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Record W3094537017 · doi:10.1063/5.0022797

Numerical characterization of Love waves dispersion in viscoelastic guiding-layer under viscous fluid

2020· article· en· W3094537017 on OpenAlexaff
Jérémy Bonhomme, Mourad Oudich, Pedro Alberto Segura Chavez, Mohamed Lamine Fayçal Bellaredj, Jean‐François Bryche, D. Beyssen, Paul G. Charette, F. Sarry

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersCentre National de la Recherche ScientifiqueCentre National d’Etudes Spatiales
KeywordsViscoelasticityAttenuationPhase velocityPiezoelectricityAnisotropyWave propagationDispersion relationMechanicsDispersion (optics)Viscous liquidWavenumberMaterials scienceViscosityPhysicsClassical mechanicsAcousticsOpticsComposite material

Abstract

fetched live from OpenAlex

We present a finite element (FE) based model to accurately investigate the dispersion and attenuation of Love waves in a multilayered structure made of a piezoelectric substrate, a guiding layer, and a viscous fluid. The numerical model solves the general form of the wave equations that includes the materials anisotropy, piezoelectricity, and viscoelasticity. We express the wave equations for elastic waves in a particular formulation in order to solve an eigenvalue problem where the eigenvalue is the complex wavenumber k from which we can derive the phase velocity [ω/Re(k)] and the attenuation rate [Im(k)]. The numerical model enables us to study the effects of the interdigitated electrodes, the materials viscoelasticity and piezoelectricity, and the fluid's viscosity on the wave phase velocity and attenuation. Our FE based model will facilitate optimizing the design of anisotropic piezoelectric platforms for Love waves propagation under viscous fluid loading.

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 score0.235
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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