Visualizing 4D Spatiotemporal Vortex Behavior Through Evolution Surfaces
Date
2019-03-20
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Abstract
Turbulent fluid flow data is often 4-dimensional (4D), spatially and temporally complex, and requires specific techniques for visualization. Common visualization techniques neglect the temporal aspect of the data, limiting their ability to convey feature motion. Existing spatiotemporal visualization techniques either do not support 3D vortices, or they must reduce temporal resolution to preserve visual clarity. In sacrificing temporal resolution these techniques can no longer accurately detect or portray feature evolution events. The objective of this thesis is to develop a method to present the spatiotemporal behavior of vortices with a focus on temporal fidelity. To achieve this goal this thesis presents an approach – evolution surfaces – which abstracts the spatial representation of vortices to render their spatiotemporal behavior with reduced visual complexity. The behavior of vortex features are presented as surfaces, with textures indicating properties of motion and evolution events (e.g., bifurcation and amalgamation) represented by the surface topology. This approach has been implemented in a prototype software system and used to examine empirical and computer-simulated turbulent flow datasets ranging from Reynolds number Re = 300 to Re = 86000. Additionally, the reduction in visual complexity offered by evolution surfaces has enabled simultaneous rendering of multiple shedding cycles for analysis of long-term vortex shedding behavior patterns. These results have been compared to existing spatiotemporal visualization techniques using qualitative and quantitative metrics. This approach has been assessed by fluid dynamicists to assert its validity and future potential. Evolution surfaces offer a compact visualization of spatiotemporal vortex behaviors, opening potential avenues for exploration and analysis of turbulent fluid flows.
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Keywords
flow visualization, feature tracking, spatiotemporal visualization, vortex extraction, pattern recognition
Citation
Ferrari, S. (2019). Visualizing 4D spatiotemporal vortex behavior through evolution surfaces (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.