Bursting at the surface: Fine droplet generation from an exploding water drop in hot oil
Bibliographic record
Abstract
The explosion of a water drop near the surface of hot cooking oil may eject fine droplets into the air. Although such a process is often observed in activity in the kitchen, the formation process of these fine droplets has not been investigated extensively [Kiyama et al., “Morphology of bubble dynamics and sound in heated oil,” Phys. Fluids 34, 062107 (2022)]. In this study, we use a high-speed camera to visualize this phenomenon, showing that the size of these droplets are of the order of 10 μm. The droplet formation process is divided into two sequential stages. The first stage, which is associated with the explosion of a submerged water drop and following rupture of the air-oil interface, largely governs the formation of fine droplets. The magnitude of the fine droplets formation seems to be related through the dimensionless relative distance from the bubble formed on the drop to the oil surface. The formation of the small droplets is driven by the Rayleigh–Taylor instability, which is similar to the fragmentation of a droplet by a laser pulse [Klein et al., “Drop fragmentation by laser-pulse impact,” J. Fluid Mech. 893, A7 (2020)]. The second stage is associated with the splash dynamics. Liquid ligaments dispatch relatively larger droplets at later times. We also recorded sounds in the surrounding air, in which the impulsive sounds are related to rupturing of a bag, which is formed due to a sudden extension of exploded drop. The subsequent splash dynamicsalso have unique acoustic signals at a higher acoustic frequency than that of the enclosed cavity after the surface sealing. These characteristics suggests the acoustics may be useful for a better understanding of this complex fluid phenomenon including fine droplets sizes and cavity closure.
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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 | 0.001 |
| Open science | 0.001 | 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".