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Record W4391733915 · doi:10.1121/10.0024767

On the area-averaged effective sound absorption coefficient of porous materials excited by a monopole

2024· article· en· W4391733915 on OpenAlexafffund
Franck Sgard, Noureddine Atalla, Olivier Robin, Alain Berry

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de SherbrookeInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
FundersInstitut de recherche Robert-Sauve
KeywordsMagnetic monopoleAcousticsAbsorption (acoustics)Sound powerExcitationAttenuation coefficientPhysicsPlane waveAcoustic waveComputational physicsPorosityExcited stateSound intensityOpticsMaterials scienceSound (geography)Atomic physics

Abstract

fetched live from OpenAlex

The area-averaged effective sound absorption coefficient (SAC) of a rigid-backed homogeneous porous material subjected to a monopole excitation is calculated as the absorbed-to-incident sound power ratio. Using Allard's model to describe the sound propagation above the porous material, an analytical model for this power-based SAC is proposed and proves to give a good approximation of the sound absorption performance under monopole excitation of sufficiently large areas of material. The impact of factors on the power-based SAC, such as monopole height, material radial dimension used to calculate the sound powers, and material properties is discussed. The power-based SAC frequency-dependent behavior is analyzed through sound intensity field assessments at the material surface and is compared to normal incident plane wave and diffuse field SACs. The sound absorption behavior of sound absorbers under monopole excitation exhibits notable distinctions and peculiar results compared to those observed under plane wave and diffuse fields, particularly at low frequencies and for sources close to the material.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.327

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207