Matching Items (149)
Description
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
ContributorsShen, Wei (Author) / Mittlemann, Hans D (Thesis advisor) / Renaut, Rosemary A. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Gelb, Anne (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Aluminum alloys are commonly used for engineering applications due to their high strength to weight ratio, low weight, and low cost. Pitting corrosion, accelerated by saltwater environments, leads to fatigue cracks and stress corrosion cracking during service. Two-dimensional (2D) characterization methods are typically used to identify and characterize corrosion; however, these methods are destructive and do not enable an efficient means of quantifying mechanisms of pit initiation and growth. In this study, lab-scale x-ray microtomography was used to non-destructively observe, quantify, and understand pit growth in three dimensions over a 20-day corrosion period in the AA7075-T651 alloy. The XRT process, capable of imaging sample volumes with a resolution near one micrometer, was found to be an ideal tool for large-volume pit examination. Pit depths were quantified over time using renderings of sample volumes, leading to an understanding of how inclusion particles, oxide breakdown, and corrosion mechanisms impact the growth and morphology of pits. This process, when carried out on samples produced with two different rolling directions and rolling extents, yielded novel insights into the long-term macroscopic corrosion behaviors impacted by alloy production and design. Key among these were the determinations that the alloy’s rolling direction produces a significant difference in the average growth rate of pits and that the corrosion product layer loses its passivating effect as a result of cyclic immersion. In addition, a new mechanism of pitting corrosion is proposed which is focused on the pseudo-random spatial distribution of iron-rich inclusion particles in the alloy matrix, which produces a random distribution of pit depths based on the occurrence of co-operative corrosion near inclusion clusters.
ContributorsSinclair, Daniel Ritchie (Author) / Chawla, Nikhilesh (Thesis director) / Jiao, Yang (Committee member) / Bale, Hrishikesh (Committee member) / School of International Letters and Cultures (Contributor) / Materials Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Solid oxide fuel cells have become a promising candidate in the development of high-density clean energy sources for the rapidly increasing demands in energy and global sustainability. In order to understand more about solid oxide fuel cells, the important step is to understand how to model heterogeneous materials. Heterogeneous materials are abundant in nature and also created in various processes. The diverse properties exhibited by these materials result from their complex microstructures, which also make it hard to model the material. Microstructure modeling and reconstruction on a meso-scale level is needed in order to produce heterogeneous models without having to shave and image every slice of the physical material, which is a destructive and irreversible process. Yeong and Torquato [1] introduced a stochastic optimization technique that enables the generation of a model of the material with the use of correlation functions. Spatial correlation functions of each of the various phases within the heterogeneous structure are collected from a two-dimensional micrograph representing a slice of a solid oxide fuel cell through computational means. The assumption is that two-dimensional images contain key structural information representative of the associated full three-dimensional microstructure. The collected spatial correlation functions, a combination of one-point and two-point correlation functions are then outputted and are representative of the material. In the reconstruction process, the characteristic two-point correlation functions is then inputted through a series of computational modeling codes and software to generate a three-dimensional visual model that is statistically similar to that of the original two-dimensional micrograph. Furthermore, parameters of temperature cooling stages and number of pixel exchanges per temperature stage are utilized and altered accordingly to observe which parameters has a higher impact on the reconstruction results. Stochastic optimization techniques to produce three-dimensional visual models from two-dimensional micrographs are therefore a statistically reliable method to understanding heterogeneous materials.
ContributorsPhan, Richard Dylan (Author) / Jiao, Yang (Thesis director) / Ren, Yi (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Widespread knowledge of fracture mechanics is mostly based on previous models that generalize crack growth in materials over several loading cycles. The objective of this project is to characterize crack growth that occurs in titanium alloys, specifically Grade 5 Ti-6Al-4V, at the sub-cycle scale, or within a single loading cycle. Using scanning electron microscopy (SEM), imaging analysis is performed to observe crack behavior at ten loading steps throughout the loading and unloading paths. Analysis involves measuring the incremental crack growth and crack tip opening displacement (CTOD) of specimens at loading ratios of 0.1, 0.3, and 0.5. This report defines the relationship between crack growth and the stress intensity factor, K, of the specimens, as well as the relationship between the R-ratio and stress opening level. The crack closure phenomena and effect of microcracks are discussed as they influence the crack growth behavior. This method has previously been used to characterize crack growth in Al 7075-T6. The results for Ti-6Al-4V are compared to these previous findings in order to strengthen conclusions about crack growth behavior.
