Gradient version of divergence theorem?

In summary, the conversation discusses the similarities and differences between the divergence/Gauss's theorem and another version called Gauss's theorem. The latter involves a scalar function p and a surface integral. The conversation then goes on to explore a possible proof for this version, using dot products and the product rule. There is a concern about freely converting between vectors and sums and the conversation ends with a clarification on the usual proof for the scalar version of the divergence theorem.
  • #1
Cygnus_A
34
2
So we all know the divergence/Gauss's theorem as
[itex]∫ (\vec∇ ⋅ \vec v) dV = ∫\vec v \cdot d\vec S[/itex]

Now I've come across something labeled as Gauss's theorem:
[itex]\int (\vec\nabla p)dV = \oint p d\vec S[/itex]
where p is a scalar function.

I was wondering if I could go about proving it in the following way (replacing dot products with implied sums):
With [itex]e_i := \hat e_i[/itex] and [itex] d\vec S := ds_1 \hat x + ds_2 \hat y + ds_3 \hat z = ds_i e_i [/itex],

[itex] \oint p d\vec S = \oint p (e_i ds_i) = \oint (p e_i)(ds_i) = \oint \vec p \cdot d\vec s[/itex] (this p vector has scalar functional dependence still, it's just [itex](p)(\vec v)[/itex], a scalar times a vector, but still overall a vector in my mind)

then applying divergence theorem and getting
[itex]= \int (\vec \nabla \cdot \vec p) dV = \int \partial_i (p e_i) dV [/itex]

and finally applying the product rule and the fact that [itex]e_i[/itex] is a unit vector
[itex]\int (e_i \partial_i p + p \partial_i e_i) dV[/itex].

The second term is zero, since it's a partial of a unit vector, which has no spatial dependence, leaving
[itex]\int (e_i \partial_i p)dV = \int (\vec \nabla p) dV[/itex]

Does that make sense? I think it seems to work out, but I'm concerned that it's flawed due to my free conversions between sums and vectors. It seems unnatural that I've said [itex]d\vec S =d\vec s = ds_i[/itex], despite defining them differently. One, I suppose has actual vector components, whereas the other is just a list of components.
 
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  • #2
I believe the gradient version of the divergence theorem would be your typical statement that the integral of the path going through a potential is just the difference in potentials.
 
  • #3
TMO said:
I believe the gradient version of the divergence theorem would be your typical statement that the integral of the path going through a potential is just the difference in potentials.

You must be thinking of the fundamental theorem of calculus for gradients -- [itex]\phi (\vec b) - \phi (\vec a) = \int_a^b \vec \nabla \phi \cdot d\vec r [/itex]

This was what was presented to me during a proof, and I had never seen it before
[itex]\int (\vec\nabla p)dV = \oint p d\vec S[/itex](oops, and I just realized I forgot to close the surface integral of divergence theorem in my original post :P)
 
  • #4
The usual proof of the scalar version of the divergence theorem involves replacing the vector field v by (p k), where p is a scalar field and k is an arbitrary constant vector. Then div v is equal to k.grad p. Since k is arbitrary it can then be removed from both sides giving the result.
 
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  • #5
Ah, ok. The steps are essentially the same then. Just slightly different in how you get your constant vector. Thanks!
 

Related to Gradient version of divergence theorem?

1. What is the gradient version of the divergence theorem?

The gradient version of the divergence theorem is a mathematical theorem that relates the flux of a vector field through a closed surface to the divergence of the same vector field within the enclosed volume.

2. How is the gradient version of the divergence theorem different from the traditional divergence theorem?

The traditional divergence theorem relates the flux of a vector field through a closed surface to the net source of the field within the enclosed volume, while the gradient version relates it to the divergence of the field within the volume.

3. What is the significance of the gradient version of the divergence theorem in vector calculus?

The gradient version of the divergence theorem is important in vector calculus as it provides a way to evaluate the divergence of a vector field without having to explicitly calculate the flux through a surface. It also allows for the application of the fundamental theorem of calculus to vector fields.

4. How is the gradient version of the divergence theorem used in practical applications?

The gradient version of the divergence theorem is used in many practical applications, such as in fluid dynamics, electromagnetism, and heat transfer. It allows for the analysis and calculation of flux and divergence in a variety of physical systems.

5. Are there any limitations to the gradient version of the divergence theorem?

Like any mathematical theorem, the gradient version of the divergence theorem has its limitations. It is only applicable to continuous vector fields and closed surfaces, and it assumes that the vector field is well-behaved within the enclosed volume. Additionally, it may not be applicable in certain non-Euclidean or curved spaces.

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