step-62.h

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) const
 *   {
 * @endcode
 * 
 * The speed of sound is defined by
 * @f[
 * c = \frac{K_e}{\rho}
 * @f]
 * where @f$K_e@f$ is the effective elastic constant and @f$\rho@f$ the density.
 * Here we consider the case in which the waveguide width is much smaller
 * than the wavelength. In this case it can be shown that for the two
 * dimensional case
 * @f[
 * K_e = 4\mu\frac{\lambda +\mu}{\lambda+2\mu}
 * @f]
 * and for the three dimensional case @f$K_e@f$ is equal to the Young's modulus.
 * @f[
 * K_e = \mu\frac{3\lambda +2\mu}{\lambda+\mu}
 * @f]
 * 
 * @code
 *     double elastic_constant;
 *     if (dim == 2)
 *       {
 *         elastic_constant = 4 * mu * (lambda + mu) / (lambda + 2 * mu);
 *       }
 *     else if (dim == 3)
 *       {
 *         elastic_constant = mu * (3 * lambda + 2 * mu) / (lambda + mu);
 *       }
 *     else
 *       {
 *         Assert(false, ExcInternalError());
 *       }
 *     const double material_a_speed_of_sound =
 *       std::sqrt(elastic_constant / material_a_rho);
 *     const double material_a_wavelength =
 *       material_a_speed_of_sound / cavity_resonance_frequency;
 *     const double material_b_speed_of_sound =
 *       std::sqrt(elastic_constant / material_b_rho);
 *     const double material_b_wavelength =
 *       material_b_speed_of_sound / cavity_resonance_frequency;
 * 
 * @endcode
 * 
 * The density @f$\rho@f$ takes the following form
 * <img alt="Phononic superlattice cavity"
 * src="https://www.dealii.org/images/steps/developer/step-62.04.svg"
 * height="200" />
 * where the brown color represents material_a and the green color
 * represents material_b.
 * 
 * @code
 *     for (unsigned int idx = 0; idx < nb_mirror_pairs; ++idx)
 *       {
 *         const double layer_transition_center =
 *           material_a_wavelength / 2 +
 *           idx * (material_b_wavelength / 4 + material_a_wavelength / 4);
 *         if (std::abs(p[0]) >=
 *               (layer_transition_center - average_rho_width / 2) &&
 *             std::abs(p[0]) <= (layer_transition_center + average_rho_width / 2))
 *           {
 *             const double coefficient =
 *               (std::abs(p[0]) -
 *                (layer_transition_center - average_rho_width / 2)) /
 *               average_rho_width;
 *             return (1 - coefficient) * material_a_rho +
 *                    coefficient * material_b_rho;
 *           }
 *       }
 * 
 * @endcode
 * 
 * Here we define the
 * [subpixel
 * smoothing](https://meep.readthedocs.io/en/latest/Subpixel_Smoothing/)
 * which improves the precision of the simulation.
 * 
 * @code
 *     for (unsigned int idx = 0; idx < nb_mirror_pairs; ++idx)
 *       {
 *         const double layer_transition_center =
 *           material_a_wavelength / 2 +
 *           idx * (material_b_wavelength / 4 + material_a_wavelength / 4) +
 *           material_b_wavelength / 4;
 *         if (std::abs(p[0]) >=
 *               (layer_transition_center - average_rho_width / 2) &&
 *             std::abs(p[0]) <= (layer_transition_center + average_rho_width / 2))
 *           {
 *             const double coefficient =
 *               (std::abs(p[0]) -
 *                (layer_transition_center - average_rho_width / 2)) /
 *               average_rho_width;
 *             return (1 - coefficient) * material_b_rho +
 *                    coefficient * material_a_rho;
 *           }
 *       }
 * 
 * @endcode
 * 
 * then the cavity
 * 
 * @code
 *     if (std::abs(p[0]) <= material_a_wavelength / 2)
 *       {
 *         return material_a_rho;
 *       }
 * 
 * @endcode
 * 
 * the material_a layers
 * 
 * @code
 *     for (unsigned int idx = 0; idx < nb_mirror_pairs; ++idx)
 *       {
 *         const double layer_center =
 *           material_a_wavelength / 2 +
 *           idx * (material_b_wavelength / 4 + material_a_wavelength / 4) +
 *           material_b_wavelength / 4 + material_a_wavelength / 8;
 *         const double layer_width = material_a_wavelength / 4;
 *         if (std::abs(p[0]) >= (layer_center - layer_width / 2) &&
 *             std::abs(p[0]) <= (layer_center + layer_width / 2))
 *           {
 *             return material_a_rho;
 *           }
 *       }
 * 
 * @endcode
 * 
 * the material_b layers
 * 
 * @code
 *     for (unsigned int idx = 0; idx < nb_mirror_pairs; ++idx)
 *       {
 *         const double layer_center =
 *           material_a_wavelength / 2 +
 *           idx * (material_b_wavelength / 4 + material_a_wavelength / 4) +
 *           material_b_wavelength / 8;
 *         const double layer_width = material_b_wavelength / 4;
 *         if (std::abs(p[0]) >= (layer_center - layer_width / 2) &&
 *             std::abs(p[0]) <= (layer_center + layer_width / 2))
 *           {
 *             return material_b_rho;
 *           }
 *       }
 * 
 * @endcode
 * 
 * and finally the default is material_a.
 * 
 * @code
 *     return material_a_rho;
 *   }
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="TheParametersclassimplementation"></a> 
 * <h4>The `Parameters` class implementation</h4>
 * 
 
 * 
 * The constructor reads all the parameters from the HDF5::Group `data` using
 * the HDF5::Group::get_attribute() function.
 * 
 * @code
 *   template <int dim>
 *   Parameters<dim>::Parameters(HDF5::Group &data)
 *     : data(data)
 *     , simulation_name(data.get_attribute<std::string>("simulation_name"))
 *     , save_vtu_files(data.get_attribute<bool>("save_vtu_files"))
 *     , start_frequency(data.get_attribute<double>("start_frequency"))
 *     , stop_frequency(data.get_attribute<double>("stop_frequency"))
 *     , nb_frequency_points(data.get_attribute<int>("nb_frequency_points"))
 *     , lambda(data.get_attribute<double>("lambda"))
 *     , mu(data.get_attribute<double>("mu"))
 *     , dimension_x(data.get_attribute<double>("dimension_x"))
 *     , dimension_y(data.get_attribute<double>("dimension_y"))
 *     , nb_probe_points(data.get_attribute<int>("nb_probe_points"))
 *     , grid_level(data.get_attribute<int>("grid_level"))
 *     , probe_start_point(data.get_attribute<double>("probe_pos_x"),
 *                         data.get_attribute<double>("probe_pos_y") -
 *                           data.get_attribute<double>("probe_width_y") / 2)
 *     , probe_stop_point(data.get_attribute<double>("probe_pos_x"),
 *                        data.get_attribute<double>("probe_pos_y") +
 *                          data.get_attribute<double>("probe_width_y") / 2)
 *     , right_hand_side(data)
 *     , pml(data)
 *     , rho(data)
 *   {}
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="TheQuadratureCacheclassimplementation"></a> 
 * <h4>The `QuadratureCache` class implementation</h4>
 * 
 
