PDE U_t = aU_xy (mixed derivatives)

In summary, the conversation is about using the centred finite difference method to solve a two-dimensional diffusion equation, with a discussion on the PDE theory, motivation for the equation, potential issues with FDM schemes, and solutions for avoiding oscillations.
  • #1
RedBranchKnight
8
0
I am trying to solve

(1) U_t = 2bU_xy (as part of U_t = aU_xx + 2bU_xy + cU_yy)

using centred finite difference method. When a > 0 everyhing is OK but when a < 0 I get some oscillation problems.

My questions are:

1. is there a pde theory for (1)?
2. what is the 'motivation' for (1) in terms of information flow to and from boundaries/
3. maybe my fdm schemes destroy ellipticity which is the reason why I get oscillations.
4. These oscillations seem to be related to the initial condition in some way but I have not figured it out

thanks for any help!
RBK
 
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  • #2
1. Yes, the PDE theory for (1) is called parabolic partial differential equations. 2. The motivation for (1) is that it is a two-dimensional diffusion equation which describes the flow of information or energy from one point to another over time. The 2bU_xy term in the equation represents the diffusion coefficient across the x and y directions. 3. It is possible that your FDM schemes are destroying the ellipticity of the equation, which can lead to oscillations. To check this, you could examine the eigenvalues of the elliptic operator associated with the equation. 4. Oscillations due to the initial condition can be avoided by using a better numerical scheme such as implicit methods or high order explicit methods.
 

1. What is a PDE?

A PDE (partial differential equation) is a type of mathematical equation that involves multiple variables and their partial derivatives. It is commonly used in physics, engineering, and other scientific fields to model complex systems and phenomena.

2. What does "U_t = aU_xy" mean?

The equation "U_t = aU_xy" is a specific form of a PDE called a mixed derivative equation. It means that the function U depends on three variables (x, y, and t) and that its partial derivative with respect to t is equal to the product of a constant "a" and the mixed partial derivative of U with respect to x and y.

3. What is the role of mixed derivatives in PDEs?

Mixed derivatives play an important role in PDEs because they allow us to model systems with multiple variables that influence each other. In other words, they help us understand how a small change in one variable can affect the behavior of another variable in a system.

4. How are PDEs used in scientific research?

PDEs are used in scientific research to model a wide range of natural phenomena and complex systems. They are particularly useful in fields such as physics, chemistry, and engineering, where many real-world problems can be described using PDEs.

5. What are some applications of the mixed derivative PDE "U_t = aU_xy"?

The mixed derivative PDE "U_t = aU_xy" has many applications in various scientific fields. For example, it can be used to model heat transfer in materials, diffusion of substances in fluids, and propagation of waves in different media. It is also commonly used in image processing and computer vision to analyze and manipulate images.

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