Are hyperbolic sines and cosines orthogonal in solving higher order PDE's?

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In summary, the conversation discusses solving the Euler Bernoulli beam equation by separation of variables and proving the separated functions of space are orthogonal. The pde is given and the separated function of space is provided. The conversation also mentions the difficulty of finding an inner product for which the solutions are orthogonal and suggests a sufficient condition for this. The approach of using boundary conditions to replicate a cantilever beam is discussed, along with the use of hyperbolic sines and cosines and the question of their orthogonality. The inner product being used is also mentioned.
  • #1
Bleakfacade
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Hello there!
So here's my problem, while you solve the Euler Bernoulli beam Equation by separation of variables, how do I have to prove the separated function of space are orthogonal? If so, are hyperbolic sines and cosines orthogonal when you have a product or a linear combination of them?

The pde is- [; u_{tt}+\alpha^{2} u_{xxxx} = o ;] where [; \alpha ;] is a constant that is material dependent. The separated function of space is [; F(x) = \sum_{n=1}^{\infty} [cosh(\beta_{n}x)-cos(\beta_{n}x)]-[sinh(\beta_{n}x)-sin(\beta_{n}x)] ;] where [; \beta_{n} ;] is some constant.
 
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  • #2
You are looking for solutions of [itex]X_\lambda^{(4)} = \lambda X_\lambda[/itex] for [itex]\lambda \in \mathbb{R}[/itex]. Now for fixed [itex]\lambda[/itex] there is a four-dimensional subspace of solutions, and given an arbitrary inner product on that space there will exist a basis which is orthogonal with respect to that inner product. (The constraint you haven't mentioned is that [itex]X_\lambda[/itex] needs to satisfy the boundary conditions at each end of the beam. That constraint may cause you to reject some of these solutions.) So the difficulty is to find an inner product with respect to which [itex]X_\lambda[/itex] and [itex]X_\mu[/itex] are necessarily orthogonal when [itex]\lambda \neq \mu[/itex].

A sufficient condition for this is that the operator [itex]f \mapsto f^{(4)}[/itex] should be self-adjoint with respect to the inner product, ie. [tex]
(f^{(4)},g) = (f,g^{(4)})[/tex] for every [itex]f[/itex] and [itex]g[/itex]. Now if you take (somewhat arbitrarily) the inner product [tex]
(f,g) = \int_{-L}^L f(x)(g(x))^{*}\,dx[/tex] where [itex]{}^{*}[/itex] denotes the complex conjugate, then repeatedly integrating [itex](f^{(4)},g)[/itex] by parts yields [tex]
(f^{(4)},g) = (f,g^{(4)}) + \left[ f'''g^{*} - f''(g^*)' + f'(g^*)'' - f(g^*)''' \right]_{-L}^{L}.[/tex]
 
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  • #3
pasmith said:
You are looking for solutions of [itex]X_\lambda^{(4)} = \lambda X_\lambda[/itex] for [itex]\lambda \in \mathbb{R}[/itex]. Now for fixed [itex]\lambda[/itex] there is a four-dimensional subspace of solutions, and given an arbitrary inner product on that space there will exist a basis which is orthogonal with respect to that inner product. (The constraint you haven't mentioned is that [itex]X_\lambda[/itex] needs to satisfy the boundary conditions at each end of the beam. That constraint may cause you to reject some of these solutions.) So the difficulty is to find an inner product with respect to which [itex]X_\lambda[/itex] and [itex]X_\mu[/itex] are necessarily orthogonal when [itex]\lambda \neq \mu[/itex].

A sufficient condition for this is that the operator [itex]f \mapsto f^{(4)}[/itex] should be self-adjoint with respect to the inner product, ie. [tex]
(f^{(4)},g) = (f,g^{(4)})[/tex] for every [itex]f[/itex] and [itex]g[/itex]. Now if you take (somewhat arbitrarily) the inner product [tex]
(f,g) = \int_{-L}^L f(x)(g(x))^{*}\,dx[/tex] where [itex]{}^{*}[/itex] denotes the complex conjugate, then repeatedly integrating [itex](f^{(4)},g)[/itex] by parts yields [tex]
(f^{(4)},g) = (f,g^{(4)}) + \left[ f'''g^{*} - f''(g^*)' + f'(g^*)'' - f(g^*)''' \right]_{-L}^{L}.[/tex]

Well, that was my approach, which was not obvious to me to begin with. But the boundary conditions were to replicate a cantilever beam that is suspended at one end. I have a rather monstrous equation which has many hyperbolic sines and cosines in it. I am not quite sure if the hyperbolic terms are orthogonal. I looked at their graphs for some insight but to no avail.
Also, the inner product I have is
[; (f,g) = \int_{0}^{L} f(x)(g(x))*dx ;]
 

1. What is a higher order partial differential equation (PDE)?

A higher order PDE is a mathematical equation that involves partial derivatives of a function with respect to multiple independent variables. It is used to describe physical phenomena such as heat transfer, fluid dynamics, and quantum mechanics.

2. What is the difference between a first order and a higher order PDE?

A first order PDE involves only first derivatives of the unknown function, while a higher order PDE involves derivatives of higher order (second, third, etc.). Higher order PDEs are generally more difficult to solve and require more complex techniques.

3. What are the main methods for solving higher order PDEs?

The main methods for solving higher order PDEs include the method of characteristics, separation of variables, and Fourier transforms. Other techniques such as finite difference methods and numerical methods are also commonly used.

4. How do boundary conditions affect the solution of a higher order PDE?

Boundary conditions are essential in solving higher order PDEs as they provide information about the behavior of the solution at the boundaries of the domain. They help to determine the unique solution to the PDE and can greatly affect the accuracy and stability of the solution method used.

5. Can all higher order PDEs be solved analytically?

No, not all higher order PDEs can be solved analytically. Some PDEs are too complex and do not have closed-form solutions, requiring numerical or approximate methods to find a solution. In these cases, it is important to choose an appropriate numerical method that can accurately approximate the solution.

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