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
Presentation Outline
Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A
Nematic Liquid Crystals
Unique state of matter Sensitivity
Liquid Crystals Physics
Orientation measured via: scalar order parameter (S),
director (n) and Q-tensor.
Uniaxial Biaxial
Biosensor Design
Design a nematic liquid crystal (NLC)-based bio-sensor.
10-30nm
Nanostructured substrate
Receptor LC
10-30nm
Nanostructured substrateReceptor pesticide
Pesticide
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?
Challenges
Explore unstructured grid
Identify defect cores
Visualize and present relevant information
Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A
Structural Defects – Disclination
Very high director (n) gradient. Defects influence physical properties.
+1/2 defect
-1/2 defect
Axial +1 defect
+1
+2
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]
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]
Existing Approaches: Contour map, Isosurface and Streamlines
Tools used:Ensight and TecPLot
Existing investigation methods
Existing Approach: S-based Analysis
Cut-planeS-mapped to color
IsosurfaceS-based
Surface boundary
Validation method for our algorithm
Tool used: FieldView
Validation Approach
Defect detection algorithm validation using
• Well understood models
• Comparative analysis
• Insights from experts
Presentation Outline
Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A
Defect Core Identification in Unstructured Grid!
Problems?
Approaches not extensible
Defect classification fails!!
NNP Sorted Spiral Winding
Existing approaches on regular structure
Regularity fails in unstructured space
Ordered Winding
Nearest-neighbor-path traversals:
random (left) and ordered (right)
Nearest Neighbor - preserve spatial orientation
Defect Detection Algorithm –Visual Flow
DATA
GRID (HDF5)
Nearest Neighbor Sorted list Visualization of Defect Nodes
Pre-process Detect Visualize
Visualization
Defect structures and orientation vectors
Colored nodes and arrow
Analyte (impurity) outline visualized
as a mesh
Presentation Outline
Introduction and Motivation Defect Detection Background Algorithm Case studies Conclusion Future Goals Q&A
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
I: Defect Detection: Structured Model
II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
II: Defect Detection: Temporal
Time Line10000 15000 20000 30000
III: Defect Detection: Immunoglobulin G Model (IgG)
Complex bio-molecular model
Existing method- isosurfacing
Our
method
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
Surface mesh (red) and isosurface (blue)
Existing Techniques: S-, b-parameter, Isosurface
Biaxial plot
Scalar order plot
Cut-plane (mesh)
Comparison of Existing and Proposed Technique
Defect nodes identified with our algorithm (new)
Surface mesh (red) and isosurface (blue) (existing)
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.
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.
Thank You!!