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- All Subjects: Mechanical Engineering
Description
Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.
Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
ContributorsRezayat Sorkhabadi, Seyed Mostafa (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Failures in the cold chain, the system of refrigerated storage and transport that provides fresh produce or other essentials to be maintained at desired temperatures and environmental conditions, lead to food and energy waste. The mini container (MC) concept is introduced as an alternative to conventional refrigerated trucks (“reefers”), particularly for small growers. The energy consumption and corresponding GHG emissions for transporting tomatoes in two cities representing contrasting climates is analyzed for conventional reefers and the proposed mini containers. The results show that, for partial reefer loads, using the MCs reduces energy consumption and GHG emissions. The transient behavior of the vapor compression refrigeration cycle is analyzed by considering each component as a “lumped” system, and the resulting sub-models are solved using the Runge Kutta 4th-order method in a MATLAB code at hot and cold ambient temperatures. The time needed to reach steady state temperatures and the temperature values are determined. The maximum required compressor work in the transient phase and at steady state are computed, and as expected, as the ambient temperature increases, both values increase. Finally, the average coefficient of performance (COP) is determined for varying heat transfer coefficient values for the condenser and for the evaporator. The results show that the average COP increases as heat transfer coefficient values for the condenser and the evaporator increase. Starting the system from rest has an adverse effect on the COP due to the higher compressor load needed to change the temperature of the condenser and the evaporator. Finally, the impact on COP is analyzed by redirecting a fraction of the cold exhaust air to provide supplemental cooling of the condenser. It is noted that cooling the condenser improves the system's performance better than cooling the fresh air at 0% of returned air to the system.To sum up, the dissertation shows that the comparison between the conventional reefer and the MC illustrates the promising advantages of the MC, then a transient analysis is developed for deeply understanding the behaviors of the system component parameters, which leads finally to improvements in the system to enhance its performance.
ContributorsSyam, Mahmmoud Muhammed (Author) / Phelan, Patrick (Thesis advisor) / Villalobos, Rene (Thesis advisor) / Huang, Huei-Ping (Committee member) / Bocanegra, Luis (Committee member) / Al Omari, Salah (Committee member) / Arizona State University (Publisher)
Created2023
Description
A finite element model that replicates the experimental procedure to test and certify soft body armor has been developed. The model consists of four components: bullet, clay, straps, and shoot pack with different material models that closely capture the behavior of each component when subjected to ballistic impact loading. To test the fidelity of the model, three metrics are used - back face signature (BFS), the number of penetrated shoot pack layers, and the number of damaged shoot pack layers on the clay side of the shoot pack assembly. In addition, the shape and size of the bullet, and the shape and size of the hole in the shoot pack are also considered as qualitative measures to assess the developed model. The focus of this research work is to improve the shoot pack material model, while the constitutive model for the components is taken from earlier work done at ASU. Results show considerable improvement in the model in terms of capturing the number of penetrated layers, the size and shape of the holes in the shoot pack layer, and the predicted BFS. The developed finite element models can be used to predict the behavior of soft body armor for different initial conditions, shoot pack materials, and arrangement of the layers.
ContributorsPechetti, Sateesh (Author) / Rajan, Subramaniam (Thesis advisor) / Mignolet, Marc (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2024
Description
The measurement of the radiation and convection that the human body experiences are important for ensuring safety in extreme heat conditions. The radiation from the surroundings on the human body is most often measured using globe or cylindrical radiometers. The large errors stemming from differences in internal and exterior temperatures and indirect estimation of convection can be resolved by simultaneously using three cylindrical radiometers (1 cm diameter, 9 cm height) with varying surface properties and internal heating. With three surface balances, the three unknowns (heat transfer coefficient, shortwave, and longwave radiation) can be solved for directly. As compared to integral radiation measurement technique, however, the bottom mounting using a wooden-dowel of the three-cylinder radiometers resulted in underestimated the total absorbed radiation. This first part of this thesis focuses on reducing the size of the three-cylinder radiometers and an alternative mounting that resolves the prior issues. In particular, the heat transfer coefficient in laminar wind tunnel with wind speed of 0.25 to 5 m/s is measured for six polished, heated cylinders with diameter of 1 cm and height of 1.5 to 9 cm mounted using a wooden dowel. For cylinders with height of 6 cm and above, the heat transfer coefficients are independent of the height and agree with the Hilpert correlation for infinitely long cylinder. Subsequently, a side-mounting for heated 6 cm tall cylinder with top and bottom metallic caps is developed and tested within the wind tunnel. The heat transfer coefficient is shown to be independent of the flow-side mounting and in agreement with the Hilpert correlation.
