Vector decomposition (Helmholtz)

In summary, the conversation discusses the decomposition of a generic vector into irrotational and solenoidal components. This can be achieved by taking the divergence/curl of the vector and using projection operators. The conversation also mentions the use of the Helmoltz' Theorem and defining the scalar and vector potentials. Ultimately, the problem is solved by using projection operators.
  • #1
Legion81
69
0
I have to show that a generic vector can be decomposed into an irrotational and solenoidal component:

V(r) = -Grad[phi(r)] + Curl[A(r)]

I'm not sure how to start. Do I need to take the curl or div of V and use a vector identity? Any help would be greatly appreciated!
 
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  • #2
Helmholtz' Theorem starts with the two components in my original post and defines the divergence and curl as:

div[V] = s(r)
and
curl[V] = c(r), where div[c(r)] = 0

But I can't find anything about how we can define a generic vector as two components:

V = -grad[phi] + curl[A], where "phi" is the scalar potential and "A" is the vector potential. I need to do this before I can show that s(r) and c(r) uniquely specify the vector.

I hope that makes my problem a little more clear.
 
  • #3
Legion81 said:
I have to show that a generic vector can be decomposed into an irrotational and solenoidal component:

V(r) = -Grad[phi(r)] + Curl[A(r)]

I'm not sure how to start. Do I need to take the curl or div of V and use a vector identity?

Taking the divergence/curl of both sides of this equation seems like a good place to start. What do you get when you do that?

P.S. You may wish to use boldface font to denote vectors, to make things clear.
 
  • #4
I actually just found an easy way of showing it using projection operators. Thanks for the reply.

Consider this question solved.
 
  • #5


Yes, you are correct. To show that a generic vector can be decomposed into an irrotational and solenoidal component, you can use the Helmholtz decomposition theorem, which states that any vector field can be decomposed into a sum of an irrotational (curl-free) component and a solenoidal (divergence-free) component. To start, you can use the vector identity:

V = -Grad[phi] + Curl[A]

where V is the generic vector, phi is a scalar potential, and A is a vector potential.

Next, you can take the curl of both sides of the equation:

Curl[V] = Curl[-Grad[phi]] + Curl[Curl[A]]

Using the vector identity Curl[Curl[A]] = -Grad[Div[A]] + Laplacian[A], we can rewrite the equation as:

Curl[V] = -Grad[phi] - Grad[Div[A]] + Laplacian[A]

Now, we know that the curl of a gradient is always zero, so we can simplify the equation to:

Curl[V] = -Grad[Div[A]] + Laplacian[A]

Since we want to show that V can be decomposed into an irrotational (curl-free) and solenoidal (divergence-free) component, we can set the right-hand side of the equation to zero:

-Grad[Div[A]] + Laplacian[A] = 0

This is known as the Helmholtz equation. By solving this equation, we can find the scalar and vector potentials, phi and A, which will allow us to decompose V into its irrotational and solenoidal components.

I hope this helps to get you started on showing the vector decomposition using the Helmholtz theorem. Remember to always check your work and make sure that your final solution satisfies all the necessary conditions.
 

1. What is vector decomposition?

Vector decomposition, also known as Helmholtz decomposition, is a mathematical technique used to break down a vector into its component parts. It involves separating a vector into two or more orthogonal vectors that, when added together, equal the original vector.

2. Why is vector decomposition important?

Vector decomposition is important because it allows us to better understand and analyze complex vector fields. By breaking down a vector into its component parts, we can study each part individually and gain insights into the behavior and properties of the overall vector field.

3. What are the two types of vector decomposition?

The two types of vector decomposition are scalar decomposition and vector decomposition. Scalar decomposition involves breaking down a vector into its magnitude (length) and direction, while vector decomposition involves breaking down a vector into its orthogonal components.

4. What is the difference between vector decomposition and vector projection?

Vector decomposition and vector projection are similar concepts, but they have different purposes. Vector decomposition breaks down a vector into its component parts, while vector projection involves finding the component of a vector that lies in a specific direction.

5. How is vector decomposition used in physics?

Vector decomposition is used in physics to analyze and solve problems involving vector fields. It is particularly useful in fields such as fluid mechanics, electromagnetism, and quantum mechanics, where vector fields play a crucial role in describing physical phenomena.

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