Penetration characteristics of a liquid droplet impacting on a narrow gap: Experimental and numerical analysis
Bibliographic record
Abstract
Experimentalists are limited in the amount of information they can derive from drop impact experiments on porous surfaces because of the short timescales involved and the normally opaque nature of porous materials. Numerical simulations can supplement experiments and provide researchers with previously unattainable information such as velocity and pressure profiles, and quantification of fluid volume flow rates into the pores. Ethanol drops, 2.0 mm in diameter, are impacted on a narrow gap at Weber numbers that match the impact of water drops, also 2.0 mm in diameter, on the same gap size in a previous study. The experiments show the ethanol drops cleaving at all Weber numbers tested, while the water drops completely enter the gap at low Weber numbers and only cleave at higher Weber numbers. A volume of fluid numerical model of the experiments is constructed in OpenFOAM and used to probe the interior of the drops during impact. For the water drop, a high-pressure region fills the drop during impact which continuously drives liquid into the gap. For the ethanol drops, the high-pressure region is smaller and quickly attenuates, which results in a near-zero vertical velocity at the entrance of the gap. Compared to water, the lower surface tension of ethanol causes these drops to spread further upon impact, recoil less, and overall have less liquid over the gap, which promotes cleaving. Against a superficial thought, when the penetration of liquids into porous materials is to be maximized, a higher surface tension liquid is therefore desirable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".