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Record W2988402832 · doi:10.1121/1.5137400

Broadband superabsorption of waterborne acoustic waves by bubble metascreens

2019· article· en· W2988402832 on OpenAlexaff
Maxime Lanoy, Valentin Leroy, Steven Squire, Anatoliy Strybulevych, Reine‐Marie Guillermic, Eric J. Lee, Fabrice Lemoult, Arnaud Tourin, J. H. Page

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2019
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsAcousticsBubbleBroadbandMetamaterialMaterials scienceAbsorption (acoustics)DissipationAttenuationUltrasonic sensorBandwidth (computing)NarrowbandReflection (computer programming)OpticsCoatingOptoelectronicsComputer scienceTelecommunicationsPhysicsNanotechnology

Abstract

fetched live from OpenAlex

Absorption of acoustic or mechanical waves is an important challenge for various applications such as noise insulation, stealth coating, seismic event mitigation and ultrasonic testing. In order to absorb sound, one needs to introduce a medium with sufficient dissipation but without significant reflection of the incoming wave. Ideally, the absorber should also be thin and light, a goal that may be realized through the use of a thin 2-D metamaterial, or metalayer. The conventional way of achieving strong absorption in a thin metamaterial is to exploit low-frequency resonant inclusions. However, most resonant structures have an intrinsically narrowband response, making it difficult to attain broadband absorption in a deeply subwavelength-thick meta-layer, and making it necessary to devise larger structures to increase the bandwidth. For waterborne acoustic waves, an exception to this common situation can be achieved through the fabrication of bubble metascreens, which consist of a single layer of bubble inclusions embedded in a soft solid. In this presentation, we re-visit the optimization of such bubble metascreens and show that, despite being resonance-based, near-perfect absorption is possible over a very wide frequency range even when the metalayer is ultrathin.

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)0.000
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 score0.701
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207