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Record W2121986875 · doi:10.1155/2014/327653

A Robust Asymptotically Based Modeling Approach for Two-Phase Flows

2014· article· en· W2121986875 on OpenAlexafffund
Mohamed M. Awad, Yuri S. Muzychka

Bibliographic record

VenueAdvances in Mechanical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPressure gradientLaminar flowMechanicsTurbulencePhase (matter)MathematicsPhysicsMaterials science

Abstract

fetched live from OpenAlex

A simple semitheoretical method for calculating two-phase frictional pressure gradient in horizontal circular pipes using asymptotic analysis to develop a robust compact model is presented. Two-phase frictional pressure gradient is expressed in terms of the asymptotic single-phase frictional pressure gradients for liquid and gas flowing alone. The proposed model can be transformed into either a two-phase frictional multiplier for liquid flowing alone (ϕ l 2 ) or two-phase frictional multiplier for gas flowing alone (ϕ g 2 ) as a function of the Lockhart-Martinelli parameter, X. Single-phase friction factors are calculated using the Churchill model which allows for prediction over the full range of laminar-transition-turbulent regions and allows for pipe roughness effects. The proposed model is compared against published data to show the asymptotic behavior. Comparison with other existing correlations for two-phase frictional pressure gradient such as the Chisholm correlation, the Friedel correlation, and the Müller-Steinhagen and Heck correlation, is also presented. Comparison with experimental data for both ϕ l and ϕ l versus X is also presented. At the end of the paper, the present asymptotic model is also extended to minichannels and microchannels.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations10
Published2014
Admission routes2
Has abstractyes

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