Animating streamlines with orthogonal advancing waves

Abstract

Self-animating image of flow through repeated asymmetric patterns (RAPs) is an innovative approach for creating illusory motion using a single image. In this paper, we present a smooth cyclic variable-speed RAP animation model that emulates orthogonal advancing waves from a geometry-based flow representation. It enables dense, accurate visualization of complex real-world flows using animated streamlines of an elegant placement coupled with visually appealing orthogonal advancing waves. The animation model first performs velocity (magnitude) integral luminance transition on individual streamlines. Then, inter-streamline synchronization in luminance varying along the tangential direction is imposed. Next, tangential flow streaks are constructed using evenly spaced hue differing in the orthogonal direction. In addition, an energy-decreasing strategy is proposed that adopts an iterative yet efficient procedure for determining the luminance phase and hue of each streamline in HSL (hue, saturation, and lightness or brightness) color space. To increase the contrast between flow streaks, adaptive luminance interleaving in the direction perpendicular to the flow is further applied. We demonstrate the effectiveness of the animation model using some synthetic and real flows. Color figures, images, and accompanying animations are available at http://graphics.csie.ncku.edu.tw/ flowvis.

Visualizing the invisible: User-centered design of a system for the visualization of flows and concentrations of particles in the air

“…Adding a temporal dimension to a 2D or 3D visualization changes it into an animation, which helps to understand temporal characteristics of a phenomenon as well as the correlation between different factors represented simultaneously. 19 Although professional software allows the visualization of results from numerical computations, they are hardly accessible to non-experts. 20 Emerging technologies such as virtual reality and augmented reality can reduce the difficulties of interpreting results, through better contextualization due to immersion in the virtual or real environment, as was suggested already in 1995.…”