Re: Thermal Diffusion with Source
Daniel, No problem - later this week I’ll make a clean notebook with a less application specific example. Cheers, John Leeman > On Apr 25, 2016, at 12:34 PM, Daniel Wheeler > wrote: > > On Tue, Apr 19, 2016 at 11:10 PM, John Leeman wrote: >> >> I was just worried I didn’t grasp the way I needed to interact with FiPy. An >> example along this vein could be a useful addition in the docs possibly? > > John, an example would be very welcome. If you would like to just make > it an IPython notebook (rather than doctest/restructured text script) > then please do that and we'll try and link to it from the > documentation. > > -- > Daniel Wheeler > > ___ > fipy mailing list > fipy@nist.gov > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] ___ fipy mailing list fipy@nist.gov http://www.ctcms.nist.gov/fipy [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
Re: understanding convection terms
Daniel,
Thank you. I am a bit surprised that the CentralDifference basically
matches the hybrid method, and is more accurate than upwind.
Kris
On Tue, Apr 26, 2016 at 8:22 AM, Daniel Wheeler
wrote:
> Hi Kris,
>
> Good to hear from you again and a very nicely coded example.
>
> I think it's a solver issue. I tried the LU solver and it gives
> perfect results. See
> https://gist.github.com/wd15/affe4d82cc2a189d894a7d774e4bc00b.
>
> This might suggest that we need to change the default solver when
> using the central difference scheme.
>
> Cheers,
>
> Daniel
>
> On Mon, Apr 25, 2016 at 11:54 AM, Kris Kuhlman
> wrote:
> > I am trying to understand the convection terms available in fipy through
> a
> > simple steady-state problem. I am surprised at the divergence of the
> > CentralDifferenceConvectionTerm, is this to be expected? As the
> > discretization in the mesh is made finer, the solution gets worse!?
> >
> > The problem is: \frac{\partial^2 u}{\partial x^2} - v \frac{\partial
> > u}{\partial x} = 0
> >
> > The script at the link below compares the solution using different
> > ConvectionTerms, and plots the figures attached for different values of
> nx.
> > For larger values of v, the solution diverges even more and becomes
> > oscillatory.
> >
> > https://gist.github.com/klkuhlm/07f9eaf52b24e103f60ae213c0944c21
> >
> > Is this expected behavior?
> >
> > Kris
> >
> >
> > ___
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> > fipy@nist.gov
> > http://www.ctcms.nist.gov/fipy
> > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
> >
>
>
>
> --
> Daniel Wheeler
> ___
> fipy mailing list
> fipy@nist.gov
> http://www.ctcms.nist.gov/fipy
> [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
>
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Re: understanding convection terms
Hi Kris,
Good to hear from you again and a very nicely coded example.
I think it's a solver issue. I tried the LU solver and it gives
perfect results. See
https://gist.github.com/wd15/affe4d82cc2a189d894a7d774e4bc00b.
This might suggest that we need to change the default solver when
using the central difference scheme.
Cheers,
Daniel
On Mon, Apr 25, 2016 at 11:54 AM, Kris Kuhlman
wrote:
> I am trying to understand the convection terms available in fipy through a
> simple steady-state problem. I am surprised at the divergence of the
> CentralDifferenceConvectionTerm, is this to be expected? As the
> discretization in the mesh is made finer, the solution gets worse!?
>
> The problem is: \frac{\partial^2 u}{\partial x^2} - v \frac{\partial
> u}{\partial x} = 0
>
> The script at the link below compares the solution using different
> ConvectionTerms, and plots the figures attached for different values of nx.
> For larger values of v, the solution diverges even more and becomes
> oscillatory.
>
> https://gist.github.com/klkuhlm/07f9eaf52b24e103f60ae213c0944c21
>
> Is this expected behavior?
>
> Kris
>
>
> ___
> fipy mailing list
> fipy@nist.gov
> http://www.ctcms.nist.gov/fipy
> [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
>
--
Daniel Wheeler
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