src/examples/ginzburg-landau.c

    The complex Ginzburg–Landau equation

    The complex Ginzburg–Landau equation \displaystyle \partial_t A = A + \left( 1 + i \alpha \right) \nabla^2 A - \left( 1 + i \beta \right) \left| A \right|^2 A with A a complex number, is a classical model for phenomena exhibiting Hopf bifurcations such as Rayleigh-Bénard convection or superconductivity.

    Posing A_r=Re(A) and A_i=Im(A) one gets the coupled reaction–diffusion equations. \displaystyle \partial_t A_r = \nabla^2 A_r + A_r \left( 1 - \left| A \right|^2 \right) - \alpha \nabla^2 A_i + \left| A \right|^2 \beta A_i \displaystyle \partial_t A_i = \nabla^2 A_i + A_i \left( 1 - \left| A \right|^2 \right) + \alpha \nabla^2 A_r - \left| A \right|^2 \beta A_r

    This system can be solved with the reaction–diffusion solver.

    #include "grid/multigrid.h"
#include "run.h"
#include "diffusion.h"

scalar Ar[], Ai[], A2[];

    In this example, we only consider the case when \alpha=0.

    double beta;

    The generic time loop needs a timestep. We will store the statistics on the diffusion solvers in mgd1 and mgd2.

    double dt;
mgstats mgd1, mgd2;

    Parameters

    We change the size of the domain L0.

    int main() {
  beta = 1.5;
  size (100);
  init_grid (256);
  run();
}

    Initial conditions

    We use a white noise in [-10^{-4}:10^{-4}] for both components.

    event init (i = 0) {
  foreach() {
    Ar[] = 1e-4*noise();
    Ai[] = 1e-4*noise();
  }
}

    Time integration

    event integration (i++) {

    We first set the timestep according to the timing of upcoming events. We choose a maximum timestep of 0.05 which ensures the stability of the reactive terms for this example.

      dt = dtnext (0.05);

    We compute |A|^2.

      foreach()
    A2[] = sq(Ar[]) + sq(Ai[]);

    We use the diffusion solver (twice) to advance the system from t to t+dt.

      scalar r[], lambda[];
  foreach() {
    r[] = A2[]*beta*Ai[];
    lambda[] = 1. - A2[];
  }
  mgd1 = diffusion (Ar, dt, r = r, beta = lambda);
  foreach() {
    r[] = - A2[]*beta*Ar[];
    lambda[] = 1. - A2[];
  }
  mgd1 = diffusion (Ai, dt, r = r, beta = lambda);
}

    Outputs

    Here we create MP4 animations for both components. The spread parameter sets the color scale to \pm twice the standard deviation.

    event movies (i += 3; t <= 150) {
  fprintf (stderr, "%g %g\n", t, sqrt(normf(A2).max));

  output_ppm (Ai, spread = 2, linear = true, file = "Ai.mp4");
  output_ppm (A2, spread = 2, linear = true, file = "A2.mp4");
}

    For the value of \beta we chose, we get a typical “frozen state” composed of “cellular structures” for |A|^2 and stationary spirals for A_i.
    |A|^2 A_i
    Evolution of the norm and imaginary part

    See also