/* ---------------------------------------------------------------------
 *
 * Copyright (C) 2006 - 2022 by the deal.II authors
 *
 * This file is part of the deal.II library.
 *
 * The deal.II library is free software; you can use it, redistribute
 * it, and/or modify it under the terms of the GNU Lesser General
 * Public License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 * The full text of the license can be found in the file LICENSE.md at
 * the top level directory of deal.II.
 *
 * ---------------------------------------------------------------------

 *
 * Authors: Yan Li, Wolfgang Bangerth, Texas A&M University, 2006
 */


// This program is an adaptation of step-20 and includes some technique of DG
// methods from step-12. A good part of the program is therefore very similar
// to step-20 and we will not comment again on these parts. Only the new stuff
// will be discussed in more detail.

// @sect3{Include files}

// All of these include files have been used before:
#include <deal.II/base/quadrature_lib.h>
#include <deal.II/base/logstream.h>
#include <deal.II/base/function.h>

#include <deal.II/lac/block_vector.h>
#include <deal.II/lac/full_matrix.h>
#include <deal.II/lac/block_sparse_matrix.h>
#include <deal.II/lac/solver_cg.h>
#include <deal.II/lac/precondition.h>
#include <deal.II/lac/affine_constraints.h>

#include <deal.II/grid/tria.h>
#include <deal.II/grid/grid_generator.h>
#include <deal.II/grid/grid_tools.h>

#include <deal.II/dofs/dof_handler.h>
#include <deal.II/dofs/dof_renumbering.h>
#include <deal.II/dofs/dof_tools.h>

#include <deal.II/fe/fe_raviart_thomas.h>
#include <deal.II/fe/fe_dgq.h>
#include <deal.II/fe/fe_system.h>
#include <deal.II/fe/fe_values.h>

#include <deal.II/numerics/vector_tools.h>
#include <deal.II/numerics/matrix_tools.h>
#include <deal.II/numerics/data_out.h>

#include <iostream>
#include <fstream>

// In this program, we use a tensor-valued coefficient. Since it may have a
// spatial dependence, we consider it a tensor-valued function. The following
// include file provides the <code>TensorFunction</code> class that offers
// such functionality:
#include <deal.II/base/tensor_function.h>

// Additionally, we use the class <code>DiscreteTime</code> to perform
// operations related to time incrementation.
#include <deal.II/base/discrete_time.h>

// The last step is as in all previous programs:
namespace Step21
{
  using namespace dealii;


  // @sect3{The <code>TwoPhaseFlowProblem</code> class}

  // This is the main class of the program. It is close to the one of step-20,
  // but with a few additional functions:
  //
  // <ul> <li><code>assemble_rhs_S</code> assembles the right hand side of the
  //   saturation equation. As explained in the introduction, this can't be
  //   integrated into <code>assemble_rhs</code> since it depends on the
  //   velocity that is computed in the first part of the time step.
  //
  //   <li><code>get_maximal_velocity</code> does as its name suggests. This
  //   function is used in the computation of the time step size.
  //
  //   <li><code>project_back_saturation</code> resets all saturation degrees
  //   of freedom with values less than zero to zero, and all those with
  //   saturations greater than one to one.  </ul>
  //
  // The rest of the class should be pretty much obvious. The
  // <code>viscosity</code> variable stores the viscosity $\mu$ that enters
  // several of the formulas in the nonlinear equations. The variable
  // <code>time</code> keeps track of the time information within the
  // simulation.
  template <int dim>
  class TwoPhaseFlowProblem
  {
  public:
    TwoPhaseFlowProblem(const unsigned int degree);
    void run();

  private:
    void   make_grid_and_dofs();
    void   assemble_system();
    void   assemble_rhs_S();
    double get_maximal_velocity() const;
    void   solve();
    void   project_back_saturation();
    void   output_results() const;

    const unsigned int degree;

    Triangulation<dim> triangulation;
    FESystem<dim>      fe;
    DoFHandler<dim>    dof_handler;

    BlockSparsityPattern      sparsity_pattern;
    BlockSparseMatrix<double> system_matrix;

    const unsigned int n_refinement_steps;

    DiscreteTime time;
    double       viscosity;

    BlockVector<double> solution;
    BlockVector<double> old_solution;
    BlockVector<double> system_rhs;
  };


  // @sect3{Equation data}

  // @sect4{Pressure right hand side}

  // At present, the right hand side of the pressure equation is simply the
  // zero function. However, the rest of the program is fully equipped to deal
  // with anything else, if this is desired:
  template <int dim>
  class PressureRightHandSide : public Function<dim>
  {
  public:
    PressureRightHandSide()
      : Function<dim>(1)
    {}

    virtual double value(const Point<dim> & /*p*/,
                         const unsigned int /*component*/ = 0) const override
    {
      return 0;
    }
  };



