$$ \newcommand{\half}{\frac{1}{2}} \newcommand{\halfi}{{1/2}} \newcommand{\uex}{{u_{\small\mbox{e}}}} \newcommand{\xpoint}{\boldsymbol{x}} \newcommand{\normalvec}{\boldsymbol{n}} \newcommand{\Oof}[1]{\mathcal{O}(#1)} \newcommand{\Ix}{\mathcal{I}_x} \newcommand{\Iy}{\mathcal{I}_y} \newcommand{\It}{\mathcal{I}_t} \newcommand{\setb}[1]{#1^0} % set begin \newcommand{\sete}[1]{#1^{-1}} % set end \newcommand{\setl}[1]{#1^-} \newcommand{\setr}[1]{#1^+} \newcommand{\seti}[1]{#1^i} \newcommand{\Real}{\mathbb{R}} $$

Study guide: Finite difference methods for wave motion

Hans Petter Langtangen [1, 2]

[1] Center for Biomedical Computing, Simula Research Laboratory
[2] Department of Informatics, University of Oslo

2016










Table of contents

Finite difference methods for waves on a string
      The complete initial-boundary value problem
      Input data in the problem
      Demo of a vibrating string (\( C=0.8 \))
      Demo of a vibrating string (\( C=1.0012 \))
      Step 1: Discretizing the domain
      The discrete solution
      Step 2: Fulfilling the equation at the mesh points
      Step 3: Replacing derivatives by finite differences
      Step 3: Algebraic version of the PDE
      Step 3: Algebraic version of the initial conditions
      Step 4: Formulating a recursive algorithm
      The Courant number
      The finite difference stencil
      The stencil for the first time level
      The algorithm
      Moving finite difference stencil
      Sketch of an implementation (1)
      PDE solvers should save memory
      Sketch of an implementation (2)
Verification
      A slightly generalized model problem
      Discrete model for the generalized model problem
      Modified equation for the first time level
      Using an analytical solution of physical significance
      Manufactured solution: principles
      Manufactured solution: example
      Testing a manufactured solution
      Constructing an exact solution of the discrete equations
      Analytical work with the PDE problem
      Analytical work with the discrete equations (1)
      Analytical work with the discrete equations (1)
      Testing with the exact discrete solution
Implementation
      The algorithm
      What do to with the solution?
      Making a solver function
      Verification: exact quadratic solution
      Visualization: animating \( u(x,t) \)
      Making movie files
      Running a case
      Implementation of the case
      Resulting movie for \( C=0.8 \)
      The benefits of scaling
Vectorization
      Operations on slices of arrays
      Test the understanding
      Vectorization of finite difference schemes (1)
      Vectorization of finite difference schemes (2)
      Vectorized implementation in the solver function
      Verification of the vectorized version
      Efficiency measurements
Generalization: reflecting boundaries
      Neumann boundary condition
      Discretization of derivatives at the boundary (1)
      Discretization of derivatives at the boundary (2)
      Visualization of modified boundary stencil
      Implementation of Neumann conditions
      Moving finite difference stencil
      Index set notation
      Index set notation in code
      Index sets in action (1)
      Index sets in action (2)
      Alternative implementation via ghost cells
      Implementation of ghost cells (1)
      Implementation of ghost cells (2)
Generalization: variable wave velocity
      The model PDE with a variable coefficient
      Discretizing the variable coefficient (1)
      Discretizing the variable coefficient (2)
      Discretizing the variable coefficient (3)
      Computing the coefficient between mesh points
      Discretization of variable-coefficient wave equation in operator notation
      Neumann condition and a variable coefficient
      Implementation of variable coefficients
      A more general model PDE with variable coefficients
      Generalization: damping
Building a general 1D wave equation solver
      Collection of initial conditions
Finite difference methods for 2D and 3D wave equations
      Examples on wave equations written out in 2D/3D
      Boundary and initial conditions
      Example: 2D propagation of Gaussian function
      Mesh
      Discretization
      Special stencil for the first time step
      Variable coefficients (1)
      Variable coefficients (2)
      Neumann boundary condition in 2D
Implementation of 2D/3D problems
      Algorithm
      Scalar computations: mesh
      Scalar computations: arrays
      Scalar computations: initial condition
      Scalar computations: primary stencil
      Vectorized computations: mesh coordinates
      Vectorized computations: stencil
      Verification: quadratic solution (1)
      Verification: quadratic solution (2)
Analysis of the difference equations
      Properties of the solution of the wave equation
      Demo of the splitting of \( I(x) \) into two waves
      Simulation of a case with variable wave velocity
      Let us change the shape of the initial condition slightly and see what happens
      Representation of waves as sum of sine/cosine waves
      A similar wave component is also a solution of the finite difference scheme (!)
      Preliminary results
      Insertion of the numerical wave component
      The equation for \( \tilde\omega \)
      The numerical dispersion relation
      The special case \( C=1 \) gives the exact solution
      Computing the error in wave velocity
      Visualizing the error in wave velocity
      Taylor expanding the error in wave velocity
      Example on effect of wrong wave velocity (1)
      Example on effect of wrong wave velocity (1)
      Stability
      Why \( C>1 \) leads to non-physical waves
      Extending the analysis to 2D (and 3D)
      Discrete wave components in 2D
      Stability criterion in 2D
      Stability criterion in 3D
      Numerical dispersion relation in 2D (1)
      Numerical dispersion relation in 2D (2)
      Numerical dispersion relation in 2D (3)









Finite difference methods for waves on a string

Waves on a string can be modeled by the wave equation $$ \frac{\partial^2 u}{\partial t^2} = c^2 \frac{\partial^2 u}{\partial x^2} $$

\( u(x,t) \) is the displacement of the string

Demo of waves on a string.









The complete initial-boundary value problem

$$ \begin{align} \frac{\partial^2 u}{\partial t^2} &= c^2 \frac{\partial^2 u}{\partial x^2}, \quad &x\in (0,L),\ t\in (0,T] \label{wave:pde1}\\ u(x,0) &= I(x), \quad &x\in [0,L] \label{wave:pde1:ic:u}\\ \frac{\partial}{\partial t}u(x,0) &= 0, \quad &x\in [0,L] \label{wave:pde1:ic:ut}\\ u(0,t) & = 0, \quad &t\in (0,T] \label{wave:pde1:bc:0}\\ u(L,t) & = 0, \quad &t\in (0,T] \label{wave:pde1:bc:L} \end{align} $$









Input data in the problem

Rule for number of initial and boundary conditions:







Demo of a vibrating string (\( C=0.8 \))









Demo of a vibrating string (\( C=1.0012 \))

Ooops!









Step 1: Discretizing the domain

Mesh in time: $$ \begin{equation} 0 = t_0 < t_1 < t_2 < \cdots < t_{N_t-1} < t_{N_t} = T \label{_auto1} \end{equation} $$

Mesh in space: $$ \begin{equation} 0 = x_0 < x_1 < x_2 < \cdots < x_{N_x-1} < x_{N_x} = L \label{_auto2} \end{equation} $$

Uniform mesh with constant mesh spacings \( \Delta t \) and \( \Delta x \): $$ \begin{equation} x_i = i\Delta x,\ i=0,\ldots,N_x,\quad t_i = n\Delta t,\ n=0,\ldots,N_t \label{_auto3} \end{equation} $$









The discrete solution









Step 2: Fulfilling the equation at the mesh points

Let the PDE be satisfied at all interior mesh points: $$ \begin{equation} \frac{\partial^2}{\partial t^2} u(x_i, t_n) = c^2\frac{\partial^2}{\partial x^2} u(x_i, t_n), \label{wave:pde1:step2} \end{equation} $$ for \( i=1,\ldots,N_x-1 \) and \( n=1,\ldots,N_t-1 \).

For \( n=0 \) we have the initial conditions \( u=I(x) \) and \( u_t=0 \), and at the boundaries \( i=0,N_x \) we have the boundary condition \( u=0 \).









Step 3: Replacing derivatives by finite differences

Widely used finite difference formula for the second-order derivative: $$ \frac{\partial^2}{\partial t^2}u(x_i,t_n)\approx \frac{u_i^{n+1} - 2u_i^n + u^{n-1}_i}{\Delta t^2}= [D_tD_t u]^n_i$$

and $$ \frac{\partial^2}{\partial x^2}u(x_i,t_n)\approx \frac{u_{i+1}^{n} - 2u_i^n + u^{n}_{i-1}}{\Delta x^2} = [D_xD_x u]^n_i $$









Step 3: Algebraic version of the PDE

Replace derivatives by differences: $$ \begin{equation} \frac{u_i^{n+1} - 2u_i^n + u^{n-1}_i}{\Delta t^2} = c^2\frac{u_{i+1}^{n} - 2u_i^n + u^{n}_{i-1}}{\Delta x^2}, \label{wave:pde1:step3b} \end{equation} $$

In operator notation: $$ \begin{equation} [D_tD_t u = c^2 D_xD_x]^{n}_i \label{wave:pde1:step3a} \end{equation} $$









Step 3: Algebraic version of the initial conditions

$$ [D_{2t} u]^n_i = 0,\quad n=0\quad\Rightarrow\quad u^{n-1}_i=u^{n+1}_i,\quad i=0,\ldots,N_x$$

The other initial condition \( u(x,0)=I(x) \) can be computed by $$ u_i^0 = I(x_i),\quad i=0,\ldots,N_x$$









Step 4: Formulating a recursive algorithm

Write out \( [D_tD_t u = c^2 D_xD_x]^{n}_i \) and solve for \( u^{n+1}_i \), $$ \begin{equation} u^{n+1}_i = -u^{n-1}_i + 2u^n_i + C^2 \left(u^{n}_{i+1}-2u^{n}_{i} + u^{n}_{i-1}\right) \label{wave:pde1:step4} \end{equation} $$









The Courant number

$$ \begin{equation} C = c\frac{\Delta t}{\Delta x}, \label{_auto4} \end{equation} $$ is known as the (dimensionless) Courant number

Observe.

