From c8b6a1efad0f75f592e836e7a0baa5c4f61ad86c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 11 May 2022 23:52:22 -0500 Subject: [PATCH] Updates to notebooks to use models and correct for API changes --- depletion.ipynb | 754 ++++++++++- mdgxs-part-i.ipynb | 494 +++---- mdgxs-part-ii.ipynb | 617 ++------- mgxs-part-i.ipynb | 569 ++++---- mgxs-part-ii.ipynb | 3072 ++++++++++++++++++++++--------------------- mgxs-part-iii.ipynb | 885 ++++++------- 6 files changed, 3338 insertions(+), 3053 deletions(-) diff --git a/depletion.ipynb b/depletion.ipynb index 0ab2f1a..f152972 100644 --- a/depletion.ipynb +++ b/depletion.ipynb @@ -125,7 +125,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -134,9 +134,9 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
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" ] }, "metadata": { @@ -284,7 +284,8 @@ "metadata": {}, "outputs": [], "source": [ - "operator = openmc.deplete.Operator(geometry, settings, \"./chain_simple.xml\")" + "model = openmc.Model(geometry=geometry, settings=settings)\n", + "operator = openmc.deplete.Operator(model, \"./chain_simple.xml\")" ] }, { @@ -346,7 +347,685 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, - "outputs": [], + "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 | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1-dev\n", + " Git SHA1 | 1be02f90bd0e9adb5a3311ee77c904a52ca7ebaa\n", + " Date/Time | 2022-05-12 00:02:21\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U234.h5\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading O17 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O17.h5\n", + " Reading U236 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U236.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr96.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading H2 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H2.h5\n", + " Reading c_H_in_H2O from\n", + " /home/pshriwise/data/xs/openmc/endfb71_hdf5/c_H_in_H2O.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + "[openmc.deplete] t=0.0 s, dt=2592000 s, source=174\n", + " Reading I135 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/I135.h5\n", + " Reading Xe135 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Xe135.h5\n", + " Reading Xe136 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Xe136.h5\n", + " Reading Cs135 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Cs135.h5\n", + " Reading Gd157 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Gd157.h5\n", + " Reading Gd156 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Gd156.h5\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.53790\n", + " 2/1 1.46903\n", + " 3/1 1.36829\n", + " 4/1 1.45600\n", + " 5/1 1.38633\n", + " 6/1 1.51748\n", + " 7/1 1.35208\n", + " 8/1 1.55790\n", + " 9/1 1.58144\n", + " 10/1 1.42114\n", + " 11/1 1.42185\n", + " 12/1 1.46298 1.44242 +/- 0.02057\n", + " 13/1 1.46751 1.45078 +/- 0.01453\n", + " 14/1 1.53137 1.47093 +/- 0.02261\n", + " 15/1 1.38387 1.45352 +/- 0.02470\n", + " 16/1 1.51570 1.46388 +/- 0.02267\n", + " 17/1 1.55438 1.47681 +/- 0.02312\n", + " 18/1 1.44013 1.47222 +/- 0.02054\n", + " 19/1 1.53542 1.47925 +/- 0.01942\n", + " 20/1 1.36717 1.46804 +/- 0.02068\n", + " 21/1 1.41912 1.46359 +/- 0.01922\n", + " 22/1 1.52130 1.46840 +/- 0.01820\n", + " 23/1 1.43972 1.46619 +/- 0.01688\n", + " 24/1 1.41279 1.46238 +/- 0.01609\n", + " 25/1 1.45216 1.46170 +/- 0.01499\n", + " 26/1 1.53062 1.46601 +/- 0.01467\n", + " 27/1 1.43426 1.46414 +/- 0.01391\n", + " 28/1 1.39795 1.46046 +/- 0.01362\n", + " 29/1 1.38172 1.45632 +/- 0.01353\n", + " 30/1 1.49705 1.45835 +/- 0.01300\n", + " 31/1 1.42760 1.45689 +/- 0.01245\n", + " 32/1 1.51946 1.45973 +/- 0.01221\n", + " 33/1 1.43375 1.45860 +/- 0.01172\n", + " 34/1 1.41749 1.45689 +/- 0.01135\n", + " 35/1 1.50162 1.45868 +/- 0.01103\n", + " 36/1 1.42764 1.45749 +/- 0.01067\n", + " 37/1 1.44483 1.45702 +/- 0.01027\n", + " 38/1 1.57292 1.46116 +/- 0.01073\n", + " 39/1 1.46150 1.46117 +/- 0.01035\n", + " 40/1 1.54874 1.46409 +/- 0.01042\n", + " 41/1 1.41823 1.46261 +/- 0.01019\n", + " 42/1 1.36083 1.45943 +/- 0.01036\n", + " 43/1 1.47536 1.45991 +/- 0.01006\n", + " 44/1 1.48400 1.46062 +/- 0.00978\n", + " 45/1 1.43449 1.45987 +/- 0.00953\n", + " 46/1 1.47191 1.46021 +/- 0.00926\n", + " 47/1 1.43326 1.45948 +/- 0.00904\n", + " 48/1 1.53823 1.46155 +/- 0.00904\n", + " 49/1 1.51592 1.46294 +/- 0.00891\n", + " 50/1 1.44665 1.46254 +/- 0.00870\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.9508e-01 seconds\n", + " Reading cross sections = 3.8826e-01 seconds\n", + " Total time in simulation = 1.4135e+00 seconds\n", + " Time in transport only = 1.4088e+00 seconds\n", + " Time in inactive batches = 2.4940e-01 seconds\n", + " Time in active batches = 1.1641e+00 seconds\n", + " Time synchronizing fission bank = 2.2862e-03 seconds\n", + " Sampling source sites = 1.9995e-03 seconds\n", + " SEND/RECV source sites = 2.7605e-04 seconds\n", + " Time accumulating tallies = 5.6571e-05 seconds\n", + " Time writing statepoints = 1.4376e-03 seconds\n", + " Total time for finalization = 2.8760e-05 seconds\n", + " Total time elapsed = 1.8154e+00 seconds\n", + " Calculation Rate (inactive) = 40095.6 particles/second\n", + " Calculation Rate (active) = 34360.8 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.46314 +/- 0.00636\n", + " k-effective (Track-length) = 1.46254 +/- 0.00870\n", + " k-effective (Absorption) = 1.46104 +/- 0.00528\n", + " Combined k-effective = 1.46184 +/- 0.00460\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n0.h5...