2.5 Sample Applications
Subsections
2.5.1 Thermal Conduction in 1D
Again, discretize the equation of thermal conduction,
$
\begin{eqnarray}
\frac{\textstyle \partial T(x,t)}{\textstyle \partial t} &=&
\lambda \frac{\textstyle \partial^{2} T(x,t)}{\textstyle \partial x^{2}}
\end{eqnarray}
$
Earlier we applied DNGF to the l.h.s. and DDST
at time $ t_{n}$ to the r.h.s.:
$
\begin{eqnarray}
\frac{\textstyle \partial T(x,t)}{\textstyle \partial x^{2}} &\approx&
\frac{\textstyle \delta_{i}^{2}T_{i}^{n}}{\textstyle (\Delta x)^{2}}
\end{eqnarray}
$
In this manner we arrived at the "FTCS"-formula.
Now we may use the DDST formula at time $t_{n+1}$,
$
\begin{eqnarray}
\frac{\textstyle \partial T(x,t)}{\textstyle \partial x^{2}} & \approx &
\frac{\textstyle \delta_{i}^{2}T_{i}^{n+1}}{\textstyle (\Delta x)^{2}}
\end{eqnarray}
$
This leads us to the "implicit scheme of first order"
$
\begin{eqnarray}
&&
\frac{\textstyle 1}{\textstyle \Delta t} [T_{i}^{n+1}-T_{i}^{n}]
=\frac{\textstyle \lambda}{\textstyle (\Delta x)^{2}}
[T_{i+1}^{n+1}-2T_{i}^{n+1}+T_{i-1}^{n+1}]
\end{eqnarray}
$
which may be written, using
$a \equiv \lambda\Delta t/(\Delta x)^{2}$,
$
\begin{eqnarray}
-a T_{i-1}^{n+1}+(1+2a)T_{i}^{n+1}-aT_{i+1}^{n+1}=T_{i}^{n}
\end{eqnarray}
$
or
$
\begin{eqnarray}
&&
A \cdot T^{n+1} = T^{n}
\end{eqnarray}
$
where (for fixed $T_{0}$ and $T_{N}$)
$
\begin{eqnarray}
&&
A \equiv
\left(
\begin{array}{cccccc}
1 & 0 & 0 & . & . & 0 \\
-a & 1+2a & -a & 0 & . & 0 \\
0 & . & . & . & 0 & . \\
. & . & . & . & . & . \\
. & . & . & 0 & 0 & 1
\end{array}
\right)
\end{eqnarray}
$
This tridiagonal system may be solved by the Recursion Method.
EXERCISE:
(See also
here)
Redo the earlier exercise on One-dimensional thermal conduction
by applying the implicit scheme in place of the FTCS method. Use various values of
$\Delta t$ (and therefore $a$.) Compare the efficiencies and stabilities
of the two methods.
2.5.2 Potential Equation in 2D
Discretize the elliptic PDE
$
\begin{eqnarray}
&& \frac{\textstyle \partial^{2}u}{\textstyle \partial x^{2}}
+ \frac{\textstyle \partial^{2}u}{\textstyle \partial y^{2}}
= - \rho
\end{eqnarray}
$
to find
$
\begin{eqnarray}
\frac{\textstyle 1}{\textstyle (\Delta x)^{2}}
\left[ u_{i+1,j} - 2u_{i,j} + u_{i-1,j} +
u_{i,j+1} - 2u_{i,j} + u_{i,j-1} \right] & = & -\rho_{i,j}
\\
&& i=1,\dots N; j=1, \dots M
\end{eqnarray}
$
Combining the $N$ row vectors $\{u_{i,j};\;j=1, \dots M \}$
sequentially to a vector $v$ of length $N.M$
we may write these equations in the form
$
\begin{eqnarray}
&&A \cdot v = b
\end{eqnarray}
$
where $A$ is a sparse matrix, and where the vector $b$
contains the charge density $\rho$ and the given boundary
values of the potential function $u$.
Solve by applying any of the Relaxation Methods.
vesely
2005-10-10