Droplet impact on a mesh wetted underneath
Bibliographic record
Abstract
Droplet impacts on meshes are ubiquitous in applications where the mesh becomes wetted following even a single impact. The wetting morphology is diverse due to the mesh's leakage characteristics, but its effects on impact outcomes are yet to be explored. Here, droplet impact on a mesh wetted underneath was investigated using high-speed photography, with special attention paid to the influence of the height of the hanging droplet, the mesh size, and the Weber number. It was found that the threshold Weber number corresponding to the generation of secondary droplets initially increased and then decreased as the height of the hanging droplet increased and decreased as the mesh pore size increased. A semi-empirical model was proposed, capable of reproducing the nonmonotonic dependence between the threshold Weber number and the height of the hanging droplet. Slightly above the threshold Weber number, only one secondary droplet was generated, whose diameter increased with the hanging droplet's height, and its magnitude was similar to the impacting droplet. A further increase in the Weber number resulted in additional secondary droplets with smaller diameters. However, the diameter of the first secondary droplet was always of the same order of magnitude as the impacting droplet. The spray efficiency increased with an increase in the Weber number and the height of the hanging droplet, even well above 1, indicating that the impact can decrease the liquid mass that remains attached underneath the mesh.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".