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Record W2007865042 · doi:10.1115/fedsm-icnmm2010-31268

A Parametric Investigation of Operating Limits in Heat Pipes Using Novel Metal Foams as Wicks

2010· article· en· W2007865042 on OpenAlexaff
Mahmood R. S. Shirazy, Luc G. Fre ́chette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceHeat pipeHeat transferCapillary actionBoilingMetal foamPorosityComposite materialMechanicsThermodynamics

Abstract

fetched live from OpenAlex

A parametric investigation has been performed to study the different operating limits of heat pipes employing a novel type of metal foam as wick for chip cooling applications. These foams have a unique spherical pore cluster microstructure with very high surface to volume ratio compared to traditional metal foams and exhibit higher operating limits in preliminary tests of heat pipes, suggesting high cooling rates for microelectronics. In the first part of this parametric study, widely used correlations are applied to calculate the five types of heat transfer limits (capillary, boiling, viscous, entrainment and sonic) as a function of temperature, type of foam, and porosity. Results show that the dominant limit is mostly the capillary limit, but for 50 pore-per-inch (PPI) foam, the boiling limit will be dominant. Also, 50 and 60 PPI foams have higher heat transfer limits than sintered copper powder. In the second part of this study, thermodynamic steady state modeling of a flat heat pipe has been done to study the effect of the different parameters on the dominant limit (capillary). A dimensionless number has been proposed to evaluate the balance between the pressure loss in the vapor and liquid phases as an additional design guideline to improve the capillary limit in flat heat pipes.

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 score0.181
Threshold uncertainty score0.385

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 head0.033
GPT teacher head0.258
Teacher spread0.224 · 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

Citations12
Published2010
Admission routes1
Has abstractyes

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