ogstools.studies.convergence package#

functions to generate a convergence study.

ogstools.studies.convergence.add_grid_spacing(mesh)[source]#
Return type:

UnstructuredGrid

ogstools.studies.convergence.convergence_metrics(meshes, reference, variable, timestep_sizes)[source]#

Calculate convergence metrics for a given reference and variable.

Parameters:
Return type:

DataFrame

Returns:

A pandas Dataframe containing all metrics.

ogstools.studies.convergence.convergence_metrics_evolution(mesh_series, variable, refinement_ratio=2.0, units=('s', 's'))[source]#

Calculate convergence evolution metrics for given mesh series.

Contains convergence order and the relative error to the Richardson extrapolation for each timestep of the coarsest mesh series. and a variable

Parameters:

  • meshes_series – The List of mesh series to be analyzed.

  • variable (Variable) – The variable of interest.

  • refinement_ratio (float) – Refinement ratio between the discretizations.

Return type:

DataFrame

Returns:

A pandas Dataframe containing all metrics.

ogstools.studies.convergence.grid_convergence(meshes, variable, topology, refinement_ratio)[source]#

Calculate the grid convergence field for the given meshes on the topology.

The calculation is based on the last three of the given meshes. For more information on this topic see <https://www.grc.nasa.gov/www/wind/valid/tutorial/spatconv.html> or <https://curiosityfluids.com/2016/09/09/establishing-grid-convergence/>.

Parameters:

  • meshes (list[UnstructuredGrid]) – At least three meshes with constant refinement.

  • variable (Variable) – The variable to be extrapolated.

  • topology (UnstructuredGrid) – The topology to evaluate.

  • refinement_ratio (float) – If not given, it is calculated automatically

Return type:

UnstructuredGrid

returns: Grid convergence field of the given variable.

ogstools.studies.convergence.log_fit(x, y)[source]#
Return type:

tuple[float, ndarray]

ogstools.studies.convergence.plot_convergence(metrics, variable)[source]#

Plot the absolute values of the convergence metrics.

Return type:

Figure

ogstools.studies.convergence.plot_convergence_error_evolution(evolution_metrics, error_type='relative')[source]#

Plot the evolution of relative errors.

Return type:

Figure

ogstools.studies.convergence.plot_convergence_errors(metrics)[source]#

Plot the relative errors of the convergence metrics in loglog scale.

Return type:

Figure

ogstools.studies.convergence.plot_convergence_order_evolution(evolution_metrics)[source]#

Plot the evolution of convergence orders.

Return type:

Figure

ogstools.studies.convergence.richardson_extrapolation(meshes, variable, topology, refinement_ratio)[source]#

Estimate a better approximation of a variable on a mesh.

This function calculates the Richardson Extrapolation based on the change in results in the last three of the given meshes. For more information on this topic see <https://www.grc.nasa.gov/www/wind/valid/tutorial/spatconv.html> or <https://curiosityfluids.com/2016/09/09/establishing-grid-convergence/>.

Parameters:

  • meshes (list[UnstructuredGrid]) – At least three meshes with constant refinement.

  • variable (Variable) – The variable to be extrapolated.

  • topology (UnstructuredGrid) – The topology on which the extrapolation is done.

  • refinement_ratio (float) – Refinement ratio (spatial or temporal).

Return type:

UnstructuredGrid

Returns:

Richardson extrapolation of the given variable.

ogstools.studies.convergence.run_convergence_study(output_name, mesh_paths, variable_name, timevalue=0.0, refinement_ratio=2.0, reference_solution_path=None, prepare_only=False, show_progress=False)[source]#

Run a convergence study.

Parameters:

  • output_name (Path) – The output path for the Jupyter notebook.

  • mesh_paths (list[Path]) – mesh paths of increasing resolutions.

  • variable_name (str) – The variable to study for convergence.

  • timevalue (float) – The time value for analysis.

  • refinement_ratio (float) – The refinement ratio between the meshes.

  • reference_solution_path (Path | None) – Optional reference solution for comparison.

  • prepare_only (bool) – If True, don’t execute the notebook.

  • show_progress (bool) – If True, display a progress bar.

Return type:

None

returns: None, but generates the convergence study notebook.

Submodules#