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Record W2158906330 · doi:10.1088/0964-1726/16/6/012

Modeling of the cyclic thermomechanical response of SMA wires at different strain rates

2007· article· en· W2158906330 on OpenAlexafffund
Mahmoud Kadkhodaei, R. K. N. D. Rajapakse, Mojtaba Mahzoon, Mahmoud Salimi

Bibliographic record

VenueSmart Materials and Structures · 2007
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSMA*Materials scienceComposite materialStrain (injury)Structural engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

A one-dimensional coupled thermomechanical model is presented for shape memory alloys (SMAs) under non quasi-static loading by defining a Helmholtz free-energy function consisting of strain energy, thermal energy, and the energy of phase transformation. The first law of thermodynamics is used to address the thermomechanical coupling due to the influence of strain rate on the SMA temperature. The convective heat transfer coefficient of an SMA wire is calculated by using temperature-dependent empirical relations, and it is shown that no single empirical formula for the heat transfer coefficient can be applied to obtain experimentally consistent results under different loading conditions. The martensite fraction is decomposed into stress-induced and temperature-induced fractions so that the model is capable of predicting both the shape memory effect and the pseudoelasticity. Cyclic loading, the effect of wire diameter and the variation of dissipated energy with strain rate are studied, and the general features of the responses are found to be in agreement with the experimental observations.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.260
Teacher spread0.241 · 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

Citations40
Published2007
Admission routes2
Has abstractyes

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