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- All Subjects: Mechanical Engineering
Description
Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.
ContributorsSchichtel, Jacob (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Ghoshal, Anindya (Committee member) / Huang, Huei-Ping (Committee member) / Jiao, Yang (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2022
Description
Advanced fibrous composite materials exhibit outstanding thermomechanical performance under extreme environments, which make them ideal for structural components that are used in a wide range of aerospace, nuclear, and defense applications. The integrity and residual useful life of these components, however, are strongly influenced by their inherent material flaws and defects resulting from the complex fabrication processes. These defects exist across multiple length scales and govern several scale-dependent inelastic deformation mechanisms of each of the constituents as well as their composite damage anisotropy. Tailoring structural components for optimal performance requires addressing the knowledge gap regarding the microstructural material morphology that governs the structural scale damage and failure response. Therefore, there is a need for a high-fidelity multiscale modeling framework and scale-specific in-situ experimental characterization that can capture complex inelastic mechanisms, including damage initiation and propagation across multiple length scales. This dissertation presents a novel multiscale computational framework that accounts for experimental information pertinent to microstructure morphology and architectural variabilities to investigate the response of ceramic matrix composites (CMCs) with manufacturing-induced defects. First, a three-dimensional orthotropic viscoplasticity creep formulation is developed to capture the complex temperature- and time-dependent constituent load transfer mechanisms in different CMC material systems. The framework also accounts for a reformulated fracture mechanics-informed matrix damage model and the Curtin progressive fiber damage model to capture the complex scale-dependent damage and failure mechanisms through crack kinetics and porosity growth. Next, in-situ experiments using digital image correlation (DIC) are performed to capture the damage and failure mechanisms in CMCs and to validate the high-fidelity modeling results.
The dissertation also presents an exhaustive experimental investigation into the effects of temperature and manufacturing-induced defects on toughened epoxy adhesives and hybrid composite-metallic bonded joints. Nondestructive evaluation techniques are utilized to characterize the inherent defects morphology of the bulk adhesives and bonded interface. This is followed by quasi-static tensile tests conducted at extreme hot and cold temperature conditions. The damage mechanisms and failure modes are investigated using in-situ DIC and a high-resolution camera. The information from the morphology characterization studies is used to reconstruct high-fidelity geometries of the test specimens for finite element analysis.
ContributorsKhafagy, Khaled Hassan Abdo (Author) / Chattopadhyay, Aditi (Thesis advisor) / Fard, Masoud Y. (Committee member) / Milcarek, Ryan (Committee member) / Stoumbos, Tom (Committee member) / Borkowski, Luke (Committee member) / Arizona State University (Publisher)
Created2022
Description
Magnetic liquids called ferrofluids have been used in applications ranging from audio speaker cooling and rotary pressure seals to retinal detachment surgery and implantable artificial glaucoma valves. Recently, ferrofluids have been investigated as a material for use in magnetically controllable liquid droplet robotics. Liquid droplet robotics is an emerging technology that aims to apply control theory to manipulate fluid droplets as robotic agents to perform a wide range of tasks. Furthermore, magnetically controlled micro-robotics is another popular area of study where manipulating a magnetic field allows for the control of magnetized micro-robots. Both of these emerging fields have potential for impact toward medical applications: liquid characteristics such as being able to dissolve various compounds, be injected via a needle, and the potential for the human body to automatically filter and remove a liquid droplet robot, make liquid droplet robots advantageous for medical applications; while the ability to remotely control the torques and forces on an untethered microrobot via modulating the magnetic field and gradient is also highly advantageous. The research described in this dissertation explores applications and methods for the electromagnetic control of ferrofluid droplet robots. First, basic electrical components built from fluidic channels containing ferrofluid are made remotely tunable via the placement of ferrofluid within the channel. Second, a ferrofluid droplet is shown to be fully controllable in position, stretch direction, and stretch length in two dimensions using proportional-integral-derivative (PID) controllers. Third, control of a ferrofluid’s position, stretch direction, and stretch length is extended to three dimensions, and control gains are optimized via a Bayesian optimization process to achieve higher accuracy. Finally, magnetic control of both single and multiple ferrofluid droplets in two dimensions is investigated via a visual model predictive control approach based on machine learning. These achievements take both liquid droplet robotics and magnetic micro-robotics fields several steps closer toward real-world medical applications such as embedded soft electronic health monitors, liquid-droplet-robot-based drug delivery, and automated magnetically actuated surgeries.
