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Record W4383342788 · doi:10.1063/5.0155777

Development of a scale-up strategy for an aerated coaxial mixer containing a non-Newtonian fluid: A mass transfer approach

2023· article· en· W4383342788 on OpenAlexafffund
Ali Rahimzadeh, Farhad Ein‐Mozaffari, Ali Lohi

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
FundersRES’EAU-WaterNET
KeywordsImpellerCoaxialMechanicsPhysicsMass transferAerationMass transfer coefficientNewtonian fluidMixing (physics)Non-Newtonian fluidComputational fluid dynamicsDispersion (optics)ThermodynamicsMechanical engineeringOpticsEngineeringWaste management

Abstract

fetched live from OpenAlex

Coaxial mixers have been shown to be effective in enhancing the hydrodynamic stress and shear environment inside the aerated systems. However, the scale-up study of the aerated coaxial mixing reactors based on a constant mass transfer coefficient has never been reported in the literature. In this study, for the first time, a practical technique is suggested to evaluate the scalability of these systems in terms of a constant mass transfer coefficient. The effects of impeller speed, impeller type, aeration rate, and pumping direction on the mass transfer, power consumption, gas holdup profile, fluid hydrodynamics, and energy dissipation rate were explored for gas dispersion in non-Newtonian fluids inside coaxial mixers through tomography, dynamic gassing-in, and computational fluid dynamics. It was found that a practical approach to preserve the mass transfer coefficient of the large-scale coaxial mixer the same as its small-scale counterpart was to maintain the volumetric aeration rate per working fluid volume constant.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score1.000

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 head0.032
GPT teacher head0.252
Teacher spread0.220 · 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.

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

Citations12
Published2023
Admission routes2
Has abstractyes

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