Experimental and numerical study of a hollow droplet impacting on inclined solid surfaces
Bibliographic record
Abstract
This study sheds light on the complex dynamics of hollow droplet impacts and highlights the unique behaviors that differentiate them from their dense counterparts. The impact dynamics of hollow droplets on surfaces at varying angles were investigated through a combination of experimental and numerical methods. Two-view imaging technique is used to capture the droplet flattening during the experimental study. A three-dimensional compressible solver is developed to model the droplet impact using the volume of fluid method to capture the liquid and gas interface. The study revealed two distinct behaviors when comparing the flattening of hollow droplets to that of dense droplets. First, a unique counter-jet formation was observed following the collision of a hollow droplet perpendicular to the surface, indicating an inherent characteristic of hollow droplet flattening. The length of this counter-jet was primarily influenced by the droplet velocity and liquid viscosity, with the perpendicular velocity component playing a key role in its size. Second, unlike dense droplets that recoil and form a dome shape upon impact on hydrophobic surfaces, hollow droplets form a donut shape due to disturbances caused by bubble rupture during spreading. These disturbances fragmented the liquid sheet, preventing the droplet from recoiling and resulting in a distinctive donut shape. On surfaces with different orientations, the hollow droplet exhibited two velocity components, where the normal component controls the counter-jet size while the tangential component induces tangential motion. The donut shape splat was also observed on surfaces with different orientations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".