findings. A combination of analytical and numerical modeling is used to interpret those findings. It is hoped that the analyses developed in this thesis will assist future scientists in understanding a variety of fracture phenomena. Furthermore, it is hoped that this inspires future scientists to do a deep dive whenever they come across an interesting experimental finding in fracture mechanics.

In the evolving landscape of biomechanics and rehabilitation science, understanding muscle function and the mechanisms behind muscle contractile dynamics remains a cornerstone for advancing therapeutic interventions, developing new musculoskeletal sensing methodologies, and enhancing human motor performance. Central to all muscle function is the ability to generate force. This fundamental attribute not only coordinates movement but also underpins the many regulatory mechanisms that muscles support. Importantly, the concept of muscle force extends beyond simple motion, allowing us to explore more specific physiological parameters such as fatigue, which reflects a change in force over time, or central drive, which measures the ratio between volitionally-generated force and the maximum force-generating ability of a muscle. When muscles contract, they deform---and this deformation signal can be measured using ultrasound to estimate muscle force during a contraction. Some sensing approaches instead measure external, surface-level muscle deformation resulting from internal changes in muscle architecture propagating through soft tissue to the surface of the skin. This thesis seeks to bridge critical gaps in our current understanding of muscle function by developing innovative methodologies for analyzing and evaluating muscle dynamics. At the heart of this investigation is the premise that muscle deformation, especially when combined with electrical stimulation, can provide comprehensive insights into muscle function, offering a new perspective on the biomechanical and neural basis of muscle performance and fatigue. This thesis starts by characterizing and validating surface-level muscle deformation, as a correlate for joint torque using a highly accurate, non-contact, 2D laser profilometer. We show that three metrics of surface-level muscle deformation---peak radial displacement: $r$ = 0.94$\pm$0.05, surface curvature: $r$ = 0.78$\pm$0.10, and surface strain: $r$ = 0.83$\pm$0.12---correlate with elbow joint torque across a range of elbow angles and measurement locations. We also find that surface-level muscle deformation leads torque generation but lags torque relaxation during volitional contractions. Finally, we demonstrate that unlike peak radial displacement and surface strain, surface curvature is not significantly affected by changes in joint angle or measurement location, exhibiting strong and consistent correlations with joint torque and range of responses across all conditions, underscoring its potential as a candidate signal for wearable estimation of joint torque during dynamic activities. Building upon the systematic characterization of surface-level muscle deformation as a correlate for force generation, we next show how ultra-sensitive soft strain gauges can be used as wearable sensors to measure changes in surface curvature. We demonstrate how the soft sensors can track plantarflexion torque by monitoring deformation of the skin above the gastrocnemius and elbow flexion torque (monitoring the biceps brachii) during volitional contractions on a dynamometer. We then show how soft sensors strongly correlate to knee extension torque ($r^2$ = 0.93$\pm$0.05) when monitoring rectus femoris deformation on eight participants. Next, to explore the viability of using sensors in more real-world scenarios, we use the sensors to measure deformation to estimate muscle coordination (peak force timing) of the quadriceps muscles during stationary cycling at different cadences and to exhaustion. The results show that the deformation-derived timing metrics are more closely aligned with ground-truth peak force application measured by the cycle ergometer as compared to surface electromyography-derived coordination metrics. Building on our research looking at surface measures of muscle deformation, we next explore the potential of using A-Mode ultrasound combined with electrical stimulation to estimate muscle fatigue. In particular, we look to address the volition-induced variability of traditional fatigue assessment by bypassing the central nervous system and directly activating muscles with electrical stimulation. We demonstrate how stimulation-induced changes in muscle thickness measured via A-Mode ultrasound correlate with simultaneously generated torque and fatigue-related deficits in overall volitional force. Through a dynamic, volitional knee extensor fatiguing protocol, we show how this combination can provide targeted and highly correlated measures of fatigue when compared to ground-truth torque ($r$ = 0.82$\pm$0.16). Finally, building upon the application of electrical stimulation, we are interested in exploring how neurological control is impacted following a stroke, and seek to understand if force-derived measures can be clinically relevant. To this end, we develop a portable, neurostimulation integrated, force measurement platform to measure plantarflexion central drive (i.e.,\ the ratio of the forces produced without and with superimposed electrical stimulation). Although not wearable, this initial work reaffirms central drive as a clinically-relevant metric which is associated with post-stroke walking ability. Future work to explore how such metrics can be measured directly with deformation sensing is ongoing. Together, this thesis contributes to a more thorough understanding of how muscle deformation can be used as a wearable measure of muscle force and demonstrates how electrical stimulation can advance muscle function assessment in both deformation-based and mechanical devices.

Aircraft platforms are continually upgraded with increasingly high quantities of high-powered electronics. As such, efficient thermal management systems are crucially important to overcome system instabilities and support pulsed power profiles. To create effective thermal control systems, it is imperative to thoroughly examine and account for a variety of potential system behaviors caused by transient changes in the flow regime. If left unconstrained, these changes will create thermal instabilities, which can severely damage electronics and hurt the overall reliability of the aircraft. These instabilities can be described by both void fraction and quality. Electrical Capacitance Tomography (ECT) allows for the collection of capacitance data from the fluid moving through a channel. The vapor, liquid, and the transition between the two, will have different permittivity values depending on the state of the fluid. These permittivity values can be found and recorded by the ECT sensor and easily translated to void fraction measurements. A series of ramped profiles with varying conditions were tested to see what types of system behaviors were present between the maximum and minimum values of the pulse profile, which in this case, resembles a square sine wave. Using data recorded by the ECT sensor, a physical understanding of what is occurring during transient conditions between pulses or throughout instabilities can be constructed. Determining the quality of the refrigerant while instabilities are present or for pulsed loads becomes more difficult, especially when trying to apply traditional methods using the Energy Balance Equation. The Energy Balance Equation provides unrealistic results for quality during transient conditions due to its dependence on the quantity of power input. Additionally, the Energy Balance Equation is unable to properly model instabilities present in the flow, particularly those present due to the flow regime. The Energy Balance Equation wrongly assumes that the system can react instantaneously to changes in conditions. In contrast, the values of void fraction are not susceptible to the same issues induced by the time lag, as present in the Energy Balance Equation, because the values of void fraction are measured from the ECT sensor. Using correlations between void fraction and quality will provide a more accurate and realistic representation of the quality of the R-134a refrigerant at a given instant in time. For this research, only the Separated Flow Model was considered. This model has six eminent correlations that each share the same basic equation but contain their own unique coefficients. The models were validated against the results obtained through the ECT sensor to identify situations in which they were optimal. A custom correlation was then calibrated using the experimental data from the ECT sensor. The general Separated Flow Model Equation, which relates quality to void fraction, was then manipulated algebraically to solve for quality in terms of void fraction. This allowed for the calculation of quality based off the experimentally measured data from the ECT sensor. This achievement proved significant because it made it possible to find quality during transient conditions. A further series of tests were then conducted to find the practical bounds of this new quality prediction method. After validating this new model over varying operating conditions, the model proved to be a robust method to accurately predict quality under a large range of operating conditions. The results of this experiment will significantly enhance the ability of future thermal control systems to swiftly and efficiently adapt to instabilities and other abrupt fluctuations in the thermal loads they are required to manage.

This dissertation presents the design and development of a novel hydrodynamic thrust bearing test rig featuring a new (patent pending) pressure-feedback control system for maintaining static bearing alignment. This research aims to provide an enhanced understanding of how the critical operational characteristics of fixed-geometry hydrodynamic thrust bearings including minimum oil film thickness (MOFT), hydrodynamic pressure distribution, and bearing temperature are affected by variability in bearing pad taper geometry under different speed, load, and oil conditions. Further, a new (patent pending) additively manufactured (AM) thrust bearing fabricated using direct metal laser sintering (DMLS) is experimentally evaluated to determine in-service viability. To support the experimental data obtained in the variable taper experiment, a Matlab simulation code is developed using the Reynolds equation to generate numerically predicted performance data for direct comparison. The AM thrust bearing is experimentally compared to a traditionally manufactured cast alloy bearing with identical surface geometry. For the variable taper study, trends in performance established by the numerical analysis show mutually agreeable results compared to experimental data. The average percent deviation of the experimentally gathered change in MOFT as load is increased with respect to the numerically predicted values is 24%. Comparison of experimental to numerical pressure distribution data shows an overall average percent deviation of 32%. For the AM vs. traditionally manufactured bearing experiment, the AM bearing showed an average increase in minimum oil film thickness of 53%, an average increase in trailing edge hydrodynamic pressure of 116%, while exhibiting an average decrease in bearing temperature of 1%.

