Effect of process parameters on droplet spreading behaviour over porous surface
Bibliographic record
Abstract
Abstract Droplet spreading behaviour over a porous surface is a complex phenomenon, and is a basic component of many industrial processes, for example the spray coating process. The coating process has wide applications and this includes coating of urea fertilizer to produce slow release urea. The quality of coating film in such applications is affected by many factors, one of them being droplet spreading on the substrate. Droplet spreading behaviour is affected by process parameters such as viscosity, density, surface tension, impact velocity, porosity, etc. Droplet spreading on a porous surface involves penetration into the porous surface and spreading on the surface. Previously, the effect of individual process parameters has been studied. The current work aims at finding the interactive effect of process parameters on droplet spreading behaviour by using response surface methodology. The combined effect of liquid viscosity, impact velocity, and surface porosity has been studied on contact angle, spreading factor, and residual drop volume. The results show that minimum contact angle can be achieved with maximum impact velocity, minimum porosity, and minimal liquid viscosity. Similar behaviour was observed with droplet residual volume. Maximum spreading factor was attained at minimum viscosity and porosity while impact velocity was at maximum level.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".