The second part of this thesis explores feasibility of employing the three-cylinder concept to measuring all air-flow parameters relevant to human convection including mean wind speed, turbulence intensity and length scale. Heated cylinders with same surface properties but varying diameters are fabricated. Uniformity of their exterior temperature, which is fundamental to the three-cylinder anemometer concept, is tested during operation using infrared camera. To provide a lab-based method to measure convection from the cylinders in turbulent flow, several designs of turbulence-generating fractal grids are laser-cut and introduced into the wind tunnel.
ContributorsGupta, Mahima (Author) / Rykaczewski, Konrad (Thesis advisor) / Pathikonda, Gokul (Thesis advisor) / Middel, Ariane (Committee member) / Arizona State University (Publisher)
Created2024
Description
Integrating advanced materials with innovative manufacturing techniques has propelled the field of additive manufacturing into new frontiers. This study explores the rapid 3D printing of reduced graphene oxide/polymer composites using Micro-Continuous Liquid Interface Production (μCLIP), a cutting-edge technology known for its speed and precision. A printable ink is formulated with reduced graphene oxide for μCLIP-based 3D printing. The research focuses on optimizing μCLIP parameters to fabricate reduced graphene composites efficiently. The study encompasses material synthesis, ink formulation and explores the resulting material's structural and electrical properties. The marriage of graphene's unique attributes with the rapid prototyping capabilities of μCLIP opens new avenues for scalable and rapid production in applications such as energy storage, sensors, and lightweight structural components. This work contributes to the evolving landscape of advanced materials and additive manufacturing, offering insights into the synthesis, characterization, and potential applications of 3D printed reduced graphene oxide/polymercomposites.
ContributorsRavishankar, Chayaank Bangalore (Author) / Chen, Xiangfan (Thesis advisor) / Bhate, Dhruv (Committee member) / Azeredo, Bruno (Committee member) / Arizona State University (Publisher)
Created2024
Description
When solving analysis, estimation, and control problems for Partial Differential Equations (PDEs) via computational methods, one must resolve three main challenges: (a) the lack of a universal parametric representation of PDEs; (b) handling unbounded differential operators that appear as parameters; and (c), enforcing auxiliary constraints such as Boundary conditions and continuity conditions. To address these challenges, an alternative representation of PDEs called the `Partial Integral Equation' (PIE) representation is proposed in this work. Primarily, the PIE representation alleviates the problem of the lack of a universal parametrization of PDEs since PIEs have, at most, $12$ Partial Integral (PI) operators as parameters. Naturally, this also resolves the challenges in handling unbounded operators because PI operators are bounded linear operators. Furthermore, for admissible PDEs, the PIE representation is unique and has no auxiliary constraints --- resolving the last of the $3$ main challenges. The PIE representation for a PDE is obtained by finding a unique unitary map from the states of the PIE to the states of the PDE. This map shows a PDE and its associated PIE have equivalent system properties, including well-posedness, internal stability, and I/O behavior. Furthermore, this unique map also allows us to construct a well-defined dual representation that can be used to solve optimal control problems for a PDE. Using the equivalent PIE representation of a PDE, mathematical and computational tools are developed to solve standard problems in Control theory for PDEs. In particular, problems such as a test for internal stability, Input-to-Output (I/O) $L_2$-gain, $\hinf$-optimal state observer design, and $\hinf$-optimal full state-feedback controller design are solved using convex-optimization and Lyapunov methods for linear PDEs in one spatial dimension. Once the PIE associated with a PDE is obtained, Lyapunov functions (or storage functions) are parametrized by positive PI operators to obtain a solvable convex formulation of the above-stated control problems. Lastly, the methods proposed here are applied to various PDE systems to demonstrate the application.
