previous up next SYLLABUS  Previous: 2 FINITE DIFFERENCES  Up: 2 FINITE DIFFERENCES  Next: 2.2 Explicit 3 levels


2.1 Explicit 2 levels


Slide : [ 2 levels Scheme - Code - Run - Stability - Upwind || VIDEO login]

A spatial difference to the left for the advection (1.3.1#eq.1) and a centered difference for the diffusion (1.3.2#eq.1) for example yields an explicit scheme for the advection-diffusion equation that involves only two time levels

Figure 2.1#fig.1: Explicit 2 levels.
\includegraphics[width=4cm]{figs/FDe2lvl.eps}

$\displaystyle \frac{f_j^{t+\Delta t} -f_j^t}{\Delta t}$ $\displaystyle +$ $\displaystyle u \frac{f_j^t - f_{j-1}^t}{\Delta x}
- D \frac{f_{j+1}^t -2f_j^t +f_{j-1}^t}{\Delta x^2} = 0$  
$\displaystyle f_j^{t+\Delta t}$ $\displaystyle =$ $\displaystyle f_j^t
- \beta\left[ f_j^t - f_{j-1}^t \right]
+ \alpha\left[ f_{j+1}^t -2f_j^t +f_{j-1}^t\right]$ (2.1.0#eq.1)

The Courant-Friedrichs-Lewy (CFL) number $ \beta=u\Delta t/\Delta x$ and the coefficient $ \alpha=D\Delta t/\Delta x^2$ measure typical advection and diffusion velocities relative to the characteristic propagation speed of the mesh $ \Delta x/\Delta t$ . For every step in time, a new value of the solution fp[j] is obtained explicitly from a linear combination involving the nearest neighbors f[j-1],f[j],f[j+1], which are known either from a previous step or the initial condition. This scheme has been implemented in JBONE as
      for (int j=1; j<n; j++) {
        fp[j]=f[j] -beta *(f[j]-f[j-1])+alpha*(f[j+1]-2.*f[j]+f[j-1]); }
      fp[0]=f[0]   -beta *(f[0]-f[ n ])+alpha*(f[ 1 ]-2.*f[0]+f[ n ]);
      fp[n]=f[n]   -beta *(f[n]-f[n-1])+alpha*(f[ 0 ]-2.*f[n]+f[n-1]);
where the last two statements take care of the periodicity.

Sharp variations of the solution should be AVOIDED to produce physically meaningful results; to study the numerical properties of a approximation, it is however often illuminating to initialize a box function and check how the intrinsic numerical dispersion / damping of a scheme affects the superposition of both the short and the long wavelengths. The example below shows the result of an evolution with a constant advection $ u=1$ and no physical diffusion $ D=\alpha=0$ .

JBONE applet:  press Start/Stop to simulate the advection of a box function and to test how the explicit 2 levels scheme affects the dispersion and the damping of short and long wavelengths superposed in a box function.

After 128 steps of duration $ \Delta t=0.5$ , the pulse propagates exactly once accross the periodic domain, which is here discretized with 64 mesh points homogeneously distributed over the length $ L=64$ so that $ \Delta x=1$ . The lowest order moment (density, (1.2.5#eq.1)) is conserved to a very good accuracy (as can be judged from the JBONE monitor under Mom[%]) and the function remains positive everywhere as it should(as can be judged from Min) . The shape, however, is strongly affected as the sharp edges are smoothed out due intrinsic numerical damping.



Numerical experiments: explicit 2-levels
  1. Change the initial condition from Box to Cosine, and vary the wavelength $ \lambda$ (parameter ICWavelength) from 2-64 mesh points per wavelength to verify that it is the short wavelengths associated with steep edges that are heavily damped: exactly what you expect from diffusion (1.3.2#eq.4) except that with $ D=0$ , this is here a numerical artifact. Without special care, numerical damping can easily cover the physical process you want to model!
  2. Looking for a quick fix, you reduce the time step and the CFL number from $ \beta=0.5$ down 0.1. What happens ? Adding to the confusion, increase the time step to exactly 1 and check what happens.

