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Record W2045499036 · doi:10.1021/ie049654x

Power Characteristics in Coaxial Mixing:  Newtonian and Non-Newtonian Fluids

2004· article· en· W2045499036 on OpenAlexafffund
S. Foucault, Gabriel Ascanio, Philippe A. Tanguy

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpellerMechanicsCoaxialNewtonian fluidNon-Newtonian fluidDispersion (optics)Power consumptionMixing (physics)Reynolds numberPower (physics)PhysicsMaterials scienceThermodynamicsMechanical engineeringOpticsEngineeringTurbulence

Abstract

fetched live from OpenAlex

The power consumption of a coaxial mixer consisting of a wall-scraping anchor and different dispersion impellers (radial discharge) operating in co- and counterrotating modes has been experimentally characterized in the case of viscous Newtonian and non-Newtonian fluids. It was found that the anchor speed did not affect the power consumption of the dispersion turbines. The power consumption of the anchor was found to increase when the dispersion impellers were used in counterrotating mode and to decrease when they were used in corotating mode. Following the Metzner and Otto approach [ AIChE J. 1957, 3 (1), 3−10], new correlations based on the impeller geometry for the generalized Reynolds number and the power number are proposed, and it is shown that a power master curve can be generated for speed ratios larger than 10.

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)0.002
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.267
Teacher spread0.241 · 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

Citations126
Published2004
Admission routes2
Has abstractyes

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