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Record W4320184335 · doi:10.3397/in_2022_0052

Numerical modelling of acoustic metamaterial made of periodic Helmholtz resonator containing a damping material in the cavity

2023· article· en· W4320184335 on OpenAlexaff
Zacharie Laly, Christopher Mechefske, Sebastian Ghinet, Charly T. Kone, Noureddine Atalla

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council CanadaQueen's UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsHelmholtz resonatorResonatorMetamaterialAcousticsMaterials scienceHelmholtz free energyNoise controlResonance (particle physics)PhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Acoustic metamaterials are frequently used in many fields such as aerospace, buildings and ground transportation industries for low frequency noise control applications. Different solutions based on membrane or Helmholtz resonators have been investigated in the past few years. In the present study, a numerical design of acoustic metamaterial made of Helmholtz resonator with a membrane in its cavity is presented. The resonator with a neck extended into the cavity is periodically embedded within a porous material. The membrane inside the resonator cavity is modelled as a linear isotropic elastic material with free and fixed boundary conditions. The transmission loss (TL) of the proposed metamaterial design, predicted by finite element method presents multiple resonant peaks while only one peak is obtained with a conventional resonator. Two TL resonance peaks are observed when the membrane circumferential boundary is free inside the resonator cavity. For fixed boundary conditions, more than two resonance peaks are obtained. The proposed metamaterial design can therefore be used in many industrial applications for low frequency noise attenuation.

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 score0.789
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.039
GPT teacher head0.257
Teacher spread0.218 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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