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Record W2022115502 · doi:10.1115/fedsm2002-31359

Mixing Enhancement of Non-Newtonian Fluids Using Unconventional Configurations

2002· article· en· W2022115502 on OpenAlexaff
Gabriel Ascanio, Magdalena Brito-Baza ́n, Edmundo Brito‐de la Fuente, Pierre J. Carreau, Philippe A. Tanguy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsImpellerMechanicsMixing (physics)Newtonian fluidHomogenization (climate)Shear thinningMaterials scienceClockwiseSlip factorPhysicsOpticsRheologyComposite material

Abstract

fetched live from OpenAlex

Dynamic perturbations and off-centered single impeller configurations have been investigated to reduce mixing time with shear thinning fluids. The use of a color-decolorization technique based on a fast acid-base reaction allowed to measure mixing times and to reveal the presence of both segregated and dead zones. A statistical design approach has been used to evaluate the effect of the impeller position as well as the dynamic conditions and their interactions. Mixing times and flow patterns for shear thinning fluids were presented and compared to conventional operating modes. Homogenization was significantly enhanced when a radial flow impeller was used under both off-centered and dynamic perturbation conditions. In the case of an axial flow impeller, a combination of a large axial displacement with long clockwise times and short counter-clockwise times gave better mixing times.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.022
GPT teacher head0.248
Teacher spread0.227 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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