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- Genre: Doctoral Dissertation
- Creators: Liu, Yongming
Description
Ultrasound has become one of the most popular non-destructive characterization tools for soft materials. Compared to conventional ultrasound imaging, quantitative ultrasound has the potential of analyzing detailed microstructural variation through spectral analysis. Because of having a better axial and lateral resolution, and high attenuation coefficient, quantitative high-frequency ultrasound analysis (HFUA) is a very effective tool for small-scale penetration depth application. One of the QUS parameters, peak density had recently shown a promising response with the variation in the soft material microstructure. Acoustic scattering is arguably the most important factor behind different parametric responses in ultrasound spectra. Therefore, to evaluate peak density, acoustic scattering at different frequency levels was investigated. Analytical, computational, and experimental analysis was conducted to observe both single and multiple scattering in different microstructural setups. It was observed that peak density was an effective tool to express different levels of acoustic scattering that occurred through microstructural variation. The feasibility of the peak density parameter was further evaluated in ultrasound C-scan imaging. The study was also extended to detect the relative position of the imaged structure in the direction of wave propagation. For this purpose, a derivative parameter of peak density named mean peak to valley distance (MPVD) was developed to address the limitations of peak density. The study was then focused on detecting soft tissue malignancy. The histology-based computational study of HFUA was conducted to detect various breast tumor (soft tissue) grades. It was observed that both peak density and MPVD parameters could identify tumor grades at a certain level. Finally, the study was focused on evaluating the feasibility of ultrasound parameters to detect asymptotic breast carcinoma i.e., ductal carcinoma in situ (DCIS) in the surgical margin of the breast tumor. In that computational study, breast pathologies were modeled by including all the phases of DCIS. From the similar analysis mentioned above, it was understood that both peak density and MPVD parameters could detect various breast pathologies like ductal hyperplasia, DCIS, and calcification during intraoperative margin analysis. Furthermore, the spectral features of the frequency spectrums from various pathologies also provided significant information to identify them conclusively.
ContributorsPaul, Koushik (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Holloway, Julianne (Committee member) / Li, Xiangjia (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2022
Description
IOsteosarcoma is the most common bone cancer and typically affects patients in the second decade of life. Current treatment methods have not proven effective for treating reoccurring or metastatic osteosarcoma (mOS) given the 5-year survival rate of 15-30%. Previous work showed that using the immune system to fight the cancer significantly improved survival of mOS in mice, but approximately 40-50% of treated mice still succumbed to disease. To further improve immunotherapy, I analyzed immune cells in the tumor bed and observed high numbers of a rare T cell subtype: CD4hiCD8αhi, or double positive (DP), T cells. While previous literature found mature DP T cells in chronic diseases, the associations and functions of this rare T cell subtype varied between studies and were unknown for mOS. Controlling for age, chronicity of disease, and environmental exposure, I found DP T cells composed a higher percentage of T cells in the cancer as tumor burden increased. I then tested whether the DP cells were pro- or anti-tumor. I found that DP cells produced the cytokines IFNγ and IL-2 when exhaustion was overcome. They also expressed FasL for cytotoxic function, although the target is unknown. These findings suggest DP T cells have multifunctionality, which could be advantageous when responding to high antigen load.
II
Course-based undergraduate research experiences (CUREs) offer students opportunities to engage in critical thinking and problem solving. However, quantitating the impact that incorporating research into undergraduate courses has on student learning has been difficult since most CUREs lack a comparable traditional course as a control. Because the overall class structure remained unaltered when our upper division immunology course transitioned to a CURE class, we realized retrospectively that we were in a unique position to quantitate the impact of incorporating research on student performance. I then analyzed the summative assessments used to assess student learning and found that students in the CURE format class performed significantly better on quizzes, exams, and reports. There were no significant differences in academic levels, degree programs, or grade point averages, suggesting improved performance was due to increased engagement of students in research.