Hydrodynamic thrust bearings are extensively used in rotary machines. It is well knownthat different environmental conditions can strongly affect these machines and theirrotating parts. Since the conditions can be static or dynamic, it is essential to identify thetrue operating conditions and its effect on the hydrodynamic thrust bearings for properdesign. Oil film pressure, oil film thickness and temperature of oil are some of the mostcrucial operating parameters. Measuring the oil film pressure especially has been a verydemanding task that many researchers have studied by mathematical means. In thepresent dissertation, tapered land hydrodynamic thrust bearing is experimentallyinvestigated to understand its behavior under dynamic loading. To the best of the author’sknowledge, published work on experimental testing of hydrodynamic fixed geometrythrust bearings under dynamic conditions for varying frequencies is non-existent.This dissertation is based on an experimental test rig that is designed, machined,and fabricated to apply and study dynamic loading conditions on hydrodynamic thrustbearings. The rig has the capability for measuring the oil film pressure, oil film thicknessand the temperature of the oil. A HAAS-VF1 machine has been re-utilized as a housingfor the dynamic test rig and a customized hydraulic loading system was used. Sinusoidalwaves with varying frequencies (2-10 Hz), peak loads (150-350 lbs), and runner speed(1500 RPM and 2000 RPM) were applied to a thrust bearing of 0.002 in. taper depth.Each experimental test has been validated by pressure trends of leading, middle, andtrailing edges of the pads of the bearing by comparing with static test rig results.In one of the presented cases, the 150 lbs peak load test at 1500 RPM showed areduction of about 59.4%, 74.6%, and 57% in leading, middle, and trailing pressures,respectively, as the load frequency increased from 2 Hz to 10 Hz. Additionally, with therise in dynamic loading frequency, there was an upward trend in the minimum oil filmthickness. Specifically, there was an increase of 71.3%, 72.5%, and 67.8% in oil filmthickness for peak load tests at 150 lbs, 250 lbs, and 350 lbs, respectively, at 1500 RPMbetween the 2 Hz and 10 Hz loading frequencies. These discoveries offer valuableperspectives on the performance attributes of hydrodynamic thrust bearings underdynamic loads, potentially aiding in the design and application considerations for suchbearings.

The world is currently experiencing a shortage of semiconductor chips. This shortage is affecting different industries that rely on electronic components that involve semiconductor chips to manufacture their products. Due to the shortage of chips, manufacturers are unable to complete the final assembly of their products, resulting in a delay in delivering the finished products to their customers. To address this issue, the US Congress passed the "Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act of 2022" on 9th August, 2022. This act aims to improve the competitiveness, innovation, and national security of the US.This dissertation focuses on addressing the chip shortage through the reduction of chronic semiconductor manufacturing process quality problems caused by wafer map surface defects. The proposed solution involves detecting mixed-type wafer map surface defect patterns using Ensemble Deformable Convolutional Neural Networks. The framework for defect detection proposed in this dissertation outperforms other machine learning models from literature, such as Conv-Pool-CNN, All-CNN, NIN-CNN, DCNN-v1, and DCNN-v2, in terms of F1-score. The proposed framework uses an industrial wafer map dataset (MixedWM38) from a semiconductor wafer manufacturing process to train the base models for the ensemble method. The results show that the proposed framework accurately identifies multi-pattern defects from the surface of wafer maps. This dissertation will contribute to advancing academic literature for the new field of detecting mixed-type defect patterns from the surface of wafer maps. Defects are indicators of process problems, and preventing quality defects in advance is the best approach to achieving positive yield. The efficient and accurate detection of wafer map mixed-type surface defect patterns is important for addressing chronic manufacturing process quality problems. The proposed framework can be used by semiconductor manufacturers for real-time defect detection on the manufacturing floor, enabling the identification of the relevant manufacturing process from where the defect originates and resolving process problems quickly. This dissertation will contribute to the advancement of semiconductor chip manufacturing by improving defect identification methods and reducing chronic manufacturing process quality issues that will lead to better yield outcomes, minimizing waste and damage. With the world facing chip shortages, improving the yield will contribute to solving the chip shortage crisis globally.

This paper focuses on designing, modeling, and controlling a suspension system for a Baja SAE vehicle with an integrated semi-active suspension damping system. The main goals in order to optimize performance are to maximize vehicle speed through various inputs and minimize stress on critical components. Sensor technology including inertial measurement units (IMU), strain gauges, and hall effects are used to measure vehicle parameters throughout road inputs. This data is then analyzed using machine learning techniques to determine the most influential parameters in maximizing speed and minimizing stress with the fastest reaction time. Once these most influential and implementable parameters are determined to be pitch and roll, they are used to create a simplified on car optimization algorithm to improve vehicle performance, as validated by data collected on the vehicle.

In icing conditions, ice accretes on aircraft components which impacts performance and stability. Researchers simulate icing in wind tunnels to understand icing physics and to develop methods of mitigating its effects. This research investigates the capabilities of the Adaptive Icing Tunnel (AIT), a small-scale tunnel designed to conduct economical and quicker tests. A test plan is developed to characterize the temperature in the AIT including objectives, instrumentation, methodology, and test matrices. Four drop sizing methods are developed that use scaling physics, LEWICE, a cloud droplet probe (CDP), and a rotating multi-cylinder. For specified ice shapes, the scaling and LEWICE methods result in average differences between the calculated and actual MVDs of 24.5% and 71.9% respectively. Methodologies and test plans are developed to use the CDP and the rotating multi-cylinder. The test plans designed will be used during the initial aerothermal and icing cloud characterizations of the AIT.

The objective of this doctoral research is to investigate and synthesize novel physics-informed machine learning methods that enable physically valid characterization of additive, subtractive, and deformative manufacturing processes and accurate prediction of process outcomes using in-process data. The inclusion of domain constraints and physical laws via physics-informed machine learning contrasts with conventional machine learning, wherein abstract and oftentimes physically spurious connections are allowed to be made during model training.To accomplish this objective, several types of physics-informed machine learning have been investigated, including physics-informed model input, physics-informed model output, physics-informed data synthesis, and physics-informed model training. The research uses physics-informed machine learning to address four fundamental questions across four manufacturing domains: 1) how to perform high-throughput surface roughness inference over large areas; 2) how to discover and verify novel relationships between additive manufacturing process observations and parts’ mechanical properties; 3) how to synthesize machining process signatures which are physically consistent with process variables; and 4) how to improve the accuracy of microstructure prediction in casting.Specific research tasks include: 1) developing a high-throughput optical image-based technique for surface roughness quantification; 2) developing a novel set of process variables to predict tensile properties of additively manufactured components and verifying the physical validity of this set using physics-informed model output techniques; and 3) developing a generative method of process signature synthesis and corresponding measures of physical consistency; and 4) developing a method of error homogenization in physics-informed neural networks for improved modeling accuracy of the partial differential equations which dictate as-cast microstructure.This research, supported by an NSF graduate research fellowship (#1937968) and partially supported by NSF grants CMMI-2040288 and EEC-2133630 (NSF ERC HAMMER), contributes to the fundamental science of manufacturing process modelling and predictive metrology. The findings provide guidance to the design of manufacturing processes and metrology procedures for improved flexibility, efficiency and productivity. They are also expected to extend to process lifecycle management paradigms, including digital twins, that require a comprehensive understanding of the product’s geometry and material properties at all points of the manufacturing system.

As deep space missions expand in scope and distance, the efficiency of propulsion systems becomes paramount. This thesis analyzes the impact of small measurement errors in the thrust profiles of Hall-Effect Thrusters, a common type of electric propulsor, known for increased efficiency compared to traditional chemical propulsion despite lower thrust. Due to their prolonged operational times, these errors compound, affecting trajectory and mission success. Through analyzing the AEPS Hall-Effect Thruster prototype, designed for NASA’s Artemis Program’s Gateway space station, using curve fitting and a Monte Carlo simulation, we assess the effects of these errors on an example mission to Alpha Centauri. Results show plasma dynamics cause the majority of error, but cause minimal trajectory deviation and propellant loss. This reinforces electric propulsion’s suitability for long-distance space travel. This work informs spacecraft mission design, providing valuable insights into fuel efficiency and system selection, as well as building upon NASA Glenn Research Center’s Electric Propulsion and Power Laboratory’s prior research.

As the world gravitates towards sustainable transportation, battery-powered electric vehicles are increasingly vital. This study focuses on the energy efficiency and life cycle analysis of three pioneering battery-powered electric vehicle technologies: in-wheel motor-driven autonomous driving technology, lithium-ion battery direct recycling, and closed-loop lithium-ion battery water-based manufacturing. In-wheel motor-driven autonomous electric vehicle represent a leap in vehicle design, integrating propulsion directly into wheels. This approach minimizes mechanical losses and vehicle weight, promising significant gains in energy efficiency. The decentralization of propulsion through in-wheel motors not only enhances performance but also contributes to an overall reduction in energy consumption, spotlighting the potential for substantial environmental benefits. Parallelly, the benefits of direct-recycling for lithium-ion batteries are analyzed. A process that conserves valuable materials without the intensive energy and environmental costs associated with conventional recycling methods. By enabling the reuse of battery components, direct recycling extends material lifecycles, diminishes the demand for virgin materials, and lowers the overall environmental impact. Additionally, the study examines the shift towards closed-loop water-based manufacturing of lithium-ion batteries, a method that eschews harmful volatile organic solvent for water-based processes, based on the direct recycling technology. This green manufacturing technique significantly reduces the environmental footprint of battery production, aligning with broader sustainability goals by cutting down hazardous emissions and energy use. Our life cycle assessment highlights the environmental advantages and pinpoints opportunities for enhancing the energy efficiency and sustainability of these battery-powered electric vehicle technologies. The findings advocate for the integration of advanced propulsion systems and eco-friendly manufacturing and recycling practices, underlining their importance in advancing sustainable transportation. This thesis provides critical insights for stakeholders in the transition towards a more sustainable automotive future, emphasizing the role of innovative technologies in reducing the environmental impact of transportation.

The Adaptive Icing Tunnel is a new refrigerated, closed-loop, vertical icing wind tunnel designed to achieve a test section airspeed of 215 knots and a temperature of 20 °C. Test section airspeed, liquid water content, and icing cloud uniformity will be tested for both spatial and temporal uniformity. The methods of these initial characterizations are developed, and analysis is conducted to determine the theoretical capabilities of the icing cloud based on data collected at a similar wind tunnel with known nozzle characteristics. It was approximated that a single Mod1 nozzle could produce an icing cloud with an LWC of 0.15 g/m3. A redesigned nozzle was created to allow for the use of multiple nozzles while maintaining a low LWC. The results of these characterization efforts will be used to provide crucial insights into the tunnel’s performance and future research opportunities.