The properties of a FD approximation are best understood from a numerical dispersion / Von Neuman stability analysis, which is carried out on a homogeneous grid $ x_j=j\Delta x$ using a hamonic ansatz $ f \sim F=\exp(i[kx-\omega t])$ . Define the amplification factor as

$\displaystyle G$ $\displaystyle =$ $\displaystyle \exp(-i\omega\Delta t)$ (2.1.0#eq.2)

and simplify by $ F$ to cast the dispersion relation into
$\displaystyle G$ $\displaystyle =$ $\displaystyle 1-\beta \left[1-\exp(-ik\Delta x)\right]
+\alpha\left[\exp(+ik\Delta x)-2+\exp(-ik\Delta x)\right]$  
  $\displaystyle =$ $\displaystyle 1- \beta \left[1-\exp(-ik\Delta x)\right]
-4\alpha\sin^2\left(\frac{k\Delta x}{2}\right)$ (2.1.0#eq.3)

Figure 2.1#fig.2 illustrates with vectors in the complex plane how the first three term are combined in the presence of advection only ($ \alpha=0$ ).
Figure 2.1#fig.2: Numerical dispersion / stability (explicit 2 levels).
\includegraphics[width=5cm]{figs/DSPFDe2lvl.eps}
Short wavelengths $ k\Delta x \simeq \pi/2$ are heavily damped $ \vert G\vert<1$ (area inside the outer circle) if the CFL number is smaller than unity $ \beta<1$ ; for values exceeding the magic time step where $ \beta=1$ , the short wavelengths however grow into a so-called numerical instability. Reload the initial applet parameters and try to reproduce this in JBONE.



Numerical experiments: explicit 2-levels (cont)
  1. Increase the TimeStep=1.01 and observe the slowly growing mode with 2 mesh points per wavelength $ \lambda=2\Delta x$ , i.e. $ k=2\pi/\lambda=\pi/\Delta x$ .
  2. Starting from the initial applet parameters, take two steps using a Cosine with ICWavelength=8 mesh points per wavelength to determine the value of the physical Diffusion that approximatively doubles the damping observed when $ D=0$ - e.g. doubling the value of the Min value displayed in the applet.
  3. Choose the value of the numerical parameters (MeshPoints, TimeStep) to compute a physical evolution of a Gaussian pulse with a ICWidth=2.5 when the advection Velocity=1 and the Diffusion=0.1 is relatively small. Make sure that the numerical dispersion and damping are both small in comparison with the physical effect you try to reproduce.

For a diffusive process ($ \beta=0$ ), the dispersion relation (2.1#eq.3) shows that the 2 levels scheme is stable for all wavelengths provided that $ \Vert G\Vert=\Vert 1-4\alpha\sin^2(\pi/2)\Vert<1$ , i.e. $ \alpha<1/2$ . Although the superposition of advection and diffusion is slightly more limiting, the overall conditions for numerical stability can conveniently be summarized as

$\displaystyle \alpha$ $\displaystyle =$ $\displaystyle \frac{D\Delta t}{\Delta x^2} < \frac{1}{2}$  
$\displaystyle \beta$ $\displaystyle =$ $\displaystyle \frac{u\Delta t}{\Delta x} < 1 \qquad\qquad \textup{(CFL condition)}$ (2.1.0#eq.4)

It is finally worth pointing out that a backward finite difference for the advection is unstable for negative velocities $ u<0$ ; this defect of non-centered schemes is sometimes cured with an upwind difference, which takes the finite difference forward or backward depending on the local direction of propagation (exercise 2.01).

SYLLABUS  Previous: 2 FINITE DIFFERENCES  Up: 2 FINITE DIFFERENCES  Next: 2.2 Explicit 3 levels

      
back up next contents bibliography Copyright © Lifelong-learners at 09:11:15, November 18th, 2017