Source code for pymor.discretizers.advection

# This file is part of the pyMOR project (
# Copyright 2013-2016 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (

from __future__ import absolute_import, division, print_function

import numpy as np

from pymor.algorithms.timestepping import ExplicitEulerTimeStepper
from pymor.analyticalproblems.advection import InstationaryAdvectionProblem
from pymor.discretizations.basic import InstationaryDiscretization
from pymor.domaindiscretizers.default import discretize_domain_default
from pymor.gui.qt import PatchVisualizer, Matplotlib1DVisualizer
from pymor.operators.numpy import NumpyGenericOperator
from pymor.operators.fv import (nonlinear_advection_lax_friedrichs_operator,
                                L2Product, L2ProductFunctional)
from pymor.vectorarrays.numpy import NumpyVectorArray

[docs]def discretize_nonlinear_instationary_advection_fv(analytical_problem, diameter=None, nt=100, num_flux='lax_friedrichs', lxf_lambda=1., eo_gausspoints=5, eo_intervals=1, num_values=None, domain_discretizer=None, grid=None, boundary_info=None): """Discretizes an |InstationaryAdvectionProblem| using the finite volume method. Explicit Euler time-stepping is used for time discretization. Parameters ---------- analytical_problem The |InstationaryAdvectionProblem| to discretize. diameter If not `None`, `diameter` is passed as an argument to the `domain_discretizer`. nt The number of time steps. num_flux The numerical flux to use in the finite volume formulation. Allowed values are `'lax_friedrichs'`, `'engquist_osher'`, `'simplified_engquist_osher'` (see :mod:`pymor.operators.fv`). lxf_lambda The stabilization parameter for the Lax-Friedrichs numerical flux (ignored, if different flux is chosen). eo_gausspoints Number of Gauss points for the Engquist-Osher numerical flux (ignored, if different flux is chosen). eo_intervals Number of sub-intervals to use for integration when using Engquist-Osher numerical flux (ignored, if different flux is chosen). num_values The number of returned vectors of the solution trajectory. If `None`, each intermediate vector that is calculated is returned. domain_discretizer Discretizer to be used for discretizing the analytical domain. This has to be a function `domain_discretizer(domain_description, diameter)`. If `None`, |discretize_domain_default| is used. grid Instead of using a domain discretizer, the |Grid| can also be passed directly using this parameter. boundary_info A |BoundaryInfo| specifying the boundary types of the grid boundary entities. Must be provided if `grid` is specified. Returns ------- discretization The |Discretization| that has been generated. data Dictionary with the following entries: :grid: The generated |Grid|. :boundary_info: The generated |BoundaryInfo|. """ assert isinstance(analytical_problem, InstationaryAdvectionProblem) assert grid is None or boundary_info is not None assert boundary_info is None or grid is not None assert grid is None or domain_discretizer is None assert num_flux in ('lax_friedrichs', 'engquist_osher', 'simplified_engquist_osher') if grid is None: domain_discretizer = domain_discretizer or discretize_domain_default if diameter is None: grid, boundary_info = domain_discretizer(analytical_problem.domain) else: grid, boundary_info = domain_discretizer(analytical_problem.domain, diameter=diameter) p = analytical_problem if num_flux == 'lax_friedrichs': L = nonlinear_advection_lax_friedrichs_operator(grid, boundary_info, p.flux_function, dirichlet_data=p.dirichlet_data, lxf_lambda=lxf_lambda) elif num_flux == 'engquist_osher': L = nonlinear_advection_engquist_osher_operator(grid, boundary_info, p.flux_function, p.flux_function_derivative, gausspoints=eo_gausspoints, intervals=eo_intervals, dirichlet_data=p.dirichlet_data) else: L = nonlinear_advection_simplified_engquist_osher_operator(grid, boundary_info, p.flux_function, p.flux_function_derivative, dirichlet_data=p.dirichlet_data) F = None if p.rhs is None else L2ProductFunctional(grid, p.rhs) if p.initial_data.parametric: def initial_projection(U, mu): I = p.initial_data.evaluate(grid.quadrature_points(0, order=2), mu).squeeze() I = np.sum(I * grid.reference_element.quadrature(order=2)[1], axis=1) * (1. / grid.reference_element.volume) I = NumpyVectorArray(I, copy=False) return I.lincomb(U).data I = NumpyGenericOperator(initial_projection, dim_range=grid.size(0), linear=True, parameter_type=p.initial_data.parameter_type) else: I = p.initial_data.evaluate(grid.quadrature_points(0, order=2)).squeeze() I = np.sum(I * grid.reference_element.quadrature(order=2)[1], axis=1) * (1. / grid.reference_element.volume) I = NumpyVectorArray(I, copy=False) products = {'l2': L2Product(grid, boundary_info)} if grid.dim == 2: visualizer = PatchVisualizer(grid=grid, bounding_box=grid.bounding_box(), codim=0) elif grid.dim == 1: visualizer = Matplotlib1DVisualizer(grid, codim=0) else: visualizer = None parameter_space = p.parameter_space if hasattr(p, 'parameter_space') else None time_stepper = ExplicitEulerTimeStepper(nt=nt) discretization = InstationaryDiscretization(operator=L, rhs=F, initial_data=I, T=p.T, products=products, time_stepper=time_stepper, parameter_space=parameter_space, visualizer=visualizer, num_values=num_values, name='{}_FV'.format( return discretization, {'grid': grid, 'boundary_info': boundary_info}