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Record W2043693829 · doi:10.1139/l07-068

Effect of depth on flow over a fixed dune

2007· article· en· W2043693829 on OpenAlexaffvenue
Ram Balachandar, Beom-Soo Hyun, V. C. Patel

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTurbulenceGeologyTurbulence kinetic energyFlow (mathematics)MechanicsCrestShear stressShear (geology)Open-channel flowGeometryOpticsPhysicsMathematics

Abstract

fetched live from OpenAlex

Laser Doppler velocimeter (LDV) measurements were carried out to study the effect of depth on the flow over a train of fixed two-dimensional dunes. Conventionally averaged velocity and turbulence parameters reveal large peaks in the streamwise and vertical components of turbulent intensities and shear stress, along the shear layer emanating from the dune crest. A secondary peak in the streamwise turbulence profiles some distance beyond the shear layer indicates maintenance of turbulence generated on the previous dune and convection of the flow history from one dune to the next. Analyses based on triple products and quadrant decomposition of velocity fluctuations reveals the central role of the shear layer in dictating the flow properties over the entire depth. The depth influences the flow in the near-bed region and the length of the separation zone is longer at a shallower depth. The streamwise mean profiles collapse onto a single curve in the outer region beyond the shear layer, indicating a degree of similarity and independence from the near-bed flow. The profiles of the vertical component of turbulence reveal a systematic dependence on flow depth, with lower turbulence intensity at larger depths. The quantitative effect of flow depth is evident in the measurements at all levels, including triple products and quadrant decomposition.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.003
GPT teacher head0.184
Teacher spread0.180 · 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.

Study designObservational
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

Citations21
Published2007
Admission routes2
Has abstractyes

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