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Record W4409688218 · doi:10.1177/10812865251329006

Evaluating the dynamic behavior of a two-dimensional metamaterial: An analytical and numerical approach

2025· article· en· W4409688218 on OpenAlexafffund
Diwakar Singh, Xiaodong Wang, Rajeev Kumar

Bibliographic record

VenueMathematics and Mechanics of Solids · 2025
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Alberta
FundersScience and Engineering Research BoardNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMetamaterialNumerical analysisMaterials scienceMechanicsPhysicsComputer scienceMathematicsApplied mathematicsMathematical analysisOptics

Abstract

fetched live from OpenAlex

Auxetic metamaterial systems have garnered significant attention for their unique properties and potential applications. This study aims to address a fundamental question concerning the suitability of the spring model in accurately describing the behavior of a disk bar metamaterial system. A comprehensive analytical derivation for spring-mass and finite-element formulation for disk-bar model has been carried out. The efficacy of the spring-mass model to capture the effective properties, dispersion relations, and band gap is compared with numerical simulation results of the disk-bar metamaterial. Varying the local structural parameter allows for the independent attainment of negative mass and negative modulus, and their unique frequency ranges can be adjusted to the desired range. Current findings shed light on the effectiveness of the spring model as a predictive tool for such complex systems and provide valuable insights into the behavior of metamaterial structures. Ultimately, this research contributes to a designing and engineering metamaterial structures with tailored negative characteristics, promising new avenues for application in diverse fields.

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 score0.952
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.047
GPT teacher head0.353
Teacher spread0.306 · 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
Published2025
Admission routes2
Has abstractyes

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