This page was generated from unit-3.1-parabolic/parabolic.ipynb.
3.1 Parabolic model problem¶
In this unit we want to turn to instationary problems. And we will start with a very basic setup: implicit Euler time stepping for the convection diffusion equation. After the main part of this tutorial we have supplementary material to extend the case in several regards.
We are solving the unsteady heat equation
with a suitable advective field \(b\) (the wind).
[1]:
#imports
from ngsolve import *
from netgen.geom2d import SplineGeometry
from ngsolve.webgui import Draw
Geometry: \((-1,1)^2\)
Dirichlet boundaries everywhere
Mesh
[2]:
from netgen.occ import *
from netgen.webgui import Draw as DrawGeo
shape = Rectangle(2,2).Face().Move((-1,-1,0))
shape.edges.Min(X).name="left"
shape.edges.Max(X).name="right"
shape.edges.Min(Y).name="bottom"
shape.edges.Max(Y).name="top"
mesh = Mesh(OCCGeometry(shape, dim=2).GenerateMesh(maxh=0.25))
[3]:
fes = H1(mesh, order=3, dirichlet="bottom|right|left|top")
u,v = fes.TnT()
time = 0.0
dt = 0.001
We define the field \(b\) (the wind) as
[4]:
b = CoefficientFunction((2*y*(1-x*x),-2*x*(1-y*y)))
Draw(b,mesh,"wind", vectors={"grid_size": 32}, order=3)
[4]:
BaseWebGuiScene
bilinear forms for
convection-diffusion stiffness and
mass matrix separately.
non-symmetric memory layout for the mass matrix so that a and m have the same sparsity pattern.
[5]:
a = BilinearForm(fes, symmetric=False)
a += 0.01*grad(u)*grad(v)*dx + b*grad(u)*v*dx
a.Assemble()
m = BilinearForm(fes, symmetric=False)
m += u*v*dx
m.Assemble()
[5]:
<ngsolve.comp.BilinearForm at 0x7ff09b4dd730>
Implicit Euler method¶
or in an incremental form:
Incremental form: \(u^{n+1} - u^n\) has homogeneous boundary conditions (unless boundary conditions are time-dependent).
For the time stepping method: set up linear combinations of matrices.
(Only) if the sparsity pattern of the matrices agree we can copy the pattern and sum up the entries.
First, we create a matrix of same format as m.mat and compare number of non-zero entries and sparsity pattern:
[6]:
mstar = m.mat.CreateMatrix()
print(f"m.mat.nze = {m.mat.nze}, a.mat.nze={a.mat.nze}, mstar.nze={mstar.nze}")
from helper import ShowPattern
print("sparsity pattern a.mat:")
ShowPattern(a.mat,binarize=True)
m.mat.nze = 11104, a.mat.nze=11104, mstar.nze=11104
sparsity pattern a.mat:

[7]:
print("sparsity pattern mstar:")
ShowPattern(mstar,binarize=True)
sparsity pattern mstar:

To access the entries we use the vector of nonzero-entries:
[8]:
print(f"mstar.nze={mstar.nze}, len(mstar.AsVector())={len(mstar.AsVector())}")
mstar.nze=11104, len(mstar.AsVector())=11104
[9]:
print(mstar.AsVector())
0.00520833
0.00260417
0.00260417
-0.000520833
8.68056e-05
-0.000520833
8.68056e-05
-0.000260417
-1.32137e-19
0.000173611
0.00870064
0.00221203
0.00213829
0.00435032
-0.000442406
7.37343e-05
-0.000427658
7.12763e-05
-0.000870064
0.000145011
-0.000221203
1.15196e-19
-0.000213829
-1.10114e-19
0.000147469
0.000142553
0.00520833
0.00260417
0.00260417
-0.000520833
8.68056e-05
-0.000520833
8.68056e-05
-0.000260417
1.32137e-19
0.000173611
0.00811466
0.00203707
0.00202026
0.00405733
-0.000407414
6.79023e-05
-0.000404052
6.73421e-05
-0.000811466
0.000135244
-0.000203707
1.04185e-19
-0.000202026
-1.01644e-19
0.000135805
0.000134684
0.00260417
0.019115
0.00322599
0.00633153
0.00695335
-0.000520833
-8.68056e-05
-0.000260417
-1.42302e-19
-0.000645197
0.000107533
-0.00126631
0.000211051
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0.000231778
-0.000322599
-1.66018e-19
-0.000372736
1.91429e-19
0.000173611
0.000215066
0.000248491
0.00322599
0.019175
0.00276575
0.00682174
0.0063615
-0.000645197
-0.000107533
-0.000322599
-1.74489e-19
-0.000553149
9.21915e-05
-0.00136435
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0.00021205
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-1.42302e-19
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1.84653e-19
0.000215066
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0.00276575
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0.005536
0.00531728
-0.000553149
-9.21915e-05
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-1.49078e-19
-0.000509407
8.49011e-05
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0.000184533
-0.00106346
0.000177243