 * 
 * We need to reserve enough space for the mass and stiffness matrices and the
 * right hand side vector.
 * 
 * @code
 *   template <int dim>
 *   QuadratureCache<dim>::QuadratureCache(const unsigned int dofs_per_cell)
 *     : dofs_per_cell(dofs_per_cell)
 *     , mass_coefficient(dofs_per_cell, dofs_per_cell)
 *     , stiffness_coefficient(dofs_per_cell, dofs_per_cell)
 *     , right_hand_side(dofs_per_cell)
 *   {}
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="ImplementationoftheElasticWaveclass"></a> 
 * <h3>Implementation of the `ElasticWave` class</h3>
 * 
 
 * 
 * 
 * <a name="Constructor"></a> 
 * <h4>Constructor</h4>
 * 
 
 * 
 * This is very similar to the constructor of @ref step_40 "step-40". In addition we create
 * the HDF5 datasets `frequency_dataset`, `position_dataset` and
 * `displacement`. Note the use of the `template` keyword for the creation of
 * the HDF5 datasets. It is a C++ requirement to use the `template` keyword in
 * order to treat `create_dataset` as a dependent template name.
 * 
 * @code
 *   template <int dim>
 *   ElasticWave<dim>::ElasticWave(const Parameters<dim> &parameters)
 *     : parameters(parameters)
 *     , mpi_communicator(MPI_COMM_WORLD)
 *     , triangulation(mpi_communicator,
 *                     typename Triangulation<dim>::MeshSmoothing(
 *                       Triangulation<dim>::smoothing_on_refinement |
 *                       Triangulation<dim>::smoothing_on_coarsening))
 *     , quadrature_formula(2)
 *     , fe(FE_Q<dim>(1), dim)
 *     , dof_handler(triangulation)
 *     , frequency(parameters.nb_frequency_points)
 *     , probe_positions(parameters.nb_probe_points, dim)
 *     , frequency_dataset(parameters.data.template create_dataset<double>(
 *         "frequency",
 *         std::vector<hsize_t>{parameters.nb_frequency_points}))
 *     , probe_positions_dataset(parameters.data.template create_dataset<double>(
 *         "position",
 *         std::vector<hsize_t>{parameters.nb_probe_points, dim}))
 *     , displacement(
 *         parameters.data.template create_dataset<std::complex<double>>(
 *           "displacement",
 *           std::vector<hsize_t>{parameters.nb_probe_points,
 *                                parameters.nb_frequency_points}))
 *     , pcout(std::cout,
 *             (Utilities::MPI::this_mpi_process(mpi_communicator) == 0))
 *     , computing_timer(mpi_communicator,
 *                       pcout,
 *                       TimerOutput::never,
 *                       TimerOutput::wall_times)
 *   {}
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWavesetup_system"></a> 
 * <h4>ElasticWave::setup_system</h4>
 * 
 
 * 
 * There is nothing new in this function, the only difference with @ref step_40 "step-40" is
 * that we don't have to apply boundary conditions because we use the PMLs to
 * truncate the domain.
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::setup_system()
 *   {
 *     TimerOutput::Scope t(computing_timer, "setup");
 * 
 *     dof_handler.distribute_dofs(fe);
 * 
 *     locally_owned_dofs = dof_handler.locally_owned_dofs();
 *     locally_relevant_dofs =
 *       DoFTools::extract_locally_relevant_dofs(dof_handler);
 * 
 *     locally_relevant_solution.reinit(locally_owned_dofs,
 *                                      locally_relevant_dofs,
 *                                      mpi_communicator);
 * 
 *     system_rhs.reinit(locally_owned_dofs, mpi_communicator);
 * 
 *     constraints.clear();
 *     constraints.reinit(locally_relevant_dofs);
 *     DoFTools::make_hanging_node_constraints(dof_handler, constraints);
 * 
 *     constraints.close();
 * 
 *     DynamicSparsityPattern dsp(locally_relevant_dofs);
 * 
 *     DoFTools::make_sparsity_pattern(dof_handler, dsp, constraints, false);
 *     SparsityTools::distribute_sparsity_pattern(dsp,
 *                                                locally_owned_dofs,
 *                                                mpi_communicator,
 *                                                locally_relevant_dofs);
 * 
 *     system_matrix.reinit(locally_owned_dofs,
 *                          locally_owned_dofs,
 *                          dsp,
 *                          mpi_communicator);
 *   }
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWaveassemble_system"></a> 
 * <h4>ElasticWave::assemble_system</h4>
 * 
 
