From: Jean-Paul Pelteret Date: Fri, 20 May 2016 20:36:00 +0000 (+0200) Subject: Updated step-44 to use modern features of deal.II. X-Git-Tag: v8.5.0-rc1~1009^2 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=c6b05aed2105ef2f6eb4018ba9c0179e5c9915e0;p=dealii.git Updated step-44 to use modern features of deal.II. The step-44 tutorial now uses the new CellDataStorage class to store and retrieve local quadrature point data. There is now also the option to use LinearOperators to solve the linear system, as well as a direct solver for the full block system. The documentation has also been extended to describe the extensions. --- diff --git a/doc/news/changes.h b/doc/news/changes.h index 1caca6a5ff..7284ff6565 100644 --- a/doc/news/changes.h +++ b/doc/news/changes.h @@ -120,6 +120,13 @@ inconvenience this causes.

General

    +
  1. Improved: The step-44 tutorial now uses the new CellDataStorage class to + store and retrieve local quadrature point data. An alternative approach to + solving the linear system using the LinearOperator class has been implemented. +
    + (Jean-Paul Pelteret, 2016/05/20) +
    2. +
  2. New: Add VectorTools::compute_global_error that computes global errors from cellwise errors obtained by VectorTools::integrate_difference() and do MPI collectives if necessary. diff --git a/examples/step-18/step-18.cc b/examples/step-18/step-18.cc index 50ea691d44..d340b8542a 100644 --- a/examples/step-18/step-18.cc +++ b/examples/step-18/step-18.cc @@ -454,6 +454,10 @@ namespace Step18 // point on those cells for which we are responsible (i.e. we don't store // history data for quadrature points on cells that are owned by other // processors). + // Note that, instead of storing and managing this data ourself, we + // could use the CellDataStorage class like is done in step-44. However, + // for the purpose of demonstration, in this case we manage the storage + // manually. std::vector > quadrature_point_history; // The way this object is accessed is through a user pointer diff --git a/examples/step-44/CMakeLists.txt b/examples/step-44/CMakeLists.txt index ba213626ba..1d6d1bf714 100644 --- a/examples/step-44/CMakeLists.txt +++ b/examples/step-44/CMakeLists.txt @@ -34,6 +34,17 @@ IF(NOT ${deal.II_FOUND}) ) ENDIF() +# +# Are all dependencies fulfilled? +# +IF(NOT DEAL_II_WITH_CXX11) + MESSAGE(FATAL_ERROR " +Error! The deal.II library found at ${DEAL_II_PATH} was not configured with + DEAL_II_WITH_CXX11 = ON +This is OFF in your installation but is required for this tutorial step." + ) +ENDIF() + DEAL_II_INITIALIZE_CACHED_VARIABLES() PROJECT(${TARGET}) DEAL_II_INVOKE_AUTOPILOT() diff --git a/examples/step-44/doc/intro.dox b/examples/step-44/doc/intro.dox index 1e7bbc4847..1660ea141c 100644 --- a/examples/step-44/doc/intro.dox +++ b/examples/step-44/doc/intro.dox @@ -30,7 +30,7 @@ Thereafter, various key stress measures are introduced and the constitutive mode We then describe the three-field formulation in detail prior to explaining the structure of the class used to manage the material. The setup of the example problem is then presented. -@note This tutorial has been developed for the problem of elasticity in three dimensions. +@note This tutorial has been developed (and is described in the introduction) for the problem of elasticity in three dimensions. While the space dimension could be changed in the main() routine, care needs to be taken. Two-dimensional elasticity problems, in general, exist only as idealisations of three-dimensional ones. That is, they are either plane strain or plane stress. @@ -49,26 +49,38 @@ A nice overview of issues pertaining to incompressible elasticity (at small stra Variational and projection methods for the volume constraint in finite deformation elasto-plasticity, Computer Methods in Applied Mechanics and Engineering , 51 , 1-3, - 177-208; + 177-208. + DOI: 10.1016/0045-7825(85)90033-7;
  3. J.C. Simo and R.L. Taylor (1991), Quasi-incompressible finite elasticity in principal stretches. Continuum basis and numerical algorithms, Computer Methods in Applied Mechanics and Engineering , 85 , 3, - 273-310; + 273-310. + DOI: 10.1016/0045-7825(91)90100-K;
  4. C. Miehe (1994), - Aspects of the formulation and finite element implementation of large strain isotropic elasticity + Aspects of the formulation and finite element implementation of large strain isotropic elasticity International Journal for Numerical Methods in Engineering 37 , 12, - 1981-2004; + 1981-2004. + DOI: 10.1002/nme.1620371202;
  5. G.A. Holzapfel (2001), Nonlinear Solid Mechanics. A Continuum Approach for Engineering, - John Wiley & Sons; + John Wiley & Sons. + ISBN: 0-471-82304-X;
  6. T.J.R. Hughes (2000), The Finite Element Method: Linear Static and Dynamic Finite Element Analysis, Dover. + ISBN: 978-0486411811
+An example where this three-field formulation is used in a coupled problem is documented in +
    +
  1. J-P. V. Pelteret, D. Davydov, A. McBride, D. K. Vu, and P. Steinmann (2016), + Computational electro- and magneto-elasticity for quasi-incompressible media immersed in free space, + International Journal for Numerical Methods in Engineering . + DOI: 10.1002/nme.5254 +

Notation

@@ -668,6 +680,7 @@ This benchmark problem is taken from Computers and Structures , 75 , 291-304. + DOI: 10.1016/S0045-7949(99)00137-6 diff --git a/examples/step-44/doc/results.dox b/examples/step-44/doc/results.dox index 93ff4e3475..dddb8a6df5 100644 --- a/examples/step-44/doc/results.dox +++ b/examples/step-44/doc/results.dox @@ -300,5 +300,10 @@ There are a number of obvious extensions for this work: (detection and stress calculations) itself. An alternative to additional penalty terms in the free-energy functional would be to use active set methods such as the one used in step-41. +- The complete condensation procedure using LinearOperators has been + coded into the linear solver routine. This could also have been + achieved through the application of the schur_complement() + operator to condense out one or more of the fields in a more + automated manner. - Finally, adaptive mesh refinement, as demonstrated in step-6 and step-18, could provide additional solution accuracy. diff --git a/examples/step-44/parameters.prm b/examples/step-44/parameters.prm index ad796b4be6..647460931f 100644 --- a/examples/step-44/parameters.prm +++ b/examples/step-44/parameters.prm @@ -28,12 +28,17 @@ subsection Linear solver # Linear solver residual (scaled by residual norm) set Residual = 1e-6 + + # Use static condensation and solve a 1-block system, or solve + # the full 3-block system using Linear Operators and the Schur + # complement + set Use static condensation = true - # Preconditioner type - set Preconditioner type = ssor + # Preconditioner type + set Preconditioner type = ssor - # Preconditioner relaxation value - set Preconditioner relaxation = 0.65 + # Preconditioner relaxation value + set Preconditioner relaxation = 0.65 # Type of solver used to solve the linear system set Solver type = CG diff --git a/examples/step-44/step-44.cc b/examples/step-44/step-44.cc index 2ea8c9e128..2dec4d7909 100644 --- a/examples/step-44/step-44.cc +++ b/examples/step-44/step-44.cc @@ -31,10 +31,13 @@ #include #include #include - #include #include +// This header gives us the functionality to store +// data at quadrature points +#include + #include #include #include @@ -57,6 +60,15 @@ #include #include +// Here are the headers necessary to use the LinearOperator class. +// These are also all conveniently packaged into a single +// header file, namely +// but we list those specifically required here for the sake +// of transparency. +#include +#include +#include + #include #include @@ -225,6 +237,7 @@ namespace Step44 std::string type_lin; double tol_lin; double max_iterations_lin; + bool