Find the gradient of 1/mod{r-r'}

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In summary, the student is trying to solve a homework equation involving derivatives, but is having difficulty. He has found a solution using the chain rule, but is unsure of what to do next. He seems to be somewhat on the right track, but needs more help.
  • #1
Ashiataka
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Homework Statement


Find [tex]\nabla\left( \dfrac{1}{\left| \vec{r}-\vec{r'}\right| }\right)[/tex]

Homework Equations





The Attempt at a Solution

[tex]
\left| \vec{r}-\vec{r'}\right| =\sqrt{(x-x^\prime)^2 + (y-y^\prime)^2 + (z-z^\prime)^2}[/tex]
and so therefore the derivative of the scalar would be 0. Of course then it seems trivial. So perhaps I need to use the chain rule, but I don't know what to make my substitution for. I can't seem to escape the fact that the bottom always ends up being a scaler.

So I seem a bit stuck. Any advice on where to go onwards would be appreciated. Thank you.
 
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  • #2
I don't understand how you get 0. What do you get for
[tex]\frac{\partial}{\partial x}((x-x^\prime)^2 + (y-y^\prime)^2 + (z-z^\prime)^2)^{-1/2}[/tex]?
Yes, you need to use the chain rule. You can break it up as finely as you like, but a reasonable choice would be
[tex]\frac{\partial f}{\partial x} \frac{\partial f^{-1/2}}{\partial f}[/tex]
where [tex]f = (x-x^\prime)^2 + (y-y^\prime)^2 + (z-z^\prime)^2[/tex]
 
  • #3
Thank you.

Okay, so doing that I get [tex]
\frac{\partial}{\partial x}\left(\frac{1}{\left| \vec{r}-\vec{r'}\right|}\right) = \frac{x^{\prime}-x}{\sqrt{(x-x^\prime)^2 + C}^3}
[/tex]

by treating x' as a constant.Does that seem along the right path?
 
  • #4
Sort of. I assume C here stands for (y-y')2 + (z-z')2, but why are you choosing to represent that with C? Suggests to me you are thinking of it as a constant, and so playing a different role from the (x-x')2 part. Yes, you had treat it as a constant for the purposes of ∂/∂x, but having done that it tells you the value of the derivative at any point (x,y,z), so they should still be considered variables on an equal footing. So I would suggest representing the whole square root part using the original modulus formula involving r.
What do you then get for the whole grad (∇) vector?
 
  • #5
I got this:

[tex]\nabla \left(\frac{1}{\left| \vec{r}-\vec{r'}\right|}\right) = \frac{ (x^\prime-x) \vec{i} + (y^\prime-y) \vec{j} + (z^\prime-z)\vec{k}} {\sqrt{(x-x^\prime)^2 + (y-y^\prime)^2 + (z-z^\prime)^2}^3}[/tex]

by adding the x, y and z parts of the same form as above.
 
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  • #6
Two things wrong with that. First, I assume you meant ∇ on the left, not ∂/∂x. Secondly, you shouldn't add the three terms up. ∇ is a vector, namely (∂/∂x, ∂/∂y, ∂/∂z), so the answer should be a vector consisting of those three terms.
 
  • #7
Yes, just typos. Corrected as above.
 
  • #8
Looks good.
 
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  • #9
Thank you for your assistance :).
 
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FAQ: Find the gradient of 1/mod{r-r'}

What is the formula for finding the gradient of 1/mod{r-r'}?

The formula for finding the gradient of 1/mod{r-r'} is ∇(1/mod{r-r'}), where ∇ represents the gradient operator.

How is the gradient of 1/mod{r-r'} calculated?

The gradient of 1/mod{r-r'} is calculated by taking the partial derivatives of the function with respect to each variable and combining them using vector addition. This can be represented as ∇(1/mod{r-r'}) = (∂/∂x + ∂/∂y + ∂/∂z) (1/mod{r-r'}).

What does the gradient of 1/mod{r-r'} represent?

The gradient of 1/mod{r-r'} represents the rate of change of the function 1/mod{r-r'} in the direction of the steepest ascent. In other words, it shows the direction and magnitude of the maximum change in the function's value at a given point.

How is the gradient of 1/mod{r-r'} used in scientific research?

The gradient of 1/mod{r-r'} is used in scientific research to analyze and understand the behavior of various physical phenomena such as electric and magnetic fields. It is also used in fields such as fluid mechanics, where it helps in predicting the direction and strength of fluid flow.

Can the gradient of 1/mod{r-r'} be negative?

Yes, the gradient of 1/mod{r-r'} can be negative. This indicates that the function is decreasing in the direction of the steepest descent. In other words, the function's value is decreasing as you move away from the given point in the direction of the gradient vector.

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