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Record W2018335811 · doi:10.1115/icnmm2008-62071

Flow Boiling Heat Transfer in a Silicon Microchannel Heat Sink Using Liquid Crystal Thermography

2008· article· en· W2018335811 on OpenAlexaff
Ayman Megahed, Ibrahim Hassan, Tariq Ahmad

Bibliographic record

VenueASME 2008 6th International Conference on Nanochannels, Microchannels, and Minichannels · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsMicrochannelHeat sinkHeat transfer coefficientMaterials scienceHeat transferPressure dropBoilingSiliconNucleate boilingMicro heat exchangerThermodynamicsHeat spreaderMechanicsOptoelectronicsNanotechnology

Abstract

fetched live from OpenAlex

The present study focuses on the experimental investigation of boiling heat transfer characteristics and pressure drop in a silicon microchannel heat sink. The microchannel heat sink consists of a rectangular silicon chip in which 45 rectangular microchannels were chemically etched with a depth of 295 μm, width of 254 μm, and a length of 16 mm. Un-encapsulated Thermochromic liquid Crystals (TLC) are used in the present work to enable nonintrusive and high spatial resolution temperature measurements. This measuring technique is used to provide accurate full and local surface-temperature and heat transfer coefficient measurements. Experiments are carried out for mass velocities ranging between 290 to 457 kg/m2.s and heat fluxes from 6.04 to 13.06 W/cm2 using FC-72 as the working fluid. Experimental results show that the pressure drop increases as the exit quality and the flow rate increase. High values of heat transfer coefficient can be obtained at low exit quality (xe < 0.2). However, the heat transfer coefficient decreases sharply and remains almost constant as the quality increases for an exit quality higher than 0.2.

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 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 score0.390
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.044
GPT teacher head0.252
Teacher spread0.208 · 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
Published2008
Admission routes1
Has abstractyes

Explore more

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