Droplet dynamics under shear flow on surfaces with different wettability
Bibliographic record
Abstract
Droplet dynamics on surfaces under shear flow, which affects the efficiency of water condensate removal in condensation , are significant for investigation. The influencing factors include air velocity, droplet volume and surface wettability . Three smooth surfaces (HI, HO-POTS, and HO-PDMS) and three semi-textured surfaces (HI+SHO, HO-POTS+SHO, and HO-PDMS+SHO) are fabricated. Experiments are performed for droplets with different volumes under different air velocities. A centrifugal fan is used to generate channel flow, and a high-speed camera is utilized to capture droplet behaviors. The results show that motion of droplets with small deformation on smooth surfaces can be divided into two stages (Acceleration and Constant Speed). Critical velocity for the onset of droplet motion, as well as the velocity that droplet can reach at Constant Speed Stage, is affected by air velocity, droplet volume and surface wettability . The mathematical expression of retention force is derived, and forces are calculated to verify the force balances at Constant Speed Stage. Droplet behaviors on semi-textured surfaces are much different from those on smooth surfaces, especially when crossing the boundaries between smooth and textured areas. The HO+SHO surfaces display a better performance of droplet removal compared with the HI+SHO surface.
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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) | 0.001 | 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".