1085 * sparsity_pattern.copy_from(dsp);
1087 * system_matrix.reinit(sparsity_pattern);
1089 * solution.reinit(dof_handler.n_dofs());
1090 * system_rhs.reinit(dof_handler.n_dofs());
1097 * In the following function, the
matrix and right hand side are
1098 * assembled. As stated in the documentation of the main
class above, it
1099 * does not
do this itself, but rather delegates to the function following
1100 * next, utilizing the
WorkStream concept discussed in @ref threads .
1104 * If you have looked through the @ref threads module, you will have
1105 * seen that assembling in
parallel does not take an incredible
1106 * amount of extra code as long as you diligently describe what the
1107 * scratch and
copy data objects are, and if you define suitable
1108 *
functions for the local assembly and the
copy operation from local
1109 * contributions to global objects. This done, the following will do
1110 * all the heavy lifting to get these operations done on multiple
1111 * threads on as many cores as you have in your system:
1114 * template <
int dim>
1115 * void AdvectionProblem<dim>::assemble_system()
1118 * dof_handler.
end(),
1120 * &AdvectionProblem::local_assemble_system,
1121 * &AdvectionProblem::copy_local_to_global,
1122 * AssemblyScratchData(fe),
1123 * AssemblyCopyData());
1130 * As already mentioned above, we need to have scratch objects for
1131 * the
parallel computation of local contributions. These objects
1133 * we will need to have constructors and
copy constructors that allow us to
1134 * create them. For the cell terms we need the
values
1136 * order to determine the source density and the advection field at
1137 * a given
point, and the weights of the quadrature points times the
1138 *
determinant of the Jacobian at these points. In contrast, for the
1139 * boundary integrals, we don't need the
gradients, but rather the
1140 * normal vectors to the cells. This determines which update flags
1141 * we will have to pass to the constructors of the members of the
1145 * template <
int dim>
1146 * AdvectionProblem<dim>::AssemblyScratchData::AssemblyScratchData(
1149 *
QGauss<dim>(fe.degree + 1),
1152 * , fe_face_values(fe,
1153 *
QGauss<dim - 1>(fe.degree + 1),
1156 * , rhs_values(fe_values.get_quadrature().size())
1157 * , advection_directions(fe_values.get_quadrature().size())
1158 * , face_boundary_values(fe_face_values.get_quadrature().size())
1159 * , face_advection_directions(fe_face_values.get_quadrature().size())
1164 * template <
int dim>
1165 * AdvectionProblem<dim>::AssemblyScratchData::AssemblyScratchData(
1166 * const AssemblyScratchData &scratch_data)
1167 * : fe_values(scratch_data.fe_values.get_fe(),
1168 * scratch_data.fe_values.get_quadrature(),
1171 * , fe_face_values(scratch_data.fe_face_values.get_fe(),
1172 * scratch_data.fe_face_values.get_quadrature(),
1175 * , rhs_values(scratch_data.rhs_values.size())
1176 * , advection_directions(scratch_data.advection_directions.size())
1177 * , face_boundary_values(scratch_data.face_boundary_values.size())
1178 * , face_advection_directions(scratch_data.face_advection_directions.size())
1185 * Now, this is the function that does the actual work. It is not very
1186 * different from the <code>assemble_system</code>
functions of previous
1187 * example programs, so we will again only comment on the differences. The
1188 * mathematical stuff closely follows what we have said in the introduction.
1192 * There are a number of points worth mentioning here, though. The
1194 * objects into the ScratchData object. We have done so because the
1195 * alternative would have been to simply create one every time we
1196 * get into this function -- i.
e., on every cell. It now turns out
1197 * that the
FEValues classes were written with the explicit goal of
1198 * moving everything that remains the same from cell to cell into
1199 * the construction of the object, and only do as little work as
1201 * cell. What this means is that it would be very expensive to
1202 * create a new object of this kind in this function as we would
1203 * have to do it for every cell -- exactly the thing we wanted to
1204 * avoid with the
FEValues class. Instead, what we do is create it
1205 * only once (or a small number of times) in the scratch objects and
1206 * then re-use it as often as we can.
