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Record W2051511517 · doi:10.1115/fedsm2007-37167

Effect of Shear Layer Interaction on Acoustic Response of Coaxial Side Branch Resonators

2007· article· en· W2051511517 on OpenAlexaff
Peter Oshkai, Alexey Velikorodny, Tian-Yu Yan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAcousticsResonatorParticle image velocimetryShear (geology)CoaxialAmplitudeAcoustic waveOscillation (cell signaling)PhysicsOpticsTurbulenceMechanicsMaterials scienceEngineeringTelecommunicationsChemistry

Abstract

fetched live from OpenAlex

Fully turbulent inflow past a coaxial side branch resonator mounted in a duct can give rise to pronounced flow oscillations due to coupling between separated shear layers and standing acoustic waves. Experimental investigation of acoustically-coupled shear layers is conducted using digital particle image velocimetry in conjunction with unsteady pressure measurements. Global instantaneous flow images, as well as phase-averaged images, are evaluated to provide insight into the flow physics during tone generation. The emphasis is on the effect of shear layer interaction on the acoustic response of the resonator during the first and second hydrodynamic modes of the shear layer oscillation. Onset of the locked-on resonant states is characterized in terms of the acoustic pressure amplitudes and the quality factors of the corresponding spectral peaks. Moreover, patterns of generated acoustic power are calculated using a semi-empirical approach. As the level of interaction between the separated shear layers is increased, spatial structure of the acoustic source undergoes a substantial transformation.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.257
Teacher spread0.251 · 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
Published2007
Admission routes1
Has abstractyes

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