Surface roughness effect on droplet impact characterization: Experimental and theoretical study
Bibliographic record
Abstract
In this paper, the effect of surface roughness on the both normal and inclined droplet impact is investigated experimentally by image processing. The impingement of water droplets with 2.9 mm diameter and 1m/s velocity impacting on three types of stainless steel surfaces with respective arithmetic average surface roughness values of 2.24 μm (Smooth), 6.04 (Medium) and 30.2 (Rough) is examined using a high-speed camera. The dynamic behavior of the impact including droplet deformation, the maximum spreading diameter and length, contact angle and the number of fingers are studied. Experimental results demonstrate that rough surfaces not only prevent secondary droplet formation but also decrease the number of fingers formed around the droplet in the normal droplet impact. Considering the inclined droplet impact scenarios, the asymmetric spreading of droplet on inclined surfaces avoids the secondary droplet formation by decreasing the fluid kinetic energy. In the inclined impact, fingers are formed around the droplet perimeter like the normal impact. The only difference between impacts onto the inclined surfaces is a gradual decrease of the number of fingers by increasing the incline angle. The experimental results are compared with those of the analytics available in the literature for the normal droplet impacts. Next, a simple analytical model for droplet impact on an inclined surface is developed; the predictions from this model was also compared to those of measurements. Calculated values from the analytical models agreed well with experimental data for both normal and inclined impact scenarios.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".