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Record W4410108841 · doi:10.1098/rsif.2024.0753

Wing extension–flexion coupled aeroelastic effects improve avian gliding performance

2025· article· en· W4410108841 on OpenAlexaff
Jasmin Wong, Vaibhav Joshi, Rajeev K. Jaiman, Douglas L. Altshuler

Bibliographic record

VenueJournal of The Royal Society Interface · 2025
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsUniversity of British Columbia
FundersAir Force Office of Scientific Research
KeywordsWingMorphingAeroelasticityAerodynamicsWing twistFlutterStiffnessStructural engineeringSwept wingFlight featherVortexMechanicsAerospace engineeringComputer scienceAngle of attackPhysicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.211
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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