ContributorsAhmed, Reza James (Author) / Marvi, Hamidreza (Thesis advisor) / Espanol, Malena (Committee member) / Rajagopalan, Jagannathan (Committee member) / Zhuang, Houlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2022
Description
The relationships between the properties of materials and their microstructures have been a central topic in materials science. The microstructure-property mapping and numerical failure prediction are critical for integrated computational material engineering (ICME). However, the bottleneck of ICME is the lack of a clear understanding of the failure mechanism as well as an efficient computational framework. To resolve these issues, research is performed on developing novel physics-based and data-driven numerical methods to reveal the failure mechanism of materials in microstructure-sensitive applications.
First, to explore the damage mechanism of microstructure-sensitive materials in general loading cases, a nonlocal lattice particle model (LPM) is adopted because of its intrinsic ability to handle the discontinuity. However, the original form of LPM is unsuitable for simulating nonlinear behavior involving tensor calculation. Therefore, a damage-augmented LPM (DLPM) is proposed by introducing the concept of interchangeability and bond/particle-based damage criteria. The proposed DLPM successfully handles the damage accumulation behavior in general material systems under static and fatigue loading cases.
Then, the study is focused on developing an efficient physics-based data-driven computational framework. A data-driven model is proposed to improve the efficiency of a finite element analysis neural network (FEA-Net). The proposed model, i.e., MFEA-Net, aims to learn a more powerful smoother in a multigrid context. The learned smoothers have good generalization properties, and the resulted MFEA-Net has linear computational complexity. The framework has been applied to efficiently predict the thermal and elastic behavior of composites with various microstructural fields. Finally, the fatigue behavior of additively manufactured (AM) Ti64 alloy is analyzed both experimentally and numerically. The fatigue experiments show the fatigue life is related with the contour process parameters, which can result in different pore defects, surface roughness, and grain structures. The fractography and grain structures are closely observed using scanning electron microscope. The sample geometry and defect/crack morphology are characterized through micro computed tomography (CT). After processing the pixel-level CT data, the fatigue crack initiation and growth behavior are numerically simulated using MFEA-Net and DLPM. The experiments and simulation results provided valuable insights in fatigue mechanism of AM Ti64 alloy.
ContributorsMeng, Changyu (Author) / Liu, Yongming (Thesis advisor) / Hoover, Christian (Committee member) / Li, Lin (Committee member) / Peralta, Pedro (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2023
Description
The study aims to develop and evaluate failure prediction models that accurately predict crack initiation sites, fatigue life in additively manufactured Ti-6Al-4V, and burst pressure in relevant applications.The first part proposes a classification model to identify crack initiation sites in AM-built Ti-6Al-4V alloy. The model utilizes surface and pore-related parameters and achieves high accuracy (0.97) and robustness (F1 score of 0.98). Leveraging CT images for characterization and data extraction from the CT-images built STL files, the model effectively detects crack initiation sites while minimizing false positives and negatives. Data augmentation techniques, including SMOTE+Tomek Links, are employed to address imbalanced data distributions and improve model performance.
This study proposes the Probabilistic Physics-guided Neural Network 2.0 (PPgNN) for probabilistic fatigue life estimation. The presented approach overcomes the limitations of classical regression machine models commonly used to analyze fatigue data. One key advantage of the proposed method is incorporating known physics constraints, resulting in accurate and physically consistent predictions.
The efficacy of the model is demonstrated by training the model with multiple fatigue S-N curve data sets from open literature with relevant morphological data and tested using the data extracted from CT-built STL files. The results illustrate that PPgNN 2.0 is a flexible and robust model for predicting fatigue life and quantifying uncertainties by estimating the mean and standard deviation of the fatigue life. The loss function that trains the proposed model can capture the underlying distribution and reduce the prediction error.
A comparison study between the performance of neural network models highlights the benefits of physics-guided learning for fatigue data analysis. The proposed model demonstrates satisfactory learning capacity and generalization, providing accurate fatigue life predictions to unseen examples.
