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- All Subjects: Materials Science
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Description
In engineering, buckling is mechanical instability of walls or columns under compression and usually is a problem that engineers try to prevent. In everyday life buckles (wrinkles) on different substrates are ubiquitous -- from human skin to a rotten apple they are a commonly observed phenomenon. It seems that buckles with macroscopic wavelengths are not technologically useful; over the past decade or so, however, thanks to the widespread availability of soft polymers and silicone materials micro-buckles with wavelengths in submicron to micron scale have received increasing attention because it is useful for generating well-ordered periodic microstructures spontaneously without conventional lithographic techniques. This thesis investigates the buckling behavior of thin stiff films on soft polymeric substrates and explores a variety of applications, ranging from optical gratings, optical masks, energy harvest to energy storage. A laser scanning technique is proposed to detect micro-strain induced by thermomechanical loads and a periodic buckling microstructure is employed as a diffraction grating with broad wavelength tunability, which is spontaneously generated from a metallic thin film on polymer substrates. A mechanical strategy is also presented for quantitatively buckling nanoribbons of piezoelectric material on polymer substrates involving the combined use of lithographically patterning surface adhesion sites and transfer printing technique. The precisely engineered buckling configurations provide a route to energy harvesters with extremely high levels of stretchability. This stiff-thin-film/polymer hybrid structure is further employed into electrochemical field to circumvent the electrochemically-driven stress issue in silicon-anode-based lithium ion batteries. It shows that the initial flat silicon-nanoribbon-anode on a polymer substrate tends to buckle to mitigate the lithiation-induced stress so as to avoid the pulverization of silicon anode. Spontaneously generated submicron buckles of film/polymer are also used as an optical mask to produce submicron periodic patterns with large filling ratio in contrast to generating only ~100 nm edge submicron patterns in conventional near-field soft contact photolithography. This thesis aims to deepen understanding of buckling behavior of thin films on compliant substrates and, in turn, to harness the fundamental properties of such instability for diverse applications.
ContributorsMa, Teng (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongyu (Committee member) / Yu, Hongbin (Committee member) / Poon, Poh Chieh Benny (Committee member) / Rajagopalan, Jagannathan (Committee member) / Arizona State University (Publisher)
Created2014
Description
ABSTRACT Electronics especially mobile electronics such as smart phones, tablet PCs, notebooks and digital cameras are undergoing rapid development nowadays and have thoroughly changed our lives. With the requirement of more transistors, higher power, smaller size, lighter weight and even bendability, thermal management of these devices became one of the key challenges. Compared to active heat management system, heat pipe, which is a passive fluidic system, is considered promising to solve this problem. However, traditional heat pipes have size, weight and capillary limitation. Thus new type of heat pipe with smaller size, lighter weight and higher capillary pressure is needed. Nanofiber has been proved with superior properties and has been applied in multiple areas. This study discussed the possibility of applying nanofiber in heat pipe as new wick structure. In this study, a needleless electrospinning device with high productivity rate was built onsite to systematically investigate the effect of processing parameters on fiber properties as well as to generate nanofiber mat to evaluate its capability in electronics cooling. Polyethylene oxide (PEO) and Polyvinyl Alcohol (PVA) nanofibers were generated. Tensiometer was used for wettability measurement. The results show that independent parameters including spinneret type, working distance, solution concentration and polymer type are strongly correlated with fiber morphology compared to other parameters. The results also show that the fabricated nanofiber mat has high capillary pressure.
ContributorsSun, Tianwei (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongyu (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2014
Description
Damage detection in heterogeneous material systems is a complex problem and requires an in-depth understanding of the material characteristics and response under varying load and environmental conditions. A significant amount of research has been conducted in this field to enhance the fidelity of damage assessment methodologies, using a wide range of sensors and detection techniques, for both metallic materials and composites. However, detecting damage at the microscale is not possible with commercially available sensors. A probable way to approach this problem is through accurate and efficient multiscale modeling techniques, which are capable of tracking damage initiation at the microscale and propagation across the length scales. The output from these models will provide an improved understanding of damage initiation; the knowledge can be used in conjunction with information from physical sensors to improve the size of detectable damage. In this research, effort has been dedicated to develop multiscale modeling approaches and associated damage criteria for the estimation of damage evolution across the relevant length scales. Important issues such as length and time scales, anisotropy and variability in material properties at the microscale, and response under mechanical and thermal loading are addressed. Two different material systems have been studied: metallic material and a novel stress-sensitive epoxy polymer.
