SfePy October 2012

sfepy@python.org

5 participants
11 discussions

Re: Torque
by Robert Cimrman 09 Mar '18

09 Mar '18

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 -- You received this message because you are subscribed to the Google Groups "sfepy-devel" group. To post to this group, send email to sfepy...(a)googlegroups.com. To unsubscribe from this group, send email to sfepy-devel...(a)googlegroups.com. For more options, visit this group at http://groups.google.com/group/sfepy-devel?hl=en.

4 7

1D line elements in SfePy
by Nimish 22 Jan '16

22 Jan '16

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

4 9

Evaluate a solution in the arbitrary point in the domain
by Alec Kalinin 03 Apr '14

03 Apr '14

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

3 25

Modeling an adjustable X-ray optic
by Tom Aldcroft 21 May '13

21 May '13

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

3 6

Porous media - Darcy Flow
by Ivan 14 Nov '12

14 Nov '12

Hi all, I've just discovered SfePy, I'm looking for an example that can be changed for Darcy flow in porous media: \vec{q} = - K\nabla \phi where K is the hydraulic conductivity and \phi the hydraulic head. Could you please point me to some examples or relevant documentation? Cheers

2 3

Material properties dependent on test field variables.
by Bjarke Dalslet 03 Nov '12

03 Nov '12

Hello sfepy users! I am using sfepy to do thermal simulations of (BIG!) electric resistors. Nothing fancy, but i would like to be able to use temperature dependent thermal conductivities as my system gets very hot. At the moment I am employing the Laplace weak term: int(s * \nabla q *\nabla p) where s is the thermal conductivity, q is the test field parameter and p is the temperature field What i want is for s to depend on the temperature. I wonder which strategy to use: 1: To limit myself to a linear s, i.e. s(q)=s0+\alpha * q. In that case i guess i can do this: int(s(q) * \nabla q *\nabla p) = int(s0 * \nabla q *\nabla p) + int(\alpha * q * \nabla q *\nabla p) Unfortunately the second term is not implemented. Looking at the source, this would be some work to implement. 2: To iterate and after each iteration assign new values of s at each point. This would not be a big deal when doing time dependent simulations, but steady-state would be much slower to calculate I guess? An added plus would be that arbitrary c(q) could be used. 3: Ask for help here, and go "Ahhh! Of course!", when you state the obvious, easily implementable and fantastic solution. I have only used sfepy for about a week, and this is the first time i am messing around with weak formulation FEM, so this question might be really silly, but I ask anyway: Is the strategy above completely crazy or am I on the right track? Kind regards Bjarke Dalslet

2 11

Re: Field variable dependent material parameter.
by Robert Cimrman 26 Oct '12

26 Oct '12

I have responded to the first message (which got posted with some delay, as I have to approve first-time posts of new mailing list members as an anti-spam measure). r. On 10/26/2012 11:15 AM, Bjarke Dalslet wrote: > Hello sfepy users. > > I am doing thermal simulations of (very big) electric resistors in sfepy. > Nothing fancy, but as the resistors get very hot, i would like to use a > thermal conductivity that depends on the temperature. > > At the moment i am using the laplace term: > int( c \nabla q \nabla p) > where c is the thermal conductivity, q is the test (temperature field) and > p is the temperature field. > > I have come up with two possible approaches to make c dependent on q: > > 1: restrict c(q) to a linear function c(q) = c_0 + \alpha q. > I think this would allow me to do this: > int( c(q) \nabla q \nabla p) = int( c_0 \nabla q \nabla p) + int( \alpha > q \nabla q \nabla p) > That would allow fast steady state solutions, although the second term > is not yet implementet (and judging from the laplace source, it seems like > some work to do it). > > 2: Iteratively seek the solution, i.e. adjusting c(x,y,z) after each > timestep. This would be fine when looking at transients, but for steady > state it would be much slower than solution 1. > > I am new to sfepy and also to weak form FEM, so I ask anybody with > experience in this: Are the above approaches silly, and does a better one > exist? > > Kind regards > Bjarke Dalslet >

1 0

Poisson problem: minus sign before volume integral
by Alec Kalinin 23 Oct '12

23 Oct '12

Hello SfePy, May be I am wrong, but it seems that there is an issue with the minus sign before volume integral in Poisson problem definition. I am trying to solve Poisson problem for the simple analytical function $u(x) = x^2$, so the $\Delta u = 2$ and the week form is $\int_\Omega u v \, d\Omega = \int_\Omega 2 v \, d\Omega$. In SfePy I used the following definition: dw_laplace.i1.Omega(m.val, v, u) = dw_volume_integrate.i1.Omega(f.val, v) but I got a quite big relative error: 1e-1 But in case I add the minus sign: dw_laplace.i1.Omega(m.val, v, u) = -dw_volume_integrate.i1.Omega(f.val, v) the accuracy of the solution becomes very good, the relative error: 1e-3 So my question, why it is necessary to add minus sign before volume integral? Yes, I know that the classical Poisson problem is $u(x) = -f(x)$. May be we assume minus sign implicitly? The test script demonstrated a problem is in the attachment. Sincerely, Alec

3 3

Possible issue with superfluous vertices
by Alec Kalinin 18 Oct '12

18 Oct '12

Hello SfePy! I am solving the Poisson problem and during my solution I got the warning: Warning: /usr/local/lib/python2.7/dist-packages/sfepy/fem/fields_base.py:1258: RuntimeWarning: invalid value encountered in divide data_vertex /= nod_vol[:,nm.newaxis] The code for this warning: def post_process(out, pb, state, extend = False): # evaluate gradient in nodes grad_data = pb.evaluate('ev_grad.i1.Omega(u)', mode = 'qp') grad_field = H1NodalVolumeField('grad', np.float64, (3, ), pb.domain.regions['Omega']) grad_var = FieldVariable('grad', 'parameter', grad_field, 3, primary_var_name='(set-to-None)') *grad_var.data_from_qp(grad_data, pb.integrals['i1']) * This warning is appeared only for the meshes with superfluous vertices. The code and the meshes is in the attachment. I think this warning my be related to the issue with superfluous vertices. See early discussion [1] and git commit [2] [1] https://groups.google.com/forum/?fromgroups=#!topic/sfepy-devel/3Qae2M5bBps [2] https://github.com/sfepy/sfepy/commit/4ef0f5dbc7fac15942ba2031debf1296a35eb… Sincerely, Alec

2 1

Question on "diffusion/poisson_functions.py" example
by Alec Kalinin 16 Oct '12

16 Oct '12

Hello SfePy users, I am solving a Poisson's equation with a free term $b(x)$: $\Delta u(x) = b(x), \quad x \in Omega$. I take "diffusion/poisson_functions.py" as the base script for my task. But something in this script is not clear for me. 1. In the script we have two definitions: $p$ is a given function and $f$ is a load parameter. What function does correspond to the free term $b(x)$? 2. The known function $f$ (in the code it is named as "load") is defined in materials, but the function $p$ is also known, but defined in the "variables" section. Usually in variable section we define functions to be find during solution. Why we define known function in the variable section? 3. For the evaluation of the function $f$ ("load") we have a python function "get_pars(ts, coors, mode=None, **kwargs)". In this python function we evaluate $f$ only if "mode == qp" condition is true. For the evaluation of the function $p$ we also have a python function "get_load_variable(ts, coors, region=None)". But in this function we do not use "mode" condition. Why those function $f$ and $p$ are evaluated in different ways? Thanks, Alec

2 3

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

SfePy October 2012