So we all know the divergence/Gauss's theorem as(adsbygoogle = window.adsbygoogle || []).push({});

[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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# Gradient version of divergence theorem?

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