For metallic material (Al 2024-T351), the methodology initiates at the microscale where extensive material characterization is conducted to capture the microstructural variability. A statistical volume element (SVE) model is constructed to represent the material properties. Geometric and crystallographic features including grain orientation, misorientation, size, shape, principal axis direction and aspect ratio are captured. This SVE model provides a computationally efficient alternative to traditional techniques using representative volume element (RVE) models while maintaining statistical accuracy. A physics based multiscale damage criterion is developed to simulate the fatigue crack initiation. The crack growth rate and probable directions are estimated simultaneously.
Mechanically sensitive materials that exhibit specific chemical reactions upon external loading are currently being investigated for self-sensing applications. The "smart" polymer modeled in this research consists of epoxy resin, hardener, and a stress-sensitive material called mechanophore The mechanophore activation is based on covalent bond-breaking induced by external stimuli; this feature can be used for material-level damage detections. In this work Tris-(Cinnamoyl oxymethyl)-Ethane (TCE) is used as the cyclobutane-based mechanophore (stress-sensitive) material in the polymer matrix. The TCE embedded polymers have shown promising results in early damage detection through mechanically induced fluorescence. A spring-bead based network model, which bridges nanoscale information to higher length scales, has been developed to model this material system. The material is partitioned into discrete mass beads which are linked using linear springs at the microscale. A series of MD simulations were performed to define the spring stiffness in the statistical network model. By integrating multiple spring-bead models a network model has been developed to represent the material properties at the mesoscale. The model captures the statistical distribution of crosslinking degree of the polymer to represent the heterogeneous material properties at the microscale. The developed multiscale methodology is computationally efficient and provides a possible means to bridge multiple length scales (from 10 nm in MD simulation to 10 mm in FE model) without significant loss of accuracy. Parametric studies have been conducted to investigate the influence of the crosslinking degree on the material behavior. The developed methodology has been used to evaluate damage evolution in the self-sensing polymer.
For metallic material (Al 2024-T351), the methodology initiates at the microscale where extensive material characterization is conducted to capture the microstructural variability. A statistical volume element (SVE) model is constructed to represent the material properties. Geometric and crystallographic features including grain orientation, misorientation, size, shape, principal axis direction and aspect ratio are captured. This SVE model provides a computationally efficient alternative to traditional techniques using representative volume element (RVE) models while maintaining statistical accuracy. A physics based multiscale damage criterion is developed to simulate the fatigue crack initiation. The crack growth rate and probable directions are estimated simultaneously.
Mechanically sensitive materials that exhibit specific chemical reactions upon external loading are currently being investigated for self-sensing applications. The "smart" polymer modeled in this research consists of epoxy resin, hardener, and a stress-sensitive material called mechanophore The mechanophore activation is based on covalent bond-breaking induced by external stimuli; this feature can be used for material-level damage detections. In this work Tris-(Cinnamoyl oxymethyl)-Ethane (TCE) is used as the cyclobutane-based mechanophore (stress-sensitive) material in the polymer matrix. The TCE embedded polymers have shown promising results in early damage detection through mechanically induced fluorescence. A spring-bead based network model, which bridges nanoscale information to higher length scales, has been developed to model this material system. The material is partitioned into discrete mass beads which are linked using linear springs at the microscale. A series of MD simulations were performed to define the spring stiffness in the statistical network model. By integrating multiple spring-bead models a network model has been developed to represent the material properties at the mesoscale. The model captures the statistical distribution of crosslinking degree of the polymer to represent the heterogeneous material properties at the microscale. The developed multiscale methodology is computationally efficient and provides a possible means to bridge multiple length scales (from 10 nm in MD simulation to 10 mm in FE model) without significant loss of accuracy. Parametric studies have been conducted to investigate the influence of the crosslinking degree on the material behavior. The developed methodology has been used to evaluate damage evolution in the self-sensing polymer.
