MétaCan
Menu
Back to cohort
Record W3127522123 · doi:10.1002/cjce.24061

Numerical investigation of liquid‐liquid two‐phase separation and pressure drop characteristics in <scp>T</scp> ‐junctions

2021· article· en· W3127522123 on OpenAlexvenueno aff
Changshuang Zhi, Liu Liu, Pengfan Chen, Yingwen Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPressure dropInletMechanicsMaterials scienceDrop (telecommunication)Separation (statistics)VortexMass fractionComposite materialPhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Based on the Eulerian and k − ε viscous models, a numerical model of liquid‐liquid two‐phase flow in a T‐junction was established. The simulation results were in reasonable agreement with the experimental data. The effects of the mass fraction taken off through branch and mixture velocity on the separation efficiency and pressure drop characteristics were investigated using the validated model. The results illustrate that the separation efficiency first increases and then decreases with an increase in the mass fraction taken off through branch. When the mass fraction taken off through branch is close to the inlet mass quality, the separation efficiency reaches its maximum. The maximum separation efficiency decreases with the increase of the mixture velocity. In addition, the pressure drop from the inlet to the outlet is primarily due to the local resistance at the intersection and frictional resistance in the pipe, and the pressure drop from the inlet to the branch is majorly caused by the gravity in the branch. It was observed that there is a vortex region at the inlet of the outlet pipe, causing pressure losses.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.403

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 head0.006
GPT teacher head0.204
Teacher spread0.197 · 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 designBench or experimental
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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicFluid Dynamics and MixingFrench-language works237,207