Visualization-Driven Structural and Statistical Analysis of Turbulent Flows

Knowledge extraction from data volumes of ever increasing size requires ever more flexible tools to facilitate interactive query. Interactivity 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 provide 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 facilities, 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 multivariate visualization of the corresponding data. All of ...

• Type: Conference
• Year: 2009
• Where: IDA
• Authors: Kenny Gruchalla, Mark Rast, Elizabeth Bradley, John Clyne, Pablo Mininni
• Keywords: Analysis, Data Volume, IDA 2009, Information Management, Terascale Data Volume