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Record W4409890122 · doi:10.1063/5.0262632

Reynold number scaling of circulation growth on flapping wings

2025· article· en· W4409890122 on OpenAlexafffund
Zahra Hajati, Maya L. Evenden, Jaime G. Wong

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsFlappingReynolds numberCirculation (fluid dynamics)ScalingMechanicsClassical mechanicsAerospace engineeringMeteorologyGeometryTurbulenceThermodynamics

Abstract

fetched live from OpenAlex

Insect scale flight is characterized by large amplitude motions and high frequencies, often described by the Strouhal number and reduced frequency. Previous studies have scaled force coefficients with Strouhal number using classical potential flow theories. However, these often include a Reynolds number term, despite the implicit inviscid assumptions in classical theory. In this study, we derive a scaling relationship between force coefficients and wing kinematics in order to investigate this apparent contradiction. The model is based on the work of von Kàrmàn and Sears and, as a consequence of this choice, also provides an estimate of circulation in the wake. We consider a Blasius-like stream function to model the circulation transported into the leading-edge vortex from the shear layer in two cases: one that scaled circulation flux by the Reynolds number and one that did not. This model was applied to an existing insect flight data set, consisting of 168 individual flights of 38 individual mountain pine beetles. The model accurately captures the relationship between force coefficient and wing kinematics, regardless of the stream function scaling with or without the Reynolds number. However, only the model that was allowed to scale with the Reynolds number was also able to predict the circulation in the wake.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.367

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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