ContributorsZhang, Jinjun (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
Identification of early damage in polymer composite materials is of significant importance so that preventative measures can be taken before the materials reach catastrophic failure. Scientists have been developing damage detection technologies over many years and recently, mechanophore-based polymers, in which mechanical energy is translated to activate a chemical transformation, have received increasing attention. More specifically, the damage can be made detectable by mechanochromic polymers, which provide a visible color change upon the scission of covalent bonds under stress. This dissertation focuses on the study of a novel self-sensing framework for identifying early and in-situ damage by employing unique stress-sensing mechanophores. Two types of mechanophores, cyclobutane and cyclooctane, were utilized, and the former formed from cinnamoyl moeities and the latter formed from anthracene upon photodimerization. The effects on the thermal and mechanical properties with the addition of the cyclobutane-based polymers into epoxy matrices were investigated. The emergence of cracks was detected by fluorescent signals at a strain level right after the yield point of the polymer blends, and the fluorescence intensified with the accumulation of strain. Similar to the mechanism of fluorescence emission from the cleavage of cyclobutane, the cyclooctane moiety generated fluorescent emission with a higher quantum yield upon cleavage. The experimental results also demonstrated the success of employing the cyclooctane type mechanophore as a potential force sensor, as the fluorescence intensification was correlated with the strain increase.
ContributorsZou, Jin (Author) / Dai, Lenore L (Thesis advisor) / Chattopadhyay, Aditi (Thesis advisor) / Lind, Mary L (Committee member) / Mu, Bin (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2014
Description
Advanced composites are being widely used in aerospace applications due to their high stiffness, strength and energy absorption capabilities. However, the assurance of structural reliability is a critical issue because a damage event will compromise the integrity of composite structures and lead to ultimate failure. In this dissertation a novel homogenization based multiscale modeling framework using semi-analytical micromechanics is presented to simulate the response of textile composites. The novelty of this approach lies in the three scale homogenization/localization framework bridging between the constituent (micro), the fiber tow scale (meso), weave scale (macro), and the global response. The multiscale framework, named Multiscale Generalized Method of Cells (MSGMC), continuously bridges between the micro to the global scale as opposed to approaches that are top-down and bottom-up. This framework is fully generalized and capable of modeling several different weave and braids without reformulation. Particular emphasis in this dissertation is placed on modeling the nonlinearity and failure of both polymer matrix and ceramic matrix composites.
ContributorsLiu, Guang (Author) / Chattopadhyay, Aditi (Thesis advisor) / Mignolet, Marc (Committee member) / Jiang, Hanqing (Committee member) / Li, Jian (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Composite materials are increasingly being used in aircraft, automobiles, and other applications due to their high strength to weight and stiffness to weight ratios. However, the presence of damage, such as delamination or matrix cracks, can significantly compromise the performance of these materials and result in premature failure. Structural components are often manually inspected to detect the presence of damage. This technique, known as schedule based maintenance, however, is expensive, time-consuming, and often limited to easily accessible structural elements. Therefore, there is an increased demand for robust and efficient Structural Health Monitoring (SHM) techniques that can be used for Condition Based Monitoring, which is the method in which structural components are inspected based upon damage metrics as opposed to flight hours. SHM relies on in situ frameworks for detecting early signs of damage in exposed and unexposed structural elements, offering not only reduced number of schedule based inspections, but also providing better useful life estimates. SHM frameworks require the development of different sensing technologies, algorithms, and procedures to detect, localize, quantify, characterize, as well as assess overall damage in aerospace structures so that strong estimations in the remaining useful life can be determined. The use of piezoelectric transducers along with guided Lamb waves is a method that has received considerable attention due to the weight, cost, and function of the systems based on these elements. The research in this thesis investigates the ability of Lamb waves to detect damage in feature dense anisotropic composite panels. Most current research negates the effects of experimental variability by performing tests on structurally simple isotropic plates that are used as a baseline and damaged specimen. However, in actual applications, variability cannot be negated, and therefore there is a need to research the effects of complex sample geometries, environmental operating conditions, and the effects of variability in material properties. This research is based on experiments conducted on a single blade-stiffened anisotropic composite panel that localizes delamination damage caused by impact. The overall goal was to utilize a correlative approach that used only the damage feature produced by the delamination as the damage index. This approach was adopted because it offered a simplistic way to determine the existence and location of damage without having to conduct a more complex wave propagation analysis or having to take into account the geometric complexities of the test specimen. Results showed that even in a complex structure, if the damage feature can be extracted and measured, then an appropriate damage index can be associated to it and the location of the damage can be inferred using a dense sensor array. The second experiment presented in this research studies the effects of temperature on damage detection when using one test specimen for a benchmark data set and another for damage data collection. This expands the previous experiment into exploring not only the effects of variable temperature, but also the effects of high experimental variability. Results from this work show that the damage feature in the data is not only extractable at higher temperatures, but that the data from one panel at one temperature can be directly compared to another panel at another temperature for baseline comparison due to linearity of the collected data.
