Experimental and numerical analysis of shear-driven droplet coalescence on surfaces with various wettabilities
Bibliographic record
Abstract
The goal of this study is to explore and analyze the concurrent shear-driven droplet shedding and coalescence under the effect of various parameters, such as droplet size and distance, as well as airflow velocity and surface wettability. To investigate and capture different aspects of droplet dynamics, both experimental modeling and numerical modeling are conducted. The volume of fluid coupled with the large-eddy simulation turbulent model in conjunction with the dynamic contact angle is implemented to model droplet shedding on different surface wettabilities. Analysis revealed a great match between the numerical and experimental outcomes. It is shown that in addition to surface wettability and airflow speed, droplet sizes, and the distance between them are crucial factors in controlling droplet dynamics during the shedding and coalescence. It is illustrated that on the aluminum (hydrophilic) surface, the second droplet (the one further from the airflow inlet) tends to move toward the first droplet (the one closer to the airflow inlet) more significantly when the distance between droplets is larger as well as the cases where the first droplet is also the larger one. It is revealed that if the first droplet is larger, after coalescence the resulting droplet will break up into smaller droplets known as satellites. On the superhydrophobic surfaces, on the other hand, droplets behaved differently, which is mainly related to initial droplet shape and dynamic contact angles. For the cases of the larger distance between the droplets, the first droplet is lifted off from the surface after a few milliseconds, and consequently, the second droplet is not prone to move toward the first one. When the first droplet is larger between the two, the second droplet tends to move toward the first one in contrast to the case where the first droplet is the smaller one. To better interpret the droplet dynamics, and the effect of different parameters on their behavior, further details on aerodynamic forces including the drag and lift forces before and after the coalescence are presented in this work.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".