{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "71007b8d-a65a-4137-ae22-c6076785885f", "metadata": {}, "outputs": [], "source": [ "cp run_10_cells_plasma/weight_windows.h5 run_10_cells_plasma/weight_windows_iter1.h5" ] }, { "cell_type": "code", "execution_count": null, "id": "18dc26a1-9114-4366-8197-06f1d238ba08", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenMC versija: 0.15.3\n", "Naudojama biblioteka: /IEVOS_STUFF_HERE/ConvertesStuff/combined_cross_sections_FENDL32b_ENDF80.xml\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another MeshBase instance already exists with id=1.\n", " warn(msg, IDWarning)\n", "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Material instance already exists with id=1.\n", " warn(msg, IDWarning)\n", "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: 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IDWarning: Another Tally instance already exists with id=55.\n", " warn(msg, IDWarning)\n", "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Tally instance already exists with id=56.\n", " warn(msg, IDWarning)\n", "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Tally instance already exists with id=58.\n", " warn(msg, IDWarning)\n", "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Tally instance already exists with id=59.\n", " warn(msg, IDWarning)\n", "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Tally instance already exists with id=60.\n", " warn(msg, IDWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Bibliotekoje rasta neutronų duomenų rinkinių: 557\n", "\n", "TIKSLINĖS CELĖS\n", " cell_id material_id material_name volume_cm3\n", " 7454 15 4490.250000\n", " 7810 125 166.272000\n", " 7990 666 116.383000\n", " 8009 666 816.075000\n", " 8005 667 1689.510000\n", " 8006 667 1421.250000\n", " 7108 5 395.097000\n", " 8070 5 2851.140000\n", " 7090 100 0.662744\n", " 8484 100 41200.400000\n", "\n", "ŠALTINIO PATIKRA\n", "Šaltinių skaičius: 1\n", "\n", "Šaltinis [0]: MeshSource\n", " strength (santykinis svoris): 1.0\n", " Šaltinio mesh tipas: CylindricalMesh\n", " Mesh origin: [0. 0. 0.]\n", " R ribos [cm]: 605.05211 1182.5478899999998\n", " Phi ribos [rad]: 0.0 0.19634954084936207\n", " Phi kampas [deg]: 11.25\n", " Z ribos [cm]: -475.695 475.695\n", "\n", "CCFE ribų skaičius: 710\n", "CCFE grupių skaičius: 709\n", "Energijos ribos: 1e-05 – 1000000000.0 eV\n", "\n", "Mesh tally: mesh_total_flux | dimension = (300, 150, 100) | be energijos filtro\n", "\n", "Sukurta tally: 31\n", " spectrum_7454 | scores: ['flux']\n", " flux_7454 | scores: ['flux']\n", " reactions_7454 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_7810 | scores: ['flux']\n", " flux_7810 | scores: ['flux']\n", " reactions_7810 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_7990 | scores: ['flux']\n", " flux_7990 | scores: ['flux']\n", " reactions_7990 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_8009 | scores: ['flux']\n", " flux_8009 | scores: ['flux']\n", " reactions_8009 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_8005 | scores: ['flux']\n", " flux_8005 | scores: ['flux']\n", " reactions_8005 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_8006 | scores: ['flux']\n", " flux_8006 | scores: ['flux']\n", " reactions_8006 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_7108 | scores: ['flux']\n", " flux_7108 | scores: ['flux']\n", " reactions_7108 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_8070 | scores: ['flux']\n", " flux_8070 | scores: ['flux']\n", " reactions_8070 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_7090 | scores: ['flux']\n", " flux_7090 | scores: ['flux']\n", " reactions_7090 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " spectrum_8484 | scores: ['flux']\n", " flux_8484 | scores: ['flux']\n", " reactions_8484 | scores: ['total', 'elastic', 'absorption', '(n,gamma)', '(n,2n)', '(n,p)', '(n,a)']\n", " mesh_total_flux | scores: ['flux']\n", "\n", "WW REŽIMAS: produkcija\n", "\n", " WW rinkinys 0\n", " Mesh ID: 990001\n", " Dimension: (68, 4, 104)\n", " Erdviniai binai: 28288\n", " Energijos grupės: 4\n", " Tikėtini weight binai: 113152\n", " Lower weight binai: 113152\n", " Upper weight binai: 113152\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/openmc_venv/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another MeshBase instance already exists with id=990001.\n", " warn(msg, IDWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Paleidžiamas OpenMC...\n", " %%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%%%%%%\n", " ##################### %%%%%%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%\n", " ################# %%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%\n", " ############ %%%%%%%%%%%%%%%\n", " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.15.3\n", " Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n", " Date/Time | 2026-07-31 16:07:43\n", " MPI Processes | 1\n", " OpenMP Threads | 24\n", "\n", " Reading model XML file\n", " '/IEVOS_STUFF_HERE/run_10_cells_plasma/model_fixed_library_checked.xml' ...\n", " Reading cross sections XML file...\n", " Reading Fe54 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2625_2\n", " 6-Fe-54.endf.h5\n", " Reading Fe56 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2631_2\n", " 6-Fe-56.endf.h5\n", " Reading Fe57 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/IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0828_8\n", " -O-17.endf.h5\n", " Reading O18 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0831_8\n", " -O-18.endf.h5\n", " Reading Al27 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1325_1\n", " 3-Al-27.endf.h5\n", " Reading Si28 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1425_1\n", " 4-Si-28.endf.h5\n", " Reading Si29 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1428_1\n", " 4-Si-29.endf.h5\n", " Reading Si30 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1431_1\n", " 4-Si-30.endf.h5\n", " Reading P31 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1525_1\n", " 5-P-31.endf.h5\n", " Reading S32 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1625_1\n", " 6-S-32.endf.h5\n", " Reading S33 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1628_1\n", " 6-S-33.endf.h5\n", " Reading S34 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1631_1\n", " 6-S-34.endf.h5\n", " Reading S36 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1637_1\n", " 6-S-36.endf.h5\n", " Reading Ti46 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2225_2\n", " 2-Ti-46.endf.h5\n", " Reading Ti47 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2228_2\n", " 2-Ti-47.endf.h5\n", " Reading Ti48 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2231_2\n", " 2-Ti-48.endf.h5\n", " Reading Ti49 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2234_2\n", " 2-Ti-49.endf.h5\n", " Reading Ti50 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2237_2\n", " 2-Ti-50.endf.h5\n", " Reading V51 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2300_2\n", " 3-V-51.endf.h5\n", " Reading Cr50 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2425_2\n", " 4-Cr-50.endf.h5\n", " Reading Cr52 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2431_2\n", " 4-Cr-52.endf.h5\n", " Reading Cr53 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2434_2\n", " 4-Cr-53.endf.h5\n", " Reading Cr54 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2437_2\n", " 4-Cr-54.endf.h5\n", " Reading Mn55 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2525_2\n", " 5-Mn-55.endf.h5\n", " Reading Co59 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2725_2\n", " 7-Co-59.endf.h5\n", " Reading Ni58 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2825_2\n", " 8-Ni-58.endf.h5\n", " Reading Ni60 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2831_2\n", " 8-Ni-60.endf.h5\n", " Reading Ni61 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2834_2\n", " 8-Ni-61.endf.h5\n", " Reading Ni62 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2837_2\n", " 8-Ni-62.endf.h5\n", " Reading Ni64 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2843_2\n", " 8-Ni-64.endf.h5\n", " Reading Cu63 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2925_2\n", " 9-Cu-63.endf.h5\n", " Reading Cu65 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2931_2\n", " 9-Cu-65.endf.h5\n", " Reading Nb93 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4125_4\n", " 1-Nb-93.endf.h5\n", " Reading Mo92 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4225_4\n", " 2-Mo-92.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Mo92 at 294K\n", " Reading Mo94 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4231_4\n", " 2-Mo-94.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Mo94 at 294K\n", " Reading Mo95 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4234_4\n", " 2-Mo-95.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Mo95 at 294K\n", " Reading Mo96 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4237_4\n", " 2-Mo-96.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Mo96 at 294K\n", " Reading Mo97 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4240_4\n", " 2-Mo-97.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Mo97 at 294K\n", " Reading Mo98 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4243_4\n", " 2-Mo-98.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Mo98 at 294K\n", " Reading Mo100 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4249_4\n", " 2-Mo-100.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Mo100 at 294K\n", " Reading Ta181 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7328_7\n", " 3-Ta-181.endf.h5\n", " Reading W180 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7425_7\n", " 4-W-180.endf.h5\n", " Reading W182 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7431_7\n", " 4-W-182.endf.h5\n", " Reading W183 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7434_7\n", " 4-W-183.endf.h5\n", " Reading W184 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7437_7\n", " 4-W-184.endf.h5\n", " Reading W186 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7443_7\n", " 4-W-186.endf.h5\n", " Reading As75 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/endfb-viii.0-neutron-hdf5/ENDF-B-VIII.0__neutro\n", " ns__n-033_As_075.endf.h5\n", " Reading Sn112 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5025_5\n", " 0-Sn-112.endf.h5\n", " Reading Sn114 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5031_5\n", " 0-Sn-114.endf.h5\n", " Reading Sn115 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5034_5\n", " 0-Sn-115.endf.h5\n", " Reading Sn116 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5037_5\n", " 0-Sn-116.endf.h5\n", " Reading Sn117 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5040_5\n", " 0-Sn-117.endf.h5\n", " Reading Sn118 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5043_5\n", " 0-Sn-118.endf.h5\n", " Reading Sn119 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5046_5\n", " 0-Sn-119.endf.h5\n", " Reading Sn120 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5049_5\n", " 0-Sn-120.endf.h5\n", " Reading Sn122 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5055_5\n", " 0-Sn-122.endf.h5\n", " Reading Sn124 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5061_5\n", " 0-Sn-124.endf.h5\n", " Reading Sb121 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5125_5\n", " 1-Sb-121.endf.h5\n", " Reading Sb123 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5131_5\n", " 1-Sb-123.endf.h5\n", " Reading Zr90 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4025_4\n", " 0-Zr-90.endf.h5\n", " Reading Zr91 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4028_4\n", " 0-Zr-91.endf.h5\n", " Reading Zr92 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4031_4\n", " 0-Zr-92.endf.h5\n", " Reading Zr94 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4037_4\n", " 0-Zr-94.endf.h5\n", " Reading Zr96 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4043_4\n", " 0-Zr-96.endf.h5\n", " Reading C12 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0625_6\n", " -C-12.endf.h5\n", " Reading C13 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0628_6\n", " -C-13.endf.h5\n", " Reading Ca40 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2025_2\n", " 0-Ca-40.endf.h5\n", " Reading Ca42 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2031_2\n", " 0-Ca-42.endf.h5\n", " Reading Ca43 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2034_2\n", " 0-Ca-43.endf.h5\n", " Reading Ca44 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2037_2\n", " 0-Ca-44.endf.h5\n", " Reading Ca46 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2043_2\n", " 0-Ca-46.endf.h5\n", " Reading Ca48 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2049_2\n", " 0-Ca-48.endf.h5\n", " Reading H1 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0125_1\n", " -H-1.endf.h5\n", " Reading H2 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0128_1\n", " -H-2.endf.h5\n", " Reading K39 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1925_1\n", " 9-K-39.endf.h5\n", " Reading K40 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1928_1\n", " 9-K-40.endf.h5\n", " Reading K41 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1931_1\n", " 9-K-41.endf.h5\n", " Reading Mg24 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1225_1\n", " 2-Mg-24.endf.h5\n", " Reading Mg25 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1228_1\n", " 2-Mg-25.endf.h5\n", " Reading Mg26 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1231_1\n", " 2-Mg-26.endf.h5\n", " Reading Na23 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_1125_1\n", " 1-Na-23.endf.h5\n", " Reading Pb206 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_8231_8\n", " 2-Pb-206.endf.h5\n", " Reading Pb207 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_8234_8\n", " 2-Pb-207.endf.h5\n", " Reading Pb208 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_8237_8\n", " 2-Pb-208.endf.h5\n", " Reading Ag107 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4725_4\n", " 7-Ag-107.endf.h5\n", " Reading Ag109 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4731_4\n", " 7-Ag-109.endf.h5\n", " Reading Ba130 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5625_5\n", " 6-Ba-130.endf.h5\n", " Reading Ba132 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5631_5\n", " 6-Ba-132.endf.h5\n", " Reading Ba134 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5637_5\n", " 6-Ba-134.endf.h5\n", " Reading Ba135 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5640_5\n", " 6-Ba-135.endf.h5\n", " Reading Ba136 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5643_5\n", " 6-Ba-136.endf.h5\n", " Reading Ba137 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5646_5\n", " 6-Ba-137.endf.h5\n", " Reading Ba138 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_5649_5\n", " 6-Ba-138.endf.h5\n", " Reading Cd106 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4825_4\n", " 8-Cd-106.endf.h5\n", " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", " Reading Cd108 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4831_4\n", " 8-Cd-108.endf.h5\n", " Reading Cd110 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4837_4\n", " 8-Cd-110.endf.h5\n", " Reading Cd111 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4840_4\n", " 8-Cd-111.endf.h5\n", " Reading Cd112 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/endfb-viii.0-neutron-hdf5/ENDF-B-VIII.0__neutro\n", " ns__n-048_Cd_112.endf.h5\n", " Reading Cd113 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4846_4\n", " 8-Cd-113.endf.h5\n", " Reading Cd114 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4849_4\n", " 8-Cd-114.endf.h5\n", " Reading Cd116 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4855_4\n", " 8-Cd-116.endf.h5\n", " Reading Zn64 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_3025_3\n", " 0-Zn-64.endf.h5\n", " Reading Zn66 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_3031_3\n", " 0-Zn-66.endf.h5\n", " Reading Zn67 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_3034_3\n", " 0-Zn-67.endf.h5\n", " Reading Zn68 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_3037_3\n", " 0-Zn-68.endf.h5\n", " Reading Zn70 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_3043_3\n", " 0-Zn-70.endf.h5\n", " Reading He4 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0228_2\n", " -He-4.endf.h5\n", " Reading Bi209 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_8325_8\n", " 3-Bi-209.endf.h5\n", " Reading Be9 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0425_4\n", " -Be-9.endf.h5\n", " Reading Pb204 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_8225_8\n", " 2-Pb-204.endf.h5\n", " Reading F19 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0925_9\n", " -F-19.endf.h5\n", " Reading U234 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_9225_9\n", " 2-U-234.endf.h5\n", " Reading U235 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_9228_9\n", " 2-U-235.endf.h5\n", " Reading U238 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_9237_9\n", " 2-U-238.endf.h5\n", " Reading Li6 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0325_3\n", " -Li-6.endf.h5\n", " Reading Li7 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_0328_3\n", " -Li-7.endf.h5\n", " Reading Au197 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7925_7\n", " 9-Au-197.endf.h5\n", " Reading Pt190 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7825_7\n", " 8-Pt-190.endf.h5\n", " Reading Pt192 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7831_7\n", " 8-Pt-192.endf.h5\n", " Reading Pt194 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7837_7\n", " 8-Pt-194.endf.h5\n", " Reading Pt195 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7840_7\n", " 8-Pt-195.endf.h5\n", " Reading Pt196 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7843_7\n", " 8-Pt-196.endf.h5\n", " Reading Pt198 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_7849_7\n", " 8-Pt-198.endf.h5\n", " Reading Rh103 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_4525_4\n", " 5-Rh-103.endf.h5\n", " Reading V50 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/fendl-3.2b-neutron-hdf5/neutron__endf__n_2325_2\n", " 3-V-50.endf.h5\n", " Reading Ta180 from\n", " /IEVOS_STUFF_HERE/ConvertesStuff/endfb-viii.0-neutron-hdf5/ENDF-B-VIII.0__neutro\n", " ns__n-073_Ta_180.endf.h5\n", " Minimum neutron data temperature: 294 K\n", " Maximum neutron data temperature: 294 K\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", " Maximum neutron transport energy: 20000000 eV for As75\n", "\n", " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", "\n", " Simulating batch 1\n", " Simulating batch 2\n", " WARNING: After particle 216207 crossed surface 7702 it could not be located in\n", " any cell and it did not leak.\n", " Simulating batch 3\n", " Simulating batch 4\n", " Simulating batch 5\n", " Simulating batch 6\n", " Simulating batch 7\n" ] } ], "source": [ "# -*- coding: utf-8 -*-\n", "\"\"\"\n", "EU DEMO HCPB: 10 celių neutronų spektrai, integralinis srautas,\n", "izotopinės reakcijų lentelės, leakage patikra ir MAGIC weight windows.\n", "\n", "NAUJA ŠIOJE VERSIJOJE:\n", "1. 30 tally (kiekvienai celei): spectrum_{cid} (CCFE-709 spektras),\n", " flux_{cid} (integralinis srautas su TIKSLIA statistine paklaida)\n", " ir reactions_{cid} (izotopinės reakcijos).\n", "2. MAGIC weight windows sistema su WW_MODE jungikliu:\n", " \"generate\" -> pigus paleidimas, sukuria weight_windows.h5\n", " \"use\" -> produkcinis paleidimas su įkeltais WW\n", " \"off\" -> be WW (analog + survival biasing)\n", "3. Survival biasing (MCNP implicit capture atitikmuo) visada įjungtas.\n", "4. Tarpiniai statepoint kas 25 batch'ius — ilgi paleidimai nepražūva.\n", "5. Statepoint failas randamas dinamiškai (naujausias), nes batch'ų\n", " skaičius skiriasi tarp generavimo ir produkcijos režimų.\n", "6. Naujas QA: flux_{cid} tally lyginamas su spektro grupių suma —\n", " jie privalo sutapti statistikos ribose (spektro sumos paklaida\n", " kvadratūroje ignoruoja koreliacijas, todėl tiksliąja laikoma\n", " tiesioginio flux tally paklaida).\n", "\n", "Normalizacija nepakeista:\n", " tally * SOURCE_STRENGTH / 32 / CELL_VOLUME\n", "\"\"\"\n", "\n", "import os\n", "import glob\n", "import xml.etree.ElementTree as ET\n", "from collections import defaultdict\n", "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import openmc\n", "import pandas as pd\n", "\n", "import warnings\n", "\n", "warnings.filterwarnings(\n", " \"ignore\",\n", " message=\"Another Surface instance already exists.*\"\n", ")\n", "warnings.filterwarnings(\n", " \"ignore\",\n", " message=\"Another Cell instance already exists*\"\n", ")\n", "\n", "# ============================================================\n", "# 1. VARTOTOJO NUSTATYMAI\n", "# ============================================================\n", "\n", "MODEL_XML = Path(\"/IEVOS_STUFF_HERE/model_fixed(1).xml\")\n", "\n", "CROSS_SECTIONS_XML = Path(\n", " \"/IEVOS_STUFF_HERE/ConvertesStuff/combined_cross_sections_FENDL32b_ENDF80.xml\"\n", ")\n", "\n", "RUN_DIR = Path(\"run_10_cells_plasma\")\n", "RESULTS_DIR = Path(\"results_10_cells_plasma\")\n", "\n", "FIXED_MODEL_XML = RUN_DIR / \"model_fixed_library_checked.xml\"\n", "MATERIAL_AUDIT_XLSX = RESULTS_DIR / \"material_library_audit.xlsx\"\n", "\n", "SOURCE_STRENGTH = 7.09e20 # n/s visam 360° modeliui\n", "SECTOR_DIVISOR = 32.0 # 360° / 11.25° = 32\n", "\n", "# Produkcinio paleidimo dydis\n", "BATCHES = 50\n", "PARTICLES = 1_000_000\n", "INACTIVE = 0\n", "\n", "# ------------------------------------------------------------\n", "# WEIGHT WINDOWS REŽIMAS:\n", "# \"generate\" -> 1 žingsnis: sugeneruoja RUN_DIR/weight_windows.h5\n", "# \"use\" -> 2 žingsnis: produkcinis paleidimas su WW\n", "# \"off\" -> be weight windows\n", "# ------------------------------------------------------------\n", "WW_MODE = \"use\"\n", "\n", "WW_FILE = RUN_DIR / \"weight_windows.h5\"\n", "\n", "# Kelias į ankstesnės iteracijos rezultatą. Nukopijuok senąjį failą,\n", "# kad naujas generavimas jo neperrašytų!\n", "WW_SEED_FILE = None #RUN_DIR / \"weight_windows_iter1.h5\"\n", "\n", "# Generavimo paleidimo dydis (pigus — MAGIC iteruoja pats)\n", "WW_GEN_BATCHES = 60\n", "WW_GEN_PARTICLES = 200_000\n", "WW_UPDATE_INTERVAL = 5\n", "\n", "# WW tinklelio ribos [cm]. TURI apgaubti visą geometriją, kurioje\n", "# vyksta transportas. Pagal šaltinio mesh (R 605–1183, Z ±476) ir\n", "# tipinę DEMO sektoriaus geometriją. PASITIKSLINK pagal savo modelio\n", "# išorinius paviršius ir, jei reikia, padidink!