Azimuthal shear instability of a liquid jet injected into a gaseous cross-flow
Bibliographic record
Abstract
Abstract We investigate azimuthal instabilities which exist on the periphery of a non-turbulent liquid jet injected transversely into a gaseous cross-flow. We predict that the temporal growth of such instabilities may lead to the formation of interface corrugations, which are eventually sheared off of the jet surface (known as the jet ‘surface breakup’). In this study we employ temporal linear stability analyses to understand the nature of these instabilities. The analysis is based on a continuous formulation of momentum equations in which the jet and cross-flow are considered to be slightly miscible at the vicinity of the interface. We identify the shear instability as the primary destabilization mechanism in the flow. This inherently inviscid mechanism opposes the previously suggested mechanism of surface breakup (known as ‘boundary-layer stripping’), which is based on a viscous interpretation. The results show that the wavelengths of instabilities increase by moving away from the jet windward stagnation point toward the leeward point. We also investigate the influence of the jet-to-cross-flow density ratio on the flow stability and find that a higher ratio leads to formation of instabilities with higher wavenumbers on the jet surface. The results show that the density may have a non-monotonic stabilizing/destabilizing effect on the flow.
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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 | 0.000 |
| 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) | 0.001 | 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".