Samir Hamdi

Revision as of 18:58, 25 September 2007

We now revisit the PDE problem of eqs. (1) to (4) to illustrate the details of constructing a MOL code and to discuss the numerical and graphical output from the code.

The specific initial condition of eq. (2) will be taken as:

<math eq_2>

u(x,t=0) = sin(\pi x/2) </math>

The analytical solution to eqs. (1) to (4) which we use to evaluate the numerical MOL output is

<math eq_2>

u(x,t)=e^{-(\pi^{2}/4)t}sin(\pi x/2) </math>

A main program in Matlab for the MOL solution of eqs. (1) to (4) with the analytical solution, eq. (42), included for comparison with the MOL solution, is listed below

   

%
% Clear previous files
  clear all
  clc
%
% Parameters shared with the ODE routine
  global ncall ndss
%
% Initial condition
  n=21;
  for i=1:n
    u0(i)=sin((pi/2.0)*(i-1)/(n-1));
  end
%
% Independent variable for ODE integration
  t0=0.0;
  tf=2.5;
  tout=linspace(t0,tf,n);
  nout=n;
  ncall=0;
%
% ODE integration
  mf=1;
  reltol=1.0e-04; abstol=1.0e-04;
  options=odeset('RelTol',reltol,'AbsTol',abstol);
  if(mf==1) % explicit FDs
    [t,u]=ode15s(@pde_1,tout,u0,options); end
  if(mf==2) ndss=4; % ndss = 2, 4, 6, 8 or 10 required
    [t,u]=ode15s(@pde_2,tout,u0,options); end
  if(mf==3) ndss=44; % ndss = 42, 44, 46, 48 or 50 required
    [t,u]=ode15s(@pde_3,tout,u0,options); end
%
% Store numerical and analytical solutions, errors at x = 1/2
  n2=(n-1)/2.0+1;
  sine=sin(pi/2.0*0.5);
  for i=1:nout
    u_plot(i)=u(i,n2);
    u_anal(i)=exp(-pi^2/4.0*t(i))*sine;
    err_plot(i)=u_plot(i)-u_anal(i);
  end
%
% Display selected output
  fprintf('\n mf = %2d   abstol = %8.1e   reltol = %8.1e\n',...
          mf,abstol,reltol);
  fprintf('\n     t       u(0.5,t)  u_anal(0.5,t)   err u(0.5,t)\n');
  for i=1:5:nout
    fprintf('%6.3f%15.6f%15.6f%15.7f\n',...
            t(i),u_plot(i),u_anal(i),err_plot(i));
  end
  fprintf('\n ncall = %4d\n',ncall);
%
% Plot numerical solution and errors at x = 1/2
  figure(1);
  subplot(1,2,1)
  plot(t,u_plot); axis tight
  title('u(0.5,t) vs t'); xlabel('t'); ylabel('u(0.5,t)')
  subplot(1,2,2)
  plot(t,err_plot); axis tight
  title('Err u(0.5,t) vs t'); xlabel('t'); ylabel('Err u(0.5,t)')
  print -deps pde.eps; print -dps pde.ps
%
% Plot numerical solution in 3D perspective
  figure(2);
  colormap('Gray');
  C=ones(n);
  g=linspace(0,1,n); % For distance x
  h1 = waterfall(t,g,u',C);
  axis('tight');
  grid off
  xlabel('t, time')
  ylabel('x, distance')
  zlabel('u(x,t)')
  s1 = sprintf('Diffusion Equation - MOL Solution');
  sTmp = sprintf('u(x,0) = sin(\\pi x/2 )');
  s2 = sprintf('Initial condition: %s', sTmp);
  title([{s1}, {s2}], 'fontsize', 12);
  v = [0.8616   -0.5076    0.0000   -0.1770
       0.3712    0.6301    0.6820   -0.8417
       0.3462    0.5876   -0.7313    8.5590
            0         0         0    1.0000];
  view(v);
  rotate3d on;

We can note the following points about this main program:

• After declaring some parameters global so that they can be shared with other routines called via this main program, initial condition (2) is computed over a 21-point grid in \( x \)

   
% Clear previous files
  clear all
  clc
%
% Parameters shared with the ODE routine
  global ncall ndss
%
% Initial condition
  n=21;
  for i=1:n
    u0(i)=sin((pi/2.0)*(i-1)/(n-1));
  end

• The independent variable \( t \) is defined over the interval \( 0 \leq t\leq 2.5 \) again, a 21-point grid is used.

