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Record W2477455275 · doi:10.2298/tsci160510185a

Particle-level simulations of flocculation in a fiber suspension flowing through a diffuser

2016· article· en· W2477455275 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThermal Science · 2016
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsEngineering Link (Canada)
Fundersnot available
KeywordsMechanicsTurbulenceFlocculationMaterials scienceFiberInertiaDragSuspension (topology)Reynolds numberPhysicsClassical mechanicsComposite materialChemistryMathematics

Abstract

fetched live from OpenAlex

We investigate flocculation in dilute suspensions of rigid, straight fibers in a decelerating flow field of a diffuser. We carry out numerical studies using a particle-level simulation technique that takes into account the fiber inertia and the non-creeping fiber-flow interactions. The fluid flow is governed by the Reynolds-averaged Navier-Stokes equations with the standard k-omega eddy-viscosity turbulence model. A one-way coupling between the fibers and the flow is considered with a stochastic model for the fiber dispersion due to turbulence. The fibers interact through short-range attractive forces that cause them to aggregate into flocs when fiber-fiber collisions occur. We show that ballistic deflection of fibers greatly increases the flocculation in the diffuser. The inlet fiber kinematics and the fiber inertia are the main parameters that affect fiber flocculation in the prediffuser region.

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.

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 score0.635
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.285
Teacher spread0.234 · 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