\n", + "[openmc.deplete] t=2592000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.40447\n", + " 2/1 1.46409\n", + " 3/1 1.41697\n", + " 4/1 1.47293\n", + " 5/1 1.40942\n", + " 6/1 1.33075\n", + " 7/1 1.51129\n", + " 8/1 1.42074\n", + " 9/1 1.42390\n", + " 10/1 1.45595\n", + " 11/1 1.44417\n", + " 12/1 1.53237 1.48827 +/- 0.04410\n", + " 13/1 1.50886 1.49513 +/- 0.02637\n", + " 14/1 1.47570 1.49027 +/- 0.01927\n", + " 15/1 1.37653 1.46753 +/- 0.02721\n", + " 16/1 1.32482 1.44374 +/- 0.03255\n", + " 17/1 1.43044 1.44184 +/- 0.02757\n", + " 18/1 1.40452 1.43718 +/- 0.02433\n", + " 19/1 1.57279 1.45224 +/- 0.02622\n", + " 20/1 1.50655 1.45768 +/- 0.02407\n", + " 21/1 1.45460 1.45740 +/- 0.02178\n", + " 22/1 1.44881 1.45668 +/- 0.01989\n", + " 23/1 1.46728 1.45750 +/- 0.01832\n", + " 24/1 1.40401 1.45368 +/- 0.01738\n", + " 25/1 1.47119 1.45484 +/- 0.01622\n", + " 26/1 1.37773 1.45002 +/- 0.01592\n", + " 27/1 1.49799 1.45285 +/- 0.01522\n", + " 28/1 1.39252 1.44949 +/- 0.01474\n", + " 29/1 1.40978 1.44740 +/- 0.01409\n", + " 30/1 1.38517 1.44429 +/- 0.01373\n", + " 31/1 1.42478 1.44336 +/- 0.01309\n", + " 32/1 1.43802 1.44312 +/- 0.01248\n", + " 33/1 1.41992 1.44211 +/- 0.01197\n", + " 34/1 1.50261 1.44463 +/- 0.01174\n", + " 35/1 1.46153 1.44531 +/- 0.01128\n", + " 36/1 1.40062 1.44359 +/- 0.01097\n", + " 37/1 1.45439 1.44399 +/- 0.01056\n", + " 38/1 1.46819 1.44485 +/- 0.01022\n", + " 39/1 1.45317 1.44514 +/- 0.00986\n", + " 40/1 1.52401 1.44777 +/- 0.00988\n", + " 41/1 1.42428 1.44701 +/- 0.00959\n", + " 42/1 1.45040 1.44712 +/- 0.00929\n", + " 43/1 1.47218 1.44788 +/- 0.00903\n", + " 44/1 1.49330 1.44921 +/- 0.00886\n", + " 45/1 1.43803 1.44889 +/- 0.00861\n", + " 46/1 1.44053 1.44866 +/- 0.00837\n", + " 47/1 1.55714 1.45159 +/- 0.00866\n", + " 48/1 1.41795 1.45071 +/- 0.00847\n", + " 49/1 1.41987 1.44992 +/- 0.00829\n", + " 50/1 1.45097 1.44994 +/- 0.00808\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 = 1.4483e+00 seconds\n", + " Time in transport only = 1.4434e+00 seconds\n", + " Time in inactive batches = 2.5623e-01 seconds\n", + " Time in active batches = 1.1921e+00 seconds\n", + " Time synchronizing fission bank = 2.2229e-03 seconds\n", + " Sampling source sites = 1.9324e-03 seconds\n", + " SEND/RECV source sites = 2.8016e-04 seconds\n", + " Time accumulating tallies = 6.3820e-05 seconds\n", + " Time writing statepoints = 2.5644e-03 seconds\n", + " Total time for finalization = 4.0910e-05 seconds\n", + " Total time elapsed = 1.4557e+00 seconds\n", + " Calculation Rate (inactive) = 39026.8 particles/second\n", + " Calculation Rate (active) = 33555.2 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.44685 +/- 0.00591\n", + " k-effective (Track-length) = 1.44994 +/- 0.00808\n", + " k-effective (Absorption) = 1.43801 +/- 0.00415\n", + " Combined k-effective = 1.43997 +/- 0.00407\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n1.h5...\n", + "[openmc.deplete] t=5184000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.49609\n", + " 2/1 1.47153\n", + " 3/1 1.41531\n", + " 4/1 1.42063\n", + " 5/1 1.46329\n", + " 6/1 1.44056\n", + " 7/1 1.38761\n", + " 8/1 1.45656\n", + " 9/1 1.45922\n", + " 10/1 1.40711\n", + " 11/1 1.46788\n", + " 12/1 1.45795 1.46292 +/- 0.00497\n", + " 13/1 1.50546 1.47710 +/- 0.01447\n", + " 14/1 1.44094 1.46806 +/- 0.01365\n", + " 15/1 1.36331 1.44711 +/- 0.02347\n", + " 16/1 1.39131 1.43781 +/- 0.02130\n", + " 17/1 1.31723 1.42058 +/- 0.02491\n", + " 18/1 1.39658 1.41758 +/- 0.02178\n", + " 19/1 1.41203 1.41696 +/- 0.01922\n", + " 20/1 1.37762 1.41303 +/- 0.01764\n", + " 21/1 1.48863 1.41990 +/- 0.01737\n", + " 22/1 1.41002 1.41908 +/- 0.01588\n", + " 23/1 1.41208 1.41854 +/- 0.01462\n", + " 24/1 1.41126 1.41802 +/- 0.01354\n", + " 25/1 1.43126 1.41890 +/- 0.01264\n", + " 26/1 1.51326 1.42480 +/- 0.01321\n", + " 27/1 1.46624 1.42724 +/- 0.01265\n", + " 28/1 1.42494 1.42711 +/- 0.01192\n", + " 29/1 1.41976 1.42672 +/- 0.01129\n", + " 30/1 1.40582 1.42568 +/- 0.01076\n", + " 31/1 1.34030 1.42161 +/- 0.01101\n", + " 32/1 1.38143 1.41979 +/- 0.01066\n", + " 33/1 1.47056 1.42199 +/- 0.01042\n", + " 34/1 1.45693 1.42345 +/- 0.01008\n", + " 35/1 1.47827 1.42564 +/- 0.00991\n", + " 36/1 1.34483 1.42253 +/- 0.01002\n", + " 37/1 1.42930 1.42278 +/- 0.00964\n", + " 38/1 1.38584 1.42147 +/- 0.00939\n", + " 39/1 1.43818 1.42204 +/- 0.00908\n", + " 40/1 1.38034 1.42065 +/- 0.00888\n", + " 41/1 1.38352 1.41945 +/- 0.00867\n", + " 42/1 1.44455 1.42024 +/- 0.00843\n", + " 43/1 1.54961 1.42416 +/- 0.00906\n", + " 44/1 1.42095 1.42406 +/- 0.00879\n", + " 45/1 1.39984 1.42337 +/- 0.00857\n", + " 46/1 1.50165 1.42555 +/- 0.00860\n", + " 47/1 1.52897 1.42834 +/- 0.00882\n", + " 48/1 1.41814 1.42807 +/- 0.00859\n", + " 49/1 1.49425 1.42977 +/- 0.00854\n", + " 50/1 1.39230 1.42883 +/- 0.00838\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 = 1.4227e+00 seconds\n", + " Time in transport only = 1.4178e+00 seconds\n", + " Time in inactive batches = 2.4900e-01 seconds\n", + " Time in active batches = 1.1737e+00 seconds\n", + " Time synchronizing fission bank = 2.2354e-03 seconds\n", + " Sampling source sites = 1.9705e-03 seconds\n", + " SEND/RECV source sites = 2.5359e-04 seconds\n", + " Time accumulating tallies = 7.8271e-05 seconds\n", + " Time writing statepoints = 2.7186e-03 seconds\n", + " Total time for finalization = 6.4002e-03 seconds\n", + " Total time elapsed = 1.4349e+00 seconds\n", + " Calculation Rate (inactive) = 40160.6 particles/second\n", + " Calculation Rate (active) = 34081.6 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.43006 +/- 0.00591\n", + " k-effective (Track-length) = 1.42883 +/- 0.00838\n", + " k-effective (Absorption) = 1.42863 +/- 0.00572\n", + " Combined k-effective = 1.42936 +/- 0.00514\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n2.h5...