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Record W1993245977 · doi:10.1139/l04-089

A numerical model of sediment-laden turbulent flow in an open channel

2005· article· en· W1993245977 on OpenAlexvenueno aff
Joon‐Yong Yoon, Seung-Kyu Kang

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceOpen-channel flowSettlingTurbulence modelingMechanicsK-epsilon turbulence modelSediment transportFlow (mathematics)Large eddy simulationShear velocityGeologyGeotechnical engineeringChézy formulaSedimentPhysicsGeomorphologyThermodynamics

Abstract

fetched live from OpenAlex

A numerical analysis of sediment-laden flow is carried out. Velocity and suspended sediment concentration profiles are compared with the experiments in open channel flow. The k-ω turbulence model is selected for the fully turbulent flow field because this model has shown good results for wall-bounded flows and for flows on rough surfaces. The concentration equation that takes into account the settling velocity is adopted for the concentration field. A new eddy viscosity model is proposed in the turbulence modeled equations to couple the velocity field and the concentration field. The bed load thickness and surface roughness are considered in this study, and reasonable predictions are achieved.Key words: numerical simulation, suspended sediment transport, open channel flow, k-ω turbulence model.

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 categoriesInsufficient payload (model declined to judge)
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 score0.068
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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 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
Published2005
Admission routes1
Has abstractyes

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