ContributorsAppel, Nicole (Author) / Blattman, Joseph (Thesis advisor) / Anderson, Karen (Committee member) / Lake, Douglas (Committee member) / Hingorani, Pooja (Committee member) / Arizona State University (Publisher)
Created2022
Description
Originally conceived as a way to scaffold molecules of interest into three-dimensional (3D) crystalline lattices for X ray crystallography, the field of deoxyribonucleic acid (DNA) nanotechnology has dramatically evolved since its inception. The unique properties of DNA nanostructures have promoted their use not only for X ray crystallography, but for a suite of biomedical applications as well. The work presented in this dissertation focuses on both of these exciting applications in the field: 1) Nucleic acid nanostructures as multifunctional drug and vaccine delivery platforms, and 2) 3D DNA crystals for structure elucidation of scaffolded guest molecules.Chapter 1 illustrates how a wide variety of DNA nanostructures have been developed for the delivery of drugs and vaccine components. However, their applications are limited under physiological conditions due to their lack of stability in low salt environments, susceptibility to enzymatic degradation, and tendency for endosomal entrapment. To address these issues, Chapter 2 describes a PEGylated peptide coating molecule was designed to electrostatically adhere to and protect DNA origami nanostructures and to facilitate their cytosolic delivery by peptide-mediated endosomal escape. The development of this molecule will aid in the use of nucleic acid nanostructures for biomedical purposes, such as the delivery of messenger ribonucleic acid (mRNA) vaccine constructs. To this end, Chapter 3 discusses the fabrication of a structured mRNA nanoparticle for more cost-efficient mRNA vaccine manufacture and proposes a multi-epitope mRNA nanostructure vaccine design for targeting human papillomavirus (HPV) type 16-induced head and neck cancers.
DNA nanotechnology was originally envisioned to serve as three-dimensional scaffolds capable of positioning proteins in a rigid array for their structure elucidation by X ray crystallography. Accordingly, Chapter 4 explores design parameters, such as sequence and Holliday junction isomeric forms, for efficient crystallization of 3D DNA lattices. Furthermore, previously published DNA crystal motifs are used to site-specifically position and structurally evaluate minor groove binding molecules with defined occupancies. The results of this study provide significant advancement towards the ultimate goal of the field.
ContributorsHenry, Skylar J.W. (Author) / Stephanopoulos, Nicholas (Thesis advisor) / Anderson, Karen (Thesis advisor) / Blattman, Joseph (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2023
Description
Tire blowout often occurs during driving, which can suddenly disturb vehicle motions and seriously threaten road safety. Currently, there is still a lack of effective methods to mitigate tire blowout risks in everyday traffic, even for automated vehicles. To fundamentally study and systematically resolve the tire blowout issue for automated vehicles, a collaborative project between General Motors (GM) and Arizona State University (ASU) has been conducted since 2018. In this dissertation, three main contributions of this project will be presented. First, to explore vehicle dynamics with tire blowout impacts and establish an effective simulation platform for close-loop control performance evaluation, high-fidelity tire blowout models are thoroughly developed by explicitly considering important vehicle parameters and variables. Second, since human cooperation is required to control Level 2/3 partially automated vehicles (PAVs), novel shared steering control schemes are specifically proposed for tire blowout to ensure safe vehicle stabilization via cooperative driving. Third, for Level 4/5 highly automated vehicles (HAVs) without human control, the development of control-oriented vehicle models, controllability study, and automatic control designs are performed based on impulsive differential systems (IDS) theories.
Co-simulations Matlab/Simulink® and CarSim® are conducted to validate performances of all models and control designs proposed in this dissertation. Moreover, a scaled test vehicle at ASU and a full-size test vehicle at GM are well instrumented for data collection and control implementation. Various tire blowout experiments for different scenarios are conducted for more rigorous validations. Consequently, the proposed high-fidelity tire blowout models can correctly and more accurately describe vehicle motions upon tire blowout. The developed shared steering control schemes for PAVs and automatic control designs for HAVs can effectively stabilize a vehicle to maintain path following performance in the driving lane after tire blowout.
