21414796804

Committed 27 Jan 2026 09:24PM UTC coverage: 81.976% (-0.02%) from 81.994%

Build # 21414796804

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Pull #3682

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web-flow

Commit Message

Merge c34922eb5 into db426b66c

Pull Request Pull Request #3682: Extend Kalbach Mann systematics to support incident photons

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84.93

/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 "xtensor/xarray.hpp"
#include "xtensor/xview.hpp"

#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)
{
  is_photon_ = false;
  // Check if projectile is a photon
  if (attribute_exists(group, "particle")) {
    std::string temp;
    read_attribute(group, "particle", temp);
    if (temp == "photon")
      is_photon_ = true;
  }
  // Open incoming energy dataset
  hid_t dset = open_dataset(group, "energy");

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

  auto temp_b = xt::view(temp, 0); // view of breakpoints
  auto temp_i = xt::view(temp, 1); // view of 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);

  xt::xarray<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 = xt::view(eout, 0, xt::range(j, j + n));
    d.p = xt::view(eout, 1, xt::range(j, j + n));
    d.c = xt::view(eout, 2, xt::range(j, j + n));
    d.r = xt::view(eout, 3, xt::range(j, j + n));
    d.a = xt::view(eout, 4, xt::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(
  double E_in, double& E_out, double& mu, 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];
  double km_r, km_a;
  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);
    }
  }

  // Sampled correlated angle from Kalbach-Mann parameters
  if (is_photon_) {
    double T = uniform_distribution(0., 1., seed);
    double sinha = std::sinh(km_a);
    mu = sinha * ((1 + T) - 2 * km_r) /
         (sinha * T + std::cosh(km_a) - std::exp(km_a * T));
  } else {
    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;
    }
  }
}

