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Record W2795227631 · doi:10.1115/1.4039781

Measurement of the Excitation Source of an Axisymmetric Shallow Cavity Shear Layer

2018· article· en· W2795227631 on OpenAlexaff
Saber Mohamed, Hans R. Graf, Samir Ziada

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAcousticsMechanicsSound pressureStanding waveResonance (particle physics)Rotational symmetryTurbulenceAcoustic resonanceOscillation (cell signaling)PhysicsMaterials scienceAtomic physics

Abstract

fetched live from OpenAlex

The interaction of a cavity shear layer with the sound field of an acoustic mode can generate an aeroacoustic source which is capable of initiating and sustaining acoustic resonances in the duct housing the cavity. This aeroacoustic source is determined experimentally for an internal axisymmetric cavity exposed to high Reynolds number, fully developed turbulent pipe flow without the need to resolve the details of neither the unsteady flow field nor the flow-sound interaction process at the cavity. The experimental technique, referred to here as the standing wave method (SWM), employs six microphones distributed upstream and downstream of the cavity to evaluate the fluctuating pressure difference generated by the oscillating cavity shear layer in the presence of an externally imposed sound wave. The results of the aeroacoustic source are in good agreement with the concepts of free shear layer instability and the fluid-resonant oscillation behavior. The accuracy of the measurement technique is evaluated by means of sensitivity tests. In addition, the measured source is used to predict the self-excited acoustic resonance of a shallow cavity in a pipeline. Comparison of the predicted and measured results shows excellent prediction of the self-excited acoustic resonance, including the resonance frequency, the lock-in velocity range, and the amplitude of the self-generated acoustic resonance.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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 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

Citations5
Published2018
Admission routes1
Has abstractyes

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