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add constructor for Linearised Nedelec space on non-conforming mesh
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,100 +1,121 @@ | ||
| function _create_ref_rule(Dc,ranks::MPIArray, num_uniform_refs) | ||
| if Dc==2 | ||
| coarse_model=CartesianDiscreteModel((0,1,0,1),(1,1)) | ||
| else | ||
| @assert Dc==3 | ||
| coarse_model=CartesianDiscreteModel((0,1,0,1,0,1),(1,1,1)) | ||
| end | ||
| new_comm=MPI.Comm_split(ranks.comm,MPI.Comm_rank(ranks.comm),0) | ||
| new_ranks=MPIArray(1,new_comm,(1,)) | ||
| model_ref=OctreeDistributedDiscreteModel(new_ranks,coarse_model,num_uniform_refs) | ||
| ref_rules=map(local_views(model_ref.dmodel)) do model | ||
| Gridap.Adaptivity.RefinementRule(Gridap.Adaptivity.GenericRefinement(), | ||
| Dc==2 ? QUAD : HEX, | ||
| get_grid(model)) | ||
| end | ||
| ref_rules.item_ref[] | ||
| end | ||
|
|
||
| function _get_order(reffe::Tuple{<:Lagrangian,Any,Any}) | ||
| reffe[2][2] | ||
| function _create_ref_rule(Dc, ranks::MPIArray, num_uniform_refs) | ||
| if Dc == 2 | ||
| coarse_model = CartesianDiscreteModel((0, 1, 0, 1), (1, 1)) | ||
| else | ||
| @assert Dc == 3 | ||
| coarse_model = CartesianDiscreteModel((0, 1, 0, 1, 0, 1), (1, 1, 1)) | ||
| end | ||
| new_comm = MPI.Comm_split(ranks.comm, MPI.Comm_rank(ranks.comm), 0) | ||
| new_ranks = MPIArray(1, new_comm, (1,)) | ||
| model_ref = OctreeDistributedDiscreteModel(new_ranks, coarse_model, num_uniform_refs) | ||
| ref_rules = map(local_views(model_ref.dmodel)) do model | ||
| Gridap.Adaptivity.RefinementRule(Gridap.Adaptivity.GenericRefinement(), | ||
| Dc == 2 ? QUAD : HEX, | ||
| get_grid(model)) | ||
| end | ||
| ref_rules.item_ref[] | ||
| end | ||
|
|
||
| function _create_adaptivity_glue(model::OctreeDistributedDiscreteModel{Dc}, | ||
| ref_model::OctreeDistributedDiscreteModel{Dc}, | ||
| num_uniform_refinements) where {Dc} | ||
| ref_rule=_create_ref_rule(Dc,model.parts,num_uniform_refinements) | ||
| num_children=Gridap.Adaptivity.num_subcells(ref_rule) | ||
| cell_gids_model = get_cell_gids(model.dmodel) | ||
| cell_gids_ref_model = get_cell_gids(ref_model.dmodel) | ||
| ref_model::OctreeDistributedDiscreteModel{Dc}, | ||
| num_uniform_refinements) where {Dc} | ||
| ref_rule = _create_ref_rule(Dc, model.parts, num_uniform_refinements) | ||
| num_children = Gridap.Adaptivity.num_subcells(ref_rule) | ||
| cell_gids_model = get_cell_gids(model.dmodel) | ||
| cell_gids_ref_model = get_cell_gids(ref_model.dmodel) | ||
|
|
||
| n2o_cell_map,n2o_cell_to_child_id=map(partition(cell_gids_model),partition(cell_gids_ref_model)) do model_partition, ref_model_partition | ||
| num_local_cells_model=local_length(model_partition) | ||
| num_owned_cells_model=own_length(model_partition) | ||
| num_local_cells_ref_model=local_length(ref_model_partition) | ||
| n2o_cell_map=Vector{Int}(undef,num_local_cells_ref_model) | ||
| n2o_cell_to_child_id=Vector{Int}(undef,num_local_cells_ref_model) | ||
| current=1 | ||
| for i=1:num_owned_cells_model | ||
| for j=1:num_children | ||
| n2o_cell_map[current]=i | ||
| n2o_cell_to_child_id[current]=j | ||
| current+=1 | ||
| end | ||
| end | ||
| n2o_cell_map,n2o_cell_to_child_id | ||
| end |> tuple_of_arrays | ||
| n2o_cell_map, n2o_cell_to_child_id = map(partition(cell_gids_model), partition(cell_gids_ref_model)) do model_partition, ref_model_partition | ||
