From 1d5bb6b1384608a2f0b6441e5b81cf803505ee0e Mon Sep 17 00:00:00 2001 From: church89 Date: Sat, 20 Apr 2024 20:20:18 +0200 Subject: [PATCH] add transfer rates example --- .../msre_depletion_transfer_rates.ipynb | 2453 +++++++++++++++++ 1 file changed, 2453 insertions(+) create mode 100644 openmc_notebooks/msre_depletion_transfer_rates.ipynb diff --git a/openmc_notebooks/msre_depletion_transfer_rates.ipynb b/openmc_notebooks/msre_depletion_transfer_rates.ipynb new file mode 100644 index 0000000..3834f53 --- /dev/null +++ b/openmc_notebooks/msre_depletion_transfer_rates.ipynb @@ -0,0 +1,2453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fafca9f7", + "metadata": {}, + "source": [ + "# MSRE U235 Power Run\n", + "In the 1966 the MSRE started power operations with U235 as main fissile material. \n", + "In this notebook we will run a fully coupled transport-depletion simulation using a CAD design version developed by [Copenhagen Atomics](https://www.copenhaagenatomics.com) using the CAE tool OnShape and made available for export here: [onshape msre model]((https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)).\n", + "\n", + "To effectively run OpenMC, the MSRE CAD geometry is meshed and converted into an h5m format readable by OpenMC, using the open-source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC). \n", + "\n", + "Furthermore, to model fission products removal we will use OpenMC [Transfer rates theory](https://docs.openmc.org/en/latest/methods/depletion.html#transfer-rates) capability available through the depletion solver and integrated into the main code from version 0.14.0. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "079fc782", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import openmc\n", + "import openmc.deplete\n", + "import numpy as np\n", + "from math import log10\n", + "import matplotlib.pyplot as plt\n", + "import os \n", + "\n", + "os.system('rm *.xml *.h5')" + ] + }, + { + "cell_type": "markdown", + "id": "bfca4d07", + "metadata": {}, + "source": [ + "For materials nuclear properties we will be using the same definitions used in the first criticality test derived from the [MSRE benchmark evaluation project](https://www.osti.gov/servlets/purl/1617123) and available in the International Reactor Physics Experiment Evaulation Project (IRPhEP) handbook. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4bc6b094", + "metadata": {}, + "outputs": [], + "source": [ + "# Define materials \n", + "salt_temp = 638.3 # in C\n", + "salt_density = 2.32556 # g/cm3\n", + "salt = openmc.Material(name=\"salt\", temperature = salt_temp + 273.15)\n", + "salt.add_nuclide('Li6', 0.0000131541127279649)\n", + "salt.add_nuclide('Li7', 0.263082254559299)\n", + "salt.add_nuclide('Be9', 0.118688703357854)\n", + "salt.add_nuclide('Zr90', 0.0105474637491834)\n", + "salt.add_nuclide('Zr91', 0.00230014661352455)\n", + "salt.add_nuclide('Zr92', 0.00351582124972781)\n", + "salt.add_nuclide('Zr94', 0.00356297220526352)\n", + "salt.add_nuclide('Zr96', 0.000574011632608622)\n", + "salt.add_nuclide('Hf174', 0.000000000838247756705461)\n", + "salt.add_nuclide('Hf176', 0.000000027557395001692)\n", + "salt.add_nuclide('Hf177', 0.0000000974463017170099)\n", + "salt.add_nuclide('Hf178', 0.000000142921242518281)\n", + "salt.add_nuclide('Hf179', 0.0000000713558402895524)\n", + "salt.add_nuclide('Hf180', 0.000000183785820657672)\n", + "salt.add_nuclide('U234', 0.000010347565467384)\n", + "salt.add_nuclide('U235', 0.00101016470221956)\n", + "salt.add_nuclide('U236', 0.00000422977321829143)\n", + "salt.add_nuclide('U238', 0.00216927473057667)\n", + "salt.add_nuclide('Fe54', 0.00000285638012142825)\n", + "salt.add_nuclide('Fe56', 0.0000448390593090724)\n", + "salt.add_nuclide('Fe57', 0.00000103552942297801)\n", + "salt.add_nuclide('Fe58', 0.000000137809956243416)\n", + "salt.add_nuclide('Cr50', 0.00000212334843928242)\n", + "salt.add_nuclide('Cr52', 0.000040946661076878)\n", + "salt.add_nuclide('Cr53', 0.00000464302267471169)\n", + "salt.add_nuclide('Cr54', 0.00000115574661884993)\n", + "salt.add_nuclide('Ni58', 0.00000586242358522768)\n", + "salt.add_nuclide('Ni60', 0.00000225819506936691)\n", + "salt.add_nuclide('Ni61', 0.0000000981621760803009)\n", + "salt.add_nuclide('Ni62', 0.00000031298397136929)\n", + "salt.add_nuclide('Ni64', 0.0000000797077903148755)\n", + "salt.add_nuclide('O16', 0.0000514608160260434)\n", + "salt.add_nuclide('O17', 0.0000000189357310970321)\n", + "salt.add_nuclide('O18', 0.0000000964845153990466)\n", + "salt.add_nuclide('F19', 0.594363006576997)\n", + "salt.set_density('g/cm3',salt_density)\n", + "\n", + "#moderator blocks170\n", + "graphite = openmc.Material(name='graphite',temperature= salt_temp + 273.15)\n", + "graphite.set_density('g/cm3',1.8492)\n", + "graphite.add_element('C',0.999992212250888) #endfb71 does not have C12 cross sections\n", + "graphite.add_nuclide('B10', 1.76873036539477E-07)\n", + "graphite.add_nuclide('B11', 7.1193229306592E-07)\n", + "graphite.add_nuclide('V51', 2.12200224802724E-06)\n", + "graphite.add_nuclide('S32', 1.77900760271203E-06)\n", + "graphite.add_nuclide('S33', 1.40462754188233E-08)\n", + "graphite.add_nuclide('S34', 7.95955607066653E-08)\n", + "graphite.add_nuclide('S36', 1.87283672250977E-10)\n", + "graphite.add_nuclide('O16', 1.85674385782835E-06)\n", + "graphite.add_nuclide('O17', 6.81675170610618E-10)\n", + "graphite.add_nuclide('Si28', 5.31911333979174E-07)\n", + "graphite.add_nuclide('Si29', 2.70215087309286E-08)\n", + "graphite.add_nuclide('Si30', 1.78336189959512E-08)\n", + "graphite.add_nuclide('Al27', 4.0117589478321E-07)\n", + "graphite.add_nuclide('Fe54', 2.31047953307142E-09)\n", + "graphite.add_nuclide('Fe56', 3.62695875239409E-08)\n", + "graphite.add_nuclide('Fe57', 8.37622947917593E-10)\n", + "graphite.add_nuclide('Fe58', 1.11472237523719E-10)\n", + "graphite.add_nuclide('Ti46', 1.12809907219147E-09)\n", + "graphite.add_nuclide('Ti47', 1.01734025419449E-09)\n", + "graphite.add_nuclide('Ti48', 1.008041983054E-08)\n", + "graphite.add_nuclide('Ti49', 7.39759512794646E-10)\n", + "graphite.add_nuclide('Ti50', 7.08309478054763E-10)\n", + "graphite.add_nuclide('Mg24', 8.53578076978748E-09)\n", + "graphite.add_nuclide('Mg25', 1.0806153652092E-09)\n", + "graphite.add_nuclide('Mg26', 1.18975751709533E-09)\n", + "graphite.add_nuclide('Ca40', 4.58305905846326E-09)\n", + "graphite.add_nuclide('Ca42', 3.05880815220158E-11)\n", + "graphite.add_nuclide('Ca43', 6.38236631448551E-12)\n", + "graphite.add_nuclide('Ca44', 9.86193787556798E-11)\n", + "graphite.add_nuclide('Ca48', 8.8407592652503E-12)\n", + "graphite.add_s_alpha_beta('c_Graphite')\n", + "\n", + "#inor-8\n", + "inor = openmc.Material(name='inor-8',temperature= salt_temp + 273.15)\n", + "inor.set_density('g/cm3',8.7745)\n", + "inor.add_element('Ni',(66+71)/2,'wo')\n", + "inor.add_element('Mo',(15+18)/2,'wo')\n", + "inor.add_element('Cr',(6+8)/2,'wo')\n", + "inor.add_element('Fe',5,'wo')\n", + "inor.add_element('C',(0.04+0.08)/2,'wo')\n", + "inor.add_element('Al',0.25,'wo')\n", + "inor.add_element('Ti',0.25,'wo')\n", + "inor.add_element('S',0.02,'wo')\n", + "inor.add_element('Mn',1.0,'wo')\n", + "inor.add_element('Si',1.0,'wo')\n", + "inor.add_element('Cu',0.35,'wo')\n", + "inor.add_element('B',0.010,'wo')\n", + "inor.add_element('W',0.5,'wo')\n", + "inor.add_element('P',0.015,'wo')\n", + "inor.add_element('Co',0.2,'wo')\n", + "\n", + "#helium\n", + "helium = openmc.Material(name='helium')\n", + "helium.add_element('He',1.0)\n", + "helium.set_density('g/cm3',1.03*(10**-4))\n", + "\n", + "#Control rods inconel clad\n", + "trace = 0.01\n", + "inconel = openmc.Material(name='inconel-600', temperature = 65.6 + 273.15)\n", + "inconel.add_element('Ni',78.5,percent_type='wo')\n", + "inconel.add_element('Cr',14.0,percent_type='wo')\n", + "inconel.add_element('Fe',6.5,percent_type='wo')\n", + "inconel.add_element('Mn',0.25,percent_type='wo')\n", + "inconel.add_element('Si',0.25,percent_type='wo')\n", + "inconel.add_element('Cu',0.2,percent_type='wo')\n", + "inconel.add_element('Co',0.2,percent_type='wo')\n", + "inconel.add_element('Al',0.2,percent_type='wo')\n", + "inconel.add_element('Ti',0.2,percent_type='wo')\n", + "inconel.add_element('Ta',0.5,percent_type='wo')\n", + "inconel.add_element('W',0.5,percent_type='wo')\n", + "inconel.add_element('Zn',0.2,percent_type='wo')\n", + "inconel.add_element('Zr',0.1,percent_type='wo')\n", + "inconel.add_element('C',trace,percent_type='wo')\n", + "inconel.add_element('Mo',trace,percent_type='wo')\n", + "inconel.add_element('Ag',trace,percent_type='wo')\n", + "inconel.add_element('B',trace,percent_type='wo')\n", + "inconel.add_element('Ba',trace,percent_type='wo')\n", + "inconel.add_element('Be',trace,percent_type='wo')\n", + "inconel.add_element('Ca',trace,percent_type='wo')\n", + "inconel.add_element('Cd',trace,percent_type='wo')\n", + "inconel.add_element('V',trace,percent_type='wo')\n", + "inconel.add_element('Sn',trace,percent_type='wo')\n", + "inconel.add_element('Mg',trace,percent_type='wo')\n", + "inconel.set_density('g/cm3',8.5)\n", + "\n", + "#Control rods bushing posion material\n", + "Gd2O3 = openmc.Material()\n", + "Gd2O3.add_element('Gd',2)\n", + "Gd2O3.add_element('O',3)\n", + "Gd2O3.set_density('g/cm3',7.41)\n", + "Al2O3 = openmc.Material()\n", + "Al2O3.add_element('Al',2)\n", + "Al2O3.add_element('O',3)\n", + "Al2O3.set_density('g/cm3',3.95)\n", + "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n", + "bush.name='gd2o3-al2o3'\n", + "bush.temperature = 65.6 +273.15\n", + "\n", + "#Concrete block\n", + "concrete = openmc.Material(name='concrete')\n", + "concrete.add_element('H',0.005,'wo')\n", + "concrete.add_element('O',0.496,'wo')\n", + "concrete.add_element('Si',0.314,'wo')\n", + "concrete.add_element('Ca',0.083,'wo')\n", + "concrete.add_element('Na',0.017,'wo')\n", + "concrete.add_element('Mn',0.002,'wo')\n", + "concrete.add_element('Al',0.046,'wo')\n", + "concrete.add_element('S',0.001,'wo')\n", + "concrete.add_element('K',0.019,'wo')\n", + "concrete.add_element('Fe',0.012,'wo')\n", + "concrete.set_density('g/cm3',2.35)\n", + "\n", + "#Thermal shielding as water and SS305 (50-50)\n", + "water = openmc.Material()\n", + "water.add_element('H',2)\n", + "water.add_element('O',1)\n", + "water.set_density('g/cm3',0.997)\n", + "\n", + "#stainless steel 304\n", + "ss304 = openmc.Material()\n", + "ss304.add_element('C',0.08,'wo')\n", + "ss304.add_element('Mn',2,'wo')\n", + "ss304.add_element('P',0.045,'wo')\n", + "ss304.add_element('S',0.03,'wo')\n", + "ss304.add_element('Si',0.75,'wo')\n", + "ss304.add_element('Cr',19,'wo')\n", + "ss304.add_element('Ni',10,'wo')\n", + "ss304.add_element('N',0.1,'wo')\n", + "ss304.add_element('Fe',67.995, 'wo')\n", + "ss304.set_density('g/cm3',7.93)\n", + "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n", + "shield.temperature = 32.2 + 273.15\n", + "shield.name='steelwater'\n", + "\n", + "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati)\n", + "# http://moltensalt.org/references/static/downloads/pdf/ORNL-TM-0728.pdf\n", + "insulation=openmc.Material(name='insulation')\n", + "insulation.add_element('Si',1)\n", + "insulation.add_element('O',2)\n", + "insulation.set_density('g/cm3',0.16) #https://www.osti.gov/servlets/purl/1411211\n", + "\n", + "# sand water, not sure about this material\n", + "sandwater=openmc.Material(name='watersand')\n", + "sandwater.add_element('Fe',3)\n", + "sandwater.add_element('O',4)\n", + "sandwater.set_density('g/cm3',6)\n", + "\n", + "#Vessel anular steel\n", + "steel = openmc.Material(name='steel')\n", + "steel.add_element('Fe',1)\n", + "steel.set_density('g/cm3',7.85)\n", + "\n", + "mats = openmc.Materials([salt, graphite, inor, helium, inconel, shield, concrete,\n", + " steel, sandwater, insulation, bush])" + ] + }, + { + "cell_type": "markdown", + "id": "7a0355f1-a513-4fc5-8812-7dffb5138c11", + "metadata": {}, + "source": [ + "The MSRE geometry was produced with the CAE tool OnShape ([onshape cad model](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)) as step files and converted into OpenMC readable h5m files using the open source mesh tool [CAD_to_OpenMC](https://pypi.org/project/CAD-to-OpenMC/). