ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 147
Description
Concrete columns constitute the fundamental supports of buildings, bridges, and various other infrastructures, and their failure could lead to the collapse of the entire structure. As such, great effort goes into improving the fire resistance of such columns. In a time sensitive fire situation, a delay in the failure of critical load bearing structures can lead to an increase in time allowed for the evacuation of occupants, recovery of property, and access to the fire. Much work has been done in improving the structural performance of concrete including reducing column sizes and providing a safer structure. As a result, high-strength (HS) concrete has been developed to fulfill the needs of such improvements. HS concrete varies from normal-strength (NS) concrete in that it has a higher stiffness, lower permeability and larger durability. This, unfortunately, has resulted in poor performance under fire. The lower permeability allows for water vapor to build up causing HS concrete to suffer from explosive spalling under rapid heating. In addition, the coefficient of thermal expansion (CTE) of HS concrete is lower than that of NS concrete. In this study, the effects of introducing a region of crumb rubber concrete into a steel-reinforced concrete column were analyzed. The inclusion of crumb rubber concrete into a column will greatly increase the thermal resistivity of the overall column, leading to a reduction in core temperature as well as the rate at which the column is heated. Different cases were analyzed while varying the positioning of the crumb-rubber region to characterize the effect of position on the improvement of fire resistance. Computer simulated finite element analysis was used to calculate the temperature and strain distribution with time across the column's cross-sectional area with specific interest in the steel - concrete region. Of the several cases which were investigated, it was found that the improvement of time before failure ranged between 32 to 45 minutes.
ContributorsZiadeh, Bassam Mohammed (Author) / Phelan, Patrick (Thesis advisor) / Kaloush, Kamil (Thesis advisor) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2011
Description
Pb-free solders are used as interconnects in various levels of micro-electronic packaging. Reliability of these interconnects is very critical for the performance of the package. One of the main factors affecting the reliability of solder joints is the presence of porosity which is introduced during processing of the joints. In this thesis, the effect of such porosity on the deformation behavior and eventual failure of the joints is studied using Finite Element (FE) modeling technique. A 3D model obtained by reconstruction of x-ray tomographic image data is used as input for FE analysis to simulate shear deformation and eventual failure of the joint using ductile damage model. The modeling was done in ABAQUS (v 6.10). The FE model predictions are validated with experimental results by comparing the deformation of the pores and the crack path as predicted by the model with the experimentally observed deformation and failure pattern. To understand the influence of size, shape, and distribution of pores on the mechanical behavior of the joint four different solder joints with varying degrees of porosity are modeled using the validated FE model. The validation technique mentioned above enables comparison of the simulated and actual deformation only. A more robust way of validating the FE model would be to compare the strain distribution in the joint as predicted by the model and as observed experimentally. In this study, to enable visualization of the experimental strain for the 3D microstructure obtained from tomography, a three dimensional digital image correlation (3D DIC) code has been implemented in MATLAB (MathWorks Inc). This developed 3D DIC code can be used as another tool to verify the numerical model predictions. The capability of the developed code in measuring local displacement and strain is demonstrated by considering a test case.
ContributorsJakkali, Vaidehi (Author) / Chawla, Nikhilesh K (Thesis advisor) / Jiang, Hanqing (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2011
Description
Increasing demand for high strength powder metallurgy (PM) steels has resulted in the development of dual phase PM steels. In this work, the effects of thermal aging on the microstructure and mechanical behavior of dual phase precipitation hardened powder metallurgy (PM) stainless steels of varying ferrite-martensite content were examined. Quantitative analyses of the inherent porosity and phase fractions were conducted on the steels and no significant differences were noted with respect to aging temperature. Tensile strength, yield strength, and elongation to fracture all increased with increasing aging temperature reaching maxima at 538oC in most cases. Increased strength and decreased ductility were observed in steels of higher martensite content. Nanoindentation of the individual microconstituents was employed to obtain a fundamental understanding of the strengthening contributions. Both the ferrite and martensite hardness values increased with aging temperature and exhibited similar maxima to the bulk tensile properties. Due to the complex non-uniform stresses and strains associated with conventional nanoindentation, micropillar compression has become an attractive method to probe local mechanical behavior while limiting strain gradients and contributions from surrounding features. In this study, micropillars of ferrite and martensite were fabricated by focused ion beam (FIB) milling of dual phase precipitation hardened powder metallurgy (PM) stainless steels. Compression testing was conducted using a nanoindenter equipped with a flat punch indenter. The stress-strain curves of the individual microconstituents were calculated from the load-displacement curves less the extraneous displacements of the system. Using a rule of mixtures approach in conjunction with porosity corrections, the mechanical properties of ferrite and martensite were combined for comparison to tensile tests of the bulk material, and reasonable agreement was found for the ultimate tensile strength. Micropillar compression experiments of both as sintered and thermally aged material allowed for investigation of the effect of thermal aging.