 * 
 * This function is also very similar to @ref step_40 "step-40", though there are notable
 * differences. We assemble the system for each frequency/omega step. In the
 * first step we set `calculate_quadrature_data = True` and we calculate the
 * mass and stiffness matrices and the right hand side vector. In the
 * subsequent steps we will use that data to accelerate the calculation.
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::assemble_system(const double omega,
 *                                          const bool   calculate_quadrature_data)
 *   {
 *     TimerOutput::Scope t(computing_timer, "assembly");
 * 
 *     FEValues<dim>      fe_values(fe,
 *                             quadrature_formula,
 *                             update_values | update_gradients |
 *                               update_quadrature_points | update_JxW_values);
 *     const unsigned int dofs_per_cell = fe.n_dofs_per_cell();
 *     const unsigned int n_q_points    = quadrature_formula.size();
 * 
 *     FullMatrix<std::complex<double>> cell_matrix(dofs_per_cell, dofs_per_cell);
 *     Vector<std::complex<double>>     cell_rhs(dofs_per_cell);
 * 
 *     std::vector<types::global_dof_index> local_dof_indices(dofs_per_cell);
 * 
 * @endcode
 * 
 * Here we store the value of the right hand side, rho and the PML.
 * 
 * @code
 *     std::vector<Vector<double>> rhs_values(n_q_points, Vector<double>(dim));
 *     std::vector<double>         rho_values(n_q_points);
 *     std::vector<Vector<std::complex<double>>> pml_values(
 *       n_q_points, Vector<std::complex<double>>(dim));
 * 
 * @endcode
 * 
 * We calculate the stiffness tensor for the @f$\lambda@f$ and @f$\mu@f$ that have
 * been defined in the jupyter notebook. Note that contrary to @f$\rho@f$ the
 * stiffness is constant among for the whole domain.
 * 
 * @code
 *     const SymmetricTensor<4, dim> stiffness_tensor =
 *       get_stiffness_tensor<dim>(parameters.lambda, parameters.mu);
 * 
 * @endcode
 * 
 * We use the same method of @ref step_20 "step-20" for vector-valued problems.
 * 
 * @code
 *     const FEValuesExtractors::Vector displacement(0);
 * 
 *     for (const auto &cell : dof_handler.active_cell_iterators())
 *       if (cell->is_locally_owned())
 *         {
 *           cell_matrix = 0;
 *           cell_rhs    = 0;
 * 
 * @endcode
 * 
 * We have to calculate the values of the right hand side, rho and
 * the PML only if we are going to calculate the mass and the
 * stiffness matrices. Otherwise we can skip this calculation which
 * considerably reduces the total calculation time.
 * 
 * @code
 *           if (calculate_quadrature_data)
 *             {
 *               fe_values.reinit(cell);
 * 
 *               parameters.right_hand_side.vector_value_list(
 *                 fe_values.get_quadrature_points(), rhs_values);
 *               parameters.rho.value_list(fe_values.get_quadrature_points(),
 *                                         rho_values);
 *               parameters.pml.vector_value_list(
 *                 fe_values.get_quadrature_points(), pml_values);
 *             }
 * 
 * @endcode
 * 
 * We have done this in @ref step_18 "step-18". Get a pointer to the quadrature
 * cache data local to the present cell, and, as a defensive
 * measure, make sure that this pointer is within the bounds of the
 * global array:
 * 
 * @code
 *           QuadratureCache<dim> *local_quadrature_points_data =
 *             reinterpret_cast<QuadratureCache<dim> *>(cell->user_pointer());
 *           Assert(local_quadrature_points_data >= &quadrature_cache.front(),
 *                  ExcInternalError());
 *           Assert(local_quadrature_points_data <= &quadrature_cache.back(),
 *                  ExcInternalError());
 *           for (unsigned int q = 0; q < n_q_points; ++q)
 *             {
 * @endcode
 * 
 * The quadrature_data variable is used to store the mass and
 * stiffness matrices, the right hand side vector and the value
 * of `JxW`.
 * 
 * @code
 *               QuadratureCache<dim> &quadrature_data =
 *                 local_quadrature_points_data[q];
 * 
 * @endcode
 * 
 * Below we declare the force vector and the parameters of the
 * PML @f$s@f$ and @f$\xi@f$.
 * 
 * @code
 *               Tensor<1, dim>                       force;
 *               Tensor<1, dim, std::complex<double>> s;
 *               std::complex<double>                 xi(1, 0);
 * 
 * @endcode
 * 
 * The following block is calculated only in the first frequency
 * step.
 * 
 * @code
 *               if (calculate_quadrature_data)
 *                 {
 * @endcode
 * 
 * Store the value of `JxW`.
 * 
 * @code
 *                   quadrature_data.JxW = fe_values.JxW(q);
 * 
 *                   for (unsigned int component = 0; component < dim; ++component)
 *                     {
 * @endcode
 * 
 * Convert vectors to tensors and calculate xi
 * 
 * @code
 *                       force[component] = rhs_values[q][component];
 *                       s[component]     = pml_values[q][component];
 *                       xi *= s[component];
 *                     }
 * 
 * @endcode
 * 
 * Here we calculate the @f$\alpha_{mnkl}@f$ and @f$\beta_{mnkl}@f$
 * tensors.
 * 
 * @code
 *                   Tensor<4, dim, std::complex<double>> alpha;
 *                   Tensor<4, dim, std::complex<double>> beta;
 *                   for (unsigned int m = 0; m < dim; ++m)
 *                     for (unsigned int n = 0; n < dim; ++n)
 *                       for (unsigned int k = 0; k < dim; ++k)
 *                         for (unsigned int l = 0; l < dim; ++l)
 *                           {
 *                             alpha[m][n][k][l] = xi *
 *                                                 stiffness_tensor[m][n][k][l] /
 *                                                 (2.0 * s[n] * s[k]);
 *                             beta[m][n][k][l] = xi *
 *                                                stiffness_tensor[m][n][k][l] /
 *                                                (2.0 * s[n] * s[l]);
 *                           }
 * 
 *                   for (unsigned int i = 0; i < dofs_per_cell; ++i)
 *                     {
 *                       const Tensor<1, dim> phi_i =
 *                         fe_values[displacement].value(i, q);
 *                       const Tensor<2, dim> grad_phi_i =
 *                         fe_values[displacement].gradient(i, q);
 * 
 *                       for (unsigned int j = 0; j < dofs_per_cell; ++j)
 *                         {
 *                           const Tensor<1, dim> phi_j =
 *                             fe_values[displacement].value(j, q);
 *                           const Tensor<2, dim> grad_phi_j =
 *                             fe_values[displacement].gradient(j, q);
 * 
 * @endcode
 * 
 * calculate the values of the mass matrix.
 * 
 * @code
 *                           quadrature_data.mass_coefficient[i][j] =
 *                             rho_values[q] * xi * phi_i * phi_j;
 * 
 * @endcode
 * 
 * Loop over the @f$mnkl@f$ indices of the stiffness
 * tensor.
 * 
 * @code
 *                           std::complex<double> stiffness_coefficient = 0;
 *                           for (unsigned int m = 0; m < dim; ++m)
 *                             for (unsigned int n = 0; n < dim; ++n)
 *                               for (unsigned int k = 0; k < dim; ++k)
 *                                 for (unsigned int l = 0; l < dim; ++l)
 *                                   {
 * @endcode
 * 
 * Here we calculate the stiffness matrix.
 * Note that the stiffness matrix is not
 * symmetric because of the PMLs. We use the
 * gradient function (see the
 * [documentation](https://www.dealii.org/current/doxygen/deal.II/group__vector__valued.html))
 * which is a <code>Tensor@<2,dim@></code>.
 * The matrix @f$G_{ij}@f$ consists of entries
 * @f[
 * G_{ij}=
 * \frac{\partial\phi_i}{\partial x_j}
 * =\partial_j \phi_i
 * @f]
 * Note the position of the indices @f$i@f$ and
 * @f$j@f$ and the notation that we use in this
 * tutorial: @f$\partial_j\phi_i@f$. As the
 * stiffness tensor is not symmetric, it is
 * very easy to make a mistake.
 * 
 * @code
 *                                     stiffness_coefficient +=
 *                                       grad_phi_i[m][n] *
 *                                       (alpha[m][n][k][l] * grad_phi_j[l][k] +
 *                                        beta[m][n][k][l] * grad_phi_j[k][l]);
 *                                   }
 * 
 * @endcode
 * 
 * We save the value of the stiffness matrix in
 * quadrature_data
 * 
 * @code
 *                           quadrature_data.stiffness_coefficient[i][j] =
 *                             stiffness_coefficient;
 *                         }
 * 
 * @endcode
 * 
 * and the value of the right hand side in
 * quadrature_data.
 * 
 * @code
 *                       quadrature_data.right_hand_side[i] =
 *                         phi_i * force * fe_values.JxW(q);
 *                     }
 *                 }
 * 
 * @endcode
 * 
 * We loop again over the degrees of freedom of the cells to
 * calculate the system matrix. These loops are really quick
 * because we have already calculated the stiffness and mass
 * matrices, only the value of @f$\omega@f$ changes.
 * 
 * @code
 *               for (unsigned int i = 0; i < dofs_per_cell; ++i)
 *                 {
 *                   for (unsigned int j = 0; j < dofs_per_cell; ++j)
 *                     {
 *                       std::complex<double> matrix_sum = 0;
 *                       matrix_sum += -std::pow(omega, 2) *
 *                                     quadrature_data.mass_coefficient[i][j];
 *                       matrix_sum += quadrature_data.stiffness_coefficient[i][j];
 *                       cell_matrix(i, j) += matrix_sum * quadrature_data.JxW;
 *                     }
 *                   cell_rhs(i) += quadrature_data.right_hand_side[i];
 *                 }
 *             }
 *           cell->get_dof_indices(local_dof_indices);
 *           constraints.distribute_local_to_global(cell_matrix,
 *                                                  cell_rhs,
 *                                                  local_dof_indices,
 *                                                  system_matrix,
 *                                                  system_rhs);
 *         }
 * 
 *     system_matrix.compress(VectorOperation::add);
 *     system_rhs.compress(VectorOperation::add);
 *   }
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWavesolve"></a> 
 * <h4>ElasticWave::solve</h4>
 * 
 
