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Record W1941258674 · doi:10.1109/intlec.1996.573414

Thermal analysis and optimization of a small, high density DC power system by finite element analysis (FEA)

2002· article· en· W1941258674 on OpenAlexaff
G.K. Crowe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsFinite element methodModular designConvectionThermal conductionHeat transferHeat transfer coefficientConvective heat transferThermalNatural convectionMechanical engineeringThermal analysisPower (physics)Power densityMechanicsMaterials scienceEngineeringComputer scienceThermodynamicsStructural engineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Today, the trend in power for is towards compact, modular systems. While packaging is small, the power density is high. Cooling by natural convection becomes marginal and designing for optimal heat dissipation is challenging. In this paper, a basic understanding of thermodynamics is presented. Heat transfer by conduction, convection and radiation and the FEA method are briefly defined. The convective heat transfer coefficient, the major factor in an accurate thermal analysis, is discussed in some detail. The thermal design of a selected compact, modular DC power supply is analyzed and optimized with the aid of FEA. Demonstrated are 3D model building, thermal analysis and results and the optimization method.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.179
Teacher spread0.170 · 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 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

Citations5
Published2002
Admission routes1
Has abstractyes

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