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Record W2079871594 · doi:10.1002/cjce.21747

Numerical modelling of convective vapour condensation with non‐condensable gases between two coaxial vertical cylinders

2012· article· en· W2079871594 on OpenAlexvenueno aff
Lazhar Merouani, Belkacem Zeghmati, Azeddine Belhamri

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCondensationLaminar flowMomentum (technical analysis)MechanicsCoaxialThermodynamicsInletMaterials scienceBoundary layerConvectionMass transferFlow (mathematics)PhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A problem of laminar film condensation from steam–gas mixtures between two coaxial cylinders is numerically analysed. A set of complete boundary layer equations is used to model simultaneous momentum, heat and mass transfer in the liquid film and the vapour–gas mixture. In order to locate accurately the liquid–mixture interface, a relevant change of coordinates is performed. Equations are solved using an implicit finite difference scheme. The liquid film thickness is determined from an iterative procedure based on the secant method. An analytical solution of the momentum equations of both phases at the end of condensation is also developed. Results presented include evolutions of field profiles and flow parameters from the inlet until the end of condensation. We also analyse the effects of the wall properties, the inlet conditions, the type of non‐condensable gas, the size of the annular space and the convective cooling coefficient.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.023
GPT teacher head0.206
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2012
Admission routes1
Has abstractyes

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