How to compute multidimensional inverse Fourier transform

In summary, the inverse Fourier transform of the pressure field is given by:\tilde{p}=-\frac{i}{{{k}^{2}}}\mathbf{F}\centerdot \mathbf{x}
  • #1
Hanyu Ye
5
0
Hello, everybody. I am currently working on deriving solutions for Stokes flows. I encounter a multidimensional inverse Fourier transform. I already known the Fourier transform of the pressure field:
[itex]\tilde{p}=-\frac{i}{{{k}^{2}}}\mathbf{F}\centerdot \mathbf{k}[/itex]
where [itex]i[/itex] is the imaginary unit, [itex]\mathbf{k}[/itex] is the frequency vector, [itex]k[/itex] is the length of [itex]\mathbf{k}[/itex] (That is, [itex]k=\left\| \mathbf{k} \right\|[/itex]), and [itex]\mathbf{F}[/itex] is a constant vector. I don't know how to perform the inverse transform, although I have found the final answer in some references, which reads
[itex]p=\frac{\mathbf{F}\centerdot \mathbf{x}}{4\pi {{r}^{3}}}[/itex]
where [itex]r=\left\| \mathbf{x} \right\|[/itex]
Does anybody has an idea? Thanks a lot.
 
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  • #2
Hanyu Ye said:
Hello, everybody. I am currently working on deriving solutions for Stokes flows. I encounter a multidimensional inverse Fourier transform. I already known the Fourier transform of the pressure field:
[itex]\tilde{p}=-\frac{i}{{{k}^{2}}}\mathbf{F}\centerdot \mathbf{k}[/itex]
where [itex]i[/itex] is the imaginary unit, [itex]\mathbf{k}[/itex] is the frequency vector, [itex]k[/itex] is the length of [itex]\mathbf{k}[/itex] (That is, [itex]k=\left\| \mathbf{k} \right\|[/itex]), and [itex]\mathbf{F}[/itex] is a constant vector. I don't know how to perform the inverse transform, although I have found the final answer in some references, which reads
[itex]p=\frac{\mathbf{F}\centerdot \mathbf{x}}{4\pi {{r}^{3}}}[/itex]
where [itex]r=\left\| \mathbf{x} \right\|[/itex]
Does anybody has an idea? Thanks a lot.
If you could show what you fear is the problem you have in solving these problems, it could help us understand where you need help.
 
  • #3
Hanyu Ye said:
Hello, everybody. I am currently working on deriving solutions for Stokes flows. I encounter a multidimensional inverse Fourier transform. I already known the Fourier transform of the pressure field:
[itex]\tilde{p}=-\frac{i}{{{k}^{2}}}\mathbf{F}\centerdot \mathbf{k}[/itex]
where [itex]i[/itex] is the imaginary unit, [itex]\mathbf{k}[/itex] is the frequency vector, [itex]k[/itex] is the length of [itex]\mathbf{k}[/itex] (That is, [itex]k=\left\| \mathbf{k} \right\|[/itex]), and [itex]\mathbf{F}[/itex] is a constant vector. I don't know how to perform the inverse transform, although I have found the final answer in some references, which reads
[itex]p=\frac{\mathbf{F}\centerdot \mathbf{x}}{4\pi {{r}^{3}}}[/itex]
where [itex]r=\left\| \mathbf{x} \right\|[/itex]
Does anybody has an idea? Thanks a lot.
Oh, I have found the answer. It is presented in the following link:
http://www.fuw.edu.pl/~mklis/publications/Hydro/oseen.pdf
 

What is a multidimensional inverse Fourier transform?

A multidimensional inverse Fourier transform is a mathematical operation that converts a signal or function from the frequency domain into the spatial or time domain. It is the inverse of the multidimensional Fourier transform, which converts a function from the time or spatial domain into the frequency domain.

What is the formula for computing a multidimensional inverse Fourier transform?

The formula for computing a multidimensional inverse Fourier transform is: f(x) = ∫∫...∫F(u1,u2,...,un)e^(i(u1x1+u2x2+...+unxn))du1du2...dun, where F(u1,u2,...,un) is the multidimensional Fourier transform of f(x).

What are the applications of a multidimensional inverse Fourier transform?

A multidimensional inverse Fourier transform has various applications in signal processing, image processing, data compression, and other fields. It is used to analyze and filter signals, recover images from blurred or distorted versions, and extract relevant information from data.

How is a multidimensional inverse Fourier transform computed?

To compute a multidimensional inverse Fourier transform, the input signal or function is first decomposed into its frequency components using the multidimensional Fourier transform. Then, the inverse transform formula is applied to each component, and the results are summed to obtain the original function in the spatial or time domain.

Are there any limitations to using a multidimensional inverse Fourier transform?

One limitation of using a multidimensional inverse Fourier transform is that it requires the signal or function to be periodic in all dimensions. Additionally, the computation can become complex and time-consuming for high-dimensional signals or functions.

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