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Thesis (Ph. D.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (C.E.)--Lehigh University, 1879. / Caption title. Also available online.

see document / Master of Engineering

This project investigates the use of a truss network, a structural mechanics model, as a metaphor for adapting a computational fluid dynamics (CFD) mesh. The objective of such adaptation is to increase computational effi- ciency by reducing the numerical error. To drive the adaptation, or to give the scheme an understanding of accuracy, computational errors are translated into forces at mesh vertices via a so-called monitor function. The ball-vertex truss network method is employed as it offers robustness and is applicable to problems in both two and three dimensions. In support of establishing a state-of-the-art adaptive meshing tool, boundary vertices are allowed to slide along geometric boundaries in an automated manner. This is achieved via feature identification followed by the construction of 3rd order bezier surface patches over boundary faces. To investigate the ability of the scheme, three numerical test cases were investigated. The first comprised an analytical case, with the aim of qualitatively assessing the ability to cluster vertices according to gradient. The developed scheme proved successful in doing this. Next, compressible transonic flow cases were considered in 2D and 3D. In both cases, the computed coefficient of lift and moment were investigated on the unrefined and refined meshes and then compared for error reduction. Improvements in accuracy of at least 60% were guaranteed, even on coarse meshes. This is viewed as a marked achievement in the sphere of robust and industrially viable r-refinement schemes.

Truss decomposition is an essential problem in graph mining, which focuses on discovering dense subgraphs of a graph. Detecting trusses in deterministic graphs is extensively studied in the literature. As most of the real-world graphs, such as social, biological, and communication networks, are associated with uncertainty, it is of great importance to study truss decomposition in a probabilistic context. However, the problem has received much less attention in a probabilistic framework. Furthermore, due to computational challenges of truss decomposition in probabilistic graphs, state-of- the-art approaches are not scalable to large graphs. Formally, given a user-defined threshold k (for truss denseness), we are interested in finding all the maximal subgraphs, which are a k-truss with high probability. In this thesis, we introduce a novel approach based on an asynchronous h-index updating process, which offers significant improvement over the state-of-the-art. Our extensive experimental results confirm the scalability and efficiency of our approach. / Graduate

This paper describes the investigation of a mill building bent of truss and column design, a redesign using a rigid-frame, and an estimated cost of each design. For sake of brevity the overall design of the building (sway bracing, lateral bracing, purlins, girts, roofing, siding, and bay spacing) was assumed to be the same for both designs. It is possible that this assumption cannot be made and the estimates of cost, therefore, are in error. In actual practice the complete redesign of the building should be considered.

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