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Record W2084697765 · doi:10.1002/cjce.21719

Heat transfer and flow pattern in co‐current downward steam condensation in vertical pipes‐II: Comparison with published work

2012· article· en· W2084697765 on OpenAlexvenueaboutno aff
Arijit A. Ganguli, Sachin K. Dahikar, Mayurkumar S. Gandhi, Jyeshtharaj B. Joshi, P.K. Vijayan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsPressure dropThermodynamicsCondensationHeat transferMechanicsWork (physics)Heat transfer coefficientMass transferMaterials scienceChemistryEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

Abstract The condensation of pure steam flowing downward inside a vertical tube has been extensively studied. Considerable amount of experimental and analytical efforts can be found due to the significance of this subject in practice. In this work, a critical review of the most important experimental, analytical and computational fluid dynamic (CFD) investigations have been presented. CFD simulations for the geometries of Goodykoontz and Dorsch [Goodykoontz and Dorsch, NASA TN D‐3326, 1966; Goodykoontz and Dorsch, NASA TN D‐3953, 1967], Kim and No [Kim and No, Int. J. Heat Mass Transf. 2000;43:4031–4042] and the present work have been performed and compared with the experimental data reported in these investigations. CFD predictions of the pressure drop and the heat transfer coefficient (HTC) were in close agreement with the experimental values. A preliminary regime map has been constructed for downward flow steam condensation inside pipes. Finally, all the published semi‐empirical correlations for the HTC have been critically analysed and compared with the CFD predictions. An attempt has been made to make specific recommendations. © 2012 Canadian Society for Chemical Engineering

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.422

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 integrity0.0000.001
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 head0.013
GPT teacher head0.207
Teacher spread0.193 · 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 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

Citations6
Published2012
Admission routes2
Has abstractyes

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