Cppcheck report - [-128-NOTFOUND]: /tmp/TeamCity-1/work/5ad443c8abe7fc0a/src/PDE/Integrate/Basis.cpp

// *****************************************************************************
/*!
  \file      src/PDE/Integrate/Basis.cpp
  \copyright 2012-2015 J. Bakosi,
             2016-2018 Los Alamos National Security, LLC.,
             2019-2021 Triad National Security, LLC.
             All rights reserved. See the LICENSE file for details.
  \brief     Functions for computing the Dubiner basis functions in DG methods
  \details   This file contains functionality for computing the basis functions
     and relating coordinates transformation functions used in discontinuous
     Galerkin methods for variaous orders of numerical representation. The basis
     functions chosen for the DG method are the Dubiner basis, which are
     Legendre polynomials modified for tetrahedra, which are defined only on
     the reference/master tetrahedron.
  \see [1] https://doi.org/10.1007/BF01060030
  \see [2] https://doi.org/10.1093/imamat/hxh111
*/
// *****************************************************************************

#include <array>
#include "QuinoaConfig.hpp"

#include "Basis.hpp"
#include "Vector.hpp"
#include "Mass.hpp"

std::array< tk::real, 3 >
tk::eval_gp ( const std::size_t igp,
              const std::array< std::array< tk::real, 3>, 3 >& coordfa,
              const std::array< std::vector< tk::real >, 2 >& coordgp )
// *****************************************************************************
//  Compute the coordinates of quadrature points for face integral in physical
//  space
//! \param[in] igp Index of quadrature points
//! \param[in] coordfa Array of nodal coordinates for face element
//! \param[in] coordgp Array of coordinates for quadrature points in reference
//!   space
//! \return Array of coordinates for quadrature points in physical space
// *****************************************************************************
{
  // Barycentric coordinates for the triangular face
  auto shp1 = 1.0 - coordgp[0][igp] - coordgp[1][igp];
  auto shp2 = coordgp[0][igp];
  auto shp3 = coordgp[1][igp];

  // Transformation of the quadrature point from the 2D reference/master
  // element to physical space, to obtain its physical (x,y,z) coordinates.
  return {{ coordfa[0][0]*shp1 + coordfa[1][0]*shp2 + coordfa[2][0]*shp3,
            coordfa[0][1]*shp1 + coordfa[1][1]*shp2 + coordfa[2][1]*shp3,
            coordfa[0][2]*shp1 + coordfa[1][2]*shp2 + coordfa[2][2]*shp3 }};
}

std::array< tk::real, 3 >
tk::eval_gp ( const std::size_t igp,
              const std::array< std::array< tk::real, 3>, 4 >& coord,
              const std::array< std::vector< tk::real >, 3 >& coordgp )
// *****************************************************************************
//  Compute the coordinates of quadrature points for volume integral in
//  physical space
//! \param[in] igp Index of quadrature points
//! \param[in] coord Array of nodal coordinates for tetrahedron element
//! \param[in] coordgp Array of coordinates for quadrature points in reference space
//! \return Array of coordinates for quadrature points in physical space
// *****************************************************************************
{
  // Barycentric coordinates for the tetradedron element
  auto shp1 = 1.0 - coordgp[0][igp] - coordgp[1][igp] - coordgp[2][igp];
  auto shp2 = coordgp[0][igp];
  auto shp3 = coordgp[1][igp];
  auto shp4 = coordgp[2][igp];

  // Transformation of the quadrature point from the reference/master
  // element to physical space, to obtain its physical (x,y,z) coordinates.
  return {{
   coord[0][0]*shp1 + coord[1][0]*shp2 + coord[2][0]*shp3 + coord[3][0]*shp4,
   coord[0][1]*shp1 + coord[1][1]*shp2 + coord[2][1]*shp3 + coord[3][1]*shp4,
   coord[0][2]*shp1 + coord[1][2]*shp2 + coord[2][2]*shp3 + coord[3][2]*shp4 }};
}

std::array< std::vector<tk::real>, 3 >
tk::eval_dBdxi( const std::size_t ndof,
  const std::array< tk::real, 3 >& coordgp )
// *****************************************************************************
//  Compute the derivatives of Dubiner basis wrt. reference coordinates
//! \param[in] ndof Number of degrees of freedom
//! \param[in] coordgp Coordinates in ref element where derivatives are needed
//! \return Array of the derivatives of basis functions
// *****************************************************************************
{
  // Initialize array
  std::array< std::vector< tk::real >, 3 > dBdxi;
  for (std::size_t idir=0; idir<3; ++idir) {
    dBdxi[idir].resize(ndof, 0.0);
  }

  // high-order basis
  if (ndof > 1) {
    dBdxi[0][0] = 0.0;
    dBdxi[1][0] = 0.0;
    dBdxi[2][0] = 0.0;

    dBdxi[0][1] = 2.0;
    dBdxi[1][1] = 1.0;
    dBdxi[2][1] = 1.0;

    dBdxi[0][2] = 0.0;
    dBdxi[1][2] = 3.0;
    dBdxi[2][2] = 1.0;

    dBdxi[0][3] = 0.0;
    dBdxi[1][3] = 0.0;
    dBdxi[2][3] = 4.0;

    if (ndof > 4) {
      dBdxi[0][4] = 12.0 * coordgp[0] + 6.0 * coordgp[1]
                  +  6.0 * coordgp[2] - 6.0;
      dBdxi[1][4] =  6.0 * coordgp[0] + 2.0 * coordgp[1]
                  +  2.0 * coordgp[2] - 2.0;
      dBdxi[2][4] =  6.0 * coordgp[0] + 2.0 * coordgp[1]
                  +  2.0 * coordgp[2] - 2.0;

      dBdxi[0][5] = 10.0 * coordgp[1] +  2.0 * coordgp[2] - 2.0;
      dBdxi[1][5] = 10.0 * coordgp[0] + 10.0 * coordgp[1]
                  +  6.0 * coordgp[2] - 6.0;
      dBdxi[2][5] =  2.0 * coordgp[0] +  6.0 * coordgp[1]
                  +  2.0 * coordgp[2] - 2.0;