| num_local_cells_model = local_length(model_partition) | ||
| num_owned_cells_model = own_length(model_partition) | ||
| num_local_cells_ref_model = local_length(ref_model_partition) | ||
| n2o_cell_map = Vector{Int}(undef, num_local_cells_ref_model) | ||
| n2o_cell_to_child_id = Vector{Int}(undef, num_local_cells_ref_model) | ||
| current = 1 | ||
| for i = 1:num_owned_cells_model | ||
| for j = 1:num_children | ||
| n2o_cell_map[current] = i | ||
| n2o_cell_to_child_id[current] = j | ||
| current += 1 | ||
| end | ||
| end | ||
| n2o_cell_map, n2o_cell_to_child_id | ||
| end |> tuple_of_arrays | ||
|
|
||
| cache = fetch_vector_ghost_values_cache(n2o_cell_map,partition(cell_gids_ref_model)) | ||
| fetch_vector_ghost_values!(n2o_cell_map,cache) |> wait | ||
| fetch_vector_ghost_values!(n2o_cell_to_child_id,cache) |> wait | ||
| cache = fetch_vector_ghost_values_cache(n2o_cell_map, partition(cell_gids_ref_model)) | ||
| fetch_vector_ghost_values!(n2o_cell_map, cache) |> wait | ||
| fetch_vector_ghost_values!(n2o_cell_to_child_id, cache) |> wait | ||
|
|
||
| adaptivity_glue=map(n2o_cell_map,n2o_cell_to_child_id) do n2o_cell_map, n2o_cell_to_child_id | ||
| n2o_faces_map = [(d==Dc) ? n2o_cell_map : Int[] for d in 0:Dc] | ||
| AdaptivityGlue(n2o_faces_map,n2o_cell_to_child_id,ref_rule) | ||
| end | ||
| end | ||
| adaptivity_glue = map(n2o_cell_map, n2o_cell_to_child_id) do n2o_cell_map, n2o_cell_to_child_id | ||
| n2o_faces_map = [(d == Dc) ? n2o_cell_map : Int[] for d in 0:Dc] | ||
| AdaptivityGlue(n2o_faces_map, n2o_cell_to_child_id, ref_rule) | ||
| end | ||
| end | ||
|
|
||
| function _setup_one_level_refined_octree_model(model::OctreeDistributedDiscreteModel{Dc,Dp}, | ||
| fmodel::OctreeDistributedDiscreteModel{Dc,Dp}, | ||
| adaptivity_glue) where {Dc,Dp} | ||
| @assert model.parts === fmodel.parts | ||
| adaptive_models = map(local_views(model), | ||
| local_views(fmodel), | ||
| adaptivity_glue) do model, fmodel, glue | ||
| Gridap.Adaptivity.AdaptedDiscreteModel(fmodel.model,model,glue) | ||
| fmodel::OctreeDistributedDiscreteModel{Dc,Dp}, | ||
| adaptivity_glue) where {Dc,Dp} | ||
| @assert model.parts === fmodel.parts | ||
| adaptive_models = map(local_views(model), | ||
| local_views(fmodel), | ||
| adaptivity_glue) do model, fmodel, glue | ||
| Gridap.Adaptivity.AdaptedDiscreteModel(fmodel.model, model, glue) | ||
| end | ||
| new_fmodel = GridapDistributed.GenericDistributedDiscreteModel(adaptive_models, get_cell_gids(fmodel)) | ||
| OctreeDistributedDiscreteModel(Dc, Dp, | ||
| model.parts, | ||
| new_fmodel, | ||
| fmodel.non_conforming_glue, | ||
| model.coarse_model, | ||
| model.ptr_pXest_connectivity, | ||
| pXest_copy(fmodel.pXest_type, fmodel.ptr_pXest), | ||
| fmodel.pXest_type, | ||
| fmodel.pXest_refinement_rule_type, | ||
| false, | ||
| fmodel) | ||
| end | ||
|
|
||
| function Gridap.LinearizedFESpace(model::OctreeDistributedDiscreteModel{Dc}, | ||
| reffe::Tuple{Gridap.ReferenceFEs.Lagrangian,Any,Any}; | ||
| kwargs...) where {Dc} | ||
| lin_reffe = Gridap._linearize_reffe(reffe) | ||
| order = reffe[2][2] | ||
| @assert floor(log2(order)) == ceil(log2(order)) "The order of the Lagrangian reference FE must be a power of 2" | ||
| num_refinements = Int(log2(order)) | ||
| ref_model = model | ||
| for i = 1:num_refinements | ||
