parabolic_module.template.h

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//
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Copyright (C) 2023 - 2026 by the ryujin authors
//
 
#pragma once
 
#include "parabolic_module.h"
 
#include <computing_timer.h>
#include <loop.h>
#include <simd.h>
 
#include <deal.II/lac/linear_operator.h>
#include <deal.II/lac/precondition.h>
#include <deal.II/lac/solver_cg.h>
#include <deal.II/matrix_free/fe_evaluation.h>
#include <deal.II/multigrid/mg_coarse.h>
#include <deal.II/multigrid/mg_matrix.h>
#include <deal.II/multigrid/mg_transfer.templates.h>
#include <deal.II/multigrid/mg_transfer_matrix_free.h>
#include <deal.II/multigrid/multigrid.h>
 
#include <atomic>
 
namespace ryujin
{
  namespace NavierStokes
  {
    using namespace dealii;
 
    template <int dim, typename Number>
    ParabolicModule<dim, Number>::ParabolicModule(
        const MPIEnsemble &mpi_ensemble,
        const OfflineData<dim, Number> &offline_data,
        const HyperbolicSystem &hyperbolic_system,
        const ParabolicSystem &parabolic_system,
        const InitialValues<HyperbolicDescription, dim, Number> &initial_values,
        const std::string &subsection /*= "ParabolicModule"*/)
        : ParameterAcceptor(subsection)
        , mpi_ensemble_(mpi_ensemble)
        , hyperbolic_system_(&hyperbolic_system)
        , parabolic_system_(&parabolic_system)
        , offline_data_(&offline_data)
        , initial_values_(&initial_values)
        , id_violation_strategy_(IDViolationStrategy::warn)
        , cycle_(0)
        , n_restarts_(0)
        , n_corrections_(0)
        , n_warnings_(0)
        , n_iterations_velocity_(0.)
        , n_iterations_internal_energy_(0.)
    {
      use_gmg_velocity_ = false;
      add_parameter("multigrid velocity",
                    use_gmg_velocity_,
                    "Use geometric multigrid for velocity component");
 
      gmg_max_iter_vel_ = 12;
      add_parameter("multigrid velocity - max iter",
                    gmg_max_iter_vel_,
                    "Maximal number of CG iterations with GMG smoother");
 
      gmg_smoother_range_vel_ = 8.;
      add_parameter("multigrid velocity - chebyshev range",
                    gmg_smoother_range_vel_,
                    "Chebyshev smoother: eigenvalue range parameter");
 
      gmg_smoother_max_eig_vel_ = 2.0;
      add_parameter("multigrid velocity - chebyshev max eig",
                    gmg_smoother_max_eig_vel_,
                    "Chebyshev smoother: maximal eigenvalue");
 
      use_gmg_internal_energy_ = false;
      add_parameter("multigrid energy",
                    use_gmg_internal_energy_,
                    "Use geometric multigrid for internal energy component");
 
      gmg_max_iter_en_ = 15;
      add_parameter("multigrid energy - max iter",
                    gmg_max_iter_en_,
                    "Maximal number of CG iterations with GMG smoother");
 
      gmg_smoother_range_en_ = 15.;
      add_parameter("multigrid energy - chebyshev range",
                    gmg_smoother_range_en_,
                    "Chebyshev smoother: eigenvalue range parameter");
 
      gmg_smoother_max_eig_en_ = 2.0;
      add_parameter("multigrid energy - chebyshev max eig",
                    gmg_smoother_max_eig_en_,
                    "Chebyshev smoother: maximal eigenvalue");
 
      gmg_smoother_degree_ = 3;
      add_parameter("multigrid - chebyshev degree",
                    gmg_smoother_degree_,
                    "Chebyshev smoother: degree");
 
      gmg_smoother_n_cg_iter_ = 10;
      add_parameter(
          "multigrid - chebyshev cg iter",
          gmg_smoother_n_cg_iter_,
          "Chebyshev smoother: number of CG iterations to approximate "
          "eigenvalue");
 
      gmg_min_level_ = 0;
      add_parameter(
          "multigrid - min level",
          gmg_min_level_,
          "Minimal mesh level to be visited in the geometric multigrid "
          "cycle where the coarse grid solver (Chebyshev) is called");
 
      tolerance_ = Number(1.0e-12);
      add_parameter("tolerance", tolerance_, "Tolerance for linear solvers");
 
      tolerance_linfty_norm_ = false;
      add_parameter("tolerance linfty norm",
                    tolerance_linfty_norm_,
                    "Use the l_infty norm instead of the l_2 norm for the "
                    "stopping criterion");
    }
 
 
    template <int dim, typename Number>
    void ParabolicModule<dim, Number>::prepare()
    {
#ifdef DEBUG_OUTPUT
      std::cout << "ParabolicModule<dim, Number>::prepare()" << std::endl;
#endif
      /*
       * The cycle_ variabe is only used for gmg reinitialization, simply
       * reset it to zero on prepare().
       */
      cycle_ = 0;
 
      const auto &discretization = offline_data_->discretization();
      AssertThrow(discretization.ansatz() == Ansatz::cg_q1,
                  dealii::ExcMessage("The Navier-Stokes module currently only "
                                     "supports cG Q1 finite elements."));
 