  
%
% Independent variable for ODE integration
  t0=0.0;
  tf=2.5;
  tout=linspace(t0,tf,n);
  nout=n;
  ncall=0;

• The 21 ODEs are then integrated by a call to the Matlab integrator ode15s

 
  mf=1;
  reltol=1.0e-04; abstol=1.0e-04;
  options=odeset('RelTol',reltol,'AbsTol',abstol);
  if(mf==1) % explicit FDs
    [t,u]=ode15s(@pde_1,tout,u0,options); end
  if(mf==2) ndss=4; % ndss = 2, 4, 6, 8 or 10 required
    [t,u]=ode15s(@pde_2,tout,u0,options); end
  if(mf==3) ndss=44; % ndss = 42, 44, 46, 48 or 50 required
    [t,u]=ode15s(@pde_3,tout,u0,options); end

Three cases are programmed corresponding to mf = 1, 2, 3 , for which three different ODEs routines, pde_1 , pde_2 , and pde_3 are called (these routines are discussed subsequently). The variable ndss refers to a library of differentiation routines for use in the MOL solution of PDEs; the use of ndss is illustrated in the subsequent discussion. Note that a stiff integrator, ode15s , was selected because the 21 ODEs are sufficiently stiff that a nonstiff integrator results in a large number of calls to the ODE routine.

• Selected numerical results are stored for subsequent tabular and plotted output

 
% Store numerical and analytical solutions, errors at x = 1/2
  n2=(n-1)/2.0+1;
  sine=sin(pi/2.0*0.5);
  for i=1:nout
    u_plot(i)=u(i,n2);
    u_anal(i)=exp(-pi^2/4.0*t(i))*sine;
    err_plot(i)=u_plot(i)-u_anal(i);
  end

• Selected tabular numerical output is first displayed

 
% Display selected output
  fprintf('\n mf = %2d   abstol = %8.1e   reltol = %8.1e\n',...
          mf,abstol,reltol);
  fprintf('\n     t       u(0.5,t)  u_anal(0.5,t)   err u(0.5,t)\n');
  for i=1:5:nout
    fprintf('%6.3f%15.6f%15.6f%15.7f\n',...
            t(i),u_plot(i),u_anal(i),err_plot(i));
  end
  fprintf('\n ncall = %4d\n',ncall);

The output from this code is

 
 mf =  1   abstol = 1.0e-004   reltol = 1.0e-004

     t       u(0.5,t)  u_anal(0.5,t)   err u(0.5,t)
 0.000       0.707107       0.707107      0.0000000
 0.625       0.151387       0.151268      0.0001182
 1.250       0.032370       0.032360      0.0000093
 1.875       0.006894       0.006923     -0.0000283
 2.500       0.001472       0.001481     -0.0000091
 ncall =   85

This output indicates that the MOL solution agrees with the analytical solution to at least three significant figures. Also, ode15s calls the derivative routine only 85 times (in contrast with the nonstiff integrator ode45 that requires approximately 5000 - 10000 calls, which clearly indicates the advantage of a stiff integrator for this problem).

• The MOL solution and its error (computed from the analytical solution) are plotted

 
% Plot numerical solution and errors at x = 1/2
  figure(1);
  subplot(1,2,1)
  plot(t,u_plot); axis tight
  title('u(0.5,t) vs t'); xlabel('t'); ylabel('u(0.5,t)')
  subplot(1,2,2)
  plot(t,err_plot); axis tight
  title('Err u(0.5,t) vs t'); xlabel('t'); ylabel('Err u(0.5,t)')
  print -deps pde.eps; print -dps pde.ps

The plotted error output below indicates that the error in the MOL solution varied between approximately \( -3\times 10^{-5} \) and \(16\times 10^{-5}\) which is not quite within the error range specified in the program

 

     reltol=1.0e-04; abstol=1.0e-04;

The fact that these error tolerances were not satisfied does not necessarily mean that ode15s failed to adjust the integration interval to meet these error tolerances. Rather, the error of approximately \( 1.6 \times 10^{-4} \) is due to the limited accuracy of the second order FD approximation of \( \frac{\partial ^2 u} {\partial x^2} \) programmed in pde_1 . This conclusion is confirmed when the main program calls pde_2 (for mf = 2 ) or pde_3 (for mf = 3 ) as discussed subsequently; these two routines have FD approximations that are more accurate than in pde_1 so the errors fall below the specified tolerances.

This analysis indicates that two sources of errors result from the MOL solution of PDEs such as eq. (1): (a) errors due to the integration in \( t \) (by ode15s ) and (b) errors due to the approximation of the spatial derivatives such as \( \frac{\partial ^2 u}{\partial x^2} \) programmed in the derivative routine such as pde_1 . In other words, we have to be attentive to integration errors in the initial and boundary value independent variables.

In summary, a comparison of the numerical and analytical solutions indicates that 21 grid points in \( x \) were not sufficient when using the second order FDs in pde_1 .

