Visualization-driven Structural and Statistical Analysis of Turbulent Flows
Kenny Gruchalla, Mark Rast, Elizabeth A. Bradley, John Clyne, Pablo D. Mininni · 2009
Abstract. Knowledge extraction from data volumes of ever increasing size requires ever more flexible tools to facilitate interactive query. In-teractivity enables real-time hypothesis testing and scientific discovery, but can generally not be achieved without some level of data reduction. The approach described in this paper combines multi-resolution access, region-of-interest extraction, and structure identification in order to pro-vide interactive spatial and statistical analysis of a terascale data volume. Unique aspects of our approach include the incorporation of both local and global statistics of the flow structures, and iterative refinement fa-cilities, which combine geometry, topology, and statistics to allow the user to effectively tailor the analysis and visualization to the science. Working together, these facilities allow a user to focus the spatial scale and domain of the analysis and perform an appropriately tailored mul-tivariate visualization of the corresponding data. All of these ideas and algorithms are instantiated in a deployed visualization and analysis tool