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Record W2166574953 · doi:10.1139/l11-042

Open channel flow recovery in the wake of a flat plate on rough beds

2011· article· en· W2166574953 on OpenAlexaffvenue
Franklin Norvisi Krampa, Ram Balachandar

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWakeFlatness (cosmology)TurbulenceMechanicsGeometryGeologyChord (peer-to-peer)Surface finishSkewnessTurbulence kinetic energyFlow (mathematics)Open-channel flowPhysicsMaterials scienceMathematicsComposite materialStatistics

Abstract

fetched live from OpenAlex

The flow recovery in the wake of a surface-mounted flat plate is investigated in an open channel. The plate with a thickness-to-chord ratio (t/c) of 0.12 is placed with the chord parallel to the flow. The characteristics of the mean velocity and higher-order statistics obtained along the wake axis upstream and downstream of the plate are discussed in the presence of bed roughness. Inner scaling of the mean velocity profile shows a depression in the outer region of the flow. The near-wall portion of the velocity profile in the plate wake region was found to recover faster to the upstream state. The profiles of the turbulent intensity in the plate wake deviate from the upstream profiles and recover gradually with downstream distance. The upstream turbulent intensities for the wire-mesh rough surface peak at farther wall normal locations compared to those of the sand grain and smooth surfaces. A similar roughness effect was observed at the downstream locations amidst the distorted flow, especially, in the intermediate and far wake regions. In the near- and intermediate-wake regions, both the velocity skewness and flatness factors data for the rough walls were higher compared to the data for the smooth surface. For all surfaces, even at the last measuring station (x = 200t) considered in the study, the skewness and flatness factors were found to be still recovering to the upstream condition.

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 score0.323
Threshold uncertainty score0.916

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 head0.018
GPT teacher head0.184
Teacher spread0.166 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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