Leveling of a Line of Paint Droplets on a Surface
Bibliographic record
Abstract
When liquid droplets are sprayed onto a solid surface during spray painting and coating, defects may be created due to non-uniformity of the liquid film. The time scale required for leveling the liquid film is of utmost importance. In this paper, the leveling of a droplet line was modeled numerically using a three-dimensional parallel code, based on a two-step projection method with a volume-of-fluid technique used to model free surface flow. Simulations and experiments were performed with a high viscosity liquid (87%wt glycerin in water solution and commercial paint) where a droplet line was deposited with varying center-to-center distance between the droplets and the time scale for leveling was measured. Predictions from the numerical model for droplet line thickness and variation in the thickness agreed well with experimental measurements. Simulation and experimental measurements clearly showed that the drastic portion of thickness change happened in the first 30 seconds. When the droplets were dispensed close to one another (initially thicker films), there was a lot of interaction between the droplets and therefore there was a huge variation in the thickness of the droplet line until the variation died down and the line was level. On the other hand, when droplets were dispensed further apart (initially thinner films), there was less interaction between the droplets and therefore less variation of the film thickness from the beginning and the leveling occurred much sooner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".