      AssertThrow(!offline_data_->dof_handler().has_hp_capabilities(),
                  dealii::ExcMessage(
                      "The Navier-Stokes module currently does not support "
                      "DoFHandlers set up with hp capabilities."));
 
      /* Initialize vectors: */
 
      typename MatrixFree<dim, Number>::AdditionalData additional_data;
      additional_data.tasks_parallel_scheme =
          MatrixFree<dim, Number>::AdditionalData::none;
 
      matrix_free_.reinit(discretization.mapping(),
                          offline_data_->dof_handler(),
                          offline_data_->affine_constraints(),
                          discretization.quadrature_1d(),
                          additional_data);
 
      const auto &scalar_partitioner =
          matrix_free_.get_dof_info(0).vector_partitioner;
 
      velocity_.reinit(dim);
      velocity_rhs_.reinit(dim);
      for (unsigned int i = 0; i < dim; ++i) {
        velocity_.block(i).reinit(scalar_partitioner);
        velocity_rhs_.block(i).reinit(scalar_partitioner);
      }
 
      internal_energy_.reinit(scalar_partitioner);
      internal_energy_rhs_.reinit(scalar_partitioner);
 
      density_.reinit(scalar_partitioner);
 
      /* Initialize multigrid: */
 
      if (!use_gmg_velocity_ && !use_gmg_internal_energy_)
        return;
 
      const unsigned int n_levels =
          offline_data_->dof_handler().get_triangulation().n_global_levels();
      const unsigned int min_level = std::min(gmg_min_level_, n_levels - 1);
      MGLevelObject<IndexSet> relevant_sets(0, n_levels - 1);
      for (unsigned int level = 0; level < n_levels; ++level)
        relevant_sets[level] =
            dealii::DoFTools::extract_locally_relevant_level_dofs(
                offline_data_->dof_handler(), level);
      mg_constrained_dofs_.initialize(offline_data_->dof_handler(),
                                      relevant_sets);
      std::set<types::boundary_id> boundary_ids;
      boundary_ids.insert(Boundary::dirichlet);
      boundary_ids.insert(Boundary::no_slip);
      mg_constrained_dofs_.make_zero_boundary_constraints(
          offline_data_->dof_handler(), boundary_ids);
 
      typename MatrixFree<dim, float>::AdditionalData additional_data_level;
      additional_data_level.tasks_parallel_scheme =
          MatrixFree<dim, float>::AdditionalData::none;
 
      level_matrix_free_.resize(min_level, n_levels - 1);
      level_density_.resize(min_level, n_levels - 1);
      for (unsigned int level = min_level; level < n_levels; ++level) {
        additional_data_level.mg_level = level;
        AffineConstraints<double> constraints(relevant_sets[level],
                                              relevant_sets[level]);
        // constraints.add_lines(mg_constrained_dofs_.get_boundary_indices(level));
        // constraints.merge(mg_constrained_dofs_.get_level_constraints(level));
        constraints.close();
        level_matrix_free_[level].reinit(discretization.mapping(),
                                         offline_data_->dof_handler(),
                                         constraints,
                                         discretization.quadrature_1d(),
                                         additional_data_level);
        level_matrix_free_[level].initialize_dof_vector(level_density_[level]);
      }
 
      mg_transfer_velocity_.build(offline_data_->dof_handler(),
                                  mg_constrained_dofs_,
                                  level_matrix_free_);
      mg_transfer_energy_.build(offline_data_->dof_handler(),
                                level_matrix_free_);
    }
 
 
    template <int dim, typename Number>
    void ParabolicModule<dim, Number>::prepare_state_vector(
        StateVector<Number> & /*state_vector*/, Number /*t*/) const
    {
      /*
       * There is no parabolic part of the state vector for Navier-Stokes,
       * so we do nothing.
       */
    }
 
 
    template <int dim, typename Number>
    template <int stages>
    void ParabolicModule<dim, Number>::backward_euler_step(
        const StateVector<Number> &old_state_vector,
        const Number old_t,
        std::array<std::reference_wrapper<const StateVector<Number>>,
                   stages> /*stage_state_vectors*/,
        const std::array<Number, stages> /*stage_weights*/,
        StateVector<Number> &new_state_vector,
        Number tau) const
    {
      /* Backward Euler step to half time step, and extrapolate: */
 
