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Record W3162313066 · doi:10.1063/5.0051226

Shear layer synchronization of aerodynamically isolated opposite cavities due to acoustic resonance excitation

2021· article· en· W3162313066 on OpenAlexafffund
Moamenbellah Abdelmwgoud, Mahmoud Shaaban, Atef Mohany

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsMechanicsResonance (particle physics)ExcitationVortexAcoustic resonanceReynolds numberAcoustic waveAcousticsTurbulenceAtomic physics

Abstract

fetched live from OpenAlex

Flow over rectangular cavities can become unstable and excite the acoustic modes of the surrounding duct, resulting in severe noise and vibration. In this work, acoustic resonance excitation by two opposite and aerodynamically isolated rectangular cavities is experimentally and numerically investigated to identify the effect of the flow-acoustic coupling on the synchronization of shear layer instabilities. Compressible unsteady Reynolds-averaged Navier–Stokes simulation is used to model the self-excitation of resonance and characterize the fully coupled flow and acoustic fields. Moreover, the location and the strength of the acoustic sources and sinks are evaluated using Howe's integral formulation of the aerodynamic sound. It is revealed that double symmetric cavities generate a higher rate of acoustic energy transfer due to the synchronization of the shear layer instabilities over the two cavities in an antisymmetric pattern, leading to a stronger acoustic resonance than all other cases. On the other hand, the two shear layers over two opposite cavities with different aspect ratios were mismatched in phase and vortex convection velocity. As a result, the net energy transfer in an asymmetric cavity configuration occurred at a similar rate to a single rectangular cavity, driving a weaker acoustic resonance excitation.

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: none
Teacher disagreement score0.587
Threshold uncertainty score0.647

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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