ASSESSMENT OF PARAMETERS FOR DISTINGUISHING DROPLET SHAPE IN A SPRAY FIELD USING IMAGE-BASED TECHNIQUES
Bibliographic record
Abstract
Quantification of droplet shape in a spray field can elucidate several characteristics and mechanisms of the atomization process such as droplet deformation, breakup, and collision. To identify an optimum parameter for accurate quantification of droplet shape using image-based measurement systems, several parameters from different applications are presented in terms of their mathematical definition, calculation procedure, and characteristics. An experimental investigation using a shadowgraph droplet analyzer is also conducted to provide visual evidence of droplet shape in a spray field. The droplets from this data set are classified based on their shape into three categories, namely, spheres, deformed droplets, and ligaments. The capability of the shape parameters in distinguishing between these droplet groups is investigated using a simulation and the collected droplet images. Many of the parameters have insufficient resolution to distinguish between different droplet shapes. A new scaling parameter is applied to each of the parameters to distinguish droplets that are purely convex (spheres and deformed droplets) from those that have concavity (ligaments). From those investigated, an optimum shape parameter is suggested to distinguish the three droplet groups.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".