Prove ∇(A x B) = (∇ x A)⋅B - (∇ x B)⋅A where A,B are vectors

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In summary, the conversation discusses the relationship between vectors A and B and the application of the divergence operator. The correct use of the Graßmann-identity or the Jacobi-identity is necessary to properly understand the behavior of the divergence operator. Additionally, it is noted that the divergence operator is an operator with components that are not numbers, and it satisfies the Leibniz rule.
  • #1
goggles31
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I can prove this relationship by defining A = (A1,A2,A3) and B=(B1,B2,B3) and expanding but I tried another approach and failed.

I read that for any 3 vectors,
a⋅(b x c) = (a x b)⋅c
and thus applying this to the equation, I only get
(A x B) = ( x A)⋅B
Can anyone explain why this is so?
 
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  • #2
That should be the divergence operator ## \nabla \cdot (A \times B ) ## ,and the divergence operator behaves quite differently from a vector dot product.
 
  • #3
Your mixture of products is wrong. Either use
##\vec{a} \times (\vec{b}\times\vec{c})=(\vec{a}\cdot\vec{c})\cdot\vec{b}-(\vec{a}\cdot\vec{b})\cdot\vec{c}\;## (Graßmann-identity) or
##\vec{a}\times(\vec{b}\times\vec{c})+\vec{b}\times(\vec{c}\times\vec{a})+\vec{c}\times(\vec{a}\times\vec{b})=0 \;## (Jacobi-identity).
 
  • #4
exact, it's not the same operation and then we cannot speak of associativity. ∇ is itself an operator noted as 3 derivators components. See its definition, its components are not numbers
 
  • #5
Derivative operators satisfy the Leibniz rule.
 
  • #6
robphy said:
Derivative operators satisfy the Leibniz rule.
... which is closely related to the Jacobi-identity or likewise the definition of a derivation.
 

1. What is the meaning of ∇?

∇ (pronounced "del") is a mathematical operator called the "nabla" or "gradient" operator. It represents the gradient of a scalar field, which is a function that assigns a scalar value to every point in space.

2. How do you interpret the equation ∇(A x B) = (∇ x A)⋅B - (∇ x B)⋅A?

This equation is known as the "vector triple product identity" and it describes the relationship between the gradient of a vector cross product and the cross product of the gradients of two vectors. In simpler terms, it shows how the gradient of a vector cross product can be broken down into two separate cross products.

3. Can you provide an example of how this equation is used in physics or engineering?

Yes, this equation is commonly used in fluid mechanics to calculate the force exerted by a fluid on a surface. By taking the gradient of the velocity field and cross producting it with the stress tensor, we can find the force per unit area on a surface due to the fluid.

4. What are some other applications of this equation?

This equation has many applications in various fields of physics and engineering, including electromagnetism, quantum mechanics, and fluid dynamics. It is also used in computer graphics to simulate fluid flow and in robotics to calculate the torque needed for a robot to manipulate an object.

5. Is this equation always true for any vectors A and B?

Yes, this equation is a fundamental mathematical identity and is always true for any vectors A and B. However, it may not always be applicable in certain situations, such as when A and B are not continuous or differentiable functions.

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