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Record W2040788455 · doi:10.1115/1.4003363

Finite Element Modeling of Beams With Surface Energy Effects

2011· article· en· W2040788455 on OpenAlexaff
C. Liu, R. K. N. D. Rajapakse, A. Srikantha Phani

Bibliographic record

VenueJournal of Applied Mechanics · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNonlocal and gradient elasticity in micro/nano structures
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsFinite element methodCantileverBeam (structure)Bending stiffnessStiffnessBucklingBendingMaterials scienceStructural engineeringNanoelectromechanical systemsTimoshenko beam theoryMechanicsPhysicsComposite materialEngineering

Abstract

fetched live from OpenAlex

A finite element formulation of a nonclassical beam theory based on the Gurtin–Murdoch model for continua with deformable elastic surfaces is presented. The governing equations for thin and thick beams are used together with a weighted residual formulation to explicitly obtain the beam stiffness and mass matrices. Numerical solutions for selected test cases are compared with the analytical results available in literature for beam static deflections, natural frequencies, and buckling loads. The modified bending stiffness corresponding to the present model agrees closely with a recently reported rigorous solution. The maximum influence of surface energy effects is observed for cantilever beams. The finite element scheme provides an efficient tool to analyze, design, and predict the mechanical response of beam elements encountered in nanoelectromechanical systems and other nanoscale devices.

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 score0.007

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 science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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

Citations54
Published2011
Admission routes1
Has abstractyes

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