Control of viscoplastic fluid dynamics in superhydrophobic channels with asymmetric groove configurations
Bibliographic record
Abstract
We study the plane Poiseuille flow of viscoplastic fluids in channels with asymmetric superhydrophobic (SH) walls featuring transverse groove configurations in the thin channel limit. We use OpenFOAM simulations and the Papanastasiou regularization method to approximate the Bingham model. Focusing on variations in the upper SH wall’s characteristics, we explore the effects of slip number ( b 2 ), groove periodicity length ( ℓ 2 ), slip area fraction ( φ 2 ), and Bingham number ( B ) on flow dynamics, flow metrics and unyielded center plug morphology. We find that increasing b 2 , φ 2 , and ℓ 2 enhances slip velocity on the upper SH wall and reduces the normalized plug area ( A / A 0 ) up to φ 2 = 0 . 5 , while higher B amplifies flow asymmetry, shifting and breaking center plugs. By introducing the concept of slippery equivalent systems , we demonstrate that varying groove configurations can yield identical effective slip lengths ( χ T ) with distinct plug morphologies, enabling precise control of viscoplastic fluid dynamics. We derive a simplified model to predict χ T and A / A 0 , identifying a critical threshold at A / A 0 ≈ 0 . 68 for regime transitions between unbroken (Regime I) and broken (Regime II) center plugs, leading to a six-dimensional manifold equation for classifying these regimes across parameter space. • Investigation of viscoplastic fluids in plane Poiseuille flow through channels with asymmetric superhydrophobic (SH) walls. • Slippery equivalence system for Poiseuille viscoplastic flow with SH walls. • Viscoplastic flow is manipulated by the asymmetry of SH walls’ hydrophobicity characteristics. • Asymmetry of the SH wall’s characteristics affects the formation of the SH wall plug. • A simplified model for predicting the total effective slip length based on the characteristics of both SH walls.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".