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Record W2922727394 · doi:10.1177/1081286519837315

The effects of surface elasticity on the thermal stress around a circular nano-hole in a thermoelectric material

2019· article· en· W2922727394 on OpenAlexaff
Kun Song, Haopeng Song, Peter Schiavone, Cun‐Fa Gao

Bibliographic record

VenueMathematics and Mechanics of Solids · 2019
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThermoelastic dampingElasticity (physics)Materials scienceSurface stressThermoelectric effectBoundary value problemMechanicsComposite materialShear stressCylinder stressStress (linguistics)ThermalCondensed matter physicsPhysicsThermodynamicsMathematicsUltimate tensile strengthMathematical analysisSurface energy

Abstract

fetched live from OpenAlex

We analyze the contribution of surface elasticity and electric current density on the thermal stress distribution around a circular nano-hole in a thermoelectric material. Using complex variable methods, we obtain closed-form solutions describing the corresponding thermoelastic fields in the vicinity of the nano-hole. Our results indicate that the effect of surface elasticity is to generate significant normal and shear stresses on the boundary of the hole, allowing for the ability to either suppress or enhance hoop stress depending on the sign of the corresponding surface material constant. In addition, we find that positive hoop stress generated on the boundary by the remote electric current density can be neutralized by the incorporation of positive surface elasticity. In the case of the remaining boundary stress components, both surface elasticity and electric current density are found to enhance normal stress, while the maximum shear stress depends largely on the contribution of surface elasticity.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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