EuroVisSTAR2020
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Browsing EuroVisSTAR2020 by Author "Bujack, Roxana"
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Item State of the Art in Time-Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bujack, Roxana; Yan, Lin; Hotz, Ingrid; Garth, Christoph; Wang, Bei; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, BeiWe present a state-of-the-art report on time-dependent flow topology. We survey representative papers in visualization and provide a taxonomy of existing approaches that generalize flow topology from time-independent to time-dependent settings. The approaches are classified based upon four categories: tracking of steady topology, reference frame adaption, pathline classification or clustering, and generalization of critical points. Our unique contributions include introducing a set of desirable mathematical properties to interpret physical meaningfulness for time-dependent flow visualization, inferring mathematical properties associated with selective research papers, and utilizing such properties for classification. The five most important properties identified in the existing literature include coincidence with the steady case, induction of a partition within the domain, Lagrangian invariance, objectivity, and Galilean invariance.Item A Survey of Seed Placement and Streamline Selection Techniques(The Eurographics Association and John Wiley & Sons Ltd., 2020) Sane, Sudhanshu; Bujack, Roxana; Garth, Christoph; Childs, Hank; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, BeiStreamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state-of-the-art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high-level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density-based, feature-based, and similarity-based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.