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Record W2530013605 · doi:10.1002/cjce.22718

Hydrodynamic difference between inline and batch operation of a rotor‐stator mixer head ‐ A CFD approach

2016· article· en· W2530013605 on OpenAlexvenueno aff
Andréas Håkansson, Dragana Arlov, Fredrik Carlsson, Fredrik Innings

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsStatorRotor (electric)MechanicsTurbulenceComputational fluid dynamicsTurbulence kinetic energyFlow (mathematics)Flow velocityPhysicsMaterials scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Rotor‐stator mixers (RSMs) can be operated in either batch or inline mode. When operating a rotor‐stator geometry in batch mode, it typically experiences an order of magnitude higher volumetric flow through the stator than in inline mode. This is expected to cause differences in the flow and turbulence in the rotor‐stator region. This study uses computational fluid dynamics (CFD) to study the hydrodynamic differences in and near the stator hole as a function of volumetric flow rates between those experienced in inline and batch modes of operation. It is concluded that both radial flow profiles and turbulent kinetic energy across a range of rotor speeds and flow rates can be described by a velocity ratio: average tangential fluid velocity in the stator hole divided by the rotor tip speed. Moreover, the position where dissipation of turbulent kinetic energy takes place—and hence the effective region of dispersion or mixing—differs between the two modes of operation. The relative importance of the two regions can be described in terms of the velocity ratio and the transition can be predicted based on the relative power input due to rotational and pumping power of the mixer. This study provides a starting point for understanding differences between emulsification efficiency between inline and batch modes of operation with relevance for both equipment design and process scale‐up.

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 score0.913
Threshold uncertainty score0.322

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 head0.175
Teacher spread0.168 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

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