There is only one parameter, \( C \), in the discrete model: \( C \) lumps mesh parameters \( \Delta t \) and \( \Delta x \) with the only physical parameter, the wave velocity \( c \). The value \( C \) and the smoothness of \( I(x) \) govern the quality of the numerical solution.









The finite difference stencil









The stencil for the first time level

Initial condition: $$ [D_{2t}u=0]^0_i\quad\Rightarrow\quad u^{-1}_i=u^1_i$$

Insert in stencil \( [D_tD_tu = c^2D_xD_x]^0_i \) to get $$ \begin{equation} u_i^1 = u^0_i + \half C^2\left(u^{n}_{i+1}-2u^{n}_{i} + u^{n}_{i-1}\right) \label{wave:pde1:step4:1} \end{equation} $$









The algorithm

  1. Compute \( u^0_i=I(x_i) \) for \( i=0,\ldots,N_x \)
  2. Compute \( u^1_i \) by \eqref{wave:pde1:step4:1} and set \( u_i^1=0 \) for the boundary points \( i=0 \) and \( i=N_x \), for \( n=1,2,\ldots,N-1 \),
  3. For each time level \( n=1,2,\ldots,N_t-1 \)

    1. apply \eqref{wave:pde1:step4} to find \( u^{n+1}_i \) for \( i=1,\ldots,N_x-1 \)
    2. set \( u^{n+1}_i=0 \) for the boundary points \( i=0 \), \( i=N_x \).








Moving finite difference stencil

web page or a movie file.









Sketch of an implementation (1)

Naming convention.

u is the unknown to be computed (a spatial mesh function), u_1 denotes the latest computed time level, u_2 corresponds to one time step back in time.









PDE solvers should save memory

Important to minimize the memory usage.

The algorithm only needs to access the three most recent time levels, so we need only three arrays for \( u_i^{n+1} \), \( u_i^n \), and \( u_i^{n-1} \), \( i=0,\ldots,N_x \). Storing all the solutions in a two-dimensional array of size \( (N_x+1)\times (N_t+1) \) would be possible in this simple one-dimensional PDE problem, but not in large 2D problems and not even in small 3D problems.









Sketch of an implementation (2)

# Given mesh points as arrays x and t (x[i], t[n])
dx = x[1] - x[0]
dt = t[1] - t[0]
C = c*dt/dx            # Courant number
Nt = len(t)-1
C2 = C**2              # Help variable in the scheme

# Set initial condition u(x,0) = I(x)
for i in range(0, Nx+1):
    u_1[i] = I(x[i])

# Apply special formula for first step, incorporating du/dt=0
for i in range(1, Nx):
    u[i] = u_1[i] + 0.5*C**2(u_1[i+1] - 2*u_1[i] + u_1[i-1])
u[0] = 0;  u[Nx] = 0   # Enforce boundary conditions

# Switch variables before next step
u_2[:], u_1[:] = u_1, u

for n in range(1, Nt):
    # Update all inner mesh points at time t[n+1]
    for i in range(1, Nx):
        u[i] = 2*u_1[i] - u_2[i] + \ 
               C**2(u_1[i+1] - 2*u_1[i] + u_1[i-1])

    # Insert boundary conditions
    u[0] = 0;  u[Nx] = 0

    # Switch variables before next step
    u_2[:], u_1[:] = u_1, u









Verification









A slightly generalized model problem

Add source term \( f \) and nonzero initial condition \( u_t(x,0) \): $$ \begin{align} u_{tt} &= c^2 u_{xx} + f(x,t), \label{wave:pde2}\\ u(x,0) &= I(x), \quad &x\in [0,L] \label{wave:pde2:ic:u}\\ u_t(x,0) &= V(x), \quad &x\in [0,L] \label{wave:pde2:ic:ut}\\ u(0,t) & = 0, \quad & t>0, \label{wave:pde2:bc:0}\\ u(L,t) & = 0, \quad &t>0 \label{wave:pde2:bc:L} \end{align} $$









Discrete model for the generalized model problem

$$ \begin{equation} [D_tD_t u = c^2 D_xD_x + f]^{n}_i \label{wave:pde2:fdop} \end{equation} $$

Writing out and solving for the unknown \( u^{n+1}_i \): $$ \begin{equation} u^{n+1}_i = -u^{n-1}_i + 2u^n_i + C^2 (u^{n}_{i+1}-2u^{n}_{i} + u^{n}_{i-1}) + \Delta t^2 f^n_i \label{wave:pde2:step3b} \end{equation} $$









Modified equation for the first time level

Centered difference for \( u_t(x,0) = V(x) \): $$ [D_{2t}u = V]^0_i\quad\Rightarrow\quad u^{-1}_i = u^{1}_i - 2\Delta t V_i,$$

Inserting this in the stencil \eqref{wave:pde2:step3b} for \( n=0 \) leads to $$ \begin{equation} u^{1}_i = u^0_i - \Delta t V_i + {\half} C^2 \left(u^{n}_{i+1}-2u^{n}_{i} + u^{n}_{i-1}\right) + \half\Delta t^2 f^n_i \label{wave:pde2:step3c} \end{equation} $$









Using an analytical solution of physical significance

$$ \begin{equation} \uex(x,y,t)) = A\sin\left(\frac{\pi}{L}x\right) \cos\left(\frac{\pi}{L}ct\right) \label{wave:pde2:test:ue} \end{equation} $$







Manufactured solution: principles









Manufactured solution: example

$$ \uex(x,t) = x(L-x)\sin t$$

PDE \( u_{tt}=c^2u_{xx}+f \): $$ -x(L-x)\sin t = -2\sin t + f\quad\Rightarrow f = (2 - x(L-x))\sin t$$

Implied initial conditions: $$ \begin{align*} u(x,0) &= I(x) = 0\\ u_t(x,0) &= V(x) = - x(L-x) \end{align*} $$

Boundary conditions: $$ u(0,t) = u(L,t) = 0 $$









Testing a manufactured solution









Constructing an exact solution of the discrete equations









Analytical work with the PDE problem

Here, choose \( \uex \) such that \( \uex(0,t)=\uex(L,t)=0 \): $$ \uex (x,t) = x(L-x)(1+{\half}t), $$

Insert in the PDE and find \( f \): $$ f(x,t)=2(1+t)c^2$$

Initial conditions: $$ I(x) = x(L-x),\quad V(x)={\half}x(L-x) $$









Analytical work with the discrete equations (1)

We want to show that \( \uex \) also solves the discrete equations!

Useful preliminary result: $$ \begin{align} \lbrack D_tD_t t^2\rbrack^n &= \frac{t_{n+1}^2 - 2t_n^2 + t_{n-1}^2}{\Delta t^2} = (n+1)^2 -n^2 + (n-1)^2 = 2 \label{_auto5}\\ \lbrack D_tD_t t\rbrack^n &= \frac{t_{n+1} - 2t_n + t_{n-1}}{\Delta t^2} = \frac{((n+1) -n + (n-1))\Delta t}{\Delta t^2} = 0 \label{_auto6} \end{align} $$

Hence, $$ [D_tD_t \uex]^n_i = x_i(L-x_i)[D_tD_t (1+{\half}t)]^n = x_i(L-x_i){\half}[D_tD_t t]^n = 0$$









Analytical work with the discrete equations (1)

$$ \begin{align*} \lbrack D_xD_x \uex\rbrack^n_i &= (1+{\half}t_n)\lbrack D_xD_x (xL-x^2)\rbrack_i = (1+{\half}t_n)\lbrack LD_xD_x x - D_xD_x x^2\rbrack_i \\ &= -2(1+{\half}t_n) \end{align*} $$

Now, \( f^n_i = 2(1+{\half}t_n)c^2 \) and we get $$ [D_tD_t \uex - c^2D_xD_x\uex - f]^n_i = 0 - c^2(-1)2(1 + {\half}t_n) + 2(1+{\half}t_n)c^2 = 0$$

Moreover, \( \uex(x_i,0)=I(x_i) \), \( \partial \uex/\partial t = V(x_i) \) at \( t=0 \), and \( \uex(x_0,t)=\uex(x_{N_x},t)=0 \). Also the modified scheme for the first time step is fulfilled by \( \uex(x_i,t_n) \).









Testing with the exact discrete solution

Later we show that the exact solution of the discrete equations can be obtained by \( C=1 \) (!)









