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 Reference documentation for deal.II version 9.3.3
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step-6.h
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1 false);
714 *
715 * @endcode
716 *
717 * Now all non-zero entries of the matrix are known (i.e. those from
718 * regularly assembling the matrix and those that were introduced by
719 * eliminating constraints). We may copy our intermediate object to the
720 * sparsity pattern:
721 *
722 * @code
723 * sparsity_pattern.copy_from(dsp);
724 *
725 * @endcode
726 *
727 * We may now, finally, initialize the sparse matrix:
728 *
729 * @code
730 * system_matrix.reinit(sparsity_pattern);
731 * }
732 *
733 *
734 * @endcode
735 *
736 *
737 * <a name="Step6assemble_system"></a>
738 * <h4>Step6::assemble_system</h4>
739 *
740
741 *
742 * Next, we have to assemble the matrix. However, to copy the local matrix and
743 * vector on each cell into the global system, we are no longer using a
744 * hand-written loop. Instead, we use
745 * AffineConstraints::distribute_local_to_global() that internally executes
746 * this loop while performing Gaussian elimination on rows and columns
747 * corresponding to constrained degrees on freedom.
748 *
749
750 *
751 * The rest of the code that forms the local contributions remains
752 * unchanged. It is worth noting, however, that under the hood several things
753 * are different than before. First, the variable <code>dofs_per_cell</code>
754 * and return value of <code>quadrature_formula.size()</code> now are 9 each,
755 * where they were 4 before. Introducing such variables as abbreviations is a
756 * good strategy to make code work with different elements without having to
757 * change too much code. Secondly, the <code>fe_values</code> object of course
758 * needs to do other things as well, since the shape functions are now
759 * quadratic, rather than linear, in each coordinate variable. Again, however,
760 * this is something that is completely handled by the library.
761 *
762 * @code
763 * template <int dim>
764 * void Step6<dim>::assemble_system()
765 * {
766 * const QGauss<dim> quadrature_formula(fe.degree + 1);
767 *
768 * FEValues<dim> fe_values(fe,
769 * quadrature_formula,
772 *
773 * const unsigned int dofs_per_cell = fe.n_dofs_per_cell();
774 *
775 * FullMatrix<double> cell_matrix(dofs_per_cell, dofs_per_cell);
776 * Vector<double> cell_rhs(dofs_per_cell);
777 *
778 * std::vector<types::global_dof_index> local_dof_indices(dofs_per_cell);
779 *
780 * for (const auto &cell : dof_handler.active_cell_iterators())
781 * {
782 * cell_matrix = 0;
783 * cell_rhs = 0;
784 *
785 * fe_values.reinit(cell);
786 *
787 * for (const unsigned int q_index : fe_values.quadrature_point_indices())
788 * {
789 * const double current_coefficient =
790 * coefficient(fe_values.quadrature_point(q_index));
791 * for (const unsigned int i : fe_values.dof_indices())
792 * {
793 * for (const unsigned int j : fe_values.dof_indices())
794 * cell_matrix(i, j) +=
795 * (current_coefficient * // a(x_q)
796 * fe_values.shape_grad(i, q_index) * // grad phi_i(x_q)
797 * fe_values.shape_grad(j, q_index) * // grad phi_j(x_q)
798 * fe_values.JxW(q_index)); // dx
799 *
800 * cell_rhs(i) += (1.0 * // f(x)
801 * fe_values.shape_value(i, q_index) * // phi_i(x_q)
802 * fe_values.JxW(q_index)); // dx
803 * }
804 * }
805 *
806 * @endcode
807 *
808 * Finally, transfer the contributions from @p cell_matrix and
809 * @p cell_rhs into the global objects.
810 *
811 * @code
812 * cell->get_dof_indices(local_dof_indices);
813 * constraints.distribute_local_to_global(
814 * cell_matrix, cell_rhs, local_dof_indices, system_matrix, system_rhs);
815 * }
816 * @endcode
817 *
818 * Now we are done assembling the linear system. The constraint matrix took
819 * care of applying the boundary conditions and also eliminated hanging node
820 * constraints. The constrained nodes are still in the linear system (there
821 * is a nonzero entry, chosen in a way that the matrix is well conditioned,
822 * on the diagonal of the matrix and all other entries for this line are set
823 * to zero) but the computed values are invalid (i.e., the corresponding
824 * entries in <code>system_rhs</code> are currently meaningless). We compute
825 * the correct values for these nodes at the end of the <code>solve</code>
826 * function.
827 *
828 * @code
829 * }
830 *
831 *
832 * @endcode
833 *
834 *
835 * <a name="Step6solve"></a>
836 * <h4>Step6::solve</h4>
837 *
838
839 *
840 * We continue with gradual improvements. The function that solves the linear
841 * system again uses the SSOR preconditioner, and is again unchanged except
842 * that we have to incorporate hanging node constraints. As mentioned above,
843 * the degrees of freedom from the AffineConstraints object corresponding to
844 * hanging node constraints and boundary values have been removed from the
845 * linear system by giving the rows and columns of the matrix a special
846 * treatment. This way, the values for these degrees of freedom have wrong,
847 * but well-defined values after solving the linear system. What we then have
848 * to do is to use the constraints to assign to them the values that they
849 * should have. This process, called <code>distributing</code> constraints,
850 * computes the values of constrained nodes from the values of the
851 * unconstrained ones, and requires only a single additional function call
852 * that you find at the end of this function:
853 *
854
855 *
856 *
857 * @code
858 * template <int dim>
859 * void Step6<dim>::solve()
860 * {
861 * SolverControl solver_control(1000, 1e-12);
862 * SolverCG<Vector<double>> solver(solver_control);
863 *
865 * preconditioner.initialize(system_matrix, 1.2);
866 *
867 * solver.solve(system_matrix, solution, system_rhs, preconditioner);
868 *
869 * constraints.distribute(solution);
870 * }
871 *
872 *
873 * @endcode
874 *
875 *
876 * <a name="Step6refine_grid"></a>
877 * <h4>Step6::refine_grid</h4>
878 *
879
880 *
881 * We use a sophisticated error estimation scheme to refine the mesh instead
882 * of global refinement. We will use the KellyErrorEstimator class which
883 * implements an error estimator for the Laplace equation; it can in principle
884 * handle variable coefficients, but we will not use these advanced features,
885 * but rather use its most simple form since we are not interested in
886 * quantitative results but only in a quick way to generate locally refined
887 * grids.