ContributorsNazareno, Alyssa Noelle (Author) / Liu, Yongming (Thesis director) / Jiao, Yang (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Electromigration, the net atomic diffusion associated with the momentum transfer from electrons moving through a material, is a major cause of device and component failure in microelectronics. The deleterious effects from electromigration rise with increased current density, a parameter that will only continue to increase as our electronic devices get smaller and more compact. Understanding the dynamic diffusional pathways and mechanisms of these electromigration-induced and propagated defects can further our attempts at mitigating these failure modes. This dissertation provides insight into the relationships between these defects and parameters of electric field strength, grain boundary misorientation, grain size, void size, eigenstrain, varied atomic mobilities, and microstructure.First, an existing phase-field model was modified to investigate the various defect modes associated with electromigration in an equiaxed non-columnar microstructure. Of specific interest was the effect of grain boundary misalignment with respect to current flow and the mechanisms responsible for the changes in defect kinetics. Grain size, magnitude of externally applied electric field, and the utilization of locally distinct atomic mobilities were other parameters investigated. Networks of randomly distributed grains, a common microstructure of interconnects, were simulated in both 2- and 3-dimensions displaying the effects of 3-D capillarity on diffusional dynamics.
Also, a numerical model was developed to study the effect of electromigration on void migration and coalescence. Void migration rates were found to be slowed from compressive forces and the nature of the deformation concurrent with migration was examined through the lens of chemical potential. Void migration was also validated with previously reported theoretical explanations. Void coalescence and void budding were investigated and found to be dependent on the magnitude of interfacial energy and electric field strength.
A grasp on the mechanistic pathways of electromigration-induced defect evolution is imperative to the development of reliable electronics, especially as electronic devices continue to miniaturize. This dissertation displays a working understanding of the mechanistic pathways interconnects can fail due to electromigration, as well as provide direction for future research and understanding.
ContributorsFarmer, William McHann (Author) / Ankit, Kumar (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jiao, Yang (Committee member) / McCue, Ian (Committee member) / Arizona State University (Publisher)
Created2022
Description本文对中国制药企业并购溢价影响因素进行了研究，提出了对制药企业并购非常重要的两个新的影响因素：可生产药品批文和在研新药批文。本文以2011年1月—2019年12月间我国制药行业上市公司并购事件为样本，对在研新药和可生产药品批文的价值从四个维度度量：是否有在研新药和可生产药品批文；在研新药数量及可生产药品批文数量；根据创新药和仿制药两个类别进行细分；标的企业所拥有的在研新药和可生产药品批文的市场价值。论文发现药品批文对企业并购溢价的影响不是很显著。进一步的，本文探究了药品批文对主并企业的对被并购公司的估值的影响。实证结果表明，我国制药企业在并购估值时确实会考虑到在研新药和可生产药品批文的价值。本文还发现对于可生产药品来说，相对创新药，被并购公司持有的仿制药批文影响更显著。而对于在研新药来说，主并企业更看重在研的创新药，在研仿制药对并购估值的影响不大。最后，本文选取了两个代表性案例进一步分析和探讨药品批文对企业并购的影响。
ContributorsYe, Tao (Author) / Shen, Wei (Thesis advisor) / Chang, Chun (Thesis advisor) / Jiang, Zhan (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Disordered many-body systems are ubiquitous in condensed matter physics, materials science and biological systems. Examples include amorphous and glassy states of matter, granular materials, and tissues composed of packings of cells in the extra-cellular matrix (ECM). Understanding the collective emergent properties in these systems is crucial to improving the capability for controlling, engineering and optimizing their behaviors, yet it is extremely challenging due to their complexity and disordered nature. The main theme of the thesis is to address this challenge by characterizing and understanding a variety of disordered many-body systems via unique statistical geometrical and topological tools and the state-of-the-art simulation methods. Two major topics of the thesis are modeling ECM-mediated multicellular dynamics and understanding hyperuniformity in 2D material systems. Collective migration is an important mode of cell movement for several biological processes, and it has been the focus of a large number of studies over the past decades. Hyperuniform (HU) state is a critical state in a many-particle system, an exotic property of condensed matter discovered recently. The main focus of this thesis is to study the mechanisms underlying collective cell migration behaviors by developing theoretical/phenomenological models that capture the features of ECM-mediated mechanical communications in vitro and investigate general conditions that can be imposed on hyperuniformity-preserving and hyperuniformity-generating operations, as well as to understand how various novel transport physical properties arise from the unique hyperuniform long-range correlations.