      step(old_state_vector,
           old_t,
           new_state_vector,
           tau,
           /*crank_nicolson_extrapolation = */ false);
    }
 
 
    template <int dim, typename Number>
    void ParabolicModule<dim, Number>::crank_nicolson_step(
        const StateVector<Number> &old_state_vector,
        const Number old_t,
        StateVector<Number> &new_state_vector,
        Number tau) const
    {
      try {
        step(old_state_vector,
             old_t,
             new_state_vector,
             tau / Number(2.),
             /*crank_nicolson_extrapolation = */ true);
 
      } catch (Correction) {
 
        /*
         * Under very rare circumstances we might fail to perform a Crank
         * Nicolson step because the extrapolation step produced
         * inadmissible states. We could correct the update now by
         * performing a limiting step (either convex limiting, or flux
         * corrected transport)... but *meh*, just perform a backward Euler
         * step:
         */
        step(old_state_vector,
             old_t,
             new_state_vector,
             tau,
             /*crank_nicolson_extrapolation = */ false);
      }
    }
 
 
    template <int dim, typename Number>
    void ParabolicModule<dim, Number>::print_solver_statistics(
        std::ostream &output) const
    {
      output << "        [ " << std::setprecision(2) << std::fixed
             << n_iterations_velocity_
             << (use_gmg_velocity_ ? " GMG vel -- " : " CG vel -- ")
             << n_iterations_internal_energy_
             << (use_gmg_internal_energy_ ? " GMG int ]" : " CG int ]")
             << std::endl;
    }
 
 
    template <int dim, typename Number>
    void ParabolicModule<dim, Number>::step(
        const StateVector<Number> &old_state_vector,
        const Number t,
        StateVector<Number> &new_state_vector,
        Number tau,
        const bool crank_nicolson_extrapolation) const
    {
#ifdef DEBUG_OUTPUT
      std::cout << "ParabolicModule<dim, Number>::step()" << std::endl;
#endif
      constexpr ScalarNumber eps = std::numeric_limits<ScalarNumber>::epsilon();
 
      using HostSpace = dealii::MemorySpace::Host;
 
      copy_to_memory_space<HostSpace>(old_state_vector,
                                      StateVectorPart::hyperbolic);
      move_to_memory_space<HostSpace>(new_state_vector,
                                      StateVectorPart::hyperbolic);
 
      const auto old_U_view =
          old_state_vector.U.template view<problem_dimension>();
      const auto new_U_view =
          new_state_vector.U.template view<problem_dimension>();
 
      const auto lumped_mass_matrix_view =
          offline_data_->lumped_mass_matrix().template view<1>();
      const auto &affine_constraints = offline_data_->affine_constraints();
 
      /* Index ranges for the iteration over the sparsity pattern : */
 
      const unsigned int n_owned = offline_data_->n_locally_owned();
 
      const auto sparsity_simd_view =
          offline_data_->sparsity_pattern_simd().view();
 
      DiagonalMatrix<dim, Number> diagonal_matrix;
 
#ifdef DEBUG_OUTPUT
      std::cout << "        perform time-step with tau = " << tau << std::endl;
      if (crank_nicolson_extrapolation)
        std::cout << "        and extrapolate to t + 2 * tau" << std::endl;
#endif
 
      /*
       * Update MG matrices all 4 time steps; this is a balance because more
       * refreshes will render the approximation better, at some additional
       * cost.
       */
      const bool reinitialize_gmg = (cycle_++ % 4 == 0);
 
      /*
       * A boolean indicating that a restart is required.
       *
       * In our current implementation we set this boolean to true if the
       * backward Euler step produces an internal energy update that
       * violates the minimum principle, i.e., the minimum of the new
       * internal energy is smaller than the minimum of the old internal
       * energy.
       *
       * Depending on the chosen "id_violation_strategy" we either signal a
       * restart by throwing a "Restart" object, or we simply increase the
       * number of warnings.
       */
      std::atomic<bool> restart_needed = false;
 
      /*
       * A boolean indicating that we have to correct the high-order Crank
       * Nicolson update. Note that this is a truly exceptional case
       * indicating that the high-order update produced an inadmissible
       * state, *boo*.
       *
       * Our current limiting strategy is to simply fall back to perform a
       * single backward Euler step...
       */
      std::atomic<bool> correction_needed = false;
 