} // 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 "xtensor/xarray.hpp"
9	#include "xtensor/xview.hpp"
10
11	#include "openmc/hdf5_interface.h"
12	#include "openmc/math_functions.h"
13	#include "openmc/random_dist.h"
14	#include "openmc/random_lcg.h"
15	#include "openmc/search.h"
16	#include "openmc/vector.h"
17
18	namespace openmc {
19
20	//==============================================================================
21	//! KalbachMann implementation
22	//==============================================================================
23
24	KalbachMann::KalbachMann(hid_t group)	68,506✔
25	{
26	is_photon_ = false;	68,506✔
27	// Check if projectile is a photon
28	if (attribute_exists(group, "particle")) {	68,506!
NEW 29	std::string temp;	×
NEW 30	read_attribute(group, "particle", temp);	×
NEW 31	if (temp == "photon")	×
NEW 32	is_photon_ = true;	×
NEW 33	}	×
34	// Open incoming energy dataset
35	hid_t dset = open_dataset(group, "energy");	68,506✔
36
37	// Get interpolation parameters
38	xt::xarray<int> temp;	68,506✔
39	read_attribute(dset, "interpolation", temp);	68,506✔
40
41	auto temp_b = xt::view(temp, 0); // view of breakpoints	68,506✔
42	auto temp_i = xt::view(temp, 1); // view of interpolation parameters	68,506✔
43
44	std::copy(temp_b.begin(), temp_b.end(), std::back_inserter(breakpoints_));	68,506✔
45	for (const auto i : temp_i)	137,012✔
46	interpolation_.push_back(int2interp(i));	68,506✔
47	n_region_ = breakpoints_.size();	68,506✔
48
49	// Get incoming energies
50	read_dataset(dset, energy_);	68,506✔
51	std::size_t n_energy = energy_.size();	68,506✔
52	close_dataset(dset);	68,506✔
53
54	// Get outgoing energy distribution data
55	dset = open_dataset(group, "distribution");	68,506✔
56	vector<int> offsets;	68,506✔
57	vector<int> interp;	68,506✔
58	vector<int> n_discrete;	68,506✔
59	read_attribute(dset, "offsets", offsets);	68,506✔
60	read_attribute(dset, "interpolation", interp);	68,506✔
61	read_attribute(dset, "n_discrete_lines", n_discrete);	68,506✔
62
63	xt::xarray<double> eout;	68,506✔
64	read_dataset(dset, eout);	68,506✔
65	close_dataset(dset);	68,506✔
66
67	for (int i = 0; i < n_energy; ++i) {	1,395,139✔
68	// Determine number of outgoing energies
69	int j = offsets[i];	1,326,633✔
70	int n;
71	if (i < n_energy - 1) {	1,326,633✔
72	n = offsets[i + 1] - j;	1,258,127✔
73	} else {
74	n = eout.shape()[1] - j;	68,506✔
75	}
76
77	// Assign interpolation scheme and number of discrete lines
78	KMTable d;	1,326,633✔
79	d.interpolation = int2interp(interp[i]);	1,326,633✔
80	d.n_discrete = n_discrete[i];	1,326,633✔
81
82	// Copy data
83	d.e_out = xt::view(eout, 0, xt::range(j, j + n));	1,326,633✔
84	d.p = xt::view(eout, 1, xt::range(j, j + n));	1,326,633✔
85	d.c = xt::view(eout, 2, xt::range(j, j + n));	1,326,633✔
86	d.r = xt::view(eout, 3, xt::range(j, j + n));	1,326,633✔
87	d.a = xt::view(eout, 4, xt::range(j, j + n));	1,326,633✔
88
89	// To get answers that match ACE data, for now we still use the tabulated
90	// CDF values that were passed through to the HDF5 library. At a later
91	// time, we can remove the CDF values from the HDF5 library and
92	// reconstruct them using the PDF
93	if (false) {
94	// Calculate cumulative distribution function -- discrete portion
95	for (int k = 0; k < d.n_discrete; ++k) {
96	if (k == 0) {
97	d.c[k] = d.p[k];
98	} else {
99	d.c[k] = d.c[k - 1] + d.p[k];
100	}
101	}
102
103	// Continuous portion
104	for (int k = d.n_discrete; k < n; ++k) {
105	if (k == d.n_discrete) {
106	d.c[k] = d.c[k - 1] + d.p[k];
107	} else {
108	if (d.interpolation == Interpolation::histogram) {
109	d.c[k] = d.c[k - 1] + d.p[k - 1] * (d.e_out[k] - d.e_out[k - 1]);
110	} else if (d.interpolation == Interpolation::lin_lin) {
111	d.c[k] = d.c[k - 1] + 0.5 * (d.p[k - 1] + d.p[k]) *
112	(d.e_out[k] - d.e_out[k - 1]);
113	}
114	}
115	}
116
117	// Normalize density and distribution functions
118	d.p /= d.c[n - 1];
119	d.c /= d.c[n - 1];
120	}
121
122	distribution_.push_back(std::move(d));	1,326,633✔
123	} // incoming energies	1,326,633✔
124	}	68,506✔
125
126	void KalbachMann::sample(	5,069,770✔
127	double E_in, double& E_out, double& mu, uint64_t* seed) const
128	{
129	// Find energy bin and calculate interpolation factor