      dBdxi[0][6] = 12.0 * coordgp[2] - 2.0;
      dBdxi[1][6] =  6.0 * coordgp[2] - 1.0;
      dBdxi[2][6] = 12.0 * coordgp[0] + 6.0 * coordgp[1]
                  + 12.0 * coordgp[2] - 7.0;

      dBdxi[0][7] = 0.0;
      dBdxi[1][7] = 20.0 * coordgp[1] + 8.0 * coordgp[2] - 8.0;
      dBdxi[2][7] =  8.0 * coordgp[1] + 2.0 * coordgp[2] - 2.0;

      dBdxi[0][8] = 0.0;
      dBdxi[1][8] = 18.0 * coordgp[2] -  3.0;
      dBdxi[2][8] = 18.0 * coordgp[1] + 12.0 * coordgp[2] - 7.0;

      dBdxi[0][9] = 0.0;
      dBdxi[1][9] = 0.0;
      dBdxi[2][9] = 30.0 * coordgp[2] - 10.0;
    }
  }

  return dBdxi;
}

std::array< std::vector<tk::real>, 3 >
tk::eval_dBdx_p1( const std::size_t ndof,
                  const std::array< std::array< tk::real, 3 >, 3 >& jacInv )
// *****************************************************************************
//  Compute the derivatives of basis functions for DG(P1)
//! \param[in] ndof Number of degrees of freedom
//! \param[in] jacInv Array of the inverse of Jacobian
//! \return Array of the derivatives of basis functions
// *****************************************************************************
{
  // The derivatives of the basis functions dB/dx are easily calculated
  // via a transformation to the reference space as,
  // dB/dx = dB/dxi . dxi/dx,
  // where, x = (x,y,z) are the physical coordinates, and
  //        xi = (xi, eta, zeta) are the reference coordinates.
  // The matrix dxi/dx is the inverse of the Jacobian of transformation
  // and the matrix vector product has to be calculated. This follows.

  std::array< std::vector<tk::real>, 3 > dBdx;
  dBdx[0].resize( ndof, 0 );
  dBdx[1].resize( ndof, 0 );
  dBdx[2].resize( ndof, 0 );

  auto db2dxi1 = 2.0;
  auto db2dxi2 = 1.0;
  auto db2dxi3 = 1.0;

  auto db3dxi1 = 0.0;
  auto db3dxi2 = 3.0;
  auto db3dxi3 = 1.0;

  auto db4dxi1 = 0.0;
  auto db4dxi2 = 0.0;
  auto db4dxi3 = 4.0;

  dBdx[0][1] =  db2dxi1 * jacInv[0][0]
              + db2dxi2 * jacInv[1][0]
              + db2dxi3 * jacInv[2][0];

  dBdx[1][1] =  db2dxi1 * jacInv[0][1]
              + db2dxi2 * jacInv[1][1]
              + db2dxi3 * jacInv[2][1];

  dBdx[2][1] =  db2dxi1 * jacInv[0][2]
              + db2dxi2 * jacInv[1][2]
              + db2dxi3 * jacInv[2][2];

  dBdx[0][2] =  db3dxi1 * jacInv[0][0]
              + db3dxi2 * jacInv[1][0]
              + db3dxi3 * jacInv[2][0];

  dBdx[1][2] =  db3dxi1 * jacInv[0][1]
              + db3dxi2 * jacInv[1][1]
              + db3dxi3 * jacInv[2][1];

  dBdx[2][2] =  db3dxi1 * jacInv[0][2]
              + db3dxi2 * jacInv[1][2]
              + db3dxi3 * jacInv[2][2];

  dBdx[0][3] =  db4dxi1 * jacInv[0][0]
              + db4dxi2 * jacInv[1][0]
              + db4dxi3 * jacInv[2][0];

  dBdx[1][3] =  db4dxi1 * jacInv[0][1]
              + db4dxi2 * jacInv[1][1]
              + db4dxi3 * jacInv[2][1];

  dBdx[2][3] =  db4dxi1 * jacInv[0][2]
              + db4dxi2 * jacInv[1][2]
              + db4dxi3 * jacInv[2][2];

  return dBdx;
}

void
tk::eval_dBdx_p2( const std::size_t igp,
                  const std::array< std::vector< tk::real >, 3 >& coordgp,
                  const std::array< std::array< tk::real, 3 >, 3 >& jacInv,
                  std::array< std::vector<tk::real>, 3 >& dBdx )
// *****************************************************************************
//  Compute the derivatives of Dubiner basis function for DG(P2)
//! \param[in] igp Index of quadrature points
//! \param[in] coordgp Gauss point coordinates for tetrahedron element
//! \param[in] jacInv Array of the inverse of Jacobian
//! \param[in,out] dBdx Array of the derivatives of basis function
// *****************************************************************************
{
  auto db5dxi1 = 12.0 * coordgp[0][igp] + 6.0 * coordgp[1][igp]
               +  6.0 * coordgp[2][igp] - 6.0;
  auto db5dxi2 =  6.0 * coordgp[0][igp] + 2.0 * coordgp[1][igp]
<--- Same expression used in consecutive assignments of 'db5dxi2' and 'db5dxi3'. [+]
Finding variables 'db5dxi2' and 'db5dxi3' that are assigned the same expression is suspicious and might indicate a cut and paste or logic error. Please examine this code carefully to determine if it is correct.
               +  2.0 * coordgp[2][igp] - 2.0;
  auto db5dxi3 =  6.0 * coordgp[0][igp] + 2.0 * coordgp[1][igp]
<--- Same expression used in consecutive assignments of 'db5dxi2' and 'db5dxi3'. [+]
Finding variables 'db5dxi2' and 'db5dxi3' that are assigned the same expression is suspicious and might indicate a cut and paste or logic error. Please examine this code carefully to determine if it is correct.
               +  2.0 * coordgp[2][igp] - 2.0;

