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Record W2080710493 · doi:10.1115/imece2014-38957

Application of Variational Iteration Method in Nonlinear Free Vibration Analysis of Multi-Layered Nano-Scale Graphene Sheets

2014· article· en· W2080710493 on OpenAlexaff
Mehran Sadri, Davood Younesian, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNonlocal and gradient elasticity in micro/nano structures
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNonlinear systemVibrationGalerkin methodGrapheneBoundary value problemMathematical analysisNano-Equations of motionIterative methodAmplitudeClassical mechanicsPhysicsMaterials scienceMathematicsComposite materialMathematical optimizationAcousticsNanotechnologyOptics

Abstract

fetched live from OpenAlex

The nonlinear free vibration of multi-layered nano-scale graphene sheets is studied. Using the von Kármán and nonlocal continuum theories, large amplitude of vibration is included in the analysis as well as the size effect of nano-structure. The SSSS boundary condition is considered for the multi-layered graphene sheet and coupled nonlinear differential equations of motion of layers are taken into account based on Galerkin method. Variational iteration method (VIM) is employed as the solution procedure and nonlinear natural frequencies of the system are analytically determined. Two different geometries are taken into account and the analytical results are compared with frequencies obtained by numerical method. Finally, influence of geometrical parameters and amplitude of vibration on nonlinear frequencies of the system is examined.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 science0.0000.001
Research integrity0.0010.000
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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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