In addition to new research findings and developments in this dissertation, a pending patent for tire blowout detection is also generated in the tire blowout project. The obtained research results have attracted interest from automotive manufacturers and could have a significant impact on driving safety enhancement for automated vehicles upon tire blowout.
ContributorsLi, Ao (Author) / Chen, Yan (Thesis advisor) / Berman, Spring (Committee member) / Kannan, Arunachala Mada (Committee member) / Liu, Yongming (Committee member) / Lin, Wen-Chiao (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2023
Mechanical Behavior of Meteorites: Multiscale Characterization of the Strength and Failure Mechanism
Description
In this research, the chemical and mineralogical compositions, physical and mechanical properties, and failure mechanisms of two ordinary chondrite (OCs) meteorites Aba Panu (L3) and Viñales (L6), and the iron meteorite called Gibeon (IVA) were studied. OCs are dominated by anhydrous silicates with lesser amounts of sulfides and native Fe-Ni metals, while Gibeon is primarily composed of Fe-Ni metals with scattered inclusions of graphite and troilite. The OCs were investigated to understand their response to compressive loading, using a three-dimensional (3-D) Digital Image Correlation (DIC) technique to measure full-field deformation and strain during compression. The DIC data were also used to identify the effects of mineralogical and structural heterogeneity on crack formation and growth. Even though Aba Panu and Viñales are mineralogically similar and are both classified as L ordinary chondrites, they exhibit differences in compressive strengths due to variations in chemical compositions, microstructure, and the presence of cracks and shock veins. DIC data of Aba Panu and Viñales show a brittle failure mechanism, consistent with the crack formation and growth from pre-existing microcracks and porosity. In contrast, the Fe-Ni phases of the Gibeon meteorite deform plastically without rupture during compression, whereas during tension, plastic deformations followed by necking lead to final failure. The Gibeon DIC results showed strain concentration in the tensile gauge region along the sample edge, resulting in the initiation of new damage surfaces that propagated perpendicular to the loading direction. Finally, an in-situ low-temperature testing method of iron meteorites was developed to study the response of their unique microstructure and failure mechanism.
ContributorsRabbi, Md Fazle (Author) / Chattopadhyay, Aditi (Thesis advisor) / Garvie, Laurence A.J. (Thesis advisor) / Liu, Yongming (Committee member) / Fard, Masoud Yekani (Committee member) / Cotto-Figueroa, Desiree (Committee member) / Arizona State University (Publisher)
Created2023
Description
Sequential event prediction or sequential pattern mining is a well-studied topic in the literature. There are a lot of real-world scenarios where the data is released sequentially. People believe that there exist repetitive patterns of event sequences so that the future events can be predicted. For example, many companies build their recommender system to predict the next possible product for the users according to their purchase history. The healthcare system discovers the relationships among patients’ sequential symptoms to mitigate the adverse effect of a treatment (drugs or surgery). Modern engineering systems like aviation/distributed computing/energy systems diagnosed failure event logs and took prompt actions to avoid disaster when a similar failure pattern occurs. In this dissertation, I specifically focus on building a scalable algorithm for event prediction and extraction in the aviation domain. Understanding the accident event is always the major concern of the safety issue in the aviation system. A flight accident is often caused by a sequence of failure events. Accurate modeling of the failure event sequence and how it leads to the final accident is important for aviation safety. This work aims to study the relationship of the failure event sequence and evaluate the risk of the final accident according to these failure events. There are three major challenges I am trying to deal with. (1) Modeling Sequential Events with Hierarchical Structure: I aim to improve the prediction accuracy by taking advantage of the multi-level or hierarchical representation of these rare events. Specifically, I proposed to build a sequential Encoder-Decoder framework with a hierarchical embedding representation of the events. (2) Lack of high-quality and consistent event log data: In order to acquire more accurate event data from aviation accident reports, I convert the problem into a multi-label classification. An attention-based Bidirectional Encoder Representations from Transformers model is developed to achieve good performance and interpretability. (3) Ontology-based event extraction: In order to extract detailed events, I proposed to solve the problem as a hierarchical classification task. I improve the model performance by incorporating event ontology. By solving these three challenges, I provide a framework to extract events from narrative reports and estimate the risk level of aviation accidents through event sequence modeling.