      /*
       * Step 1:
       *
       * Build right hand side for the velocity update.
       * Also initialize solution vectors for internal energy and velocity
       * update.
       */
      {
        ComputingTimer::Scope scope("time step [P] 1 - update velocities");
 
        const auto body = [&](auto sentinel, unsigned int i) {
          using T = decltype(sentinel);
 
          const auto view = hyperbolic_system_->template view<dim, T>();
 
          const auto U_i = old_U_view.template read_tensor<T>(i);
          const auto rho_i = view.density(U_i);
          const auto M_i = view.momentum(U_i);
          const auto rho_e_i = view.internal_energy(U_i);
          const auto m_i = lumped_mass_matrix_view.template read_entry<T>(i);
 
          write_entry<T>(density_, rho_i, i);
          /* (5.4a) */
          for (unsigned int d = 0; d < dim; ++d) {
            write_entry<T>(velocity_.block(d), M_i[d] / rho_i, i);
            write_entry<T>(velocity_rhs_.block(d), m_i * (M_i[d]), i);
          }
          write_entry<T>(internal_energy_, rho_e_i / rho_i, i);
        };
 
        cpu_simd_loop<Number>(
            "time_step_parabolic_1", body, 0, n_owned, n_owned);
 
        /*
         * Set up "strongly enforced" boundary conditions that are not stored
         * in the AffineConstraints map. In this case we enforce boundary
         * values by imposing them strongly in the iteration by setting the
         * initial vector and the right hand side to the right value:
         */
 
        const auto &boundary_map = offline_data_->boundary_map();
 
        for (auto entry : boundary_map) {
          // [i, normal, normal_mass, boundary_mass, id, position] = entry
          const auto i = std::get<0>(entry);
          if (i >= n_owned)
            continue;
 
          const auto normal = std::get<1>(entry);
          const auto id = std::get<4>(entry);
          const auto position = std::get<5>(entry);
 
          if (id == Boundary::slip) {
            /* Remove normal component of velocity: */
            Tensor<1, dim, Number> V_i;
            Tensor<1, dim, Number> RHS_i;
            for (unsigned int d = 0; d < dim; ++d) {
              V_i[d] = velocity_.block(d).local_element(i);
              RHS_i[d] = velocity_rhs_.block(d).local_element(i);
            }
            V_i -= 1. * (V_i * normal) * normal;
            RHS_i -= 1. * (RHS_i * normal) * normal;
            for (unsigned int d = 0; d < dim; ++d) {
              velocity_.block(d).local_element(i) = V_i[d];
              velocity_rhs_.block(d).local_element(i) = RHS_i[d];
            }
 
          } else if (id == Boundary::no_slip) {
 
            /* Set velocity to zero: */
            for (unsigned int d = 0; d < dim; ++d) {
              velocity_.block(d).local_element(i) = Number(0.);
              velocity_rhs_.block(d).local_element(i) = Number(0.);
            }
 
          } else if (id == Boundary::dirichlet) {
 
            /* Prescribe velocity: */
            const auto U_i = initial_values_->initial_state(position, t + tau);
            const auto view = hyperbolic_system_->template view<dim, Number>();
            const auto rho_i = view.density(U_i);
            const auto V_i = view.momentum(U_i) / rho_i;
            const auto e_i = view.internal_energy(U_i) / rho_i;
 
            for (unsigned int d = 0; d < dim; ++d) {
              velocity_.block(d).local_element(i) = V_i[d];
              velocity_rhs_.block(d).local_element(i) = V_i[d];
            }
 
            internal_energy_.local_element(i) = e_i;
          }
        }
 
        /*
         * Zero out constrained degrees of freedom due to hanging nodes and
         * periodic boundary conditions. These boundary conditions are
         * enforced by modifying the stencil - consequently we have to
         * remove constrained dofs from the linear system.
         */
 
        affine_constraints.set_zero(density_);
        affine_constraints.set_zero(internal_energy_);
        for (unsigned int d = 0; d < dim; ++d) {
          affine_constraints.set_zero(velocity_.block(d));
          affine_constraints.set_zero(velocity_rhs_.block(d));
        }
 