888 *
889
890 *
891 * Although the error estimator derived by Kelly et al. was originally
892 * developed for the Laplace equation, we have found that it is also well
893 * suited to quickly generate locally refined grids for a wide class of
894 * problems. This error estimator uses the solution gradient's jump at
895 * cell faces (which is a measure for the second derivatives) and
896 * scales it by the size of the cell. It is therefore a measure for the local
897 * smoothness of the solution at the place of each cell and it is thus
898 * understandable that it yields reasonable grids also for hyperbolic
899 * transport problems or the wave equation as well, although these grids are
900 * certainly suboptimal compared to approaches specially tailored to the
901 * problem. This error estimator may therefore be understood as a quick way to
902 * test an adaptive program.
903 *
904
905 *
906 * The way the estimator works is to take a <code>DoFHandler</code> object
907 * describing the degrees of freedom and a vector of values for each degree of
908 * freedom as input and compute a single indicator value for each active cell
909 * of the triangulation (i.e. one value for each of the active cells). To do
910 * so, it needs two additional pieces of information: a face quadrature formula,
911 * i.e., a quadrature formula on <code>dim-1</code> dimensional objects. We use
912 * a 3-point Gauss rule again, a choice that is consistent and appropriate with
913 * the bi-quadratic finite element shape functions in this program.
914 * (What constitutes a suitable quadrature rule here of course depends on
915 * knowledge of the way the error estimator evaluates the solution field. As
916 * said above, the jump of the gradient is integrated over each face, which
917 * would be a quadratic function on each face for the quadratic elements in
918 * use in this example. In fact, however, it is the square of the jump of the
919 * gradient, as explained in the documentation of that class, and that is a
920 * quartic function, for which a 3 point Gauss formula is sufficient since it
921 * integrates polynomials up to order 5 exactly.)
922 *
923
924 *
925 * Secondly, the function wants a list of boundary indicators for those
926 * boundaries where we have imposed Neumann values of the kind
927 * @f$\partial_n u(\mathbf x) = h(\mathbf x)@f$, along with a function @f$h(\mathbf
928 * x)@f$ for each such boundary. This information is represented by a map from
929 * boundary indicators to function objects describing the Neumann boundary
930 * values. In the present example program, we do not use Neumann boundary
931 * values, so this map is empty, and in fact constructed using the default
932 * constructor of the map in the place where the function call expects the
933 * respective function argument.
934 *
935
936 *
937 * The output is a vector of values for all active cells. While it may
938 * make sense to compute the <b>value</b> of a solution degree of freedom
939 * very accurately, it is usually not necessary to compute the <b>error
940 * indicator</b> corresponding to the solution on a cell particularly
941 * accurately. We therefore typically use a vector of floats instead of a vector
942 * of doubles to represent error indicators.
943 *
944 * @code
945 * template <int dim>
946 * void Step6<dim>::refine_grid()
947 * {
948 * Vector<float> estimated_error_per_cell(triangulation.n_active_cells());
949 *
950 * KellyErrorEstimator<dim>::estimate(dof_handler,
951 * QGauss<dim - 1>(fe.degree + 1),
952 * {},
953 * solution,
954 * estimated_error_per_cell);
955 *
956 * @endcode
957 *
958 * The above function returned one error indicator value for each cell in
959 * the <code>estimated_error_per_cell</code> array. Refinement is now done
960 * as follows: refine those 30 per cent of the cells with the highest error
961 * values, and coarsen the 3 per cent of cells with the lowest values.
962 *
963
964 *
965 * One can easily verify that if the second number were zero, this would
966 * approximately result in a doubling of cells in each step in two space
967 * dimensions, since for each of the 30 per cent of cells, four new would be
968 * replaced, while the remaining 70 per cent of cells remain untouched. In
969 * practice, some more cells are usually produced since it is disallowed
970 * that a cell is refined twice while the neighbor cell is not refined; in
971 * that case, the neighbor cell would be refined as well.
972 *
973
974 *
975 * In many applications, the number of cells to be coarsened would be set to
976 * something larger than only three per cent. A non-zero value is useful
977 * especially if for some reason the initial (coarse) grid is already rather
978 * refined. In that case, it might be necessary to refine it in some
979 * regions, while coarsening in some other regions is useful. In our case
980 * here, the initial grid is very coarse, so coarsening is only necessary in
981 * a few regions where over-refinement may have taken place. Thus a small,
982 * non-zero value is appropriate here.
983 *
984
985 *
986 * The following function now takes these refinement indicators and flags
987 * some cells of the triangulation for refinement or coarsening using the
988 * method described above. It is from a class that implements several
989 * different algorithms to refine a triangulation based on cell-wise error
990 * indicators.
991 *
992 * @code
993 * GridRefinement::refine_and_coarsen_fixed_number(triangulation,
994 * estimated_error_per_cell,
995 * 0.3,
996 * 0.03);
997 *
998 * @endcode
999 *
1000 * After the previous function has exited, some cells are flagged for
1001 * refinement, and some other for coarsening. The refinement or coarsening
1002 * itself is not performed by now, however, since there are cases where
1003 * further modifications of these flags is useful. Here, we don't want to do
1004 * any such thing, so we can tell the triangulation to perform the actions
1005 * for which the cells are flagged:
1006 *
1007 * @code
1008 * triangulation.execute_coarsening_and_refinement();
1009 * }
1010 *
1011 *
1012 * @endcode
1013 *
1014 *
1015 * <a name="Step6output_results"></a>
1016 * <h4>Step6::output_results</h4>
1017 *
1018
1019 *
1020 * At the end of computations on each grid, and just before we continue the
1021 * next cycle with mesh refinement, we want to output the results from this
1022 * cycle.
1023 *
1024
1025 *
1026 * We have already seen in @ref step_1 "step-1" how this can be achieved for the
1027 * mesh itself. Here, we change a few things:
1028 * <ol>
1029 * <li>We use two different formats: gnuplot and VTU.</li>
1030 * <li>We embed the cycle number in the output file name.</li>
1031 * <li>For gnuplot output, we set up a GridOutFlags::Gnuplot object to
1032 * provide a few extra visualization arguments so that edges appear
1033 * curved. This is explained in further detail in @ref step_10 "step-10".</li>
1034 * </ol>
1035 *
1036 * @code
1037 * template <int dim>
1038 * void Step6<dim>::output_results(const unsigned int cycle) const
1039 * {
1040 * {
1041 * GridOut grid_out;
1042 * std::ofstream output("grid-" + std::to_string(cycle) + ".gnuplot");
1043 * GridOutFlags::Gnuplot gnuplot_flags(false, 5);
1044 * grid_out.set_flags(gnuplot_flags);
1045 * MappingQGeneric<dim> mapping(3);
1046 * grid_out.write_gnuplot(triangulation, output, &mapping);
1047 * }
1048 *
1049 * {
1050 * DataOut<dim> data_out;
1051 * data_out.attach_dof_handler(dof_handler);
1052 * data_out.add_data_vector(solution, "solution");
1053 * data_out.build_patches();
1054 *
1055 * std::ofstream output("solution-" + std::to_string(cycle) + ".vtu");
1056 * data_out.write_vtu(output);
1057 * }
1058 * }
1059 *
1060 *
1061 * @endcode
1062 *
1063 *
1064 * <a name="Step6run"></a>
1065 * <h4>Step6::run</h4>
1066 *
1067
1068 *
1069 * The final function before <code>main()</code> is again the main driver of
1070 * the class, <code>run()</code>. It is similar to the one of @ref step_5 "step-5", except
1071 * that we generate a file in the program again instead of reading it from
1072 * disk, in that we adaptively instead of globally refine the mesh, and that
1073 * we output the solution on the final mesh in the present function.
