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Record W2119853423 · doi:10.1139/l01-089

Velocity distribution in decelerating flow over rough surfaces

2002· article· en· W2119853423 on OpenAlexvenueno aff
Ram Balachandar, K. Hagel, Deighen Blakely

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
Fundersnot available
KeywordsFroude numberReynolds numberBoundary layerMechanicsShear velocityOpen-channel flowTurbulenceWakeBoundary layer thicknessLaw of the wallGeometryMathematicsSurface roughnessPhysicsFlow (mathematics)GeologyThermodynamics

Abstract

fetched live from OpenAlex

An experimental program was undertaken to study turbulent boundary layers formed in decelerating open channel flows. The flows over a smooth surface and three rough surfaces were examined. Tests were conducted at a subcritical Froude number (~0.2) and varying depth Reynolds numbers (64 000 < Re d < 88 000). The corresponding momentum thickness Reynolds numbers were small (1000 < Re θ < 2100). The velocity measurements were undertaken using a one-component laser-Doppler anemometer. Variables such as the shear velocity, the longitudinal mean velocity, Coles' wake parameter, and Clauser's shape parameter were examined. Three different methods for determining the friction velocity were investigated for use in sloping channels. The inner region of the boundary layer was found not to be influenced by the channel slope. The log-law slope and intercept were found to be the same as that noted for canonical boundary layers. The skin friction coefficient for the sloping smooth surface tests was found to be slightly higher than that noticed for flow over a horizontal surface. As indicated by the wake parameter, the free surface, the channel slope, and the roughness of the channel affected the outer region of the boundary layer.Key words: decelerating flow, open channel, log-law, friction velocity, power law.

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 score0.051
Threshold uncertainty score0.966

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 head0.007
GPT teacher head0.162
Teacher spread0.155 · 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

Citations24
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207