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "edfa5bd1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10000.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10001.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10002.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10003.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10004.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10005.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# CAD h5m files\n", + "core_h5m = 'msre_full.h5m'\n", + "control_rod_h5m = 'msre_control_rod.h5m'\n", + "\n", + "#Geometry\n", + "core = openmc.DAGMCUniverse(filename = core_h5m, auto_geom_ids = True,\n", + " universe_id = 1)\n", + "control_rod = openmc.DAGMCUniverse(filename = control_rod_h5m,\n", + " auto_geom_ids = True, universe_id=2)\n", + "core_region = core.bounding_region()\n", + "cr_region = control_rod.bounding_region(boundary_type = 'transmission')\n", + "\n", + "_offset_xy = 10.163255 #cm, _offset_xy between cr1, cr2 and cr3 (from Onshape model)\n", + "_lower_rod = 61.728 # distance to lower limit (from OnShape model)\n", + "_upper_rod = 51 * 2.54 # escursion to upper rod limit (from ORNL)\n", + "start_rod = 56 * 2.54 # rod iniitial position (user input)\n", + "\n", + "# Create control rod regions w\n", + "cr1_region = cr_region.translate([_offset_xy/2, _offset_xy/2, _lower_rod + _upper_rod])\n", + "cr2_region = cr_region.translate([_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n", + "cr3_region = cr_region.translate([-_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n", + "\n", + "# Extend control rod region1 to include downloads translations\n", + "cr1_region = cr1_region | cr1_region.translate([0, 0, -_upper_rod])\n", + "\n", + "#Define cells\n", + "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region ,\n", + " fill=core)\n", + "\n", + "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=control_rod)\n", + "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=control_rod)\n", + "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=control_rod)\n", + "\n", + "#translate cells to regions\n", + "setattr(cr1_cell, 'translation', [_offset_xy/2, _offset_xy/2 , _lower_rod + start_rod])\n", + "setattr(cr2_cell, 'translation', [_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n", + "setattr(cr3_cell, 'translation', [-_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n", + "geometry = openmc.Geometry([core_cell,cr1_cell,cr2_cell,cr3_cell])" + ] + }, + { + "cell_type": "markdown", + "id": "0abbe529-1d22-438a-aa58-b083f8faac87", + "metadata": {}, + "source": [ + "Let's set some generic settings " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ba99fb4b", + "metadata": {}, + "outputs": [], + "source": [ + "settings = openmc.Settings()\n", + "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n", + "settings.batches = 50\n", + "settings.inactive = 20\n", + "settings.particles = 30000\n", + "settings.photon_transport = False\n", + "source_area = openmc.stats.Box([-100., -100., 0.],[ 100., 100., 200.],only_fissionable = True)\n", + "settings.source = openmc.IndependentSource(space=source_area)" + ] + }, + { + "cell_type": "markdown", + "id": "dd3cd25c", + "metadata": {}, + "source": [ + "# Transfer Rates\n", + "The circulation of molten salt along the main fuel circuit of the MSRE made it possible to have a continuos separation of fission products. \n", + "In particular it is reported that volatile noble gasses would bubble out in the off-gas system removal and noble metals plate out on the surface of the heat-exchangers. \n", + "\n", + "In this particular and simplified case, we can set removal rates as negative transfer rates from the main fuel salt with removal rate coefficients that are function of the time characteristic and efficiency of the removal methods for the two set of materials. \n", + "\n", + "This depletion capability has been added to OpenMC main branch from version 0.14.0.\n", + "\n", + "We will use the same removal rates as the one derived in this [ornl paper](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b9f8eada", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\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 | 2011-2024 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.14.1-dev\n", + " Git SHA1 | d93ac83697e7fdb4fd4cc0f2705795398bf804bd\n", + " Date/Time | 2024-04-19 17:18:01\n", + " MPI Processes | 1\n", + " OpenMP Threads | 56\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", + "Loading file msre_full.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.01\n", + "Building acceleration data structures...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", + "Loading file msre_control_rod.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0\n", + "Building acceleration data structures...\n", + " Reading Li6 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li6.h5\n", + " Reading Li7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li7.h5\n", + " Reading Be9 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be9.h5\n", + " Reading O16 from /home/lorenzo/nuclear_data/endfb80_hdf5/O16.h5\n", + " Reading O17 from /home/lorenzo/nuclear_data/endfb80_hdf5/O17.h5\n", + " Reading O18 from /home/lorenzo/nuclear_data/endfb80_hdf5/O18.h5\n", + " Reading F19 from /home/lorenzo/nuclear_data/endfb80_hdf5/F19.h5\n", + " Reading Cr50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr50.h5\n", + " Reading Cr52 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr52.h5\n", + " Reading Cr53 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr53.h5\n", + " Reading Cr54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr54.h5\n", + " Reading Fe54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe58.h5\n", + " Reading Ni58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni58.h5\n", + " Reading Ni60 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni60.h5\n", + " Reading Ni61 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni61.h5\n", + " Reading Ni62 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni62.h5\n", + " Reading Ni64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni64.h5\n", + " Reading Zr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr96.h5\n", + " Reading Hf174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf174.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Hf176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf176.h5\n", + " Reading Hf177 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf177.h5\n", + " Reading Hf178 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf178.h5\n", + " Reading Hf179 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf179.h5\n", + " Reading Hf180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf180.h5\n", + " Reading U234 from /home/lorenzo/nuclear_data/endfb80_hdf5/U234.h5\n", + " Reading U235 from /home/lorenzo/nuclear_data/endfb80_hdf5/U235.h5\n", + " Reading U236 from /home/lorenzo/nuclear_data/endfb80_hdf5/U236.h5\n", + " Reading U238 from /home/lorenzo/nuclear_data/endfb80_hdf5/U238.h5\n", + " Reading B10 from /home/lorenzo/nuclear_data/endfb80_hdf5/B10.h5\n", + " Reading B11 from /home/lorenzo/nuclear_data/endfb80_hdf5/B11.h5\n", + " Reading C12 from /home/lorenzo/nuclear_data/endfb80_hdf5/C12.h5\n", + " Reading C13 from /home/lorenzo/nuclear_data/endfb80_hdf5/C13.h5\n", + " Reading Mg24 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg24.h5\n", + " Reading Mg25 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg25.h5\n", + " Reading Mg26 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg26.h5\n", + " Reading Al27 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al27.h5\n", + " Reading Si28 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si28.h5\n", + " Reading Si29 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si29.h5\n", + " Reading Si30 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si30.h5\n", + " Reading S32 from /home/lorenzo/nuclear_data/endfb80_hdf5/S32.h5\n", + " Reading S33 from /home/lorenzo/nuclear_data/endfb80_hdf5/S33.h5\n", + " Reading S34 from /home/lorenzo/nuclear_data/endfb80_hdf5/S34.h5\n", + " Reading S36 from /home/lorenzo/nuclear_data/endfb80_hdf5/S36.h5\n", + " Reading Ca40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca40.h5\n", + " Reading Ca42 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca42.h5\n", + " Reading Ca43 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca43.h5\n", + " Reading Ca44 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca44.h5\n", + " Reading Ca48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca48.h5\n", + " Reading Ti46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti46.h5\n", + " Reading Ti47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti47.h5\n", + " Reading Ti48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti48.h5\n", + " Reading Ti49 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti49.h5\n", + " Reading Ti50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti50.h5\n", + " Reading V51 from /home/lorenzo/nuclear_data/endfb80_hdf5/V51.h5\n", + " Reading P31 from /home/lorenzo/nuclear_data/endfb80_hdf5/P31.h5\n", + " Reading Mn55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn55.h5\n", + " Reading Co59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co59.h5\n", + " Reading Cu63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu63.h5\n", + " Reading Cu65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu65.h5\n", + " Reading Mo92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo92.h5\n", + " Reading Mo94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo94.h5\n", + " Reading Mo95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo95.h5\n", + " Reading Mo96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo96.h5\n", + " Reading Mo97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo97.h5\n", + " Reading Mo98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo98.h5\n", + " Reading Mo100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo100.h5\n", + " Reading W180 from /home/lorenzo/nuclear_data/endfb80_hdf5/W180.h5\n", + " Reading W182 from /home/lorenzo/nuclear_data/endfb80_hdf5/W182.h5\n", + " Reading W183 from /home/lorenzo/nuclear_data/endfb80_hdf5/W183.h5\n", + " Reading W184 from /home/lorenzo/nuclear_data/endfb80_hdf5/W184.h5\n", + " Reading W186 from /home/lorenzo/nuclear_data/endfb80_hdf5/W186.h5\n", + " Reading He3 from /home/lorenzo/nuclear_data/endfb80_hdf5/He3.h5\n", + " Reading He4 from /home/lorenzo/nuclear_data/endfb80_hdf5/He4.h5\n", + " Reading Ca46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca46.h5\n", + " Reading V50 from /home/lorenzo/nuclear_data/endfb80_hdf5/V50.h5\n", + " Reading Zn64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn64.h5\n", + " Reading Zn66 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn66.h5\n", + " Reading Zn67 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn67.h5\n", + " Reading Zn68 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn68.h5\n", + " Reading Zn70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn70.h5\n", + " Reading Ag107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag107.h5\n", + " Reading Ag109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag109.h5\n", + " Reading Cd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd106.h5\n", + " Reading Cd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd108.h5\n", + " Reading Cd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd110.h5\n", + " Reading Cd111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd111.h5\n", + " Reading Cd112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd112.h5\n", + " Reading Cd113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd113.