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Record W3193673850 · doi:10.1103/physrevb.104.054302

Effective inertia spring tensor model for acoustic materials with coupled local resonances

2021· article· en· W3193673850 on OpenAlexafffund
Kenny L. S. Yip, Sajeev John

Bibliographic record

VenuePhysical review. B./Physical review. B · 2021
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResonatorPhysicsTensor (intrinsic definition)MetamaterialInertiaSpring (device)AcousticsCoupling (piping)Moment of inertiaRepresentation (politics)Computational physicsClassical mechanicsMaterials scienceOpticsGeometry

Abstract

fetched live from OpenAlex

We present a simple physical picture and precise model for the low-frequency acoustic modes of phononic crystals with multiple local resonances within each unit cell and their coupling to spatially separated resonators. The physical picture is a generalization of the widely quoted mass-in-a-box representation of resonant acoustic metamaterials. Our model consists of an array of frequency-dependent effective masses and moments of inertia coupled to near and distant neighbors by a wave-vector-dependent effective spring constant matrix. We demonstrate, using several two-dimensional models, that our simple representation accurately recaptures exact phononic band structure involving coupled translational and rotational modes. Our simplified but precise description is ideally suited for resonators consisting of multiple rigid cores and shells embedded in a softer elastic background. This enables a rich spectrum of acoustic modes and band gaps at audible frequencies, using millimeter to centimeter scale resonators. Our model is readily generalized to three-dimensional phononic crystals. It is suggested that suitable modifications of the spring tensor may enable description of disordered resonant acoustic media.

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 score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.336
Teacher spread0.320 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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