        /* Prepare preconditioner: */
 
        diagonal_matrix.reinit(
            lumped_mass_matrix_view, density_, affine_constraints);
 
        if (use_gmg_velocity_ && reinitialize_gmg) {
          MGLevelObject<typename PreconditionChebyshev<
              VelocityMatrix<dim, float, Number>,
              LinearAlgebra::distributed::BlockVector<float>,
              DiagonalMatrix<dim, float>>::AdditionalData>
              smoother_data(level_matrix_free_.min_level(),
                            level_matrix_free_.max_level());
 
          level_velocity_matrices_.resize(level_matrix_free_.min_level(),
                                          level_matrix_free_.max_level());
          mg_transfer_velocity_.interpolate_to_mg(
              offline_data_->dof_handler(), level_density_, density_);
 
          for (unsigned int level = level_matrix_free_.min_level();
               level <= level_matrix_free_.max_level();
               ++level) {
            level_velocity_matrices_[level].initialize(
                *parabolic_system_,
                *offline_data_,
                level_matrix_free_[level],
                level_density_[level],
                tau,
                level);
            level_velocity_matrices_[level].compute_diagonal(
                smoother_data[level].preconditioner);
            if (level == level_matrix_free_.min_level()) {
              smoother_data[level].degree = numbers::invalid_unsigned_int;
              smoother_data[level].eig_cg_n_iterations = 500;
              smoother_data[level].smoothing_range = 1e-3;
            } else {
              smoother_data[level].degree = gmg_smoother_degree_;
              smoother_data[level].eig_cg_n_iterations =
                  gmg_smoother_n_cg_iter_;
              smoother_data[level].smoothing_range = gmg_smoother_range_vel_;
              if (gmg_smoother_n_cg_iter_ == 0)
                smoother_data[level].max_eigenvalue = gmg_smoother_max_eig_vel_;
            }
          }
          mg_smoother_velocity_.initialize(level_velocity_matrices_,
                                           smoother_data);
        }
      }
 
      Number e_min_old;
 
      {
        ComputingTimer::Scope scope(
            "time step [X] _ - synchronization barriers");
 
        /* Compute the global minimum of the internal energy: */
 
        // .begin() and .end() denote the locally owned index range:
        e_min_old =
            *std::min_element(internal_energy_.begin(), internal_energy_.end());
 
        e_min_old = Utilities::MPI::min(e_min_old,
                                        mpi_ensemble_.ensemble_communicator());
 
        // FIXME: create a meaningful relaxation based on global mesh size min.
        constexpr Number eps = std::numeric_limits<Number>::epsilon();
        e_min_old *= (1. - 1000. * eps);
      }
 
      /*
       * Step 1: Solve velocity update:
       */
      {
        ComputingTimer::Scope scope("time step [P] 1 - update velocities");
 
        VelocityMatrix<dim, Number, Number> velocity_operator;
        velocity_operator.initialize(
            *parabolic_system_, *offline_data_, matrix_free_, density_, tau);
 
        const auto tolerance_velocity =
            (tolerance_linfty_norm_ ? velocity_rhs_.linfty_norm()
                                    : velocity_rhs_.l2_norm()) *
            tolerance_;
 
        /*
         * Multigrid might lack robustness for some cases, so in case it takes
         * too many iterations we better switch to the more robust plain
         * conjugate gradient method.
         */
        try {
          if (!use_gmg_velocity_)
            throw SolverControl::NoConvergence(0, 0.);
 
          using bvt_float = LinearAlgebra::distributed::BlockVector<float>;
 
          MGCoarseGridApplySmoother<bvt_float> mg_coarse;
          mg_coarse.initialize(mg_smoother_velocity_);
 
          mg::Matrix<bvt_float> mg_matrix(level_velocity_matrices_);
 
          Multigrid<bvt_float> mg(mg_matrix,
                                  mg_coarse,
                                  mg_transfer_velocity_,
                                  mg_smoother_velocity_,
                                  mg_smoother_velocity_,
                                  level_velocity_matrices_.min_level(),
                                  level_velocity_matrices_.max_level());
 
          const auto &dof_handler = offline_data_->dof_handler();
          PreconditionMG<dim, bvt_float, MGTransferVelocity<dim, float>>
              preconditioner(dof_handler, mg, mg_transfer_velocity_);
 
          SolverControl solver_control(gmg_max_iter_vel_, tolerance_velocity);
          SolverCG<BlockHostVector> solver(solver_control);
          solver.solve(
              velocity_operator, velocity_, velocity_rhs_, preconditioner);
 
          /* update exponential moving average */
          n_iterations_velocity_ =
              0.9 * n_iterations_velocity_ + 0.1 * solver_control.last_step();
 
        } catch (SolverControl::NoConvergence &) {
 
          SolverControl solver_control(1000, tolerance_velocity);
          SolverCG<BlockHostVector> solver(solver_control);
          solver.solve(
              velocity_operator, velocity_, velocity_rhs_, diagonal_matrix);
 
          /* update exponential moving average, counting also GMG iterations */
          n_iterations_velocity_ *= 0.9;
          n_iterations_velocity_ +=
              0.1 * (use_gmg_velocity_ ? gmg_max_iter_vel_ : 0) +
              0.1 * solver_control.last_step();
        }
      }
 