This dissertation introduces the nodal honeycomb lattice structure, inspired by conventional honeycomb and chiral structures, and designed for assembly compatibility, serving as a foundation for integrating additional system components. This structure facilitated the development of soft robots with shape-morphing skins and an untethered, soft-bodied robot. It addresses the gap in research on the practical applications of shape morphing in robotics by proposing a novel design approach that enhances the mobility and efficiency of soft robots through dynamic shape-changing capabilities. This allows robots to navigate through confined spaces more effectively than traditional designs. The work also details a metamaterial-based approach for developing an untethered, soft-bodied robot that overcomes the challenges of traditional fabrication methods through a semi-automated process involving 3D printing and discrete assembly. This process results in a customizable and scalable robot with multimodal and omnidirectional locomotion, addressing the need for fully integrated on-board systems for applications in diverse and unstructured environments. This dissertation demonstrates that the nodal honeycomb structure has the potential to serve as a foundational concept for application in various soft robotic designs.

Diaphyseal long bone fractures have been identified in very young children (1-3 years) due to accidental and non-accidental trauma. Failure to diagnose a child’s fracture as non-accidental upon first presentation is associated with an increased likelihood of repeat injury and death. Computational modeling may be used to diagnose if a fracture was caused by accidental or non-accidental trauma. Due to differences in composition, microstructure, and mechanical behavior, the properties of mature bone cannot be scaled and applied to immature bone. The objective of this dissertation was to investigate the evolution of composition, microstructure, tissue-level and whole-bone mechanical behavior of cortical bone in porcine long bones as a function of early maturation (newborn, 1 month, 3 months). Tissue-level elastic modulus, fracture stress, and energy absorption increased as a function of maturation at a quasistatic rate. Fracture stress increased as a function of maturation at a faster rate and increased as a function of increasing displacement rate. Fracture strain decreased with increasing displacement rate. Carbonate-to-phosphate ratio was linearly related to elastic modulus. Fracture stress was related to carbonate-to-phosphate ratio and matrix maturation ratio. Second moment of area and flexural rigidity increased from newborn to 1 month. Second moment of area increased between 1 and 3 months. Whole bone stiffness, fracture load, energy absorption, and maximum bending moment increased from newborn to 1 month. Fracture displacement decreased between 1 and 3 months. Qualitative assessment of fracture pattern suggested that jagged fracture patterns and partial fractures arise with maturation.Mathematical relationships between Hounsfield Units and density, and between density and elastic modulus were determined. These relationships were used to configure finite element models. The models using elastic moduli intended for immature bone underestimated the experimental 3-point bending conditions at each age, whereas the load-displacement behavior was overestimated when mature elastic moduli were applied. Taken together, these studies indicate that the mechanical behavior of immature bone differs from mature bone, and that immature bone must be studied independently. This dissertation serves as a basis to motivate future work to further understand the mechanical behavior of immature bone in response to traumatic loading scenarios.

As technology continues to evolve and diversify, so too does the complexity of soft polymeric devices. It has become increasingly important to develop a set of design criteria for polymer synthesis and fabrication that corresponds to success in electrical engineering and robotics spaces. Polymer material interfaces are widely employed in such devices due to their versatility and excellent characteristics. One goal is to expand the repertoire of polymeric substrates suitable for fabrication of inkjet-printed electronics. This work will focus on strategies for maintaining high conductivity in hydrophilic flexible electronics, while other strategies have been employed previously for hydrophobic substrates. It was found that by pre-treating poly(vinyl alcohol) solutions with trace quantities of an inorganic salt such as magnesium chloride, electronic sensors could be fabricated with high resolution and a low fabrication temperature of 80°C. This demonstrates the importance of the organic-inorganic interfaces in electronic properties. Mg-treated films maintained sheet resistance values of 0.2 Ohms/square, comparable to bulk silver, under both wet and dry conditions, 0-90° curvature, and 0-6% strain.To better understand the design criteria for polymers in soft robotics, a model system of a bilayer composed of an elastic and viscoelastic film was investigated. Bilayer films of SEPS elastomer and butyl rubber demonstrate reversible curvature upon strain-hold-release mechanical cycling. These curvatures and relaxation times can be described with linear and nonlinear spring-dashpot viscoelastic models. Simulations from FEA based on these models showed excellent correlation to the measured values, especially in the generalized Maxwell model. Generalized Maxwell predicts curvature over time with the lowest overall mean absolute scaled error of 0.519, which corresponds to a 4.9% difference from the second lowest error model and 76.8% difference from the highest error model.Based on the elastic/viscoelastic framework, a bilayer capable of responding to moisture was investigated. An interpolymer complex of polyacrylic acid and polyethylene oxide was chosen as the active component. Bilayers of these films melt-pressed onto an elastomeric layer of Tecoflex demonstrated similar curvature as the previously reported elastic/viscoelastic bilayers. PAA-PEO/Tecoflex bilayers can switch reversibly between two stable configurations with both deformation and moisture.

The aim of this study is to understand the intricate flow physics and conditions leading to critical heat flux (CHF) inside a horizontally oriented rectangular channel. This study is divided into three parts: design of experimental setup, effect of inlet conditions on critical heat flux and two-phase heat transfer coefficient and understanding the effect of vapor generation on liquid-phase velocity using Particle Image Velocimetry (PIV).The experimental setup allows both upward and downward-facing single-sided heating configurations. Various inlet mass fluxes are examined, along with different levels of inlet sub-cooling. The impact of gravity is studied using flow visualization. During downward facing heating, vapor accumulates along the copper heater wall due to buoyancy effects resulting in notably low CHF and heat transfer values. In contrast, with upward facing heating, buoyancy assists in extraction of vapor from the copper heater wall, facilitating increased liquid contact and higher CHF and heat transfer values. CHF values begin to converge at high inlet mass flux for both orientations, which can be attributed to inertia dominating gravity. Inlet sub-cooling also influences CHF, with highly sub-cooled conditions yielding higher CHF. The impact of orientation and sub-cooled inlet on CHF andheat transfer coefficients are captured. Experimental CHF data was used to validate the Hydrodynamic Instability-Based Model for CHF prediction demonstrating a Mean Absolute Error of 10.8%. Effectively considering gravity forces, heated wall orientation, and flow regimes, the model demonstrates a proficiency in its comprehensive approach.Particle Image Velocimetry (PIV) is utilized to understand the flow physics and the impact of vapor phase on the liquid flow velocity. Four mass flow rates ranging from 5 – 20 g/s with sub-cooled inlet conditions are investigated in a rectangular channel with single-sided heating. Three regions of interest along the heated channel are investigated for instantaneous PIV analysis. The PIV system captures detailed velocity profiles, illustrating the impact of varying mass flow rates and heat flux levels on flow behavior. Vapor masking techniques are introduced to enhance the precision of PIV data by mitigating interference from the vapor phase. Results demonstrate the influence of vapor bubbles on flow resistance, revealing non-uniform velocity distributions and turbulence near the liquid-vapor interface. The study emphasizes the critical role of inertia and buoyancy forces in shaping the velocity profiles. Moreover, the investigation sheds light on the effects of flow rates on the inter-facial behaviors, hinting at a transition point between 10 and 15 g/s. In summary, this research contributes valuable insights into the nuanced dynamics of flow boiling, laying the foundation for future studies on turbulence, heat transfer, and phase-change phenomena in two-phase thermal management systems.

The acoustic guitar is a stringed instrument, often made of wood, that transduces vibrational energy of steel strings into coupled vibrations of the wood and acoustic pressure waves in the air. Variations in wood selection and instrument geometry have been shown to affect the timbre of the acoustic guitar. Computational methods were utilized to investigate the impact of material properties on acoustic performance. Sitka spruce was deemed the most suitable wood for guitar soundboards due to its acoustic characteristics, strength, and uniform aesthetic. Mahogany was deemed to be the best wood for the back and sides of the guitar body due to its greater sensitivity to pressure difference, strength, and impedance matching with Sitka spruce.

Direct energy deposition (DED) is an additive manufacturing technique which is gaining traction for manufacturing metal components. This study investigated the influence of build orientation and process parameters on the mechanical properties and microstructure of SS316L components fabricated using Laser Hot Wire deposition (LHWD) technique. Experimental methodologies include tensile testing, hardness analysis, microstructural characterization, and bead analysis that provides valuable insights into the effects of process parameters and build orientation on material properties. The results revealed a noticeable variation in tensile strength and hardness across various build orientations, with Unidirectional 0° (U0) demonstrating superior tensile strength which was followed by Unidirectional 90° (U90) and Bidirectional 0° (B0). Microstructural analysis further gives the information on the impact of thermal gradients on grain structure and phase distribution, highlighting the role of build orientation in determining mechanical performance. Additionally, bead analysis and microscopy provide detailed observations of melt pool formation and interlayer bonding thus contributing to a deeper understanding of the LHWD fabrication process. XRF and elemental analysis confirmed that the fabrication process successfully preserved the elemental composition of the SS316L wire feedstock. XRD analysis explained the expected face-centered cubic (FCC) austenitic structure in both the as-received wire and the fabricated components. However, a minor shift in peak positions and lower intensity for some peaks were observed in the fabricated parts. These variations could be due to residual stress or minor microstructural changes introduced during the printing process.

