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Record W4220745993 · doi:10.37934/cfdl.14.2.19

Effects of Operating Conditions on Evaporation Rate and Wall Shear Stress Development in a Micro-gap Heat Sink with Internal Micro-Fins

2022· article· en· W4220745993 on OpenAlexaff
Shugata Ahmed, Erwin Sulaeman, Ahmad Faris Ismail, Muhammad Hasibul Hasan, Zahir Hanouf

Bibliographic record

VenueCFD Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceHeat sinkHeat fluxHeat transferPressure dropHeat transfer coefficientMechanicsShear stressCritical heat fluxFinCoolantComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Evaporation in the micro-gap heat sink has a very high heat transfer coefficient. As a result, it is significant for high heat flux management. Heat transfer rate can be enhanced further by including internal micro-fins. However, the pressure drop penalty due to the small gap height and fin surfaces is a major concern. Wall shear stress development is responsible for pressure drop. This paper investigates the effects of operating conditions, e.g., wall heat flux, pumping power, and inlet void fraction, on evaporation rate and wall shear stress development in a micro-gap heat sink with internal micro-fins of rectangular and triangular profiles, while the cross-sectional area (21.8 mm2) is kept constant. R-134a is considered as coolant. Results show that the evaporation rate from per unit volume increases with the increment of wall heat flux and decreases with the enhancement of pumping power. However, after a threshold value of the pumping power (2×10-4 W), the decrement rate falls. Again, the wall shear stress rises with the increasing wall heat flux and pumping power while reduces for escalating inlet void fraction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.393

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 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
Published2022
Admission routes1
Has abstractyes

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