Experimental investigation of spreading dynamics of glycerol droplets on a heated surface
Bibliographic record
Abstract
Abstract The dynamics of droplets impacting on solid surfaces are widely encountered in industrial applications. This paper presents an experimental study of the spreading dynamics of droplets of liquids with a temperature‐dependent viscosity on a heated horizontal sapphire surface. This surface can be heated between 40 and 90°C and allows for usual observation from different directions. The used liquids are glycerol water solutions with a volume percentage of 80%, 85.2%, and 92.8% glycerol, which feature a large dependency of the viscosity on the temperature. Using high‐speed imaging and infrared thermography, the shape of the droplet and the temperature of the droplet surface in contact with the surface are measured, respectively. Our experiments reveal that the spreading of the droplets increases with an increase in the surface temperature, which is also expected as the effective viscosity of the liquid will decrease with an increase in the droplet temperature. However, the heating of the droplets ensures that these effects only are apparent after sufficient time to heat the droplet. In addition, the amplitude of the oscillations in the spreading of the surface will increase when the temperature of the surface is increased, which is also related to the decreased viscosity. Finally, the effects on the local spreading cannot be directly correlated to the temperature of the droplet in contact with the surface, which indicates that the viscosity in the droplet is not homogeneous during the experiment and local gradients in the viscosity are important in the overall spreading behaviour of the droplet.
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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".