Fourier representation of fields

The only difference is that, for our case, the coefficients a(n,x,y) will be functions of x and y. This is just like in the example you would find that the coefficients cn are functions of x and y.To determine those coefficients a(n,x,y) you would use the boundary conditionsa(x,y,0) = a(x,y,L) = 0 ,that are given in the problem. That's how it works.The idea is that the sin(nπz/L)'s form a "basis" for the space
  • #1
MadMax
99
0
Hello,

From Jean Zinn-Justin:

"The conducting plates impose to the electric field to be perpendicular to the plates. It is easy to verify that this condition is satisfied if the vector field [itex]A_\mu[/itex] itself vanishes on the plates. Calling L the distance between the plates, z=0 and z=L the plate positions, we thus integrate over fields which have the Fourier representation,

[tex]A_\mu (\mathbf{x}_{\perp},z)=\int d^{d-1}p_{\perp}\sum_{n\ge1} e^{ip_{\perp} \cdot \mathbf{x}_{\perp}} \sin(\pi z/L) \tilde{A}(\mathbf{p}_{\perp},n)[/tex],

where [itex]\mathbf{x_{\perp}}[/itex] are the space coordinates in the remaining directions."

OK so the bit that I don't understand is the Fourier representation buisness. Am I right in thinking that the exponential factor is the imaginary part of the fields?

Also what is meant by [itex]\tilde{A}()[/itex]?

Answers to these questions or comments that will help to make this equation clear to me would be very much appreciated.

Thank you for your time.
 
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  • #2
Please? *insert post filler here*
 
  • #3
MadMax said:
Please? *insert post filler here*

The Fourier representation is a representation of the function in the sinusoidal basis. The quantity [tex]\tilde{A}(\mathbf{p},n)[/tex] is the (Fourier) amplitude at coordinate (p, n). Because the vector potential [tex] A[/tex] is real, the Fourier amplitudes satisfy certain symmetry conditions.

BTW, I think you are missing a factor of n inside you sine function.
 
  • #4
Consider a function f(x) of one real variable x. We define the "Fourier transform of f(x)" as the function F(p), of the single real variable p, which is given by

F(p) ≡ (2π)-1 ∫dx e-ipx f(x) .

It then follows by theorem that

f(x) = ∫dp eipx F(p) ,

which is called the "inverse Fourier transform of F(p)".
____________________

This pattern can be applied to any number of dimensions m. In that case,

F(p) ≡ (2π)-m ∫dmx e-ipx f(x) ,

and again, by theorem, it follows that

f(x) = ∫dmp eipx F(p) .
____________________

For the case at hand, we are dealing with a function

a(x,y,z) = ∑n≥1 sin(nπz/L) a(n,x,y) ... [1] ,

and with Fourier transforms A(n,p1,p2), in m=2 dimensions, of the functions
a(n,x,y) such that

A(n,p1,p2) ≡ (2π)-2 ∫dx dy exp[-ip1∙x] exp[-ip2∙y] a(n,x,y) ... [2] ,

so that, by theorem,

a(n,x,y) = ∫ dp1 dp2 exp[ip1∙x] exp[ip2∙y] A(n,p1,p2) ... [3] .

Substituting [3] into [1] gives the original expression posted at the beginning of this thread, which in the current notation is just

a(x,y,z) = ∫ dp1 dp2n≥1 exp[ip1∙x] exp[ip2∙y] sin(nπz/L) A(n,p1,p2) ... [4] .
_____________________________

IN ANSWER TO THE QUESTIONS:

The exponential factors

exp[ip1∙x] exp[ip2∙y]

that appear in [4] are not the imaginary parts of the field. Those exponentials are just parts of the "inverse Fourier transform" which takes

A(n,p1,p2) → a(n,x,y) ,

for each value of n, according to equation [3].

Also, what is meant by A(n,p1,p2) is just, quite simply, the Fourier transform of
a(n,x,y), according to equation [2], for each value of n.
 
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  • #5
MadMax,

What is your end goal by applying this Fourier method to a parallel plate geometry.

I am interested in calculating capacitance of parallel plates, taking into account fringing fields etc... all the 'non-ideal' properties of real life capacitances.

My methods so far have only included a very basic Boundary element method, but I am beginning to look into conformal mapping and I am always looking for new modelling techniques.

