Finite Element Methods: a first example

In the foloowing we introduce the basic components of a finite element method:

  • constructing a tesselation of the computational domain
  • setting up a discrete function space and working with functions defined over the grid
  • defining the mathematical model to solve
  • solving the (non linear) system arising from the discretization of the model by the Galerkin method
import time, numpy, math, sys
import dune.plotting
dune.plotting.block = True
import matplotlib
matplotlib.rc( 'image', cmap='jet' )
from matplotlib import pyplot

Setting up the Mesh

from dune.grid import structuredGrid as leafGridView
gridView = leafGridView([0, 0], [1, 1], [4, 4])

Grid Functions

We can integrate grid function

from ufl import SpatialCoordinate, triangle
x = SpatialCoordinate(triangle)

exact = 1/2*(x[0]**2+x[1]**2) - 1/3*(x[0]**3 - x[1]**3) + 1

from dune.fem.function import integrate
mass = integrate(gridView, exact, order=5)

and plot them using matplotlib or write a vtk file for postprocessing

from dune.fem.plotting import plotPointData as plot
plot(exact, grid=gridView)
gridView.writeVTK('exact', pointdata={'exact': exact})

from dune.fem.function import uflFunction
exact_gf = uflFunction(gridView, name="ufl", order=1, ufl=exact)
mass = 0
for element in gridView.elements:
  mass += exact_gf(element,[0.5,0.5]) * element.geometry.volume

Discrete Spaces

Setting up a discrete function space and some grid function

from import lagrange as solutionSpace
space = solutionSpace(gridView, order=2)

So far we used grid functions defined globally. An important subclass of grid functions are discrete functions over a given discrete function space. The easiest way to construct such functions is to use the interpolate method on the discrete function space.

u_h = space.interpolate(exact, name='u_h')

If a discrete function is already available it is possible to call copy to obtain further discrete functions:

u_h_n = u_h.copy(name="previous")

It is possible to plot a discrete function using matplotlib or write a vtk file for postprocessing

gridView.writeVTK('uh', pointdata=[u_h])

Note: the discrete function u_h already has a name attribute given in the interpolate call. This is used by default in the vtk file. An alternative name can be given by using a dictionary as shown previously.

Models and Schemes

We consider a scalar boundary value problem \begin{align*} -\triangle u &= f & \text{in}\;\Omega:=(0,1)^2 \\ \nabla u\cdot n &= g_N & \text{on}\;\Gamma_N \\ u &= g_D & \text{on}\;\Gamma_D \end{align*} and \(f=f(x)\) is some forcing term. For the boundary conditions we set \(\Gamma_D={0}\times[0,1]\) and take \(\Gamma_N\) to be the remaining boundary of \(\Omega\).

We will solve this problem in variational form \begin{align*} \int \nabla u \cdot \nabla \varphi \ - \int_{\Omega} f(x) \varphi\ dx - \int_{\Gamma_N} g_N(x) v\ ds = 0. \end{align*} We choose \(f,g_N,g_D\) so that the exact solution is given by \begin{align*} u(x) = \left(\frac{1}{2}(x^2 + y^2) - \frac{1}{3}(x^3 - y^3)\right) + 1 \end{align*}

from ufl import TestFunction, TrialFunction
from dune.ufl import DirichletBC
u = TrialFunction(space)
v = TestFunction(space)

from ufl import dx, grad, div, grad, dot, inner, sqrt, conditional, FacetNormal, ds
a = dot(grad(u), grad(v)) * dx

f   = -div( grad(exact) )
g_N = grad(exact)
n   = FacetNormal(space)
b   = f*v*dx + dot(g_N,n)*conditional(x[0]>=1e-8,1,0)*v*ds
dbc = DirichletBC(space,exact,x[0]<=1e-8)

With the model described as a ufl form, we can construct a scheme class that provides the solve method which we can use to compute the solution:

from dune.fem import parameter
parameter.append({"fem.verboserank": -1})
from dune.fem.scheme import galerkin as solutionScheme
scheme = solutionScheme([a == b, dbc], solver='cg')
scheme.solve(target = u_h)
{'converged': True, 'iterations': 1, 'linear_iterations': 48}

