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238 lines
8.8 KiB
C++
238 lines
8.8 KiB
C++
//! \file scattdata.h
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//! A collection of multi-group scattering data classes
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#ifndef OPENMC_SCATTDATA_H
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#define OPENMC_SCATTDATA_H
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#include "openmc/tensor.h"
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#include "openmc/constants.h"
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#include "openmc/vector.h"
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namespace openmc {
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// forward declarations so we can name our friend functions
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class ScattDataLegendre;
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class ScattDataTabular;
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//==============================================================================
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// SCATTDATA contains all the data needed to describe the scattering energy and
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// angular distribution data
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//==============================================================================
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class ScattData {
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public:
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virtual ~ScattData() = default;
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protected:
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//! \brief Initializes the attributes of the base class.
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void base_init(int order, const tensor::Tensor<int>& in_gmin,
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const tensor::Tensor<int>& in_gmax, const double_2dvec& in_energy,
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const double_2dvec& in_mult);
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//! \brief Combines microscopic ScattDatas into a macroscopic one.
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void base_combine(size_t max_order, size_t order_dim,
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const vector<ScattData*>& those_scatts, const vector<double>& scalars,
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tensor::Tensor<int>& in_gmin, tensor::Tensor<int>& in_gmax,
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double_2dvec& sparse_mult, double_3dvec& sparse_scatter);
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public:
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double_2dvec energy; // Normalized p0 matrix for sampling Eout
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double_2dvec mult; // nu-scatter multiplication (nu-scatt/scatt)
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double_3dvec dist; // Angular distribution
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tensor::Tensor<int> gmin; // minimum outgoing group
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tensor::Tensor<int> gmax; // maximum outgoing group
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tensor::Tensor<double> scattxs; // Isotropic Sigma_{s,g_{in}}
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//! \brief Calculates the value of normalized f(mu).
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//!
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//! The value of f(mu) is normalized as in the integral of f(mu)dmu across
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//! [-1,1] is 1.
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//!
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//! @param gin Incoming energy group of interest.
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//! @param gout Outgoing energy group of interest.
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//! @param mu Cosine of the change-in-angle of interest.
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//! @return The value of f(mu).
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virtual double calc_f(int gin, int gout, double mu) = 0;
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//! \brief Samples the outgoing energy and angle from the ScattData info.
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//!
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//! @param gin Incoming energy group.
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//! @param gout Sampled outgoing energy group.
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//! @param mu Sampled cosine of the change-in-angle.
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//! @param wgt Weight of the particle to be adjusted.
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//! @param seed Pseudorandom number seed pointer
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virtual void sample(
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int gin, int& gout, double& mu, double& wgt, uint64_t* seed) = 0;
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//! \brief Initializes the ScattData object from a given scatter and
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//! multiplicity matrix.
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//!
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//! @param in_gmin List of minimum outgoing groups for every incoming group
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//! @param in_gmax List of maximum outgoing groups for every incoming group
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//! @param in_mult Input sparse multiplicity matrix
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//! @param coeffs Input sparse scattering matrix
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virtual void init(const tensor::Tensor<int>& in_gmin,
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const tensor::Tensor<int>& in_gmax, const double_2dvec& in_mult,
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const double_3dvec& coeffs) = 0;
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//! \brief Combines the microscopic data.
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//!
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//! @param those_scatts Microscopic objects to combine.
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//! @param scalars Scalars to multiply the microscopic data by.
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virtual void combine(
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const vector<ScattData*>& those_scatts, const vector<double>& scalars) = 0;
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//! \brief Getter for the dimensionality of the scattering order.
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//!
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//! If Legendre this is the "n" in "Pn"; for Tabular, this is the number
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//! of points, and for Histogram this is the number of bins.
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//!
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//! @return The order.
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virtual size_t get_order() = 0;
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//! \brief Builds a dense scattering matrix from the constituent parts
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//!
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//! @param max_order If Legendre this is the maximum value of "n" in "Pn"
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//! requested; ignored otherwise.
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//! @return The dense scattering matrix.
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virtual tensor::Tensor<double> get_matrix(size_t max_order) = 0;
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//! \brief Samples the outgoing energy from the ScattData info.
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//!
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//! @param gin Incoming energy group.
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//! @param gout Sampled outgoing energy group.
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//! @param i_gout Sampled outgoing energy group index.
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//! @param seed Pseudorandom number seed pointer
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void sample_energy(int gin, int& gout, int& i_gout, uint64_t* seed);
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//! \brief Provides a cross section value given certain parameters
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//!
