Derivative of angular momentum

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The discussion focuses on the derivation of angular momentum expressed as Iω, where I is the inertia matrix and ω is the angular velocity vector. The author successfully derives a key formula related to Euler's equations using a moving Cartesian frame and differentiates the angular momentum over time. The differentiation process reveals relationships involving the inertia matrix and the angular velocity, leading to insights about the complexity of rotational motion in multiple directions. The author seeks clarification on the practical applications of this formula and its simplifications in calculations. The conversation highlights the intricate nature of angular momentum in rigid body dynamics.
DavideGenoa
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Hi, friends! Let the quantity ##I\boldsymbol{\omega}## be given, where ##I## is an inertia matrix and ##\boldsymbol{\omega}## a column vector representing angular velocity; ##I\boldsymbol{\omega}## can be the angular momentum of a rigid body rotating around a static point or around its -even moving- centre of mass, with ##I## calculated with respect to such a point. I have tried to derive, without any difficulty, a key formula from which Euler 's equation derive in the following way. By using a moving Cartesian frame, dextrogyre as is usual for angular quantities, having the basis ##(\mathbf{i},\mathbf{j},\mathbf{k})=(\mathbf{i}(t),\mathbf{j}(t),\mathbf{k}(t))##, solidal with the rigid body, whose vectors I write as the columns of a matrix ##E=\left( \begin{array}{ccc}\mathbf{i}&\mathbf{j}&\mathbf{k} \end{array} \right)##, we see that

##I\boldsymbol{\omega}=EI_m E^{-1} \boldsymbol{\omega}=EI_m \boldsymbol{\omega}_{m}##​

where the index ##m## means that the respective quantities are expressed with respect to basis ##\{\mathbf{i},\mathbf{j},\mathbf{k}\}##. By differentiating we get

##\frac{d(I\boldsymbol{\omega})}{dt}=\frac{dE}{dt}I_m\boldsymbol{\omega}_{m}+E\frac{d(I_m\boldsymbol{\omega}_{m})}{dt}##
and, since Poisson formulae give us ##\frac{d}{dt}\left( \begin{array}{ccc}\mathbf{i}&\mathbf{j}&\mathbf{k} \end{array} \right)=\left( \begin{array}{ccc}\boldsymbol{\omega}\times\mathbf{i}&\boldsymbol{\omega}\times\mathbf{j}&\boldsymbol{\omega}\times\mathbf{k} \end{array} \right)##, if we call the ##i##-th row of matrix ##I_m## as ##I_m^{(i)}##, we see that

##\frac{d(I\boldsymbol{\omega})}{dt}=\frac{dE}{dt}I_m\boldsymbol{\omega}_{m}+E\frac{d(I_m\boldsymbol{\omega}_{m})}{dt}=\boldsymbol{\omega}\times( I_m^{(1)}\cdot\boldsymbol{\omega}_m\mathbf{i})+\boldsymbol{\omega}\times( I_m^{(2)}\cdot\boldsymbol{\omega}_m\mathbf{j})+\boldsymbol{\omega}\times( I_m^{(3)}\cdot\boldsymbol{\omega}_m\mathbf{k})++E\frac{d(I_m\boldsymbol{\omega}_{m})}{dt}##
##=\boldsymbol{\omega}\times (I\boldsymbol{\omega})+E\frac{d(I_m\boldsymbol{\omega}_{m})}{dt}##​

and, since ##I_m## does not depend upon ##t## because it is calculated according to the moving basis,
##\frac{d(I\boldsymbol{\omega})}{dt}=\boldsymbol{\omega}\times (I\boldsymbol{\omega})+\left( \begin{array}{ccc}\mathbf{i}&\mathbf{j}&\mathbf{k} \end{array} \right)I_m\frac{d\boldsymbol{\omega}_{m}}{dt}##​

or, equivalently,​

##\frac{d(I\boldsymbol{\omega})}{dt}=\boldsymbol{\omega}\times (I\boldsymbol{\omega})+I \left( \begin{array}{ccc}\mathbf{i}&\mathbf{j}&\mathbf{k} \end{array} \right) \frac{d\big(\left( \begin{array}{ccc}\mathbf{i}&\mathbf{j}&\mathbf{k} \end{array} \right)^{-1}\boldsymbol{\omega}\big)}{dt}=\boldsymbol{\omega}\times (I\boldsymbol{\omega})+I\Big(\frac{d(\boldsymbol{\omega}\cdot\mathbf{i})}{dt}\mathbf{i}+\frac{d(\boldsymbol{\omega}\cdot\mathbf{j})}{dt}\mathbf{j}+\frac{d(\boldsymbol{\omega}\cdot\mathbf{k})}{dt}\mathbf{k}\Big)##​

I suppose that such a formula is used to simplify some calculations, but I am not sure where the simplification is. Is it used because it is easier to differentiate ##\boldsymbol{\omega}_m=(\boldsymbol{\omega}\cdot\mathbf{i},\boldsymbol{\omega}\cdot\mathbf{j},\boldsymbol{\omega}\cdot\mathbf{k})## (which does not seem so easy to compute to my eyes...) and left-multiply by ##\left( \begin{array}{ccc}\mathbf{i}&\mathbf{j}&\mathbf{k} \end{array} \right)I_m##, where ##I_m## can be easy to compute, than acting upon ##I##? I would like to collect ideas on how such formula is used. Thank you very much for any answer!
 
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A motion with rotation on more than one main direction must be very complex. Rotation on one axis make forces on the others:
$$ \textbf{τ} = \frac{\partial}{\partial{t}}\mathbf{L} = I\frac{\partial}{\partial{t}}\textbf{ω} $$
 
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For simple comparison, I think the same thought process can be followed as a block slides down a hill, - for block down hill, simple starting PE of mgh to final max KE 0.5mv^2 - comparing PE1 to max KE2 would result in finding the work friction did through the process. efficiency is just 100*KE2/PE1. If a mousetrap car travels along a flat surface, a starting PE of 0.5 k th^2 can be measured and maximum velocity of the car can also be measured. If energy efficiency is defined by...

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