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Area of Science:

Computational physics and materials science.
Scientific visualization and data analysis.

Background:

Visualizing alignment tensor fields, crucial for nematic liquid crystal (LC) simulations, is challenging, especially near orientational defects.
Existing hyperstreamline integration methods can fail in regions with high alignment degeneracy.

Purpose of the Study:

To present a novel, topologically-informed hyperstreamline seeding method for visualizing alignment tensor fields.
To apply this method to nematic liquid crystal (LC) orientation dynamics simulations, improving defect region analysis.

Main Methods:

Distributes hyperstreamlines along domain boundaries and edges of a nearest-neighbor graph.
Graph vertices represent degenerate regions (orientational defects) in the alignment tensor field.
Method operates without iteration and conforms to user-specified hyperstreamline spacing.

Main Results:

Successfully enables automated hyperstreamline-based visualization of diverse alignment tensor fields.
Demonstrates effective visualization in nematic liquid crystal (LC) orientation dynamics simulations.
Avoids failure modes associated with hyperstreamline integration near alignment degeneracies.

Conclusions:

The proposed seeding method enhances researchers' ability to interpret complex alignment tensor fields.
Offers a robust alternative to traditional glyph-based visualization techniques for LC simulations.
Facilitates a deeper understanding of orientational defect dynamics in nematic materials.