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Variation of Flow Pattern with Sinuosity in Sine-Generated Meandering Streams

2006· article· en· W2039031175 on OpenAlex
Ana Maria Ferreira da Silva, Tarek El-Tahawy, William D. Tape

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Hydraulic Engineering · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of WindsorQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSinuosityGeologyTurbulenceFlow (mathematics)Open-channel flowGeometrySTREAMSMechanicsSecondary flowFlumeConvectionFlow conditionsHydrology (agriculture)GeomorphologyGeotechnical engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

The effect of channel sinuosity on flow pattern in meandering streams is investigated. The centerlines of the idealized meandering streams under consideration follow sine-generated curves, and the banks are rigid; the flow is turbulent and subcritical. This study focuses on the vertically averaged flow over a flat (horizontal at any cross section) bed formed by a granular material. The “flat bed” is viewed as the initial surface of a moveable bed at the beginning of an experiment (at time t=0 ). A series of laboratory flow measurements involving the systematic variation of the deflection angle θ0 from 30 to 110° , is used. It is found that every different sinuosity (every different θ0 ) has its own convective flow pattern, i.e., its own distribution in plan of (the L∕2 long) convergence–divergence zones of flow. As θ0 increases, a gradual change in flow pattern is observed. Two expressions defining the observed θ0 variation of the convective flow pattern are introduced. It is shown, with the aid of the sediment transport continuity equation, that the geometry of the developed bed at the end of an experiment is strongly related to the convective behavior of the vertically averaged (initial) flow over the flat bed at t=0 . In particular, information on the θ0 variation of the convective pattern of the initial flow can be used to estimate the location of erosion–deposition zones and the location(s) of the most intense erosion–deposition corresponding to any θ0 .

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.288

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 head0.003
GPT teacher head0.164
Teacher spread0.161 · 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