  // @sect4{Pressure boundary values}

  // The next are pressure boundary values. As mentioned in the introduction,
  // we choose a linear pressure field:
  template <int dim>
  class PressureBoundaryValues : public Function<dim>
  {
  public:
    PressureBoundaryValues()
      : Function<dim>(1)
    {}

    virtual double value(const Point<dim> &p,
                         const unsigned int /*component*/ = 0) const override
    {
      return 1 - p[0];
    }
  };



  // @sect4{Saturation boundary values}

  // Then we also need boundary values on the inflow portions of the
  // boundary. The question whether something is an inflow part is decided
  // when assembling the right hand side, we only have to provide a functional
  // description of the boundary values. This is as explained in the
  // introduction:
  template <int dim>
  class SaturationBoundaryValues : public Function<dim>
  {
  public:
    SaturationBoundaryValues()
      : Function<dim>(1)
    {}

    virtual double value(const Point<dim> &p,
                         const unsigned int /*component*/ = 0) const override
    {
      if (p[0] == 0)
        return 1;
      else
        return 0;
    }
  };



  // @sect4{Initial data}

  // Finally, we need initial data. In reality, we only need initial data for
  // the saturation, but we are lazy, so we will later, before the first time
  // step, simply interpolate the entire solution for the previous time step
  // from a function that contains all vector components.
  //
  // We therefore simply create a function that returns zero in all
  // components. We do that by simply forward every function to the
  // Functions::ZeroFunction class. Why not use that right away in the places of
  // this program where we presently use the <code>InitialValues</code> class?
  // Because this way it is simpler to later go back and choose a different
  // function for initial values.
  template <int dim>
  class InitialValues : public Function<dim>
  {
  public:
    InitialValues()
      : Function<dim>(dim + 2)
    {}

    virtual double value(const Point<dim> & p,
                         const unsigned int component = 0) const override
    {
      return Functions::ZeroFunction<dim>(dim + 2).value(p, component);
    }

    virtual void vector_value(const Point<dim> &p,
                              Vector<double> &  values) const override
    {
      Functions::ZeroFunction<dim>(dim + 2).vector_value(p, values);
    }
  };



  // @sect3{The inverse permeability tensor}

  // As announced in the introduction, we implement two different permeability
  // tensor fields. Each of them we put into a namespace of its own, so that
  // it will be easy later to replace use of one by the other in the code.

  // @sect4{Single curving crack permeability}

  // The first function for the permeability was the one that models a single
  // curving crack. It was already used at the end of step-20, and its
  // functional form is given in the introduction of the present tutorial
  // program. As in some previous programs, we have to declare a (seemingly
  // unnecessary) default constructor of the KInverse class to avoid warnings
  // from some compilers:
  namespace SingleCurvingCrack
  {
    template <int dim>
    class KInverse : public TensorFunction<2, dim>
    {
    public:
      KInverse()
        : TensorFunction<2, dim>()
      {}

      virtual void
      value_list(const std::vector<Point<dim>> &points,
                 std::vector<Tensor<2, dim>> &  values) const override
      {
        AssertDimension(points.size(), values.size());

        for (unsigned int p = 0; p < points.size(); ++p)
          {
            values[p].clear();

            const double distance_to_flowline =
              std::fabs(points[p][1] - 0.5 - 0.1 * std::sin(10 * points[p][0]));

            const double permeability =
              std::max(std::exp(-(distance_to_flowline * distance_to_flowline) /
                                (0.1 * 0.1)),
                       0.01);

            for (unsigned int d = 0; d < dim; ++d)
              values[p][d][d] = 1. / permeability;
          }
      }
    };
  } // namespace SingleCurvingCrack


  // @sect4{Random medium permeability}

  // This function does as announced in the introduction, i.e. it creates an
  // overlay of exponentials at random places. There is one thing worth
  // considering for this class. The issue centers around the problem that the
  // class creates the centers of the exponentials using a random function. If
  // we therefore created the centers each time we create an object of the
  // present type, we would get a different list of centers each time. That's
  // not what we expect from classes of this type: they should reliably
  // represent the same function.
  //
  // The solution to this problem is to make the list of centers a static
  // member variable of this class, i.e. there exists exactly one such
  // variable for the entire program, rather than for each object of this
  // type. That's exactly what we are going to do.
  //
  // The next problem, however, is that we need a way to initialize this
  // variable. Since this variable is initialized at the beginning of the
  // program, we can't use a regular member function for that since there may
  // not be an object of this type around at the time. The C++ standard
  // therefore says that only non-member and static member functions can be
  // used to initialize a static variable. We use the latter possibility by
  // defining a function <code>get_centers</code> that computes the list of
  // center points when called.
  //
  // Note that this class works just fine in both 2d and 3d, with the only
  // difference being that we use more points in 3d: by experimenting we find
  // that we need more exponentials in 3d than in 2d (we have more ground to
  // cover, after all, if we want to keep the distance between centers roughly
  // equal), so we choose 40 in 2d and 100 in 3d. For any other dimension, the
  // function does presently not know what to do so simply throws an exception
  // indicating exactly this.
  namespace RandomMedium
  {
    template <int dim>
    class KInverse : public TensorFunction<2, dim>
    {
    public:
      KInverse()
        : TensorFunction<2, dim>()
      {}

