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Record W4389241286 · doi:10.3397/in_2023_0151

Numerical study on honeycomb membrane-type acoustic metamaterial with added mass

2023· article· en· W4389241286 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
KeywordsHoneycombMaterials scienceMetamaterialIsotropyHoneycomb structureComposite materialTransmission lossMembraneAcousticsOpticsOptoelectronicsChemistryPhysics

Abstract

fetched live from OpenAlex

This paper presents numerical investigations on the transmission loss (TL) of a honeycomb structure with embedded membrane to which small masses are attached. The structure is a lightweight honeycomb membrane-type acoustic metamaterial that presents excellent transmission loss especially at low frequencies. The added mass is a solid material that is attached to the membrane, which is modelled as a linear isotropic elastic material with fixed boundary conditions. The influences of the membrane material properties and of the added mass parameters on the transmission loss are presented. It is demonstrated that the resonant peak amplitude of the transmission loss and its frequency band can be controlled by the properties of the added mass. The various numerical simulation results of configurations with and without added masses as well as various membrane elastic properties are compared. The honeycomb structure used in this study constitutes generally the core layer in sandwich honeycomb panels. While the TL of the honeycomb structure alone is zero, due to the open cell distribution, the integration of a membrane within the honeycomb with added masses induces a significant improvement of the transmission loss. This lightweight metamaterial is shown to attenuate the low frequency noise.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.655
Threshold uncertainty score1.000

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)0.0000.001

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 head0.024
GPT teacher head0.257
Teacher spread0.232 · 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.

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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