With the revolution of additive manufacturing (AM), researchers are working to adopt the benefits of AM for manufacturing flexibility and reduced wastage, which are some of the disadvantages of subtractive manufacturing. AM has introduced design flexibility during manufacturing of fiber reinforced polymer composites with complex geometries and varied internal structures. AM allows designing orientations and percentages of reinforcement fiber to achieve desired mechanical properties of parts. To obtain a high level of surface finish and tolerance, machining is still considered as a post-processing technique for 3D-printed composite parts. Finite element analysis (FEA) is a widely used numerical analysis tool that has the capability of revealing the physics of failure mechanisms of fiber, matrix, and interface during machining of polymer composites. A commercially available powerful FEA software ABAQUS/Explicit package has been used in this study. The objective of this research is to investigate the post-processing machinability of AM-CFRPs with various make strategies, infill patterns, layer widths and orientations. The machinability of AM-CFRPs has been compared to that of traditional laminated CFRP composite to understand how AM process adds further challenges during machining or post-processing of CFRPs. The effect of different machining conditions like variation in depth of cut was also studied to understand the machining behavior as well as failure mechanisms of additively manufactured carbon fiber reinforced polymer composites during post-process machining.

The average physical therapy clinic lacks the funding and resources to install highlyspecific movement assessment and rehabilitation tools. Moreover, engagement of patients duringrehabilitation is difficult to maintain due to the mundane nature of the routines. Virtual Reality(VR) systems have the capacity to become an all-in-one system that gives an engaging and highlycustomizable experience for each user. VR also incorporates wearable sensors that allow fortracking the position and orientation of individual segments. This study has two primary aims, thefirst is to validate that a VR system is capable of upper extremity movement motion capturecomparable to the golden standard of infrared motion capture. The second aim is to assessmovement task data extracted from a VR game to see if quantification of a cohort with spinalcord injury (SCI) is possible through a simulated task. Two cohorts were included in this study, agroup of persons with history of SCI (n=7), and a control group (n=9). Each participant wasasked to play a modified commercially available VR game known as BeatSaber. The levels wereseparated into therapy-based mirrored, opposing, and unilateral tasks. Moreover, each task wasdefined by its position and orientation relative to the user. Additionally, task color was used todistinguish which hand to perform the task with. Results from the VR system compared to the IRsystem showed that the overall error between the two systems was on average between 4.2°-8.6°and showed small instantaneous errors with all joint angles being less than 2°. Moreover, theinstantaneous error was even lower at peak values reported in the IR system. Results allowed fora comparison of performance data for a combination of seven SCI with seven age and gendermatched control groups. Task related data showed that SCI tended to have asymmetrical impactfrom injury and performed worse compared to the control group.

This work discusses the experimental testing of a nominal six-inch diameter single airstream rotating detonation engine that utilizes a hollow core to split the airstream into two separate flow paths and causes the test article to self-balance. Hydrogen and air were selected as the fuel/oxidizer combination for this test. The hollow core is designed to mimic a venturi nozzle, enabling the collection of pressure and temperature at the throat of nozzle to determine the mass flow rate through each flow path. Two Laval nozzles with varying contraction ratios, 7.5 and 11, and three fuel rings of varying injection are experimentally examined and their impact on the operability of the self-balancing RDE is investigated. This work highlights the calculation and determination of the mass flow split between the flow paths during cold flow and hot flow conditions, the operating map across six experimental configurations, and the impact of fuel injection penetration and momentum flux on the operability of the test article.

This research introduces an Origami-inspired dynamic spacecraft radiator,capable of adjusting heat rejection in response to orbital variations and extremetemperature fluctuations in lunar environments. The research centers around thesquare twist origami tessellation, an adaptable geometric structure withsignificant potential for revolutionizing radiative heat control in space.The investigative involves simulations of square twist origami tessellation panelsusing vector math and algebra. This study examines both a two-dimensional (2-D), infinitely thin tessellation, and a three-dimensional (3-D), rigidly-foldabletessellation, each characterized by an adjustable closure or actuation angle “φ”.Meticulously analyzed the heat loss characteristics of both the 2D and 3Dradiators over a 180-degree range of actuation.Utilizing Monte Carlo Ray Tracing and the concept of “view factors”, the studyquantifies radiative heat loss, exploring the interplay of emitted, interrupted, andescaped rays as the geometry adapts to various positions. This method allowedfor an in-depth understanding of the changing radiative heat loss behavior asthe tessellation actuates from fully closed to fully deployed. The findings reveal asignificant divergence between the 2D and 3D square twist origami radiators.With an emissivity of 1, the 3D model demonstrated a slower decrease in theratio of escaped to emitted rays (Ψ) as the closure/actuation angle increased,while the 2D model exhibited a more linear decline. This divergence underscoresthe superior radiative heat loss control capabilities of the 2D square twist origamigeometry, offering a promising turndown ratio of 4.42, validating the model’sefficiency and practicality for radiative heat loss control.Further exploration involved both non-rigidly and rigidly foldable radiator models.The non-rigidly foldable geometry, initially a theoretical concept, is realizedthrough 3D modeling and physical prototyping, demonstrating effective foldabilityand radiative heat loss control. The rigidly foldable model, enhanced withsophisticated techniques for increasing 3D height, showed full actuationcapabilities in a physical prototype, confirming its practical application potential.The research also extends to derivative geometries such as “Star Twist”,“Sloped-Edge Twist”, “Octa Twist”, and “Min-core Square Twist”. Extensivesimulations for these geometries are conducted, exploring their potential foradvanced thermal management systems.In conclusion, this research significantly advances the understanding of squaretwist origami geometry in radiative heat loss control. The study highlights thedistinct behavior and advantages of origami-based designs over conventionalradiators, demonstrating efficient thermal management capabilities andadaptability for space exploration. These findings illuminate the path towarddynamic heat rejection in future space missions, powered by origami radiatortechnology, and open avenues for further optimization and enhanced thermalefficiency.

This research investigates the optimization of vertical axis wind turbine (VAWT) systems to harvest energy from vehicle-induced highway winds. The primary objective is to enhance the efficiency of small-scale VAWTs mounted on the side of highways, enabling the generation of electrical energy or clean hydrogen production.Computational fluid dynamics (CFD) modeling was employed to systematically optimize the turbine design and to develop wind guides that further increase the efficiency. The study found that an elliptical VAWT design demonstrated a 4.4% higher power coefficient compared to a Savonius VAWT. Introducing a single flat or curved guide between the turbine and the road increased the power output by 145.33%. Further refinements, including the use of three guides with optimized angles and radii, culminated in a remarkable 393.16% improvement over the initial non-guided-guided configuration.In the non-guided-guided scenario, simulating the VAWT's exposure to the wake flow induced by a bus traveling at 32 m/s, the CFD analysis predicted an energy output of 30.41 Nm. However, when the three guide vane configuration was employed, the energy output exhibited a substantial increase, reaching 100.41 Nm under the same bus speed conditions.The comparative analysis between the Non-guided-guided and three-guide vane setups for the bus wake simulations revealed a remarkable 230% enhancement in energy capture when the guide vanes were incorporated. This significant performance improvement highlights the favorable impact of the optimized guide vane arrangement on the aerodynamic behavior of the VAWT, facilitating more effective extraction of energy from the wake flows generated by larger vehicles such as buses.The results showcase the significant potential of vehicle-induced highway winds as a viable source of renewable energy. The optimized VAWT system, incorporating multiple flow guides, demonstrates the ability to effectively harness this untapped resource and contribute to sustainable transportation solutions.To move forward, validating the technology with more advanced methods is crucial. While the 2D CFD simulations offered valuable insights, 3D CFD modeling would provide a more realistic representation of the complex airflow. Additionally, manufacturing and testing a physical prototype in a controlled environment would validate the numerical predictions and offer real-world performance data. These additional steps would significantly strengthen the understanding of this technology and its potential for practical highway applications, bridging the gap between computational models and real-world hardware.

