Journal article 224 views 26 downloads
Visual Analysis of Spatio-temporal Relations of Pairwise Attributes in Unsteady Flow / Bob, Laramee
IEEE Transactions on Visualization and Computer Graphics, Pages: 1 - 1
Swansea University Author: Bob, Laramee
PDF | Accepted ManuscriptDownload (33.23MB)
Despite significant advances in the analysis and visualization of unsteady flow, the interpretation of it’s behavior still remains a challenge.In this work, we focus on the linear correlation and non-linear dependency of different physical attributes of unsteady flows to aid theirstudy from a new pe...
|Published in:||IEEE Transactions on Visualization and Computer Graphics|
Check full text
No Tags, Be the first to tag this record!
Despite significant advances in the analysis and visualization of unsteady flow, the interpretation of it’s behavior still remains a challenge.In this work, we focus on the linear correlation and non-linear dependency of different physical attributes of unsteady flows to aid theirstudy from a new perspective. Specifically, we extend the existing spatial correlation quantification, i.e. the Local Correlation Coefficient(LCC), to the spatio-temporal domain to study the correlation of attribute-pairs from both the Eulerian and Lagrangian views. To studythe dependency among attributes, which need not be linear, we extend and compute the mutual information (MI) among attributes overtime. To help visualize and interpret the derived correlation and dependency among attributes associated with a particle, we encodethe correlation and dependency values on individual pathlines. Finally, to utilize the correlation and MI computation results to identifyregions with interesting flow behavior, we propose a segmentation strategy of the flow domain based on the ranking of the strengthof the attributes relations. We have applied our correlation and dependency metrics to a number of 2D and 3D unsteady flows withvarying spatio-temporal kernel sizes to demonstrate and assess their effectiveness.
flow visualization, scientific visualization, information theory
College of Science