An elastic-plastic Finite Element Model (FEM) is developed in the second part to assess pipeline integrity, focusing on burst pressure estimation in high-pressure gas pipelines with interactive corrosion defects. The FEM accurately predicts burst pressure and evaluates the remaining useful life by considering the interaction between corrosion defects and neighboring pits. The FEM outperforms the well-known ASME-B31G method in handling interactive corrosion threats.
ContributorsBalamurugan, Rakesh (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Bhate, Dhruv (Committee member) / Arizona State University (Publisher)
Created2023
Description
Computing the fluid phase interfaces in multiphase flow is a challenging area of research in fluids. The Volume of Fluid andLevel Set methods are a few algorithms that have been developed for reconstructing the multiphase fluid flow interfaces.
The thesis work focuses on exploring the ability of neural networks to reconstruct the multiphase fluid flow interfaces using
a data-driven approach.
The neural network model has liquid volume fraction stencils as an input, and it predicts the radius of the circle as an
output of the network which represents a phase interface separating two immiscible fluids inside a fluid domain. The liquid
volume fraction stencils are generated for randomly varying circle radii within a 1x1 domain using an open-source VOFI
library. These datasets are used to train the neural network. Once the model is trained, the predicted circular phase
interface from the neural network output is used to generate back the predicted liquid volume fraction stencils.
Error norms values are calculated to assess the error in the neural network model’s predicted liquid volume fraction
stencils with the actual liquid volume fraction stencils from the VOFI library. The neural network parameters are optimized
by testing them for different hyper-parameters to reduce the error norms. So as to minimize the difference between the
predicted and the actual liquid volume fraction stencils and errors in reconstructing the fluid phase interface geometry.
ContributorsPawar, Pranav Rajesh (Author) / Herrmann, Marcus (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation introduces and examines Soft Curved Reconfigurable Anisotropic Mechanisms (SCRAMs) as a solution to address actuation, manufacturing, and modeling challenges in the field of soft robotics, with the aim of facilitating the broader implementation of soft robots in various industries. SCRAM systems utilize the curved geometry of thin elastic structures to tackle these challenges in soft robots. SCRAM devices can modify their dynamic behavior by incorporating reconfigurable anisotropic stiffness, thereby enabling tailored locomotion patterns for specific tasks. This approach simplifies the actuation of robots, resulting in lighter, more flexible, cost-effective, and safer soft robotic systems. This dissertation demonstrates the potential of SCRAM devices through several case studies. These studies investigate virtual joints and shape change propagation in tubes, as well as anisotropic dynamic behavior in vibrational soft twisted beams, effectively demonstrating interesting locomotion patterns that are achievable using simple actuation mechanisms. The dissertation also addresses modeling and simulation challenges by introducing a reduced-order modeling approach. This approach enables fast and accurate simulations of soft robots and is compatible with existing rigid body simulators. Additionally, this dissertation investigates the prototyping processes of SCRAM devices and offers a comprehensive framework for the development of these devices. Overall, this dissertation demonstrates the potential of SCRAM devices to overcome actuation, modeling, and manufacturing challenges in soft robotics. The innovative concepts and approaches presented have implications for various industries that require cost-effective, adaptable, and safe robotic systems. SCRAM devices pave the way for the widespread application of soft robots in diverse domains.
ContributorsJiang, Yuhao (Author) / Aukes, Daniel (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2023
Description
The conversion of H2S enables the recycling of a waste gas into a potential source of hydrogen at a lower thermodynamic energy cost as compared to water splitting. However, studies on the photocatalytic decomposition of H2S focus on traditional deployment of catalyst materials to facilitate this conversion, and operation only when a light source is available. In this study, the efficacy of Direct Ink Written (DIW) luminous structures for H2S conversion has been investigated, with the primary objective of sustaining H2S conversion when a light source has been terminated. Additionally, as a secondary objective, improving light distribution within monoliths for photocatalytic applications is desired. The intrinsic illumination of the 3D printed monoliths developed in this work could serve as an alternative to monolith systems that employ light transmitting fiber optic cables that have been previously proposed to improve light distribution in photocatalytic systems. The results that were obtained demonstrate that H2S favorable adsorbents, a wavelength compatible long afterglow phosphor, and a photocatalyst can form viscoelastic inks that are printable into DIW luminous monolithic contactors. Additionally, rheological, optical and porosity analyses conducted, provide design guidelines for future studies seeking to develop DIW luminous monoliths from compatible catalyst-phosphor pairs. The monoliths that were developed demonstrate not only improved conversion when exposed to light, but more significantly, extended H2S conversion from the afterglow of the monoliths when an external light source was removed. Lastly, considering growing interests in attaining a global circular economy, the techno-economic feasibility of a H2S-CO2 co-utilization plant leveraging hydrogen from H2S photocatalysis as a feed source for a downstream CO2 methanation plant has been assessed. The work provides preliminary information to guide future chemical kinetic design characteristics that are important to strive for if using H2S as a source of hydrogen in a CO2 methanation facility.
