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Record W3193567910 · doi:10.2495/cmem-v9-n3-261-275

Spherical particle migration evaluation in low reynolds number couette flow using smooth profile method

2021· article· en· W3193567910 on OpenAlexaff
Mahyar Pourghasemi, Nima Fathi, Peter Vorobieff, Goodarz Ahmadi, Seyed Sobhan Aleyasin, Luís Eça

Bibliographic record

VenueInternational Journal of Computational Methods and Experimental Measurements · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Windsor
FundersNational Science Foundation
KeywordsReynolds numberCouette flowTaylor–Couette flowFlow (mathematics)MechanicsParticle (ecology)PhysicsHele-Shaw flowParticle methodClassical mechanicsMathematicsGeologyMathematical analysisTurbulence

Abstract

fetched live from OpenAlex

An Eulerian-Lagrangian model is developed to investigate the solid particle migration in low Reynolds number shear flows between two parallel plates. A continuous kernel function with a predefined thickness is applied in the implemented numerical model to smooth the discontinuity at the interface between primary and secondary phases. At each time step, the solid particle's rotation and displacement are calculated to directly capture the interaction between the solid particle and primary liquid phase without simplification. Solution verification is performed using the global deviation grid convergence index approach. The observed order of accuracy for the primary phase solver approaches 2, consistent with the formal order of accuracy of the applied discretization scheme. The obtained velocity profiles from the implemented numerical approach show a good agreement with the analytical solution, confirming the single-phase flow solver's reliability. The obtained numerical results from the applied Eulerian-Lagrangian multiphase model are also compared with experimental data from a linear shear flow apparatus with suspended buoyant particles, and good agreement was found.

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)0.002
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 score0.155
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.074
GPT teacher head0.428
Teacher spread0.354 · 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
Published2021
Admission routes1
Has abstractyes

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