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Record W4282579859 · doi:10.1002/cjce.24498

Calculation of the phase separation rate in two‐phase flow of non‐ <scp>Newtonian</scp> power‐law fluid and gas bubbles flowing inside the <scp>T‐</scp> and <scp>Y</scp> ‐junctions using random vortex method ( <scp>RVM</scp> )

2022· article· en· W4282579859 on OpenAlexvenueno aff
Yaser Noori, Ali Reza Teymourtash, Behrooz Zafarmand

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsPower lawReynolds numberPower-law fluidNon-Newtonian fluidSeparation (statistics)Flow (mathematics)Phase (matter)VortexInletNewtonian fluidVolumetric flow ratePower (physics)Materials scienceThermodynamicsLawPhysicsTurbulenceMathematicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract In this paper, the phase separation rate in a two‐phase flow of non‐Newtonian power‐law fluid and gas bubbles flowing inside T‐ and Y‐junction channels with branch angles of 45° and 135° is numerically investigated. The numerical simulation is carried out using the random vortex method (RVM) for the Reynolds number of liquid phase at the inlet of the channel Re m = 250 for various power‐law indexes ( n = 0.2–1.4). The result of this research represents valuable information about the effect of branch angle and also the power‐law index on the phase separation. The result shows that when increasing the branch angle, the phase separation is increased, while when increasing the power‐law index, the phase separation is decreased. The acceptable conformation between this study and the experimental results shows the capability of the evaluated method.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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
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

Citations2
Published2022
Admission routes1
Has abstractyes

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