ContributorsZheng, Yu (Author) / Jiao, Yang (Thesis advisor) / Zhuang, Houlong (Committee member) / Beckstein, Oliver (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2022
Description汽车行业属于国家支柱型产业，创造了高额的产值，增加了就业岗位。随着汽车生产行业竞争日趋激烈的趋势影响，汽车经销商在未来会出现明显的分化，并且逐步向头部集中。基于这样的行业背景，本项研究开展汽车经销商整体经营和盈利能力等方面的详细深入分析，即系统整合汽车经销商业务运营层面和财务层面数据，结合统计研究方法，对经销商盈利能力进行系统且详实归因分析，从而试别驱动盈利能力的关键业务要素。其研究成果能够完善对行业发展规律和经营模式系统性理解，从而进一步指导该领域的相关业务实践，提高经销商整体经营业绩。本课题通过四个阶段来开展经销商整体经营与盈利归因的相关研究。首先，本课题梳理了中国汽车消费行业发展的历史，同时阐述样本期内（2018-2020年）国内宏观经济和汽车消费市场的特征进行，并介绍X品牌汽车经销商的地理分布、资质和业绩评级体系、自身经营特征以及汽车生产商对经销商扶持政策等方面。在第二阶段，本课题聚焦研究假设、模型与方法，通过对X品牌汽车经销商的业务结构和运营管理开展分析，并逐步识别影响经销商盈利的关键指标变量，并提出研究假设和相关模型（即时间序列模型和面板回归模型）。在第三阶段，本课题首先开展经销商相关信息整体性统计分析，获得关键业务指标在样本期内动态特征，并结合时间序列回归模型探讨各项业务指标对经销商整体盈利能力的影响程度。在第四阶段，本课题采用（个体）固定效应的面板回归模型来研究不同组别（控制）条件下经销商盈利能力的影响因素以及其盈利能力对这些因素的敏感程度，从而更深入和全面地揭示影响经销商盈利能力的潜在因素。
基于上述四阶段的研究结果，本研究进一步就提升经销商盈利能力展开讨论，并提出相应对策。本课题相关结论仅从X品牌汽车经销商经营和财务数据进行定性和定量分析获得，但衷心希望本研究的成果能够对汽车经销商改善经营业务方面能起到实践上的借鉴和指导意义。
ContributorsPan, Guangxiong (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Zhu, Qigui (Committee member) / Arizona State University (Publisher)
Created2022
Description
The increasing availability of data and advances in computation have spurred the development of data-driven approaches for modeling complex dynamical systems. These approaches are based on the idea that the underlying structure of a complex system can be discovered from data using mathematical and computational techniques. They also show promise for addressing the challenges of modeling high-dimensional, nonlinear systems with limited data. In this research expository, the state of the art in data-driven approaches for modeling complex dynamical systems is surveyed in a systemic way. First the general formulation of data-driven modeling of dynamical systems is discussed. Then several representative methods in feature engineering and system identification/prediction are reviewed, including recent advances and key challenges.
ContributorsShi, Wenlong (Author) / Ren, Yi (Thesis advisor) / Hong, Qijun (Committee member) / Jiao, Yang (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
Applications such as heat exchangers, surface-based cellular structures, rotating blades, and waveguides rely on thin metal walls as crucial constituent elements of the structure. The design freedom enabled by laser powder bed fusion has led to an interest in exploiting this technology to further the performance of these components, many of which retain their as-built surface morphologies on account of their design complexity. However, there is limited understanding of how and why mechanical properties vary by wall thickness for specimens that are additively manufactured and maintain an as-printed surface finish. Critically, the contributions of microstructure and morphology to the mechanical behavior of thin wall laser powder bed fusion structures have yet to be systematically identified and decoupled. This work focuses on elucidating the room temperature quasi-static tensile and high cycle fatigue properties of as-printed, thin-wall Inconel 718 fabricated using laser powder bed fusion, with the aim of addressing this critical gap in the literature. Wall thicknesses studied range from 0.3 - 2.0 mm, and the effects of Hot Isostatic Pressing are also examined, with sheet metal specimens used as a baseline for comparison. Statistical analyses are conducted to identify the significance of the dependence of properties on wall thickness and Hot Isostatic Pressing, as well as to examine correlations of these properties to section area, porosity, and surface roughness. A thorough microstructural study is complemented with a first-of-its-kind study of surface morphology to decouple their contributions and identify underlying causes for observed changes in mechanical properties. This thesis finds that mechanical properties in the quasi-static and fatigue framework do not see appreciable declines until specimen thickness is under 0.75 mm in thickness. The added Hot Isostatic Pressing heat treatment effectively closed pores, recrystallized the grain structure, and provided a more homogenous microstructure that benefits the modulus, tensile strength, elongation, and fatigue performance at higher stresses. Stress heterogeneities, primarily caused by surface defects, negatively affected the thinner specimens disproportionately. Without the use of the Hot Isostatic Pressing, the grain structure remained much more refined and benefitted the yield strength and fatigue endurance limit.
ContributorsParadise, Paul David (Author) / Bhate, Dhruv (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Azeredo, Bruno (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2022