inverse square
Table of Contents
1. Derivation
Say you're doing some physics modeling, and you want to describe a force that has the following properties (and one more that I will introduce later, and many of which inherit from the definition of a central force):
- The force happens at a distance between two particles.
- The force gives both the particles an equal amount of force but in opposite directions (Newton's Third Law).
- The force magnitude only depends on the distance between the two particles in question, and maybe some other property intrinsic to both the particles.
- If you draw a straight line between the two points, the force vector has to be parallel to that line.
- The force works in the same way no matter where in the universe you are.
We will call our mysterious force field
If we imagine drawing
Think about the implications of this last rule. The circumference of a circle is
but in three dimensions, that isn't quite right.
Spheres have a surface area of
by property 3, there might be some property intrinsic to each particle in question that makes the force vector have a higher magnitude (like mass for gravity), so we have to scale this force by our measurement of that property. By property 2, the force must be the same magnitude but opposite direction on the two particles. In order to preserve that property, this force must scale by both particles' properties:
in this article I am going to consider this
And from 6 properties that seem like reasonable constraints, we derive the inverse square law.
To formalize the sixth property by taking the flux through a sphere and showing it doesn't depend on
Which is a result we will use later.
Also, from here on out, we will be dealing with a field that is not dependent on
2. Definition
Inverse square fields for point particles are fields which have the form:
where
Inverse square laws follow the superposition principle, and therefore:
Which means statements made about fields of single point particles is most likely true of systems of more than one particle. Inverse square fields also have a continuous distribution form:
Where sigma becomes a smooth distribution. The integral is for a continuous distribution of points that follow the inverse square law, where the field generated by each individual point contributes in a weighted sum.
3. Divergence of Inverse Square Fields
Using the del operator:
In this example, I will only be using the Cartesian del operator because although the calculation is harder
the math needed in order to understand Cartesian coordinates is lesser.
If we are using the Cartesian del operator, we must also use Cartesian coordinates. Therefore, we do unit
conversions to replace
If we can solve for one of these partial derivative terms, we can solve for the other two by symmetry:
We use the chain rule to solve for this:
Then it will look like this inside the integral:
If we factor out the
So is the divergence of this field zero? Well, not exactly. In order to understand why, we must ask: What happens to the divergence at
We can model this behavior with the dirac delta distribution. Our actual divergence is:
So now we can take the volume integral of this quantity:
As an analogy, if we have a point mass (an object with finite mass in a single point), the density of any volume containing the point will be zero, but
the gravitational field generated by that point mass will not be zero. Obviously just taking the density blindly will not accurately account for the
actual distribution of mass within space. You need to use something like the dirac delta distribution to model this behavior. Also, since the
Where
4. Curl of Inverse Square Fields
Because an inverse square field is a special case of a central force, this result is also the same as for all general central fields. That is:
Which implies the field in question is a conservative force:
It also implies:
Where
5. Potential of Inverse Square fields
Now we want to find the specific potential scalar field for inverse square fields. In an inverse square field, given the identity:
We can set a reference point as
It is easy to prove that
Note that because this field does not require keeping track of vector orientation, it is significantly easier to solve for