      /*
       * Step 2: Build internal energy right hand side:
       */
      {
        ComputingTimer::Scope scope("time step [P] 2 - update internal energy");
 
        /* Compute m_i K_i^{n+1/2}:  (5.5) */
        matrix_free_
            .template cell_loop<ScalarHostVector<Number>, BlockHostVector>(
                [this](const auto &data,
                       auto &dst,
                       const auto &src,
                       const auto cell_range) {
                  FEEvaluation<dim, order_fe, order_quad, dim, Number> velocity(
                      data);
                  FEEvaluation<dim, order_fe, order_quad, 1, Number> energy(
                      data);
 
                  const auto mu = parabolic_system_->mu();
                  const auto lambda = parabolic_system_->lambda();
 
                  for (unsigned int cell = cell_range.first;
                       cell < cell_range.second;
                       ++cell) {
                    velocity.reinit(cell);
                    energy.reinit(cell);
                    velocity.gather_evaluate(src, EvaluationFlags::gradients);
 
                    for (unsigned int q = 0; q < velocity.n_q_points; ++q) {
                      if constexpr (dim == 1) {
                        /* Workaround: no symmetric gradient for dim == 1: */
                        const auto gradient = velocity.get_gradient(q);
                        auto S = (4. / 3. * mu + lambda) * gradient;
                        energy.submit_value(gradient * S, q);
 
                      } else {
 
                        const auto symmetric_gradient =
                            velocity.get_symmetric_gradient(q);
                        const auto divergence = trace(symmetric_gradient);
                        auto S = 2. * mu * symmetric_gradient;
                        for (unsigned int d = 0; d < dim; ++d)
                          S[d][d] += (lambda - 2. / 3. * mu) * divergence;
                        energy.submit_value(symmetric_gradient * S, q);
                      }
                    }
                    energy.integrate_scatter(EvaluationFlags::values, dst);
                  }
                },
                internal_energy_rhs_,
                velocity_,
                /* zero destination */ true);
 
        const auto lumped_mass_matrix_view =
            offline_data_->lumped_mass_matrix().template view<1>();
 
        const auto body = [&](auto sentinel, unsigned int i) {
          using T = decltype(sentinel);
 
          const auto view = hyperbolic_system_->template view<dim, T>();
 
          const auto rhs_i = read_entry<T>(internal_energy_rhs_, i);
          const auto m_i = lumped_mass_matrix_view.template read_entry<T>(i);
          const auto rho_i = read_entry<T>(density_, i);
          const auto e_i = read_entry<T>(internal_energy_, i);
 
          const auto U_i = old_U_view.template read_tensor<T>(i);
          const auto V_i = view.momentum(U_i) / rho_i;
 
          dealii::Tensor<1, dim, T> V_i_new;
          for (unsigned int d = 0; d < dim; ++d) {
            V_i_new[d] = read_entry<T>(velocity_.block(d), i);
          }
 
          /*
           * For backward Euler we have to add this algebraic correction
           * to ensure conservation of total energy.
           */
          const auto correction =
              crank_nicolson_extrapolation
                  ? T(0.)
                  : Number(0.5) * (V_i - V_i_new).norm_square();
 
          /* rhs_i contains already m_i K_i^{n+1/2} */
          const auto result = m_i * rho_i * (e_i + correction) + tau * rhs_i;
          write_entry<T>(internal_energy_rhs_, result, i);
        };
 
        cpu_simd_loop<Number>(
            "time_step_parabolic_2", body, 0, n_owned, n_owned);
 
        /*
         * Set up "strongly enforced" boundary conditions that are not stored
         * in the AffineConstraints map: We enforce Neumann conditions (i.e.,
         * insulating boundary conditions) everywhere except for Dirichlet
         * boundaries where we have to enforce prescribed conditions:
         */
 
        const auto &boundary_map = offline_data_->boundary_map();
 
        for (auto entry : boundary_map) {
          // [i, normal, normal_mass, boundary_mass, id, position] = entry
          const auto i = std::get<0>(entry);
          if (i >= n_owned)
            continue;
 
          const auto id = std::get<4>(entry);
          const auto position = std::get<5>(entry);
 
          if (id == Boundary::dirichlet) {
            /* Prescribe internal energy: */
            const auto U_i = initial_values_->initial_state(position, t + tau);
            const auto view = hyperbolic_system_->template view<dim, Number>();
            const auto rho_i = view.density(U_i);
            const auto e_i = view.internal_energy(U_i) / rho_i;
            internal_energy_rhs_.local_element(i) = e_i;
          }
        }
 
