<a name="step-26-Intro"></a>
<h1>Introduction</h1>

@dealiiVideoLecture{29,30}
(@dealiiVideoLectureSeeAlso{31.7})


This program implements the heat equation
@f{align*}{
  \frac{\partial u(\mathbf x, t)}{\partial t}
  -
  \Delta u(\mathbf x, t)
  &=
  f(\mathbf x, t),
  \qquad\qquad &&
  \forall \mathbf x \in \Omega, t\in (0,T),
  \\
  u(\mathbf x, 0) &= u_0(\mathbf x) &&
  \forall \mathbf x \in \Omega,
  \\
  u(\mathbf x, t) &= g(\mathbf x,t) &&
  \forall \mathbf x \in \partial\Omega, t \in (0,T).
@f}
In some sense, this equation is simpler than the ones we have discussed in the
preceding programs step-23, step-24, step-25, namely the wave equation. This
is due to the fact that the heat equation smoothes out the solution over time,
and is consequently more forgiving in many regards. For example, when using
implicit time stepping methods, we can actually take large time steps, we have
less trouble with the small disturbances we introduce through adapting the
mesh every few time steps, etc.

Our goal here will be to solve the equations above using the theta-scheme that
discretizes the equation in time using the following approach, where we would
like $u^n(\mathbf x)$ to approximate $u(\mathbf x, t_n)$ at some time $t_n$:
@f{align*}{
  \frac{u^n(\mathbf x)-u^{n-1}(\mathbf x)}{k_n}
  -
  \left[
  (1-\theta)\Delta u^{n-1}(\mathbf x)
  +
  \theta\Delta u^n(\mathbf x)
  \right]
  &=
  \left[
  (1-\theta)f(\mathbf x, t_{n-1})
  +
  \theta f(\mathbf x, t_n)
  \right].
@f}
Here, $k_n=t_n-t_{n-1}$ is the time step size. The theta-scheme generalizes
the explicit Euler ($\theta=0$), implicit Euler ($\theta=1$) and
Crank-Nicolson ($\theta=\frac 12$) time discretizations. Since the latter has
the highest convergence order, we will choose $\theta=\frac 12$ in the program
below, but make it so that playing with this parameter remains simple. (If you
are interested in playing with higher order methods, take a look at step-52.)

Given this time discretization, space discretization happens as it always
does, by multiplying with test functions, integrating by parts, and then
restricting everything to a finite dimensional subspace. This yields the
following set of fully discrete equations after multiplying through with
$k_n$:
@f{align*}{
  M U^n-MU^{n-1}
  +
  k_n \left[
  (1-\theta)A U^{n-1}
  +
  \theta A U^n
  \right]
  &=
  k_n
  \left[
  (1-\theta)F^{n-1}
  +
  \theta F^n
  \right],
@f}
where $M$ is the @ref GlossMassMatrix "mass matrix" and $A$ is the @ref GlossStiffnessMatrix "stiffness matrix" that results from
discretizing the Laplacian. Bringing all known quantities to the right hand
side yields the linear system we have to solve in every step:
@f{align*}{
  (M
  +
  k_n \theta A) U^n
  &=
  MU^{n-1}
  -
  k_n
  (1-\theta)A U^{n-1}
  +
  k_n
  \left[
  (1-\theta)F^{n-1}
  +
  \theta F^n
  \right].
@f}
The linear system on the left hand side is symmetric and positive definite, so
we should have no trouble solving it with the Conjugate Gradient method.

We can start the iteration above if we have the set of nodal coefficients
$U^0$ at the initial time. Here, we take the ones we get by interpolating the
initial values $u_0(\mathbf x)$ onto the mesh used for the first time step. We
will also need to choose a time step; we will here just choose it as fixed,
but clearly advanced simulators will want to choose it adaptively. We will
briefly come back to this in the <a href="#Results">results section
below</a>.


<h3> Adapting meshes for time dependent problems </h3>

When solving the wave equation and its variants in the previous few programs,
we kept the mesh fixed. Just as for stationary equations, one can make a good
case that this is not the smartest approach and that significant savings can
be had by adapting the mesh. There are, however, significant difficulties
compared to the stationary case. Let us go through them in turn:

<ul>
  <li><i>Time step size and minimal mesh size</i>: For stationary problems, the
  general approach is "make the mesh as fine as it is necessary". For problems
  with singularities, this often leads to situations where we get many levels
  of refinement into corners or along interfaces. The very first tutorial to
  use adaptive meshes, step-6, is a point in case already.

