Authors: Selcuk Sumengen, Ekrem Serin, Selim Balcisoy
Abstract: Flow fields produced by physically based simulations are subsets of multivariate spatio-temporal data, and have been in interest of many researchers for visualization, since the data complexity makes it difficult to extract representative views for the interpretation of fluid behavior. We utilize Information Theory to find entropy maps for vector flow fields, and use entropy maps to aid visualization and analysis of the flow fields. Our major contribution is to use Principal Component Analyses (PCA) to find a projection that has the maximal directional variation in polar coordinates for each sampling window in order to generate histograms according to the projected 3D vector field, producing results with fewer artifacts than the traditional methods. Entropy guided visualization of different data sets are presented to evaluate proposed method for the generation of entropy maps. High entropy regions and coherent directional components of the flow fields are visible without cluttering to reveal fluid behavior in rendered images. In addition to using data sets those are available for research purposes, we developed a fluid simulation framework using Smoothed Particle Hydrodynamics.