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Record W4400919022 · doi:10.1139/cjp-2023-0275

Numerical investigations of heat transfer augmentation in a microchannel heat sink with different shaped periodic reentrant cavities on channel sidewalls

2024· article· en· W4400919022 on OpenAlexvenueno aff
Yu Tingfang, Xing Guo, Yicun Tang

Bibliographic record

VenueCanadian Journal of Physics · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsReentrancyMicrochannelHeat transferHeat sinkMechanicsChannel (broadcasting)ThermodynamicsHeat transfer coefficientCondensed matter physics

Abstract

fetched live from OpenAlex

Microchannel heat sink is widely applied in high-density heat flux devices for its high efficiency in heat dissipation. However, the most effective geometrical configuration of microchannels for best heat transfer augmentation with least pump power penalty is still unknown. In this research, the heat transfer augmentation in a microchannel heat sink with different shaped periodic reentrant cavities on channel sidewalls are numerically studied. Three different kinds of channel structures are introduced, including convex quarter-circular, concave quarter-circular, and inclined shaped reentrant cavities. The comprehensive performances of the proposed microchannels are compared with that of the smooth straight channel, with respect to the pressure drop, friction coefficient, Nusselt number, and performance factor. The results indicate that the comprehensive performance of the microchannel heat sink with periodic reentrant cavities is superior to the smooth straight channel, because its reentrant cavities can strengthen the mixing of fluids and lower the pressure loss. The proposed microchannels can enhance the heat transfer by 1.7 times, reduce the pressure drop by 3.7%–22.4% at Re 648.2, with the heat transfer entropy production decreased by 15.9%–48.2%. The inclined shaped microchannel has the best overall performance factor among the three proposed channels, which achieves up to 1.59 at Re 648.2.

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 score0.173
Threshold uncertainty score0.522

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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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