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rTAMAAS tamaas
bem_gigi.cpp
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/**
*
* @author Guillaume Anciaux <guillaume.anciaux@epfl.ch>
*
* @section LICENSE
*
* Copyright (©) 2016 EPFL (Ecole Polytechnique Fédérale de
* Lausanne) Laboratory (LSMS - Laboratoire de Simulation en Mécanique des
* Solides)
*
* Tamaas is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Tamaas is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Tamaas. If not, see <http://www.gnu.org/licenses/>.
*
*/
/* -------------------------------------------------------------------------- */
#include "bem_gigi.hh"
#include "surface.hh"
#include <fstream>
#include <iomanip>
#include <iostream>
#include <sstream>
#include <vector>
/* -------------------------------------------------------------------------- */
namespace
tamaas
{
Real
BemGigi
::
computeEquilibrium
(
Real
epsilon
,
Real
mean_displacement
)
{
this
->
computeSpectralInfluenceOverDisplacement
();
Real
Rold
=
1.
;
Real
f
=
1e300
;
this
->
search_direction
=
0.
;
this
->
true_displacements
=
0
;
this
->
true_displacements
=
mean_displacement
;
this
->
computeGaps
();
this
->
optimizeToMeanDisplacement
(
mean_displacement
);
this
->
computeGaps
();
convergence_iterations
.
clear
();
nb_iterations
=
0
;
max_iterations
=
50
;
Real
R
=
0
;
while
(
f
>
epsilon
&&
nb_iterations
++
<
max_iterations
)
{
this
->
computeGaps
();
this
->
functional
->
computeGradFU
();
this
->
updateSearchDirection
(
R
/
Rold
);
Rold
=
R
;
Real
alpha
=
this
->
computeOptimalStep
();
std
::
cout
<<
"alpha vaut "
<<
alpha
<<
std
::
endl
;
this
->
old_displacements
=
this
->
true_displacements
;
this
->
updateDisplacements
(
alpha
);
this
->
computeGaps
();
this
->
optimizeToMeanDisplacement
(
mean_displacement
);
// espace admissible
this
->
computeGaps
();
this
->
computePressures
(
mean_displacement
);
f
=
computeStoppingCriterion
();
}
this
->
computePressures
(
mean_displacement
);
return
f
;
}
/* -------------------------------------------------------------------------- */
Real
BemGigi
::
computeStoppingCriterion
()
{
Real
rho
=
this
->
functional
->
getParameter
(
"rho"
);
Real
surface_energy
=
this
->
functional
->
getParameter
(
"surface_energy"
);
Real
crit
=
0.
;
// Real disp_norm = 0.;
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
//#pragma omp parallel for reduction(+:crit, disp_norm)
#pragma omp parallel for reduction(+ : crit)
for
(
UInt
i
=
0
;
i
<
size
;
++
i
)
{
crit
+=
std
::
abs
(
this
->
gap
(
i
)
*
(
this
->
surface_tractions
(
i
)
+
surface_energy
/
rho
*
exp
(
-
(
gap
(
i
))
/
rho
)));
}
return
crit
;
}
/* -------------------------------------------------------------------------- */
/* -------------------------------------------------------------------------- */
void
BemGigi
::
optimizeToMeanDisplacement
(
Real
imposed_mean
)
{
Real
target_value
=
imposed_mean
-
SurfaceStatistics
::
computeAverage
(
surface
,
0
);
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
// Initial guesses for upper and lower bound
Real
step_min
=
-
10
;
Real
step_max
=
10
;
// Gaps for upper and lower bound
Real
gap_min
=
positiveGapAverage
(
step_min
);
Real
gap_max
=
positiveGapAverage
(
step_max
);
UInt
max_expansion
=
8
;
// Expand bounds if necessary
for
(
UInt
i
=
0
;
gap_max
<
target_value
&&
i
<
max_expansion
;
i
++
,
step_max
*=
10
)
gap_max
=
positiveGapAverage
(
step_max
);
for
(
UInt
i
=
0
;
gap_min
>
target_value
&&
i
<
max_expansion
;
i
++
,
step_min
*=
10
)
gap_min
=
positiveGapAverage
(
step_min
);
Real
g
=
0.
;
Real
epsilon
=
1e-12
;
Real
step
=
0
;
while
(
std
::
abs
(
g
-
target_value
)
>
epsilon
)
{
step
=
(
step_min
+
step_max
)
/
2.
;
g
=
positiveGapAverage
(
step
);
if
(
g
>
target_value
)
step_max
=
step
;
else
if
(
g
<
target_value
)
step_min
=
step
;
else
{
step_max
=
step
;
step_min
=
step
;
}
}
step
=
(
step_min
+
step_max
)
/
2.