However, in general, we will not have an analytical solution such as eq. (42) to determine if the number of spatial grid points is adequate. In this case, some experimentation with the number of grid points, and the observation of the resulting solutions to infer the degree of accuracy or spatial convergence, may be required.

• A 3D plot is also produced

 
% Plot numerical solution in 3D perspective
  figure(2);
  colormap('Gray');
  C=ones(n);
  g=linspace(0,1,n); % For distance x
  h1 = waterfall(t,g,u',C);
  axis('tight');
  grid off
  xlabel('t, time')
  ylabel('x, distance')
  zlabel('u(x,t)')
  s1 = sprintf('Diffusion Equation - MOL Solution');
  sTmp = sprintf('u(x,0) = sin(\\pi x/2 )');
  s2 = sprintf('Initial condition: %s', sTmp);
  title([{s1}, {s2}], 'fontsize', 12);
  v = [0.8616   -0.5076    0.0000   -0.1770
       0.3712    0.6301    0.6820   -0.8417
       0.3462    0.5876   -0.7313    8.5590
            0         0         0    1.0000];
  view(v);
  rotate3d on;

The plotted output below clearly indicates the origin of the lines in the method of lines

The programming of the approximating MOL/ODEs is in one of the three routines called by ode15s . We now consider each of these routines. For mf = 1, pde_1 calls function ut=pde_1(t,u)

 

function ut=pde_1(t,u)
%
% Problem parameters
  global ncall
  xl=0.0;
  xu=1.0;
%
% PDE
  n=length(u);
  dx2=((xu-xl)/(n-1))^2;
  for i=1:n
    if(i==1)     ut(i)=0.0;
    elseif(i==n) ut(i)=2.0*(u(i-1)-u(i))/dx2;
    else         ut(i)=(u(i+1)-2.0*u(i)+u(i-1))/dx2;
    end
  end
  ut=ut';
%
% Increment calls to pde_1
  ncall=ncall+1;

We can note the following points about tt pde_1 :

• Some problem parameters are first defined

 

function ut=pde_1(t,u)
%
% Problem parameters
  global ncall
  xl=0.0;
  xu=1.0;

xl and xu could have also been set in the main program and passed to pde_1 as global variables. The defining statement at the beginning of pde_1 indicates that the independent variable t and dependent variable vector u are inputs to pde_1 , while the output is the vector of tt t dervatives, ut ; in other words, all of the n ODE derivatives in t must be defined in pde_1 .

• The finite difference approximation of eq. (1) is then programmed

 
% PDE
  n=length(u);
  dx2=((xu-xl)/(n-1))^2;
  for i=1:n
    if(i==1)     ut(i)=0.0;
    elseif(i==n) ut(i)=2.0*(u(i-1)-u(i))/dx2;
    else         ut(i)=(u(i+1)-2.0*u(i)+u(i-1))/dx2;
  end
  end
  ut=ut';

The number of ODEs (21) is determined by the length command n=length(u); so that the programming is general (the number of ODEs can easily be changed in the main program). The square of the FD interval, dx2 , is then computed.

• The MOL programming of the 21 ODEs is done in the for loop. For BC

(3), the coding is

 

    if(i==1) ut(i)=0.0;

since the value of \( u(x=0,t) = 0 \) does not change after being set as an initial condition in the main program (and therefore its time derivative is zero).

• For BC (4), the coding is

 

    elseif(i==n) ut(i)=2.0*(u(i-1)-u(i))/dx2;

which follows directly from the FD approximation of BC (4) (eq. (37))

\[ u_x \approx \frac{u(i+1) - u(i-1)}{\Delta x} = 0 \]

or with \( i = n \)

\[ u(n+1) = u(n-1) \]

Note that the fictitious value \(u(n+1) \) can then be replaced in the ODE at \(i=n\) by \(u(n-1)\).

• For the remaining interior points, the programming is

\begin{verbatim} else ut(i)=(u(i+1)-2.0*u(i)+u(i-1))/dx2; \end{verbatim}

\end{itemize}

which follows from the FD approximation of the second derivative (eq. (33))

\begin{equation*} u_{xx} \approx \frac{(u(i+1) - 2u(i) + u(i-1))}{\Delta x^2} \end{equation*}

\begin{itemize} \item Since the Matlab ODE integrators require a column vector of derivatives, a final transpose of {\tt ut} is required \begin{verbatim} ut=ut'; % % Increment calls to pde_1 ncall=ncall+1; \end{verbatim} \end{itemize}

Finally, the number of calls to {\tt pde\_1} is incremented so that at the end of the solution, the value of {\tt ncall} displayed by the main program gives an indication of the computational effort required to produce the entire solution. The numerical and graphical output for this case ({\tt mf = 1}) was discussed subsequently.

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