Checking $\nabla x (uv(hat))$ Equation

  • Thread starter squenshl
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In summary, to check the equation \nabla x (uv(hat)) = (\nabla u) x v(hat) + u(\nabla x v(hat)), we need to express both sides in terms of components and compare. This can be done using Cartesian coordinates and the definitions of the determinant, gradient, and cross product of two vectors. It is important to show an attempt at solving the problem before seeking further help, following the guidelines for Homework & Coursework questions.
  • #1
squenshl
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4
How do I check [tex]\nabla[/tex] x (uv(hat)) = ([tex]\nabla[/tex]u) x v(hat) + u([tex]\nabla[/tex] x v(hat)).
 
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  • #2
squenshl said:
How do I check [tex]\nabla[/tex] x (uv(hat)) = ([tex]\nabla[/tex]u) x v(hat) + u([tex]\nabla[/tex] x v(hat)).

Changing your notation a little, if f(x,y,z) is a scalar and

V(x,y,z) = <u(x,y,z),v(x,y,z),w(x,y,z)> is a vector, to show

[tex]\nabla \times f\vec V = \nabla f \times\vec V + f\nabla \times \vec V[/tex]

just work both sides out in terms of components and compare. I don't think it matters whether V is a unit vector.
 
  • #3
The same way you prove every other vector calculus identity... express everything in terms of components, calculate the derivatives and simplify.

Even if this isn't an assigned homework problem, it's still a homework type problem and you should follow the homework template.
 
  • #4
What if I wanted to use Cartesian coordinates, the definitions of the determinant, gradient and cross product of 2 vectors.
 
  • #5
squenshl said:
What if I wanted to use Cartesian coordinates, the definitions of the determinant, gradient and cross product of 2 vectors.
As a matter of fact, you pretty much have to use all of those. And work things out in terms of components of vectors, as others have said.

Is this homework?
 
  • #6
Na. Studying for a test. It was a on a practice test.
 
  • #7
squenshl said:
Na. Studying for a test. It was a on a practice test.

Moderator's note:

I have moved this thread to the Homework & Coursework Questions area. We have guidelines on what belongs there, and this definitely qualifies.

At this point, normal rules for Homework & Coursework apply. The OP should show an attempt at solving the problem before further help is given.
 

What is the significance of checking the gradient of the equation $\nabla x (uv(hat))$?

The gradient of a mathematical equation represents the rate of change in each of its variables. By checking the gradient of the equation $\nabla x (uv(hat))$, we can better understand how the output changes with respect to variations in the input variables. This helps us analyze the behavior of the equation and make predictions about its solutions.

How do you calculate the gradient of the equation $\nabla x (uv(hat))$?

The gradient of an equation can be calculated by taking the partial derivatives of the equation with respect to each of its variables. In the case of $\nabla x (uv(hat))$, we would take the partial derivatives of the equation with respect to x, u, and v. This will give us a vector of values representing the rate of change in each variable.

What does it mean if the gradient of the equation $\nabla x (uv(hat))$ is zero?

If the gradient of an equation is zero, it means that the equation has reached a critical point where there is no change in the output with respect to variations in the input variables. This could indicate a maximum, minimum, or saddle point in the equation. Further analysis would be needed to determine the nature of the critical point.

Why is it important to check the gradient of the equation $\nabla x (uv(hat))$?

Checking the gradient of an equation is important because it helps us understand the behavior of the equation and make predictions about its solutions. It also allows us to identify critical points and determine the nature of these points, which can be useful in optimization problems.

What are some common methods for checking the gradient of the equation $\nabla x (uv(hat))$?

There are several methods for checking the gradient of an equation, including using analytical techniques, numerical methods, and software tools. Analytical techniques involve taking the partial derivatives of the equation, while numerical methods involve approximating the gradient using finite differences. Software tools, such as MATLAB or Python, have built-in functions for calculating gradients and can be useful for more complex equations.

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