How to Modelize Column Buckling with Coupled Differential Equations?

In summary, the author is trying to model a column buckling problem and has come up with a system where the coefficients (thetas) are not constants. However, he doesn't know how to diagonalize A and the equations are non-linear and matrix methods cannot be used.
  • #1
tdcaupv
7
0
Hi,
I have to modelize the buckling of a column and I've come up with this system:
[tex] N'(x) + N(x) \theta ' (x) \theta (x) - Q \theta ' (x) + f = 0 [/tex]
[tex] Q'(x) + N(x) \theta ' (x) + Q \theta ' (x) \theta (x) = 0 [/tex]

with f a constant

The coefficients (thetas) are not constants.
I've written it as X' = A X + f But I don't know how to diagonalize A since coefficients are not constants.

Thank You for helping me.
 
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  • #2
Actually, it's worse than that. Not only are the coefficients variable, but the second equation includes [itex]\theta \theta'[/itex] so the equations are non-linear and matrix methods cannot be used at all.
 
  • #4
edit
 
  • #5
θ is a known function right?
 
  • #6
Yes.
 
  • #7
How complicated is θ?
 
  • #8
Well, i don't know, I am a bit lost.
Actually, theta is my slope angle which is very small.
 
  • #9
May I ask why you just don't solve it numerically? I mean really, he didn't say nothing about analytic solution else I'd keep my mouth shut. You know, numerical methods are perfectly fine for the real-world.

So if I may be the practical voice in here: work it first numerically even if you need an analytic solution just to get a handle on it, then do it analytically if you have to.
 
  • #10
First I need to solve it analytically. Maybe equations are not good. Maybe I should get simplier equations, i don't know ...
 
  • #11
No you don't even if you have to solve it analytically. You know if you're going to drive your truck in the dark without lights, it's a good idea to first walk the path with a flash light to see if there are any holes and stuf.

I'm tellin' you the right way to approach this: solve it first numerically even if you have to just dream-up initial conditions. Get a handle on it, then attempt to solve it analytically.

Also, I do not believe this is a non-linear system if [itex]\theta(x)[/itex] is known. You effectively have:

[tex]\frac{dN}{dx}=-Ngh+Qh-f[/tex]

[tex]\frac{dQ}{dx}=-Nh+Qhg[/tex]

where g=g(x) and h=h(x) are known functions. Well there you go: I'm dreamin' up g and h, N(0) and Q(0) too, bingo-bango: Numeric solution in hand.
 
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FAQ: How to Modelize Column Buckling with Coupled Differential Equations?

What are coupled differential equations?

Coupled differential equations are a set of differential equations that are interconnected and cannot be solved independently. They involve multiple variables and their rates of change are dependent on each other.

What is the difference between coupled and uncoupled differential equations?

Uncoupled differential equations are a set of differential equations that can be solved independently, as they do not depend on each other. Coupled differential equations, on the other hand, are interconnected and cannot be solved independently.

What are some real-life applications of coupled differential equations?

Coupled differential equations are commonly used in physics, chemistry, engineering, and other scientific fields to model systems with multiple interacting variables. Examples include the movement of planets in the solar system, the spread of diseases in a population, and the flow of electricity in circuits.

How do you solve coupled differential equations?

Solving coupled differential equations involves finding a set of equations that describe the relationships between the variables and their rates of change. This can be done analytically or numerically using techniques such as substitution, elimination, and numerical integration.

What are some challenges in solving coupled differential equations?

One of the main challenges in solving coupled differential equations is finding a closed-form solution, as it may not always be possible. This means numerical methods are often necessary, which can be computationally intensive and require careful selection of step sizes and tolerances. Additionally, the complexity of the equations and the number of variables involved can also make the problem challenging.

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