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Record W2066226109 · doi:10.1080/00221686.2007.9521762

Flow recovery in the wake of a suspended flat plate

2007· article· en· W2066226109 on OpenAlexaff
F. N. Krampa-Morlu, Ram Balachandar

Bibliographic record

VenueJournal of Hydraulic Research · 2007
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of WindsorUniversity of Saskatchewan
Fundersnot available
KeywordsWakeTurbulenceMechanicsOpen-channel flowMean flowVortexFlow (mathematics)Reynolds numberReynolds stressShear stressPhysicsTurbulence kinetic energyFlow separationJet (fluid)Geology

Abstract

fetched live from OpenAlex

The flow field behind a flat plate suspended in an open channel is investigated using LDV. The formation of a gap between the plate and channel bed produces a flow that is different from the flow around a wall-mounted bluff body. The gap can be perceived as an end condition of the plate that suppresses the formation of the horseshoe vortex and modifies the interaction between the plate and the wall bounded flow. As the flow plunges under the plate through the gap, a wall jet is formed. As the wall jet grows and interacts with the wake-like flow, the effect of the wall jet diminishes with increasing distance downstream of the plate, beyond which the turbulent flow in the wake may be considered to be quasi-two-dimensional. Flow recovery is examined in terms of the mean velocity, turbulence intensity and quadrant decomposition. The present results indicate that the near-wall region of the mean flow recovers faster than the outer region. The turbulent intensities showed a similar tendency. The Reynolds shear stress distributions indicated full recovery while turbulence intensities did not recover completely, especially in the outer regions of the flow. However, a quadrant analysis reveals that at the last measuring station and away from the wall region, the flow is still being influenced by the disturbance generated in the wake region.

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)0.006
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 score0.144
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.328
Teacher spread0.291 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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