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", + " 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cd114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd114.h5\n", + " Reading Cd116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd116.h5\n", + " Reading Sn112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn112.h5\n", + " Reading Sn114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn114.h5\n", + " Reading Sn115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn115.h5\n", + " Reading Sn116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn116.h5\n", + " Reading Sn117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn117.h5\n", + " Reading Sn118 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn118.h5\n", + " Reading Sn119 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn119.h5\n", + " Reading Sn120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn120.h5\n", + " Reading Sn122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn122.h5\n", + " Reading Sn124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn124.h5\n", + " Reading Ba130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba130.h5\n", + " Reading Ba132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba132.h5\n", + " Reading Ba134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba134.h5\n", + " Reading Ba135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba135.h5\n", + " Reading Ba136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba136.h5\n", + " Reading Ba137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba137.h5\n", + " Reading Ba138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba138.h5\n", + " Reading Ta180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta180.h5\n", + " Reading Ta181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta181.h5\n", + " Reading Gd152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd152.h5\n", + " Reading Gd154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd154.h5\n", + " Reading Gd155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd155.h5\n", + " Reading Gd156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd156.h5\n", + " Reading Gd157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd157.h5\n", + " Reading Gd158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd158.h5\n", + " Reading Gd160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd160.h5\n", + " Reading H1 from /home/lorenzo/nuclear_data/endfb80_hdf5/H1.h5\n", + " Reading H2 from /home/lorenzo/nuclear_data/endfb80_hdf5/H2.h5\n", + " Reading Na23 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na23.h5\n", + " Reading K39 from /home/lorenzo/nuclear_data/endfb80_hdf5/K39.h5\n", + " Reading K40 from /home/lorenzo/nuclear_data/endfb80_hdf5/K40.h5\n", + " Reading K41 from /home/lorenzo/nuclear_data/endfb80_hdf5/K41.h5\n", + " Reading N14 from /home/lorenzo/nuclear_data/endfb80_hdf5/N14.h5\n", + " Reading N15 from /home/lorenzo/nuclear_data/endfb80_hdf5/N15.h5\n", + " Reading c_Graphite from /home/lorenzo/nuclear_data/endfb80_hdf5/c_Graphite.h5\n", + " Minimum neutron data temperature: 250 K\n", + " Maximum neutron data temperature: 2500 K\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + "[openmc.deplete] t=0.0 s, dt=432000 s, source=8000000.0\n", + " Reading H3 from /home/lorenzo/nuclear_data/endfb80_hdf5/H3.h5\n", + " Reading Be7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be7.h5\n", + " Reading Ne20 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne20.h5\n", + " Reading Ne21 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne21.h5\n", + " Reading Ne22 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne22.h5\n", + " Reading Na22 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na22.h5\n", + " Reading Al26_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al26_m1.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Si31 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si31.h5\n", + " Reading Si32 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si32.h5\n", + " Reading S35 from /home/lorenzo/nuclear_data/endfb80_hdf5/S35.h5\n", + " Reading Cl35 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl35.h5\n", + " Reading Cl36 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl36.h5\n", + " Reading Cl37 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl37.h5\n", + " Reading Ar36 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar36.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Ar37 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar37.h5\n", + " Reading Ar38 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar38.h5\n", + " Reading Ar39 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar39.h5\n", + " Reading Ar40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar40.h5\n", + " Reading Ar41 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar41.h5\n", + " Reading Ca41 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca41.h5\n", + " Reading Ca45 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca45.h5\n", + " Reading Ca47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca47.h5\n", + " Reading Sc45 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sc45.h5\n", + " Reading V49 from /home/lorenzo/nuclear_data/endfb80_hdf5/V49.h5\n", + " Reading Cr51 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr51.h5\n", + " Reading Mn54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn54.h5\n", + " Reading Fe55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe55.h5\n", + " Reading Co58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co58.h5\n", + " Reading Co58_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co58_m1.h5\n", + " Reading Ni59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni59.h5\n", + " Reading Ni63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni63.h5\n", + " Reading Cu64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu64.h5\n", + " Reading Zn65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn65.h5\n", + " Reading Zn69 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn69.h5\n", + " Reading Ga69 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga69.h5\n", + " Reading Ga70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga70.h5\n", + " Reading Ga71 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga71.h5\n", + " Reading Ge70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge70.h5\n", + " Reading Ge71 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge71.h5\n", + " Reading Ge72 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge72.h5\n", + " Reading Ge73 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge73.h5\n", + " Reading Ge74 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge74.h5\n", + " Reading Ge75 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge75.h5\n", + " Reading Ge76 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge76.h5\n", + " Reading As73 from /home/lorenzo/nuclear_data/endfb80_hdf5/As73.h5\n", + " Reading As74 from /home/lorenzo/nuclear_data/endfb80_hdf5/As74.h5\n", + " Reading As75 from /home/lorenzo/nuclear_data/endfb80_hdf5/As75.h5\n", + " Reading Se74 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se74.h5\n", + " Reading Se75 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se75.h5\n", + " Reading Se76 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se76.h5\n", + " Reading Se77 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se77.h5\n", + " Reading Se78 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se78.h5\n", + " Reading Se79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se79.h5\n", + " Reading Se80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se80.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Se81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se81.h5\n", + " Reading Se82 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se82.h5\n", + " Reading Br79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br79.h5\n", + " Reading Br80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br80.h5\n", + " Reading Br81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br81.h5\n", + " Reading Kr78 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr78.h5\n", + " Reading Kr79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr79.h5\n", + " Reading Kr80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr80.h5\n", + " Reading Kr81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr81.h5\n", + " Reading Kr82 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr82.h5\n", + " Reading Kr83 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr83.h5\n", + " Reading Kr84 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr84.h5\n", + " Reading Kr85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr85.h5\n", + " Reading Kr86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr86.h5\n", + " Reading Rb85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb85.h5\n", + " Reading Rb86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb86.h5\n", + " Reading Rb87 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb87.h5\n", + " Reading Sr84 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr84.h5\n", + " Reading Sr85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr85.h5\n", + " Reading Sr86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr86.h5\n", + " Reading Sr87 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr87.h5\n", + " Reading Sr88 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr88.h5\n", + " Reading Sr89 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr89.h5\n", + " Reading Sr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr90.h5\n", + " Reading Y89 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y89.h5\n", + " Reading Y90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y90.h5\n", + " Reading Y91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y91.h5\n", + " Reading Zr93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr93.h5\n", + " Reading Zr95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr95.h5\n", + " Reading Nb93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb93.h5\n", + " Reading Nb94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb94.h5\n", + " Reading Nb95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb95.