ContributorsShivakumar, Sachin (Author) / Peet, Matthew (Thesis advisor) / Nedich, Angelia (Committee member) / Marvi, Hamidreza (Committee member) / Platte, Rodrigo (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2024
Description
Carbon nanotubes (CNTs) have emerged as compelling materials for enhancing both electrical and mechanical properties of aerospace structures. Buckypaper (BP), a porous
membrane consisting of a highly cross-linked network of CNTs, can be effectively
integrated with carbon fiber reinforced polymer (CFRP) composites to simultaneously
enhance their electromagnetic interference (EMI) shielding effectiveness (SE) and
mechanical properties. In existing literature, CNT based nanocomposites are shown to
improve the flexural strength and stiffness of CFRP laminates. However, a limited amount
of research has been reported in predicting the EMI SE of hybrid BP embedded CFRP
composites.
To characterize the EMI shielding response of hybrid BP/CFRP laminates, a novel
modeling approach based on equivalent electrical circuits is employed to estimate the
electrical conductivity of unidirectional CFRP plies. This approach uses Monte Carlo
simulations and accounts for the effects of quantum tunneling at the fiber-fiber contact
region. This study specifically examines a signal frequency range of 50 MHz to 12 GHz,
corresponding to the very high to X band spectrum. The results indicate that at a frequency
of 12 GHz, the longitudinal conductivity decreases to around ~3,300 S/m from an initial
DC value of 40,000 S/m, while the transverse conductivity concurrently increases from
negligible to approximately ~12.67 S/m. These results are then integrated into Ansys High
Frequency Structure Simulator (HFSS) to predict EMI SE by simulating the propagation
of electromagnetic waves through a semi-infinite composite shield representative volume
element. The numerical simulations illustrate that incorporating BP allows for significant
ii
improvements in SE of hybrid BP/CFRP composites. At 12 GHz signal frequency, for
example, the incorporation of a single BP interleave enhances the SE of a [90,0] laminate
by up to ~64%, while the incorporation of two BP interleaves in a [90,0,+45,-45,0,90]s
balanced symmetric laminate enhances its SE by ~20% . This enhancement is due to the
high conductivity of BP at high frequencies.
Additionally, to evaluate the flexural property enhancements due to BP, experimental
three-point bend tests were conducted on different configurations of hybrid BP/CFRP
laminates, and their strength and stiffness were compared with the non-BP samples.
Micrographs of failed samples are acquired using an optical microscope, which provides
insights into their underlying damage mechanisms. Fractography analysis confirms the role
of BP in preventing through-thickness crack propagation, attributed to the excellent crack
retardation properties of CNTs.
ContributorsTripathi, Kartik (Author) / Chattopadhyay, Aditi (Thesis advisor) / Henry, Todd C. (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2024
Description
Human thermoregulation is substantially based on sweat evaporation, yet little is known about this process at the microscopic level. Midwave infrared thermography (MWIR) and optical coherence tomography (OCT) can assess the sweat evaporation dynamics from the skin, from the onset of a sweat droplet emergence from the sweat pores to its filmwise stage. In physiological studies, the rate of sweat evaporation is frequently determined using ventilated capsules or using technical absorbent pads. The first part of this thesis compares flow fields and water film evaporation rates from a capsule with a sudden expansion transition section, from a round tube to a rectangular evaporation section and one with a transition consisting of a wind tunnel-like diffuser section. The comparative study shows that the ventilated capsule with the diffuser transition section is effective at minimizing the flow disturbances as compared to the chaotic flow occurring with the capsule with a sudden expansion transition section. The second part of this thesis focuses on optimization and implementation of the ventilated capsule with diffuser transition section in pilot human trials. The experimental setup and protocol for pilot human trials are also described. The capsule geometry is altered to increase the imaging field and include calibration or alignment marks on the skin to enable quantitative image analysis. Based on pilot human trials, several additional improvements to the ventilated capsule, including a soft gasket and alternative sapphire window, are proposed to further refine this sweat evaporation measurement and imaging technique.
