Gradient (1/r): Homework Equation Solutions

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In summary, the gradient of 1/r can be expressed as -r(hat) / r^2 in spherical coordinates, while in Cartesian coordinates it is equal to -1/r^2 multiplied by the unit vector in the direction of r. This can be proven by taking the derivative of 1/r in Cartesian coordinates and simplifying.
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Homework Statement



gradient(1/r) = r(hat) / r^2

Homework Equations


r = (x-x')i + (y-y')j + (z-z')k

[Mentor Note -- Poster was reminded to always show effort on schoolwork questions]
 
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  • #2
gradient(1/r) = -r(hat) / r^2
 
  • #3
how do you prove that?
 
  • #4
In spherical coordinates, the gradient of a scalar function f is:
[itex]\vec\nabla f(r, \theta, \phi) = \frac{\partial f}{\partial r}\hat r+ \frac{1}{r}\frac{\partial f}{\partial \theta}\hat\theta+ \frac{1}{r \sin\theta}\frac{\partial f}{\partial \phi}\hat\phi [/itex].
And we have [itex] \frac{d}{dr}\frac 1 r=-\frac{1}{r^2} [/itex].
 
  • #5
can this be done in cartesian coordinates?
 
  • #6
Or, in Cartesian coordinates,
[tex]\frac{1}{r}= \frac{1}{x^2+ y^2+ z^2}= (x^2+ y^2+ z^2)^{-1/2}[/tex]

[tex]\left(\frac{1}{r}\right)_x= -\frac{1}{2}(x^2+ y^2+ z^2)^{-3/2}(2x)= -\frac{rcos(\theta)sin(\phi)}{r^3}= -\frac{1}{r^3} (rcos(\theta)sin(\phi))[/tex]

[tex]\left(\frac{1}{r}\right)_y= -\frac{1}{2}(x^2+ y^2+ z^2)^{-3/2}(2y)= -\frac{rsin(\theta)sin(\phi)}{r^3}= -\frac{1}{r^3} (rsin(\theta)sin(\phi)([/tex]

[tex]\left(\frac{1}{r}\right)_z= -\frac{1}{2}(x^2+ y^2+ z^2)^{-3/2}(2z)= -\frac{rcos(\phi)}{r^3}= -\frac{1}{r^3} (rcos(\phi))[/tex]

So that [tex]\nabla \frac{1}{r}= -\frac{1}{r^3}\vec{r}= -\frac{1}{r^2}\frac{\vec{r}}{r}= -\frac{1}{r^2}\hat{r}[/tex]

Where [itex]\hat{r}[/itex] is the unit vector in the direction of [itex]\vec{r}[/itex].
 
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  • #7
but this negative
 
  • #8
kunu said:
but this negative
Yes it is. 1/r decreases with increasing r.
 
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  • #9
Sometimes it's easier to work in Cartesian coordinates. For any function ##\Phi(r)## you have
$$\vec{\nabla} \Phi(r)=\Phi'(r) \vec{\nabla} r.$$
Now
$$\vec{\nabla} r= \begin{pmatrix} \partial_1 \\ \partial_2 \\ \partial_3 \end{pmatrix} \sqrt{x_1^2+x_2^2+x_3^2}.$$
The first component thus reads
$$\partial_1 r=2 x_1 \frac{1}{2 \sqrt{x_1^2+x_2^2+x_3^2}}=\frac{x_1}{r}.$$
In the same way you get the other two components, leading to
$$\vec{\nabla} r= \frac{\vec{r}}{r}=\hat{r}.$$
Thus
$$\vec{\nabla} \frac{1}{r} = -\frac{1}{r^2} \hat{r}.$$
 
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1. What is the gradient of a function?

The gradient of a function is a vector that points in the direction of maximum increase of the function at a particular point. It is also known as the slope or the rate of change of the function.

2. How is the gradient calculated?

The gradient is calculated by taking the partial derivatives of the function with respect to each variable and creating a vector with these values. For a function of two variables, the gradient can be represented as ∇f(x,y) = (∂f/∂x, ∂f/∂y).

3. What is the significance of the "1/r" term in the gradient equation?

The "1/r" term represents the distance between the point where the gradient is being calculated and the point where the function is being evaluated. It is typically used in situations where the function is dependent on the distance from a specific point, such as in gravitational or electrical fields.

4. How is the gradient used in physics and engineering?

The gradient is commonly used in physics and engineering to determine the direction and magnitude of a physical quantity, such as force or potential energy. It is also used in vector calculus to solve problems related to motion, heat flow, and fluid mechanics.

5. Can the gradient be negative?

Yes, the gradient can be negative. A negative gradient indicates that the function is decreasing in the direction of the gradient. In other words, the function is decreasing as you move in the direction of maximum decrease indicated by the negative gradient vector.

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