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Record W2041877626 · doi:10.1002/cjce.22058

Experimental study of a time‐varying turbulent cross‐flow near a two‐dimensional rough wall with narrow apertures

2014· article· en· W2041877626 on OpenAlexafffundvenue
Hayder Salem, Mark Martinez, Robert W. Gooding, James A. Olson

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsParticle image velocimetryTurbulenceWakeVortexAperture (computer memory)PhysicsOpticsFlow (mathematics)Materials scienceSuctionAcousticsMeteorology

Abstract

fetched live from OpenAlex

Turbulent flow over a rough wall with suction or blowing is an industrially important fluid mechanics problem. In the screening of wood pulp fibre suspensions, for example, turbulent flow is induced by a rotor adjacent to a slotted screen cylinder. To better understand the complex hydrodynamics in the critical region between the pulp screen rotor and the slotted screen wall, the stream‐wise velocity and aperture velocities were measured using particle image velocimetry. The vortex generated above the aperture is shown to be strongly dependent on aperture velocity and wall roughness. The vortex diminishes in size at higher aperture velocities and increased exit layer height. The experiments also show that the reversal flow in the slot decreases with lower rotor speeds and increased mean slot velocities. This observation challenges the existing models of apertures being cleared by flow reversal driven by a Bernoulli‐type suction pulse. In its place, this paper identifies elements of a more sophisticated flow model that considers the depletion of the zone below the rotor as well as the flow in the wake of the foil.

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 score0.015
Threshold uncertainty score0.484

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations2
Published2014
Admission routes3
Has abstractyes

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