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Record W2036303768 · doi:10.1115/mnhmt2009-18010

Experimental Investigation of Flow Boiling Heat Transfer Characteristics of FC-72 in Cross-Linked Microchannel Heat Sinks Using Thermochromatic Liquid Crystals

2009· article· en· W2036303768 on OpenAlexaff
Ayman Megahed, Ibrahim Hassan, Kristina J. Cook

Bibliographic record

VenueASME 2009 Second International Conference on Micro/Nanoscale Heat and Mass Transfer, Volume 2 · 2009
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsNucleate boilingMicrochannelHeat transfer coefficientHeat sinkHeat fluxBoilingCritical heat fluxThermodynamicsHeat transferMaterials scienceHydraulic diameterMass fluxMechanicsChemistryPhysics

Abstract

fetched live from OpenAlex

The present study investigates the effect of cross-links on flow boiling heat transfer characteristics in rectangular microchannel heat sinks, using FC-72 as the working fluid. The silicon test section consists of 45 cross-linked microchannels, measuring 16 mm in length, with a hydraulic diameter of 248 μm. The parameters investigated include mass flux, heat flux, and exit quality, ranging from 99–275 kg/m2s, 7.2–88.8 kW/m2, and 0.01–0.71, respectively. Thermochromatic liquid crystals have been used in the present study as full-field surface temperature sensors to map the temperature distribution on the heat sink surface. The flow boiling heat transfer coefficient shows a different trend in the cross-linked design relative to the straight microchannel design. The flow boiling heat transfer coefficient increases with increasing exit quality at a constant mass flux, which is caused by the domination of the nucleation boiling mechanism in the cross-link region. The predictions obtained from the existing heat transfer correlations found in the literature significantly under-estimate the present heat transfer measurements, except for the Yu et al. (2002) correlation.

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 categoriesMeta-epidemiology (narrow)
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 score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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.

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
Published2009
Admission routes1
Has abstractyes

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