Effect of interfacial slip on the thin film drainage time for two equal-sized, surfactant-free drops undergoing a head-on collision: A scaling analysis
Bibliographic record
Abstract
Using a scaling analysis, we assess the impact of interfacial slip on the time required for the thin liquid film between two drops undergoing a head-on collision to drain to the critical thickness for rupture by van der Waals forces. Interfacial slip is included in our continuum development using a Navier slip boundary condition, with the slip coefficient modeled using previous theories [Helfand and Tagami, J. Chem. Phys. 57, 1812 (1972); Goveas and Fredrickson, Eur. Phys. J. B 2, 79 (1998)]. Slip decreases hydrodynamic resistance and speeds up film drainage. It renders the dependence of the drainage time on capillary number stronger in the spherical-film regime, but, interestingly, this dependence is altered only weakly in the dimpled-film regime. A subtle effect of slip is that it increases the range of capillary numbers in which the film remains predominantly spherical in shape during drainage (as opposed to being dimpled), leading to significantly faster drainage for these capillary numbers. Slip also leads to an increase in the critical capillary number beyond which coalescence is not possible in a head-collision.1 MoreReceived 21 January 2016DOI:https://doi.org/10.1103/PhysRevFluids.1.064204©2016 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasComplex fluidsInterfacial flowsParticle-laden flowsFluid Dynamics
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".