On Wed, Feb 19, 2014 at 8:10 PM, Caleb Hattingh
wrote:
> On 20 February 2014 01:48, Daniel Wheeler wrote:
> Thank you very much for your time.
Thanks for all the feedback and good luck with your work.
--
Daniel Wheeler
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On 20 February 2014 01:48, Daniel Wheeler wrote:
> It depends on the boundary condition. If you have natural boundary
> conditions, the values should be -1, but with fixed value, the values
> are -3. This is because the distance to the edge is dx / 2.
>
Perfect, I checked the math and that works
On Tue, Feb 18, 2014 at 6:04 PM, Caleb Hattingh
wrote:
> On 19 February 2014 02:25, Daniel Wheeler wrote:
> And this continues all the way down into the bottom right-hand
> corner such that J[nx,nx] * dx^2 is again -3. I found this by using
> perturbation (forward differencing) to calculate th
On 19 February 2014 02:25, Daniel Wheeler wrote:
> The difficulty is of course that the
> coefficients are functions of the variables so this would have to be
> automated somehow, probably with sympy.
>
Many thanks for taking the time to respond. I've thought about it a
little more. Say your e
On Sun, Feb 16, 2014 at 11:50 PM, Caleb Hattingh
wrote:
> Given a typical DiffustionTerm(coeff=D), and typical CellVariable phi, is
> there an easy way to obtain the Jacobian of such a system?
This is a good question. It would be really nice if FiPy could do
this, but it can't at present. The dif
Given a typical DiffustionTerm(coeff=D), and typical CellVariable phi, is
there an easy way to obtain the Jacobian of such a system? In other words,
the matrix of derivatives of all the residuals over all the cell variables.
Obviously this will be quite sparse.
I am using justResidualVector() to