ContributorsVizzini, Anthony James, II (Author) / Chattopadhyay, Aditi (Thesis advisor) / Fard, Masoud (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
Energy harvesting from ambient is important to configuring Wireless Sensor Networks (WSN) for environmental data collecting. In this work, highly flexible thermoelectric generators (TEGs) have been studied and fabricated to supply power to the wireless sensor notes used for data collecting in hot spring environment. The fabricated flexible TEGs can be easily deployed on the uneven surface of heated rocks at the rim of hot springs. By employing the temperature gradient between the hot rock surface and the air, these TEGs can generate power to extend the battery lifetime of the sensor notes and therefore reduce multiple batteries changes where the environment is usually harsh in hot springs. Also, they show great promise for self-powered wireless sensor notes. Traditional thermoelectric material bismuth telluride (Bi2Te3) and advanced MEMS (Microelectromechanical systems) thin film techniques were used for the fabrication. Test results show that when a flexible TEG array with an area of 3.4cm2 was placed on the hot plate surface of 80°C in the air under room temperature, it had an open circuit voltage output of 17.6mV and a short circuit current output of 0.53mA. The generated power was approximately 7mW/m2.
On the other hand, high pressure, temperatures that can reach boiling, and the pH of different hot springs ranging from <2 to >9 make hot spring ecosystem a unique environment that is difficult to study. WSN allows many scientific studies in harsh environments that are not feasible with traditional instrumentation. However, wireless pH sensing for long time in situ data collection is still challenging for two reasons. First, the existing commercial-off-the-shelf pH meters are frequent calibration dependent; second, biofouling causes significant measurement error and drift. In this work, 2-dimentional graphene pH sensors were studied and calibration free graphene pH sensor prototypes were fabricated. Test result shows the resistance of the fabricated device changes linearly with the pH values (in the range of 3-11) in the surrounding liquid environment. Field tests show graphene layer greatly prevented the microbial fouling. Therefore, graphene pH sensors are promising candidates that can be effectively used for wireless pH sensing in exploration of hot spring ecosystems.
On the other hand, high pressure, temperatures that can reach boiling, and the pH of different hot springs ranging from <2 to >9 make hot spring ecosystem a unique environment that is difficult to study. WSN allows many scientific studies in harsh environments that are not feasible with traditional instrumentation. However, wireless pH sensing for long time in situ data collection is still challenging for two reasons. First, the existing commercial-off-the-shelf pH meters are frequent calibration dependent; second, biofouling causes significant measurement error and drift. In this work, 2-dimentional graphene pH sensors were studied and calibration free graphene pH sensor prototypes were fabricated. Test result shows the resistance of the fabricated device changes linearly with the pH values (in the range of 3-11) in the surrounding liquid environment. Field tests show graphene layer greatly prevented the microbial fouling. Therefore, graphene pH sensors are promising candidates that can be effectively used for wireless pH sensing in exploration of hot spring ecosystems.
ContributorsHan, Ruirui (Author) / Yu, Hongyu (Thesis advisor) / Jiang, Hanqing (Committee member) / Yu, Hongbin (Committee member) / Garnero, Edward (Committee member) / Li, Mingming (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the maturity of advanced composites as feasible structural materials for various applications there is a critical need to solve the challenge of designing these material systems for optimal performance. However, determining superior design methods requires a deep understanding of the material-structure properties at various length scales. Due to the length-scale dependent behavior of advanced composites, multiscale modeling techniques may be used to describe the dominant mechanisms of damage and failure in these material systems. With polymer matrix fiber composites and nanocomposites it becomes essential to include even the atomic length scale, where the resin-hardener-nanofiller molecules interact, in the multiscale modeling framework. Additionally, sources of variability are also critical to be included in these models due to the important role of uncertainty in advance composite behavior. Such a methodology should be able to describe length scale dependent mechanisms in a computationally efficient manner for the analysis of practical composite structures.
