Hydrodynamics of chiral nematics in a channel and sudden contraction geometry
Bibliographic record
Abstract
This study investigates the influence of chirality, viscous effects, and confinement geometry on the flow dynamics and defect structures of cholesteric liquid crystals (CLCs) using numerical simulations. As chiral strength increases, π-twist defects form at low chirality and progressively organize into hexagonal domains resembling blue phase-like structures at higher chirality. At sufficiently high chirality and aspect ratios, localized defect arrangements emerge that are structurally analogous to half-skyrmion configurations observed in confined cholesteric systems. Although the skyrmion number is not explicitly calculated, the defect arrangement mirrors known equilibrium patterns. These results indicate dynamic analogs of equilibrium phases formed and stabilized by flow–structure interactions. Sudden contraction geometries reveal how aspect ratio and elastic interactions promote flow disturbances and defect development. Analyses of velocity and scalar order parameter (S) distributions demonstrate strong coupling between molecular alignment and hydrodynamic fields, with localized vortices forming around τ− defects in regions of low S and velocity. Temporal evaluations of velocity fluctuations uncover irregular or quasi-chaotic flow regimes at low Ericksen numbers (Er), characterized by irregular defect motion and skewed probability density functions. These findings offer new insight into CLC structure formation and flow behavior, with potential applications in defect engineering, microfluidics, soft robotics, and photonic materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".