 * 
 * This is even more simple than in @ref step_40 "step-40". We use the parallel direct solver
 * MUMPS which requires less options than an iterative solver. The drawback is
 * that it does not scale very well. It is not straightforward to solve the
 * Helmholtz equation with an iterative solver. The shifted Laplacian
 * multigrid method is a well known approach to precondition this system, but
 * this is beyond the scope of this tutorial.
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::solve()
 *   {
 *     TimerOutput::Scope              t(computing_timer, "solve");
 *     LinearAlgebraPETSc::MPI::Vector completely_distributed_solution(
 *       locally_owned_dofs, mpi_communicator);
 * 
 *     SolverControl                    solver_control;
 *     PETScWrappers::SparseDirectMUMPS solver(solver_control, mpi_communicator);
 *     solver.solve(system_matrix, completely_distributed_solution, system_rhs);
 * 
 *     pcout << "   Solved in " << solver_control.last_step() << " iterations."
 *           << std::endl;
 *     constraints.distribute(completely_distributed_solution);
 *     locally_relevant_solution = completely_distributed_solution;
 *   }
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWaveinitialize_position_vector"></a> 
 * <h4>ElasticWave::initialize_position_vector</h4>
 * 
 
 * 
 * We use this function to calculate the values of the position vector.
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::initialize_probe_positions_vector()
 *   {
 *     for (unsigned int position_idx = 0;
 *          position_idx < parameters.nb_probe_points;
 *          ++position_idx)
 *       {
 * @endcode
 * 
 * Because of the way the operator + and - are overloaded to subtract
 * two points, the following has to be done:
 * `Point_b<dim> + (-Point_a<dim>)`
 * 
 * @code
 *         const Point<dim> p =
 *           (position_idx / ((double)(parameters.nb_probe_points - 1))) *
 *             (parameters.probe_stop_point + (-parameters.probe_start_point)) +
 *           parameters.probe_start_point;
 *         probe_positions[position_idx][0] = p[0];
 *         probe_positions[position_idx][1] = p[1];
 *         if (dim == 3)
 *           {
 *             probe_positions[position_idx][2] = p[2];
 *           }
 *       }
 *   }
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWavestore_frequency_step_data"></a> 
 * <h4>ElasticWave::store_frequency_step_data</h4>
 * 
 