      virtual void
      value_list(const std::vector<Point<dim>> &points,
                 std::vector<Tensor<2, dim>> &  values) const override
      {
        AssertDimension(points.size(), values.size());

        for (unsigned int p = 0; p < points.size(); ++p)
          {
            values[p].clear();

            double permeability = 0;
            for (unsigned int i = 0; i < centers.size(); ++i)
              permeability += std::exp(-(points[p] - centers[i]).norm_square() /
                                       (0.05 * 0.05));

            const double normalized_permeability =
              std::min(std::max(permeability, 0.01), 4.);

            for (unsigned int d = 0; d < dim; ++d)
              values[p][d][d] = 1. / normalized_permeability;
          }
      }

    private:
      static std::vector<Point<dim>> centers;

      static std::vector<Point<dim>> get_centers()
      {
        const unsigned int N =
          (dim == 2 ? 40 : (dim == 3 ? 100 : throw ExcNotImplemented()));

        std::vector<Point<dim>> centers_list(N);
        for (unsigned int i = 0; i < N; ++i)
          for (unsigned int d = 0; d < dim; ++d)
            centers_list[i][d] = static_cast<double>(rand()) / RAND_MAX;

        return centers_list;
      }
    };



    template <int dim>
    std::vector<Point<dim>>
      KInverse<dim>::centers = KInverse<dim>::get_centers();
  } // namespace RandomMedium



  // @sect3{The inverse mobility and saturation functions}

  // There are two more pieces of data that we need to describe, namely the
  // inverse mobility function and the saturation curve. Their form is also
  // given in the introduction:
  double mobility_inverse(const double S, const double viscosity)
  {
    return 1.0 / (1.0 / viscosity * S * S + (1 - S) * (1 - S));
  }

  double fractional_flow(const double S, const double viscosity)
  {
    return S * S / (S * S + viscosity * (1 - S) * (1 - S));
  }



  // @sect3{Linear solvers and preconditioners}

  // The linear solvers we use are also completely analogous to the ones used
  // in step-20. The following classes are therefore copied verbatim from
  // there. Note that the classes here are not only copied from
  // step-20, but also duplicate classes in deal.II. In a future version of this
  // example, they should be replaced by an efficient method, though. There is a
  // single change: if the size of a linear system is small, i.e. when the mesh
  // is very coarse, then it is sometimes not sufficient to set a maximum of
  // <code>src.size()</code> CG iterations before the solver in the
  // <code>vmult()</code> function converges. (This is, of course, a result of
  // numerical round-off, since we know that on paper, the CG method converges
  // in at most <code>src.size()</code> steps.) As a consequence, we set the
  // maximum number of iterations equal to the maximum of the size of the linear
  // system and 200.
  template <class MatrixType>
  class InverseMatrix : public Subscriptor
  {
  public:
    InverseMatrix(const MatrixType &m)
      : matrix(&m)
    {}

    void vmult(Vector<double> &dst, const Vector<double> &src) const
    {
      SolverControl solver_control(std::max<unsigned int>(src.size(), 200),
                                   1e-8 * src.l2_norm());
      SolverCG<Vector<double>> cg(solver_control);

      dst = 0;

      cg.solve(*matrix, dst, src, PreconditionIdentity());
    }

  private:
    const SmartPointer<const MatrixType> matrix;
  };



  class SchurComplement : public Subscriptor
  {
  public:
    SchurComplement(const BlockSparseMatrix<double> &          A,
                    const InverseMatrix<SparseMatrix<double>> &Minv)
      : system_matrix(&A)
      , m_inverse(&Minv)
      , tmp1(A.block(0, 0).m())
      , tmp2(A.block(0, 0).m())
    {}

    void vmult(Vector<double> &dst, const Vector<double> &src) const
    {
      system_matrix->block(0, 1).vmult(tmp1, src);
      m_inverse->vmult(tmp2, tmp1);
      system_matrix->block(1, 0).vmult(dst, tmp2);
    }

  private:
    const SmartPointer<const BlockSparseMatrix<double>>           system_matrix;
    const SmartPointer<const InverseMatrix<SparseMatrix<double>>> m_inverse;

    mutable Vector<double> tmp1, tmp2;
  };



  class ApproximateSchurComplement : public Subscriptor
  {
  public:
    ApproximateSchurComplement(const BlockSparseMatrix<double> &A)
      : system_matrix(&A)
      , tmp1(A.block(0, 0).m())
      , tmp2(A.block(0, 0).m())
    {}

    void vmult(Vector<double> &dst, const Vector<double> &src) const
    {
      system_matrix->block(0, 1).vmult(tmp1, src);
      system_matrix->block(0, 0).precondition_Jacobi(tmp2, tmp1);
      system_matrix->block(1, 0).vmult(dst, tmp2);
    }

  private:
    const SmartPointer<const BlockSparseMatrix<double>> system_matrix;

    mutable Vector<double> tmp1, tmp2;
  };



  // @sect3{<code>TwoPhaseFlowProblem</code> class implementation}

  // Here now the implementation of the main class. Much of it is actually
  // copied from step-20, so we won't comment on it in much detail. You should
  // try to get familiar with that program first, then most of what is
  // happening here should be mostly clear.

