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Record W4320183647 · doi:10.3397/in_2022_0053

Numerical design of Helmholtz resonators with multiple necks for multi-tonal noise control

2023· article· en· W4320183647 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
KeywordsResonatorHelmholtz resonatorAcousticsHelmholtz free energyMetamaterialNoise controlNoise (video)Transmission lossNoise reductionMufflerReduction (mathematics)Materials sciencePhysicsTransmission (telecommunications)Sound transmission classResonance (particle physics)OpticsComputer scienceMathematicsTelecommunicationsGeometry

Abstract

fetched live from OpenAlex

The reduction of multi-tonal noise at multiple frequencies simultaneously is a challenge in many industrial fields. Different solutions such as metamaterials consisting of periodic Helmholtz resonators embedded into a porous layer have been studied in the literature. Generally, a classical resonator made of a cavity connected to a neck provides only one resonant transmission loss peak. In this study, a design of acoustic metamaterials is proposed numerically using the finite element method for multi-tonal noise reduction. The resonator is made of multiple necks extended into the cavity and is periodically distributed within a porous material. The cylindrical global cavity of the resonator is partitioned into several sub-cavities, which are separated from one another by a rigid wall, and each sub-cavity is connected to one neck. Helmholtz resonators with 2, 3, 4, and 5 necks are presented, they exhibit multiple resonance transmission loss peaks which correspond respectively to the number of the resonator necks. The proposed acoustic metamaterial designs can be used to reduce multi-total noise at several frequencies simultaneously.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.038
GPT teacher head0.264
Teacher spread0.227 · 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 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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