Different implementation methods of enrich()

Enrich_methods/example.py

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+import argparse
+from pyne import material
+from pyne.material import Material, MultiMaterial
+from pyne.material_library import MaterialLibrary
+from material_db_tools import mix_by_volume
+import material_db_tools as mdbt
+from material_library import (
+    mat_data as pure,
+)  # this is importing the material dictionaries
+
+mat_lib_test = MaterialLibrary()
+# you may need to adjust this path for your setup
+mat_lib_test.from_json("material_library.json")
+
+
+simulation_lib = MaterialLibrary()
+
+
+################################### Method 3
+
+
+# Currently how Edgar and I are enriching out materials
+def enriched_lithium(enrichment):
+    enriched_li = Material({"Li6": enrichment, "Li7": 1 - enrichment})
+    return enriched_li
+
+
+def Li2TiO3Li_mat(enrichment=0.60):
+    Li2TiO3Li60 = Material()
+    Li2TiO3Li60.from_atom_frac(
+        {enriched_lithium(enrichment): 2, 80000000: 3, 220000000: 1}
+    )
+    Li2TiO3Li60.density = 3.43
+    Li2TiO3Li60 = Li2TiO3Li60.expand_elements()
+    Li2TiO3Li60.metadata["citation"] = "HernandezFusEngDes_2018"
+    return Li2TiO3Li60
+
+
+def Li4SiO4Li_mat(enrichment=0.60):
+    Li2TiO3Li60 = Material()
+    Li2TiO3Li60.from_atom_frac(
+        {enriched_lithium(enrichment): 4, 80000000: 4, 140000000: 1}
+    )
+    Li2TiO3Li60.density = 2.40
+    Li2TiO3Li60 = Li2TiO3Li60.expand_elements()
+    Li2TiO3Li60.metadata["citation"] = "HernandezFusEngDes_2018"
+    return Li2TiO3Li60
+
+
+# I also need to have another version of this function for the pin breeder zone.
+# I will if need to either make a version of this for each material I'm testing
+# or I will need a universial function that will require me to pass all the arugments in hcpb_pin_bw()
+# plus whatever material I'm testing for the case. Currently I'm testing around 8 different materials
+# How I make the universal function will depend on how I can import the library or if I can use enrich
+def hcpb_pin_bw(
+    enrichment=0.6,
+    Li4SiO4_frac=0.065,
+    Li2TiO3_frac=0.035,
+    he_frac=0.482,
+    EUROFER97_frac=0.418,
+):
+
+    Li4SiO4Li = Li4SiO4Li_mat(enrichment)
+    Li2TiO3Li = Li2TiO3Li_mat(enrichment)
+
+    mix = MultiMaterial(
+        {
+            mat_lib_test["EUROFER97"]: EUROFER97_frac,
+            Li4SiO4Li: Li4SiO4_frac,
+            mat_lib_test["HeT410P80"]: he_frac,
+            Li2TiO3Li: Li2TiO3_frac,
+        }
+    )
+    hcpb_pin_bw = mix.mix_by_volume()
+    hcpb_pin_bw.metadata["constituentcitation"] = "HernandezFusEngDes_2018"
+    return hcpb_pin_bw
+
+
+################################### Method 2
+
+# Method if you want to use enrich function without it being called in mix_by_volume or in make_mat_from_atom
+# But this version uses the output json file which will give you the same answer as rebuiling the material to the second last decimal
+# Earlier you said that you wanted to avoid that method since you don't trust it
+enriched_mat = mdbt.Material({30060000: 0.6, 30070000: 0.4})
+collapsed = mat_lib_test["Li2TiO3nat"].collapse_elements({3})
+atom_frac_enriched = mdbt.enrich(
+    collapsed.to_atom_frac(), 30000000, enriched_mat.to_atom_frac()
+)
+
+mat_input2 = {
+    "atom_frac": atom_frac_enriched,
+    "density": 3.42,
+    "citation": "HernandezFusEngDes_2018",
+}
+material2 = mdbt.make_mat_from_atom(**mat_input2)
+# From here you can either add it to the input library or call it in def hcpb_pin_bw
+# Benefit of adding to input library you can have it included in your output material library like ss 316 currently
+
+
+################################### Method 3
+
+# You can either use "from material_library import mat_data as pure" or just redefine the material which would be the same as the current method
+
+enriched_mat = mdbt.Material({30060000: 0.6, 30070000: 0.4})
+atom_frac_enriched = mdbt.enrich(
+    pure["Li2TiO3nat"]["atom_frac"], 30000000, enriched_mat.to_atom_frac()
+)