  However, for time dependent problems, we typically need to choose the time
  step related to the mesh size. For explicit time discretizations, this is
  obvious, since we need to respect a CFL condition that ties the time step
  size to the smallest mesh size. For implicit time discretizations, no such
  hard restriction exists, but in practice we still want to make the time step
  smaller if we make the mesh size smaller since we typically have error
  estimates of the form $\|e\| \le {\cal O}(k^p + h^q)$ where $p,q$ are the
  convergence orders of the time and space discretization, respectively. We
  can only make the error small if we decrease both terms. Ideally, an
  estimate like this would suggest to choose $k \propto h^{q/p}$. Because, at
  least for problems with non-smooth solutions, the error is typically
  localized in the cells with the smallest mesh size, we have to indeed choose
  $k \propto h_{\text{min}}^{q/p}$, using the <i>smallest</i> mesh size.

  The consequence is that refining the mesh further in one place implies not
  only the moderate additional effort of increasing the number of degrees of
  freedom slightly, but also the much larger effort of having the solve the
  <i>global</i> linear system more often because of the smaller time step.

  In practice, one typically deals with this by acknowledging that we can not
  make the time step arbitrarily small, and consequently can not make the
  local mesh size arbitrarily small. Rather, we set a maximal level of
  refinement and when we flag cells for refinement, we simply do not refine
  those cells whose children would exceed this maximal level of refinement.

  There is a similar problem in that we will choose a right hand side that
  will switch on in different parts of the domain at different times. To avoid
  being caught flat footed with too coarse a mesh in areas where we suddenly
  need a finer mesh, we will also enforce in our program a <i>minimal</i> mesh
  refinement level.

  <li><i>Test functions from different meshes</i>: Let us consider again the
  semi-discrete equations we have written down above:
  @f{align*}{
    \frac{u^n(\mathbf x)-u^{n-1}(\mathbf x)}{k_n}
    -
    \left[
    (1-\theta)\Delta u^{n-1}(\mathbf x)
    +
    \theta\Delta u^n(\mathbf x)
    \right]
    &=
    \left[
    (1-\theta)f(\mathbf x, t_{n-1})
    +
    \theta f(\mathbf x, t_n)
    \right].
  @f}
  We can here consider $u^{n-1}$ as data since it has presumably been computed
  before. Now, let us replace
  @f{align*}{
    u^n(\mathbf x)\approx u_h^n(\mathbf x)
    =
    \sum_j U^n \varphi_j(\mathbf x),
  @f}
  multiply with test functions $\varphi_i(\mathbf x)$ and integrate by parts
  where necessary. In a process as outlined above, this would yield
  @f{align*}{
    \sum_j
    (M
    +
    k_n \theta A)_{ij} U^n_j
    &=
    (\varphi_i, u_h^{n-1})
    -
    k_n
    (1-\theta)(\nabla \varphi_i, \nabla u_h^{n-1})
    +
    k_n
    \left[
    (1-\theta)F^{n-1}
    +
    \theta F^n
    \right].
  @f}
  Now imagine that we have changed the mesh between time steps $n-1$ and
  $n$. Then the problem is that the basis functions we use for $u_h^n$ and
  $u^{n-1}$ are different! This pertains to the terms on the right hand side,
  the first of which we could more clearly write as (the second follows the
  same pattern)
  @f{align*}{
    (\varphi_i, u_h^{n-1})
    =
    (\varphi_i^n, u_h^{n-1})
    =
    \sum_{j=1}^{N_{n-1}}
    (\varphi_i^n, \varphi_j^{n-1}) U^{n-1}_j,
    \qquad\qquad
    i=1\ldots N_n.
  @f}
  If the meshes used in these two time steps are the same, then
  $(\varphi_i^n, \varphi_j^{n-1})$ forms a square mass matrix
  $M_{ij}$. However, if the meshes are not the same, then in general the matrix
  is rectangular. Worse, it is difficult to even compute these integrals
  because if we loop over the cells of the mesh at time step $n$, then we need
  to evaluate $\varphi_j^{n-1}$ at the quadrature points of these cells, but
  they do not necessarily correspond to the cells of the mesh at time step
  $n-1$ and $\varphi_j^{n-1}$ is not defined via these cells; the same of
  course applies if we wanted to compute the integrals via integration on the
  cells of mesh $n-1$.

