Fourier series damped driven oscillator ODE

In summary, the conversation discusses how to justify removing the summations and solving for $C_n$ in a given equation using the fact that a well-behaved function has a unique Fourier expansion. This can be achieved by combining the summations and equating the coefficients on each side, resulting in the equation $-n^2\omega^2C_n + 2\beta in\omega C_n + \omega_0^2 C_n = f_n$ for each $n$. It is also mentioned that one can multiply through by $\frac{1}{2\pi}\overline{e^{in\omega t}}$ and use Sturm-Liouville to integrate the summation to 1.
  • #1
Dustinsfl
2,281
5
$$
-\sum_{n = 0}^{\infty}n^2\omega^2C_ne^{in\omega t} + 2\beta\sum_{n = 0}^{\infty}in\omega C_ne^{in\omega t} + \omega_0^2\sum_{n = 0}^{\infty}C_ne^{in\omega t} = \sum_{n = 0}^{\infty}f_ne^{in\omega t}
$$
How can I justify removing the summations and solving for $C_n$?
$$
-n^2\omega^2C_ne^{in\omega t} + 2\beta in\omega C_ne^{in\omega t} + \omega_0^2C_ne^{in\omega t} = f_ne^{in\omega t}
$$
 
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  • #2
dwsmith said:
$$
-\sum_{n = 0}^{\infty}n^2\omega^2C_ne^{in\omega t} + 2\beta\sum_{n = 0}^{\infty}in\omega C_ne^{in\omega t} + \omega_0^2\sum_{n = 0}^{\infty}C_ne^{in\omega t} = \sum_{n = 0}^{\infty}f_ne^{in\omega t}
$$
How can I justify removing the summations and solving for $C_n$?
$$
-n^2\omega^2C_ne^{in\omega t} + 2\beta in\omega C_ne^{in\omega t} + \omega_0^2C_ne^{in\omega t} = f_ne^{in\omega t}
$$
Combining the summations, you can write this as $$\sum_{n = 0}^{\infty}\bigl(-n^2\omega^2C_n + 2\beta in\omega C_n + \omega_0^2 C_n \bigr) e^{in\omega t} = \sum_{n = 0}^{\infty}f_ne^{in\omega t}.$$
Now use the fact that a (reasonably well-behaved) function has a unique Fourier expansion to conclude that the coefficients on each side must be the same, to conclude that $-n^2\omega^2C_n + 2\beta in\omega C_n + \omega_0^2 C_n = f_n$ for each $n.$
 
  • #3
Opalg said:
Combining the summations, you can write this as $$\sum_{n = 0}^{\infty}\bigl(-n^2\omega^2C_n + 2\beta in\omega C_n + \omega_0^2 C_n \bigr) e^{in\omega t} = \sum_{n = 0}^{\infty}f_ne^{in\omega t}.$$
Now use the fact that a (reasonably well-behaved) function has a unique Fourier expansion to conclude that the coefficients on each side must be the same, to conclude that $-n^2\omega^2C_n + 2\beta in\omega C_n + \omega_0^2 C_n = f_n$ for each $n.$

Could I also just multiple through by $\frac{1}{2\pi}\overline{e^{in\omega t}}$, and by Sturm-Liouville, the summation integrates to 1?
 
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FAQ: Fourier series damped driven oscillator ODE

What is a Fourier series?

A Fourier series is a mathematical representation of a periodic function as a sum of sinusoidal functions. It is commonly used to analyze and model signals and systems in various fields, including physics, engineering, and mathematics.

What is a damped driven oscillator?

A damped driven oscillator is a system that consists of a mass attached to a spring and subjected to an external force. The damping term in the system represents the dissipation of energy, while the driving force causes the system to oscillate with a specific frequency.

What is an ODE?

An ODE (ordinary differential equation) is a mathematical equation that describes the relationship between a function and its derivatives. In the context of a Fourier series damped driven oscillator, the ODE represents the motion of the system over time.

How is a Fourier series used to solve the ODE for a damped driven oscillator?

A Fourier series can be used to express the solution of an ODE in terms of sinusoidal functions. By substituting the Fourier series into the ODE and solving for the coefficients, we can obtain a solution that satisfies the given initial conditions and accurately describes the behavior of the damped driven oscillator.

What are the applications of Fourier series damped driven oscillator ODEs?

Fourier series damped driven oscillator ODEs have various applications in the fields of physics, engineering, and mathematics. They can be used to model and analyze mechanical systems, electrical circuits, and acoustic phenomena. They are also widely used in signal processing, image compression, and data analysis.

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