The code for the definition of the region of interest below worked with
the adaptive algorithm. The integral is taken over a region surrounding
the point where the solution is evaluated.
How can the cell closest to (vbar, 0) be picked?
--Wilhelm
#######################################################
#code for using a cell integral over a certain region of interest to
define a goal function
#######################################################
class region_of_interest(SubDomain):
def inside(self, x, on_boundary):
return (between(x[0], (vbar-0.5, vbar+0.5)) and between(x[1], (-0.5,
0.5)))
# Initialize sub-domain instances
ROI = region_of_interest()
cell_domains = CellFunction("size_t", mesh)
cell_domains.set_all(0)
ROI.mark(cell_domains, 1)
dx_roi = Measure('dx')[cell_domains]
#check whether ROI is empty or not
if not ROI:
print "problem, region of interest empty"
exit()
# Plot cell_domains
#plot(cell_domains, interactive=True)
M = u * dx_roi(1)
On 23/02/15 10:19, Martin Sandve Alnæs wrote:
> I would suggest using a cell integral and just marking a single cell
> containing your point.
>
> Martin
>
> On 23 February 2015 at 11:09, Wilhelm Braun <[email protected]
> <mailto:[email protected]>> wrote:
>
> I just checked this, and I got
>
> AssertionError
>
>
> which means that the domain is indeed empty.
>
> Is there a way to fix this by redefining the domain or by simply
> refining the mesh at the point of interest before the start of the
> computation?
>
> --Wilhelm
>
> On 21/02/15 18:46, Jan Blechta wrote:
> > On Fri, 20 Feb 2015 17:22:36 +0000
> > Wilhelm Braun <[email protected]
> <mailto:[email protected]>> wrote:
> >
> >> I tried to implement the goal function using the point integral as
> >> outlined in the point integral demo in DOLFIN.
> >>
> >> However, the point integral evaluated to zero, and hence, the
> adaptive
> >> algorithm stopped after one iteration, but did not throw an error.
> >>
> >> Please find the code for the definition of the goal function M
> below.
> >>
> >> --Wilhelm
> >>
> >>
>
> ----------------------------------------------------------------------------------------------------
> >> code for goal function M
> >>
>
> ----------------------------------------------------------------------------------------------------
> >>
> >>
> >> def center_func(x):
> >> return (vbar-0.05 <= x[0] and x[0] <= vbar + 0.05 and
> >> near(x[1], 0.0)) or -0.05 <= x[1] and x[1] <= 0.05 and near(x[0],
> >> vbar)
> >>
> >> # Define domain for point integral
> >> center_domain = VertexFunction("size_t", mesh, 0)
> >> center = AutoSubDomain(center_func)
> >> center.mark(center_domain, 1)
> >> dPP = dP[center_domain]
> > Have you tested that center_domain is non-trivial? For example
> >
> > assert center_domain.array().max() > 0
> >
> > Jan
> >
> >> M = u* dPP(1)
> >>
> >>
> >>
>
> ----------------------------------------------------------------------------------------------------
> >>
>
> ----------------------------------------------------------------------------------------------------
> >>
> >>
> >>
> >> On 20/02/15 11:23, Anders Logg wrote:
> >>> I suggest looking at the point integral demo in DOLFIN:
> >>>
> >>> demo/undocumented/point-integral/python/demo_point-integral.py
> >>>
> >>> Then use the point integral to define you goal functional.
> Warning:
> >>> I haven't tested this myself and don't know if it will break the
> >>> adaptive algorithm somehow. It would be interesting to get
> feedback
> >>> on whether it works (without any promise that I will fix it if it
> >>> does not... ;-)
> >>>
> >>> --
> >>> Anders
> >>>
> >>>
> >>> Fri Feb 20 2015 at 11:47:10 AM skrev Wilhelm Braun
> >>> <[email protected] <mailto:[email protected]>
> <mailto:[email protected] <mailto:[email protected]>>>:
> >>>
> >>> Dear all,
> >>>
> >>> I have posted a question in the FEniCS QA forum, and it was
> >>> recommended that I bring up the issue on this mailing list.
> >>>
> >>> Please have a look here for my question.
> >>>
> >>>
>
> http://fenicsproject.org/qa/6555/automated-oriented-adaptivity-point-computational-domain
> >>>
> >>> Thank you very much!
> >>>
> >>> Wilhelm Braun
> >>>
> >>>
> >>>
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> >>
> >>
> >>
> >>
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