Implementation









The algorithm

  1. Compute \( u^0_i=I(x_i) \) for \( i=0,\ldots,N_x \)
  2. Compute \( u^1_i \) by \eqref{wave:pde1:step4:1} and set \( u_i^1=0 \) for the boundary points \( i=0 \) and \( i=N_x \), for \( n=1,2,\ldots,N-1 \),
  3. For each time level \( n=1,2,\ldots,N_t-1 \)

    1. apply \eqref{wave:pde1:step4} to find \( u^{n+1}_i \) for \( i=1,\ldots,N_x-1 \)
    2. set \( u^{n+1}_i=0 \) for the boundary points \( i=0 \), \( i=N_x \).








What do to with the solution?

def user_action(u, x, t, n):
    # u[i] at spatial mesh points x[i] at time t[n]
    # plot u
    # or store u









Making a solver function

import numpy as np

def solver(I, V, f, c, L, dt, C, T, user_action=None):
    """Solve u_tt=c^2*u_xx + f on (0,L)x(0,T]."""
    Nt = int(round(T/dt))
    t = np.linspace(0, Nt*dt, Nt+1)   # Mesh points in time
    dx = dt*c/float(C)
    Nx = int(round(L/dx))
    x = np.linspace(0, L, Nx+1)       # Mesh points in space
    C2 = C**2                      # Help variable in the scheme
    if f is None or f == 0 :
        f = lambda x, t: 0
    if V is None or V == 0:
        V = lambda x: 0

    u   = np.zeros(Nx+1)   # Solution array at new time level
    u_1 = np.zeros(Nx+1)   # Solution at 1 time level back
    u_2 = np.zeros(Nx+1)   # Solution at 2 time levels back

    import time;  t0 = time.clock()  # for measuring CPU time

    # Load initial condition into u_1
    for i in range(0,Nx+1):
        u_1[i] = I(x[i])

    if user_action is not None:
        user_action(u_1, x, t, 0)

    # Special formula for first time step
    n = 0
    for i in range(1, Nx):
        u[i] = u_1[i] + dt*V(x[i]) + \ 
               0.5*C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1]) + \ 
               0.5*dt**2*f(x[i], t[n])
    u[0] = 0;  u[Nx] = 0

    if user_action is not None:
        user_action(u, x, t, 1)

    # Switch variables before next step
    u_2[:] = u_1;  u_1[:] = u

    for n in range(1, Nt):
        # Update all inner points at time t[n+1]
        for i in range(1, Nx):
            u[i] = - u_2[i] + 2*u_1[i] + \ 
                     C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1]) + \ 
                     dt**2*f(x[i], t[n])

        # Insert boundary conditions
        u[0] = 0;  u[Nx] = 0
        if user_action is not None:
            if user_action(u, x, t, n+1):
                break

        # Switch variables before next step
        u_2[:] = u_1;  u_1[:] = u

    cpu_time = t0 - time.clock()
    return u, x, t, cpu_time









Verification: exact quadratic solution

Exact solution of the PDE problem and the discrete equations: \( \uex (x,t) = x(L-x)(1+{\half}t) \)

def test_quadratic():
    """Check that u(x,t)=x(L-x)(1+t/2) is exactly reproduced."""

    def u_exact(x, t):
        return x*(L-x)*(1 + 0.5*t)

    def I(x):
        return u_exact(x, 0)

    def V(x):
        return 0.5*u_exact(x, 0)

    def f(x, t):
        return 2*(1 + 0.5*t)*c**2

    L = 2.5
    c = 1.5
    C = 0.75
    Nx = 6  # Very coarse mesh for this exact test
    dt = C*(L/Nx)/c
    T = 18

    def assert_no_error(u, x, t, n):
        u_e = u_exact(x, t[n])
        diff = np.abs(u - u_e).max()
        tol = 1E-13
        assert diff < tol

    solver(I, V, f, c, L, dt, C, T,
           user_action=assert_no_error)









Visualization: animating \( u(x,t) \)

Make a viz function for animating the curve, with plotting in a user_action function plot_u:

def viz(
    I, V, f, c, L, dt, C, T,  # PDE paramteres
    umin, umax,               # Interval for u in plots
    animate=True,             # Simulation with animation?
    tool='matplotlib',        # 'matplotlib' or 'scitools'
    solver_function=solver,   # Function with numerical algorithm
    ):
    """Run solver and visualize u at each time level."""

    def plot_u_st(u, x, t, n):
        """user_action function for solver."""
        plt.plot(x, u, 'r-',
                 xlabel='x', ylabel='u',
                 axis=[0, L, umin, umax],
                 title='t=%f' % t[n], show=True)
        # Let the initial condition stay on the screen for 2
        # seconds, else insert a pause of 0.2 s between each plot
        time.sleep(2) if t[n] == 0 else time.sleep(0.2)
        plt.savefig('frame_%04d.png' % n)  # for movie making

    class PlotMatplotlib:
        def __call__(self, u, x, t, n):
            """user_action function for solver."""
            if n == 0:
                plt.ion()
                self.lines = plt.plot(x, u, 'r-')
                plt.xlabel('x');  plt.ylabel('u')
                plt.axis([0, L, umin, umax])
                plt.legend(['t=%f' % t[n]], loc='lower left')
            else:
                self.lines[0].set_ydata(u)
                plt.legend(['t=%f' % t[n]], loc='lower left')
                plt.draw()
            time.sleep(2) if t[n] == 0 else time.sleep(0.2)
            plt.savefig('tmp_%04d.png' % n)  # for movie making

    if tool == 'matplotlib':
        import matplotlib.pyplot as plt
        plot_u = PlotMatplotlib()
    elif tool == 'scitools':
        import scitools.std as plt  # scitools.easyviz interface
        plot_u = plot_u_st
    import time, glob, os

    # Clean up old movie frames
    for filename in glob.glob('tmp_*.png'):
        os.remove(filename)

    # Call solver and do the simulaton
    user_action = plot_u if animate else None
    u, x, t, cpu = solver_function(
        I, V, f, c, L, dt, C, T, user_action)

    # Make video files
    fps = 4  # frames per second
    codec2ext = dict(flv='flv', libx264='mp4', libvpx='webm',
                     libtheora='ogg')  # video formats
    filespec = 'tmp_%04d.png'
    movie_program = 'ffmpeg'  # or 'avconv'
    for codec in codec2ext:
        ext = codec2ext[codec]
        cmd = '%(movie_program)s -r %(fps)d -i %(filespec)s '\ 
              '-vcodec %(codec)s movie.%(ext)s' % vars()
        os.system(cmd)

    if tool == 'scitools':
        # Make an HTML play for showing the animation in a browser
        plt.movie('tmp_*.png', encoder='html', fps=fps,
                  output_file='movie.html')
    return cpu

Note: plot_u is function inside function and remembers the local variables in viz (known as a closure).









Making movie files

Terminal> scitools movie encoder=html output_file=movie.html \ 
          fps=4 frame_*.png  # web page with a player
Terminal> avconv -r 4 -i frame_%04d.png -c:v flv       movie.flv
Terminal> avconv -r 4 -i frame_%04d.png -c:v libtheora movie.ogg
Terminal> avconv -r 4 -i frame_%04d.png -c:v libx264   movie.mp4
Terminal> avconv -r 4 -i frame_%04d.png -c:v libpvx    movie.webm

Important.









Running a case

$$ \begin{equation} I(x) = \left\lbrace \begin{array}{ll} ax/x_0, & x < x_0\\ a(L-x)/(L-x_0), & \hbox{otherwise} \end{array}\right. \label{wave:pde1:guitar:I} \end{equation} $$

Appropriate data:









Implementation of the case

def guitar(C):
    """Triangular wave (pulled guitar string)."""
    L = 0.75
    x0 = 0.8*L
    a = 0.005
    freq = 440
    wavelength = 2*L
    c = freq*wavelength
    omega = 2*pi*freq
    num_periods = 1
    T = 2*pi/omega*num_periods
    # Choose dt the same as the stability limit for Nx=50
    dt = L/50./c

    def I(x):
        return a*x/x0 if x < x0 else a/(L-x0)*(L-x)

    umin = -1.2*a;  umax = -umin
    cpu = viz(I, 0, 0, c, L, dt, C, T, umin, umax,
              animate=True, tool='scitools')

Program: wave1D_u0.py.









Resulting movie for \( C=0.8 \)

Movie of the vibrating string









The benefits of scaling

Introduce new \( x \), \( t \), and \( u \) without dimension: $$ \bar x = \frac{x}{L},\quad \bar t = \frac{c}{L}t,\quad \bar u = \frac{u}{a} $$

Insert this in the PDE (with \( f=0 \)) and dropping bars $$ u_{tt} = u_{xx}$$

Initial condition: set \( a=1 \), \( L=1 \), and \( x_0\in [0,1] \) in \eqref{wave:pde1:guitar:I}.

In the code: set a=c=L=1, x0=0.8, and there is no need to calculate with wavelengths and frequencies to estimate \( c \)!

Just one challenge: determine the period of the waves and an appropriate end time (see the text for details).









