25033551588

Committed 28 Apr 2026 04:15AM UTC coverage: 81.361% (-0.2%) from 81.566%

Build # 25033551588

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Pull #3860

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web-flow

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Merge 3714a06cb into 806fb4ce7

Pull Request Pull Request #3860: Support multiple meshes in R2S calculations

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88.48

/src/secondary_kalbach.cpp

#include "openmc/secondary_kalbach.h"

#include <algorithm> // for copy, move
#include <cmath>     // for log, sqrt, sinh
#include <cstddef>   // for size_t
#include <iterator>  // for back_inserter

#include "openmc/tensor.h"

#include "openmc/hdf5_interface.h"
#include "openmc/math_functions.h"
#include "openmc/random_dist.h"
#include "openmc/random_lcg.h"
#include "openmc/search.h"
#include "openmc/vector.h"

namespace openmc {

//==============================================================================
//! KalbachMann implementation
//==============================================================================

KalbachMann::KalbachMann(hid_t group)
{
  // Open incoming energy dataset
  hid_t dset = open_dataset(group, "energy");

  // Get interpolation parameters
  tensor::Tensor<int> temp;
  read_attribute(dset, "interpolation", temp);

  tensor::View<int> temp_b = temp.slice(0); // breakpoints
  tensor::View<int> temp_i = temp.slice(1); // interpolation parameters

  std::copy(temp_b.begin(), temp_b.end(), std::back_inserter(breakpoints_));
  for (const auto i : temp_i)
    interpolation_.push_back(int2interp(i));
  n_region_ = breakpoints_.size();

  // Get incoming energies
  read_dataset(dset, energy_);
  std::size_t n_energy = energy_.size();
  close_dataset(dset);

  // Get outgoing energy distribution data
  dset = open_dataset(group, "distribution");
  vector<int> offsets;
  vector<int> interp;
  vector<int> n_discrete;
  read_attribute(dset, "offsets", offsets);
  read_attribute(dset, "interpolation", interp);
  read_attribute(dset, "n_discrete_lines", n_discrete);

  tensor::Tensor<double> eout;
  read_dataset(dset, eout);
  close_dataset(dset);

  for (int i = 0; i < n_energy; ++i) {
    // Determine number of outgoing energies
    int j = offsets[i];
    int n;
    if (i < n_energy - 1) {
      n = offsets[i + 1] - j;
    } else {
      n = eout.shape(1) - j;
    }

    // Assign interpolation scheme and number of discrete lines
    KMTable d;
    d.interpolation = int2interp(interp[i]);
    d.n_discrete = n_discrete[i];

    // Copy data
    d.e_out = eout.slice(0, tensor::range(j, j + n));
    d.p = eout.slice(1, tensor::range(j, j + n));
    d.c = eout.slice(2, tensor::range(j, j + n));
    d.r = eout.slice(3, tensor::range(j, j + n));
    d.a = eout.slice(4, tensor::range(j, j + n));

    // To get answers that match ACE data, for now we still use the tabulated
    // CDF values that were passed through to the HDF5 library. At a later
    // time, we can remove the CDF values from the HDF5 library and
    // reconstruct them using the PDF
    if (false) {
      // Calculate cumulative distribution function -- discrete portion
      for (int k = 0; k < d.n_discrete; ++k) {
        if (k == 0) {
          d.c[k] = d.p[k];
        } else {
          d.c[k] = d.c[k - 1] + d.p[k];
        }
      }

      // Continuous portion
      for (int k = d.n_discrete; k < n; ++k) {
        if (k == d.n_discrete) {
          d.c[k] = d.c[k - 1] + d.p[k];
        } else {
          if (d.interpolation == Interpolation::histogram) {
            d.c[k] = d.c[k - 1] + d.p[k - 1] * (d.e_out[k] - d.e_out[k - 1]);
          } else if (d.interpolation == Interpolation::lin_lin) {
            d.c[k] = d.c[k - 1] + 0.5 * (d.p[k - 1] + d.p[k]) *
                                    (d.e_out[k] - d.e_out[k - 1]);
          }
        }
      }

