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Record W4389228176 · doi:10.3397/in_2023_0465

Numerical prediction of the sound transmission loss of double panel configurations with acoustic structured and poroelastic materials

2023· article· en· W4389228176 on OpenAlexaff
Tenon Charly Kone, Sebastian Ghinet, Raymond Panneton, Anant Grewal

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de SherbrookeNational Research Council Canada
Fundersnot available
KeywordsMetamaterialMultiphysicsSound transmission classAcousticsPoromechanicsTransmission lossFinite element methodMaterials scienceStructural acousticsStructural engineeringPhysicsEngineeringVibrationOpticsPorous medium

Abstract

fetched live from OpenAlex

The sound transmission loss of multilayer system assemblies made of Noise Control Treatments comprising metamaterials and poroelastic materials, sandwiched between two structural panels is of utmost importance in aerospace, automotive, building and several other engineering applications. The integration of acoustic metamaterials in these systems introduces a number of challenges at the level of numerical simulation in order to predict their acoustic and structural behavior. This paper presents a methodology for the numerical simulation of the sound transmission loss of structural configurations comprising acoustic metamaterials embedded in a glass wools layer, sandwiched between two elastic panels. The metamaterial is designed as a parallel assembly of four sub-metamaterials. Each of these sub-metamaterials is also a series assembly of a periodic unit cell having a neck + cavity + neck type configuration. The four sub-metamaterials are designed so that their first resonance frequencies are grouped together in order to improve the sound transmission loss at the first two natural frequencies of the double panel. The COMSOL Multiphysics finite element method solver is used. The normal incidence and diffuse field sound transmission loss numerical results are presented and discussed in comparison with results from literature for validation.

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 score0.332
Threshold uncertainty score0.397

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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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
Published2023
Admission routes1
Has abstractyes

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