1210 * This begs the question of whether there are other objects we
1211 * create in this function whose creation is expensive compared to
1212 * its use. Indeed, at the top of the function, we declare all sorts
1213 * of objects. The <code>AdvectionField</code>,
1214 * <code>RightHandSide</code> and <code>BoundaryValues</code> do not
1215 * cost much to create, so there is no harm here. However,
1216 * allocating memory in creating the <code>rhs_values</code> and
1217 * similar variables below typically costs a significant amount of
1218 * time, compared to just accessing the (temporary)
values we store
1219 * in them. Consequently, these would be candidates for moving into
1220 * the <code>AssemblyScratchData</code> class. We will leave this as
1224 * template <
int dim>
1225 * void AdvectionProblem<dim>::local_assemble_system(
1226 * const typename
DoFHandler<dim>::active_cell_iterator &cell,
1227 * AssemblyScratchData & scratch_data,
1228 * AssemblyCopyData & copy_data)
1232 * We define some abbreviations to avoid unnecessarily long lines:
1235 * const unsigned
int dofs_per_cell = fe.n_dofs_per_cell();
1236 *
const unsigned int n_q_points =
1237 * scratch_data.fe_values.get_quadrature().size();
1238 *
const unsigned int n_face_q_points =
1239 * scratch_data.fe_face_values.get_quadrature().size();
1243 * We declare cell
matrix and cell right hand side...
1246 * copy_data.cell_matrix.reinit(dofs_per_cell, dofs_per_cell);
1247 * copy_data.cell_rhs.reinit(dofs_per_cell);
1251 * ... an array to hold the global indices of the degrees of freedom of
1252 * the cell on which we are presently working...
1255 * copy_data.local_dof_indices.resize(dofs_per_cell);
1259 * ... then initialize the <code>
FEValues</code>
object...
1262 * scratch_data.fe_values.
reinit(cell);
1266 * ... obtain the
values of right hand side and advection directions
1267 * at the quadrature points...
1270 * scratch_data.advection_field.value_list(
1271 * scratch_data.fe_values.get_quadrature_points(),
1272 * scratch_data.advection_directions);
1273 * scratch_data.right_hand_side.value_list(
1274 * scratch_data.fe_values.get_quadrature_points(), scratch_data.rhs_values);
1278 * ...
set the
value of the streamline diffusion parameter as
1279 * described in the introduction...
1282 *
const double delta = 0.1 * cell->diameter();
1286 * ... and
assemble the local contributions to the system
matrix and
1287 * right hand side as also discussed above:
1290 *
for (
unsigned int q_point = 0; q_point < n_q_points; ++q_point)
1291 *
for (
unsigned int i = 0; i < dofs_per_cell; ++i)
1295 * Alias the AssemblyScratchData
object to keep the lines from
1299 *
const auto &sd = scratch_data;
1300 *
for (
unsigned int j = 0; j < dofs_per_cell; ++j)
1301 * copy_data.cell_matrix(i, j) +=
1302 * ((sd.fe_values.shape_value(i, q_point) +
1303 * delta * (sd.advection_directions[q_point] *
1304 * sd.fe_values.shape_grad(i, q_point))) *
1305 * sd.advection_directions[q_point] *
1306 * sd.fe_values.shape_grad(j, q_point)) *
1307 * sd.fe_values.JxW(q_point);
1309 * copy_data.cell_rhs(i) +=
1310 * (sd.fe_values.shape_value(i, q_point) +
1311 * delta * (sd.advection_directions[q_point] *
1312 * sd.fe_values.shape_grad(i, q_point))) *
1313 * sd.rhs_values[q_point] *
1314 * sd.fe_values.JxW(q_point);
1319 * Besides the cell terms which we have built up now, the bilinear
1320 * form of the present problem also contains terms on the boundary of
1321 * the domain. Therefore, we have to
check whether any of the faces of
1322 *
this cell are on the boundary of the domain, and
if so
assemble the
1323 * contributions of
this face as well. Of course, the bilinear form
1324 * only contains contributions from the <code>inflow</code> part of
1325 * the boundary, but to find out whether a certain part of a face of
1326 * the present cell is part of the inflow boundary, we have to have
1327 * information on the exact location of the quadrature points and on
1328 * the direction of flow at
this point; we obtain
this information
1330 * whether a quadrature
point is on the inflow boundary.
1333 *
for (
const auto &face : cell->face_iterators())
1334 *
if (face->at_boundary())
1338 * Ok,
this face of the present cell is on the boundary of the
1339 * domain. Just as
for the usual
FEValues object which we have
1340 * used in previous examples and also above, we have to
1341 * reinitialize the
FEFaceValues object for the present face:
1344 * scratch_data.fe_face_values.
