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Record W2982376104 · doi:10.3390/w11112288

Combined Use of High-Resolution Numerical Schemes to Reduce Numerical Diffusion in Coupled Nonhydrostatic Hydrodynamic and Solute Transport Model

2019· article· en· W2982376104 on OpenAlexfundno aff
Augusto Hugo Farias da Cunha, Carlos Ruberto Fragoso, Matheus Henrique Tavares, José Rafael Cavalcanti, Marie‐Paule Bonnet, David da Motta Marques

Bibliographic record

VenueWater · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoGoverno BrasilCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorH2020 Marie Skłodowska-Curie ActionsGlobal Lake Ecological Observatory Network
KeywordsNumerical diffusionMechanicsAdvectionDiffusionComputer simulationInterpolation (computer graphics)Reynolds-averaged Navier–Stokes equationsNumerical analysisFlux limiterConvection–diffusion equationBilinear interpolationFlow (mathematics)PhysicsComputational fluid dynamicsClassical mechanicsMathematicsThermodynamicsMathematical analysis

Abstract

fetched live from OpenAlex

In three-dimensional simulations of free-surface flow where the vertical velocities are relevant, such as in lakes, estuaries, reservoirs, and coastal zones, a nonhydrostatic hydrodynamic approach may be necessary. Although the nonhydrostatic hydrodynamic approach improves the physical representation of pressure, acceleration and velocity fields, it is not free of numerical diffusion. This numerical issue stems from the numerical solution employed in the advection and diffusion terms of the Reynolds-averaged Navier–Stokes (RANS) and solute transport equations. The combined use of high-resolution schemes in coupled nonhydrostatic hydrodynamic and solute transport models is a promising alternative to minimize these numerical issues and determine the relationship between numerical diffusion in the two solutions. We evaluated the numerical diffusion in three numerical experiments, for different purposes: The first two experiments evaluated the potential for reducing numerical diffusion in a nonhydrostatic hydrodynamic solution, by applying a quadratic interpolator over a Bilinear, applied in the Eulerian–Lagrangian method (ELM) step-ii interpolation, and the capability of representing the propagation of complex waves. The third experiment evaluated the effect on numerical diffusion of using flux-limiter schemes over a first-order Upwind in solute transport solution, combined with the interpolation methods applied in a coupled hydrodynamic and solute transport model. The high-resolution methods were able to substantially reduce the numerical diffusion in a solute transport problem. This exercise showed that the numerical diffusion of a nonhydrostatic hydrodynamic solution has a major influence on the ability of the model to simulate stratified internal waves, indicating that high-resolution methods must be implemented in the numerical solution to properly simulate real situations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.192
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

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