Why Does the Contraction Term Vanish in the Divergence Theorem on Manifolds?

In summary: So if we take the dual of \hat{N}_x, we get \hat{N}_x=g_x(N(x),\cdot) \wedge \iota_X(\omega) = \langle X, N \rangle \omega - \hat{N(x)} \wedge \iota_X(\omega)
  • #1
mathmeat
2
0
Hi,

I'm having some trouble understanding this theorem in Lang's book, (pp. 497) "Fundamentals of Differential Geometry." It goes as follows:

[tex] \int_{M} \mathcal{L}_X(\Omega)= \int_{\partial M} \langle X, N \rangle \omega [/tex]

where [tex] N [/tex] is the unit outward normal vector to [tex] \partial M [/tex], [tex] X [/tex] is a vector field on [tex] M [/tex], [tex] \Omega [/tex] is the volume element on [tex] M [/tex], [tex] \omega [/tex] is the volume element on the boundary [tex]\partial M[/tex], and [tex]\mathcal{L}_X[/tex] is the lie derivative along $X$.

I understand that you can do the following:

[tex]
\[
\int_{M} \mathcal{L}_X(\Omega) &= \int_{M} d(\iota_{X}(\Omega))) \\
&= \int_{\partial M} \iota_{X}(\Omega)
\]
[/tex]

by Stokes' theorem. Now, we can take [tex] N(x)[/tex] with an appropriate sign so that if [tex]\hat N(x)[/tex] is the dual of $N$, then

[tex] \hat N(x) \wedge \omega = \Omega [/tex].

By the formula for the contraction, we know that

[tex] \iota_X (\Omega) = \langle X, N \rangle \omega - \hat{N(x)} \wedge \iota_X(\omega) [/tex]

Lang claims that [tex] \hat{N(x)} \wedge \iota_X(\omega) [/tex] vanishes on the boundary at this point, and doesn't give an explanation. Can anyone help me understand why? Of course, this proves the theorem.

Thank you.
 
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  • #2
If I understand you correctly, [tex]\hat{N}[/tex] is the image of N by the musical isomorphism; that is, the 1-form [tex]\hat{N}_x=g_x(N(x),\cdot)[/tex]. Clearly this vanishes on [itex]\partial M[/itex] (that is, [tex]\hat{N}_x|_{T_x(\partial M)}=0[/tex] for all x in dM) since N is, by definition, normal to dM.
 
  • #3
quasar987 said:
If I understand you correctly, [tex]\hat{N}[/tex] is the image of N by the musical isomorphism; that is, the 1-form [tex]\hat{N}_x=g_x(N(x),\cdot)[/tex]. Clearly this vanishes on [itex]\partial M[/itex] (that is, [tex]\hat{N}_x|_{T_x(\partial M)}=0[/tex] for all x in dM) since N is, by definition, normal to dM.

Thank you!

I guess I forgot about the isomorphism.
 

Related to Why Does the Contraction Term Vanish in the Divergence Theorem on Manifolds?

1. What is the Divergence Theorem on Manifolds?

The Divergence Theorem on Manifolds is a mathematical concept that relates the divergence of a vector field to the flux of that vector field through a surface in a higher-dimensional space known as a manifold. It is a generalization of the traditional Divergence Theorem in three-dimensional space.

2. How is the Divergence Theorem on Manifolds different from the traditional Divergence Theorem?

The Divergence Theorem on Manifolds is different from the traditional Divergence Theorem in that it applies to vector fields in higher-dimensional spaces, rather than just three-dimensional space. It also takes into account the curvature of the surface, making it a more general and versatile theorem.

3. What is the importance of the Divergence Theorem on Manifolds in mathematics?

The Divergence Theorem on Manifolds is an important concept in mathematics because it allows for the evaluation of flux integrals in higher dimensions. It also has applications in physics, particularly in the study of fluid flow and electromagnetism.

4. Are there any limitations to the Divergence Theorem on Manifolds?

Yes, the Divergence Theorem on Manifolds has some limitations. It can only be applied to vector fields that are continuously differentiable, and the surface must have a well-defined orientation. Additionally, the surface must be a closed manifold, meaning it has no boundary.

5. Can the Divergence Theorem on Manifolds be extended to higher dimensions?

Yes, the Divergence Theorem on Manifolds can be extended to higher dimensions. It is often used in the study of n-dimensional manifolds, where n is any positive integer. However, the process becomes more complicated as the number of dimensions increases, and special cases may need to be considered.

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