 * 
 * This function stores in the HDF5 file the measured energy by the probe.
 * 
 * @code
 *   template <int dim>
 *   void
 *   ElasticWave<dim>::store_frequency_step_data(const unsigned int frequency_idx)
 *   {
 *     TimerOutput::Scope t(computing_timer, "store_frequency_step_data");
 * 
 * @endcode
 * 
 * We store the displacement in the @f$x@f$ direction; the displacement in the
 * @f$y@f$ direction is negligible.
 * 
 * @code
 *     const unsigned int probe_displacement_component = 0;
 * 
 * @endcode
 * 
 * The vector coordinates contains the coordinates in the HDF5 file of the
 * points of the probe that are located in locally owned cells. The vector
 * displacement_data contains the value of the displacement at these points.
 * 
 * @code
 *     std::vector<hsize_t>              coordinates;
 *     std::vector<std::complex<double>> displacement_data;
 * 
 *     const auto &mapping = get_default_linear_mapping(triangulation);
 *     GridTools::Cache<dim, dim> cache(triangulation, mapping);
 *     typename Triangulation<dim, dim>::active_cell_iterator cell_hint{};
 *     std::vector<bool>                                      marked_vertices = {};
 *     const double                                           tolerance = 1.e-10;
 * 
 *     for (unsigned int position_idx = 0;
 *          position_idx < parameters.nb_probe_points;
 *          ++position_idx)
 *       {
 *         Point<dim> point;
 *         for (unsigned int dim_idx = 0; dim_idx < dim; ++dim_idx)
 *           {
 *             point[dim_idx] = probe_positions[position_idx][dim_idx];
 *           }
 *         bool point_in_locally_owned_cell = false;
 *         {
 *           auto cell_and_ref_point = GridTools::find_active_cell_around_point(
 *             cache, point, cell_hint, marked_vertices, tolerance);
 *           if (cell_and_ref_point.first.state() == IteratorState::valid)
 *             {
 *               cell_hint = cell_and_ref_point.first;
 *               point_in_locally_owned_cell =
 *                 cell_and_ref_point.first->is_locally_owned();
 *             }
 *         }
 *         if (point_in_locally_owned_cell)
 *           {
 * @endcode
 * 
 * Then we can store the values of the displacement in the points of
 * the probe in `displacement_data`.
 * 
 * @code
 *             Vector<std::complex<double>> tmp_vector(dim);
 *             VectorTools::point_value(dof_handler,
 *                                      locally_relevant_solution,
 *                                      point,
 *                                      tmp_vector);
 *             coordinates.emplace_back(position_idx);
 *             coordinates.emplace_back(frequency_idx);
 *             displacement_data.emplace_back(
 *               tmp_vector(probe_displacement_component));
 *           }
 *       }
 * 
 * @endcode
 * 
 * We write the displacement data in the HDF5 file. The call
 * HDF5::DataSet::write_selection() is MPI collective which means that all
 * the processes have to participate.
 * 
 * @code
 *     if (coordinates.size() > 0)
 *       {
 *         displacement.write_selection(displacement_data, coordinates);
 *       }
 * @endcode
 * 
 * Therefore even if the process has no data to write it has to participate
 * in the collective call. For this we can use HDF5::DataSet::write_none().
 * Note that we have to specify the data type, in this case
 * `std::complex<double>`.
 * 
 * @code
 *     else
 *       {
 *         displacement.write_none<std::complex<double>>();
 *       }
 * 
 * @endcode
 * 
 * If the variable `save_vtu_files` in the input file equals `True` then all
 * the data will be saved as vtu. The procedure to write `vtu` files has
 * been described in @ref step_40 "step-40".
 * 
 * @code
 *     if (parameters.save_vtu_files)
 *       {
 *         std::vector<std::string> solution_names(dim, "displacement");
 *         std::vector<DataComponentInterpretation::DataComponentInterpretation>
 *           interpretation(
 *             dim, DataComponentInterpretation::component_is_part_of_vector);
 * 
 *         DataOut<dim> data_out;
 *         data_out.add_data_vector(dof_handler,
 *                                  locally_relevant_solution,
 *                                  solution_names,
 *                                  interpretation);
 *         Vector<float> subdomain(triangulation.n_active_cells());
 *         for (unsigned int i = 0; i < subdomain.size(); ++i)
 *           subdomain(i) = triangulation.locally_owned_subdomain();
 *         data_out.add_data_vector(subdomain, "subdomain");
 * 
 *         std::vector<Vector<double>> force(
 *           dim, Vector<double>(triangulation.n_active_cells()));
 *         std::vector<Vector<double>> pml(
 *           dim, Vector<double>(triangulation.n_active_cells()));
 *         Vector<double> rho(triangulation.n_active_cells());
 * 
 *         for (auto &cell : triangulation.active_cell_iterators())
 *           {
 *             if (cell->is_locally_owned())
 *               {
 *                 for (unsigned int dim_idx = 0; dim_idx < dim; ++dim_idx)
 *                   {
 *                     force[dim_idx](cell->active_cell_index()) =
 *                       parameters.right_hand_side.value(cell->center(), dim_idx);
 *                     pml[dim_idx](cell->active_cell_index()) =
 *                       parameters.pml.value(cell->center(), dim_idx).imag();
 *                   }
 *                 rho(cell->active_cell_index()) =
 *                   parameters.rho.value(cell->center());
 *               }
 * @endcode
 * 
 * And on the cells that we are not interested in, set the
 * respective value to a bogus value in order to make sure that if
 * we were somehow wrong about our assumption we would find out by
 * looking at the graphical output:
 * 
 * @code
 *             else
 *               {
 *                 for (unsigned int dim_idx = 0; dim_idx < dim; ++dim_idx)
 *                   {
 *                     force[dim_idx](cell->active_cell_index()) = -1e+20;
 *                     pml[dim_idx](cell->active_cell_index())   = -1e+20;
 *                   }
 *                 rho(cell->active_cell_index()) = -1e+20;
 *               }
 *           }
 * 
 *         for (unsigned int dim_idx = 0; dim_idx < dim; ++dim_idx)
 *           {
 *             data_out.add_data_vector(force[dim_idx],
 *                                      "force_" + std::to_string(dim_idx));
 *             data_out.add_data_vector(pml[dim_idx],
 *                                      "pml_" + std::to_string(dim_idx));
 *           }
 *         data_out.add_data_vector(rho, "rho");
 * 
 *         data_out.build_patches();
 * 
 *         std::stringstream  frequency_idx_stream;
 *         const unsigned int nb_number_positions =
 *           ((unsigned int)std::log10(parameters.nb_frequency_points)) + 1;
 *         frequency_idx_stream << std::setw(nb_number_positions)
 *                              << std::setfill('0') << frequency_idx;
 *         std::string filename = (parameters.simulation_name + "_" +
 *                                 frequency_idx_stream.str() + ".vtu");
 *         data_out.write_vtu_in_parallel(filename.c_str(), mpi_communicator);
 *       }
 *   }
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWaveoutput_results"></a> 
 * <h4>ElasticWave::output_results</h4>
 * 
 
 * 
 * This function writes the datasets that have not already been written.
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::output_results()
 *   {
 * @endcode
 * 
 * The vectors `frequency` and `position` are the same for all the
 * processes. Therefore any of the processes can write the corresponding
 * `datasets`. Because the call HDF5::DataSet::write is MPI collective, the
 * rest of the processes will have to call HDF5::DataSet::write_none.
 * 
 * @code
 *     if (Utilities::MPI::this_mpi_process(mpi_communicator) == 0)
 *       {
 *         frequency_dataset.write(frequency);
 *         probe_positions_dataset.write(probe_positions);
 *       }
 *     else
 *       {
 *         frequency_dataset.write_none<double>();
 *         probe_positions_dataset.write_none<double>();
 *       }
 *   }
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWavesetup_quadrature_cache"></a> 
 * <h4>ElasticWave::setup_quadrature_cache</h4>
 * 
 
 * 
 * We use this function at the beginning of our computations to set up initial
 * values of the cache variables. This function has been described in @ref step_18 "step-18".
 * There are no differences with the function of @ref step_18 "step-18".
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::setup_quadrature_cache()
 *   {
 *     triangulation.clear_user_data();
 * 
 *     {
 *       std::vector<QuadratureCache<dim>> tmp;
 *       quadrature_cache.swap(tmp);
 *     }
 * 
 *     quadrature_cache.resize(triangulation.n_locally_owned_active_cells() *
 *                               quadrature_formula.size(),
 *                             QuadratureCache<dim>(fe.n_dofs_per_cell()));
 *     unsigned int cache_index = 0;
 *     for (const auto &cell : triangulation.active_cell_iterators())
 *       if (cell->is_locally_owned())
 *         {
 *           cell->set_user_pointer(&quadrature_cache[cache_index]);
 *           cache_index += quadrature_formula.size();
 *         }
 *     Assert(cache_index == quadrature_cache.size(), ExcInternalError());
 *   }
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWavefrequency_sweep"></a> 
 * <h4>ElasticWave::frequency_sweep</h4>
 * 
 
