Experimental study of the unsteady aerodynamics during flapping flight of birds: European Starling, Western Sandpiper and American Robin
Bibliographic record
Abstract
Birds’ unique characteristics such as wing shape, flexibility, feathers, flapping motion, etc., result in high aerodynamic performance. Using various flight modes such as gliding, bounding, and flapping, birds can use a single propulsion system for multiple functions. In low Reynolds number flyers using flapping flight mechanisms, the contribution of unsteady effects on lift and drag is not entirely understood. To gain insight about the unsteady contributions, a controlled study on the near wake flow behind freely flying birds was performed. Long duration, time resolved particle image velocimetry (PIV), combined with high speed imaging has been used to characterize the various flow features in the wake that are associated with flapping flight. The specially designed PIV system can sample the flow field for twenty minutes yielding a continuous measurement, sampling several wingbeat cycles consecutively. Time series of the vorticity fields have been expressed as composite wake plots, which reveal various characteristics of the wake during the upstroke (US) and down stroke (DS) phase of the flapping as well as the transition between US to DS and vice versa. Comparison between the near wake fields behind the three birds show remarkable similarity in their wake structure. We have identified over multiple wing beat cycles the presence of what appears to be an overlap of two distinct wakes during the transition from US toDS, named “double branch”. Over the region of the double branch, the majority of net positive circulation is accumulated. Indicating this may be a key feature in producing lift, and thus contribute to the observed high aerodynamic performance.
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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".