Flow Features of the Near Wake of the Australian Boobook Owl (Ninox boobook) During Flapping Flight Suggest an Aerodynamic Mechanism of Sound Suppression for Stealthy Flight
Bibliographic record
Abstract
Synopsis The mechanisms associated with the ability of owls to fly silently have been the subject of scientific interest for many decades and may be relevant to bio-inspired design to reduce noise of flapping and non-flapping flying devices. Here, we characterize the near wake dynamics and the associated flow structures produced during flight of the Australian boobook owl (Ninox boobook). Three individual owls were flown at 8 ms−1 in a climatic avian wind tunnel. The velocity field in the wake was sampled at 500 Hz using long-duration high-speed particle image velocimetry (PIV) while the wing kinematics were imaged simultaneously using high speed video. The time series of velocity maps that were acquired over several consecutive wingbeat cycles enabled us to characterize the wake patterns and to associate them with the phases of the wingbeat cycle. We found that the owl wake was dramatically different from other birds measured under the same flow conditions (i.e., western sandpiper, Calidris mauri and European starling, Sturnus vulgaris). The near wake of the owl did not exhibit any apparent shedding of organized vortices. Instead, a more chaotic wake pattern was observed, in which the characteristic scales of vorticity (associated with turbulence) are substantially smaller in comparison to other birds. Estimating the pressure field developed in the wake shows that owls reduce the pressure Hessian (i.e., the pressure distribution) to approximately zero. We hypothesize that owls manipulate the near wake to suppress the aeroacoustic signal by controlling the size of vortices generated in the wake, which are associated with noise reduction through suppression of the pressure field. Understanding how specialized feather structures, wing morphology, or flight kinematics of owls contribute to this effect remains a challenge for additional study.
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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.
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