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Record W2029081996 · doi:10.1115/pvp2007-26404

Acoustic Power Calculation in Deep Cavity Flows: A Semi-Empirical Approach

2007· article· en· W2029081996 on OpenAlexaff
Peter Oshkai, T. Yan, Alexey Velikorodny, S. VanCaeseele

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAcousticsSound powerAcoustic source localizationPhysicsAcoustic wave equationVorticityAcoustic radiationParticle image velocimetryDuct (anatomy)Particle velocityTurbulenceAcoustic waveMechanicsOpticsVortexRadiation

Abstract

fetched live from OpenAlex

Acoustic power generated by turbulent flow over a coaxial side branch (deep cavity) resonator mounted in a rectangular duct is calculated using a semi-empirical approach. Instantaneous flow velocity is decomposed into an irrotational acoustic component and vorticity-bearing hydrodynamic field. The total velocity at several phases of the acoustic oscillation cycle is measured using digital particle image velocimetry. The acoustic velocity field is calculated numerically. The emphasis is on the effect of the accurate geometry representation for the acoustic field modeling on the calculated acoustic power. Despite the generally low levels of acoustic radiation from the coaxial side branches, when the main duct is incorporated into the model for calculation of the acoustic velocity, the acoustic velocity exhibits substantial horizontal (streamwise) components in the vicinity of the cavity corners. This streamwise acoustic velocity correlates with hydrodynamic horizontal velocity fluctuations, thus contributing to the calculated acoustic power. In addition, spatial structure and strength of the acoustic source changes as the distance between the side branches varies. The transformation of the acoustic source structure is characterized in terms of patterns of instantaneous and phase-averaged flow velocity, vorticity, and streamline topology.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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