Turbulence example

In this notebook we show how to perform a simulation using a soundspeed field based on a Gaussian turbulence spectrum.



In [1]:

    
import numpy as np
from pstd import PSTD, PML, Medium, Position2D, PointSource
from pstd import PSTD
from acoustics import Signal
import seaborn as sns
%matplotlib inline

Configuration

The following are the parameters for our simulation



In [2]:

    
x = 30.0
y = 20.0
z = 0.0

c = 343.2
maximum_frequency_target = 200.0

Create model

We now create a model. Waves propagate in a medium which we define first.



In [3]:

    
medium = Medium(soundspeed=c, density=1.296)

The model is only finite and to prevent aliasing we need a Perfectly Matched Layer.



In [4]:

    
pml = PML((1000.0, 1000.0), depth=10.0)

Now we create the actual model.



In [5]:

    
model = PSTD(maximum_frequency=maximum_frequency_target, pml=pml, medium=medium, cfl=0.05, size=[x, y])

In this example our source excites a pulse.



In [6]:

    
source_position = Position2D(x/4.0, y/2.0)

source = model.addObject('source', 'PointSource', position=source_position, 
                         excitation='pulse', quantity='pressure', amplitude=0.1)

We also add a receiver on the other side of the domain



In [7]:

    
receiver_position = Position2D(x*3.0/4.0, y/2.0)

receiver = model.addObject('receiver', 'Receiver', position=receiver_position, quantity='pressure')

Check model

To get a quick overview of all parameters, for example to check them, we can print one



In [8]:

    
print(model.overview())









    



Model timestep: 0.000125 
Maximum frequency: 200.0
Grid spacing: 0.858
Grid shape: (59, 48)
Grid shape without PML: (35, 24)
Grid shape with PML: (59, 48)
PML nodes: 12
PML depth target: 10.00
PML depth actual: 10.30
Grid size: (50.622, 41.183999999999997)
Grid size without PML: (30.030000000000001, 20.591999999999999)
Grid size with PML: (50.622, 41.183999999999997)
Amount of sources: 1
Amount of receivers: 1

To check whether the geometry is as we want it to be, we can simply draw it.



In [9]:

    
_ = model.plot_scene()

Running the simulation

Now that we've defined and checked our model we can run it.

With model.run() you can specify the amount of time steps or amount of seconds it should run.



In [10]:

    
model.run(steps=10)









    Out[10]:





<pstd.pstd.PSTD at 0x7f1ec3ef7e48>

Let's see how the sound pressure field looks like now.



In [11]:

    
_ = model.plot_field()

It might happen that you realize that you actually need to calculate a bit further. This can easily be done, since the state is remembered. Simply use model.run() again and the simulation continues.



In [12]:

    
model.run(steps=500)









    Out[12]:





<pstd.pstd.PSTD at 0x7f1ec3ef7e48>

as you can see.



In [13]:

    
_ = model.plot_field()

We can also check the signal recorded by the receiver, which in this case is the impulse response. The method receiver.recording() returns an instance of acoustics.Signal.



In [14]:

    
_ = receiver.recording().plot()

If however, you want to restart the simulation you can do so with model.restart().



In [15]:

    
model.restart()









    Out[15]:





<pstd.pstd.PSTD at 0x7f1ec3ef7e48>