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Record W4389244651 · doi:10.3397/in_2023_0150

Sound attenuation analysis of a honeycomb structure with extended necks

2023· article· en· W4389244651 on OpenAlexaff
Zacharie Laly, Christopher Mechefske, Sebastian Ghinet, Behnam Ashrafi, 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 resonatorHoneycombHelmholtz free energyAcousticsMaterials scienceHoneycomb structureResonatorNoise reduction coefficientStiffnessAbsorption (acoustics)Acoustic attenuationAttenuationMetamaterialTransmission lossAttenuation coefficientSound transmission classFinite element methodOpticsComposite materialStructural engineeringPhysicsOptoelectronicsEngineering

Abstract

fetched live from OpenAlex

In this paper, a honeycomb structure metamaterial consisting of 95 necks that are attached to the perforated top panel is presented and its sound absorption coefficient and transmission loss are investigated using the finite element method. Helmholtz resonators are therefore created by each honeycomb cell (cavity) and the attached neck, which is protruding within each cell. The structure has therefore a high bending stiffness due to the honeycomb mechanical performance and constitutes a sound absorber based on the parallel assembly of multiple Helmholtz resonators. This study demonstrates the importance of properly designing the diameter and the length of each neck to create a broadband sound absorption. One resonant sound absorption peak is observed when all the necks are identical, and two absorption peaks are obtained using two different sets of neck parameters. When the number of different necks increases, the sound absorption frequency band improves and when the parameters of all the necks are different, resulting in a parallel assembly of 95 different Helmholtz resonators, the sound absorption frequency band broadens. The material design studied in this paper can be useful in various applications where available space is limited and high mechanical stiffness and noise reduction are required in one structural element.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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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