Linear ODE Systems in Numerical Methods.

In summary, the matrix in your problem is composed of a particular solution in the top row and a homogenous solution in the bottom row. This satisfies the requirement for two equations.
  • #1
arizonian
18
2
I feel so embarrased asking this question, but this is the place to get answers.

I have a 2nd order ODE with a forcing function that needs to be manipulated and put into a matrix for a numerical method solution, ie Matlab. My question is: Is the matrix composed of a particular solution in the top row and a homogenous solution in the bottom row? Does this satisfy the requirement for two equations? Maybe I should say equation, not solution.

My work:

m d^2x/dt^2 + c dx/dt + kx = 0

d^2x/dt^2 = dx/dt

Substituting y2 for d^2t/dx^2 and y1 for dx/dt, and realizing that y2 is the derivative of y1, I end up with, in matrix form:

(I am using periods to hold the spacing)

[-1/k...-c/mk]..[y2]...=[x2]
[1...-1]..[y1]...=[x1]

Thank you

Bill

On edit, I realized I forgot the signs in the first equation.
On second edit, I changed the lower equation to simplify what I was after.
 
Last edited:
Physics news on Phys.org
  • #2
Any body care to comment?

Bill
 
  • #3
The way you describe the problem is a little confusing. Let me try to paraphrase.
Your original problem is:
[tex] m \frac{d^2 x}{d t^2} + c \frac{dx}{dt} + kx = 0 [/tex]
[tex] m \frac{d^2 x}{d t^2} - \frac{dx}{dt} = 0 [/tex]

And you would like to transfer it to matrix form, right ?
You might do something as follows:

[tex] y_{1}=x [/tex]
[tex] y_{2}= \frac{dx}{dt} [/tex]
And let
[tex] X = \left(\begin{array}{c} y_{1} \\ y_{2} \end{array}\right) [/tex]
[tex] Y = \left(\begin{array}{c}\frac{d y_{1}}{dt} \\ \frac{dy_{2}}{dt} \end{array}\right) [/tex]

And you might turn this problem into

[tex] \left(\begin{array}{cc} c & m \\ -1 & m \end{array}\right) Y=
\left(\begin{array}{cc} -k & 0 \\ 0 & 00 \end{array}\right)X
[/tex]

Is this what you are trying to ask ?

Probably you can just start from here and check the other websites
to solve this question in martrix form. You might as well just take a look
at

http://www.ScienceOxygen.com/math409.html

It might not solve your question directly. But it is with a lot of links
on differential equation. You could start from there to collect some information...
 
  • #4
I think the original question is a bit confussing. This is how I'd present converting a high-order ODE to a system of first-order ODEs:


[tex]
\frac {d^2y} {dx^2} + \ln{y} = yx
[/tex]

To convert this to a system of ODEs,

let:

[tex]
z[x]=\frac{dy} {dx}
[/tex]

Then:

[tex]
\frac{dy} {dx} = z
[/tex]

[tex]
\frac {dz} {dx} =yx-\ln{y}
[/tex]

You can use the same method for higher-order ODEs, just assigning different variables to each higher-order derivative. I used Mathematica to solve this system with initial conditions:

[tex]
y[0]=1
[/tex]

[tex]
y'[0]=1
[/tex]

I attached a plot of the solution which you can find in "additional options".

SD
 

Attachments

  • plot1.JPG
    plot1.JPG
    5.3 KB · Views: 472
  • #5
Oh yea, I thought the talking dictionary on line was cool, but LaTeX rocks!

SD
 

1. What are linear ODE systems in numerical methods?

Linear ODE (ordinary differential equation) systems are a set of equations that describe the relationships between variables in a system. In numerical methods, these equations are approximated using numerical techniques to solve for the values of the variables.

2. Why are linear ODE systems important in numerical methods?

Linear ODE systems are important because they can model many real-world systems such as chemical reactions, population growth, and electrical circuits. Solving these systems using numerical methods allows us to predict the behavior of these systems and make informed decisions.

3. How are linear ODE systems solved using numerical methods?

There are various numerical methods used to solve linear ODE systems, such as Euler's method, Runge-Kutta methods, and multistep methods. These methods involve approximating the solution at discrete points and using iterative calculations to find the values of the variables at each point.

4. What are the advantages of using numerical methods to solve linear ODE systems?

Numerical methods offer a more efficient and accurate way to solve linear ODE systems compared to analytical methods. They also allow us to solve complex systems that may not have an analytical solution. Additionally, numerical methods can handle systems with varying parameters, making them versatile for different applications.

5. What are some common challenges when using numerical methods to solve linear ODE systems?

Some common challenges include choosing an appropriate numerical method for a specific system, ensuring stability and accuracy of the numerical solution, and dealing with stiff systems that have a wide range of time scales. It is also important to consider the limitations of numerical methods and the potential for round-off errors.

Similar threads

Replies
3
Views
776
Replies
4
Views
2K
Replies
2
Views
1K
Replies
7
Views
2K
Replies
10
Views
256
  • Differential Equations
Replies
9
Views
2K
Replies
3
Views
1K
Replies
2
Views
1K
  • Differential Equations
Replies
16
Views
865
Replies
6
Views
2K
Back
Top