-0.000254703
-1.32137e-19
-0.000277025
1.42302e-19
0.000184383
0.000169802
0.000184683
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0.0144748
0.00230512
0.0049323
0.00469039
-0.000509407
-8.49011e-05
-0.000254703
-1.40607e-19
-0.000461025
7.68375e-05
-0.000986459
0.00016441
-0.000938077
0.000156346
-0.000230512
-1.20279e-19
-0.000238526
1.18585e-19
0.000169802
0.000153675
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0.0128165
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0.0043454
0.00410312
-0.000461025
-7.68375e-05
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-1.23667e-19
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6.87613e-05
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0.000136771
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-1.05032e-19
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1.06726e-19
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0.0122623
0.00211946
0.00401168
0.00406831
-0.000412568
-6.87613e-05
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-1.10114e-19
-0.000423892
7.06487e-05
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0.000133723
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0.00013561
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-1.09267e-19
-0.000194884
9.74088e-20
0.000137523
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0.00221203
0.00211946
0.0123988
0.00398737
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9.48677e-20
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0.010775
0.00163783
0.00374969
0.00324924
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5.45944e-05
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0.000108308
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-8.47033e-20
-0.00016114
8.30092e-20
0.000142553
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0.00320108
0.00358066
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6.7247e-05
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-1.04185e-19
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7.87741e-20
0.000109189
0.000134494
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0.00201741
0.012647
0.0022013
0.00412219
0.00430608
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7.33767e-05
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1.0842e-19
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1.25361e-19
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1.25361e-19
0.000156438
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0.000192332
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0.000147479
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1.28749e-19
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9.14796e-20
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-1.27055e-21
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8.78797e-21
1.08323e-05
0.000158677
0.000158677
0.000158677
-2.26681e-05
-1.0482e-20
-2.26681e-05
-1.48231e-21
-2.26681e-05
9.635e-21
1.13341e-05
0.000172582
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-2.46546e-05
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0.000176573
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0.000165828
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0.000198918
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0.000186829
0.000186829
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-1.27055e-21
-2.66899e-05
-1.24937e-20
-2.66899e-05
-1.14349e-20
1.3345e-05
Using the vector we can build the linear combination of the a and the m matrix:
[10]:
mstar.AsVector().data = m.mat.AsVector() + dt * a.mat.AsVector()
# corresponds to M* = M + dt * A
invmstar = mstar.Inverse(freedofs=fes.FreeDofs())
We set the r.h.s. \(f = exp(-6 ((x+\frac12)^2+y^2)) - exp(-6 ((x-\frac12)^2+y^2))\)
[11]:
f = LinearForm(fes)
gaussp = exp(-6*((x+0.5)*(x+0.5)+y*y))-exp(-6*((x-0.5)*(x-0.5)+y*y))
Draw(gaussp,mesh,"f", deformation=True)
f += gaussp*v*dx
f.Assemble()
[11]:
<ngsolve.comp.LinearForm at 0x7ff098ef55b0>
and the initial data: \(u_0 = (1-y^2)x\)
[12]:
gfu = GridFunction(fes)
gfu.Set((1-y*y)*x) # note that boundary conditions remain
scene = Draw(gfu,mesh,"u")
we define a simple time loop including some visualization sampling:
[13]:
