Line data    Source code

       1            0 : // Distributed under the MIT License.
       2              : // See LICENSE.txt for details.
       3              : 
       4              : #pragma once
       5              : 
       6              : #include <array>
       7              : #include <boost/functional/hash.hpp>
       8              : #include <iterator>
       9              : #include <limits>
      10              : #include <memory>
      11              : #include <pup.h>
      12              : #include <type_traits>
      13              : #include <unordered_map>
      14              : #include <utility>
      15              : 
      16              : #include "DataStructures/DataVector.hpp"
      17              : #include "DataStructures/Tags.hpp"
      18              : #include "DataStructures/Tensor/Tensor.hpp"
      19              : #include "Domain/Structure/Direction.hpp"
      20              : #include "Domain/Structure/DirectionMap.hpp"
      21              : #include "Domain/Structure/Element.hpp"
      22              : #include "Domain/Structure/ElementId.hpp"
      23              : #include "Domain/Structure/OrientationMap.hpp"
      24              : #include "Evolution/DiscontinuousGalerkin/Limiters/MinmodHelpers.hpp"
      25              : #include "Evolution/DiscontinuousGalerkin/Limiters/MinmodType.hpp"
      26              : #include "NumericalAlgorithms/LinearOperators/MeanValue.hpp"
      27              : #include "NumericalAlgorithms/Spectral/Mesh.hpp"
      28              : #include "Utilities/ErrorHandling/Assert.hpp"
      29              : #include "Utilities/Gsl.hpp"
      30              : #include "Utilities/Literals.hpp"
      31              : #include "Utilities/Numeric.hpp"
      32              : #include "Utilities/TMPL.hpp"
      33              : 
      34              : namespace Limiters::Minmod_detail {
      35              : 
      36              : // This function combines the evaluation of the troubled-cell indicator with the
      37              : // computation of the post-limiter reduced slopes. The returned bool indicates
      38              : // whether the slopes are to be reduced. The slopes themselves are returned by
      39              : // pointer.
      40              : //
      41              : // Note: This function is only made available in this header file to facilitate
      42              : // testing.
      43              : template <size_t VolumeDim>
      44              : bool minmod_limited_slopes(
      45              :     gsl::not_null<DataVector*> u_lin_buffer,
      46              :     gsl::not_null<BufferWrapper<VolumeDim>*> buffer,
      47              :     gsl::not_null<double*> u_mean,
      48              :     gsl::not_null<std::array<double, VolumeDim>*> u_limited_slopes,
      49              :     Limiters::MinmodType minmod_type, double tvb_constant, const DataVector& u,
      50              :     const Mesh<VolumeDim>& mesh, const Element<VolumeDim>& element,
      51              :     const std::array<double, VolumeDim>& element_size,
      52              :     const DirectionMap<VolumeDim, double>& effective_neighbor_means,
      53              :     const DirectionMap<VolumeDim, double>& effective_neighbor_sizes);
      54              : 
      55              : // Implements the minmod limiter for one Tensor<DataVector> at a time.
      56              : template <size_t VolumeDim, typename Tag, typename PackagedData>
      57              : bool minmod_impl(
      58              :     const gsl::not_null<DataVector*> u_lin_buffer,
      59              :     const gsl::not_null<BufferWrapper<VolumeDim>*> buffer,
      60              :     const gsl::not_null<typename Tag::type*> tensor,
      61              :     const Limiters::MinmodType minmod_type, const double tvb_constant,
      62              :     const Mesh<VolumeDim>& mesh, const Element<VolumeDim>& element,
      63              :     const tnsr::I<DataVector, VolumeDim, Frame::ElementLogical>& logical_coords,
      64              :     const std::array<double, VolumeDim>& element_size,
      65              :     const std::unordered_map<DirectionalId<VolumeDim>, PackagedData,
      66              :                              boost::hash<DirectionalId<VolumeDim>>>&
      67              :         neighbor_data) {
      68              :   // True if the mesh is linear-order in every direction
      69              :   const bool mesh_is_linear = (mesh.extents() == Index<VolumeDim>(2));
      70              :   const bool minmod_type_is_linear =
      71              :       (minmod_type != Limiters::MinmodType::LambdaPiN);
      72              :   const bool using_linear_limiter_on_non_linear_mesh =
      73              :       minmod_type_is_linear and not mesh_is_linear;
      74              : 
      75              :   // In each direction, average the size of all different neighbors in that
      76              :   // direction. Note that only the component of neighor_size that is normal
      77              :   // to the face is needed (and, therefore, computed). Note that this average
      78              :   // does not depend on the solution on the neighboring elements, so could be
      79              :   // precomputed outside of `limit_one_tensor`. Changing the code to
      80              :   // precompute the average may or may not be a measurable optimization.
      81              :   const auto effective_neighbor_sizes =
      82              :       compute_effective_neighbor_sizes(element, neighbor_data);
      83              : 
      84              :   bool some_component_was_limited = false;
      85              :   for (size_t i = 0; i < tensor->size(); ++i) {
      86              :     // In each direction, average the mean of the i'th tensor component over
      87              :     // all different neighbors in that direction. This produces one effective
      88              :     // neighbor per direction.
      89              :     const auto effective_neighbor_means =
      90              :         compute_effective_neighbor_means<Tag>(i, element, neighbor_data);
      91              : 
      92              :     DataVector& u = (*tensor)[i];
      93              :     double u_mean = std::numeric_limits<double>::signaling_NaN();
      94              :     std::array<double, VolumeDim> u_limited_slopes{};
      95              :     const bool reduce_slopes = minmod_limited_slopes(
      96              :         u_lin_buffer, buffer, make_not_null(&u_mean),
      97              :         make_not_null(&u_limited_slopes), minmod_type, tvb_constant, u, mesh,
      98              :         element, element_size, effective_neighbor_means,
      99              :         effective_neighbor_sizes);
     100              : 
     101              :     if (reduce_slopes or using_linear_limiter_on_non_linear_mesh) {
     102              :       u = u_mean;
     103              :       for (size_t d = 0; d < VolumeDim; ++d) {
     104              :         u += logical_coords.get(d) * gsl::at(u_limited_slopes, d);
     105              :       }
     106              :       some_component_was_limited = true;
     107              :     }
     108              :   }  // end for loop over tensor components
     109              : 
     110              :   return some_component_was_limited;
     111              : }
     112              : 
     113              : }  // namespace Limiters::Minmod_detail