Source code for gusto.diffusion

from abc import ABCMeta, abstractmethod
from firedrake import (TestFunction, TrialFunction, Function,
                       inner, outer, grad, avg, dx, dS_h, dS_v,
                       FacetNormal, LinearVariationalProblem,
                       LinearVariationalSolver, action)

__all__ = ["InteriorPenalty"]

class Diffusion(object, metaclass=ABCMeta):
    Base class for diffusion schemes for gusto.

    :arg state: :class:`.State` object.

    def __init__(self, state):
        self.state = state

    def apply(self, x, x_out):
        Function takes x as input, computes F(x) and returns x_out
        as output.

        :arg x: :class:`.Function` object, the input Function.
        :arg x_out: :class:`.Function` object, the output Function.

[docs]class InteriorPenalty(Diffusion): """ Interior penalty diffusion method :arg state: :class:`.State` object. :arg V: Function space of diffused field :arg direction: list containing directions in which function space :arg: mu: the penalty weighting function, which is :recommended to be proportional to 1/dx :arg: kappa: strength of diffusion :arg: bcs: (optional) a list of boundary conditions to apply """ def __init__(self, state, V, kappa, mu, bcs=None): super(InteriorPenalty, self).__init__(state) dt = state.timestepping.dt gamma = TestFunction(V) phi = TrialFunction(V) self.phi1 = Function(V) n = FacetNormal(state.mesh) a = inner(gamma, phi)*dx + dt*inner(grad(gamma), grad(phi)*kappa)*dx def get_flux_form(dS, M): fluxes = (-inner(2*avg(outer(phi, n)), avg(grad(gamma)*M)) - inner(avg(grad(phi)*M), 2*avg(outer(gamma, n))) + mu*inner(2*avg(outer(phi, n)), 2*avg(outer(gamma, n)*kappa)))*dS return fluxes a += dt*get_flux_form(dS_v, kappa) a += dt*get_flux_form(dS_h, kappa) L = inner(gamma, phi)*dx problem = LinearVariationalProblem(a, action(L, self.phi1), self.phi1, bcs=bcs) self.solver = LinearVariationalSolver(problem)
[docs] def apply(self, x_in, x_out): self.phi1.assign(x_in) self.solver.solve() x_out.assign(self.phi1)