        /*
         * Zero out constrained degrees of freedom due to hanging nodes and
         * periodic boundary conditions. These boundary conditions are
         * enforced by modifying the stencil - consequently we have to
         * remove constrained dofs from the linear system.
         */
        affine_constraints.set_zero(internal_energy_);
        affine_constraints.set_zero(internal_energy_rhs_);
 
        /*
         * Update MG matrices all 4 time steps; this is a balance because more
         * refreshes will render the approximation better, at some additional
         * cost.
         */
        if (use_gmg_internal_energy_ && reinitialize_gmg) {
          MGLevelObject<typename PreconditionChebyshev<
              EnergyMatrix<dim, float, Number>,
              LinearAlgebra::distributed::Vector<float>>::AdditionalData>
              smoother_data(level_matrix_free_.min_level(),
                            level_matrix_free_.max_level());
 
          level_energy_matrices_.resize(level_matrix_free_.min_level(),
                                        level_matrix_free_.max_level());
 
          for (unsigned int level = level_matrix_free_.min_level();
               level <= level_matrix_free_.max_level();
               ++level) {
            level_energy_matrices_[level].initialize(
                *offline_data_,
                level_matrix_free_[level],
                level_density_[level],
                tau * parabolic_system_->cv_inverse_kappa(),
                level);
            level_energy_matrices_[level].compute_diagonal(
                smoother_data[level].preconditioner);
            if (level == level_matrix_free_.min_level()) {
              smoother_data[level].degree = numbers::invalid_unsigned_int;
              smoother_data[level].eig_cg_n_iterations = 500;
              smoother_data[level].smoothing_range = 1e-3;
            } else {
              smoother_data[level].degree = gmg_smoother_degree_;
              smoother_data[level].eig_cg_n_iterations =
                  gmg_smoother_n_cg_iter_;
              smoother_data[level].smoothing_range = gmg_smoother_range_en_;
              if (gmg_smoother_n_cg_iter_ == 0)
                smoother_data[level].max_eigenvalue = gmg_smoother_max_eig_en_;
            }
          }
          mg_smoother_energy_.initialize(level_energy_matrices_, smoother_data);
        }
      }
 
      /*
       * Step 2: Solve internal energy update:
       */
      {
        ComputingTimer::Scope scope("time step [P] 2 - update internal energy");
 
        EnergyMatrix<dim, Number, Number> energy_operator;
        const auto &kappa = parabolic_system_->cv_inverse_kappa();
        energy_operator.initialize(
            *offline_data_, matrix_free_, density_, tau * kappa);
 
        const auto tolerance_internal_energy =
            (tolerance_linfty_norm_ ? internal_energy_rhs_.linfty_norm()
                                    : internal_energy_rhs_.l2_norm()) *
            tolerance_;
 
        try {
          if (!use_gmg_internal_energy_)
            throw SolverControl::NoConvergence(0, 0.);
 
          using vt_float = LinearAlgebra::distributed::Vector<float>;
          MGCoarseGridApplySmoother<vt_float> mg_coarse;
          mg_coarse.initialize(mg_smoother_energy_);
          mg::Matrix<vt_float> mg_matrix(level_energy_matrices_);
 
          Multigrid<vt_float> mg(mg_matrix,
                                 mg_coarse,
                                 mg_transfer_energy_,
                                 mg_smoother_energy_,
                                 mg_smoother_energy_,
                                 level_energy_matrices_.min_level(),
                                 level_energy_matrices_.max_level());
 
          const auto &dof_handler = offline_data_->dof_handler();
          PreconditionMG<dim, vt_float, MGTransferEnergy<dim, float>>
              preconditioner(dof_handler, mg, mg_transfer_energy_);
 
          SolverControl solver_control(gmg_max_iter_en_,
                                       tolerance_internal_energy);
          SolverCG<ScalarHostVector<Number>> solver(solver_control);
          solver.solve(energy_operator,
                       internal_energy_,
                       internal_energy_rhs_,
                       preconditioner);
 
          /* update exponential moving average */
          n_iterations_internal_energy_ = 0.9 * n_iterations_internal_energy_ +
                                          0.1 * solver_control.last_step();
 
        } catch (SolverControl::NoConvergence &) {
 
          SolverControl solver_control(1000, tolerance_internal_energy);
          SolverCG<ScalarHostVector<Number>> solver(solver_control);
          solver.solve(energy_operator,
                       internal_energy_,
                       internal_energy_rhs_,
                       diagonal_matrix);
 
          /* update exponential moving average, counting also GMG iterations */
          n_iterations_internal_energy_ *= 0.9;
          n_iterations_internal_energy_ +=
              0.1 * (use_gmg_internal_energy_ ? gmg_max_iter_en_ : 0) +
              0.1 * solver_control.last_step();
        }
      }
 