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Mo93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo93.h5\n", + " Reading Mo99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo99.h5\n", + " Reading Tc98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc98.h5\n", + " Reading Tc99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc99.h5\n", + " Reading Ru96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru96.h5\n", + " Reading Ru97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru97.h5\n", + " Reading Ru98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru98.h5\n", + " Reading Ru99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru99.h5\n", + " Reading Ru100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru100.h5\n", + " Reading Ru101 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru101.h5\n", + " Reading Ru102 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru102.h5\n", + " Reading Ru103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru103.h5\n", + " Reading Ru104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru104.h5\n", + " Reading Ru105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru105.h5\n", + " Reading Ru106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru106.h5\n", + " Reading Rh103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh103.h5\n", + " Reading Rh104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh104.h5\n", + " Reading Rh105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh105.h5\n", + " Reading Pd102 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd102.h5\n", + " Reading Pd103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd103.h5\n", + " Reading Pd104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd104.h5\n", + " Reading Pd105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd105.h5\n", + " Reading Pd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd106.h5\n", + " Reading Pd107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd107.h5\n", + " Reading Pd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd108.h5\n", + " Reading Pd109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd109.h5\n", + " Reading Pd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd110.h5\n", + " Reading Ag108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag108.h5\n", + " Reading Ag110_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag110_m1.h5\n", + " Reading Ag111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag111.h5\n", + " Reading Ag112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag112.h5\n", + " Reading Ag113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag113.h5\n", + " Reading Ag114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag114.h5\n", + " Reading Ag115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag115.h5\n", + " Reading Ag116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag116.h5\n", + " Reading Ag117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag117.h5\n", + " Reading Ag118_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag118_m1.h5\n", + " Reading Cd107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd107.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cd109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd109.h5\n", + " Reading Cd115_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd115_m1.h5\n", + " Reading In113 from /home/lorenzo/nuclear_data/endfb80_hdf5/In113.h5\n", + " Reading In114 from /home/lorenzo/nuclear_data/endfb80_hdf5/In114.h5\n", + " Reading In115 from /home/lorenzo/nuclear_data/endfb80_hdf5/In115.h5\n", + " Reading Sn113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn113.h5\n", + " Reading Sn121_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn121_m1.h5\n", + " Reading Sn123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn123.h5\n", + " Reading Sn125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn125.h5\n", + " Reading Sn126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn126.h5\n", + " Reading Sb121 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb121.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Sb122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb122.h5\n", + " Reading Sb123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb123.h5\n", + " Reading Sb124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb124.h5\n", + " Reading Sb125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb125.h5\n", + " Reading Sb126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb126.h5\n", + " Reading Te120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te120.h5\n", + " Reading Te121 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te121.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Te120 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Te120 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Te121_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te121_m1.h5\n", + " Reading Te122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te122.h5\n", + " Reading Te123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te123.h5\n", + " Reading Te124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te124.h5\n", + " Reading Te125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te125.h5\n", + " Reading Te126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te126.h5\n", + " Reading Te127_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te127_m1.h5\n", + " Reading Te128 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te128.h5\n", + " Reading Te129_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te129_m1.h5\n", + " Reading Te130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te130.h5\n", + " Reading Te131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te131.h5\n", + " Reading Te131_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te131_m1.h5\n", + " Reading Te132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te132.h5\n", + " Reading I127 from /home/lorenzo/nuclear_data/endfb80_hdf5/I127.h5\n", + " Reading I128 from /home/lorenzo/nuclear_data/endfb80_hdf5/I128.h5\n", + " Reading I129 from /home/lorenzo/nuclear_data/endfb80_hdf5/I129.h5\n", + " Reading I130 from /home/lorenzo/nuclear_data/endfb80_hdf5/I130.h5\n", + " Reading I131 from /home/lorenzo/nuclear_data/endfb80_hdf5/I131.h5\n", + " Reading I132 from /home/lorenzo/nuclear_data/endfb80_hdf5/I132.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide I131 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading I132_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/I132_m1.h5\n", + " Reading I133 from /home/lorenzo/nuclear_data/endfb80_hdf5/I133.h5\n", + " Reading I134 from /home/lorenzo/nuclear_data/endfb80_hdf5/I134.h5\n", + " Reading I135 from /home/lorenzo/nuclear_data/endfb80_hdf5/I135.h5\n", + " Reading Xe123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe123.h5\n", + " Reading Xe124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe124.h5\n", + " Reading Xe125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe125.h5\n", + " Reading Xe126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe126.h5\n", + " Reading Xe127 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe127.h5\n", + " Reading Xe128 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe128.h5\n", + " Reading Xe129 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe129.h5\n", + " Reading Xe130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe130.h5\n", + " Reading Xe131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe131.h5\n", + " Reading Xe132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe132.h5\n", + " Reading Xe133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe133.h5\n", + " Reading Xe134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe134.h5\n", + " Reading Xe135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe135.h5\n", + " Reading Xe136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe136.h5\n", + " Reading Cs133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs133.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Xe133 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cs134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs134.h5\n", + " Reading Cs135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs135.h5\n", + " Reading Cs136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs136.h5\n", + " Reading Cs137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs137.h5\n", + " Reading Ba131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba131.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Ba133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba133.h5\n", + " Reading Ba139 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba139.h5\n", + " Reading Ba140 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba140.h5\n", + " Reading La138 from /home/lorenzo/nuclear_data/endfb80_hdf5/La138.h5\n", + " Reading La139 from /home/lorenzo/nuclear_data/endfb80_hdf5/La139.h5\n", + " Reading La140 from /home/lorenzo/nuclear_data/endfb80_hdf5/La140.h5\n", + " Reading Ce136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce136.h5\n", + " Reading Ce137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce137.h5\n", + " Reading Ce137_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce137_m1.h5\n", + " Reading Ce138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce138.h5\n", + " Reading Ce139 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce139.h5\n", + " Reading Ce140 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce140.h5\n", + " Reading Ce141 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce141.h5\n", + " Reading Ce142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce142.h5\n", + " Reading Ce143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce143.h5\n", + " Reading Ce144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce144.h5\n", + " Reading Pr141 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr141.h5\n", + " Reading Pr142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr142.h5\n", + " Reading Pr143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr143.h5\n", + " Reading Nd142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd142.h5\n", + " Reading Nd143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd143.h5\n", + " Reading Nd144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd144.h5\n", + " Reading Nd145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd145.h5\n", + " Reading Nd146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd146.h5\n", + " Reading Nd147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd147.h5\n", + " Reading Nd148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd148.h5\n", + " Reading Nd149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd149.h5\n", + " Reading Nd150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd150.h5\n", + " Reading Pm143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm143.h5\n", + " Reading Pm144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm144.h5\n", + " Reading Pm145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm145.h5\n", + " Reading Pm146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm146.h5\n", + " Reading Pm147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm147.h5\n", + " Reading Pm148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm148.h5\n", + " Reading Pm148_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm148_m1.h5\n", + " Reading Pm149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm149.h5\n", + " Reading Pm150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm150.h5\n", + " Reading Pm151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm151.h5\n", + " Reading Sm144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm144.h5\n", + " Reading Sm145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm145.h5\n", + " Reading Sm146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm146.h5\n", + " Reading Sm147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm147.h5\n", + " Reading Sm148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm148.h5\n", + " Reading Sm149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm149.h5\n", + " Reading Sm150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm150.h5\n", + " Reading Sm151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm151.h5\n", + " Reading Sm152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm152.h5\n", + " Reading Sm153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm153.h5\n", + " Reading Sm154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm154.h5\n", + " Reading Eu151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu151.h5\n", + " Reading Eu152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu152.h5\n", + " Reading Eu153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu153.h5\n", + " Reading Eu154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu154.h5\n", + " Reading Eu155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu155.h5\n", + " Reading Eu156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu156.h5\n", + " Reading Eu157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu157.