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![Page 1: Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.](https://reader035.fdocuments.in/reader035/viewer/2022062417/5514ec9f550346935c8b5b77/html5/thumbnails/1.jpg)
Detection and Visualization of Defects in
3D Unstructured Modelsof Nematic Liquid Crystals
Ketan Mehta* & T. J. Jankun-Kelly
Viz Lab,
Computer Science & Engineering Department,
Mississippi State University
* Now at Vital Images. Inc
![Page 2: Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.](https://reader035.fdocuments.in/reader035/viewer/2022062417/5514ec9f550346935c8b5b77/html5/thumbnails/2.jpg)
Presentation Outline
Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A
![Page 3: Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.](https://reader035.fdocuments.in/reader035/viewer/2022062417/5514ec9f550346935c8b5b77/html5/thumbnails/3.jpg)
Nematic Liquid Crystals
Unique state of matter Sensitivity
![Page 4: Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.](https://reader035.fdocuments.in/reader035/viewer/2022062417/5514ec9f550346935c8b5b77/html5/thumbnails/4.jpg)
Liquid Crystals Physics
Orientation measured via: scalar order parameter (S),
director (n) and Q-tensor.
Uniaxial Biaxial
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Biosensor Design
Design a nematic liquid crystal (NLC)-based bio-sensor.
10-30nm
Nanostructured substrate
Receptor LC
10-30nm
Nanostructured substrateReceptor pesticide
Pesticide
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Facilitate Bio-sensor Design Process
Computational simulation
(physics)
Existing analysis methods• Partial views• Cutting-plane • Isosurface
Extend and provide both correct and deeper
How does Visualization beneift design process?
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Challenges
Explore unstructured grid
Identify defect cores
Visualize and present relevant information
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Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A
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Structural Defects – Disclination
Very high director (n) gradient. Defects influence physical properties.
+1/2 defect
-1/2 defect
Axial +1 defect
+1
+2
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Defect Detection Methods – Physical Sciences
Toyoki and Zapotocky’s
2D approach.
Hobdell and Windel
extended 2D to 3D.
Fukuda et al. demonstrated
benefit of adaptive grids• Unstructured
motivation
[M. Zapotocky et al. Feb’95]
[J. Hobdell et al. ’97]
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Defect Detection Methods – Scientific Visualization
Sparavigna et al. • Oriented LIC and
streamlines
[A. Sparavigna et al. Oct’99 ]
Slavin et al. used • Streamlines• Streamtubes• Ellipsoids• Smooth field
[V. Slavin et al. Oct’04]
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Existing Approaches: Contour map, Isosurface and Streamlines
Tools used:Ensight and TecPLot
Existing investigation methods
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Existing Approach: S-based Analysis
Cut-planeS-mapped to color
IsosurfaceS-based
Surface boundary
Validation method for our algorithm
Tool used: FieldView
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Validation Approach
Defect detection algorithm validation using
• Well understood models
• Comparative analysis
• Insights from experts
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Presentation Outline
Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A
![Page 16: Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.](https://reader035.fdocuments.in/reader035/viewer/2022062417/5514ec9f550346935c8b5b77/html5/thumbnails/16.jpg)
Defect Core Identification in Unstructured Grid!
Problems?
Approaches not extensible
Defect classification fails!!
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NNP Sorted Spiral Winding
Existing approaches on regular structure
Regularity fails in unstructured space
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Ordered Winding
Nearest-neighbor-path traversals:
random (left) and ordered (right)
Nearest Neighbor - preserve spatial orientation
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Defect Detection Algorithm –Visual Flow
DATA
GRID (HDF5)
Nearest Neighbor Sorted list Visualization of Defect Nodes
Pre-process Detect Visualize
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Visualization
Defect structures and orientation vectors
Colored nodes and arrow
Analyte (impurity) outline visualized
as a mesh
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Presentation Outline
Introduction and Motivation Defect Detection Background Algorithm Case studies Conclusion Future Goals Q&A
![Page 22: Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.](https://reader035.fdocuments.in/reader035/viewer/2022062417/5514ec9f550346935c8b5b77/html5/thumbnails/22.jpg)
Effectiveness and Validation
Verification using comparative analysis
Based on three Case studies• Regular structured
• backward compatibility• Unstructured temporal
• detecting changing features• Unstructured complex model
• real-life application
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I: Defect Detection: Structured Model
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II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
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II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
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II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
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II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
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III: Defect Detection: Immunoglobulin G Model (IgG)
Complex bio-molecular model
Existing method- isosurfacing
Our
method
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Summary So Far
Semi-automatic defect detection feasible• On structured and un-structured grids
Easier to navigate – reduces analysis time
Provides correct and deeper insight
![Page 30: Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.](https://reader035.fdocuments.in/reader035/viewer/2022062417/5514ec9f550346935c8b5b77/html5/thumbnails/30.jpg)
Surface mesh (red) and isosurface (blue)
Existing Techniques: S-, b-parameter, Isosurface
Biaxial plot
Scalar order plot
Cut-plane (mesh)
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Comparison of Existing and Proposed Technique
Defect nodes identified with our algorithm (new)
Surface mesh (red) and isosurface (blue) (existing)
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Future Work
Integration with Q-tensor based analysis and NLCGlyph [Jankun-Kelly 06 – very soon].
Further user study based validation.
Defect classification scheme for unstructured grids.
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Acknowledgement
Collaborators• Dr. Rajendran Mohanraj, • Huang Li
Faculty and Staff of the
Computer Science & Engineering Department, MSU
National Science Foundation EPSCoR program via award #0132618.
Vital Images, Inc.
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Thank You!!