ContributorsStewart, Jennifer (Author) / Chawla, Nikhilesh (Thesis advisor) / Jiang, Hanqing (Committee member) / Krause, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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
This thesis investigated two different thermal flow sensors for intravascular shear stress analysis. They were based on heat transfer principle, which heat convection from the resistively heated element to the flowing fluid was measured as a function of the changes in voltage. For both sensors, the resistively heated elements were made of Ti/Pt strips with the thickness 0.12 µm and 0.02 µm. The resistance of the sensing element was measured at approximately 1.6-1.7 kohms;. A linear relation between the resistance and temperature was established over the temperature ranging from 22 degree Celsius to 80 degree Celsius and the temperature coefficient of resistance (TCR) was at approximately 0.12 %/degree Celsius. The first thermal flow sensor was one-dimensional (1-D) flexible shear stress sensor. The structure was sensing element sandwiched by a biocompatible polymer "poly-para-xylylene", also known as Parylene, which provided both insulation of electrodes and flexibility of the sensors. A constant-temperature (CT) circuit was designed as the read out circuit based on 0.6 µm CMOS (Complementary metal-oxide-semiconductor) process. The 1-D shear stress sensor suffered from a large measurement error. Because when the sensor was inserted into blood vessels, it was impossible to mount the sensor to the wall as calibrated in micro fluidic channels. According to the previous simulation work, the shear stress was varying and the sensor itself changed the shear stress distribution. We proposed a three-dimensional (3-D) thermal flow sensor, with three-axis of sensing elements integrated in one sensor. It was in the similar shape as a hexagonal prism with diagonal of 1000 µm. On the top of the sensor, there were five bond pads for external wires over 500 µm thick silicon substrate. In each nonadjacent side surface, there was a bended parylene branch with one sensing element. Based on the unique 3-D structure, the sensor was able to obtain data along three axes. With computational fluid dynamics (CFD) model, it is possible to locate the sensor in the blood vessels and give us a better understanding of shear stress distribution in the presence of time-varying component of blood flow and realize more accurate assessment of intravascular convective heat transfer.
ContributorsTang, Rui (Author) / Yu, Hongyu (Thesis advisor) / Jiang, Hanqing (Committee member) / Pan, George (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis focuses on the continued extension, validation, and application of combined thermal-structural reduced order models for nonlinear geometric problems. The first part of the thesis focuses on the determination of the temperature distribution and structural response induced by an oscillating flux on the top surface of a flat panel. This flux is introduced here as a simplified representation of the thermal effects of an oscillating shock on a panel of a supersonic/hypersonic vehicle. Accordingly, a random acoustic excitation is also considered to act on the panel and the level of the thermo-acoustic excitation is assumed to be large enough to induce a nonlinear geometric response of the panel. Both temperature distribution and structural response are determined using recently proposed reduced order models and a complete one way, thermal-structural, coupling is enforced. A steady-state analysis of the thermal problem is first carried out that is then utilized in the structural reduced order model governing equations with and without the acoustic excitation. A detailed validation of the reduced order models is carried out by comparison with a few full finite element (Nastran) computations. The computational expedience of the reduced order models allows a detailed parametric study of the response as a function of the frequency of the oscillating flux. The nature of the corresponding structural ROM equations is seen to be of a Mathieu-type with Duffing nonlinearity (originating from the nonlinear geometric effects) with external harmonic excitation (associated with the thermal moments terms on the panel). A dominant resonance is observed and explained. The second part of the thesis is focused on extending the formulation of the combined thermal-structural reduced order modeling method to include temperature dependent structural properties, more specifically of the elasticity tensor and the coefficient of thermal expansion. These properties were assumed to vary linearly with local temperature and it was found that the linear stiffness coefficients and the "thermal moment" terms then are cubic functions of the temperature generalized coordinates while the quadratic and cubic stiffness coefficients were only linear functions of these coordinates. A first validation of this reduced order modeling strategy was successfully carried out.