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Gd153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd153.h5\n", + " Reading Gd159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd159.h5\n", + " Reading Tb158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb158.h5\n", + " Reading Tb159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb159.h5\n", + " Reading Tb160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb160.h5\n", + " Reading Tb161 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb161.h5\n", + " Reading Dy154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy154.h5\n", + " Reading Dy155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy155.h5\n", + " Reading Dy156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy156.h5\n", + " Reading Dy157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy157.h5\n", + " Reading Dy158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy158.h5\n", + " Reading Dy159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy159.h5\n", + " Reading Dy160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy160.h5\n", + " Reading Dy161 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy161.h5\n", + " Reading Dy162 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy162.h5\n", + " Reading Dy163 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy163.h5\n", + " Reading Dy164 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy164.h5\n", + " Reading Ho165 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ho165.h5\n", + " Reading Ho166_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ho166_m1.h5\n", + " Reading Er162 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er162.h5\n", + " Reading Er163 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er163.h5\n", + " Reading Er164 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er164.h5\n", + " Reading Er165 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er165.h5\n", + " Reading Er166 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er166.h5\n", + " Reading Er167 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er167.h5\n", + " Reading Er168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er168.h5\n", + " Reading Er169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er169.h5\n", + " Reading Er170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er170.h5\n", + " Reading Tm168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm168.h5\n", + " Reading Tm169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm169.h5\n", + " Reading Tm170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm170.h5\n", + " Reading Tm171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm171.h5\n", + " Reading Yb168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb168.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Yb169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb169.h5\n", + " Reading Yb170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb170.h5\n", + " Reading Yb171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb171.h5\n", + " Reading Yb172 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb172.h5\n", + " Reading Yb173 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb173.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Yb174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb174.h5\n", + " Reading Yb175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb175.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Yb176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb176.h5\n", + " Reading Lu175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Lu175.h5\n", + " Reading Lu176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Lu176.h5\n", + " Reading Hf175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf175.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Hf181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf181.h5\n", + " Reading Hf182 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf182.h5\n", + " Reading Ta182 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta182.h5\n", + " Reading W181 from /home/lorenzo/nuclear_data/endfb80_hdf5/W181.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading W185 from /home/lorenzo/nuclear_data/endfb80_hdf5/W185.h5\n", + " Reading Re185 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re185.h5\n", + " Reading Re186_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re186_m1.h5\n", + " Reading Re187 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re187.h5\n", + " Reading Os184 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os184.h5\n", + " Reading Os185 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os185.h5\n", + " Reading Os186 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os186.h5\n", + " Reading Os187 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os187.h5\n", + " Reading Os188 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os188.h5\n", + " Reading Os189 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os189.h5\n", + " Reading Os190 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os190.h5\n", + " Reading Os191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os191.h5\n", + " Reading Os192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os192.h5\n", + " Reading Ir191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir191.h5\n", + " Reading Ir192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir192.h5\n", + " Reading Ir193 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir193.h5\n", + " Reading Ir194_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir194_m1.h5\n", + " Reading Pt190 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt190.h5\n", + " Reading Pt191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt191.h5\n", + " Reading Pt192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt192.h5\n", + " Reading Pt193 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt193.h5\n", + " Reading Pt194 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt194.h5\n", + " Reading Pt195 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt195.h5\n", + " Reading Pt196 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt196.h5\n", + " Reading Pt197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt197.h5\n", + " Reading Pt198 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt198.h5\n", + " Reading Au197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Au197.h5\n", + " Reading Hg196 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg196.h5\n", + " Reading Hg197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg197.h5\n", + " Reading Hg197_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg197_m1.h5\n", + " Reading Hg198 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg198.h5\n", + " Reading Hg199 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg199.h5\n", + " Reading Hg200 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg200.h5\n", + " Reading Hg201 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg201.h5\n", + " Reading Hg202 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg202.h5\n", + " Reading Hg203 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg203.h5\n", + " Reading Hg204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg204.h5\n", + " Reading Tl203 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl203.h5\n", + " Reading Tl204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl204.h5\n", + " Reading Tl205 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl205.h5\n", + " Reading Pb204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb204.h5\n", + " Reading Pb205 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb205.h5\n", + " Reading Pb206 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb206.h5\n", + " Reading Pb207 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb207.h5\n", + " Reading Pb208 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb208.h5\n", + " Reading Bi209 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bi209.h5\n", + " Reading Bi210_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bi210_m1.h5\n", + " Reading Po208 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po208.h5\n", + " Reading Po209 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po209.h5\n", + " Reading Po210 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po210.h5\n", + " Reading Ra223 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra223.h5\n", + " Reading Ra224 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra224.h5\n", + " Reading Ra225 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra225.h5\n", + " Reading Ra226 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra226.h5\n", + " Reading Ac225 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac225.h5\n", + " Reading Ac226 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac226.h5\n", + " Reading Ac227 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac227.h5\n", + " Reading Th227 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th227.h5\n", + " Reading Th228 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th228.h5\n", + " Reading Th229 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th229.h5\n", + " Reading Th230 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th230.h5\n", + " Reading Th231 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th231.h5\n", + " Reading Th232 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th232.h5\n", + " Reading Th233 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th233.h5\n", + " Reading Th234 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th234.h5\n", + " Reading Pa229 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa229.h5\n", + " Reading Pa230 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa230.h5\n", + " Reading Pa231 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa231.h5\n", + " Reading Pa232 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa232.h5\n", + " Reading Pa233 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa233.h5\n", + " Reading U230 from /home/lorenzo/nuclear_data/endfb80_hdf5/U230.h5\n", + " Reading U231 from /home/lorenzo/nuclear_data/endfb80_hdf5/U231.h5\n", + " Reading U232 from /home/lorenzo/nuclear_data/endfb80_hdf5/U232.h5\n", + " Reading U233 from /home/lorenzo/nuclear_data/endfb80_hdf5/U233.h5\n", + " Reading U237 from /home/lorenzo/nuclear_data/endfb80_hdf5/U237.h5\n", + " Reading U239 from /home/lorenzo/nuclear_data/endfb80_hdf5/U239.h5\n", + " Reading U240 from /home/lorenzo/nuclear_data/endfb80_hdf5/U240.h5\n", + " Reading U241 from /home/lorenzo/nuclear_data/endfb80_hdf5/U241.h5\n", + " Reading Np234 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np234.h5\n", + " Reading Np235 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np235.h5\n", + " Reading Np236 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np236.h5\n", + " Reading Np236_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np236_m1.h5\n", + " Reading Np237 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np237.h5\n", + " Reading Np238 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np238.h5\n", + " Reading Np239 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np239.h5\n", + " Reading Pu236 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu236.h5\n", + " Reading Pu237 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu237.h5\n", + " Reading Pu238 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu238.h5\n", + " Reading Pu239 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu239.h5\n", + " Reading Pu240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu240.h5\n", + " Reading Pu241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu241.h5\n", + " Reading Pu242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu242.h5\n", + " Reading Pu243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu243.h5\n", + " Reading Pu244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu244.h5\n", + " Reading