Formation of fingers around the edges of a drop hitting a metal plate with high velocity
Bibliographic record
Abstract
Water droplets (0.55 or 1.3 mm diameter) were photographed as they impinged on a stainless steel surface. The droplet impact velocity (10–50 m s−1) and the average roughness (0.03 or 0.23 μm) of the test surfaces were varied. The stainless steel substrate was mounted on the end of a rotating arm, giving linear velocities of up to 50 m s−1. Different stages of droplet impact were photographed by synchronizing the ejection of a single droplet with the position of the rotating arm and triggering of a camera. Finger-shape perturbations were observed around the edges of spreading droplets. The maximum diameter to which a droplet spread and the number of fingers formed around it were measured. The size and number of fingers increased with impact velocity and droplet diameter. At sufficiently high velocities, the tips of these fingers detached, producing satellite droplets. By increasing surface roughness, both the number of fingers and the maximum extent of spreading were decreased. At high impact velocities the spreading liquid film became so thin that it ruptured in several places. A mathematical model, based on linear Rayleigh–Taylor instability theory, was used to predict the wavelength of the fastest growing perturbation around a spreading droplet. The corresponding wavenumber agreed reasonably well with the number of fingers around the droplet.
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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) | 0.001 | 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".