We can compute the error between the exact and the discrete solution by using the integrate function described above:

h1error = dot(grad(u_h - exact), grad(u_h - exact))
error = sqrt(integrate(gridView, h1error, order=5))
print("Number of elements:",gridView.size(0),
      "number of dofs:",space.size,"H^1 error:", error)
Number of elements: 16 number of dofs: 81 H^1 error: 0.00658807845868413

To verify that the discrete scheme is converging to the exact solution we can compute the experimental order of convergence (EOC): \begin{align*} {\rm eoc} = \frac{\log{e_h/e_H}}{\log{h/H}} \end{align*} where \(h,H\) refer to the spacing of two grids.

from math import log
loops = 2
for eocLoop in range(loops):
    error_old = error
    scheme.solve(target = u_h)
    error = sqrt(integrate(gridView, h1error, order=5))
    eoc = round(log(error/error_old)/log(0.5),2)
          "Number of elements:", gridView.size(0),
          "number of dofs:", space.size,"H^1 error:", error)
EOC: 2.0 Number of elements: 64 number of dofs: 289 H^1 error: 0.0016470196151710534
EOC: 2.0 Number of elements: 256 number of dofs: 1089 H^1 error: 0.00041175491468679274

Coarsen the mesh again up to the macro-level such that subsequent example start with a “clean” setup.


Time dependent Problems

Now we can set up our PDE model As an example we will study the Forchheimer problem [Kie15] which is a scalar, nonlinear parabolic equation \begin{equation} \partial_tu - \nabla\cdot K(\nabla u)\nabla u = f \end{equation} where the diffusion tensor is given by \begin{equation} K(\nabla u) = \frac{2}{1+\sqrt{1+4|\nabla u|}} \end{equation} and \(f=f(x,t)\) is some time dependent forcing term. On the boundary we prescribe Neumann boundary as before and initial conditions \(u=u_0\).

We will solve this problem in variational form and using Crank Nicholson in time \begin{equation} \begin{split} \int_{\Omega} \frac{u^{n+1}-u^n}{\Delta t} \varphi + \frac{1}{2}K(\nabla u^{n+1}) \nabla u^{n+1} \cdot \nabla \varphi \ + \frac{1}{2}K(\nabla u^n) \nabla u^n \cdot \nabla \varphi v\ dx \\ - \int_{\Omega} \frac{1}{2}(f(x,t^n)+f(x,t^n+\Delta t) \varphi\ dx - \int_{\partial \Omega} \frac{1}{2}(g(x,t^n)+g(x,t^n+\Delta t)) v\ ds = 0. \end{split} \end{equation} on a domain \(\Omega=[0,1]^2\). We choose \(f,g\) so that the exact solution is given by \begin{align*} u(x,t) = e^{-2t}\left(\frac{1}{2}(x^2 + y^2) - \frac{1}{3}(x^3 - y^3)\right) + 1 \end{align*}

from ufl import exp
initial = 1/2*(x[0]**2+x[1]**2) - 1/3*(x[0]**3 - x[1]**3) + 1
exact   = lambda t: exp(-2*t)*(initial - 1) + 1

from dune.ufl import Constant
u = TrialFunction(space)
v = TestFunction(space)
dt = Constant(0, name="dt")    # time step
t  = Constant(0, name="t")     # current time

abs_du = lambda u: sqrt(inner(grad(u), grad(u)))
K = lambda u: 2/(1 + sqrt(1 + 4*abs_du(u)))
a = ( dot((u - u_h_n)/dt, v) \
    + 0.5*dot(K(u)*grad(u), grad(v)) \
    + 0.5*dot(K(u_h_n)*grad(u_h_n), grad(v)) ) * dx

f = lambda s: -2*exp(-2*s)*(initial - 1) - div( K(exact(s))*grad(exact(s)) )
g = lambda s: K(exact(s))*grad(exact(s))
n = FacetNormal(space)
b = 0.5*(f(t)+f(t+dt))*v*dx + 0.5*dot(g(t)+g(t+dt),n)*v*ds

With the model described as a ufl form, we can construct a scheme class that provides the solve method which we can use to evolve the solution from one time step to the next:

from dune.fem.scheme import galerkin as solutionScheme
scheme = solutionScheme(a == b, solver='cg')

scheme.model.dt = 0.005

def evolve(scheme, u_h, u_h_n, endTime):
    time = 0
    while time < (endTime - 1e-6):
        scheme.model.t = time
        time += scheme.model.dt

