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Record W2278411610 · doi:10.1080/00221686.2015.1085919

A coupled two-dimensional numerical model for rapidly varying flow, sediment transport and bed morphology

2015· article· en· W2278411610 on OpenAlexaff
Xin Liu, Julio Ángel Infante Sedano, Abdolmajid Mohammadian

Bibliographic record

VenueJournal of Hydraulic Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Ottawa
FundersQatar National Research Fund
KeywordsBed loadFlux limiterSediment transportShallow water equationsJacobian matrix and determinantMechanicsFlow (mathematics)ErosionGeologySedimentMathematicsApplied mathematicsPhysicsGeomorphology

Abstract

fetched live from OpenAlex

This paper presents a coupled two-dimensional model that can produce a more stable numerical simulation of rapid bed evolution than the conventional decoupled model. To solve the coupled bed-load sediment transport terms using a Godunov-type central-upwind method, a novel scheme to estimate the bed-load fluxes which can produce more accurate results than the previously reported coupled model is proposed using a pair of local wave speeds different from those used for the flow. The two-dimensional shallow water equations are used to solve the flow velocities and water depth. The bed level is solved by an Exner-based equation containing bed-load sediment transport as numerical flux terms and sediment entrainment and deposition as source terms. Analytical formulas to compute the eigenvalues of the Jacobian matrix are developed. The linear reconstruction of variables with a multi-dimensional slope limiter, and the second-order Runge-Kutta scheme are employed to achieve higher accuracy in space and time. For the case of rapid bed-erosion, the accuracy and stability of the proposed coupled model are verified by several numerical tests.

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)0.004
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 score0.030
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.001
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 head0.068
GPT teacher head0.340
Teacher spread0.272 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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