Dear all --
A warning about using the above code. Since the choice of triangles
formed by a Delaunay triangulation can be somewhat arbitrary in the case of
a somewhat regular distribution of points, it will choose some
triangulations that lead to severe orthogonality errors in fipy, even if
Thanks.
And no, I'm not sure about the normalization for grid spacing. I very well
could have calculated the error incorrectly. I just reported the root mean
square error of the points and didn't weight by volume or anything like
that. I can change that, but I'm not sure of the correct approach.
On Wed, Jul 20, 2016 at 1:30 PM, Raymond Smith wrote:
> Hi, FiPy.
>
> I was looking over the diffusion term documentation,
> http://www.ctcms.nist.gov/fipy/documentation/numerical/discret.html#diffusion-term
> and I was wondering, do we lose second order spatial accuracy as soon
FiPy has issues with non-orthogonal meshes (i.e. when the vector
between the cell center isn't parallel to the face normal). We did
make an attempt at fixing this, which resulted in two diffusion terms,
https://github.com/usnistgov/fipy/blob/develop/fipy/terms/diffusionTermCorrection.py
and
Hi Raymond,
I was just corresponding with James Pringle off the list about the
same issue for a Delaunay triangulated mesh. Here is my explanation to
him.
I think this is the non-orthogonality issue. I should have realized
this much sooner. The only place we seem to reference this issue is in
Hi, FiPy.
I was looking over the diffusion term documentation,
http://www.ctcms.nist.gov/fipy/documentation/numerical/discret.html#diffusion-term
and I was wondering, do we lose second order spatial accuracy as soon as we
introduce any non-uniform spacing (anywhere) into our mesh? I think the