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Record W2073341458 · doi:10.1115/ht2013-17240

Inlet and Wall Effects on Fluid Flow in Doubly-Periodic Arrays of Spacer-Filled Passages

2013· article· en· W2073341458 on OpenAlexafffund
S. M. Mojab, Steven Beale, A. Pollard, E. Hanff, Jon G. Pharoah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsNational Research Council CanadaQueen's University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsReynolds numberMaterials scienceInletPressure dropMechanicsTransverse planeBoundary layerFlow (mathematics)Open-channel flowChannel (broadcasting)Flow velocityComposite materialPhysicsTurbulenceStructural engineeringElectrical engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

A numerical investigation has been carried out to study the effect of the sidewalls and the number of cells in arrays of spacer-filled channels on the local flow distribution, for Reynolds number, Re = 100, for a spacer-configuration typically employed in process industries. It was found that the channel sidewalls have a significant effect on the velocity profile near the walls. Numerically calculated values of velocity are compared with those measured experimentally, with good agreement being obtained; a maximum deviation of 4.5% was observed. Particle traces emitted from a cell at the channel entrance revealed that, unexpectedly, the flow moves parallel to the spacer filaments within each channel layer and changes 90° direction mostly at the cell adjacent to the channel side walls. The effects of the number of cells and the type of boundary condition imposed on the channel transverse sidewalls on the pressure drop and friction factor are considered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes2
Has abstractyes

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