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//! @param xstype Type of cross section requested, according to the
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//! enumerated constants.
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//! @param gin Incoming energy group.
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//! @param gout Outgoing energy group; use nullptr if irrelevant, or if a
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//! sum is requested.
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//! @param mu Cosine of the change-in-angle, for scattering quantities;
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//! use nullptr if irrelevant.
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//! @return Requested cross section value.
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double get_xs(MgxsType xstype, int gin, const int* gout, const double* mu);
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};
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//==============================================================================
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// ScattDataLegendre represents the angular distributions as Legendre kernels
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//==============================================================================
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class ScattDataLegendre : public ScattData {
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protected:
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// Maximal value for rejection sampling from a rectangle
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double_2dvec max_val;
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// Friend convert_legendre_to_tabular so it has access to protected
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// parameters
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friend void convert_legendre_to_tabular(
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ScattDataLegendre& leg, ScattDataTabular& tab);
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public:
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void init(const tensor::Tensor<int>& in_gmin,
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const tensor::Tensor<int>& in_gmax, const double_2dvec& in_mult,
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const double_3dvec& coeffs) override;
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void combine(const vector<ScattData*>& those_scatts,
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const vector<double>& scalars) override;
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//! \brief Find the maximal value of the angular distribution to use as a
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// bounding box with rejection sampling.
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void update_max_val();
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double calc_f(int gin, int gout, double mu) override;
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void sample(
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int gin, int& gout, double& mu, double& wgt, uint64_t* seed) override;
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size_t get_order() override { return dist[0][0].size() - 1; };
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tensor::Tensor<double> get_matrix(size_t max_order) override;
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};
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//==============================================================================
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// ScattDataHistogram represents the angular distributions as a histogram, as it
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// would be if it came from a "mu" tally in OpenMC
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//==============================================================================
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class ScattDataHistogram : public ScattData {
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protected:
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tensor::Tensor<double> mu; // Angle distribution mu bin boundaries
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double dmu; // Quick storage of the mu spacing
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double_3dvec fmu; // The angular distribution histogram
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public:
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void init(const tensor::Tensor<int>& in_gmin,
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const tensor::Tensor<int>& in_gmax, const double_2dvec& in_mult,
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const double_3dvec& coeffs) override;
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void combine(const vector<ScattData*>& those_scatts,
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const vector<double>& scalars) override;
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double calc_f(int gin, int gout, double mu) override;
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void sample(
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int gin, int& gout, double& mu, double& wgt, uint64_t* seed) override;
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size_t get_order() override { return dist[0][0].size(); };
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tensor::Tensor<double> get_matrix(size_t max_order) override;
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};
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//==============================================================================
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// ScattDataTabular represents the angular distributions as a table of mu and
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// f(mu)
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//==============================================================================
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class ScattDataTabular : public ScattData {
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protected:
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tensor::Tensor<double> mu; // Angle distribution mu grid points
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double dmu; // Quick storage of the mu spacing
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double_3dvec fmu; // The angular distribution function
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// Friend convert_legendre_to_tabular so it has access to protected
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// parameters
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friend void convert_legendre_to_tabular(
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ScattDataLegendre& leg, ScattDataTabular& tab);
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public:
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void init(const tensor::Tensor<int>& in_gmin,
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const tensor::Tensor<int>& in_gmax, const double_2dvec& in_mult,
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const double_3dvec& coeffs) override;
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void combine(const vector<ScattData*>& those_scatts,
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const vector<double>& scalars) override;
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double calc_f(int gin, int gout, double mu) override;
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void sample(
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int gin, int& gout, double& mu, double& wgt, uint64_t* seed) override;
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size_t get_order() override { return dist[0][0].size(); };
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tensor::Tensor<double> get_matrix(size_t max_order) override;
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};
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//==============================================================================
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// Function to convert Legendre functions to tabular
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//==============================================================================
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//! \brief Converts a ScattDatalegendre to a ScattDataHistogram
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//!
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//! @param leg The initial ScattDataLegendre object.
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//! @param leg The resultant ScattDataTabular object.
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//! @param n_mu The number of mu points to use when building the
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//! ScattDataTabular object.
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void convert_legendre_to_tabular(
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ScattDataLegendre& leg, ScattDataTabular& tab, int n_mu);
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} // namespace openmc
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#endif // OPENMC_SCATTDATA_H
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