Wing extension–flexion coupled aeroelastic effects improve avian gliding performance
Bibliographic record
Abstract
During flight, birds instigate remarkably large changes in wing shape, commonly termed ‘wing morphing’. These changes in shape, particularly extension–flexion, have been well documented to influence the production of aerodynamic forces. However, it is unknown how wing stiffness changes as a result of the structural rearrangements needed for morphing. We address this gap in knowledge through mechanical testing of in situ flight feathers in anaesthetized pigeons and found that while the most distal portion of the feathered wing remained unaffected, proximal areas saw an increase in out-of-plane stiffness due to wing folding. Following this, we used computational fluid–structure interaction simulations to evaluate how this morphing-coupled change in stiffness might modulate local flow patterns to affect aerodynamic performance. We found that flexible wings perform better than entirely rigid wings as an increase in near-wall vorticity delayed flow separation. Furthermore, an increase in stiffness in a folded wing during high-speed flight prevented the reduction in lift seen in more flexible cases caused by aeroelastic flutter modes destructively interfering with shed leading-edge vortices. Collectively, these results reveal that mechanical changes coupled with wing morphing can provide a speed-dependent mechanism to enhance flight 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".