ContributorsZhao, Xinyu (Author) / Yan, Hao (Thesis advisor) / Liu, Yongming (Committee member) / Ju, Feng (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022
Description
Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst devices, magnetic shielding, etc. For the engineering of the cellular foam architectures, closed-form models that can be used to predict the mechanical and thermal properties of foams are highly desired especially for the recently developed ultralight weight shellular architectures. Herein, for the first time, a novel packing three-dimensional (3D) hollow pentagonal dodecahedron (HPD) model is proposed to simulate the cellular architecture with hollow struts. An electrochemical deposition process is utilized to manufacture the metallic hollow foam architecture. Mechanical and thermal testing of the as-manufactured foams are carried out to compare with the HPD model. Timoshenko beam theory is utilized to verify and explain the derived power coefficient relation. Our HPD model is proved to accurately capture both the topology and the physical properties of hollow stochastic foam. Understanding how the novel HPD model packing helps break the conventional impression that 3D pentagonal topology cannot fulfill the space as a representative volume element. Moreover, the developed HPD model can predict the mechanical and thermal properties of the manufactured hollow metallic foams and elucidating of how the inevitable manufacturing defects affect the physical properties of the hollow metallic foams. Despite of the macro-scale stochastic foam architecture, nano gradient gyroid lattices are studied using Molecular Dynamics (MD) simulation. The simulation result reveals that, unlike homogeneous architecture, gradient gyroid not only shows novel layer-by-layer deformation behavior, but also processes significantly better energy absorption ability. The deformation behavior and energy absorption are predictable and designable, which demonstrate its highly programmable potential.
ContributorsDai, Rui (Author) / Nian, Qiong (Thesis advisor) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Liu, Yongming (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2021
Description
Ultra-fast 2D/3D material microstructure reconstruction and quantitative structure-property mapping are crucial components of integrated computational material engineering (ICME). It is particularly challenging for modeling random heterogeneous materials such as alloys, composites, polymers, porous media, and granular matters, which exhibit strong randomness and variations of their material properties due to the hierarchical uncertainties associated with their complex microstructure at different length scales. Such uncertainties also exist in disordered hyperuniform systems that are statistically isotropic and possess no Bragg peaks like liquids and glasses, yet they suppress large-scale density fluctuations in a similar manner as in perfect crystals. The unique hyperuniform long-range order in these systems endow them with nearly optimal transport, electronic and mechanical properties. The concept of hyperuniformity was originally introduced for many-particle systems and has subsequently been generalized to heterogeneous materials such as porous media, composites, polymers, and biological tissues for unconventional property discovery. An explicit mixture random field (MRF) model is proposed to characterize and reconstruct multi-phase stochastic material property and microstructure simultaneously, where no additional tuning step nor iteration is needed compared with other stochastic optimization approaches such as the simulated annealing. The proposed method is shown to have ultra-high computational efficiency and only requires minimal imaging and property input data. Considering microscale uncertainties, the material reliability will face the challenge of high dimensionality. To deal with the so-called “curse of dimensionality”, efficient material reliability analysis methods are developed. Then, the explicit hierarchical uncertainty quantification model and efficient material reliability solvers are applied to reliability-based topology optimization to pursue the lightweight under reliability constraint defined based on structural mechanical responses. Efficient and accurate methods for high-resolution microstructure and hyperuniform microstructure reconstruction, high-dimensional material reliability analysis, and reliability-based topology optimization are developed. The proposed framework can be readily incorporated into ICME for probabilistic analysis, discovery of novel disordered hyperuniform materials, material design and optimization.
