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Record W2043157736 · doi:10.1115/fedsm2005-77432

The Effect of Wire Shape on the Flow Through Narrow Apertures in a Pulp Screen Cylinder

2005· article· en· W2043157736 on OpenAlexaff
Satya Mokamati, J. A. Oslon, Richard Gooding

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTurbulenceVortexMechanicsMaterials scienceAperture (computer memory)CylinderSurface roughnessSurface finishEddyFlow (mathematics)Turbulence kinetic energyOpticsFluid dynamicsGeometryPhysicsComposite materialAcousticsMathematics

Abstract

fetched live from OpenAlex

Turbulent flow over a rough surface with suction or blowing is a common fluid mechanics problem that has many practical applications including pulp screening. The present study is motivated by the optimization of aperture geometry in pulp screens used in the pulp and paper industry to separate unwanted contaminants from pulp fibres. In these devices, a dilute suspension of fibres is forced through fine slots to remove the oversized contaminants. To better understand the complex hydrodynamics at the critical region near the screen surface, a Computational Fluid Dynamics study has been conducted to examine how the geometry of the aperture entry, characterized by contour height and wire width, affect the details of the flow field. The results indicate that the flow is dominated by a separation vortex that covers the aperture. For low contour heights the flow is accelerated upstream of the aperture due to the high vorticity at the aperture entry and as profile height increases the turbulence intensity at the screen cylinder surface increases. Although the wire width has less of a impact on the flow field than contour height, the results show that decreasing wire width increases turbulence intensity at the surface due to a increase in apparent roughness of the cylinder.

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 score0.112
Threshold uncertainty score0.259

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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
Published2005
Admission routes1
Has abstractyes

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