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Record W2139867082 · doi:10.1088/0964-1726/17/4/045005

Finite element analysis of a shape memory alloy three-dimensional beam based on a finite strain description

2008· article· en· W2139867082 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmart Materials and Structures · 2008
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsShape-memory alloyPseudoelasticitySMA*Finite element methodStructural engineeringBendingCurvatureMaterials scienceBeam (structure)Constitutive equationDisplacement (psychology)Transformation (genetics)MartensiteGeometryEngineeringComputer scienceMathematicsComposite materialAlgorithmMicrostructure

Abstract

fetched live from OpenAlex

In the present work a modified phenomenological model of the shape memory alloy (SMA) constitutive law is proposed that is capable of reproducing some aspects of SMA thermomechanical behavior like superelasticity and the one-way shape memory effect. The modified law uses strain and temperature as control variables, which eliminates the need for transformation correctors in finite element analysis. It is implemented in a structural model developed to analyze three-dimensional (3D) structures made out of thick beam elements with features such as taper and curvature, given that SMA products typically fall in this category of shapes. Moreover, a finite strain and large displacement description is adopted to account for the large deformations exhibited during phase transformation. Results, produced by the proposed model, of simulated tensile, three-point and four-point bending tests are presented and compared with experimental data taken from the literature.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0050.000

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 head0.025
GPT teacher head0.238
Teacher spread0.213 · 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