Issues Solving Pure Neumann Problems in dolfinx

Is there any currently recommended way to solve pure neumann problems in Dolfinx?

This seems to be a major limitation for me using dolfinx. There are many cases were we would need to enforce that some average condition for the solution (contraining rigid body modes in mechanics, solving the pressure-poisson equation with incompressible fluid flows, etc), but currently I don’t know of a recommended way to solve these types of problems in dolfinx. The normal way (involving the use of LaGrange multipliers) works fine in legacy dolfin:

but it is well know that this is not possible in dolfinx because of the Real Type element does not exist:

Does anyone have a work around or a best practice until real type elements are fully implemented?

You can remove the constant nullspace with PETSc. See for instance

1 Like

Thanks @dokken.

I was able to replicate the Singular Poisson problem in dolfinx using your help. The dolfin example is here.

It might be nice to include this example in the dolfinx tutorial. Either way, I’ll just post the code I was able to get working here (apologies if this already exists elsewhere, it just took me a bit to put the pieces together):

from mpi4py import MPI
from dolfinx import mesh,plot
from dolfinx.fem import FunctionSpace,Function,form
from dolfinx.fem.petsc import create_vector,assemble_matrix,assemble_vector
from ufl import inner,TrialFunction,TestFunction,ds,dx,grad,exp,sin,SpatialCoordinate
from petsc4py import PETSc
import pyvista

# Create mesh and define function space
domain = mesh.create_unit_square(MPI.COMM_WORLD, 20,20, mesh.CellType.quadrilateral)
V = FunctionSpace(domain, ("CG", 1))

u = TrialFunction(V)
v = TestFunction(V)

x = SpatialCoordinate(domain)
f = 10*exp(-(((x[0] - 0.5)**2) + (x[1] - 0.5)**2) / 0.02)
g = -sin(5*x[0])

a = inner(grad(u), grad(v))*dx
L = f*v*dx + g*v*ds

# Assemble system
A = assemble_matrix(form(a))
with b.localForm() as b_loc:

# Solution Function
uh = Function(V)

# Create Krylov solver
solver = PETSc.KSP().create(A.getComm())

# Create vector that spans the null space
nullspace = PETSc.NullSpace().create(constant=True,comm=MPI.COMM_WORLD)

# orthogonalize b with respect to the nullspace ensures that 
# b does not contain any component in the nullspace

# Finally we are able to solve our linear system ::

#plotting stuff
tdim = domain.topology.dim
topology, cell_types, geometry = plot.create_vtk_mesh(domain, tdim)
grid = pyvista.UnstructuredGrid(topology, cell_types, geometry)
plotter = pyvista.Plotter()
plotter.add_mesh(grid, show_edges=True,opacity=0.25)
u_topology, u_cell_types, u_geometry = plot.create_vtk_mesh(V)
u_grid = pyvista.UnstructuredGrid(u_topology, u_cell_types, u_geometry)
u_grid.point_data["u"] = uh.x.array.real
plotter.add_mesh(u_grid.warp_by_scalar("u",factor=.5), show_edges=True)
if not pyvista.OFF_SCREEN:
1 Like

If you want to add it to the tutorial, feel free to make a Pull request at:
and I will review it.

If I want to change the above code to make the average of u equal to 7 instead of 0, how would I do it? This would help me to understand what is in the code.

You can simply add the constant that makes the average 7 after the fact, as a Poisson problem with pure Neumann conditions are determined only up to a constant.

That’s cheating. This is a baby problem, where we already know the answer. The motivation for this is a somewhat more complicated problem, where we don’t know the answer. The version of this I am trying to solve (another baby problem…) is here

As I understand it, the old fenics had the capability to introduce additional constraints into the PDE, which fenicsx apparently doesn’t allow. Is there some workaround for this which doesn’t rely on already knowing what the solution in the null space looks like?

I guess you are talking about a Lagrange multiplier from the real space?

There are ways of implementing this in DOLFINx,

The problem you are trying to solve seem related to:

I am locking this to preserve the discussion as close as possible to the original question. @Daniel.Baker please answer to @dokken in the other post that you linked.