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Record W4389228345 · doi:10.3397/in_2023_0937

Analysis of Helmholtz resonator wall elasticity effects on the performance of periodic acoustic metamaterial

2023· article· en· W4389228345 on OpenAlexaff
Zacharie Laly, Christopher Mechefske, Sebastian Ghinet, Behnam Ashrafi, Charly T. Kone

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council CanadaQueen's UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsMetamaterialResonatorMaterials scienceElasticity (physics)Transmission lossHelmholtz resonatorTransfer-matrix method (optics)AcousticsTransfer matrixAttenuationHelmholtz free energyAcoustic attenuationFinite element methodSplit-ring resonatorOpticsPhysicsComposite materialOptoelectronicsComputer science

Abstract

fetched live from OpenAlex

In this paper, a metamaterial made of a porous layer with embedded periodic Helmholtz resonators is studied and the effects of the resonator wall elasticity on the acoustic attenuation performance of the metamaterial are investigated numerically. The analytical transmission loss results using the parallel transfer matrix method show good agreement with finite element results for the rigid wall case. Elastic materials with different thicknesses are considered for the wall of the neck and the top, lateral and bottom walls of the resonator cavity. It is shown that the transmission loss of the metamaterial degrades when the bottom wall of the resonator cavity is elastic. The elasticity of the bottom wall has a significant impact on the performance of the metamaterial. This analysis will help for better design of acoustic metamaterials with periodic Helmholtz resonators.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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