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Record W2074282024 · doi:10.1115/icnmm2011-58038

Investigation of Cooling Performance of a Swirl Microchannel Heat Sink by Numerical Simulation

2011· article· en· W2074282024 on OpenAlexaff
Yanfeng Fan, Ibrahim Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsHeat sinkMaterials scienceMechanicsMicrochannelHeat fluxPressure dropCoolantThermodynamicsMicro heat exchangerCritical heat fluxHeat transferHeat transfer coefficientPhysics

Abstract

fetched live from OpenAlex

High heat fluxes have been created by the semiconductor devices due to the high power generation and shrank size. The large heat flux causes the circuit to exceed its allowable temperature and may experience both working efficiency loss and irreversible damage due to excess in their temperatures. In this paper, a swirl microchannel heat sink is designed to dissipate the large heat flux from the devices. The numerical simulation is carried out to investigate the cooling performance. Uniform heating boundary condition is applied and single phase water is selected as coolant. The present micro heat sink applies multiple swirl microchannels positioned in a circular flat plate to enhance the heat convection by creating the secondary flow at high Reynolds numbers. Copper is selected as the material of heat sink. The channel depth and width are fixed as 0.5 mm and 0.4 mm, respectively. The heat is injected into the system from the bottom of heat sink at the heat fluxes from 10 to 60 W/cm2. Flow is supplied from the top of micro heat sink through a jet hole with a diameter of 2 mm and enters swirl microchannels at the volume flow rates varying from 47 to 188 ml/min. The cooling performances of swirl microchannel heat sinks with different curvatures and channel numbers are evaluated based on the targets of low maximum temperature, temperature gradient and pressure drop.

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 score0.392
Threshold uncertainty score0.247

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)0.0000.000

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 head0.022
GPT teacher head0.199
Teacher spread0.177 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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