\n", "WW_R_MAX = 1700.0\n", "WW_Z_MIN = -1300.0\n", "WW_Z_MAX = 1300.0\n", "WW_ENERGY_BOUNDS = [0.0, 1.0e2, 1.0e5, 1.0e6, 2.0e7] # eV, 4 grupės\n", "\n", "TARGET_CELL_IDS = [\n", " 7454, 7810, 7990, 8009, 8005,\n", " 8006, 7108, 8070, 7090, 8484\n", "]\n", "\n", "CELL_VOLUMES_CM3 = {\n", " 7454: 4490.25,\n", " 7810: 166.272,\n", " 7990: 116.383,\n", " 8009: 816.075,\n", " 8005: 1689.51,\n", " 8006: 1421.25,\n", " 7108: 395.097,\n", " 8070: 2851.14,\n", " 7090: 0.662744,\n", " 8484: 41200.4,\n", "}\n", "\n", "ENERGY_STRUCTURE = \"CCFE-709\"\n", "\n", "\n", "# ------------------------------------------------------------\n", "# BENDRO SRAUTO MESH TALLY (be energijos filtro)\n", "# Ribos atkartoja MCNP FMESH24:n:\n", "# ORIGIN 510 -50 -776; IMESH 940; JMESH 200; KMESH -544\n", "# ------------------------------------------------------------\n", "ENABLE_MESH_TALLY = True\n", "MESH_LOWER_LEFT = (510.0, -50.0, -776.0)\n", "MESH_UPPER_RIGHT = (940.0, 200.0, -544.0)\n", "MESH_DIMENSION = (300, 150, 100)\n", "MESH_TALLY_NAME = \"mesh_total_flux\"\n", "\n", "# X-Z pjūvis, artimiausias šiai Y koordinatei\n", "MESH_SLICE_Y_CM = 72.5\n", "\n", "# Paveiksle paklaidos apribojamos iki 100 %. Nulinio srauto\n", "# vokseliai paliekami balti, nes jų santykinė paklaida neapibrėžta.\n", "MESH_MAX_REL_STD_PERCENT = 100.0\n", "\n", "REACTION_SCORES = [\n", " \"total\",\n", " \"elastic\",\n", " \"absorption\",\n", " \"(n,gamma)\",\n", " \"(n,2n)\",\n", " \"(n,p)\",\n", " \"(n,a)\",\n", "]\n", "\n", "DELETE_OLD_OUTPUT = True\n", "\n", "\n", "# ============================================================\n", "# 2. BENDRI PATIKRINIMAI\n", "# ============================================================\n", "\n", "def validate_inputs():\n", " if not MODEL_XML.exists():\n", " raise FileNotFoundError(f\"Nerastas modelio failas: {MODEL_XML.resolve()}\")\n", "\n", " if not CROSS_SECTIONS_XML.exists():\n", " raise FileNotFoundError(\n", " \"Nerastas cross_sections.xml:\\n\"\n", " f\"{CROSS_SECTIONS_XML}\\n\"\n", " \"Pataisyk CROSS_SECTIONS_XML kelią.\"\n", " )\n", "\n", " if WW_MODE not in (\"generate\", \"use\", \"off\"):\n", " raise ValueError(f\"Neteisingas WW_MODE: {WW_MODE!r}\")\n", "\n", " if WW_MODE == \"use\" and not WW_FILE.exists():\n", " raise FileNotFoundError(\n", " f\"Nerastas {WW_FILE.resolve()}.\\n\"\n", " \"Pirmiausia paleisk su WW_MODE = 'generate'.\"\n", " )\n", "\n", " missing_volumes = [\n", " cid for cid in TARGET_CELL_IDS\n", " if cid not in CELL_VOLUMES_CM3\n", " ]\n", " if missing_volumes:\n", " raise ValueError(f\"Nenurodyti celių tūriai: {missing_volumes}\")\n", "\n", " invalid_volumes = {\n", " cid: vol for cid, vol in CELL_VOLUMES_CM3.items()\n", " if cid in TARGET_CELL_IDS and (not np.isfinite(vol) or vol <= 0.0)\n", " }\n", " if invalid_volumes:\n", " raise ValueError(f\"Neteisingi celių tūriai: {invalid_volumes}\")\n", "\n", " RUN_DIR.mkdir(parents=True, exist_ok=True)\n", " RESULTS_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", " os.environ[\"OPENMC_CROSS_SECTIONS\"] = str(CROSS_SECTIONS_XML.resolve())\n", " os.environ[\"HDF5_USE_FILE_LOCKING\"] = \"FALSE\"\n", "\n", "\n", "# ============================================================\n", "# 3. BRANDUOLINIŲ DUOMENŲ BIBLIOTEKOS AUDITAS\n", "# ============================================================\n", "\n", "def read_library_nuclides(cross_sections_xml: Path) -> set[str]:\n", " root = ET.parse(cross_sections_xml).getroot()\n", " available = set()\n", "\n", " for lib in root.findall(\".//library\"):\n", " lib_type = (lib.get(\"type\") or \"neutron\").lower()\n", " if lib_type != \"neutron\":\n", " continue\n", " materials = (lib.get(\"materials\") or \"\").split()\n", " available.update(materials)\n", "\n", " if not available:\n", " raise RuntimeError(\n", " \"cross_sections.xml faile nerasta neutronų bibliotekų.\"\n", " )\n", "\n", " return available\n", "\n", "\n", "def merge_duplicate_nuclides(material: openmc.Material):\n", " merged = defaultdict(float)\n", " percent_type_by_name = {}\n", "\n", " for nuclide in material.nuclides:\n", " name = nuclide.name\n", " value = float(nuclide.percent)\n", " percent_type = nuclide.percent_type\n", "\n", " if (\n", " name in percent_type_by_name\n", " and percent_type_by_name[name] != percent_type\n", " ):\n", " raise ValueError(\n", " f\"Medžiaga {material.id}: nuklidas {name} turi ir \"\n", " f\"{percent_type_by_name[name]}, ir {percent_type} dalis.\"\n", " )\n", "\n", " percent_type_by_name[name] = percent_type\n", " merged[name] += value\n", "\n", " if len(merged) == len(material.nuclides):\n", " return\n", "\n", " for nuclide in list(material.nuclides):\n", " material.remove_nuclide(nuclide.name)\n", "\n", " for name, value in merged.items():\n", " material.add_nuclide(\n", " name,\n", " value,\n", " percent_type=percent_type_by_name[name]\n", " )\n", "\n", "\n", "def replace_c0_using_available_data(\n", " material: openmc.Material,\n", " available_nuclides: set[str]\n", ") -> str:\n", " c0_entries = [n for n in material.nuclides if n.name == \"C0\"]\n", " if not c0_entries:\n", " return \"C0 nėra\"\n", "\n", " if \"C0\" in available_nuclides:\n", " return \"C0 paliktas, nes yra bibliotekoje\"\n", "\n", " percent_types = {n.percent_type for n in c0_entries}\n", " if len(percent_types) != 1:\n", " raise ValueError(\n", " f\"Medžiaga {material.id}: C0 turi skirtingus dalies tipus.\"\n", " )\n", "\n", " percent_type = percent_types.pop()\n", " total_c = sum(float(n.percent) for n in c0_entries)\n", "\n", " material.remove_nuclide(\"C0\")\n", "\n", " if \"C12\" in available_nuclides and \"C13\" in available_nuclides:\n", " if percent_type == \"ao\":\n", " material.add_nuclide(\"C12\", total_c * 0.9893, \"ao\")\n", " material.add_nuclide(\"C13\", total_c * 0.0107, \"ao\")\n", " elif percent_type == \"wo\":\n", " material.add_nuclide(\"C12\", total_c * 0.9885, \"wo\")\n", " material.add_nuclide(\"C13\", total_c * 0.0115, \"wo\")\n", " else:\n", " raise ValueError(\n", " f\"Medžiaga {material.id}: neatpažintas C0 tipas \"\n", " f\"{percent_type}.\"\n", " )\n", " return \"C0 -> C12 + C13\"\n", "\n", " if \"C12\" in available_nuclides:\n", " material.add_nuclide(\"C12\", total_c, percent_type)\n", " return \"C0 -> C12 (C13 bibliotekoje nėra)\"\n", "\n", " raise RuntimeError(\n", " f\"Medžiaga {material.id}: bibliotekoje nėra nei C0, \"\n", " \"nei tinkamo C12 rinkinio.\"\n", " )\n", "\n", "\n", "def audit_and_fix_materials(\n", " model: openmc.Model,\n", " available_nuclides: set[str]\n", ") -> pd.DataFrame:\n", " rows = []\n", "\n", " for material in model.materials:\n", " merge_duplicate_nuclides(material)\n", "\n", " carbon_action = replace_c0_using_available_data(\n", " material,\n", " available_nuclides\n", " )\n", "\n", " merge_duplicate_nuclides(material)\n", "\n", " for nuclide in material.nuclides:\n", " rows.append({\n", " \"material_id\": material.id,\n", " \"material_name\": material.name or \"\",\n", " \"nuclide\": nuclide.name,\n", " \"fraction\": float(nuclide.percent),\n", " \"fraction_type\": nuclide.percent_type,\n", " \"available_in_library\": (\n", " nuclide.name in available_nuclides\n", " ),\n", " \"carbon_action\": carbon_action,\n", " })\n", "\n", " audit = pd.DataFrame(rows)\n", "\n", " missing = audit.loc[\n", " ~audit[\"available_in_library\"],\n", " [\"material_id\", \"material_name\", \"nuclide\"]\n", " ].drop_duplicates()\n", "\n", " with pd.ExcelWriter(MATERIAL_AUDIT_XLSX) as writer:\n", " audit.to_excel(writer, sheet_name=\"all_nuclides\", index=False)\n", " missing.to_excel(writer, sheet_name=\"missing\", index=False)\n", "\n", " if not missing.empty:\n", " print(\"\\nBIBLIOTEKOJE TRŪKSTA ŠIŲ NUKLIDŲ:\")\n", " print(missing.to_string(index=False))\n", " raise RuntimeError(\n", " \"\\nModelis nepaleistas, nes cross_sections.xml neturi \"\n", " \"visų modelyje naudojamų nuklidų. Visas sąrašas išsaugotas:\\n\"\n", " f\"{MATERIAL_AUDIT_XLSX}\"\n", " )\n", "\n", " return audit\n", "\n", "\n", "# ============================================================\n", "# 4. ŠALTINIO IR GEOMETRIJOS PATIKRA\n", "# ============================================================\n", "\n", "def print_source_summary(model: openmc.Model):\n", " sources = model.settings.source\n", " if not sources:\n", " raise RuntimeError(\"Modelio faile nėra šaltinio.\")\n", "\n", " print(\"\\nŠALTINIO PATIKRA\")\n", " print(f\"Šaltinių skaičius: {len(sources)}\")\n", "\n", " for i, source in enumerate(sources):\n", " print(f\"\\nŠaltinis [{i}]: {type(source).__name__}\")\n", "\n", " strength = getattr(source, \"strength\", None)\n", " if strength is not None:\n", " print(\" strength (santykinis svoris):\", strength)\n", "\n", " mesh = getattr(source, \"mesh\", None)\n", " if mesh is None:\n", " space = getattr(source, \"space\", None)\n", " if space is not None:\n", " space_name = type(space).__name__\n", " print(\" Erdvinis skirstinys:\", space_name)\n", " if space_name == \"Point\":\n", " raise RuntimeError(\n", " \"Šaltinis yra taškinis (Point) — tai NĖRA \"\n", " \"plazmos šaltinis. Modelio XML šaltinio neturi \"\n", " \"arba jis buvo prarastas konvertuojant.\"\n", " )\n", " continue\n", "\n", " print(\" Šaltinio mesh tipas:\", type(mesh).__name__)\n", " print(\" Mesh origin:\", getattr(mesh, \"origin\", None))\n", "\n", " if hasattr(mesh, \"r_grid\"):\n", " print(\n", " \" R ribos [cm]:\",\n", " float(mesh.r_grid[0]),\n", " float(mesh.r_grid[-1])\n", " )\n", " if hasattr(mesh, \"phi_grid\"):\n", " phi0 = float(mesh.phi_grid[0])\n", " phi1 = float(mesh.phi_grid[-1])\n", " phi_deg = np.rad2deg(phi1 - phi0)\n", " print(\" Phi ribos [rad]:\", phi0, phi1)\n", " print(\" Phi kampas [deg]:\", phi_deg)\n", "\n", " expected_deg = 360.0 / SECTOR_DIVISOR\n", " if not np.isclose(phi_deg, expected_deg, rtol=0.02):\n", " print(\n", " f\" ĮSPĖJIMAS: šaltinio phi kampas {phi_deg:.3f}° \"\n", " f\"nesutampa su sektoriaus kampu {expected_deg:.3f}°. \"\n", " \"Patikrink SECTOR_DIVISOR — normalizacija gali būti \"\n", " \"neteisinga!\"\n", " )\n", " if hasattr(mesh, \"z_grid\"):\n", " print(\n", " \" Z ribos [cm]:\",\n", " float(mesh.z_grid[0]),\n", " float(mesh.z_grid[-1])\n", " )\n", "\n", "\n", "def validate_target_cells(model: openmc.Model):\n", " all_cells = model.geometry.get_all_cells()\n", " missing = [cid for cid in TARGET_CELL_IDS if cid not in all_cells]\n", "\n", " if missing:\n", " raise RuntimeError(f\"Geometrijoje nerastos celės: {missing}\")\n", "\n", " rows = []\n", " for cid in TARGET_CELL_IDS:\n", " cell = all_cells[cid]\n", " if not isinstance(cell.fill, openmc.Material):\n", " raise RuntimeError(\n", " f\"Celė {cid} nėra tiesiogiai užpildyta medžiaga. \"\n", " f\"fill={type(cell.fill).__name__}\"\n", " )\n", "\n", " rows.append({\n", " \"cell_id\": cid,\n", " \"material_id\": cell.fill.id,\n", " \"material_name\": cell.fill.name or \"\",\n", " \"volume_cm3\": CELL_VOLUMES_CM3[cid],\n", " \"region\": str(cell.region),\n", " })\n", "\n", " cell_table = pd.DataFrame(rows)\n", " cell_table.to_excel(\n", " RESULTS_DIR / \"target_cells.xlsx\",\n", " index=False\n", " )\n", " print(\"\\nTIKSLINĖS CELĖS\")\n", " print(cell_table[[\n", " \"cell_id\", \"material_id\", \"material_name\", \"volume_cm3\"\n", " ]].to_string(index=False))\n", "\n", "\n", "# ============================================================\n", "# 5. TALLY SUKŪRIMAS (30 tally: 3 kiekvienai celei)\n", "# ============================================================\n", "\n", "def build_tallies(model: openmc.Model):\n", " all_cells = model.geometry.get_all_cells()\n", "\n", " energy_edges = np.asarray(\n", " openmc.mgxs.GROUP_STRUCTURES[ENERGY_STRUCTURE],\n", " dtype=float\n", " )\n", " energy_filter = openmc.EnergyFilter(energy_edges)\n", "\n", " print(\"\\nCCFE ribų skaičius:\", len(energy_edges))\n", " print(\"CCFE grupių skaičius:\", len(energy_edges) - 1)\n", " print(\n", " \"Energijos ribos:\",\n", " energy_edges[0], \"–\", energy_edges[-1], \"eV\"\n", " )\n", "\n", " tallies = openmc.Tallies()\n", "\n", " for cid in TARGET_CELL_IDS:\n", " cell = all_cells[cid]\n", " cell_filter = openmc.CellFilter([cell])\n", "\n", " # ---- 1. Energijos spektras (CCFE-709) ----\n", " spectrum = openmc.Tally(name=f\"spectrum_{cid}\")\n", " spectrum.filters = [cell_filter, energy_filter]\n", " spectrum.scores = [\"flux\"]\n", " tallies.append(spectrum)\n", "\n", " # ---- 2. Integralinis srautas (be energijos filtro) ----\n", " # Duoda TIKSLIĄ integralinio srauto paklaidą — spektro\n", " # grupių sumos paklaida kvadratūroje ignoruoja grupių\n", " # koreliacijas, o šis tally jų nepraranda.