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Record W4246928821 · doi:10.1121/2.0000599

Characterization of micro-perforated panel at high sound pressure levels using rigid frame porous models

2017· article· en· W4246928821 on OpenAlexaff
Zacharie Laly, Noureddine Atalla, Sid-Ali Meslioui

Bibliographic record

VenueProceedings of meetings on acoustics · 2017
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTortuositySound pressureAcousticsMaterials scienceAcoustic impedanceElectrical impedanceRigid framePorous mediumPorosityFrame (networking)EngineeringPhysicsComposite materialUltrasonic sensorTelecommunications

Abstract

fetched live from OpenAlex

An acoustic impedance model to predict the acoustic response of micro-perforated panels at high sound pressure levels is proposed using rigid frame porous models. The micro-perforated panel is modeled following Johnson-Allard approach with an effective density which depends on the frequency. The incident sound pressure on the surface of the perforations is considered as a main variable in the model and the parameters of the equivalent fluid such as the tortuosity and the flow resistivity are expressed as functions of this incidence pressure. The proposed model shows good agreement by comparison with other existing nonlinear impedance models for sound pressure level up to 150 dB. Experimental measurements were performed on several micro-perforated panels backed by air cavities using an impedance tube equipped with a high sound speaker capable of delivering a high sound pressure level up to 155 dB. A good correlation between theoretical and experimental results is obtained. Micro-perforated panel backed by porous material is modeled and validated experimentally using an equivalent tortuosity of the micro-perforated panel which depends on the dynamic tortuosity of the porous layer.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.525
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.059
GPT teacher head0.263
Teacher spread0.204 · 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.

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
Published2017
Admission routes1
Has abstractyes

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