Vectorization

Next: vectorized loops









Operations on slices of arrays

n = u.size
for i in range(0, n-1):
    d[i] = u[i+1] - u[i]









Test the understanding

Newcomers to vectorization are encouraged to choose a small array u, say with five elements, and simulate with pen and paper both the loop version and the vectorized version.









Vectorization of finite difference schemes (1)

Finite difference schemes basically contains differences between array elements with shifted indices. Consider the updating formula

for i in range(1, n-1):
    u2[i] = u[i-1] - 2*u[i] + u[i+1]

The vectorization consists of replacing the loop by arithmetics on slices of arrays of length n-2:

u2 = u[:-2] - 2*u[1:-1] + u[2:]
u2 = u[0:n-2] - 2*u[1:n-1] + u[2:n]   # alternative

Note: u2 gets length n-2.

If u2 is already an array of length n, do update on "inner" elements

u2[1:-1]  = u[:-2] - 2*u[1:-1] + u[2:]
u2[1:n-1] = u[0:n-2] - 2*u[1:n-1] + u[2:n]   # alternative









Vectorization of finite difference schemes (2)

Include a function evaluation too:

def f(x):
    return x**2 + 1

# Scalar version
for i in range(1, n-1):
    u2[i] = u[i-1] - 2*u[i] + u[i+1] + f(x[i])

# Vectorized version
u2[1:-1] = u[:-2] - 2*u[1:-1] + u[2:] + f(x[1:-1])









Vectorized implementation in the solver function

Scalar loop:

for i in range(1, Nx):
    u[i] = 2*u_1[i] - u_2[i] + \ 
           C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1])

Vectorized loop:

u[1:-1] = - u_2[1:-1] + 2*u_1[1:-1] + \ 
          C2*(u_1[:-2] - 2*u_1[1:-1] + u_1[2:])

or

u[1:Nx] = 2*u_1[1:Nx]- u_2[1:Nx] + \ 
          C2*(u_1[0:Nx-1] - 2*u_1[1:Nx] + u_1[2:Nx+1])

Program: wave1D_u0v.py









Verification of the vectorized version

def test_quadratic():
    """
    Check the scalar and vectorized versions work for
    a quadratic u(x,t)=x(L-x)(1+t/2) that is exactly reproduced.
    """
    # The following function must work for x as array or scalar
    u_exact = lambda x, t: x*(L - x)*(1 + 0.5*t)
    I = lambda x: u_exact(x, 0)
    V = lambda x: 0.5*u_exact(x, 0)
    # f is a scalar (zeros_like(x) works for scalar x too)
    f = lambda x, t: np.zeros_like(x) + 2*c**2*(1 + 0.5*t)

    L = 2.5
    c = 1.5
    C = 0.75
    Nx = 3  # Very coarse mesh for this exact test
    dt = C*(L/Nx)/c
    T = 18

    def assert_no_error(u, x, t, n):
        u_e = u_exact(x, t[n])
        tol = 1E-13
        diff = np.abs(u - u_e).max()
        assert diff < tol

    solver(I, V, f, c, L, dt, C, T,
           user_action=assert_no_error, version='scalar')
    solver(I, V, f, c, L, dt, C, T,
           user_action=assert_no_error, version='vectorized')

Note:









Efficiency measurements

Much bigger improvements for 2D and 3D codes!









Generalization: reflecting boundaries

Demo of boundary conditions









Neumann boundary condition

$$ \begin{equation} \frac{\partial u}{\partial n} \equiv \normalvec\cdot\nabla u = 0 \label{wave:pde1:Neumann:0} \end{equation} $$

For a 1D domain \( [0,L] \): $$ \left.\frac{\partial}{\partial n}\right\vert_{x=L} = \frac{\partial}{\partial x},\quad \left.\frac{\partial}{\partial n}\right\vert_{x=0} = - \frac{\partial}{\partial x} $$

Boundary condition terminology:









Discretization of derivatives at the boundary (1)

$$ \begin{equation} \frac{u_{-1}^n - u_1^n}{2\Delta x} = 0 \label{wave:pde1:Neumann:0:cd} \end{equation} $$









Discretization of derivatives at the boundary (2)

$$ \frac{u_{-1}^n - u_1^n}{2\Delta x} = 0 $$ $$ \begin{equation} u^{n+1}_i = -u^{n-1}_i + 2u^n_i + 2C^2 \left(u^{n}_{i+1}-u^{n}_{i}\right),\quad i=0 \label{_auto7} \end{equation} $$









Visualization of modified boundary stencil

Discrete equation for computing \( u^3_0 \) in terms of \( u^2_0 \), \( u^1_0 \), and \( u^2_1 \):

Animation in a web page or a movie file.









Implementation of Neumann conditions

i = 0
ip1 = i+1
im1 = ip1  # i-1 -> i+1
u[i] = u_1[i] + C2*(u_1[im1] - 2*u_1[i] + u_1[ip1])

i = Nx
im1 = i-1
ip1 = im1  # i+1 -> i-1
u[i] = u_1[i] + C2*(u_1[im1] - 2*u_1[i] + u_1[ip1])

# Or just one loop over all points

for i in range(0, Nx+1):
    ip1 = i+1 if i < Nx else i-1
    im1 = i-1 if i > 0  else i+1
    u[i] = u_1[i] + C2*(u_1[im1] - 2*u_1[i] + u_1[ip1])

Program wave1D_dn0.py









Moving finite difference stencil

web page or a movie file.









Index set notation









Index set notation in code

Notation Python
\( \Ix \) Ix
\( \setb{\Ix} \) Ix[0]
\( \sete{\Ix} \) Ix[-1]
\( \setr{\Ix} \) Ix[1:]
\( \setl{\Ix} \) Ix[:-1]
\( \seti{\Ix} \) Ix[1:-1]









Index sets in action (1)

Index sets for a problem in the \( x,t \) plane: $$ \begin{equation} \Ix = \{0,\ldots,N_x\},\quad \It = \{0,\ldots,N_t\}, \label{_auto8} \end{equation} $$

Implemented in Python as

Ix = range(0, Nx+1)
It = range(0, Nt+1)









Index sets in action (2)

A finite difference scheme can with the index set notation be specified as $$ \begin{align*} u^{n+1}_i &= -u^{n-1}_i + 2u^n_i + C^2 \left(u^{n}_{i+1}-2u^{n}_{i}+u^{n}_{i-1}\right), \quad i\in\seti{\Ix},\ n\in\seti{\It}\\ u^{n+1}_i &= 0, \quad i=\setb{\Ix},\ n\in\seti{\It}\\ u^{n+1}_i &= 0, \quad i=\sete{\Ix},\ n\in\seti{\It} \end{align*} $$

Corresponding implementation:

for n in It[1:-1]:
    for i in Ix[1:-1]:
        u[i] = - u_2[i] + 2*u_1[i] + \ 
               C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1])
    i = Ix[0];  u[i] = 0
    i = Ix[-1]; u[i] = 0

Program wave1D_dn.py









Alternative implementation via ghost cells









Implementation of ghost cells (1)

Add ghost points:

u   = zeros(Nx+3)
u_1 = zeros(Nx+3)
u_2 = zeros(Nx+3)

x = linspace(0, L, Nx+1)  # Mesh points without ghost points








Implementation of ghost cells (2)

u = zeros(Nx+3)
Ix = range(1, u.shape[0]-1)

# Boundary values: u[Ix[0]], u[Ix[-1]]

# Set initial conditions
for i in Ix:
    u_1[i] = I(x[i-Ix[0]])  # Note i-Ix[0]

# Loop over all physical mesh points
for i in Ix:
    u[i] = - u_2[i] + 2*u_1[i] + \ 
           C2*(u_1[i-1] - 2*u_1[i] + u_1[i+1])

# Update ghost values
i = Ix[0]          # x=0 boundary
u[i-1] = u[i+1]
i = Ix[-1]         # x=L boundary
u[i+1] = u[i-1]

Program: wave1D_dn0_ghost.py.









Generalization: variable wave velocity

Heterogeneous media: varying \( c=c(x) \)









The model PDE with a variable coefficient

$$ \begin{equation} \frac{\partial^2 u}{\partial t^2} = \frac{\partial}{\partial x}\left( q(x) \frac{\partial u}{\partial x}\right) + f(x,t) \label{wave:pde2:var:c:pde} \end{equation} $$

This equation sampled at a mesh point \( (x_i,t_n) \): $$ \frac{\partial^2 }{\partial t^2} u(x_i,t_n) = \frac{\partial}{\partial x}\left( q(x_i) \frac{\partial}{\partial x} u(x_i,t_n)\right) + f(x_i,t_n), $$









Discretizing the variable coefficient (1)

The principal idea is to first discretize the outer derivative.

