Effect of Stagnation Flow on an Impacting Water Droplet on a Superhydrophobic Surface
Bibliographic record
Abstract
One of the relevant applications of this study is related to designing anti-icing surfaces. Supercooled water droplet impact at high Weber number on a wing of airplane is one of the main concerns in aircraft ice accumulation. In order to address this issue, an experimental setup which generates co-flow is designed to mimic the real scenario of droplet impact in practical icing condition. This process is observed using a high-speed camera to capture the correct moment of sliding at 10000 frames per second and 120000 1/s shutter speed. Different air stream velocities are generated by a convergent nozzle with a maximum Mach number of 0.1. Two different cases are considered. First, droplet impact in still air with an impact velocity of 2 m/s is performed as the base case. Then droplet impact accompanied with 10, 18 and 20 m/s air stream velocities having the same impact velocity are conducted. Droplet impact velocity will change either by air flow or gravity. So, it means that an increase in air speed must be accompanied with a decrease in needle height to substrate. It is clearly demonstrated that for Weber number around 140 (e.g. impact velocity equal to 2.1 m/s and droplet size equal to 2.3 mm) in still air there is no droplet break up either in spreading or in receding stages. On the other hand, violent splashing of water droplet impacting with the same Weber number accompanied with 18 and 20 m/s airstream velocities were observed during the recoiling stage.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".