Flow Visualization and Analysis: From Geometry to Physics

dc.contributor.advisorChen, Guoning
dc.contributor.committeeMemberThompson, David S.
dc.contributor.committeeMemberDeng, Zhigang
dc.contributor.committeeMemberEick, Christoph F.
dc.creatorZhang, Lei 1983-
dc.creator.orcid0000-0001-5450-0412
dc.date.accessioned2019-09-18T02:14:22Z
dc.date.available2019-09-18T02:14:22Z
dc.date.createdAugust 2017
dc.date.issued2017-08
dc.date.submittedAugust 2017
dc.date.updated2019-09-18T02:14:23Z
dc.description.abstractAs the size and complexity of flow data sets continuously increase, many vector field visualization techniques aim to generate an abstract representation of the geometric characteristics of the flow to simplify its interpretation. However, most of the geometric-based visualization techniques lack the ability to reveal the physically important features. Additional efforts are needed to interpret the physical characteristics from the geometric representation of the flow. In this work, the Lagrangian accumulation framework is introduced first, which accumulates various local physical and geometric properties of individual particles along the associated integral curves. This accumulation process results in a number of attribute fields that encode the information of certain global behaviors of particles, which can be used to achieve an abstract representation of the flow data. This framework is utilized to aid the classification of integral curves, produce texture-based visualizations, study property transport structures, and identify discontinuous behaviors among neighboring integral curves. Although the accumulation framework is simple and effective, the detailed flow behavior at individual integration points (and times) along the integral curves is suppressed, leading to incomplete analysis and visualization of flow data. In order to achieve a more detailed exploration, a new flow-exploration framework is investigated based on the time-series data or Time Activity Curves (TAC) of local properties. In this framework, the physical behavior of the individual particles can be described via their respective TACs. An event detector based on TACs is proposed to capture the local and global similarity of any spatial point with its neighboring points with a new dissimilarity metric. A hierarchical clustering framework is then developed based on this metric, upon which a level-of-detail representation of the flow can be obtained. This new framework is applied to a number of 2D and 3D unsteady-flow data sets to demonstrate its effectiveness.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Zhang, Lei, Robert S. Laramee, David Thompson, Adrian Sescu, and Guoning Chen. "Compute and visualize discontinuity among neighboring integral curves of 2D vector fields." In Topological Methods in Data Analysis and Visualization, pp. 187-203. Springer, Cham, 2015. And in: Zhang, Lei, Robert S. Laramee, David Thompson, Adrian Sescu, and Guoning Chen. "An integral curve attribute based flow segmentation." Journal of Visualization 19, no. 3 (2016): 423-436. And in: Zhang, Lei, Guoning Chen, Robert S. Laramee, David Thompson, and Adrian Sescu. "Flow visualization based on a derived rotation field." Electronic Imaging 2016, no. 1 (2016): 1-10.
dc.identifier.urihttps://hdl.handle.net/10657/4811
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectFlow visualization
dc.subjectAttributes
dc.subjectIntegral Curves
dc.titleFlow Visualization and Analysis: From Geometry to Physics
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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