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Record W2332842173 · doi:10.1299/kikaib.74.1807

Study on Improving the Performance of a Pulsating Heat Pipe Using Self-Rewetting Material

2008· article· en· W2332842173 on OpenAlexaff
Koji Fumoto, Masahiro Kawaji

Bibliographic record

VenueTRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series B · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHeat pipeWorking fluidSurface tensionBoilingMaterials scienceHeat fluxThermosiphonMechanicsThermodynamicsComposite materialHeat transfer

Abstract

fetched live from OpenAlex

In the present study, new experimental results will be reported on the enhancement of heat transport by a pulsating heat pipe (PHP) using a self-rewetting fluid as a working fluid. Self-rewetting fluids have a property that the surface tension increases with temperature unlike other common liquids. The increasing surface tension at a higher temperature means that the liquid will be drawn towards a heated surface if a dry spot appears. Thus, in boiling, a dryout phenomenon may be prevented at a higher heat flux. In the present experiment, butanol was added to water at a concentration of less than 1wt% to make the self-rewetting fluid. A pulsating heat pipe made from extruded multiport tubing was partially filled with the butanol-water mixture and tested for its heat transport capability at different input power levels. One end of the PHP was heated using a copper block equipped with two cartridge heaters. A fin was attached to the opposite end to be cooled by a fan. The experiments showed that the maximum heat transport capability was enhanced by a factor of four when the maximum heater temperature was limited to 110 degrees C. Thus, the use of a self-rewetting fluid in a PHP has been shown to be highly effective in improving the heat transport capability of pulsating heat pipes.

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 score0.258
Threshold uncertainty score0.530

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueTRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series BSame topicHeat Transfer and Boiling StudiesFrench-language works237,207