Shear driven droplet shedding and coalescence on a superhydrophobic surface
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The interest on shedding and coalescence of sessile droplets arises from the importance of these phenomena in various scientific problems and industrial applications such as ice formation on wind turbine blades, power lines, nacelles, and aircraft wings. It is shown recently that one of the ways to reduce the probability of ice accretion on industrial components is using superhydrophobic coatings due to their low adhesion to water droplets. In this study, a combined experimental and numerical approach is used to investigate droplet shedding and coalescence phenomena under the influence of air shear flow on a superhydrophobic surface. Droplets with a size of 2 mm are subjected to various air speeds ranging from 5 to 90 m/s. A numerical simulation based on the Volume of Fluid method coupled with the Large Eddy Simulation turbulent model is carried out in conjunction with the validating experiments to shed more light on the coalescence of droplets and detachment phenomena through a detailed analysis of the aerodynamics forces and velocity vectors on the droplet and the streamlines around it. The results indicate a contrast in the mechanism of two-droplet coalescence and subsequent detachment with those related to the case of a single droplet shedding. At lower speeds, the two droplets coalesce by attracting each other with successive rebounds of the merged droplet on the substrate, while at higher speeds, the detachment occurs almost instantly after coalescence, with a detachment time decreasing exponentially with the air speed. It is shown that coalescence phenomenon assists droplet detachment from the superhydrophobic substrate at lower air speeds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it