  // @sect4{TwoPhaseFlowProblem::TwoPhaseFlowProblem}

  // First for the constructor. We use $RT_k \times DQ_k \times DQ_k$
  // spaces. For initializing the DiscreteTime object, we don't set the time
  // step size in the constructor because we don't have its value yet.
  // The time step size is initially set to zero, but it will be computed
  // before it is needed to increment time, as described in a subsection of
  // the introduction. The time object internally prevents itself from being
  // incremented when $dt = 0$, forcing us to set a non-zero desired size for
  // $dt$ before advancing time.
  template <int dim>
  TwoPhaseFlowProblem<dim>::TwoPhaseFlowProblem(const unsigned int degree)
    : degree(degree)
    , fe(FE_RaviartThomas<dim>(degree),
         1,
         FE_DGQ<dim>(degree),
         1,
         FE_DGQ<dim>(degree),
         1)
    , dof_handler(triangulation)
    , n_refinement_steps(5)
    , time(/*start time*/ 0., /*end time*/ 1.)
    , viscosity(0.2)
  {}



  // @sect4{TwoPhaseFlowProblem::make_grid_and_dofs}

  // This next function starts out with well-known functions calls that create
  // and refine a mesh, and then associate degrees of freedom with it. It does
  // all the same things as in step-20, just now for three components instead
  // of two.
  template <int dim>
  void TwoPhaseFlowProblem<dim>::make_grid_and_dofs()
  {
    GridGenerator::hyper_cube(triangulation, 0, 1);
    triangulation.refine_global(n_refinement_steps);

    dof_handler.distribute_dofs(fe);
    DoFRenumbering::component_wise(dof_handler);

    const std::vector<types::global_dof_index> dofs_per_component =
      DoFTools::count_dofs_per_fe_component(dof_handler);
    const unsigned int n_u = dofs_per_component[0],
                       n_p = dofs_per_component[dim],
                       n_s = dofs_per_component[dim + 1];

    std::cout << "Number of active cells: " << triangulation.n_active_cells()
              << std::endl
              << "Number of degrees of freedom: " << dof_handler.n_dofs()
              << " (" << n_u << '+' << n_p << '+' << n_s << ')' << std::endl
              << std::endl;

    const std::vector<types::global_dof_index> block_sizes = {n_u, n_p, n_s};
    BlockDynamicSparsityPattern                dsp(block_sizes, block_sizes);
    DoFTools::make_sparsity_pattern(dof_handler, dsp);

    sparsity_pattern.copy_from(dsp);
    system_matrix.reinit(sparsity_pattern);

    solution.reinit(block_sizes);
    old_solution.reinit(block_sizes);
    system_rhs.reinit(block_sizes);
  }


  // @sect4{TwoPhaseFlowProblem::assemble_system}

  // This is the function that assembles the linear system, or at least
  // everything except the (1,3) block that depends on the still-unknown
  // velocity computed during this time step (we deal with this in
  // <code>assemble_rhs_S</code>). Much of it is again as in step-20, but we
  // have to deal with some nonlinearity this time.  However, the top of the
  // function is pretty much as usual (note that we set matrix and right hand
  // side to zero at the beginning &mdash; something we didn't have to do for
  // stationary problems since there we use each matrix object only once and
  // it is empty at the beginning anyway).
  //
  // Note that in its present form, the function uses the permeability
  // implemented in the RandomMedium::KInverse class. Switching to the single
  // curved crack permeability function is as simple as just changing the
  // namespace name.
  template <int dim>
  void TwoPhaseFlowProblem<dim>::assemble_system()
  {
    system_matrix = 0;
    system_rhs    = 0;

    QGauss<dim>     quadrature_formula(degree + 2);
    QGauss<dim - 1> face_quadrature_formula(degree + 2);

    FEValues<dim>     fe_values(fe,
                            quadrature_formula,
                            update_values | update_gradients |
                              update_quadrature_points | update_JxW_values);
    FEFaceValues<dim> fe_face_values(fe,
                                     face_quadrature_formula,
                                     update_values | update_normal_vectors |
                                       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();
    const unsigned int n_face_q_points = face_quadrature_formula.size();

