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Record W2056439086 · doi:10.1115/ihtc14-22714

Effect of Working Fluid on Pulsating Heat Pipe Thermal Performance

2010· article· en· W2056439086 on OpenAlexaff
Koji Fumoto, Masahiro Kawaji, Tsuyoshi Kawanami

Bibliographic record

Venue2010 14th International Heat Transfer Conference, Volume 5 · 2010
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorking fluidHeat sinkBoilingSurface tensionHeat pipeMaterials scienceHeat transferAdiabatic processFluid dynamicsHeat transfer enhancementThermalMechanicsThermodynamicsHeat transfer coefficient

Abstract

fetched live from OpenAlex

Pulsating heat pipes (PHPs) are complex heat transfer devices whose thermal performance is governed by a strong thermohydrodynamic coupling. Recently, PHPs have attracted attention as novel electronic cooling devices. In this study, we used a self-rewetting fluid and obtained new experimental results for the improvement of the heat transport efficiency in PHPs. In contrast to the case of common liquids, the surface tension of self-rewetting fluids increases with temperature. Because of the increase in the surface tension at high temperatures, these fluids tend to flow toward the dry spot appearing on a heated surface, and thus, the boiling heat transfer is improved. We constructed PHPs from multiport extruded aluminum tubes with square channels. The PHPs consisted of a heating section, an adiabatic section, and a condensation section with a heat sink. We investigated the effect of the type of working fluid and the fluid fill ratio on the device performance. The working fluids employed were a self-rewetting fluid, water, and ethanol. The thermophysical properties of the working fluid affected the device performance, which also depended strongly on the boundary conditions employed during the PHP operation. In particular, the use of a self-rewetting fluid in the PHPs helped enhance the heat transport efficiency to a considerable extent.

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 categoriesMeta-epidemiology (narrow)
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 score0.181
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.230
Teacher spread0.214 · 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.

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

Citations6
Published2010
Admission routes1
Has abstractyes

Explore more

Same venue2010 14th International Heat Transfer Conference, Volume 5Same topicHeat Transfer and Boiling StudiesFrench-language works237,207