Method of Characteristics for Hyperbolic PDE

In summary, The conversation discusses using the method of characteristics to solve a hyperbolic PDE with initial and boundary conditions. The method involves finding the characteristic curve and using it to calculate the values of n at different sizes. However, the problem arises when there is no function to describe the initial and boundary conditions, making it difficult to incorporate them into the equation. The solution involves finding the characteristic curve and using it to calculate n at different sizes, but this is challenging without a function to describe the initial and boundary conditions.
  • #1
Graham Power
4
0
I am trying to build a program in Matlab to solve the following hyperbolic PDE by the method of characteristics

∂n/∂t + G(t)∂n/∂L = 0

with the inital and boundary conditions

n(t,0)=B(t)/G(t) and
n(0,L)=ns

Here ns is an intial distribution (bell curve) but I don't have a function to fit it. I just have different values of n at various sizes, L (from 0 to 1000μm).

I know that the method of characterisitics gives the solution

∂n/∂s=0
∂t/∂s=1
∂L/∂s=G(t)

which gives the characteristic curve as L-G(t)*t=L0. So the values of n @ any initial size, L0, remain constant along the characteristic curve.

My problem is how do I incorporate the original n values at time zero and various sizes into the equation. I want to say:

n@(L0)=n@(L-G(t)*t)

If the initial condition was given as a function of L0, say n(0,L)= f(L0)=sin(L0), then I could just sub in the characteristic equation and calculate n(t,L) as sin(L-G(t)*t). But I don't have a function that describes the initial condition, just vaues of n at various sizes, L as said above.

The other problem is the boundary condition, this should make up one section of the solution and the initial condition the other.

But again how do I actually say n@(t,0)=B(t)/G(t)=n@(L-G(t)*t).

Say if n(t,0)=g(t), so the boundary condition is a function of time explicitly, not the case for me, B and G are actually a function of concentration which is changing with time, then the solution for the PDE above subject to the boundary would be;

n(t,L)=g(t-(L/G(t))

So I get the method overall, I am just confused how I propagate each value when I don't have functions to describe the initial and boundary conditions, like f(L0) and g(t) shown above.

I posted about this ages ago but if anyone had some feedback on this it would be great.
 
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  • #2
The characteristic curve is

[tex]L=L_0+\int_0^t{G(\xi)d\xi}[/tex]
 

Related to Method of Characteristics for Hyperbolic PDE

1. What is the Method of Characteristics for Hyperbolic PDE?

The Method of Characteristics is a mathematical technique used to solve hyperbolic partial differential equations (PDEs). It involves transforming the PDE into a system of ordinary differential equations (ODEs) along specific characteristic curves, which can then be solved using standard ODE techniques.

2. When is the Method of Characteristics used?

The Method of Characteristics is typically used to solve initial value problems for hyperbolic PDEs. It is particularly useful when the PDE is linear and has constant coefficients, as the transformation into a system of ODEs simplifies the solution process.

3. What are the advantages of using the Method of Characteristics?

One advantage of the Method of Characteristics is that it can handle non-rectangular boundaries and discontinuities in the solution. It also allows for a more intuitive approach to solving hyperbolic PDEs, as it involves finding solutions along specific curves rather than in the entire domain.

4. Are there any limitations to using the Method of Characteristics?

The Method of Characteristics can only be applied to hyperbolic PDEs, which are a type of second-order PDE with two distinct families of characteristic curves. It also requires the PDE to have constant coefficients, which is not always the case in real-world applications.

5. Can the Method of Characteristics be applied to higher-dimensional PDEs?

Yes, the Method of Characteristics can be extended to higher-dimensional PDEs, but it becomes more complex and computationally intensive. It is also limited to certain types of higher-dimensional PDEs, such as quasilinear PDEs.

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