Restructuring and Tally Fixes

Moved initialization of IFP tallies to the add_ifp_kinetics_tallies method in the Model class

Fixed multi-precursor tallying of beta_numerator to work with the delayed group tallying system.

Author: realmisch

docs/source/usersguide/kinetics.rst

@@ -68,9 +68,10 @@ Obtaining kinetics parameters
 -----------------------------
 
 The ``Model`` class can be used to automatically generate all IFP tallies using the Python API
-with ``settings.ifp_n_generation`` greater than 0::
+with ``settings.ifp_n_generation`` greater than 0 and the ``Model.add_ifp_kinetics_tallies`` method::
 
-    model = openmc.model.Model(settings = settings)
+    model = openmc.model.Model(geometry = geometry, materials = materials, settings = settings)
+    model.add_ifp_kinetics_tallies(num_groups = 6) #Add 6 precursor groups
     model.export_to_xml()
 
 Additionally, each of the tallies can be manually defined individually with group-wise or total 

openmc/model/model.py

@@ -81,9 +81,6 @@ def __init__(
         self.tallies = openmc.Tallies() if tallies is None else tallies
         self.plots = openmc.Plots() if plots is None else plots
 
-        if self.settings._ifp_n_generation is not None:
-            self._init_ifp()
-
     @property
     def geometry(self) -> openmc.Geometry | None:
         return self._geometry
@@ -200,8 +197,14 @@ def _materials_by_name(self) -> dict[int, openmc.Material]:
             result[mat.name].add(mat)
         return result
 
-    def _init_ifp(self) -> None:
-        """Automate tally creation for calculating Iterated Fission Probability kinetics parameters"""
+    def add_ifp_kinetics_tallies(self, num_groups: int = 0):
+        """Add necessary tallies for calculating kinetics parameters using Iterated Fission Probability 
+
+        Parameters
+        ----------
+        num_groups : int
+            Number of precursor groups to filter the delayed neutron fraction.
+        """
         existing_tallies = {'time-num':False, 'beta-num':False, 'ifp-denom':False}
 
         dg_filter = None
@@ -223,10 +226,10 @@ def _init_ifp(self) -> None:
         if not existing_tallies['beta-num']:
             beta_tally = openmc.Tally()
             beta_tally.scores = ['ifp-beta-numerator']
-            if dg_filter is None:
-                beta_tally.filters = [openmc.DelayedGroupFilter(list(range(1,7)))]
-            else:
+            if dg_filter is not None:
                 beta_tally.filters = [dg_filter]
+            elif num_groups > 1:
+                beta_tally.filters = [openmc.DelayedGroupFilter(list(range(1, num_groups + 1)))]
             self._tallies.append(beta_tally)
         if not existing_tallies['ifp-denom']:
             denom_tally = openmc.Tally()

src/tallies/tally_scoring.cpp

@@ -963,10 +963,15 @@ void score_general_ce_nonanalog(Particle& p, int i_tally, int start_index,
               simulation::ifp_source_delayed_group_bank[p.current_work() - 1];
             if (delayed_groups.size() == settings::ifp_n_generation) {
               if (delayed_groups[0] > 0) {
+                score = p.wgt_last();
                 if (tally.delayedgroup_filter_ != C_NONE) {
-                  filter_index = delayed_groups[0] - 1;
+                  auto i_dg_filt = tally.filters()[tally.delayedgroup_filter_];
+                  const DelayedGroupFilter& filt {*dynamic_cast<DelayedGroupFilter*>(
+                      model::tally_filters[i_dg_filt].get())};
+                  score_fission_delayed_dg(
+                      i_tally, delayed_groups[0] - 1, score, score_index, p.filter_matches());
+                  continue;
                 }
-                score = p.wgt_last();
               }
             }
           }

tests/regression_tests/ifp/groupwise/test.py

@@ -32,6 +32,7 @@ def ifp_model():
     space = openmc.stats.Box(*cell.bounding_box)
     model.settings.source = openmc.IndependentSource(
         space=space, constraints={'fissionable': True})
+    model.add_ifp_kinetics_tallies(num_groups = 6)
     return model
 
 def test_iterated_fission_probability(ifp_model):