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| Formato: | Recurso digital |
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Zenodo
2025
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| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.17758705 |
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- <p>This archive provides the reproducibility package for the manuscript</p> <p>"Vortex-Merger Thermodynamics in 2D Turbulence:<br> A Metriplectic Scenario for the Onsager Anomaly".</p> <p>It contains a minimal, self-contained set of data and Python scripts needed to<br>reproduce</p> <p>- the dissipation–saturation figure (viscous, event, and total dissipation<br> versus effective Reynolds number), and<br>- the robustness diagnostics with respect to particle number N and random seeds,</p> <p>without requiring access to the production solver or large-scale computing<br>resources.</p> <p>Contents (top-level layout):</p> <p>- data/raw/epsilon_events_ensemble.csv:<br> pre-computed ensemble of metriplectic point-vortex runs, generated offline<br> with the Average Discrete Gradient (ADG) solver described in the paper;<br>- data/processed/*.csv:<br> processed summary tables (ensemble means and standard deviations,<br> dissipation vs Re_eff) used directly for plotting;<br>- NS_reference/:<br> lightweight 2D pseudo-spectral Navier–Stokes solver and the corresponding<br> viscous dissipation vs Re_eff datasets, including optional high-resolution<br> runs at 256^2 and 512^2;<br>- src/:<br> small helper scripts for rebuilding the processed tables and producing<br> all figures from the raw and processed CSV files;<br>- docs/:<br> short notes documenting the discrete vortex metriplectic model and the<br> simulation protocol used to generate the ensemble.</p> <p>Quick usage:</p> <p> python -m src.parameter_sweep<br> python NS_reference/generate_viscous_data.py # optional; regenerates NS curve<br> python -m src.plot_dissipation_saturation<br> python -m src.plot_robustness</p> <p>This will regenerate the main dissipation-saturation figure and the robustness<br>plots reported in the accompanying Letter.</p>