\n", + "[openmc.deplete] t=7776000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.44525\n", + " 2/1 1.39725\n", + " 3/1 1.49818\n", + " 4/1 1.43473\n", + " 5/1 1.55325\n", + " 6/1 1.49043\n", + " 7/1 1.44001\n", + " 8/1 1.47096\n", + " 9/1 1.33569\n", + " 10/1 1.43309\n", + " 11/1 1.40207\n", + " 12/1 1.40982 1.40594 +/- 0.00388\n", + " 13/1 1.46991 1.42727 +/- 0.02144\n", + " 14/1 1.27811 1.38998 +/- 0.04025\n", + " 15/1 1.41299 1.39458 +/- 0.03152\n", + " 16/1 1.44922 1.40369 +/- 0.02730\n", + " 17/1 1.40275 1.40355 +/- 0.02307\n", + " 18/1 1.40665 1.40394 +/- 0.01998\n", + " 19/1 1.46326 1.41053 +/- 0.01882\n", + " 20/1 1.40650 1.41013 +/- 0.01684\n", + " 21/1 1.37708 1.40712 +/- 0.01552\n", + " 22/1 1.40019 1.40655 +/- 0.01418\n", + " 23/1 1.42877 1.40826 +/- 0.01316\n", + " 24/1 1.37671 1.40600 +/- 0.01239\n", + " 25/1 1.40717 1.40608 +/- 0.01153\n", + " 26/1 1.46304 1.40964 +/- 0.01136\n", + " 27/1 1.37648 1.40769 +/- 0.01085\n", + " 28/1 1.36343 1.40523 +/- 0.01052\n", + " 29/1 1.52641 1.41161 +/- 0.01182\n", + " 30/1 1.32142 1.40710 +/- 0.01208\n", + " 31/1 1.48502 1.41081 +/- 0.01208\n", + " 32/1 1.40243 1.41043 +/- 0.01152\n", + " 33/1 1.48399 1.41363 +/- 0.01147\n", + " 34/1 1.49694 1.41710 +/- 0.01151\n", + " 35/1 1.46212 1.41890 +/- 0.01119\n", + " 36/1 1.50103 1.42206 +/- 0.01120\n", + " 37/1 1.47787 1.42413 +/- 0.01098\n", + " 38/1 1.40935 1.42360 +/- 0.01059\n", + " 39/1 1.42517 1.42365 +/- 0.01022\n", + " 40/1 1.39736 1.42277 +/- 0.00991\n", + " 41/1 1.41014 1.42237 +/- 0.00960\n", + " 42/1 1.45449 1.42337 +/- 0.00935\n", + " 43/1 1.38649 1.42225 +/- 0.00913\n", + " 44/1 1.40900 1.42186 +/- 0.00886\n", + " 45/1 1.50622 1.42427 +/- 0.00894\n", + " 46/1 1.43840 1.42467 +/- 0.00869\n", + " 47/1 1.41787 1.42448 +/- 0.00846\n", + " 48/1 1.39728 1.42377 +/- 0.00826\n", + " 49/1 1.44498 1.42431 +/- 0.00807\n", + " 50/1 1.43243 1.42451 +/- 0.00787\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 = 1.4724e+00 seconds\n", + " Time in transport only = 1.4674e+00 seconds\n", + " Time in inactive batches = 2.6190e-01 seconds\n", + " Time in active batches = 1.2105e+00 seconds\n", + " Time synchronizing fission bank = 2.2506e-03 seconds\n", + " Sampling source sites = 1.9510e-03 seconds\n", + " SEND/RECV source sites = 2.8790e-04 seconds\n", + " Time accumulating tallies = 6.0710e-05 seconds\n", + " Time writing statepoints = 2.8315e-03 seconds\n", + " Total time for finalization = 4.5860e-05 seconds\n", + " Total time elapsed = 1.4844e+00 seconds\n", + " Calculation Rate (inactive) = 38181.9 particles/second\n", + " Calculation Rate (active) = 33043.1 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.42805 +/- 0.00568\n", + " k-effective (Track-length) = 1.42451 +/- 0.00787\n", + " k-effective (Absorption) = 1.42588 +/- 0.00400\n", + " Combined k-effective = 1.42628 +/- 0.00367\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n3.h5...\n", + "[openmc.deplete] t=10368000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.42372\n", + " 2/1 1.36392\n", + " 3/1 1.56019\n", + " 4/1 1.41091\n", + " 5/1 1.44355\n", + " 6/1 1.42948\n", + " 7/1 1.43458\n", + " 8/1 1.42146\n", + " 9/1 1.35445\n", + " 10/1 1.42498\n", + " 11/1 1.43976\n", + " 12/1 1.42404 1.43190 +/- 0.00786\n", + " 13/1 1.38913 1.41764 +/- 0.01496\n", + " 14/1 1.43466 1.42190 +/- 0.01140\n", + " 15/1 1.48155 1.43383 +/- 0.01484\n", + " 16/1 1.37786 1.42450 +/- 0.01529\n", + " 17/1 1.46886 1.43084 +/- 0.01440\n", + " 18/1 1.38946 1.42566 +/- 0.01350\n", + " 19/1 1.41925 1.42495 +/- 0.01193\n", + " 20/1 1.38620 1.42108 +/- 0.01135\n", + " 21/1 1.35242 1.41484 +/- 0.01201\n", + " 22/1 1.48247 1.42047 +/- 0.01233\n", + " 23/1 1.44192 1.42212 +/- 0.01146\n", + " 24/1 1.34402 1.41654 +/- 0.01199\n", + " 25/1 1.44718 1.41859 +/- 0.01135\n", + " 26/1 1.47729 1.42225 +/- 0.01123\n", + " 27/1 1.52300 1.42818 +/- 0.01210\n", + " 28/1 1.41466 1.42743 +/- 0.01143\n", + " 29/1 1.51501 1.43204 +/- 0.01175\n", + " 30/1 1.47697 1.43429 +/- 0.01138\n", + " 31/1 1.45582 1.43531 +/- 0.01087\n", + " 32/1 1.44561 1.43578 +/- 0.01037\n", + " 33/1 1.45486 1.43661 +/- 0.00995\n", + " 34/1 1.40667 1.43536 +/- 0.00960\n", + " 35/1 1.54290 1.43966 +/- 0.01017\n", + " 36/1 1.35465 1.43639 +/- 0.01030\n", + " 37/1 1.43986 1.43652 +/- 0.00991\n", + " 38/1 1.40741 1.43548 +/- 0.00961\n", + " 39/1 1.35348 1.43265 +/- 0.00969\n", + " 40/1 1.34159 1.42962 +/- 0.00984\n", + " 41/1 1.47708 1.43115 +/- 0.00964\n", + " 42/1 1.49474 1.43314 +/- 0.00955\n", + " 43/1 1.40445 1.43227 +/- 0.00929\n", + " 44/1 1.36593 1.43032 +/- 0.00922\n", + " 45/1 1.38073 1.42890 +/- 0.00907\n", + " 46/1 1.42638 1.42883 +/- 0.00881\n", + " 47/1 1.40671 1.42823 +/- 0.00859\n", + " 48/1 1.36251 1.42650 +/- 0.00854\n", + " 49/1 1.43370 1.42669 +/- 0.00832\n", + " 50/1 1.39115 1.42580 +/- 0.00816\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 = 1.4343e+00 seconds\n", + " Time in transport only = 1.4296e+00 seconds\n", + " Time in inactive batches = 2.5415e-01 seconds\n", + " Time in active batches = 1.1801e+00 seconds\n", + " Time synchronizing fission bank = 2.1958e-03 seconds\n", + " Sampling source sites = 1.9276e-03 seconds\n", + " SEND/RECV source sites = 2.5595e-04 seconds\n", + " Time accumulating tallies = 4.9060e-05 seconds\n", + " Time writing statepoints = 2.6309e-03 seconds\n", + " Total time for finalization = 4.0060e-05 seconds\n", + " Total time elapsed = 1.4403e+00 seconds\n", + " Calculation Rate (inactive) = 39346.2 particles/second\n", + " Calculation Rate (active) = 33894.5 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.41659 +/- 0.00659\n", + " k-effective (Track-length) = 1.42580 +/- 0.00816\n", + " k-effective (Absorption) = 1.40992 +/- 0.00492\n", + " Combined k-effective = 1.41334 +/- 0.00460\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n4.h5...