ContributorsGao, Yi (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Ren, Yi (Committee member) / Pan, Rong (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2021
Description
Advanced Polymer and Ceramic Matrix Composites (PMCs and CMCs) are currently employed in a variety of airframe and engine applications. This includes PMC jet engine fan cases and CMC hot gas path turbine components. In an impact event, such as a jet engine fan blade-out, PMCs exhibit significant deformation-induced temperature rises in addition to strain rate, temperature, and pressure dependence. CMC turbine components experience elevated temperatures, large thermal gradients, and sustained loading for long time periods in service, where creep is a major issue. However, the complex nature of woven and braided composites presents significant challenges for deformation, progressive damage, and failure prediction, particularly under extreme service conditions where global response is heavily driven by competing time and temperature dependent phenomena at the constituent level. In service, the constituents in these advanced composites experience history-dependent inelastic deformation, progressive damage, and failure, which drive global nonlinear constitutive behavior. In the case of PMCs, deformation-induced heating under impact conditions is heavily influenced by the matrix. The creep behavior of CMCs is a complex manifestation of time-dependent load transfer due to the differing creep rates of the constituents; simultaneous creep and relaxation at the constituent level govern macroscopic CMC creep. The disparity in length scales associated with the constituent materials, woven and braided tow architectures, and composite structural components therefore necessitates the development of robust multiscale computational tools. In this work, multiscale computational tools are developed to gain insight into the deformation, progressive damage, and failure of advanced PMCs and CMCs. This includes multiscale modeling of the impact response of PMCs, including adiabatic heating due to the conversion of plastic work to heat at the constituent level, as well as elevated temperature creep in CMCs as a result of time-dependent constituent load transfer. It is expected that the developed models and methods will provide valuable insight into the challenges associated with the design and certification of these advanced material systems.
ContributorsSorini, Christopher (Author) / Chattopadhyay, Adit (Thesis advisor) / Goldberg, Robert K (Committee member) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Yekani-Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2021
Description
Damage and failure of advanced composite materials and structures are often manifestations of nonlinear deformation that involve multiple mechanisms and their interactions at the constituent length scale. The presence and interactions of inelastic microscale constituents strongly influence the macroscopic damage anisotropy and useful residual life. The mechano-chemical interactions between constituents at the atomistic length scale play a more critical role with nanoengineered composites. Therefore, it is desirable to link composite behavior to specific microscopic constituent properties explicitly and lower length scale features using high-fidelity multiscale modeling techniques.In the research presented in this dissertation, an atomistically-informed multiscale modeling framework is developed to investigate damage evolution and failure in composites with radially-grown carbon nanotube (CNT) architecture. A continuum damage mechanics (CDM) model for the radially-grown CNT interphase region is developed with evolution equations derived using atomistic simulations. The developed model is integrated within a high-fidelity generalized method of cells (HFGMC) micromechanics theory and is used to parametrically investigate the influence of various input micro and nanoscale parameters on the mechanical properties, such as elastic stiffness, strength, and toughness. In addition, the inter-fiber stresses and the onset of damage in the presence of the interphase region are investigated to better understand the energy dissipation mechanisms that attribute to the enhancement in the macroscopic out-of-plane strength and toughness. Note that the HFGMC theory relies heavily on the description of microscale features and requires many internal variables, leading to high computational costs. Therefore, a novel reduced-order model (ROM) is also developed to surrogate full-field nonlinear HFGMC simulations and decrease the computational time and memory requirements of concurrent multiscale simulations significantly.
The accurate prediction of composite sandwich materials' thermal stability and durability remains a challenge due to the variability of thermal-related material coefficients at different temperatures and the extensive use of bonded fittings. Consequently, the dissertation also investigates the thermomechanical performance of a complex composite sandwich space structure subject to thermal cycling. Computational finite element (FE) simulations are used to investigate the intrinsic failure mechanisms and damage precursors in honeycomb core composite sandwich structures with adhesively bonded fittings.
ContributorsVenkatesan, Karthik Rajan (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Yekani Fard, Masoud (Committee member) / Stoumbos, Tom (Committee member) / Arizona State University (Publisher)
Created2021