      /*
       * Step 3: Copy vectors and check for local minimum principle on
       * internal energy:
       *
       * FIXME: Memory access is suboptimal...
       */
      {
        ComputingTimer::Scope scope("time step [P] 3 - write back vectors");
 
        const auto body = [&](auto sentinel, unsigned int i) {
          using T = decltype(sentinel);
 
          const auto view = hyperbolic_system_->template view<dim, T>();
 
          /* Skip constrained degrees of freedom: */
          const unsigned int row_length = sparsity_simd_view.row_length(i);
          if (row_length == 1)
            return;
 
          auto U_i = old_U_view.template read_tensor<T>(i);
          const auto rho_i = view.density(U_i);
 
          Tensor<1, dim, T> m_i_new;
          for (unsigned int d = 0; d < dim; ++d) {
            m_i_new[d] = rho_i * read_entry<T>(velocity_.block(d), i);
          }
 
          auto rho_e_i_new = rho_i * read_entry<T>(internal_energy_, i);
 
          /*
           * Check that the backward Euler step itself (which is our "low
           * order" update) satisfies bounds. If not, signal a restart.
           */
 
          if (!(T(0.) == std::max(T(0.), rho_i * e_min_old - rho_e_i_new))) {
#ifdef DEBUG_OUTPUT
            std::cout << std::fixed << std::setprecision(16);
            const auto e_i_new = rho_e_i_new / rho_i;
            std::cout << "Bounds violation: internal energy (critical)!\n"
                      << "\t\te_min_old:         " << e_min_old << "\n"
                      << "\t\te_min_old (delta): "
                      << negative_part(e_i_new - e_min_old) << "\n"
                      << "\t\te_min_new:         " << e_i_new << "\n"
                      << std::endl;
#endif
            restart_needed = true;
          }
 
          if (crank_nicolson_extrapolation) {
            m_i_new = Number(2.0) * m_i_new - view.momentum(U_i);
            rho_e_i_new = Number(2.0) * rho_e_i_new - view.internal_energy(U_i);
 
            /*
             * If we do perform an extrapolation step for Crank Nicolson
             * we have to check whether we maintain admissibility
             */
 
            if (!(T(0.) ==
                  std::max(T(0.), eps * rho_i * e_min_old - rho_e_i_new))) {
#ifdef DEBUG_OUTPUT
              std::cout << std::fixed << std::setprecision(16);
              const auto e_i_new = rho_e_i_new / rho_i;
 
              std::cout << "Bounds violation: high-order internal energy!"
                        << "\t\te_min_new:         " << e_i_new << "\n"
                        << "\t\t-- correction required --" << std::endl;
#endif
              correction_needed = true;
            }
          }
 
          const auto E_i_new = rho_e_i_new + 0.5 * m_i_new * m_i_new / rho_i;
 
          for (unsigned int d = 0; d < dim; ++d)
            U_i[1 + d] = m_i_new[d];
          U_i[1 + dim] = E_i_new;
 
          new_U_view.template write_tensor<T>(U_i, i);
        };
 
        cpu_simd_loop<Number>(
            "time_step_parabolic_3", body, 0, n_owned, n_owned);
 
        new_U_view.update_ghost_values();
      }
 
      {
        ComputingTimer::Scope scope(
            "time step [X] _ - synchronization barriers");
 
        /*
         * Synchronize whether we have to restart or correct the time step.
         * Even though the restart/correction condition itself only affects
         * the local ensemble we nevertheless need to synchronize the
         * boolean in case we perform synchronized global time steps.
         * (Otherwise different ensembles might end up with a different
         * time step.)
         */
 
        restart_needed.store(Utilities::MPI::logical_or(
            restart_needed.load(),
            mpi_ensemble_.synchronization_communicator()));
 
        correction_needed.store(Utilities::MPI::logical_or(
            correction_needed.load(),
            mpi_ensemble_.synchronization_communicator()));
      }
 
      if (correction_needed) {
        /* If we can do a restart try that first: */
        if (id_violation_strategy_ == IDViolationStrategy::raise_exception) {
          n_restarts_++;
          /* Half step size is a good heuristic: */
          throw Restart{Number(0.5) * tau};
        } else {
          n_corrections_++;
          throw Correction();
        }
      }
 
      if (restart_needed) {
        switch (id_violation_strategy_) {
        case IDViolationStrategy::warn:
          n_warnings_++;
          break;
        case IDViolationStrategy::raise_exception:
          n_restarts_++;
          /* Half step size is a good heuristic: */
          throw Restart{Number(0.5) * tau};
        }
      }
    }
 
  } // namespace NavierStokes
} /* namespace ryujin */