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Record W2042175934 · doi:10.1080/00221680209499902

Two dimensional vertically averaged and moment equations for rapidly varied flows

2002· article· en· W2042175934 on OpenAlexafffund
Haitham Ghamry, P. M. Steffler

Bibliographic record

VenueJournal of Hydraulic Research · 2002
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsOpen-channel flowMathematicsMethod of mean weighted residualsFlow (mathematics)Galerkin methodMechanicsQuadratic equationHydrostatic equilibriumMoment (physics)ResidualFinite element methodGeometryMathematical analysisPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

The classical depth averaged De St. Venant equations, which are used for most of the computational models in open channels, are based on the fundamental assumptions of uniform velocity and hydrostatic pressure distributions. They are thus limited in their applicability to cases where vertical details are not of importance. Alternative two-dimensional vertically averaged and moment equations are developed, by a moment weighted residual method from the fundamental 3D Reynolds equations, to account for problems where more vertical details are significant and essential. The proposed model is applied to rapidly varied flow problems involved in open channel flow. These problems include flow in channel transitions with rapid contraction and/or expansion and flow over a hemispherical hump. Linear distribution shapes are proposed for the horizontal velocity components, while quadratic distribution shapes are considered for vertical velocity and pressure. The implicit Petrov-Galerkin finite element scheme is used in these simulations. A good agreement is attained. In addition, the obtained results show that more details are gained and the flow is better represented by the proposed model compared to the classical De St. Venant 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)0.001
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 score0.091
Threshold uncertainty score0.337

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)0.0000.000

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 head0.046
GPT teacher head0.300
Teacher spread0.254 · 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

Citations40
Published2002
Admission routes2
Has abstractyes

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