 * 
 * For clarity we divide the function `run` of @ref step_40 "step-40" into the functions
 * `run` and `frequency_sweep`. In the function `frequency_sweep` we place the
 * iteration over the frequency vector.
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::frequency_sweep()
 *   {
 *     for (unsigned int frequency_idx = 0;
 *          frequency_idx < parameters.nb_frequency_points;
 *          ++frequency_idx)
 *       {
 *         pcout << parameters.simulation_name + " frequency idx: "
 *               << frequency_idx << '/' << parameters.nb_frequency_points - 1
 *               << std::endl;
 * 
 * 
 * 
 *         setup_system();
 *         if (frequency_idx == 0)
 *           {
 *             pcout << "   Number of active cells :       "
 *                   << triangulation.n_active_cells() << std::endl;
 *             pcout << "   Number of degrees of freedom : "
 *                   << dof_handler.n_dofs() << std::endl;
 *           }
 * 
 *         if (frequency_idx == 0)
 *           {
 * @endcode
 * 
 * Write the simulation parameters only once
 * 
 * @code
 *             parameters.data.set_attribute("active_cells",
 *                                           triangulation.n_active_cells());
 *             parameters.data.set_attribute("degrees_of_freedom",
 *                                           dof_handler.n_dofs());
 *           }
 * 
 * @endcode
 * 
 * We calculate the frequency and omega values for this particular step.
 * 
 * @code
 *         const double current_loop_frequency =
 *           (parameters.start_frequency +
 *            frequency_idx *
 *              (parameters.stop_frequency - parameters.start_frequency) /
 *              (parameters.nb_frequency_points - 1));
 *         const double current_loop_omega =
 *           2 * numbers::PI * current_loop_frequency;
 * 
 * @endcode
 * 
 * In the first frequency step we calculate the mass and stiffness
 * matrices and the right hand side. In the subsequent frequency steps
 * we will use those values. This improves considerably the calculation
 * time.
 * 
 * @code
 *         assemble_system(current_loop_omega,
 *                         (frequency_idx == 0) ? true : false);
 *         solve();
 * 
 *         frequency[frequency_idx] = current_loop_frequency;
 *         store_frequency_step_data(frequency_idx);
 * 
 *         computing_timer.print_summary();
 *         computing_timer.reset();
 *         pcout << std::endl;
 *       }
 *   }
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="ElasticWaverun"></a> 
 * <h4>ElasticWave::run</h4>
 * 
 
 * 
 * This function is very similar to the one in @ref step_40 "step-40".
 * 
 * @code
 *   template <int dim>
 *   void ElasticWave<dim>::run()
 *   {
 * #ifdef DEBUG
 *     pcout << "Debug mode" << std::endl;
 * #else
 *     pcout << "Release mode" << std::endl;
 * #endif
 * 
 *     {
 *       Point<dim> p1;
 *       p1(0) = -parameters.dimension_x / 2;
 *       p1(1) = -parameters.dimension_y / 2;
 *       if (dim == 3)
 *         {
 *           p1(2) = -parameters.dimension_y / 2;
 *         }
 *       Point<dim> p2;
 *       p2(0) = parameters.dimension_x / 2;
 *       p2(1) = parameters.dimension_y / 2;
 *       if (dim == 3)
 *         {
 *           p2(2) = parameters.dimension_y / 2;
 *         }
 *       std::vector<unsigned int> divisions(dim);
 *       divisions[0] = int(parameters.dimension_x / parameters.dimension_y);
 *       divisions[1] = 1;
 *       if (dim == 3)
 *         {
 *           divisions[2] = 1;
 *         }
 *       GridGenerator::subdivided_hyper_rectangle(triangulation,
 *                                                 divisions,
 *                                                 p1,
 *                                                 p2);
 *     }
 * 
 *     triangulation.refine_global(parameters.grid_level);
 * 
 *     setup_quadrature_cache();
 * 
 *     initialize_probe_positions_vector();
 * 
 *     frequency_sweep();
 * 
 *     output_results();
 *   }
 * } // namespace step62
 * 
 * 
 * 
 * @endcode
 * 
 * 
 * <a name="Themainfunction"></a> 
 * <h4>The main function</h4>
 * 
 
 * 
 * The main function is very similar to the one in @ref step_40 "step-40".
 * 
 * @code
 * int main(int argc, char *argv[])
 * {
 *   try
 *     {
 *       using namespace dealii;
 *       const unsigned int dim = 2;
 * 
 *       Utilities::MPI::MPI_InitFinalize mpi_initialization(argc, argv, 1);
 * 
 *       HDF5::File data_file("results.h5",
 *                            HDF5::File::FileAccessMode::create,
 *                            MPI_COMM_WORLD);
 *       auto       data = data_file.create_group("data");
 * 
 * @endcode
 * 
 * Each of the simulations (displacement and calibration) is stored in a
 * separate HDF5 group:
 * 
 * @code
 *       const std::vector<std::string> group_names = {"displacement",
 *                                                     "calibration"};
 *       for (auto group_name : group_names)
 *         {
 * @endcode
 * 
 * For each of these two group names, we now create the group and put
 * attributes into these groups.
 * Specifically, these are:
 * - The dimensions of the waveguide (in @f$x@f$ and @f$y@f$ directions)
 * - The position of the probe (in @f$x@f$ and @f$y@f$ directions)
 * - The number of points in the probe
 * - The global refinement level
 * - The cavity resonance frequency
 * - The number of mirror pairs
 * - The material properties
 * - The force parameters
 * - The PML parameters
 * - The frequency parameters
 * 
 
