See video: Ellipsoids and the Bizarre Nature of Rotating Bodies Very good resource on the weirdness of rotating bodies

Continued Rigid Body and Inertia Tensors

Uniform Solid Cube Example: Given length , mass , and the corner of the cube at the origin of the system

figure 1 here

Kronecker delta review

Finding

We can pull out because the square is uniform

Performing the integration:

Because

Now finding the component

This can be proven with symmetry or math

Or simplified:

Angular Momentum

Previously in physics 1, angular momentum

and Angular kinetic energy

In the past, we have treated the moment of inertia as a scalar, but that is only true in a special case where

In general:

or

Angular momentum of a rigid body with respect to the origin of rotating coordinate system.

Where

and

the component only remains due to the Kronecker delta

Multiply by 1… ( on both sides)

Principle Axis of Inertia

For the case when none of the components of are 0 then the calculation of energy and angular momentum can be annoying/complicated. (annoying if simple, etc)

Instead, if we assume our inertia tensor only has non-zero diagonal elements such that

Much easier. Would be nice if this is the case. Examples of how nice everything is:

How do we get here?

The diagonal elements are the principal moments of inertia and the principle axes of inertia are the coordinates (axes) used such that is diagonal.

Since we can pick the coordinate system and axes, we can make selections such that we diagonalize

There are a few ways to go about this:

Method 1: Simply choose the right axes such that there is symmetry about the axes. Just literally pick better axes.

Method 2: If no obvious symmetries exist, pick arbitrary coordinate system, then diagonalize

The eigenvalues of in the arbitrary coordinate system are the principal moments of inertia and the corresponding eigenvectors are the principal axes. The inertia tensor should always have real eigenvalues

For a matrix :

Where are the eigenvalues and are eigenvectors

for an matrix, has n eigenvalues and eigenvectors

  1. So So for a non-trivial this is true when Use this to solve for

In which is the identity matrix 2) Use this to construct the matrix from the eigenvectors

  1. Find
  2. Diagonalize A: