Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 2 of 2
Filtering by
- All Subjects: Damage Localization
- All Subjects: Liquid-Liquid System
Description
There is a concerted effort in developing robust systems health monitoring/management (SHM) technology as a means to reduce the life cycle costs, improve availability, extend life and minimize downtime of various platforms including aerospace and civil infrastructure. The implementation of a robust SHM system requires a collaborative effort in a variety of areas such as sensor development, damage detection and localization, physics based models, and prognosis models for residual useful life (RUL) estimation. Damage localization and prediction is further complicated by geometric, material, loading, and environmental variabilities. Therefore, it is essential to develop robust SHM methodologies by taking into account such uncertainties. In this research, damage localization and RUL estimation of two different physical systems are addressed: (i) fatigue crack propagation in metallic materials under complex multiaxial loading and (ii) temporal scour prediction near bridge piers. With little modifications, the methodologies developed can be applied to other systems.
Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature.
For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions.
Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature.
For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions.
ContributorsNeerukatti, Rajesh Kumar (Author) / Chattopadhyay, Aditi (Thesis advisor) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Rajadas, John (Committee member) / Yekani Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2016
Description
A comprehensive and systematic investigation on the diffusion and phase behaviors of nanoparticles and macromolecules in two component liquid-liquid systems via Molecule Dynamic (MD) simulations is presented in this dissertation.
The interface of biphasic liquid systems has attracted great attention because it offers a simple, flexible, and highly reproducible template for the assembly of a variety of nanoscale objects. However, certain important fundamental issues at the interface have not been fully explored, especially when the size of the object is comparable with the liquid molecules. In the first MD simulation system, the diffusion and self-assembly of nanoparticles with different size, shape and surface composition were studied in an oil/water system. It has been found that a highly symmetrical nanoparticle with uniform surface (e.g. buckyball) can lead to a better-defined solvation shell which makes the “effective radius” of the nanoparticle larger than its own radius, and thus, lead to slower transport (diffusion) of the nanoparticles across the oil-water interface. Poly(N-isopropylacrylamide) (PNIPAM) is a thermoresponsive polymer with a Lower Critical Solution Temperature (LCST) of 32°C in pure water. It is one of the most widely studied stimulus-responsive polymers which can be fabricated into various forms of smart materials. However, current understanding about the diffusive and phase behaviors of PNIPAM in ionic liquids/water system is very limited. Therefore, two biphasic water-ionic liquids (ILs) systems were created to investigate the interfacial behavior of PNIPAM in such unique liquid-liquid interface. It was found the phase preference of PNIPAM below/above its LCST is dependent on the nature of ionic liquids. This potentially allows us to manipulate the interfacial behavior of macromolecules by tuning the properties of ionic liquids and minimizing the need for expensive polymer functionalization. In addition, to seek a more comprehensive understanding of the effects of ionic liquids on the phase behavior of PNIPAM, PNIPAM was studied in two miscible ionic liquids/water systems. The thermodynamic origin causes the reduction of LCST of PNIPAM in imidazolium based ionic liquids/water system was found. Energy analysis, hydrogen boding calculation and detailed structural quantification were presented in this study to support the conclusions.
The interface of biphasic liquid systems has attracted great attention because it offers a simple, flexible, and highly reproducible template for the assembly of a variety of nanoscale objects. However, certain important fundamental issues at the interface have not been fully explored, especially when the size of the object is comparable with the liquid molecules. In the first MD simulation system, the diffusion and self-assembly of nanoparticles with different size, shape and surface composition were studied in an oil/water system. It has been found that a highly symmetrical nanoparticle with uniform surface (e.g. buckyball) can lead to a better-defined solvation shell which makes the “effective radius” of the nanoparticle larger than its own radius, and thus, lead to slower transport (diffusion) of the nanoparticles across the oil-water interface. Poly(N-isopropylacrylamide) (PNIPAM) is a thermoresponsive polymer with a Lower Critical Solution Temperature (LCST) of 32°C in pure water. It is one of the most widely studied stimulus-responsive polymers which can be fabricated into various forms of smart materials. However, current understanding about the diffusive and phase behaviors of PNIPAM in ionic liquids/water system is very limited. Therefore, two biphasic water-ionic liquids (ILs) systems were created to investigate the interfacial behavior of PNIPAM in such unique liquid-liquid interface. It was found the phase preference of PNIPAM below/above its LCST is dependent on the nature of ionic liquids. This potentially allows us to manipulate the interfacial behavior of macromolecules by tuning the properties of ionic liquids and minimizing the need for expensive polymer functionalization. In addition, to seek a more comprehensive understanding of the effects of ionic liquids on the phase behavior of PNIPAM, PNIPAM was studied in two miscible ionic liquids/water systems. The thermodynamic origin causes the reduction of LCST of PNIPAM in imidazolium based ionic liquids/water system was found. Energy analysis, hydrogen boding calculation and detailed structural quantification were presented in this study to support the conclusions.
ContributorsGao, Wei (Author) / Dai, Lenore (Thesis advisor) / Jiao, Yang (Committee member) / Liu, Yongming (Committee member) / Green, Matthew (Committee member) / Emady, Heather (Committee member) / Arizona State University (Publisher)
Created2017