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Journal of Flow Visualization and Image Processing
CiteScore™: 0.1 SNIP: 0.312 SJR: 0.11

ISSN Print: 1065-3090
ISSN Online: 1940-4336

Journal of Flow Visualization and Image Processing

DOI: 10.1615/JFlowVisImageProc.2017022282
pages 253-273


Zhanping Liu
Department of Modeling, Simulation, and Visualization Engineering, Old Dominion University, ODU-MSVE 1300 ECSB, 4700 Elkhorn Avenue, Norfolk, VA 23529, USA
Robert J. Moorhead II
Department of Electrical and Computer Engineering, Mississippi State University, USA


There are frequent user studies in flow visualization for comparing different algorithms in the effectiveness. While much work is focused on statistical analysis of the collected results, considerable bias arising from both flow data and task design is seldom addressed, compromising and even invalidating the subsequent conclusion. This paper presents topology-based synthesis of symmetric flows for explicit data generation, which then facilitates implicit task design, so as to enable convincing user studies. This method can produce a large number of (x-/y-/center-)symmetric flows with different structures but with nearly the same degree of topological complexity, and geometrically symmetric yet topologically asymmetric flows. The wide variety of topological constitution requires local + global visual perception, comprehensive feature recognition, and discreet pattern classification to gain a proper understanding of such symmetric and asymmetric flows. To visualize these flows, we propose to use a set of representative techniques including sparse geometry-based methods (i.e., arrow plots and evenly spaced streamlines) and dense texture-based approaches (i.e., three variants of line integral convolution) coupled with two color map schemes (i.e., rainbow band and color wheel). This selection, out of a broad range of candidate techniques, is based on thorough investigation of the working mechanisms, in-depth analysis of perceptual factors, and extensive observation of the advances in flow visualization. Thus, the rationale helps identify strengths and weaknesses of the existing flow visualization methods and provides hints for designing new effective algorithms.