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Record W4317635581 · doi:10.2514/6.2023-2123

Optimization of Band Gap Area in the Low-Frequency In-Plane Elastic/Acoustic Passive Adaptive Metamaterial

2023· article· en· W4317635581 on OpenAlexaff
H. Jafari, Ramin Sedaghati

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsMetamaterialAttenuationPeriodic boundary conditionsAcousticsCenter frequencyFrequency bandVibrationFinite element methodBand gapLow frequencySquare (algebra)Boundary value problemOpticsPhysicsMaterials scienceBand-pass filterGeometryComputer scienceBandwidth (computing)OptoelectronicsTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-2123.vid The present research aims to enhance and control bandgaps in a periodic metamaterial of square shape by adding a cylindrical core to the center of the conventional geometry and changing its arm angles. The base unit cell was unable to exhibit bandgaps mainly due to uniform mass distribution in the unit cell. The introduced modified unit cell incorporating a cylindrical core at the center with isosceles trapezoid arms permitted the generation of new bandgap areas capable of attenuation of propagating waves in desirable frequency ranges. Bloch's theorem and finite element method with periodic boundary conditions are employed to analyze the band structure and also to investigate the effect of unit cell geometrical parameters on the low-frequency band gap area. The present research outcomes may provide an essential guide to designing architected periodic filters with the ability to attenuate low-frequency elastic/acoustic vibrations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.240
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes1
Has abstractyes

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Same venueAIAA SCITECH 2023 ForumSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207