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Record W4308917457 · doi:10.1115/1.4056216

Circumferential Wrinkling of Elastic Cylinders With Negative Surface Tension

2022· article· en· W4308917457 on OpenAlexaff
C. Q. Ru

Bibliographic record

VenueJournal of Applied Mechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOgdenMaterials scienceCompressibilitySurface tensionCylinderPoisson's ratioElasticity (physics)MechanicsShear modulusModulusLinear elasticitySurface stressSurface (topology)Composite materialPoisson distributionMathematicsGeometryPhysicsThermodynamicsFinite element methodSurface energy

Abstract

fetched live from OpenAlex

Abstract The present paper studies the critical condition for negative surface tension-driven circumferential wrinkling of soft cylinders based on the linearized Steigmann–Ogden model of surface elasticity. A simple negative surface tension-mode number relation is derived explicitly for arbitrary Poisson ratios of the cylinder and its surface layer and their shear modulus ratio, on which the critical surface residual strain and the associated mode number can be determined easily. For an incompressible solid cylinder with an incompressible thin surface layer, the critical values of surface residual strain and the mode number predicted by the present model are in good agreement with available numerical results based on the popular neo-Hooken nonlinear model for a wide range of material and geometrical parameters. In addition, the critical condition for circumferential wrinkling of the inner surface of a cylindrical hole within an infinite body is also derived. The present work addresses the key role of negative surface tension in circumferential wrinkling of soft cylinders and offers supporting evidence for the efficiency and accuracy of the linear Steigmann–Ogden model for the determination of the critical values for circumferential wrinkling.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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