VISCOELASTIC AIR-BLAST SPRAYS IN A CROSS-FLOW. PART 2: DROPLET VELOCITIES
Bibliographic record
Abstract
To better understand spray coating in field conditions, we have examined the effect of a cross-flow on two different airblast sprays, one comprising water and the other a viscoelastic industrial coating. Using particle image velocimetry, we measured the time-averaged spatial distribution of droplet velocity 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 caused the droplet velocity magnitude to decay more rapidly with streamwise distance. Along the deflecting spray centerline, the decay followed jetlike scaling in the near field but wakelike scaling in the far field. The transverse droplet velocity, meanwhile, showed a local maximum in the main spray body, which was always substantially higher than the cross-flow velocity; normalized values of the local maximum increased with qab, reaching as high as 65%. As for differences between the two sprays, the coating droplets were markedly faster than the water droplets because they had lower drag-momentum ratios and could thus better preserve their initial momentum. For a similar reason, the water droplets were able to more closely track the carrier airflow, approaching the jet/wakelike velocity scaling earlier than did the coating droplets.
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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 | 0.001 |
| 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".