    FullMatrix<double> local_matrix(dofs_per_cell, dofs_per_cell);
    Vector<double>     local_rhs(dofs_per_cell);

    std::vector<types::global_dof_index> local_dof_indices(dofs_per_cell);

    const PressureRightHandSide<dim>  pressure_right_hand_side;
    const PressureBoundaryValues<dim> pressure_boundary_values;
    const RandomMedium::KInverse<dim> k_inverse;

    std::vector<double>         pressure_rhs_values(n_q_points);
    std::vector<double>         boundary_values(n_face_q_points);
    std::vector<Tensor<2, dim>> k_inverse_values(n_q_points);

    std::vector<Vector<double>>              old_solution_values(n_q_points,
                                                    Vector<double>(dim + 2));
    std::vector<std::vector<Tensor<1, dim>>> old_solution_grads(
      n_q_points, std::vector<Tensor<1, dim>>(dim + 2));

    const FEValuesExtractors::Vector velocities(0);
    const FEValuesExtractors::Scalar pressure(dim);
    const FEValuesExtractors::Scalar saturation(dim + 1);

    for (const auto &cell : dof_handler.active_cell_iterators())
      {
        fe_values.reinit(cell);
        local_matrix = 0;
        local_rhs    = 0;

        // Here's the first significant difference: We have to get the values
        // of the saturation function of the previous time step at the
        // quadrature points. To this end, we can use the
        // FEValues::get_function_values (previously already used in step-9,
        // step-14 and step-15), a function that takes a solution vector and
        // returns a list of function values at the quadrature points of the
        // present cell. In fact, it returns the complete vector-valued
        // solution at each quadrature point, i.e. not only the saturation but
        // also the velocities and pressure:
        fe_values.get_function_values(old_solution, old_solution_values);

        // Then we also have to get the values of the pressure right hand side
        // and of the inverse permeability tensor at the quadrature points:
        pressure_right_hand_side.value_list(fe_values.get_quadrature_points(),
                                            pressure_rhs_values);
        k_inverse.value_list(fe_values.get_quadrature_points(),
                             k_inverse_values);

        // With all this, we can now loop over all the quadrature points and
        // shape functions on this cell and assemble those parts of the matrix
        // and right hand side that we deal with in this function. The
        // individual terms in the contributions should be self-explanatory
        // given the explicit form of the bilinear form stated in the
        // introduction:
        for (unsigned int q = 0; q < n_q_points; ++q)
          for (unsigned int i = 0; i < dofs_per_cell; ++i)
            {
              const double old_s = old_solution_values[q](dim + 1);

              const Tensor<1, dim> phi_i_u = fe_values[velocities].value(i, q);
              const double div_phi_i_u = fe_values[velocities].divergence(i, q);
              const double phi_i_p     = fe_values[pressure].value(i, q);
              const double phi_i_s     = fe_values[saturation].value(i, q);

              for (unsigned int j = 0; j < dofs_per_cell; ++j)
                {
                  const Tensor<1, dim> phi_j_u =
                    fe_values[velocities].value(j, q);
                  const double div_phi_j_u =
                    fe_values[velocities].divergence(j, q);
                  const double phi_j_p = fe_values[pressure].value(j, q);
                  const double phi_j_s = fe_values[saturation].value(j, q);

                  local_matrix(i, j) +=
                    (phi_i_u * k_inverse_values[q] *
                       mobility_inverse(old_s, viscosity) * phi_j_u -
                     div_phi_i_u * phi_j_p - phi_i_p * div_phi_j_u +
                     phi_i_s * phi_j_s) *
                    fe_values.JxW(q);
                }

              local_rhs(i) +=
                (-phi_i_p * pressure_rhs_values[q]) * fe_values.JxW(q);
            }


        // Next, we also have to deal with the pressure boundary values. This,
        // again is as in step-20:
        for (const auto &face : cell->face_iterators())
          if (face->at_boundary())
            {
              fe_face_values.reinit(cell, face);

              pressure_boundary_values.value_list(
                fe_face_values.get_quadrature_points(), boundary_values);

              for (unsigned int q = 0; q < n_face_q_points; ++q)
                for (unsigned int i = 0; i < dofs_per_cell; ++i)
                  {
                    const Tensor<1, dim> phi_i_u =
                      fe_face_values[velocities].value(i, q);

                    local_rhs(i) +=
                      -(phi_i_u * fe_face_values.normal_vector(q) *
                        boundary_values[q] * fe_face_values.JxW(q));
                  }
            }

        // The final step in the loop over all cells is to transfer local
        // contributions into the global matrix and right hand side vector:
        cell->get_dof_indices(local_dof_indices);
        for (unsigned int i = 0; i < dofs_per_cell; ++i)
          for (unsigned int j = 0; j < dofs_per_cell; ++j)
            system_matrix.add(local_dof_indices[i],
                              local_dof_indices[j],
                              local_matrix(i, j));

        for (unsigned int i = 0; i < dofs_per_cell; ++i)
          system_rhs(local_dof_indices[i]) += local_rhs(i);
      }
  }


  // So much for assembly of matrix and right hand side. Note that we do not
  // have to interpolate and apply boundary values since they have all been
  // taken care of in the weak form already.