Pu245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu245.h5\n", + " Reading Pu246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu246.h5\n", + " Reading Am240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am240.h5\n", + " Reading Am241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am241.h5\n", + " Reading Am242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am242.h5\n", + " Reading Am242_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am242_m1.h5\n", + " Reading Am243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am243.h5\n", + " Reading Am244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am244.h5\n", + " Reading Am244_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am244_m1.h5\n", + " Reading Cm240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm240.h5\n", + " Reading Cm241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm241.h5\n", + " Reading Cm242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm242.h5\n", + " Reading Cm243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm243.h5\n", + " Reading Cm244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm244.h5\n", + " Reading Cm245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm245.h5\n", + " Reading Cm246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm246.h5\n", + " Reading Cm247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm247.h5\n", + " Reading Cm248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm248.h5\n", + " Reading Cm249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm249.h5\n", + " Reading Cm250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm250.h5\n", + " Reading Bk245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk245.h5\n", + " Reading Bk246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk246.h5\n", + " Reading Bk247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk247.h5\n", + " Reading Bk248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk248.h5\n", + " Reading Bk249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk249.h5\n", + " Reading Bk250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk250.h5\n", + " Reading Cf246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf246.h5\n", + " Reading Cf247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf247.h5\n", + " Reading Cf248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf248.h5\n", + " Reading Cf249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf249.h5\n", + " Reading Cf250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf250.h5\n", + " Reading Cf251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf251.h5\n", + " Reading Cf252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf252.h5\n", + " Reading Cf253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf253.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at\n", + " 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cf254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf254.h5\n", + " Reading Es251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es251.h5\n", + " Reading Es252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es252.h5\n", + " Reading Es253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es253.h5\n", + " Reading Es254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es254.h5\n", + " Reading Es254_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es254_m1.h5\n", + " Reading Es255 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es255.h5\n", + " Reading Fm255 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fm255.h5\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60942\n", + " 2/1 0.89106\n", + " 3/1 0.97799\n", + " 4/1 0.99502\n", + " 5/1 0.99819\n", + " 6/1 1.00437\n", + " 7/1 0.99348\n", + " 8/1 1.00257\n", + " 9/1 1.01441\n", + " 10/1 1.01353\n", + " 11/1 1.00178\n", + " 12/1 1.01576\n", + " 13/1 1.01754\n", + " 14/1 1.00683\n", + " 15/1 1.00505\n", + " 16/1 0.99786\n", + " 17/1 1.01767\n", + " 18/1 1.00034\n", + " 19/1 1.00494\n", + " 20/1 1.01117\n", + " 21/1 1.00252\n", + " 22/1 1.00592 1.00422 +/- 0.00170\n", + " 23/1 1.01500 1.00781 +/- 0.00372\n", + " 24/1 1.01347 1.00923 +/- 0.00299\n", + " 25/1 1.01031 1.00944 +/- 0.00233\n", + " 26/1 1.00326 1.00841 +/- 0.00216\n", + " 27/1 1.00972 1.00860 +/- 0.00184\n", + " 28/1 1.01373 1.00924 +/- 0.00171\n", + " 29/1 1.00611 1.00889 +/- 0.00155\n", + " 30/1 1.01140 1.00914 +/- 0.00141\n", + " 31/1 0.99045 1.00744 +/- 0.00212\n", + " 32/1 1.00671 1.00738 +/- 0.00194\n", + " 33/1 1.01682 1.00811 +/- 0.00193\n", + " 34/1 1.00605 1.00796 +/- 0.00179\n", + " 35/1 1.00573 1.00781 +/- 0.00167\n", + " 36/1 1.00131 1.00741 +/- 0.00162\n", + " 37/1 1.00832 1.00746 +/- 0.00152\n", + " 38/1 1.01443 1.00785 +/- 0.00148\n", + " 39/1 1.00582 1.00774 +/- 0.00141\n", + " 40/1 0.98830 1.00677 +/- 0.00165\n", + " 41/1 0.99523 1.00622 +/- 0.00166\n", + " 42/1 1.00353 1.00610 +/- 0.00159\n", + " 43/1 1.00226 1.00593 +/- 0.00153\n", + " 44/1 1.01347 1.00624 +/- 0.00150\n", + " 45/1 1.00294 1.00611 +/- 0.00144\n", + " 46/1 1.01234 1.00635 +/- 0.00141\n", + " 47/1 1.01298 1.00660 +/- 0.00138\n", + " 48/1 1.00300 1.00647 +/- 0.00133\n", + " 49/1 1.01010 1.00659 +/- 0.00129\n", + " 50/1 0.99917 1.00635 +/- 0.00127\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.6673e+01 seconds\n", + " Reading cross sections = 1.5091e+01 seconds\n", + " Total time in simulation = 1.4075e+03 seconds\n", + " Time in transport only = 1.4071e+03 seconds\n", + " Time in inactive batches = 6.3276e+01 seconds\n", + " Time in active batches = 1.3442e+03 seconds\n", + " Time synchronizing fission bank = 1.9756e-01 seconds\n", + " Sampling source sites = 1.5885e-01 seconds\n", + " SEND/RECV source sites = 3.7839e-02 seconds\n", + " Time accumulating tallies = 6.4644e-02 seconds\n", + " Time writing statepoints = 9.7757e-03 seconds\n", + " Total time for finalization = 1.3423e-04 seconds\n", + " Total time elapsed = 1.4453e+03 seconds\n", + " Calculation Rate (inactive) = 9482.26 particles/second\n", + " Calculation Rate (active) = 669.536 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00599 +/- 0.00121\n", + " k-effective (Track-length) = 1.00635 +/- 0.00127\n", + " k-effective (Absorption) = 1.00602 +/- 0.00110\n", + " Combined k-effective = 1.00598 +/- 0.00110\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n0.h5...\n", + "[openmc.deplete] t=432000.0 s, dt=432000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60115\n", + " 2/1 0.88928\n", + " 3/1 0.94715\n", + " 4/1 0.97733\n", + " 5/1 0.99749\n", + " 6/1 1.00479\n", + " 7/1 0.99471\n", + " 8/1 0.99993\n", + " 9/1 0.99051\n", + " 10/1 1.01665\n", + " 11/1 0.99415\n", + " 12/1 1.01018\n", + " 13/1 1.01106\n", + " 14/1 1.01588\n", + " 15/1 1.00526\n", + " 16/1 1.00077\n", + " 17/1 1.00117\n", + " 18/1 1.00009\n", + " 19/1 0.99434\n", + " 20/1 1.00243\n", + " 21/1 1.00150\n", + " 22/1 0.99410 0.99780 +/- 0.00370\n", + " 23/1 1.00698 1.00086 +/- 0.00373\n", + " 24/1 1.00999 1.00314 +/- 0.00349\n", + " 25/1 1.01735 1.00598 +/- 0.00392\n", + " 26/1 1.00480 1.00579 +/- 0.00321\n", + " 27/1 1.00722 1.00599 +/- 0.00272\n", + " 28/1 0.99473 1.00458 +/- 0.00274\n", + " 29/1 1.00419 1.00454 +/- 0.00242\n", + " 30/1 1.00796 1.00488 +/- 0.00219\n", + " 31/1 1.00857 1.00522 +/- 0.00201\n", + " 32/1 0.99732 1.00456 +/- 0.00195\n", + " 33/1 1.00652 1.00471 +/- 0.00180\n", + " 34/1 0.99580 1.00407 +/- 0.00178\n", + " 35/1 0.99530 1.00349 +/- 0.00176\n", + " 36/1 1.01447 1.00418 +/- 0.00178\n", + " 37/1 1.01762 1.00497 +/- 0.00185\n", + " 38/1 0.99450 1.00439 +/- 0.00184\n", + " 39/1 0.99973 1.00414 +/- 0.00176\n", + " 40/1 0.99579 1.00372 +/- 0.00172\n", + " 41/1 1.00942 1.00399 +/- 0.00166\n", + " 42/1 1.00841 1.00420 +/- 0.00159\n", + " 43/1 0.99084 1.00361 +/- 0.00163\n", + " 44/1 1.00087 1.00350 +/- 0.00156\n", + " 45/1 1.00956 1.00374 +/- 0.00152\n", + " 46/1 1.00058 1.00362 +/- 0.00147\n", + " 47/1 1.00990 1.00385 +/- 0.00143\n", + " 48/1 1.00305 1.00382 +/- 0.00138\n", + " 49/1 1.00704 1.00394 +/- 0.00133\n", + " 50/1 1.01104 1.00417 +/- 0.00131\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8414e+03 seconds\n", + " Time in transport only = 1.8410e+03 seconds\n", + " Time in inactive batches = 2.8855e+02 seconds\n", + " Time in active batches = 1.5529e+03 seconds\n", + " Time synchronizing fission bank = 1.8210e-01 seconds\n", + " Sampling source sites = 1.4562e-01 seconds\n", + " SEND/RECV source sites = 3.5746e-02 seconds\n", + " Time accumulating tallies = 7.8137e-02 seconds\n", + " Time writing statepoints = 1.1256e-02 seconds\n", + " Total time for finalization = 1.4153e-04 seconds\n", + " Total time elapsed = 1.8424e+03 seconds\n", + " Calculation Rate (inactive) = 2079.33 particles/second\n", + " Calculation Rate (active) = 579.574 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00479 +/- 0.00124\n", + " k-effective (Track-length) = 1.00417 +/- 0.00131\n", + " k-effective (Absorption) = 1.00426 +/- 0.00105\n", + " Combined k-effective = 1.00450 +/- 0.00092\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n1.h5...\n", + "[openmc.deplete] t=864000.0 s, dt=2592000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.59346\n", + " 2/1 0.87731\n", + " 3/1 0.96109\n", + " 4/1 0.97331\n", + " 5/1 0.98716\n", + " 6/1 0.99491\n", + " 7/1 0.99851\n", + " 8/1 1.00039\n", + " 9/1 0.99998\n", + " 10/1 0.99704\n", + " 11/1 1.00638\n", + " 12/1 1.00982\n", + " 13/1 1.01091\n", + " 14/1 1.01678\n", + " 15/1 0.98923\n", + " 16/1 0.99254\n", + " 17/1 1.00578\n", + " 18/1 1.01132\n", + " 19/1 1.01314\n", + " 20/1 1.01694\n", + " 21/1 1.00736\n", + " 22/1 1.01141 1.00939 +/- 0.00203\n", + " 23/1 1.00158 1.00678 +/- 0.00285\n", + " 24/1 1.00589 1.00656 +/- 0.00203\n", + " 25/1 1.00737 1.00672 +/- 0.00158\n", + " 26/1 1.01017 1.00730 +/- 0.00141\n", + " 27/1 0.99982 1.00623 +/- 0.00160\n", + " 28/1 1.01095 1.00682 +/- 0.00151\n", + " 29/1 1.01166 1.00736 +/- 0.00143\n", + " 30/1 1.00725 1.00735 +/- 0.00128\n", + " 31/1 0.99733 1.00643 +/- 0.00148\n", + " 32/1 1.01437 1.00710 +/- 0.00150\n", + " 33/1 1.00339 1.00681 +/- 0.00141\n", + " 34/1 0.98085 1.00496 +/- 0.00227\n", + " 35/1 1.01442 1.00559 +/- 0.00220\n", + " 36/1 1.00294 1.00542 +/- 0.00207\n", + " 37/1 0.99744 1.00495 +/- 0.00200\n", + " 38/1 1.01074 1.00527 +/- 0.00191\n", + " 39/1 0.99945 1.00497 +/- 0.00183\n", + " 40/1 1.00435 1.00494 +/- 0.00174\n", + " 41/1 1.00735 1.00505 +/- 0.00166\n", + " 42/1 1.00603 1.00510 +/- 0.00158\n", + " 43/1 1.00323 1.00501 +/- 0.00151\n", + " 44/1 0.99800 1.00472 +/- 0.00148\n", + " 45/1 1.01305 1.00506 +/- 0.00146\n", + " 46/1 1.00507 1.00506 +/- 0.00140\n", + " 47/1 1.02376 1.00575 +/- 0.00151\n", + " 48/1 1.00153 1.00560 +/- 0.00147\n", + " 49/1 1.00894 1.00571 +/- 0.00142\n", + " 50/1 1.01304 1.00596 +/- 0.00139\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8190e+03 seconds\n", + " Time in transport only = 1.8186e+03 seconds\n", + " Time in inactive batches = 2.8913e+02 seconds\n", + " Time in active batches = 1.5299e+03 seconds\n", + " Time synchronizing fission bank = 1.8696e-01 seconds\n", + " Sampling source sites = 1.4822e-01 seconds\n", + " SEND/RECV source sites = 3.7953e-02 seconds\n", + " Time accumulating tallies = 9.6822e-02 seconds\n", + " Time writing statepoints = 1.1563e-02 seconds\n", + " Total time for finalization = 1.2487e-04 seconds\n", + " Total time elapsed = 1.8201e+03 seconds\n", + " Calculation Rate (inactive) = 2075.23 particles/second\n", + " Calculation Rate (active) = 588.286 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00533 +/- 0.00143\n", + " k-effective (Track-length) = 1.00596 +/- 0.00139\n", + " k-effective (Absorption) = 1.00445 +/- 0.00114\n", + " Combined k-effective = 1.00527 +/- 0.00103\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n2.h5...