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Record W4402728295 · doi:10.1063/5.0226876

On particle-modified velocity fields of particulate Taylor–Couette flow

2024· article· en· W4402728295 on OpenAlexafffund
C. Q. Ru

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsTaylor–Couette flowMechanicsParticulatesCouette flowParticle (ecology)Flow (mathematics)Classical mechanicsFlow velocity

Abstract

fetched live from OpenAlex

Particulate Taylor–Couette flow of a particle-laden viscous fluid between two coaxial rotating cylinders is studied using a novel hydrodynamic model. With the volume fraction of particles as the dimensionless small parameter, explicit leading-order solutions are derived for the general case of dispersed particles heavier or lighter than the carrier fluid. It is shown that, unlike the classical azimuthal velocity field of a clear fluid without particles, dispersed particles generally have a radial velocity toward the outer or inner cylinder depending on the angular velocities and radii of the two cylinders and whether the particles are heavier or lighter than the carrier fluid, in qualitative agreement with some known results reported in literature on heavier or lighter particles, respectively. In some cases, such as the flow driven by rotating inner cylinder with a wider gap between the two cylinders and a moderate value of Stokes number of particles, our results predict the existence of a circular ring between two cylinders, which attracts or repels heavier or lighter particles that could have relevant physical implications. Beyond existing literature on the Taylor–Couette flow with neutrally buoyant particles, these results could offer new insight and useful explicit solutions to the Taylor–Couette flow with particles heavier or lighter than the carrier fluid.

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 score0.159
Threshold uncertainty score0.735

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 head0.016
GPT teacher head0.248
Teacher spread0.231 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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