1074 *
1075
1076 *
1077 * The first block in the main loop of the function deals with mesh generation.
1078 * If this is the first cycle of the program, instead of reading the grid from
1079 * a file on disk as in the previous example, we now again create it using a
1080 * library function. The domain is again a circle with center at the origin and
1081 * a radius of one (these are the two hidden arguments to the function, which
1082 * have default values).
1083 *
1084
1085 *
1086 * You will notice by looking at the coarse grid that it is of inferior
1087 * quality than the one which we read from the file in the previous example:
1088 * the cells are less equally formed. However, using the library function this
1089 * program works in any space dimension, which was not the case before.
1090 *
1091
1092 *
1093 * In case we find that this is not the first cycle, we want to refine the
1094 * grid. Unlike the global refinement employed in the last example program, we
1095 * now use the adaptive procedure described above.
1096 *
1097
1098 *
1099 * The rest of the loop looks as before:
1100 *
1101 * @code
1102 * template <int dim>
1103 * void Step6<dim>::run()
1104 * {
1105 * for (unsigned int cycle = 0; cycle < 8; ++cycle)
1106 * {
1107 * std::cout << "Cycle " << cycle << ':' << std::endl;
1108 *
1109 * if (cycle == 0)
1110 * {
1112 * triangulation.refine_global(1);
1113 * }
1114 * else
1115 * refine_grid();
1116 *
1117 *
1118 * std::cout << " Number of active cells: "
1119 * << triangulation.n_active_cells() << std::endl;
1120 *
1121 * setup_system();
1122 *
1123 * std::cout << " Number of degrees of freedom: " << dof_handler.n_dofs()
1124 * << std::endl;
1125 *
1126 * assemble_system();
1127 * solve();
1128 * output_results(cycle);
1129 * }
1130 * }
1131 *
1132 *
1133 * @endcode
1134 *
1135 *
1136 * <a name="Thecodemaincodefunction"></a>
1137 * <h3>The <code>main</code> function</h3>
1138 *
1139
1140 *
1141 * The main function is unaltered in its functionality from the previous
1142 * example, but we have taken a step of additional caution. Sometimes,
1143 * something goes wrong (such as insufficient disk space upon writing an
1144 * output file, not enough memory when trying to allocate a vector or a
1145 * matrix, or if we can't read from or write to a file for whatever reason),
1146 * and in these cases the library will throw exceptions. Since these are
1147 * run-time problems, not programming errors that can be fixed once and for
1148 * all, this kind of exceptions is not switched off in optimized mode, in
1149 * contrast to the <code>Assert</code> macro which we have used to test
1150 * against programming errors. If uncaught, these exceptions propagate the
1151 * call tree up to the <code>main</code> function, and if they are not caught
1152 * there either, the program is aborted. In many cases, like if there is not
1153 * enough memory or disk space, we can't do anything but we can at least print
1154 * some text trying to explain the reason why the program failed. A way to do
1155 * so is shown in the following. It is certainly useful to write any larger
1156 * program in this way, and you can do so by more or less copying this
1157 * function except for the <code>try</code> block that actually encodes the
1158 * functionality particular to the present application.
1159 *
1160 * @code
1161 * int main()
1162 * {
1163 * @endcode
1164 *
1165 * The general idea behind the layout of this function is as follows: let's
1166 * try to run the program as we did before...
1167 *
1168 * @code
1169 * try
1170 * {
1171 * Step6<2> laplace_problem_2d;
1172 * laplace_problem_2d.run();
1173 * }
1174 * @endcode
1175 *
1176 * ...and if this should fail, try to gather as much information as
1177 * possible. Specifically, if the exception that was thrown is an object of
1178 * a class that is derived from the C++ standard class
1179 * <code>exception</code>, then we can use the <code>what</code> member
1180 * function to get a string which describes the reason why the exception was
1181 * thrown.
1182 *
1183
1184 *
1185 * The deal.II exception classes are all derived from the standard class,
1186 * and in particular, the <code>exc.what()</code> function will return
1187 * approximately the same string as would be generated if the exception was
1188 * thrown using the <code>Assert</code> macro. You have seen the output of
1189 * such an exception in the previous example, and you then know that it
1190 * contains the file and line number of where the exception occurred, and
1191 * some other information. This is also what the following statements would
1192 * print.
1193 *
1194
1195 *
1196 * Apart from this, there isn't much that we can do except exiting the
1197 * program with an error code (this is what the <code>return 1;</code>
1198 * does):
1199 *
1200 * @code
1201 * catch (std::exception &exc)
1202 * {
1203 * std::cerr << std::endl
1204 * << std::endl
1205 * << "----------------------------------------------------"
1206 * << std::endl;
1207 * std::cerr << "Exception on processing: " << std::endl
1208 * << exc.what() << std::endl
1209 * << "Aborting!" << std::endl
1210 * << "----------------------------------------------------"
1211 * << std::endl;
1212 *
1213 * return 1;
1214 * }
1215 * @endcode
1216 *
1217 * If the exception that was thrown somewhere was not an object of a class
1218 * derived from the standard <code>exception</code> class, then we can't do
1219 * anything at all. We then simply print an error message and exit.
1220 *
1221 * @code
1222 * catch (...)
1223 * {
1224 * std::cerr << std::endl
1225 * << std::endl
1226 * << "----------------------------------------------------"
1227 * << std::endl;
1228 * std::cerr << "Unknown exception!" << std::endl
1229 * << "Aborting!" << std::endl
1230 * << "----------------------------------------------------"
1231 * << std::endl;
1232 * return 1;
1233 * }
1234 *
1235 * @endcode
1236 *
1237 * If we got to this point, there was no exception which propagated up to
1238 * the main function (there may have been exceptions, but they were caught
1239 * somewhere in the program or the library). Therefore, the program
1240 * performed as was expected and we can return without error.