\n", + "[openmc.deplete] t=3456000.0 s, dt=2592000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.61365\n", + " 2/1 0.90212\n", + " 3/1 0.95962\n", + " 4/1 1.00423\n", + " 5/1 1.00033\n", + " 6/1 1.00771\n", + " 7/1 1.00453\n", + " 8/1 1.00135\n", + " 9/1 1.01025\n", + " 10/1 1.01089\n", + " 11/1 1.00141\n", + " 12/1 1.00329\n", + " 13/1 1.00327\n", + " 14/1 1.00053\n", + " 15/1 1.00595\n", + " 16/1 1.00520\n", + " 17/1 1.00244\n", + " 18/1 1.01770\n", + " 19/1 1.00163\n", + " 20/1 1.01013\n", + " 21/1 1.01999\n", + " 22/1 1.00433 1.01216 +/- 0.00783\n", + " 23/1 1.00672 1.01035 +/- 0.00487\n", + " 24/1 0.99404 1.00627 +/- 0.00534\n", + " 25/1 1.01133 1.00728 +/- 0.00426\n", + " 26/1 1.00606 1.00708 +/- 0.00348\n", + " 27/1 0.99274 1.00503 +/- 0.00358\n", + " 28/1 1.00750 1.00534 +/- 0.00312\n", + " 29/1 1.00673 1.00549 +/- 0.00276\n", + " 30/1 1.01539 1.00648 +/- 0.00266\n", + " 31/1 1.00000 1.00589 +/- 0.00247\n", + " 32/1 1.01101 1.00632 +/- 0.00230\n", + " 33/1 1.01284 1.00682 +/- 0.00217\n", + " 34/1 0.99580 1.00603 +/- 0.00216\n", + " 35/1 1.00903 1.00623 +/- 0.00202\n", + " 36/1 1.01458 1.00676 +/- 0.00196\n", + " 37/1 1.00325 1.00655 +/- 0.00185\n", + " 38/1 0.99058 1.00566 +/- 0.00196\n", + " 39/1 1.00662 1.00571 +/- 0.00185\n", + " 40/1 0.98884 1.00487 +/- 0.00195\n", + " 41/1 1.01278 1.00525 +/- 0.00189\n", + " 42/1 0.99109 1.00460 +/- 0.00192\n", + " 43/1 0.99064 1.00400 +/- 0.00193\n", + " 44/1 1.01222 1.00434 +/- 0.00188\n", + " 45/1 1.01213 1.00465 +/- 0.00183\n", + " 46/1 1.00018 1.00448 +/- 0.00177\n", + " 47/1 0.99712 1.00421 +/- 0.00172\n", + " 48/1 1.00972 1.00440 +/- 0.00167\n", + " 49/1 1.00204 1.00432 +/- 0.00161\n", + " 50/1 0.98755 1.00376 +/- 0.00166\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8207e+03 seconds\n", + " Time in transport only = 1.8203e+03 seconds\n", + " Time in inactive batches = 2.8771e+02 seconds\n", + " Time in active batches = 1.5330e+03 seconds\n", + " Time synchronizing fission bank = 1.8680e-01 seconds\n", + " Sampling source sites = 1.4770e-01 seconds\n", + " SEND/RECV source sites = 3.8463e-02 seconds\n", + " Time accumulating tallies = 1.1871e-01 seconds\n", + " Time writing statepoints = 1.1733e-02 seconds\n", + " Total time for finalization = 1.0574e-04 seconds\n", + " Total time elapsed = 1.8217e+03 seconds\n", + " Calculation Rate (inactive) = 2085.41 particles/second\n", + " Calculation Rate (active) = 587.09 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00440 +/- 0.00161\n", + " k-effective (Track-length) = 1.00376 +/- 0.00166\n", + " k-effective (Absorption) = 1.00555 +/- 0.00094\n", + " Combined k-effective = 1.00539 +/- 0.00101\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n3.h5...\n", + "[openmc.deplete] t=6048000.0 s, dt=2592000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60296\n", + " 2/1 0.88599\n", + " 3/1 0.95124\n", + " 4/1 0.98034\n", + " 5/1 0.99482\n", + " 6/1 0.99609\n", + " 7/1 1.00739\n", + " 8/1 0.99006\n", + " 9/1 1.00694\n", + " 10/1 1.00602\n", + " 11/1 1.00145\n", + " 12/1 1.00226\n", + " 13/1 1.01503\n", + " 14/1 1.01195\n", + " 15/1 1.01079\n", + " 16/1 1.00054\n", + " 17/1 0.99742\n", + " 18/1 1.00577\n", + " 19/1 1.02088\n", + " 20/1 1.01246\n", + " 21/1 1.01198\n", + " 22/1 0.99621 1.00409 +/- 0.00788\n", + " 23/1 1.01483 1.00767 +/- 0.00579\n", + " 24/1 1.00061 1.00591 +/- 0.00446\n", + " 25/1 1.00577 1.00588 +/- 0.00345\n", + " 26/1 1.02079 1.00836 +/- 0.00376\n", + " 27/1 0.99468 1.00641 +/- 0.00373\n", + " 28/1 1.01004 1.00686 +/- 0.00326\n", + " 29/1 1.00174 1.00629 +/- 0.00293\n", + " 30/1 1.00836 1.00650 +/- 0.00263\n", + " 31/1 1.00349 1.00623 +/- 0.00240\n", + " 32/1 1.00414 1.00605 +/- 0.00219\n", + " 33/1 1.00559 1.00602 +/- 0.00202\n", + " 34/1 0.99914 1.00553 +/- 0.00193\n", + " 35/1 0.99346 1.00472 +/- 0.00197\n", + " 36/1 1.01869 1.00559 +/- 0.00204\n", + " 37/1 1.00135 1.00534 +/- 0.00193\n", + " 38/1 1.02210 1.00628 +/- 0.00205\n", + " 39/1 1.01476 1.00672 +/- 0.00199\n", + " 40/1 0.99363 1.00607 +/- 0.00199\n", + " 41/1 1.00109 1.00583 +/- 0.00191\n", + " 42/1 1.01019 1.00603 +/- 0.00183\n", + " 43/1 1.01389 1.00637 +/- 0.00178\n", + " 44/1 1.00938 1.00650 +/- 0.00171\n", + " 45/1 0.99871 1.00618 +/- 0.00167\n", + " 46/1 0.99567 1.00578 +/- 0.00166\n", + " 47/1 1.00367 1.00570 +/- 0.00160\n", + " 48/1 1.00425 1.00565 +/- 0.00154\n", + " 49/1 1.00738 1.00571 +/- 0.00149\n", + " 50/1 0.98480 1.00501 +/- 0.00160\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8171e+03 seconds\n", + " Time in transport only = 1.8167e+03 seconds\n", + " Time in inactive batches = 2.8780e+02 seconds\n", + " Time in active batches = 1.5293e+03 seconds\n", + " Time synchronizing fission bank = 1.8174e-01 seconds\n", + " Sampling source sites = 1.4617e-01 seconds\n", + " SEND/RECV source sites = 3.4910e-02 seconds\n", + " Time accumulating tallies = 8.8733e-02 seconds\n", + " Time writing statepoints = 9.8809e-03 seconds\n", + " Total time for finalization = 9.3226e-05 seconds\n", + " Total time elapsed = 1.8182e+03 seconds\n", + " Calculation Rate (inactive) = 2084.77 particles/second\n", + " Calculation Rate (active) = 588.496 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00510 +/- 0.00149\n", + " k-effective (Track-length) = 1.00501 +/- 0.00160\n", + " k-effective (Absorption) = 1.00332 +/- 0.00142\n", + " Combined k-effective = 1.00403 +/- 0.00136\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n4.h5...\n", + "[openmc.deplete] t=8640000.0 s, dt=15552000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60086\n", + " 2/1 0.86972\n", + " 3/1 0.96642\n", + " 4/1 0.97718\n", + " 5/1 0.99563\n", + " 6/1 0.99663\n", + " 7/1 0.99786\n", + " 8/1 1.00361\n", + " 9/1 0.99566\n", + " 10/1 1.01047\n", + " 11/1 0.99560\n", + " 12/1 1.01017\n", + " 13/1 1.00995\n", + " 14/1 1.00569\n", + " 15/1 0.99830\n", + " 16/1 1.00247\n", + " 17/1 1.00797\n", + " 18/1 1.01169\n", + " 19/1 0.98977\n", + " 20/1 1.00191\n", + " 21/1 1.00061\n", + " 22/1 1.01345 1.00703 +/- 0.00642\n", + " 23/1 0.99995 1.00467 +/- 0.00439\n", + " 24/1 1.00920 1.00580 +/- 0.00331\n", + " 25/1 0.99803 1.00425 +/- 0.00300\n", + " 26/1 1.01122 1.00541 +/- 0.00271\n", + " 27/1 0.98902 1.00307 +/- 0.00327\n", + " 28/1 0.99650 1.00225 +/- 0.00295\n", + " 29/1 1.01064 1.00318 +/- 0.00277\n", + " 30/1 1.00402 1.00326 +/- 0.00248\n", + " 31/1 0.99900 1.00288 +/- 0.00227\n", + " 32/1 1.01320 1.00374 +/- 0.00225\n", + " 33/1 1.00294 1.00367 +/- 0.00207\n", + " 34/1 1.01290 1.00433 +/- 0.00202\n", + " 35/1 1.00241 1.00421 +/- 0.00189\n", + " 36/1 0.99250 1.00347 +/- 0.00191\n", + " 37/1 0.99637 1.00306 +/- 0.00184\n", + " 38/1 1.01621 1.00379 +/- 0.00189\n", + " 39/1 1.00358 1.00378 +/- 0.00178\n", + " 40/1 1.00271 1.00372 +/- 0.00169\n", + " 41/1 1.01014 1.00403 +/- 0.00164\n", + " 42/1 1.00411 1.00403 +/- 0.00156\n", + " 43/1 1.01082 1.00433 +/- 0.00152\n", + " 44/1 1.01002 1.00456 +/- 0.00148\n", + " 45/1 1.01622 1.00503 +/- 0.00149\n", + " 46/1 0.99330 1.00458 +/- 0.00150\n", + " 47/1 1.00098 1.00445 +/- 0.00145\n", + " 48/1 1.01513 1.00483 +/- 0.00145\n", + " 49/1 1.00437 1.00481 +/- 0.00140\n", + " 50/1 1.00083 1.00468 +/- 0.00136\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8105e+03 seconds\n", + " Time in transport only = 1.8100e+03 seconds\n", + " Time in inactive batches = 2.8790e+02 seconds\n", + " Time in active batches = 1.5226e+03 seconds\n", + " Time synchronizing fission bank = 1.7775e-01 seconds\n", + " Sampling source sites = 1.4078e-01 seconds\n", + " SEND/RECV source sites = 3.6356e-02 seconds\n", + " Time accumulating tallies = 1.1440e-01 seconds\n", + " Time writing statepoints = 1.1593e-02 seconds\n", + " Total time for finalization = 1.8903e-04 seconds\n", + " Total time elapsed = 1.8115e+03 seconds\n", + " Calculation Rate (inactive) = 2084.08 particles/second\n", + " Calculation Rate (active) = 591.112 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00489 +/- 0.00143\n", + " k-effective (Track-length) = 1.00468 +/- 0.00136\n", + " k-effective (Absorption) = 1.00411 +/- 0.00115\n", + " Combined k-effective = 1.00418 +/- 0.00112\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n5.h5...\n", + "[openmc.deplete] t=24192000.0 s, dt=8208000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.59708\n", + " 2/1 0.88271\n", + " 3/1 0.95662\n", + " 4/1 0.96192\n", + " 5/1 1.00702\n", + " 6/1 0.97276\n", + " 7/1 1.01247\n", + " 8/1 0.99829\n", + " 9/1 0.99887\n", + " 10/1 0.99240\n", + " 11/1 1.01194\n", + " 12/1 0.99297\n", + " 13/1 1.01286\n", + " 14/1 1.00783\n", + " 15/1 1.01223\n", + " 16/1 0.99347\n", + " 17/1 1.00024\n", + " 18/1 0.99622\n", + " 19/1 1.00014\n", + " 20/1 1.00125\n", + " 21/1 0.99719\n", + " 22/1 0.97588 0.98654 +/- 0.01065\n", + " 23/1 1.00592 0.99300 +/- 0.00892\n", + " 24/1 1.00911 0.99703 +/- 0.00748\n", + " 25/1 1.00455 0.99853 +/- 0.00599\n", + " 26/1 0.99838 0.99851 +/- 0.00489\n", + " 27/1 1.00942 1.00007 +/- 0.00442\n", + " 28/1 0.99898 0.99993 +/- 0.00383\n", + " 29/1 0.98653 0.99844 +/- 0.00369\n", + " 30/1 1.00646 0.99924 +/- 0.00340\n", + " 31/1 0.99763 0.99910 +/- 0.00308\n", + " 32/1 1.00461 0.99956 +/- 0.00285\n", + " 33/1 1.00540 1.00001 +/- 0.00266\n", + " 34/1 1.00215 1.00016 +/- 0.00246\n", + " 35/1 1.01281 1.00100 +/- 0.00244\n", + " 36/1 0.99520 1.00064 +/- 0.00231\n", + " 37/1 1.00471 1.00088 +/- 0.00219\n", + " 38/1 0.99427 1.00051 +/- 0.00209\n", + " 39/1 1.01149 1.00109 +/- 0.00206\n", + " 40/1 1.00084 1.00108 +/- 0.00196\n", + " 41/1 1.00232 1.00114 +/- 0.00186\n", + " 42/1 1.00407 1.00127 +/- 0.00178\n", + " 43/1 1.00735 1.00153 +/- 0.00172\n", + " 44/1 0.98486 1.00084 +/- 0.00179\n", + " 45/1 0.99805 1.00073 +/- 0.00172\n", + " 46/1 0.99078 1.00035 +/- 0.00170\n", + " 47/1 1.00270 1.00043 +/- 0.00163\n", + " 48/1 0.99237 1.00015 +/- 0.00160\n", + " 49/1 0.99284 0.99989 +/- 0.00157\n", + " 50/1 0.99736 0.99981 +/- 0.00151\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8224e+03 seconds\n", + " Time in transport only = 1.8220e+03 seconds\n", + " Time in inactive batches = 2.8825e+02 seconds\n", + " Time in active batches = 1.5341e+03 seconds\n", + " Time synchronizing fission bank = 1.8792e-01 seconds\n", + " Sampling source sites = 1.5020e-01 seconds\n", + " SEND/RECV source sites = 3.6929e-02 seconds\n", + " Time accumulating tallies = 8.4246e-02 seconds\n", + " Time writing statepoints = 1.1513e-02 seconds\n", + " Total time for finalization = 1.3029e-04 seconds\n", + " Total time elapsed = 1.8235e+03 seconds\n", + " Calculation Rate (inactive) = 2081.5 particles/second\n", + " Calculation Rate (active) = 586.658 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 0.99966 +/- 0.00148\n", + " k-effective (Track-length) = 0.99981 +/- 0.00151\n", + " k-effective (Absorption) = 0.99907 +/- 0.00130\n", + " Combined k-effective = 0.99935 +/- 0.00121\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n6.h5...