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Compact Models for Transient Thermal Spreading Resistance in Half-space, Flux Tube, and Flux Channel Regions

2023· article· en· W4384129886 on OpenAlexaff
Yuri S. Muzychka, M. M. Yovanovich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of WaterlooMemorial University of Newfoundland
Fundersnot available
KeywordsHeat fluxTransient (computer programming)Isothermal processThermal resistanceMechanicsConvergence (economics)Channel (broadcasting)Flux (metallurgy)Boundary value problemThermalBoundary (topology)Space (punctuation)Heat transferComputer scienceMathematicsMaterials sciencePhysicsThermodynamicsMathematical analysisTelecommunications

Abstract

fetched live from OpenAlex

Transient thermal spreading resistance is of great importance in thermal management of electronic components and devices. A small number of solutions exist for heat sources on a half-space for isoflux or isothermal boundary conditions. In addition, a finite number of solutions exist for semi-infinite flux tubes and flux channels. In this paper we review the available solutions and propose simple compact models for each class of problems using asymptotic correlations. These models offer several advantages. First, they provide an efficient and easy prediction tool. Second, by taking advantage of an appropriate length scale, the models can be applied for heat sources of more complex shape for which no solutions exist. Finally, in the case of flux tubes and channels, these models provide a simpler alternative to the exact series solutions which often require many terms for convergence. Models are developed for both types of heat sources: isothermal and isoflux.

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)0.001
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.246
Teacher spread0.207 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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