# 2D Wave Equation¶

This tutorial explains the code wave_2d_DAB.m and requires the use of optimal_cosines.m

## Introduction¶

This Matlab code implements a second order finite difference approximation to the 2D wave equation. On one side, the grid is terminated with a Double Absorbing Boundary (DAB).

The primary thing to notice here is that the DAB is essentially identical to the 1D case described in the 1D Klein-Gordon example. This is because we only need to use the appropriate wave equation to update the interior of the layer and all remaining updates are in the normal direction.

### What this program does¶

For this example, we consider the 2D wave equation,

\begin{align} \frac{d^2 u}{d t^2} &= c^2 \left(\frac{d^2 u}{d x^2} + \frac{d^2 u}{d y^2} \right), \end{align}

where $$c>0$$.

For this example, we will impose Dirichlet boundary conditions on the both sides in the x-direction and at the bottom in the y-direction. We truncate the domain at the top in the y-direction with a DAB. We will use zero initial conditions.

\begin{align} u(x,y,0) = 0, \\ \frac{du}{dt}(x,y,0) = 0, \\ u(0,y,t) = 0, \\ u(0.1, y,t) = 0, \\ u(x,0,t) = 0. \end{align}

We discretize this equation using second order finite differences on the domain $$[0, .1]$$ with mesh spacing of $$\Delta x = \Delta y$$ and we define

\begin{align} x_i &= i\Delta x, \qquad i=0,...,n-1, y_i &= i\Delta y, \qquad i=0,...,n-1, \end{align}

We choose a time step size, $$\Delta t$$, satisfying

\begin{align} \Delta t \leq \frac{dx}{c \sqrt{2}}. \end{align}

Letting

\begin{align} t_n = n \Delta t, \end{align}

$$u$$ is on the grid by:

\begin{align} \frac{u(x_i,y_j,t_{n-1}) - 2u(x_i,y_j,t_{n}) + u(x_i,y_j,t_{n+1})}{\Delta t} = c\left( \frac{u(x_{i-1},y_j,t_{n}) - 2u(x_i,y_j,t_{n}) + u(x_{i+1},y_j,t_{n})}{\Delta x} + \frac{u(x_{i},y_{j-1},t_{n}) - 2u(x_i,y_j,t_{n}) + u(x_{i},y_{j+1},t_{n})}{\Delta y}\right) \end{align}

We use the discretization of the DAB described in theory overview

We drive the simulation with a point source that takes the form of a differentiated Gaussian.

## The commented program¶

We begin by choosing some basic simulation parameters. First we choose the number of grid points to use in the discetization. Then we choose the number of grid points to extend the domain by so we can compare to a larger simulation to check error. In this case, we use 300 grid points and extend the larger simulation by 300 grid points, which corresponds to running on the domain [0,0.2]. Then, we choose the problem and source parameters.

% domain parameters
n = 300;           % number of grid points in domain [0,0.1]
m = 300;           % number of grid points to extent the domain by for a reference
% solution using a larger simulation
c = 1;             % wave speed
nsteps = 700;     % number of time steps
cfl = 0.99; % cfl ratio (1 is exact dispersion relation, but num. unstable)

% compute grid spacing / time step
dx = 0.1 / (n - 1); % we'll just use dx=dy
dt = cfl / (c*sqrt(2/dx^2));

% source paramters
tw = 25*dt;             % pulse width
t0 = 5*tw;              % pulse delay (roughly need t0 > 4*tw to be smooth)
amp = 1;                % pulse "amplitude"
sloc = [210, 175];      % source location


Next, we choose the DAB parameters. We choose the number of recursions to use, $$p$$ and how wide the DAB layer should be in grid points. We require at least three points to support the update to the wave equation. For efficiency and accuracy, 3 is the best choice; however, if the auxiliary variables are to be plotted, increasing the thickness is desireable.

We present two ways to choose the parameter values, the first is simplistic and sets all of the values to either 1 or 0. This generally works well for short times. The alternative is to choose the optimal parameters, which provide an error estimate valid until the provided time.