Solving the System

Since we have forced the system towards a given solution, we can compute the discretization error. First we define ufl expressions for the \(L^2\) and \(H^1\) norms and will use those to compute the experimental order of convergence of the scheme by computing the time evolution on different grid levels. We first reset the grid to start computing on the coarsest level:

endTime    = 0.25
exact_end  = exact(endTime)
l2error = dot(u_h - exact_end, u_h - exact_end)
h1error = dot(grad(u_h - exact_end), grad(u_h - exact_end))

from math import log
errors = 0,0
loops = 2
for eocLoop in range(loops):
    evolve(scheme, u_h, u_h_n, endTime)
    errors_old = errors
    errors = [sqrt(e) for e in integrate(gridView, [l2error,h1error], order=5)]
    if eocLoop == 0:
        eocs = ['-','-']
        eocs = [ round(log(e/e_old)/log(0.5),2) \
                 for e,e_old in zip(errors,errors_old) ]
    print('step:', eocLoop, ', size:', gridView.size(0))
    print('\t | u_h - u | =', '{:0.5e}'.format(errors[0]), ', eoc =', eocs[0])
    print('\t | grad(uh - u) | =', '{:0.5e}'.format(errors[1]), ', eoc =', eocs[1])
    gridView.writeVTK('forchheimer', pointdata={'u': u_h, 'l2error':
                  l2error, 'h1error': h1error}, number=eocLoop)
    if eocLoop < loops-1:
        scheme.model.dt /= 2
step: 0 , size: 16
         | u_h - u | = 1.30402e-04 , eoc = -
         | grad(uh - u) | = 3.99892e-03 , eoc = -
step: 1 , size: 64
         | u_h - u | = 1.61395e-05 , eoc = 3.01
         | grad(uh - u) | = 9.99160e-04 , eoc = 2.0

Alternate Solve Methods

Here we look at different ways of solving PDEs using external packages and python functionality. Different linear algebra backends can be accessed by changing setting the storage parameter during construction of the discrete space. All discrete functions and operators/schemes based on this space will then use this backend. Available backends are fem,istl,petsc. The default is fem which uses simple data structures and linear solvers implemented in the dune-fem package. The simplicity of the data structure makes it possible to use the buffer protocol to seamlessly move between C++ and Numpy/Scipy data structures on the python side. A degrees of freedom vector (dof vector) can be retrieved # from a discrete function over the fem space by using the as_numpy method. Similar methods are available for the other storages, i.e., as_istl,as_petsc. The same methods are also available to retrieve the underlying matrix structures of linear operators.

Using Scipy

We implement a simple Newton Krylov solver using a linear solver from Scipy. We can use the as_numpy method to access the degrees of freedom as Numpy vector based on the python buffer protocol. So no data is copied and changes to the dofs made on the python side are automatically carried over to the C++ side. from Scipy.

from dune.fem.operator import linear as linearOperator
import numpy as np
from scipy.sparse.linalg import spsolve as solver
class Scheme:
  def __init__(self, scheme):
      self.model = scheme.model
      self.jacobian = linearOperator(scheme)

  def solve(self, target):
      # create a copy of target for the residual
      res = target.copy(name="residual")

      # extract numpy vectors from target and res
      sol_coeff = target.as_numpy
      res_coeff = res.as_numpy

      n = 0
      while True:
          scheme(target, res)
          absF = math.sqrt(,res_coeff) )
          if absF < 1e-10:
          sol_coeff -= solver(self.jacobian.as_numpy, res_coeff)
          n += 1

scheme_cls = Scheme(scheme)

u_h.interpolate(initial)                # reset u_h to initial
evolve(scheme_cls, u_h, u_h_n, endTime)
error = u_h - exact_end
print("size: ", gridView.size(0), "L^2, H^1 error:",'{:0.5e}, {:0.5e}'.format(
  *[ sqrt(e) for e in integrate(gridView,[error**2,inner(grad(error),grad(error))], order=5) ]))
size:  64 L^2, H^1 error: 1.61627e-05, 9.99158e-04

Using a non linear solver from the Scipy package

from scipy.optimize import newton_krylov
from scipy.sparse.linalg import LinearOperator
from scipy.sparse.linalg import cg as solver

class Scheme2:
    def __init__(self, scheme):
        self.scheme = scheme
        self.model = scheme.model
        self.res = u_h.copy(name="residual")

