NLCVIZ: Tensor Visualization and Defect Detection
Ketan Mehta
Dr. T.J. Jankun-Kelly (advisor), Mississippi State University, August 2006.
Abstract
Visualization and exploration of nematic liquid crystal (NLC) data is a challenging task due to the multidimensional and multivariate nature of the data. Traditionally, scientists have used a combination of different tools and techniques like 2D plots, histograms, cut views, etc. for data visualization and analysis. However, such an environment does not provide the required insight into NLC datasets. This thesis addresses two areas of the study of NLC data---understanding of the tensor order field (the Q-tensor) and defect detection in this field. Tensor field understanding is enhanced by using a new glyph (NLCGlyph) based on a new design metric which is closely related to the underlying physical properties of an NLC, described using the Q-tensor. A new defect detection algorithm for 3D unstructured grids based on the orientation change of the director is developed. This method has been
used successfully in detecting defects for both structured and unstructured models with varying grid complexity.
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Copyright
Copyright 2006, Ketan Mehta
Affiliated Projects
None
BibTeX Citation
@mastersthesis{Mehta:2006:NTV,
Author = {Ketan Mehta},
Title = {NLCVIZ: Tensor Visualization and Defect Detection },
Abstract = {Visualization and exploration of nematic liquid crystal (NLC) data is a challenging task due to the multidimensional and multivariate nature of the data. Traditionally, scientists have used a combination of different tools and techniques like 2D plots, histograms, cut views, etc. for data visualization and analysis. However, such an environment does not provide the required insight into NLC datasets. This thesis addresses two areas of the study of NLC data---understanding of the tensor order field (the Q-tensor) and defect detection in this field. Tensor field understanding is enhanced by using a new glyph (NLCGlyph) based on a new design metric which is closely related to the underlying physical properties of an NLC, described using the Q-tensor. A new defect detection algorithm for 3D unstructured grids based on the orientation change of the director is developed. This method has been
used successfully in detecting defects for both structured and unstructured models with varying grid complexity.},
Keywords = {scientific visualization, nematic liquid crystals},
Pages = {},
Year = {2006}
Institution = {Mississippi State University},
Advisor = {Dr. T.J. Jankun-Kelly},
Month = {August},
}
Contact
Dr. T.J. Jankun-Kelly [tjk@acm.org], Department of Computer Science and Engineering, Mississippi State University