ContributorsMatney, Andrew (Author) / Mignolet, Marc (Thesis advisor) / Jiang, Hanqing (Committee member) / Spottswood, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Damage assessment and residual useful life estimation (RULE) are essential for aerospace, civil and naval structures. Structural Health Monitoring (SHM) attempts to automate the process of damage detection and identification. Multiscale modeling is a key element in SHM. It not only provides important information on the physics of failure, such as damage initiation and growth, the output can be used as "virtual sensing" data for detection and prognosis. The current research is part of an ongoing multidisciplinary effort to develop an integrated SHM framework for metallic aerospace components. In this thesis a multiscale model has been developed by bridging the relevant length scales, micro, meso and macro (or structural scale). Micro structural representations obtained from material characterization studies are used to define the length scales and to capture the size and orientation of the grains at the micro level. Parametric studies are conducted to estimate material parameters used in this constitutive model. Numerical and experimental simulations are performed to investigate the effects of Representative Volume Element (RVE) size, defect area fraction and distribution. A multiscale damage criterion accounting for crystal orientation effect is developed. This criterion is applied for fatigue crack initial stage prediction. A damage evolution rule based on strain energy density is modified to incorporate crystal plasticity at the microscale (local). Optimization approaches are used to calculate global damage index which is used for the RVE failure prediciton. Potential cracking directions are provided from the damage criterion simultaneously. A wave propagation model is incorporated with the damage model to detect changes in sensing signals due to plastic deformation and damage growth.
ContributorsLuo, Chuntao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Dai, Lenore (Committee member) / Li, Jian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
Nuclear magnetic resonance (NMR) is an important phenomenon involving nuclear magnetic moments in magnetic field, which can provide much information about a wide range of materials, including their chemical composition, chemical environments and nuclear spin interactions. The NMR spectrometer has been extensively developed and used in many areas of research. In this thesis, studies in two different areas using NMR are presented. First, a new kind of nanoparticle, Gd(DTPA) intercalated layered double hydroxide (LDH), has been successfully synthesized in the laboratory of Prof. Dey in SEMTE at ASU. In Chapter II, the NMR relaxation studies of two types of LDH (Mg, Al-LDH and Zn, Al-LDH) are presented and the results show that when they are intercalated with Gd(DTPA) they have a higher relaxivity than current commercial magnetic resonance imaging (MRI) contrast agents, such as DTPA in water solution. So this material may be useful as an MRI contrast agent. Several conditions were examined, such as nanoparticle size, pH and intercalation percentage, to determine the optimal relaxivity of this nanoparticle. Further NMR studies and simulations were conducted to provide an explanation for the high relaxivity. Second, fly ash is a kind of cementitious material, which has been of great interest because, when activated by an alkaline solution, it exhibits the capability for replacing ordinary Portland cement as a concrete binder. However, the reaction of activated fly ash is not fully understood. In chapter III, pore structure and NMR studies of activated fly ash using different activators, including NaOH and KOH (4M and 8M) and Na/K silicate, are presented. The pore structure, degree of order and proportion of different components in the reaction product were obtained, which reveal much about the reaction and makeup of the final product.
ContributorsPeng, Zihui (Author) / Marzke, Robert F (Thesis advisor) / Dey, Sandwip Kumar (Committee member) / Neithalath, Narayanan (Committee member) / Chamberlin, Ralph Vary (Committee member) / Mccartney, Martha Rogers (Committee member) / Arizona State University (Publisher)
Created2013