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Record W4415455680 · doi:10.3397/in_2025_1074275

A broadband sound absorption resonator made of acoustic black hole

2025· article· en· W4415455680 on OpenAlexaff
Zacharie Laly, Raymond Panneton, Noureddine Atalla, Sebastian Ghinet, Kévin Verdière

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council CanadaUniversité de Sherbrooke
Fundersnot available
KeywordsResonatorBroadbandAcoustic impedanceAttenuation coefficientAbsorption (acoustics)Noise reduction coefficientElectrical impedanceImpedance matchingAcoustic attenuation

Abstract

fetched live from OpenAlex

In this paper, an acoustic resonator designed for broadband sound absorption is proposed. The resonator features a neck that extends into the cavity, forming a sonic black hole. This design incorporates a central pore profile that decreases linearly across a series of interconnected annular cavities. The radii of the necks connecting these cavities progressively decrease from the inlet, while the resonator's wall is considered rigid. The finite element method is used to calculate both the sound absorption coefficient and the surface impedance of the proposed resonator. When the neck comprises fifteen annular cavities, the sound absorption coefficient exhibits fifteen different resonant peaks. By reducing the thickness of each annular cavity to approximately 1 millimeter, a broadband sound absorption coefficient is achieved with a surface impedance closely matching that of air. The proposed resonator demonstrates very good performance in noise attenuation across a wide frequency range, making it a promising solution for effective noise insulation in various applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.895

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)0.0000.000

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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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
Published2025
Admission routes1
Has abstractyes

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