10.1063/5.0063049.4
Bibliographic record
Abstract
The dynamic behavior and disintegration mechanisms of a fan liquid sheet in a quiescent atmosphere are investigated over a broad range of differential injection pressures up to Δp≈70 bar through experiments, proper orthogonal decomposition (POD) and spectral analyses, and linear stability analysis (LSA). By fan liquid sheet, we mean a diverging and attenuating liquid stream emanating from a flat fan nozzle with high velocity. High spatiotemporal resolution backlit images reveal the formation-growth-fragmentation process of bag-like structures along the fan liquid sheets, which we predict to be responsible for the overall breakup of the sheets through a mechanism known as “wavy corridor.” Therefore, we propose a conjugate model based on LSA to take into account the role of different shear and surface tension-driven instabilities in defining the liquid sheet intact radius and primary droplet sizes. The predictions of LSA from the dynamic features of the liquid sheets, which mainly depend on the sheet Weber number We, are consistent with the quantitative results obtained from the POD and spectral analyses of the images. While the Strouhal number St and the intact radius R of the fan liquid sheets reduce like We−1/3 with increasing We, the volume median diameter of primary droplets decreases like We−11/12. An image feature consolidation technique along with a machine-learning technique, receiver operating characteristic curve analysis, was used to estimate the mean diameter of spray droplets with a large range of sizes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.808 | 0.715 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".