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Record W2783886944 · doi:10.1115/imece2017-72138

A Comparative Finite Element Analysis of Hart-Smith Hyperelastic Model Under Uniaxial, Planar and Equi-Biaxial Tension

2017· article· en· W2783886944 on OpenAlexafffund
Rohan Thakkar, Aleksander Czekanski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHyperelastic materialUniaxial tensionSubroutineTension (geology)Finite element methodCompressibilityPlanarTensile testingMaterials scienceUltimate tensile strengthStructural engineeringMathematicsMechanicsMathematical analysisPhysicsComposite materialComputer scienceEngineering

Abstract

fetched live from OpenAlex

The classical phenomenological compressible Hart-Smith model expressed in exponential-logarithmic terms of stretch invariants is compared with substantial hyperelastic models available in Abaqus. It is implemented in Abaqus Explicit using a customized user subroutine. Compressible Hart-Smith model together with selected acclaimed models available in Abaqus are evaluated under uniaxial tension, equi-biaxial extension and planar tension modes of deformations. The required material constants are determined from a simple uniaxial tension test. In order to investigate mode-independent characteristics of considered models, predictive planar tension and biaxial extension simulations are performed using the material constants derived from a uniaxial tensile test. Obtained numerical results are validated with respect to classical experimental data for natural rubber reported by Treloar.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.488

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 head0.056
GPT teacher head0.270
Teacher spread0.214 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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