INFLUENCE OF SOME GEOMETRICAL PARAMETERS ON THE CHARACTERISTICS OF PREFILMING TWIN-FLUID ATOMIZATION
Bibliographic record
Abstract
Geometries are considered to have a great influence on the spray characteristics of atomizers. In the present study, we studied a prefilming twin-fluid atomizer patented by Sadatomi and Kawahara (2012), in which liquid atomization is implemented by supplying compressed air alone into an internal mixing chamber, and water is automatically sucked by the negative pressure induced by an orifice. In the experiments, we studied spray characteristics influenced by the geometrical parameters, such as orifices in different opening area ratios and different shapes, porous rings with different porous diameters, and different atomizer sizes. Higher spray performance can be obtained by a small sized atomizer with a circular orifice in opening area ratio of 0.429 and a porous fiber ring with porosity of 25 μm. The present results provide a significant guidance for practical applications with different requirements of spray characteristics.
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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".