    # non linear function
    def f(self, x_coeff):
        # the following converts a given numpy array
        # into a discrete function over the given space
        x = space.function("tmp", dofVector=x_coeff)
        scheme(x, self.res)
        return self.res.as_numpy

    # class for the derivative DS of S
    class Df(LinearOperator):
        def __init__(self, x_coeff):
            self.shape = (x_coeff.shape[0], x_coeff.shape[0])
            self.dtype = x_coeff.dtype
            x = space.function("tmp", dofVector=x_coeff)
            self.jacobian = linearOperator(scheme, ubar=x)
        # reassemble the matrix DF(u) given a DoF vector for u
        def update(self, x_coeff, f):
            x = space.function("tmp", dofVector=x_coeff)
            scheme.jacobian(x, self.jacobian)
        # compute DS(u)^{-1}x for a given DoF vector x
        def _matvec(self, x_coeff):
            return solver(self.jacobian.as_numpy, x_coeff, tol=1e-10)[0]

    def solve(self, target):
        sol_coeff = target.as_numpy
        # call the newton krylov solver from scipy
        sol_coeff[:] = newton_krylov(self.f, sol_coeff,
                    verbose=0, f_tol=1e-8,

scheme2_cls = Scheme2(scheme)
evolve(scheme2_cls, u_h, u_h_n, endTime)
error = u_h - exact_end
print("size: ", gridView.size(0), "L^2, H^1 error:",'{:0.5e}, {:0.5e}'.format(
  *[ sqrt(e) for e in integrate(gridView,[error**2,inner(grad(error),grad(error))], order=5) ]))
size:  64 L^2, H^1 error: 1.61627e-05, 9.99158e-04

Using Petsc and Petsc4Py

Switching to a storage based on the PETSc solver package and solving the system using the dune-fem bindings

    spacePetsc = solutionSpace(gridView, order=2, storage='petsc')
    # first we will use the petsc solver available in the `dune-fem` package
    # (using the sor preconditioner)
    schemePetsc = solutionScheme(a == b, space=spacePetsc,
    schemePetsc.model.dt = scheme.model.dt
    u_h = spacePetsc.interpolate(initial, name='u_h')
    u_h_n = u_h.copy(name="previous")
    evolve(schemePetsc, u_h, u_h_n, endTime)
    error = u_h - exact_end
    print("size: ", gridView.size(0), "L^2, H^1 error:",'{:0.5e}, {:0.5e}'.format(
      *[ sqrt(e) for e in integrate(gridView,[error**2,inner(grad(error),grad(error))], order=5) ]))
except dune.generator.ConfigurationError:
    print("petsc was not found during configuration of dune-py: skipping example")
    spacePetsc = None
size:  64 L^2, H^1 error: 1.61612e-05, 9.99160e-04

Implementing a Newton Krylov solver using the binding provided by petsc4py

    import petsc4py, sys
    from petsc4py import PETSc
except ModuleNotFoundError:
    print("petsc4py not found: skipping example")
    petsc4py = None

if petsc4py is not None and spacePetsc is not None:
    class Scheme3:
      def __init__(self, scheme):
          self.model = scheme.model
          self.jacobian = linearOperator(scheme)
          self.ksp = PETSc.KSP()
          # use conjugate gradients method
          # and incomplete Cholesky
      def solve(self, target):
          res = target.copy(name="residual")
          sol_coeff = target.as_petsc
          res_coeff = res.as_petsc
          n = 0
          while True:
              schemePetsc(target, res)
              absF = math.sqrt( )
              if absF < 1e-10:
              schemePetsc.jacobian(target, self.jacobian)
              self.ksp.solve(res_coeff, res_coeff)
              sol_coeff -= res_coeff
              n += 1

    scheme3_cls = Scheme3(schemePetsc)
    evolve(scheme3_cls, u_h, u_h_n, endTime)
    error = u_h - exact_end
    print("size: ", gridView.size(0), "L^2, H^1 error:",'{:0.5e}, {:0.5e}'.format(
      *[ sqrt(e) for e in integrate(gridView,[error**2,inner(grad(error),grad(error))], order=5) ]))
size:  64 L^2, H^1 error: 1.61627e-05, 9.99158e-04