use_static_condensation; std::string preconditioner_type; double preconditioner_relaxation; @@ -251,6 +264,10 @@ namespace Step44 Patterns::Double(0.0), "Linear solver iterations (multiples of the system matrix size)"); + prm.declare_entry("Use static condensation", "true", + Patterns::Bool(), + "Solve the full block system or a reduced problem"); + prm.declare_entry("Preconditioner type", "ssor", Patterns::Selection("jacobi|ssor"), "Type of preconditioner"); @@ -269,6 +286,7 @@ namespace Step44 type_lin = prm.get("Solver type"); tol_lin = prm.get_double("Residual"); max_iterations_lin = prm.get_double("Max iteration multiplier"); + use_static_condensation = prm.get_bool("Use static condensation"); preconditioner_type = prm.get("Preconditioner type"); preconditioner_relaxation = prm.get_double("Preconditioner relaxation"); } @@ -890,7 +908,7 @@ namespace Step44 void assemble_system_tangent_one_cell(const typename DoFHandler::active_cell_iterator &cell, ScratchData_K &scratch, - PerTaskData_K &data); + PerTaskData_K &data) const; void copy_local_to_global_K(const PerTaskData_K &data); @@ -901,7 +919,7 @@ namespace Step44 void assemble_system_rhs_one_cell(const typename DoFHandler::active_cell_iterator &cell, ScratchData_RHS &scratch, - PerTaskData_RHS &data); + PerTaskData_RHS &data) const; void copy_local_to_global_rhs(const PerTaskData_RHS &data); @@ -957,23 +975,24 @@ namespace Step44 // Finally, some member variables that describe the current state: A // collection of the parameters used to describe the problem setup... - Parameters::AllParameters parameters; + Parameters::AllParameters parameters; - // ...the volume of the reference and current configurations... - double vol_reference; - double vol_current; + // ...the volume of the reference configuration... + double vol_reference; // ...and description of the geometry on which the problem is solved: - Triangulation triangulation; + Triangulation triangulation; // Also, keep track of the current time and the time spent evaluating // certain functions - Time time; - TimerOutput timer; + Time time; + mutable TimerOutput timer; - // A storage object for quadrature point information. See step-18 for - // more on this: - std::vector > quadrature_point_history; + // A storage object for quadrature point information. As opposed to + // step-18, deal.II's native quadrature point data manager is employed + // here. + CellDataStorage::cell_iterator, + PointHistory > quadrature_point_history; // A description of the finite-element system including the displacement // polynomial degree, the degree-of-freedom handler, number of DoFs per @@ -990,11 +1009,11 @@ namespace Step44 // Description of how the block-system is arranged. There are 3 blocks, // the first contains a vector DOF $\mathbf{u}$ while the other two // describe scalar DOFs, $\widetilde{p}$ and $\widetilde{J}$. - static const unsigned int n_blocks = 3; - static const unsigned int n_components = dim + 2; - static const unsigned int first_u_component = 0; - static const unsigned int p_component = dim; - static const unsigned int J_component = dim + 1; + static const unsigned int n_blocks = 3; + static const unsigned int n_components = dim + 2; + static const unsigned int first_u_component = 0; + static const unsigned int p_component = dim; + static const unsigned int J_component = dim + 1; enum { @@ -1003,27 +1022,27 @@ namespace Step44 J_dof = 2 }; - std::vector dofs_per_block; - std::vector element_indices_u; - std::vector element_indices_p; - std::vector element_indices_J; + std::vector dofs_per_block; + std::vector element_indices_u; + std::vector element_indices_p; + std::vector element_indices_J; // Rules for Gauss-quadrature on both the cell and faces. The number of // quadrature points on both cells and faces is recorded. - const QGauss qf_cell; - const QGauss qf_face; - const unsigned int n_q_points; - const unsigned int n_q_points_f; + const QGauss qf_cell; + const QGauss qf_face; + const unsigned int n_q_points; + const unsigned int n_q_points_f; // Objects that store the converged solution and right-hand side vectors, // as well as the tangent matrix. There is a ConstraintMatrix object used // to keep track of constraints. We make use of a sparsity pattern // designed for a block system. - ConstraintMatrix constraints; - BlockSparsityPattern sparsity_pattern; - BlockSparseMatrix tangent_matrix; - BlockVector system_rhs; - BlockVector solution_n; + ConstraintMatrix constraints; + BlockSparsityPattern sparsity_pattern; + BlockSparseMatrix tangent_matrix; + BlockVector system_rhs; + BlockVector solution_n; // Then define a number of variables to store norms and update norms and // normalisation factors. @@ -1068,7 +1087,11 @@ namespace Step44 Errors &error_update); std::pair - get_error_dilation(); + get_error_dilation() const; + + // Compute the volume in the spatial configuration + double + compute_vol_current () const; // Print information to screen in a pleasing way... static @@ -1521,7 +1544,6 @@ namespace Step44 triangulation.refine_global(std::max (1U, parameters.global_refinement)); vol_reference = GridTools::volume(triangulation); - vol_current = vol_reference; std::cout << "Grid:\n\t Reference volume: " << vol_reference << std::endl; // Since we wish to apply a Neumann BC to a patch on the top surface, we @@ -1609,21 +1631,21 @@ namespace Step44 // The global system matrix initially has the following structure // @f{align*} // \underbrace{\begin{bmatrix} - // \mathbf{\mathsf{K}}_{uu} & \mathbf{\mathsf{K}}_{u\widetilde{p}} & \mathbf{0} - // \\ \mathbf{\mathsf{K}}_{\widetilde{p}u} & \mathbf{0} & \mathbf{\mathsf{K}}_{\widetilde{p}\widetilde{J}} - // \\ \mathbf{0} & \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}} & \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} - // \end{bmatrix}}_{\mathbf{\mathsf{K}}(\mathbf{\Xi}_{\textrm{i}})} + // \mathsf{\mathbf{K}}_{uu} & \mathsf{\mathbf{K}}_{u\widetilde{p}} & \mathbf{0} + // \\ \mathsf{\mathbf{K}}_{\widetilde{p}u} & \mathbf{0} & \mathsf{\mathbf{K}}_{\widetilde{p}\widetilde{J}} + // \\ \mathbf{0} & \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}} & \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // \end{bmatrix}}_{\mathsf{\mathbf{K}}(\mathbf{\Xi}_{\textrm{i}})} // \underbrace{\begin{bmatrix} - // d \mathbf{\mathsf{u}} - // \\ d \widetilde{\mathbf{\mathsf{p}}} - // \\ d \widetilde{\mathbf{\mathsf{J}}} + // d \mathsf{u} + // \\ d \widetilde{\mathsf{\mathbf{p}}} + // \\ d \widetilde{\mathsf{\mathbf{J}}} // \end{bmatrix}}_{d \mathbf{\Xi}} // = // \underbrace{\begin{bmatrix} - // \mathbf{\mathsf{F}}_{u}(\mathbf{u}_{\textrm{i}}) - // \\ \mathbf{\mathsf{F}}_{\widetilde{p}}(\widetilde{p}_{\textrm{i}}) - // \\ \mathbf{\mathsf{F}}_{\widetilde{J}}(\widetilde{J}_{\textrm{i}}) - //\end{bmatrix}}_{ \mathbf{\mathsf{F}}(\mathbf{\Xi}_{\textrm{i}}) } \, . + // \mathsf{\mathbf{F}}_{u}(\mathbf{u}_{\textrm{i}}) + // \\ \mathsf{\mathbf{F}}_{\widetilde{p}}(\widetilde{p}_{\textrm{i}}) + // \\ \mathsf{\mathbf{F}}_{\widetilde{J}}(\widetilde{J}_{\textrm{i}}) + //\end{bmatrix}}_{ \mathsf{\mathbf{F}}(\mathbf{\Xi}_{\textrm{i}}) } \, . // @f} // We optimise the sparsity pattern to reflect this structure // and prevent unnecessary data creation for the right-diagonal @@ -1706,42 +1728,22 @@ namespace Step44 { std::cout << " Setting up quadrature point data..." << std::endl; - { - triangulation.clear_user_data(); - { - std::vector > tmp; - tmp.swap(quadrature_point_history); - } - - quadrature_point_history - .resize(triangulation.n_active_cells() * n_q_points); - - unsigned int history_index = 0; - for (typename Triangulation::active_cell_iterator cell = - triangulation.begin_active(); cell != triangulation.end(); - ++cell) - { - cell->set_user_pointer(&quadrature_point_history[history_index]); - history_index += n_q_points; - } - - Assert(history_index == quadrature_point_history.size(), - ExcInternalError()); - } + quadrature_point_history.initialize(triangulation.begin_active(), + triangulation.end(), + n_q_points); - // Next we setup the initial quadrature - // point data: + // Next we setup the initial quadrature point data. + // Note that when the quadrature point data is retrieved, + // it is returned as a vector of smart pointers. for (typename Triangulation::active_cell_iterator cell = triangulation.begin_active(); cell != triangulation.end(); ++cell) { - PointHistory *lqph = - reinterpret_cast*>(cell->user_pointer()); - - Assert(lqph >= &quadrature_point_history.front(), ExcInternalError()); - Assert(lqph <= &quadrature_point_history.back(), ExcInternalError()); + const std::vector > > lqph = + quadrature_point_history.get_data(cell); + Assert(lqph.size() == n_q_points, ExcInternalError()); for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) - lqph[q_point].setup_lqp(parameters); + lqph[q_point]->setup_lqp(parameters); } } @@ -1787,11 +1789,9 @@ namespace Step44 ScratchData_UQPH &scratch, PerTaskData_UQPH &/*data*/) { - PointHistory *lqph = - reinterpret_cast*>(cell->user_pointer()); - - Assert(lqph >= &quadrature_point_history.front(), ExcInternalError()); - Assert(lqph <= &quadrature_point_history.back(), ExcInternalError()); + const std::vector > > lqph = + quadrature_point_history.get_data(cell); + Assert(lqph.size() == n_q_points, ExcInternalError()); Assert(scratch.solution_grads_u_total.size() == n_q_points, ExcInternalError()); @@ -1815,9 +1815,9 @@ namespace Step44 scratch.solution_values_J_total); for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) - lqph[q_point].update_values(scratch.solution_grads_u_total[q_point], - scratch.solution_values_p_total[q_point], - scratch.solution_values_J_total[q_point]); + lqph[q_point]->update_values(scratch.solution_grads_u_total[q_point], + scratch.solution_values_p_total[q_point], + scratch.solution_values_J_total[q_point]); } @@ -1966,19 +1966,55 @@ namespace Step44 std::cout << "_"; std::cout << std::endl; - const std::pair error_dil = get_error_dilation(); + const std::pair error_dil = get_error_dilation(); std::cout << "Relative errors:" << std::endl << "Displacement:\t" << error_update.u / error_update_0.u << std::endl << "Force: \t\t" << error_residual.u / error_residual_0.u << std::endl << "Dilatation:\t" << error_dil.first << std::endl - << "v / V_0:\t" << vol_current << " / " << vol_reference + << "v / V_0:\t" << error_dil.second *vol_reference << " / " << vol_reference << " = " << error_dil.second << std::endl; } // @sect4{Solid::get_error_dilation} +// Calculate the volume of the domain in the spatial configuration + template + double + Solid::compute_vol_current() const + { + double vol_current = 0.0; + + FEValues fe_values_ref(fe, qf_cell, update_JxW_values); + + for (typename Triangulation::active_cell_iterator + cell = triangulation.begin_active(); + cell != triangulation.end(); ++cell) + { + fe_values_ref.reinit(cell); + + // In contrast to that which was previously called for, + // in this instance the quadrature point data is specifically + // non-modifiable since we will only be accessing data. + // We ensure that the right get_data function is called by + // marking this update function as constant. + const std::vector > > lqph = + quadrature_point_history.get_data(cell); + Assert(lqph.size() == n_q_points, ExcInternalError()); + + for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) + { + const double det_F_qp = lqph[q_point]->get_det_F(); + const double JxW = fe_values_ref.JxW(q_point); + + vol_current += det_F_qp * JxW; + } + } + Assert(vol_current > 0.0, ExcInternalError()); + return vol_current; + } + // Calculate how well the dilatation $\widetilde{J}$ agrees with $J := // \textrm{det}\ \mathbf{F}$ from the $L^2$ error $ \bigl[ \int_{\Omega_0} {[ J // - \widetilde{J}]}^{2}\textrm{d}V \bigr]^{1/2}$. @@ -1988,10 +2024,9 @@ namespace Step44 // enforced. template std::pair - Solid::get_error_dilation() + Solid::get_error_dilation() const { double dil_L2_error = 0.0; - vol_current = 0.0; FEValues fe_values_ref(fe, qf_cell, update_JxW_values); @@ -2001,31 +2036,24 @@ namespace Step44 { fe_values_ref.reinit(cell); - PointHistory *lqph = - reinterpret_cast*>(cell->user_pointer()); - - Assert(lqph >= &quadrature_point_history.front(), ExcInternalError()); - Assert(lqph <= &quadrature_point_history.back(), ExcInternalError()); + const std::vector > > lqph = + quadrature_point_history.get_data(cell); + Assert(lqph.size() == n_q_points, ExcInternalError()); for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) { - const double det_F_qp = lqph[q_point].get_det_F(); - const double J_tilde_qp = lqph[q_point].get_J_tilde(); + const double det_F_qp = lqph[q_point]->get_det_F(); + const double J_tilde_qp = lqph[q_point]->get_J_tilde(); const double the_error_qp_squared = std::pow((det_F_qp - J_tilde_qp), 2); const double JxW = fe_values_ref.JxW(q_point); dil_L2_error += the_error_qp_squared * JxW; - vol_current += det_F_qp * JxW; } - Assert(vol_current > 0, ExcInternalError()); } - std::pair error_dil; - error_dil.first = std::sqrt(dil_L2_error); - error_dil.second = vol_current / vol_reference; - - return error_dil; + return std::make_pair(std::sqrt(dil_L2_error), + compute_vol_current() / vol_reference); } @@ -2051,7 +2079,7 @@ namespace Step44 } -// @sect4{Solid::get_error_udpate} +// @sect4{Solid::get_error_update} // Determine the true Newton update error for the problem template @@ -2108,11 +2136,21 @@ namespace Step44 PerTaskData_K per_task_data(dofs_per_cell); ScratchData_K scratch_data(fe, qf_cell, uf_cell); + // The syntax used here to pass data to the WorkStream class + // is discussed in step-14. We need to use this particular + // call to WorkStream because assemble_system_tangent_one_cell + // is a constant function and copy_local_to_global_K is + // non-constant. WorkStream::run(dof_handler_ref.begin_active(), dof_handler_ref.end(), - *this, - &Solid::assemble_system_tangent_one_cell, - &Solid::copy_local_to_global_K, + std_cxx11::bind(&Solid::assemble_system_tangent_one_cell, + this, + std_cxx11::_1, + std_cxx11::_2, + std_cxx11::_3), + std_cxx11::bind(&Solid::copy_local_to_global_K, + this, + std_cxx11::_1), scratch_data, per_task_data); @@ -2145,18 +2183,20 @@ namespace Step44 void Solid::assemble_system_tangent_one_cell(const typename DoFHandler::active_cell_iterator &cell, ScratchData_K &scratch, - PerTaskData_K &data) + PerTaskData_K &data) const { data.reset(); scratch.reset(); scratch.fe_values_ref.reinit(cell); cell->get_dof_indices(data.local_dof_indices); - PointHistory *lqph = - reinterpret_cast*>(cell->user_pointer()); + + const std::vector > > lqph = + quadrature_point_history.get_data(cell); + Assert(lqph.size() == n_q_points, ExcInternalError()); for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) { - const Tensor<2, dim> F_inv = lqph[q_point].get_F_inv(); + const Tensor<2, dim> F_inv = lqph[q_point]->get_F_inv(); for (unsigned int k = 0; k < dofs_per_cell; ++k) { const unsigned int k_group = fe.system_to_base_index(k).first.first; @@ -2186,17 +2226,17 @@ namespace Step44 // \widetilde{p}} = \mathbf{0}$, $\mathsf{\mathbf{k}}_{\widetilde{J} // \widetilde{J}}$ blocks, while the whole // $\mathsf{\mathbf{k}}_{\widetilde{p} \widetilde{J}}$, - // $\mathsf{\mathbf{k}}_{\mathbf{u} \widetilde{J}} = \mathbf{0}$, - // $\mathsf{\mathbf{k}}_{\mathbf{u} \widetilde{p}}$ blocks are built. + // $\mathsf{\mathbf{k}}_{u \widetilde{J}} = \mathbf{0}$, + // $\mathsf{\mathbf{k}}_{u \widetilde{p}}$ blocks are built. // // In doing so, we first extract some configuration dependent variables - // from our QPH history objects for the current quadrature point. + // from our quadrature history objects for the