In the research presented in this dissertation, a comprehensive nano to macro multiscale framework is developed for the mechanical and multifunctional analysis of advanced composite materials and structures. An atomistically informed statistical multiscale model is developed for linear problems, to estimate and scale elastic properties of carbon fiber reinforced polymer composites (CFRPs) and carbon nanotube (CNT) enhanced CFRPs using information from molecular dynamics simulation of the resin-hardener-nanofiller nanoscale system. For modeling inelastic processes, an atomistically informed coupled damage-plasticity model is developed using the framework of continuum damage mechanics, where fundamental nanoscale covalent bond disassociation information is scaled up as a continuum scale damage identifying parameter. This damage model is coupled with a nanocomposite microstructure generation algorithm to study the sub-microscale damage mechanisms in CNT/CFRP microstructures. It is further integrated in a generalized method of cells (GMC) micromechanics model to create a low-fidelity computationally efficient nonlinear multiscale method with imperfect interfaces between the fiber and matrix, where the interface behavior is adopted from nanoscale MD simulations. This algorithm is used to understand damage mechanisms in adhesively bonded composite joints as a case study for the comprehensive nano to macroscale structural analysis of practical composites structures. At each length scale sources of variability are identified, characterized, and included in the multiscale modeling framework.
In the research presented in this dissertation, a comprehensive nano to macro multiscale framework is developed for the mechanical and multifunctional analysis of advanced composite materials and structures. An atomistically informed statistical multiscale model is developed for linear problems, to estimate and scale elastic properties of carbon fiber reinforced polymer composites (CFRPs) and carbon nanotube (CNT) enhanced CFRPs using information from molecular dynamics simulation of the resin-hardener-nanofiller nanoscale system. For modeling inelastic processes, an atomistically informed coupled damage-plasticity model is developed using the framework of continuum damage mechanics, where fundamental nanoscale covalent bond disassociation information is scaled up as a continuum scale damage identifying parameter. This damage model is coupled with a nanocomposite microstructure generation algorithm to study the sub-microscale damage mechanisms in CNT/CFRP microstructures. It is further integrated in a generalized method of cells (GMC) micromechanics model to create a low-fidelity computationally efficient nonlinear multiscale method with imperfect interfaces between the fiber and matrix, where the interface behavior is adopted from nanoscale MD simulations. This algorithm is used to understand damage mechanisms in adhesively bonded composite joints as a case study for the comprehensive nano to macroscale structural analysis of practical composites structures. At each length scale sources of variability are identified, characterized, and included in the multiscale modeling framework.
ContributorsRai, Ashwin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Rajadas, John (Committee member) / Fard, Masoud Yekani (Committee member) / Arizona State University (Publisher)
Created2018
Description
Although wind turbine bearings are designed to operate 18-20 years, in the recent years premature failure among these bearings has caused this life to reduce to as low as a few months to a year. One of the leading causes of premature failure called white structure flaking is a mechanism that was first cited in literature decades ago but not much is understood about it even today. The cause of this mode of failure results from the initiation of white etched cracks (WECs). In this report, different failure mechanisms, especially premature failure mechanisms that were tested and analyzed are demonstrated as a pathway to understanding this phenomenon. Through the use of various tribometers, samples were tested in diverse and extreme conditions in order to study the effect of these different operational conditions on the specimen. Analysis of the tested samples allowed for a comparison of the microstructure alterations in the tested samples to the field bearings affected by WSF.
ContributorsSharma, Aman (Author) / Foy, Joseph (Thesis director) / Adams, James (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
A model has been developed to modify Euler-Bernoulli beam theory for wooden beams, using visible properties of wood knot-defects. Treating knots in a beam as a system of two ellipses that change the local bending stiffness has been shown to improve the fit of a theoretical beam displacement function to edge-line deflection data extracted from digital imagery of experimentally loaded beams. In addition, an Ellipse Logistic Model (ELM) has been proposed, using L1-regularized logistic regression, to predict the impact of a knot on the displacement of a beam. By classifying a knot as severely positive or negative, vs. mildly positive or negative, ELM can classify knots that lead to large changes to beam deflection, while not over-emphasizing knots that may not be a problem. Using ELM with a regression-fit Young's Modulus on three-point bending of Douglass Fir, it is possible estimate the effects a knot will have on the shape of the resulting displacement curve.
ContributorsSexton, Thurston Bryant (Author) / Takahashi, Timothy (Thesis director) / Jones, Donald (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-05