  auto db6dxi1 = 10.0 * coordgp[1][igp] +  2.0 * coordgp[2][igp] - 2.0;
  auto db6dxi2 = 10.0 * coordgp[0][igp] + 10.0 * coordgp[1][igp]
               +  6.0 * coordgp[2][igp] - 6.0;
  auto db6dxi3 =  2.0 * coordgp[0][igp] +  6.0 * coordgp[1][igp]
               +  2.0 * coordgp[2][igp] - 2.0;

  auto db7dxi1 = 12.0 * coordgp[2][igp] - 2.0;
  auto db7dxi2 =  6.0 * coordgp[2][igp] - 1.0;
  auto db7dxi3 = 12.0 * coordgp[0][igp] + 6.0 * coordgp[1][igp]
               + 12.0 * coordgp[2][igp] - 7.0;

  auto db8dxi1 =  0;
  auto db8dxi2 = 20.0 * coordgp[1][igp] + 8.0 * coordgp[2][igp] - 8.0;
  auto db8dxi3 =  8.0 * coordgp[1][igp] + 2.0 * coordgp[2][igp] - 2.0;

  auto db9dxi1 =  0;
  auto db9dxi2 = 18.0 * coordgp[2][igp] -  3.0;
  auto db9dxi3 = 18.0 * coordgp[1][igp] + 12.0 * coordgp[2][igp] - 7.0;

  auto db10dxi1 =  0;
  auto db10dxi2 =  0;
  auto db10dxi3 = 30.0 * coordgp[2][igp] - 10.0;

  dBdx[0][4] =  db5dxi1 * jacInv[0][0]
              + db5dxi2 * jacInv[1][0]
              + db5dxi3 * jacInv[2][0];

  dBdx[1][4] =  db5dxi1 * jacInv[0][1]
              + db5dxi2 * jacInv[1][1]
              + db5dxi3 * jacInv[2][1];

  dBdx[2][4] =  db5dxi1 * jacInv[0][2]
              + db5dxi2 * jacInv[1][2]
              + db5dxi3 * jacInv[2][2];

  dBdx[0][5] =  db6dxi1 * jacInv[0][0]
              + db6dxi2 * jacInv[1][0]
              + db6dxi3 * jacInv[2][0];

  dBdx[1][5] =  db6dxi1 * jacInv[0][1]
              + db6dxi2 * jacInv[1][1]
              + db6dxi3 * jacInv[2][1];

  dBdx[2][5] =  db6dxi1 * jacInv[0][2]
              + db6dxi2 * jacInv[1][2]
              + db6dxi3 * jacInv[2][2];

  dBdx[0][6] =  db7dxi1 * jacInv[0][0]
              + db7dxi2 * jacInv[1][0]
              + db7dxi3 * jacInv[2][0];

  dBdx[1][6] =  db7dxi1 * jacInv[0][1]
              + db7dxi2 * jacInv[1][1]
              + db7dxi3 * jacInv[2][1];

  dBdx[2][6] =  db7dxi1 * jacInv[0][2]
              + db7dxi2 * jacInv[1][2]
              + db7dxi3 * jacInv[2][2];

  dBdx[0][7] =  db8dxi1 * jacInv[0][0]
              + db8dxi2 * jacInv[1][0]
              + db8dxi3 * jacInv[2][0];

  dBdx[1][7] =  db8dxi1 * jacInv[0][1]
              + db8dxi2 * jacInv[1][1]
              + db8dxi3 * jacInv[2][1];

  dBdx[2][7] =  db8dxi1 * jacInv[0][2]
              + db8dxi2 * jacInv[1][2]
              + db8dxi3 * jacInv[2][2];

  dBdx[0][8] =  db9dxi1 * jacInv[0][0]
              + db9dxi2 * jacInv[1][0]
              + db9dxi3 * jacInv[2][0];

  dBdx[1][8] =  db9dxi1 * jacInv[0][1]
              + db9dxi2 * jacInv[1][1]
              + db9dxi3 * jacInv[2][1];

  dBdx[2][8] =  db9dxi1 * jacInv[0][2]
              + db9dxi2 * jacInv[1][2]
              + db9dxi3 * jacInv[2][2];

  dBdx[0][9] =  db10dxi1 * jacInv[0][0]
              + db10dxi2 * jacInv[1][0]
              + db10dxi3 * jacInv[2][0];

  dBdx[1][9] =  db10dxi1 * jacInv[0][1]
              + db10dxi2 * jacInv[1][1]
              + db10dxi3 * jacInv[2][1];

  dBdx[2][9] =  db10dxi1 * jacInv[0][2]
              + db10dxi2 * jacInv[1][2]
              + db10dxi3 * jacInv[2][2];
}

std::vector< tk::real >
tk::eval_basis( const std::size_t ndof,
                const tk::real xi,
                const tk::real eta,
                const tk::real zeta )
// *****************************************************************************
//  Compute the Dubiner basis functions
//! \param[in] ndof Number of degrees of freedom
//! \param[in] xi,eta,zeta Coordinates for quadrature points in reference space
//! \return Vector of basis functions
// *****************************************************************************
{
  // Array of basis functions
  std::vector< tk::real > B( ndof, 1.0 );

  if ( ndof > 1 )           // DG(P1)
  {
    B[1] = 2.0 * xi + eta + zeta - 1.0;
    B[2] = 3.0 * eta + zeta - 1.0;
    B[3] = 4.0 * zeta - 1.0;