  // @sect4{TwoPhaseFlowProblem::assemble_rhs_S}

  // As explained in the introduction, we can only evaluate the right hand
  // side of the saturation equation once the velocity has been computed. We
  // therefore have this separate function to this end.
  template <int dim>
  void TwoPhaseFlowProblem<dim>::assemble_rhs_S()
  {
    QGauss<dim>       quadrature_formula(degree + 2);
    QGauss<dim - 1>   face_quadrature_formula(degree + 2);
    FEValues<dim>     fe_values(fe,
                            quadrature_formula,
                            update_values | update_gradients |
                              update_quadrature_points | update_JxW_values);
    FEFaceValues<dim> fe_face_values(fe,
                                     face_quadrature_formula,
                                     update_values | update_normal_vectors |
                                       update_quadrature_points |
                                       update_JxW_values);
    FEFaceValues<dim> fe_face_values_neighbor(fe,
                                              face_quadrature_formula,
                                              update_values);

    const unsigned int dofs_per_cell   = fe.n_dofs_per_cell();
    const unsigned int n_q_points      = quadrature_formula.size();
    const unsigned int n_face_q_points = face_quadrature_formula.size();

    Vector<double> local_rhs(dofs_per_cell);

    std::vector<Vector<double>> old_solution_values(n_q_points,
                                                    Vector<double>(dim + 2));
    std::vector<Vector<double>> old_solution_values_face(n_face_q_points,
                                                         Vector<double>(dim +
                                                                        2));
    std::vector<Vector<double>> old_solution_values_face_neighbor(
      n_face_q_points, Vector<double>(dim + 2));
    std::vector<Vector<double>> present_solution_values(n_q_points,
                                                        Vector<double>(dim +
                                                                       2));
    std::vector<Vector<double>> present_solution_values_face(
      n_face_q_points, Vector<double>(dim + 2));

    std::vector<double>                  neighbor_saturation(n_face_q_points);
    std::vector<types::global_dof_index> local_dof_indices(dofs_per_cell);

    SaturationBoundaryValues<dim> saturation_boundary_values;

    const FEValuesExtractors::Scalar saturation(dim + 1);

    for (const auto &cell : dof_handler.active_cell_iterators())
      {
        local_rhs = 0;
        fe_values.reinit(cell);

        fe_values.get_function_values(old_solution, old_solution_values);
        fe_values.get_function_values(solution, present_solution_values);

        // First for the cell terms. These are, following the formulas in the
        // introduction, $(S^n,\sigma)-(F(S^n) \mathbf{v}^{n+1},\nabla
        // \sigma)$, where $\sigma$ is the saturation component of the test
        // function:
        for (unsigned int q = 0; q < n_q_points; ++q)
          for (unsigned int i = 0; i < dofs_per_cell; ++i)
            {
              const double   old_s = old_solution_values[q](dim + 1);
              Tensor<1, dim> present_u;
              for (unsigned int d = 0; d < dim; ++d)
                present_u[d] = present_solution_values[q](d);

              const double         phi_i_s = fe_values[saturation].value(i, q);
              const Tensor<1, dim> grad_phi_i_s =
                fe_values[saturation].gradient(i, q);

              local_rhs(i) +=
                (time.get_next_step_size() * fractional_flow(old_s, viscosity) *
                   present_u * grad_phi_i_s +
                 old_s * phi_i_s) *
                fe_values.JxW(q);
            }

        // Secondly, we have to deal with the flux parts on the face
        // boundaries. This was a bit more involved because we first have to
        // determine which are the influx and outflux parts of the cell
        // boundary. If we have an influx boundary, we need to evaluate the
        // saturation on the other side of the face (or the boundary values,
        // if we are at the boundary of the domain).
        //
        // All this is a bit tricky, but has been explained in some detail
        // already in step-9. Take a look there how this is supposed to work!
        for (const auto face_no : cell->face_indices())
          {
            fe_face_values.reinit(cell, face_no);

            fe_face_values.get_function_values(old_solution,
                                               old_solution_values_face);
            fe_face_values.get_function_values(solution,
                                               present_solution_values_face);

            if (cell->at_boundary(face_no))
              saturation_boundary_values.value_list(
                fe_face_values.get_quadrature_points(), neighbor_saturation);
            else
              {
                const auto         neighbor = cell->neighbor(face_no);
                const unsigned int neighbor_face =
                  cell->neighbor_of_neighbor(face_no);

                fe_face_values_neighbor.reinit(neighbor, neighbor_face);

                fe_face_values_neighbor.get_function_values(
                  old_solution, old_solution_values_face_neighbor);

                for (unsigned int q = 0; q < n_face_q_points; ++q)
                  neighbor_saturation[q] =
                    old_solution_values_face_neighbor[q](dim + 1);
              }


            for (unsigned int q = 0; q < n_face_q_points; ++q)
              {
                Tensor<1, dim> present_u_face;
                for (unsigned int d = 0; d < dim; ++d)
                  present_u_face[d] = present_solution_values_face[q](d);

                const double normal_flux =
                  present_u_face * fe_face_values.normal_vector(q);

                const bool is_outflow_q_point = (normal_flux >= 0);

                for (unsigned int i = 0; i < dofs_per_cell; ++i)
                  local_rhs(i) -=
                    time.get_next_step_size() * normal_flux *
                    fractional_flow((is_outflow_q_point == true ?
                                       old_solution_values_face[q](dim + 1) :
                                       neighbor_saturation[q]),
                                    viscosity) *
                    fe_face_values[saturation].value(i, q) *
                    fe_face_values.JxW(q);
              }
          }

        cell->get_dof_indices(local_dof_indices);
        for (unsigned int i = 0; i < dofs_per_cell; ++i)
          system_rhs(local_dof_indices[i]) += local_rhs(i);
      }
  }



