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SUBCOOLED FLOW BOILING HEAT TRANSFER CHARACTERISTICS OF R134A IN HORIZONTAL HELICALLY COILED TUBES

2015· article· en· W2562948919 on OpenAlexaff
Lingjian Kong, Jitian Han, Changnian Chen, Kewei Xing, Gang Lei, Ri Li

Bibliographic record

VenueEnhanced heat transfer/Journal of enhanced heat transfer · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsSubcoolingNucleate boilingThermodynamicsMaterials scienceHeat transfer coefficientHeat fluxHeat transferBoilingMass fluxMechanicsCritical heat fluxPhysics

Abstract

fetched live from OpenAlex

Subcooled flow boiling heat transfer of R134a in helically coiled tubes was investigated experimentally. Experiments were carried out at pressure ranging from 412 to 653 kPa, inlet subcooled temperature from 5.0 to 11.0° C, heat flux from 0.11 to 15.4 kWm−2 and mass flux from 147 to 249 kg m−2s−1. The wall temperature distribution was analyzed. In the case of single-phase flows, the wall temperature distribution is attributed to the secondary flow and the velocity profile of the main flow. In the case of two-phase flows, the temperature distribution is explained based on the buoyance and drag forces acting on the bubble. An increase of the inlet subcooling leads, for early subcooled boiling, to an increase in the heat transfer coefficient. However, the pressure has an opposite function on heat flux at the onset of nucleate boiling (ONB) and heat transfer coefficient. Besides, raising the mass flux can cause a substantial increase in the heat flux at ONB, but the effect on the heat transfer coefficient was negligible. The correlations of heat flux at ONB and the subcooled boiling heat transfer coefficient in a horizontal, helically coiled tube were developed based on the experimental data.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.017
GPT teacher head0.239
Teacher spread0.221 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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