\n", + "[openmc.deplete] t=12960000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.50840\n", + " 2/1 1.37871\n", + " 3/1 1.44147\n", + " 4/1 1.37502\n", + " 5/1 1.51166\n", + " 6/1 1.36480\n", + " 7/1 1.37474\n", + " 8/1 1.50120\n", + " 9/1 1.48437\n", + " 10/1 1.43839\n", + " 11/1 1.42606\n", + " 12/1 1.36163 1.39384 +/- 0.03222\n", + " 13/1 1.43083 1.40617 +/- 0.02232\n", + " 14/1 1.43214 1.41266 +/- 0.01706\n", + " 15/1 1.39529 1.40919 +/- 0.01367\n", + " 16/1 1.40656 1.40875 +/- 0.01117\n", + " 17/1 1.45853 1.41586 +/- 0.01182\n", + " 18/1 1.38835 1.41242 +/- 0.01080\n", + " 19/1 1.37392 1.40814 +/- 0.01044\n", + " 20/1 1.44311 1.41164 +/- 0.00997\n", + " 21/1 1.37689 1.40848 +/- 0.00955\n", + " 22/1 1.44170 1.41125 +/- 0.00915\n", + " 23/1 1.42896 1.41261 +/- 0.00853\n", + " 24/1 1.40808 1.41229 +/- 0.00790\n", + " 25/1 1.38138 1.41023 +/- 0.00764\n", + " 26/1 1.47825 1.41448 +/- 0.00831\n", + " 27/1 1.50549 1.41983 +/- 0.00947\n", + " 28/1 1.44024 1.42097 +/- 0.00900\n", + " 29/1 1.55001 1.42776 +/- 0.01089\n", + " 30/1 1.45065 1.42890 +/- 0.01039\n", + " 31/1 1.44314 1.42958 +/- 0.00991\n", + " 32/1 1.53581 1.43441 +/- 0.01061\n", + " 33/1 1.34584 1.43056 +/- 0.01085\n", + " 34/1 1.46262 1.43189 +/- 0.01047\n", + " 35/1 1.40896 1.43098 +/- 0.01008\n", + " 36/1 1.43938 1.43130 +/- 0.00969\n", + " 37/1 1.42939 1.43123 +/- 0.00933\n", + " 38/1 1.36832 1.42898 +/- 0.00927\n", + " 39/1 1.41975 1.42866 +/- 0.00895\n", + " 40/1 1.44832 1.42932 +/- 0.00867\n", + " 41/1 1.42290 1.42911 +/- 0.00839\n", + " 42/1 1.39116 1.42793 +/- 0.00821\n", + " 43/1 1.51719 1.43063 +/- 0.00840\n", + " 44/1 1.37441 1.42898 +/- 0.00832\n", + " 45/1 1.40624 1.42833 +/- 0.00810\n", + " 46/1 1.43779 1.42859 +/- 0.00788\n", + " 47/1 1.46176 1.42949 +/- 0.00771\n", + " 48/1 1.35875 1.42763 +/- 0.00773\n", + " 49/1 1.47829 1.42893 +/- 0.00765\n", + " 50/1 1.45400 1.42955 +/- 0.00748\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 = 1.4193e+00 seconds\n", + " Time in transport only = 1.4146e+00 seconds\n", + " Time in inactive batches = 2.5120e-01 seconds\n", + " Time in active batches = 1.1681e+00 seconds\n", + " Time synchronizing fission bank = 2.1310e-03 seconds\n", + " Sampling source sites = 1.8703e-03 seconds\n", + " SEND/RECV source sites = 2.4971e-04 seconds\n", + " Time accumulating tallies = 5.0790e-05 seconds\n", + " Time writing statepoints = 2.6284e-03 seconds\n", + " Total time for finalization = 3.8810e-05 seconds\n", + " Total time elapsed = 1.4249e+00 seconds\n", + " Calculation Rate (inactive) = 39808.3 particles/second\n", + " Calculation Rate (active) = 34244.1 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.42351 +/- 0.00647\n", + " k-effective (Track-length) = 1.42955 +/- 0.00748\n", + " k-effective (Absorption) = 1.42123 +/- 0.00460\n", + " Combined k-effective = 1.42301 +/- 0.00453\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n5.h5...\n", + "[openmc.deplete] t=15552000.0 (final operator evaluation)\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.45179\n", + " 2/1 1.33474\n", + " 3/1 1.40530\n", + " 4/1 1.34120\n", + " 5/1 1.46186\n", + " 6/1 1.48744\n", + " 7/1 1.47086\n", + " 8/1 1.40613\n", + " 9/1 1.42509\n", + " 10/1 1.41891\n", + " 11/1 1.37455\n", + " 12/1 1.41770 1.39613 +/- 0.02158\n", + " 13/1 1.43778 1.41001 +/- 0.01865\n", + " 14/1 1.38670 1.40418 +/- 0.01442\n", + " 15/1 1.53620 1.43059 +/- 0.02867\n", + " 16/1 1.34925 1.41703 +/- 0.02705\n", + " 17/1 1.29340 1.39937 +/- 0.02889\n", + " 18/1 1.46871 1.40804 +/- 0.02648\n", + " 19/1 1.43752 1.41131 +/- 0.02358\n", + " 20/1 1.44796 1.41498 +/- 0.02141\n", + " 21/1 1.35480 1.40951 +/- 0.02012\n", + " 22/1 1.41545 1.41000 +/- 0.01837\n", + " 23/1 1.38735 1.40826 +/- 0.01699\n", + " 24/1 1.31729 1.40176 +/- 0.01702\n", + " 25/1 1.35917 1.39892 +/- 0.01610\n", + " 26/1 1.45585 1.40248 +/- 0.01547\n", + " 27/1 1.44240 1.40483 +/- 0.01472\n", + " 28/1 1.37028 1.40291 +/- 0.01401\n", + " 29/1 1.40159 1.40284 +/- 0.01325\n", + " 30/1 1.44674 1.40503 +/- 0.01276\n", + " 31/1 1.40583 1.40507 +/- 0.01214\n", + " 32/1 1.35239 1.40268 +/- 0.01182\n", + " 33/1 1.42400 1.40360 +/- 0.01133\n", + " 34/1 1.47846 1.40672 +/- 0.01129\n", + " 35/1 1.40109 1.40650 +/- 0.01083\n", + " 36/1 1.39432 1.40603 +/- 0.01042\n", + " 37/1 1.29680 1.40198 +/- 0.01081\n", + " 38/1 1.42088 1.40266 +/- 0.01044\n", + " 39/1 1.39365 1.40235 +/- 0.01008\n", + " 40/1 1.44057 1.40362 +/- 0.00982\n", + " 41/1 1.37402 1.40267 +/- 0.00954\n", + " 42/1 1.40850 1.40285 +/- 0.00924\n", + " 43/1 1.49684 1.40570 +/- 0.00940\n", + " 44/1 1.35129 1.40410 +/- 0.00926\n", + " 45/1 1.48278 1.40635 +/- 0.00927\n", + " 46/1 1.43966 1.40727 +/- 0.00905\n", + " 47/1 1.47286 1.40904 +/- 0.00898\n", + " 48/1 1.37405 1.40812 +/- 0.00879\n", + " 49/1 1.40086 1.40794 +/- 0.00856\n", + " 50/1 1.45317 1.40907 +/- 0.00842\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 = 1.4688e+00 seconds\n", + " Time in transport only = 1.4635e+00 seconds\n", + " Time in inactive batches = 2.6092e-01 seconds\n", + " Time in active batches = 1.2078e+00 seconds\n", + " Time synchronizing fission bank = 2.3775e-03 seconds\n", + " Sampling source sites = 2.0713e-03 seconds\n", + " SEND/RECV source sites = 2.9489e-04 seconds\n", + " Time accumulating tallies = 6.9540e-05 seconds\n", + " Time writing statepoints = 2.7717e-03 seconds\n", + " Total time for finalization = 5.0230e-05 seconds\n", + " Total time elapsed = 1.4749e+00 seconds\n", + " Calculation Rate (inactive) = 38326.4 particles/second\n", + " Calculation Rate (active) = 33116.9 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.41453 +/- 0.00635\n", + " k-effective (Track-length) = 1.40907 +/- 0.00842\n", + " k-effective (Absorption) = 1.41404 +/- 0.00479\n", + " Combined k-effective = 1.41418 +/- 0.00459\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n6.h5...