def TimeStepping(invmstar, initial_cond = None, t0 = 0, tend = 2,
nsamples = 10):
if initial_cond:
gfu.Set(initial_cond)
cnt = 0; time = t0
sample_int = int(floor(tend / dt / nsamples)+1)
gfut = GridFunction(gfu.space,multidim=0)
gfut.AddMultiDimComponent(gfu.vec)
while time < tend - 0.5 * dt:
res = dt * f.vec - dt * a.mat * gfu.vec
gfu.vec.data += invmstar * res
print("\r",time,end="")
scene.Redraw()
if cnt % sample_int == 0:
gfut.AddMultiDimComponent(gfu.vec)
cnt += 1; time = cnt * dt
return gfut
[14]:
%%time
gfut = TimeStepping(invmstar, (1-y*y)*x)
1.999CPU times: user 564 ms, sys: 16.1 ms, total: 580 ms
Wall time: 567 ms
[15]:
Draw(gfut, mesh, interpolate_multidim=True, animate=True)
[15]:
BaseWebGuiScene
Supplementary material:¶
The simple time stepping can be extended in several directions:
Instead of direct one could use iterative solvers
R.h.s. data time-dependent
boundary data time-dependent
Replace implicit Euler time stepping with (Singly diagonal) Runge-Kutta methods
Supplementary 1: Using iterative solvers¶
For a factorization of \(M^\ast\) (and \({M^\ast}^{-1}\)) we required a sparse matrix \(M^\ast\)
To store \(M^\ast\) as a sparse matrix requires new storage (and same memory layout of \(A\) and \(M\))
For iterative solvers we only require the matrix (and preconditioner) applications and it suffices to have :math:`M^ast` available as a linear operator
mstar_alt = m.mat + dt * a.mat
has no storage but defines the operator action: sum of two matrix-vector multiplications
iterative solver version (with Jacobi preconditining):
[16]:
mstar_alt = m.mat + dt * a.mat
premstar_alt = m.mat.CreateSmoother() + dt * a.mat.CreateSmoother()
print(premstar_alt)
Sum of
Scale a = 1
Print base-matrix
Scale b = 1
Scale with 0.001:
Print base-matrix
Now, replace the action of the inverse Matrix from the previous script with a solution of a conjugate gradient method:
[17]:
from ngsolve.krylovspace import CGSolver
invmstar_alt = CGSolver(mstar_alt, premstar_alt, tol=1e-8, printrates="\r", maxiter=200)
Now, we can repeat the time stepping. As in 2D direct solvers are quite efficient, this simple preconditioned solver can hardly compete. We hence only do a few time steps:
[18]:
%%time
gfut_a1 = TimeStepping(invmstar_alt, (1-y*y)*x, tend=25*dt, nsamples=5)
CG converged in 107 iterations to residual 3.4506237382489186e-11
CG converged in 107 iterations to residual 3.434400568946877e-11
CG converged in 107 iterations to residual 3.41904053343005e-11
CG converged in 107 iterations to residual 3.404224595837282e-11
CG converged in 107 iterations to residual 3.389722956134242e-11
CG converged in 107 iterations to residual 3.3753751774369674e-11
CG converged in 107 iterations to residual 3.3610733933076795e-11
CG converged in 107 iterations to residual 3.3467487850259067e-11
CG converged in 107 iterations to residual 3.332361060800224e-11
CG converged in 107 iterations to residual 3.3178904755819595e-11
CG converged in 107 iterations to residual 3.303331891114304e-11
CG converged in 107 iterations to residual 3.288690415261776e-11
CG converged in 107 iterations to residual 3.273978230993873e-11
CG converged in 107 iterations to residual 3.259212302910974e-11
CG converged in 107 iterations to residual 3.244412720384711e-11
CG converged in 107 iterations to residual 3.2296014963767106e-11
CG converged in 107 iterations to residual 3.2148016889930035e-11
CG converged in 107 iterations to residual 3.200036749909559e-11
CG converged in 107 iterations to residual 3.185330031735189e-11
CG converged in 107 iterations to residual 3.1707044070111576e-11
CG converged in 107 iterations to residual 3.156181966549319e-11
CG converged in 107 iterations to residual 3.1417837755470876e-11
CG converged in 107 iterations to residual 3.1275296734879405e-11
CG converged in 107 iterations to residual 3.113438109072801e-11
CG converged in 107 iterations to residual 3.0995260049605705e-11
0.024CPU times: user 586 ms, sys: 0 ns, total: 586 ms
Wall time: 582 ms
[19]:
Draw(gfut_a1, mesh, interpolate_multidim=True, animate=True)
[19]:
BaseWebGuiScene
Supplementary 2: time-dependent r.h.s. data¶
Next: time-dependent r.h.s. data \(f=f(t)\):
Use
Parameter
t representing the time.A
Parameter
is a constantCoefficientFunction
the value of which can be changed with theSet
-function.
