Another approach which has been tested is
const FEValuesExtractors::Vector surface_N(Free_Carrier_Density);
const FEValuesExtractors::Vector surface_TE(Electron_Temperature);
const FEValuesExtractors::Vector surface_TL(Lattice_Temperature);
const ComponentMask surface_N_mask = fe.component_mask(surface_N);
const ComponentMask surface_TE_mask = fe.component_mask(surface_TE);
const ComponentMask surface_TL_mask = fe.component_mask(surface_TL);
std::vector<bool> TE_value_mask(dof_handler.n_locally_owned_dofs()),
TL_value_mask(dof_handler.n_locally_owned_dofs()),
N_value_mask(dof_handler.n_locally_owned_dofs());
DoFTools::extract_dofs(dof_handler, surface_N_mask, N_value_mask);
DoFTools::extract_dofs(dof_handler, surface_TE_mask, TE_value_mask);
DoFTools::extract_dofs(dof_handler, surface_TL_mask, TL_value_mask);
LinearAlgebraTrilinos::MPI::Vector unified_test_vector(dof_handler.
locally_owned_dofs(), mpi_communicator);
for(auto index : scaled_solution.locally_owned_elements())
{
if(N_value_mask[index])
{
unified_test_vector(index) = scaled_solution(index) *
(unified_max_N_value
- unified_min_N_value) * unified_N_value + unified_N_value;
}
if(TE_value_mask[index])
{
unified_test_vector(index) = scaled_solution(index) *
(unified_max_TE_value
- unified_min_TE_value) * unified_TE_value + unified_TE_value;
}
if(TL_value_mask[index])
{
unified_test_vector(index) = scaled_solution(index) *
(unified_max_TL_value
- unified_min_TL_value) * unified_TL_value + unified_TL_value;
}
}
unified_test_vector.compress(VectorOperation::insert);
scaled_solution = unified_test_vector;
but when looking at the output, some parts of the solution are scaled
correctly, some other parts are not. I suspect the fault is due to parallel
processing, after the results are correct for single thread calculations,
but not for multithread calculations.
Thanks!
Am Donnerstag, 19. April 2018 10:58:36 UTC+2 schrieb Maxi Miller:
>
> I try to scale a distributed vector (LinearAlgebraTrilinos::MPI::Vector)
> by another vector which contents are set based on masks. I tried to use the
> following code:
> const FEValuesExtractors::Vector surface_N(Free_Carrier_Density);
> const FEValuesExtractors::Vector surface_TE(Electron_Temperature);
> const FEValuesExtractors::Vector surface_TL(Lattice_Temperature);
>
> const ComponentMask surface_N_mask = fe.component_mask(surface_N);
> const ComponentMask surface_TE_mask = fe.component_mask(surface_TE);
> const ComponentMask surface_TL_mask = fe.component_mask(surface_TL);
>
> std::vector<bool> TE_value_mask(dof_handler.n_locally_owned_dofs()),
> TL_value_mask(dof_handler.n_locally_owned_dofs()),
> N_value_mask(dof_handler.n_locally_owned_dofs());
>
> DoFTools::extract_dofs(dof_handler, surface_N_mask, N_value_mask);
> DoFTools::extract_dofs(dof_handler, surface_TE_mask, TE_value_mask);
> DoFTools::extract_dofs(dof_handler, surface_TL_mask, TL_value_mask);
>
> LinearAlgebraTrilinos::MPI::Vector unified_add_vector(dof_handler.
> locally_owned_dofs(), mpi_communicator);
> LinearAlgebraTrilinos::MPI::Vector unified_mult_vector(dof_handler.
> locally_owned_dofs(), mpi_communicator);
>
> for(size_t i = 0; i < N_value_mask.size(); ++i)
> {
> if(N_value_mask[i])
> {
> unified_mult_vector(i) = (unified_max_N_value -
> unified_min_N_value) * unified_N_value;
> unified_add_vector(i) = unified_N_value;
> }
> }
> for(size_t i = 0; i < TE_value_mask.size(); ++i)
> {
> if(TE_value_mask[i])
> {
> unified_mult_vector(i) = (unified_max_TE_value -
> unified_min_TE_value) * unified_TE_value;
> unified_add_vector(i) = unified_TE_value;
> }
> }
> for(size_t i = 0; i < TL_value_mask.size(); ++i)
> {
> if(TL_value_mask[i])
> {
> unified_mult_vector(i) = (unified_max_TL_value -
> unified_min_TL_value) * unified_TL_value;
> unified_add_vector(i) = unified_TL_value;
> }
> }
> //scaled_solution.scale(unified_mult_vector);
> unified_add_vector.add(scaled_solution, true);
> scaled_solution = unified_add_vector;
> Nevertheless, unlike with .add() there is no such option for
> .mult-functions, allowing me to multiply a distributed vector to another
> distributed vector, elementwise. What can I use instead?
> Or is the whole approach wrong?
>
> Thanks!
>
>
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