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Record W4399527193 · doi:10.1109/tcpmt.2024.3412794

Analytical Solutions for Transient Thermal Spreading Resistance of a 3-D Flux Channel

2024· article· en· W4399527193 on OpenAlexaff
Belal Al-Khamaiseh, Yuri S. Muzychka

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTransient (computer programming)Channel (broadcasting)MechanicsFlux (metallurgy)Thermal resistanceThermalTransient analysisMaterials sciencePhysicsTransient responseComputer scienceThermodynamicsEngineeringElectrical engineeringHeat transfer

Abstract

fetched live from OpenAlex

In microelectronic devices, the moment a high-power current is transmitted into the system, heat is simultaneously generated, and the thermal field keeps developing until it reaches a steady-state field after a period of time. In this work, transient analytical solutions for the temperature field and thermal resistance of a rectangular 3-D flux channel are obtained. The flux channel is assumed to have a small heat source on the top surface, whereas convective effects are considered on the bottom surface and lateral edges. The time-dependent solutions are presented explicitly as infinite Fourier series forms. In addition, the solutions are also presented in dimensionless forms as generalized solutions. Moreover, an existing, well-established simple model that represents the profile of the transient thermal spreading resistance for a semi-infinite flux tube is used to verify the presented forms of the analytical solutions, and the results compare very well when considering a flux channel of large thickness. Further, the solutions are used to study the behavior of temperature and thermal resistance over time for some dimensional and nondimensional problems.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.241
Teacher spread0.214 · 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 designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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