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Record W4230841980 · doi:10.32920/ryerson.14663865.v1

Mixing of complex fluids with coaxial mixers composed of two central impellers and an anchor

2021· preprint· en· W4230841980 on OpenAlexafffund
Argang Kazemzadeh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpellerCoaxialShear thinningMixing (physics)TurbineMechanicsComputational fluid dynamicsMaterials scienceRushton turbineMechanical engineeringNewtonian fluidRheologyEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The coaxial mixers composed of a high-speed central impeller and a low-speed anchor have been recommended by the previous researchers for the mixing of highly viscous and non-Newtonian fluids. However, no study has been reported in the literature regarding the use of the coaxial mixing systems composed of two central impellers and an anchor in the agitation of complex fluids. Thus, the main objective of this study was to investigate the performance of coaxial mixers composed of two central impellers and an anchor in the agitation of the xanthan gum solution, which is a yield-pseudoplastic fluid, through electrical resistance tomography (ERT), the computational fluid dynamics (CFD), and design of experiments (DOE) combined with the response surface methodology (RSM). In the first stage of this study, the hydrodynamic performance of coaxial mixers, the single and double Scaba impellers in combination with an anchor impeller, was investigated in the mixing of yield-pseudoplastic fluids. Considering the mixing efficiency criteria, it was found that the double Scaba-anchor coaxial system was more efficient than the single Scaba-anchor coaxial mixer in the mixing of yield pseudoplastic fluids with regard to the mixing time and power drawn. In the second stage of this research project, the performances of three different coaxial mixers, namely, double Scaba-anchor coaxial (DSAC), double Rushton turbine-anchor coaxial (DRAC), and double pitched blade turbine-anchor coaxial (DPAC) mixers were assessed. It was found that the double Scaba-anchor coaxial (DSAC) mixer was more efficient system compared to the others at the same operating conditions. To evaluate the influence of the impeller spacing on the hydrodynamics of the double Scaba-anchor coaxial mixer, the lower impeller clearance and the spacing between two central impellers were changed within a wide range. The results demonstrated that a coaxial mixer with the impeller spacing of almost equal to the central impeller diameter was the most efficient configuration compared to the other cases. When the impeller spacing was varied, the merging flow and parallel flow patterns were observed. Finally, the hydrodynamic performances of different configurations of coaxial mixers composed of a wall scraping anchor impeller in combination with two different or identical central high-speed impellers were analyzed. The coaxial mixers utilized in this stage were the Scaba–Scaba-anchor (SSAC), Scaba-Rushton-anchor (SRAC), Rushton-Scaba-anchor (RSAC), Scaba-pitched blade-anchor (SPBAC), and pitched blade-Scaba-anchor (PBSAC). A new correlation was introduced for these complex configurations of the coaxial mixers by incorporating the Metzner-Otto constants (Ks) of the different types of the central impellers into the Reynolds number. The analysis of the collected data revealed that the Scaba-pitched blade-anchor coaxial (SPBAC) mixer was the most efficient mixing system in the mixing of the highly viscous non-Newtonian fluids.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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 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

Citations4
Published2021
Admission routes2
Has abstractyes

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