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Record W1992783609 · doi:10.1115/icnmm2011-58144

Heat Transfer in Spiral Channel Heat Sinks

2011· article· en· W1992783609 on OpenAlexafffund
Mehdi Ghobadi, Yuri S. Muzychka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNusselt numberMechanicsPrandtl numberHeat sinkHeat transferMaterials scienceLaminar flowReynolds numberHeat transfer enhancementThermodynamicsHeat fluxDimensionless quantityHeat transfer coefficientTurbulencePhysics

Abstract

fetched live from OpenAlex

Heat transfer in a spiral heat sink is examined experimentally and analytically. The spiral channel was fabricated on a base plate of copper. The cross section of the channel is square with 1 mm sides. A copper cap plate was bolted tight to seal the channel. Water and four low viscosity silicone oils (0.65 cSt, 1 cSt, 3 cSt and 10 cSt) were used as a medium; thus a Prandtl number from 5 to 100 was examined. Tests considered fluid entering from the side of the heat sink and exiting from the middle of heat sink and entering from the side and exiting from the middle. Heat transfer behavior over a wide range of flow rates from laminar to turbulent has been examined. Enhancement due to the spiral geometry was observed, and no significant difference was reported between the side and middle inlet condition. The dimensionless mean wall flux and the dimensionless thermal flow length were used to analyse the experimental data instead of Nusselt number and channel length. The spiral channel has been discretized, so that a single Dean number can be assumed in each cell, and two current models were applied to obtain the average Nusselt number. These are used to obtain the dimensionless mean wall flux and comparisons made with the experimental points.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.197
Teacher spread0.173 · 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 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

Citations2
Published2011
Admission routes2
Has abstractyes

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