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====== Differences ====== This shows you the differences between two versions of the page.

--- gibson:teaching:spring-2014:iam950:hw1 [2014/02/24 19:22]
gibson
+++ gibson:teaching:spring-2014:iam950:hw1 [2014/02/27 09:39] (current)
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@@ Line 1: / Line 1: @@
 ====== IAM 950 HW1  ======
-**1.** Write numerical simulation for the heat eqn $u_t = \nu u_{xx}$ on a periodic domain $[-L/2,L/2]$ using
+**Problem 1.** Write numerical simulation for the heat eqn $u_t = \nu u_{xx}$ on a periodic domain
-Fourier spatial discretization and Crank-Nicolson temporal discretization. Verify that your code works
+$[-L_x/2, L_x/2]$ using Fourier spatial discretization and Crank-Nicolson temporal discretization.
-correctly by simulating the Gaussian-decay solution
+Verify that your code works correctly by simulating the Gaussian-decay solution
 <latex>
@@ Line 9: / Line 9: @@
 </latex>
-from t = 1 to 100 for parameters $\nu$ = 2, L=100, dt = 1/16, and 128 gridpoints.
+from t = 1 to 100 for parameters $\nu = 2$, Lx=100, dt=1/16, Nx=128 (number of x gridpoints).
-That is, initialize your numerical code with u(x,1) and then compare the results of the
+Initialize your numerical code with u(x,1) and then compare the results of the
 numerical time integration with u(x,t) evaluated from the above formula. I suggest plotting
-both quantities versus x at regular intervals in time.
+both quantities versus x at regular intervals in time during the simulation, using "pause" (matlab)
+to stop the simulation temporarily.
 Make a plot of the numerical solution and the Gaussian solution versus x at t=100 to turn in.
 Are the two entirely consistent? If not, why not?
-**2.** Write a numerical simulation for the 1d Swift-Hohenberg equation, $u_t = (r-1) u - u_{xx} - u_{xxxx}$ on
+**Problem 2.** Copy and revise your heat equation code so that it simulated the 1d Swift-Hohenberg equation,
-a periodic domain $[-L/2, L/2]$ using Fourier spatial discretization and Crank-Nicolson/Adams-Bashforth semi-implicit temporal discretization. The above heat equation code is a good starting point.
+$u_t = (r-1) u - 2 u_{xx} - u_{xxxx} - u^3$  on a periodic domain $[0,L_x]$ using Fourier spatial
+discretization and Crank-Nicolson/Adams-Bashforth semi-implicit temporal discretization. Use the
+initial condition $u(x,0) = \cos(ax) + 0.1 \cos(2ax)$ where $a=2\pi/L_x$, parameter values
+r = 0.2, Lx = 100, Nx = 256 (number of x gridpoints), dt = 1/16, and integrate from t=0 to 100.
-**3.** Write a numerical simulation for the 1d Kuramoto-Sivashinsky equation, $u_t = - u_{xx} - u_{xxxx}- u u_x$
+Make a colorplot of $u(x,t)$ in the $x,t$ plane (using every x gridpoint but plotting t at a larger
-on a periodic domain $[-L/2, L/2]$ using Fourier spatial discretization and Crank-Nicolson/Adams-Bashforth semi-implicit temporal discretization. This should be a very minor modification of the above Swift-Hohenberg code.
+interval than dt, perhaps at intervals of 1/2 or 1. It should look something like this
+{{:gibson:teaching:spring-2014:iam950:sh.png?direct&400|Simulations of Swift-Hohenberg eqn}}
+When you are satisfied with the correctness of your simulation, make a plot of $u(x,100)$ versus x for
+the above parameters and initial conditions to turn in.
+Now play around with the forcing parameter in the range 0 < r < 1 and the initial condition,
+by changing the wavelengths and magnitudes of the the cosines, and possibly the total integration
+time, if you don't see patterns develop by t=100. Answer the following questions
+  - Does the wavelength of the final pattern depend on r or the initial condition?
+  - Does the wavelength of the final pattern match you expectation from linear analysis?
+  - Can you relate the amplitude of the final pattern or the time scale of its growth from the initial condition to the Swift-Hohenberg equation?