      // Normalize density and distribution functions
      d.p /= d.c[n - 1];
      d.c /= d.c[n - 1];
    }

    distribution_.push_back(std::move(d));
  } // incoming energies
}

void KalbachMann::sample_params(
  double E_in, double& E_out, double& km_a, double& km_r, uint64_t* seed) const
{
  // Find energy bin and calculate interpolation factor
  int i;
  double r;
  get_energy_index(energy_, E_in, i, r);

  // Sample between the ith and [i+1]th bin
  int l = r > prn(seed) ? i + 1 : i;

  // Interpolation for energy E1 and EK
  int n_energy_out = distribution_[i].e_out.size();
  int n_discrete = distribution_[i].n_discrete;
  double E_i_1 = distribution_[i].e_out[n_discrete];
  double E_i_K = distribution_[i].e_out[n_energy_out - 1];

  n_energy_out = distribution_[i + 1].e_out.size();
  n_discrete = distribution_[i + 1].n_discrete;
  double E_i1_1 = distribution_[i + 1].e_out[n_discrete];
  double E_i1_K = distribution_[i + 1].e_out[n_energy_out - 1];

  double E_1 = E_i_1 + r * (E_i1_1 - E_i_1);
  double E_K = E_i_K + r * (E_i1_K - E_i_K);

  // Determine outgoing energy bin
  n_energy_out = distribution_[l].e_out.size();
  n_discrete = distribution_[l].n_discrete;
  double r1 = prn(seed);
  double c_k = distribution_[l].c[0];
  int k = 0;
  int end = n_energy_out - 2;

  // Discrete portion
  for (int j = 0; j < n_discrete; ++j) {
    k = j;
    c_k = distribution_[l].c[k];
    if (r1 < c_k) {
      end = j;
      break;
    }
  }

  // Continuous portion
  double c_k1;
  for (int j = n_discrete; j < end; ++j) {
    k = j;
    c_k1 = distribution_[l].c[k + 1];
    if (r1 < c_k1)
      break;
    k = j + 1;
    c_k = c_k1;
  }

  double E_l_k = distribution_[l].e_out[k];
  double p_l_k = distribution_[l].p[k];
  if (distribution_[l].interpolation == Interpolation::histogram) {
    // Histogram interpolation
    if (p_l_k > 0.0 && k >= n_discrete) {
      E_out = E_l_k + (r1 - c_k) / p_l_k;
    } else {
      E_out = E_l_k;
    }

    // Determine Kalbach-Mann parameters
    km_r = distribution_[l].r[k];
    km_a = distribution_[l].a[k];

  } else {
    // Linear-linear interpolation
    double E_l_k1 = distribution_[l].e_out[k + 1];
    double p_l_k1 = distribution_[l].p[k + 1];

    double frac = (p_l_k1 - p_l_k) / (E_l_k1 - E_l_k);
    if (frac == 0.0) {
      E_out = E_l_k + (r1 - c_k) / p_l_k;
    } else {
      E_out =
        E_l_k +
        (std::sqrt(std::max(0.0, p_l_k * p_l_k + 2.0 * frac * (r1 - c_k))) -
          p_l_k) /
          frac;
    }

    // Determine Kalbach-Mann parameters
    km_r = distribution_[l].r[k] +
           (E_out - E_l_k) / (E_l_k1 - E_l_k) *
             (distribution_[l].r[k + 1] - distribution_[l].r[k]);
    km_a = distribution_[l].a[k] +
           (E_out - E_l_k) / (E_l_k1 - E_l_k) *
             (distribution_[l].a[k + 1] - distribution_[l].a[k]);
  }

  // Now interpolate between incident energy bins i and i + 1
  if (k >= n_discrete) {
    if (l == i) {
      E_out = E_1 + (E_out - E_i_1) * (E_K - E_1) / (E_i_K - E_i_1);
    } else {
      E_out = E_1 + (E_out - E_i1_1) * (E_K - E_1) / (E_i1_K - E_i1_1);
    }
  }
}

void KalbachMann::sample(
  double E_in, double& E_out, double& mu, uint64_t* seed) const
{
  double km_r, km_a;
  sample_params(E_in, E_out, km_a, km_r, seed);

