Determining the relative stability of leading-edge vortices on nominally two-dimensional flapping profiles
Bibliographic record
Abstract
Abstract It is hypothesized that the relative stability of leading-edge vortices (LEVs) on flapping profiles can be improved by moderating LEV growth through spanwise vorticity convection and vortex stretching. Moreover, it is hypothesized that the reduced frequency $k$ and profile sweep ${\it\Lambda}$ are critical in predicting relative LEV stability as determined by the aforementioned effects. These hypotheses are then confirmed experimentally with phase-averaged particle image velocimetry (PIV) and three-dimensional particle tracking velocimetry. In particular, more stable LEVs are observed at higher reduced frequencies, which is argued to represent the ratio between the limiting vortex size and the rate of vorticity feeding. The introduction of profile sweep increased both relative LEV stability and spanwise vorticity transport. It is thought that spanwise vorticity transport improved LEV stability by acting as a sink for vorticity generated in the leading-edge shear layer.
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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 | 0.001 |
| 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 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".