# Bad Discretizations for 2D Stokes¶

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## </details>¶

Follows Braess, Section III.7.

Of note: This notebook contains a recipe for how to compute negative Sobolev norms, in one of the folds in part II, below.

(Thanks to Colin Cotter and Matt Knepley for tips!)

In [23]:
import numpy as np
import numpy.linalg as la
import firedrake.mesh as fd_mesh
import matplotlib.pyplot as plt

from firedrake import *

In [44]:
nx = 7

triplot(mesh)
plt.gca().set_aspect("equal")
plt.legend()

Out[44]:
<matplotlib.legend.Legend at 0x7fa71527ab90>

## Part I: The Checkerboard Instability¶

$\let\b=\boldsymbol$Build a $Q^1$-$Q^0$ mixed space:

In [45]:
V = VectorFunctionSpace(mesh, "CG", 1)
W = FunctionSpace(mesh, "DG", 0)

Z = V * W

In [46]:
x = SpatialCoordinate(mesh)
x_w = interpolate(x[0], W)
y_w = interpolate(x[1], W)

In [47]:
x_array = x_w.dat.data.copy()
y_array = y_w.dat.data.copy()

x_idx = (np.round(x_array * nx * 2) - 1)//2
y_idx = (np.round(y_array * nx * 2) - 1)//2

checkerboard = (x_idx+y_idx) % 2 - 0.5

q = Function(W, checkerboard)

In [48]:
ax = plt.gca()
l = tricontourf(q, axes=ax)
triplot(mesh, axes=ax, interior_kw=dict(alpha=0.05))
plt.colorbar(l)

Out[48]:
<matplotlib.colorbar.Colorbar at 0x7fa6dce34590>

Assemble the discrete coefficients representing $\int (\nabla \cdot \boldsymbol u) q$:

In [54]:
utest, ptest = TestFunctions(Z)
bcs = [DirichletBC(Z.sub(0), Constant((0, 0)), (1, 2, 3, 4))]
coeffs = assemble(div(utest)*q*dx, bcs=bcs)
coeffs.dat.data[0].round(5).T

Out[54]:
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
-0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,  0.,  0.,
0.,  0.,  0., -0.,  0., -0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,
0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,  0.,  0.,  0., -0.,  0.,
0.,  0.,  0., -0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,  0.,  0.,  0.,  0.,
0.,  0., -0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,
0.,  0.,  0.,  0.,  0.,  0., -0.,  0.,  0.,  0.,  0.,  0.,  0.,
-0.,  0.,  0.,  0., -0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])

$$b (\b{v}, q) = \int_{\Omega} \nabla \cdot \b{v}q.$$

Needed:

There exists a constant $c_2 > 0$ so that (inf-sup or LBB condition): $$\inf_{\mu \in M} \sup_{v \in X} \frac{b (v, \mu)}{||v||_X ||\mu||_M} \geqslant c_2 .$$

## Part II: Is Removing the Checkerboard Sufficient?¶

Suppose we consider the space that has the checkerboard projected out. Is that better?

In [8]:
ramp_checkers = (x_idx-(nx//2))*(-1)**(x_idx+y_idx)
print(ramp_checkers)

q_ramp = Function(W, ramp_checkers)

ax = plt.gca()
l = tricontourf(q_ramp, axes=ax)
triplot(mesh, axes=ax, interior_kw=dict(alpha=0.05))
plt.colorbar(l)

[-3.  2.  3. -1. -2. -3. -0.  1.  2.  3.  1.  0. -1. -2. -3. -2. -1. -0.
1.  2.  3.  3.  2.  1.  0. -1. -2. -3. -3. -2. -1. -0.  1.  2.  3.  2.
1.  0. -1. -3. -2. -1. -0.  3.  2.  1. -3. -2.  3.]

Out[8]:
<matplotlib.colorbar.Colorbar at 0x7fcdfb9e8f50>

Check that this new function q_ramp is orthogonal to the checkerboard q:

In [9]:
assemble(q_ramp*q*dx)

Out[9]:
-4.5102810375396984e-17

We would like to computationally check whether the inf-sup condition is obeyed.

