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Record W1998106865 · doi:10.1002/cjce.20231

Enhanced mixing of Newtonian fluids in a stirred vessel using impeller speed modulation

2009· article· en· W1998106865 on OpenAlexvenueno aff
Wei Ming Yek, Mohamed Nabil Noui-Mehidi, Rajarathinam Parthasarathy, Sati N. Bhattacharya, Jie Wu, Naoto Ohmura, Nami Nishioka

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsReynolds numberMechanicsRushton turbineMixing (physics)ImpellerAmplitudeNewtonian fluidSine waveNon-Newtonian fluidWaveformSquare wavePhysicsMathematicsMaterials scienceControl theory (sociology)OpticsComputer scienceTurbulence

Abstract

fetched live from OpenAlex

Abstract This paper reports on an experimental study of mixing intensification using speed modulation of a six‐blade Rushton turbine in a stirred vessel. Mixing times were measured using a non‐intrusive technique based on direct visualisation of an acid‐base reaction in a Newtonian fluid. The impeller speed modulation was achieved by using two waveforms: a square wave and a sine wave. The amplitude was fixed between a maximum Reynolds number of Re max = 60 and minimum Reynolds numbers of Re min = 40 or 30. The wave periods were varied (10, 20, or 40 s) in order to compare the effects of unsteady stirring on mixing performance. It was observed that a square wave protocol with the shortest wave period and the larger amplitude resulted in the shortest time to destroy the observed isolated mixing regions (IMRs), which are known to exist in stirred vessels operating at low Reynolds number. However, the sine wave protocol led to a slow diffusive mechanism in which IMR structures reached an asymptotic volume and remained visible even after several hours. The results are presented and discussed using digital photographs taken at different time intervals during experimentation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.495

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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 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

Citations26
Published2009
Admission routes1
Has abstractyes

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