Bibliographic record
Abstract
Droplet motion induced by external forces such as mechanical vibrations, shear flows or gravitational forces has a major importance in many industrial applications.The present study introduces a setup for a tilting plane experiment, which enables investigations of the droplet behaviour due to the balance between surface tension and gravitational force.Furthermore, droplet motion measurements on a tilting acrylic glass surface are presented.The main aspect of this study is the influence of the droplet volume and the angular velocity of the inclining plane on the droplet detachment.To quantify this influence, different moving regimes are detected and specified.Further, flow maps due to the rotational velocity are obtained.The results show that the inclination angle needed to initiate drop motions decreases when the droplet volume increases.In addition to that, the droplet motion is initiated at a smaller inclination angle if the angular velocity decreases.This behaviour has no influence when rotational velocity increases above a certain threshold.The study concludes that the detachment of water droplets on a tilting acrylic surface can be forced and amplified if either the droplet volume is increased or the angular velocity of the surface is decreased.
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.
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".