\n", " flux = openmc.Tally(name=f\"flux_{cid}\")\n", " flux.filters = [cell_filter]\n", " flux.scores = [\"flux\"]\n", " tallies.append(flux)\n", "\n", " # ---- 3. Izotopinės reakcijos ----\n", " reactions = openmc.Tally(name=f\"reactions_{cid}\")\n", " reactions.filters = [cell_filter]\n", " reactions.nuclides = list(cell.fill.get_nuclides())\n", " reactions.scores = REACTION_SCORES\n", " tallies.append(reactions)\n", "\n", " if ENABLE_MESH_TALLY:\n", " mesh = openmc.RegularMesh()\n", " mesh.lower_left = MESH_LOWER_LEFT\n", " mesh.upper_right = MESH_UPPER_RIGHT\n", " mesh.dimension = MESH_DIMENSION\n", "\n", " mesh_tally = openmc.Tally(name=MESH_TALLY_NAME)\n", " mesh_tally.filters = [openmc.MeshFilter(mesh)]\n", " mesh_tally.scores = [\"flux\"]\n", " tallies.append(mesh_tally)\n", "\n", " print(\n", " \"\\nMesh tally:\",\n", " MESH_TALLY_NAME,\n", " \"| dimension =\", MESH_DIMENSION,\n", " \"| be energijos filtro\"\n", " )\n", "\n", " model.tallies = tallies\n", "\n", " print(f\"\\nSukurta tally: {len(tallies)}\")\n", " for tally in tallies:\n", " print(f\" {tally.name} | scores: {tally.scores}\")\n", "\n", "\n", "# ============================================================\n", "# 6. WEIGHT WINDOWS (MAGIC)\n", "# ============================================================\n", "\n", "WW_MESH_ID = 990001\n", "\n", "def build_ww_mesh() -> openmc.CylindricalMesh:\n", " \"\"\"Cilindrinis WW tinklelis, apimantis visą sektorių.\"\"\"\n", "\n", " return openmc.CylindricalMesh(\n", " r_grid=np.linspace(0.0, WW_R_MAX, 69),\n", " phi_grid=np.linspace(\n", " 0.0,\n", " np.deg2rad(360.0 / SECTOR_DIVISOR),\n", " 5\n", " ),\n", " z_grid=np.linspace(\n", " WW_Z_MIN,\n", " WW_Z_MAX,\n", " 105\n", " ),\n", " origin=(0.0, 0.0, 0.0),\n", " mesh_id=WW_MESH_ID\n", " )\n", "\n", "def configure_weight_windows(model: openmc.Model):\n", "\n", " if WW_MODE == \"generate\":\n", " print(\"\\nWW REŽIMAS: generavimas (MAGIC)\")\n", "\n", " model.settings.survival_biasing = False\n", " model.settings.cutoff = {}\n", "\n", " model.settings.batches = WW_GEN_BATCHES\n", " model.settings.particles = WW_GEN_PARTICLES\n", "\n", " # SVARBU:\n", " # seno seed failo nenaudojame, jeigu pasikeitė mesh arba jo ID\n", " if WW_SEED_FILE is not None and WW_SEED_FILE.exists():\n", "\n", " seed_wws = openmc.WeightWindowsList.from_hdf5(\n", " WW_SEED_FILE.resolve()\n", " )\n", "\n", " for index, ww in enumerate(seed_wws):\n", " ww.mesh.id = WW_MESH_ID + index\n", " ww.id = 990100 + index\n", "\n", " print(\n", " f\" Seed WW {index}: \"\n", " f\"mesh ID={ww.mesh.id}, \"\n", " f\"dimension={ww.mesh.dimension}, \"\n", " f\"energy bins={ww.num_energy_bins}, \"\n", " f\"weights={ww.lower_ww_bounds.size}\"\n", " )\n", "\n", " model.settings.weight_windows = seed_wws\n", " model.settings.weight_windows_on = True\n", "\n", " print(\n", " f\" SEED: įkelta {len(seed_wws)} WW rinkinių iš \"\n", " f\"{WW_SEED_FILE.name}\"\n", " )\n", "\n", " else:\n", " # Generuojama nuo nulio\n", " model.settings.weight_windows = []\n", " model.settings.weight_windows_on = True\n", "\n", " print(\n", " \" SEED: nėra – WW generuojami nuo nulio\"\n", " )\n", "\n", " ww_mesh = build_ww_mesh()\n", "\n", " expected_spatial_bins = int(\n", " np.prod(ww_mesh.dimension)\n", " )\n", "\n", " expected_weight_bins = (\n", " expected_spatial_bins *\n", " (len(WW_ENERGY_BOUNDS) - 1)\n", " )\n", "\n", " print(\" WW mesh ID:\", ww_mesh.id)\n", " print(\" WW mesh dimension:\", ww_mesh.dimension)\n", " print(\" Erdviniai binai:\", expected_spatial_bins)\n", " print(\" Energijos grupės:\", len(WW_ENERGY_BOUNDS) - 1)\n", " print(\" Tikėtinas WW skaičius:\", expected_weight_bins)\n", "\n", " wwg = openmc.WeightWindowGenerator(\n", " mesh=ww_mesh,\n", " energy_bounds=WW_ENERGY_BOUNDS,\n", " particle_type=\"neutron\",\n", " method=\"magic\",\n", " max_realizations=WW_GEN_BATCHES,\n", " update_interval=WW_UPDATE_INTERVAL,\n", " on_the_fly=True\n", " )\n", "\n", " model.settings.weight_window_generators = [wwg]\n", "\n", " elif WW_MODE == \"use\":\n", " print(\"\\nWW REŽIMAS: produkcija\")\n", "\n", " model.settings.survival_biasing = False\n", " model.settings.cutoff = {}\n", "\n", " wws = openmc.WeightWindowsList.from_hdf5(\n", " WW_FILE.resolve()\n", " )\n", "\n", " # Neleidžiame WW mesh ID susidurti su modelyje jau\n", " # esančių tally mesh ID\n", " for index, ww in enumerate(wws):\n", "\n", " ww.mesh.id = WW_MESH_ID + index\n", " ww.id = 990100 + index\n", "\n", " spatial_bins = int(\n", " np.prod(ww.mesh.dimension)\n", " )\n", "\n", " expected_weights = (\n", " spatial_bins * ww.num_energy_bins\n", " )\n", "\n", " actual_lower = ww.lower_ww_bounds.size\n", " actual_upper = ww.upper_ww_bounds.size\n", "\n", " print(f\"\\n WW rinkinys {index}\")\n", " print(\" Mesh ID:\", ww.mesh.id)\n", " print(\" Dimension:\", ww.mesh.dimension)\n", " print(\" Erdviniai binai:\", spatial_bins)\n", " print(\" Energijos grupės:\", ww.num_energy_bins)\n", " print(\" Tikėtini weight binai:\", expected_weights)\n", " print(\" Lower weight binai:\", actual_lower)\n", " print(\" Upper weight binai:\", actual_upper)\n", "\n", " if actual_lower != expected_weights:\n", " raise ValueError(\n", " f\"Netinkamas WW failas: tikėtasi \"\n", " f\"{expected_weights} lower binų, \"\n", " f\"bet rasta {actual_lower}.\"\n", " )\n", "\n", " if actual_upper != expected_weights:\n", " raise ValueError(\n", " f\"Netinkamas WW failas: tikėtasi \"\n", " f\"{expected_weights} upper binų, \"\n", " f\"bet rasta {actual_upper}.\"\n", " )\n", "\n", " model.settings.weight_windows = wws\n", " model.settings.weight_windows_on = True\n", "\n", " model.settings.weight_window_checkpoints = {\n", " \"collision\": True,\n", " \"surface\": True\n", " }\n", "\n", " model.settings.max_history_splits = 100_000\n", "\n", " else:\n", " print(\"\\nWW REŽIMAS: išjungta\")\n", "\n", " model.settings.weight_windows = []\n", " model.settings.weight_window_generators = []\n", " model.settings.weight_windows_on = False\n", "\n", " model.settings.survival_biasing = True\n", " model.settings.cutoff = {\n", " \"weight\": 0.25,\n", " \"weight_avg\": 0.5\n", " }\n", " \n", "# ============================================================\n", "# 7. MODELIO PARUOŠIMAS IR PALEIDIMAS\n", "# ============================================================\n", "\n", "def prepare_model() -> openmc.Model:\n", " validate_inputs()\n", "\n", " print(\"OpenMC versija:\", openmc.__version__)\n", " print(\"Naudojama biblioteka:\", CROSS_SECTIONS_XML.resolve())\n", "\n", " model = openmc.Model.from_model_xml(MODEL_XML)\n", "\n", " model.materials.cross_sections = str(CROSS_SECTIONS_XML.resolve())\n", "\n", " available_nuclides = read_library_nuclides(CROSS_SECTIONS_XML)\n", " print(\n", " \"Bibliotekoje rasta neutronų duomenų rinkinių:\",\n", " len(available_nuclides)\n", " )\n", "\n", " audit_and_fix_materials(model, available_nuclides)\n", " validate_target_cells(model)\n", " print_source_summary(model)\n", "\n", " model.settings.run_mode = \"fixed source\"\n", " model.settings.batches = BATCHES\n", " model.settings.particles = PARTICLES\n", " model.settings.inactive = INACTIVE\n", "\n", " model.settings.max_lost_particles = 1000\n", " model.settings.rel_max_lost_particles = 1.0e-5\n", "\n", " build_tallies(model)\n", "\n", " # WW konfigūracija (generavimo režime perrašo batches/particles)\n", " configure_weight_windows(model)\n", "\n", " # Tarpiniai statepoint — ilgas paleidimas nepražūva nutrūkus.\n", " n_batches = model.settings.batches\n", " checkpoints = sorted(set(\n", " list(range(25, n_batches, 25)) + [n_batches]\n", " ))\n", " model.settings.statepoint = {\"batches\": checkpoints}\n", "\n", " model.export_to_model_xml(FIXED_MODEL_XML)\n", "\n", " return model\n", "\n", "\n", "def run_openmc(path_input=\"model_fixed_2.xml\"):\n", " if DELETE_OLD_OUTPUT:\n", " for pattern in (\"statepoint.*.h5\", \"summary.h5\", \"tallies.out\"):\n", " for file_name in glob.glob(str(RUN_DIR / pattern)):\n", " Path(file_name).unlink(missing_ok=True)\n", "\n", " print(\"\\nPaleidžiamas OpenMC...\")\n", " openmc.run(\n", " cwd=str(RUN_DIR),\n", " path_input=str(FIXED_MODEL_XML.resolve())\n", " )\n", "\n", "\n", "def find_latest_statepoint() -> Path:\n", " candidates = sorted(\n", " RUN_DIR.glob(\"statepoint.*.h5\"),\n", " key=lambda p: int(p.stem.split(\".\")[-1])\n", " )\n", " if not candidates:\n", " raise FileNotFoundError(\n", " f\"Aplanke {RUN_DIR} nerastas statepoint failas.\"\n", " )\n", " return candidates[-1]\n", "\n", "\n", "# ============================================================\n", "# 8. SPEKTRŲ APDOROJIMAS\n", "# ============================================================\n", "\n", "def safe_relative_std_percent(mean, std):\n", " mean = np.asarray(mean, dtype=float)\n", " std = np.asarray(std, dtype=float)\n", "\n", " result = np.full(mean.shape, np.nan, dtype=float)\n", " valid = np.isfinite(mean) & np.isfinite(std) & (mean > 0.0)\n", " result[valid] = 100.0 * std[valid] / mean[valid]\n", " return result\n", "\n", "\n", "def process_spectra(sp: openmc.StatePoint):\n", " summary_rows = []\n", "\n", " spectra_dir = RESULTS_DIR / \"spectra\"\n", " spectra_dir.mkdir(exist_ok=True)\n", "\n", " for cid in TARGET_CELL_IDS:\n", " volume = CELL_VOLUMES_CM3[cid]\n", " tally = sp.get_tally(name=f\"spectrum_{cid}\")\n", "\n", " mean = tally.mean.ravel()\n", " std = tally.std_dev.ravel()\n", "\n", " energy_filter = tally.find_filter(openmc.EnergyFilter)\n", " bins = np.asarray(energy_filter.bins, dtype=float)\n", "\n", " e_low_eV = bins[:, 0]\n", " e_high_eV = bins[:, 1]\n", " e_mid_MeV = np.sqrt(e_low_eV * e_high_eV) / 1.0e6\n", "\n", " norm_factor = SOURCE_STRENGTH / SECTOR_DIVISOR / volume\n", "\n", " raw_flux = mean\n", " raw_std = std\n", "\n", " flux = raw_flux * norm_factor\n", " flux_std = raw_std * norm_factor\n", "\n", " rel_std_percent = safe_relative_std_percent(flux, flux_std)\n", "\n", " total_raw = float(np.sum(raw_flux))\n", " total_raw_std = float(np.sqrt(np.sum(raw_std ** 2)))\n", "\n", " total_flux = total_raw * norm_factor\n", " total_flux_std = total_raw_std * norm_factor\n", "\n", " total_rel_std_percent = (\n", " 100.0 * total_flux_std / total_flux\n", " if total_flux > 0.0 else np.nan\n", " )\n", "\n", " df = pd.DataFrame({\n", " \"E_low_eV\": e_low_eV,\n", " \"E_high_eV\": e_high_eV,\n", " \"E_mid_MeV\": e_mid_MeV,\n", " \"raw_flux_per_source_particle\": raw_flux,\n", " \"raw_std_dev\": raw_std,\n", " \"normalized_flux_n_cm2_s\": flux,\n", " \"normalized_std_dev_n_cm2_s\": flux_std,\n", " \"relative_std_percent\": rel_std_percent,\n", " \"cell_volume_cm3\": volume,\n", " \"source_strength_n_s\": SOURCE_STRENGTH,\n", " \"sector_divisor\": SECTOR_DIVISOR,\n", " })\n", "\n", " df.to_excel(\n", " spectra_dir / f\"cell_{cid}_{ENERGY_STRUCTURE}_spectrum.xlsx\",\n", " index=False\n", " )\n", "\n", " mask = np.isfinite(flux) & (flux > 0.0)\n", "\n", " if np.any(mask):\n", " plt.figure(figsize=(9, 6))\n", " plt.loglog(\n", " e_mid_MeV[mask],\n", " flux[mask],\n", " linestyle=\"\",\n", " marker=\"o\",\n", " markersize=2.5,\n", " )\n", " plt.xlabel(\"Energy (MeV)\")\n", " plt.ylabel(r\"Neutron