ContributorsAbdullahi, Adnan (Author) / Andino, Jean (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Bhate, Dhruv (Committee member) / Wang, Robert (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023
Description
Sweat evaporation is fundamental to human thermoregulation, yet our knowledge of the microscale sweat droplet evaporation dynamics is very limited. To study sweat droplet evaporation, a reliable way to measure sweat evaporation rate from skin and simultaneously image the droplet dynamics through midwave infrared thermography (MWIR) or optical coherence tomography (OCT) is required. Ventilated capsule is a common device employed for measuring sweat evaporation rates in physiological studies. However, existing designs of ventilated capsules with cylindrical flow chambers create unrealistic flow conditions that include flow separation and swirling. To address this problem, this thesis introduces a ventilated capsule with rectangular sweat evaporation area preceded by a diffuser section with geometry based on wind tunnel design guidelines. To allow for OCT or MWIR imaging, a provision to install an acrylic or a sapphire window directly over the exposed skin surface being measured is incorporated in the design. In addition to the capsule, a simplified artificial sweating surface that can supply water in a filmwise, single or multiple droplet form was developed. The performance of the capsule is demonstrated using the artificial sweating surface along with example MWIR imaging.
ContributorsRamesh, Rajesh (Author) / Rykaczewski, Konrad (Thesis advisor) / Kavouras, Stavros (Committee member) / Phelan, Patrick (Committee member) / Burke, Richard (Committee member) / Arizona State University (Publisher)
Created2023
Description
Human exposure to extreme heat is becoming more prevalent due to increasing urbanization and changing climate. In many extreme heat conditions, thermal radiation (from solar to emitted by the surrounding) is a significant contributor to heating the body, among other modes of heat transfer. Therefore, accurately measuring radiative heat flux on a human body is becoming increasingly important for calculating human thermal comfort and heat safety in extreme conditions. Most often, radiant heat exchange between the human body and surroundings is quantified using mean radiant temperature, T_mrt. This value is commonly measured using globe or cylindrical radiometers. It is based on radiation absorbed by the surface of the radiometer, which can be calculated using a surface energy balance involving both convection and emitted radiation at steady state. This convection must be accounted for and is accomplished using a traditional heat transfer coefficient correlation with measured wind speed. However, the utilized correlations are based on wind tunnel measurements and do not account for any turbulence present in the air. The latter can even double the heat transfer coefficient, so not accounting for it can introduce major errors in T_mrt. This Thesis focuses on the development, and testing of a cost-effective heated cylinder to directly measure the convection heat transfer coefficient in field conditions, which can be used for accounting convection in measuring T_mrt using a cylindrical radiometer. An Aluminum cylinder of similar dimensions as that of a cylindrical radiometer was heated using strip heaters, and the surface temperature readings were recorded to estimate the convection heat transfer coefficient, h. Various tests were conducted to test this concept. It was observed that heated cylinders take significantly less time to reach a steady state and respond to velocity change quicker than existing regular-sized globe thermometers. It was also shown that, for accurate estimation of h, it is required to measure the outer surface temperature than the center temperature. Furthermore, the value calculated matches well in range with classic correlations that include velocity, showing proof of concept.
ContributorsGuddanti, Sai Susmitha (Author) / Rykaczewski, Konrad (Thesis advisor) / Vanos, Jennifer (Committee member) / Wang, Robert (Committee member) / Burke, Richard (Committee member) / Arizona State University (Publisher)
Created2023