reinit(cell, face);
1348 * For the quadrature points at hand, we ask
for the
values of
1349 * the inflow function and
for the direction of flow:
1352 * scratch_data.boundary_values.value_list(
1353 * scratch_data.fe_face_values.get_quadrature_points(),
1354 * scratch_data.face_boundary_values);
1355 * scratch_data.advection_field.value_list(
1356 * scratch_data.fe_face_values.get_quadrature_points(),
1357 * scratch_data.face_advection_directions);
1361 * Now
loop over all quadrature points and see whether
this face is on
1362 * the inflow or outflow part of the boundary. The normal
1363 * vector points out of the cell: since the face is at
1364 * the boundary, the normal vector points out of the domain,
1365 * so
if the advection direction points into the domain, its
1366 *
scalar product with the normal vector must be
negative (to see why
1367 *
this is
true, consider the scalar product definition that uses a
1371 * for (unsigned
int q_point = 0; q_point < n_face_q_points; ++q_point)
1372 *
if (scratch_data.fe_face_values.normal_vector(q_point) *
1373 * scratch_data.face_advection_directions[q_point] <
1377 * If the face is part of the inflow boundary, then compute the
1378 * contributions of
this face to the global matrix and right
1379 * hand side,
using the values obtained from the
1380 *
FEFaceValues object and the formulae discussed in the
1384 *
for (
unsigned int i = 0; i < dofs_per_cell; ++i)
1386 *
for (
unsigned int j = 0; j < dofs_per_cell; ++j)
1387 * copy_data.cell_matrix(i, j) -=
1388 * (scratch_data.face_advection_directions[q_point] *
1389 * scratch_data.fe_face_values.normal_vector(q_point) *
1390 * scratch_data.fe_face_values.shape_value(i, q_point) *
1391 * scratch_data.fe_face_values.shape_value(j, q_point) *
1392 * scratch_data.fe_face_values.JxW(q_point));
1394 * copy_data.cell_rhs(i) -=
1395 * (scratch_data.face_advection_directions[q_point] *
1396 * scratch_data.fe_face_values.normal_vector(q_point) *
1397 * scratch_data.face_boundary_values[q_point] *
1398 * scratch_data.fe_face_values.shape_value(i, q_point) *
1399 * scratch_data.fe_face_values.JxW(q_point));
1405 * The
final piece of information the
copy routine needs is the global
1406 * indices of the degrees of freedom on
this cell, so we
end by writing
1407 * them to the local array:
1410 * cell->get_dof_indices(copy_data.local_dof_indices);
1417 * The
second function we needed to write was the one that copies
1418 * the local contributions the previous function computed (and
1419 * put into the AssemblyCopyData
object) into the global
matrix and right
1420 * hand side vector objects. This is essentially what we
always had
1421 * as the last block of code when assembling something on every
1422 * cell. The following should therefore be pretty obvious:
1425 *
template <
int dim>
1427 * AdvectionProblem<dim>::copy_local_to_global(
const AssemblyCopyData ©_data)
1429 * hanging_node_constraints.distribute_local_to_global(
1430 * copy_data.cell_matrix,
1431 * copy_data.cell_rhs,
1432 * copy_data.local_dof_indices,
1439 * Here comes the linear solver routine. As the system is no longer
1441 * use the Conjugate Gradient method anymore. Rather, we use a solver that
1442 * is more
general and does not rely on any special properties of the
1443 *
matrix: the GMRES method. GMRES, like the conjugate
gradient method,
1444 *
requires a decent preconditioner: we use a Jacobi preconditioner here,
1445 * which works well enough
for this problem.
1448 *
template <
int dim>
1449 *
void AdvectionProblem<dim>::solve()
1452 * system_rhs.size() / 10),
1453 * 1e-10 * system_rhs.l2_norm());
1456 * preconditioner.
initialize(system_matrix, 1.0);
1457 * solver.solve(system_matrix, solution, system_rhs, preconditioner);
1461 * system_matrix.vmult(residual, solution);
1462 * residual -= system_rhs;
1463 * std::cout <<
" Iterations required for convergence: "
1464 * << solver_control.last_step() <<
'\n'
1465 * <<
" Max norm of residual: "
1466 * << residual.linfty_norm() <<
'\n';
1468 * hanging_node_constraints.distribute(solution);
1473 * The following function refines the grid according to the quantity
1474 * described in the introduction. The respective computations are made in
1475 * the class <code>GradientEstimation</code>.
1478 *
template <
int dim>
1479 *
void AdvectionProblem<dim>::refine_grid()
1483 * GradientEstimation::estimate(dof_handler,
1485 * estimated_error_per_cell);
1488 * estimated_error_per_cell,
1497 * This function is similar to the one in step 6, but since we use a higher
1498 * degree finite element we save the solution in a different
1499 * way. Visualization programs like VisIt and Paraview typically only
1500 * understand data that is associated with nodes: they cannot plot
1501 * fifth-degree basis
functions, which results in a very inaccurate picture
1502 * of the solution we computed. To get around
this we save multiple
1503 * <em>patches</em> per cell: in 2D we save 64 bilinear `cells
' to the VTU
1504 * file for each cell, and in 3D we save 512. The end result is that the
1505 * visualization program will use a piecewise linear interpolation of the
1506 * cubic basis functions: this captures the solution detail and, with most
1507 * screen resolutions, looks smooth. We save the grid in a separate step
1508 * with no extra patches so that we have a visual representation of the cell
1513 * Version 9.1 of deal.II gained the ability to write higher degree
1514 * polynomials (i.e., write piecewise bicubic visualization data for our
1515 * piecewise bicubic solution) VTK and VTU output: however, not all recent
1516 * versions of ParaView and VisIt (as of 2018) can read this format, so we
1517 * use the older, more general (but less efficient) approach here.
