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Record W2943899402 · doi:10.23977/jeis.2018.31013

Surrounding Boards Effects on Component Temperatures of Target Board in Telecom Rack with Active or Passive Cooling

2018· article· en· W2943899402 on OpenAlexvenueno aff
Lian-Tuu Yeh

Bibliographic record

VenueJournal of Electronics and Information Science · 2018
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
Fundersnot available
KeywordsNatural convectionRackActive coolingThermalElectronic componentComponent (thermodynamics)Passive coolingWater coolingMaterials scienceMechanical engineeringForced convectionConvectionStructural engineeringEngineeringMechanicsMeteorologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

The understanding of the effect of the empty slots in the card cage on the thermal analysis and the tests is extremely important. The detailed full scale thermal model of the rack is often too large to be for the practical applications. Similarly, the fillers which are often referred to as the dummy boards are frequently required during the system thermal tests to insure the proper flow distribution to all slots (or boards). The purpose of this study is to examine the effect of filler on the component temperatures of the target board under active (forced convection) and passive (natural convection) cooling. The results indicated that no noticeable difference in the component temperatures between the cases of the fully loaded and partially filled card cages under the forced air cooling with the fans. However, the cases with the partially filled card cage significantly reduce the component temperatures of the heated boards as compared with the fully loaded card cage for natural convection cooling. Therefore, the thermal simulations and/or tests for the passive cooling should be conducted under the actual operation conditions with the full power on all boards in the card cage.

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 score0.138
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Electronics and Information ScienceSame topicAerosol Filtration and Electrostatic PrecipitationFrench-language works237,207