  // Sampled correlated angle from Kalbach-Mann parameters
  if (prn(seed) > km_r) {
    double T = uniform_distribution(-1., 1., seed) * std::sinh(km_a);
    mu = std::log(T + std::sqrt(T * T + 1.0)) / km_a;
  } else {
    double r1 = prn(seed);
    mu = std::log(r1 * std::exp(km_a) + (1.0 - r1) * std::exp(-km_a)) / km_a;
  }
}
double KalbachMann::sample_energy_and_pdf(
  double E_in, double mu, double& E_out, uint64_t* seed) const
{
  double km_r, km_a;
  sample_params(E_in, E_out, km_a, km_r, seed);

  // https://docs.openmc.org/en/latest/methods/neutron_physics.html#equation-KM-pdf-angle
  return km_a / (2 * std::sinh(km_a)) *
         (std::cosh(km_a * mu) + km_r * std::sinh(km_a * mu));
}

} // namespace openmc

1	#include "openmc/secondary_kalbach.h"
2
3	#include <algorithm> // for copy, move
4	#include <cmath> // for log, sqrt, sinh
5	#include <cstddef> // for size_t
6	#include <iterator> // for back_inserter
7
8	#include "openmc/tensor.h"
9
10	#include "openmc/hdf5_interface.h"
11	#include "openmc/math_functions.h"
12	#include "openmc/random_dist.h"
13	#include "openmc/random_lcg.h"
14	#include "openmc/search.h"
15	#include "openmc/vector.h"
16
17	namespace openmc {
18
19	//==============================================================================
20	//! KalbachMann implementation
21	//==============================================================================
22
23	KalbachMann::KalbachMann(hid_t group)	70,779✔
24	{
25	// Open incoming energy dataset
26	hid_t dset = open_dataset(group, "energy");	70,779✔
27
28	// Get interpolation parameters
29	tensor::Tensor<int> temp;	70,779✔
30	read_attribute(dset, "interpolation", temp);	70,779✔
31
32	tensor::View<int> temp_b = temp.slice(0); // breakpoints	70,779✔
33	tensor::View<int> temp_i = temp.slice(1); // interpolation parameters	70,779✔
34
35	std::copy(temp_b.begin(), temp_b.end(), std::back_inserter(breakpoints_));	70,779✔
36	for (const auto i : temp_i)	141,558✔
37	interpolation_.push_back(int2interp(i));	70,779✔
38	n_region_ = breakpoints_.size();	70,779✔
39
40	// Get incoming energies
41	read_dataset(dset, energy_);	70,779✔
42	std::size_t n_energy = energy_.size();	70,779✔
43	close_dataset(dset);	70,779✔
44
45	// Get outgoing energy distribution data
46	dset = open_dataset(group, "distribution");	70,779✔
47	vector<int> offsets;	70,779✔
48	vector<int> interp;	70,779✔
49	vector<int> n_discrete;	70,779✔
50	read_attribute(dset, "offsets", offsets);	70,779✔
51	read_attribute(dset, "interpolation", interp);	70,779✔
52	read_attribute(dset, "n_discrete_lines", n_discrete);	70,779✔
53
54	tensor::Tensor<double> eout;	70,779✔
55	read_dataset(dset, eout);	70,779✔
56	close_dataset(dset);	70,779✔
57
58	for (int i = 0; i < n_energy; ++i) {	1,442,225✔
59	// Determine number of outgoing energies
60	int j = offsets[i];	1,371,446✔
61	int n;	1,371,446✔
62	if (i < n_energy - 1) {	1,371,446✔
63	n = offsets[i + 1] - j;	1,300,667✔
64	} else {
65	n = eout.shape(1) - j;	141,558!
66	}
67
68	// Assign interpolation scheme and number of discrete lines
69	KMTable d;	1,371,446✔
70	d.interpolation = int2interp(interp[i]);	1,371,446✔
