Line data    Source code

       1             : //* This file is part of the MOOSE framework
       2             : //* https://mooseframework.inl.gov
       3             : //*
       4             : //* All rights reserved, see COPYRIGHT for full restrictions
       5             : //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
       6             : //*
       7             : //* Licensed under LGPL 2.1, please see LICENSE for details
       8             : //* https://www.gnu.org/licenses/lgpl-2.1.html
       9             : 
      10             : #include "GBDependentAnisotropicTensor.h"
      11             : 
      12             : registerMooseObject("PhaseFieldApp", GBDependentAnisotropicTensor);
      13             : 
      14             : InputParameters
      15           0 : GBDependentAnisotropicTensor::validParams()
      16             : {
      17           0 :   InputParameters params = GBDependentTensorBase::validParams();
      18           0 :   params.addClassDescription("Compute anisotropic rank two tensor based on GB phase variable");
      19           0 :   return params;
      20           0 : }
      21             : 
      22           0 : GBDependentAnisotropicTensor::GBDependentAnisotropicTensor(const InputParameters & parameters)
      23           0 :   : GBDependentTensorBase(parameters)
      24             : {
      25           0 : }
      26             : 
      27             : void
      28           0 : GBDependentAnisotropicTensor::initQpStatefulProperties()
      29             : {
      30           0 :   _gb_dependent_tensor[_qp].zero();
      31           0 : }
      32             : 
      33             : void
      34           0 : GBDependentAnisotropicTensor::computeQpProperties()
      35             : {
      36           0 :   RankTwoTensor iden(RankTwoTensor::initIdentity);
      37           0 :   RankTwoTensor gb_tensor;
      38             : 
      39           0 :   gb_tensor =
      40           0 :       (1.0 - _gb[_qp]) * _bulk_parameter * iden + _gb[_qp] * _gb_parameter * _gb_normal_tensor[_qp];
      41           0 :   gb_tensor.fillRealTensor(_gb_dependent_tensor[_qp]);
      42           0 : }