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Record W2085320084 · doi:10.1115/ht2008-56446

Formulation of Film Theory Equations for Modeling of Condensation of Steam-Air Mixtures in a Shell and Tube Condenser

2008· article· en· W2085320084 on OpenAlexaff
Yousef Haseli, İbrahim Dinçer, G.F. Naterer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCondensationCondenser (optics)BaffleThermodynamicsMechanicsVolumetric flow rateHeat transferMaterials scienceLeakage (economics)Water vaporMass transferChemistryPhysicsOptics

Abstract

fetched live from OpenAlex

Through development of the fundamental equations of Film Theory, condensation of steam in the presence of air in a horizontal counter-current shell and one-path tube condenser is modeled. The interaction between heat and mass transfer and hydrodynamics in the shell-side is taken into consideration. A comparison between the predictions of the model and a set of experimental data available in the archival literature indicates excellent accuracy of the new formulation. The accuracy of the method is further validated by generating profiles of the temperature and pressure drops of the gas flow through the baffles, at various air leakages. Additionally, the effects of air leakage and upstream cooling water temperature are investigated to determine how they influence the total condensation rate, shell-side gas temperature and pressure drops. The results show that the total condensation rate decreases 5% and 20.5% for an air leakage of 1% and 5%, respectively, compared to the situation of pure vapor. Also, increasing the inlet cooling water temperature from 46.5°C to 48.5°C leads to 16.2% reduction in the total condensation rate, i.e., 8.1% per °C. However, this ratio is higher at high temperatures. For example, as the cooling water temperature rises from 50°C to 51°C under identical process conditions, the total condensation rate decreases 11.7% (per °C).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.198

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 integrity0.0000.000
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 head0.031
GPT teacher head0.242
Teacher spread0.211 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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