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Record W2051844209 · doi:10.1002/cjce.20235

Effect of impeller clearance on liquid flow within an unbaffled vessel agitated with a forward–reverse rotating impeller

2009· article· en· W2051844209 on OpenAlexvenueno aff
Masanori Yoshida, Yuma Wakura, Kazuaki Yamagiwa, Akira Ohkawa, Shuichi Tezura

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsImpellerMechanicsSlip factorRushton turbineTurbineTip clearanceRotation (mathematics)Particle image velocimetryFlow (mathematics)Materials scienceFlow visualizationAccelerationPhysicsVortexTurbulenceGeometryClassical mechanicsMathematicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract For an unbaffled agitated vessel with an unsteadily forward–reverse rotating impeller whose rotation proceeds with repeated acceleration, deceleration, and stop–reverse processes, liquid flow was studied through visualisation and measurement using particle tracking velocimetry (PTV). A disk turbine impeller with six flat blades was used with varied height settings. The impeller clearance and its forward–reverse rotation cycle characterised the impeller region flow: the radially outward flow in the deceleration process for the larger clearance relative to the vessel diameter of 1/3, and the axially downward flow in the acceleration process for the smaller clearance relative to the vessel diameter of 1/8. The flow patterns within the vessel resulting from the impeller's larger and smaller clearances were outlined, respectively, by double loops and a single loop of circulation, resembling the pattern produced by unidirectionally rotating turbine‐type impellers. The discharge flow was revealed to contain a comparable level of periodic circumferential velocity component, irrespective of the 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 score0.011
Threshold uncertainty score0.677

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 integrity0.0000.001
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 head0.003
GPT teacher head0.177
Teacher spread0.174 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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