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 - 2 of 2
Filtering by
- Creators: Chawla, Nikhilesh
- Creators: Squires, Kyle D.
Description
The objective of this research is to develop robust, accurate, and adaptive algorithms in the framework of the extended finite element method (XFEM) for fracture analysis of highly heterogeneous materials with complex internal geometries. A key contribution of this work is the creation of novel methods designed to automate the incorporation of high-resolution data, e.g. from X-ray tomography, that can be used to better interpret the enormous volume of data generated in modern in-situ experimental testing. Thus new algorithms were developed for automating analysis of complex microstructures characterized by segmented tomographic images.
A centrality-based geometry segmentation algorithm was developed to accurately identify discrete inclusions and particles in composite materials where limitations in imaging resolution leads to spurious connections between particles in close contact.To allow for this algorithm to successfully segment geometry independently of particle size and shape, a relative centrality metric was defined to allow for a threshold centrality criterion for removal of voxels that spuriously connect distinct geometries.
To automate incorporation of microstructural information from high-resolution images, two methods were developed that initialize signed distance fields on adaptively-refined finite element meshes. The first method utilizes a level set evolution equation that is directly solved on the finite element mesh through Galerkins method. The evolution equation is formulated to produce a signed distance field that matches geometry defined by a set of voxels segmented from tomographic images. The method achieves optimal convergence for the order of elements used. In a second approach, the fast marching method is employed to initialize a distance field on a uniform grid which is then projected by least squares onto a finite element mesh. This latter approach is shown to be superior in speed and accuracy.
Lastly, extended finite element method simulations are performed for the analysis of particle fracture in metal matrix composites with realistic particle geometries initialized from X-ray tomographic data. In the simulations, particles fracture probabilistically through a Weibull strength distribution. The model is verified through comparisons with the experimentally-measured stress-strain response of the material as well as analysis of the fracture. Further, simulations are then performed to analyze the effect of mesh sensitivity, the effect of fracture of particles on their neighbors, and the role of a particles shape on its fracture probability.
A centrality-based geometry segmentation algorithm was developed to accurately identify discrete inclusions and particles in composite materials where limitations in imaging resolution leads to spurious connections between particles in close contact.To allow for this algorithm to successfully segment geometry independently of particle size and shape, a relative centrality metric was defined to allow for a threshold centrality criterion for removal of voxels that spuriously connect distinct geometries.
To automate incorporation of microstructural information from high-resolution images, two methods were developed that initialize signed distance fields on adaptively-refined finite element meshes. The first method utilizes a level set evolution equation that is directly solved on the finite element mesh through Galerkins method. The evolution equation is formulated to produce a signed distance field that matches geometry defined by a set of voxels segmented from tomographic images. The method achieves optimal convergence for the order of elements used. In a second approach, the fast marching method is employed to initialize a distance field on a uniform grid which is then projected by least squares onto a finite element mesh. This latter approach is shown to be superior in speed and accuracy.
Lastly, extended finite element method simulations are performed for the analysis of particle fracture in metal matrix composites with realistic particle geometries initialized from X-ray tomographic data. In the simulations, particles fracture probabilistically through a Weibull strength distribution. The model is verified through comparisons with the experimentally-measured stress-strain response of the material as well as analysis of the fracture. Further, simulations are then performed to analyze the effect of mesh sensitivity, the effect of fracture of particles on their neighbors, and the role of a particles shape on its fracture probability.
ContributorsYuan, Rui (Author) / Oswald, Jay (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Liu, Yongming (Committee member) / Solanki, Kiran (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2015
Description
Conventional fluid dynamics models such as the Navier-Stokes equations are derived for prediction of fluid motion at or near equilibrium, classic examples being the motion of fluids for which inter-molecular collisions are dominant. Flows at equilibrium permit simplifications such as the introduction of viscosity and also lead to solutions that are single-valued. However, many other regimes of interest include "fluids"' far from equilibrium; for example, rarefied gases or particle-laden flows in which the dispersed phase can be comprised of granular solids, droplets, or bubbles. Particle motion in these flows is not typically dominated by collisions and may exhibit significant memory effects; therefore, is often poorly described using continuum, field-based (Eulerian) approaches. Non-equilibrium flows generally lack a straightforward counterpart to viscosity and their multi-valued solutions cannot be represented by most Eulerian methods. This strongly motivates different strategies to address current shortcomings and the novel approach adopted in this work is based on the Conditional Quadrature Method of Moments (CQMOM). In CQMOM, moment equations are derived from the Boltzmann equation using a quadrature approximation of the velocity probability density function (PDF). CQMOM circumvents the drawbacks of current methods and leads to multivariate and multidimensional solutions in an Eulerian frame of reference. In the present work, the discretized PDF is resolved using an adaptive two-point quadrature in three-dimensional velocity space. The method is applied to computation of a series of non-equilibrium flows, ranging from simple two-dimensional test cases to fully-turbulent three-dimensional wall-bounded particle-laden flows. The primary contribution of the present effort is on development, application, and assessment of CQMOM for predicting the key features of dilute particle-laden flows. Statistical descriptors such as mean concentration and mean velocity are in good agreement with previous results, for both collision-less and collisional flows at varying particle Stokes numbers. Turbulent statistics and measures of local accumulation agree less favorably with prior results and identify areas for improvement in the modeling strategy.
ContributorsDunn, Dennis Martin (Author) / Squires, Kyle D. (Thesis advisor) / Calhoun, Ronald J. (Committee member) / Chen, Kangping (Committee member) / Dai, Lenore L. (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2015