There exists a constant $c_2 > 0$ so that (inf-sup or LBB condition): $$\inf_{\mu \in M} \sup_{v \in X} \frac{b (v, \mu)}{\|v\|_X \|\mu\|_M} \geqslant c_2 .$$

What are the $X$ and $M$ norms?

Show answer $\|\cdot\|_X=\|\cdot\|_{H^1}$ and $\|\cdot\|_M=\|\cdot\|_L^2$.

How do we check the inf-sup condition?

Show answer We're choosing a specific $\mu$ (=q) here, so we need to check that a (mesh-independent $$\sup_{v \in X} \frac{b (v, \mu)}{\|v\|_X } =\sup_{v \in H^1} \frac{b (v, \mu)}{\|v\|_{H^1} } \ge c_2 \|\mu\|_{L^2}$$ So we should really be computing the $H^{-1}$ norm of the functional $b(\cdot, \mu)$.

How do we evaluate that quantity?

Show answer Find the $(H^1)^2$ Riesz representer $\b u$ of $f(\b v):=b(\b v, \mu)$, i.e. $\b u$ so that $$(\b u,\b v)_{(H^1)^2} = b(\b v, \mu) \qquad(\b v\in (H^1_0)^2).$$ Then, evaluate $\|u\|_{(H^1)^2}$. This works because $$\|f\|_{H^{-1}} =\sup_{v\in H^1}\frac{|f(v)|}{\|v\|_{H^1}} =\sup_{v\in H^1}\frac{|(u,v)|_{H^1}}{\|v\|_{H^1}} =\|u\|_{H^1}.$$ Equivalently, we may evaluate $\sqrt{f(u)}=\sqrt{(u,u)_{H^1}} =\|u\|_{H^1}$.

Write a function with arguments (V, q) to evaluate that quantity.

In [13]:
def hminus1_norm(V, q):
# Evaluates the H^{-1} norm of b, where V is
# the function space for the first argument.

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

riesz_rep = Function(V)

return norm(riesz_rep, "H1")

In [20]:
h_values = []
h1_norms = []

for e in range(6):
nx = 10 * 2**e - 1
print(f"Now computing nx={nx}...")

V = VectorFunctionSpace(mesh, "CG", 1)
W = FunctionSpace(mesh, "DG", 0)

Z = V * W

x = SpatialCoordinate(mesh)
x_w = interpolate(x[0], W)
y_w = interpolate(x[1], W)

x_array = x_w.dat.data.copy()
y_array = y_w.dat.data.copy()

x_idx = (np.round(x_array * nx * 2) - 1)//2
y_idx = (np.round(y_array * nx * 2) - 1)//2

odd_checkers = (x_idx-(nx//2))*(-1)**(x_idx+y_idx)
q_ramp = Function(W, odd_checkers)

h_values.append(1/nx)
h1_norms.append(hminus1_norm(V, q_ramp)/norm(q_ramp, "L2"))

Now computing nx=9...
Now computing nx=19...
Now computing nx=39...
Now computing nx=79...
Now computing nx=159...
Now computing nx=319...

In [21]:
plt.figure(figsize=(4,3), dpi=100)
plt.loglog(h_values, h1_norms, "o-")
plt.xlabel("$h$")
z = plt.ylabel(r"$\sup_{v\in X}\frac{b(v,q)}{\|q\|}$")


What does this mean?

Show answer Since, apparently, $$\sup_{v \in X} \frac{b (v, \mu)}{\|v\|_X \|\mu\|_M} =O(h)$$ as $h\to 0$, there cannot be a mesh-independent lower bound $c_2$ for this quantity. A discrete inf-sup condition does not hold for this discretization.

Matt Knepley added this follow-up: (on the Firedrake Slack channel):

You can also see the instability by looking at the condition number of the Schur complement of the full system, which grows with N.

There's also the ASCOT approach (Automated testing of saddle point stability conditions in the FEniCS book). [Florian Wechsung] forward-ported that code to Firedrake. Additionally in the case of the Stokes problem, see this nice paper: https://www.waves.kit.edu/downloads/CRC1173_Preprint_2017-15.pdf

Note that none of these approaches require manually identifying the problematic functions.

In [ ]: