Prediction of the droplet size distribution in aerodynamic droplet breakup
Bibliographic record
Abstract
The rim and bag dynamics in aerodynamic droplet breakup are investigated experimentally and theoretically. Three main modes contribute to the breakup sizes in aerodynamic droplet breakup: the rim node, the remaining rim and the bag breakup modes. However, existing models only consider one mode and are, therefore, unable to predict the size distribution. The present theoretical work seeks to model the dominant breakup mechanisms of each mode and to relate these mechanisms to the size distribution. It is shown that the nodes can be modelled using either the Rayleigh–Taylor or Rayleigh–Plateau instabilities with comparable results and that the variation in the node sizes results from the variation in the amount of mass in the rim that flows into the node prior to the rim breakup. The breakup of the rim is shown to be a result of a combination of the Rayleigh–Plateau instability and a newly proposed collision mechanism, wherein the impact of the corrugated receding rim of the bag with the main rim forces the main rim to break with the same wavelength as the receding rim. The resulting size distribution of the droplet breakup is estimated assuming that the relative weighting of the breakup mechanisms for each mode follows a two-parameter gamma distribution. The volume of each geometry is used to estimate the volume weighting of the modes, giving a reasonable prediction of the size distribution resulting from aerodynamic droplet breakup.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".