\n", + "[openmc.deplete] t=32400000.0 (final operator evaluation)\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60121\n", + " 2/1 0.88427\n", + " 3/1 0.95151\n", + " 4/1 0.96807\n", + " 5/1 0.97437\n", + " 6/1 0.98574\n", + " 7/1 0.99351\n", + " 8/1 0.99847\n", + " 9/1 1.01221\n", + " 10/1 0.99740\n", + " 11/1 0.99622\n", + " 12/1 1.00647\n", + " 13/1 1.00011\n", + " 14/1 0.98830\n", + " 15/1 1.00141\n", + " 16/1 0.99886\n", + " 17/1 0.99064\n", + " 18/1 0.98695\n", + " 19/1 0.99649\n", + " 20/1 0.98039\n", + " 21/1 1.00017\n", + " 22/1 1.00425 1.00221 +/- 0.00204\n", + " 23/1 0.98771 0.99738 +/- 0.00498\n", + " 24/1 1.01080 1.00073 +/- 0.00486\n", + " 25/1 0.99855 1.00030 +/- 0.00379\n", + " 26/1 1.00262 1.00068 +/- 0.00312\n", + " 27/1 1.00268 1.00097 +/- 0.00265\n", + " 28/1 1.00079 1.00095 +/- 0.00230\n", + " 29/1 0.99842 1.00067 +/- 0.00205\n", + " 30/1 0.99646 1.00024 +/- 0.00188\n", + " 31/1 0.99804 1.00004 +/- 0.00171\n", + " 32/1 0.98251 0.99858 +/- 0.00214\n", + " 33/1 0.98509 0.99755 +/- 0.00222\n", + " 34/1 0.99858 0.99762 +/- 0.00206\n", + " 35/1 1.00299 0.99798 +/- 0.00195\n", + " 36/1 0.99394 0.99773 +/- 0.00184\n", + " 37/1 0.99468 0.99755 +/- 0.00174\n", + " 38/1 0.99469 0.99739 +/- 0.00165\n", + " 39/1 0.99689 0.99736 +/- 0.00156\n", + " 40/1 1.02072 0.99853 +/- 0.00188\n", + " 41/1 0.99978 0.99859 +/- 0.00179\n", + " 42/1 1.00440 0.99885 +/- 0.00173\n", + " 43/1 0.98447 0.99823 +/- 0.00177\n", + " 44/1 0.99226 0.99798 +/- 0.00171\n", + " 45/1 0.99634 0.99791 +/- 0.00164\n", + " 46/1 0.99276 0.99771 +/- 0.00159\n", + " 47/1 0.99572 0.99764 +/- 0.00153\n", + " 48/1 1.00497 0.99790 +/- 0.00150\n", + " 49/1 1.01988 0.99866 +/- 0.00163\n", + " 50/1 1.00250 0.99879 +/- 0.00158\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8043e+03 seconds\n", + " Time in transport only = 1.8039e+03 seconds\n", + " Time in inactive batches = 2.8726e+02 seconds\n", + " Time in active batches = 1.5170e+03 seconds\n", + " Time synchronizing fission bank = 1.8194e-01 seconds\n", + " Sampling source sites = 1.4365e-01 seconds\n", + " SEND/RECV source sites = 3.7646e-02 seconds\n", + " Time accumulating tallies = 8.7450e-02 seconds\n", + " Time writing statepoints = 1.7304e-02 seconds\n", + " Total time for finalization = 1.0928e-04 seconds\n", + " Total time elapsed = 1.8054e+03 seconds\n", + " Calculation Rate (inactive) = 2088.7 particles/second\n", + " Calculation Rate (active) = 593.257 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 0.99850 +/- 0.00153\n", + " k-effective (Track-length) = 0.99879 +/- 0.00158\n", + " k-effective (Absorption) = 0.99839 +/- 0.00119\n", + " Combined k-effective = 0.99850 +/- 0.00118\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n7.h5...\n" + ] + } + ], + "source": [ + "model = openmc.model.Model(geometry,mats,settings)\n", + "\n", + "#Depletion general settings\n", + "depletion_days = [5,5,30,30,30,180,95]\n", + "power = 8e6 #total thermal power [W]\n", + "salt_mass = 4590 * 10**3 #grams\n", + "salt_volume =salt_mass/salt_density #cm3\n", + "salt.volume = salt_volume\n", + "\n", + "# Initialize depletion operator\n", + "op = openmc.deplete.CoupledOperator(model, normalization_mode = \"energy-deposition\")\n", + "\n", + "# Initialize integrator object and start depletion calculation \n", + "integrator = openmc.deplete.PredictorIntegrator(op, depletion_days, timestep_units='d', power=power)\n", + "integrator.add_transfer_rate(salt, ['Xe','Kr'], 4.067e-5)\n", + "integrator.add_transfer_rate(salt, ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'], 8.777e-3)\n", + "integrator.integrate()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "41d076d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's store some results that will be used later for comparison\n", + "results = openmc.deplete.Results('depletion_results.h5')\n", + "t, k = results.get_keff()\n", + "n_xe = 0\n", + "n_kr = 0\n", + "for nuc,_ in openmc.data.isotopes('Xe'):\n", + " n_xe += results.get_atoms(str(salt.id), nuc)[1]\n", + "for nuc,_ in openmc.data.isotopes('Kr'):\n", + " n_kr += results.get_atoms(str(salt.id), nuc)[1]" + ] + }, + { + "cell_type": "markdown", + "id": "415f0334", + "metadata": {}, + "source": [ + "# Critical Factor" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "826c26f9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's convert time from sec to days \n", + "t /= (3600 * 24)\n", + "\n", + "plt.figure()\n", + "ax = plt.subplot()\n", + "k1, = ax.plot(t, [k[0] for k in k], '--', c='red', label='keff wo removal rates')\n", + "ax1 = ax.twinx()\n", + "n1, = ax1.plot(t, n_xe, '--', c='green', label='Xe wo removal rates')\n", + "n3, = ax1.plot(t, n_kr, '--', c='blue', label='Kr wo removal rates')\n", + "ax.set_xlabel('Time[d]')\n", + "ax.set_ylabel(r'$k_{eff}$', color='r')\n", + "ax.tick_params(axis='y', colors='red')\n", + "ax1.set_yscale('log')\n", + "ax1.set_ylabel('Nuclides [atoms]')\n", + "ax1.legend(handles=[k1, n1, n3])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cc827041", + "metadata": {}, + "source": [ + "# Fission products\n", + "The removal rate for gaseous fission products has been tuned (see [here](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf)) to obtain a Xenon poison fractions matching the measurements reported during the MSRE U235 operation, of 0.3%-0.4%. \n", + "\n", + "The Xenon poison fraction is defined as:\n", + "$FP = \\frac{\\Sigma_a^{135}Xe}{\\Sigma_a^{235}U}$\n", + "\n", + "Let's plot the same quantity and see if we obtain values that matches the reference :" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dc07279e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Microscopic absorption cross section at 0.0253 eV\n", + "xs_xe135 = 2664214.0\n", + "xs_u235 = 686.006994850397\n", + "_, n_xe135 = results.get_atoms(str(salt.id), 'Xe135')\n", + "_, n_u235 = results.get_atoms(str(salt.id), 'U235')\n", + "# Poison fraction\n", + "pf = (xs_xe135*n_xe135)/(xs_u235*n_u235)*100\n", + "plt.figure()\n", + "plt.plot(t, pf)\n", + "plt.xlabel('Time [d]')\n", + "plt.ylabel('Xe posion fraction [%]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9d28beee", + "metadata": {}, + "source": [ + "# Inventory \n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "aa32c623", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inventory = dict()\n", + "for nuc,_ in openmc.data.isotopes('U'):\n", + " inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n", + "\n", + "for nuc in ['Pu238','Pu239','Pu240','Pu241','Pu242']:\n", + " inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n", + "\n", + "plt.figure()\n", + "for nuc, mass in inventory.items():\n", + " plt.plot(t, mass, label=nuc)\n", + "plt.xlabel('Time [y]')\n", + "plt.ylabel('Mass [g]')\n", + "plt.yscale('log')\n", + "plt.ylim(1e-5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7434b75d", + "metadata": {}, + "source": [ + "# Neutron absorption in the fuel" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0c5bd844", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import seaborn as sns\n", + "regex = re.compile(r'(\\d+|\\s+)')\n", + "\n", + "# All nuclides present in the fuel at last time-step\n", + "nucs = results.export_to_materials(-1)[0].get_nuclides()\n", + "\n", + "# Let's begin by making some useful groupings\n", + "gaseos = ['H', 'He', 'Ne', 'Ar', 'Kr', 'Xe', 'Rn'] #gaseous fission products\n", + "noble_metals = ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'] # noble metals fission products\n", + "metals = ['Cr','Mn','Fe','Co','Ni','Cu','Zn','Hf','Zr','W',]\n", + "halogens = ['F','Cl','Br','I','At']\n", + "alkali_metals = ['Li','Na','K','Rb','Cs']\n", + "alkali_earths= ['Be','Mg','Ca','Sr','Ba','Ra']\n", + "lanthanides = ['Y','La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb','Lu']\n", + "m_a = ['Ac','Th','Pa','Np','Am','Cm','Bk','Cf','Es','Fm','Md','No','Lr']\n", + "# Get fissile nuclides in the fuel, based on Ronen's rule for determining fissile isotopes\n", + "fissile = []\n", + "\n", + "for nuc in nucs:\n", + " elm = regex.split(nuc)[0]\n", + " a = round(openmc.data.atomic_mass(nuc))\n", + " z = openmc.data.ATOMIC_NUMBER[elm]\n", + " if 90 <= z <= 100:\n", + " ronen = 2*z -(a-z)\n", + " if ronen in [41,43,45]:\n", + " fissile.append(nuc) \n", + "\n", + "# Calculate totat absorption rate of fissile nuclides\n", + "tot_abs_rate = 0\n", + "for nuc in fissile:\n", + " tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + "\n", + "nuclides_stack = dict()\n", + "groups_stack = {'Gaseos':0, 'Noble metals':0, 'Metals':0, 'Halogens':0 , 'Alkali metals':0, 'Alkali earths':0, 'Lanthanides':0, 'MA':0, 'Others':0}\n", + "for nuc in nucs:\n", + " \n", + " if regex.split(nuc)[0] in ['U','Pu']:\n", + " nuclides_stack[nuc] = results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " nuclides_stack[nuc] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " nuclides_stack[nuc] /= tot_abs_rate\n", + " \n", + " \n", + " elif regex.split(nuc)[0] in gaseos:\n", + " groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in noble_metals:\n", + " groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in metals:\n", + " groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in halogens:\n", + " groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in alkali_metals:\n", + " groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in alkali_earths:\n", + " groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in lanthanides:\n", + " groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in m_a:\n", + " groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " else:\n", + " groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + "\n", + "# Divide each array by the total absorption reaction rate of fissile nuclides\n", + "for g in groups_stack.keys():\n", + " groups_stack[g] /= tot_abs_rate\n", + "\n", + "# Sort dictionary groups\n", + "groups_stack=dict(reversed(sorted(groups_stack.items(), key=lambda item: item[1][len(item)])))\n", + "\n", + "# Create red color palette for groups_stack\n", + "colors = list(reversed(sns.color_palette(\"Reds\", len(groups_stack))))\n", + "\n", + "# Order uranium series\n", + "u_series = {key:value for key,value in nuclides_stack.items() if key.startswith('U')}\n", + "u_series = dict(reversed(sorted(u_series.items(), key=lambda item: item[1][len(item)])))\n", + "# Create green color palette for Uranium isotopes\n", + "colors += list(reversed(sns.color_palette(\"Greens\", len(u_series))))\n", + "\n", + "# Order plutionium series\n", + "pu_series = {key:value for key,value in nuclides_stack.items() if key.startswith('Pu')}\n", + "pu_series = dict(reversed(sorted(pu_series.items(), key=lambda item: item[1][len(item)])))\n", + "# Create blue color palette for plutonium isotopes\n", + "colors += list(reversed(sns.color_palette(\"Blues\", len(pu_series))))\n", + "# Add uramium and plutonium series to the stack\n", + "groups_stack.update(u_series)\n", + "groups_stack.update(pu_series)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cc4b980e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "plt.stackplot(t, groups_stack.values(), labels=groups_stack.keys(), \n", + " edgecolor=\"black\", linewidth=0.5,colors=colors, alpha=0.8)\n", + "handles, labels = plt.gca().get_legend_handles_labels()\n", + "legend = plt.legend([handles[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n", + " [labels[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n", + " bbox_to_anchor=(1.05,1), loc='upper left',\n", + " borderaxespad=0, ncol=2,\n", + " fontsize=15)\n", + "plt.xlabel('Time [d]',weight='bold',fontsize=17)\n", + "plt.title('Neutrons absorption distribution per neutron absorbed in fissile isotopes',\n", + " weight='bold', fontsize=17)\n", + "plt.xticks(fontsize=13)\n", + "plt.yticks(fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c0c7c200", + "metadata": {}, + "source": [ + "Here we can visualize the neutrons absorption for each nuclide present in the fuel per neutrons absorption by fissile isotopes. In other words we can see, out of the total neutrons generated, where and how many we end up losing.\n", + "\n", + "It is useful to group together those isotopes with similar characteristics that individually wouldn't represent a big contribution to capture.\n", + "\n", + "Due to the low burnup and relatively short simulation time, we are far from equilibrium. However, we can notice the quick increase of absorption in the Pu isotopes, created from neutron capture of U238, and in particular of Pu239, due to its higher absorption cross section than U235.\n", + "\n", + "**Note**: Neutrons lost to leakage out of the core and capture in other isotopes other than fuel are not represented. Thus, approximately 1 neutron is missing from the counting (we know that a fission event releases approximately 2.3 neutrons). This is simply due to the fact that we have defined the fuel salt as our only depletable material." + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}