+# I would need to call enrich() each time I wanted to enrich a different element
+mat_input3 = {
+    "atom_frac": atom_frac_enriched,
+    "density": 3.42,
+    "citation": "HernandezFusEngDes_2018",
+}
+material3 = mdbt.make_mat_from_atom(**mat_input3)
+# From here you can either add it to the input library or call it in def hcpb_pin_bw
+
+
+################################### Method 4
+
+# In this method make_mat_from_atom can call the enrich function
+# I can enrich multiple isotopes at the same time with this method
+
+pure["Li2TiO3nat"]["mass_enrichment"] = {30000000: {30060000: 0.6, 30070000: 0.4}}
+# I can enrich multiple isotopes at the same time with this method
+material4 = mdbt.make_mat_from_atom(**pure["Li2TiO3nat"])
+mat_lib_test["material4_Li2TiO3"] = material4
+
+# Can also be either added to the input library or call it in def hcpb_pin_bw
+# Inputting to the library allows for the mix
+pure["Li4SiO4nat"]["mass_enrichment"] = {30000000: {30060000: 0.6, 30070000: 0.4}}
+mat_lib_test["material4_Li3SiO4"] = mdbt.make_mat_from_atom(**pure["Li4SiO4nat"])
+
+# You can theoretically enrich the material with one line
+#mat_lib_test["material4_Li3SiO4"] = mdbt.make_mat_from_atom(**{**pure.setdefault("Li4SiO4nat", {}), "mass_enrichment": {30000000: {30060000: 0.6, 30070000: 0.4}}}})
+
+mixed_materials = {}
+
+mixed_materials["hcpb_enrich_method_4"] = {
+    "vol_fracs": {
+        "EUROFER97": 0.418,
+        "material4_Li3SiO4": 0.065,
+        "material4_Li2TiO3": 0.035,
+        "HeT410P80": 0.482,
+    },
+    "mixture_citation": "ZhouEnergies_2023 and ???",
+}
+
+# If added to the input library you can quite easily combine it with Method 5 but not need to call the mass enrichment function
+
+################################### Method 5
+
+# In this method make_mat_from_atom can be called by mix_by_volume, and make_mat_from_atom can call the the enrich function
+# Using Method I'm suggesting, one issue is that it requires the mdbt to import the library that has the materials before they have been created
+# With this method I can enrich multiple materials and multiple elements in those materials at the same time.
+# It also makes it trivial to add other materials and enrich them
+# It would be also fairly easy to add some of the other features such as adjusting the density of materials in this format
+
+
+mixed_materials["hcpb_enrich_method_5"] = {
+    "vol_fracs": {
+        "EUROFER97": 0.418,
+        "Li4SiO4nat": 0.065,
+        "Li2TiO3nat": 0.035,
+        "HeT410P80": 0.482,
+    },
+    "mixture_citation": "ZhouEnergies_2023 and ???",
+    "mass_enrichment": {
+        "Li2TiO3nat": {30000000: {30060000: 0.6, 30070000: 0.4}},
+        "Li4SiO4nat": {30000000: {30060000: 0.6, 30070000: 0.4}},
+    },
+}
+
+
+# running results
+# I looked at using enrich() to enrich mixed materials after they were made.
+# But theres a pretty big difference between making a material and multmaterial even with the same input fraction and overall density.
+# I didn't spend a ton of time on it but I could get it to work
+def main():
+    # Need this function for method 1
+    simulation_lib["HCPB_Pin_BW"] = hcpb_pin_bw(
+        enrichment=0.6,
+        Li4SiO4_frac=0.065,
+        Li2TiO3_frac=0.035,
+        he_frac=0.482,
+        EUROFER97_frac=0.418,
+    )
+
+    for material, material_def in mixed_materials.items():
+        mat = mix_by_volume(
+            mat_lib_test,
+            material_def["vol_fracs"],
+            citation=material_def.get("citation"),
+            mass_enrichment=material_def.get("mass_enrichment"),
+        )
+        simulation_lib[material] = mat
+
+    # Proof that all methods produce the same enriched material
+
+    # Base comparison from json input
+    simulation_lib["Li2TiO3Li60.0"] = mat_lib_test["Li2TiO3Li60.0"]
+    # Created from methods 2-4
+    simulation_lib["material2"] = material2
+    simulation_lib["material3"] = material3
+    simulation_lib["material4"] = material4
+
+    simulation_lib.write_openmc("materials_example.xml")
+
+
+if __name__ == "__main__":
+    main()