 * 
 * 
 * @code
 *           auto group = data.create_group(group_name);
 * 
 *           group.set_attribute<double>("dimension_x", 2e-5);
 *           group.set_attribute<double>("dimension_y", 2e-8);
 *           group.set_attribute<double>("probe_pos_x", 8e-6);
 *           group.set_attribute<double>("probe_pos_y", 0);
 *           group.set_attribute<double>("probe_width_y", 2e-08);
 *           group.set_attribute<unsigned int>("nb_probe_points", 5);
 *           group.set_attribute<unsigned int>("grid_level", 1);
 *           group.set_attribute<double>("cavity_resonance_frequency", 20e9);
 *           group.set_attribute<unsigned int>("nb_mirror_pairs", 15);
 * 
 *           group.set_attribute<double>("poissons_ratio", 0.27);
 *           group.set_attribute<double>("youngs_modulus", 270000000000.0);
 *           group.set_attribute<double>("material_a_rho", 3200);
 * 
 *           if (group_name == std::string("displacement"))
 *             group.set_attribute<double>("material_b_rho", 2000);
 *           else
 *             group.set_attribute<double>("material_b_rho", 3200);
 * 
 *           group.set_attribute(
 *             "lambda",
 *             group.get_attribute<double>("youngs_modulus") *
 *               group.get_attribute<double>("poissons_ratio") /
 *               ((1 + group.get_attribute<double>("poissons_ratio")) *
 *                (1 - 2 * group.get_attribute<double>("poissons_ratio"))));
 *           group.set_attribute("mu",
 *                               group.get_attribute<double>("youngs_modulus") /
 *                                 (2 * (1 + group.get_attribute<double>(
 *                                             "poissons_ratio"))));
 * 
 *           group.set_attribute<double>("max_force_amplitude", 1e26);
 *           group.set_attribute<double>("force_sigma_x", 1e-7);
 *           group.set_attribute<double>("force_sigma_y", 1);
 *           group.set_attribute<double>("max_force_width_x", 3e-7);
 *           group.set_attribute<double>("max_force_width_y", 2e-8);
 *           group.set_attribute<double>("force_x_pos", -8e-6);
 *           group.set_attribute<double>("force_y_pos", 0);
 * 
 *           group.set_attribute<bool>("pml_x", true);
 *           group.set_attribute<bool>("pml_y", false);
 *           group.set_attribute<double>("pml_width_x", 1.8e-6);
 *           group.set_attribute<double>("pml_width_y", 5e-7);
 *           group.set_attribute<double>("pml_coeff", 1.6);
 *           group.set_attribute<unsigned int>("pml_coeff_degree", 2);
 * 
 *           group.set_attribute<double>("center_frequency", 20e9);
 *           group.set_attribute<double>("frequency_range", 0.5e9);
 *           group.set_attribute<double>(
 *             "start_frequency",
 *             group.get_attribute<double>("center_frequency") -
 *               group.get_attribute<double>("frequency_range") / 2);
 *           group.set_attribute<double>(
 *             "stop_frequency",
 *             group.get_attribute<double>("center_frequency") +
 *               group.get_attribute<double>("frequency_range") / 2);
 *           group.set_attribute<unsigned int>("nb_frequency_points", 400);
 * 
 *           if (group_name == std::string("displacement"))
 *             group.set_attribute<std::string>(
 *               "simulation_name", std::string("phononic_cavity_displacement"));
 *           else
 *             group.set_attribute<std::string>(
 *               "simulation_name", std::string("phononic_cavity_calibration"));
 * 
 *           group.set_attribute<bool>("save_vtu_files", false);
 *         }
 * 
 *       {
 * @endcode
 * 
 * Displacement simulation. The parameters are read from the
 * displacement HDF5 group and the results are saved in the same HDF5
 * group.
 * 
 * @code
 *         auto                    displacement = data.open_group("displacement");
 *         step62::Parameters<dim> parameters(displacement);
 * 
 *         step62::ElasticWave<dim> elastic_problem(parameters);
 *         elastic_problem.run();
 *       }
 * 
 *       {
 * @endcode
 * 
 * Calibration simulation. The parameters are read from the calibration
 * HDF5 group and the results are saved in the same HDF5 group.
 * 
 * @code
 *         auto                    calibration = data.open_group("calibration");
 *         step62::Parameters<dim> parameters(calibration);
 * 
 *         step62::ElasticWave<dim> elastic_problem(parameters);
 *         elastic_problem.run();
 *       }
 *     }
 *   catch (std::exception &exc)
 *     {
 *       std::cerr << std::endl
 *                 << std::endl
 *                 << "----------------------------------------------------"
 *                 << std::endl;
 *       std::cerr << "Exception on processing: " << std::endl
 *                 << exc.what() << std::endl
 *                 << "Aborting!" << std::endl
 *                 << "----------------------------------------------------"
 *                 << std::endl;
 * 
 *       return 1;
 *     }
 *   catch (...)
 *     {
 *       std::cerr << std::endl
 *                 << std::endl
 *                 << "----------------------------------------------------"
 *                 << std::endl;
 *       std::cerr << "Unknown exception!" << std::endl
 *                 << "Aborting!" << std::endl
 *                 << "----------------------------------------------------"
 *                 << std::endl;
 *       return 1;
 *     }
 * 
 *   return 0;
 * }
 * @endcode
<a name="Results"></a><h1>Results</h1>
 
 
<a name="Resonancefrequencyandbandgap"></a><h3>Resonance frequency and bandgap</h3>
 
 
The results are analyzed in the
[jupyter notebook](https://github.com/dealii/dealii/blob/master/examples/step-62/step-62.ipynb)
with the following code
@code{.py}
h5_file = h5py.File('results.h5', 'r')
data = h5_file['data']
 
# Gaussian function that we use to fit the resonance
def resonance_f(freq, freq_m, quality_factor, max_amplitude):
    omega = 2 * constants.pi * freq
    omega_m = 2 * constants.pi * freq_m
    gamma = omega_m / quality_factor
    return max_amplitude * omega_m**2 * gamma**2 / (((omega_m**2 - omega**2)**2 + gamma**2 * omega**2))
 
frequency = data['displacement']['frequency'][...]
# Average the probe points
displacement = np.mean(data['displacement']['displacement'], axis=0)
calibration_displacement = np.mean(data['calibration']['displacement'], axis=0)
reflection_coefficient = displacement / calibration_displacement
reflectivity = (np.abs(np.mean(data['displacement']['displacement'][...]**2, axis=0))/
                np.abs(np.mean(data['calibration']['displacement'][...]**2, axis=0)))
 
try:
    x_data = frequency
    y_data = reflectivity
    quality_factor_guess = 1e3
    freq_guess = x_data[np.argmax(y_data)]
    amplitude_guess = np.max(y_data)
    fit_result, covariance = scipy.optimize.curve_fit(resonance_f, x_data, y_data,
                                                      [freq_guess, quality_factor_guess, amplitude_guess])
    freq_m = fit_result[0]
    quality_factor = np.abs(fit_result[1])
    max_amplitude = fit_result[2]
    y_data_fit = resonance_f(x_data, freq_m, quality_factor, max_amplitude)
 
    fig = plt.figure()
    plt.plot(frequency / 1e9, reflectivity, frequency / 1e9, y_data_fit)
    plt.xlabel('frequency (GHz)')
    plt.ylabel('amplitude^2 (a.u.)')
    plt.title('Transmission\n' + 'freq = ' + "%.7g" % (freq_guess / 1e9) + 'GHz Q = ' + "%.6g" % quality_factor)
except:
    fig = plt.figure()
    plt.plot(frequency / 1e9, reflectivity)
    plt.xlabel('frequency (GHz)')
    plt.ylabel('amplitude^2 (a.u.)')
    plt.title('Transmission')
 
fig = plt.figure()
plt.plot(frequency / 1e9, np.angle(reflection_coefficient))
plt.xlabel('frequency (GHz)')
plt.ylabel('phase (rad)')
plt.title('Phase (transmission coefficient)\n')
 
plt.show()
h5_file.close()
@endcode
 
A phononic cavity is characterized by the
[resonance frequency](https://en.wikipedia.org/wiki/Resonance) and the
[the quality factor](https://en.wikipedia.org/wiki/Q_factor).
The quality factor is equal to the ratio between the stored energy in the resonator and the energy
dissipated energy per cycle, which is approximately equivalent to the ratio between the
resonance frequency and the
[full width at half maximum (FWHM)](https://en.wikipedia.org/wiki/Full_width_at_half_maximum).
The FWHM is equal to the bandwidth over which the power of vibration is greater than half the
power at the resonant frequency.
@f[
Q = \frac{f_r}{\Delta f} = \frac{\omega_r}{\Delta \omega} =
2 \pi \times \frac{\text{energy stored}}{\text{energy dissipated per cycle}}
@f]
 
The square of the amplitude of the mechanical resonance @f$a^2@f$ as a function of the frequency
has a gaussian shape
@f[
a^2 = a_\textrm{max}^2\frac{\omega^2\Gamma^2}{(\omega_r^2-\omega^2)^2+\Gamma^2\omega^2}
@f]
where @f$f_r = \frac{\omega_r}{2\pi}@f$ is the resonance frequency and @f$\Gamma=\frac{\omega_r}{Q}@f$ is the dissipation rate.
We used the previous equation in the jupyter notebook to fit the mechanical resonance.
 
