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tamaas/src/model/adhesion_functional.cpp
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adhesion_functional.cpp
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/**
* @file
*
* @author Lucas Frérot <lucas.frerot@epfl.ch>
* @author Valentine Rey
*
* @section LICENSE
*
* Copyright (©) 2017 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 "adhesion_functional.hh"
/* -------------------------------------------------------------------------- */
__BEGIN_TAMAAS__
namespace functional {
Real ExponentialAdhesionFunctional::computeF(
GridBase<Real>& gap, GridBase<Real>& /*pressure*/) const {
auto rho_inv = 1. / getParameters().at("rho");
auto gamma = getParameters().at("surface_energy");
return -gamma * Loop::reduce<operation::plus>(
[rho_inv] CUDA_LAMBDA(const Real& g) {
return std::exp(-g * rho_inv);
},
gap);
}
void ExponentialAdhesionFunctional::computeGradF(
GridBase<Real>& gap, GridBase<Real>& gradient) const {
auto rho_inv = 1. / getParameters().at("rho");
auto gamma = getParameters().at("surface_energy");
Loop::loop(
[rho_inv, gamma] CUDA_LAMBDA(const Real& g, Real& grad) {
grad += gamma * std::exp(-g * rho_inv) * rho_inv;
},
gap, gradient);
}
Real MaugisAdhesionFunctional::computeF(GridBase<Real>& gap,
GridBase<Real>& /*pressure*/) const {
auto rho = getParameters().at("rho");
auto rho_inv = 1. / rho;
auto gamma = getParameters().at("surface_energy");
return -gamma * Loop::reduce<operation::plus>(
[rho, rho_inv] CUDA_LAMBDA(const Real& g) {
return (g > rho) ? 0 : 1 - g * rho_inv;
},
gap);
}
void MaugisAdhesionFunctional::computeGradF(GridBase<Real>& gap,
GridBase<Real>& gradient) const {
auto rho = getParameters().at("rho");
auto rho_inv = 1. / rho;
auto gamma = getParameters().at("surface_energy");
Loop::loop(
[rho, rho_inv, gamma] CUDA_LAMBDA(const Real& g, Real& grad) {
grad += (g > rho) ? 0 : gamma * rho_inv;
},
gap, gradient);
}
Real SquaredExponentialAdhesionFunctional::computeF(
GridBase<Real>& gap, GridBase<Real>& /*pressure*/) const {
auto rho_inv = 1. / getParameters().at("rho");
auto rho_inv_2 = rho_inv * rho_inv;
auto gamma = getParameters().at("surface_energy");
return -gamma * Loop::reduce<operation::plus>(
[rho_inv_2] CUDA_LAMBDA(const Real& g) {
return std::exp(-0.5 * g * g * rho_inv_2);
},
gap);
}
void SquaredExponentialAdhesionFunctional::computeGradF(
GridBase<Real>& gap, GridBase<Real>& gradient) const {
auto rho_inv = 1. / getParameters().at("rho");
auto rho_inv_2 = rho_inv * rho_inv;
auto gamma = getParameters().at("surface_energy");
Loop::loop(
[rho_inv_2, gamma] CUDA_LAMBDA(const Real& g, Real& grad) {
grad += g * rho_inv_2 * gamma * std::exp(-0.5 * g * g * rho_inv_2);
},
gap, gradient);
}
} // namespace functional
__END_TAMAAS__
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