This dissertation explores the kinematic synthesis of continuum robots and the design of a specific continuum robot for use in laparoscopy. The kinematic synthesis of continuum robots is studied based on either the desired target end-effector pose or the backbone shape of the robot. Initiating with an investigation into the mathematical relationships among positions and orientations at the segment tips, this study focuses on piecewise constant curvature (PCC) continuum robots with up to three segments. For a continuum robot with more than three segments, a method is introduced for generating the backbone of a continuum robot that closely approximates a given spatial curve. Furthermore, the kinematic synthesis methodology is investigated for designing a chain of three-dimensional bodies to match a set of arbitrary spatial curves.For a one-segment PCC, a reachability criterion is proposed, simplifying the calculation of the neighboring orientation. For a two-segment PCC, a reachability criterion is proposed and the redundancy of its inverse kinematics solution is found, establishing a circle of tip locations. For a three-segment PCC, the redundancy of the inverse kinematics includes tips that lie on a sphere providing a closed-form solution to the inverse kinematics problem. These relationships are derived from the unique characteristics of the bisecting plane of a single segment. The degenerate cases for the solutions are also addressed. These outcomes stem from a specific PCC parametrization, with implications extending to the general PCC model. Note that this study is grounded solely in simulation.Shape-changing mechanisms provide the theoretical tools for the synthesis of spatial chains of rigid bodies that position themselves along curves of any complexity. This study investigates the kinematic synthesis methodology for designing a chain of three-dimensional bodies to match a set of arbitrary spatial curves. The bodies synthesized can be one of three types: a rigid segment, a helical segment with constant curvature and torsion but varying length, and a growth segment that maintains its geometry but may be scaled to become larger or smaller. To realize mechanical chains, only rigid and helical segments are used. After designing the segments, they may be aligned with the original spatial curves with their ends connected via an optimization. For two curves, these connections may be made with revolute joints to obtain high accuracy. For three or more curves, spherical joint connections allow for the best accuracy. To compare curves as is useful in morphometry, all three segment types may be employed. In this case, an accurate description of the changes between curves is important, and optimizing to connect the segments is not needed. The study proposes the process of redefining curves to enable the application of the outlined techniques, as well as the methodologies for synthesizing the three segment types. Examples include a continuum robot problem and the morphometric analyses of cochlear curves and the lambdoidal suture. This work extends the established planar techniques for synthesizing mechanisms and addressing morphometric issues that are motivated by curves in two dimensions.For the shape inverse of a continuum robot with more than three segments, the approach combines the kinematic model of the continuum robot, which operates under the constant curvature assumption, with the design profile and target profile concepts from established spatial shape-changing mechanism theory. This approach involves an analysis of the accessibility of the terminal orientation of the spatial curve, which subsequently identifies a rapid synthesis method. This method facilitates the creation of a backbone with a predetermined number of segments that closely align with the tangential direction of the reference points of the target profile while simultaneously approximating the target profile itself. Optimization techniques demonstrate the efficacy of using the generated backbone of the rapid approximation method as the initial design for the optimization. In a situation where a close approximation is not needed at interim steps, optimization is not needed to provide a displacement sequence to move the robot.Finally, a novel soft robot laparoscope design is presented that enables accurate and safe deployment of the photodynamic therapy (PDT) optical fiber for pancreatic tumor treatment. To achieve this, the proposed soft robot is able to produce 3 degrees of freedom (DoF) motions: linear translation, in-plane bending, and axial rotation. A microcamera is integrated with the soft robot to provide intraoperative image guidance during the procedure. The robot forward kinematics model was obtained via a generalized calibration method, and the differential kinematics was derived analytically. Note that the proposed calibration method can be applied to general models including the PCC model. Benchtop characterization results indicate that the soft robot is able to achieve 1.4±0.4 mm position and 1.5±1.1 degrees orientation accuracy, respectively. The robot was experimentally validated in two in vivo mice models, and the results indicate that the proposed system successfully reduced the tumor size from 15.3 mm³ to 12.1 mm³ and 11.8 mm³ to 4.4 mm³.

Platooning is a strategy that aims to reduce aerodynamic drag and fuel consumption by driving trucks closely behind each other. However, the advantages of platooning can be compromised by braking loss, which refers to the extra braking needed to follow trucks on hills and in traffic. Conducting experiments to determine the benefits of platooning is time-consuming and resource-intensive, prompting the search for a method to estimate these benefits. This thesis introduces a framework that allows operators to predict energy savings during platooning, even when braking is involved, providing immediate feedback to platoon operators. The physics-based framework applies to various types of road vehicles. The dissertation outlines practical approaches to implement the framework in real-time, such as a new adaptive estimation of braking loss based on vehicle wheelspeed and a method to query hyperlocal wind data, although the latter did not enhance correlation. The validity of the framework is confirmed through robust regression analysis of more than 8000 different pairwise comparisons from experimental platoon trials, with the estimated energy change often closely matching the actual energy change and an R-Squared value exceeding 0.68 in the model. The proposed methods for estimating braking losses are relatively resistant to errors in drag and rolling resistance, but errors in mass can skew the results by a factor of two. Furthermore, a logistic regression classification approach is introduced to use the framework for making go/no-go decisions, enabling users to specify their desired confidence level. The classification approach demonstrated a 73.5% accuracy when applied to unseen on-road platooning data. The framework effectively distinguishes the energy impacts of platooning from other vehicle energy consumption. It is grounded in physics, adaptable, capable of real-time operation without the need for a simultaneous reference, sensitive to various parameterizations and signal inaccuracies, and can provide clear feedback to platoon operators on energy savings with a binary outcome. Ultimately, this research can help platoon operators optimize their energy efficiency by offering realistic fuel savings expectations and guiding decisions about when to engage in platooning.

The friction and wear properties of bilayer graphene on silicon substrate with diamond atomic force microscope tip were investigated using molecular dynamic simulation with three independent variables of tip velocity, temperature, and normal load. Based on isolated experimental results, it is determined that graphene friction is velocity, temperature, and normal load dependent. Velocity and normal load increase lead to positive friction correlations while temperature increase leads to negative friction correlations, thus leaving the mechanism to be determined. Combined studies reveal similar results, with each variable maintaining its isolated effect in chorus with the other utilized. Upon obtaining the contact area from these experiments it is evident that velocity and temperature change do not hold direct bearing on the contact area, rather that it is the normal load and size of the sliding surfaces that can fluctuate both contact area and friction in tandem. Hence, the mechanism with respect to velocity and temperature dependence of graphene friction is determined to be variation in interatomic potentials associated with interatomic interactions. Varying the contact area can increase or decrease the quantity of atoms in contact, therefore also having an impact on graphene friction.

Mission and flight planning problems for uncrewed aircraft systems (UASs) are typically large and complex in space and computational requirements. With enough time and computing resources, some of these problems may be solvable offline and then executed during flight. In dynamic or uncertain environments, however, the mission may require online adaptation and replanning. In this work, we will discuss methods of creating MDPs for online applications, and a method of using a sliding resolution and receding horizon approach to build and solve Markov Decision Processes (MDPs) in practical planing applications for UASs. In this strategy, called a Sliding Markov Decision Processes (SMDP), the underlying state space is regularly rediscretized according to its informational proximity and utility while a receding horizon algorithm allows us to consider immediate next steps while keeping the primary goal state in mind. This approach allows for dynamic decision making and replanning by a UAS in an uncertain and dynamic environment in which mission objectives or the environment could change. The SMDP method shows an ability to create recursively optimal policies, under conditions of limited computing power and time, that perform similarly to the optimal policy of the associated fully-modeled flat MDP. Advisor: Justin Bradley

In a post-9/11 world, hostile actors are using vehicles as weapons to inflict civilian casualties. Pedestrians, vulnerable road users, military personnel, and police may be targeted using a vehicle as a weapon. The United States Department of Defense (DoD) also utilizes Entry Control Facilities (ECFs) which are carefully designed transportation corridors to manage how threat vehicles are able to access military facilities. In 2004, DoD started to upgrade all ECFs using both passive and active barriers. Many anti-ram vehicle barriers were evaluated according to the criteria established in ASTM F2656, but to date few options are available which are (1) adaptable to different lengths; (2) non-proprietary; and (3) reduced overall cost compared to proprietary counterparts. The objective of this research study is to design, evaluate, simulate, and full-scale crash test a threat mitigation barrier which provides dual functionality: safely capture errant vehicles involved in unintentional collisions, and prevent threats from gaining unauthorized access to military bases. The work includes generating engineering designs, performing computer simulations, performing data analysis and reporting, and performing material characterization in the Midwest Roadside Safety Facility (MwRSF) Small Structures Laboratory. At the MwRSF Outdoor Test Site, the conducting of component and full-scale crash testing of roadside features and evaluation of device combinations according to MASH criteria occurred. The research was structured around four pivotal components: a literature review, dynamic post-soil interaction simulations, dynamic anchor pullout simulations, and dynamic component testing. An optimal configuration of component dimensions, strengths, and material properties for a novel non-proprietary anti-ram barrier system was developed and proposed. This system is required to effectively restrain a truck under ASTM M50 impact conditions, adhering to either P2 or P3 deflection limits. This study augmented the understanding of the dynamic interactions between reinforced concrete anchors and soil, particularly focusing on the resistive forces and energy absorption characteristics. This enhanced understanding was intended to facilitate the engineering of the mentioned barrier system to meet ASTM M50 P2/P3 specifications. Advisor: Cody S. Stolle

Microfluidic lab-on-a-chip (LoC) technology has driven numerous innovations due to their ability to perform laboratory-scale experiments on a single chip using microchannels. Although LoC technology has been innovative, it still suffers from limitations related to its fabrication and design flexibility. Typical LoC fabrication, with photolithography, is time consuming, expensive, and inflexible. To overcome the limitations of LoC devices, modular microfluidic platforms have been developed where multiple microfluidic modules, each with a specific function or group of functions, can be combined on a single platform. Modular microfluidics have overcome some of the limitations of LoC devices, but currently, their fabrication is complex, and they remain relatively inaccessible. This study attempts to address the limitations of both LoC microfluidics and modular microfluidic platforms by implementing stereolithography 3D printing for ease of fabrication, design flexibility, and increased accessibility. A modular platform with reversible connections was developed that could accommodate two microfluidic modules in series. The optimal printing parameters and fabrication procedure for printing both microfluidic molds and microfluidic devices was found. The base to a modular platform was designed and 3D printed with integrated O-ring module attachments and luer-lock mechanisms for connection to external flow sources. To demonstrate the capability of the developed platform, a microfluidic device previously used to compress cells by inflating a thin PDMS membrane, like a balloon, was reconfigured to serve as an active mixing microfluidic module. Next, the height response of the balloons of the active mixer was characterized in response to an applied pressure to ensure the performance of the mixer fabricated with 3D printing was comparable to that of the previous study. Separately, a straight-channel microfluidic module was fabricated. The flow through the straight-channel microfluidic was measured using particle tracking velocimetry because many studies require shear stress application. Finally, the modular platform, mixing module, and straight-channel module was combined, and a mixing experiment was performed by dynamically actuating the balloons of the mixing module at varying frequencies. The fabricated mixing module could mix two samples before they flowed into the second module, and the platform was capable of quick, reversible, and leak-free connections. Advisor: Sangjin Ryu

