Explaining Chaos in Constant Accelerating Systems

In summary, the conversation discusses the possibility of chaos appearing in a system due to nonlinearity, and questions why a constant accelerating system with a nonlinear equation does not exhibit chaos. The speaker mentions the importance of the difference and differential equation being nonlinear and expresses confusion about the concept of sensitivity in this context. They also bring up the concept of Lyapunov exponent and provide an example of a non-chaotic system with a nonlinear equation of motion. The speaker concludes by noting that the constant accelerating system can be seen as a linear mechanical system, despite the trajectory being a nonlinear function of time.
  • #1
LagrangeEuler
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Chaos could appear in the system if there is some nonlinearity. My question is how to explain that there is no chaos in constant acceletating system
[tex]s(t)=v_0t+\frac{at^2}{2}[/tex]
when equation is nonlinear? Why is important only that difference and differential equation be nonlinear. It confusing me.
 
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  • #2
LagrangeEuler said:
Chaos could appear in the system if there is some nonlinearity. My question is how to explain that there is no chaos in constant acceletating system
[tex]s(t)=v_0t+\frac{at^2}{2}[/tex]
when equation is nonlinear? Why is important only that difference and differential equation be nonlinear. It confusing me.
The crucial point is probably, that ##s(t)## isn't sensitive on the initial conditions. To answer why, I'll have to ask you which definition of sensitivity do you want to use (I've found three on Wikipedia). I assume the entire trajectory will be the underlying topological space as well as its dense ##s-##invariant subset in order to clear the topological assumptions.
 
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  • #3
Exponential sensitivity. Calculation of largest Lyapunov exponent.
 
  • #4
A back of the envelope calculation with the first differential as quotient of rates, the formula ##\log (1+t) = - \log (1 - (\frac{t}{1+t}))## and the power series of ##\log## gave me a Lyapunov exponent zero.
 
  • #5
An anharmonic 1D oscillator with Lagrangian ##\frac{p^2}{2m}-kx^4## isn't chaotic either, even though the equation of motion is nonlinear in ##x(t)##. On the other hand, you can make a harmonic oscillator chaotically sensitive to the initial condition by choosing a negative "spring constant" (a slightest deviation from the equilibrium position will start growing exponentially) . Note that in the constant accelerating system the force is an s-independent constant, which means that it's actually a mechanical system with a linear equation of motion even though the trajectory is a nonlinear function of ##t##.
 
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1. What is chaos in constant accelerating systems?

Chaos in constant accelerating systems refers to the unpredictable and seemingly random behavior that occurs in a system when it experiences constant acceleration, even though the equations governing the system are deterministic.

2. How does chaos arise in constant accelerating systems?

Chaos arises in constant accelerating systems due to the sensitive dependence on initial conditions, where small changes in the starting conditions can lead to large differences in the final outcome of the system.

3. What are some examples of constant accelerating systems that exhibit chaos?

Some examples of constant accelerating systems that exhibit chaos include the double pendulum, the Lorenz system, and the driven damped pendulum. These systems are commonly used as models to study chaos in various scientific fields.

4. Can chaos in constant accelerating systems be predicted?

No, chaos in constant accelerating systems cannot be predicted with certainty. However, researchers have developed methods such as chaos theory and bifurcation analysis to better understand and predict the behavior of chaotic systems.

5. How is chaos in constant accelerating systems relevant in real-world applications?

Chaos in constant accelerating systems has many practical applications, such as in weather forecasting, stock market analysis, and population dynamics. Understanding chaos can also help engineers design more efficient and stable systems, such as in aircraft and spacecraft control.

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