Poisson equation

This demo is implemented in a single Python file, demo_poisson.py, which contains both the variational forms and the solver. It illustrates how to:

Equation and problem definition

For a domain $$\Omega \subset \mathbb{R}^n$$ with boundary $$\partial \Omega = \Gamma_{D} \cup \Gamma_{N}$$, the Poisson equation with particular boundary conditions reads:

\begin{split} \begin{align} - \nabla^{2} u &= f \quad {\rm in} \ \Omega, \\ u &= 0 \quad {\rm on} \ \Gamma_{D}, \\ \nabla u \cdot n &= g \quad {\rm on} \ \Gamma_{N}. \\ \end{align} \end{split}

where $$f$$ and $$g$$ are input data and $$n$$ denotes the outward directed boundary normal. The variational problem reads: find $$u \in V$$ such that

$a(u, v) = L(v) \quad \forall \ v \in V,$

where $$V$$ is a suitable function space and

\begin{split} \begin{align} a(u, v) &:= \int_{\Omega} \nabla u \cdot \nabla v \, {\rm d} x, \\ L(v) &:= \int_{\Omega} f v \, {\rm d} x + \int_{\Gamma_{N}} g v \, {\rm d} s. \end{align} \end{split}

The expression $$a(u, v)$$ is the bilinear form and $$L(v)$$ is the linear form. It is assumed that all functions in $$V$$ satisfy the Dirichlet boundary conditions ($$u = 0 \ {\rm on} \ \Gamma_{D}$$).

In this demo we consider:

• $$\Omega = [0,2] \times [0,1]$$ (a rectangle)

• $$\Gamma_{D} = \{(0, y) \cup (1, y) \subset \partial \Omega\}$$

• $$\Gamma_{N} = \{(x, 0) \cup (x, 1) \subset \partial \Omega\}$$

• $$g = \sin(5x)$$

• $$f = 10\exp(-((x - 0.5)^2 + (y - 0.5)^2) / 0.02)$$

Implementation

The modules that will be used are imported:

import numpy as np

import ufl
from dolfinx import fem, io, mesh, plot
from ufl import ds, dx, grad, inner

from mpi4py import MPI
from petsc4py.PETSc import ScalarType


We begin by using create_rectangle to create a rectangular Mesh of the domain, and creating a finite element FunctionSpace $$V$$ on the mesh.

msh = mesh.create_rectangle(comm=MPI.COMM_WORLD,
points=((0.0, 0.0), (2.0, 1.0)), n=(32, 16),
cell_type=mesh.CellType.triangle,)
V = fem.FunctionSpace(msh, ("Lagrange", 1))


The second argument to FunctionSpace is a tuple consisting of (family, degree), where family is the finite element family, and degree specifies the polynomial degree. in this case V consists of first-order, continuous Lagrange finite element functions.

Next, we locate the mesh facets that lie on the boundary $$\Gamma_D$$. We do this using using locate_entities_boundary and providing a marker function that returns True for points x on the boundary and False otherwise.

facets = mesh.locate_entities_boundary(msh, dim=1,
marker=lambda x: np.logical_or(np.isclose(x[0], 0.0),
np.isclose(x[0], 2.0)))


We now find the degrees-of-freedom that are associated with the boundary facets using locate_dofs_topological

dofs = fem.locate_dofs_topological(V=V, entity_dim=1, entities=facets)


and use dirichletbc to create a DirichletBCMetaClass class that represents the boundary condition

bc = fem.dirichletbc(value=ScalarType(0), dofs=dofs, V=V)


Next, we express the variational problem using UFL.

u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
x = ufl.SpatialCoordinate(msh)
f = 10 * ufl.exp(-((x[0] - 0.5) ** 2 + (x[1] - 0.5) ** 2) / 0.02)
g = ufl.sin(5 * x[0])
L = inner(f, v) * dx + inner(g, v) * ds


We create a LinearProblem object that brings together the variational problem, the Dirichlet boundary condition, and which specifies the linear solver. In this case we use a direct (LU) solver. The solve will compute a solution.

problem = fem.petsc.LinearProblem(a, L, bcs=[bc], petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
uh = problem.solve()


The solution can be written to a XDMFFile file visualization with ParaView ot VisIt

with io.XDMFFile(msh.comm, "out_poisson/poisson.xdmf", "w") as file:
file.write_mesh(msh)
file.write_function(uh)


and displayed using pyvista.

try:
import pyvista
cells, types, x = plot.create_vtk_mesh(V)
grid = pyvista.UnstructuredGrid(cells, types, x)
grid.point_data["u"] = uh.x.array.real
grid.set_active_scalars("u")
plotter = pyvista.Plotter()