  In any case, what we have to face is a situation where we need to integrate
  shape functions defined on two different meshes. This can be done, and is in
  fact demonstrated in step-28, but the process is at best described by the
  word "awkward".

  In practice, one does not typically want to do this. Rather, we avoid the
  whole situation by interpolating the solution from the old to the new mesh
  every time we adapt the mesh. In other words, rather than solving the
  equations above, we instead solve the problem
  @f{align*}{
    \sum_j
    (M
    +
    k_n \theta A)_{ij} U^n_j
    &=
    (\varphi_i, I_h^n u_h^{n-1})
    -
    k_n
    (1-\theta)(\nabla \varphi_i, \nabla I_h^n u_h^{n-1})
    +
    k_n
    \left[
    (1-\theta)F^{n-1}
    +
    \theta F^n
    \right],
  @f}
  where $I_h^n$ is the interpolation operator onto the finite element space
  used in time step $n$. This is not the optimal approach since it introduces
  an additional error besides time and space discretization, but it is a
  pragmatic one that makes it feasible to do time adapting meshes.
</ul>



<h3> What could possibly go wrong? Verifying whether the code is correct </h3>

There are a number of things one can typically get wrong when implementing a
finite element code. In particular, for time dependent problems, the following
are common sources of bugs:
- The time integration, for example by getting the coefficients in front of
  the terms involving the current and previous time steps wrong (e.g., mixing
  up a factor $\theta$ for $1-\theta$).
- Handling the right hand side, for example forgetting a factor of $k_n$ or
  $\theta$.
- Mishandling the boundary values, again for example forgetting a factor of
  $k_n$ or $\theta$, or forgetting to apply nonzero boundary values not only
  to the right hand side but also to the system matrix.

A less common problem is getting the initial conditions wrong because one can
typically see that it is wrong by just outputting the first time step. In any
case, in order to verify the correctness of the code, it is helpful to have a
testing protocol that allows us to verify each of these components
separately. This means:
- Testing the code with nonzero initial conditions but zero right hand side
  and boundary values and verifying that the time evolution is correct.
- Then testing with zero initial conditions and boundary values but nonzero
  right hand side and again ensuring correctness.
- Finally, testing with zero initial conditions and right hand side but
  nonzero boundary values.

This sounds complicated, but fortunately, for linear partial differential
equations without coefficients (or constant coefficients) like the one here,
there is a fairly standard protocol that rests on the following observation:
if you choose as your domain a square $[0,1]^2$ (or, with slight
modifications, a rectangle), then the exact solution can be written as
@f{align*}{
  u(x,y,t) = a(t) \sin(n_x \pi x) \sin(n_y \pi y)
@f}
(with integer constants $n_x,n_y$)
if only the initial condition, right hand side and boundary values are all
of the form $\sin(n_x \pi x) \sin(n_y \pi y)$ as well. This is due to the fact
that the function $\sin(n_x \pi x) \sin(n_y \pi y)$ is an eigenfunction of the
Laplace operator and allows us to compute things like the time factor $a(t)$
analytically and, consequently, compare with what we get numerically.

As an example, let us consider the situation where we have
$u_0(x,y)=\sin(n_x \pi x) \sin(n_x \pi y)$ and
$f(x,y,t)=0$. With the claim (ansatz) of the form for
$u(x,y,t)$ above, we get that
@f{align*}{
  \left(\frac{\partial}{\partial t} -\Delta\right)
  u(x,y,t)
  &=
  \left(\frac{\partial}{\partial t} -\Delta\right)
  a(t) \sin(n_x \pi x) \sin(n_y \pi y)
  \\
  &=
  \left(a'(t) + (n_x^2+n_y^2)\pi^2 a(t) \right) \sin(n_x \pi x) \sin(n_y \pi y).
@f}
For this to be equal to $f(x,y,t)=0$, we need that
@f{align*}{
  a'(t) + (n_x^2+n_y^2)\pi^2 a(t) = 0
@f}
and due to the initial conditions, $a(0)=1$. This differential equation can be
integrated to yield
@f{align*}{
  a(t) = e^{-(n_x^2+n_y^2)\pi^2 t}.
@f}
In other words, if the initial condition is a product of sines, then the
solution has exactly the same shape of a product of sines that decays to zero
with a known time dependence. This is something that is easy to test if you
have a sufficiently fine mesh and sufficiently small time step.

