Hover over the menu bar to pick a physics animation.

3D Momentum Distribution Evolution

For my analysis of Maxwell's derivation of the speed distribution see Velocity Distribution Derivation

For our 3D momentum case, the kinetic energy `E` can be written

`E=(p_x^2+p_y^2+p_z^2)/(2m)`

where `p_x`, `p_y` and `p_z` are the momentum components in the x, y and z directions and `m` is the mass of an atom which I have set to 1 for all atoms here.

From the 3D Maxwell-Boltzmann distribution we can write for the energy distribution probability `P(E)`:

`P(EltE'ltE+deltaE)=sqrt(E/E_(avg))exp(-(3E)/(2E_(avg)))`

where `E_(avg)` is the average kinetic energy which is the same as the initial value of `(p_x^2+p_y^2+p_z^2)/(2m)`.

The peak value of `(dN)/(dE)` is obtained by taking its derivative:

`"set " r=E/E_(avg)`
`(d^2N)/(dE^2)=d/(dr)(sqrt(r)exp(-3/2r))=0`
`1/2(1/(sqrt(r)))-3/2sqrt(r)=0`
`1/(sqrt(r))-3sqrt(r)=0`
`3r=1`
`r=1/3`
`"thus "E_(peak)=1/3E_(avg)`

The peak value of `(dN)/(dE)` is clearly marked on the energy bin plot. `P(EltE'ltE+deltaE)` is proportional to `(dN(E))/(dE)` where `N(E)` is the sum of the number of atoms in all the `E` bins starting from `E=0` and going to the `E"th"` bin. In integral form this is the same as `N(E)=int_0^E(dN(E'))/(dE')dE'`.

To convert `(dN(E))/(dE)` to `(dN)/(dp)` we just use the chain rule

`(dN(p))/(dp)=(dN(E))/(dE)(dE)/(dp)=(dN(E))/(dE)(p/m)`

where `p=sqrt(p_x^2+p_y^2+p_z^2)`

The signed values of `p_x`, `p_y`, and `p_z` are accumulated in bins of width `deltap=p_(max)/n_B` and these bins are plotted here on the range `-2p_(rms)->+2p_(rms)`.

Also I have plotted an algebraic curve.

First let:

`p_(avg)^2=2msum_(i=0)^(i=N)(E_i)`

Then if we let:

`sigma=4/5sqrt(p_(avg)^2)`

Then the curve

`f(p)=bbp/(|p|)exp(-p^2/(sigma^2))`

fits the bin data for either `p_x,p_y, or p_z` very well.

To initiate the animation I choose all of the starting kinetic energies the same so you would see that the initial momenta form a circle in the `(p_x,p_y)` plane. The particles will usually collide with each other so that their energies get dispersed, some going higher and some going lower. In order to condense into the expected Maxwell-Boltzmann distribution, the lower energy particles will participate less in collisions since they are moving more slowly.

The learner has access to sliders with which to adjust the program parameters:
1. The Initial Kinetic Energy, `E_0`, per particle
2. Red Particle Radius
3. Blue Particle Radius
4. Total Number of Particles

Note that increasing any one of these parameters increases the rate of equilibration of the momentum distribution and also increases the probability of atoms escaping the container.

Results

The momentum bins data fits a gaussian of width `sigma` where for three dimensions (3D) `sigma` is

`sigma=4/5sqrt(1/(N)sum_(i=1)^(i=N)bbvecp_i*bbvecp_i`

`E_(avg)=1/(2m)1/(N)sum_(i=1)^(i=N)bbvecp_i*bbvecp_i`

`4E_(avg)`

`E_(avg)`

`0`

`sigma=4/5p_(rms)=4/5sqrt(1/(N)sum_(i=1)^(i=N)bbvecp_i*bbvecp_i`      

`(dN)/(dbbvecp)=(bbvecp/|p|)exp(-(bbvecp*bbvecp)/sigma^2)`

`-sigma`

`+sigma`

`-2sigma`

`+2sigma`