Homec4science
Diffusion µSpectre (origin/feat/muLib_integration_T2237) Browse

muSpectre/tests/test_goodies.hh
f26b16ccc17borigin/feat/muLib_integ…

Last Change
Raw File

test_goodies.hh
View Options

/**
* @file test_goodies.hh
*
* @author Till Junge <till.junge@epfl.ch>
*
* @date 27 Sep 2017
*
* @brief helpers for testing
*
* Copyright © 2017 Till Junge
*
* µSpectre is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License as
* published by the Free Software Foundation, either version 3, or (at
* your option) any later version.
*
* µSpectre 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
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with GNU Emacs; see the file COPYING. If not, write to the
* Free Software Foundation, Inc., 59 Temple Place - Suite 330,
* Boston, MA 02111-1307, USA.
*/
#ifndef TEST_GOODIES_H
#define TEST_GOODIES_H
#include "common/tensor_algebra.hh"
#include <boost/mpl/list.hpp>
#include <random>
#include <type_traits>
namespace muSpectre {
namespace testGoodies {
template <Dim_t Dim>
struct dimFixture{
constexpr static Dim_t dim{Dim};
};
using dimlist = boost::mpl::list<dimFixture<oneD>,
dimFixture<twoD>,
dimFixture<threeD>>;
/* ---------------------------------------------------------------------- */
template<typename T>
class RandRange {
public:
RandRange(): rd(), gen(rd()) {}
template <typename dummyT = T>
std::enable_if_t<std::is_floating_point<dummyT>::value, dummyT>
randval(T&& lower, T&& upper) {
static_assert(std::is_same<T, dummyT>::value, "SFINAE");
auto distro = std::uniform_real_distribution<T>(lower, upper);
return distro(this->gen);
}
template <typename dummyT = T>
std::enable_if_t<std::is_integral<dummyT>::value, dummyT>
randval(T&& lower, T&& upper) {
static_assert(std::is_same<T, dummyT>::value, "SFINAE");
auto distro = std::uniform_int_distribution<T>(lower, upper);
return distro(this->gen);
}
private:
std::random_device rd;
std::default_random_engine gen;
};
/**
* explicit computation of linearisation of PK1 stress for an
* objective Hooke's law. This implementation is not meant to be
* efficient, but te reflect exactly the formulation in Curnier
* 2000, "Méthodes numériques en mécanique des solides" for
* reference and testing
*/
template <Dim_t Dim>
decltype(auto) objective_hooke_explicit(Real lambda, Real mu,
const Matrices::Tens2_t<Dim>& F) {
using namespace Matrices;
using T2 = Tens2_t<Dim>;
using T4 = Tens4_t<Dim>;
T2 P;
T2 I = P.Identity();
T4 K;
// See Curnier, 2000, "Méthodes numériques en mécanique des
// solides", p 252, (6.95b)
Real Fjrjr = (F.array()*F.array()).sum();
T2 Fjrjm = F.transpose()*F;
P.setZero();
for (Dim_t i = 0; i < Dim; ++i) {
for (Dim_t m = 0; m < Dim; ++m) {
P(i,m) += lambda/2*(Fjrjr-Dim)*F(i,m);
for (Dim_t r = 0; r < Dim; ++r) {
P(i,m) += mu*F(i,r)*(Fjrjm(r,m) - I(r,m));
}
}
}
// See Curnier, 2000, "Méthodes numériques en mécanique des solides", p 252
Real Fkrkr = (F.array()*F.array()).sum();
T2 Fkmkn = F.transpose()*F;
T2 Fisjs = F*F.transpose();
K.setZero();
for (Dim_t i = 0; i < Dim; ++i) {
for (Dim_t j = 0; j < Dim; ++j) {
for (Dim_t m = 0; m < Dim; ++m) {
for (Dim_t n = 0; n < Dim; ++n) {
get(K, i, m, j, n) =
(lambda*((Fkrkr-Dim)/2 * I(i,j)*I(m,n) + F(i,m)*F(j,n)) +
mu * (I(i,j)*Fkmkn(m,n) + Fisjs(i,j)*I(m,n) -
I(i,j) *I(m,n) + F(i,n)*F(j,m)));
}
}
}
}
return std::make_tuple(P,K);
}
} // testGoodies
} // muSpectre
#endif /* TEST_GOODIES_H */
c4science · Help