71	d.n_discrete = n_discrete[i];	1,371,446✔
72
73	// Copy data
74	d.e_out = eout.slice(0, tensor::range(j, j + n));	1,371,446✔
75	d.p = eout.slice(1, tensor::range(j, j + n));	1,371,446✔
76	d.c = eout.slice(2, tensor::range(j, j + n));	1,371,446✔
77	d.r = eout.slice(3, tensor::range(j, j + n));	1,371,446✔
78	d.a = eout.slice(4, tensor::range(j, j + n));	1,371,446✔
79
80	// To get answers that match ACE data, for now we still use the tabulated
81	// CDF values that were passed through to the HDF5 library. At a later
82	// time, we can remove the CDF values from the HDF5 library and
83	// reconstruct them using the PDF
84	if (false) {	1,371,446✔
85	// Calculate cumulative distribution function -- discrete portion
86	for (int k = 0; k < d.n_discrete; ++k) {
87	if (k == 0) {
88	d.c[k] = d.p[k];
89	} else {
90	d.c[k] = d.c[k - 1] + d.p[k];
91	}
92	}
93
94	// Continuous portion
95	for (int k = d.n_discrete; k < n; ++k) {
96	if (k == d.n_discrete) {
97	d.c[k] = d.c[k - 1] + d.p[k];
98	} else {
99	if (d.interpolation == Interpolation::histogram) {
100	d.c[k] = d.c[k - 1] + d.p[k - 1] * (d.e_out[k] - d.e_out[k - 1]);
101	} else if (d.interpolation == Interpolation::lin_lin) {
102	d.c[k] = d.c[k - 1] + 0.5 * (d.p[k - 1] + d.p[k]) *
103	(d.e_out[k] - d.e_out[k - 1]);
104	}
105	}
106	}
107
108	// Normalize density and distribution functions
109	d.p /= d.c[n - 1];
110	d.c /= d.c[n - 1];
111	}
112
113	distribution_.push_back(std::move(d));	1,371,446✔
114	} // incoming energies	1,371,446✔
115	}	283,116✔
116
117	void KalbachMann::sample_params(	6,796,847✔
118	double E_in, double& E_out, double& km_a, double& km_r, uint64_t* seed) const
119	{
120	// Find energy bin and calculate interpolation factor
121	int i;	6,796,847✔
122	double r;	6,796,847✔
123	get_energy_index(energy_, E_in, i, r);	6,796,847✔
124
125	// Sample between the ith and [i+1]th bin
126	int l = r > prn(seed) ? i + 1 : i;	6,796,847✔
127
128	// Interpolation for energy E1 and EK
129	int n_energy_out = distribution_[i].e_out.size();	6,796,847✔
130	int n_discrete = distribution_[i].n_discrete;	6,796,847✔
131	double E_i_1 = distribution_[i].e_out[n_discrete];	6,796,847✔
132	double E_i_K = distribution_[i].e_out[n_energy_out - 1];	6,796,847✔
133
134	n_energy_out = distribution_[i + 1].e_out.size();	6,796,847✔
135	n_discrete = distribution_[i + 1].n_discrete;	6,796,847✔
136	double E_i1_1 = distribution_[i + 1].e_out[n_discrete];	6,796,847✔
137	double E_i1_K = distribution_[i + 1].e_out[n_energy_out - 1];	6,796,847✔
138
139	double E_1 = E_i_1 + r * (E_i1_1 - E_i_1);	6,796,847✔
140	double E_K = E_i_K + r * (E_i1_K - E_i_K);	6,796,847✔
141
142	// Determine outgoing energy bin
143	n_energy_out = distribution_[l].e_out.size();	6,796,847✔
144	n_discrete = distribution_[l].n_discrete;	6,796,847✔
145	double r1 = prn(seed);	6,796,847✔
146	double c_k = distribution_[l].c[0];	6,796,847✔
147	int k = 0;	6,796,847✔
148	int end = n_energy_out - 2;	6,796,847✔
149
150	// Discrete portion
151	for (int j = 0; j < n_discrete; ++j) {	6,796,847!
152	k = j;	×
153	c_k = distribution_[l].c[k];	×
154	if (r1 < c_k) {	×
155	end = j;
156	break;