Natural protective materials offer unparalleled solutions for impact-resistant material designs that are simultaneously lightweight, strong, and tough. Particularly, the dactyl club of mantis shrimp features chitin nanofibrils organized in a Bouligand structure, which has been shown to effectively dissipate high-impact energy during powerful strikes. The mollusk shells also achieve excellent mechanical strength, toughness, and impact resistance with a staggered, layer-by-layer structure. Previous studies have shown that hybrid designs, by combining different bioinspired microstructures, can lead to enhanced mechanical strength and energy dissipation capabilities. Nevertheless, it remains unknown whether combining Bouligand and staggered structures in nanofibrillar cellulose (NFC) films, forming a discontinuous fibrous Bouligand (DFB) architecture, can achieve enhanced impact resistance under localized ballistic impact. Additionally, the failure mechanisms under such dynamic loading conditions have been minimally understood. In this thesis, I present a comprehensive study to investigate the dynamic failure mechanisms and quantify the impact resistance of NFC thin films with DFB architecture by leveraging previously developed coarse-grained models and explicit projectile impact molecular dynamics simulations. The results show that when nanofibrils achieve a critical length with the DFB architecture, the impact resistance of NFC films outperforms the counterpart films with continuous fibrils by comparing their specific ballistic limit velocities and penetration energies. I also look into the underlying mechanisms contributing to this improvement in impact resistance, which include iii enhanced fibril sliding initiated at the discontinuous sites, intralayer and interlayer crack bridging, and crack twisting mechanism in the thickness direction enabled by the DFB architecture. Overall, the results in this thesis show that by combining Bouligand and staggered structures in NFC films, their potential for protective applications can be further improved. The findings presented in this thesis can provide practical guidelines for the design of protective films made of nanofibrils.

Digital twin technology builds upon virtual engineering models, computer simulation, and real-time field data streaming to enable next-generation designs and predictive maintenance. A digital twin is a computer-based high-fidelity collection of models that predicts the performance of dynamic systems per operating cycles, input feature parameters, and data communication from a physical plant. Product Lifecycle Management (PLM) is growing in importance and is central to virtual design processes where the digital twin toolset fits into this emerging architecture. The product design process can be advanced using digital twin resources by eliminating the need for, and cost from, continual physical prototyping, reliability testing, and outdated maintenance practices. Digital twin virtual tools enable improved product performance evaluations early in the design cycle which leads to manpower savings for the enterprise. The mobility needs of individuals, corporations, and government entities require careful attention to product design. With increasing ground vehicle complexity in electrical components and propulsion hybridization, the digital twin plays a role in predicting, understanding, and designing vehicle systems. The ability to couple streaming field data with digital twin estimates generates a powerful tool for diagnostic and prognostic methods. In this research project, digital twin technology is explored, developed, and applied to off- road ground vehicles for design engineering studies and predictive health maintenance. The research goals included integrating mathematical models for ground vehicle components into a digital twin, application of the digital twin for vehicle design, examination of predictive maintenance methods, and creation of two surveys to measure usefulness and time savings metrics available with digital twin technology. The modeling of wheeled and tracked vehicles in MATLAB/Simulink/Simscape enabled the assembly of a 14-degree-of-freedom virtual vehicle system consisting of body dynamics, engine curves, wheel kinematics, driveline systems, and suspension characteristics piloted by a virtual driver and environment inputs. The digital twin tool assisted in tradespace analysis studies within the Clemson University VIPR-GS Center. Predictive maintenance, with machine learning, was applied to the off-road digital twin by seeding 6 anomalies into a virtual model and leveraging statistical algorithms based on neural networks. For the numerical study, 176 logged signals across 275 total simulations were utilized in the predictive maintenance framework to successfully predict 92% of trained validation results and 40% accuracy of untested compound anomalies. Metric surveys evaluating usefulness and time savings were deployed to DO13/14 vehicle engineering teams on the Clemson University International Center for Automotive Research (CUICAR) campus and the results showed a positive Likert scale response to digital twin usefulness with the greatest perceived benefits in model organization and design validation. The advantages and opportunities available with digital twin technology have been explored in these individual studies and deployed in the VIPR-GS Center. Given the growing awareness of digital engineering design methodologies, digital twins represent a cornerstone of many future endeavors.

With the constant innovation of technology and the need for more power generation, the need for improved methods of heat transfer are also needed. These innovations have always driven the research into new and improved methods of heat transfer. The topics covered in this research mainly being porous media, submerged two-phase jet impingement and boiling have all been seen to create improvements in cooling but have not been used in conjunction with each other. Using this combination to find the possible heat transfer improvement is the goal of this research. Experiments are done in both a non-boiling and boiling scenario. This allowed for verification that the two-phase flow had an effect on the surface before performing a boiling experiment. Two surfaces were tested, these were a plain surface and a columnar post-wick porous structure. For both sets of experiments, water flow rates were chosen from Reynolds numbers of 729 and 2929. The air flow rates were calculated using values of the volumetric quality () that ranged from 0 ≤ ≤ 0.9 and the previously mentioned water flow rates. The results of this experiment were quantified by looking at the heat transfer coefficient (HTC) compared to the change in volumetric quality for both experiments. The non-boiling experiment showed that the added two-phase impinging jet created improvements in the HTC of porous media. An improvement of 81.94% over a single-phase jet was observed at a volumetric quality of = 0.9. The boiling experiment showed that the added two-phase impinging jet made minimal improvements on each surface. The plain surface saw an improvement of 9.50% over a single-phase jet at a volumetric quality of =0.9. The post-wick surface saw a maximum improvement of only 2.94% at a volumetric quality of = 0.7.

Lithium-ion batteries (LIBs) have been a vital technology since they were introduced to the world in the 1990s. Despite significant advancements in cost-effectiveness and production efficiency, there are still some obstacles that need to be addressed. Significantly, as lithium-ion battery (LIB) technology is increasingly used in the transportation industry to enable electric vehicles, the issue of industrial ethics and environmental sustainability becomes of extreme importance. We are currently developing water-based manufacturing procedures to achieve more environmentally friendly production of lithium-ion batteries. Our research focuses on analyzing the design elements and process dynamics involved in removing solvents from the electrode coatings of these batteries. We demonstrate the impact of substituting N-Methyl-2-pyrrolidone (NMP) with an aqueous solvent, specifically water, in the electrode. To describe the process of cathode drying, we employ a mathematical model at the continuous level. This model accounts for the simultaneous transmission of heat and mass, as well as phase change. The utilization of aqueous processing for electrode material has the potential of cost reduction and environmental effect reduction in existing lithium-ion battery (LIB) manufacturing processes. By substituting costly and hazardous binder solvents like N-methyl-2-pyrrolidone (NMP) with water-based processing, both material expenses and processing and capital equipment costs can be minimized. The optimization model will determine the most efficient factors for the energy consumption of the solvent drying process, which constitutes a significant component of the overall energy consumption in the drying process.

Motion in low Reynolds number flows, where viscous forces dominate over inertia, is profoundly important in many fields of colloid sciences and the rheology of suspensions. Additionally, self-propelling microparticles, primarily operating within this hydrodynamic regime, have gained significant attention over the past two decades. These self-propelled entities, also referred to as microswimmers, hold immense potential for transformative applications in targeted drug delivery, environmental sensing, and microrobotics. Contributing to the field by developing analytical techniques to study these particles is a necessity. This dissertation addresses three main problems. The first problem concerns an axisymmetric Stokes flow past a highly deformed sphere. Analytical techniques to tackle this issue are typically limited to slight deformation of reference geometries like spheres and spheroids within a perturbative framework, usually to the first order. In this dissertation, a novel spectral method has been developed to provide an analytical framework for treating highly deformed spheres. This method combines the spectral approach for a sphere in low Reynolds number hydrodynamics with Brenner’s technique for Stokes resistance of a slightly deformed sphere, developing two mesh-free formalisms—perturbative and non-perturbative—to address highly deformed spheres. The perturbative approachiiiis applicable for an arbitrary order of perturbation, unlike earlier work on deformed spheres, which limited the analysis to only the first order of perturbation and slight deformations. The second problem presents a novel perspective on the study of particle phoresis by introducing a unified framework for both active and non-polarizable passive particles. This framework bypasses the conventional calculations of slip velocity and introduces integral kernels, thereby establishing a direct connection between the distribution of the driving field or its gradient and particle velocity. These integral kernels, independent of the driving field, are determined by the particle’s geometry and surface composition, quantifying the local contribution of the field or its gradient to the particle’s velocity. Understanding these kernels offers valuable insights for developing design principles for phoretic particles and their compositions. Thus, the work embodies innovative research on the fundamental science of low Reynolds number hydrodynamics and soft matter, promising a significant contribution to the field. The third problem addresses modulating the motion of circle swimmers in a one-dimensional background potential and directional flow. We observed a quantized drift velocity in the absence of noise that approaches continuum behavior in the presence of noise.mmm

High temperature materials such as Ceramic matrix composites (CMCs) have grown more and more popular for aerospace applications due to their superior strength to weight ratio and enhanced damage tolerance when compared to super alloy and monolithic ceramic materials. However, there are notable challenges when it comes to their successful implementation of these materials in hot-section aero-engine components, primarily stemming from their susceptibility to severe damage caused by solid particle erosion due to repeated particle impacts. Although extensive efforts have been dedicated in understanding erosion in CMCs, the majority of this research has been conducted under standard ambient conditions. Given the elevated temperature environments in which CMCs will ultimately operate, it becomes crucial to comprehend how high-temperature conditions impact the lifespan of CMCs subjected to high velocity particle impacts. Therefore, the primary focus of this research is on evaluating what parameters affect erosion behavior of oxide and non-oxide CMC at temperatures of 800°C and 1200°C with different velocity of 200m/s and 350m/s, respectively. On a different note, CMCs are prone to significant strength degradation upon material removal resulting from the repeated or cumulative impingement of solid particles. While erosion in ceramics and composites has been extensively studied, existing research may not be directly applicable to strength degradation caused due to repeated particle impacts on CMCs due to their more complex architecture and failure mechanisms at different operating temperatures. Therefore, second objective of this work is to investigate relationship between high temperature erosion behavior and strength degradation in several non-oxide and oxide-based CMCs and to evaluate erosion response caused by solid particle impacts under stress induced conditions involving fatigue and creep loading.

