Experimental and Mathematical Tools to Predict Droplet Size and Velocity Distribution for a Two-Fluid Nozzle
Bibliographic record
Abstract
Despite progress in laser-based and computational tools, an accessible model that relies on fundamentals and offers a reasonably accurate estimation of droplet size and velocity is lacking, primarily due to entangled complex breakup mechanisms. Therefore, this study aims at using the integral form of the conservation equations to create a system of equations by solving which, the far-field secondary atomization can be analyzed through predicting droplet size and velocity distributions of the involved phases. To validate the model predictions, experiments are conducted at ambient conditions using water, methanol, and acetone as model fluids with varying formulation properties, such as density, viscosity, and surface tension. Droplet size distribution and velocity are measured with laser diffraction and a high-speed camera, respectively. Finally, an attempt is made to utilize non-scaled parameters to characterize the atomization process, useful for extrapolating the sensitivity analysis to other scales. The merit of this model lies in its simplicity for use in process control and optimization.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".