# 1.6 Error estimation & adaptive refinement¶

In this tutorial, we apply a Zienkiewicz-Zhu type error estimator and run an adaptive loop with these steps:

$\text{Solve}\rightarrow \text{Estimate}\rightarrow \text{Mark}\rightarrow \text{Refine}\rightarrow \text{Solve} \rightarrow \ldots$
[1]:

import netgen.gui
from ngsolve import *
from netgen.geom2d import SplineGeometry
import matplotlib.pyplot as plt


## Geometry¶

The following geometry represents a heated chip embedded in another material that conducts away the heat.

[2]:

#   point numbers 0, 1, ... 11
#   sub-domain numbers (1), (2), (3)
#
#
#             7-------------6
#             |             |
#             |     (2)     |
#             |             |
#      3------4-------------5------2
#      |                           |
#      |             11            |
#      |           /   \           |
#      |         10 (3) 9          |
#      |           \   /     (1)   |
#      |             8             |
#      |                           |
#      0---------------------------1
#

def MakeGeometry():
geometry = SplineGeometry()

# point coordinates ...
pnts = [ (0,0), (1,0), (1,0.6), (0,0.6), \
(0.2,0.6), (0.8,0.6), (0.8,0.8), (0.2,0.8), \
(0.5,0.15), (0.65,0.3), (0.5,0.45), (0.35,0.3) ]
pnums = [geometry.AppendPoint(*p) for p in pnts]

# start-point, end-point, boundary-condition, left-domain, right-domain:
lines = [ (0,1,1,1,0), (1,2,2,1,0), (2,5,2,1,0), (5,4,2,1,2), (4,3,2,1,0), (3,0,2,1,0), \
(5,6,2,2,0), (6,7,2,2,0), (7,4,2,2,0), \
(8,9,2,3,1), (9,10,2,3,1), (10,11,2,3,1), (11,8,2,3,1) ]

for p1,p2,bc,left,right in lines:
geometry.Append(["line", pnums[p1], pnums[p2]], bc=bc, leftdomain=left, rightdomain=right)

geometry.SetMaterial(1,"base")
geometry.SetMaterial(2,"chip")
geometry.SetMaterial(3,"top")

return geometry

mesh = Mesh(MakeGeometry().GenerateMesh(maxh=0.2))


## Spaces & forms¶

The problem is to find $$u$$ in $$H_{0,D}^1$$ satisfying

$\int_\Omega \lambda \nabla u \cdot \nabla v = \int_\Omega f v$

for all $$v$$ in $$H_{0,D}^1$$. We expect the solution to have singularities due to the nonconvex re-enrant angles and discontinuities in $$\lambda$$.

[3]:

fes = H1(mesh, order=3, dirichlet=[1])
u, v = fes.TnT()

# one heat conductivity coefficient per sub-domain
lam = CoefficientFunction([1, 1000, 10])
a = BilinearForm(fes)

# heat-source in inner subdomain
f = LinearForm(fes)
f += CoefficientFunction([0, 0, 1])*v * dx

c = Preconditioner(a, type="multigrid", inverse="sparsecholesky")

gfu = GridFunction(fes)
Draw (gfu)


Note that the linear system is not yet assembled above.

## Solve¶

Since we must solve multiple times, we define a function to solve the boundary value problem, where assembly, update, and solve occurs.

[4]:

def SolveBVP():
fes.Update()
gfu.Update()
a.Assemble()
f.Assemble()
inv = CGSolver(a.mat, c.mat)
gfu.vec.data = inv * f.vec
Redraw (blocking=True)

[5]:

SolveBVP()


## Estimate¶

We implement a gradient-recovery-type error estimator. For this, we need an H(div) space for flux recovery. We must compute the flux of the computed solution and interpolate it into this H(div) space.

[6]:

space_flux = HDiv(mesh, order=2)
gf_flux = GridFunction(space_flux, "flux")

gf_flux.Set(flux)


Element-wise error estimator: On each element $$T$$, set

$\eta_T^2 = \int_T \frac{1}{\lambda} |\lambda \nabla u_h - I_h(\lambda \nabla u_h) |^2$

where $$u_h$$ is the computed solution gfu and $$I_h$$ is the interpolation performed by Set in NGSolve.

