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School of Computational Science and Engineering

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Now showing 1 - 4 of 4
  • Item
    Geometric feature extraction in support of the single digital thread approach to detailed design
    (Georgia Institute of Technology, 2016-12-08) Gharbi, Aroua
    Aircraft design is a multi-disciplinary and complicated process that takes a long time and requires a large number of trade-offs between customer requirements, various types of constraints and market competition. Particularly detailed design is the phase that takes most of the time due to the high number of iterations between the component design and the structural analysis that need to be run before reaching an optimal design. In this thesis, an innovative approach for detailed design is suggested. It promotes a collaborative framework in which knowledge from the small scale level of components is shared and transferred to the subsystems and systems level leading to more robust and real time decisions that speed up the design time. This approach is called the Single Digital Thread Approach to Detailed Design or shortly STAnDD. The implementation of this approach is laid over a bottom-up plan, starting from the component level up to the aircraft level. In the component level and from a detailed design perspective, three major operations need to be executed in order to deploy the Single Digital Thread approach. The first one is the automatic geometric extraction of component features from a solid with no design history, the second phase is building an optimizer around the design and analysis iterations and the third one is the automatic update of the solid. This thesis suggests a methodology to implement the first phase. Extracting geometric features automatically from a solid with no history(also called dumb solid) is not an easy process especially in aircraft industry where most of the components have very complex shapes. Innovative techniques from Machine Learning were used allowing a consistent and robust extraction of the data.
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    Simulations of binary black holes in scalar field cosmologies
    (Georgia Institute of Technology, 2016-08-01) Tallaksen, Katharine Christina
    Numerical relativity allows us to solve Einstein's equations and study astrophysical phenomena we may not be able to observe directly, such as the very early universe. In this work, we examine the effect of scalar field cosmologies on binary black hole systems. These scalar field cosmologies were studied using cosmological bubbles, spherically symmetric structures that may have powered inflationary phase transitions. The Einstein Toolkit and Maya, developed at Georgia Tech, were used to simulate these systems. Systems studied include cosmological bubbles, binary black holes in vacuum, and binary black holes embedded within cosmological bubbles. Differences in mass accretion, merger trajectories, and characteristic gravitational waveforms will be presented for these systems. In the future, analyzing the parameter space of these waveforms may present a method to discover a gravitational wave signature characteristic to these systems and possibly detectable by the Laser Interferometer Gravitational-Wave Observatory.
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    Agglomerative clustering for community detection in dynamic graphs
    (Georgia Institute of Technology, 2016-05-10) Godbole, Pushkar J.
    Agglomerative Clustering techniques work by recursively merging graph vertices into communities, to maximize a clustering quality metric. The metric of Modularity coined by Newman and Girvan, measures the cluster quality based on the premise that, a cluster has collections of vertices more strongly connected internally than would occur from random chance. Various fast and efficient algorithms for community detection based on modularity maximization have been developed for static graphs. However, since many (contemporary) networks are not static but rather evolve over time, the static approaches are rendered inappropriate for clustering of dynamic graphs. Modularity optimization in changing graphs is a relatively new field that entails the need to develop efficient algorithms for detection and maintenance of a community structure while minimizing the “Size of change” and computational effort. The objective of this work was to develop an efficient dynamic agglomerative clustering algorithm that attempts to maximize modularity while minimizing the “size of change” in the transitioning community structure. First we briefly discuss the previous memoryless dynamic reagglomeration approach with localized vertex freeing and illustrate its performance and limitations. Then we describe the new backtracking algorithm followed by its performance results and observations. In experimental analysis of both typical and pathological cases, we evaluate and justify various backtracking and agglomeration strategies in context of the graph structure and incoming stream topologies. Evaluation of the algorithm on social network datasets, including Facebook (SNAP) and PGP Giant Component networks shows significantly improved performance over its conventional static counterpart in terms of execution time, Modularity and Size of Change.
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    Implementation and analysis of a parallel vertex-centered finite element segmental refinement multigrid solver
    (Georgia Institute of Technology, 2016-04-28) Henneking, Stefan
    In a parallel vertex-centered finite element multigrid solver, segmental refinement can be used to avoid all inter-process communication on the fine grids. While domain decomposition methods generally require coupled subdomain processing for the numerical solution to a nonlinear elliptic boundary value problem, segmental refinement exploits that subdomains are almost decoupled with respect to high-frequency error components. This allows to perform multigrid with fully decoupled subdomains on the fine grids, which was proposed as a sequential low-storage algorithm by Brandt in the 1970s, and as a parallel algorithm by Brandt and Diskin in 1994. Adams published the first numerical results from a multilevel segmental refinement solver in 2014, confirming the asymptotic exactness of the scheme for a cell-centered finite volume implementation. We continue Brandt’s and Adams’ research by experimentally investigating the scheme’s accuracy with a vertex-centered finite element segmental refinement solver. We confirm that full multigrid accuracy can be preserved for a few segmental refinement levels, although we observe a different dependency on the segmental refinement parameter space. We show that various strategies for the grid transfers between the finest conventional multigrid level and the segmental refinement subdomains affect the solver accuracy. Scaling results are reported for a Cray XC30 with up to 4096 cores.