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Record W2955792683 · doi:10.1002/cjce.23583

Liquid‐liquid two‐phase flow patterns in a new helical microchannel device

2019· article· en· W2955792683 on OpenAlexvenueno aff
Yan Cao, Jun Li, Jin Yang, Jianhong Luo, Ming Chen, Yubin Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMicrochannelSlug flowFlow (mathematics)Mass transferTwo-phase flowVolumetric flow rateMaterials scienceMechanicsOpen-channel flowChemistryThermodynamicsChromatographyPhysics

Abstract

fetched live from OpenAlex

Flow patterns of liquid‐liquid two‐phase fluids in a new helical microchannel device were presented in this paper. Three conventional systems were considered: kerosene‐water, n ‐butyl acetate‐water, and butanol‐water. Six different flow patterns, slug flow, continuous parallel flow, discontinuous deformation parallel flow, discontinuous deformation parallel‐droplet flow, droplet‐slug flow, and filiform‐droplet flow, were observed. The influence of interfacial tension, microchannel structure, and rotation rate on two‐phase flow patterns were studied, and a universal flow pattern map was presented and discussed. The systems without mass transfer (0.1 g/g (10 %) tri‐ n ‐butyl phosphate (TBP)‐water, 0.2 g/g (20 %) TBP‐water, and 0.8 g/g (80 %) TBP‐water) and the system with mass transfer (0.8 g/g (80 %) TBP‐0.62 g/g (62 %) H 3 PO 4 ) were used to verify the validity of the proposed universal flow pattern map in predicting flow patterns. The results showed that the former compared with the latter can be predicted more accurately by the universal flow pattern map.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2019
Admission routes1
Has abstractyes

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