The BEM method allows me to create a surf / mesh plot of the variation in charge across the plates using MATLAB - my aim is to include field lines and the such like to these models.

Regards

Tom
 
  • #6
Wow, thank you very much Eye_in_the_ sky.

I should have said earlier when beautiful1 mentioned it but there wasn't an n missing in the sin.. at least the book didn't have an n in there.. :S do you think there is an erratum?

Also you say that the electric field before Fourier transformation was given by a(x,y,z) = ∑n≥1 sin(nπz/L) a(n,x,y) . Is that simply a sum over all possible standing waves? If not where does that come from?

tommyers: I'm looking to learn and understand calculation of the cassimir force for various geommetries and materials. I thought I'd start with parallel conducting plates since it was the simplest example in the textbook recommended to me =)
 
  • #7
Tom,

MadMax is considering here something very different from electrostatic fields.

He considers time-dependent fields inside an extremely narrow gap in between two plates, in the absence of 'externally' applied fields. In quantum mechanics, there is a so-called "vacuum field", extremely weak that pervades everywhere. This vacuum field cannot be used as a source of energy (although some people pretend the contrary) but it is necessary for the consistency of quantum mechanics as well as to explain some experiments. One of these experiments is the Casimir force.

The Casimir force, extremely small, occurs because in between the parallel plates some "vacuum photons" cannot propagate, they cannot exist, they are short-circuited by the plates (so to speak). At the same time, these same photons can exist outside of the plates. Therefore, the (radiation) pressure inside the plates is smaller than outside and this produces a small attraction of the two plates, the so-called Casimir force.

Michel
 
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  • #8
I think I once read an article on this - it discussed the collision of two closely moored ships!

Tom
 
  • #9
MadMax said:
I should have said earlier when beautiful1 mentioned it but there wasn't an n missing in the sin.. at least the book didn't have an n in there.. :S do you think there is an erratum?
There is a typo in that book.

Also you say that the electric field before Fourier transformation was given by a(x,y,z) = ∑n≥1 sin(nπz/L) a(n,x,y) . Is that simply a sum over all possible standing waves? If not where does that come from?
We have boundary conditions

a(x,y,0) = a(x,y,L) = 0 .

This is just like saying for a function f(z)

f(0) = f(L) = 0 ,

in which case we would want to write (presuming that doing so would simplify the problem for us)

f(z) = ∑n≥1 cnsin(nπz/L) .

Those functions sin(nπz/L), for n=1,2,... , are "complete" on the interval [0,L]. That is, any (reasonably well-behaved) function f(z) can be written in terms of them for appropriate choices of the coefficients cn.

So, similarly in our case we write

a(x,y,z) = ∑n≥1 sin(nπz/L) a(n,x,y) .
 
  • #10
wow, that's awesome Eye_in_the_Sky. :O :O

You have explained everything beautifully =)

Thank you very very much =)
 

Related to Fourier representation of fields

1. What is the Fourier representation of fields?

The Fourier representation of fields is a mathematical tool used to describe a function or field in terms of its frequency components. It involves breaking down a complex signal or field into simpler sinusoidal components, which are then combined to reconstruct the original signal or field.

2. Why is the Fourier representation important in science?

The Fourier representation is important in science because it allows us to analyze and manipulate complex signals and fields in a more manageable way. It is used in a variety of fields such as physics, engineering, and mathematics, to study and solve problems related to waves, vibrations, and other periodic phenomena.

3. What is the difference between a Fourier series and a Fourier transform?

A Fourier series is used to represent a periodic function as a sum of sinusoidal functions with different frequencies, whereas a Fourier transform is used to represent a non-periodic function as a continuous spectrum of frequencies. In other words, a Fourier series is used for periodic signals and a Fourier transform is used for non-periodic signals.

4. How is the Fourier representation used in image and signal processing?

In image and signal processing, Fourier representation is used to decompose a signal or image into its frequency components and then apply filters or transformations to these components. This allows for tasks such as image enhancement, noise reduction, and compression to be performed more efficiently.

5. Are there any limitations to the Fourier representation?

Yes, there are some limitations to the Fourier representation. It assumes that the signal or field is stationary, meaning that it does not change over time. It also assumes that the signal or field is linear, which means that the output is directly proportional to the input. In reality, many signals and fields are non-stationary and non-linear, so the Fourier representation may not accurately capture their behavior.

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