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Record W2784015047 · doi:10.1002/cjce.23135

CFD simulation of hydrodynamics characteristics in a tank stirred by a hollow self‐inducing impeller

2018· article· en· W2784015047 on OpenAlexvenueno aff
Liangchao Li, Ning Chen, Beiping Xiang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsImpellerSlip factorComputational fluid dynamicsMechanicsAerationFlow (mathematics)Materials sciencePower consumptionPower (physics)AgitatorVolumetric flow rateMechanical engineeringEnvironmental scienceEngineeringPhysicsThermodynamicsWaste management

Abstract

fetched live from OpenAlex

Abstract Hollow self‐inducing impellers are often used in gas‐liquid stirred tanks in many industrial processes. To understand the hydrodynamics characteristics of this type of stirred tank under aerated and unaerated conditions, a computational fluid dynamics (CFD) simulation study was conducted. The predicted gas self‐inducing flow rate, power consumption, and impeller power number were compared with the experimental data in the literature and discussed. Under unaerated conditions, liquid level in the hollow shaft relies on the impeller speed, and is slightly influenced by the impeller clearance. Self‐inducing impeller power number remains almost unchanged with impeller speed, while increasing with the rise of the impeller clearance. Under aerated conditions, gas is easy to accumulate in the centre of the upper and lower circulation loops. Gas self‐inducing flow rate, global gas holdup, and power consumption increase with higher impeller speed. The critical impeller speed for gas self‐induction decreases with higher impeller clearance. At the same impeller speed, gas self‐inducing flow rate and global gas holdup increase, while power consumption reduces with increasing of impeller clearance.

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 score0.312
Threshold uncertainty score0.507

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 head0.173
Teacher spread0.169 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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