157	}
158	}
159
160	// Continuous portion
161	double c_k1;	6,796,847✔
162	for (int j = n_discrete; j < end; ++j) {	190,853,032✔
163	k = j;	190,622,011✔
164	c_k1 = distribution_[l].c[k + 1];	190,622,011✔
165	if (r1 < c_k1)	190,622,011✔
166	break;
167	k = j + 1;
168	c_k = c_k1;
169	}
170
171	double E_l_k = distribution_[l].e_out[k];	6,796,847✔
172	double p_l_k = distribution_[l].p[k];	6,796,847✔
173	if (distribution_[l].interpolation == Interpolation::histogram) {	6,796,847✔
174	// Histogram interpolation
175	if (p_l_k > 0.0 && k >= n_discrete) {	6,777,091!
176	E_out = E_l_k + (r1 - c_k) / p_l_k;	6,777,091✔
177	} else {
UNCOV 178	E_out = E_l_k;	×
179	}
180
181	// Determine Kalbach-Mann parameters
182	km_r = distribution_[l].r[k];	6,777,091✔
183	km_a = distribution_[l].a[k];	6,777,091✔
184
185	} else {
186	// Linear-linear interpolation
187	double E_l_k1 = distribution_[l].e_out[k + 1];	19,756!
188	double p_l_k1 = distribution_[l].p[k + 1];	19,756✔
189
190	double frac = (p_l_k1 - p_l_k) / (E_l_k1 - E_l_k);	19,756✔
191	if (frac == 0.0) {	19,756!
UNCOV 192	E_out = E_l_k + (r1 - c_k) / p_l_k;	×
193	} else {
194	E_out =	39,512✔
195	E_l_k +	19,756✔
196	(std::sqrt(std::max(0.0, p_l_k * p_l_k + 2.0 * frac * (r1 - c_k))) -	39,512!
197	p_l_k) /	19,756✔
198	frac;
199	}
200
201	// Determine Kalbach-Mann parameters
202	km_r = distribution_[l].r[k] +	19,756✔
203	(E_out - E_l_k) / (E_l_k1 - E_l_k) *	19,756✔
204	(distribution_[l].r[k + 1] - distribution_[l].r[k]);	19,756✔
205	km_a = distribution_[l].a[k] +	19,756✔
206	(E_out - E_l_k) / (E_l_k1 - E_l_k) *	19,756✔
207	(distribution_[l].a[k + 1] - distribution_[l].a[k]);	19,756✔
208	}
209
210	// Now interpolate between incident energy bins i and i + 1
211	if (k >= n_discrete) {	6,796,847!
212	if (l == i) {	6,796,847✔
213	E_out = E_1 + (E_out - E_i_1) * (E_K - E_1) / (E_i_K - E_i_1);	3,411,151✔
214	} else {
215	E_out = E_1 + (E_out - E_i1_1) * (E_K - E_1) / (E_i1_K - E_i1_1);	3,385,696✔
216	}
217	}
218	}	6,796,847✔
219
220	void KalbachMann::sample(	6,796,847✔
221	double E_in, double& E_out, double& mu, uint64_t* seed) const
222	{
223	double km_r, km_a;	6,796,847✔
224	sample_params(E_in, E_out, km_a, km_r, seed);	6,796,847✔
225
226	// Sampled correlated angle from Kalbach-Mann parameters
227	if (prn(seed) > km_r) {	6,796,847✔
228	double T = uniform_distribution(-1., 1., seed) * std::sinh(km_a);	6,682,075✔
229	mu = std::log(T + std::sqrt(T * T + 1.0)) / km_a;	6,682,075✔
230	} else {
231	double r1 = prn(seed);	114,772✔
232	mu = std::log(r1 * std::exp(km_a) + (1.0 - r1) * std::exp(-km_a)) / km_a;	114,772✔
233	}
234	}	6,796,847✔
UNCOV 235	double KalbachMann::sample_energy_and_pdf(	×
236	double E_in, double mu, double& E_out, uint64_t* seed) const
237	{
UNCOV 238	double km_r, km_a;	×
UNCOV 239	sample_params(E_in, E_out, km_a, km_r, seed);	×
240
241	// https://docs.openmc.org/en/latest/methods/neutron_physics.html#equation-KM-pdf-angle
UNCOV 242	return km_a / (2 * std::sinh(km_a)) *	×
UNCOV 243	(std::cosh(km_a * mu) + km_r * std::sinh(km_a * mu));	×
244	}
245
246	} // namespace openmc

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