Define $$ \phi = q(x) \frac{\partial u}{\partial x} $$

Then use a centered derivative around \( x=x_i \) for the derivative of \( \phi \): $$ \left[\frac{\partial\phi}{\partial x}\right]^n_i \approx \frac{\phi_{i+\half} - \phi_{i-\half}}{\Delta x} = [D_x\phi]^n_i $$









Discretizing the variable coefficient (2)

Then discretize the inner operators: $$ \phi_{i+\half} = q_{i+\half} \left[\frac{\partial u}{\partial x}\right]^n_{i+\half} \approx q_{i+\half} \frac{u^n_{i+1} - u^n_{i}}{\Delta x} = [q D_x u]_{i+\half}^n $$

Similarly, $$ \phi_{i-\half} = q_{i-\half} \left[\frac{\partial u}{\partial x}\right]^n_{i-\half} \approx q_{i-\half} \frac{u^n_{i} - u^n_{i-1}}{\Delta x} = [q D_x u]_{i-\half}^n $$









Discretizing the variable coefficient (3)

These intermediate results are now combined to $$ \begin{equation} \left[ \frac{\partial}{\partial x}\left( q(x) \frac{\partial u}{\partial x}\right)\right]^n_i \approx \frac{1}{\Delta x^2} \left( q_{i+\half} \left({u^n_{i+1} - u^n_{i}}\right) - q_{i-\half} \left({u^n_{i} - u^n_{i-1}}\right)\right) \label{wave:pde2:var:c:formula} \end{equation} $$

In operator notation: $$ \begin{equation} \left[ \frac{\partial}{\partial x}\left( q(x) \frac{\partial u}{\partial x}\right)\right]^n_i \approx [D_xq D_x u]^n_i \label{wave:pde2:var:c:formula:op} \end{equation} $$

Remark.

Many are tempted to use the chain rule on the term \( \frac{\partial}{\partial x}\left( q(x) \frac{\partial u}{\partial x}\right) \), but this is not a good idea!









Computing the coefficient between mesh points

$$ \begin{align} q_{i+\half} &\approx \half\left( q_{i} + q_{i+1}\right) = [\overline{q}^{x}]_i \quad &\hbox{(arithmetic mean)} \label{wave:pde2:var:c:mean:arithmetic}\\ q_{i+\half} &\approx 2\left( \frac{1}{q_{i}} + \frac{1}{q_{i+1}}\right)^{-1} \quad &\hbox{(harmonic mean)} \label{wave:pde2:var:c:mean:harmonic}\\ q_{i+\half} &\approx \left(q_{i}q_{i+1}\right)^{1/2} \quad &\hbox{(geometric mean)} \label{wave:pde2:var:c:mean:geometric} \end{align} $$

The arithmetic mean in \eqref{wave:pde2:var:c:mean:arithmetic} is by far the most used averaging technique.









Discretization of variable-coefficient wave equation in operator notation

$$ \begin{equation} \lbrack D_tD_t u = D_x\overline{q}^{x}D_x u + f\rbrack^{n}_i \label{wave:pde2:var:c:scheme:op} \end{equation} $$

We clearly see the type of finite differences and averaging!

Write out and solve wrt \( u_i^{n+1} \): $$ \begin{align} u^{n+1}_i &= - u_i^{n-1} + 2u_i^n + \left(\frac{\Delta t}{\Delta x}\right)^2\times \nonumber\\ &\quad \left( \half(q_{i} + q_{i+1})(u_{i+1}^n - u_{i}^n) - \half(q_{i} + q_{i-1})(u_{i}^n - u_{i-1}^n)\right) + \nonumber\\ & \quad \Delta t^2 f^n_i \label{wave:pde2:var:c:scheme:impl} \end{align} $$









Neumann condition and a variable coefficient

Consider \( \partial u/\partial x=0 \) at \( x=L=N_x\Delta x \): $$ \frac{u_{i+1}^{n} - u_{i-1}^n}{2\Delta x} = 0\quad u_{i+1}^n = u_{i-1}^n, \quad i=N_x $$

Insert \( u_{i+1}^n=u_{i-1}^n \) in the stencil \eqref{wave:pde2:var:c:scheme:impl} for \( i=N_x \) and obtain $$ u^{n+1}_i \approx - u_i^{n-1} + 2u_i^n + \left(\frac{\Delta t}{\Delta x}\right)^2 2q_{i}(u_{i-1}^n - u_{i}^n) + \Delta t^2 f^n_i $$

(We have used \( q_{i+\half} + q_{i-\half}\approx 2q_i \).)

Alternative: assume \( dq/dx=0 \) (simpler).









Implementation of variable coefficients

Assume c[i] holds \( c_i \) the spatial mesh points

for i in range(1, Nx):
    u[i] = - u_2[i] + 2*u_1[i] + \ 
           C2*(0.5*(q[i] + q[i+1])*(u_1[i+1] - u_1[i])  - \ 
               0.5*(q[i] + q[i-1])*(u_1[i] - u_1[i-1])) + \ 
           dt2*f(x[i], t[n])

Here: C2=(dt/dx)**2

Vectorized version:

u[1:-1] = - u_2[1:-1] + 2*u_1[1:-1] + \ 
          C2*(0.5*(q[1:-1] + q[2:])*(u_1[2:] - u_1[1:-1]) -
              0.5*(q[1:-1] + q[:-2])*(u_1[1:-1] - u_1[:-2])) + \ 
          dt2*f(x[1:-1], t[n])

Neumann condition \( u_x=0 \): same ideas as in 1D (modified stencil or ghost cells).









A more general model PDE with variable coefficients

$$ \begin{equation} \varrho(x)\frac{\partial^2 u}{\partial t^2} = \frac{\partial}{\partial x}\left( q(x) \frac{\partial u}{\partial x}\right) + f(x,t) \label{wave:pde2:var:c:pde2} \end{equation} $$

A natural scheme is $$ \begin{equation} [\varrho D_tD_t u = D_x\overline{q}^xD_x u + f]^n_i \label{_auto9} \end{equation} $$

Or $$ \begin{equation} [D_tD_t u = \varrho^{-1}D_x\overline{q}^xD_x u + f]^n_i \label{_auto10} \end{equation} $$

No need to average \( \varrho \), just sample at \( i \)









Generalization: damping

Why do waves die out?

Simplest damping model (for physical behavior, see demo): $$ \begin{equation} \frac{\partial^2 u}{\partial t^2} + \color{red}{b\frac{\partial u}{\partial t}} = c^2\frac{\partial^2 u}{\partial x^2} + f(x,t), \label{wave:pde3} \end{equation} $$

\( b \geq 0 \): prescribed damping coefficient.

Discretization via centered differences to ensure \( \Oof{\Delta t^2} \) error: $$ \begin{equation} [D_tD_t u + bD_{2t}u = c^2D_xD_x u + f]^n_i \label{wave:pde3:fd} \end{equation} $$

Need special formula for \( u^1_i \) + special stencil (or ghost cells) for Neumann conditions.









Building a general 1D wave equation solver

The program wave1D_dn_vc.py solves a fairly general 1D wave equation: $$ \begin{align} u_{tt} &= (c^2(x)u_x)_x + f(x,t),\quad &x\in (0,L),\ t\in (0,T] \label{wave:pde2:software:ueq}\\ u(x,0) &= I(x),\quad &x\in [0,L] \label{wave:pde2:software:u0}\\ u_t(x,0) &= V(t),\quad &x\in [0,L] \label{wave:pde2:software:ut0}\\ u(0,t) &= U_0(t)\hbox{ or } u_x(0,t)=0,\quad &t\in (0,T] \label{wave:pde2:software:bc0}\\ u(L,t) &= U_L(t)\hbox{ or } u_x(L,t)=0,\quad &t\in (0,T] \label{wave:pde2:software:bcL} \end{align} $$

Can be adapted to many needs.









Collection of initial conditions

The function pulse in wave1D_dn_vc.py offers four initial conditions:

  1. a rectangular pulse ("plug")
  2. a Gaussian function (gaussian)
  3. a "cosine hat": one period of \( 1 + \cos (\pi x \), \( x\in [-1,1] \)
  4. half a "cosine hat": half a period of \( \cos \pi x \), \( x\in [-{\half},{\half}] \)
Can locate the initial pulse at \( x=0 \) or in the middle

>>> import wave1D_dn_vc as w
>>> w.pulse(loc='left', pulse_tp='cosinehat', Nx=50, every_frame=10)









Finite difference methods for 2D and 3D wave equations

Constant wave velocity \( c \): $$ \begin{equation} \frac{\partial^2 u}{\partial t^2} = c^2\nabla^2 u\hbox{ for }\xpoint\in\Omega\subset\Real^d,\ t\in (0,T] \label{wave:2D3D:model1} \end{equation} $$

Variable wave velocity: $$ \begin{equation} \varrho\frac{\partial^2 u}{\partial t^2} = \nabla\cdot (q\nabla u) + f\hbox{ for }\xpoint\in\Omega\subset\Real^d,\ t\in (0,T] \label{wave:2D3D:model2} \end{equation} $$









Examples on wave equations written out in 2D/3D

3D, constant \( c \): $$ \begin{equation*} \nabla^2 u = \frac{\partial^2 u}{\partial x^2} + \frac{\partial^2 u}{\partial y^2} + \frac{\partial^2 u}{\partial z^2} \end{equation*} $$

2D, variable \( c \): $$ \begin{equation} \varrho(x,y) \frac{\partial^2 u}{\partial t^2} = \frac{\partial}{\partial x}\left( q(x,y) \frac{\partial u}{\partial x}\right) + \frac{\partial}{\partial y}\left( q(x,y) \frac{\partial u}{\partial y}\right) + f(x,y,t) \label{_auto11} \end{equation} $$

Compact notation: $$ \begin{align} u_{tt} &= c^2(u_{xx} + u_{yy} + u_{zz}) + f, \label{wave:2D3D:model1:v2}\\ \varrho u_{tt} &= (q u_x)_x + (q u_z)_z + (q u_z)_z + f \label{wave:2D3D:model2:v2} \end{align} $$









Boundary and initial conditions

We need one boundary condition at each point on \( \partial\Omega \):

  1. \( u \) is prescribed (\( u=0 \) or known incoming wave)
  2. \( \partial u/\partial n = \normalvec\cdot\nabla u \) prescribed (\( =0 \): reflecting boundary)
  3. open boundary (radiation) condition: \( u_t + \boldsymbol{c}\cdot\nabla u =0 \) (let waves travel undisturbed out of the domain)
PDEs with second-order time derivative need two initial conditions:

  1. \( u=I \),
  2. \( u_t = V \).








