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Record W2143598824 · doi:10.1109/itherm.2010.5501387

A hybrid thermoelectric cooler thermal management system for electronic packaging

2010· article· en· W2143598824 on OpenAlexafffund
M.K. Russel, D. Ewing, C.Y. Ching

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsMcMaster University
FundersOntario Centres of Excellence
KeywordsThermoelectric coolingTECActive coolingThermalThermoelectric effectThermal resistancePassive coolingOperating temperatureWater coolingMechanical engineeringNuclear engineeringMaterials scienceEnvironmental scienceElectrical engineeringEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Conventional cooling techniques, such as the use of heat pipes and forced convective cooling can be inadequate for many high performance electronic chips or when the operating ambient temperature is high. In such cases, there is a need for active cooling of the chip to keep its operating temperature below the design point. Thermoelectric coolers (TEC) provide an attractive option in such instances, and have been developed and used for thermal management in electronic packaging systems. In this paper a hybrid thermal management system is considered that incorporates a TEC system for active cooling in parallel with a conventional passive system. A thermal resistance network model is developed for that hybrid system that takes into account the governing equations for the TEC. The advantage of the hybrid system is that it can be operated with a higher overall system coefficient of performance for partial loads, while extending the range of operating conditions. System performance curves are obtained for change in heat load from the chip and ambient temperature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.222
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2010
Admission routes2
Has abstractyes

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