  // @sect4{TwoPhaseFlowProblem::solve}

  // After all these preparations, we finally solve the linear system for
  // velocity and pressure in the same way as in step-20. After that, we have
  // to deal with the saturation equation (see below):
  template <int dim>
  void TwoPhaseFlowProblem<dim>::solve()
  {
    const InverseMatrix<SparseMatrix<double>> m_inverse(
      system_matrix.block(0, 0));
    Vector<double> tmp(solution.block(0).size());
    Vector<double> schur_rhs(solution.block(1).size());
    Vector<double> tmp2(solution.block(2).size());


    // First the pressure, using the pressure Schur complement of the first
    // two equations:
    {
      m_inverse.vmult(tmp, system_rhs.block(0));
      system_matrix.block(1, 0).vmult(schur_rhs, tmp);
      schur_rhs -= system_rhs.block(1);


      SchurComplement schur_complement(system_matrix, m_inverse);

      ApproximateSchurComplement approximate_schur_complement(system_matrix);

      InverseMatrix<ApproximateSchurComplement> preconditioner(
        approximate_schur_complement);


      SolverControl            solver_control(solution.block(1).size(),
                                   1e-12 * schur_rhs.l2_norm());
      SolverCG<Vector<double>> cg(solver_control);

      cg.solve(schur_complement, solution.block(1), schur_rhs, preconditioner);

      std::cout << "   " << solver_control.last_step()
                << " CG Schur complement iterations for pressure." << std::endl;
    }

    // Now the velocity:
    {
      system_matrix.block(0, 1).vmult(tmp, solution.block(1));
      tmp *= -1;
      tmp += system_rhs.block(0);

      m_inverse.vmult(solution.block(0), tmp);
    }

    // Finally, we have to take care of the saturation equation. The first
    // business we have here is to determine the time step using the formula
    // in the introduction. Knowing the shape of our domain and that we
    // created the mesh by regular subdivision of cells, we can compute the
    // diameter of each of our cells quite easily (in fact we use the linear
    // extensions in coordinate directions of the cells, not the
    // diameter). Note that we will learn a more general way to do this in
    // step-24, where we use the GridTools::minimal_cell_diameter function.
    //
    // The maximal velocity we compute using a helper function to compute the
    // maximal velocity defined below, and with all this we can evaluate our
    // new time step length. We use the method
    // DiscreteTime::set_desired_next_time_step() to suggest the new
    // calculated value of the time step to the DiscreteTime object. In most
    // cases, the time object uses the exact provided value to increment time.
    // It some case, the step size may be modified further by the time object.
    // For example, if the calculated time increment overshoots the end time,
    // it is truncated accordingly.
    time.set_desired_next_step_size(std::pow(0.5, double(n_refinement_steps)) /
                                    get_maximal_velocity());

    // The next step is to assemble the right hand side, and then to pass
    // everything on for solution. At the end, we project back saturations
    // onto the physically reasonable range:
    assemble_rhs_S();
    {
      SolverControl            solver_control(system_matrix.block(2, 2).m(),
                                   1e-8 * system_rhs.block(2).l2_norm());
      SolverCG<Vector<double>> cg(solver_control);
      cg.solve(system_matrix.block(2, 2),
               solution.block(2),
               system_rhs.block(2),
               PreconditionIdentity());

      project_back_saturation();

      std::cout << "   " << solver_control.last_step()
                << " CG iterations for saturation." << std::endl;
    }


    old_solution = solution;
  }


  // @sect4{TwoPhaseFlowProblem::output_results}

  // There is nothing surprising here. Since the program will do a lot of time
  // steps, we create an output file only every fifth time step and skip all
  // other time steps at the top of the file already.
  //
  // When creating file names for output close to the bottom of the function,
  // we convert the number of the time step to a string representation that
  // is padded by leading zeros to four digits. We do this because this way
  // all output file names have the same length, and consequently sort well
  // when creating a directory listing.
  template <int dim>
  void TwoPhaseFlowProblem<dim>::output_results() const
  {
    if (time.get_step_number() % 5 != 0)
      return;

    std::vector<std::string> solution_names;
    switch (dim)
      {
        case 2:
          solution_names = {"u", "v", "p", "S"};
          break;

        case 3:
          solution_names = {"u", "v", "w", "p", "S"};
          break;

        default:
          Assert(false, ExcNotImplemented());
      }

    DataOut<dim> data_out;

    data_out.attach_dof_handler(dof_handler);
    data_out.add_data_vector(solution, solution_names);

    data_out.build_patches(degree + 1);

    std::ofstream output("solution-" +
                         Utilities::int_to_string(time.get_step_number(), 4) +
                         ".vtk");
    data_out.write_vtk(output);
  }



