This page was generated from unit-11.2-bem-Laplace/Laplace_NtD_direct.ipynb.
11.2.4. Neumann Laplace Direct Method¶
keys: homogeneous Neumann bvp, hypersingular operator, unknown Dirichlet data
[1]:
from netgen.occ import *
from ngsolve import *
from ngsolve.webgui import Draw
from ngsolve.bem import *
from ngsolve.solvers import CG
Consider the Neumann boundary value problem
The unique solution \(u\in H^1(\Omega)\) can be written in the following form (representation forumula)
Let’s carefully apply the Neumann trace \(\gamma_1\) to the representation formula and solve the resulting boundary integral equation for the Dirichlet trace \(u_0\) of \(u\) by discretisation of the following variational formulation:
Define the geometry \(\Omega \subset \mathbb R^3\) and create a mesh:
[2]:
sp = Glue(Sphere( (0,0,0), 1).faces)
mesh = Mesh( OCCGeometry(sp).GenerateMesh(maxh=0.2)).Curve(2)
Define conforming finite element spaces for \(H^{\frac12}(\Gamma)\) and \(H^{-\frac12}(\Gamma)\) respectively:
[3]:
fesL2 = SurfaceL2(mesh, order=2, dual_mapping=False)
u,v = fesL2.TnT()
fesH1 = H1(mesh, order=2, definedon=mesh.Boundaries(".*"))
uH1,vH1 = fesH1.TnT()
print ("ndofL2 = ", fesL2.ndof, "ndof H1 = ", fesH1.ndof)
ndofL2 = 4332 ndof H1 = 1446
Project the given Neumann data \(u_1\) in finite element space and have a look at the boundary data:
[4]:
uexa = 1/ sqrt( (x-1)**2 + (y-1)**2 + (z-1)**2 )
graduexa = CF( (uexa.Diff(x), uexa.Diff(y), uexa.Diff(z)) )
n = specialcf.normal(3)
u1 = GridFunction(fesL2)
u1.Interpolate(graduexa*n, definedon=mesh.Boundaries(".*"))
Draw (u1, mesh, draw_vol=False, order=3);
The discretisation of the variational formulation leads to a system of linear equations, ie
where the linear operators are as follows
\(\mathrm{D}\) is the hypersingular operator and the stabilisation \((\mathrm D + \mathrm{S})\) is regular and symmetric.
\(\mathrm{M}\) is a mass matrix.
\(\mathrm{K}'\) is the adjoint double layer operator.
The stabilisation matrix \(S\) is needed to cope with the kernel of the hypersingular operator. The following stabilisation matrix is used here:
where \(v_i, v_j\) being \(H^{\frac12}(\Gamma)\)-conforming shape functions.
[5]:
vH1m1 = LinearForm(vH1*1*ds(bonus_intorder=3)).Assemble()
S = (BaseMatrix(Matrix(vH1m1.vec.Reshape(1))))@(BaseMatrix(Matrix(vH1m1.vec.Reshape(fesH1.ndof))))
Now we assemble the stabilised system matrix and the right hand side and solve for the Dirichlet data \(u_0\) with an iterative solver:
[6]:
u0 = GridFunction(fesH1)
pre = BilinearForm(uH1*vH1*ds).Assemble().mat.Inverse(freedofs=fesH1.FreeDofs())
with TaskManager():
D = HypersingularOperator(fesH1, intorder=12)
M = BilinearForm( v.Trace() * uH1 * ds(bonus_intorder=3)).Assemble()
Kt = DoubleLayerPotentialOperator(fesL2, fesH1, intorder=12)
rhs = ( (0.5 * M.mat.T - Kt.mat) * u1.vec ).Evaluate()
CG(mat=D.mat+S, pre=pre, rhs=rhs, sol=u0.vec, tol=1e-8, maxsteps=200, printrates=True)
CG iteration 1, residual = 0.6734144317722746
CG iteration 2, residual = 0.35705393795235857
CG iteration 3, residual = 0.20848860722515924
CG iteration 4, residual = 0.16312663219142814
CG iteration 5, residual = 0.12857379575539418
CG iteration 6, residual = 0.09481253218480512
CG iteration 7, residual = 0.06093958329330442
CG iteration 8, residual = 0.048206460212526045
CG iteration 9, residual = 0.029786740712189667
CG iteration 10, residual = 0.021211301857303664
CG iteration 11, residual = 0.013534492769772451
CG iteration 12, residual = 0.00828948726187365
CG iteration 13, residual = 0.005443966188702543
CG iteration 14, residual = 0.003211926426276996
CG iteration 15, residual = 0.002038548583006044
CG iteration 16, residual = 0.0011894769220963275
CG iteration 17, residual = 0.0006848870433749013
CG iteration 18, residual = 0.00040443301406021577
CG iteration 19, residual = 0.00022073436851851347
CG iteration 20, residual = 0.0001268194381931518
CG iteration 21, residual = 6.90729759915886e-05
CG iteration 22, residual = 3.7360197645122824e-05
CG iteration 23, residual = 1.9761100557879294e-05
CG iteration 24, residual = 9.964135469278866e-06
CG iteration 25, residual = 4.933925387273146e-06
CG iteration 26, residual = 2.4787199668585203e-06
CG iteration 27, residual = 1.190054871278854e-06
CG iteration 28, residual = 5.710376983675366e-07
CG iteration 29, residual = 2.6226614954751086e-07
CG iteration 30, residual = 1.2089348611283862e-07
CG iteration 31, residual = 5.487460049090238e-08
CG iteration 32, residual = 2.724822748063751e-08
CG iteration 33, residual = 1.818781826974935e-08
CG iteration 34, residual = 1.8157784458292067e-08
CG iteration 35, residual = 2.095186858894127e-08
CG iteration 36, residual = 1.581771734781331e-08
CG iteration 37, residual = 7.829425924950727e-09
CG iteration 38, residual = 3.4516503033658875e-09
Let’s have a look at the numerical and exact Dirichlet data and compare them quantitatively:
[7]:
Draw (u0, mesh, draw_vol=False, order=3);
u0exa = GridFunction(fesH1)
u0exa.Interpolate (uexa, definedon=mesh.Boundaries(".*"))
Draw (u0exa, mesh, draw_vol=False, order=3);
[8]:
print ("L2 error of surface gradients =", sqrt (Integrate ( (grad(u0exa)-grad(u0))**2, mesh, BND)))
L2 error of surface gradients = 0.004449847820701414
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