Understanding the drop impact on moving hydrophilic and hydrophobic surfaces
Bibliographic record
Abstract
In this paper, a systematic study was performed to understand the drop impact on hydrophilic and hydrophobic surfaces that were moving in the horizontal direction. Drops (D0 = 2.5 mm) of liquids with three different viscosities were used. Wide ranges of drop normal velocity (0.5 to 3.4 m s−1) and surface velocity (0 to 17 m s−1) were studied. High speed imaging from the top and side was used to capture the impact phenomena. It was found that drop impact behavior on a moving surface significantly differs from that on a stationary surface at both the lamella extension stage (i.e. t ≤ tmax) and the retraction stage (t > tmax). Starting with the lamella extension stage, it was observed that the drop spreads asymmetrically over a moving surface. It was also found that the splashing behavior of the drop upon impact on a moving surface, unlike the understanding in the literature, is azimuthally different along the lamella contact line. In the case of the drop spreading over a moving surface, the surface movement stretches the expanded lamella in the direction of the surface motion. For hydrophilic surfaces, the stretched lamella pins to the surface and moves with the surface velocity; however, for hydrophobic surfaces, the lamella recoils during such stretching. A new model was developed to determine the splashing threshold of the drop impact on a moving surface. The model is capable of describing the azimuthally different behavior of the splashing which is a function of normal capillary and Weber numbers, surface velocity, and surface wettability. It was also found that the increase of the viscosity decreases the splashing threshold. Finally, comprehensive regime maps of the drop impact outcome on a moving surface were provided for both t ≤ tmax and t > tmax stages.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".