In examples/large_deformation/hyperelastic.py a rotation by displacements is applied. By using a similar function the vectors defining the force couples could be defined for dw_surface_ltr (IMHO). Does it make sense?
r.
----- Reply message -----
From: "Andre Smit" <freev...(a)gmail.com>
To: <sfepy...(a)googlegroups.com>
Subject: Torque
Date: Sat, Dec 18, 2010 05:10
What is the best way to apply a torque load to a model?
--
Andre
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I am currrently looking for FEM packages to help me solve a system of
beams and columns, basically a collection of 1D bernoulli/timoshenko
line elements.
I started reading SfePy docs and i am getting the idea that doing the
above is not really possible here, am i right?
Are only 2D area elements permitted in SfePy?
Or is there any direct support for solving 1D line elements too..
Cheers
Nimish
Dear SfePy users,
Is it possible to evaluate a solution not only in the FEM mesh node, but in
any arbitrary point in the domain with the given (x, y, z) coordinates?
For example, consider Dirichlet problem for Poisson equation. We apply
essential boundary conditions on the surface nodes and after the problem
has been solved we have the solution vector, i.e. vector of values in the
FEM mesh nodes. But I want to know the solution in point v(x, y, z) that is
not FEM mesh node. What is the best way to obtain solution in this point v?
Sincerely,
Alec Kalinin
I'm working on modeling a next-generation X-ray mirror for which the
shape can be actively controlled by use of many thin piezo-electric
actuators mounted on the mirror surface. The mirror is basically a
glass conical paraboloid with a 1 meter radius and 200 micron
thickness (e.g. http://en.wikipedia.org/wiki/X-ray_optics). Our
project is currently using a proprietary FEA package, but the model
setup and turnaround time is slow, in part because there is only one
part-time engineer who can run it.
SfePy looks like a great package and we're hoping that it could be
used to automate running a large number of different cases. I've
spent some time reading the documentation but I have a few questions
that I hope can be answered before going too much further. I want to
apologize in advance if some of my wording is imprecise, I have a
physics background but this topic is a bit outside my realm...
- Is SfePy appropriate for this problem?
- If a specify a grid with about 800 x 400 points (azimuthal, axial)
and about 10 boundary conditions (corresponding to mount points), what
is the rough order of magnitude of time to compute the solution? Is
it seconds, minutes, hours, or days?
- The linear elastic examples show a problem with a specified
displacement. How do I specify an input force? The piezo essentially
provides a tensile force along the surface.
- Is there a way to specify the problem and solve in cylindrical
coordinates? This is the natural coordinate system.
- How do I specify 6-DOF constraints which correspond to the mirror
mounts?
Thanks in advance for any help!
Tom Aldcroft
Hello,
In the previous topic [1] I promised to David to write the description of
the weak form terms for the Poisson equation. So, here is it. You can
freely use and modify this description without any restrictions. But I am
not very sure that the text is very accurate.
[1] https://groups.google.com/d/topic/sfepy-devel/9-cf42ZKsog/discussion
Very sad news.
-------- Original Message --------
Subject: [Numpy-discussion] A sad day for our community. John Hunter: 1968-2012.
Date: Wed, 29 Aug 2012 19:57:22 -0700
From: Fernando Perez <fpere...(a)gmail.com>
Reply-To: Discussion of Numerical Python <numpy-di...(a)scipy.org>
To: IPython Development list <ipyth...(a)scipy.org>, IPython User list
<ipytho...(a)scipy.org>, Discussion of Numerical Python
<numpy-di...(a)scipy.org>
Dear friends and colleagues,
[please excuse a possible double-post of this message, in-flight
internet glitches]
I am terribly saddened to report that yesterday, August 28 2012 at
10am, John D. Hunter died from complications arising from cancer
treatment at the University of Chicago hospital, after a brief but
intense battle with this terrible illness. John is survived by his
wife Miriam, his three daughters Rahel, Ava and Clara, his sisters
Layne and Mary, and his mother Sarah.
Note: If you decide not to read any further (I know this is a long
message), please go to this page for some important information about
how you can thank John for everything he gave in a decade of generous
contributions to the Python and scientific communities:
http://numfocus.org/johnhunter.
Just a few weeks ago, John delivered his keynote address at the SciPy
2012 conference in Austin centered around the evolution of matplotlib:
http://www.youtube.com/watch?v=e3lTby5RI54
but tragically, shortly after his return home he was diagnosed with
advanced colon cancer. This diagnosis was a terrible discovery to us
all, but John took it with his usual combination of calm and resolve,
and initiated treatment procedures. Unfortunately, the first round of
chemotherapy treatments led to severe complications that sent him to
the intensive care unit, and despite the best efforts of the
University of Chicago medical center staff, he never fully recovered
from these. Yesterday morning, he died peacefully at the hospital
with his loved ones at his bedside. John fought with grace and
courage, enduring every necessary procedure with a smile on his face
and a kind word for all of his caretakers and becoming a loved patient
of the many teams that ended up involved with his case. This was no
surprise for those of us who knew him, but he clearly left a deep and
lasting mark even amongst staff hardened by the rigors of oncology
floors and intensive care units.