1241 *
1242 * @code
1243 * return 0;
1244 * }
1245 * @endcode
1246<a name="Results"></a><h1>Results</h1>
1247
1248
1249
1250The output of the program looks as follows:
1251@code
1252Cycle 0:
1253 Number of active cells: 20
1254 Number of degrees of freedom: 89
1255Cycle 1:
1256 Number of active cells: 44
1257 Number of degrees of freedom: 209
1258Cycle 2:
1259 Number of active cells: 92
1260 Number of degrees of freedom: 449
1261Cycle 3:
1262 Number of active cells: 200
1263 Number of degrees of freedom: 921
1264Cycle 4:
1265 Number of active cells: 440
1266 Number of degrees of freedom: 2017
1267Cycle 5:
1268 Number of active cells: 956
1269 Number of degrees of freedom: 4425
1270Cycle 6:
1271 Number of active cells: 1916
1272 Number of degrees of freedom: 8993
1273Cycle 7:
1274 Number of active cells: 3860
1275 Number of degrees of freedom: 18353
1276@endcode
1277
1278
1279
1280As intended, the number of cells roughly doubles in each cycle. The
1281number of degrees is slightly more than four times the number of
1282cells; one would expect a factor of exactly four in two spatial
1283dimensions on an infinite grid (since the spacing between the degrees
1284of freedom is half the cell width: one additional degree of freedom on
1285each edge and one in the middle of each cell), but it is larger than
1286that factor due to the finite size of the mesh and due to additional
1287degrees of freedom which are introduced by hanging nodes and local
1288refinement.
1289
1290
1291
1292The program outputs the solution and mesh in each cycle of the
1293refinement loop. The solution looks as follows:
1294
1295<img src="https://www.dealii.org/images/steps/developer/step-6.solution.9.2.png" alt="">
1296
1297It is interesting to follow how the program arrives at the final mesh:
1298
1299<div class="twocolumn" style="width: 80%">
1300 <div>
1301 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_0.svg"
1302 alt="Initial grid: the five-cell circle grid with one global refinement."
1303 width="300" height="300">
1304 </div>
1305 <div>
1306 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_1.svg"
1307 alt="First grid: the five-cell circle grid with two global refinements."
1308 width="300" height="300">
1309 </div>
1310 <div>
1311 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_2.svg"
1312 alt="Second grid: the five-cell circle grid with one adaptive refinement."
1313 width="300" height="300">
1314 </div>
1315 <div>
1316 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_3.svg"
1317 alt="Third grid: the five-cell circle grid with two adaptive
1318 refinements, showing clustering around the inner circle."
1319 width="300" height="300">
1320 </div>
1321 <div>
1322 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_4.svg"
1323 alt="Fourth grid: the five-cell circle grid with three adaptive
1324 refinements, showing clustering around the inner circle."
1325 width="300" height="300">
1326 </div>
1327 <div>
1328 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_5.svg"
1329 alt="Fifth grid: the five-cell circle grid with four adaptive
1330 refinements, showing clustering around the inner circle."
1331 width="300" height="300">
1332 </div>
1333 <div>
1334 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_6.svg"
1335 alt="Sixth grid: the five-cell circle grid with five adaptive
1336 refinements, showing clustering around the inner circle."
1337 width="300" height="300">
1338 </div>
1339 <div>
1340 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_7.svg"
1341 alt="Last grid: the five-cell circle grid with six adaptive
1342 refinements, showing that most cells are clustered around the inner circle."
1343 width="300" height="300">
1344 </div>
1345</div>
1346
1347
1348It is clearly visible that the region where the solution has a kink,
1349i.e. the circle at radial distance 0.5 from the center, is
1350refined most. Furthermore, the central region where the solution is
1351very smooth and almost flat, is almost not refined at all, but this
1352results from the fact that we did not take into account that the
1353coefficient is large there. The region outside is refined rather
1354arbitrarily, since the second derivative is constant there and refinement
1355is therefore mostly based on the size of the cells and their deviation
1356from the optimal square.
1357
1358
1359
1360<a name="extensions"></a>
1361<a name="Possibilitiesforextensions"></a><h3>Possibilities for extensions</h3>
1362
1363
1364<a name="Solversandpreconditioners"></a><h4>Solvers and preconditioners</h4>
1365
1366
1367
1368One thing that is always worth playing around with if one solves
1369problems of appreciable size (much bigger than the one we have here)
1370is to try different solvers or preconditioners. In the current case,
1371the linear system is symmetric and positive definite, which makes the
1372CG algorithm pretty much the canonical choice for solving. However,
1373the SSOR preconditioner we use in the <code>solve()</code> function is
1374up for grabs.
1375
1376In deal.II, it is relatively simple to change the preconditioner. For
1377example, by changing the existing lines of code
1378@code
1379 PreconditionSSOR<SparseMatrix<double>> preconditioner;
1380 preconditioner.initialize(system_matrix, 1.2);
1381@endcode
1382into
1383@code
1384 PreconditionSSOR<SparseMatrix<double>> preconditioner;
1385 preconditioner.initialize(system_matrix, 1.0);
1386@endcode
1387we can try out different relaxation parameters for SSOR. By using
1388@code
1389 PreconditionJacobi<SparseMatrix<double>> preconditioner;
1390 preconditioner.initialize(system_matrix);
1391@endcode
1392we can use Jacobi as a preconditioner. And by using
1393@code
1394 SparseILU<double> preconditioner;
1395 preconditioner.initialize(system_matrix);
1396@endcode
1397we can use a simple incomplete LU decomposition without any thresholding or
1398strengthening of the diagonal (to use this preconditioner, you have to also
1399add the header file <code>deal.II/lac/sparse_ilu.h</code> to the include list
1400at the top of the file).
1401
1402Using these various different preconditioners, we can compare the
1403number of CG iterations needed (available through the
1404<code>solver_control.last_step()</code> call, see
1405@ref step_4 "step-4") as well as CPU time needed (using the Timer class,
1406discussed, for example, in @ref step_28 "step-28") and get the
1407following results (left: iterations; right: CPU time):
1408
1409<table width="60%" align="center">
1410 <tr>
1411 <td align="center">
1412 <img src="https://www.dealii.org/images/steps/developer/step-6.q2.dofs_vs_iterations.png" alt="">
1413 </td>
1414 <td align="center">
1415 <img src="https://www.dealii.org/images/steps/developer/step-6.q2.dofs_vs_time.png" alt="">
1416 </td>
1417 </tr>
1418</table>
1419
1420As we can see, all preconditioners behave pretty much the same on this
1421simple problem, with the number of iterations growing like @f${\cal
1422O}(N^{1/2})@f$ and because each iteration requires around @f${\cal
1423O}(N)@f$ operations the total CPU time grows like @f${\cal
1424O}(N^{3/2})@f$ (for the few smallest meshes, the CPU time is so small
1425that it doesn't record). Note that even though it is the simplest
1426method, Jacobi is the fastest for this problem.
