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Record W2002720676 · doi:10.1109/nano.2013.6720962

Nonlinear vibration of fluid-conveying carbon nanotube using homotopy analysis method

2013· article· en· W2002720676 on OpenAlexaff
H.R. Askari, D. Zhang, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNonlocal and gradient elasticity in micro/nano structures
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHomotopy analysis methodNonlinear systemTimoshenko beam theoryGalerkin methodVibrationBoundary value problemNatural frequencyCarbon nanotubeMathematical analysisHomotopyMathematicsParametric statisticsMechanicsMaterials sciencePhysicsAcousticsComposite material

Abstract

fetched live from OpenAlex

Nonlinear vibration analysis of a single-walled carbon nanotube, using the Eringen nonlocal elasticity and Euler-Bernoulli beam theories, is carried out. Pasternak-type foundation and the simply-supported boundary conditions are assumed for the carbon nanotube and the governing equation of motion is developed using those theories. The Galerkin method is utilized to obtain the nonlinear ordinary differential equation of vibration of the single-walled carbon nanotube and the homotopy analysis method is employed to determine its nonlinear natural frequency. A parametric sensitivity study is then carried out. Few of the parameters were the axial tension, nonlocal parameter, fluid velocity and the foundation stiffness. The parametric study is mainly focused on the nonlinear natural frequency of single-walled carbon nanotube. Finally, a numerical simulation is carried out to determine the accuracy of the obtained results. Furthermore, an elliptical integral is utilized to verify the nonlinear natural frequency, which was obtained using the homotopy analysis method.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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