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Record W4408778408 · doi:10.3390/birds6020015

Aerodynamics of Flight Formations in Birds: A Quest for Energy Efficiency

2025· article· en· W4408778408 on OpenAlexaboutno aff
Fabien Beaumont, Sébastien Murer, Fabien Bogard, Guillaume Polidori

Bibliographic record

VenueBirds · 2025
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsDownwashAerodynamicsDragLift-induced dragWakeAirspeedFlappingAerospace engineeringVortexLift-to-drag ratioLift (data mining)MechanicsWingPhysicsMarine engineeringEnvironmental scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

While the formation flight of birds offers numerous benefits, such as reduced predation risk, improved orientation, and enhanced communication, the aerodynamic interactions between birds are not fully understood due to their inherent complexity. This study explores the wake vortex dynamics of two flying birds and their influence on aerodynamic forces, based on their relative positions in a group. Using a computational finite volume method, the 3D vortex patterns in the wake of Canada Geese (Branta canadensis) flying at 1000 m altitude and 13.9 m/s airspeed were modeled. The results reveal a complex, undulating vortex structure shaped by the wingbeat amplitude and frequency. The analysis shows that trailing birds positioning their wingtips within the upwash region of vortices that are generated by a leading bird significantly reduce drag and enhance lift, achieving up to a 32% improvement in aerodynamic efficiency, calculated as the lift-to-drag ratio. An optimal separation distance of approximately one wavelength (3.47 m) between birds has been identified, leading to a 7% reduction in both mean drag force and aerodynamic power requirements. These findings, extrapolated to larger bird groups, offer valuable insights into the organization and optimal positioning of birds flying in V-formations, advancing our understanding of collective flight dynamics.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.311

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 integrity0.0000.000
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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations3
Published2025
Admission routes1
Has abstractyes

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