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Record W2971475423 · doi:10.1115/pvp2019-93576

A Fluidelastic Model for the Nonlinear Dynamics of Two-Dimensional Inverted Flags

2019· preprint· en· W2971475423 on OpenAlexaff
Mohammad Tavallaeinejad, Michael P. Paı̈doussis, Mathias Legrand, Mojtaba Kheiri

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsFlappingPhysicsNonlinear systemMechanicsClassical mechanicsBifurcationFlag (linear algebra)Inviscid flowMathematicsWing

Abstract

fetched live from OpenAlex

Abstract A nonlinear fluid-elastic model is proposed for the study of the dynamics of inverted flags. The quasi-steady version of Theodorsen’s unsteady aerodynamic theory is used for inviscid fluid-dynamic modelling of the deforming flag in axial flow. Polhamus’s leading edge suction analogy is employed to model flow separation effects from the free end at moderate angles of attack via a nonlinear vortex-lift force. The flag is modelled structurally via a geometrically-exact Euler-Bernoulli beam theory. Using the extended Hamilton’s principle, the nonlinear partial-integro-differential equation governing the dynamics of the inverted flag in terms of the angle of rotation of the flag is obtained. The equation of motion is discretised spatially via the Galerkin method and is integrated in time via Gear’s backward differentiation formula. The bifurcation diagrams are obtained using a time-integration method and pseudo-arclength continuation. It is shown that inverted flags undergo multiple bifurcations with respect to flow velocity, and they generally exhibit four dynamical states: (i) stretched-straight, (ii) buckled, (iii) deflected-flapping, and (iv) large-amplitude flapping. Also, flapping of inverted flags probably develops through fluid-elastic instabilities. Our findings suggest that the system dynamics is sensitive to the mass ratio. It is shown that the mass ratio parameter does not affect the stability of the stretched-straight state and the onset of divergence; however, it controls the possibility of a direct transition from static undeflected equilibrium to large-amplitude flapping motion and it affects the amplitude of large-amplitude flapping.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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