How do these operations with Del operator work?

In summary: You can just use the standard product rule, i.e.∇(rm)=(∇r)m+r∇mand so on.In summary, the conversation discusses the use of the Del operator in various expressions, including (A.∇)B and B(∇.A). The confusion arises from how to expand these expressions, with the correct expansion being a scalar multiplication of the components of B and the gradient of A. The conversation also touches on the use of the identity ∇×(a×b) = (b.∇)a - (a.∇)b - a(∇.b) + b(∇.a) and its limitations in different coordinate systems.
  • #1
supermiedos
63
0
How do these operations with Del operator work??

Homework Statement


Let's say A and B are expressed by their cartesian components as:
A = <P, Q, R> and B = <M, N, O>

what would be the differente between (A.∇)B and B(∇.A) ?


Homework Equations





The Attempt at a Solution


I tried to expand the expression (A.∇)B as:
[itex](P\partial_{x} + Q\partial_{y} + R\partial_{z})B[/itex]

Where [itex]\partial_{x}[/itex] is an abbreviation for [itex]\partial/\partial_{x}[/itex] but I don't know how to proceed next. Should I multiply the inside of the parenthesis with every component o B? That looks strange.
 
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  • #2
Think of it like a dot product with the B vector. You have B as a component vector so it should be obvious.
 
  • #3
But that's what I don't understand. I already made the dot product, and now I have to multiply the scalar
[itex](P\partial_{x} + Q\partial_{y} + R\partial_{z})[/itex] by B? My confusion is created because now I see the product like this:

[itex](P\partial_{x} + Q\partial_{y} + R\partial_{z})B =
(P\partial_{x} + Q\partial_{y} + R\partial_{z})<M, N, O>[/itex]

Since the outside is scalar, we don't have a dot product, so it would be expanded like this?:
[itex]=<P\partial_{x}M + Q\partial_{y}M + R\partial_{z}M, P\partial_{x}N + Q\partial_{y}N + R\partial_{z}N, P\partial_{x}O + Q\partial_{y}O + R\partial_{z}O>[/itex]

or should I expand it something like this?
[itex]=P\partial_{x}M + Q\partial_{y}N + R\partial_{z}O[/itex]
 
  • #4
Are you sure that B is indeed a vector function? "[itex](A\cdot\nabla)[/itex]" is usually "applied" to a scalar function f so that

[tex](\textbf{A}\cdot\nabla)f=P\partial_xf+Q\partial_yf+R\partial_zf[/tex]

If B is actually vector field, then I'd say (in cartesian coordinates, where the scale factors are constant) that what you initially wrote is correct,

[tex]\nabla \textbf{B} = \sum_{ij} \partial_j B_i \textbf{e}_i \textbf{e}_j \implies ((\textbf{A}\cdot\nabla)\textbf{B})_i=\sum_j A_j\partial_j B_i [/tex]

However, I don't recall seeing that exact expression anywhere, so I might be mistaken. It does look like the second term of the material derivative, but I'm not sure why the parentheses would be written like that.

The second expression evaluates to

[tex]\textbf{B}(\nabla\cdot \textbf{A})=\textbf{B}(\partial_x P+\partial_y Q+\partial_z R)[/tex]

Where you simply multiply every component of B with the divergence of A.

EDIT: I'd say it's reasonable for you to be confused, especially if you don't know the definition of a gradient of a vector field. Even if you do, the notation used is not the clearest, in my opinion.

EDIT2: That is to say, that this
[itex]=<P\partial_{x}M + Q\partial_{y}M + R\partial_{z}M, P\partial_{x}N + Q\partial_{y}N + R\partial_{z}N, P\partial_{x}O + Q\partial_{y}O + R\partial_{z}O>[/itex]
expression is the correct one from what I can tell.
 
Last edited:
  • #5
  • #6
supermiedos said:
Oh, sorry for my notation.

I wasn't really blaming you, and I'm not even sure if it's a valid complaint : P

supermiedos said:
when I tried to evaluate ∇x(m x r) i used the identity:

∇x(m x r) = (r . ∇)m - (m . ∇)r - r(∇ . m) + m(∇ . r)

and that's where I got confused about how (r . ∇)m and m(∇ . r) worked

Ahh, that's what it was? Sure, what I (and you) said earlier is definitely the correct way to expand them. My reply would've been much shorter if I knew that ∇×(a×b) was what you were calculating.

Do remember that these only work "well" in cartesian coordinates; for example with strong spherical symmetries simply writing out the cross products is often the quicker way of doing things.
 
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  • #7
Thank you :) I'll see how far i'll get with this problem.
 
  • #8
By the usual convention ∇ acts to the right so
(r . ∇)m and m(∇ . r)
have m and r being acted on.
 

Related to How do these operations with Del operator work?

1. What is the Del operator and how is it used in scientific operations?

The Del operator, denoted as ∇, is a vector operator commonly used in mathematical and scientific operations. It represents the gradient, divergence, and curl of a vector function in three-dimensional space. It is used to perform vector calculus operations such as finding the rate of change, direction of maximum change, and rotational behavior of a vector field.

2. How do you calculate the gradient using the Del operator?

The gradient of a scalar function is calculated by taking the dot product of the Del operator with the function. This is represented as ∇f or grad(f), where f is the scalar function. The result is a vector that shows the direction of maximum change and the magnitude of the change at a given point in the function.

3. What is the purpose of the divergence operation with the Del operator?

The divergence operation, denoted as ∇ · F, is used to measure the rate at which a vector field is flowing outward from a given point. It is calculated by taking the dot product of the Del operator with the vector function F. The result is a scalar value that indicates the strength and direction of the flow at that point.

4. How does the curl operation with the Del operator work?

The curl operation, denoted as ∇ x F, is used to measure the rotational behavior of a vector field at a given point. It is calculated by taking the cross product of the Del operator with the vector function F. The result is a vector that shows the axis of rotation and the magnitude of the rotation at that point.

5. Can the Del operator be used in other dimensions besides three-dimensional space?

Yes, the Del operator can be used in any number of dimensions. In one dimension, it becomes a simple derivative, in two dimensions it becomes a 2D vector, and in three dimensions it becomes the traditional Del operator. In higher dimensions, it is known as the gradient operator and is used to represent the gradient in that particular dimension.

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