Time Evolution of a Square Pulse - Fourier

In summary, the conversation discusses the evolution of a square pulse of light and how to graph it in Mathematica. The suggested approach involves using a Fourier transform to find the wave number spectrum and then using an inverse transformation with a time progression factor to calculate the time evolution. However, this may be difficult and it is suggested to discretize space and use a FFT to go to reciprocal space for easier visualization.
  • #1
skynelson
58
4
Hi There,
A square pulse of light has the form f(x) = A exp(ik0x) for |x| < L/2
and f(x) = 0 everywhere else.

I want to know how that pulse evolves overtime. I want to graph it in Mathematica.

I did a Fourier transform to find the wave number spectrum, with the general form:

F(k) = Sin((k - k0) L/2) / (k - k0)

this is a sinusoid that decays inversely, centered on k0. I figure that this wave packet will disperse over time, due to the different wave number components.

my question is "how do I calculate the time evolution of this pulse?

my attempted answer (thanks to Lewis A. Riley): calculate the inverse transformation, with the addition of a factor to account for the time progression of the wave:

y(x,t) = Integral[ { sin((k-k0) L/2) / (k-k0) } * exp (ik {x - vt}) ] dk

this is not a trivial calculation (I'm using Mathematica) and it returns an answer in the form of the exponential integral. I'm not sure how to make sense of this. I expect I should be able to graph a real function here, since we are talking about the evolution of real waves. I don't know how to do that.
 
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  • #2
The approach is basically correct. However, I am not sure this can be accomplished easily. I suggest instead of discretizing space and using a FFT to go to reciprocal space, where the time-dependent phase factor can be applied for the desired time, and doing an inverse FFT to recover the pulse shape. This can then be repeated for different times to visualize the evolution.
 

1. What is the Fourier transform?

The Fourier transform is a mathematical operation that decomposes a function into its constituent frequencies. It is often used to analyze signals or data in the frequency domain.

2. How does the Fourier transform relate to the time evolution of a square pulse?

The Fourier transform of a square pulse is a sinc function, which shows the amplitude of each frequency component present in the pulse. This allows us to see how the pulse evolves over time in terms of its constituent frequencies.

3. Can the Fourier transform be used to analyze other types of signals?

Yes, the Fourier transform can be applied to any signal that can be represented as a function of time. This includes audio signals, images, and even complex data sets.

4. What is the inverse Fourier transform?

The inverse Fourier transform is the mathematical operation that reconstructs a signal from its frequency components. It is used to convert a signal from the frequency domain back to the time domain.

5. Are there any limitations to using the Fourier transform?

The Fourier transform assumes that the signal being analyzed is periodic and infinite. In reality, most signals are not truly infinite, so the results may not be completely accurate. Additionally, the Fourier transform is limited by the resolution of the data and can only reveal frequencies that are present in the signal.

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