[7]:

err = 1/lam*(flux-gf_flux)*(flux-gf_flux)
Draw(err, mesh, 'error_representation')

[8]:

eta2 = Integrate(err, mesh, VOL, element_wise=True)
print(eta2)

 4.77713e-10
3.62503e-08
4.73264e-06
1.39888e-10
2.56218e-10
1.68139e-08
3.89785e-08
1.7684e-07
1.2484e-07
2.62242e-07
1.30604e-07
6.66851e-09
4.49293e-07
3.21719e-06
6.65763e-07
2.49744e-08
2.56941e-07
4.0863e-06
1.39265e-07
5.13219e-07
9.93691e-08
1.22606e-06
4.89833e-10
1.05638e-06
5.005e-07
1.03982e-06
9.39242e-10
1.62172e-06
3.01971e-08
5.35648e-07
4.09083e-09
2.08152e-07
1.12974e-10
1.18657e-11
1.58514e-10
1.45496e-11
1.91237e-11
2.16696e-12
8.31556e-07
8.72613e-07



The above values, one per element, lead us to identify elements which might have large error.

## Mark¶

We mark elements with large error estimator for refinement.

[9]:

maxerr = max(eta2)
print ("maxerr = ", maxerr)

for el in mesh.Elements():
mesh.SetRefinementFlag(el, eta2[el.nr] > 0.25*maxerr)

Draw(gfu)

maxerr =  4.7326381931136415e-06


## Refine & solve again¶

Refine marked elements:

[10]:

mesh.Refine()
SolveBVP()
Redraw()


## Automate the above steps¶

[11]:

l = []    # l = list of estimated total error

def CalcError():

# compute the flux:
space_flux.Update()
gf_flux.Update()
gf_flux.Set(flux)

# compute estimator:
err = 1/lam*(flux-gf_flux)*(flux-gf_flux)
eta2 = Integrate(err, mesh, VOL, element_wise=True)
maxerr = max(eta2)
l.append ((fes.ndof, sqrt(sum(eta2))))
print("ndof =", fes.ndof, " maxerr =", maxerr)

# mark for refinement:
for el in mesh.Elements():
mesh.SetRefinementFlag(el, eta2[el.nr] > 0.25*maxerr)

[12]:

CalcError()
mesh.Refine()

ndof = 454  maxerr = 2.7688817994871465e-06


[13]:

while fes.ndof < 50000:
SolveBVP()
CalcError()
mesh.Refine()

ndof = 547  maxerr = 9.146694004280165e-07
ndof = 1168  maxerr = 3.309564045285505e-07
ndof = 1984  maxerr = 1.198905593185445e-07
ndof = 2890  maxerr = 4.345864087436736e-08
ndof = 3760  maxerr = 1.8502847107724702e-08
ndof = 4567  maxerr = 9.244842649020178e-09
ndof = 5176  maxerr = 4.620482787247039e-09
ndof = 5821  maxerr = 2.3091632777063502e-09
ndof = 6493  maxerr = 1.1540222393413299e-09
ndof = 7120  maxerr = 5.767311562199095e-10
ndof = 7828  maxerr = 2.882221612084236e-10
ndof = 8623  maxerr = 1.4405135479310204e-10
ndof = 9478  maxerr = 7.199432111940317e-11
ndof = 10426  maxerr = 3.598117400058386e-11
ndof = 11533  maxerr = 1.7982714291495978e-11
ndof = 12796  maxerr = 8.987384603535622e-12
ndof = 14356  maxerr = 4.4917214476686066e-12
ndof = 16321  maxerr = 2.2448916112602706e-12
ndof = 18976  maxerr = 1.1219587713697675e-12
ndof = 21676  maxerr = 5.607214905266367e-13
ndof = 25231  maxerr = 2.8023556039314704e-13
ndof = 28792  maxerr = 1.400539375632888e-13
ndof = 33070  maxerr = 6.999476352988298e-14
ndof = 39082  maxerr = 3.4981400215190116e-14
ndof = 45721  maxerr = 1.7482171334947613e-14
ndof = 52705  maxerr = 8.736925640586115e-15


## Plot history of adaptive convergence¶

[14]:

plt.yscale('log')
plt.xscale('log')
plt.xlabel("ndof")
plt.ylabel("H1 error-estimate")
ndof,err = zip(*l)
plt.plot(ndof,err, "-*")

plt.ion()
plt.show()