Turbulent Wake-Flow Characteristics in the Near Wake of Freely Flying Raptors: A Comparative Analysis Between an Owl and a Hawk
Bibliographic record
Abstract
Owl flight has been studied over multiple decades associated with bio-inspiration for silent flight. However, their aerodynamics has been less researched. The aerodynamic noise generated during flight depends on the turbulent state of the flow. In order to document the turbulent characteristics of the owl during flapping flight, we measured the wake flow behind a freely flying great horned owl (Bubo virginianus). For comparison purposes, we chose to fly a similar-sized raptor a Harris's hawk (Parabuteo unicinctus): one is nocturnal and the other is a diurnal bird of prey. Here, we focus on the wake turbulent aspects and their impact on the birds' flight performances. The birds were trained to fly inside a large-scale wind tunnel in a perch-to-perch flight mode. The near wake of the freely flying birds was characterized using a long duration time-resolved particle image velocimetry system. The velocity fields in the near wake were acquired simultaneously with the birds' motion during flight which was sampled using multiple high-speed cameras. The turbulent momentum fluxes, turbulent kinetic energy production, and dissipation profiles are examined in the wake and compared. The near wake of the owl exhibited significantly higher turbulent activity than the hawk in all cases, though both birds are similar in size and followed similar flight behavior. It is suggested that owls modulate the turbulence activity of the near wake in the vicinity of the wing, resulting in rapid decay before radiating into the far-field; thus, suppressing the aerodynamic noise at the far wake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".