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Record W2804679511 · doi:10.1121/1.5037362

Experimental prediction of the vibration response of panels under a turbulent boundary layer excitation from sensitivity functions

2018· article· en· W2804679511 on OpenAlexaff
Christophe Marchetto, Laurent Maxit, Olivier Robin, Alain Berry

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsBoundary layerExcitationWind tunnelSensitivity (control systems)MechanicsTurbulenceAcousticsVibrationMicrophonePhysicsMaterials scienceComputational physicsSound pressureEngineering

Abstract

fetched live from OpenAlex

This study aims at validating an experimental method for characterizing the vibration behavior of panels excited by a turbulent boundary layer (TBL) excitation as a possible alternative to standard means like wind tunnels or in situ tests. The approach takes advantage of an explicit separation of the excitation contribution from the dynamic behavior of the panel. Based on the measurement of deterministic transfer functions on the panel, called "sensitivity functions," which are then combined with either measurements or a model of the wall-pressure fluctuations induced by the TBL excitation, the vibration response under such an excitation can be retrieved. For validation purposes, the wall-pressure fluctuations of the turbulent flow generated in an anechoic wind tunnel are measured with a flush-mounted microphone array. The decay rates and the convection velocity, which mainly characterize the excitation, are extracted from these measurements. The plate velocity response to this excitation is estimated following the proposed method using the measured sensitivity functions and the model of Mellen fed with experimentally estimated decay rates and convection velocity. A comparison between a directly measured vibration auto-spectrum under the actual flow and the one predicted following the suggested method shows satisfactory agreement.

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 score0.782
Threshold uncertainty score0.632

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 studies0.0000.002
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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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