% DAB parameters
p = 5;                  % DAB/CRBC order
ndab = 3;               % DAB width
a = ones(p,1);          % choose all the cosines to be one for simplicity
ab = ones(p,1);         % choose all the cosines to be one for simplicity
sig = zeros(p,1);       % choose all the sigmas to be zero for simplicity
sigb = zeros(p,1);      % choose all the sigmas to be zero for simplicity

% or choose optimal cosines
T = nsteps * dt;
eta = (n - sloc(2))*dx / (c * T);
if (p>0)
[at, errest] = optimal_cosines(eta, p-1);
a = at(1:2:2*p);
ab = at(2:2:2*p);
sig = 0.5*dt*(1 - a.*a) ./ (T*a);
sigb = 0.5*dt*(1 - ab.*ab) ./ (T*ab);
end


Next, we allocate the storage for all of the field values and auxiliary variables we will use.

% allocate storage
unew = zeros(n); % field values
ucur = zeros(n);
uold = zeros(n);

runew = zeros(n, n+m); % for larger reference simulation
rucur = zeros(n, n+m);
ruold = zeros(n, n+m);

udabnew = zeros(n, ndab, p+1); % dab aux. variables
udabcur = zeros(n, ndab, p+1);
udabold = zeros(n, ndab, p+1);


We begin time stepping by updating all of the internal field values and adding a source term.

% time step
for t=1:nsteps

% internal updates --- eqn 54, in DAB paper
unew(2:n-1, 2:n-1) = 2*ucur(2:n-1, 2:n-1) - uold(2:n-1, 2:n-1) ...
+ ((c*dt)/dx)^2 * (ucur(3:n, 2:n-1) - 4*ucur(2:n-1,2:n-1) ...
+ ucur(1:n-2, 2:n-1) + ucur(2:n-1, 1:n-2) + ucur(2:n-1, 3:n));

% reference solution (same thing on a bigger domain)
runew(2:n-1, 2:n+m-1) = 2*rucur(2:n-1, 2:n+m-1) - ruold(2:n-1, 2:n+m-1) ...
+ ((c*dt)/dx)^2 * (rucur(3:n, 2:n+m-1) - 4*rucur(2:n-1,2:n+m-1) ...
+ rucur(1:n-2, 2:n+m-1) + rucur(2:n-1, 1:n+m-2) + rucur(2:n-1, 3:n+m));

unew(sloc(1), sloc(2)) = unew(sloc(1), sloc(2)) ...
- 2*((t*dt - t0)/tw)*amp*exp(-((t*dt - t0)/tw)^2);
runew(sloc(1), sloc(2)) = runew(sloc(1), sloc(2)) ...
- 2*((t*dt - t0)/tw)*amp*exp(-((t*dt - t0)/tw)^2);


To begin the DAB update, in all auxiliary variables we use the same update equation that we use to evolve the interior points.

% perform wave equation update for the interior of the DAB --- eqn 54, in DAB paper
udabnew(2:n-1, 2:ndab-1,:) = 2*udabcur(2:n-1, 2:ndab-1,:) - udabold(2:n-1, 2:ndab-1,:) ...
+ ((c*dt)/dx)^2 * (udabcur(3:n, 2:ndab-1,:) - 4*udabcur(2:n-1,2:ndab-1,:) ...
+ udabcur(1:n-2, 2:ndab-1,:) + udabcur(2:n-1, 1:ndab-2,:) + udabcur(2:n-1, 3:ndab,:));


Next, we copy in the topmost row of points that the interior was able to update into the first level of the auxiliary variables.

% copy data to DAB boundary for
% the right boundary in the y-direction
udabnew(:,1,1) = unew(:,n-1);


Now, we run the CRBC recursions in the increasing direction of the auxiliary index to get updates to the bottommost points in the DAB layer.