1520 * template <int dim>
1521 * void AdvectionProblem<dim>::output_results(const unsigned int cycle) const
1525 * std::ofstream output("grid-" + std::to_string(cycle) + ".vtu");
1526 * grid_out.write_vtu(triangulation, output);
1530 * DataOut<dim> data_out;
1531 * data_out.attach_dof_handler(dof_handler);
1532 * data_out.add_data_vector(solution, "solution");
1533 * data_out.build_patches(8);
1537 * VTU output can be expensive, both to compute and to write to
1538 * disk. Here we ask ZLib, a compression library, to compress the data
1539 * in a way that maximizes throughput.
1542 * DataOutBase::VtkFlags vtk_flags;
1543 * vtk_flags.compression_level =
1544 * DataOutBase::VtkFlags::ZlibCompressionLevel::best_speed;
1545 * data_out.set_flags(vtk_flags);
1547 * std::ofstream output("solution-" + std::to_string(cycle) + ".vtu");
1548 * data_out.write_vtu(output);
1555 * ... as is the main loop (setup -- solve -- refine), aside from the number
1556 * of cycles and the initial grid:
1559 * template <int dim>
1560 * void AdvectionProblem<dim>::run()
1562 * for (unsigned int cycle = 0; cycle < 10; ++cycle)
1564 * std::cout << "Cycle " << cycle << ':
' << std::endl;
1568 * GridGenerator::hyper_cube(triangulation, -1, 1);
1569 * triangulation.refine_global(3);
1577 * std::cout << " Number of active cells: "
1578 * << triangulation.n_active_cells() << std::endl;
1582 * std::cout << " Number of degrees of freedom: "
1583 * << dof_handler.n_dofs() << std::endl;
1585 * assemble_system();
1587 * output_results(cycle);
1596 * <a name="GradientEstimationclassimplementation"></a>
1597 * <h3>GradientEstimation class implementation</h3>
1601 * Now for the implementation of the <code>GradientEstimation</code> class.
1602 * Let us start by defining constructors for the
1603 * <code>EstimateScratchData</code> class used by the
1604 * <code>estimate_cell()</code> function:
1607 * template <int dim>
1608 * GradientEstimation::EstimateScratchData<dim>::EstimateScratchData(
1609 * const FiniteElement<dim> &fe,
1610 * const Vector<double> & solution,
1611 * Vector<float> & error_per_cell)
1612 * : fe_midpoint_value(fe,
1614 * update_values | update_quadrature_points)
1615 * , solution(solution)
1616 * , error_per_cell(error_per_cell)
1617 * , cell_midpoint_value(1)
1618 * , neighbor_midpoint_value(1)
1622 * We allocate a vector to hold iterators to all active neighbors of
1623 * a cell. We reserve the maximal number of active neighbors in order to
1624 * avoid later reallocations. Note how this maximal number of active
1625 * neighbors is computed here.
1628 * active_neighbors.reserve(GeometryInfo<dim>::faces_per_cell *
1629 * GeometryInfo<dim>::max_children_per_face);
1633 * template <int dim>
1634 * GradientEstimation::EstimateScratchData<dim>::EstimateScratchData(
1635 * const EstimateScratchData &scratch_data)
1636 * : fe_midpoint_value(scratch_data.fe_midpoint_value.get_fe(),
1637 * scratch_data.fe_midpoint_value.get_quadrature(),
1638 * update_values | update_quadrature_points)
1639 * , solution(scratch_data.solution)
1640 * , error_per_cell(scratch_data.error_per_cell)
1641 * , cell_midpoint_value(1)
1642 * , neighbor_midpoint_value(1)
1648 * Next comes the implementation of the <code>GradientEstimation</code>
1649 * class. The first function does not much except for delegating work to the
1650 * other function, but there is a bit of setup at the top.
1654 * Before starting with the work, we check that the vector into
1655 * which the results are written has the right size. Programming
1656 * mistakes in which one forgets to size arguments correctly at the
1657 * calling site are quite common. Because the resulting damage from
1658 * not catching such errors is often subtle (e.g., corruption of
1659 * data somewhere in memory, or non-reproducible results), it is
1660 * well worth the effort to check for such things.
