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Record W2626924329 · doi:10.1299/jsmepes.2016.21.a222

Study of Condensing Heat Transfer in Horizontal Quadrilobed Tube

2016· article· en· W2626924329 on OpenAlexaff
Taihei Kawaguchi, Yuma MURATA, Hitoshi Asano, Nobuhiro Takeda, Masaki Kondo, K. Nishimura, Hitoshi Hara

Bibliographic record

VenueDoryoku, Enerugi Gijutsu Shinpojiumu koen ronbunshu/Doryoku, enerugi gijutsu no saizensen koen ronbunshu · 2016
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsHeat transfer coefficientTube (container)Condenser (optics)Heat transferThermodynamicsMaterials scienceMechanicsRefrigerantHeat fluxVapor qualityThermocoupleConcentric tube heat exchangerNucleate boilingChemistryComposite materialHeat exchangerOpticsPhysics

Abstract

fetched live from OpenAlex

This study deals with condensing heat transfer in a quadrilobed tube. HFC-134a was used as the working fluid. The effect of the tube configuration on the heat transfer coefficient was evaluated in horizontal flow. Superheated vapor or wet vapor was supplied to the condenser, and heated in water. A straight and twisted quadrilobed tubes were used. The local heat transfer coefficient at the center in the length was evaluated from the wall temperature measured by mounted thermocouples. As the result, for low refrigerant mass flux in the straight tube, deterioration in local heat transfer coefficient under low quality condition was suppressed, because condensate might be accumulated along the center by surface tension. For the twisted tube, on the other hand, heat transfer coefficient was lower than the straight one. In this experiment, since the twist tube improved the water heat transfer, the twist tube produced higher heat transfer rate than the straight one.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0040.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.228
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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueDoryoku, Enerugi Gijutsu Shinpojiumu koen ronbunshu/Doryoku, enerugi gijutsu no saizensen koen ronbunshuSame topicEngineering Applied ResearchFrench-language works237,207