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Record W4415455471 · doi:10.3397/in_2025_1074286

Diffuse field sound absorption performance of porous material with embedded periodic Helmholtz resonators

2025· article· en· W4415455471 on OpenAlexaff
Zacharie Laly, Christopher Mechefske, Sebastian Ghinet, Tenon Charly Kone, Raymond Panneton, Noureddine Atalla

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council CanadaQueen's UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsMetamaterialHelmholtz resonatorResonatorHelmholtz free energyAbsorption (acoustics)Acoustic impedanceElectrical impedanceNoise (video)Metamaterial absorber

Abstract

fetched live from OpenAlex

This study investigates the diffuse field sound absorption performance of acoustic metamaterial composed of a porous layer with embedded periodic Helmholtz resonators. The periodic unit cell of the metamaterial consists of a porous layer integrated with one, four and nine distinct Helmholtz resonators having extended necks. Although the resonators have cavities of the same volume, they differ in neck parameters and are arranged in parallel. The finite element method is used to evaluate the sound absorption coefficients and the surface impedance of the metamaterial under both oblique and random incidence (diffuse field) conditions. Compared to the conventional porous layer, the metamaterial exhibits significantly enhanced diffuse field sound absorption at the resonant frequencies of the resonators. With one, four and nine embedded periodic resonators, the diffuse field sound absorption shows one, four and nine resonant peaks, respectively. This metamaterial effectively attenuates low-frequency tonal noise across multiple frequencies simultaneously.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.803

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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