Example: 2D propagation of Gaussian function









Mesh









Discretization

$$ [D_tD_t u = c^2(D_xD_x u + D_yD_yu) + f]^n_{i,j,k}, $$ Written out in detail: $$ \begin{align*} \frac{u^{n+1}_{i,j} - 2u^{n}_{i,j} + u^{n-1}_{i,j}}{\Delta t^2} &= c^2 \frac{u^{n}_{i+1,j} - 2u^{n}_{i,j} + u^{n}_{i-1,j}}{\Delta x^2} + \nonumber\\ &\quad c^2\frac{u^{n}_{i,j+1} - 2u^{n}_{i,j} + u^{n}_{i,j-1}}{\Delta y^2} + f^n_{i,j}, \end{align*} $$

\( u^{n-1}_{i,j} \) and \( u^n_{i,j} \) are known, solve for \( u^{n+1}_{i,j} \): $$ u^{n+1}_{i,j} = 2u^n_{i,j} + u^{n-1}_{i,j} + c^2\Delta t^2[D_xD_x u + D_yD_y u]^n_{i,j}$$









Special stencil for the first time step

$$ [D_{2t}u = V]^0_{i,j}\quad\Rightarrow\quad u^{-1}_{i,j} = u^1_{i,j} - 2\Delta t V_{i,j} $$ $$ u^{n+1}_{i,j} = u^n_{i,j} -\Delta t V_{i,j} + {\half} c^2\Delta t^2[D_xD_x u + D_yD_y u]^n_{i,j}$$









Variable coefficients (1)

3D wave equation: $$ \varrho u_{tt} = (qu_x)_x + (qu_y)_y + (qu_z)_z + f(x,y,z,t) $$

Just apply the 1D discretization for each term: $$ \begin{equation} [\varrho D_tD_t u = (D_x\overline{q}^x D_x u + D_y\overline{q}^y D_yu + D_z\overline{q}^z D_z u) + f]^n_{i,j,k} \label{_auto12} \end{equation} $$

Need special formula for \( u^1_{i,j,k} \) (use \( [D_{2t}u=V]^0 \) and stencil for \( n=0 \)).









Variable coefficients (2)

Written out: $$ \begin{align*} u^{n+1}_{i,j,k} &= - u^{n-1}_{i,j,k} + 2u^{n}_{i,j,k} \\ &+ \frac{\Delta t^2}{\varrho_{i,j,k}}\frac{1}{\Delta x^2} ( \half(q_{i,j,k} + q_{i+1,j,k})(u^{n}_{i+1,j,k} - u^{n}_{i,j,k}) - \\ &\qquad\quad \half(q_{i-1,j,k} + q_{i,j,k})(u^{n}_{i,j,k} - u^{n}_{i-1,j,k})) \\ &+ \frac{\Delta t^2}{\varrho_{i,j,k}}\frac{1}{\Delta y^2} ( \half(q_{i,j,k} + q_{i,j+1,k})(u^{n}_{i,j+1,k} - u^{n}_{i,j,k}) - \\ &\qquad\quad\half(q_{i,j-1,k} + q_{i,j,k})(u^{n}_{i,j,k} - u^{n}_{i,j-1,k})) \\ &+ \frac{\Delta t^2}{\varrho_{i,j,k}}\frac{1}{\Delta z^2} ( \half(q_{i,j,k} + q_{i,j,k+1})(u^{n}_{i,j,k+1} - u^{n}_{i,j,k}) -\\ &\qquad\quad \half(q_{i,j,k-1} + q_{i,j,k})(u^{n}_{i,j,k} - u^{n}_{i,j,k-1})) + \\ &+\qquad \Delta t^2 f^n_{i,j,k} \end{align*} $$









Neumann boundary condition in 2D

Use ideas from 1D! Example: \( \frac{\partial u}{\partial n}=0 \) at \( y=0 \), \( \frac{\partial u}{\partial n} = -\frac{\partial u}{\partial y} \)

Boundary condition discretization: $$ [-D_{2y} u = 0]^n_{i,0}\quad\Rightarrow\quad \frac{u^n_{i,1}-u^n_{i,-1}}{2\Delta y} = 0,\ i\in\Ix $$

Insert \( u^n_{i,-1}=u^n_{i,1} \) in the stencil for \( u^{n+1}_{i,j=0} \) to obtain a modified stencil on the boundary.

Pattern: use interior stencil also on the bundary, but replace \( j-1 \) by \( j+1 \)

Alternative: use ghost cells and ghost values









Implementation of 2D/3D problems

$$ \begin{align} u_{tt} &= c^2(u_{xx} + u_{yy}) + f(x,y,t),\quad &(x,y)\in \Omega,\ t\in (0,T] \label{_auto13}\\ u(x,y,0) &= I(x,y),\quad &(x,y)\in\Omega \label{_auto14}\\ u_t(x,y,0) &= V(x,y),\quad &(x,y)\in\Omega \label{_auto15}\\ u &= 0,\quad &(x,y)\in\partial\Omega,\ t\in (0,T] \label{_auto16} \end{align} $$

\( \Omega = [0,L_x]\times [0,L_y] \)

Discretization: $$ [D_t D_t u = c^2(D_xD_x u + D_yD_y u) + f]^n_{i,j}, $$









Algorithm

  1. Set initial condition \( u^0_{i,j}=I(x_i,y_j) \)
  2. Compute \( u^1_{i,j} = \cdots \) for \( i\in\seti{\Ix} \) and \( j\in\seti{\Iy} \)
  3. Set \( u^1_{i,j}=0 \) for the boundaries \( i=0,N_x \), \( j=0,N_y \)
  4. For \( n=1,2,\ldots,N_t \):

    1. Find \( u^{n+1}_{i,j} = \cdots \) for \( i\in\seti{\Ix} \) and \( j\in\seti{\Iy} \)
    2. Set \( u^{n+1}_{i,j}=0 \) for the boundaries \( i=0,N_x \), \( j=0,N_y \)








Scalar computations: mesh

Program: wave2D_u0.py

def solver(I, V, f, c, Lx, Ly, Nx, Ny, dt, T,
           user_action=None, version='scalar'):

Mesh:

x = linspace(0, Lx, Nx+1)                  # mesh points in x dir
y = linspace(0, Ly, Ny+1)                  # mesh points in y dir
dx = x[1] - x[0]
dy = y[1] - y[0]
Nt = int(round(T/float(dt)))
t = linspace(0, N*dt, N+1)                 # mesh points in time
Cx2 = (c*dt/dx)**2;  Cy2 = (c*dt/dy)**2    # help variables
dt2 = dt**2









Scalar computations: arrays

Store \( u^{n+1}_{i,j} \), \( u^{n}_{i,j} \), and \( u^{n-1}_{i,j} \) in three two-dimensional arrays:

u   = zeros((Nx+1,Ny+1))   # solution array
u_1 = zeros((Nx+1,Ny+1))   # solution at t-dt
u_2 = zeros((Nx+1,Ny+1))   # solution at t-2*dt

\( u^{n+1}_{i,j} \) corresponds to u[i,j], etc.









Scalar computations: initial condition

Ix = range(0, u.shape[0])
Iy = range(0, u.shape[1])
It = range(0, t.shape[0])

for i in Ix:
    for j in Iy:
        u_1[i,j] = I(x[i], y[j])

if user_action is not None:
    user_action(u_1, x, xv, y, yv, t, 0)

Arguments xv and yv: for vectorized computations









Scalar computations: primary stencil

def advance_scalar(u, u_1, u_2, f, x, y, t, n, Cx2, Cy2, dt2,
                   V=None, step1=False):
    Ix = range(0, u.shape[0]);  Iy = range(0, u.shape[1])
    if step1:
        dt = sqrt(dt2)  # save
        Cx2 = 0.5*Cx2;  Cy2 = 0.5*Cy2; dt2 = 0.5*dt2  # redefine
        D1 = 1;  D2 = 0
    else:
        D1 = 2;  D2 = 1
    for i in Ix[1:-1]:
        for j in Iy[1:-1]:
            u_xx = u_1[i-1,j] - 2*u_1[i,j] + u_1[i+1,j]
            u_yy = u_1[i,j-1] - 2*u_1[i,j] + u_1[i,j+1]
            u[i,j] = D1*u_1[i,j] - D2*u_2[i,j] + \ 
                     Cx2*u_xx + Cy2*u_yy + dt2*f(x[i], y[j], t[n])
            if step1:
                u[i,j] += dt*V(x[i], y[j])
    # Boundary condition u=0
    j = Iy[0]
    for i in Ix: u[i,j] = 0
    j = Iy[-1]
    for i in Ix: u[i,j] = 0
    i = Ix[0]
    for j in Iy: u[i,j] = 0
    i = Ix[-1]
    for j in Iy: u[i,j] = 0
    return u

D1 and D2: allow advance_scalar to be used also for \( u^1_{i,j} \):

u = advance_scalar(u, u_1, u_2, f, x, y, t,
                   n, 0.5*Cx2, 0.5*Cy2, 0.5*dt2, D1=1, D2=0)









Vectorized computations: mesh coordinates

Mesh with \( 30\times 30 \) cells: vectorization reduces the CPU time by a factor of 70 (!).