    if( ndof > 4 )         // DG(P2)
    {
      B[4] =  6.0 * xi * xi + eta * eta + zeta * zeta
            + 6.0 * xi * eta + 6.0 * xi * zeta + 2.0 * eta * zeta
            - 6.0 * xi - 2.0 * eta - 2.0 * zeta + 1.0;
      B[5] =  5.0 * eta * eta + zeta * zeta
            + 10.0 * xi * eta + 2.0 * xi * zeta + 6.0 * eta * zeta
            - 2.0 * xi - 6.0 * eta - 2.0 * zeta + 1.0;
      B[6] =  6.0 * zeta * zeta + 12.0 * xi * zeta + 6.0 * eta * zeta - 2.0 * xi
            - eta - 7.0 * zeta + 1.0;
      B[7] =  10.0 * eta * eta + zeta * zeta + 8.0 * eta * zeta
            - 8.0 * eta - 2.0 * zeta + 1.0;
      B[8] =  6.0 * zeta * zeta + 18.0 * eta * zeta - 3.0 * eta - 7.0 * zeta
            + 1.0;
      B[9] =  15.0 * zeta * zeta - 10.0 * zeta + 1.0;
    }
  }

  return B;
}

std::vector< tk::real >
tk::eval_state ( ncomp_t ncomp,
                 const std::size_t ndof,
                 const std::size_t ndof_el,
                 const std::size_t e,
                 const Fields& U,
                 const std::vector< tk::real >& B )
// *****************************************************************************
//  Compute the state variables for the tetrahedron element
//! \param[in] ncomp Number of scalar components in this PDE system
//! \param[in] ndof Maximum number of degrees of freedom
//! \param[in] ndof_el Number of degrees of freedom for the local element
//! \param[in] e Index for the tetrahedron element
//! \param[in] U Solution vector at recent time step
//! \param[in] B Vector of basis functions
//! \return Vector of state variable for tetrahedron element
// *****************************************************************************
{
  // This is commented for now because that when p0/p1 adaptive with limiter
  // applied, the size of basis will be 10. However, ndof_el will be 4 which
  // leads to a size mismatch in limiter function.
  //Assert( B.size() == ndof_el, "Size mismatch" );

  if (U.empty()) return {};

  // Array of state variable for tetrahedron element
  std::vector< tk::real > state( ncomp, 0.0 );

  for (ncomp_t c=0; c<ncomp; ++c)
  {
    auto mark = c*ndof;
    state[c] = U( e, mark );

    if(ndof_el > 1)        //DG(P1)
    {
      state[c] += U( e, mark+1 ) * B[1]
                + U( e, mark+2 ) * B[2]
                + U( e, mark+3 ) * B[3];
    }

    if(ndof_el > 4)        //DG(P2)
    {
      state[c] += U( e, mark+4 ) * B[4]
                + U( e, mark+5 ) * B[5]
                + U( e, mark+6 ) * B[6]
                + U( e, mark+7 ) * B[7]
                + U( e, mark+8 ) * B[8]
                + U( e, mark+9 ) * B[9];
    }
  }

  return state;
}

std::vector< std::vector< tk::real > >
tk::DubinerToTaylor( ncomp_t ncomp,
                     const std::size_t e,
                     const std::size_t ndof,
                     const tk::Fields& U,
                     const std::vector< std::size_t >& inpoel,
                     const tk::UnsMesh::Coords& coord )
// *****************************************************************************
//  Transform the solution with Dubiner basis to the solution with Taylor basis
//! \param[in] ncomp Number of scalar components in this PDE system
//! \param[in] e Id of element whose solution is to be limited
//! \param[in] ndof Maximum number of degrees of freedom
//! \param[in] U High-order solution vector with Dubiner basis
//! \param[in] inpoel Element connectivity
//! \param[in] coord Array of nodal coordinates
//! \return High-order solution vector with Taylor basis
// *****************************************************************************
{
  std::vector< std::vector< tk::real > >
    unk(ncomp, std::vector<tk::real>(ndof, 0.0));

  const auto& cx = coord[0];
  const auto& cy = coord[1];
  const auto& cz = coord[2];

  std::array< std::vector< tk::real >, 3 > center;
  center[0].resize(1, 0.25);
  center[1].resize(1, 0.25);
  center[2].resize(1, 0.25);

  // Evaluate the cell center solution
  for(ncomp_t icomp = 0; icomp < ncomp; icomp++)
  {
    auto mark = icomp * ndof;
    unk[icomp][0] = U(e, mark);
  }

  // Evaluate the first order derivative
  std::array< std::array< tk::real, 3>, 4 > coordel {{
    {{ cx[ inpoel[4*e  ] ], cy[ inpoel[4*e  ] ], cz[ inpoel[4*e  ] ] }},
    {{ cx[ inpoel[4*e+1] ], cy[ inpoel[4*e+1] ], cz[ inpoel[4*e+1] ] }},
    {{ cx[ inpoel[4*e+2] ], cy[ inpoel[4*e+2] ], cz[ inpoel[4*e+2] ] }},
    {{ cx[ inpoel[4*e+3] ], cy[ inpoel[4*e+3] ], cz[ inpoel[4*e+3] ] }}
  }};

  auto jacInv =
              tk::inverseJacobian( coordel[0], coordel[1], coordel[2], coordel[3] );

  // Compute the derivatives of basis function for DG(P1)
  auto dBdx = tk::eval_dBdx_p1( ndof, jacInv );

  if(ndof > 4) {
    tk::eval_dBdx_p2(0, center, jacInv, dBdx);
  }

  for(ncomp_t icomp = 0; icomp < ncomp; icomp++)
  {
    auto mark = icomp * ndof; 
    for(std::size_t idir = 0; idir < 3; idir++)
    {
      unk[icomp][idir+1] = 0;
      for(std::size_t idof = 1; idof < ndof; idof++)
        unk[icomp][idir+1] += U(e, mark+idof) * dBdx[idir][idof];
    }
  }

  // Evaluate the second order derivative if DGP2 is applied
  // The basic idea of the computation follows
  //    d2Udx2 = /sum u_i * (d2B_i/dx2)
  // where d2B_i/dx2 = d( dB_i/dxi * dxi/dx ) / dxi * dxi/dx
  if(ndof > 4)
  {
    // Matrix to store the second order derivatives of basis functions in
    // reference domain
    tk::real d2Bdxi2[6][6] =
    { { 12.0,  0.0,  0.0,  0.0,  0.0,  0.0 },
      {  2.0, 10.0,  0.0, 20.0,  0.0,  0.0 },
      {  2.0,  2.0, 12.0,  2.0, 12.0, 30.0 },
      {  6.0, 10.0,  0.0,  0.0,  0.0,  0.0 },
      {  6.0,  2.0, 12.0,  0.0,  0.0,  0.0 },
      {  2.0,  6.0,  6.0,  8.0, 18.0,  0.0 } };

