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- Creators: Barrett, The Honors College
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
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
This paper explores how changing the color of chocolate can affect its perceived taste. While color psychology and its effects on food industry marketing are widely studied, this experiment focuses on blue, red, green, and purple striped chocolates. The study conducted for this paper focuses on these four colors based on their utilization in previously conducted experiments. Each color of chocolate involved 25 participants, for a total of 100 total individuals, who each taste tested one piece and immediately filled out a survey. The survey asked demographic questions, colored chocolate preferences, and questions ranking the chocolate's appeal. While the outcome showed that blue, green, red, and purple was indeed the order of appealing colors, the study results indicate the participants' color preferences did not affect their perceived taste of the chocolate they sampled. Rather, their preference was based on experiences they associated with the color of the chocolate they tasted.
ContributorsChan, Sydney (Author) / Gray, Nancy (Thesis director) / Giard, Jacques (Committee member) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The purpose of this project is to raise awareness for children with social anxiety. As a book directed to children around the age of 12, it will give them a character they can relate to, so they can feel less alone. Throughout the story, the main character experiences symptoms of social anxiety and is subject to events that exacerbate those symptoms. Despite her challenges, the main character is able to effectively cope with her social anxiety through her own hard work, and help from her family members, teachers, and peers. The intent is to show children with social anxiety that, contrary to what their disorder makes them feel, they are special and have the capacity to develop skills that are relevant to their talents and interests, and overcome their fears. They should know that parents, teachers, and peers will be there to help and support them and will not judge them as harshly as they suspect. The supporting characters in this story show how a strong support base can influence the success of children with social anxiety. By the end of the story, the main character still has social anxiety, but has gained confidence and her symptoms are less severe. This illustrates that, although social anxiety cannot simply be overcome—that is, it doesn’t go away completely—it can be effectively managed with assistance from close others, and perseverance.
ContributorsDillard, Bethlehem (Author) / Lewis, Stephen (Thesis director) / Gaffney, Cynthia (Committee member) / School of Social and Behavioral Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The current study investigated whether intermittent restraint stress (IRS) would impair fear extinction learning and lead to increased anxiety and depressive- like behaviors and then be attenuated when IRS ends and a post- stress rest period ensues for 6 weeks. Young adult, male Sprague Dawley rats underwent restraint stress using wire mesh (6hr/daily) for five days with two days off before restraint resumed for three weeks for a total of 23 restraint days. The groups consisted of control (CON) with no restraint other than food and water restriction yoked to the restrained groups, stress immediate (STR-IMM), which were restrained then fear conditioned soon after the end of the IRS paradigm, and stress given a rest for 6 weeks before fear conditioning commenced (STR-R6). Rats were fear conditioned by pairing a 20 second tone with a footshock, then given extinction training for two days (15 tone only on each day). On the first day of extinction, all groups discriminated well on the first trial, but then as trials progressed, STR-R6 discriminated between tone and context less than did CON. On the second day of extinction, STR- IMM froze more to context in the earlier trials than compared to STR-R6 and CON. As trials progressed STR-IMM and STR-R6 froze more to context than compared to CON. Together, CON discriminated between tone and context better than did STR-IMM and STR-R6. Sucrose preference, novelty suppressed feeding, and elevated plus maze was performed after fear extinction was completed. No statistical differences were observed among groups for sucrose preference or novelty suppressed feeding. For the elevated plus maze, STR-IMM entered the open arms and the sum of both open and closed arms fewer than did STR- R6 and CON. We interpret the findings to suggest that the stress groups displayed increased hypervigilance and anxiety with STR-R6 exhibiting a unique phenotype than that of STR-IMM and CON.
ContributorsShah, Vrishti Bimal (Author) / Conrad, Cheryl (Thesis director) / Newbern, Jason (Committee member) / Judd, Jessica (Committee member) / School of Life Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The purpose of this study was to examine whether positive affirmations can lower depressive symptoms amongst male and female Arizona State University (ASU) honors students. Male and female ASU honors students (20-22 years of age; N=40) were recruited from Barrett, the Honors College, through email and online newsletters. Students who had been previously or were at the time diagnosed with clinical depression were not permitted to participate in the study. Only 9 female and 14 males completed the entire study. Participants completed a pre- and post- test that each consisting of reading aloud questions and their answers from the Beck's Depression Inventory (BDI) while being video and audio recorded. Participants were given a list of 20 affirmations after the pre-test and were instructed to choose and read to themselves a new affirmation three times a day, 3 times a week for a total of 6 weeks. There was an average increase among all participants' BDI scores, but no significance was found in the improvement. Emotional responses were captured using the facial recognition software, Noldus FaceReader, and was used to observe whether there was emotional dissonance in the BDI answers. The correlation between the emotion "sad" and the answer chosen was found by using Pearson's r for each participants. There were only 2 total interviews that indicated a strong positive correlation and 1 interview that indicated strong negative correlation. All others were either moderate or minimal correlation, showing that the majority of participants' emotions may have not affected their answer choices. Results indicated there is no significant improvement when using affirmations to improve depressive symptoms and mood.
ContributorsChan, Angie (Co-author) / Duran, Jose (Co-author) / Chisum, Jack (Thesis director) / Hrncir, Micki (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05
Description
There is a disconnect between the way people are taught to find success and happiness, and the results observed. Society teaches us that success will lead to happiness. Instead, it is argued that success is engrained in happiness. Case studies of four, established, successful people: Jack Ma, Elon Musk, Ricardo Semler, and William Gore, have been conducted in order to observe an apparent pattern. This data, coupled with the data from Michael Boehringer's story, is used to formulate a solution to the proposed problem. Each case study is designed to observe characteristics of the individuals that allow them to be successful and exhibit traits of happiness. Happiness will be analyzed in terms of passion and desire to perform consistently. Someone who does what they love, paired with the ability to perform on a regular basis, is considered to be a happy person. The data indicates that there is an observable pattern within the results. From this pattern, certain traits have been highlighted and used to formulate guidelines that will aid someone falling short of success and happiness in their lives. The results indicate that there are simple questions that can guide people to a happier life. Three basic questions are defined: is it something you love, can you see yourself doing this every day and does it add value? If someone can answer yes to all three requirements, the person will be able to find happiness, with success following. These guidelines can be taken and applied to those struggling with unhappiness and failure. By creating such a formula, the youth can be taught a new way of thinking that will help to eliminate these issues, that many people are facing.
ContributorsBoehringer, Michael Alexander (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Department of Management (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05