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Record W2131235213 · doi:10.1109/itherm.2010.5501388

Thermoelectrics enhanced air cooling limits and improvement techniques: Experimental and computational studies

2010· article· en· W2131235213 on OpenAlexaff
Hengyun Zhang, Y.C. Mui, Gamal Refai-Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsTECThermoelectric coolingHeat sinkWork (physics)Thermoelectric effectThermal conductionEnvironmental scienceAir coolingComputer coolingNuclear engineeringThermalMechanical engineeringJunction temperatureMaterials scienceMeteorologyEngineeringThermodynamicsPhysicsThermal management of electronic devices and systems

Abstract

fetched live from OpenAlex

The use of thermoelectric cooler (TEC) as enhanced air cooling technique for electronic generating devices has been of increasing interest. In this paper, experimental and computational studies of TEC enhanced air cooling limits are conducted. The experimental work was first conducted as a baseline study. The setup consisted of a fan-cooled heat sink, a thermal test board mounted with a test die, and a TEC of 50 mm × 50 mm. Experiments were run at different power inputs and operation currents. The cross-over power with TEC enhancement cases was identified to be about 62W under nonuniform die heating. The computational model was constructed according to the experimental study. The TEC module was modeled as heat conduction blocks with respective volumetric heat generations. Good agreement was achieved between computational predictions and measurements. The size of TEC module, which was seldom addressed in literature, was then varied to examine its effect on the junction temperature. Improving techniques such as the use of better TIM1 or indium TIM1 and liquid cooling technique are also discussed.

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 score0.010
Threshold uncertainty score0.459

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 head0.013
GPT teacher head0.297
Teacher spread0.283 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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