\n" + ] + } + ], "source": [ "integrator.integrate()" ] @@ -369,10 +1048,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "depletion_results.h5\t openmc_simulation_n3.h5 statepoint.50.h5\r\n", - "openmc_simulation_n0.h5 openmc_simulation_n4.h5 summary.h5\r\n", - "openmc_simulation_n1.h5 openmc_simulation_n5.h5\r\n", - "openmc_simulation_n2.h5 openmc_simulation_n6.h5\r\n" + "c5g7.h5\t\t\t openmc_simulation_n2.h5 openmc_simulation_n6.h5\n", + "depletion_results.h5\t openmc_simulation_n3.h5 statepoint.50.h5\n", + "openmc_simulation_n0.h5 openmc_simulation_n4.h5 summary.h5\n", + "openmc_simulation_n1.h5 openmc_simulation_n5.h5\n" ] } ], @@ -400,7 +1079,16 @@ "cell_type": "code", "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/deplete/results_list.py:202: FutureWarning: The get_eigenvalue(...) function has been renamed get_keff and will be removed in a future version of OpenMC.\n", + " warn(\"The get_eigenvalue(...) function has been renamed get_keff and \"\n" + ] + } + ], "source": [ "time, k = results.get_eigenvalue()" ] @@ -463,7 +1151,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -575,7 +1263,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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8BSxz95lm1gVYGttYIuE7oW0mL98yjGOPacrYp2bx4HvL1WkuAlh9+IeQm5vreXl5YceQBFVYVMxPnp/Lq/M2cmH/HH53QS/SUpLDjiUSc2Y2K7j4+yuOOP+UmWUB3wM6lV3f3cdE8d5VwB6gGDhUPoCZjQLuAEqAQ8AP3X1amdczgEXAS+5+q5k1Bp4Hugbb/I+7336kHCLV0TA1mfsuPZGuWU3426SlrN62l4euHEDLJmlhRxMJRTSnql4GMoF3gdfKPKI1wt37VVS1gElAX3fvB4wBJpR7/Q5garlld7v78cCJwDAzO7sKWUSOipnxozOP5d5LT2T++l2MGvchn+fvCTuWSCiimfG2sbv/LBY7d/eCMt+mU+Ze5mY2AMgG3gRyg/X3AVOC5wfNbDaQE4tsIhX5Tt+2tG/eiO89MYsLHviI+y47kRHHtQ47lkitiqbF8aqZnXOU23fgbTObZWY3VLSCmZ1vZkuItGLGBMuSgHuAn1S2YTNrBpxLpNVS0es3mFmemeVt2bLlKOOLfN2JHZrzyq3DaN+iMdc9NpNHp61Up7nUK9EUjh8QKR6FZrYneOyOcvvD3b0/cDZwi5mdUn4Fd58YnHo6j8ipKYCbgdfdfV1FGw3uDfI0cK+7r6hoHXd/2N1z3T03Kysryrgi0WnbrBEvjB3KGT2y+c2ri/jFSwsoKi4JO5ZIrTjiqSp3b3q0G3f39cHXzWY2ERjE1/ssStedamZdzKwVMBQ42cxuBpoADcysoExH+MPAUnf/69FmE6mu9LQUxl8xgD++9Rnj31/O6m17eeCyAWQ2Tg07mkhMRTVXlZl9x8zuDh7fjvI96WbWtPQ5cBawoNw63czMguf9gTRgm7tf7u4d3L0TkdNVT5QWDTO7k0hn/Q+jySESS0lJxu1nH8+fLurDjJXbOf+BD1m5dW/YsURiKpq5qv5A5HTVouDxAzP7fRTbzgammdlcYAbwmru/aWZjzWxssM6FwAIzmwOMA0b7YU4Wm1kO8AugJzDbzOaY2fVRZBGJqYtz2/PP64ewY99Bzhv3IR8t2xp2JJGYOeIFgGY2D+jn7iXB98nAp+6eMLPk6gJAqS1rtu3jusdnsnLrXn4zqheXDdZdliVxVXYBYLTTqjcr8zyzRhKJ1EEdWjbmxZtPYli3VvzPxPn8+j8LKS7RiCupW6K5juP3wKdmNgUw4BRAV2uLVCKjYSqPXJ3Lna8t5h8frmLl1r3cd+mJNG2oTnOpG6KaqyqYHXdg8O0Md0+oqUJ1qkrC8tT01fzylYV0zUrnkasH0r5F47AjiUStyqeqzOz44Gt/oA2wLni0DZaJyBFcMaQjT4wZxKZdhYwa9yF5q7aHHUmk2iptcZjZw+5+Q3CKqjx395GxjVZz1OKQsC3fUsD1j+exfsd+/nBhby7or5lyJP5V1uKIZlRVQ3cvPNKyeKbCIfFg576D3PTUbD5esY2bT+vKT846jqQkCzuWSKWqM6rqoyiXichhNGvcgCeuG8Slg9rzwHvLuemfs9h38FDYsUSqrNJRVWZ2DNAOaGRmJxIZUQWQAaiHT+QopCYn8bvze9OtdVN++9oivvHXqVw2qCMXDmhH66YNw44nEpXD9XFcDVxDZErzmXxZOHYDj7v7v2sjYE3QqSqJRx8s3cK9k5Yyc9UOkpOMkce3ZnRue047LouU5GgvsRKJner0cVzo7i/GLFktUOGQeLZscwHP563lxdnr2FpwkNZN07g4N4dLctvTsWV62PGkHqtO4fgd8Ed33xl83xz4sbv/v1gEjQUVDkkERcUlTFq8mefy1vLeZ5spcRjapSWjB7bnm72OoWGq7nMutas6heNTdz+x3LLZwX02EoIKhySaTbsKeWHWWp7NW8va7fvJaJjCeSe245Lc9vRqp1l/alpJiTNv/S7eXZTPp2t38JtRveia1STsWKGrTuGYBwx09wPB942APHc/ISZJY0CFQxJVSYkzfcU2npm5ljcXbuLgoRJ6tctgdG57vtOvHZmNNI3J0SosKubj5dt4e1E+kxbns3nPAZKTjCSDs044hnGXJcz/jWOmssIRzVxV/wQmmdk/gu+vBR6vyXAiUrGkJOOkbq04qVsrdu47yEufrufZvHX878sLufO1xZzTuw2jB7ZncOcWBLe2kcPYvvcgU5Zs5p1F+UxduoV9B4tJb5DMqcdlcWbPbEYc15qHpq5g/PvLWbZ5D91aH/V97Oq0aOeqOhs4Pfj2HXd/K6apaphaHFKXuDsL1u/mmZlreGXOBvYcOETnVulcnJvDRf1zaJ2hYb1lrdq6l3cW5fPO4nzyVm2nxOGYjIac0bM1Z/TIZmjXlqSlfNl/tK3gAMPvmsI3ex3DX0b3Cy94HDjqU1V1gQqH1FX7Dxbz+vyNPJu3lhkrt5OcZIw4rjWjB7ZnRD0d1ltS4ny6difvLs7nnUX5LNtcAECPNhmc2aM1Z/Y8hl7tMg7bQrvz1UU8+uFKJv/4NDq1qr8j26rTxzEEuA/oATQAkoG97p4Ri6CxoMIh9cGKLQU8l7eOF2atY2vBAVo3TePCAZFhvZ3r+B+/wqJipi3dyruL83l38Wa2FhwgJckY3KUFZ/bI5vQe2VWamXjz7kKG/3EK5/Vryx8v6hvD5PGtOoUjD/gu8DyRiwGvAo5195/HImgsqHBIfVJUXMKUJZt5duZapgTDegd3bsF3B7Xn7F5t6syw3q0FB5gc9Fd8sHQLhUUlNE1L+aK/4rTjWldr8MAvX17APz9Zw5SfnFZvp8OvVuFw91wzm1d6u9iKhujGMxUOqa827SrkxdnreC5vLau37aNpwxTO69eO0QMTc1jv8i0FvLMon3cX5TNrzQ7coW1mQ87smc0ZPbMZ3LklDVJq5vTchp37OfVPU7gktz2/Pb93jWwz0VSncEwFzgAmAJuAjcA17p4w7TcVDqnvSkqc6Su38dzMtby+IDKs94S2GYwe2J5RfduR2Tg+h/UWlziz1+zg3UWR/ooVW/cC0KtdBmf0yObMntn0bHP4/orq+Pm/5/PirHVM/e8RHJNZ/wYdVKdwdATyifRv/IjIPccfcPdlsQgaCyocIl/ata+Il+eu55kZa1m0cTdpKUmc3esYRg/swJAu4Q/r3XfwEB8s3co7i/KZvGQz2/ceJDXZGNKlJWf1jPRXtG3WqFayrNm2jxH3vMdVQzvyy3MT5tK1GlOtUVXBRX8d3P2zWISLNRUOkYotWL+LZ2eu5aU569lTeIiOLRtzSW57LhqQQ3YtDuvdvKeQyYsj/RXTlm3lwKESMhqmMOL41pzZM5tTj80K7Z7tP35uLq/O28C0n40kq2laKBnCUp0Wx7nA3UADd+9sZv2A37j7d2KSNAZUOEQOb//BYt5cuJFnZqzlky+G9WZxSW57RhzfmtQaHtbr7izbXMDbi/J5d3E+c9buxB1ymjfizJ7ZnNkjm4GdW9T4fo/Gii0FnPHn9/neyV34+Tk9wo5Tq6pTOGYBI4H3SjvEzWy+uydMb5EKh0j0Vm7dy3N5a3lh1jq27DlAVtM0Luyfw+iB1RvWe6i4hFmrd3xxMd7qbfsA6JuTGemvOCGb47Kbhn6qrCK3Pf0p7y7OZ9rPRtIivUHYcWpNdQrHdHcfUnYkVdkRVolAhUOk6g4VlzDlsy1fDOstLnEGdW7BdwdGhvU2anDkYb17Dxxi6udbeGdxPlOWbGbHviIaJCdxUreWnNEjmzN6ZCdEp/Pn+Xs46y9TuXVEN37yjePCjlNrqlM4HgEmAbcDFwK3AanuPjYWQWNBhUOkejbvLuSF2et4buZaVm3bR9O0FEad2JbRuR2+dhV2/u7CL67a/mjZNg4Wl9CscSojj4v0V5x8bBZN0qKZJi++3PTULKYt3cq020fWm8klq1M4GgO/AM4KFr0F3OnuhTWeMkZUOERqhrvzycrtPDtzLa/P38iBQyX0aJPBJbk5FBQe4t3F+cxdtwuAji0bc2aPyPUVuR2bJ/z0Jws37OJb907jR2ccyw/O6B52nFpxVIXDzJKBd919RCzDxZoKh0jN27W/iFfmbuDZmWtYsH43AP3aN+PMntmc1TObbq2bxGV/RXVc//hMZq7awbSfjQhtlFdtOqpp1d292MxKzCzT3XfFLp6IJJrMRqlcOaQjVw7pyLLNBWQ0TKnzM/N+f2R3Ro37kCenr+bm07qFHSc00ZxoLADmm9k7wN7She5+W8xSiUhC6da6ftwtr2/7ZpxybBYTPljJNSd1onGDxOurqQnRnHT8N/C/wFRgVpmHiEi9c9vIbmzfe5B/fbIm7CihOWK5dHfd7U9EJJDbqQVDurTg4akruGJIxzoz23BVJPYwBxGRENw2sjub9xzguby1YUcJhQqHiEgVDe3akgEdmzP+veUcPFQSdpxaF9PCYWarzGy+mc0JbghV/vVRZjav9HUzG17u9QwzW2dm95dZNiDY5jIzu9fq2ng/EYl7Zsb3R3ZjQ3C/k/rmiIXDzI41s7+b2dtmNrn0UYV9jHD3fhWNBSZyRXpfd+8HjCFyz4+y7iDSKV/Wg8D3gO7B45tVyCIiUiNOPTaLPjmZPPDeMoqK61erI5oWx/PAbOD/AT8t86g2dy/wL69ATAe+uBrRzAYA2cDbZZa1ATLcfXrwvieA82oii4hIVURaHd1Zu30/L8/ZEHacWhVN4Tjk7g+6+wx3n1X6iHL7DrxtZrPM7IaKVjCz881sCfAakVYHZpYE3AP8pNzq7YCy7cJ1wbKKtntDcPorb8uWLVHGFRGJ3hk9WtOjTQYPTFlGccmR721UV0RTOP5jZjebWRsza1H6iHL7w929P3A2cIuZnVJ+BXef6O7HE2k53BEsvhl43d2P+uShuz/s7rnunpuVlXW0mxERqVRpX8eKrXt5dV79aXVEc9nj1cHXsqenHOhypDe6+/rg62YzmwgM4ut9FqXrTjWzLmbWChgKnGxmNwNNgAZmVgD8Dcgp87YcYH0Un0FEJCa+ecIxdG/dhHFTlnFun7YkJdX98TpHbHG4e+cKHkcsGmaWbmZNS58TmV13Qbl1upWOijKz/kAasM3dL3f3Du7eicjpqifc/XZ33wjsNrMhwfuuAl6u2kcWEak5SUnGrSO78Xl+AW8v2hR2nFoRzaiqVDO7zcxeCB63mlk000JmA9PMbC4wA3jN3d80s7FmVnovjwuBBWY2BxgHjC7TWV6Zm4mMvloGLAfeiCKLiEjMfLtPWzq3Sue+ycs48p+wxBfN/TgmAKlA6dQjVwLF7n59jLPVGE2rLiKx9lzeWv77hXk8cnUup/fIDjtOjahsWvVoOscHuvvV7j45eFwLDKz5iCIiiev8E9uR07wR99aDVkc0haPYzLqWfmNmXYDi2EUSEUk8qclJ3HRaV+au3ckHS7eGHSemoikcPwWmmNl7ZvY+MBn4cWxjiYgknosG5NAmsyH3TV5ap1sd0YyqmkRkao/bgO8Dx7n7lFgHExFJNGkpydx4ShdmrtrB9BXbw44TM5UWDjMbGXy9APgW0C14fCtYJiIi5Xx3UAdaNUnjvslLw44SM4e7APBUIqelzq3gNSdyZ0ARESmjYWqk1fHb1xcza/V2BnSMdqKNxHHE4bh1gYbjikht2nfwEMPvmkKfnEweu3ZQ2HGO2lEPxzWzHwT3xTAzm2Bms83srNjEFBFJfI0bpHDd8M6899kW5q3bGXacGhfNqKox7r6byJQhLYlcAPiHmKYSEUlwVw3tSGajVO6bvCzsKDUumsJROmPXOUTmjFpYZpmIiFSgacNUrh3WiXcW5bN44+6w49SoaArHLDN7m0jheCuYuLB+3e5KROQoXHtSZ5qkpXB/HWt1RFM4rgNuJzL1yD4i81ZdG9NUIiJ1QGbjVK4a2pHXF2xk2eY9YcepMdEUjqHAZ+6+08yuIHIL2V2xjSUiUjdcN7wzDVOS61SrI5rC8SCwz8z6EplqZDmRe32LiMgRtGySxhVDOvDK3A2s3Lo37Dg1Itp7jjswCrjf3ccBTWMbS0Sk7vjeKV1ITU7igSl1o9URTeHYY2Y/B64AXjOzJCL9HCIiEoXWTRty6aAOTPx0PWu37ws7TrVFUzhGAweA69x9E5H7fP8ppqlEROqYG0/tQpIZ499fHnaUaotmdtxN7v5nd/8g+H6Nu6uPQ0SkCtpkNuKi3Byez1vHpl2FYceplsPNjjst+LrHzHaXeewxs7p1NYuISC246dSulLgnfKvjcC2OywHcvam7Z5R5NHX3jFrKJyJSZ7Rv0ZjzT2zH0zPWsHlP4rY6Dlc4JpY+MbMXayGLiEidd/OIbhQVlzDhg5VhRzlqhyscZeej6hLrICIi9UHnVumc27ctT01fzfa9B8OOc1QOVzi8kuciIlINt47oxv6iYh6ZtiLsKEflcIWjb2lnONBHneMiIjWje3ZTzu51DI9/tJpd+4rCjlNllRYOd08u0xmeos5xEZGac+uI7hQcOMQ/Pkq8vo5oLgAUEZEa1rNtBmf0yObRaSvZU5hYrQ4VDhGRkNx2ejd2Fx7iyemrw45SJSocIiIh6ZPTjFOPzWLCByvZd/BQ2HGipsIhIhKi207vxva9B/nXJ2vCjhI1FQ4RkRAN6NiCk7q25KGpKygsKg47TlRUOEREQvb9kd3ZsucAz85cG3aUqKhwiIiEbEiXFgzs1Jzx7y/nwKH4b3WocIiIhMzM+P7I7mzcVciLs9aHHeeIVDhEROLAyd1b0TcnkwfeW0ZRcUnYcQ4rpoXDzFaZ2Xwzm2NmeRW8PsrM5pW+bmbDg+UdzWx2sHyhmY0t855Lg23OM7M3zaxVLD+DiEhtKG11rNuxn5c+je9WR220OEa4ez93z63gtUlAX3fvB4wBJgTLNwJDg+WDgdvNrK2ZpQB/C7bZB5gH3BrrDyAiUhtO79Ganm0yeOC95RSXxO/csqGeqnL3AncvPTrpBLPwuvtBdz8QLE/jy5wWPNLNzIAMYEMtRhYRiZlIq6MbK7fu5dV58funLdaFw4G3zWyWmd1Q0Qpmdr6ZLQFeI9LqKF3e3szmAWuBu9x9g7sXATcB84kUjJ7AI5Vs94bg9Ffeli1bavZTiYjEyDdOOIZjs5swbsoySuK01RHrwjHc3fsDZwO3mNkp5Vdw94nufjxwHnBHmeVrg9NR3YCrzSzbzFKJFI4TgbZETlX9vKIdu/vD7p7r7rlZWVk1/blERGIiKcm4ZUQ3Ps8v4K2Fm8KOU6GYFg53Xx983UzkVrSDDrPuVKBL+c5ud98ALABOBvoFy5YHp7ieA06KSXgRkZB8u09burRK577Jy/jybH78iFnhMLN0M2ta+hw4i0gBKLtOt6CvAjPrT6Q/Y5uZ5ZhZo2B5c2A48BmwHuhpZqVNiDOBxbH6DCIiYUhOMm4e0Y1FG3czafHmsON8TUoMt50NTAzqQgrwL3d/s3RorbuPBy4ErjKzImA/MNrd3cx6APeYmRPpDL/b3ecDmNmvganBe1YD18TwM4iIhGJUv7b8bdLn3Dd5Kaf3aE3wtzQuWDw2g2pabm6u5+V97TISEZG49vSMNfz83/N5fMwgTj229vtqzWxWRZdS6MpxEZE4dWH/HNpkNuS+SUvjqq9DhUNEJE41SEli7KldyVu9g49XbAs7zhdUOERE4tjoge3JaprGfZOWhR3lCyocIiJxrGFqMjee0oWPV2wjb9X2sOMAKhwiInHvssEdaJHegPsmx0erQ4VDRCTONW6QwvUnd+b9z7cwd+3OsOOocIiIJIKrhnYis1FqXLQ6VDhERBJAk7QUxgzrzLuL81m0YXeoWVQ4REQSxDXDOtE0LYX7pywNNYcKh4hIgshslMrVJ3XijQWbWJq/J7QcKhwiIglkzPDONEpN5v4p4fV1qHCIiCSQFukNuHJIR/4zdwMrt+4NJYMKh4hIgrnu5M6kJicxLqRWhwqHiEiCad20IZcO6sDET9ezdvu+Wt+/CoeISAIae2pXks148P3ltb5vFQ4RkQR0TGZDLs7N4YW8dWzctb9W963CISKSoG46rSsl7jz0/opa3a8Kh4hIgspp3pgL+rfj6Rlr2LynsNb2q8IhIpLAbj6tG0XFJfx9au21OlQ4REQSWKdW6Yzq146npq9hW8GBWtmnCoeISIK7ZUQ3Cg8V88i0lbWyPxUOEZEE1611E87p3YYnPl7Nzn0HY74/FQ4RkTrg1hHdKDhwiH98uCrm+1LhEBGpA3q0yeDMntn848OV7Cksium+VDhEROqI20Z2Z3fhIZ74eHVM96PCISJSR/TOyeS047J4ZNpK9h08FLP9qHCIiNQh3x/Zne17D/LP6Wtitg8VDhGROmRAx+YM69aSh6auoLCoOCb7UOEQEaljvj+yO1sLDvDMjNi0OlQ4RETqmCFdWjKoUwvGv7+CA4dqvtWhwiEiUgfddnp3Bndpwd4DNV84Ump8iyIiErrh3VsxvHurmGxbLQ4REakSFQ4REakSFQ4REamSmBYOM1tlZvPNbI6Z5VXw+igzm1f6upkND5Z3NLPZwfKFZja2zHsamNnDZva5mS0xswtj+RlEROSraqNzfIS7b63ktUnAK+7uZtYHeA44HtgIDHX3A2bWBFhgZq+4+wbgF8Bmdz/WzJKAFrXwGUREJBDqqCp3LyjzbTrgwfKyE8qn8dWW0RgixQV3LwEqK0oiIhIDse7jcOBtM5tlZjdUtIKZnW9mS4DXiBSF0uXtzWwesBa4y903mFmz4OU7glNZz5tZdiXbvSE4/ZW3ZcuWGv1QIiL1WawLx3B37w+cDdxiZqeUX8HdJ7r78cB5wB1llq919z5AN+DqoECkADnAR8F2PwburmjH7v6wu+e6e25WVlZNfy4RkXrL3L12dmT2K6DA3Sv8Qx+sswIYVL5PxMweBV4HXgQKgKbuXmJm7YE33f2EI+x7C3C0E9S3IjFOhylnzUuUrMpZ8xIla6xzdnT3r/3PO2Z9HGaWDiS5+57g+VnAb8qt0w1YHnSO9yfSn7HNzHKAbe6+38yaA8OBvwTr/Qc4DZgMnA4sOlKWij54FT5HnrvnHu37a4ty1rxEyaqcNS9RsoaVM5ad49nARDMr3c+/3P3N0qG17j4euBC4ysyKgP3A6KA49ADuMTMHDLjb3ecH2/0Z8KSZ/RXYAlwbw88gIiLlxKxwuPsKoG8Fy8eXeX4XcFcF67wD9Klku6uBr/WViIhI7dCV40f2cNgBoqScNS9RsipnzUuUrKHkrLXOcRERqRvU4hARkSpR4RARkSpR4aiEmX3TzD4zs2VmdnvYecoKrqqfYmaLgkkgfxAs/5WZrQ8mh5xjZufEQdavTXRpZi3M7B0zWxp8bR5yxuPKHLM5ZrbbzH4YL8fTzB41s81mtqDMsgqPoUXcG/zezguGuYeZ80/BZKTzzGxi6ewPZtbJzPaXObbjK91w7eSs9GdtZj8PjudnZvaNkHM+WybjKjObEyyv3ePp7nqUewDJwHKgC9AAmAv0DDtXmXxtgP7B86bA50BP4FfAT8LOVy7rKqBVuWV/BG4Pnt9OZEqZ0LOW+dlvAjrGy/EkMoqwP7DgSMcQOAd4g8gw9iHAJyHnPAtICZ7fVSZnp7LrxcHxrPBnHfy7mkvkGrPOwd+F5LBylnv9HuD/wjieanFUbBCwzN1XeGTCxWeAUSFn+oK7b3T32cHzPcBioF24qapkFPB48PxxItPNxIvTiVyUerQzDdQ4d58KbC+3uLJjOAp4wiOmA83MrE1YOd39bXc/FHw7nciUQaGq5HhWZhTwjLsfcPeVwDIifx9i7nA5LXKB3CXA07WRpTwVjoq1IzK5Yql1xOkfZjPrBJwIfBIsujU4LfBo2KeAAhVNdJnt7huD55uIXCwaL77LV/8xxtvxLFXZMYzn390xRFpDpTqb2adm9r6ZnRxWqDIq+lnH6/E8Gch396VlltXa8VThSGAWuVfJi8AP3X038CDQFehH5J4m94SX7guHnejSI+3suBgTbmYNgO8AzweL4vF4fk08HcPKmNkvgEPAP4NFG4EO7n4i8F/Av8wsI6x8JMjPuoxL+ep/cGr1eKpwVGw90L7M9znBsrhhZqlEisY/3f3fAO6e7+7FHrlPyd+ppSb14bj7+uDrZmAikUz5padPgq+bw0v4FWcDs909H+LzeJZR2TGMu99dM7sG+DZweVDkCE79bAuezyLSd3BsWBkP87OOx+OZAlwAPFu6rLaPpwpHxWYC3c2sc/C/0O8Cr4Sc6QvB+c1HgMXu/ucyy8ueyz4fWFD+vbXJzNLNrGnpcyIdpQuIHMurg9WuBl4OJ+HXfOV/cfF2PMup7Bi+QmT+NzOzIcCuMqe0ap2ZfRP4b+A77r6vzPIsM0sOnncBugMrwkl52J/1K8B3zSzNzDoTyTmjtvOVcwawxN3XlS6o9eNZW73wifYgMjrlcyKV+xdh5ymXbTiRUxPzgDnB4xzgSWB+sPwVoE3IObsQGZEyF1hYehyBlkRuG7wUeBdoEQfHNB3YBmSWWRYXx5NIMdsIFBE5x35dZceQyGiqccHv7XwgN+Scy4j0EZT+no4P1r0w+J2YA8wGzg05Z6U/ayK3q14OfAacHWbOYPljwNhy69bq8dSUIyIiUiU6VSUiIlWiwiEiIlWiwiEiIlWiwiEiIlWiwiEiIlWiwiEiIlWiwiESJTNrWWba6k1lpuEuMLMHYrC/x8xspZmNreT1guBr19IcNZ1BpCK6jkPkKJjZr4ACd787hvt4DHjV3V+o5PUCd29S2fcisaIWh0g1mdlpZvZq8PxXZva4mX1gZqvN7AIz+6NFbmb1ZjDHGGY2IJjFdJaZvRXN1OfBFDgfB9u6M9afS6QyKhwiNa8rMJLITLtPAVPcvTewH/hWUDzuAy5y9wHAo8Bvo9ju34AHg22FNv+USErYAUTqoDfcvcjM5hO5o+CbwfL5RO7UdhzQC3gnMl8lyURXCIYRmZMIInMr3VWDmUWipsIhUvMOALh7iZkV+ZcdiSVE/s0ZsNDdhx7FttUpKaHTqSqR2vcZkGVmQyFybxUzOyGK931IZIp/gMtjFU7kSFQ4RGqZR+5jfxFwl5nNJTIV9klRvPUHRO6iOJ/4uH2p1FMajisSp440HLeC9TUcV2qFWhwi8WsXcEdlFwCWKr0AEMivlVRS76nFISIiVaIWh4iIVIkKh4iIVIkKh4iIVIkKh4iIVMn/B+pIpvSHzW8DAAAAAElFTkSuQmCC\n", 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -701,10 +1389,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "\r\n", - "\r\n", - " \r\n", - "\r\n" + "\n", + "\n", + " \n", + "\n" ] } ], @@ -725,7 +1413,8 @@ "metadata": {}, "outputs": [], "source": [ - "new_op = openmc.deplete.Operator(geometry, settings)" + "model = openmc.Model(geometry=geometry, settings=settings)\n", + "new_op = openmc.deplete.Operator(model, \"./chain_simple.xml\")" ] }, { @@ -736,7 +1425,7 @@ { "data": { "text/plain": [ - "228" + "9" ] }, "execution_count": 35, @@ -756,7 +1445,7 @@ { "data": { "text/plain": [ - "['B10', 'B11', 'O16', 'Br81', 'Br82', 'Kr82', 'Kr83', 'Kr84', 'Kr85', 'Kr86']" + "['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']" ] }, "execution_count": 36, @@ -776,16 +1465,7 @@ { "data": { "text/plain": [ - "['Am242',\n", - " 'Am242_m1',\n", - " 'Am243',\n", - " 'Am244',\n", - " 'Am244_m1',\n", - " 'Cm242',\n", - " 'Cm243',\n", - " 'Cm244',\n", - " 'Cm245',\n", - " 'Cm246']" + "['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']" ] }, "execution_count": 37, @@ -864,7 +1544,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -878,9 +1558,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.1" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/mdgxs-part-i.ipynb b/mdgxs-part-i.ipynb index c37547a..d474e1e 100644 --- a/mdgxs-part-i.ipynb +++ b/mdgxs-part-i.ipynb @@ -169,8 +169,7 @@ "outputs": [], "source": [ "# Instantiate a Materials collection and export to XML\n", - "materials_file = openmc.Materials([inf_medium])\n", - "materials_file.export_to_xml()" + "materials = openmc.Materials([inf_medium])" ] }, { @@ -230,10 +229,7 @@ "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry([cell])\n", - "\n", - "# Export to \"geometry.xml\"\n", - "openmc_geometry.export_to_xml()" + "geometry = openmc.Geometry([cell])" ] }, { @@ -255,19 +251,16 @@ "particles = 5000\n", "\n", "# Instantiate a Settings object\n", - "settings_file = openmc.Settings()\n", - "settings_file.batches = batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", - "settings_file.output = {'tallies': True}\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': True}\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings_file.source = openmc.Source(space=uniform_dist)\n", - "\n", - "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" + "settings.source = openmc.Source(space=uniform_dist)" ] }, { @@ -406,43 +399,43 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=7.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=13.\n", " warn(msg, IDWarning)\n" ] } ], "source": [ "# Instantiate an empty Tallies object\n", - "tallies_file = openmc.Tallies()\n", + "tallies = openmc.Tallies()\n", "\n", "# Add chi-prompt tallies to the tallies file\n", - "tallies_file += chi_prompt.tallies.values()\n", + "tallies += chi_prompt.tallies.values()\n", "\n", "# Add prompt-nu-fission tallies to the tallies file\n", - "tallies_file += prompt_nu_fission.tallies.values()\n", + "tallies += prompt_nu_fission.tallies.values()\n", "\n", "# Add chi-delayed tallies to the tallies file\n", - "tallies_file += chi_delayed.tallies.values()\n", + "tallies += chi_delayed.tallies.values()\n", "\n", "# Add delayed-nu-fission tallies to the tallies file\n", - "tallies_file += delayed_nu_fission.tallies.values()\n", + "tallies += delayed_nu_fission.tallies.values()\n", "\n", "# Add beta tallies to the