Given the values we have chosen for the parameters, one could estimate the resonance frequency
analytically. Indeed, this is then confirmed by what we get in this program:
the phononic superlattice cavity exhibits a mechanical resonance at 20GHz and a quality factor of 5046.
The following images show the transmission amplitude and phase as a function of frequency in the
vicinity of the resonance frequency:
 
<img alt="Phononic superlattice cavity" src="https://www.dealii.org/images/steps/developer/step-62.05.png" height="400" />
<img alt="Phononic superlattice cavity" src="https://www.dealii.org/images/steps/developer/step-62.06.png" height="400" />
 
The images above suggest that the periodic structure has its intended effect: It really only lets waves of a very
specific frequency pass through, whereas all other waves are reflected. This is of course precisely what one builds
these sorts of devices for.
But it is not quite this easy. In practice, there is really only a "band gap", i.e., the device blocks waves other than
the desired one at 20GHz only within a certain frequency range. Indeed, to find out how large this "gap" is within
which waves are blocked, we can extend the frequency range to 16 GHz through the appropriate parameters in the
input file. We then obtain the following image:
 
<img alt="Phononic superlattice cavity" src="https://www.dealii.org/images/steps/developer/step-62.07.png" height="400" />
 
What this image suggests is that in the range of around 18 to around 22 GHz, really only the waves with a frequency
of 20 GHz are allowed to pass through, but beyond this range, there are plenty of other frequencies that can pass
through the device.
 
<a name="Modeprofile"></a><h3>Mode profile</h3>
 
 
We can inspect the mode profile with Paraview or VisIt.
As we have discussed, at resonance all the mechanical
energy is transmitted and the amplitude of motion is amplified inside the cavity.
It can be observed that the PMLs are quite effective to truncate the solution.
The following image shows the mode profile at resonance:
 
<img alt="Phononic superlattice cavity" src="https://www.dealii.org/images/steps/developer/step-62.08.png" height="400" />
 
On the other hand,  out of resonance all the mechanical energy is
reflected. The following image shows the profile at 19.75 GHz.
Note the interference between the force pulse and the reflected wave
at the position @f$x=-8\mu\textrm{m}@f$.
 
<img alt="Phononic superlattice cavity" src="https://www.dealii.org/images/steps/developer/step-62.09.png" height="400" />
 
<a name="Experimentalapplications"></a><h3>Experimental applications</h3>
 
 
Phononic superlattice cavities find application in
[quantum optomechanics](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.86.1391).
Here we have presented the simulation of a 2D superlattice cavity,
but this code can be used as well to simulate "real world" 3D devices such as
[micropillar superlattice cavities](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.99.060101),
which are promising candidates to study macroscopic quantum phenomena.
The 20GHz mode of a micropillar superlattice cavity is essentially a mechanical harmonic oscillator that is very well isolated
from the environment. If the device is cooled down to 20mK in a dilution fridge, the mode would then become a
macroscopic quantum harmonic oscillator.
 
 
<a name="Possibilitiesforextensions"></a><h3>Possibilities for extensions</h3>
 
 
Instead of setting the parameters in the C++ file we could set the parameters
using a python script and save them in the HDF5 file that we will use for
the simulations. Then the deal.II program will read the parameters from the
HDF5 file.
 
@code{.py}
import numpy as np
import h5py
import matplotlib.pyplot as plt
import subprocess
import scipy.constants as constants
import scipy.optimize
 
# This considerably reduces the size of the svg data
plt.rcParams['svg.fonttype'] = 'none'
 
h5_file = h5py.File('results.h5', 'w')
data = h5_file.create_group('data')
displacement = data.create_group('displacement')
calibration = data.create_group('calibration')
 
# Set the parameters
for group in [displacement, calibration]:
    # Dimensions of the domain
    # The waveguide length is equal to dimension_x
    group.attrs['dimension_x'] = 2e-5
    # The waveguide width is equal to dimension_y
    group.attrs['dimension_y'] = 2e-8
 
    # Position of the probe that we use to measure the flux
    group.attrs['probe_pos_x']   = 8e-6
    group.attrs['probe_pos_y']   = 0
    group.attrs['probe_width_y'] = 2e-08
 
    # Number of points in the probe
    group.attrs['nb_probe_points'] = 5
 
    # Global refinement
    group.attrs['grid_level'] = 1
 
    # Cavity
    group.attrs['cavity_resonance_frequency'] = 20e9
    group.attrs['nb_mirror_pairs']            = 15
 
    # Material
    group.attrs['poissons_ratio'] = 0.27
    group.attrs['youngs_modulus'] = 270000000000.0
    group.attrs['material_a_rho'] = 3200
    if group == displacement:
        group.attrs['material_b_rho'] = 2000
    else:
        group.attrs['material_b_rho'] = 3200
    group.attrs['lambda'] = (group.attrs['youngs_modulus'] * group.attrs['poissons_ratio'] /
                           ((1 + group.attrs['poissons_ratio']) *
                           (1 - 2 * group.attrs['poissons_ratio'])))
    group.attrs['mu']= (group.attrs['youngs_modulus'] / (2 * (1 + group.attrs['poissons_ratio'])))
 
    # Force
    group.attrs['max_force_amplitude'] = 1e26
    group.attrs['force_sigma_x']       = 1e-7
    group.attrs['force_sigma_y']       = 1
    group.attrs['max_force_width_x']   = 3e-7
    group.attrs['max_force_width_y']   = 2e-8
    group.attrs['force_x_pos']         = -8e-6
    group.attrs['force_y_pos']         = 0
 
    # PML
    group.attrs['pml_x']            = True
    group.attrs['pml_y']            = False
    group.attrs['pml_width_x']      = 1.8e-6
    group.attrs['pml_width_y']      = 5e-7
    group.attrs['pml_coeff']        = 1.6
    group.attrs['pml_coeff_degree'] = 2
 
    # Frequency sweep
    group.attrs['center_frequency']    = 20e9
    group.attrs['frequency_range']     = 0.5e9
    group.attrs['start_frequency']     = group.attrs['center_frequency'] - group.attrs['frequency_range'] / 2
    group.attrs['stop_frequency']      = group.attrs['center_frequency'] + group.attrs['frequency_range'] / 2
    group.attrs['nb_frequency_points'] = 400
 
    # Other parameters
    if group == displacement:
        group.attrs['simulation_name'] = 'phononic_cavity_displacement'
    else:
        group.attrs['simulation_name'] = 'phononic_cavity_calibration'
    group.attrs['save_vtu_files'] = False
 
h5_file.close()
@endcode
 
In order to read the HDF5 parameters we have to use the
HDF5::File::FileAccessMode::open flag.
@code{.py}
      HDF5::File data_file("results.h5",
                           HDF5::File::FileAccessMode::open,
                           MPI_COMM_WORLD);
      auto       data = data_file.open_group("data");
@endcode
 *
 *
<a name="PlainProg"></a>
<h1> The plain program</h1>
@include "step-62.cc"
*/