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16 The acoustic wave equation governs the propagation of pressure waves. Wave
17 types that obey this law tend to travel in liquids or gases where shear waves
18 or longitudinal style wave motion is not possible. An obvious example is sound
19 waves.
21 The acoustic wave equation is defined as;
22 \begin{equation}
23 \nabla ^2 p - \frac{1}{c^2} \frac{\partial ^2 p}{\partial t^2} = 0
24 \label{eqn:acswave}
25 \end{equation}
26 where $p$ is the pressure, $t$ is the time and $c$ is the wave velocity. In this
27 chapter the acoustic wave equation is demonstrated. Important steps include the
28 translation of the Laplacian $\nabla^2$ to the \esc general form, the stiff
29 equation stability criterion and solving for the displacement or acceleration solution.
31 \section{The Laplacian in \esc}
32 The Laplacian operator which can be written as $\Delta$ or $\nabla^2$, is
33 calculated via the divergence of the gradient of the object, which in this
34 example is the scalar $p$. Thus we can write;
35 \begin{equation}
36 \nabla^2 p = \nabla \cdot \nabla p =
37 \sum_{i}^n
38 \frac{\partial^2 p}{\partial x^2_{i}}
39 \label{eqn:laplacian}
40 \end{equation}
41 For the two dimensional case in Cartesian coordinates \autoref{eqn:laplacian}
42 becomes;
43 \begin{equation}
44 \nabla^2 p = \frac{\partial^2 p}{\partial x^2}
45 + \frac{\partial^2 p}{\partial y^2}
46 \end{equation}
48 In \esc the Laplacian is calculated using the divergence representation and the
49 intrinsic functions \textit{grad()} and \textit{trace()}. The function
50 \textit{grad{}} will return the spatial gradients of an object.
51 For a rank 0 solution, this is of the form;
52 \begin{equation}
53 \nabla p = \left[
54 \frac{\partial p}{\partial x _{0}},
55 \frac{\partial p}{\partial x _{1}}
56 \right]
57 \label{eqn:grad}
58 \end{equation}
59 Larger ranked solution objects will return gradient tensors. For example, a
60 pressure field which acts in the directions $p _{0}$ and $p
61 _{1}$ would return;
62 \begin{equation}
63 \nabla p = \begin{bmatrix}
64 \frac{\partial p _{0}}{\partial x _{0}} &
65 \frac{\partial p _{1}}{\partial x _{0}} \\
66 \frac{\partial p _{0}}{\partial x _{1}} &
67 \frac{\partial p _{1}}{\partial x _{1}}
68 \end{bmatrix}
69 \label{eqn:gradrank1}
70 \end{equation}
72 \autoref{eqn:grad} corresponds to the Linear PDE general form value
73 $X$. Notice however, that the general form contains the term $X
74 _{i,j}$\footnote{This is the first derivative in the $j^{th}$
75 direction for the $i^{th}$ component of the solution.},
76 hence for a rank 0 object there is no need to do more then calculate the
77 gradient and submit it to the solver. In the case of the rank 1 or greater
78 object, it is also necessary to calculate the trace. This is the sum of the
79 diagonal in \autoref{eqn:gradrank1}.
81 Thus when solving for equations containing the Laplacian one of two things must
82 be completed. If the object \verb!p! is less then rank 1 the gradient is
83 calculated via;
84 \begin{python}
85 gradient=grad(p)
86 \end{python}
87 and if the object is greater then or equal to a rank 1 tensor, the trace of
88 the gradient is calculated.
89 \begin{python}
90 gradient=trace(grad(p))
91 \end{python}
92 These values can then be submitted to the PDE solver via the general form term
93 $X$. The Laplacian is then computed in the solution process by taking the
94 divergence of $X$.
96 Note, if you are unsure about the rank of your tensor, the \textit{getRank}
97 command will return the rank of the PDE object.
98 \begin{python}
99 rank = p.getRank()
100 \end{python}
103 \section{Numerical Solution Stability} \label{sec:nsstab}
104 Unfortunately, the wave equation belongs to a class of equations called
105 \textbf{stiff} PDEs. These types of equations can be difficult to solve
106 numerically as they tend to oscillate about the exact solution, which can
107 eventually lead to a catastrophic failure. To counter this problem, explicitly
108 stable schemes like the backwards Euler method, and correct parameterisation of
109 the problem are required.
