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Record W2802286357 · doi:10.1139/tcsme-2016-0078

THE INFLUENCE FACTORS ON HEAT TRANSFER PERFORMANCE OF LOOP THERMOSYPHON SYSTEM

2016· article· en· W2802286357 on OpenAlexvenueno aff
Li-Chieh Hsu, Guo-Wei Wong, Kung-Ting Chen

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporatorThermosiphonCondenser (optics)Heat transferMaterials scienceBoilingHeat transfer enhancementMechanicsBoiling heat transferThermodynamicsLoop (graph theory)Heat transfer coefficientNucleate boilingOpticsPhysicsHeat exchangerMathematics

Abstract

fetched live from OpenAlex

The influence factors on the heat transfer performance of a loop thermosyphon system, a passive cooling device, are studied systematically. The parameters investigated include types of enhanced boiling structure, the depth to width ratio of enhanced boiling structures, the gap of evaporator, the condenser height and the inclination of evaporator. The results show the depth to width ratio and the condenser height has positive influences on the heat transfer performance. An optimal channel gap of evaporator exists and possesses better heat transfer performance. The inclination effect of evaporator may not be favorable to heat transfer. Among those, the horizontal and 90° inclination of evaporator has better cooling performance.

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: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.346

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 head0.009
GPT teacher head0.181
Teacher spread0.172 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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