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Record W2332070668 · doi:10.2514/6.2014-1423

A numerical study of the influence of aspect ratio and gap on 3D galloping of square prisms

2014· article· en· W2332070668 on OpenAlexaff
Simon Corbeil-Létourneau, Stéphane Étienne, Alexander Hay, Dominique Pelletier, André Garon

Bibliographic record

Venue52nd Aerospace Sciences Meeting · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSquare (algebra)Aspect ratio (aeronautics)Materials scienceStructural engineeringGeometryEngineeringMathematicsComposite material

Abstract

fetched live from OpenAlex

Transverse galloping of square sections cylinders is a movement induced oscillation. Generally, it is predicted from quasi-steady theory by using polar of loads resulting from static tests or simulations. In static we mean that the body is kept fixed in a constant cross-flow. Transverse galloping arises for high values of the reduced velocities. In fact the higher the reduced velocity, the larger the amplitude of oscillation. The vibration frequency is close to the natural frequency of the cylinder if we take into account the added mass effects. Our work focuses on the low mass ratio galloping. For this range of mass ratios, quasi-steady theory predicts that the galloping amplitude increases substantially as the mass ratio is decreased down to zero. We showed in Joly et al. that as the mass ratio is reduced, galloping is perturbed by vortex-induced vibrations (VIV) such that the resulting amplitude comes only from vibrations induced by vortex-shedding. Experimental tests on truncated square prisms showed that it does not reveal this phenomenon. We show in this paper that if we consider the flow around the extremities of the prism, as is the case in the experiments considered, vortex-shedding is delayed further in the wake. Vortex-shedding induced loads are thus largely reduced and large amplitude galloping is recovered.

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)0.001
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: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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