[20]:
t = Parameter(0.0)
An example of a time-dependent coefficient that we want to use as r.h.s. in the following is
[21]:
omega=1
gausspt = exp(-6*((x+sin(omega*t))*(x+sin(omega*t))+y*y))-exp(-6*((x-sin(omega*t))*(x-sin(omega*t))+y*y))
gff = GridFunction(L2(mesh,order=gfu.space.globalorder+1))
gfft = GridFunction(gff.space,multidim=0)
time = 0.0
for i in range(7):
t.Set(3*i/6)
gff.Set(gausspt)
gfft.AddMultiDimComponent(gff.vec)
Draw(gfft, mesh, interpolate_multidim=True, animate=True, autoscale=False, min=-1, max=1)
[21]:
BaseWebGuiScene
Accordingly, we define a different linear form which then has to be assembled in every time step.
[22]:
ft = LinearForm(fes)
ft += gausspt*v*dx
[23]:
def TimeStepping_app2(invmstar, initial_cond = None, t0 = 0, tend = 2,
nsamples = 10):
if initial_cond:
gfu.Set(initial_cond)
cnt = 0; time = t0
sample_int = int(floor(tend / dt / nsamples)+1)
gfut = GridFunction(gfu.space,multidim=0)
gfut.AddMultiDimComponent(gfu.vec)
while time < tend - 0.5 * dt:
t.Set(time)
ft.Assemble()
res = dt * ft.vec - dt * a.mat * gfu.vec
gfu.vec.data += invmstar * res
print("\r",time,end="")
if cnt % sample_int == 0:
gfut.AddMultiDimComponent(gfu.vec)
cnt += 1; time = cnt * dt
return gfut
[24]:
%%time
gfut_a2 = TimeStepping_app2(invmstar, initial_cond=CF(0),tend=2)
Draw(gfut_a2, mesh, interpolate_multidim=True, animate=True,
min=-0.75,max=0.75,autoscale=False)
1.999
CPU times: user 1.47 s, sys: 15.3 ms, total: 1.49 s
Wall time: 3.61 s
[24]:
BaseWebGuiScene
Supplementary 3: Time dependent boundary conditions¶
\(u|_{\partial \Omega} = u_D(t)\), \(f=0\)
implicit Euler time stepping method, non-incremental form:
\[M^\ast u^{n+1} = (M + \Delta t A) u^{n+1} = M u^n\]Homogenize w.r.t. to boundary conditions, i.e. we split
\[u^{n+1} = u^{n+1}_0 + u^{n+1}_D\]where \(u^{n+1}_D\) is a (discrete) function with correct boundary condition:
[25]:
uD = CoefficientFunction( [(1-x*x)*IfPos(sin(0.3*pi*t),sin(0.3*pi*t),0),0,0,0])
time = 0.0
t.Set(0.0)
gfu.Set(uD,BND)
[26]:
def TimeStepping_app3(invmstar, initial_cond = None, t0 = 0, tend = 2,
nsamples = 10):
if initial_cond:
gfu.Set(initial_cond)
cnt = 0; time = t0
sample_int = int(floor(tend / dt / nsamples)+1)
gfuD = GridFunction(gfu.space)
gfut = GridFunction(gfu.space,multidim=0)
gfut.AddMultiDimComponent(gfu.vec)
while time < tend - 0.5 * dt:
t.Set(time)
gfuD.Set(uD,BND)
res = m.mat * gfu.vec - mstar *gfuD.vec
gfu.vec.data = gfuD.vec + invmstar * res
print("\r",time,end="")
if cnt % sample_int == 0:
gfut.AddMultiDimComponent(gfu.vec)
cnt += 1; time = cnt * dt
return gfut
[27]:
%%time
gfut_a3 = TimeStepping_app3(invmstar, initial_cond=CF(0),tend=10)
Draw(gfut_a3, mesh, interpolate_multidim=True, animate=True,
#settings = {"subdivision" : 10},
deformation = True, min = 0, max = 0.2, autoscale = False)
9.999
CPU times: user 4.01 s, sys: 30.8 ms, total: 4.04 s
Wall time: 6.14 s
[27]:
BaseWebGuiScene
Supplementary 4: Singly diagonally implicit Runge-Kutta methods¶
Let us consider more sophisticated time integration methods, SDIRK methods. To simplify presentation we set \(f=0\).