flux (n cm$^{-2}$ s$^{-1}$)\")\n", " plt.title(\n", " f\"Cell {cid} — {ENERGY_STRUCTURE} neutron spectrum\\n\"\n", " f\"integral relative std = {total_rel_std_percent:.2f}%\"\n", " )\n", " plt.grid(True, which=\"both\", linestyle=\"--\", alpha=0.35)\n", " plt.tight_layout()\n", " plt.savefig(\n", " spectra_dir / f\"cell_{cid}_{ENERGY_STRUCTURE}_spectrum.png\",\n", " dpi=300,\n", " bbox_inches=\"tight\"\n", " )\n", " plt.close()\n", "\n", " std_mask = mask & np.isfinite(rel_std_percent)\n", " plt.figure(figsize=(9, 5))\n", " plt.semilogx(\n", " e_mid_MeV[std_mask],\n", " rel_std_percent[std_mask],\n", " linestyle=\"\",\n", " marker=\"o\",\n", " markersize=2.5,\n", " )\n", " plt.axhline(10.0, linestyle=\"--\", linewidth=1.0)\n", " plt.axhline(100.0, linestyle=\"--\", linewidth=1.0)\n", " plt.xlabel(\"Energy (MeV)\")\n", " plt.ylabel(\"Relative standard deviation (%)\")\n", " plt.title(f\"Cell {cid} — statistical uncertainty\")\n", " plt.ylim(bottom=0.0)\n", " plt.grid(True, which=\"both\", linestyle=\"--\", alpha=0.35)\n", " plt.tight_layout()\n", " plt.savefig(\n", " spectra_dir / f\"cell_{cid}_{ENERGY_STRUCTURE}_std_percent.png\",\n", " dpi=300,\n", " bbox_inches=\"tight\"\n", " )\n", " plt.close()\n", "\n", " summary_rows.append({\n", " \"cell_id\": cid,\n", " \"volume_cm3\": volume,\n", " \"raw_spectrum_sum\": total_raw,\n", " \"raw_sum_std_dev\": total_raw_std,\n", " \"normalized_integral_flux_n_cm2_s\": total_flux,\n", " \"normalized_integral_std_dev_n_cm2_s\": total_flux_std,\n", " \"integral_relative_std_percent\": total_rel_std_percent,\n", " \"nonzero_energy_bins\": int(np.count_nonzero(mean > 0.0)),\n", " \"total_energy_bins\": int(mean.size),\n", " })\n", "\n", " summary = pd.DataFrame(summary_rows)\n", " summary.to_excel(\n", " RESULTS_DIR / \"spectra_summary.xlsx\",\n", " index=False\n", " )\n", "\n", " print(\"\\nSPEKTRŲ SANTRAUKA\")\n", " print(summary.to_string(index=False))\n", "\n", "\n", "# ============================================================\n", "# 9. INTEGRALINIO SRAUTO QA (flux tally vs spektro suma)\n", "# ============================================================\n", "\n", "def process_total_flux(sp: openmc.StatePoint):\n", " rows = []\n", "\n", " for cid in TARGET_CELL_IDS:\n", " volume = CELL_VOLUMES_CM3[cid]\n", " norm_factor = SOURCE_STRENGTH / SECTOR_DIVISOR / volume\n", "\n", " flux_tally = sp.get_tally(name=f\"flux_{cid}\")\n", " flux_raw = float(flux_tally.mean.ravel()[0])\n", " flux_raw_std = float(flux_tally.std_dev.ravel()[0])\n", "\n", " spec_tally = sp.get_tally(name=f\"spectrum_{cid}\")\n", " spec_sum_raw = float(np.sum(spec_tally.mean.ravel()))\n", "\n", " flux_norm = flux_raw * norm_factor\n", " flux_norm_std = flux_raw_std * norm_factor\n", " spec_sum_norm = spec_sum_raw * norm_factor\n", "\n", " rel_std = (\n", " 100.0 * flux_norm_std / flux_norm\n", " if flux_norm > 0.0 else np.nan\n", " )\n", "\n", " # Sutapimo patikra: spektro suma ir tiesioginis flux tally\n", " # matuoja tą patį dydį, todėl privalo sutapti (skirtumas\n", " # < ~1e-9 santykinai; didesnis skirtumas = klaida apdorojime).\n", " agreement_percent = (\n", " 100.0 * abs(flux_norm - spec_sum_norm) / flux_norm\n", " if flux_norm > 0.0 else np.nan\n", " )\n", "\n", " rows.append({\n", " \"cell_id\": cid,\n", " \"volume_cm3\": volume,\n", " \"flux_tally_n_cm2_s\": flux_norm,\n", " \"flux_tally_std_dev_n_cm2_s\": flux_norm_std,\n", " \"flux_tally_relative_std_percent\": rel_std,\n", " \"spectrum_sum_n_cm2_s\": spec_sum_norm,\n", " \"difference_percent\": agreement_percent,\n", " })\n", "\n", " table = pd.DataFrame(rows)\n", " table.to_excel(\n", " RESULTS_DIR / \"total_flux_qa.xlsx\",\n", " index=False\n", " )\n", "\n", " print(\"\\nINTEGRALINIS SRAUTAS (tikslios paklaidos) IR QA\")\n", " print(table.to_string(index=False))\n", "\n", " bad = table[table[\"difference_percent\"] > 0.5]\n", " if not bad.empty:\n", " print(\n", " \"\\nĮSPĖJIMAS: šių celių flux tally ir spektro suma \"\n", " \"nesutampa daugiau nei 0.5 % — patikrink apdorojimą:\"\n", " )\n", " print(bad[[\"cell_id\", \"difference_percent\"]].to_string(index=False))\n", "\n", "\n", "# ============================================================\n", "# 10. IZOTOPINIŲ REAKCIJŲ LENTELĖS\n", "# ============================================================\n", "\n", "def process_reactions(sp: openmc.StatePoint):\n", " reactions_dir = RESULTS_DIR / \"reactions\"\n", " reactions_dir.mkdir(exist_ok=True)\n", "\n", " summary_rows = []\n", "\n", " for cid in TARGET_CELL_IDS:\n", " volume = CELL_VOLUMES_CM3[cid]\n", " norm_factor = SOURCE_STRENGTH / SECTOR_DIVISOR / volume\n", "\n", " tally = sp.get_tally(name=f\"reactions_{cid}\")\n", " df = tally.get_pandas_dataframe()\n", "\n", " std_col = \"std. dev.\" if \"std. dev.\" in df.columns else \"std_dev\"\n", "\n", " result = pd.DataFrame({\n", " \"cell_id\": cid,\n", " \"nuclide\": df[\"nuclide\"],\n", " \"reaction\": df[\"score\"],\n", " \"raw_reaction_per_source_particle\": df[\"mean\"],\n", " \"raw_std_dev\": df[std_col],\n", " })\n", "\n", " result[\"reaction_rate_cm3_s\"] = (\n", " result[\"raw_reaction_per_source_particle\"] * norm_factor\n", " )\n", " result[\"reaction_rate_std_dev_cm3_s\"] = (\n", " result[\"raw_std_dev\"] * norm_factor\n", " )\n", " result[\"relative_std_percent\"] = safe_relative_std_percent(\n", " result[\"reaction_rate_cm3_s\"].to_numpy(),\n", " result[\"reaction_rate_std_dev_cm3_s\"].to_numpy()\n", " )\n", " result[\"cell_volume_cm3\"] = volume\n", " result[\"source_strength_n_s\"] = SOURCE_STRENGTH\n", " result[\"sector_divisor\"] = SECTOR_DIVISOR\n", "\n", " wide_rate = result.pivot_table(\n", " index=\"nuclide\",\n", " columns=\"reaction\",\n", " values=\"reaction_rate_cm3_s\",\n", " aggfunc=\"sum\"\n", " )\n", " wide_std_percent = result.pivot_table(\n", " index=\"nuclide\",\n", " columns=\"reaction\",\n", " values=\"relative_std_percent\",\n", " aggfunc=\"first\"\n", " )\n", "\n", " output_path = reactions_dir / f\"cell_{cid}_isotope_reactions.xlsx\"\n", " with pd.ExcelWriter(output_path) as writer:\n", " result.to_excel(writer, sheet_name=\"long_format\", index=False)\n", " wide_rate.to_excel(writer, sheet_name=\"rates_cm3_s\")\n", " wide_std_percent.to_excel(\n", " writer,\n", " sheet_name=\"relative_std_percent\"\n", " )\n", "\n", " cell_totals = (\n", " result.groupby(\"reaction\", as_index=False)[\n", " [\"reaction_rate_cm3_s\", \"reaction_rate_std_dev_cm3_s\"]\n", " ]\n", " .agg({\n", " \"reaction_rate_cm3_s\": \"sum\",\n", " \"reaction_rate_std_dev_cm3_s\": (\n", " lambda x: float(np.sqrt(np.sum(np.asarray(x) ** 2)))\n", " ),\n", " })\n", " )\n", " cell_totals[\"cell_id\"] = cid\n", " cell_totals[\"relative_std_percent\"] = safe_relative_std_percent(\n", " cell_totals[\"reaction_rate_cm3_s\"].to_numpy(),\n", " cell_totals[\"reaction_rate_std_dev_cm3_s\"].to_numpy()\n", " )\n", " summary_rows.append(cell_totals)\n", "\n", " reaction_summary = pd.concat(summary_rows, ignore_index=True)\n", " reaction_summary.to_excel(\n", " RESULTS_DIR / \"reaction_summary_all_cells.xlsx\",\n", " index=False\n", " )\n", "\n", " print(\"\\nREAKCIJŲ SANTRAUKA\")\n", " print(reaction_summary.to_string(index=False))\n", "\n", "\n", "\n", "# ============================================================\n", "# 11. BENDRO NEUTRONŲ SRAUTO MESH TALLY\n", "# ============================================================\n", "\n", "def process_mesh_tally(sp: openmc.StatePoint):\n", " \"\"\"Išsaugo visą 3D mesh, X-Z srauto pjūvį ir paklaidos žemėlapį.\"\"\"\n", " if not ENABLE_MESH_TALLY:\n", " return\n", "\n", " mesh_dir = RESULTS_DIR / \"mesh_tally\"\n", " mesh_dir.mkdir(parents=True, exist_ok=True)\n", "\n", " tally = sp.get_tally(name=MESH_TALLY_NAME)\n", " mesh_filter = tally.find_filter(openmc.MeshFilter)\n", " mesh = mesh_filter.mesh\n", "\n", " nx, ny, nz = tuple(int(v) for v in mesh.dimension)\n", " lower = np.asarray(mesh.lower_left, dtype=float)\n", " upper = np.asarray(mesh.upper_right, dtype=float)\n", "\n", " x_edges = np.linspace(lower[0], upper[0], nx + 1)\n", " y_edges = np.linspace(lower[1], upper[1], ny + 1)\n", " z_edges = np.linspace(lower[2], upper[2], nz + 1)\n", " x_mid = 0.5 * (x_edges[:-1] + x_edges[1:])\n", " y_mid = 0.5 * (y_edges[:-1] + y_edges[1:])\n", " z_mid = 0.5 * (z_edges[:-1] + z_edges[1:])\n", "\n", " voxel_volume = float(np.prod((upper - lower) / np.asarray(mesh.dimension)))\n", " normalization = SOURCE_STRENGTH / SECTOR_DIVISOR / voxel_volume\n", "\n", " # OpenMC RegularMesh duomenų indeksavimo tvarka: x keičiasi greičiausiai.\n", " # order=\"F\" atkuria (nx, ny, nz) gardelę.\n", " mean = tally.mean.ravel().astype(float).reshape((nx, ny, nz), order=\"F\")\n", " std = tally.std_dev.ravel().astype(float).reshape((nx, ny, nz), order=\"F\")\n", "\n", " flux = mean * normalization\n", " flux_std = std * normalization\n", " rel_std = safe_relative_std_percent(flux, flux_std)\n", "\n", " iy = int(np.argmin(np.abs(y_mid - MESH_SLICE_Y_CM)))\n", " y_selected = float(y_mid[iy])\n", "\n", " flux_xz = flux[:, iy, :].T\n", " flux_std_xz = flux_std[:, iy, :].T\n", " rel_std_xz = rel_std[:, iy, :].T\n", "\n", " # Nuliniai / netinkami rezultatai balti.\n", " flux_masked = np.ma.masked_where(\n", " (~np.isfinite(flux_xz)) | (flux_xz <= 0.0),\n", " flux_xz\n", " )\n", " rel_std_masked = np.ma.masked_where(\n", " (~np.isfinite(rel_std_xz)) | (flux_xz <= 0.0),\n", " np.clip(rel_std_xz, 0.0, MESH_MAX_REL_STD_PERCENT)\n", " )\n", "\n", " positive = flux_xz[np.isfinite(flux_xz) & (flux_xz > 0.0)]\n", " if positive.size == 0:\n", " raise RuntimeError(\n", " f\"{MESH_TALLY_NAME} pjūvyje ties y={y_selected:.2f} cm \"\n", " \"nėra nė vienos teigiamos reikšmės.\"\n", " )\n", "\n", " from matplotlib.colors import LogNorm\n", "\n", " cmap_flux = plt.get_cmap(\"turbo\").copy()\n", " cmap_flux.set_bad(\"white\")\n", "\n", " fig, ax = plt.subplots(figsize=(12, 7))\n", " pcm = ax.pcolormesh(\n", " x_edges, z_edges, flux_masked,\n", " shading=\"auto\",\n", " cmap=cmap_flux,\n", " norm=LogNorm(vmin=positive.min(), vmax=positive.max())\n", " )\n", " cbar = fig.colorbar(pcm, ax=ax)\n", " cbar.set_label(r\"Neutronų srautas, n/(cm$^2$·s)\")\n", " ax.set_title(\n", " \"Bendras visų energijų neutronų srautas\\n\"\n", " f\"y = {y_selected:.2f} cm\"\n", " )\n", " ax.set_xlabel(\"x, cm\")\n", " ax.set_ylabel(\"z, cm\")\n", " fig.tight_layout()\n", " out_flux = mesh_dir / \"mesh_total_flux_XZ.png\"\n", " fig.savefig(out_flux, dpi=300, bbox_inches=\"tight\")\n", " plt.show()\n", " plt.close(fig)\n", "\n", " cmap_error = plt.get_cmap(\"turbo\").copy()\n", " cmap_error.set_bad(\"white\")\n", "\n", " fig, ax = plt.subplots(figsize=(12, 7))\n", " pcm = ax.pcolormesh(\n", " x_edges, z_edges, rel_std_masked,\n", " shading=\"auto\",\n", " cmap=cmap_error,\n", " vmin=0.0,\n", " vmax=MESH_MAX_REL_STD_PERCENT\n", " )\n", " cbar = fig.colorbar(pcm, ax=ax)\n", " cbar.set_label(\"Santykinė statistinė paklaida, %\")\n", " ax.set_title(\n", " \"Bendro neutronų srauto santykinė statistinė paklaida\\n\"\n", " f\"y = {y_selected:.2f} cm\"\n", " )\n", " ax.set_xlabel(\"x, cm\")\n", " ax.set_ylabel(\"z, cm\")\n", " fig.tight_layout()\n", " out_error = mesh_dir / \"mesh_total_flux_relative_std_XZ.png\"\n", " fig.savefig(out_error, dpi=300, bbox_inches=\"tight\")\n", " plt.show()\n", " plt.close(fig)\n", "\n", " valid = np.isfinite(rel_std_xz) & (flux_xz > 0.0)\n", " weighted_error = (\n", " np.average(rel_std_xz[valid], weights=flux_xz[valid])\n", " if np.any(valid) and np.sum(flux_xz[valid]) > 0.0\n", " else np.nan\n", " )\n", "\n", " summary = pd.DataFrame([{\n", " \"tally_name\": MESH_TALLY_NAME,\n", " \"slice_y_cm\": y_selected,\n", " \"voxel_volume_cm3\": voxel_volume,\n", " \"normalization_n_per_s_per_voxel_cm3\": normalization,\n", " \"nonzero_voxels_in_slice\": int(positive.size),\n", " \"mean_relative_std_percent\": (\n", " float(np.mean(rel_std_xz[valid])) if np.any(valid) else np.nan\n", " ),\n", " \"median_relative_std_percent\": (\n", " float(np.median(rel_std_xz[valid])) if np.any(valid) else np.nan\n", " ),\n", " \"flux_weighted_relative_std_percent\": float(weighted_error),\n", " \"voxels_rel_std_le_10_percent\": int(\n", " np.count_nonzero(valid & (rel_std_xz <= 10.0))\n", " ),\n", " \"voxels_rel_std_le_100_percent\": int(\n", " np.count_nonzero(valid & (rel_std_xz <= 100.0))\n", " ),\n", " }])\n", " summary.to_excel(mesh_dir / \"mesh_slice_summary.xlsx\", index=False)\n", "\n", " # Ilgo formato X-Z pjūvis, skirtas MCNP sugretinimui.