111 There are two variables which must be considered for
112 stability when numerically trying to solve the wave equation. For linear media,
113 the two variables are related via;
114 \begin{equation} \label{eqn:freqvel}
115 f=\frac{v}{\lambda}
116 \end{equation}
117 The velocity $v$ that a wave travels in a medium is an important variable. For
118 stability the analytical wave must not propagate faster then the numerical wave
119 is able to, and in general, needs to be much slower then the numerical wave.
120 For example, a line 100m long is discretised into 1m intervals or 101 nodes. If
121 a wave enters with a propagation velocity of 100m/s then the travel time for
122 the wave between each node will be 0.01 seconds. The time step, must therefore
123 be significantly less then this. Of the order $10E-4$ would be appropriate.
125 The wave frequency content also plays a part in numerical stability. The
126 nyquist-sampling theorem states that a signals bandwidth content will be
127 accurately represented when an equispaced sampling rate $f _{n}$ is
128 equal to or greater then twice the maximum frequency of the signal
129 $f_{s}$, or;
130 \begin{equation} \label{eqn:samptheorem}
131 f_{n} \geqslant f_{s}
132 \end{equation}
133 For example a 50Hz signal will require a sampling rate greater then 100Hz or
134 one sample every 0.01 seconds. The wave equation relies on a spatial frequency,
135 thus the sampling theorem in this case applies to the solution mesh spacing.
136 This relationship confirms that the frequency content of the input signal
137 directly affects the time discretisation of the problem.
139 To accurately model the wave equation with high resolutions and velocities
140 means that very fine spatial and time discretisation is necessary for most
141 problems.
142 This requirement makes the wave equation arduous to
143 solve numerically due to the large number of time iterations required in each
144 solution. Models with very high velocities and frequencies will be the worst
145 affected by this problem.
147 \section{Displacement Solution}
148 \sslist{example07a.py}
150 We begin the solution to this PDE with the centred difference formula for the
151 second derivative;
152 \begin{equation}
153 f''(x) \approx \frac{f(x+h - 2f(x) + f(x-h)}{h^2}
154 \label{eqn:centdiff}
155 \end{equation}
156 substituting \autoref{eqn:centdiff} for $\frac{\partial ^2 p }{\partial t ^2}$
157 in \autoref{eqn:acswave};
158 \begin{equation}
159 \nabla ^2 p - \frac{1}{c^2h^2} \left[p_{(t+1)} - 2p_{(t)} +
160 p_{(t-1)} \right]
161 = 0
162 \label{eqn:waveu}
163 \end{equation}
164 Rearranging for $p_{(t+1)}$;
165 \begin{equation}
166 p_{(t+1)} = c^2 h^2 \nabla ^2 p_{(t)} +2p_{(t)} -
167 p_{(t-1)}
168 \end{equation}
169 this can be compared with the general form of the \modLPDE module and it
170 becomes clear that $D=1$, $X_{i,j}=-c^2 h^2 \nabla ^2 p_{(t)}$ and
171 $Y=2p_{(t)} - p_{(t-1)}$.
173 The solution script is similar to others that we have created in previous
174 chapters. The general steps are;
175 \begin{enumerate}
176 \item The necessary libraries must be imported.
177 \item The domain needs to be defined.
178 \item The time iteration and control parameters need to be defined.
179 \item The PDE is initialised with source and boundary conditions.
180 \item The time loop is started and the PDE is solved at consecutive time steps.
181 \item All or select solutions are saved to file for visualisation later on.
182 \end{enumerate}
184 Parts of the script which warrant more attention are the definition of the
185 source, visualising the source, the solution time loop and the VTK data export.
