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Record W3120566331 · doi:10.1002/cjce.24022

Experimental and numerical investigation of the scale‐up criterion of solid‐viscous fluid mixing in a stirred tank

2021· article· en· W3120566331 on OpenAlexvenueno aff
Daiqi Lin, Hongyuan Wei, Erjia Song, Boonho Ng, Ting He, Leping Dang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsReynolds numberMixing (physics)Newtonian fluidMechanicsComputational fluid dynamicsNon-Newtonian fluidCarboxymethyl celluloseThermodynamicsSCALE-UPScale (ratio)Flow (mathematics)Materials scienceChemistryClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract The scale‐up of mixing processes is crucial for process engineering and power optimization in the chemical industry. Scale‐up of fluid mixing in stirred systems becomes a challenging task, especially when using non‐Newtonian fluids. In this study, computational fluid dynamics (CFD) has been applied to simulate the solid‐liquid mixing of silica particles with non‐Newtonian fluids of carboxymethyl cellulose (CMC) solutions (1.0, 1.5, and 2.0). The model is validated by experimental measurement of power consumption and solid‐phase concentration. The scale‐up criterion based on geometric similarity and constant tip speed is proved to be valid for non‐Newtonian systems by comparing kinematic similarity. The transformation of flow regime is discussed based on modified Reynolds number and local Reynolds number. The scale‐up criterion performs well in terms of the scale effect of power consumption, especially for the CMC2.0 solution system. This study is instructive for the scale‐up of non‐Newtonian fluid mixing in stirred tanks.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.308

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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 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

Citations9
Published2021
Admission routes1
Has abstractyes

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