Droplet impact onto a solid sphere in mid-air: Effect of viscosity, gas density, and diameter ratio on impact outcomes
Bibliographic record
Abstract
Collision of a droplet and a hydrophobic particle in mid-air was investigated. To study the impact outcomes, specifically the lamella formation, a numerical simulation tool was developed and verified with impact experiments (water droplets and glass particles, ρrel = 0.41). The velocity field within the lamella showed that the flow inside the liquid film moves in two opposite directions along the lamella axis of symmetry: one is generated through the momentum transfer from the particle, and the other is due to the droplet initial velocity. This causes the lamella to be stretched in the same direction as the particle moves and forms a rim at the end of the lamella. Although a larger droplet-to-particle diameter ratio (Dr) increased the impact duration, it did not change the collision outcomes and two opposite flows still exist inside a thicker liquid film. However, the liquid viscosity affects impact outcomes; as viscosity increased, a thicker film remained on the particle, the liquid film became shorter, and the lamella formation was hindered accordingly. The pressure of the ambient gas also affects the liquid film formation. Unlike the literature of the drop impact on a flat surface, our results indicate that by increasing the ambient pressure, the lamella formation will be suppressed (hence chance of splashing). The pressure gradient around the liquid film creates a downward force that hinders the stretching of the liquid film. The effect of the ambient pressure on lamella formation is only significant for relatively higher gas pressures (i.e., Pamb > 2 Patm).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".