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Record W2050775257 · doi:10.1115/pvp2013-97021

Effect of Sound on the Wake of Side-by-Side Cylinders in a Duct

2013· article· en· W2050775257 on OpenAlexafffund
David Arthurs, Michael Bolduc, Adam Ritcey, Samir Ziada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDuct (anatomy)Acoustic resonanceWakeBistabilityAcousticsPipingPhysicsAmplitudeSound pressureMechanicsResonance (particle physics)CylinderFlow visualizationVortexFlow (mathematics)ResonatorOpticsGeometryAtomic physicsThermodynamics

Abstract

fetched live from OpenAlex

The phenomenon of flow-excited acoustic resonance, where periodic flow oscillations are enhanced by a resonant sound field, is a design concern in many engineering applications such as in heat exchangers, piping systems, and cavity flows. This study experimentally examines the phenomenon of flow-excited acoustic resonance for two side-by-side cylinders in a duct with cross-flow. This geometry has been investigated for three cylinder spacing ratios, defined as the center-to-center distance, normalized by the diameter, of T/D = 1.25, 1.46 and 2.5, and for a range of acoustic pressure amplitudes. Intermediate and small spacing ratios have been given special attention, as these cases have been found to exhibit bistable flow in the wake in the absence of acoustic resonance. Phase-locked PIV measurements reveal that the self-excited sound field produces a strong oscillatory flow pattern in the cylinder wakes, which is symmetric for large spacing ratios and high acoustic amplitudes, but remains bistable for small spacing ratios, even during very intense acoustic resonances. The aeroacoustic sources and sinks within the flow have been computed using Howe’s theory of vortex sound, and the distribution of these sources will be compared for the range of spacing ratios examined in this study.

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 score0.340
Threshold uncertainty score0.251

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 head0.005
GPT teacher head0.202
Teacher spread0.198 · 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
Published2013
Admission routes2
Has abstractyes

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