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Record W1587614399 · doi:10.4271/2007-01-3196

A High Performance Miniature Loop Heat Pipe

2007· article· en· W1587614399 on OpenAlexafffundabout
Donatas Mishkinis, Guanghan Wang, Darius Nikanpour

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsCanadian Space Agency
FundersNational Research Council Canada
KeywordsLoop (graph theory)Loop heat pipeHeat pipeComputer scienceMaterials scienceHeat transferMechanicsPhysics

Abstract

fetched live from OpenAlex

This work presents the results of an experimental High Performance Miniature Loop Heat Pipe. The evaporator utilizes a wick structure with the non-inverted meniscus evaporation concept, which allows using high thermal conductivity materials for the evaporator case and capillary wick structure, and hence will further reduce the thermal resistance between the evaporator elements. The heat fluxes at the evaporator can therefore be significantly higher than that of a LHP using inverted meniscus evaporation approach. Tests were conducted in the Material and Thermal Laboratory at the Canadian Space Agency. The evaporator heat input cross-section area was 2.4 cm2. When water is used as the working liquid the heat transfer rate has reached values as high as 215W, corresponding to a heat flux density of 90W/cm2 (temperature drop between heat source and LHP evaporator was ∼7°C). Working temperature oscillations (with amplitude ∼2-10°C) were observed for steady state regimes of LHP operation.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.254
Teacher spread0.242 · 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

Citations2
Published2007
Admission routes3
Has abstractyes

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