Reference documentation for deal.II version Git ce1d1e1 20170628 06:17:25 0500

Functions  
template<int dim, class VectorType , int spacedim>  
void  refine_and_coarsen_fixed_number (parallel::distributed::Triangulation< dim, spacedim > &tria, const VectorType &criteria, const double top_fraction_of_cells, const double bottom_fraction_of_cells, const unsigned int max_n_cells=std::numeric_limits< unsigned int >::max()) 
template<int dim, class VectorType , int spacedim>  
void  refine_and_coarsen_fixed_fraction (parallel::distributed::Triangulation< dim, spacedim > &tria, const VectorType &criteria, const double top_fraction_of_error, const double bottom_fraction_of_error) 
Collection of functions controlling refinement and coarsening of parallel::distributed::Triangulation objects. This namespace provides similar functionality to the GridRefinement namespace, except that it works for meshes that are parallel and distributed.
void parallel::distributed::GridRefinement::refine_and_coarsen_fixed_number  (  parallel::distributed::Triangulation< dim, spacedim > &  tria, 
const VectorType &  criteria,  
const double  top_fraction_of_cells,  
const double  bottom_fraction_of_cells,  
const unsigned int  max_n_cells = std::numeric_limits<unsigned int>::max() 

) 
Like GridRefinement::refine_and_coarsen_fixed_number, but for parallel distributed triangulations.
The vector of criteria needs to be a vector of refinement criteria for all cells active on the current triangulation, i.e., it needs to be of length tria.n_active_cells()
(and not tria.n_locally_owned_active_cells()
). In other words, the vector needs to include entries for ghost and artificial cells. However, the current function will only look at the indicators that correspond to those cells that are actually locally owned, and ignore the indicators for all other cells. The function will then coordinate among all processors that store part of the triangulation so that at the end a fraction top_fraction_of_cells
of all Triangulation::n_global_active_cells() active cells are refined, rather than a fraction of the Triangulation::n_locally_active_cells on each processor individually. In other words, it may be that on some processors, no cells are refined at all.
The same is true for the fraction of cells that is coarsened.
Definition at line 464 of file grid_refinement.cc.
void parallel::distributed::GridRefinement::refine_and_coarsen_fixed_fraction  (  parallel::distributed::Triangulation< dim, spacedim > &  tria, 
const VectorType &  criteria,  
const double  top_fraction_of_error,  
const double  bottom_fraction_of_error  
) 
Like GridRefinement::refine_and_coarsen_fixed_fraction, but for parallel distributed triangulations.
The vector of criteria needs to be a vector of refinement criteria for all cells active on the current triangulation, i.e., it needs to be of length tria.n_active_cells()
(and not tria.n_locally_owned_active_cells()
). In other words, the vector needs to include entries for ghost and artificial cells. However, the current function will only look at the indicators that correspond to those cells that are actually locally owned, and ignore the indicators for all other cells. The function will then coordinate among all processors that store part of the triangulation so that at the end the smallest fraction of Triangulation::n_global_active_cells (not Triangulation::n_locally_owned_active_cells() on each processor individually) is refined that together make up a total of top_fraction_of_error
of the total error. In other words, it may be that on some processors, no cells are refined at all.
The same is true for the fraction of cells that is coarsened.
Definition at line 551 of file grid_refinement.cc.