    // Transform matrix to convert the second order derivatives of basis
    // function in reference domain to the one in physical domain
    tk::real d2xdxi2[6][6];

    d2xdxi2[0][0] = jacInv[0][0] * jacInv[0][0];
    d2xdxi2[0][1] = jacInv[1][0] * jacInv[1][0];
    d2xdxi2[0][2] = jacInv[2][0] * jacInv[2][0];
    d2xdxi2[0][3] = jacInv[0][0] * jacInv[1][0] * 2.0;
    d2xdxi2[0][4] = jacInv[0][0] * jacInv[2][0] * 2.0;
    d2xdxi2[0][5] = jacInv[1][0] * jacInv[2][0] * 2.0;

    d2xdxi2[1][0] = jacInv[0][1] * jacInv[0][1];
    d2xdxi2[1][1] = jacInv[1][1] * jacInv[1][1];
    d2xdxi2[1][2] = jacInv[2][1] * jacInv[2][1];
    d2xdxi2[1][3] = jacInv[0][1] * jacInv[1][1] * 2.0;
    d2xdxi2[1][4] = jacInv[0][1] * jacInv[2][1] * 2.0;
    d2xdxi2[1][5] = jacInv[1][1] * jacInv[2][1] * 2.0;

    d2xdxi2[2][0] = jacInv[0][2] * jacInv[0][2];
    d2xdxi2[2][1] = jacInv[1][2] * jacInv[1][2];
    d2xdxi2[2][2] = jacInv[2][2] * jacInv[2][2];
    d2xdxi2[2][3] = jacInv[0][2] * jacInv[1][2] * 2.0;
    d2xdxi2[2][4] = jacInv[0][2] * jacInv[2][2] * 2.0;
    d2xdxi2[2][5] = jacInv[1][2] * jacInv[2][2] * 2.0;

    d2xdxi2[3][0] = jacInv[0][0] * jacInv[0][1];
    d2xdxi2[3][1] = jacInv[1][0] * jacInv[1][1];
    d2xdxi2[3][2] = jacInv[2][0] * jacInv[2][1];
    d2xdxi2[3][3] = jacInv[0][0] * jacInv[1][1] + jacInv[1][0] * jacInv[0][1];
    d2xdxi2[3][4] = jacInv[0][0] * jacInv[2][1] + jacInv[2][0] * jacInv[0][1];
    d2xdxi2[3][5] = jacInv[1][0] * jacInv[2][1] + jacInv[2][0] * jacInv[1][1];

    d2xdxi2[4][0] = jacInv[0][0] * jacInv[0][2];
    d2xdxi2[4][1] = jacInv[1][0] * jacInv[1][2];
    d2xdxi2[4][2] = jacInv[2][0] * jacInv[2][2];
    d2xdxi2[4][3] = jacInv[0][0] * jacInv[1][2] + jacInv[1][0] * jacInv[0][2];
    d2xdxi2[4][4] = jacInv[0][0] * jacInv[2][2] + jacInv[2][0] * jacInv[0][2];
    d2xdxi2[4][5] = jacInv[1][0] * jacInv[2][2] + jacInv[2][0] * jacInv[1][2];

    d2xdxi2[5][0] = jacInv[0][1] * jacInv[0][2];
    d2xdxi2[5][1] = jacInv[1][1] * jacInv[1][2];
    d2xdxi2[5][2] = jacInv[2][1] * jacInv[2][2];
    d2xdxi2[5][3] = jacInv[0][1] * jacInv[1][2] + jacInv[1][1] * jacInv[0][2];
    d2xdxi2[5][4] = jacInv[0][1] * jacInv[2][2] + jacInv[2][1] * jacInv[0][2];
    d2xdxi2[5][5] = jacInv[1][1] * jacInv[2][2] + jacInv[2][1] * jacInv[1][2];

    // Matrix to store the second order derivatives of basis functions in
    // physical domain
    tk::real d2Bdx2[6][6];
    for(std::size_t ibasis = 0; ibasis < 6; ibasis++) {
      for(std::size_t idir = 0; idir < 6; idir++) {
        d2Bdx2[idir][ibasis] = 0;
        for(std::size_t k = 0; k < 6; k++)
          d2Bdx2[idir][ibasis] += d2xdxi2[idir][k] * d2Bdxi2[k][ibasis];
      }
    }

    for(ncomp_t icomp = 0; icomp < ncomp; icomp++)
    {
      auto mark = icomp * ndof;
      for(std::size_t idir = 0; idir < 6; idir++)
      {
        unk[icomp][idir+4] = 0;
        for(std::size_t ibasis = 0; ibasis < 6; ibasis++)
          unk[icomp][idir+4] += U(e, mark+4+ibasis) * d2Bdx2[idir][ibasis];
      }
    }
  }
  return unk;
}

void
tk::TaylorToDubiner( ncomp_t ncomp,
                     std::size_t e,
                     std::size_t ndof,
                     const std::vector< std::size_t >& inpoel,
                     const tk::UnsMesh::Coords& coord,
                     const tk::Fields& geoElem,
                     std::vector< std::vector< tk::real > >& unk )
// *****************************************************************************
//  Convert the solution with Taylor basis to the solution with Dubiner basis by
//    projection method
//! \param[in] ncomp Number of scalar components in this PDE system
//! \param[in] e Id of element whose solution is to be limited
//! \param[in] ndof Maximum number of degrees of freedom
//! \param[in] inpoel Element connectivity
//! \param[in] coord Array of nodal coordinates
//! \param[in, out] unk High-order solution vector with Taylor basis
// *****************************************************************************
{
  Assert( ncomp > 0, "Number of scalar components is incorrect" );