I don't need to explain to this community the impact of John's work,
but allow me to briefly recap, in case this is read by some who don't
know the whole story. In 2002, John was a postdoc at the University
of Chicago hospital working on the analysis of epilepsy seizure data
in children. Frustrated with the state of the existing proprietary
solutions for this class of problems, he started using Python for his
work, back when the scientific Python ecosystem was much, much smaller
than it is today and this could have been seen as a crazy risk.
Furthermore, he found that there were many half-baked solutions for
data visualization in Python at the time, but none that truly met his
needs. Undeterred, he went on to create matplotlib
(http://matplotlib.org) and thus overcome one of the key obstacles for
Python to become the best solution for open source scientific and
technical computing. Matplotlib is both an amazing technical
achievement and a shining example of open source community building,
as John not only created its backbone but also fostered the
development of a very strong development team, ensuring that the
talent of many others could also contribute to this project. The
value and importance of this are now painfully clear: despite having
lost John, matplotlib continues to thrive thanks to the leadership of
Michael Droetboom, the support of Perry Greenfield at the Hubble
Telescope Science Institute, and the daily work of the rest of the
team. I want to thank Perry and Michael for putting their resources
and talent once more behind matplotlib, securing the future of the
project.
It is difficult to overstate the value and importance of matplotlib,
and therefore of John's contributions (which do not end in matplotlib,
by the way; but a biography will have to wait for another day...).
Python has become a major force in the technical and scientific
computing world, leading the open source offers and challenging
expensive proprietary platforms with large teams and millions of
dollars of resources behind them. But this would be impossible without
a solid data visualization tool that would allow both ad-hoc data
exploration and the production of complex, fine-tuned figures for
papers, reports or websites. John had the vision to make matplotlib
easy to use, but powerful and flexible enough to work in graphical
user interfaces and as a server-side library, enabling a myriad use
cases beyond his personal needs. This means that now, matplotlib
powers everything from plots in dissertations and journal articles to
custom data analysis projects and websites. And despite having left
his academic career a few years ago for a job in industry, he remained
engaged enough that as of today, he is still the top committer to
matplotlib; this is the git shortlog of those with more than 1000
commits to the project:
2145 John Hunter <jdh...(a)gmail.com>
2130 Michael Droettboom <mdb...(a)gmail.com>
1060 Eric Firing <efi...(a)hawaii.edu>
All of this was done by a man who had three children to raise and who
still always found the time to help those on the mailing lists, solve
difficult technical problems in matplotlib, teach courses and seminars
about scientific Python, and more recently help create the NumFOCUS
foundation project. Despite the challenges that raising three
children in an expensive city like Chicago presented, he never once
wavered from his commitment to open source. But unfortunately now he
is not here anymore to continue providing for their well-being, and I
hope that all those who have so far benefited from his generosity,
will thank this wonderful man who always gave far more than he
received. Thanks to the rapid action of Travis Oliphant, the NumFOCUS
foundation is now acting as an escrow agent to accept donations that
will go into a fund to support the education and care of his wonderful
girls Rahel, Ava and Clara.
If you have benefited from John's many contributions, please say
thanks in the way that would matter most to him, by helping Miriam
continue the task of caring for and educating Rahel, Ava and Clara.
You will find all the information necessary to make a donation here:
http://numfocus.org/johnhunter
Remember that even a small donation helps! If all those who ever use
matplotlib give just a little bit, in the long run I am sure that we
can make a difference.
If you are a company that benefits in a serious way from matplotlib,
remember that John was a staunch advocate of keeping all scientific
Python projects under the BSD license so that commercial users could
benefit from them without worry. Please say thanks to John in a way
commensurate with your resources (and check how much a yearly matlab
license would cost you in case you have any doubts about the value you
are getting...).
John's family is planning a private burial in Tennessee, but (most
likely in September) there will also be a memorial service in Chicago
that friends and members of the community can attend. We don't have
the final scheduling details at this point, but I will post them once
we know.
I would like to again express my gratitude to Travis Oliphant for
moving quickly with the setup of the donation support, and to Eric
Jones (the founder of Enthought and another one of the central figures
in our community) who immediately upon learning of John's plight
contributed resources to support the family with everyday logistics
while John was facing treatment as well as my travel to Chicago to
assist. This kind of immediate urge to come to the help of others
that Eric and Travis displayed is a hallmark of our community.
