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Record W1589203358 · doi:10.1109/apec.2003.1179344

Characterization of swaged mixed metal heat sinks

2004· article· en· W1589203358 on OpenAlexaff
A. Zaghlol, William Leonard, Richard Culham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHeat sinkThermal resistanceCopperMaterials scienceFinThermal conductivityAluminiumHeat transferMetal foamComposite materialAirflowMetallurgyMechanicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The present experimental study investigates the thermal performance of four heatsink combinations based on the forced convection heat transfer mode. The four designs consist of an all aluminum, all copper, copper baseplate/aluminum fin and aluminum baseplate/copper fin heatsink. Each heat sink combination was tested in pairs of heatsinks placed within a vertical wind tunnel of Plexiglas walls such that the fins were positioned vertically and parallel to the airflow inside the tunnel. A block heater providing 800 watts and covering 60% of the baseplate was placed in between two identical heat sinks. Experiments were performed for an approach velocity ranging from 2 m/s to 8 m/s. The average rise in temperature of eight measured locations was used to calculate the thermal resistance. The all copper heatsink provided the lowest thermal resistance while the all aluminum heatsink returned the highest value. The experiments show that there is marginal improvement of 3% in the performance of the copper-base/aluminum-fin heatsink due to the higher conductivity of the copper base. The experiments show that the thermal performance can be improved by up to 14% by increasing the thermal conductivity of the fin material, as in the case of the aluminum-base/copper-fin heatsink.

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 score0.411
Threshold uncertainty score0.209

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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