Why isn't Omega Varying in the Duffing Equation Output?

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In summary, the conversation is about trying to achieve a particular set of graphs based on the Duffing equation. The code and formula used correctly show the variation of X(1) at every step, but the value of omega remains constant. The person is wondering why this is and wants it to change along with X. They are also confused about the concept of "amplitude" in the equation. The expert suggests solving for x(t) and calculating the amplitude for different values of omega. The expert also notes that there is no point in printing F(1) and F(2) in the main program.
  • #1
adishpatel
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Hi there,
I am trying to achieve a particular set of graphs based on duffing equation graphs which you can find here:
2ih2p3r.jpg


It shows a graph which is Amplitude (X(1)) vs. Omega graph. With the code that I have deduced and the formula used, I am able to see that X(1) is varying at every step, however the omega value isn't?

What might be wrong in there? I want it to vary as X changes at every step.

Code can be found at http://sysden.com/dufing.f

Here are the first 10 lines of my output:

1st column is omega, second is X(1), third is F(1) and fourth is F(2)


Code:
   2.0000000000000000        1.0203714984602674E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.0563376279129973E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.1078731279483987E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.1749503127865479E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.2575390789204062E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.3556069126132030E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.4691188981781366E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.5980377265090756E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.7423237040156399E-003   0.0000000000000000        0.0000000000000000     
   2.0000000000000000        1.9019347618415869E-003   0.0000000000000000        0.0000000000000000
 
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  • #2
Why should omega change? You set it equal to 2 at the beginning of your program, and then don't change the value. By what magic would you expect it to be modified?

In the Duffing equation, ##x## is a function of ##t##, and ##\omega## is a parameter. The "amplitude" you are after is not defined. What does it mean? My guess is that you need to solve for ##x(t)## for a given ##\omega##, calculate something on that ##x(t)## to get the "amplitude", and repeat for different values of ##\omega##.

And by the way, there is not point in printing F(1) and F(2) in the main program, as you do not have access to those values, which are used internally by ODE2.
 

FAQ: Why isn't Omega Varying in the Duffing Equation Output?

1. What is the Duffing equation?

The Duffing equation is a mathematical model that describes the behavior of a damped, driven oscillator. It is a second-order nonlinear differential equation that includes both a linear and a cubic term, making it a highly versatile and widely studied equation in the field of nonlinear dynamics.

2. How is the Duffing equation used in output analysis?

The Duffing equation is often used in output analysis to study the behavior of complex systems, such as mechanical and electrical systems, as well as biological and chemical systems. Its solutions can provide insights into the stability, bifurcations, and chaos of these systems, and can be used to predict their behavior under different conditions.

3. What are the key characteristics of the Duffing equation?

The Duffing equation has several key characteristics that make it a useful tool in understanding nonlinear systems. These include its ability to exhibit both regular and chaotic behavior, its sensitivity to initial conditions, and its ability to exhibit bifurcations, which are sudden changes in the system's behavior as a parameter is varied.

4. How is the Duffing equation solved?

The Duffing equation can be solved analytically in some special cases, but in most cases, numerical methods are used to find approximate solutions. These methods involve discretizing the equation and using iterative techniques to approximate the solution at each time step. Advanced numerical techniques, such as bifurcation analysis and stability analysis, can also be used to analyze the behavior of the system over a range of parameter values.

5. What are the practical applications of the Duffing equation?

The Duffing equation has a wide range of practical applications in various fields, including physics, engineering, biology, and economics. It has been used to study the behavior of mechanical systems, such as pendulums and springs, as well as electrical circuits, chemical reactions, and biological systems. Its solutions can also be applied in control and optimization problems, making it a versatile tool in many areas of science and engineering.

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