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Record W2781687651 · doi:10.2514/1.t5337

Spreading Resistance in Multilayered Orthotropic Flux Channel with Temperature-Dependent Thermal Conductivities

2018· article· en· W2781687651 on OpenAlexafffund
Belal Al-Khamaiseh, Yuri S. Muzychka, Serpil Kocabiyik

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrthotropic materialThermal conductionHeat fluxMaterials scienceThermal conductivityThermal resistanceMechanicsHeat sinkHeat transferHeat transfer coefficientThermal transmittanceThermalConvective heat transferConvectionOverheating (electricity)ThermodynamicsFinite element methodComposite materialPhysics

Abstract

fetched live from OpenAlex

Anisotropic materials have received remarkable attention in the development of microelectronic devices. In many materials, the thermal conductivities are temperature-dependent and usually are approximated by constant thermal conductivities when considering thermal analysis. In this paper, analytical solutions of the temperature rise and the thermal resistance of a multilayered three-dimensional flux channel with orthotropic temperature-dependent thermal conductivities are addressed by means of the Kirchhoff transform, which is considered as a powerful method for dealing with nonlinear conduction problems with temperature-dependent thermal conductivities. A single eccentric heat source is considered in the source plane of the flux channel, where heat enters the system and flows by conduction through the layers to reach a convective heat sink with uniform heat transfer coefficient. The solutions are extended to account for multiple eccentric heat sources in the source plane. Further, the analytical solutions have been validated by solving the problem numerically with the finite element method using the ANSYS commercial software package.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.545

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 head0.008
GPT teacher head0.200
Teacher spread0.193 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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