\n", " xx, zz = np.meshgrid(x_mid, z_mid)\n", " slice_table = pd.DataFrame({\n", " \"x_cm\": xx.ravel(),\n", " \"y_cm\": y_selected,\n", " \"z_cm\": zz.ravel(),\n", " \"openmc_flux_n_cm2_s\": flux_xz.ravel(),\n", " \"openmc_std_dev_n_cm2_s\": flux_std_xz.ravel(),\n", " \"openmc_relative_std_percent\": rel_std_xz.ravel(),\n", " })\n", " slice_table.to_csv(mesh_dir / \"mesh_OpenMC_XZ_slice.csv\", index=False)\n", "\n", " # Visas 3D masyvas taupiai išsaugomas NPZ.\n", " np.savez_compressed(\n", " mesh_dir / \"mesh_OpenMC_3D.npz\",\n", " flux=flux,\n", " std_dev=flux_std,\n", " relative_std_percent=rel_std,\n", " x_edges=x_edges,\n", " y_edges=y_edges,\n", " z_edges=z_edges,\n", " )\n", "\n", " print(\"\\nMESH TALLY\")\n", " print(f\"Pjūvis: y = {y_selected:.2f} cm (indeksas {iy})\")\n", " print(f\"Vokselio tūris: {voxel_volume:.6g} cm3\")\n", " print(f\"Nenulinių X-Z vokselių: {positive.size}/{flux_xz.size}\")\n", " print(\n", " \"Srautu pasverta santykinė paklaida: \"\n", " f\"{weighted_error:.3f} %\"\n", " )\n", " print(\"Išsaugota:\", out_flux.resolve())\n", " print(\"Išsaugota:\", out_error.resolve())\n", "\n", "\n", "# ============================================================\n", "# 12. LEAKAGE PATIKRA\n", "# ============================================================\n", "\n", "def process_leakage(sp: openmc.StatePoint):\n", " gt = sp.global_tallies\n", "\n", " names = [\n", " n.decode() if isinstance(n, (bytes, np.bytes_)) else str(n)\n", " for n in gt[\"name\"]\n", " ]\n", " if \"leakage\" not in names:\n", " raise RuntimeError(\n", " \"Statepoint global_tallies masyve nerasta 'leakage' eilutė. \"\n", " f\"Rastos eilutės: {names}\"\n", " )\n", "\n", " idx = names.index(\"leakage\")\n", " leakage = float(gt[\"mean\"][idx])\n", " leakage_std = float(gt[\"std_dev\"][idx])\n", " leakage_rel_std = (\n", " 100.0 * leakage_std / abs(leakage)\n", " if leakage != 0.0 else np.nan\n", " )\n", "\n", " table = pd.DataFrame([{\n", " \"leakage_fraction_per_source_particle\": leakage,\n", " \"std_dev\": leakage_std,\n", " \"relative_std_percent\": leakage_rel_std,\n", " \"particles\": PARTICLES,\n", " \"batches\": BATCHES,\n", " \"ww_mode\": WW_MODE,\n", " }])\n", " table.to_excel(\n", " RESULTS_DIR / \"leakage_result.xlsx\",\n", " index=False\n", " )\n", "\n", " print(\"\\nLEAKAGE (iš global tallies)\")\n", " print(f\"Leakage fraction = {leakage:.8e}\")\n", " print(f\"Std. dev. = {leakage_std:.8e}\")\n", " print(f\"Relative std = {leakage_rel_std:.3f}%\")\n", "\n", " if leakage > 0.05:\n", " print(\n", " \"\\nĮSPĖJIMAS: leakage yra didesnis nei 5 %. \"\n", " \"Tai paprastai rodo, kad daug šaltinio neutronų nepatenka į \"\n", " \"reaktoriaus medžiagas arba iš modelio išeina per vakuuminę ribą.\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 12. PAGRINDINĖ PROGRAMA\n", "# ============================================================\n", "\n", "def main():\n", " prepare_model()\n", " run_openmc()\n", "\n", " if WW_MODE == \"generate\":\n", " ww_path = RUN_DIR / \"weight_windows.h5\"\n", " if not ww_path.exists():\n", " raise RuntimeError(\n", " \"Generavimo paleidimas baigėsi, bet weight_windows.h5 \"\n", " f\"nerastas aplanke {RUN_DIR.resolve()}.\"\n", " )\n", " print(\n", " \"\\nWEIGHT WINDOWS SUGENERUOTI:\",\n", " ww_path.resolve(),\n", " \"\\n\\nDabar nustatyk WW_MODE = 'use' ir paleisk produkcinį\"\n", " \"\\nskaičiavimą. Generavimo paleidimo tally rezultatai\"\n", " \"\\nNEAPDOROJAMI — jie skirti tik WW gamybai.\"\n", " )\n", " return\n", "\n", " statepoint_path = find_latest_statepoint()\n", " print(\"\\nSkaitomas:\", statepoint_path)\n", "\n", " with openmc.StatePoint(statepoint_path) as sp:\n", " process_spectra(sp)\n", " process_total_flux(sp)\n", " process_reactions(sp)\n", " process_mesh_tally(sp)\n", " process_leakage(sp)\n", "\n", " print(\"\\nBAIGTA\")\n", " print(\"Pataisytas modelis:\", FIXED_MODEL_XML.resolve())\n", " print(\"Rezultatai:\", RESULTS_DIR.resolve())\n", "\n", "\n", "if __name__ == \"__main__\":\n", " main()" ] }, { "cell_type": "code", "execution_count": 9, "id": "501c208b-7a2f-4bf3-87be-31781f3068b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nuskaityta 10 celių spektrai iš /IEVOS_STUFF_HERE/results_10_cells_plasma/spectra\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Išsaugota: /IEVOS_STUFF_HERE/results_10_cells_plasma/combined_spectra_CCFE-709.png\n", "Išsaugota: /IEVOS_STUFF_HERE/results_10_cells_plasma/combined_spectra_CCFE-709.xlsx\n" ] } ], "source": [ "# -*- coding: utf-8 -*-\n", "\"\"\"\n", "Sujungtas 10 celių neutronų spektrų grafikas (Fig. 39 stiliaus).\n", "\n", "Skaito Excel failus, kuriuos sukūrė pagrindinis skriptas:\n", " results_10_cells_plasma/spectra/cell_{cid}_CCFE-709_spectrum.xlsx\n", "\n", "Įklijuok visą šį failą į vieną Jupyter langelį ir paleisk.\n", "\"\"\"\n", "\n", "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# ============================================================\n", "# NUSTATYMAI\n", "# ============================================================\n", "\n", "SPECTRA_DIR = Path(\"results_10_cells_plasma/spectra\")\n", "OUT_DIR = Path(\"results_10_cells_plasma\")\n", "ENERGY_STRUCTURE = \"CCFE-709\"\n", "\n", "# Celės, medžiagos ir aprašymai (tavo lentelė)\n", "CELL_INFO = {\n", " 7454: (\"W\", \"PFC apsauginis įdėklas\"),\n", " 7810: (\"W+CuCrZr+H2O\", \"PFC antrasis sluoksnis\"),\n", " 7990: (\"Ti lydinys\", \"Kasetės korpusas\"),\n", " 8009: (\"Ti lydinys\", \"Kasetės korpusas\"),\n", " 8005: (\"INCONEL\", \"Kasetės korpusas\"),\n", " 8006: (\"INCONEL\", \"Kasetės korpusas\"),\n", " 7108: (\"EUROFER\", \"EUROFER vamzdynas\"),\n", " 8070: (\"EUROFER\", \"Kasetės korpusas\"),\n", " 7090: (\"H2O\", \"Aušinimo vamzdynas\"),\n", " 8484: (\"H2O\", \"Kasetės aušinimo kanalas\"),\n", "}\n", "\n", "# True -> flux / delta(lethargy) (fizikai \"teisingesnė\" spektro forma)\n", "# False -> grynas grupinis srautas, kaip Excel failuose (Fig. 39 stilius,\n", "# nes CCFE-709 grupės yra ~vienodo letargijos pločio)\n", "PER_LETHARGY = False\n", "\n", "# Paslėpti taškus, kurių santykinė paklaida didesnė už šį % (None = rodyti visus)\n", "MAX_REL_STD_PERCENT = 100.0\n", "\n", "# Rodyti klaidų juostas (esant 709 grupių, gali būti vizualiai tanku)\n", "SHOW_ERRORBARS = False\n", "\n", "# Spalvos ir žymekliai kiekvienai celei (10 aiškiai atskiriamų porų)\n", "STYLE = {\n", " 7454: (\"#d62728\", \"x\"), # raudona\n", " 7810: (\"#ff7f0e\", \"o\"), # oranžinė\n", " 7990: (\"#2ca02c\", \"s\"), # žalia\n", " 8009: (\"#98df8a\", \"^\"), # šviesiai žalia\n", " 8005: (\"#9467bd\", \"o\"), # violetinė\n", " 8006: (\"#c5b0d5\", \"v\"), # šviesiai violetinė\n", " 7108: (\"#8c564b\", \"D\"), # ruda\n", " 8070: (\"#e377c2\", \"p\"), # rožinė\n", " 7090: (\"#1f77b4\", \"s\"), # mėlyna\n", " 8484: (\"#17becf\", \"*\"), # žydra\n", "}\n", "\n", "# ============================================================\n", "# DUOMENŲ NUSKAITYMAS\n", "# ============================================================\n", "\n", "def load_cell_spectrum(cid: int) -> pd.DataFrame:\n", " path = SPECTRA_DIR / f\"cell_{cid}_{ENERGY_STRUCTURE}_spectrum.xlsx\"\n", " if not path.exists():\n", " raise FileNotFoundError(f\"Nerastas failas: {path.resolve()}\")\n", " df = pd.read_excel(path)\n", "\n", " required = {\n", " \"E_low_eV\", \"E_high_eV\", \"E_mid_MeV\",\n", " \"normalized_flux_n_cm2_s\", \"normalized_std_dev_n_cm2_s\",\n", " \"relative_std_percent\",\n", " }\n", " missing = required - set(df.columns)\n", " if missing:\n", " raise ValueError(f\"Faile {path.name} trūksta stulpelių: {missing}\")\n", " return df\n", "\n", "\n", "data = {cid: load_cell_spectrum(cid) for cid in CELL_INFO}\n", "print(f\"Nuskaityta {len(data)} celių spektrai iš {SPECTRA_DIR.resolve()}\")\n", "\n", "# ============================================================\n", "# GRAFIKAS\n", "# ============================================================\n", "\n", "fig, ax = plt.subplots(figsize=(10, 11))\n", "\n", "combined_rows = []\n", "\n", "for cid, df in data.items():\n", " material, description = CELL_INFO[cid]\n", " color, marker = STYLE[cid]\n", "\n", " e_mid = df[\"E_mid_MeV\"].to_numpy(dtype=float)\n", " flux = df[\"normalized_flux_n_cm2_s\"].to_numpy(dtype=float)\n", " flux_std = df[\"normalized_std_dev_n_cm2_s\"].to_numpy(dtype=float)\n", " rel_std = df[\"relative_std_percent\"].to_numpy(dtype=float)\n", "\n", " y = flux.copy()\n", " y_std = flux_std.copy()\n", " ylabel = r\"Neutron spectra (n/cm$^2$ s)\"\n", "\n", " if PER_LETHARGY:\n", " du = np.log(df[\"E_high_eV\"].to_numpy(dtype=float)\n", " / df[\"E_low_eV\"].to_numpy(dtype=float))\n", " y = flux / du\n", " y_std = flux_std / du\n", " ylabel = r\"Neutron spectra per unit lethargy (n/cm$^2$ s)\"\n", "\n", " mask = np.isfinite(y) & (y > 0.0)\n", " if MAX_REL_STD_PERCENT is not None:\n", " mask &= np.isfinite(rel_std) & (rel_std <= MAX_REL_STD_PERCENT)\n", "\n", " label = f\"{cid} — {material} ({description})\"\n", "\n", " if SHOW_ERRORBARS:\n", " ax.errorbar(\n", " e_mid[mask], y[mask], yerr=y_std[mask],\n", " linestyle=\"\", marker=marker, markersize=3,\n", " color=color, ecolor=color, elinewidth=0.5,\n", " capsize=0, alpha=0.85, label=label,\n", " )\n", " else:\n", " ax.plot(\n", " e_mid[mask], y[mask],\n", " linestyle=\"\", marker=marker, markersize=3,\n", " color=color, alpha=0.85, label=label,\n", " )\n", "\n", " # Kaupiam sujungtą lentelę\n", " out = df.copy()\n", " out.insert(0, \"cell_id\", cid)\n", " out.insert(1, \"material\", material)\n", " out.insert(2, \"description\", description)\n", " if PER_LETHARGY:\n", " out[\"flux_per_lethargy_n_cm2_s\"] = y\n", " out[\"flux_per_lethargy_std_dev\"] = y_std\n", " combined_rows.append(out)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "ax.set_xlim(1e-11, 2e1)\n", "\n", "ax.set_xlabel(\"Energy (MeV)\", fontsize=13, fontweight=\"bold\")\n", "ax.set_ylabel(ylabel, fontsize=13, fontweight=\"bold\")\n", "ax.set_title(\n", " \"Neutron spectra in EU DEMO HCPB divertor cells\\n\"\n", " f\"({ENERGY_STRUCTURE} group structure, \"\n", " f\"{'per unit lethargy' if PER_LETHARGY else 'group flux'})\",\n", " fontsize=12,\n", ")\n", "\n", "ax.grid(True, which=\"major\", linestyle=\"-\", alpha=0.35)\n", "ax.grid(True, which=\"minor\", linestyle=\":\", alpha=0.15)\n", "\n", "ax.legend(\n", " loc=\"lower center\",\n", " fontsize=8.5,\n", " ncol=2,\n", " framealpha=0.9,\n", " markerscale=1.8,\n", ")\n", "\n", "plt.tight_layout()\n", "\n", "png_path = OUT_DIR / f\"combined_spectra_{ENERGY_STRUCTURE}.png\"\n", "plt.savefig(png_path, dpi=300, bbox_inches=\"tight\")\n", "plt.show()\n", "print(\"Išsaugota:\", png_path.resolve())\n", "\n", "# ============================================================\n", "# SUJUNGTA EXCEL LENTELĖ (visos celės viename