ContributorsJose, Cibin Thomas (Author) / Rykaczewski, Konrad KR (Thesis advisor) / Pathikonda, Gokul GP (Committee member) / Kavouras, Stavros SK (Committee member) / Arizona State University (Publisher)
Created2024
Description
The increasing demands of air travel and the escalating complexity of air traffic management (ATM) necessitate advanced air traffic flow prediction and optimization methodologies. This dissertation delves into integrating physics-guided machine learning techniques to address these challenges. By encompassing four pivotal studies, it contributes to the ATM field, showcasing how data-driven insights and physical principles can revolutionize our understanding and management of air traffic density, state predictions, flight delays, and airspace sectorization. The first study investigates the Bayesian Ensemble Graph Attention Network (BEGAN), a novel machine learning framework designed for precise air traffic density prediction. BEGAN combines spatial-temporal analysis with domain knowledge, enabling the model to interpret complex air traffic patterns in a highly dynamic and regulated airspace environment. The second study introduces the Physics-Informed Graph Attention Transformer, a novel approach integrating graph-based spatial learning with temporal Transformers. This model excels in capturing dynamic spatial-temporal interdependencies and integrates partial differential equations from fluid mechanics, enhancing the predictive accuracy and interpretability in ATM. The third study shifts focus to predictive modeling of aircraft delays, employing Physics-Informed Neural Networks. By utilizing sparse regression for system identification, this approach adeptly deciphers the intricate partial differential equations that dictate near-terminal air traffic dynamics, providing a novel perspective in forecasting flight delays with enhanced precision. The final study focuses on dynamic airspace sectorization, deploying an attention-based deep learning model that adeptly navigates the complexities of workload dynamics. In conjunction with constrained K-means clustering and evolutionary algorithms, it facilitates a more efficient and adaptable approach to airspace management, ensuring optimal traffic flow and safety. The findings of these studies demonstrate the significant impact of physics-guided machine learning in advancing ATM's safety and efficiency. They mark a shift from traditional empirical methods to innovative, data-driven approaches for air traffic management. This research enhances current practices and charts new paths for future technological advancements in aviation, especially in autonomous systems and digital transformation.
ContributorsXu, Qihang (Author) / Liu, Yongming YL (Thesis advisor) / Yan, Hao HY (Committee member) / Zhou, Xuesong XZ (Committee member) / Huang, Huei-Ping HH (Committee member) / Zhuang, Houlong HZ (Committee member) / Arizona State University (Publisher)
Created2024
Description
This dissertation contributes to uncertainty-aware neural networks using multi-modality data, with a focus on industrial and aviation applications. Drawing from seminal works in recent years that have significantly advanced the field, this dissertation develops techniques for incorporating uncertainty estimation and leveraging multi-modality information into neural networks for tasks such as fault detection and environmental perception. The escalating complexity of data in engineering contexts demands models that predict accurately and quantify uncertainty in these predictions. The methods proposed in this document utilize various techniques, including Bayesian Deep Learning, multi-task regularization and feature fusion, and efficient use of unlabeled data. Popular methods of uncertainty quantification are analyzed empirically to derive important insights on their use in real world engineering problems. The primary objective is to develop and refine Bayesian neural network models for enhanced predictive accuracy and decision support in engineering. This involves exploring novel architectures, regularization methods, and data fusion techniques. Significant attention is given to data handling challenges in deep learning, particularly in the context of quality inspection systems. The research integrates deep learning with vision systems for engineering risk assessment and decision support tasks, and introduces two novel benchmark datasets designed for semantic segmentation and classification tasks. Additionally, the dissertation delves into RGB-Depth data fusion for pipeline defect detection and the use of semi-supervised learning algorithms for manufacturing inspection tasks with imaging data. The dissertation contributes to bridging the gap between advanced statistical methods and practical engineering applications.
ContributorsRathnakumar, Rahul (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Jayasuriya, Suren (Committee member) / Zhuang, Houlong (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2024