1663 * template <int dim>
1664 * void GradientEstimation::estimate(const DoFHandler<dim> &dof_handler,
1665 * const Vector<double> & solution,
1666 * Vector<float> & error_per_cell)
1669 * error_per_cell.size() == dof_handler.get_triangulation().n_active_cells(),
1670 * ExcInvalidVectorLength(error_per_cell.size(),
1671 * dof_handler.get_triangulation().n_active_cells()));
1673 * WorkStream::run(dof_handler.begin_active(),
1674 * dof_handler.end(),
1675 * &GradientEstimation::template estimate_cell<dim>,
1676 * std::function<void(const EstimateCopyData &)>(),
1677 * EstimateScratchData<dim>(dof_handler.get_fe(),
1680 * EstimateCopyData());
1686 * Here comes the function that estimates the local error by computing the
1687 * finite difference approximation of the gradient. The function first
1688 * computes the list of active neighbors of the present cell and then
1689 * computes the quantities described in the introduction for each of
1690 * the neighbors. The reason for this order is that it is not a one-liner
1691 * to find a given neighbor with locally refined meshes. In principle, an
1692 * optimized implementation would find neighbors and the quantities
1693 * depending on them in one step, rather than first building a list of
1694 * neighbors and in a second step their contributions but we will gladly
1695 * leave this as an exercise. As discussed before, the worker function
1696 * passed to WorkStream::run works on "scratch" objects that keep all
1697 * temporary objects. This way, we do not need to create and initialize
1698 * objects that are expensive to initialize within the function that does
1699 * the work every time it is called for a given cell. Such an argument is
1700 * passed as the second argument. The third argument would be a "copy-data"
1701 * object (see @ref threads for more information) but we do not actually use
1702 * any of these here. Since WorkStream::run() insists on passing three
1703 * arguments, we declare this function with three arguments, but simply
1704 * ignore the last one.
1708 * (This is unsatisfactory from an aesthetic perspective. It can be avoided
1709 * by using an anonymous (lambda) function. If you allow, let us here show
1710 * how. First, assume that we had declared this function to only take two
1711 * arguments by omitting the unused last one. Now, WorkStream::run still
1712 * wants to call this function with three arguments, so we need to find a
1713 * way to "forget" the third argument in the call. Simply passing
1714 * WorkStream::run the pointer to the function as we do above will not do
1715 * this -- the compiler will complain that a function declared to have two
1716 * arguments is called with three arguments. However, we can do this by
1717 * passing the following as the third argument to WorkStream::run():
1718 * <div class=CodeFragmentInTutorialComment>
1720 * [](const typename DoFHandler<dim>::active_cell_iterator &cell,
1721 * EstimateScratchData<dim> & scratch_data,
1722 * EstimateCopyData &)
1724 * GradientEstimation::estimate_cell<dim>(cell, scratch_data);
1728 * This is not much better than the solution implemented below: either the
1729 * routine itself must take three arguments or it must be wrapped by
1730 * something that takes three arguments. We don't use
this since adding the
1731 * unused argument at the beginning is simpler.
1735 * Now
for the details:
1738 *
template <
int dim>
1739 *
void GradientEstimation::estimate_cell(
1741 * EstimateScratchData<dim> & scratch_data,
1742 *
const EstimateCopyData &)
1746 * We need space
for the tensor <code>Y</code>, which is the
sum of
1747 * outer products of the y-vectors.
1754 * First initialize the <code>
FEValues</code> object, as well as the
1755 * <code>Y</code> tensor:
1758 * scratch_data.fe_midpoint_value.
reinit(cell);
1762 * Now, before we go on, we
first compute a list of all active neighbors
1763 * of the present cell. We
do so by
first looping over all faces and see
1764 * whether the neighbor there is active, which would be the
case if it
1765 * is on the same
level as the present cell or one
level coarser (note
1766 * that a neighbor can only be once coarser than the present cell, as
1767 * we only allow a maximal difference of one refinement over a face in
1768 * deal.II). Alternatively, the neighbor could be on the same
level
1769 * and be further refined; then we have to find which of its children
1770 * are next to the present cell and select these (note that
if a child
1771 * of a neighbor of an active cell that is next to
this active cell,
1772 * needs necessarily be active itself, due to the one-refinement rule
1777 * Things are slightly different in one space dimension, as there the
1778 * one-refinement rule does not exist: neighboring active cells may
1779 * differ in as many refinement levels as they like. In
this case, the
1780 * computation becomes a little more difficult, but we will explain
1785 * Before starting the
loop over all neighbors of the present cell, we
1786 * have to clear the array storing the iterators to the active
1787 * neighbors, of course.
1790 * scratch_data.active_neighbors.clear();
1791 *
for (
const auto face_n : cell->face_indices())
1792 *
if (!cell->at_boundary(face_n))
1796 * First define an abbreviation
for the iterator to the face and
1800 *
const auto face = cell->face(face_n);
1801 *
const auto neighbor = cell->neighbor(face_n);
1805 * Then
check whether the neighbor is active. If it is, then it
1806 * is on the same
level or one
level coarser (
if we are not in
1807 * 1D), and we are interested in it in any
case.