faile)\n", "# ============================================================\n", "\n", "combined = pd.concat(combined_rows, ignore_index=True)\n", "\n", "xlsx_path = OUT_DIR / f\"combined_spectra_{ENERGY_STRUCTURE}.xlsx\"\n", "with pd.ExcelWriter(xlsx_path) as writer:\n", " combined.to_excel(writer, sheet_name=\"all_cells_long\", index=False)\n", "\n", " # Plati lentelė: eilutė = energijos grupė, stulpelis = celė\n", " wide = combined.pivot_table(\n", " index=\"E_mid_MeV\",\n", " columns=\"cell_id\",\n", " values=\"normalized_flux_n_cm2_s\",\n", " ).sort_index()\n", " wide.to_excel(writer, sheet_name=\"flux_wide\")\n", "\n", "print(\"Išsaugota:\", xlsx_path.resolve())" ] }, { "cell_type": "markdown", "id": "c54b54fd", "metadata": {}, "source": [ "## Galutinė celių spektrų, paklaidų ir MCNP palyginimo analizė" ] }, { "cell_type": "code", "execution_count": 10, "id": "bdcf5217", "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "Nerastas MCNP spektrų failas: /IEVOS_STUFF_HERE/spectraHCPB.xlsx\nĮkelk spectraHCPB.xlsx šalia notebooko arba pakeisk MCNP_FILE.", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 71\u001b[39m\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[32m 63\u001b[39m \u001b[38;5;28mfloat\u001b[39m(np.mean(e)),\n\u001b[32m 64\u001b[39m \u001b[38;5;28mfloat\u001b[39m(np.median(e)),\n\u001b[32m 65\u001b[39m \u001b[38;5;28mfloat\u001b[39m(np.average(e, weights=f)),\n\u001b[32m 66\u001b[39m \u001b[38;5;28mint\u001b[39m(np.count_nonzero(valid)),\n\u001b[32m 67\u001b[39m )\n\u001b[32m 70\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m MCNP_FILE.exists():\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[32m 72\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNerastas MCNP spektrų failas: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mMCNP_FILE.resolve()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 73\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mĮkelk spectraHCPB.xlsx šalia notebooko arba pakeisk MCNP_FILE.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 74\u001b[39m )\n\u001b[32m 76\u001b[39m mcnp_df = pd.read_excel(MCNP_FILE, sheet_name=MCNP_SHEET)\n\u001b[32m 77\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m MCNP_ENERGY_COLUMN \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mcnp_df.columns:\n", "\u001b[31mFileNotFoundError\u001b[39m: Nerastas MCNP spektrų failas: /IEVOS_STUFF_HERE/spectraHCPB.xlsx\nĮkelk spectraHCPB.xlsx šalia notebooko arba pakeisk MCNP_FILE." ] } ], "source": [ "# -*- coding: utf-8 -*-\n", "\"\"\"\n", "GALUTINĖ 10 CELIŲ OpenMC–MCNP SPEKTRŲ ANALIZĖ\n", "\n", "Sukuria kiekvienai celei:\n", "1) OpenMC spektrą ir santykinę statistinę paklaidą;\n", "2) OpenMC ir MCNP spektrų palyginimą;\n", "3) OpenMC / MCNP santykį su šilumine, epitermine ir greitąja sritimi;\n", "4) vidutines, medianines ir srautu pasvertas paklaidas;\n", "5) bendras Excel lenteles.\n", "\n", "MCNP_FILE pakeisk į tikrą savo spectraHCPB.xlsx kelią.\n", "\"\"\"\n", "\n", "from pathlib import Path\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "MCNP_FILE = Path(\"spectraHCPB.xlsx\")\n", "MCNP_SHEET = 0\n", "MCNP_ENERGY_COLUMN = \"Unnamed: 0\"\n", "MCNP_ENERGY_UNIT = \"MeV\" # \"MeV\" arba \"eV\"\n", "\n", "ANALYSIS_DIR = RESULTS_DIR / \"galutine_OpenMC_MCNP_analize\"\n", "ANALYSIS_DIR.mkdir(parents=True, exist_ok=True)\n", "(ANALYSIS_DIR / \"spektrai_ir_paklaidos\").mkdir(exist_ok=True)\n", "(ANALYSIS_DIR / \"spektro_palyginimai\").mkdir(exist_ok=True)\n", "(ANALYSIS_DIR / \"ratio\").mkdir(exist_ok=True)\n", "\n", "THERMAL_LIMIT_MEV = 0.625e-6\n", "FAST_LIMIT_MEV = 0.1\n", "MAX_SHOWN_REL_STD_PERCENT = 100.0\n", "\n", "\n", "def _mcnp_column(df, cid):\n", " for col in df.columns:\n", " if str(col).strip() == str(cid):\n", " return col\n", " raise KeyError(\n", " f\"MCNP faile nerastas celės {cid} stulpelis. \"\n", " f\"Stulpeliai: {df.columns.tolist()}\"\n", " )\n", "\n", "\n", "def _regions(energy_mev):\n", " out = np.full(len(energy_mev), \"greitieji\", dtype=object)\n", " out[energy_mev < FAST_LIMIT_MEV] = \"epiterminiai\"\n", " out[energy_mev < THERMAL_LIMIT_MEV] = \"šiluminiai\"\n", " return out\n", "\n", "\n", "def _uncertainty_stats(flux, rel_std):\n", " valid = (\n", " np.isfinite(flux) & (flux > 0.0) &\n", " np.isfinite(rel_std)\n", " )\n", " if not np.any(valid):\n", " return np.nan, np.nan, np.nan, 0\n", " f = flux[valid]\n", " e = rel_std[valid]\n", " return (\n", " float(np.mean(e)),\n", " float(np.median(e)),\n", " float(np.average(e, weights=f)),\n", " int(np.count_nonzero(valid)),\n", " )\n", "\n", "\n", "if not MCNP_FILE.exists():\n", " raise FileNotFoundError(\n", " f\"Nerastas MCNP spektrų failas: {MCNP_FILE.resolve()}\\n\"\n", " \"Įkelk spectraHCPB.xlsx šalia notebooko arba pakeisk MCNP_FILE.\"\n", " )\n", "\n", "mcnp_df = pd.read_excel(MCNP_FILE, sheet_name=MCNP_SHEET)\n", "if MCNP_ENERGY_COLUMN not in mcnp_df.columns:\n", " MCNP_ENERGY_COLUMN = mcnp_df.columns[0]\n", "\n", "mcnp_energy_all = pd.to_numeric(\n", " mcnp_df[MCNP_ENERGY_COLUMN], errors=\"coerce\"\n", ").to_numpy(dtype=float)\n", "if MCNP_ENERGY_UNIT.lower() == \"ev\":\n", " mcnp_energy_all = mcnp_energy_all / 1e6\n", "\n", "uncertainty_rows = []\n", "integral_rows = []\n", "region_rows = []\n", "all_groups = []\n", "\n", "for cid in TARGET_CELL_IDS:\n", " openmc_path = (\n", " RESULTS_DIR / \"spectra\" /\n", " f\"cell_{cid}_{ENERGY_STRUCTURE}_spectrum.xlsx\"\n", " )\n", " odf = pd.read_excel(openmc_path)\n", "\n", " energy_o = odf[\"E_mid_MeV\"].to_numpy(dtype=float)\n", " flux_o = odf[\"normalized_flux_n_cm2_s\"].to_numpy(dtype=float)\n", " std_o = odf[\"normalized_std_dev_n_cm2_s\"].to_numpy(dtype=float)\n", " err_o = odf[\"relative_std_percent\"].to_numpy(dtype=float)\n", " low_o = odf[\"E_low_eV\"].to_numpy(dtype=float) / 1e6\n", " high_o = odf[\"E_high_eV\"].to_numpy(dtype=float) / 1e6\n", "\n", " mcol = _mcnp_column(mcnp_df, cid)\n", " flux_m_all = pd.to_numeric(\n", " mcnp_df[mcol], errors=\"coerce\"\n", " ).to_numpy(dtype=float)\n", " valid_m = (\n", " np.isfinite(mcnp_energy_all) &\n", " np.isfinite(flux_m_all) &\n", " (mcnp_energy_all > 0.0)\n", " )\n", " energy_m = mcnp_energy_all[valid_m]\n", " flux_m = flux_m_all[valid_m]\n", "\n", " oo = np.argsort(energy_o)\n", " mo = np.argsort(energy_m)\n", " energy_o, flux_o, std_o, err_o = (\n", " energy_o[oo], flux_o[oo], std_o[oo], err_o[oo]\n", " )\n", " low_o, high_o = low_o[oo], high_o[oo]\n", " energy_m, flux_m = energy_m[mo], flux_m[mo]\n", "\n", " # CCFE-709 abiejuose rezultatuose: sugretinama pagal indeksą.\n", " # Papildoma MCNP integralo eilutė automatiškai atmetama.\n", " n = min(len(energy_o), len(flux_m))\n", " energy = energy_o[:n]\n", " flux_o, std_o, err_o = flux_o[:n], std_o[:n], err_o[:n]\n", " flux_m = flux_m[:n]\n", " low_o, high_o = low_o[:n], high_o[:n]\n", " region = _regions(energy)\n", "\n", " valid_ratio = (\n", " np.isfinite(flux_o) & np.isfinite(flux_m) &\n", " (flux_o > 0.0) & (flux_m > 0.0)\n", " )\n", " ratio = np.full(n, np.nan)\n", " ratio[valid_ratio] = flux_o[valid_ratio] / flux_m[valid_ratio]\n", "\n", " mean_e, median_e, weighted_e, nonzero = _uncertainty_stats(\n", " flux_o, err_o\n", " )\n", " uncertainty_rows.append({\n", " \"cell_id\": cid,\n", " \"mean_relative_std_percent\": mean_e,\n", " \"median_relative_std_percent\": median_e,\n", " \"flux_weighted_relative_std_percent\": weighted_e,\n", " \"nonzero_groups\": nonzero,\n", " \"groups_rel_std_le_10_percent\": int(\n", " np.count_nonzero(np.isfinite(err_o) & (err_o <= 10.0))\n", " ),\n", " \"groups_rel_std_le_100_percent\": int(\n", " np.count_nonzero(np.isfinite(err_o) & (err_o <= 100.0))\n", " ),\n", " })\n", "\n", " valid_sum = (\n", " np.isfinite(flux_o) & np.isfinite(flux_m) &\n", " (flux_o >= 0.0) & (flux_m >= 0.0)\n", " )\n", " sum_o = float(np.sum(flux_o[valid_sum]))\n", " sum_m = float(np.sum(flux_m[valid_sum]))\n", " integrated_ratio = sum_o / sum_m if sum_m > 0.0 else np.nan\n", " integral_rows.append({\n", " \"cell_id\": cid,\n", " \"openmc_integrated_flux\": sum_o,\n", " \"mcnp_integrated_flux\": sum_m,\n", " \"openmc_div_mcnp_integrated\": integrated_ratio,\n", " })\n", "\n", " for region_name in [\"šiluminiai\", \"epiterminiai\", \"greitieji\"]:\n", " rm = region == region_name\n", " region_valid = rm & valid_sum\n", " ro = float(np.sum(flux_o[region_valid]))\n", " rmc = float(np.sum(flux_m[region_valid]))\n", " _, _, re, _ = _uncertainty_stats(flux_o[rm], err_o[rm])\n", " region_rows.append({\n", " \"cell_id\": cid,\n", " \"energy_region\": region_name,\n", " \"openmc_flux_sum\": ro,\n", " \"mcnp_flux_sum\": rmc,\n", " \"openmc_div_mcnp\": ro / rmc if rmc > 0.0 else np.nan,\n", " \"openmc_flux_weighted_rel_std_percent\": re,\n", " \"valid_ratio_groups\": int(np.count_nonzero(rm & valid_ratio)),\n", " })\n", "\n", " detail = pd.DataFrame({\n", " \"cell_id\": cid,\n", " \"E_low_MeV\": low_o,\n", " \"E_high_MeV\": high_o,\n", " \"E_mid_MeV\": energy,\n", " \"energy_region\": region,\n", " \"openmc_flux\": flux_o,\n", " \"openmc_std_dev\": std_o,\n", " \"openmc_relative_std_percent\": err_o,\n", " \"mcnp_flux\": flux_m,\n", " \"openmc_div_mcnp\": ratio,\n", " })\n", " detail.to_excel(\n", " ANALYSIS_DIR / f\"cell_{cid}_OpenMC_MCNP_analysis.xlsx\",\n", " index=False\n", " )\n", " all_groups.append(detail)\n", "\n", " # 1. OpenMC spektras + paklaida\n", " fig, axes = plt.subplots(\n", " 2, 1, figsize=(10, 8), sharex=True,\n", " gridspec_kw={\"height_ratios\": [2, 1]}\n", " )\n", " pm = np.isfinite(flux_o) & (flux_o > 0.0)\n", " axes[0].plot(\n", " energy[pm], flux_o[pm],\n", " linestyle=\"\", marker=\".\", markersize=3, color=\"navy\"\n", " )\n", " axes[0].set_yscale(\"log\")\n", " axes[0].set_ylabel(r\"Neutronų srautas, n/(cm$^2$·s)\")\n", " axes[0].set_title(f\"Celė {cid} – OpenMC spektras ir statistinė paklaida\")\n", "\n", " em = np.isfinite(err_o) & (energy > 0.0)\n", " axes[1].plot(\n", " energy[em], np.clip(err_o[em], 0, MAX_SHOWN_REL_STD_PERCENT),\n", " linestyle=\"\", marker=\".\", markersize=3, color=\"darkred\"\n", " )\n", " axes[1].axhline(10, color=\"green\", ls=\"--\", lw=0.9, label=\"10 %\")\n", " axes[1].axhline(100, color=\"orange\", ls=\"--\", lw=0.9, label=\"100 %\")\n", " axes[1].set_ylim(0, 105)\n", " axes[1].set_ylabel(\"Santykinė paklaida, %\")\n", " axes[1].set_xlabel(\"Neutronų energija, MeV\")\n", " axes[1].legend(loc=\"upper right\")\n", " axes[1].text(\n", " 0.02, 0.94,\n", " f\"Vidutinė: {mean_e:.2f} %\\n\"\n", " f\"Mediana: {median_e:.2f} %\\n\"\n", " f\"Srautu pasverta: {weighted_e:.2f} %\",\n", " transform=axes[1].transAxes, va=\"top\",\n", " bbox=dict(facecolor=\"white\", edgecolor=\"gray\", alpha=0.9)\n", " )\n", " for ax in axes:\n", " ax.set_xscale(\"log\")\n", " ax.axvline(THERMAL_LIMIT_MEV, color=\"gray\", ls=\":\", lw=1)\n", " ax.axvline(FAST_LIMIT_MEV, color=\"gray\", ls=\":\", lw=1)\n", " ax.grid(True, which=\"major\", ls=\"--\", alpha=0.3)\n", " fig.tight_layout()\n", " fig.savefig(\n", " ANALYSIS_DIR / \"spektrai_ir_paklaidos\" /\n", " f\"cell_{cid}_spectrum_relative_std.png\",\n", " dpi=300, bbox_inches=\"tight\"\n", " )\n", " plt.show()\n", " plt.close(fig)\n", "\n", " # 2. Abu spektrai\n", " cm = valid_ratio & (energy > 0.0)\n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " ax.plot(\n", " energy[cm], flux_o[cm], linestyle=\"\", marker=\"o\",\n", " markersize=2.5, color=\"red\", label=\"OpenMC\"\n", " )\n", " ax.plot(\n", " energy[cm], flux_m[cm], linestyle=\"\", marker=\"x\",\n", " markersize=3, color=\"black\", label=\"MCNP\"\n", " )\n", " ax.set_xscale(\"log\")\n", " ax.set_yscale(\"log\")\n", " ax.axvline(THERMAL_LIMIT_MEV, color=\"gray\", ls=\"--\", lw=1)\n", " ax.axvline(FAST_LIMIT_MEV, color=\"gray\", ls=\"--\", lw=1)\n", " ax.set_title(f\"Celė {cid} – OpenMC ir MCNP neutronų spektrai\")\n", " ax.set_xlabel(\"Neutronų energija, MeV\")\n", " ax.set_ylabel(r\"Neutronų srautas, n/(cm$^2$·s)\")\n", " ax.legend()\n", " ax.grid(True, which=\"major\", ls=\"--\", alpha=0.3)\n", " fig.tight_layout()\n", " fig.savefig(\n", " ANALYSIS_DIR / \"spektro_palyginimai\" /\n", " f\"cell_{cid}_OpenMC_MCNP_spectra.png\",\n", " dpi=300, bbox_inches=\"tight\"\n", " )\n", " plt.show()\n", " plt.close(fig)\n", "\n", " # 3. Santykis – taškai be jungiamųjų linijų\n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " ax.plot(\n", " energy[valid_ratio], ratio[valid_ratio],\n", " linestyle=\"\", marker=\".