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Record W2014901095 · doi:10.1002/cjce.22121

CFD simulation of flow and mixing in‐inline rotor‐stator mixers with complex fluids

2014· article· en· W2014901095 on OpenAlexvenueno aff
Christophe Vial, Youssef Stiriba, Zaineb Trad

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsComputational fluid dynamicsLaminar flowRotor (electric)Dimensionless quantityNewtonian fluidFlow (mathematics)StatorMaterials sciencePhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The objective is to apply CFD methodology for the validation of the scale‐up of rotor‐stator units formed by flat blades and used as in‐line mixers under laminar flow conditions. The comparison between simulated and experimental data as a function of rotor geometry, rotation speed and flow rate has shown a good agreement in terms of power input and RTD curves both for Newtonian and power‐law fluids with a flow index between 0.2 and 1. The applicability of the virtual Couette analogy has been validated quantitatively and explained by the analysis of the local flow in the mixer. As a result, a shear coefficient independent of fluid rheology has been deduced from CFD data. This has been shown to depend only on the dimensionless gap when the length‐to‐diameter ratio is higher than 2.5. In this case, fast 2D simulations can provide a good approximation of the shear coefficient obtained from 3D calculations from which a master power curve can be deduced, but these are not able to predict flow transition, contrary to 3D computations. Finally, original correlations able to estimate the power and shear coefficients as a function of the mixer geometry have been established.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.345

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 head0.005
GPT teacher head0.174
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

Citations18
Published2014
Admission routes1
Has abstractyes

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