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Progressive Visualization-Driven Multivariate Feature Definition and Analysis

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One of the barriers to visualization-enabled scientific discovery is the difficulty in clearly and quantitatively articulating the meaning of a visualization, particularly in the exploration of relationships between multiple variables in large-scale data sets. This issue becomes more complicated in the visualization of three-dimensional turbu- lence, since geometry, topology, and statistics play complicated, intertwined roles in the definitions of the features of interest, making them difficult or impossible to precisely describe. This dissertation develops and evaluates a novel interactive multivariate volume visualization framework that allows features to be progressively isolated and defined using a combination of global and feature-local properties. I argue that a progressive and interactive multivariate feature-local approach is advantageous when investigating ill-defined features because it provides a physically meaningful, quantitatively rich en- vironment within which to examine the sensitivity of the structure properties to the identification parameters. The efficacy of this approach is demonstrated in the analysis of vortical structures in Taylor-Green turbulence. Through this analysis, two distinct structure populations have been discovered in these data: structures with minimal and maximal local absolute helicity distributions. These populations cannot be distinguished via global distributions; however, they were readily identified by this approach, since their feature-local statistics are distinctive.
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