Hello,
I have trouble trying to install PySparse on a Linux cluster. The pip command:
pip install --user pysparse
did not work.
I found on a forum this command:
pip install --user git+http://git.code.sf.net/p/pysparse/git#egg=PySparse
Dockerfile so you might
> not want to use it as is, however, it might give you some clues about
> what's necessary to get FiPy working even if you don't use Docker.
>
> I hope this helps.
>
> Cheers,
>
> Daniel
>
> On Thu, Jul 20, 2017 at 9:38 AM, Clara Maurel <c
>
>
>> On Jul 25, 2017, at 1:50 PM, Clara Maurel <cmau...@mit.edu> wrote:
>>
>> Hello,
>>
>> Sorry in advance if my question is very basic. I am using fipy to solve a
>> Cahn-Hilliard equation problem, and it worked perfectly fine on a 1D dom
Hello,
Sorry in advance if my question is very basic. I am using fipy to solve a
Cahn-Hilliard equation problem, and it worked perfectly fine on a 1D domain of
size nx*dx where nx=500 and dx=0.5.
I wanted to run on a larger domain, but in order to reduce the computation
time, I increase the
Hello,
I would like to model the Cahn Hilliard equation with a diffusion coefficient
that depends on the cell variable (concentration X) and time.
The diffusion coefficient (Diff) is proportional to the second derivative of
the free energy (d2G), for which I have the values in an external file,
2G(Xf))
>
>
> Maybe 'Diff' should be FaceVariable? whichever way it should be, it should be
> fine in the equation definition then.
>
> Best,
> -Mike
>
>
>
>
> On 8/9/17 4:48 PM, Clara Maurel wrote:
>> Hello,
>>
>> I would like to model
Hello,
I asked a similar questions several months ago and someone nicely answered with
some ideas, which however did not solve my problem. So I am just trying another
time!
I want to model the dynamics of a system using the Cahn Hilliard equation,
with a diffusion coefficient that depends on
_var.setValue(K)
> print 'Mob, Diff, K'
> print Mob, Diff, K
> print 'MAX CONCENTRATION'
> print max(X_var)
>
> -eq = TransientTerm(coeff=1.) == DiffusionTerm(Diff) -
> DiffusionTerm(coeff=(Mob, K))
> +
>
>
> print datetime.now() - startTime
> (E
te))],[np.log(D_tmp[k]) for k in np.arange(len(T_taenite))],1)
slope_logD_T.append(slope)
intercept_logD_T.append(intercept)
D.append(D_tmp)
a_lattice_para.append(a_lattice_para_tmp)
a_lattice_ferro.append(a_lattice_ferro_tmp)
return Tc, D, slope_logD_T, inte
Hello everyone,
I apologize in advance for this very basic question…
I am trying to solve a 1D diffusion problem where I have a cooling planet
(core+mantle) with an initial temperature profile and a diffusion coefficient
that takes different values in the core and in the mantle. I want a fixed
9*dr**2/(2.0*Diff[0]) is a BinaryOperator Variable
>> * so, dt *= 1.1/dt *= 0.8 is a BinaryOperator Variable that gets nested
>> deeper
>>and deeper every time step
>>
>> Diff[0].value short circuits that
>>
>> - Your problem is completely
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