MétaCan
Menu
Back to cohort
Record W4321372747 · doi:10.1080/02678292.2023.2179121

Numerical simulation of the 2D lid-driven cavity flow of chiral liquid crystals

2023· article· en· W4321372747 on OpenAlexaff
Shancheng Li, Dana Grecov

Bibliographic record

VenueLiquid Crystals · 2023
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceLiquid crystalFlow (mathematics)Hexagonal crystal systemVortexMicrostructureCondensed matter physicsTexture (cosmology)Composite materialMechanicsCrystallographyPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

In this study, the two-dimensional (2D), lid-driven cavity flow of chiral liquid crystals (LCs) was modelled using the Landau de-Gennes (LdG) theory. Parametric studies investigating the effect of Er (Ericksen number) and Θ (chiral strength) on the microstructure of chiral LCs were performed. In this study, we observed that an increase in Θ caused the chiral texture to have more striations and shorter pitch lengths as causes for an increased number of defects. When Θ was held constant, an increase in Er disrupted the chiral structure and even broke it at very high Er. Interestingly, a transition from low Er (10) to moderate Er (1,000) increased the number of defects; however, further increases in Er reduced the number of defects since much of the chiral structure was destroyed by the high viscous flow effects. In particular, even at very high Er, the chiral structure and defects were still present as a vortex was always present with lower velocities, where the viscous flow effect was smaller. We also found that a hexagonal structure with penta-hepta defects formed at high chiral strengths.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.322
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueLiquid CrystalsSame topicLiquid Crystal Research AdvancementsFrench-language works237,207