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Design and Experimental Verification of Air Cooled Server Enclosure: A Novel Approach for Heat Sink Characterization

2023· article· en· W4384158448 on OpenAlexaff
Ghazal Mohsenian, Yaman Manaserh, Mohammad Tradat, Srikanth Rangarjan, Ayushman Singh, Koroush Nemati, Alfonso Ortega, Bahgat Sammakia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsFuture Earth
Fundersnot available
KeywordsHeat sinkElectronicsElectronics coolingEnclosureReliability (semiconductor)BottleneckComputer scienceMechanical engineeringThermalElectronic componentCharacterization (materials science)Automotive engineeringReliability engineeringEngineeringMaterials scienceEmbedded systemElectrical engineeringNanotechnologyTelecommunications

Abstract

fetched live from OpenAlex

Semiconductor thermal management is becoming a bottleneck challenge that restricts further development in the electronics industry. Compromising processor thermal requirements will impact the processor performance and reliability. Heat sinks are designed to increase the available surface area of an electronic component and allow for more heat to be easily dissipated. As a result, the thermal characterization of the heat sinks plays a critical role in electronics thermal management. In this study, a flexible experimental apparatus is designed, built, and assembled to characterize and test various electronics components in different aerodynamics and thermal conditions. This novel experimental apparatus allows for controlled characterization of the various heat sinks with different heights as well as realistic scenarios with air bypass at server level. Moreover, a general guideline on precise experimental procedure to characterize air cooled heat sinks is developed. The results show that introduced method reduces the experimental error by 26%.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.301

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 head0.028
GPT teacher head0.228
Teacher spread0.200 · 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
Published2023
Admission routes1
Has abstractyes

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