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Record W3046807792 · doi:10.23977/jeis.2020.51009

Innovative Design of Light-Weight Finned Heat Sinks for Air Cooling of Electronics by Natural Convection

2020· article· en· W3046807792 on OpenAlexvenueno aff
Lian-Tuu Yeh

Bibliographic record

VenueJournal of Electronics and Information Science · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat sinkPassive coolingNatural convectionElectronics coolingHeat transferFinElectronicsMechanical engineeringThermosiphonConvectionEnvironmental scienceEngineeringMeteorologyMechanicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

For the tower or poled or even roof top mounted electronics, the heat sink weight is extremely important. This study describes the analysis and development of the light weight plain fin heat sink for cooling of electronics, especially for those outdoor wireless systems. A CFD analysis is performed to investigate the thermal performance of this finned heat sink at the vertical and horizontal positions under the passive cooling scheme, i.e. combined natural convection and radiation heat transfer. In addition, unlike most of previous work which is limited to either a uniform heating or uniform temperature at the base of the heat sink, the present investigation considers discrete heat sources with various power densities (heat loads) from electronics in contact with a section of the heat sink base. It should also be noted that all discussion, including the design, analysis and results can be applied to both the indoor and outdoor equipment. However, for any outdoor equipment, one must include the solar heating to the system into the design and analysis. It is found that the solar heating adds about 4 oC to all parts of the system.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.007
GPT teacher head0.214
Teacher spread0.206 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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