Tensor Visualization and Defect Detection for Nematic Liquid Crystals using Shape Characteristics
Two alternate sets of tensor shape characteristics are introduced for the study of nematic liquid crystals, a little studied problem in tensor visualization. One set of characteristics are based on the physics of the liquid crystal system (a real, symmetric, traceless tensor); the other set is an application of the well known Westin DT-MRI shape characteristics. These shape metrics are used both for direct tensor visualization and for detection of defects within the liquid crystal matrix.
KeywordsLiquid Crystal Defect Detection Nematic Liquid Crystal Topological Defect Defect Core
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We thank Ketan Mehta for his work on unstructured grid-based disclination detection and Gordon Kindlmann for our in-depth discussions. Rajendran Mohanraj and Huangli Wu, formerly of the Mississippi State SimCenter, provided the human Ig anitibody simulation data. This work was partially funded through the National Science Foundation EPSCoR program via award No. 0132618.
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