What is typically going to happen if you get the time integration scheme wrong
(e.g., by having the wrong factors of $\theta$ or $k$ in front of the various
terms) is that you don't get the right temporal behavior of the
solution. Double check the various factors until you get the right
behavior. You may also want to verify that the temporal decay rate (as
determined, for example, by plotting the value of the solution at a fixed
point) does not double or halve each time you double or halve the time step or
mesh size. You know that it's not the handling of the
boundary conditions or right hand side because these were both zero.

If you have so verified that the time integrator is correct, take the
situation where the right hand side is nonzero but the initial conditions are
zero: $u_0(x,y)=0$ and
$f(x,y,t)=\sin(n_x \pi x) \sin(n_x \pi y)$. Again,
@f{align*}{
  \left(\frac{\partial}{\partial t} -\Delta\right)
  u(x,y,t)
  &=
  \left(\frac{\partial}{\partial t} -\Delta\right)
  a(t) \sin(n_x \pi x) \sin(n_y \pi y)
  \\
  &=
  \left(a'(t) + (n_x^2+n_y^2)\pi^2 a(t) \right) \sin(n_x \pi x) \sin(n_y \pi y),
@f}
and for this to be equal to $f(x,y,t)$, we need that
@f{align*}{
  a'(t) + (n_x^2+n_y^2)\pi^2 a(t) = 1
@f}
and due to the initial conditions, $a(0)=0$. Integrating this equation in time
yields
@f{align*}{
  a(t) = \frac{1}{(n_x^2+n_y^2)\pi^2} \left[ 1 - e^{-(n_x^2+n_y^2)\pi^2 t} \right].
@f}

Again, if you have the wrong factors of $\theta$ or $k$ in front of the right
hand side terms you will either not get the right temporal behavior of the
solution, or it will converge to a maximum value other than
$\frac{1}{(n_x^2+n_y^2)\pi^2}$.

Once we have verified that the time integration and right hand side handling
are correct using this scheme, we can go on to verifying that we have the
boundary values correct, using a very similar approach.



<h3> The testcase </h3>

Solving the heat equation on a simple domain with a simple right hand side
almost always leads to solutions that are exceedingly boring, since they
become very smooth very quickly and then do not move very much any
more. Rather, we here solve the equation on the L-shaped domain with zero
Dirichlet boundary values and zero initial conditions, but as right hand side
we choose
@f{align*}{
  f(\mathbf x, t)
  =
  \left\{
  \begin{array}{ll}
    \chi_1(\mathbf x)
    & \text{if \(0\le t \le 0.2\tau\) or \(\tau\le t \le 1.2\tau\) or \(2\tau\le t
    \le 2.2\tau\), etc}
    \\
    \chi_2(\mathbf x)
    & \text{if \(0.5\le t \le 0.7\tau\) or \(1.5\tau\le t \le 1.7\tau\) or \(2.5\tau\le t
    \le 2.7\tau\), etc}
    \\
    0
    & \text{otherwise}
  \end{array}
  \right.
@f}
Here,
@f{align*}{
  \chi_1(\mathbf x) &=
  \left\{
  \begin{array}{ll}
    1
    & \text{if \(x>0.5\) and \(y>-0.5\)}
    \\
    0
    & \text{otherwise}
  \end{array}
  \right.
  \\
  \chi_2(\mathbf x) &=
  \left\{
  \begin{array}{ll}
    1
    & \text{if \(x>-0.5\) and \(y>0.5\)}
    \\
    0
    & \text{otherwise}
  \end{array}
  \right.
@f}
In other words, in every period of length $\tau$, the right hand side first
flashes on in domain 1, then off completely, then on in domain 2, then off
completely again. This pattern is probably best observed via the little
animation of the solution shown in the <a href="#Results">results
section</a>.

If you interpret the heat equation as finding the spatially and temporally
variable temperature distribution of a conducting solid, then the test case
above corresponds to an L-shaped body where we keep the boundary at zero
temperature, and heat alternatingly in two parts of the domain. While heating
is in effect, the temperature rises in these places, after which it diffuses
and diminishes again. The point of these initial conditions is that they
provide us with a solution that has singularities both in time (when sources
switch on and off) as well as time (at the reentrant corner as well as at the
edges and corners of the regions where the source acts).
