Thank you.
I am busy for the week, but then will get a chance to think about this :-)
Cheers,
Jamie
On Tue, Oct 4, 2016 at 1:16 PM, Daniel Wheeler
wrote:
> Hi James,
>
> Thanks for this example. I think I understand what is happening. Let's
> start with the code for
Hi James,
Thanks for this example. I think I understand what is happening. Let's
start with the code for solving the problem,
import fipy
import numpy
mesh = fipy.Grid1D(nx=10)
var = fipy.CellVariable(mesh=mesh)
var[:] = 2 * numpy.random.random(mesh.numberOfCells)
var.constrain(1.,
Interesting. One addition to Zhekai's comment. When I read it, I thought
Upwind might be doing better because it is adding additional numerical
diffusion (as upwind usually does, at least in finite difference codes).
However this is not the case. If you keep the advection u and diffusion D
the 1D
Hi James,
Thanks for providing this demo to illustrate the problem. I don't have any
particular ideas exactly why the initial value of psi helps to reduce the
error and why transient problem "advect" out. However, I have some
findings that may help on this.
Finding # 1: I noticed you have
tried
No worries -- I had to do it to figure out the problem in my more complex
domain and equation... The issue which surprised me was that the value the
variable was initialized to had an effect on the steady solution.
Jamie
On Fri, Sep 16, 2016 at 8:14 AM, Guyer, Jonathan E. Dr. (Fed) <
James -
I think Daniel will have more insight into why this is happening and if there
is anything to be done about it besides artificial relaxation, but I just want
to say how much I appreciate your putting this together. This is a very lucid
illustration.
- Jon
> On Sep 15, 2016, at 5:13
Dear FiPy users --
This is a simple example of how and why fipy may fail to solve a
steady advection diffusion problem, and how solving the transient
problem can reduce the error. I also found something that was a
surprise -- the "initial" condition of a steady problem can affect