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Record W2001313355 · doi:10.1002/cjce.21652

Effect of baffles on fluid flow field in stirred tank with floating particles by using PIV

2012· article· en· W2001313355 on OpenAlexvenueaboutno aff
Rajab Abd alsalam Atibeni, Zhengming Gao, Yuyun Bao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsBaffleMechanicsFlow (mathematics)ImpellerShieldMaterials scienceDrawdown (hydrology)Power consumptionPower (physics)EngineeringMechanical engineeringPhysicsGeotechnical engineeringGeologyThermodynamics

Abstract

fetched live from OpenAlex

Abstract PIV technique was applied to elucidate the effect of baffles at different shaft positions and different impeller off‐bottom clearances on the flow field in a stirred tank with floating particles. The investigation was carried out in a cylindrical tank with a flat base, and five different baffle configurations: standard baffles, narrow baffles with a width of 15 mm, narrow baffles with a width of 10 mm, down triangular baffles and up triangular baffles. The measurements show that down triangular baffles offers several advantages over standard baffles at C = T /3: high axial and radial velocities, relatively Low critical agitation speed and power consumption for just drawdown of 1.0 vol.% floating particles. While at C = T /2 this superiority disappears and the fluid flow field is similar to that for standard baffles. The other baffles are similar in performance except for a small difference in the critical agitation speed. An off‐centred shaft helps reduce the critical just drawdown speed and the corresponding power consumption for baffle configurations considered. With down triangular baffles, the critical power consumption to draw down the floating particles for the most eccentric shaft is about 42% of that for a centred shaft. © 2012 Canadian Society for Chemical Engineering

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.379

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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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

Citations20
Published2012
Admission routes2
Has abstractyes

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