1427
1428The situation changes slightly when the finite element is not a
1429bi-quadratic one as set in the constructor of this program, but a
1430bi-linear one. If one makes this change, the results are as follows:
1431
1432<table width="60%" align="center">
1433 <tr>
1434 <td align="center">
1435 <img src="https://www.dealii.org/images/steps/developer/step-6.q1.dofs_vs_iterations.png" alt="">
1436 </td>
1437 <td align="center">
1438 <img src="https://www.dealii.org/images/steps/developer/step-6.q1.dofs_vs_time.png" alt="">
1439 </td>
1440 </tr>
1441</table>
1442
1443In other words, while the increase in iterations and CPU time is as
1444before, Jacobi is now the method that requires the most iterations; it
1445is still the fastest one, however, owing to the simplicity of the
1446operations it has to perform. This is not to say that Jacobi
1447is actually a good preconditioner -- for problems of appreciable size, it is
1448definitely not, and other methods will be substantially better -- but really
1449only that it is fast because its implementation is so simple that it can
1450compensate for a larger number of iterations.
1451
1452The message to take away from this is not that simplicity in
1453preconditioners is always best. While this may be true for the current
1454problem, it definitely is not once we move to more complicated
1455problems (elasticity or Stokes, for examples @ref step_8 "step-8" or
1456@ref step_22 "step-22"). Secondly, all of these preconditioners still
1457lead to an increase in the number of iterations as the number @f$N@f$ of
1458degrees of freedom grows, for example @f${\cal O}(N^\alpha)@f$; this, in
1459turn, leads to a total growth in effort as @f${\cal O}(N^{1+\alpha})@f$
1460since each iteration takes @f${\cal O}(N)@f$ work. This behavior is
1461undesirable: we would really like to solve linear systems with @f$N@f$
1462unknowns in a total of @f${\cal O}(N)@f$ work; there is a class
1463of preconditioners that can achieve this, namely geometric (@ref step_16 "step-16",
1464@ref step_37 "step-37", @ref step_39 "step-39")
1465or algebraic multigrid (@ref step_31 "step-31", @ref step_40 "step-40", and several others)
1466preconditioners. They are, however, significantly more complex than
1467the preconditioners outlined above.
1468
1469Finally, the last message to take
1470home is that when the data shown above was generated (in 2018), linear
1471systems with 100,000 unknowns are
1472easily solved on a desktop machine in about a second, making
1473the solution of relatively simple 2d problems even to very high
1474accuracy not that big a task as it used to be even in the
1475past. At the time, the situation for 3d problems was entirely different,
1476but even that has changed substantially in the intervening time -- though
1477solving problems in 3d to high accuracy remains a challenge.
1478
1479
1480<a name="Abettermesh"></a><h4>A better mesh</h4>
1481
1482
1483If you look at the meshes above, you will see even though the domain is the
1484unit disk, and the jump in the coefficient lies along a circle, the cells
1485that make up the mesh do not track this geometry well. The reason, already hinted
1486at in @ref step_1 "step-1", is that in the absence of other information,
1487the Triangulation class only sees a bunch of
1488coarse grid cells but has, of course, no real idea what kind of geometry they
1489might represent when looked at together. For this reason, we need to tell
1490the Triangulation what to do when a cell is refined: where should the new
1491vertices at the edge midpoints and the cell midpoint be located so that the
1492child cells better represent the desired geometry than the parent cell.
1493
1494To visualize what the triangulation actually knows about the geometry,
1495it is not enough to just output the location of vertices and draw a
1496straight line for each edge; instead, we have to output both interior
1497and boundary lines as multiple segments so that they look
1498curved. We can do this by making one change to the gnuplot part of
1499<code>output_results</code>:
1500@code
1501{
1502 GridOut grid_out;
1503 std::ofstream output("grid-" + std::to_string(cycle) + ".gnuplot");
1504 GridOutFlags::Gnuplot gnuplot_flags(false, 5, /*curved_interior_cells*/true);
1505 grid_out.set_flags(gnuplot_flags);
1506 MappingQGeneric<dim> mapping(3);
1507 grid_out.write_gnuplot(triangulation, output, &mapping);
1508}
1509@endcode
1510
1511In the code above, we already do this for faces that sit at the boundary: this
1512happens automatically since we use GridGenerator::hyper_ball, which attaches a
1513SphericalManifold to the boundary of the domain. To make the mesh
1514<i>interior</i> also track a circular domain, we need to work a bit harder,
1515though. First, recall that our coarse mesh consists of a central square
1516cell and four cells around it. Now first consider what would happen if we
1517also attached the SphericalManifold object not only to the four exterior faces
1518but also the four cells at the perimeter as well as all of their faces. We can
1519do this by adding the following snippet (testing that the center of a cell is
1520larger than a small multiple, say one tenth, of the cell diameter away from
1521center of the mesh only fails for the central square of the mesh):
1522@code
1524// after GridGenerator::hyper_ball is called the Triangulation has
1525// a SphericalManifold with id 0. We can use it again on the interior.
1526const Point<dim> mesh_center;
1527for (const auto &cell : triangulation.active_cell_iterators())
1528 if (mesh_center.distance (cell->center()) > cell->diameter()/10)
1529 cell->set_all_manifold_ids(0);
1530
1531triangulation.refine_global(1);
1532@endcode
1533
1534After a few global refinement steps, this would lead to a mesh of the following
1535kind:
1536
1537
1538 <div class="onecolumn" style="width: 80%">
1539 <div>
1540 <img src="https://www.dealii.org/images/steps/developer/step_6_bad_grid_4.svg"
1541 alt="Grid where some central cells are nearly triangular."
1542 width="300" height="300">
1543 </div>
1544 </div>
1545
1546This is not a good mesh: the central cell has been refined in such a way that
1547the children located in the four corners of the original central cell
1548<i>degenerate</i>: they all tend towards triangles as mesh refinement
1549continues. This means that the Jacobian matrix of the transformation from
1550reference cell to actual cell degenerates for these cells, and because
1551all error estimates for finite element solutions contain the norm of the
1552inverse of the Jacobian matrix, you will get very large errors on these
1553cells and, in the limit as mesh refinement, a loss of convergence order because
1554the cells in these corners become worse and worse under mesh refinement.
