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GAS-LIQUID FILM DISTRIBUTION AND SEPARATION OF CONDENSATION FROM A DOWNWARD-FLOWING STEAM-NONCONDENSABLE GAS MIXTURE ONTO A HORIZONTAL TUBE AT SUBATMOSPHERIC PRESSURE

2023· article· en· W4378194984 on OpenAlexaff
Jingran Wang, Fang Liu, Junhui Lu, Fengyan Cao, Suilin Wang, Yanyan Liu

Bibliographic record

VenueHeat Transfer Research · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSubcoolingMaterials scienceCondensationThermodynamicsMass transferHeat transferMass fractionCompressed fluidMechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Based on the boundary layer, a mathematical model was established to investigate the thicknesses of liquid and gas films and the steam condensate distribution in the presence of noncondensable gas outside a horizontal tube at pressures lower than atmospheric pressure. The model accurately predicts the steam condensation heat transfer with noncondensable gas. The influences of the noncondensable gas mass fraction, pressure, velocity, and surface subcooling upon the condensation heat transfer, the thicknesses of liquid and gas films, and condensate distribution around the horizontal tube have been studied. The liquid film thickness rises as the surface subcooling raises but reduces with raising pressure and mass fraction of noncondensable gas. The gas film thickness rises with rising surface subcooling and noncondensable gas mass fraction, reducing as the pressure rises. The thicknesses of gas and liquid films and condensate distribution significantly affect the local heat transfer. The liquid film separation velocity reduces with raising pressure, surface subcooling, and mass fraction of noncondensable gas, which is most influenced by pressure.

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)0.001
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 score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.300
Teacher spread0.268 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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