Diffusion equation and a system of equations with reciprocal unknowns?

In summary, the conversation discusses the normal diffusion equation and its explicit and implicit solutions using finite differences. The focus shifts to a more complicated equation involving the variational derivative of a function G, which acts as the force for diffusion. The approach of using the concentration c as the unknown instead of the known phi is explored, but the presence of reciprocal and non-reciprocal unknowns presents challenges. The possibility of using different approximations for the derivative is also mentioned.
  • #1
Hypatio
151
1
TL;DR Summary
Need to find an implicit numerical solution to a diffusion equation with concentration replaced with the derivative of a function of the concentration
So the normal diffusion equation looks like
[tex]\frac{\partial c}{\partial t} = k\frac{\partial}{\partial x}\left(\frac{\partial c}{\partial x}\right)[/tex]
I know how to get explicit and implicit solutions to this equation using finite differences. However, I am trying to do the same for an equation of the following form:
[tex]\frac{\partial c}{\partial t} = k\frac{\partial}{\partial x}\left(\frac{\partial}{\partial x}\frac{\delta G}{\delta c}\right)[/tex]
So, the term [tex]\delta G/ \delta c[/tex] is a variational derivative of some function G. Spatial variation of this, instead of the concentration itself, acts as the force for diffusion. In my application, I need to find a solution in which G and [tex]\delta G/ \delta c[/tex] are arbitrarily complicated, so I can probably settle for a solution in which it is a constant in time for each solve.

A 1D, forward in time, centered in space, finite-difference equation for an implicit solve might be:
[tex]c^1_i=c^0_i + \Delta t \frac{k}{\Delta x^2}\left(\phi^1_{i-1}-2\phi^1_{i}+\phi^1_{i+1}\right)[/tex]
where superscript 0 indicates the old/reference time, and superscript 1 indicates the forward time being solved for, subscripts indicate position in space, and
[tex]\phi_i= \left(\partial G/ \partial c\right)_i[/tex]
But maybe this is incorrect. I'm not sure how to write the equations so that the unknowns are the concentrations, c, and not phi, which is known. One approach I've tried is to write
[tex]\phi= \left(\delta G/ \delta c\right) = \frac{G-G_0}{c-c_0}= \frac{\Delta G}{c-c_0} =\frac{\Delta G}{c}[/tex]
but this results in
[tex]c^1_i-\Delta t \frac{k}{\Delta x^2}\left(\Delta G_{i-1}\frac{1}{c^1_{i-1}}-2\Delta G_{i}\frac{1}{c^1_{i}}+\Delta G_{i+1}\frac{1}{c^1_{i+1}}\right) = c^0_i[/tex]
which would result in a matrix of reciprocal and non-reciprocal unknowns, which I'm not sure how to solve for or rewrite to solve normally.

Any ideas about how to solve this type of equation implicitly?

Thanks for any help.
 
Last edited:
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  • #2
I've realized I may have made a mistake and I should have wrote the approximation of the derivative in a different way. For example
[tex]\phi_i = x_i+y_ic_i[/tex]
for a first order approximation
[tex]\phi_i = x_i+y_ic_i+z_ic_i^2[/tex]
for a second order approximation, or
[tex]\phi_i = x_i \ln (c)+y_i[/tex]
for a logarithmic approximation.

because of the way c appears in the terms I can manage to write the coefficients that will go into the stiffness matrix. Does this make sense now?

On the other hand, I don't know how to deal with the squared term in the second order approximation, and even less how to deal with the logarithmic term in the third approximation. Any ideas?
 
  • #3
This seems to be in the wrong forum. Should be in Differential Equations.
 

1. What is the diffusion equation?

The diffusion equation is a partial differential equation that describes the movement of particles through a medium due to random thermal motion. It is commonly used in physics, chemistry, and engineering to model diffusion processes.

2. How is the diffusion equation derived?

The diffusion equation can be derived from Fick's law, which states that the flux of particles is proportional to the concentration gradient. By considering the conservation of mass, the diffusion equation can be obtained through mathematical manipulation and simplification.

3. What is a system of equations with reciprocal unknowns?

A system of equations with reciprocal unknowns is a set of equations where the variables are inversely related to each other. This means that when one variable increases, the other decreases in proportion. These types of systems are often used to model phenomena such as supply and demand or predator-prey relationships.

4. How is a system of equations with reciprocal unknowns solved?

To solve a system of equations with reciprocal unknowns, one approach is to use substitution. This involves solving one equation for a variable in terms of the other and then substituting that expression into the other equation. Another method is to graph the equations and find the point of intersection, which represents the solution.

5. What are some real-world applications of the diffusion equation and systems of equations with reciprocal unknowns?

The diffusion equation is used in various fields, such as material science, biology, and environmental engineering, to model diffusion processes such as heat transfer, mass transfer, and chemical reactions. Systems of equations with reciprocal unknowns are commonly used in economics, ecology, and population dynamics to study relationships between variables and make predictions about future behavior.

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