Experimental research on dynamic characteristics of viscous droplets impacting rough solid surfaces at different temperatures
Bibliographic record
Abstract
In view of different factors that influence dynamic behavior of a droplet impacting a solid surface, high-speed photography technology was used to capture oscillation processes and splash forms after changing seven kinds of physical properties, droplets impacting different roughness and temperature walls, and with different velocities and sizes. A physical model was established, and the theoretical value correlation of maximum spreading factor was derived to compare the theoretical analysis with experimental results. The effects of viscosity, surface tension, impact velocity, diameter of droplet, roughness, and temperature of the substrates on movement characteristics during the droplet’s impact on the wall surface were investigated. As the research indicates, the rebound and oscillatory phenomena of the fluid become more obvious with an increase in surface tension, and viscous force restricts the spreading of droplets. The higher the impact velocity, the greater the spreading factor at the same time, and the more pronounced the splashing phenomenon will be. The growth rate of maximum spreading factor (β max ) increases at first and then decreases with increasing initial diameter (d 0 ) of the droplets. The smaller the d 0 , the more consistent the experimental results with the analytical solutions. The equilibrium contact angle (θ e ) of the droplet increases with surface roughness (Ra), whereas the surface wettability degrades. θ e decreases with rising wall temperature. The increase of Ra promotes the “finger-like edge” and the splash motion of droplets; moreover, the critical velocity of splash declines with Ra. The optimum temperature (T c ) of a droplet impacting the high-temperature wall reduces with a decrease of Re. Furthermore, the greater the difference between wall temperature and T c , the more significantly β max changes. Droplet spreading is hindered on the low-temperature wall, and the lower Re is, the smaller the decrease in amplitude of β max with dropping wall temperature.
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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".