MétaCan
Menu
Back to cohort
Record W2966525908 · doi:10.1093/qjmam/hbz012

Mechanical Performance of a Thermoelectric Composite in the Vicinity of an Elliptic Inhomogeneity

2019· article· en· W2966525908 on OpenAlexafffund
Kun Song, Hao Song, Peter Schiavone, Cun‐Fa Gao

Bibliographic record

VenueThe Quarterly Journal of Mechanics and Applied Mathematics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Alberta
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsThermoelectric effectMaterials scienceComposite numberReliability (semiconductor)ThermalElectric fieldStress (linguistics)Matrix (chemical analysis)Current densityField (mathematics)Composite materialMechanicsPhysicsThermodynamicsMathematicsPower (physics)

Abstract

fetched live from OpenAlex

Summary Thermal stress induced by an uneven temperature field and mismatched thermal expansion is known to be a dominating factor in the debonding mechanism that threatens reliability and ultimately leads to failure in thermoelectric (TE) composites. Accordingly, we analyse the stress distributions in a TE composite induced by the presence of an elliptic inhomogeneity embedded in the surrounding matrix material. Using complex variable methods, we obtain closed-form representations of the thermal–electric and thermal–elastic fields and find that the temperature field around the inhomogeneity is reduced dramatically by the application of a remote electric current density without affecting the temperature difference across the inhomogeneity–matrix interface. This ensures the conversion efficiency of the TE composite while improving its reliability. Numerical results illustrate that a suitable choice of electric current density can prevent interfacial debonding via the suppression of the maximum positive normal stress on the interface.

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)0.004
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 score0.024
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
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 head0.014
GPT teacher head0.221
Teacher spread0.208 · 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 designBench or experimental
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

Citations19
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueThe Quarterly Journal of Mechanics and Applied MathematicsSame topicThermal properties of materialsFrench-language works237,207