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Record W4391527887 · doi:10.1115/imece2023-112706

Dynamics of Two Parallel Inverted Flags in Axial Flow

2023· article· en· W4391527887 on OpenAlexafffund
Shaoguang Wang, Mathias Legrand, Michael P. Paı̈doussis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlutterFlag (linear algebra)MechanicsInstabilityCritical ionization velocityBifurcationPhysicsChoked flowFlow (mathematics)Pitchfork bifurcationFlow velocityPotential flowEuler's formulaClassical mechanicsMathematicsHopf bifurcationMathematical analysisAerodynamicsNonlinear systemSupersonic speed

Abstract

fetched live from OpenAlex

Abstract Two identical thin flexible plates, referred to as “inverted flags”, in a side-by-side arrangement are investigated theoretically. A linear model is developed to predict the onset of instability. This linear model elucidates the mechanism of instability and the sensitivity of the critical flow velocity to the gap between the two flags. The dynamics of a single flag has been studied massively, but the coupling of multiple flags is seldom reported. The Euler-Bernoulli beam theory and incompressible potential flow theory are adopted in the model. The Galerkin method and Fourier transform technique are used to solve flag displacements and fluid potentials, respectively. As the flow velocity is increased, the first mode becomes unstable via a pitchfork bifurcation. At higher flow velocities, higher modes lose stability via Hopf bifurcations. Out-of-phase and in-phase motions are predicted for the two flags. It is found that the critical velocity is independent of the flag gap-to-length ratio when it is approximately greater than 1. When the ratio is reduced, the critical velocity becomes smaller. When the ratio is extremely small, flutter occurs to the first mode before static divergence.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes2
Has abstractyes

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