Understanding the Product Rule: A Guide to \nabla × (A×B)

In summary, the product rule states that the dot product of two vectors is equal to the scalar product of the vectors.
  • #1
qspeechc
844
15
Hello everyone. I'm trying to get my head around this product rule:

[tex] \nabla \times (A\times B) = (B\cdot \nabla )A - (A\cdot \nabla )B + A(\nabla \cdot B) - B(\nabla \cdot A) [/tex]

Ok, we have this

[tex] \nabla = (\partial /\partial x,\partial/\partial y,\partial /\partial z) [/tex]

and for dot products

[tex] a\cdot b = b\cdot a [/tex]

Therefore in the product rule given above, is it not the case

[tex] (B\cdot \nabla )A = A(\nabla \cdot B) [/tex]

and similarly, the other two terms on the RHS are equal?
Thank-you for your help.
 
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  • #2
qspeechc said:
Therefore in the product rule given above, is it not the case

[tex] (B\cdot \nabla )A = A(\nabla \cdot B) [/tex]

Be careful. [tex]\nabla[/tex] is a vector operator not a vector. It will not commute the way you expect it to.
 
  • #3
In fact, I would much prefer the notation [itex]B\cdot(\nabla A)[/itex] to [itex](B\cdot\nabla)A[/itex].
 
  • #4
HallsofIvy said:
In fact, I would much prefer the notation [itex]B\cdot(\nabla A)[/itex] to [itex](B\cdot\nabla)A[/itex].

Aha! I think I get it now! The brackets were confusing, because usually we have to evaluate the stuff in the brackets first right?

Does this mean [tex]\nabla [/tex] always acts on the vector directly to its right?
 
  • #5
HallsofIvy said:
In fact, I would much prefer the notation [itex]B\cdot(\nabla A)[/itex] to [itex](B\cdot\nabla)A[/itex].


Hold on, this can't be right can it? Then we would have
[tex]\nabla \times (A\times B) = 0 [/tex]

wouldn't we? Can someone please tell me what [tex](B\cdot\nabla)A[/itex] is?
 
  • #6
qspeechc said:
Can someone please tell me what [tex](B\cdot\nabla)A[/itex] is?

First you evaluate [tex] \nabla A [/tex]. You get a vector field, i.e. a vector at every point of space. Then [tex] B \cdot(\nabla A) [/tex] would be vector field whose value at any point is the dot of B with vector [tex] \nabla A [/tex] at that point.
 
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  • #7
I'm sorry for being extremely thick, but then doesn't that mean that

[tex] \nabla \times (A\times B) = 0 [/tex]?

This makes no sense, because it means the cross product of any two vectors has zero curl? Surely [tex](B\cdot \nabla)\cdot A[/tex] is not the same thing as [tex]B\cdot (\nabla \cdot A)[/tex]?
 
  • #8
qspeechc said:
I'm sorry for being extremely thick, but then doesn't that mean that

[tex] \nabla \times (A\times B) = 0 [/tex]?

why does it mean that?
 
  • #9
The second operation not a dot product. del.A is a scalar. So (del.A)B is the scalar (d/dxAx + d/dyAy + d/dzAz) times the vector B = d/dxAx*B + d/dyAy*B + d/dzAz*B

Also, remember the del is an operator so del.A is not the same as A.del. A.del is still a scalar though being applied to B to it gets pretty messy looking.

http://mathworld.wolfram.com/ConvectiveOperator.html
 
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  • #10
qspeechc said:
Surely [tex](B\cdot \nabla)\cdot A[/tex] is not the same thing as [tex]B\cdot (\nabla \cdot A)[/tex]?


No, what I said was [tex] B \cdot (\nabla A) [/tex] is the same thing as [tex](B \cdot \nabla)A[/tex]
 
  • #11
Er, ok, what's the difference? A and B are vectors.
 
  • #12
Vid said:
The second operation not a dot product. del.A is a scalar. So (del.A)B is the scalar (d/dxAx + d/dyAy + d/dzAz) times the vector B = d/dxAx*B + d/dyAy*B + d/dzAz*B

Also, remember the del is an operator so del.A is not the same as A.del. A.del is still a scalar though being applied to B to it gets pretty messy looking.

http://mathworld.wolfram.com/ConvectiveOperator.html


Oh, ok, this site explains it to me. Thanks for all your help everyone, taking time to answer my stupid questions :biggrin:
 
  • #13
HallsofIvy said:
In fact, I would much prefer the notation [itex]B\cdot(\nabla A)[/itex] to [itex](B\cdot\nabla)A[/itex].

Some people do not like to take the gradient of a vector since it is a dyad, and it makes them feel ickky inside.
 
  • #14
qspeechc said:
Aha! I think I get it now! The brackets were confusing, because usually we have to evaluate the stuff in the brackets first right?

Does this mean [tex]\nabla [/tex] always acts on the vector directly to its right?

This is a confusing aspect of vector calculus.
[tex]\nabla [/tex] acts to the right only.
One may define a bidirectional del/nabla
consider this bidirectional derivative
Dab=aDb=abD
it is a good and correct habit when working with vectors to switch to bidirectional form
-hold all function left of operators constant
-change operators to birectional
-perfom manipulations ending with a form easy to conver to unidirectional form
-convert

recall this identity when working with products

[tex]\mathbf{(a\times\nabla)\times b+a\nabla\cdot b=a\times(\nabla\times b)+(a\cdot\nabla)b}[/tex]
 
  • #15
lurflurf said:
Some people do not like to take the gradient of a vector since it is a dyad, and it makes them feel ickky inside.

It's only ickky if you don't know that there are things which aren't vectors or scalars. There are also tensors (or dyads). Then it could make you queasy.
 

1. What is the product rule for calculating the curl of a vector field?

The product rule for calculating the curl of a vector field, denoted by ∇ × (A×B), is a mathematical formula used in vector calculus. It states that the curl of a cross product is equal to the cross product of the curl of the individual vectors, minus the dot product of the two vectors. In other words, ∇ × (A×B) = (B∇) × A - (A∇) × B.

2. How is the product rule useful in understanding vector fields?

The product rule is useful in understanding vector fields because it allows us to calculate the curl of a vector field in terms of its component functions. This can be helpful in solving problems involving fluid flow, electromagnetism, and other physical systems.

3. What is the geometric interpretation of the product rule?

The geometric interpretation of the product rule is that it represents the rotation of a vector field around a given point. This rotation is determined by the cross product of the individual vectors in the field and the dot product between them.

4. Can the product rule be applied to any type of vector field?

Yes, the product rule can be applied to any type of vector field, as long as the field is differentiable and has well-defined components. It is a fundamental rule in vector calculus and is used in many different applications.

5. How does the product rule relate to other differentiation rules?

The product rule is a generalization of the chain rule and the cross product rule. It can also be derived from the quotient rule by rewriting the cross product as a quotient of determinants. Additionally, the product rule is closely related to the gradient, divergence, and Laplacian operators, which are all commonly used in vector calculus.

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