current quadrature point. for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) { - const Tensor<2, dim> tau = lqph[q_point].get_tau(); - const SymmetricTensor<4, dim> Jc = lqph[q_point].get_Jc(); - const double d2Psi_vol_dJ2 = lqph[q_point].get_d2Psi_vol_dJ2(); - const double det_F = lqph[q_point].get_det_F(); + const Tensor<2, dim> tau = lqph[q_point]->get_tau(); + const SymmetricTensor<4, dim> Jc = lqph[q_point]->get_Jc(); + const double d2Psi_vol_dJ2 = lqph[q_point]->get_d2Psi_vol_dJ2(); + const double det_F = lqph[q_point]->get_det_F(); // Next we define some aliases to make the assembly process easier to // follow @@ -2218,7 +2258,7 @@ namespace Step44 const unsigned int component_j = fe.system_to_component_index(j).first; const unsigned int j_group = fe.system_to_base_index(j).first.first; - // This is the $\mathsf{\mathbf{k}}_{\mathbf{u} \mathbf{u}}$ + // This is the $\mathsf{\mathbf{k}}_{uu}$ // contribution. It comprises a material contribution, and a // geometrical stress contribution which is only added along // the local matrix diagonals: @@ -2230,7 +2270,7 @@ namespace Step44 data.cell_matrix(i, j) += grad_Nx[i][component_i] * tau * grad_Nx[j][component_j] * JxW; } - // Next is the $\mathsf{\mathbf{k}}_{ \widetilde{p} \mathbf{u}}$ contribution + // Next is the $\mathsf{\mathbf{k}}_{ \widetilde{p} u}$ contribution else if ((i_group == p_dof) && (j_group == u_dof)) { data.cell_matrix(i, j) += N[i] * det_F @@ -2285,9 +2325,14 @@ namespace Step44 WorkStream::run(dof_handler_ref.begin_active(), dof_handler_ref.end(), - *this, - &Solid::assemble_system_rhs_one_cell, - &Solid::copy_local_to_global_rhs, + std_cxx11::bind(&Solid::assemble_system_rhs_one_cell, + this, + std_cxx11::_1, + std_cxx11::_2, + std_cxx11::_3), + std_cxx11::bind(&Solid::copy_local_to_global_rhs, + this, + std_cxx11::_1), scratch_data, per_task_data); @@ -2309,18 +2354,20 @@ namespace Step44 void Solid::assemble_system_rhs_one_cell(const typename DoFHandler::active_cell_iterator &cell, ScratchData_RHS &scratch, - PerTaskData_RHS &data) + PerTaskData_RHS &data) const { data.reset(); scratch.reset(); scratch.fe_values_ref.reinit(cell); cell->get_dof_indices(data.local_dof_indices); - PointHistory *lqph = - reinterpret_cast*>(cell->user_pointer()); + + const std::vector > > lqph = + quadrature_point_history.get_data(cell); + Assert(lqph.size() == n_q_points, ExcInternalError()); for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) { - const Tensor<2, dim> F_inv = lqph[q_point].get_F_inv(); + const Tensor<2, dim> F_inv = lqph[q_point]->get_F_inv(); for (unsigned int k = 0; k < dofs_per_cell; ++k) { @@ -2343,11 +2390,11 @@ namespace Step44 for (unsigned int q_point = 0; q_point < n_q_points; ++q_point) { - const SymmetricTensor<2, dim> tau = lqph[q_point].get_tau(); - const double det_F = lqph[q_point].get_det_F(); - const double J_tilde = lqph[q_point].get_J_tilde(); - const double p_tilde = lqph[q_point].get_p_tilde(); - const double dPsi_vol_dJ = lqph[q_point].get_dPsi_vol_dJ(); + const SymmetricTensor<2, dim> tau = lqph[q_point]->get_tau(); + const double det_F = lqph[q_point]->get_det_F(); + const double J_tilde = lqph[q_point]->get_J_tilde(); + const double p_tilde = lqph[q_point]->get_p_tilde(); + const double dPsi_vol_dJ = lqph[q_point]->get_dPsi_vol_dJ(); const std::vector &N = scratch.Nx[q_point]; @@ -2608,344 +2655,31 @@ namespace Step44 constraints.close(); } -// @sect4{Solid::solve_linear_system} +// @sect4{Solid::assemble_sc} // Solving the entire block system is a bit problematic as there are no -// contributions to the $\mathsf{\mathbf{k}}_{ \widetilde{J} \widetilde{J}}$ -// block, rendering it noninvertible. +// contributions to the $\mathsf{\mathbf{K}}_{ \widetilde{J} \widetilde{J}}$ +// block, rendering it noninvertible (when using an iterative solver). // Since the pressure and dilatation variables DOFs are discontinuous, we can // condense them out to form a smaller displacement-only system which // we will then solve and subsequently post-process to retrieve the // pressure and dilatation solutions. -// -// At the top, we allocate two temporary vectors to help with the static -// condensation, and variables to store the number of linear solver iterations -// and the (hopefully converged) residual. -// -// For the following, recall that -// @f{align*} -// \mathbf{\mathsf{K}}_{\textrm{store}} -//:= -// \begin{bmatrix} -// \mathbf{\mathsf{K}}_{\textrm{con}} & \mathbf{\mathsf{K}}_{u\widetilde{p}} & \mathbf{0} -// \\ \mathbf{\mathsf{K}}_{\widetilde{p}u} & \mathbf{0} & \mathbf{\mathsf{K}}_{\widetilde{p}\widetilde{J}}^{-1} -// \\ \mathbf{0} & \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}} & \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} -// \end{bmatrix} \, . -// @f} -// and -// @f{align*} -// d \widetilde{\mathbf{\mathsf{p}}} -// & = \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \bigl[ -// \mathbf{\mathsf{F}}_{\widetilde{J}} -// - \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} d \widetilde{\mathbf{\mathsf{J}}} \bigr] -// \\ d \widetilde{\mathbf{\mathsf{J}}} -// & = \mathbf{\mathsf{K}}_{\widetilde{p}\widetilde{J}}^{-1} \bigl[ -// \mathbf{\mathsf{F}}_{\widetilde{p}} -// - \mathbf{\mathsf{K}}_{\widetilde{p}u} d \mathbf{\mathsf{u}} -// \bigr] -// \\ \Rightarrow d \widetilde{\mathbf{\mathsf{p}}} -// &= \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \mathbf{\mathsf{F}}_{\widetilde{J}} -// - \underbrace{\bigl[\mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} -// \mathbf{\mathsf{K}}_{\widetilde{p}\widetilde{J}}^{-1}\bigr]}_{\overline{\mathbf{\mathsf{K}}}}\bigl[ \mathbf{\mathsf{F}}_{\widetilde{p}} -// - \mathbf{\mathsf{K}}_{\widetilde{p}u} d \mathbf{\mathsf{u}} \bigr] -// @f} -// and thus -// @f[ -// \underbrace{\bigl[ \mathbf{\mathsf{K}}_{uu} + \overline{\overline{\mathbf{\mathsf{K}}}}~ \bigr] -// }_{\mathbf{\mathsf{K}}_{\textrm{con}}} d \mathbf{\mathsf{u}} -// = -// \underbrace{ -// \Bigl[ -// \mathbf{\mathsf{F}}_{u} -// - \mathbf{\mathsf{K}}_{u\widetilde{p}} \bigl[ \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \mathbf{\mathsf{F}}_{\widetilde{J}} -// - \overline{\mathbf{\mathsf{K}}}\mathbf{\mathsf{F}}_{\widetilde{p}} \bigr] -// \Bigr]}_{\mathbf{\mathsf{F}}_{\textrm{con}}} -// @f] -// where -// @f[ -// \overline{\overline{\mathbf{\mathsf{K}}}} := -// \mathbf{\mathsf{K}}_{u\widetilde{p}} \overline{\mathbf{\mathsf{K}}} \mathbf{\mathsf{K}}_{\widetilde{p}u} \, . -// @f] - template - std::pair - Solid::solve_linear_system(BlockVector &newton_update) - { - BlockVector A(dofs_per_block); - BlockVector B(dofs_per_block); - - unsigned int lin_it = 0; - double lin_res = 0.0; - - // In the first step of this function, we solve for the incremental - // displacement $d\mathbf{u}$. To this end, we perform static - // condensation to make - // $\mathbf{\mathsf{K}}_{\textrm{con}} - // = \bigl[ \mathbf{\mathsf{K}}_{uu} + \overline{\overline{\mathbf{\mathsf{K}}}}~ \bigr]$ - // and put - // $\mathsf{\mathbf{k}}^{-1}_{\widetilde{p} \widetilde{J}}$ - // in the original $\mathsf{\mathbf{k}}_{\widetilde{p} \widetilde{J}}$ block. - // That is, we make $\mathbf{\mathsf{K}}_{\textrm{store}}$. - { - assemble_sc(); - - // $ - // \mathsf{\mathbf{A}}_{\widetilde{J}} - // = - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{F}}_{\widetilde{p}} - // $ - tangent_matrix.block(p_dof, J_dof).vmult(A.block(J_dof), - system_rhs.block(p_dof)); - // $ - // \mathsf{\mathbf{B}}_{\widetilde{J}} - // = - // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{F}}_{\widetilde{p}} - // $ - tangent_matrix.block(J_dof, J_dof).vmult(B.block(J_dof), - A.block(J_dof)); - // $ - // \mathsf{\mathbf{A}}_{\widetilde{J}} - // = - // \mathsf{\mathbf{F}}_{\widetilde{J}} - // - - // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{F}}_{\widetilde{p}} - // $ - A.block(J_dof) = system_rhs.block(J_dof); - A.block(J_dof) -= B.block(J_dof); - // $ - // \mathsf{\mathbf{A}}_{\widetilde{J}} - // = - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{J} \widetilde{p}} - // [ - // \mathsf{\mathbf{F}}_{\widetilde{J}} - // - - // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{F}}_{\widetilde{p}} - // ] - // $ - tangent_matrix.block(p_dof, J_dof).Tvmult(A.block(p_dof), - A.block(J_dof)); - // $ - // \mathsf{\mathbf{A}}_{\mathbf{u}} - // = - // \mathsf{\mathbf{K}}_{\mathbf{u} \widetilde{p}} - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{J} \widetilde{p}} - // [ - // \mathsf{\mathbf{F}}_{\widetilde{J}} - // - - // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{F}}_{\widetilde{p}} - // ] - // $ - tangent_matrix.block(u_dof, p_dof).vmult(A.block(u_dof), - A.block(p_dof)); - // $ - // \mathsf{\mathbf{F}}_{\text{con}} - // = - // \mathsf{\mathbf{F}}_{\mathbf{u}} - // - - // \mathsf{\mathbf{K}}_{\mathbf{u} \widetilde{p}} - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{J} \widetilde{p}} - // [ - // \mathsf{\mathbf{F}}_{\widetilde{J}} - // - - // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} - // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{K}}_{\widetilde{p}} - // ] - // $ - system_rhs.block(u_dof) -= A.block(u_dof); - - timer.enter_subsection("Linear solver"); - std::cout << " SLV " << std::flush; - if (parameters.type_lin == "CG") - { - const int solver_its = tangent_matrix.block(u_dof, u_dof).m() - * parameters.max_iterations_lin; - const double tol_sol = parameters.tol_lin - * system_rhs.block(u_dof).l2_norm(); - - SolverControl solver_control(solver_its, tol_sol); - - GrowingVectorMemory > GVM; - SolverCG > solver_CG(solver_control, GVM); - - // We've chosen by default a SSOR preconditioner as it appears to - // provide the fastest solver convergence characteristics for this - // problem on a single-thread machine. However, this might not be - // true for different problem sizes. - PreconditionSelector, Vector > - preconditioner (parameters.preconditioner_type, - parameters.preconditioner_relaxation); - preconditioner.use_matrix(tangent_matrix.block(u_dof, u_dof)); - - solver_CG.solve(tangent_matrix.block(u_dof, u_dof), - newton_update.block(u_dof), - system_rhs.block(u_dof), - preconditioner); - - lin_it = solver_control.last_step(); - lin_res = solver_control.last_value(); - } - else if (parameters.type_lin == "Direct") - { - // Otherwise if the problem is small - // enough, a direct solver can be - // utilised. - SparseDirectUMFPACK A_direct; - A_direct.initialize(tangent_matrix.block(u_dof, u_dof)); - A_direct.vmult(newton_update.block(u_dof), system_rhs.block(u_dof)); - - lin_it = 1; - lin_res = 0.0; - } - else - Assert (false, ExcMessage("Linear solver type not implemented")); - - timer.leave_subsection(); - } - - // Now that we have the displacement update, distribute the constraints - // back to the Newton update: - constraints.distribute(newton_update); - - timer.enter_subsection("Linear solver postprocessing"); - std::cout << " PP " << std::flush; - - // The next step after solving the displacement - // problem is to post-process to get the - // dilatation solution from the - // substitution: - // $ - // d \widetilde{\mathbf{\mathsf{J}}} - // = \mathbf{\mathsf{K}}_{\widetilde{p}\widetilde{J}}^{-1} \bigl[ - // \mathbf{\mathsf{F}}_{\widetilde{p}} - // - \mathbf{\mathsf{K}}_{\widetilde{p}u} d \mathbf{\mathsf{u}} - // \bigr] - // $ - { - // $ - // \mathbf{\mathsf{A}}_{\widetilde{p}} - // = - // \mathbf{\mathsf{K}}_{\widetilde{p}u} d \mathbf{\mathsf{u}} - // $ - tangent_matrix.block(p_dof, u_dof).vmult(A.block(p_dof), - newton_update.block(u_dof)); - // $ - // \mathbf{\mathsf{A}}_{\widetilde{p}} - // = - // -\mathbf{\mathsf{K}}_{\widetilde{p}u} d \mathbf{\mathsf{u}} - // $ - A.block(p_dof) *= -1.0; - // $ - // \mathbf{\mathsf{A}}_{\widetilde{p}} - // = - // \mathbf{\mathsf{F}}_{\widetilde{p}} - // -\mathbf{\mathsf{K}}_{\widetilde{p}u} d \mathbf{\mathsf{u}} - // $ - A.block(p_dof) += system_rhs.block(p_dof); - // $ - // d\mathbf{\mathsf{\widetilde{J}}} - // = - // \mathbf{\mathsf{K}}^{-1}_{\widetilde{p}\widetilde{J}} - // [ - // \mathbf{\mathsf{F}}_{\widetilde{p}} - // -\mathbf{\mathsf{K}}_{\widetilde{p}u} d \mathbf{\mathsf{u}} - // ] - // $ - tangent_matrix.block(p_dof, J_dof).vmult(newton_update.block(J_dof), - A.block(p_dof)); - } - - // we insure here that any Dirichlet constraints - // are distributed on the updated solution: - constraints.distribute(newton_update); - - // Finally we solve for the pressure - // update with the substitution: - // $ - // d \widetilde{\mathbf{\mathsf{p}}} - // = - // \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}}^{-1} - // \bigl[ - // \mathbf{\mathsf{F}}_{\widetilde{J}} - // - \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} - // d \widetilde{\mathbf{\mathsf{J}}} - // \bigr] - // $ - { - // $ - // \mathsf{\mathbf{A}}_{\widetilde{J}} - // = - // \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} - // d \widetilde{\mathbf{\mathsf{J}}} - // $ - tangent_matrix.block(J_dof, J_dof).vmult(A.block(J_dof), - newton_update.block(J_dof)); - // $ - // \mathsf{\mathbf{A}}_{\widetilde{J}} - // = - // -\mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} - // d \widetilde{\mathbf{\mathsf{J}}} - // $ - A.block(J_dof) *= -1.0; - // $ - // \mathsf{\mathbf{A}}_{\widetilde{J}} - // = - // \mathsf{\mathbf{F}}_{\widetilde{J}} - // - - // \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} - // d \widetilde{\mathbf{\mathsf{J}}} - // $ - A.block(J_dof) += system_rhs.block(J_dof); - // and finally.... - // $ - // d \widetilde{\mathbf{\mathsf{p}}} - // = - // \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{p}}^{-1} - // \bigl[ - // \mathbf{\mathsf{F}}_{\widetilde{J}} - // - \mathbf{\mathsf{K}}_{\widetilde{J}\widetilde{J}} - // d \widetilde{\mathbf{\mathsf{J}}} - // \bigr] - // $ - tangent_matrix.block(p_dof, J_dof).Tvmult(newton_update.block(p_dof), - A.block(J_dof)); - } - - // We are now at the end, so we distribute all - // constrained dofs back to the Newton - // update: - constraints.distribute(newton_update); - - timer.leave_subsection(); - - return std::make_pair(lin_it, lin_res); - } - -// @sect4{Solid::assemble_system_SC} // The static condensation process could be performed at a global level but we // need the inverse of one of the blocks. However, since the pressure and // dilatation variables are discontinuous, the static condensation (SC) -// operation can be done on a per-cell basis and we can produce the inverse of -// the block-diagonal $ \mathbf{\mathsf{K}}_{\widetilde{p}\widetilde{J}}$ - // block by inverting the local blocks. We can again -// use TBB to do this since each operation will be independent of one another. +// operation can also be done on a per-cell basis and we can produce the inverse of +// the block-diagonal $\mathsf{\mathbf{K}}_{\widetilde{p}\widetilde{J}}$ +// block by inverting the local blocks. We can again use TBB to do this since +// each operation will be independent of one another. // // Using the TBB via the WorkStream class, we assemble the contributions to form // $ -// \mathbf{\mathsf{K}}_{\textrm{con}} -// = \bigl[ \mathbf{\mathsf{K}}_{uu} + \overline{\overline{\mathbf{\mathsf{K}}}}~ \bigr] +// \mathsf{\mathbf{K}}_{\textrm{con}} +// = \bigl[ \mathsf{\mathbf{K}}_{uu} + \overline{\overline{\mathsf{\mathbf{K}}}}~ \bigr] // $ -// from each element's contributions. These -// contributions are then added to the global stiffness matrix. Given this -// description, the following two functions should be clear: +// from each element's contributions. These contributions are then added to the +// global stiffness matrix. Given this description, the following two functions +// should be clear: template void Solid::assemble_sc() { @@ -2980,7 +2714,7 @@ namespace Step44 } -// Now we describe the static condensation process. As per usual, we must +// Now we describe the static condensation process. As per usual, we must // first find out which global numbers the degrees of freedom on this cell // have and reset some data structures: template @@ -2997,12 +2731,12 @@ namespace Step44 // cell to the global stiffness matrix. The discontinuous nature of the // $\widetilde{p}$ and $\widetilde{J}$ interpolations mean that their is // no coupling of the local contributions at the global level. This is not - // the case with the u dof. In other words, + // the case with the $\mathbf{u}$ dof. In other words, // $\mathsf{\mathbf{k}}_{\widetilde{J} \widetilde{p}}$, // $\mathsf{\mathbf{k}}_{\widetilde{p} \widetilde{p}}$ and // $\mathsf{\mathbf{k}}_{\widetilde{J} \widetilde{p}}$, when extracted // from the global stiffness matrix are the element contributions. This - // is not the case for $\mathsf{\mathbf{k}}_{\mathbf{u} \mathbf{u}}$ + // is not the case for $\mathsf{\mathbf{k}}_{uu}$. // // Note: A lower-case symbol is used to denote element stiffness matrices. @@ -3012,30 +2746,30 @@ namespace Step44 // is of the form: // @f{align*} // \begin{bmatrix} - // \mathbf{\mathsf{k}}_{uu} & \mathbf{\mathsf{k}}_{u\widetilde{p}} & \mathbf{0} - // \\ \mathbf{\mathsf{k}}_{\widetilde{p}u} & \mathbf{0} & \mathbf{\mathsf{k}}_{\widetilde{p}\widetilde{J}} - // \\ \mathbf{0} & \mathbf{\mathsf{k}}_{\widetilde{J}\widetilde{p}} & \mathbf{\mathsf{k}}_{\widetilde{J}\widetilde{J}} + // \mathsf{\mathbf{k}}_{uu} & \mathsf{\mathbf{k}}_{u\widetilde{p}} & \mathbf{0} + // \\ \mathsf{\mathbf{k}}_{\widetilde{p}u} & \mathbf{0} & \mathsf{\mathbf{k}}_{\widetilde{p}\widetilde{J}} + // \\ \mathbf{0} & \mathsf{\mathbf{k}}_{\widetilde{J}\widetilde{p}} & \mathsf{\mathbf{k}}_{\widetilde{J}\widetilde{J}} // \end{bmatrix} // @f} // // We now need to modify it such that it appear as // @f{align*} // \begin{bmatrix} - // \mathbf{\mathsf{k}}_{\textrm{con}} & \mathbf{\mathsf{k}}_{u\widetilde{p}} & \mathbf{0} - // \\ \mathbf{\mathsf{k}}_{\widetilde{p}u} & \mathbf{0} & \mathbf{\mathsf{k}}_{\widetilde{p}\widetilde{J}}^{-1} - // \\ \mathbf{0} & \mathbf{\mathsf{k}}_{\widetilde{J}\widetilde{p}} & \mathbf{\mathsf{k}}_{\widetilde{J}\widetilde{J}} + // \mathsf{\mathbf{k}}_{\textrm{con}} & \mathsf{\mathbf{k}}_{u\widetilde{p}} & \mathbf{0} + // \\ \mathsf{\mathbf{k}}_{\widetilde{p}u} & \mathbf{0} & \mathsf{\mathbf{k}}_{\widetilde{p}\widetilde{J}}^{-1} + // \\ \mathbf{0} & \mathsf{\mathbf{k}}_{\widetilde{J}\widetilde{p}} & \mathsf{\mathbf{k}}_{\widetilde{J}\widetilde{J}} // \end{bmatrix} // @f} - // with $\mathbf{\mathsf{k}}_{\textrm{con}} = \bigl[ \mathbf{\mathsf{k}}_{uu} +\overline{\overline{\mathbf{\mathsf{k}}}}~ \bigr]$ + // with $\mathsf{\mathbf{k}}_{\textrm{con}} = \bigl[ \mathsf{\mathbf{k}}_{uu} +\overline{\overline{\mathsf{\mathbf{k}}}}~ \bigr]$ // where - // $ \overline{\overline{\mathbf{\mathsf{k}}}} := - // \mathbf{\mathsf{k}}_{u\widetilde{p}} \overline{\mathbf{\mathsf{k}}} \mathbf{\mathsf{k}}_{\widetilde{p}u} + // $ \overline{\overline{\mathsf{\mathbf{k}}}} := + // \mathsf{\mathbf{k}}_{u\widetilde{p}} \overline{\mathsf{\mathbf{k}}} \mathsf{\mathbf{k}}_{\widetilde{p}u} // $ // and // $ - // \overline{\mathbf{\mathsf{k}}} = - // \mathbf{\mathsf{k}}_{\widetilde{J}\widetilde{p}}^{-1} \mathbf{\mathsf{k}}_{\widetilde{J}\widetilde{J}} - // \mathbf{\mathsf{k}}_{\widetilde{p}\widetilde{J}}^{-1} + // \overline{\mathsf{\mathbf{k}}} = + // \mathsf{\mathbf{k}}_{\widetilde{J}\widetilde{p}}^{-1} \mathsf{\mathbf{k}}_{\widetilde{J}\widetilde{J}} + // \mathsf{\mathbf{k}}_{\widetilde{p}\widetilde{J}}^{-1} // $. // // At this point, we need to take note of @@ -3056,12 +2790,12 @@ namespace Step44 // // This is the strategy we will employ to get the sub-blocks we want: // - // - $ {\mathbf{\mathsf{k}}}_{\textrm{store}}$: + // - $ {\mathsf{\mathbf{k}}}_{\textrm{store}}$: // Since we don't have access to $\mathsf{\mathbf{k}}_{uu}$, // but we know its contribution is added to // the global $\mathsf{\mathbf{K}}_{uu}$ matrix, we just want // to add the element wise - // static-condensation $\overline{\overline{\mathbf{\mathsf{k}}}}$. + // static-condensation $\overline{\overline{\mathsf{\mathbf{k}}}}$. // // - $\mathsf{\mathbf{k}}^{-1}_{\widetilde{p} \widetilde{J}}$: // Similarly, $\mathsf{\mathbf{k}}_{\widetilde{p} \widetilde{J}}$ exists in @@ -3091,7 +2825,7 @@ namespace Step44 data.local_dof_indices, data.local_dof_indices); // and next the local matrices for - // $\mathsf{\mathbf{k}}_{ \widetilde{p} \mathbf{u}}$ + // $\mathsf{\mathbf{k}}_{ \widetilde{p} u}$ // $\mathsf{\mathbf{k}}_{ \widetilde{p} \widetilde{J}}$ // and // $\mathsf{\mathbf{k}}_{ \widetilde{J} \widetilde{J}}$: @@ -3112,14 +2846,14 @@ namespace Step44 data.k_pJ_inv.invert(data.k_pJ); // Now we can make condensation terms to - // add to the $\mathsf{\mathbf{k}}_{\mathbf{u} \mathbf{u}}$ + // add to the $\mathsf{\mathbf{k}}_{uu}$ // block and put them in // the cell local matrix // $ // \mathsf{\mathbf{A}} // = // \mathsf{\mathbf{k}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{k}}_{\widetilde{p} \mathbf{u}} + // \mathsf{\mathbf{k}}_{\widetilde{p} u} // $: data.k_pJ_inv.mmult(data.A, data.k_pu); // $ @@ -3127,7 +2861,7 @@ namespace Step44 // = // \mathsf{\mathbf{k}}^{-1}_{\widetilde{J} \widetilde{J}} // \mathsf{\mathbf{k}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{k}}_{\widetilde{p} \mathbf{u}} + // \mathsf{\mathbf{k}}_{\widetilde{p} u} // $ data.k_JJ.mmult(data.B, data.A); // $ @@ -3136,17 +2870,17 @@ namespace Step44 // \mathsf{\mathbf{k}}^{-1}_{\widetilde{J} \widetilde{p}} // \mathsf{\mathbf{k}}^{-1}_{\widetilde{J} \widetilde{J}} // \mathsf{\mathbf{k}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{k}}_{\widetilde{p} \mathbf{u}} + // \mathsf{\mathbf{k}}_{\widetilde{p} u} // $ data.k_pJ_inv.Tmmult(data.C, data.B); // $ // \overline{\overline{\mathsf{\mathbf{k}}}} // = - // \mathsf{\mathbf{k}}_{\mathbf{u} \widetilde{p}} + // \mathsf{\mathbf{k}}_{u \widetilde{p}} // \mathsf{\mathbf{k}}^{-1}_{\widetilde{J} \widetilde{p}} // \mathsf{\mathbf{k}}^{-1}_{\widetilde{J} \widetilde{J}} // \mathsf{\mathbf{k}}^{-1}_{\widetilde{p} \widetilde{J}} - // \mathsf{\mathbf{k}}_{\widetilde{p} \mathbf{u}} + // \mathsf{\mathbf{k}}_{\widetilde{p} u} // $ data.k_pu.Tmmult(data.k_bbar, data.C); data.k_bbar.scatter_matrix_to(element_indices_u, @@ -3166,6 +2900,473 @@ namespace Step44 data.cell_matrix); } +// @sect4{Solid::solve_linear_system} +// We now have all of the necessary components to use one of two possible +// methods to solve the linearised system. The first is to perform static +// condensation on an element level, which requires some alterations +// to the tangent matrix and RHS vector. Alternatively, the full block +// system can be solved by performing condensation on a global level. +// Below we implement both approaches. + template + std::pair + Solid::solve_linear_system(BlockVector &newton_update) + { + unsigned int lin_it = 0; + double lin_res = 0.0; + + if (parameters.use_static_condensation == true) + { + // Firstly, here is the approach using the (permanent) augmentation of the + // tangent matrix. + // For the following, recall that + // @f{align*} + // \mathsf{\mathbf{K}}_{\textrm{store}} + //:= + // \begin{bmatrix} + // \mathsf{\mathbf{K}}_{\textrm{con}} & \mathsf{\mathbf{K}}_{u\widetilde{p}} & \mathbf{0} + // \\ \mathsf{\mathbf{K}}_{\widetilde{p}u} & \mathbf{0} & \mathsf{\mathbf{K}}_{\widetilde{p}\widetilde{J}}^{-1} + // \\ \mathbf{0} & \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}} & \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // \end{bmatrix} \, . + // @f} + // and + // @f{align*} + // d \widetilde{\mathsf{\mathbf{p}}} + // & = \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \bigl[ + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} d \widetilde{\mathsf{\mathbf{J}}} \bigr] + // \\ d \widetilde{\mathsf{\mathbf{J}}} + // & = \mathsf{\mathbf{K}}_{\widetilde{p}\widetilde{J}}^{-1} \bigl[ + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // - \mathsf{\mathbf{K}}_{\widetilde{p}u} d \mathsf{\mathbf{u}} + // \bigr] + // \\ \Rightarrow d \widetilde{\mathsf{\mathbf{p}}} + // &= \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \mathsf{\mathbf{F}}_{\widetilde{J}} + // - \underbrace{\bigl[\mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // \mathsf{\mathbf{K}}_{\widetilde{p}\widetilde{J}}^{-1}\bigr]}_{\overline{\mathsf{\mathbf{K}}}}\bigl[ \mathsf{\mathbf{F}}_{\widetilde{p}} + // - \mathsf{\mathbf{K}}_{\widetilde{p}u} d \mathsf{\mathbf{u}} \bigr] + // @f} + // and thus + // @f[ + // \underbrace{\bigl[ \mathsf{\mathbf{K}}_{uu} + \overline{\overline{\mathsf{\mathbf{K}}}}~ \bigr] + // }_{\mathsf{\mathbf{K}}_{\textrm{con}}} d \mathsf{\mathbf{u}} + // = + // \underbrace{ + // \Bigl[ + // \mathsf{\mathbf{F}}_{u} + // - \mathsf{\mathbf{K}}_{u\widetilde{p}} \bigl[ \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}^{-1} \mathsf{\mathbf{F}}_{\widetilde{J}} + // - \overline{\mathsf{\mathbf{K}}}\mathsf{\mathbf{F}}_{\widetilde{p}} \bigr] + // \Bigr]}_{\mathsf{\mathbf{F}}_{\textrm{con}}} + // @f] + // where + // @f[ + // \overline{\overline{\mathsf{\mathbf{K}}}} := + // \mathsf{\mathbf{K}}_{u\widetilde{p}} \overline{\mathsf{\mathbf{K}}} \mathsf{\mathbf{K}}_{\widetilde{p}u} \, . + // @f] + + // At the top, we allocate two temporary vectors to help with the + // static condensation, and variables to store the number of + // linear solver iterations and the (hopefully converged) residual. + BlockVector A(dofs_per_block); + BlockVector B(dofs_per_block); + + + // In the first step of this function, we solve for the incremental + // displacement $d\mathbf{u}$. To this end, we perform static + // condensation to make + // $\mathsf{\mathbf{K}}_{\textrm{con}} + // = \bigl[ \mathsf{\mathbf{K}}_{uu} + \overline{\overline{\mathsf{\mathbf{K}}}}~ \bigr]$ + // and put + // $\mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}}$ + // in the original $\mathsf{\mathbf{K}}_{\widetilde{p} \widetilde{J}}$ block. + // That is, we make $\mathsf{\mathbf{K}}_{\textrm{store}}$. + { + + assemble_sc(); + + // $ + // \mathsf{\mathbf{A}}_{\widetilde{J}} + // = + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // $ + tangent_matrix.block(p_dof, J_dof).vmult(A.block(J_dof), + system_rhs.block(p_dof)); + // $ + // \mathsf{\mathbf{B}}_{\widetilde{J}} + // = + // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // $ + tangent_matrix.block(J_dof, J_dof).vmult(B.block(J_dof), + A.block(J_dof)); + // $ + // \mathsf{\mathbf{A}}_{\widetilde{J}} + // = + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - + // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // $ + A.block(J_dof) = system_rhs.block(J_dof); + A.block(J_dof) -= B.block(J_dof); + // $ + // \mathsf{\mathbf{A}}_{\widetilde{J}} + // = + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{J} \widetilde{p}} + // [ + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - + // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // ] + // $ + tangent_matrix.block(p_dof, J_dof).Tvmult(A.block(p_dof), + A.block(J_dof)); + // $ + // \mathsf{\mathbf{A}}_{u} + // = + // \mathsf{\mathbf{K}}_{u \widetilde{p}} + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{J} \widetilde{p}} + // [ + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - + // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // ] + // $ + tangent_matrix.block(u_dof, p_dof).vmult(A.block(u_dof), + A.block(p_dof)); + // $ + // \mathsf{\mathbf{F}}_{\text{con}} + // = + // \mathsf{\mathbf{F}}_{u} + // - + // \mathsf{\mathbf{K}}_{u \widetilde{p}} + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{J} \widetilde{p}} + // [ + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - + // \mathsf{\mathbf{K}}_{\widetilde{J} \widetilde{J}} + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p} \widetilde{J}} + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // ] + // $ + system_rhs.block(u_dof) -= A.block(u_dof); + + timer.enter_subsection("Linear solver"); + std::cout << " SLV " << std::flush; + if (parameters.type_lin == "CG") + { + const int solver_its = tangent_matrix.block(u_dof, u_dof).m() + * parameters.max_iterations_lin; + const double tol_sol = parameters.tol_lin + * system_rhs.block(u_dof).l2_norm(); + + SolverControl solver_control(solver_its, tol_sol); + + GrowingVectorMemory > GVM; + SolverCG > solver_CG(solver_control, GVM); + + // We've chosen by default a SSOR preconditioner as it appears to + // provide the fastest solver convergence characteristics for this + // problem on a single-thread machine. However, this might not be + // true for different problem sizes. + PreconditionSelector, Vector > + preconditioner (parameters.preconditioner_type, + parameters.preconditioner_relaxation); + preconditioner.use_matrix(tangent_matrix.block(u_dof, u_dof)); + + solver_CG.solve(tangent_matrix.block(u_dof, u_dof), + newton_update.block(u_dof), + system_rhs.block(u_dof), + preconditioner); + + lin_it = solver_control.last_step(); + lin_res = solver_control.last_value(); + } + else if (parameters.type_lin == "Direct") + { + // Otherwise if the problem is small + // enough, a direct solver can be + // utilised. + SparseDirectUMFPACK A_direct; + A_direct.initialize(tangent_matrix.block(u_dof, u_dof)); + A_direct.vmult(newton_update.block(u_dof), system_rhs.block(u_dof)); + + lin_it = 1; + lin_res = 0.0; + } + else + Assert (false, ExcMessage("Linear solver type not implemented")); + + timer.leave_subsection(); + } + + // Now that we have the displacement update, distribute the constraints + // back to the Newton update: + constraints.distribute(newton_update); + + timer.enter_subsection("Linear solver postprocessing"); + std::cout << " PP " << std::flush; + + // The next step after solving the displacement + // problem is to post-process to get the + // dilatation solution from the + // substitution: + // $ + // d \widetilde{\mathsf{\mathbf{J}}} + // = \mathsf{\mathbf{K}}_{\widetilde{p}\widetilde{J}}^{-1} \bigl[ + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // - \mathsf{\mathbf{K}}_{\widetilde{p}u} d \mathsf{\mathbf{u}} + // \bigr] + // $ + { + // $ + // \mathsf{\mathbf{A}}_{\widetilde{p}} + // = + // \mathsf{\mathbf{K}}_{\widetilde{p}u} d \mathsf{\mathbf{u}} + // $ + tangent_matrix.block(p_dof, u_dof).vmult(A.block(p_dof), + newton_update.block(u_dof)); + // $ + // \mathsf{\mathbf{A}}_{\widetilde{p}} + // = + // -\mathsf{\mathbf{K}}_{\widetilde{p}u} d \mathsf{\mathbf{u}} + // $ + A.block(p_dof) *= -1.0; + // $ + // \mathsf{\mathbf{A}}_{\widetilde{p}} + // = + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // -\mathsf{\mathbf{K}}_{\widetilde{p}u} d \mathsf{\mathbf{u}} + // $ + A.block(p_dof) += system_rhs.block(p_dof); + // $ + // d\mathsf{\mathbf{\widetilde{J}}} + // = + // \mathsf{\mathbf{K}}^{-1}_{\widetilde{p}\widetilde{J}} + // [ + // \mathsf{\mathbf{F}}_{\widetilde{p}} + // -\mathsf{\mathbf{K}}_{\widetilde{p}u} d \mathsf{\mathbf{u}} + // ] + // $ + tangent_matrix.block(p_dof, J_dof).vmult(newton_update.block(J_dof), + A.block(p_dof)); + } + + // we ensure here that any Dirichlet constraints + // are distributed on the updated solution: + constraints.distribute(newton_update); + + // Finally we solve for the pressure + // update with the substitution: + // $ + // d \widetilde{\mathsf{\mathbf{p}}} + // = + // \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}^{-1} + // \bigl[ + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // d \widetilde{\mathsf{\mathbf{J}}} + // \bigr] + // $ + { + // $ + // \mathsf{\mathbf{A}}_{\widetilde{J}} + // = + // \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // d \widetilde{\mathsf{\mathbf{J}}} + // $ + tangent_matrix.block(J_dof, J_dof).vmult(A.block(J_dof), + newton_update.block(J_dof)); + // $ + // \mathsf{\mathbf{A}}_{\widetilde{J}} + // = + // -\mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // d \widetilde{\mathsf{\mathbf{J}}} + // $ + A.block(J_dof) *= -1.0; + // $ + // \mathsf{\mathbf{A}}_{\widetilde{J}} + // = + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - + // \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // d \widetilde{\mathsf{\mathbf{J}}} + // $ + A.block(J_dof) += system_rhs.block(J_dof); + // and finally.... + // $ + // d \widetilde{\mathsf{\mathbf{p}}} + // = + // \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}^{-1} + // \bigl[ + // \mathsf{\mathbf{F}}_{\widetilde{J}} + // - \mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{J}} + // d \widetilde{\mathsf{\mathbf{J}}} + // \bigr] + // $ + tangent_matrix.block(p_dof, J_dof).Tvmult(newton_update.block(p_dof), + A.block(J_dof)); + } + + // We are now at the end, so we distribute all + // constrained dofs back to the Newton + // update: + constraints.distribute(newton_update); + + timer.leave_subsection(); + } + else + { + std::cout << " ------ " << std::flush; + + timer.enter_subsection("Linear solver"); + std::cout << " SLV " << std::flush; + + if (parameters.type_lin == "CG") + { + // Manual condensation of the dilatation and pressure fields on + // a local level, and subsequent post-processing, took quite a + // bit of effort to achieve. To recap, we had to produce the + // inverse matrix $\mathsf{\mathbf{K}}_{\widetilde{p}\widetilde{J}}^{-1}$, + // which was permanently written into the global tangent matrix. + // We then permanently modified $\mathsf{\mathbf{K}}_{uu}$ to + // produce $\mathsf{\mathbf{K}}_{\textrm{con}}$. This involved + // the extraction and manipulation of local sub-blocks of the + // tangent matrix. After solving for the displacement, the + // individual matrix-vector operations required to solve for + // dilatation and pressure were carefully implemented. + // Contrast these many sequence of steps to the much simpler and + // transparent implementation using functionality provided by the + // LinearOperator class. + + // For ease of later use, we define some aliases for + // blocks in the RHS vector + const Vector &f_u = system_rhs.block(u_dof); + const Vector &f_p = system_rhs.block(p_dof); + const Vector &f_J = system_rhs.block(J_dof); + + // ... and for blocks in the Newton update vector. + Vector &d_u = newton_update.block(u_dof); + Vector &d_p = newton_update.block(p_dof); + Vector &d_J = newton_update.block(J_dof); + + // We next define some linear operators for the tangent matrix sub-blocks + // We will exploit the symmetry of the system, so not all blocks + // are required. + const auto K_uu = linear_operator(tangent_matrix.block(u_dof, u_dof)); + const auto K_up = linear_operator(tangent_matrix.block(u_dof, p_dof)); + const auto K_pu = linear_operator(tangent_matrix.block(p_dof, u_dof)); + const auto K_Jp = linear_operator(tangent_matrix.block(J_dof, p_dof)); + const auto K_JJ = linear_operator(tangent_matrix.block(J_dof, J_dof)); + + // We then construct a LinearOperator that represents the inverse of (square block) + // $\mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}$. Since it is diagonal (or, + // when a higher order ansatz it used, nearly diagonal), a Jacobi preconditioner + // is suitable. + PreconditionSelector< SparseMatrix, Vector > + preconditioner_K_Jp_inv ("jacobi"); + preconditioner_K_Jp_inv.use_matrix(tangent_matrix.block(J_dof, p_dof)); + ReductionControl solver_control_K_Jp_inv (tangent_matrix.block(J_dof, p_dof).m() * parameters.max_iterations_lin, + 1.0e-30, parameters.tol_lin); + SolverSelector< Vector > solver_K_Jp_inv; + solver_K_Jp_inv.select("cg"); + solver_K_Jp_inv.set_control(solver_control_K_Jp_inv); + const auto K_Jp_inv = inverse_operator(K_Jp, + solver_K_Jp_inv, + preconditioner_K_Jp_inv); + + // Now we can construct that transpose of $\mathsf{\mathbf{K}}_{\widetilde{J}\widetilde{p}}^{-1}$ + // and a linear operator that represents the condensed operations + // $\overline{\mathsf{\mathbf{K}}}$ and + // $\overline{\overline{\mathsf{\mathbf{K}}}}$ and the final augmented matrix + // $\mathsf{\mathbf{K}}_{\textrm{con}}$. + // Note that the schur_complement() operator could also be of use here, but + // for clarity and the purpose of demonstrating the similarities between the + // formulation and implementation of the linear solution scheme, we will perform + // these operations manually. + const auto K_pJ_inv = transpose_operator(K_Jp_inv); + const auto K_pp_bar = K_Jp_inv * K_JJ * K_pJ_inv; + const auto K_uu_bar_bar = K_up * K_pp_bar * K_pu; + const auto K_uu_con = K_uu + K_uu_bar_bar; + + // Lastly, we define an operator for inverse of augmented stiffness matrix, + // namely $\mathsf{\mathbf{K}}_{\textrm{con}}^{-1}$. + // Note that the preconditioner for the augmented stiffness matrix is + // different to the case when we use static condensation. In this instance, + // the preconditioner is based on a non-modified $\mathsf{\mathbf{K}}_{uu}$, + // while with the first approach we actually modified the entries of this + // sub-block. However, since $\mathsf{\mathbf{K}}_{\textrm{con}}$ and + // $\mathsf{\mathbf{K}}_{uu}$ operate on the same space, it remains adequate + // for this problem. + PreconditionSelector< SparseMatrix, Vector > + preconditioner_K_con_inv (parameters.preconditioner_type, + parameters.preconditioner_relaxation); + preconditioner_K_con_inv.use_matrix(tangent_matrix.block(u_dof, u_dof)); + ReductionControl solver_control_K_con_inv (tangent_matrix.block(u_dof, u_dof).m() * parameters.max_iterations_lin, + 1.0e-30, parameters.tol_lin); + SolverSelector< Vector > solver_K_con_inv; + solver_K_con_inv.select("cg"); + solver_K_con_inv.set_control(solver_control_K_con_inv); + const auto K_uu_con_inv = inverse_operator(K_uu_con, + solver_K_con_inv, + preconditioner_K_con_inv); + + // Now we are in a position to solve for the displacement field. + // We can nest the linear operations, and the result is immediately + // written to the Newton update vector. + // It is clear that the implementation closely mimics the derivation + // stated in the introduction. + d_u = K_uu_con_inv*(f_u - K_up*(K_Jp_inv*f_J - K_pp_bar*f_p)); + + timer.leave_subsection(); + + // The operations need to post-process for the dilatation and pressure + // fields are just as easy to express. + timer.enter_subsection("Linear solver postprocessing"); + std::cout << " PP " << std::flush; + + d_J = K_pJ_inv*(f_p - K_pu*d_u); + d_p = K_Jp_inv*(f_J - K_JJ*d_J); + + lin_it = solver_control_K_con_inv.last_step(); + lin_res = solver_control_K_con_inv.last_value(); + } + else if (parameters.type_lin == "Direct") + { + // Solve the full block system with + // a direct solver. As it is relatively + // robust, it may be immune to problem + // arising from the presence of the zero + // $\mathsf{\mathbf{K}}_{ \widetilde{J} \widetilde{J}}$ + // block. + SparseDirectUMFPACK A_direct; + A_direct.initialize(tangent_matrix); + A_direct.vmult(newton_update, system_rhs); + + lin_it = 1; + lin_res = 0.0; + + std::cout << " -- " << std::flush; + } + else + Assert (false, ExcMessage("Linear solver type not implemented")); + + timer.leave_subsection(); + + // Finally, we again ensure here that any Dirichlet + // constraints are distributed on the updated solution: + constraints.distribute(newton_update); + } + + return std::make_pair(lin_it, lin_res); + } + // @sect4{Solid::output_results} // Here we present how the results are written to file to be viewed // using ParaView or Visit. The method is similar to that shown in previous