Using the petsc4py bindings for the non linear KSP solvers from PETSc

if petsc4py is not None and spacePetsc is not None:
    class Scheme4:
        def __init__(self, scheme):
            self.model = scheme.model
            self.res =[0],name="residual")
            self.scheme = scheme
            self.jacobian = linearOperator(self.scheme)
            self.snes = PETSc.SNES().create()
            self.snes.setFunction(self.f, self.res.as_petsc.duplicate())
            self.snes.setJacobian(self.Df, self.jacobian.as_petsc, self.jacobian.as_petsc)

        def f(self, snes, x, f):
            # setup discrete function using the provide petsc vectors
            inDF ="tmp",dofVector=x)
            outDF ="tmp",dofVector=f)

        def Df(self, snes, x, m, b):
            inDF ="tmp",dofVector=x)
            self.scheme.jacobian(inDF, self.jacobian)
            return PETSc.Mat.Structure.SAME_NONZERO_PATTERN

        def solve(self, target):
            sol_coeff = target.as_petsc
            self.snes.solve(self.res.as_petsc, sol_coeff)

    scheme4_cls = Scheme4(schemePetsc)
    evolve(scheme4_cls, u_h, u_h_n, endTime)
    error = u_h - exact_end
    print("size: ", gridView.size(0), "L^2, H^1 error:",'{:0.5e}, {:0.5e}'.format(
      *[ sqrt(e) for e in integrate(gridView,[error**2,inner(grad(error),grad(error))], order=5) ]))
size:  64 L^2, H^1 error: 1.61627e-05, 9.99158e-04

More General Boundary Conditions

So far we only used natural boundary conditions. Here we discuss how to set Dirichlet boundary conditions and use different conditions for different components of the solution.

To fix Dirichlet boundary conditions \(u=g\) on part of the boundary \(\Gamma\subset\partial\Omega\) the central class is dune.ufl.DirichletBC which takes three arguments: the discrete function space for \(u\), the function \(g\) given by a UFL expression, and a description of \(\Gamma\). There are different ways to do this. If it is omitted or None the condition is applied to the whole domain, a integer \(s>0\) can be provided which can be set to describe a part of the boundary during grid construction as described in another place. Finally a UFL condition can be used, i.e., x[0]<0.

For vector valued functions \(u\) the value function \(g\) can be a UFL vector or a list. In the later case a component of None can be used to describe components which are not to be constrained by the boundary condition.

from ufl import sin
from dune.ufl import DirichletBC
from dune.fem.plotting import plotComponents
from matplotlib import ticker

vecSpace = solutionSpace(gridView, dimRange=2, order=2)
x = SpatialCoordinate(vecSpace)
vec = vecSpace.interpolate([0,0], name='u_h')
uVec,vVec = TrialFunction(vecSpace), TestFunction(vecSpace)
a  = ( inner(grad(uVec), grad(vVec)) + inner(uVec,vVec) ) * dx
f  = ( uVec[0]*(1-uVec[1])*vVec[0] + uVec[1]*(1-uVec[0])*vVec[1] ) * dx
f  = f + uVec[0]*uVec[0] * vVec[1] * ds
bc = DirichletBC(vecSpace,[sin(4*(x[0]+x[1])),None])
vecScheme = solutionScheme( [a == f, bc],
        parameters={"newton.linear.tolerance": 1e-9} )
plotComponents(vec, gridLines=None, level=2,
               colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})

To prescribe \(u_2=0\) at the bottom boundary is also straightforward

bcBottom = DirichletBC(vecSpace,[sin(4*(x[0]+x[1])),0],x[1]<1e-10)
vecScheme = solutionScheme( [a == f, bc, bcBottom],
        parameters={"newton.linear.tolerance": 1e-9} )
plotComponents(vec, gridLines=None, level=2,
               colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})

Discontinuous Galerkin methods

So far we have been using Lagrange spaces of different order to solve our PDE. In the following we show how to use Discontinuous Galerkin method to solve an advection dominated advection-diffusion probllem: \begin{align*} -\varepsilon\triangle u + b\cdot\nabla u &= f \end{align*} with Dirichlet boundary conditions. Here \(\varepsilon\) is a small constant and \(b\) a given vector.

