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Record W2322892230 · doi:10.2514/6.2004-2851

Active Control of a Self-sustained Pressure Fluctuation due to Flow over a Cavity

2004· article· en· W2322892230 on OpenAlexaff
Philippe Micheau, Ludovic Chatellier, J. Laumonier, Yves Gervais

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTrailing edgeVortexMechanicsVortex sheddingWind tunnelLeading edgePhysicsPressure sensorPressure measurementMicrophoneAcousticsOpticsSound pressureMeteorologyReynolds numberTurbulence

Abstract

fetched live from OpenAlex

The problem addressed in this paper is to control the self-sustained pressure fluctuations due to flow over a cavity. The vortex sound theory describes how sound can be induced by periodic shedding of discrete vortices at the leading edge and their interaction with the downstream edge after convection across the cavity. The pressure sound generated by the vortex-edge interaction propagates upstream and trigger the vortex shedding upstream of the leading edge. An active device can act on this feedback loop in order to break the reinforcement mechanism, and thus to attenuate the self-sustained pressure fluctuations. The main originality of the presented active device is to use a vibrating surface located at the trailing edge in order to control the volume velocity generated by the interaction of vortex with the wall. The command of the vibrating surface is synchronized with the pressure fluctuations measured at the bottom of the cavity with a microphone. The describing function analysis is used to predict the self-sustained sinusoidal pressure and to explain the action of the active device on the feedback loop. Experimental data were obtained using the system in the test section of an Eiffel type wind-tunnel. The pressure fluctuation spectrum measured in the cavity without and with active control show an attenuation of 20 dB.

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 score0.858
Threshold uncertainty score0.295

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 head0.002
GPT teacher head0.193
Teacher spread0.190 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

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