1810 *
if (neighbor->is_active())
1811 * scratch_data.active_neighbors.push_back(neighbor);
1816 * If the neighbor is not active, then
check its children.
1823 * To find the child of the neighbor which bounds to the
1824 * present cell, successively go to its right child
if
1825 * we are left of the present cell (n==0), or go to the
1826 * left child
if we are on the right (n==1), until we
1827 * find an active cell.
1830 *
auto neighbor_child = neighbor;
1831 *
while (neighbor_child->has_children())
1832 * neighbor_child = neighbor_child->child(face_n == 0 ? 1 : 0);
1836 * As
this used some non-trivial geometrical intuition,
1837 * we might want to
check whether we did it right,
1838 * i.e.,
check whether the neighbor of the cell we found
1839 * is indeed the cell we are presently working
1840 * on. Checks like
this are often useful and have
1841 * frequently uncovered errors both in algorithms like
1842 * the line above (where it is simple to involuntarily
1843 * exchange <code>n==1</code> for <code>n==0</code> or
1844 * the like) and in the library (the assumptions
1845 * underlying the algorithm above could either be wrong,
1846 * wrongly documented, or are violated due to an error
1847 * in the library). One could in principle remove such
1848 * checks after the program works
for some time, but it
1849 * might be a good things to leave it in anyway to
check
1850 *
for changes in the library or in the algorithm above.
1854 * Note that
if this check fails, then
this is certainly
1855 * an error that is irrecoverable and probably qualifies
1856 * as an
internal error. We therefore use a predefined
1857 * exception
class to throw here.
1860 *
Assert(neighbor_child->neighbor(face_n == 0 ? 1 : 0) == cell,
1861 * ExcInternalError());
1865 * If the
check succeeded, we push the active neighbor
1866 * we just found to the stack we keep:
1869 * scratch_data.active_neighbors.push_back(neighbor_child);
1874 * If we are not in 1
d, we collect all neighbor children
1875 * `behind
' the subfaces of the current face and move on:
1878 * for (unsigned int subface_n = 0; subface_n < face->n_children();
1880 * scratch_data.active_neighbors.push_back(
1881 * cell->neighbor_child_on_subface(face_n, subface_n));
1887 * OK, now that we have all the neighbors, lets start the computation
1888 * on each of them. First we do some preliminaries: find out about the
1889 * center of the present cell and the solution at this point. The
1890 * latter is obtained as a vector of function values at the quadrature
1891 * points, of which there are only one, of course. Likewise, the
1892 * position of the center is the position of the first (and only)
1893 * quadrature point in real space.
1896 * const Point<dim> this_center =
1897 * scratch_data.fe_midpoint_value.quadrature_point(0);
1899 * scratch_data.fe_midpoint_value.get_function_values(
1900 * scratch_data.solution, scratch_data.cell_midpoint_value);
1904 * Now loop over all active neighbors and collect the data we
1908 * Tensor<1, dim> projected_gradient;
1909 * for (const auto &neighbor : scratch_data.active_neighbors)
1913 * Then get the center of the neighbor cell and the value of the
1914 * finite element function at that point. Note that for this
1915 * information we have to reinitialize the <code>FEValues</code>
1916 * object for the neighbor cell.
1919 * scratch_data.fe_midpoint_value.reinit(neighbor);
1920 * const Point<dim> neighbor_center =
1921 * scratch_data.fe_midpoint_value.quadrature_point(0);
1923 * scratch_data.fe_midpoint_value.get_function_values(
1924 * scratch_data.solution, scratch_data.neighbor_midpoint_value);
1928 * Compute the vector <code>y</code> connecting the centers of the
1929 * two cells. Note that as opposed to the introduction, we denote
1930 * by <code>y</code> the normalized difference vector, as this is
1931 * the quantity used everywhere in the computations.
1934 * Tensor<1, dim> y = neighbor_center - this_center;
1935 * const double distance = y.norm();
1940 * Then add up the contribution of this cell to the Y matrix...
1943 * for (unsigned int i = 0; i < dim; ++i)
1944 * for (unsigned int j = 0; j < dim; ++j)
1945 * Y[i][j] += y[i] * y[j];
1949 * ... and update the sum of difference quotients:
1952 * projected_gradient += (scratch_data.neighbor_midpoint_value[0] -
1953 * scratch_data.cell_midpoint_value[0]) /
1959 * If now, after collecting all the information from the neighbors, we
1960 * can determine an approximation of the gradient for the present
1961 * cell, then we need to have passed over vectors <code>y</code> which
1962 * span the whole space, otherwise we would not have all components of
1963 * the gradient. This is indicated by the invertibility of the matrix.