gridView      = leafGridView([-1, -1], [1, 1], [20, 20])
order = 2
from import dglegendre as dgSpace
space = dgSpace(gridView, order=order)

from ufl import avg, jump, dS, ds,\
         CellVolume, FacetArea, FacetNormal,\
         as_vector, atan
u    = TrialFunction(space)
v    = TestFunction(space)
n    = FacetNormal(space)
he   = avg( CellVolume(space) ) / FacetArea(space)
hbnd = CellVolume(space) / FacetArea(space)
x    = SpatialCoordinate(space)

# diffusion factor
eps = Constant(0.1,"eps")
# transport direction and upwind flux
b    = as_vector([1,0])
hatb = (dot(b, n) + abs(dot(b, n)))/2.0
# boundary values (for left/right boundary)
dD   = conditional((1+x[0])*(1-x[0])<1e-10,1,0)
g    = conditional(x[0]<0,atan(10*x[1]),0)
# penalty parameter
beta = 10*order*order

aInternal     = dot(eps*grad(u) - b*u, grad(v)) * dx
diffSkeleton  = eps*beta/he*jump(u)*jump(v)*dS -\
                eps*dot(avg(grad(u)),n('+'))*jump(v)*dS -\
diffSkeleton += eps*beta/hbnd*(u-g)*v*dD*ds -\
advSkeleton   = jump(hatb*u)*jump(v)*dS
advSkeleton  += ( hatb*u + (dot(b,n)-hatb)*g )*v*dD*ds
form          = aInternal + diffSkeleton + advSkeleton

scheme = solutionScheme(form==0, solver="gmres",
uh = space.interpolate(0, name="solution")

So far the example was not really advection dominated so we now repeat the experiment but set \(\varepsilon=1e-5\)

eps.value = 1e-5 # could also use scheme.model.eps = 1e-5

A 3D example using a GMesh file

In this example we use pygmsh to construct a tetrahedral mesh and olve a simple laplace problem

# The following code is taken from the `pygmsh` homepage
  import pygmsh
  geom = pygmsh.built_in.Geometry()
  poly = geom.add_polygon([
      [ 0.0,  0.5, 0.0], [-0.1,  0.1, 0.0], [-0.5,  0.0, 0.0],
      [-0.1, -0.1, 0.0], [ 0.0, -0.5, 0.0], [ 0.1, -0.1, 0.0],
      [ 0.5,  0.0, 0.0], [ 0.1,  0.1, 0.0] ], lcar=0.05)
  axis = [0, 0, 1]
  geom.extrude( poly, translation_axis=axis, rotation_axis=axis,
      point_on_axis=[0, 0, 0], angle=2.0 / 6.0 * numpy.pi)
  mesh = pygmsh.generate_mesh(geom, verbose=False)
  points, cells = mesh.points, mesh.cells
  domain3d = {"vertices":points, "simplices":cells["tetra"]}
except ImportError: # pygmsh not installed - use a simple cartesian domain
  from dune.grid import cartesianDomain
  domain3d = cartesianDomain([-0.25,-0.25,0],[0.25,0.25,1],[30,30,60])

from dune.alugrid import aluSimplexGrid as leafGridView3d
gridView3d  = leafGridView3d(domain3d)
space3d = solutionSpace(gridView3d, order=1)

from ufl import conditional
u = TrialFunction(space3d)
v = TestFunction(space3d)
x = SpatialCoordinate(space3d)
scheme3d = solutionScheme((inner(grad(u),grad(v))+inner(u,v))*dx ==

uh3d = space3d.interpolate([0],name="solution")
# note: plotting with matplotlib not yet available for 3d grids
gridView3d.writeVTK('3dexample', pointdata=[uh3d])

3d laplace problem

Listing installed components

The available realization of a given interface, i.e., the available grid implementations, depends on the modules found during configuration. Getting access to all available components is straightforward:

from dune.utility import components
# to get a list of all available components:
# to get for example all available grid implementations:
available categories are:
available entries for this category are:
entry      function       module
alu        aluGrid        dune.alugrid
aluconform aluConformGrid dune.alugrid
alucube    aluCubeGrid    dune.alugrid
alusimplex aluSimplexGrid dune.alugrid
oned       onedGrid       dune.grid
polygon    polygonGrid    dune.polygongrid
polygrid   polyGrid       dune.vem
ug         ugGrid         dune.grid
yasp       yaspGrid       dune.grid