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Record W4403270247 · doi:10.3397/in_2024_3082

Numerical study of acoustic metamaterial made of Helmholtz resonators with complex necks for low frequencies noise attenuation

2024· article· en· W4403270247 on OpenAlexaff
Zacharie Laly, Christopher Mechefske, Sebastian Ghinet, Tenon Charly Kone, Raymond Panneton

Bibliographic record

VenueNOISE-CON proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council CanadaQueen's UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsAcousticsAttenuationAcoustic attenuationResonatorHelmholtz free energyHelmholtz resonatorNoise (video)MetamaterialPhysicsMaterials scienceOpticsComputer science

Abstract

fetched live from OpenAlex

In this paper, an acoustic metamaterial consisting of Helmholtz resonators with complex necks is proposed for low frequency noise reduction. The resonators are made of a cavity and a complex neck, which is a periodic succession of necks with small and large diameters. The acoustic attenuation performance of the proposed metamaterial design is demonstrated using finite element method. For such a shaped neck, the sound absorption coefficient presents multiple peaks. With a succession of N small and (N - 1) large neck diameters, N peaks in the sound absorption coefficient are obtained. By increasing the number of successions, the number of peaks increases. At the peaks, the surface acoustic impedance of the metamaterial nearly matches with the characteristic impedance of the air. This makes the sound absorption coefficient being nearly 100%. Keeping the same cavity volume, we increase the number of sound absorption peaks by the complex shape of the neck, while the classic Helmholtz resonator presents only one peak.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.268
Teacher spread0.240 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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