+**Problem 3.** Copy and revise your Swift-Hohenberg code so that it simulates the 1d Kuramoto-Sivashinsky equation, $u_t = - u_{xx} - u_{xxxx}- u u_x$, with parameters Lx = 100, Nx = 512, and dt = 1/16,
+initial condition $u(x,0) = \cos(ax) + 0.1 \cos(2ax)$ (same as before), and integrate from t=0 to t=100. Make a colorplot of $u(x,t)$ as before. It should look like this
+{{:gibson:teaching:spring-2014:iam950:ks.png?direct&400|Kuramoto-Sivashinsky simulation}}
+Make a plot of $u(x,100)$ versus x to turn in. Now play around with the domain size Lx (adjusting
+Nx so that Lx/Nx is approximately constant), try replacing the initial condition with random numbers
+of order 0.01, and answer the following questions
+  - Does the wavelength of the final pattern depend on r or the initial condition?
+  - Does the wavelength of the final pattern match you expectation from linear analysis?
+  - What qualitative changes in behavior do you observe as L increases from small (L=1) to large (L=100)?
+**Problem 4.** For each of the above codes, fix the initial condition and parameters, and run the simulation
+with dt = 1/8, 1/4, .... What happens? Why?
+**Some help on Matlab's FFT:** Matlab documentation is not very helpful about the ordering or normalization of Fourier coefficients in the FFT. However, a small numerical experiment clarifies. Let's construct a function with energy in known wavenumbers and observe where nonzero coefficients appear in the numerical FFT. I'll do 8 gridpoints for the periodic domain [0, 2pi].
+<code>
+>> x = 1/8*(0:7)*2*pi
+x =
+    0.7854    1.5708    2.3562    3.1416    3.9270    4.7124    5.4978
+>> k = 0;
+>> u = cos(k*x) + 0.1*sin(k*x);
+>> [real(fft(u)); imag(fft(u))]
+ans =
+     0     0     0     0     0     0     0
+     0     0     0     0     0     0     0
+>> k = 1;
+>> u = cos(k*x) + 0.1*sin(k*x);
+>> [real(fft(u)); imag(fft(u))]
+ans =
+   -0.0000    4.0000    0.0000         0    0.0000         0    0.0000    4.0000
+   -0.4000         0   -0.0000         0    0.0000         0    0.4000
+>> k = 2;
+>> u = cos(k*x) + 0.1*sin(k*x);
+>> [real(fft(u)); imag(fft(u))]
+ans =
+   -0.0000   -0.0000    4.0000    0.0000    0.0000    0.0000    4.0000   -0.0000
+         0   -0.4000         0         0         0    0.4000         0
+>> k = 3;
+>> u = cos(k*x) + 0.1*sin(k*x);
+>> [real(fft(u)); imag(fft(u))]
+ans =
+   -0.0000    0.0000   -0.0000    4.0000    0.0000    4.0000   -0.0000    0.0000
+   -0.0000    0.0000   -0.4000         0    0.4000   -0.0000    0.0000
+>> k = 4;
+>> u = cos(k*x) + 0.1*sin(k*x);
+>> [real(fft(u)); imag(fft(u))]
+ans =
+         0         0         0    8.0000         0         0         0
+    0.0000         0    0.0000         0   -0.0000         0   -0.0000
+</code>
+So the ordering of Fourier modes in the FFT for N=8 is k = [0 1 2 3 -4 -3 -2 -1]
+--not what I suggested it would be in class. The normalization is also different:
+the kth and -kth FFT coefficients for u(x) = cos(kx) are not 1/2, but N/2.
+Also observe that the treatment of the highest-order Fourier mode k=-4 is a little weird,
+in that the 8 represents the sum of the k=4 and k=-4 cosine modes, with no representation
+of the k=4 and k=-4 sin modes. Without going into too much detail on this, Fourier spectral
+PDE codes typically just zero the highest-order mode at every time step. We can ignore this
+subtlety.
+Based on the above, here's a short example of how to do Fourier differentiation in Matlab on
+a [0,L] periodic domain. This should be plenty to get you started for the time-stepping codes.
+<code>
+% A demonstration of Fourier differentiation in Matlab
+% Parameters
+L = 10;
+N = 16;
+x = L/N*(0:N-1);
+% Set up u(x) and du/dx for some known L-periodic function
+a = 2*pi/L
+u    =   sin(a*x) + 0.2*cos(a*2*x);
+dudx = a*cos(a*x) - 0.4*a*sin(a*2*x);
+% Now compute dudx via FFT
+k = [0:N/2-1 -N/2:-1];
+alpha = 2*pi*k/L;
+D = i*alpha;
+dudx_fft = real(ifft(D.*fft(u)));
+% Plot everybody
+plot(x,u, 'b', x,dudx, 'g', x, dudx_fft, 'r.')
+legend('u','du/dx','du/dx via FFT','location','southwest')
+xlabel('x')
+</code>

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