Before closing, I want to take a moment to publicly thank the
incredible staff of the University of Chicago medical center. The
last two weeks were an intense and brutal ordeal for John and his
loved ones, but the hospital staff offered a sometimes hard to
believe, unending supply of generosity, care and humanity in addition
to their technical competence. The latter is something we expect from
a first-rate hospital at a top university, where the attending
physicians can be world-renowned specialists in their field. But the
former is often forgotten in a world often ruled by a combination of
science and concerns about regulations and liability. Instead, we
found generous and tireless staff who did everything in their power to
ease the pain, always putting our well being ahead of any mindless
adherence to protocol, patiently tending to every need we had and
working far beyond their stated responsibilities to support us. To
name only one person (and many others are equally deserving), I want
to thank Dr. Carla Moreira, chief surgical resident, who spent the
last few hours of John's life with us despite having just completed a
solid night shift of surgical work. Instead of resting she came to
the ICU and worked to ensure that those last hours were as comfortable
as possible for John; her generous actions helped us through a very
difficult moment.
It is now time to close this already too long message...
John, thanks for everything you gave all of us, and for the privilege
of knowing you.
Fernando.
ps - I have sent this with my 'mailing lists' email. If you need to
contact me directly for anything regarding the above, please write to
my regular address at Fernand...(a)berkeley.edu, where I do my best
to reply more promptly.
_______________________________________________
NumPy-Discussion mailing list
NumPy-Di...(a)scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi,
I wrote an extension for sfepy, that requires compiling and linking additional
fortran sources (invoked by cython). I found out, that these libraries can be
easily added using distutils
config.add_library()
function, but there is the problem.
The --fcompiler command doesn't propagate to the stage where the
libraries are compiled, so the default (and in my case wrong) compiler is used.
(If I try compile the files using add_extension method, they are compiled by
desired compiler, but I need combine more sources in one extension so I think
that I can't use add_extension).
Is there any way how to force python to use the right compiler, or at
least hardcode the compiler in the setup.py?
Thanks a lot for suggestions,
Matyas
My setup.py:
def configuration(parent_package='', top_path=None):
import os.path as op
from numpy.distutils.misc_util import Configuration
from sfepy import Config
site_config = Config()
auto_dir = op.dirname(__file__)
auto_name = op.split(auto_dir)[-1]
config = Configuration(auto_name, parent_package, top_path)
flags= site_config.compile_flags()
flags.append('-I'+os.path.dirname(__file__))
config.add_library('xc_default',
['default/dft.c'],
extra_compile_args=flags,
f2py_options = '--fcompiler=gnu95',
extra_link_args=site_config.link_flags()
)
config.add_library('xc_vackar',
['vackar/vscxc.f90'],
extra_compile_args=flags,
f2py_options = {'fcompiler' : 'gnu95'},
extra_link_args=site_config.link_flags()
Hi !
I could install sfepy and run the examples wihout any issue so far.
Congratulations !
In the "examples/diffusion/poisson.py" example, the Dirichlet conditions
are explicitly specified on the "Gamma_Left" and "Gamma_Right" regions, but
the Von Neumann boundary conditions (grad(T).n = 0) on "all other" surfaces
are not specified although the solution does complies with them...
How is this working ? Are the "0 Von Neumann conditions" implicit in that
case ? What am I missing ?
David.
Dear SfePy users,
I am new to the SfePy and first of all I want to say thank you to the
developers of this FEM engine. Now I am trying to use SfePy for my tasks
and I got several questions.
Consider problem in the example ``examples/diffusion/poisson.py'' (I will
use LaTeX notations further for the math). We need to find unknown function
$t(x)$ such that
$$
c \Delta t = 0, x \in \Omega,
$$
$$
t(x) = T_1, x \in \Gamma_{D1},
$$
$$
t(x) = T_2, x \in \Gamma_{D2},
$$
$$
\frac{\partial t(x)}{\partial n} = 0, x \in \Gamma_{N},
$$
where $T_1$ and $T_2$ is known functions, $\Gamma_{D1} = \{x \mid x <
0.00001\}$, $\Gamma_{D2} = \{x \mid x > 0.099999\}$, $\Gamma_{N} = \partial
\Omega \setminus (\Gamma_{D1} \cup \Gamma_{D2})$. This is the mixed
boundary value problem with both Dirichlet and Neumann boundary conditions.
Could you, please, answer me on following questions:
1. Is it possible to get a flux on the $\Gamma_{D1}$ and $\Gamma_{D2}$
surfaces after the problem has been solved?
2. Suppose we solve pure Dirichlet problem (no Neumann conditions). We need
to specify essential boundary conditions on the all surface nodes? What is
the best way to select all surface nodes?
3. Suppose the Neumann boundary conditions is not zero. In this case we
need to add surface integral with the flux. Do we need to add new term for
the flux in the problem description? And this term will be the surface
integral but in the problem description we have only tetrahedral domain 3D
mesh. What is the best way to select only surface part of the mesh for the
surface integration?
Thanks,
Alec Kalinin
