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Record W2959085616 · doi:10.18280/i2m.180216

Parametric Study of Uniform Heat Sink Design

2019· article· fr· W2959085616 on OpenAlexvenueno aff
Mahmoud Huleihil

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2019
Typearticle
Languagefr
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat sinkParametric statisticsSink (geography)Environmental scienceMechanicsComputer scienceMathematicsMechanical engineeringEngineeringPhysicsGeographyStatistics

Abstract

fetched live from OpenAlex

The aim of this study is to analyze a single component heat sink design numerically and analytically.The analysis included various modeling assumptions: lumped parameter; one dimensional and two dimensional models.It is shown that the lumped model for the considered problem is capable to accurately predict the steady state temperature of the heat sink with a small discrepancy at the center of the heat sink.Some results are presented graphically for various parameters to show their effect on the steady state temperature of the heat sink including power input; convective heat transfer coefficient; ambient temperature; thickness of the heat sink and the shape of the cross section of the heat sink (circular, ellipsoidal, squared).A few particular applications include single solar cell under high concentration; thermoelectric cooling device and/or high-power led.The findings of the research are twofold: educational and the derived formulae serve as a quick estimate tool to predict the effect of the various parameters on the heat sink temperature and thus aids to estimate the thermodynamic efficiency of the system.

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)0.003
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.034
GPT teacher head0.273
Teacher spread0.239 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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