1555
1556So we need something smarter. To this end, consider the following solution
1557originally developed by Konstantin Ladutenko. We will use the following code:
1558@code
1560
1561const Point<dim> mesh_center;
1562const double core_radius = 1.0/5.0,
1563 inner_radius = 1.0/3.0;
1564
1565// Step 1: Shrink the inner cell
1566//
1567// We cannot get a circle out of the inner cell because of
1568// the degeneration problem mentioned above. Rather, shrink
1569// the inner cell to a core radius of 1/5 that stays
1570// sufficiently far away from the place where the
1571// coefficient will have a discontinuity and where we want
1572// to have cell interfaces that actually lie on a circle.
1573// We do this shrinking by just scaling the location of each
1574// of the vertices, given that the center of the circle is
1575// simply the origin of the coordinate system.
1576for (const auto &cell : triangulation.active_cell_iterators())
1577 if (mesh_center.distance(cell->center()) < 1e-5)
1578 {
1579 for (const auto v : cell->vertex_indices())
1580 cell->vertex(v) *= core_radius/mesh_center.distance(cell->vertex(v));
1581 }
1582
1583// Step 2: Refine all cells except the central one
1584for (const auto &cell : triangulation.active_cell_iterators())
1585 if (mesh_center.distance(cell->center()) >= 1e-5)
1586 cell->set_refine_flag();
1587triangulation.execute_coarsening_and_refinement();
1588
1589// Step 3: Resize the inner children of the outer cells
1590//
1591// The previous step replaced each of the four outer cells
1592// by its four children, but the radial distance at which we
1593// have intersected is not what we want to later refinement
1594// steps. Consequently, move the vertices that were just
1595// created in radial direction to a place where we need
1596// them.
1597for (const auto &cell : triangulation.active_cell_iterators())
1598 for (const auto v : cell->vertex_indices())
1599 {
1600 const double dist = mesh_center.distance(cell->vertex(v));
1601 if (dist > core_radius*1.0001 && dist < 0.9999)
1602 cell->vertex(v) *= inner_radius/dist;
1603 }
1604
1605// Step 4: Apply curved manifold description
1606//
1607// As discussed above, we can not expect to subdivide the
1608// inner four cells (or their faces) onto concentric rings,
1609// but we can do so for all other cells that are located
1610// outside the inner radius. To this end, we loop over all
1611// cells and determine whether it is in this zone. If it
1612// isn't, then we set the manifold description of the cell
1613// and all of its bounding faces to the one that describes
1614// the spherical manifold already introduced above and that
1615// will be used for all further mesh refinement.
1616for (const auto &cell : triangulation.active_cell_iterators())
1617 {
1618 bool is_in_inner_circle = false;
1619 for (const auto v : cell->vertex_indices())
1620 if (mesh_center.distance(cell->vertex(v)) < inner_radius)
1621 {
1622 is_in_inner_circle = true;
1623 break;
1624 }
1625
1626 if (is_in_inner_circle == false)
1627 // The Triangulation already has a SphericalManifold with
1628 // manifold id 0 (see the documentation of
1629 // GridGenerator::hyper_ball) so we just attach it to the outer
1630 // ring here:
1631 cell->set_all_manifold_ids(0);
1632 }
1633@endcode
1634
1635This code then generates the following, much better sequence of meshes:
1636
1637<div class="twocolumn" style="width: 80%">
1638 <div>
1639 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_0_ladutenko.svg"
1640 alt="Initial grid: the Ladutenko grid with one global refinement."
1641 width="300" height="300">
1642 </div>
1643 <div>
1644 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_1_ladutenko.svg"
1645 alt="First adaptively refined Ladutenko grid."
1646 width="300" height="300">
1647 </div>
1648 <div>
1649 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_2_ladutenko.svg"
1650 alt="Second adaptively refined Ladutenko grid."
1651 width="300" height="300">
1652 </div>
1653 <div>
1654 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_3_ladutenko.svg"
1655 alt="Third adaptively refined Ladutenko grid."
1656 width="300" height="300">
1657 </div>
1658 <div>
1659 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_4_ladutenko.svg"
1660 alt="Fourth adaptively refined Ladutenko grid. The cells are clustered
1661 along the inner circle."
1662 width="300" height="300">
1663 </div>
1664 <div>
1665 <img src="https://www.dealii.org/images/steps/developer/step_6_grid_5_ladutenko.svg"
1666 alt="Fifth adaptively refined Ladutenko grid: the cells are clustered
1667 along the inner circle."
1668 width="300" height="300">
1669 </div>
1670</div>
1671
1672Creating good meshes, and in particular making them fit the geometry you
1673want, is a complex topic in itself. You can find much more on this in
1674@ref step_49 "step-49", @ref step_53 "step-53", and @ref step_54 "step-54", among other tutorial programs that cover
1675the issue. @ref step_65 "step-65" shows another, less manual way to achieve a mesh
1676well fit to the problem here.
1677Information on curved domains can also be found in the
1678documentation module on @ref manifold "Manifold descriptions".
1679
1680Why does it make sense to choose a mesh that tracks the internal
1681interface? There are a number of reasons, but the most essential one
1682comes down to what we actually integrate in our bilinear
1683form. Conceptually, we want to integrate the term @f$A_{ij}^K=\int_K
1684a(\mathbf x) \nabla \varphi_i(\mathbf x) \nabla \varphi_j(\mathbf x) ; dx@f$ as the
1685contribution of cell @f$K@f$ to the matrix entry @f$A_{ij}@f$. We can not
1686compute it exactly and have to resort to quadrature. We know that
1687quadrature is accurate if the integrand is smooth. That is because
1688quadrature in essence computes a polynomial approximation to the
1689integrand that coincides with the integrand in the quadrature points,
1690and then computes the volume under this polynomial as an approximation
1691to the volume under the original integrand. This polynomial
1692interpolant is accurate if the integrand is smooth on a cell, but it
1693is usually rather inaccurate if the integrand is discontinuous on a
1694cell.
1695
1696Consequently, it is worthwhile to align cells in such a way that the
1697interfaces across which the coefficient is discontinuous are aligned
1698with cell interfaces. This way, the coefficient is constant on each
1699cell, following which the integrand will be smooth, and its polynomial
1700approximation and the quadrature approximation of the integral will
1701both be accurate. Note that such an alignment is common in many
1702practical cases, so deal.II provides a number of functions (such as
1703@ref GlossMaterialId "material_id") to help manage such a scenario.
