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Record W2045553051 · doi:10.1177/1045389x11401450

Modeling of Shape Memory Alloy Actuators Using Likhachev’s Formulation

2011· article· en· W2045553051 on OpenAlexafffund
Patrick Terriault, Vladimir Braïlovski

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2011
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShape-memory alloyActuatorAirfoilFinite element methodPseudoelasticityMechanical engineeringJoule heatingMaterials scienceStructural engineeringComputer scienceEngineeringComposite materialArtificial intelligence

Abstract

fetched live from OpenAlex

This article presents a simplified version of Likhachev’s micromechanical model and its integration in ANSYS, a commercial finite element software package used to simulate the response of a structure equipped with shape memory alloy wire actuators controlled by direct Joule heating. The original Likhachev’s formulation is adapted to obtain a model that is easy to characterize, numerically efficient, and general in the sense that it can simulate all the shape memory-related features using the same formulation (superelasticity, shape memory effect, stress generation, etc.). The adapted Likhachev’s formulation is coupled to an electro-thermal model to simulate temporal response of actuators heated by an electrical current. An experimental result obtained with a deformable airfoil powered by shape memory actuators is used to validate the proposed electro-thermo-mechanical model.

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)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.062
GPT teacher head0.272
Teacher spread0.210 · 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

Citations20
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Intelligent Material Systems and StructuresSame topicShape Memory Alloy TransformationsFrench-language works237,207