Viscous droplets impact on rough surfaces
Bibliographic record
Abstract
We experimentally investigate the dynamics of viscous droplets impacting on rough surfaces under a broad range of Weber number ( 2 ≤ We ≤ 1 , 194 ), Ohnesorge number ( 0 . 002 ≤ Oh ≤ 2 . 630 ), and average surface roughness ( 9 . 7 μ m ≤ R a ≤ 19 . 5 μ m ). Three primary impact outcomes—jetting, spreading, and splashing—are observed. Our findings reveal that surface roughness promotes splashing by amplifying perturbations, while liquid viscosity counters this effect by dissipating the kinetic energy of the advancing lamella. We empirically describe the splashing threshold with the relation as Oh Re χ ( R a ) = K ( R a ) , where the fitting parameter K ( R a ) increases and χ ( R a ) decreases with greater surface roughness. Moreover, the maximum spreading factor ( β m ), defined as the ratio of the droplet’s maximum spreading diameter to its initial diameter, shows a pronounced dependence on surface roughness in low-viscosity conditions ( Oh < 0 . 050 ), but this dependence diminishes in high-viscosity regimes ( Oh ≥ 0 . 050 ). This trend results from the interplay between viscous dissipation induced by surface roughness and the intrinsic liquid viscosity. In the low-viscosity regime, the experimental β m is consistent with the empirical scaling law of β m = a ( We / Oh ) b , with the fitting constants, a and b , varying with surface roughness and liquid properties. In the regime of 0 . 050 < Oh < 1 , β m approximates ( We / Oh ) 1 / 6 . These findings elucidate the significant role of surface roughness and liquid viscosity in governing droplet impact dynamics and spreading.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".