130	int i;
131	double r;
132	get_energy_index(energy_, E_in, i, r);	5,069,770✔
133
134	// Sample between the ith and [i+1]th bin
135	int l = r > prn(seed) ? i + 1 : i;	5,069,770✔
136
137	// Interpolation for energy E1 and EK
138	int n_energy_out = distribution_[i].e_out.size();	5,069,770✔
139	int n_discrete = distribution_[i].n_discrete;	5,069,770✔
140	double E_i_1 = distribution_[i].e_out[n_discrete];	5,069,770✔
141	double E_i_K = distribution_[i].e_out[n_energy_out - 1];	5,069,770✔
142
143	n_energy_out = distribution_[i + 1].e_out.size();	5,069,770✔
144	n_discrete = distribution_[i + 1].n_discrete;	5,069,770✔
145	double E_i1_1 = distribution_[i + 1].e_out[n_discrete];	5,069,770✔
146	double E_i1_K = distribution_[i + 1].e_out[n_energy_out - 1];	5,069,770✔
147
148	double E_1 = E_i_1 + r * (E_i1_1 - E_i_1);	5,069,770✔
149	double E_K = E_i_K + r * (E_i1_K - E_i_K);	5,069,770✔
150
151	// Determine outgoing energy bin
152	n_energy_out = distribution_[l].e_out.size();	5,069,770✔
153	n_discrete = distribution_[l].n_discrete;	5,069,770✔
154	double r1 = prn(seed);	5,069,770✔
155	double c_k = distribution_[l].c[0];	5,069,770✔
156	int k = 0;	5,069,770✔
157	int end = n_energy_out - 2;	5,069,770✔
158
159	// Discrete portion
160	for (int j = 0; j < n_discrete; ++j) {	5,069,770!
UNCOV 161	k = j;	×
UNCOV 162	c_k = distribution_[l].c[k];	×
163	if (r1 < c_k) {	×
164	end = j;	×
165	break;	×
166	}
167	}
168
169	// Continuous portion
170	double c_k1;
171	for (int j = n_discrete; j < end; ++j) {	128,292,900✔
172	k = j;	128,097,566✔
173	c_k1 = distribution_[l].c[k + 1];	128,097,566✔
174	if (r1 < c_k1)	128,097,566✔
175	break;	4,874,436✔
176	k = j + 1;	123,223,130✔
177	c_k = c_k1;	123,223,130✔
178	}
179
180	double E_l_k = distribution_[l].e_out[k];	5,069,770✔
181	double p_l_k = distribution_[l].p[k];	5,069,770✔
182	double km_r, km_a;
183	if (distribution_[l].interpolation == Interpolation::histogram) {	5,069,770✔
184	// Histogram interpolation
185	if (p_l_k > 0.0 && k >= n_discrete) {	5,050,014!
186	E_out = E_l_k + (r1 - c_k) / p_l_k;	5,050,014✔
187	} else {
UNCOV 188	E_out = E_l_k;	×
189	}
190
191	// Determine Kalbach-Mann parameters
192	km_r = distribution_[l].r[k];	5,050,014✔
193	km_a = distribution_[l].a[k];	5,050,014✔
194
195	} else {
196	// Linear-linear interpolation
197	double E_l_k1 = distribution_[l].e_out[k + 1];	19,756✔
198	double p_l_k1 = distribution_[l].p[k + 1];	19,756✔
199
200	double frac = (p_l_k1 - p_l_k) / (E_l_k1 - E_l_k);	19,756✔
201	if (frac == 0.0) {	19,756!
UNCOV 202	E_out = E_l_k + (r1 - c_k) / p_l_k;	×
203	} else {
204	E_out =	19,756✔
205	E_l_k +	19,756✔
206	(std::sqrt(std::max(0.0, p_l_k * p_l_k + 2.0 * frac * (r1 - c_k))) -	19,756✔
207	p_l_k) /	19,756✔
208	frac;
209	}
210
211	// Determine Kalbach-Mann parameters
212	km_r = distribution_[l].r[k] +	19,756✔
213	(E_out - E_l_k) / (E_l_k1 - E_l_k) *	39,512✔
214	(distribution_[l].r[k + 1] - distribution_[l].r[k]);	19,756✔
215	km_a = distribution_[l].a[k] +	19,756✔
216	(E_out - E_l_k) / (E_l_k1 - E_l_k) *	39,512✔
217	(distribution_[l].a[k + 1] - distribution_[l].a[k]);	19,756✔
218	}
219
220	// Now interpolate between incident energy bins i and i + 1
221	if (k >= n_discrete) {	5,069,770!
222	if (l == i) {	5,069,770✔
223	E_out = E_1 + (E_out - E_i_1) * (E_K - E_1) / (E_i_K - E_i_1);	2,523,461✔
224	} else {
225	E_out = E_1 + (E_out - E_i1_1) * (E_K - E_1) / (E_i1_K - E_i1_1);	2,546,309✔
226	}
227	}
228
229	// Sampled correlated angle from Kalbach-Mann parameters
230	if (is_photon_) {	5,069,770!
NEW UNCOV 231	double T = uniform_distribution(0., 1., seed);	×
NEW 232	double sinha = std::sinh(km_a);	×
NEW 233	mu = sinha * ((1 + T) - 2 * km_r) /	×
NEW 234	(sinha * T + std::cosh(km_a) - std::exp(km_a * T));	×
235	} else {
236	if (prn(seed) > km_r) {	5,069,770✔
237	double T = uniform_distribution(-1., 1., seed) * std::sinh(km_a);	4,988,392✔
238	mu = std::log(T + std::sqrt(T * T + 1.0)) / km_a;	4,988,392✔
239	} else {
240	double r1 = prn(seed);	81,378✔
241	mu = std::log(r1 * std::exp(km_a) + (1.0 - r1) * std::exp(-km_a)) / km_a;	81,378✔
242	}
243	}
244	}	5,069,770✔
245
246	} // namespace openmc

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