Yield stress fluid flows in superhydrophobic channels: From creeping to inertial regime
Bibliographic record
Abstract
In this work, inertial flows of a yield stress fluid in a channel equipped with a superhydrophobic groovy wall are studied through numerical computations. Assuming an ideal Cassie state, the superhydrophobic wall is modeled via arrays of slip, quantified using the Navier slip law, and arrays of stick, corresponding to the no-slip boundary condition. The viscoplastic rheology is modeled using the Bingham constitutive model, implemented via the Papanastasiou regularization technique. The focus is on inertial flows in the thin channel limit, where the groove period is much larger than the half-channel height. The effects of the flow parameters are quantified on the flow variables of interest, including the slip and axial velocity profiles, unyielded plug zones, regime classifications, flow asymmetry indices, effective slip lengths, and friction factors. In particular, an increase in the flow inertia quantified via the Reynolds number affects the flow in several ways, such as reducing the dimensionless slip velocity and effective slip length, increasing the friction factor, inducing an asymmetry in the velocity profile, and showing a non-monotonic effect on the yielding of the center plug. The present work addresses the complex interplay between the yield stress fluid rheology, the wall superhydrophobicity, and the flow inertia, and it can find applications in macro-/micro-transports of non-Newtonian fluids, from oil and gas to health-related industries.
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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 | 0.001 | 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".