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Record W4312553976 · doi:10.1115/pvp2022-84907

Control of Vortex Shedding and Acoustic Resonance of a Circular Cylinder in Cross-Flow

2022· article· en· W4312553976 on OpenAlexaff
Rasha Noufal, Mohammed Alziadeh, Atef Mohany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsStrouhal numberVortex sheddingCylinderResonance (particle physics)Acoustic resonanceParticle image velocimetryPhysicsVortexExcitationAcousticsOpticsSound pressureMechanicsMaterials scienceGeometryAtomic physicsTurbulenceMathematics

Abstract

fetched live from OpenAlex

Abstract This paper presents an experimental study of the aeroacoustics response from a circular cylinder in cross-flow with a control rod. The effectiveness of the control rod on suppressing acoustic resonance excitation is investigated. The control rod diameter-to-circular cylinder diameter ratio (d/D) is fixed at 0.21. The gap (G) between the circular cylinder and the control rod is taken at 0.11D. The rod is placed at several angular positions, which varied from 0° (front stagnation of the main cylinder) to 180° (base of the main cylinder). Phase-locked particle image velocimetry (PIV) measurements are performed during acoustic resonance to visualize the coherent vortex structures downstream of the cylinders. The results show that the placement of the control rod significantly influences the Strouhal periodicity with great dependence on the rod’s angular orientation. Moreover, at some angular positions, the existence of the rod has resulted in a reduction of the sound pressure level (SPL) generated during acoustic resonance excitation. However, at other angular positions, the rod has resulted in a stronger resonance excitation. This behavior is due to the profound effect of the control rod’s angular position on the formation of vortex cores during acoustic resonance. A brief summary of the results is presented herein.

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 score0.197
Threshold uncertainty score0.225

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 head0.211
Teacher spread0.207 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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