Additive manufacturing, considered as a future manufacturing technique, provides an unique advantage of fabricating near-net-shape parts without any additional processes. While there are extensive efforts to improve mechanical properties of additively manufactured materials, their fatigue strength, particularly those with an unmachined surface condition, still remains significantly low. It delays the adoption of additive manufacturing in practical applications and weakens its primary advantage of direct usage after fabrication as the low fatigue strength requires post-surface treatments. This dissertation aims to investigate the effects of surface texture on fatigue behavior of additively manufactured materials. Firstly, the surface texture of unmachined additively manufactured parts is examined to understand its effect on fatigue behavior under force-controlled and strain-controlled cyclic loadings. In addition to unmachined surface condition, the fatigue behavior of additively manufactured parts after different post-surface treatments is investigated. Based on the experimental findings, several objectives have been identified and addressed. These include the development of a hybrid surface roughness parameter tailored to represent the surface texture of additively manufactured parts. Additionally, surface features that are critical to fatigue strength of additively manufactured material have been investigated through various surface measurement techniques. Using the surface texture analyses and fatigue test outcomes, a non-destructive surface texture based fatigue prediction model has been proposed. The prediction model has been validated by comparing predicted fatigue lives with empirical data. It has further been employed to estimate fatigue lives under multiaxial loading conditions. The developed hybrid surface roughness parameter effectively correlates the surface texture of additively manufactured parts with various surface conditions to their fatigue strength. Finally, most of the fatigue lives predicted by the surface texture based fatigue prediction model fall within scatter bands of 3 compared to empirical data.

With the growth of electronic packaging industries, ranging from automobiles to hand-held products, reliability is a major concern. Also, to keep pace with the increasing demand of end users for integration, miniaturization, light weight, high speed, and multi-functionality of portable electronic devices such as mobile phones, digital cameras, as well as personal digital assistant (PDA), electronic packaging industries are intended to manufacture packages of high density and smaller dimension. These packages consist of smaller solder interconnects with solder balls in each area and short distance between the solder balls. The reliability of smaller electronic packages is greatly affected by fundamental crystallographic configuration of beta-tin crystals in solder joints. Most of the studies in the field of mechanics of electronic packaging consider solder interconnects as isotropic material. However, solder alloys are highly anisotropic in terms of elastic modulus, rate-dependent plastic deformation behavior, and coefficient of thermal expansion. Hence, to predict the deformation behavior of solder joints in microelectronic packages more accurately, beta-tin crystallographic orientation needs to be considered in modeling and analysis of the deformation mechanics of solder interconnects. Solder joints provide mechanical support, electrical and thermal interconnection between packaging levels in microelectronics assembly systems. The proper functioning of these interconnections and the reliability of the electronic packages depend largely on the anisotropic mechanical properties of the solder joints. A few studies were conducted to investigate the effect of material orientation on lead free solder under the application of external shear loads. In addition, different crystallographic slip systems were incorporated in the modeling to assess the anisotropic deformation behavior of solder interconnects. However, no study has focused on the detailed analysis of beta-tin grain size and orientation on the overall deformation behavior of SAC solder joints. This research mostly involves several projects to define the deformation behavior of solder joints considering different grain size and orientation. Prior to that, experiments and simulations were conducted to evaluate anisotropic elastic stiffness matrix coefficients for SAC305. Because SAC alloys contain intermetallic particles and these intermetallic compounds (IMC) affect the overall deformation significantly. Therefore, investigations need to be carried out acknowledging that fact that SAC material domains contain explicit IMC particles which are embedded in anisotropic beta-tin matrix. In the first project, micromechanical behavior evolution of SAC305 lead free solder material was investigated using the nanoindentation technique and then micromechanical modeling was performed to evaluate the direction dependent elastic moduli. The dimensions of solder joints are typically in the sub-millimeter range. Hence, size effects must be taken into account when evaluating constitutive properties of lead-free solder joints. However, it is difficult to evaluate constitutive behavior by using traditional uniaxial tests for material samples that are so small. Thus, instrumented indentation or nanoindentation techniques have proven to be convenient in investigating mechanical properties such as elastic moduli and hardness of different materials. Samples of solder joints were collected from ball grid array packages. The samples were then cross sectioned and prepared for electron back-scatter diffraction (EBSD) experiments to record the orientations of the various single and multiple-grained solder balls. After characterization of the joint orientations, nanoindentation experiments were performed on individual grains with various material orientations to obtain the elastic moduli along different directions. Using the recorded experimental modulus data, calculations were performed to evaluate the elastic compliance and stiffness matrices, which incorporate the directional material properties. Next a micromechanical model with the presence of explicit nonspherical IMC particles was developed that takes inputs pertaining to individual constituents' mechanical properties and volume fractions. Then computations were performed to generate outputs in the form of effective properties of bulk solder. Studies show that IMC particles work as reinforcement agents to strengthen the overall mechanical behavior. The representative volume elements (RVE) were considered which contained all the constituents according to their volume fractions. Size, shape, and volume fractions of IMCs were determined from Scanning Electron Microscope – Focused Ion Beam (SEM-FIB) technology. Following the model with explicit IMC particles, another finite element model was generated with homogenized properties obtained from nanoindentation tests. Finally, stress-strain responses obtained from these two approaches were compared from the overall directional elastic moduli point of view. Almost 97% weight percentage is β-Sn which is a highly directional material in terms of elastic modulus (E) and coefficient of thermal expansion (CTE). In the second project, a physics-based crystal plasticity finite element (CPFE) model with homogenized SAC properties was used to explain the mesoscale deformation behavior of solder joints that have dimensions in the sub-millimeter range. A crystal plasticity theory-based subroutine was implemented in ABAQUS finite element (FE) software to forecast the effects of β-Sn crystal orientations on overall deformation behavior of SAC305 BGA solder joints. Since the crystal c-axis is the strongest axis in terms of elastic modulus, several finite element models were developed and run with varying c-axis orientation in the x-y plane to assess the effects of various slip systems on the deformation patterns. Since β-Sn is a crystalline material, deformation beyond the elastic limit is described by various atomic planes and directions, which are termed as slip systems together. Ten different slip families have been recognized in literature for β-Sn body-centered tetragonal (BCT) crystals. When external load is applied on solder balls, plastic deformation is dictated by the movement of dislocations and direction of deformation is defined by slip properties. Later in this project, solder joint models with multiple grains were developed and deformation behavior was explained under the application of mechanical shear. In the third project, a crystal plasticity-based DAMASK code was used to explain the mesoscale deformation behavior of polycrystal beta-tin samples. Analyzed polycrystalline beta-tin samples were obtained using Voronoi tessellation in NEPER with random grain orientations, which is the case for real samples. Tensile simulations were performed for a sample with reasonably large number of grains. The model was then calibrated with experimental stress-strain data for a reflowed SAC305 alloy to determine a set of beta-tin slip properties. Subsequently, multiple polycrystalline models were generated with varying number of Sn grains, and a spectral solver was used to determine the sample responses under tensile and shear loads along three orthogonal directions. Samples with large number of grains exhibited isotropic deformation behavior. However, samples with fewer number of grains demonstrated anisotropic behavior. This study determined the overall average grain size to sample size ratio at which isotropic behavior breaks down. In this project, size effects were analyzed for polycrystal models subjected to both tensile and shear loads. In the fourth project, a data-driven machine learning (ML) model was developed to predict the mesoscale deformation behavior of single crystal SAC samples. The ML model was trained using a large number of solder stress-strain curves for various crystal orientations obtained using simulations performed with the crystal plasticity-based DAMASK code. The constants in the crystal plasticity (CP) code were first calibrated with experimental solder stress-strain data (both elastic and plastic regions) to obtain the model constants that describe the overall material deformation of SAC305 alloy from a crystallographic standpoint. For a single crystal SAC sample, there are an infinite number of possible orientations for uniaxial loading that can lead to an infinite number of different overall mechanical deformation behaviors. To account for this, a large number of CP simulations (15,000) were performed using the DAMASK code for samples with various single crystal orientations. A time-series based machine learning model was then trained on the simulated stress-strain plots for the 10,000 known orientations. The performance of the trained ML model was then evaluated by predicting the stress-strain behavior of a set of 2500 additional orientations that were not in the training set. The accuracy of the ML predictions was shown to be very good, so that the ML model can be used in the future without the need to perform computationally expensive and time-consuming CP simulations. In particular, the machine learning model can help to bypass the complicated pre-processing, computation, and post-processing aspects of numerical crystal plasticity modeling. In the final project, a comparison was made between the conventional modeling approach of package level solder joint reliability and the mesoscale method utilized throughout the significant part of this dissertation. At first, a plastic ball grid array package (PBGA) was created using CAD software SolidWorks and then the model was imported to ABAQUS finite element software for running simulations in both techniques. For the conventional technique, material properties of solder joints were assigned in terms of isotropic and homogeneous elastic properties and rate dependent plasticity using Anand model parameters. For mesoscale modeling, the solder joints’ properties were assigned in terms of anisotropic elastic stiffness matrix and beta-tin slip properties. Finally, a relative comparison was analyzed between the mesoscale simulation predictions and the conventional technique. In both approaches, elastic properties were considered for other package elements such as mold compound, die, die attachment adhesives, solder mask, substrate/PCB material and copper pad.