| cell_gids = get_cell_gids(ref_model.dmodel) | ||
| ref_coarse_flags = map(partition(cell_gids)) do indices | ||
| flags = Vector{Cint}(undef, local_length(indices)) | ||
| flags .= refine_flag | ||
| end | ||
| new_fmodel = GridapDistributed.GenericDistributedDiscreteModel(adaptive_models,get_cell_gids(fmodel)) | ||
| OctreeDistributedDiscreteModel(Dc,Dp, | ||
| model.parts, | ||
| new_fmodel, | ||
| fmodel.non_conforming_glue, | ||
| model.coarse_model, | ||
| model.ptr_pXest_connectivity, | ||
| pXest_copy(fmodel.pXest_type,fmodel.ptr_pXest), | ||
| fmodel.pXest_type, | ||
| fmodel.pXest_refinement_rule_type, | ||
| false, | ||
| fmodel) | ||
| end | ||
| ref_model, glue = Gridap.Adaptivity.adapt(ref_model, ref_coarse_flags) | ||
| end | ||
| adaptivity_glue = _create_adaptivity_glue(model, ref_model, num_refinements) | ||
| one_level_ref_model = _setup_one_level_refined_octree_model(model, ref_model, adaptivity_glue) | ||
| Gridap.FESpace(one_level_ref_model, lin_reffe; kwargs...), one_level_ref_model | ||
| end | ||
|
|
||
| function Gridap.LinearizedFESpace(model::OctreeDistributedDiscreteModel{Dc}, | ||
| reffe::Tuple{Gridap.ReferenceFEs.Lagrangian,Any,Any}; | ||
| kwargs...) where {Dc} | ||
| lin_reffe=Gridap._linearize_reffe(reffe) | ||
| order=_get_order(reffe) | ||
| @assert floor(log2(order)) == ceil(log2(order)) "The order of the Lagrangian reference FE must be a power of 2" | ||
| num_refinements=Int(log2(order)) | ||
| ref_model=model | ||
| for i=1:num_refinements | ||
| cell_gids=get_cell_gids(ref_model.dmodel) | ||
| ref_coarse_flags=map(partition(cell_gids)) do indices | ||
| flags=Vector{Cint}(undef,local_length(indices)) | ||
| flags.=refine_flag | ||
| end | ||
| ref_model,glue=Gridap.Adaptivity.adapt(ref_model,ref_coarse_flags) | ||
| function Gridap.LinearizedFESpace(model::OctreeDistributedDiscreteModel{Dc}, | ||
| reffe::Tuple{Gridap.ReferenceFEs.Nedelec,Any,Any}; | ||
| kwargs...) where {Dc} | ||
| lin_reffe = (reffe[1], (reffe[2][1], 0), reffe[3]) | ||
| order = reffe[2][2] + 1 | ||
| @assert floor(log2(order)) == ceil(log2(order)) "The order of the Nedelec reference FE plus one must be a power of 2" | ||
| _build_linearized_space(model, lin_reffe, order; kwargs...) | ||
| end | ||
|
|
||
| function _build_linearized_space(model, lin_reffe, order; kwargs...) | ||
| num_refinements = Int(log2(order)) | ||
| ref_model = model | ||
| for _ in 1:num_refinements | ||
| cell_gids = get_cell_gids(ref_model.dmodel) | ||
| ref_coarse_flags = map(partition(cell_gids)) do indices | ||
| flags = Vector{Cint}(undef, local_length(indices)) | ||
| flags .= refine_flag | ||
| end | ||
| adaptivity_glue=_create_adaptivity_glue(model,ref_model,num_refinements) | ||
| one_level_ref_model=_setup_one_level_refined_octree_model(model,ref_model,adaptivity_glue) | ||
| Gridap.FESpace(one_level_ref_model,lin_reffe; kwargs...), one_level_ref_model | ||
| end | ||
| ref_model, _ = Gridap.Adaptivity.adapt(ref_model, ref_coarse_flags) | ||
| end | ||
| adaptivity_glue = _create_adaptivity_glue(model, ref_model, num_refinements) | ||
| one_level_ref_model = _setup_one_level_refined_octree_model(model, ref_model, adaptivity_glue) | ||
| Gridap.FESpace(one_level_ref_model, lin_reffe; kwargs...), one_level_ref_model | ||
| end |
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