  // @sect4{TwoPhaseFlowProblem::project_back_saturation}

  // In this function, we simply run over all saturation degrees of freedom
  // and make sure that if they should have left the physically reasonable
  // range, that they be reset to the interval $[0,1]$. To do this, we only
  // have to loop over all saturation components of the solution vector; these
  // are stored in the block 2 (block 0 are the velocities, block 1 are the
  // pressures).
  //
  // It may be instructive to note that this function almost never triggers
  // when the time step is chosen as mentioned in the introduction. However,
  // if we choose the timestep only slightly larger, we get plenty of values
  // outside the proper range. Strictly speaking, the function is therefore
  // unnecessary if we choose the time step small enough. In a sense, the
  // function is therefore only a safety device to avoid situations where our
  // entire solution becomes unphysical because individual degrees of freedom
  // have become unphysical a few time steps earlier.
  template <int dim>
  void TwoPhaseFlowProblem<dim>::project_back_saturation()
  {
    for (unsigned int i = 0; i < solution.block(2).size(); ++i)
      if (solution.block(2)(i) < 0)
        solution.block(2)(i) = 0;
      else if (solution.block(2)(i) > 1)
        solution.block(2)(i) = 1;
  }


  // @sect4{TwoPhaseFlowProblem::get_maximal_velocity}

  // The following function is used in determining the maximal allowable time
  // step. What it does is to loop over all quadrature points in the domain
  // and find what the maximal magnitude of the velocity is.
  template <int dim>
  double TwoPhaseFlowProblem<dim>::get_maximal_velocity() const
  {
    QGauss<dim>        quadrature_formula(degree + 2);
    const unsigned int n_q_points = quadrature_formula.size();

    FEValues<dim> fe_values(fe, quadrature_formula, update_values);
    std::vector<Vector<double>> solution_values(n_q_points,
                                                Vector<double>(dim + 2));
    double                      max_velocity = 0;

    for (const auto &cell : dof_handler.active_cell_iterators())
      {
        fe_values.reinit(cell);
        fe_values.get_function_values(solution, solution_values);

        for (unsigned int q = 0; q < n_q_points; ++q)
          {
            Tensor<1, dim> velocity;
            for (unsigned int i = 0; i < dim; ++i)
              velocity[i] = solution_values[q](i);

            max_velocity = std::max(max_velocity, velocity.norm());
          }
      }

    return max_velocity;
  }


  // @sect4{TwoPhaseFlowProblem::run}

  // This is the final function of our main class. Its brevity speaks for
  // itself. There are only two points worth noting: First, the function
  // projects the initial values onto the finite element space at the
  // beginning; the VectorTools::project function doing this requires an
  // argument indicating the hanging node constraints. We have none in this
  // program (we compute on a uniformly refined mesh), but the function
  // requires the argument anyway, of course. So we have to create a
  // constraint object. In its original state, constraint objects are
  // unsorted, and have to be sorted (using the AffineConstraints::close
  // function) before they can be used. This is what we do here, and which is
  // why we can't simply call the VectorTools::project function with an
  // anonymous temporary object <code>AffineConstraints<double>()</code> as the
  // second argument.
  //
  // The second point worth mentioning is that we only compute the length of
  // the present time step in the middle of solving the linear system
  // corresponding to each time step. We can therefore output the present
  // time of a time step only at the end of the time step.
  // We increment time by calling the method DiscreteTime::advance_time()
  // inside the loop. Since we are reporting the time and dt after we
  // increment it, we have to call the method
  // DiscreteTime::get_previous_step_size() instead of
  // DiscreteTime::get_next_step_size(). After many steps, when the simulation
  // reaches the end time, the last dt is chosen by the DiscreteTime class in
  // such a way that the last step finishes exactly at the end time.
  template <int dim>
  void TwoPhaseFlowProblem<dim>::run()
  {
    make_grid_and_dofs();

    {
      AffineConstraints<double> constraints;
      constraints.close();

      VectorTools::project(dof_handler,
                           constraints,
                           QGauss<dim>(degree + 2),
                           InitialValues<dim>(),
                           old_solution);
    }

    do
      {
        std::cout << "Timestep " << time.get_step_number() + 1 << std::endl;

        assemble_system();

        solve();

        output_results();

        time.advance_time();
        std::cout << "   Now at t=" << time.get_current_time()
                  << ", dt=" << time.get_previous_step_size() << '.'
                  << std::endl
                  << std::endl;
      }
    while (time.is_at_end() == false);
  }
} // namespace Step21


// @sect3{The <code>main</code> function}

// That's it. In the main function, we pass the degree of the finite element
// space to the constructor of the TwoPhaseFlowProblem object.  Here, we use
// zero-th degree elements, i.e. $RT_0\times DQ_0 \times DQ_0$. The rest is as
// in all the other programs.
int main()
{
  try
    {
      using namespace Step21;

      TwoPhaseFlowProblem<2> two_phase_flow_problem(0);
      two_phase_flow_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;
}
