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Record W4409742701 · doi:10.1002/cjce.25737

Investigation of mixing performance and flow characteristics of oil–water two‐phase flow in a novel swirling shear‐type static mixer

2025· article· en· W4409742701 on OpenAlexvenueno aff
Xiaoli Zhu, Guosheng Song, Zhenbo Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Shandong Province
KeywordsMixing (physics)Flow (mathematics)MechanicsMaterials scienceStatic mixerShear (geology)Two-phase flowPhase (matter)Geotechnical engineeringGeologyTurbulencePhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract Multiphase flow mixing plays a crucial role in industrial applications, enhancing reaction efficiency, product quality, and process stability. However, traditional static mixers often face challenges such as high pressure loss and stringent fluid property requirements. This study introduces a novel static mixer based on swirling and shear effects to address these issues. An experimental setup was developed to evaluate the mixer, which incorporates intersecting baffle plates and multiple columns of cutting teeth. Both experiments and computational fluid dynamics (CFD) simulations were conducted to analyze the internal flow field and mixing performance of the oil–water two‐phase system. The mixing performance of the sequential and cross arrangements of cutting teeth was compared. The results show that the combination of the baffle plate and cutting teeth significantly influences the oil–water flow pattern, creating a complex vortex system that enhances mixing efficiency. Notably, the cross arrangement of cutting teeth provides more uniform mixing and a lower pressure drop compared to the sequential arrangement. These findings offer valuable insights into the optimal design and scale‐up of the newly developed static mixer for industrial applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.358

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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
Published2025
Admission routes1
Has abstractyes

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