1704Refer to @ref step_28 "step-28" and @ref step_46 "step-46" for examples of how material ids can be
1705applied.
1706
1707Finally, let us consider the case of a coefficient that has a smooth
1708and non-uniform distribution in space. We can repeat once again all of
1709the above discussion on the representation of such a function with the
1710quadrature. So, to simulate it accurately there are a few readily
1711available options: you could reduce the cell size, increase the order
1712of the polynomial used in the quadrature formula, select a more
1713appropriate quadrature formula, or perform a combination of these
1714steps. The key is that providing the best fit of the coefficient's
1715spatial dependence with the quadrature polynomial will lead to a more
1716accurate finite element solution of the PDE.
1717
1718As a final note: The discussion in the previous paragraphs shows, we here
1719have a very concrete way of stating what we think of a good mesh -- it should
1720be aligned with the jump in the coefficient. But one could also have asked
1721this kind of question in a more general setting: Given some equation with
1722a smooth solution and smooth coefficients, can we say what a good mesh
1723would look like? This is a question for which the answer is easier to state
1724in intuitive terms than mathematically: A good mesh has cells that all,
1725by and large, look like squares (or cubes, in 3d). A bad mesh would contain
1726cells that are very elongated in some directions or, more generally, for which
1727there are cells that have both short and long edges. There are many ways
1728in which one could assign a numerical quality index to each cell that measures
1729whether the cell is "good" or "bad"; some of these are often chosen because
1730they are cheap and easy to compute, whereas others are based on what enters
1731into proofs of convergence. An example of the former would be the ratio of
1732the longest to the shortest edge of a cell: In the ideal case, that ratio
1733would be one; bad cells have values much larger than one. An example of the
1734latter kind would consider the gradient (the "Jacobian") of the mapping
1735from the reference cell @f$\hat K=[0,1]^d@f$ to the real cell @f$K@f$; this
1736gradient is a matrix, and a quantity that enters into error estimates
1737is the maximum over all points on the reference cell of the ratio of the
1738largest to the smallest eigenvalue of this matrix. It is again not difficult
1739to see that this ratio is constant if the cell @f$K@f$ is an affine image of
1740@f$\hat K@f$, and that it is one for squares and cubes.
1741
1742In practice, it might be interesting to visualize such quality measures.
1743The function GridTools::compute_aspect_ratio_of_cells() provides one
1744way to get this kind of information. Even better, visualization tools
1745such as VisIt often allow you to visualize this sort of information
1746for a variety of measures from within the visualization software
1747itself; in the case of VisIt, just add a "pseudo-color" plot and select
1748one of the mesh quality measures instead of the solution field.
1749
1750
1751<a name="Playingwiththeregularityofthesolution"></a><h4>Playing with the regularity of the solution</h4>
1752
1753
1754From a mathematical perspective, solutions of the Laplace equation
1755@f[
1756 -\Delta u = f
1757@f]
1758on smoothly bounded, convex domains are known to be smooth themselves. The exact degree
1759of smoothness, i.e., the function space in which the solution lives, depends
1760on how smooth exactly the boundary of the domain is, and how smooth the right
1761hand side is. Some regularity of the solution may be lost at the boundary, but
1762one generally has that the solution is twice more differentiable in
1763compact subsets of the domain than the right hand side.
1764If, in particular, the right hand side satisfies @f$f\in C^\infty(\Omega)@f$, then
1765@f$u \in C^\infty(\Omega_i)@f$ where @f$\Omega_i@f$ is any compact subset of @f$\Omega@f$
1766(@f$\Omega@f$ is an open domain, so a compact subset needs to keep a positive distance
1767from @f$\partial\Omega@f$).
1768
1769The situation we chose for the current example is different, however: we look
1770at an equation with a non-constant coefficient @f$a(\mathbf x)@f$:
1771@f[
1772 -\nabla \cdot (a \nabla u) = f.
1773@f]
1774Here, if @f$a@f$ is not smooth, then the solution will not be smooth either,
1775regardless of @f$f@f$. In particular, we expect that wherever @f$a@f$ is discontinuous
1776along a line (or along a plane in 3d),
1777the solution will have a kink. This is easy to see: if for example @f$f@f$
1778is continuous, then @f$f=-\nabla \cdot (a \nabla u)@f$ needs to be
1779continuous. This means that @f$a \nabla u@f$ must be continuously differentiable
1780(not have a kink). Consequently, if @f$a@f$ has a discontinuity, then @f$\nabla u@f$
1781must have an opposite discontinuity so that the two exactly cancel and their
1782product yields a function without a discontinuity. But for @f$\nabla u@f$ to have
1783a discontinuity, @f$u@f$ must have a kink. This is of course exactly what is
1784happening in the current example, and easy to observe in the pictures of the
1785solution.
1786
1787In general, if the coefficient @f$a(\mathbf x)@f$ is discontinuous along a line in 2d,
1788or a plane in 3d, then the solution may have a kink, but the gradient of the
1789solution will not go to infinity. That means, that the solution is at least
1790still in the <a href="https://en.wikipedia.org/wiki/Sobolev_space">Sobolev space</a>
1791@f$W^{1,\infty}@f$ (i.e., roughly speaking, in the
1792space of functions whose derivatives are bounded). On the other hand,
1793we know that in the most
1794extreme cases -- i.e., where the domain has reentrant corners, the
1795right hand side only satisfies @f$f\in H^{-1}@f$, or the coefficient @f$a@f$ is only in
1796@f$L^\infty@f$ -- all we can expect is that @f$u\in H^1@f$ (i.e., the
1797<a
1798href="https://en.wikipedia.org/wiki/Sobolev_space#Sobolev_spaces_with_integer_k">Sobolev
1799space</a> of functions whose derivative is square integrable), a much larger space than
1800@f$W^{1,\infty}@f$. It is not very difficult to create cases where
1801the solution is in a space @f$H^{1+s}@f$ where we can get @f$s@f$ to become as small
1802as we want. Such cases are often used to test adaptive finite element
1803methods because the mesh will have to resolve the singularity that causes
1804the solution to not be in @f$W^{1,\infty}@f$ any more.
1805
1806The typical example one uses for this is called the <i>Kellogg problem</i>
1807(referring to @cite Kel74), which in the commonly used form has a coefficient
1808@f$a(\mathbf x)@f$ that has different values in the four quadrants of the plane
1809(or eight different values in the octants of @f${\mathbb R}^3@f$). The exact degree
1810of regularity (the @f$s@f$ in the index of the Sobolev space above) depends on the
1811values of @f$a(\mathbf x)@f$ coming together at the origin, and by choosing the
1812jumps large enough, the regularity of the solution can be made as close as
1813desired to @f$H^1@f$.
