Eigenvectors of Inertia tensor

Click For Summary

Discussion Overview

The discussion revolves around the properties of eigenvectors of the inertia tensor in a system of N particles, particularly focusing on the orthogonality of derived vectors formed from cross products of these eigenvectors and particle position vectors. Participants explore the implications of these properties and seek analytical explanations for observed numerical results.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant describes a numerical observation that the vectors \vec{\omega}_j, formed from the cross products of eigenvectors and particle positions, appear to be mutually orthogonal for non-linear configurations with N >= 3.
  • Another participant expresses confusion regarding the definition and physical significance of the \vec{\omega}_j vectors.
  • A participant challenges the assumption that the dot product in the original claim will vanish, providing a counterexample for N=1.
  • One participant suggests that the sum of products in the original claim might relate to the components of the inertia tensor in the eigenvector coordinate system.
  • A later reply proposes that the off-diagonal components of the inertia tensor, expressed in its eigenframe, could explain why the sum vanishes, suggesting a potential connection to the diagonal nature of the inertia tensor in this frame.

Areas of Agreement / Disagreement

Participants express differing views on the validity of the original claim regarding the vanishing of the sum, with some supporting the idea and others questioning it. The discussion remains unresolved, with no consensus reached on the analytical explanation for the observed orthogonality.

Contextual Notes

The discussion highlights the complexity of relating numerical observations to analytical properties of the inertia tensor, with participants noting the dependence on the configuration of particles and the definitions used.

Derivator
Messages
147
Reaction score
0
Hi,

I've written a little fortran code that computes the three Eigenvectors \vec{v}_1, \vec{v}_2, \vec{v}_3 of the inertia tensor of a N-Particle system.
Now I observed something that I cannot explain analytically:
Assume the position vector \vec{r}_i of each particle to be given with respect to the center of mass of the system.
Then define three new vectors \vec{\omega}_j := (\vec{v}_j\times\vec{r}_1,\dots,\vec{v}_j\times\vec{r}_N) where j=1,...,3. These new vectors are of length 3*N.
Now, for a non-linear configuration of the \vec{r}_i and N>=3, the \vec{\omega}_j seem to be mutually orthogonal, that is \vec{\omega}_j \cdot \vec{\omega}_i = 0 for i \neq j (At least, I obtain this numerically up to machine precision)

I have no analytical explanation for this...

The most promising ansatz I tried so far is:
\vec{\omega}_i \cdot \vec{\omega}_j = \sum_{l=1}^N (\vec{v}_i\times \vec{r}_l)(\vec{v}_j \times \vec{r}_l) = \sum_{l=1}^N (\vec{v}_i\cdot\vec{v}_j)(\vec{r}_l\cdot\vec{r}_l)-(\vec{v}_i\cdot\vec{r}_l)(\vec{r}_l\cdot\vec{v}_j)= -\sum_{l=1}^N (\vec{v}_i\cdot\vec{r}_l)(\vec{r}_l\cdot\vec{v}_j)

Where I have used the relation
(\mathbf{a \times b})\mathbf {\cdot}(\mathbf{c}\times \mathbf{d}) = (\mathbf{a \cdot c})(\mathbf{b \cdot d}) - (\mathbf{a \cdot d})(\mathbf{b \cdot c})
(see: http://en.wikipedia.org/wiki/Quadruple_product)

and the fact that the eigenvectors of the inertia tensor are mutually orthogonal:

\vec{v}_i\cdot\vec{v}_j = 0

Unfortunately, this is the point where I'm stuck. I don't see why -\sum_{l=1}^N (\vec{v}_i\cdot\vec{r}_l)(\vec{r}_l\cdot\vec{v}_j) should vanish.derivator
 
Last edited:
Physics news on Phys.org
I'm a bit confused on how you're defining your ##\vec{w}_i##. Can you explain? I'm not quite seeing how you're defining it (maybe it's that I don't understand the physical significance).

Also, the dot product won't vanish in your ansatz. The simplest case I can think of is considering a space in ##\mathbb{R}^3## when ##N=1##. Dotting a random vector in the x-y plane with both the x-axis and y-axis. You'll get a vectors along the positive and/or negative z-axis. The dot product of those two vectors isn't zero.
 
Last edited:
Hi,
the \vec{\omega}_i are just the hypervectors build from the cross products of the eigenvectors of the inertia tensor and the particle positions (length 3*N). Frankly, I don't know, if they have any physical significance. They just happen to be an intermediate step in my calculation and I noticed their orthogonality. while playing around with my code.

Of corse, you are right, that for N=1 the dot product will not vanish. However, for N >=3 , as long as the particles are not in one line, it does.

Cheers,
derivator
 
Taking the eigenvectors to be normalized your last sum is a sum on the product of the i-th and j-th components (with the eigenvectors as the basis) of the position vectors of the particles. Since you have defined the position vectors relative to the center of mass you know the following holds for each component: $$ \sum_l r_l^x=0. $$ I don't know the solution to the problem, but maybe this will help cast it in a new light.
 
Hi Haborix,

thanks a lot for your idea. Unfortunately, I also don't see how to use this relation...

Still hoping, someone might see why the sum is vanishing.

Cheers,
derivator
 
Maybe, I just had an idea:
Am I right, that the -\sum_{l=1}^N (\vec{v}_i\cdot\vec{r}_l)(\vec{r}_l\cdot\vec{v}_j) are nothing else than the off-diagonal components of the inertia tensor when being expressed in the coordinate system defined by the eigenvectors of the inertia tensor? (for the components of the inertia tensor see: http://farside.ph.utexas.edu/teaching/336k/Newtonhtml/node64.html)
Now, if I did not overlook anything, then its trivial why the
-\sum_{l=1}^N (\vec{v}_i\cdot\vec{r}_l)(\vec{r}_l\cdot\vec{v}_j) vanish: It's simply because the inertia tensor is (trivially) diagonal in its own `eigenframe'.

Let me know what you think.

Cheers,
derivator
 

Similar threads

Replies
6
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 30 ·
2
Replies
30
Views
4K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 62 ·
3
Replies
62
Views
8K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 42 ·
2
Replies
42
Views
7K
  • · Replies 6 ·
Replies
6
Views
2K