  // The diagonal of mass matrix
  std::vector< tk::real > L(ndof, 0.0);

  tk::real vol = 1.0 / 6.0;

  L[0] = vol;

  if(ndof > 1) {
    Assert( (ndof == 4)||(ndof == 10),
      "Mismatch in number of degrees of freedom" );
    L[1] = vol / 10.0;
    L[2] = vol * 3.0/10.0;
    L[3] = vol * 3.0/5.0;
  }

  if(ndof > 4) {
    Assert( ndof == 10, "Mismatch in number of degrees of freedom" );
    L[4] = vol / 35.0;
    L[5] = vol / 21.0;
    L[6] = vol / 14.0;
    L[7] = vol / 7.0;
    L[8] = vol * 3.0/14.0;
    L[9] = vol * 3.0/7.0;
  }

  // Coordinates of the centroid in physical domain
  std::array< tk::real, 3 > x_c{geoElem(e,1), geoElem(e,2), geoElem(e,3)};

  const auto& cx = coord[0];
  const auto& cy = coord[1];
  const auto& cz = coord[2];

  std::array< std::array< tk::real, 3>, 4 > coordel {{
    {{ cx[ inpoel[4*e  ] ], cy[ inpoel[4*e  ] ], cz[ inpoel[4*e  ] ] }},
    {{ cx[ inpoel[4*e+1] ], cy[ inpoel[4*e+1] ], cz[ inpoel[4*e+1] ] }},
    {{ cx[ inpoel[4*e+2] ], cy[ inpoel[4*e+2] ], cz[ inpoel[4*e+2] ] }},
    {{ cx[ inpoel[4*e+3] ], cy[ inpoel[4*e+3] ], cz[ inpoel[4*e+3] ] }}
  }};

  // Number of quadrature points for volume integration
  auto ng = tk::NGvol(ndof);

  // arrays for quadrature points
  std::array< std::vector< tk::real >, 3 > coordgp;
  std::vector< tk::real > wgp;

  coordgp[0].resize( ng );
  coordgp[1].resize( ng );
  coordgp[2].resize( ng );
  wgp.resize( ng );

  // get quadrature point weights and coordinates for triangle
  tk::GaussQuadratureTet( ng, coordgp, wgp );

  // right hand side vector
  std::vector< tk::real > R( ncomp*ndof, 0.0 );

  // Gaussian quadrature
  for (std::size_t igp=0; igp<ng; ++igp)
  {
    auto wt = wgp[igp] * vol;

    auto gp = tk::eval_gp( igp, coordel, coordgp );

    auto B_taylor = eval_TaylorBasis( ndof, gp, x_c, coordel);

    // Compute high order solution at gauss point
    std::vector< tk::real > state( ncomp, 0.0 );
    for (ncomp_t c=0; c<ncomp; ++c)
    {
      state[c] = unk[c][0];
      state[c] += unk[c][1] * B_taylor[1]
                + unk[c][2] * B_taylor[2]
                + unk[c][3] * B_taylor[3];

      if(ndof > 4)
        state[c] += unk[c][4] * B_taylor[4] + unk[c][5] * B_taylor[5]
                  + unk[c][6] * B_taylor[6] + unk[c][7] * B_taylor[7]
                  + unk[c][8] * B_taylor[8] + unk[c][9] * B_taylor[9];
    }

    auto B = tk::eval_basis( ndof, coordgp[0][igp], coordgp[1][igp], coordgp[2][igp] );

    for (ncomp_t c=0; c<ncomp; ++c)
    {
      auto mark = c*ndof;
      R[mark] += wt * state[c];

      if(ndof > 1)
      {
        R[mark+1] += wt * state[c] * B[1];
        R[mark+2] += wt * state[c] * B[2];
        R[mark+3] += wt * state[c] * B[3];

        if(ndof > 4)
        {
          R[mark+4] += wt * state[c] * B[4];
          R[mark+5] += wt * state[c] * B[5];
          R[mark+6] += wt * state[c] * B[6];
          R[mark+7] += wt * state[c] * B[7];
          R[mark+8] += wt * state[c] * B[8];
          R[mark+9] += wt * state[c] * B[9];
        }
      }
    }
  }

  for (ncomp_t c=0; c<ncomp; ++c)
  {
    auto mark = c*ndof;
    for(std::size_t idof = 0; idof < ndof; idof++)
      unk[c][idof] = R[mark+idof] / L[idof];
  }
}

std::vector< tk::real >
tk::eval_TaylorBasis( const std::size_t ndof,
                      const std::array< tk::real, 3 >& x,
                      const std::array< tk::real, 3 >& x_c,
                      const std::array< std::array< tk::real, 3>, 4 >& coordel )
// *****************************************************************************
//  Evaluate the Taylor basis at points
//! \param[in] ndof Maximum number of degrees of freedom
//! \param[in] x Nodal coordinates
//! \param[in] x_c Coordinates of the centroid
//! \param[in] coordel Array of nodal coordinates for the tetrahedron
// *****************************************************************************
{
  std::vector< tk::real > avg( 6, 0.0 );
  if(ndof > 4)
  {
    Assert( ndof == 10, "Mismatch in number of degrees of freedom" );
    auto ng = tk::NGvol(ndof);

    std::array< std::vector< tk::real >, 3 > coordgp;
    std::vector< tk::real > wgp;

    coordgp[0].resize( ng );
    coordgp[1].resize( ng );
    coordgp[2].resize( ng );
    wgp.resize( ng );

    tk::GaussQuadratureTet( ng, coordgp, wgp );

    for (std::size_t igp=0; igp<ng; ++igp)
    {
      // Compute the coordinates of quadrature point at physical domain
      auto gp = tk::eval_gp( igp, coordel, coordgp );

