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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 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 score0.527
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

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

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 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

Citations2
Published2011
Admission routes2
Has abstractyes

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