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Record W3206749990 · doi:10.1177/1045389x211048228

A phenomenological model for thermally-induced hysteresis in polycrystalline shape memory alloys with internal loops

2021· article· en· W3206749990 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

VenueJournal of Intelligent Material Systems and Structures · 2021
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsWilfrid Laurier University
FundersNational Natural Science Foundation of China
KeywordsShape-memory alloyHysteresisPhenomenological modelCrystalliteAusteniteMaterials scienceMartensiteAnisotropyCondensed matter physicsPhase transitionDiffusionless transformationStatistical physicsPhysicsMetallurgyMicrostructureOptics

Abstract

fetched live from OpenAlex

In the current study, a 1-D phenomenological model is constructed to capture the temperature-induced hysteretic response in polycrystalline shape memory alloys (SMAs). The martensitic and austenitic transformations are regarded as the first-order transitions. A differential single-crystal model is formulated on the basis of Landau theory. It is assumed that the transformation temperatures follow the normal distribution among the grains due to the anisotropic stress field developed in the material. The polycrystalline hysteretic response is expressed as the integration of single-crystal responses. Besides, the prediction strategy for incomplete transitions is presented, and the first-order reversal curves are obtained via density reassignment. The proposed model is numerically implemented for validation. Comparisons between the modeling results and the experimental ones demonstrate the capability of the proposed model in addressing the hysteresis in thermally-induced phase transformations.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.034
GPT teacher head0.263
Teacher spread0.229 · 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