1967 * If the matrix is not invertible, then the present
1968 * cell had an insufficient number of active neighbors. In contrast to
1969 * all previous cases (where we raised exceptions) this is, however,
1970 * not a programming error: it is a runtime error that can happen in
1971 * optimized mode even if it ran well in debug mode, so it is
1972 * reasonable to try to catch this error also in optimized mode. For
1973 * this case, there is the <code>AssertThrow</code> macro: it checks
1974 * the condition like the <code>Assert</code> macro, but not only in
1975 * debug mode; it then outputs an error message, but instead of
1976 * aborting the program as in the case of the <code>Assert</code>
1977 * macro, the exception is thrown using the <code>throw</code> command
1978 * of C++. This way, one has the possibility to catch this error and
1979 * take reasonable counter actions. One such measure would be to
1980 * refine the grid globally, as the case of insufficient directions
1981 * can not occur if every cell of the initial grid has been refined at
1985 * AssertThrow(determinant(Y) != 0, ExcInsufficientDirections());
1989 * If, on the other hand, the matrix is invertible, then invert it,
1990 * multiply the other quantity with it, and compute the estimated error
1991 * using this quantity and the correct powers of the mesh width:
1994 * const Tensor<2, dim> Y_inverse = invert(Y);
1996 * const Tensor<1, dim> gradient = Y_inverse * projected_gradient;
2000 * The last part of this function is the one where we write into
2001 * the element of the output vector what we have just
2002 * computed. The address of this vector has been stored in the
2003 * scratch data object, and all we have to do is know how to get
2004 * at the correct element inside this vector -- but we can ask the
2005 * cell we're on the how-manyth active cell it is
for this:
2008 * scratch_data.error_per_cell(cell->active_cell_index()) =
2017 * <a name=
"Mainfunction"></a>
2018 * <h3>Main function</h3>
2022 * The <code>main</code> function is similar to the previous examples. The
2024 * number of threads (see the documentation module @ref threads
2025 *
"Parallel computing with multiple processors accessing shared memory"
2026 *
for more information). The number of threads used is the minimum of the
2027 * environment variable DEAL_II_NUM_THREADS and the parameter of
2028 * <code>set_thread_limit</code>. If no
value is given to
2029 * <code>set_thread_limit</code>, the
default value from the Intel Threading
2030 * Building Blocks (TBB) library is used. If the
call to
2031 * <code>set_thread_limit</code> is omitted, the number of threads will be
2032 * chosen by TBB independently of DEAL_II_NUM_THREADS.
2037 *
using namespace dealii;
2042 * Step9::AdvectionProblem<2> advection_problem_2d;
2043 * advection_problem_2d.run();
2045 *
catch (std::exception &exc)
2047 * std::cerr << std::endl
2049 * <<
"----------------------------------------------------"
2051 * std::cerr <<
"Exception on processing: " << std::endl
2052 * << exc.what() << std::endl
2053 * <<
"Aborting!" << std::endl
2054 * <<
"----------------------------------------------------"
2060 * std::cerr << std::endl
2062 * <<
"----------------------------------------------------"
2064 * std::cerr <<
"Unknown exception!" << std::endl
2065 * <<
"Aborting!" << std::endl
2066 * <<
"----------------------------------------------------"
2074<a name=
"Results"></a><h1>Results</h1>
2078The results of
this program are not particularly spectacular. They
2079consist of the console output, some grid files, and the solution on
2080each of these grids. First
for the console output:
2083 Number of active cells: 64
2084 Number of degrees of freedom: 1681
2085 Iterations required
for convergence: 298
2086 Max
norm of residual: 3.60316e-12
2088 Number of active cells: 124
2089 Number of degrees of freedom: 3537
2090 Iterations required
for convergence: 415
2091 Max
norm of residual: 3.70682e-12
2093 Number of active cells: 247
2094 Number of degrees of freedom: 6734
2095 Iterations required
for convergence: 543
2096 Max
norm of residual: 7.19716e-13
2098 Number of active cells: 502
2099 Number of degrees of freedom: 14105
2100 Iterations required
for convergence: 666
2101 Max
norm of residual: 3.45628e-13
2103 Number of active cells: 1003
2104 Number of degrees of freedom: 27462
2105 Iterations required
for convergence: 1064
2106 Max
norm of residual: 1.86495e-13
2108 Number of active cells: 1993
2109 Number of degrees of freedom: 55044
2110 Iterations required
for convergence: 1251
2111 Max
norm of residual: 1.28765e-13
2113 Number of active cells: 3985
2114 Number of degrees of freedom: 108492
2115 Iterations required
for convergence: 2035
2116 Max
norm of residual: 6.78085e-14
2118 Number of active cells: 7747
2119 Number of degrees of freedom: 210612
2120 Iterations required
for convergence: 2187
2121 Max
norm of residual: 2.61457e-14
2123 Number of active cells: 15067
2124 Number of degrees of freedom: 406907
2125 Iterations required
for convergence: 3079
2126 Max
norm of residual: 2.9932e-14
2128 Number of active cells: 29341
2129 Number of degrees of freedom: 780591
2130 Iterations required
for convergence: 3913
2131 Max
norm of residual: 8.15689e-15
2134Quite a number of cells are used on the finest
level to resolve the features of
2135the solution. Here are the fourth and tenth grids:
2136<div
class=
"twocolumn" style=
"width: 80%">
2138 <img src=
"https://www.dealii.org/images/steps/developer/step-9-grid-3.png"
2139 alt=
"Fourth grid in the refinement cycle, showing some adaptivity to features."