187 \subsection{Pressure Sources}
188 As the pressure is a scalar, one need only define the pressure for two
189 time steps prior to the start of the solution loop. Two known solutions are
190 required because the wave equation contains a double partial derivative with
191 respect to time. This is often a good opportunity to introduce a source to the
192 solution. This model has the source located at it's centre. The source should
193 be smooth and cover a number of samples to satisfy the frequency stability
194 criterion. Small sources will generate high frequency signals. Here, when using
195 a rectangular domain, the source is defined by a cosine function.
196 \begin{python}
197 U0=0.01 # amplitude of point source
198 xc=[500,500] #location of point source
199 # define small radius around point xc
200 src_radius = 30
201 # for first two time steps
202 u=U0*(cos(length(x-xc)*3.1415/src_radius)+1)*\
203 whereNegative(length(x-xc)-src_radius)
204 u_m1=u
205 \end{python}
207 \subsection{Visualising the Source}
208 There are two options for visualising the source. The first is to export the
209 initial conditions of the model to VTK, which can be interpreted as a scalar
210 surface in Mayavi2. The second is to take a cross section of the model which
211 will require the \textit{Locator} function.
212 First \verb!Locator! must be imported;
213 \begin{python}
214 from esys.escript.pdetools import Locator
215 \end{python}
216 The function can then be used on the domain to locate the nearest domain node
217 to the point or points of interest.
219 It is now necessary to build a list of $(x,y)$ locations that specify where are
220 model slice will go. This is easily implemented with a loop;
221 \begin{python}
222 cut_loc=[]
223 src_cut=[]
224 for i in range(ndx/2-ndx/10,ndx/2+ndx/10):
225 cut_loc.append(xstep*i)
226 src_cut.append([xstep*i,xc[1]])
227 \end{python}
228 We then submit the output to \verb!Locator! and finally return the appropriate
229 values using the \verb!getValue! function.
230 \begin{python}
231 src=Locator(mydomain,src_cut)
232 src_cut=src.getValue(u)
233 \end{python}
234 It is then a trivial task to plot and save the output using \mpl
235 (\autoref{fig:cxsource}).
236 \begin{python}
237 pl.plot(cut_loc,src_cut)
238 pl.axis([xc[0]-src_radius*3,xc[0]+src_radius*3,0.,2*U0])
239 pl.savefig(os.path.join(savepath,"source_line.png"))
240 \end{python}
241 \begin{figure}[h]
242 \centering
243 \includegraphics[width=6in]{figures/sourceline.png}
244 \caption{Cross section of the source function.}
245 \label{fig:cxsource}
246 \end{figure}
249 \subsection{Point Monitoring}
250 In the more general case where the solution mesh is irregular or specific
251 locations need to be monitored, it is simple enough to use the \textit{Locator}
252 function.
253 \begin{python}
254 rec=Locator(mydomain,[250.,250.])
255 \end{python}
256 When the solution \verb u is updated we can extract the value at that point
257 via;
258 \begin{python}
259 u_rec=rec.getValue(u)
260 \end{python}
261 For consecutive time steps one can record the values from \verb!u_rec! in an
262 array initialised as \verb!u_rec0=[]! with;
263 \begin{python}
264 u_rec0.append(rec.getValue(u))
265 \end{python}
267 It can be useful to monitor the value at a single or multiple individual points
268 in the model during the modelling process. This is done using
269 the \verb!Locator! function.
272 \section{Acceleration Solution}
273 \sslist{example07b.py}
275 An alternative method to the displacement solution, is to solve for the
276 acceleration $\frac{\partial ^2 p}{\partial t^2}$ directly. The displacement can
277 then be derived from the acceleration after a solution has been calculated
278 The acceleration is given by a modified form of \autoref{eqn:waveu};
279 \begin{equation}
280 \nabla ^2 p - \frac{1}{c^2} a = 0
281 \label{eqn:wavea}
282 \end{equation}
283 and can be solved directly with $Y=0$ and $X=-c^2 \nabla ^2 p_{(t)}$.