SDIRK methods for the semi-discrete problem \(\frac{d}{dt} u = M^{-1} F(u) = -M^{-1} \cdot A u\) are of the form:
with
The coefficients a,b and c are stored in the butcher tableau:
For an SDIRK method we have \(a^\ast = a_{ii},~i=0,..,s-1\).
Simplest example: Implicit Euler
[28]:
class sdirk1: #order 1 (implicit Euler)
stages = 1
a = [[1]]
b = [1]
c = [1]
astar = 1
Midpoint rule:
[29]:
class MR: #order 2 (midpoint rule)
stages = 1
a = [[0.5]]
b = [1]
c = [0.5]
astar = 0.5
We can use for example the 2 stage SDIRK (order 3) method
with \(p = \frac{3 - \sqrt{3}}{6}\).
[30]:
class sdirk2: #order 3
p = (3 - sqrt(3))/6
stages = 2
a = [[p, 0],
[1 - 2*p, p]]
b = [1/2, 1/2]
c = [p, 1 - p]
astar = p
or the 5 stage SDIRK (order 4) method
[31]:
class sdirk5: #order 4
stages = 5
a = [[1/4, 0, 0, 0, 0],
[1/2, 1/4, 0, 0, 0],
[17/50,-1/25, 1/4, 0, 0],
[371/1360, -137/2720, 15/544, 1/4,0],
[25/24, -49/48, 125/16, -85/12, 1/4]]
b = [25/24, -49/48, 125/16, -85/12, 1/4]
c = [1/4, 3/4, 11/20, 1/2, 1]
astar = 1/4
We have to update the \(M^\ast\) matrix due to the different diagonal in the butcher tableau. Further, we will also need the inverse mass matrix to compute the slope states \(k_i\).
In the next function, we put setup of the matrices, their inverses and the time stepping in one (larger) loop:
[32]:
def TimeStepping_app4(m, a, butchertab, dt,
initial_cond = None, t0 = 0, tend = 2,
nsamples = 10):
mstar.AsVector().data = m.mat.AsVector() + butchertab.astar * dt * a.mat.AsVector()
invmstar = mstar.Inverse(freedofs=fes.FreeDofs())
invmass = m.mat.Inverse(freedofs=fes.FreeDofs())
rhsi, Mu0, ui = gfu.vec.CreateVector(),gfu.vec.CreateVector(),gfu.vec.CreateVector()
k = [gfu.vec.CreateVector() for i in range(butchertab.stages)]
if initial_cond:
gfu.Set(initial_cond)
cnt = 0; time = t0
sample_int = int(floor(tend / dt / nsamples)+1)
gfuD = GridFunction(gfu.space)
gfut = GridFunction(gfu.space,multidim=0)
gfut.AddMultiDimComponent(gfu.vec)
while time < tend - 0.5 * dt:
Mu0.data = m.mat * gfu.vec
for i in range(butchertab.stages):
rhsi.data = Mu0
for j in range(0,i):
rhsi.data += dt * butchertab.a[i][j] * k[j]
t.Set(time+butchertab.c[i]*dt)
gfu.Set(uD,BND)
ui.data = gfu.vec; rhsi.data -= mstar * ui
ui.data += invmstar * rhsi
k[i].data = - a.mat * ui
t.Set(time+dt)
gfu.Set(uD,BND)
Mu0.data -= m.mat * gfu.vec
for i in range(0,butchertab.stages):
Mu0.data += dt * butchertab.b[i] * k[i]
gfu.vec.data += invmass * Mu0
print("\r",time,end="")
if cnt % sample_int == 0:
gfut.AddMultiDimComponent(gfu.vec)
cnt += 1; time = cnt * dt
return gfut
Next, we simply apply the time stepping:
[33]:
gfut_a4 = TimeStepping_app4(m, a, butchertab = sdirk5(),
dt = 0.25, initial_cond=CF(0),tend=10)
Draw(gfut_a4, mesh, interpolate_multidim=True, animate=True,
order = 3,
deformation = True, min = 0, max = 0.2, autoscale = False)
9.75
[33]:
BaseWebGuiScene
Tasks
If you want to play around and explore this tutorial unit, here are a few suggestions:
Make a convergence study (for convergence in space, time and space and time)
Re-introduce non-zero r.h.s. in the RK-script
Generalize the RK script to fully implicit RK schemes (using product spaces, or block matrices, …)