2140 width=
"400" height=
"400">
2143 <img src=
"https://www.dealii.org/images/steps/developer/step-9-grid-9.png"
2144 alt=
"Tenth grid in the refinement cycle, showing that the waves are fully captured."
2145 width=
"400" height=
"400">
2148and the fourth and tenth solutions:
2149<div
class=
"twocolumn" style=
"width: 80%">
2151 <img src=
"https://www.dealii.org/images/steps/developer/step-9-solution-3.png"
2152 alt=
"Fourth solution, showing that we resolve most features but some
2153 are sill unresolved and appear blury."
2154 width=
"400" height=
"400">
2157 <img src=
"https://www.dealii.org/images/steps/developer/step-9-solution-9.png"
2158 alt=
"Tenth solution, showing a fully resolved flow."
2159 width=
"400" height=
"400">
2162and both the grid and solution zoomed in:
2163<div
class=
"twocolumn" style=
"width: 80%">
2165 <img src=
"https://www.dealii.org/images/steps/developer/step-9-solution-3-zoom.png"
2166 alt=
"Detail of the fourth solution, showing that we resolve most
2167 features but some are sill unresolved and appear blury. In particular,
2168 the larger cells need to be refined."
2169 width=
"400" height=
"400">
2172 <img src=
"https://www.dealii.org/images/steps/developer/step-9-solution-9-zoom.png"
2173 alt=
"Detail of the tenth solution, showing that we needed a lot more
2174 cells than were present in the fourth solution."
2175 width=
"400" height=
"400">
2179The solution is created by that part that is transported along the wiggly
2180advection field from the left and lower boundaries to the top right, and the
2181part that is created by the source in the lower left corner, and the results of
2182which are also transported along. The grid shown above is well-adapted to
2183resolve these features. The comparison between plots shows that, even though we
2184are
using a high-order approximation, we still need adaptive mesh refinement to
2185fully resolve the wiggles.
2188<a name=
"PlainProg"></a>
2189<h1> The plain program</h1>
void reinit(const TriaIterator< DoFCellAccessor< dim, spacedim, level_dof_access > > &cell, const unsigned int face_no)
void reinit(const TriaIterator< DoFCellAccessor< dim, spacedim, level_dof_access > > &cell)
static void set_thread_limit(const unsigned int max_threads=numbers::invalid_unsigned_int)
@ update_values
Shape function values.
@ update_normal_vectors
Normal vectors.
@ update_JxW_values
Transformed quadrature weights.
@ update_gradients
Shape function gradients.
@ update_quadrature_points
Transformed quadrature points.
__global__ void set(Number *val, const Number s, const size_type N)
#define Assert(cond, exc)
typename ActiveSelector::active_cell_iterator active_cell_iterator
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())
void initialize(const MatrixType &A, const AdditionalData ¶meters=AdditionalData())
void refine_and_coarsen_fixed_number(Triangulation< dim, spacedim > &triangulation, const Vector< Number > &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())
@ matrix
Contents is actually a matrix.
@ symmetric
Matrix is symmetric.
@ general
No special properties.
double norm(const FEValuesBase< dim > &fe, const ArrayView< const std::vector< Tensor< 1, dim > > > &Du)
Point< spacedim > point(const gp_Pnt &p, const double tolerance=1e-10)
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)
T sum(const T &t, const MPI_Comm &mpi_communicator)
void run(const std::vector< std::vector< Iterator > > &colored_iterators, Worker worker, Copier copier, const ScratchData &sample_scratch_data, const CopyData &sample_copy_data, const unsigned int queue_length=2 *MultithreadInfo::n_threads(), const unsigned int chunk_size=8)
bool check(const ConstraintKinds kind_in, const unsigned int dim)
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)
::VectorizedArray< Number, width > pow(const ::VectorizedArray< Number, width > &, const Number p)
const ::parallel::distributed::Triangulation< dim, spacedim > * triangulation
constexpr Number determinant(const SymmetricTensor< 2, dim, Number > &)