VISCOELASTIC AIR-BLAST SPRAYS IN A CROSS-FLOW. PART 1: PENETRATION AND DISPERSION
Bibliographic record
Abstract
Viscoelastic liquids, such as paints and coatings, are widely known to be more difficult to atomize than typical Newtonian liquids. What is not known, however, is how such a difference affects spray coating in field conditions. To address this, we have examined the effect of a cross-flow on two different air-blast sprays, one comprising water and the other an industrial coating. Using particle image velocimetry and raw Mie scattering, we measured the penetration and dispersion of both sprays over a wide range of spray:cross-flow momentum-flux ratios: 134 ≤ qab ≤ 1382. For both sprays, increasing the relative momentum-flux of the cross-flow led to reduced penetration but enhanced dispersion. The leeward boundary, meanwhile, consistently outspread the windward boundary, creating a bias for which we have proposed several plausible mechanisms. As for differences between the two sprays, the coating droplets outpenetrated the water droplets because they had lower drag-momentum ratios, due to their larger size. In response to this finding, we have proposed a new regression model for predicting the penetration of sprays differing in mean droplet size. The coating spray also spread later and more abruptly than did the water spray, particularly at low qab. To explain this behavior, we have suggested a physical mechanism based on the delayed breakup of the coating ligaments.
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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) | 0.001 | 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".