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Record W3177173110 · doi:10.1002/aic.17360

Which impeller should be chosen for efficient solid–liquid mixing in the laminar and transitional regime?

2021· article· en· W3177173110 on OpenAlexafffund
Bastien Delacroix, Louis Fradette, François Bertrand, Bruno Blais

Bibliographic record

VenueAIChE Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsImpellerLaminar flowMixing (physics)MechanicsReynolds numberViscosityMaterials scienceViscous liquidAgitatorTurbineRibbonSlip factorMechanical engineeringEngineeringTurbulencePhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract The vast majority of solid–liquid mixing studies have focused on high Reynolds number applications with configurations and impeller geometries adapted to this type of regime. However, the mixing of particles in a viscous fluid is an essential element of many contemporary industries. We used the computational fluid dynamics‐discrete element method model previously developed in our group to investigate solid–liquid mixing with close‐clearance impellers in the laminar regime of operation. We compared different geometries, that is, the double helical ribbon, anchor, Paravisc ™ , and Maxblend ™ impellers. We investigated the impact of fluid viscosity and compared the results with those obtained with the pitched blade turbine, a more commonly used impeller, based on power consumption for equivalent mixing states. This study highlights that the higher the viscosity of the fluid, the more interesting it is to use close‐clearance impellers for their ability to generate a strong shear stress and a strong bulk flow in the entire vessel.

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)0.001
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 score0.386
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.252
Teacher spread0.234 · 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
Published2021
Admission routes2
Has abstractyes

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