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Record W1981396553 · doi:10.1115/1.4001575

Investigation of the Flow Field in a Rectangular Vessel Equipped With a Side-Entering Agitator

2010· article· en· W1981396553 on OpenAlexaff
Clara Gómez, Chad P. J. Bennington, Fariborz Taghipour

Bibliographic record

VenueJournal of Fluids Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImpellerAgitatorComputational fluid dynamicsParticle image velocimetryMechanicsLaminar flowMixing (physics)Slip factorMaterials scienceFluid dynamicsMechanical engineeringEngineeringPhysicsTurbulence

Abstract

fetched live from OpenAlex

The hydrodynamics of stirred vessels with side-entering impellers, which are used in numerous process industries including petroleum, foods, and pulp and paper manufacturing, have received limited attention. In the present work, the flow in a reduced size rectangular tank equipped with a side-entering axial flow impeller, scaled down from the industrial agitation of low consistency pulp fiber suspensions, was investigated using particle image velocimetry (PIV) and computational fluid dynamics (CFD), in the laminar regime (18≤Re≤120). Tuning of the PIV measuring parameters for an optimum capture of valid velocity vectors within a representative portion of the vessel is described. A detailed description of the construction and refinement of the grid and quantification of the discretization error in the CFD results is also presented. The simulation predictions were extensively evaluated by comparing the measured planar flow patterns and velocity fields at various locations in the mixing vessel. Very good agreement was found between PIV measurements and computed velocities confirming the efficiency of CFD in the analysis of mixing systems. The prediction of global mixing parameters was also evaluated. The computed impeller torque and impeller power number agreed very well with experimental measurements over the range of Re studied.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.374

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 head0.171
Teacher spread0.167 · 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 designBench or experimental
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

Citations21
Published2010
Admission routes1
Has abstractyes

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