1814
1815To implement something like this, one could replace the coefficient
1816function by the following (shown here only for the 2d case):
1817@code
1818template <int dim>
1819double coefficient (const Point<dim> &p)
1820{
1821 if ((p[0] < 0) && (p[1] < 0)) // lower left quadrant
1822 return 1;
1823 else if ((p[0] >= 0) && (p[1] < 0)) // lower right quadrant
1824 return 10;
1825 else if ((p[0] < 0) && (p[1] >= 0)) // upper left quadrant
1826 return 100;
1827 else if ((p[0] >= 0) && (p[1] >= 0)) // upper right quadrant
1828 return 1000;
1829 else
1830 {
1831 Assert(false, ExcInternalError());
1832 return 0;
1833 }
1834}
1835@endcode
1836(Adding the <code>Assert</code> at the end ensures that either an exception
1837is thrown or that the program aborts if we ever get to that point
1838-- which of course we shouldn't,
1839but this is a good way to insure yourself: we all make mistakes by
1840sometimes not thinking of all cases, for example by checking
1841for <code>p[0]</code> to be less than and greater than zero,
1842rather than greater-or-equal to zero, and thereby forgetting
1843some cases that would otherwise lead to bugs that are awkward
1844to find. The <code>return 0;</code> at the end is only there to
1845avoid compiler warnings that the function does not end in a
1846<code>return</code> statement -- the compiler cannot see that the
1847function would never actually get to that point because of the
1848preceding <code>Assert</code> statement.)
1849
1850By playing with such cases where four or more sectors come
1851together and on which the coefficient has different values, one can
1852construct cases where the solution has singularities at the
1853origin. One can also see how the meshes are refined in such cases.
1854 *
1855 *
1856<a name="PlainProg"></a>
1857<h1> The plain program</h1>
1858@include "step-6.cc"
1859*/
void distribute_local_to_global(const InVector &local_vector, const std::vector< size_type > &local_dof_indices, OutVector &global_vector) const
void attach_dof_handler(const DoFHandlerType &)
void add_data_vector(const VectorType &data, const std::vector< std::string > &names, const DataVectorType type=type_automatic, const std::vector< DataComponentInterpretation::DataComponentInterpretation > &data_component_interpretation=std::vector< DataComponentInterpretation::DataComponentInterpretation >())
virtual void build_patches(const unsigned int n_subdivisions=0)
Definition: data_out.cc:1085
void set_flags(const GridOutFlags::DX &flags)
Definition: grid_out.cc:471
void write_gnuplot(const Triangulation< dim, spacedim > &tria, std::ostream &out, const Mapping< dim, spacedim > *mapping=nullptr) const
Definition: grid_out.cc:4572
Definition: point.h:111
numbers::NumberTraits< Number >::real_type distance(const Point< dim, Number > &p) const
Definition: vector.h:110
Point< 3 > center
Point< 3 > vertices[4]
@ update_values
Shape function values.
@ update_JxW_values
Transformed quadrature weights.
@ update_gradients
Shape function gradients.
@ update_quadrature_points
Transformed quadrature points.
Point< 2 > second
Definition: grid_out.cc:4588
Point< 2 > first
Definition: grid_out.cc:4587
__global__ void set(Number *val, const Number s, const size_type N)
#define Assert(cond, exc)
Definition: exceptions.h:1465
std::string to_string(const T &t)
Definition: patterns.h:2329
void write_vtu(std::ostream &out) const
void loop(ITERATOR begin, typename identity< ITERATOR >::type end, DOFINFO &dinfo, INFOBOX &info, const std::function< void(DOFINFO &, typename INFOBOX::CellInfo &)> &cell_worker, const std::function< void(DOFINFO &, typename INFOBOX::CellInfo &)> &boundary_worker, const std::function< void(DOFINFO &, DOFINFO &, typename INFOBOX::CellInfo &, typename INFOBOX::CellInfo &)> &face_worker, ASSEMBLER &assembler, const LoopControl &lctrl=LoopControl())
Definition: loop.h:439
void initialize(const MatrixType &A, const typename BaseClass::AdditionalData &parameters=typename BaseClass::AdditionalData())
void hyper_ball(Triangulation< dim > &tria, const Point< dim > &center=Point< dim >(), const double radius=1., const bool attach_spherical_manifold_on_boundary_cells=false)
void refine(Triangulation< dim, spacedim > &tria, const Vector< Number > &criteria, const double threshold, const unsigned int max_to_mark=numbers::invalid_unsigned_int)
double volume(const Triangulation< dim, spacedim > &tria, const Mapping< dim, spacedim > &mapping=(ReferenceCells::get_hypercube< dim >() .template get_default_linear_mapping< dim, spacedim >()))
Definition: grid_tools.cc:137
double diameter(const Triangulation< dim, spacedim > &tria)
Definition: grid_tools.cc:81
static const types::blas_int zero
@ matrix
Contents is actually a matrix.
static const char A
@ diagonal
Matrix is diagonal.
@ general
No special properties.
static const char N
static const types::blas_int one
static const char O
void cell_matrix(FullMatrix< double > &M, const FEValuesBase< dim > &fe, const FEValuesBase< dim > &fetest, const ArrayView< const std::vector< double > > &velocity, const double factor=1.)
Definition: advection.h:75
double norm(const FEValuesBase< dim > &fe, const ArrayView< const std::vector< Tensor< 1, dim > > > &Du)
Definition: divergence.h:472
Point< spacedim > point(const gp_Pnt &p, const double tolerance=1e-10)
Definition: utilities.cc:188
SymmetricTensor< 2, dim, Number > e(const Tensor< 2, dim, Number > &F)
SymmetricTensor< 2, dim, Number > d(const Tensor< 2, dim, Number > &F, const Tensor< 2, dim, Number > &dF_dt)
void call(const std::function< RT()> &function, internal::return_value< RT > &ret_val)
VectorType::value_type * end(VectorType &V)
void run(const Iterator &begin, const typename identity< Iterator >::type &end, Worker worker, Copier copier, const ScratchData &sample_scratch_data, const CopyData &sample_copy_data, const unsigned int queue_length, const unsigned int chunk_size)
Definition: work_stream.h:472
void copy(const T *begin, const T *end, U *dest)
int(&) functions(const void *v1, const void *v2)
void assemble(const MeshWorker::DoFInfoBox< dim, DOFINFO > &dinfo, A *assembler)
Definition: loop.h:71
const ::parallel::distributed::Triangulation< dim, spacedim > * triangulation