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Record W2140637270 · doi:10.1080/00221686.2014.928805

Open-channel turbulent flow through bar racks

2014· article· en· W2140637270 on OpenAlexafffundabout
Jonathan M. Tsikata, Mark F. Tachie, Christos Katopodis

Bibliographic record

VenueJournal of Hydraulic Research · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaBureau of ReclamationManitoba Hydro
KeywordsBar (unit)TurbulenceFlow (mathematics)MechanicsGeometryScale (ratio)Open-channel flowMaterials scienceGeologyStructural engineeringPhysicsMathematicsEngineeringMeteorology

Abstract

fetched live from OpenAlex

ABSTRACTThe paper reports on an experimental study of turbulent open-channel flow near small-scale and large-scale bar rack models. The experiments were conducted for a wide range of bar depth, bar shape, bar spacing and bar inclination to the approach flow. The contours of the mean velocity near the small-scale models revealed that bar inclination produced asymmetric flow, a potential threat for vibration failure. The reconstructed fluctuating velocity fields obtained from proper orthogonal decomposition revealed strong organization of vortical structure downstream of the aligned bars compared to the inclined bars. The head loss coefficient generally increased monotonically with blockage ratio and bar inclination. Significant reduction in head losses was observed when square leading edges of rectangular bars are replaced by round leading edges, or if streamlined profile cross-section bars were used instead of rectangular bars.Keywords: Bar inclinationbar rackbar shapehead lossproper orthogonal decomposition AcknowledgementsFinancial support from NSERC CRD and Manitoba Hydro is greatly acknowledged.

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)0.003
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 score0.442
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.321
Teacher spread0.277 · 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

Citations23
Published2014
Admission routes3
Has abstractyes

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