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Record W4408378427 · doi:10.1098/rspa.2024.0353

Thermoelastic fields of an inhomogeneity embedded in a matrix under thermoelectric loads based on a temperature-dependent model

2025· article· en· W4408378427 on OpenAlexafffund
Kunkun Xie, Haopeng Song, Peter Schiavone, Cunfa Gao

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2025
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsUniversity of Alberta
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsThermoelastic dampingThermoelectric effectMatrix (chemical analysis)Materials scienceCondensed matter physicsMechanicsPhysicsThermalComposite materialThermodynamics

Abstract

fetched live from OpenAlex

We consider the thermoelastic problem of an elliptical inhomogeneity embedded in an infinite matrix subjected to uniform remote electric-thermal loading in which the temperature dependency of thermoelastic parameters, including thermal conductivity, thermal expansion coefficient and elastic modulus are considered. Complex variable methods are applied to develop analytical solutions under the assumption that both the corresponding transport and constitutive equations incorporate nonlinear effects. Numerical results reveal that the temperature dependency of the corresponding material parameters will significantly affect the distribution of thermal stress around the inhomogeneity. Under the temperature dependency assumption and with severe temperature gradients attributed to the remote electric-thermal loading, stresses along the interface may present a significantly different distribution than those obtained under the assumption of temperature independence. We mention that in the research area dealing with thermal stress induced by electric-thermal loading, our results provide a new theoretical tool for predicting stress concentration phenomena in heterogeneous materials.

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 score0.144
Threshold uncertainty score0.415

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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

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