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Record W2895054269

Suppression of Flow-Excited Acoustic Resonance in Rectangular Cavities Using Spanwise High Frequency Vortex Generators

2018· article· en· W2895054269 on OpenAlexaff
Moamen Abdelmwgoud, Atef Mohany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsVortexVortex sheddingAcousticsDuct (anatomy)Materials scienceAcoustic resonanceOpticsMechanicsResonance (particle physics)Vortex generatorFlow (mathematics)Enhanced Data Rates for GSM EvolutionStatic pressurePhysicsTurbulenceResonatorEngineeringAnatomyReynolds numberTelecommunicationsAtomic physics
DOInot available

Abstract

fetched live from OpenAlex

The effectiveness of spanwise high frequency vortex generators in suppressing the acoustic resonance in flow over ducted rectangular cavities is experimentally investigated. Acoustic pressure measurements are performed where it is found that placing a circular rod upstream of the cavity edge can reduce the generated acoustic pressure significantly. By studying the flow structures in the region over the cavity, effective rod configurations can be identified and the effectiveness of this suppression method can be enhanced. Accordingly, hotwire measurements have been performed to understand the interaction mechanism between the shear layer developed over the cavity mouth and the vortex shedding generated by the rod. The velocity distribution along the cavity mid-plane when no rod is placed and when the rod is placed at different locations relative to the cavity upstream edge are discussed. It was found that the most effective rod locations mainly depend on the rod axis location with respect to the upstream edge as well as the vertical gap between the duct wall and the rod.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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 designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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