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Record W2770998296 · doi:10.1515/johh-2017-0025

Effect of transverse perforations on fluid loading on a long, slender plate at zero incidence

2017· article· en· W2770998296 on OpenAlexfundno aff
Filippo Carlo Bossi, Oleksandr Barannyk, Mostafa Rahimpour, Stefano Malavasi, Peter Oshkai

Bibliographic record

VenueJournal of Hydrology and Hydromechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStrouhal numberVortex sheddingDrag coefficientTransverse planeMechanicsReynolds numberLift coefficientWakeMaterials sciencePerforationDragLift (data mining)PhysicsStructural engineeringComposite materialEngineeringTurbulence

Abstract

fetched live from OpenAlex

Abstract This paper reports the results of experimental investigations of flow-induced loading on perforated and solid flat plates at zero incidence with respect to the incoming flow. The plates had a streamwise length to transverse thickness ratio of 23.5. The effect of the perforations was investigated for three different perforation diameters. The results corresponding to the perforated plates were compared with the reference case of the solid plate (no perforations) at five inflow velocities. We quantified the effect of the perforations on the unsteady fluid loading on the plate in terms of the variations of the corresponding Strouhal number, the mean drag coefficient and the fluctuating lift coefficient as functions of the Reynolds number and the perforation diameter. The results indicate that the loading was dominated by the dynamics of the wake. In particular, increasing the perforation diameter resulted in a wider wake, corresponding to the increase in mean drag coefficient and the decrease in the Strouhal number. Onset of coupling between the vortex shedding and the transverse oscillations of the plate was manifested as a rapid increase in the fluctuating lift coefficient, as the perforation diameter exceeds the plate thickness.

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: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.461

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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