Need special coordinate arrays xv and yv such that \( I(x,y) \) and \( f(x,y,t) \) can be vectorized:

from numpy import newaxis
xv = x[:,newaxis]
yv = y[newaxis,:]

u_1[:,:] = I(xv, yv)
f_a[:,:] = f(xv, yv, t)









Vectorized computations: stencil

def advance_vectorized(u, u_1, u_2, f_a, Cx2, Cy2, dt2,
                       V=None, step1=False):
    if step1:
        dt = sqrt(dt2)  # save
        Cx2 = 0.5*Cx2;  Cy2 = 0.5*Cy2; dt2 = 0.5*dt2  # redefine
        D1 = 1;  D2 = 0
    else:
        D1 = 2;  D2 = 1
    u_xx = u_1[:-2,1:-1] - 2*u_1[1:-1,1:-1] + u_1[2:,1:-1]
    u_yy = u_1[1:-1,:-2] - 2*u_1[1:-1,1:-1] + u_1[1:-1,2:]
    u[1:-1,1:-1] = D1*u_1[1:-1,1:-1] - D2*u_2[1:-1,1:-1] + \ 
                   Cx2*u_xx + Cy2*u_yy + dt2*f_a[1:-1,1:-1]
    if step1:
        u[1:-1,1:-1] += dt*V[1:-1, 1:-1]
    # Boundary condition u=0
    j = 0
    u[:,j] = 0
    j = u.shape[1]-1
    u[:,j] = 0
    i = 0
    u[i,:] = 0
    i = u.shape[0]-1
    u[i,:] = 0
    return u

def quadratic(Nx, Ny, version):
    """Exact discrete solution of the scheme."""

    def exact_solution(x, y, t):
        return x*(Lx - x)*y*(Ly - y)*(1 + 0.5*t)

    def I(x, y):
        return exact_solution(x, y, 0)

    def V(x, y):
        return 0.5*exact_solution(x, y, 0)

    def f(x, y, t):
        return 2*c**2*(1 + 0.5*t)*(y*(Ly - y) + x*(Lx - x))

    Lx = 5;  Ly = 2
    c = 1.5
    dt = -1 # use longest possible steps
    T = 18

    def assert_no_error(u, x, xv, y, yv, t, n):
        u_e = exact_solution(xv, yv, t[n])
        diff = abs(u - u_e).max()
        tol = 1E-12
        msg = 'diff=%g, step %d, time=%g' % (diff, n, t[n])
        assert diff < tol, msg

    new_dt, cpu = solver(
        I, V, f, c, Lx, Ly, Nx, Ny, dt, T,
        user_action=assert_no_error, version=version)
    return new_dt, cpu


def test_quadratic():
    # Test a series of meshes where Nx > Ny and Nx < Ny
    versions = 'scalar', 'vectorized', 'cython', 'f77', 'c_cy', 'c_f2py'
    for Nx in range(2, 6, 2):
        for Ny in range(2, 6, 2):
            for version in versions:
                print 'testing', version, 'for %dx%d mesh' % (Nx, Ny)
                quadratic(Nx, Ny, version)

def run_efficiency(nrefinements=4):
    def I(x, y):
        return sin(pi*x/Lx)*sin(pi*y/Ly)

    Lx = 10;  Ly = 10
    c = 1.5
    T = 100
    versions = ['scalar', 'vectorized', 'cython', 'f77',
               'c_f2py', 'c_cy']
    print ' '*15, ''.join(['%-13s' % v for v in versions])
    for Nx in 15, 30, 60, 120:
        cpu = {}
        for version in versions:
            dt, cpu_ = solver(I, None, None, c, Lx, Ly, Nx, Nx,
                              -1, T, user_action=None,
                              version=version)
            cpu[version] = cpu_
        cpu_min = min(list(cpu.values()))
        if cpu_min < 1E-6:
            print 'Ignored %dx%d grid (too small execution time)' \ 
                  % (Nx, Nx)
        else:
            cpu = {version: cpu[version]/cpu_min for version in cpu}
            print '%-15s' % '%dx%d' % (Nx, Nx),
            print ''.join(['%13.1f' % cpu[version] for version in versions])

def gaussian(plot_method=2, version='vectorized', save_plot=True):
    """
    Initial Gaussian bell in the middle of the domain.
    plot_method=1 applies mesh function, =2 means surf, =0 means no plot.
    """
    # Clean up plot files
    for name in glob('tmp_*.png'):
        os.remove(name)

    Lx = 10
    Ly = 10
    c = 1.0

    def I(x, y):
        """Gaussian peak at (Lx/2, Ly/2)."""
        return exp(-0.5*(x-Lx/2.0)**2 - 0.5*(y-Ly/2.0)**2)

    if plot_method == 3:
        from mpl_toolkits.mplot3d import axes3d
        import matplotlib.pyplot as plt
        from matplotlib import cm
        plt.ion()
        fig = plt.figure()
        u_surf = None

    def plot_u(u, x, xv, y, yv, t, n):
        if t[n] == 0:
            time.sleep(2)
        if plot_method == 1:
            mesh(x, y, u, title='t=%g' % t[n], zlim=[-1,1],
                 caxis=[-1,1])
        elif plot_method == 2:
            surfc(xv, yv, u, title='t=%g' % t[n], zlim=[-1, 1],
                  colorbar=True, colormap=hot(), caxis=[-1,1],
                  shading='flat')
        elif plot_method == 3:
            print 'Experimental 3D matplotlib...under development...'
            #plt.clf()
            ax = fig.add_subplot(111, projection='3d')
            u_surf = ax.plot_surface(xv, yv, u, alpha=0.3)
            #ax.contourf(xv, yv, u, zdir='z', offset=-100, cmap=cm.coolwarm)
            #ax.set_zlim(-1, 1)
            # Remove old surface before drawing
            if u_surf is not None:
                ax.collections.remove(u_surf)
            plt.draw()
            time.sleep(1)
        if plot_method > 0:
            time.sleep(0) # pause between frames
            if save_plot:
                filename = 'tmp_%04d.png' % n
                savefig(filename)  # time consuming!

    Nx = 40; Ny = 40; T = 20
    dt, cpu = solver(I, None, None, c, Lx, Ly, Nx, Ny, -1, T,
                     user_action=plot_u, version=version)



if __name__ == '__main__':
    test_quadratic()









Verification: quadratic solution (1)

Manufactured solution: $$ \begin{equation} \uex(x,y,t) = x(L_x-x)y(L_y-y)(1+{\half}t) \label{wave2D3D:impl:verify:quadratic} \end{equation} $$

Requires \( f=2c^2(1+{\half}t)(y(L_y-y) + x(L_x-x)) \).

This \( \uex \) is ideal because it also solves the discrete equations!









Verification: quadratic solution (2)

$$ \begin{align*} [D_xD_x \uex]^n_{i,j} &= [y(L_y-y)(1+{\half}t) D_xD_x x(L_x-x)]^n_{i,j}\\ &= y_j(L_y-y_j)(1+{\half}t_n)2 \end{align*} $$







Analysis of the difference equations









Properties of the solution of the wave equation

$$ \begin{equation*} \frac{\partial^2 u}{\partial t^2} = c^2 \frac{\partial^2 u}{\partial x^2} \end{equation*} $$

Solutions: $$ u(x,t) = g_R(x-ct) + g_L(x+ct) $$

If \( u(x,0)=I(x) \) and \( u_t(x,0)=0 \): $$ u(x,t) = \half I(x-ct) + \half I(x+ct) $$

Two waves: one traveling to the right and one to the left









Demo of the splitting of \( I(x) \) into two waves









Simulation of a case with variable wave velocity

A wave propagates perfectly (\( C=1 \)) and hits a medium with 1/4 of the wave velocity (\( C=0.25 \)). A part of the wave is reflected and the rest is transmitted.









Let us change the shape of the initial condition slightly and see what happens









Representation of waves as sum of sine/cosine waves

Build \( I(x) \) of wave components \( e^{ikx} = \cos kx + i\sin kx \): $$ I(x) \approx \sum_{k\in K} b_k e^{ikx} $$

Since \( u(x,t)=\half I(x-ct) + \half I(x+ct) \), the exact solution is $$ u(x,t) = \half \sum_{k\in K} b_k e^{ik(x - ct)} + \half \sum_{k\in K} b_k e^{ik(x + ct)} $$

Our interest: one component \( e^{i(kx -\omega t)} \), \( \omega = kc \)









A similar wave component is also a solution of the finite difference scheme (!)

