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Record W3003181969 · doi:10.1615/jpormedia.2020026859

HEAT ENHANCEMENT USING ALUMINUM METAL FOAM: EXPERIMENTAL AND NUMERICAL APPROACH

2020· article· en· W3003181969 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Porous Media · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMetal foamHeat sinkMaterials sciencePressure dropLiquid metalHeat transferComputer coolingThermalAluminiumHeat transfer enhancementElectronicsComposite materialPerformance enhancementMechanicsMechanical engineeringHeat transfer coefficientThermodynamicsThermal management of electronic devices and systems

Abstract

fetched live from OpenAlex

As a result of increasing power demands and the decreasing size of computational hardware, the need for an effective cooling technique is more urgent now than ever. Despite this need, there is insufficient research on metal foams operating as liquid cooling heat sinks within electronic systems and documented cases where the results are reported and verified using both experimental and numerical analyses. Operating within the Forchheimer flow regime, the present paper assesses the effect of varying pore densities on the thermal effectiveness of metal foams as liquid cooling heat sinks for electronics. The effectiveness of the system is evaluated based on the pressure drop that occurs across the metal foam and the effective heat transfer rate. These results are then verified both numerically and experimentally. The results revealed that a sample with a linear pore density of 10 PPI is most effective when all the evaluation parameters are taken into consideration.

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.

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 score0.102
Threshold uncertainty score0.697

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 head0.030
GPT teacher head0.247
Teacher spread0.216 · 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