      avg[0] += wgp[igp] * (gp[0] - x_c[0]) * (gp[0] - x_c[0]) * 0.5;
      avg[1] += wgp[igp] * (gp[1] - x_c[1]) * (gp[1] - x_c[1]) * 0.5;
      avg[2] += wgp[igp] * (gp[2] - x_c[2]) * (gp[2] - x_c[2]) * 0.5;
      avg[3] += wgp[igp] * (gp[0] - x_c[0]) * (gp[1] - x_c[1]);
      avg[4] += wgp[igp] * (gp[0] - x_c[0]) * (gp[2] - x_c[2]);
      avg[5] += wgp[igp] * (gp[1] - x_c[1]) * (gp[2] - x_c[2]);
    }
  }

  std::vector< tk::real > B( ndof, 1.0 );

  if(ndof > 1) {
    Assert( (ndof == 4)||(ndof == 10) ,
      "Mismatch in number of degrees of freedom" );
    B[1] = x[0] - x_c[0];
    B[2] = x[1] - x_c[1];
    B[3] = x[2] - x_c[2];
  }

  if(ndof > 4) {
    B[4] = B[1] * B[1] * 0.5 - avg[0];
    B[5] = B[2] * B[2] * 0.5 - avg[1];
    B[6] = B[3] * B[3] * 0.5 - avg[2];
    B[7] = B[1] * B[2] - avg[3];
    B[8] = B[1] * B[3] - avg[4];
    B[9] = B[2] * B[3] - avg[5];
  }

  return B;
}

// -----------------------------------------------------------------------------
// Functions for reference element Taylor basis and related Xforms
// -----------------------------------------------------------------------------

std::vector< std::vector< tk::real > >
tk::DubinerToTaylorRefEl( ncomp_t ncomp,
  const std::size_t e,
  const std::size_t ndof,
  const std::size_t ndof_el,
  const std::vector< std::vector< tk::real > >& mtInv,
  const tk::Fields& U )
// *****************************************************************************
//  Transform the solution from Dubiner basis to Taylor basis
//! \param[in] ncomp Number of scalar components in this PDE system
//! \param[in] e Id of element whose solution is to be limited
//! \param[in] ndof Maximum number of degrees of freedom
//! \param[in] ndof_el Local number of degrees of freedom for the element
//! \param[in] mtInv Inverse of Taylor mass matrix
//! \param[in] U High-order solution vector with Dubiner basis
//! \return High-order solution vector with Taylor basis (ref element)
// *****************************************************************************
{
  auto vol = 1.0/6.0;

  // 1. Get rhs for L2-projection
  // Quadrature setup
  auto ng = tk::NGvol(ndof_el);
  std::array< std::vector< real >, 3 > coordgp;
  std::vector< real > wgp;
  coordgp[0].resize( ng );
  coordgp[1].resize( ng );
  coordgp[2].resize( ng );
  wgp.resize( ng );
  GaussQuadratureTet( ng, coordgp, wgp );

  // Gaussian quadrature
  std::vector< std::vector< tk::real > >
    R(ncomp, std::vector<tk::real>(ndof_el, 0.0));
  for (std::size_t igp=0; igp<ng; ++igp)
  {
    // Dubiner basis functions
    auto B = eval_basis( ndof_el, coordgp[0][igp], coordgp[1][igp],
                         coordgp[2][igp] );
    // Taylor basis functions
    auto Bt = eval_TaylorBasisRefEl(ndof_el, coordgp[0][igp], coordgp[1][igp],
      coordgp[2][igp]);

    auto state = tk::eval_state(ncomp, ndof, ndof_el, e, U, B);

    for (std::size_t c=0; c<ncomp; ++c) {
      for (std::size_t id=0; id<ndof_el; ++id) {
        R[c][id] += wgp[igp] * vol * state[c] * Bt[id];
      }
    }
  }


  // 2. Get Taylor solution by premultiplying by mass matrix inverse
  std::vector< std::vector< tk::real > >
    unk(ncomp, std::vector<tk::real>(ndof_el, 0.0));
  for (std::size_t c=0; c<ncomp; ++c) {
    for (std::size_t id=0; id<ndof_el; ++id) {
      for (std::size_t jd=0; jd<ndof_el; ++jd) {
        unk[c][id] += mtInv[id][jd] * R[c][jd];
      }
    }
  }

  return unk;
}

void
tk::TaylorToDubinerRefEl( ncomp_t ncomp,
  const std::size_t ndof,
  std::vector< std::vector< tk::real > >& unk )
// *****************************************************************************
//  Transform the solution from Taylor to Dubiner basis
//! \param[in] ncomp Number of scalar components in this PDE system
//! \param[in] ndof Number of degrees of freedom
//! \param[in,out] unk High-order solution vector with Taylor basis that gets
//!   transformed to solution with Dubiner basis
// *****************************************************************************
{
  auto vol = 1.0/6.0;

  auto M = massMatrixDubiner(ndof, vol);

  // 1. Get rhs for L2-projection
  // Quadrature setup
  auto ng = tk::NGvol(ndof);
  std::array< std::vector< real >, 3 > coordgp;
  std::vector< real > wgp;
  coordgp[0].resize( ng );
  coordgp[1].resize( ng );
  coordgp[2].resize( ng );
  wgp.resize( ng );
  GaussQuadratureTet( ng, coordgp, wgp );

  // Gaussian quadrature
  std::vector< std::vector< tk::real > >
    R(ncomp, std::vector<tk::real>(ndof, 0.0));
  for (std::size_t igp=0; igp<ng; ++igp)
  {
    // Dubiner basis functions
    auto B = eval_basis( ndof, coordgp[0][igp], coordgp[1][igp],
                         coordgp[2][igp] );
    // Taylor basis functions
    auto Bt = eval_TaylorBasisRefEl(ndof, coordgp[0][igp], coordgp[1][igp],
      coordgp[2][igp]);

    for (std::size_t c=0; c<ncomp; ++c) {
      real state(0.0);
      for (std::size_t id=0; id<ndof; ++id) {
        state += unk[c][id] * Bt[id];
      }
      for (std::size_t id=0; id<ndof; ++id) {
        R[c][id] += wgp[igp] * vol * state * B[id];
      }
    }
  }