A new method is proposed to mitigate ice accretions on wind turbine blades via the creation of a microstructural gradient surface geometry that facilitates spontaneous water droplet motion along the surface. The wettability gradients are formed by laser etching 35m wide, 35m deep channels into aluminum to form a surface with a gradually increasing solid area fraction. Different design permutations are then proposed and systematically evaluated on the merits of their performance. An analytical model is also derived based on a balance of hysteresis and drag forces to predict the critical airspeed necessary for droplet movement as a function of the droplet size and surface contact angle. Experimentation has shown good agreement with the model for both the baseline and fixed-pitch channel surfaces and has also demonstrated that, in certain cases, up to 70% lower critical airspeeds are needed to initiate droplet motion on these microstructured surfaces. Finally, the effects of frozen droplets on aerodynamic performance were studied via 3D-printed airfoil prototypes. This work demonstrated that at airspeeds under

The rise of electric vehicles (EVs) has led to new engineering challenges for electric motors and new design opportunities for lubricants and tribological elements like bearings and gears. High power density EV motors require dual fluids: a high heat capacity, low viscosity coolant fluid and a highly effective lubricant capable of protecting ultra-high-speed bearings and gears. This dual fluid practice requires two different fluid systems (sumps, pumps, pipes, and filters) adding cost, weight and complexity. Compared to combustion engines, EVs fluids (oils and coolants) do not require similarly extreme high temperature capabilities. Thus, the opportunity exists to consider new fluids (coolants/lubricants) to enhance EV system performance.Towards this end, low viscosity, high heat capacity fluids such as water-based lubricants (WBLs) have gained popularity, as they can cool electric components and lubricate moving parts fulfilling the single fluid approach. However, WBLs have limitations such as low viscosity, evaporation, freezing point, microbiological growth, oxidation, corrosion, and high electrical conductivity. To mitigate these limitations, different additives such as ionic liquids, bio-based oils, and nanoparticles have been incorporated into WBLs. This has resulted in significant improvements in coefficient of friction and wear reduction.Limited literature is available on the rheological and tribological behavior of WBLs in steels, and the wear mechanisms for these lubricants are not fully understood. The proposed work aims to test different WBLs by characterizing their rheological properties and conducting tribological tests such as fretting and sliding experiments. Posttest analyses will be performed via SEM/EDX, XRD, and AFM to characterize the tribofilms and surface morphology. The results will be compared with traditional lubricants to gain a better understanding of the mechanisms and propose adjustments to current modeling tools. The study underscores the efficacy of certain WBL formulations, particularly those containing glycerol or polyalkylene glycol, in reducing friction and wear on 440C steel, while emphasizing the need for careful consideration of additives under specific operating conditions.

Laminar flow airfoils represent a possible method of greatly increasing the efficiency and thus flight time of small civilian aircraft and drones. This is theoretically accomplished by allowing the boundary layer to remain laminar and attached over a wing, thus reducing the effects of drag over the wing. Unfortunately, laminar flow boundary layers are easily disturbed by a variety of outside effects, including particles, rainfall, transient pressure changes, and other phenomena. Disruptions to flow are able to cause transient or lasting disturbances, both of these scenarios working to prematurely trigger transition over a surface. For this reason it is important to understand the impact these conditions have on boundary layers, and to discover possible methods of mitigation. The focus of these studies lies in examining the differences of behavior and interactions between spots and wedges created by a variety of scenarios.These scenarios may be divided into the ricochet and the adhesion scenarios. The difference being that within the ricochet scenario the droplet rebounds from the surface while in the adhesion scenario some amount of the droplet volume remains attached. The ricochet scenario serves as comparison to a spot initiated by an impulsive disturbance, with the aim to investigate the importance of methodology to the disturbance creation. The adhesion scenarios investigate the interaction between the initial disturbance and the lasting disturbance that follows, and has three subcategories in which the droplet:1) retains sufficient mass to instigate a turbulent wedge.2) loses sufficient mass to only form a low-speed streak.3) retains sufficient mass to create an unstable low speed streak, capable of turning into a wedge. Raindrops will be abstracted into time-evolving body forces moving through channel flow making use of an in-house direct numerical simulation code. This code solves the Navier Stokes equations without utilizing any turbulencemodels by resolving the relevant length and time scales using a pseudo spectral algorithm based on the channel code algorithm of Kim Moin and Moser. The simulations will be examined through a turbulence and vorticity field of view, analysis of both large turbulent structures and smaller phenomena.

Precision finishing of hard/brittle materials is of ever-increasing relevance in industry today. Silicon nitride is such a material that has been used increasingly in demanding applications such as bearing rolling elements and in microelectronics. Silicon nitride is considered a difficult-to-grind material due to its high hardness, requiring the use of superabrasives. Several studies have documented the grinding or scratching of silicon nitride and the resulting mechanism of material removal. In general, those studies do not include abrasives typical in grinding process or were not conducted at typical grinding speeds. It is important to understand the interaction between the abrasive grain and the workpiece at grinding speed to better design manufacturing processes of silicon nitride material. A main objective of this research was to create a multi-grit scratch machine capable of producing speeds in the range of actual grinding processes for fast data generation. This machine utilizes a partially electroplated arbor to enable fast generation of individual scratches. In addition, the second objective of the research was to investigate the influence of various factors - wheel speed, depth of cut, workspeed – on grinding outcomes.

Reciprocating compressors are critical components for the natural gas energy infrastructure throughout the world. Proper lubrication of PTFE and PEEK based packings and piston rings (seals) in the compressor cylinder is necessary to prevent leaks of methane to the environment and allow efficient, reliable, and extended operation of the compressor. Cylinder lubrication is poorly understood and commonly cited as the reason for premature seal failures while being a major annual expense for compressor station operators. An industry priority is to reduce the amount of lubricant injected into the compressor cylinder. The lubricant is carried away by the process gas and collects in pipelines or other downstream equipment. To better understand the tribological aspects and physical phenomena of compressor cylinder lubrication, the effect of high pressure methane on the surface tension of common lubricants and sealing materials was investigated. Methane gas pressure exhibited an inverse relationship with liquid lubricant surface tension through a pendant drop test. Methane pressure also increased the contact angles of sessile drops of lubricants on the solid surfaces. Nitrogen pressure exhibited the same trends but to a lesser extent than methane. Analysis using the Owens, Wendt, Rabel and Kaelble calculation method revealed that as methane pressure increased, the surface free energy of the seal materials decreased. These results further revealed that increased gas pressure decreased the wettability of lubricants on the materials. PTFE based materials decreased much more than PEEK based materials. Spontaneous adsorption of the pressurized gases on the surface of the lubricant drops and sealing materials was determined to be the underlying cause of the reductions of surface energy. This new understanding is significant because it better explains the observed lubrication phenomena and may be able to guide the industry toward optimized lubrication, more efficient seal designs or better selection of lubricants and materials specifically suited for high pressure service.

Gigantic off-the-road (OTR) tires used in mining industries are usually damaged and both their damage resistance and damage mechanisms of fiber-reinforced rubbers have not been well understood. In order to optimize OTR tire fillers and mechanical properties, the damage resistance needs to be studied under consideration of size independence and size dependence. In this research, rubbers reinforced with different fiber stiffness and varied fiber amount fabrics were fabricated by vulcanization. The mechanical properties including performance of tensile, scratch, impact, fracture, and debonding were studied under size-independent condition. Natural rubber (NR) having a lower stiffness was better than styrene-butadiene rubber (SBR) in tensile and fracture performance. Increasing fiber stiffness and fiber amount improved tensile, scratch, and impact resistance. Reinforcing Kevlar fabric reduced the matrix crack occurrence in the impact test and the damage level in the scratch test. Carbon fiber-reinforced rubber had the most serious damage level in the scratch and impact test. The fracture toughness decreases with increasing fiber volume ratio and fiber stiffness. The delamination resistance rises as fiber ductility reduces. In the studies of fracture performance, we studied both the thickness effect and the length-width effect. Increasing the SBR thickness to fourfold (3 mm, 6 mm, 9 mm, and 12 mm) exhibited decreased fracture toughness and the varied stress state from plane stress to plane strain in compact tension (CT) tests. For delamination resistance, a higher matrix thickness caused more energy dissipation to increase the adhesion strength. In the study of the length-width effect, specimens tested using single-edge-notched tension (SENT) had increased the same proportion length and width, and unvaried thickness (3mm) to evaluate the size effect under plane-stress condition. The fracture toughness of the unreinforced rubbers was independent of reduced length and width. Reinforcing more fibers mitigated the size effect in plane-stress conditions. Both reinforcing a stiffer fiber and improving the fiber volume ratio are effective in reducing the size effect; however, the fracture toughness is lower. Overall, tensile, scratch, impact, CT, SENT, and T-peel tests were adopted to estimate the damage resistance. Fiber amount, fiber stiffness, specimen thickness, and both length and width were considered parameters in this study. The contribution of this research is to establish damage resistance and mechanisms of fiber-reinforced rubber under size effect consideration. The relationship between fiber amount, fiber stiffness, and damage resistance was also established. The summarized conclusions might be useful for tire and rubber structural optimization.