Discrete Lagrangian Homework: Minimize S, Find EoM's & Discrete Trajectory

In summary, the student is trying to solve for the discrete equations of motions for a potential energy function. They are not sure if the last term on the right is correct.
  • #1
Christoffelsymbol100
19
1

Homework Statement


In this exercise, we are given a discrete Lagrangian which looks like this: http://imgur.com/TL0P61r. We have to minimize the discrete S with fixed point [tex]r_i[/tex] and [tex]r_f[/tex] and find the the discrete equations of motions.
In the second part we should derive a discrete trajectory for U(r) = 0 and U(r) = mgz.

Homework Equations


[tex] j = t_i + j \Delta t[/tex] ; [tex] \Delta t = (t_f - t_i)/N [/tex]
[tex]r_j = r(t_i + j \Delta t)[/tex]
[tex]v_j = \frac{r_j - r_{j-1}}{\Delta t}[/tex][/B]
j = 0, ..., N
U(r) is the potential energy
And the discrete action:
[tex]S(r_j) = \sum_{j=1}^N \Delta t \left(\frac{1}{2}m\left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)^2 - \frac{1}{2}\left(U(\boldsymbol{r_j})+ U(\boldsymbol{r_{j-1}})\right)\right)[/tex]

The Attempt at a Solution


Since S is a function, I thought I could take the gradient and set that to 0 to derive the discrete equations of motions: [tex]
0 = \frac{\partial}{\partial r_k} S = \sum_{j=1}^N \Delta t \left(\frac{1}{2}m\frac{\partial}{\partial r_k}\left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)^2 - \frac{1}{2}\left(\frac{\partial}{\partial r_k} U(\boldsymbol{r_j})+ \frac{\partial}{\partial r_k}U(\boldsymbol{r_{j-1}})\right)\right)
\\
\rightarrow \sum_{j=1}^N \Delta t \left(m \left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)\left(\frac{\delta_{jk}-\delta_{j-1,k}}{\Delta t}\right) -\frac{1}{2} \left(\frac{\partial U(\boldsymbol{r_j})}{\partial r_k}\delta_{jk} + \frac{\partial U(\boldsymbol{r_{j-1}})}{\partial r_k}\delta_{j-1,k}\right)\right)
\\
\rightarrow 0 = m \left(\frac{-\boldsymbol{r_{k+1}}-\boldsymbol{r_{k-1}}+2\boldsymbol{r_k}}{\Delta t^2}\right) - \frac{1}{2} \left(\frac{\partial U(r_k)}{\partial r_k} + \frac{\partial U(r_{k+1})}{\partial r_k}\right)
[/tex]

The first term seems to be right, it looks like F = ma at least. I am not sure about the second part though because of that factor of 1/2 and I am not sure if the derivatives of the potential energy are correct.
For part b, I think I would have to insert the potential energy in the discrete equations of motions and integrate two times with respect to the time. But I am not sure how an integral would work here because of those indices.
Anyways, I would be really grateful for any advice! Thank you
 
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  • #2
Paumi said:
Since S is a function, I thought I could take the gradient and set that to 0 to derive the discrete equations of motions: [tex]
0 = \frac{\partial}{\partial r_k} S = \sum_{j=1}^N \Delta t \left(\frac{1}{2}m\frac{\partial}{\partial r_k}\left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)^2 - \frac{1}{2}\left(\frac{\partial}{\partial r_k} U(\boldsymbol{r_j})+ \frac{\partial}{\partial r_k}U(\boldsymbol{r_{j-1}})\right)\right)
\\
\rightarrow \sum_{j=1}^N \Delta t \left(m \left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)\left(\frac{\delta_{jk}-\delta_{j-1,k}}{\Delta t}\right) -\frac{1}{2} \left(\frac{\partial U(\boldsymbol{r_j})}{\partial r_k}\delta_{jk} + \frac{\partial U(\boldsymbol{r_{j-1}})}{\partial r_k}\delta_{j-1,k}\right)\right)[/tex]
I think everything is good so far.
[tex]\rightarrow 0 = m \left(\frac{-\boldsymbol{r_{k+1}}-\boldsymbol{r_{k-1}}+2\boldsymbol{r_k}}{\Delta t^2}\right) - \frac{1}{2} \left(\frac{\partial U(r_k)}{\partial r_k} + \frac{\partial U(r_{k+1})}{\partial r_k}\right)[/tex]
I don't think the very last term on the right is correct. You might reconsider what the expression ##\sum_{j=1}^N \frac{\partial U(\boldsymbol{r_{j-1}})}{\partial r_k}\delta_{j-1,k}## reduces to.
 
  • #3
Wouldn't it reduce to [tex] \frac{\partial U(r_{k})}{\partial r_k} [/tex] [tex]j = r_{k+1}[/tex] because only for j=k+1 the kroenecker delta would give me a 1? And when you would have [tex] \frac{\partial U(r_{k+1-1})}{\partial r_k} = \frac{\partial U(r_{k})}{\partial r_k} [/tex] and then I would have two [tex]\frac{\partial U(r_{k})}{\partial r_k}[/tex] and I could cancle the 1/2 out?
 
  • #4
Yes.
 
  • #5
Hey thank you very much! Now the discrete equation looks like Newtons 2nd law just as was asked :')
 

1. What is a discrete Lagrangian and why is it important in physics?

A discrete Lagrangian is a mathematical function that describes the energy of a physical system in terms of its position and velocity. It is important in physics because it allows us to model and analyze the behavior of complex systems, such as particles interacting with each other, using a simplified and systematic approach.

2. How do you minimize S (action) in a discrete Lagrangian homework?

To minimize S (action) in a discrete Lagrangian homework, you need to find the values of the variables that make the action as small as possible. This can be done by using the Euler-Lagrange equations, which are a set of differential equations that describe the behavior of the system.

3. What are the equations of motion (EoM) in a discrete Lagrangian system?

The equations of motion (EoM) in a discrete Lagrangian system are the Euler-Lagrange equations, which are a set of differential equations that describe the behavior of the system. These equations are derived from the principle of least action, which states that the path a system takes between two points is the one that minimizes the action.

4. How do you find the discrete trajectory in a discrete Lagrangian system?

To find the discrete trajectory in a discrete Lagrangian system, you need to solve the equations of motion (EoM) for the given initial conditions. This will give you the position and velocity of the system at different points in time, allowing you to plot the trajectory of the system.

5. Can a discrete Lagrangian system be used to study any type of physical system?

Yes, a discrete Lagrangian system can be used to study a wide range of physical systems, from simple particle dynamics to more complex systems such as fluids, gases, and even quantum mechanical systems. As long as the system can be described in terms of position and velocity, a discrete Lagrangian approach can be applied to analyze its behavior.

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