  // 2. Get Dubiner solution by premultiplying by mass matrix inverse
  for (std::size_t c=0; c<ncomp; ++c) {
    for (std::size_t id=0; id<ndof; ++id) {
      unk[c][id] = R[c][id] / M[id];
    }
  }
}

std::vector< tk::real >
tk::eval_TaylorBasisRefEl( std::size_t ndof, tk::real x, tk::real y,
  tk::real z )
// *****************************************************************************
//  Evaluate the Taylor basis at a point in the reference element
//! \param[in] ndof Number of degrees of freedom
//! \param[in] x Xi coordinate of point in reference element
//! \param[in] y Eta coordinate of point in reference element
//! \param[in] z Zeta coordinate of point in reference element
// *****************************************************************************
{
  // Get averages required for P2 basis functions
  std::vector< tk::real > avg( 6, 0.0 );
  if(ndof > 4)
  {
    auto ng = tk::NGvol(ndof);
    std::array< std::vector< tk::real >, 3 > coordgp;
    std::vector< tk::real > wgp;
    coordgp[0].resize( ng );
    coordgp[1].resize( ng );
    coordgp[2].resize( ng );
    wgp.resize( ng );
    tk::GaussQuadratureTet( ng, coordgp, wgp );

    for (std::size_t igp=0; igp<ng; ++igp)
    {
      avg[0] += wgp[igp] * (coordgp[0][igp] - 0.25) * (coordgp[0][igp] - 0.25) * 0.5;
      avg[1] += wgp[igp] * (coordgp[1][igp] - 0.25) * (coordgp[1][igp] - 0.25) * 0.5;
      avg[2] += wgp[igp] * (coordgp[2][igp] - 0.25) * (coordgp[2][igp] - 0.25) * 0.5;
      avg[3] += wgp[igp] * (coordgp[0][igp] - 0.25) * (coordgp[1][igp] - 0.25);
      avg[4] += wgp[igp] * (coordgp[0][igp] - 0.25) * (coordgp[2][igp] - 0.25);
      avg[5] += wgp[igp] * (coordgp[1][igp] - 0.25) * (coordgp[2][igp] - 0.25);
    }
  }

  // Get Taylor basis functions
  std::vector< tk::real > B( ndof, 1.0 );
  if(ndof > 1) {
    B[1] = x - 0.25;
    B[2] = y - 0.25;
    B[3] = z - 0.25;
    if(ndof > 4) {
      B[4] = B[1] * B[1] * 0.5 - avg[0];
      B[5] = B[2] * B[2] * 0.5 - avg[1];
      B[6] = B[3] * B[3] * 0.5 - avg[2];
      B[7] = B[1] * B[2] - avg[3];
      B[8] = B[1] * B[3] - avg[4];
      B[9] = B[2] * B[3] - avg[5];
    }
  }

  return B;
}

std::vector< std::vector< tk::real > >
tk::invMassMatTaylorRefEl( std::size_t dof )
// *****************************************************************************
//  Obtain inverse mass matrix for Taylor basis in reference element
//! \param[in] dof Number of degrees of freedom
//! \return Inverse mass matrix
// *****************************************************************************
{
  // Get Taylor mass matrix
  auto Mt = massMatrixTaylorRefEl(dof);

  // Only invert if DGP2
  if (dof > 4) {
    double mtInv[10*10];
    for (std::size_t i=0; i<Mt.size(); ++i) {
      for (std::size_t j=0; j<Mt[i].size(); ++j) {
        std::size_t idx = 10*i+j;
        mtInv[idx] = Mt[i][j];
      }
    }
    lapack_int ipiv[10];
    // LU-factorization for inversion
    lapack_int info1 = LAPACKE_dgetrf(LAPACK_ROW_MAJOR, 10, 10, mtInv, 10, ipiv);
    if (info1 != 0) Throw("Taylor mass matrix is singular");
    // Inversion
    lapack_int info2 = LAPACKE_dgetri(LAPACK_ROW_MAJOR, 10, mtInv, 10, ipiv);
    if (info2 != 0) Throw("Error while inverting Taylor mass matrix");

    // Get 2D vector from 1D array mass matrix inverse
    for (std::size_t i=0; i<Mt.size(); ++i) {
      for (std::size_t j=0; j<Mt[i].size(); ++j) {
        std::size_t idx = 10*i+j;
        Mt[i][j] = mtInv[idx];
      }
    }
  }

  return Mt;
}

std::vector< std::vector< tk::real > >
tk::massMatrixTaylorRefEl(std::size_t dof)
// *****************************************************************************
//  Obtain mass matrix for Taylor basis in reference element
//! \param[in] dof Number of degrees of freedom
//! \return Mass matrix
// *****************************************************************************
{
  std::vector< std::vector< tk::real > >
    Mt(dof, std::vector<tk::real>(dof,0.0));

  // Mt(1,1)
  tk::real vol = 1.0/6.0;
  Mt[0][0] = vol;

  // Mt(i,j) for i,j > 1
  if (dof > 1) {
    // Quadrature information
    auto ng = tk::NGvol(dof);
    std::array< std::vector< tk::real >, 3 > coordgp;
    std::vector< tk::real > wgp;
    coordgp[0].resize( ng );
    coordgp[1].resize( ng );
    coordgp[2].resize( ng );
    wgp.resize( ng );
    tk::GaussQuadratureTet( ng, coordgp, wgp );

    for (std::size_t igp=0; igp<ng; ++igp)
    {
      auto Bt = eval_TaylorBasisRefEl(dof, coordgp[0][igp], coordgp[1][igp],
        coordgp[2][igp]);
      for (std::size_t id=1; id<dof; ++id) {
        for (std::size_t jd=1; jd<dof; ++jd) {
          Mt[id][jd] += vol*wgp[igp]*Bt[id]*Bt[jd];
        }
      }
    }
  }

  return Mt;
}