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Record W2884770243 · doi:10.2514/1.t5418

Effect of Temperature-Dependent Thermal Conductivity on Spreading Resistance in Flux Channels

2018· article· en· W2884770243 on OpenAlexafffund
Belal Al-Khamaiseh, Yuri S. Muzychka, Serpil Kocabiyik

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermal conductivityThermal conductionHeat fluxMaterials scienceThermal resistanceThermodynamicsMechanicsHeat transferHeat sinkThermalThermal effusivityConvectionThermal transmittanceThermal contact conductanceComposite materialPhysics

Abstract

fetched live from OpenAlex

When the thermal conductivity of a material varies with temperature, the governing heat conduction equation becomes nonlinear and the use of constant thermal conductivity may produce unreliable results in thermal analysis. In this paper, analytical solutions for the temperature distribution and thermal resistance of a three-dimensional flux channel with temperature-dependent thermal conductivity are discussed and used to study the effect of the temperature-dependent thermal conductivity on the temperature rise and thermal spreading resistance for different conductivity functions. A single eccentric heat source is considered in the source plane of the flux channel that spreads the heat into a convective heat sink. The analytical solutions of the problem are illustrated by means of the Kirchhoff transform, which is considered a powerful technique for solving nonlinear conduction problems with temperature-dependent thermal conductivity. For validation purposes of the analytical results, the results are compared with numerical solution results obtained by solving the problem based on 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 score0.011
Threshold uncertainty score0.426

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 head0.225
Teacher spread0.217 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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