284 After each iteration the displacement is re-evaluated via;
285 \begin{equation}
286 p_{(t+1)}=2p_{(t)} - p_{(t-1)} + h^2a
287 \end{equation}
289 \subsection{Lumping}
290 For \esc, the acceleration solution is prefered as it allows the use of matrix
291 lumping. Lumping or mass lumping as it is sometimes known, is the process of
292 aggressively approximating the density elements of a mass matrix into the main
293 diagonal. The use of Lumping is motivaed by the simplicity of diagonal matrix
294 inversion. As a result, Lumping can significantly reduce the computational
295 requirements of a problem. Care should be taken however, as this
296 function can only be used when the $A$, $B$ and $C$ coefficients of the
297 general form are zero.
299 To turn lumping on in \esc one can use the command;
300 \begin{python}
301 mypde.getSolverOptions().setSolverMethod(mypde.getSolverOptions().LUMPING)
302 \end{python}
303 It is also possible to check if lumping is set using;
304 \begin{python}
305 print mypde.isUsingLumping()
306 \end{python}
308 \section{Stability Investigation}
309 It is now prudent to investigate the stability limitations of this problem.
310 First, we let the frequency content of the source be very small. If we define
311 the source as a cosine input, then the wavlength of the input is equal to the
312 radius of the source. Let this value be 5 meters. Now, if the maximum velocity
313 of the model is $c=380.0ms^{-1}$, then the source
314 frequency is $f_{r} = \frac{380.0}{5} = 76.0 Hz$. This is a worst case
315 scenario with a small source and the models maximum velocity.
317 Furthermore, we know from \autoref{sec:nsstab}, that the spatial sampling
318 frequency must be at least twice this value to ensure stability. If we assume
319 the model mesh is a square equispaced grid,
320 then the sampling interval is the side length divided by the number of samples,
321 given by $\Delta x = \frac{1000.0m}{400} = 2.5m$ and the maximum sampling
322 frequency capable at this interval is
323 $f_{s}=\frac{380.0ms^{-1}}{2.5m}=152Hz$ this is just equal to the
324 required rate satisfying \autoref{eqn:samptheorem}.
326 \autoref{fig:ex07sampth} depicts three examples where the grid has been
327 undersampled, sampled correctly, and over sampled. The grids used had
328 200, 400 and 800 nodes per side respectively. Obviously, the oversampled grid
329 retains the best resolution of the modelled wave.
331 The time step required for each of these examples is simply calculated from
332 the propagation requirement. For a maximum velocity of $380.0ms^{-1}$,
333 \begin{subequations}
334 \begin{equation}
335 \Delta t \leq \frac{1000.0m}{200} \frac{1}{380.0} = 0.013s
336 \end{equation}
337 \begin{equation}
338 \Delta t \leq \frac{1000.0m}{400} \frac{1}{380.0} = 0.0065s
339 \end{equation}
340 \begin{equation}
341 \Delta t \leq \frac{1000.0m}{800} \frac{1}{380.0} = 0.0032s
342 \end{equation}
343 \end{subequations}
344 Observe that for each doubling of the number of nodes in the mesh, we halve
345 the time step. To illustrate the impact this has, consider our model. If the
346 source is placed at the center, it is $500m$ from the nearest boundary. With a
347 velocity of $380.0ms^{-1}$ it will take $\approx1.3s$ for the wavefront to
348 reach that boundary. In each case, this equates to $100$, $200$ and $400$ time
349 steps. This is again, only a best case scenario, for true stability these time
350 values may need to be halved and possibly halved again.
352 \begin{figure}[ht]
353 \centering
354 \subfigure[Undersampled Example]{
355 \includegraphics[width=0.45\textwidth,trim=0cm 6cm 5cm 6cm
356 ,clip]{figures/ex07usamp.png}
357 \label{fig:ex07usamp}
358 }
359 \subfigure[Just sampled Example]{
360 \includegraphics[width=0.45\textwidth,trim=0cm 6cm 5cm 6cm
361 ,clip]{figures/ex07jsamp.png}
362 \label{fig:ex07jsamp}
363 }
364 \subfigure[Over sampled Example]{
365 \includegraphics[width=0.45\textwidth,trim=0cm 6cm 5cm 6cm
366 ,clip]{figures/ex07nsamp.png}
367 \label{fig:ex07nsamp}
368 }
369 \caption{Sampling Theorem example for stability
370 investigation.}
371 \label{fig:ex07sampth}
372 \end{figure}

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