;
#pragma omp parallel for
for
(
UInt
i
=
0
;
i
<
size
;
i
++
)
{
gap
(
i
)
+=
step
;
if
(
gap
(
i
)
<
0
)
gap
(
i
)
=
0
;
true_displacements
(
i
)
=
gap
(
i
)
+
surface
(
i
);
}
}
/* -------------------------------------------------------------------------- */
Real
BemGigi
::
positiveGapAverage
(
Real
shift
)
{
UInt
n
=
surface
.
size
();
Real
res
=
0
;
#pragma omp parallel for reduction(+ : res)
for
(
UInt
i
=
0
;
i
<
n
*
n
;
i
++
)
{
Real
shifted_gap
=
gap
(
i
)
+
shift
;
res
+=
shifted_gap
*
(
shifted_gap
>
0
);
}
return
res
/
(
n
*
n
);
}
/* -------------------------------------------------------------------------- */
void
BemGigi
::
updateSearchDirection
(
Real
/*factor*/
)
{
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
const
Surface
<
Real
>&
gradF
=
this
->
functional
->
getGradF
();
#pragma omp parallel for
for
(
UInt
i
=
0
;
i
<
size
;
++
i
)
{
this
->
search_direction
(
i
)
=
gradF
(
i
);
}
}
/* -------------------------------------------------------------------------- */
Real
BemGigi
::
computeOptimalStep
()
{
this
->
applyInverseInfluenceFunctions
(
search_direction
,
projected_direction
);
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
const
Surface
<
Real
>&
gradF
=
this
->
functional
->
getGradF
();
Real
numerator
=
0.
,
denominator
=
0.
;
#pragma omp parallel for reduction(+ : numerator, denominator)
for
(
UInt
i
=
0
;
i
<
size
;
++
i
)
{
numerator
+=
gradF
(
i
)
*
search_direction
(
i
);
denominator
+=
projected_direction
(
i
)
*
search_direction
(
i
);
}
Real
alpha
=
numerator
/
denominator
;
return
alpha
;
}
/* -------------------------------------------------------------------------- */
Real
BemGigi
::
computeTau
()
{
return
computeOptimalStep
();
}
/* -------------------------------------------------------------------------- */
void
BemGigi
::
updateDisplacements
(
Real
alpha
)
{
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
#pragma omp parallel for
for
(
UInt
i
=
0
;
i
<
size
;
++
i
)
{
this
->
true_displacements
(
i
)
-=
alpha
*
this
->
search_direction
(
i
);
}
}
/* -------------------------------------------------------------------------- */
void
BemGigi
::
emptyOverlap
()
{
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
#pragma omp parallel for
for
(
UInt
i
=
0
;
i
<
size
;
++
i
)
{
if
(
gap
(
i
)
<
0
)
this
->
true_displacements
(
i
)
=
this
->
surface
(
i
);
}
}
/* -------------------------------------------------------------------------- */
void
BemGigi
::
enforceMeanDisplacement
(
Real
mean_displacement
)
{
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
Real
moyenne_surface
=
SurfaceStatistics
::
computeAverage
(
this
->
surface
,
0
);
Real
average
=
SurfaceStatistics
::
computeAverage
(
this
->
true_displacements
,
0
);
Real
factor
=
(
mean_displacement
-
moyenne_surface
)
/
(
average
-
moyenne_surface
);
#pragma omp parallel for
for
(
UInt
i
=
0
;
i
<
size
;
++
i
)
{
this
->
true_displacements
(
i
)
=
factor
*
(
this
->
true_displacements
(
i
)
-
this
->
surface
(
i
))
+
this
->
surface
(
i
);
}
}
/* -------------------------------------------------------------------------- */
void
BemGigi
::
computePressures
(
Real
/*mean_displacement*/
)
{
this
->
computeTractionsFromDisplacements
();
UInt
n
=
surface
.
size
();
UInt
size
=
n
*
n
;
Real
rho
=
this
->
functional
->
getParameter
(
"rho"
);
Real
surface_energy
=
this
->
functional
->
getParameter
(
"surface_energy"
);
Real
mini
=
3000
;
for
(
UInt
i
=
0
;
i
<
size
;
++
i
)
{
if
(
gap
(
i
)
<
rho
)
{
if
(
this
->
surface_tractions
(
i
)
+
surface_energy
/
rho
<
mini
)
mini
=
this
->
surface_tractions
(
i
)
+
surface_energy
/
rho
;
}
else
{
if
(
this
->
surface_tractions
(
i
)
<
mini
)
mini
=
this
->
surface_tractions
(
i
);
}
}
this
->
surface_tractions
-=
mini
;
}
}
// namespace tamaas
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