Idea: a similar discrete \( u^n_q = e^{i(kx_q - \tilde\omega t_n)} \) solution (corresponding to the exact \( e^{i(kx - \omega t)} \)) solves $$ [D_tD_t u = c^2 D_xD_x u]^n_q $$

Note: we expect numerical frequency \( \tilde\omega\neq\omega \)









Preliminary results

$$ [D_tD_t e^{i\omega t}]^n = -\frac{4}{\Delta t^2}\sin^2\left( \frac{\omega\Delta t}{2}\right)e^{i\omega n\Delta t} $$

By \( \omega\rightarrow k \), \( t\rightarrow x \), \( n\rightarrow q \)) it follows that $$ [D_xD_x e^{ikx}]_q = -\frac{4}{\Delta x^2}\sin^2\left( \frac{k\Delta x}{2}\right)e^{ikq\Delta x} $$









Insertion of the numerical wave component

Inserting a basic wave component \( u=e^{i(kx_q-\tilde\omega t_n)} \) in the scheme requires computation of $$ \begin{align*} \lbrack D_tD_t e^{ikx}e^{-i\tilde\omega t}\rbrack^n_q &= \lbrack D_tD_t e^{-i\tilde\omega t}\rbrack^ne^{ikq\Delta x}\nonumber\\ &= -\frac{4}{\Delta t^2}\sin^2\left( \frac{\tilde\omega\Delta t}{2}\right)e^{-i\tilde\omega n\Delta t}e^{ikq\Delta x}\\ \lbrack D_xD_x e^{ikx}e^{-i\tilde\omega t}\rbrack^n_q &= \lbrack D_xD_x e^{ikx}\rbrack_q e^{-i\tilde\omega n\Delta t}\nonumber\\ &= -\frac{4}{\Delta x^2}\sin^2\left( \frac{k\Delta x}{2}\right)e^{ikq\Delta x}e^{-i\tilde\omega n\Delta t} \end{align*} $$









The equation for \( \tilde\omega \)

The complete scheme, $$ \lbrack D_tD_t e^{ikx}e^{-i\tilde\omega t} = c^2D_xD_x e^{ikx}e^{-i\tilde\omega t}\rbrack^n_q $$

leads to an equation for \( \tilde\omega \) (which can readily be solved): $$ \sin^2\left(\frac{\tilde\omega\Delta t}{2}\right) = C^2\sin^2\left(\frac{k\Delta x}{2}\right),\quad C = \frac{c\Delta t}{\Delta x} \mbox{ (Courant number)} $$

Taking the square root: $$ \sin\left(\frac{\tilde\omega\Delta t}{2}\right) = C\sin\left(\frac{k\Delta x}{2}\right) $$









The numerical dispersion relation

Can easily solve for an explicit formula for \( \tilde\omega \): $$ \tilde\omega = \frac{2}{\Delta t} \sin^{-1}\left( C\sin\left(\frac{k\Delta x}{2}\right)\right) $$

Note:









The special case \( C=1 \) gives the exact solution









Computing the error in wave velocity

$$ r(C, p) = \frac{\tilde c}{c} = \frac{2}{kc\Delta t} \sin^{-1}(C\sin p) = \frac{2}{kC\Delta x} \sin^{-1}(C\sin p) = \frac{1}{Cp}{\sin}^{-1}\left(C\sin p\right) $$

Can plot \( r(C,p) \) for \( p\in (0,\pi/2] \), \( C\in (0,1] \)









Visualizing the error in wave velocity

def r(C, p):
    return 1/(C*p)*asin(C*sin(p))

Note: the shortest waves have the largest error, and short waves move too slowly.









Taylor expanding the error in wave velocity

For small \( p \), Taylor expand \( \tilde\omega \) as polynomial in \( p \):

>>> C, p = symbols('C p')
>>> rs = r(C, p).series(p, 0, 7)
>>> print rs
1 - p**2/6 + p**4/120 - p**6/5040 + C**2*p**2/6 -
C**2*p**4/12 + 13*C**2*p**6/720 + 3*C**4*p**4/40 -
C**4*p**6/16 + 5*C**6*p**6/112 + O(p**7)

>>> # Drop the remainder O(...) term
>>> rs = rs.removeO()
>>> # Factorize each term
>>> rs = [factor(term) for term in rs.as_ordered_terms()]
>>> rs = sum(rs)
>>> print rs
p**6*(C - 1)*(C + 1)*(225*C**4 - 90*C**2 + 1)/5040 +
p**4*(C - 1)*(C + 1)*(3*C - 1)*(3*C + 1)/120 +
p**2*(C - 1)*(C + 1)/6 + 1

Leading error term is \( \frac{1}{6}(C^2-1)p^2 \) or $$ \frac{1}{6}\left(\frac{k\Delta x}{2}\right)^2(C^2-1) = \frac{k^2}{24}\left( c^2\Delta t^2 - \Delta x^2\right) = \Oof{\Delta t^2, \Delta x^2} $$









Example on effect of wrong wave velocity (1)

Smooth wave, few short waves (large \( k \)) in \( I(x) \):









Example on effect of wrong wave velocity (1)

Not so smooth wave, significant short waves (large \( k \)) in \( I(x) \):









Stability

$$ \sin\left(\frac{\tilde\omega\Delta t}{2}\right) = C\sin\left(\frac{k\Delta x}{2}\right) $$ Stability criterion: $$ C = \frac{c\Delta t}{\Delta x} \leq 1 $$









Why \( C>1 \) leads to non-physical waves

Recall that right-hand side is in \( [-C,C] \). Then \( C>1 \) means $$ \underbrace{\sin\left(\frac{\tilde\omega\Delta t}{2}\right)}_{>1} = C\sin\left(\frac{k\Delta x}{2}\right) $$









Extending the analysis to 2D (and 3D)

$$ u(x,y,t) = g(k_xx + k_yy - \omega t) $$

is a typically solution of $$ u_{tt} = c^2(u_{xx} + u_{yy}) $$

Can build solutions by adding complex Fourier components of the form $$ e^{i(k_xx + k_yy - \omega t)} $$









Discrete wave components in 2D

$$ \lbrack D_tD_t u = c^2(D_xD_x u + D_yD_y u)\rbrack^n_{q,r} $$

This equation admits a Fourier component $$ u^n_{q,r} = e^{i(k_x q\Delta x + k_y r\Delta y - \tilde\omega n\Delta t)} $$

Inserting the Fourier component into the dicrete 2D wave equation, and using formulas from the 1D analysis: $$ \sin^2\left(\frac{\tilde\omega\Delta t}{2}\right) = C_x^2\sin^2 p_x + C_y^2\sin^2 p_y $$

where $$ C_x = \frac{c\Delta t}{\Delta x},\quad C_y = \frac{c\Delta t}{\Delta y}, \quad p_x = \frac{k_x\Delta x}{2},\quad p_y = \frac{k_y\Delta y}{2} $$









Stability criterion in 2D

Ensuring real-valued \( \tilde\omega \) requires $$ C_x^2 + C_y^2 \leq 1 $$

or $$ \Delta t \leq \frac{1}{c} \left( \frac{1}{\Delta x^2} + \frac{1}{\Delta y^2}\right)^{-\halfi} $$









Stability criterion in 3D

$$ \Delta t \leq \frac{1}{c}\left( \frac{1}{\Delta x^2} + \frac{1}{\Delta y^2} + \frac{1}{\Delta z^2}\right)^{-\halfi} $$

For \( c^2=c^2(\xpoint) \) we must use the worst-case value \( \bar c = \sqrt{\max_{\xpoint\in\Omega} c^2(\xpoint)} \) and a safety factor \( \beta\leq 1 \): $$ \Delta t \leq \beta \frac{1}{\bar c} \left( \frac{1}{\Delta x^2} + \frac{1}{\Delta y^2} + \frac{1}{\Delta z^2}\right)^{-\halfi} $$









Numerical dispersion relation in 2D (1)

$$ \tilde\omega = \frac{2}{\Delta t}\sin^{-1}\left( \left( C_x^2\sin^2 p_x + C_y^2\sin^ p_y\right)^\half\right) $$

For visualization, introduce \( k=\sqrt{k_x^2+k_y^2} \) and \( \theta \) such that $$ k_x = k\sin\theta,\quad k_y=k\cos\theta, \quad p_x=\half kh\cos\theta,\quad p_y=\half kh\sin\theta$$

Also: \( \Delta x=\Delta y=h \). Then \( C_x=C_y=c\Delta t/h\equiv C \).

Now \( \tilde\omega \) depends on









Numerical dispersion relation in 2D (2)

$$ \frac{\tilde c}{c} = \frac{1}{Ckh} \sin^{-1}\left(C\left(\sin^2 ({\half}kh\cos\theta) + \sin^2({\half}kh\sin\theta) \right)^\half\right) $$

Can make color contour plots of \( 1-\tilde c/c \) in polar coordinates with \( \theta \) as the angular coordinate and \( kh \) as the radial coordinate.









Numerical dispersion relation in 2D (3)