% run the "forward" recursion --- from eqn. 60-61 (a=ab=1,sig=sigb=0)
w = 1/dt + c/dx;

% run the "forward" recursion --- from eqn. 60-61, generalized
for q=1:p
udabnew(2:n-1,1,q+1) = ...
(ab(q) - c*dt/dx - sigb(q))/(ab(q) + c*dt/dx + sigb(q)) * udabcur(2:n-1,1,q+1) ...
+(ab(q) + c*dt/dx - sigb(q))/(ab(q) + c*dt/dx + sigb(q)) * udabcur(2:n-1,2,q+1) ...
+(-a(q) + c*dt/dx + sig(q))/(ab(q) + c*dt/dx + sigb(q)) * udabcur(2:n-1,2,q) ...
+(-a(q) - c*dt/dx + sig(q))/(ab(q) + c*dt/dx + sigb(q)) * udabcur(2:n-1,1,q) ...
+(-ab(q) + c*dt/dx - sigb(q))/(ab(q) + c*dt/dx + sigb(q)) * udabnew(2:n-1,2,q+1) ...
+(a(q) + c*dt/dx + sig(q))/(ab(q) + c*dt/dx + sigb(q)) * udabnew(2:n-1,2,q) ...
+(a(q) - c*dt/dx + sig(q))/(ab(q) + c*dt/dx + sigb(q)) * udabnew(2:n-1,1,q);
end


We begin the CRBC recursions at the topmost points at the highest auxilliary order by applying the Sommerfeld radiation condition. Then we run the CRBC recursions in decreasing auxiliary order.

% apply the termination conditon, sommerfeld --- from eqn 56-57
udabnew(2:n-1,ndab, p+1) = ((udabcur(2:n-1,ndab-1, p+1) - udabnew(2:n-1,ndab-1, p+1) + udabcur(2:n-1,ndab, p+1)) / dt ...
+ c*(udabcur(2:n-1,ndab-1,p+1) - udabcur(2:n-1,ndab, p+1) + udabnew(2:n-1,ndab-1, p+1))/dx)/w;

% run the "backward" recursions --- from eqn. 58-59, generalized
for q=p:-1:1
udabnew(2:n-1,ndab,q) = ...
(a(q) - c*dt/dx - sig(q))/(a(q) + c*dt/dx + sig(q)) * udabcur(2:n-1,ndab,q) ...
+(a(q) + c*dt/dx - sig(q))/(a(q) + c*dt/dx + sig(q)) * udabcur(2:n-1,ndab-1,q) ...
+(-ab(q) + c*dt/dx + sigb(q))/(a(q) + c*dt/dx + sig(q)) * udabcur(2:n-1,ndab-1,q+1) ...
+(-ab(q) - c*dt/dx + sigb(q))/(a(q) + c*dt/dx + sig(q)) * udabcur(2:n-1,ndab,q+1) ...
+(-a(q) + c*dt/dx - sig(q))/(a(q) + c*dt/dx + sig(q)) * udabnew(2:n-1,ndab-1,q) ...
+(ab(q) + c*dt/dx + sigb(q))/(a(q) + c*dt/dx + sig(q)) * udabnew(2:n-1,ndab-1,q+1) ...
+(ab(q) - c*dt/dx + sigb(q))/(a(q) + c*dt/dx + sig(q)) * udabnew(2:n-1,ndab,q+1);
end


Finally, we copy the updated first level auxiliary variables into the internal solver.

% copy DAB value back into the field
unew(:,n) = udabnew(:,2,1);


We plot the field values and the error by comparing to the larger simulation. The commented out portion plots the field values and the auxiliary layers (these plots are clearer if the DAB layer is relatively wide).

  % figures

% field and comparison to larger simulation
figure(1)
subplot(1,2,1)
surf(unew);
view(2)
colorbar;
title('field values')
subplot(1,2,2)
surf(unew - runew(1:n,1:n))
colorbar;
view(2)
title('Error compared to larger simulation')
drawnow

% field and auxiliary fields
%     figure(2)
%     subplot(1, p+4, 1:3)
%     surf(unew);
%     view(2)
%     colorbar;
%     title('field values')
%     for i=1:p+1
%         subplot(1, p+4, i+3)
%         surf(udabnew(:,:,i));
%         view(2)
%         colorbar;
%         title(sprintf('p = %i', i-1))
%     end
%     drawnow


Lastly, we rotate the storage arrays so we can procede to the next time step.

  % swap old, new, and current values
uold = ucur;
ucur = unew;

ruold = rucur;
rucur = runew;

udabold = udabcur;
udabcur = udabnew;

end


References

1. Thomas Hagstrom, Dan Givoli, Daniel Rabinovich, and Jacobo Bielak. The double absorbing boundary method. Journal of Computational Physics, 259(0):220 – 241, 2014.