\", markersize=3, color=\"red\"\n", " )\n", " ax.axhline(1.0, color=\"black\", lw=1.1)\n", " ax.axhline(0.9, color=\"green\", ls=\"--\", lw=0.8)\n", " ax.axhline(1.1, color=\"green\", ls=\"--\", lw=0.8, label=\"±10 %\")\n", " ax.axvline(THERMAL_LIMIT_MEV, color=\"gray\", ls=\"--\", lw=1)\n", " ax.axvline(FAST_LIMIT_MEV, color=\"gray\", ls=\"--\", lw=1)\n", " ax.set_xscale(\"log\")\n", " ax.set_title(f\"Celė {cid} – OpenMC ir MCNP spektrų santykis\")\n", " ax.set_xlabel(\"Neutronų energija, MeV\")\n", " ax.set_ylabel(\"OpenMC / MCNP\")\n", " # Linijinė Y ašis išlaiko 1.0 vizualiai aiškų. Ekstremalūs\n", " # artimo nuliui MCNP taškai ribojami tik vaizdavime, ne lentelėse.\n", " finite_ratio = ratio[np.isfinite(ratio)]\n", " if finite_ratio.size:\n", " upper = min(2.5, max(1.2, float(np.nanpercentile(finite_ratio, 98))))\n", " ax.set_ylim(0.0, upper)\n", " ax.text(\n", " 0.02, 0.04, f\"Integruotas santykis: {integrated_ratio:.3f}\",\n", " transform=ax.transAxes,\n", " bbox=dict(facecolor=\"white\", edgecolor=\"gray\", alpha=0.9)\n", " )\n", " y_top = ax.get_ylim()[1] * 0.96\n", " xmin = np.nanmin(energy[energy > 0])\n", " xmax = np.nanmax(energy)\n", " ax.text(np.sqrt(xmin * THERMAL_LIMIT_MEV), y_top, \"šiluminiai\",\n", " ha=\"center\", va=\"top\", fontsize=8)\n", " ax.text(np.sqrt(THERMAL_LIMIT_MEV * FAST_LIMIT_MEV), y_top,\n", " \"epiterminiai\", ha=\"center\", va=\"top\", fontsize=8)\n", " ax.text(np.sqrt(FAST_LIMIT_MEV * xmax), y_top, \"greitieji\",\n", " ha=\"center\", va=\"top\", fontsize=8)\n", " ax.grid(True, which=\"major\", ls=\"--\", alpha=0.3)\n", " ax.legend(loc=\"upper right\")\n", " fig.tight_layout()\n", " fig.savefig(\n", " ANALYSIS_DIR / \"ratio\" / f\"cell_{cid}_OpenMC_MCNP_ratio.png\",\n", " dpi=300, bbox_inches=\"tight\"\n", " )\n", " plt.show()\n", " plt.close(fig)\n", "\n", "uncertainty_df = pd.DataFrame(uncertainty_rows)\n", "integral_df = pd.DataFrame(integral_rows)\n", "regions_df = pd.DataFrame(region_rows)\n", "groups_df = pd.concat(all_groups, ignore_index=True)\n", "\n", "with pd.ExcelWriter(ANALYSIS_DIR / \"bendra_OpenMC_MCNP_analize.xlsx\") as writer:\n", " uncertainty_df.to_excel(writer, sheet_name=\"Celiu paklaidos\", index=False)\n", " integral_df.to_excel(writer, sheet_name=\"Integralai\", index=False)\n", " regions_df.to_excel(writer, sheet_name=\"Energijos sritys\", index=False)\n", " groups_df.to_excel(writer, sheet_name=\"Visos grupes\", index=False)\n", "\n", "display(uncertainty_df)\n", "display(integral_df)\n", "display(regions_df)\n", "print(\"Galutinė spektrų analizė išsaugota:\", ANALYSIS_DIR.resolve())\n" ] }, { "cell_type": "markdown", "id": "1e563660", "metadata": {}, "source": [ "## Pasirenkamas OpenMC–MCNP mesh tally palyginimas" ] }, { "cell_type": "code", "execution_count": null, "id": "0f0cc6e7", "metadata": {}, "outputs": [], "source": [ "# -*- coding: utf-8 -*-\n", "\"\"\"\n", "PASIRENKAMAS OPENMC–MCNP MESH TALLY PALYGINIMAS\n", "\n", "Paleisk tik turėdama MCNP FMESH X-Z pjūvio CSV arba XLSX lentelę.\n", "Reikalingi stulpeliai: x_cm, z_cm, mcnp_flux.\n", "Pasirinktinai: mcnp_relative_std_percent.\n", "\n", "Jeigu MCNP tinklelis skiriasi, MCNP reikšmės interpoliuojamos į\n", "OpenMC X-Z taškus. Už interpoliuoto MCNP diapazono taškai paliekami NaN.\n", "\"\"\"\n", "\n", "from pathlib import Path\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "MCNP_MESH_FILE = Path(\"mcnp_mesh_XZ_slice.xlsx\")\n", "MCNP_MESH_SHEET = 0\n", "MCNP_X_COLUMN = \"x_cm\"\n", "MCNP_Z_COLUMN = \"z_cm\"\n", "MCNP_FLUX_COLUMN = \"mcnp_flux\"\n", "MCNP_REL_STD_COLUMN = \"mcnp_relative_std_percent\" # gali nebūti\n", "\n", "mesh_dir = RESULTS_DIR / \"mesh_tally\"\n", "openmc_mesh_file = mesh_dir / \"mesh_OpenMC_XZ_slice.csv\"\n", "\n", "if not MCNP_MESH_FILE.exists():\n", " print(\n", " \"MCNP mesh palyginimas praleistas: nerastas\",\n", " MCNP_MESH_FILE.resolve(),\n", " \"\\nĮkelk MCNP X-Z lentelę ir pakeisk MCNP_MESH_FILE.\"\n", " )\n", "else:\n", " from scipy.interpolate import griddata\n", " from matplotlib.colors import LogNorm, TwoSlopeNorm\n", "\n", " om = pd.read_csv(openmc_mesh_file)\n", " if MCNP_MESH_FILE.suffix.lower() in {\".xlsx\", \".xls\"}:\n", " mm = pd.read_excel(MCNP_MESH_FILE, sheet_name=MCNP_MESH_SHEET)\n", " else:\n", " mm = pd.read_csv(MCNP_MESH_FILE)\n", "\n", " required = {MCNP_X_COLUMN, MCNP_Z_COLUMN, MCNP_FLUX_COLUMN}\n", " missing = required - set(mm.columns)\n", " if missing:\n", " raise ValueError(f\"MCNP mesh faile trūksta stulpelių: {missing}\")\n", "\n", " opoints = om[[\"x_cm\", \"z_cm\"]].to_numpy(dtype=float)\n", " mpoints = mm[[MCNP_X_COLUMN, MCNP_Z_COLUMN]].to_numpy(dtype=float)\n", " mflux = pd.to_numeric(mm[MCNP_FLUX_COLUMN], errors=\"coerce\").to_numpy()\n", "\n", " good_m = np.all(np.isfinite(mpoints), axis=1) & np.isfinite(mflux)\n", " mcnp_on_openmc = griddata(\n", " mpoints[good_m], mflux[good_m], opoints,\n", " method=\"linear\", fill_value=np.nan\n", " )\n", "\n", " openmc_flux = om[\"openmc_flux_n_cm2_s\"].to_numpy(dtype=float)\n", " ratio = np.full(len(om), np.nan)\n", " valid = (\n", " np.isfinite(openmc_flux) & np.isfinite(mcnp_on_openmc) &\n", " (openmc_flux > 0.0) & (mcnp_on_openmc > 0.0)\n", " )\n", " ratio[valid] = openmc_flux[valid] / mcnp_on_openmc[valid]\n", "\n", " xs = np.sort(om[\"x_cm\"].unique())\n", " zs = np.sort(om[\"z_cm\"].unique())\n", " nx, nz = len(xs), len(zs)\n", " ratio_xz = ratio.reshape((nz, nx))\n", " openmc_xz = openmc_flux.reshape((nz, nx))\n", " mcnp_xz = mcnp_on_openmc.reshape((nz, nx))\n", "\n", " def _edges(mid):\n", " d = np.diff(mid)\n", " return np.r_[mid[0] - d[0] / 2, 0.5 * (mid[:-1] + mid[1:]),\n", " mid[-1] + d[-1] / 2]\n", "\n", " x_edges, z_edges = _edges(xs), _edges(zs)\n", "\n", " # Santykio žemėlapis centruotas ties 1.\n", " ratio_masked = np.ma.masked_invalid(ratio_xz)\n", " finite_ratio = ratio_xz[np.isfinite(ratio_xz) & (ratio_xz > 0)]\n", " vmax = min(5.0, max(1.1, float(np.nanpercentile(finite_ratio, 98))))\n", " vmin = max(0.0, min(0.9, float(np.nanpercentile(finite_ratio, 2))))\n", "\n", " cmap = plt.get_cmap(\"coolwarm\").copy()\n", " cmap.set_bad(\"white\")\n", " fig, ax = plt.subplots(figsize=(12, 7))\n", " pcm = ax.pcolormesh(\n", " x_edges, z_edges, ratio_masked, shading=\"auto\", cmap=cmap,\n", " norm=TwoSlopeNorm(vmin=vmin, vcenter=1.0, vmax=vmax)\n", " )\n", " cbar = fig.colorbar(pcm, ax=ax)\n", " cbar.set_label(\"OpenMC / MCNP\")\n", " ax.set_title(\"Bendro neutronų srauto OpenMC / MCNP santykis\")\n", " ax.set_xlabel(\"x, cm\")\n", " ax.set_ylabel(\"z, cm\")\n", " fig.tight_layout()\n", " fig.savefig(\n", " mesh_dir / \"mesh_OpenMC_MCNP_ratio_XZ.png\",\n", " dpi=300, bbox_inches=\"tight\"\n", " )\n", " plt.show()\n", " plt.close(fig)\n", "\n", " # Simetrinis procentinis skirtumas neleidžia vienai programai\n", " # esant arti nulio gauti klaidinančiai begalinio procento.\n", " sym_diff = np.full(len(om), np.nan)\n", " denom = openmc_flux + mcnp_on_openmc\n", " vd = valid & (denom > 0.0)\n", " sym_diff[vd] = (\n", " 200.0 * (openmc_flux[vd] - mcnp_on_openmc[vd]) / denom[vd]\n", " )\n", " sym_xz = sym_diff.reshape((nz, nx))\n", "\n", " fig, ax = plt.subplots(figsize=(12, 7))\n", " pcm = ax.pcolormesh(\n", " x_edges, z_edges, np.ma.masked_invalid(sym_xz),\n", " shading=\"auto\", cmap=cmap, vmin=-100.0, vmax=100.0\n", " )\n", " cbar = fig.colorbar(pcm, ax=ax)\n", " cbar.set_label(\"Simetrinis skirtumas, %\")\n", " ax.set_title(\"OpenMC ir MCNP bendro srauto skirtumas\")\n", " ax.set_xlabel(\"x, cm\")\n", " ax.set_ylabel(\"z, cm\")\n", " fig.tight_layout()\n", " fig.savefig(\n", " mesh_dir / \"mesh_OpenMC_MCNP_symmetric_difference_XZ.png\",\n", " dpi=300, bbox_inches=\"tight\"\n", " )\n", " plt.show()\n", " plt.close(fig)\n", "\n", " comparison = om.copy()\n", " comparison[\"mcnp_flux_n_cm2_s\"] = mcnp_on_openmc\n", " comparison[\"openmc_div_mcnp\"] = ratio\n", " comparison[\"symmetric_difference_percent\"] = sym_diff\n", "\n", " if MCNP_REL_STD_COLUMN in mm.columns:\n", " merr = pd.to_numeric(\n", " mm[MCNP_REL_STD_COLUMN], errors=\"coerce\"\n", " ).to_numpy()\n", " good_e = np.all(np.isfinite(mpoints), axis=1) & np.isfinite(merr)\n", " comparison[\"mcnp_relative_std_percent\"] = griddata(\n", " mpoints[good_e], merr[good_e], opoints,\n", " method=\"linear\", fill_value=np.nan\n", " )\n", "\n", " comparison.to_csv(\n", " mesh_dir / \"mesh_OpenMC_MCNP_comparison_XZ.csv\",\n", " index=False\n", " )\n", " print(\n", " \"Mesh palyginimas baigtas. Palygintų vokselių:\",\n", " np.count_nonzero(valid)\n", " )\n" ] }, { "cell_type": "markdown", "id": "f47ef4f0", "metadata": {}, "source": [ "## Kaip aiškinti sugeneruotus grafikus\n", "\n", "**Celių spektrai.** Aukštų energijų srityje matoma 14,1 MeV plazmos neutronų įtaka. Arčiau plazmos esanti volframo PFC celė 7454 paprastai išlaiko stipresnę greitųjų neutronų dedamąją. Neutronams pereinant per volframą, plieną, titaną, INCONEL ir vandenį, jie sklaidos metu praranda energiją, todėl tolimesnėse ar vandens turinčiose celėse padidėja epiterminių ir šiluminių neutronų dalis.\n", "\n", "**Statistinės paklaidos.** Paklaida mažiausia ten, kur srautas didelis ir į energijos grupę patenka daug dalelių istorijų. Spektro uodegose, mažose celėse ir nuo šaltinio nutolusiose srityse paklaida padidėja. Balti mesh žemėlapio vokseliai reiškia nulinį srautą arba neapibrėžtą `std/mean`, o ne nulinę paklaidą. Ataskaitoje patikimiausia nurodyti srautu pasvertą paklaidą, nes paprastą vidurkį stipriai padidina beveik tuščios energijos grupės ar vokseliai.\n", "\n", "**OpenMC / MCNP santykis.** Santykis 1 reiškia sutapimą, 0,9–1,1 – skirtumą iki maždaug 10 %. Pavieniai labai aukšti ar žemi taškai dažniausiai atsiranda, kai vienos programos reikšmė artima nuliui arba jos statistinė paklaida didelė. Beveik vienodas viso spektro poslinkis labiau rodo normalizacijos skirtumą; energijai priklausanti struktūra labiau siejama su branduolinių duomenų bibliotekomis, medžiagomis, geometrija arba energijos grupių sugretinimu.\n", "\n", "**Energijos sritys.**\n", "\n", "- Šiluminėje srityje svarbūs vandens kiekis ir tankis, vandenilio `S(α,β)` terminio sklaidymo duomenys bei ribota statistika.\n", "- Epiterminėje srityje skirtumus gali sustiprinti medžiagų rezonansai ir nevienodos FENDL ar ENDF skerspjūvių versijos.\n", "- Greitųjų neutronų srityje svarbiausi plazmos šaltinio erdvinis ir kryptinis pasiskirstymas, 14,1 MeV šaltinio energija, geometrija ir slenkstinės reakcijos, pvz., `(n,2n)`.\n", "\n", "**Mesh tally.** Didelis srautas viršutinėje ir centrinėje divertoriaus pjūvio dalyje rodo zonas, kurias neutronai pasiekia tiesiogiau. Srautui mažėjant už storesnių konstrukcijų ir ties modelio pakraščiais, santykinė paklaida didėja, nes į tuos vokselius patenka mažiau efektyvių dalelių istorijų. OpenMC ir MCNP mesh santykį verta interpretuoti tik ten, kur abiejų kodų srautai yra nenuliniai ir paklaidos priimtinos.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }