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Record W4296087113 · doi:10.1002/cjce.24645

An integrated <scp>CFD</scp> methodology for tracking fluid interfaces and solid distributions in a vortexing stirred tank

2022· article· en· W4296087113 on OpenAlexvenueno aff
Arjun Kumar Pukkella, Sivakumar Subramanian

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersTata Consultancy Services
KeywordsImpellerComputational fluid dynamicsSuspension (topology)Dispersion (optics)Volume (thermodynamics)Materials scienceMechanicsWork (physics)Particle (ecology)Phase (matter)VortexTracking (education)SCALE-UPScale (ratio)Mechanical engineeringEngineeringChemistryThermodynamicsPhysicsMathematicsClassical mechanicsOptics

Abstract

fetched live from OpenAlex

Abstract Distribution of solid phase in a solid–liquid suspension being mixed in a vortexing, unbaffled stirred tank is difficult to model numerically. The need is to be able to predict the shape of the vortex (air–water interface) and the distribution of solids in the liquid domain. Typically, the problem is approached with assumptions about the shape of the interface to capture the solid distribution through a multi‐phase Eulerian model (doi: 10.1016/j.ces.2018.07.023 ). In this work, a multi‐step modelling framework for multi‐phase systems that have a free surface along with the dispersion of secondary phase(s) in the liquid domain is proposed. To demonstrate the method, it is applied to a laboratory‐scale vortexing unbaffled system reported in the literature (doi: 10.1021/ie071225m ) and a pilot‐scale tank (doi: 10.1016/j.cej.2018.10.020 ). The predictions from the computational fluid dynamics model are compared with the experimental profiles of solid volume fractions. Using the model, the effects of solid density, particle size, particle loading, and impeller speed are investigated for the laboratory‐scale system. An interesting self‐similar nature in the axial distribution of solid is observed when the loading is varied from 0.5 to 10 volume percent.

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)0.001
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 score0.412
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.240
Teacher spread0.221 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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