tallies file\n", - "tallies_file += beta.tallies.values()\n", + "tallies += beta.tallies.values()\n", "\n", "# Add decay rate tallies to the tallies file\n", - "tallies_file += decay_rate.tallies.values()\n", + "tallies += decay_rate.tallies.values()\n", "\n", "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" + "tallies.export_to_xml()" ] }, { @@ -456,6 +449,19 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, + "outputs": [], + "source": [ + "# tie geometry, materials, settings, and tallies together into a model object\n", + "model = openmc.Model(geometry=geometry,\n", + " materials=materials,\n", + " settings=settings,\n", + " tallies=tallies)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -485,112 +491,113 @@ " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2021 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.0-dev\n", - " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-28 08:47:21\n", - " OpenMP Threads | 2\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1-dev\n", + " Git SHA1 | 1be02f90bd0e9adb5a3311ee77c904a52ca7ebaa\n", + " Date/Time | 2022-05-11 17:26:27\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", - " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", - " Reading Pu239 from /opt/data/xs/nndc_hdf5/Pu239.h5\n", - " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.0 K\n", - " Maximum neutron data temperature: 294.0 K\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading Pu239 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Pu239.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for H1\n", + " Maximum neutron transport energy: 20000000 eV for H1\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.26527\n", - " 2/1 1.23398\n", - " 3/1 1.25383\n", - " 4/1 1.24224\n", - " 5/1 1.22254\n", - " 6/1 1.20745\n", - " 7/1 1.22674\n", - " 8/1 1.22724\n", - " 9/1 1.23185\n", - " 10/1 1.22019\n", - " 11/1 1.24342\n", - " 12/1 1.23258 1.23800 +/- 0.00542\n", - " 13/1 1.20626 1.22742 +/- 0.01103\n", - " 14/1 1.19410 1.21909 +/- 0.01141\n", - " 15/1 1.24556 1.22439 +/- 0.01030\n", - " 16/1 1.27632 1.23304 +/- 0.01207\n", - " 17/1 1.25083 1.23558 +/- 0.01051\n", - " 18/1 1.23155 1.23508 +/- 0.00912\n", - " 19/1 1.27501 1.23952 +/- 0.00918\n", - " 20/1 1.24863 1.24043 +/- 0.00827\n", - " 21/1 1.18837 1.23569 +/- 0.00885\n", - " 22/1 1.21978 1.23437 +/- 0.00819\n", - " 23/1 1.22815 1.23389 +/- 0.00754\n", - " 24/1 1.24244 1.23450 +/- 0.00701\n", - " 25/1 1.21128 1.23295 +/- 0.00671\n", - " 26/1 1.22836 1.23267 +/- 0.00628\n", - " 27/1 1.21573 1.23167 +/- 0.00598\n", - " 28/1 1.19115 1.22942 +/- 0.00607\n", - " 29/1 1.24854 1.23042 +/- 0.00583\n", - " 30/1 1.24486 1.23115 +/- 0.00558\n", - " 31/1 1.23967 1.23155 +/- 0.00532\n", - " 32/1 1.24406 1.23212 +/- 0.00511\n", - " 33/1 1.24808 1.23282 +/- 0.00493\n", - " 34/1 1.23056 1.23272 +/- 0.00472\n", - " 35/1 1.24209 1.23310 +/- 0.00454\n", - " 36/1 1.23203 1.23305 +/- 0.00437\n", - " 37/1 1.21629 1.23243 +/- 0.00425\n", - " 38/1 1.22928 1.23232 +/- 0.00409\n", - " 39/1 1.23665 1.23247 +/- 0.00395\n", - " 40/1 1.24100 1.23276 +/- 0.00383\n", - " 41/1 1.26373 1.23375 +/- 0.00384\n", - " 42/1 1.25002 1.23426 +/- 0.00375\n", - " 43/1 1.24100 1.23447 +/- 0.00364\n", - " 44/1 1.25701 1.23513 +/- 0.00359\n", - " 45/1 1.23027 1.23499 +/- 0.00349\n", - " 46/1 1.25747 1.23562 +/- 0.00345\n", - " 47/1 1.24960 1.23599 +/- 0.00338\n", - " 48/1 1.23535 1.23598 +/- 0.00329\n", - " 49/1 1.21318 1.23539 +/- 0.00325\n", - " 50/1 1.27184 1.23630 +/- 0.00330\n", + " 1/1 1.24970\n", + " 2/1 1.21100\n", + " 3/1 1.19756\n", + " 4/1 1.25555\n", + " 5/1 1.24295\n", + " 6/1 1.21186\n", + " 7/1 1.25633\n", + " 8/1 1.24522\n", + " 9/1 1.21656\n", + " 10/1 1.26028\n", + " 11/1 1.20898\n", + " 12/1 1.23185 1.22041 +/- 0.01143\n", + " 13/1 1.25350 1.23144 +/- 0.01285\n", + " 14/1 1.22167 1.22900 +/- 0.00941\n", + " 15/1 1.24549 1.23230 +/- 0.00800\n", + " 16/1 1.24976 1.23521 +/- 0.00715\n", + " 17/1 1.18269 1.22771 +/- 0.00963\n", + " 18/1 1.23822 1.22902 +/- 0.00845\n", + " 19/1 1.23413 1.22959 +/- 0.00747\n", + " 20/1 1.21361 1.22799 +/- 0.00687\n", + " 21/1 1.24244 1.22930 +/- 0.00635\n", + " 22/1 1.21414 1.22804 +/- 0.00593\n", + " 23/1 1.21809 1.22727 +/- 0.00551\n", + " 24/1 1.19780 1.22517 +/- 0.00552\n", + " 25/1 1.24190 1.22628 +/- 0.00526\n", + " 26/1 1.24078 1.22719 +/- 0.00500\n", + " 27/1 1.21557 1.22651 +/- 0.00475\n", + " 28/1 1.26431 1.22861 +/- 0.00494\n", + " 29/1 1.27196 1.23089 +/- 0.00520\n", + " 30/1 1.24033 1.23136 +/- 0.00496\n", + " 31/1 1.24532 1.23203 +/- 0.00476\n", + " 32/1 1.22646 1.23177 +/- 0.00455\n", + " 33/1 1.23791 1.23204 +/- 0.00436\n", + " 34/1 1.21230 1.23122 +/- 0.00425\n", + " 35/1 1.22857 1.23111 +/- 0.00408\n", + " 36/1 1.22386 1.23083 +/- 0.00393\n", + " 37/1 1.25504 1.23173 +/- 0.00388\n", + " 38/1 1.24488 1.23220 +/- 0.00377\n", + " 39/1 1.24251 1.23255 +/- 0.00366\n", + " 40/1 1.19482 1.23130 +/- 0.00375\n", + " 41/1 1.20078 1.23031 +/- 0.00376\n", + " 42/1 1.24233 1.23069 +/- 0.00366\n", + " 43/1 1.29614 1.23267 +/- 0.00406\n", + " 44/1 1.23726 1.23281 +/- 0.00394\n", + " 45/1 1.24222 1.23307 +/- 0.00384\n", + " 46/1 1.24097 1.23329 +/- 0.00374\n", + " 47/1 1.27425 1.23440 +/- 0.00380\n", + " 48/1 1.25510 1.23495 +/- 0.00374\n", + " 49/1 1.23654 1.23499 +/- 0.00364\n", + " 50/1 1.23369 1.23495 +/- 0.00355\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.6337e-01 seconds\n", - " Reading cross sections = 2.5312e-01 seconds\n", - " Total time in simulation = 2.6057e+01 seconds\n", - " Time in transport only = 2.6028e+01 seconds\n", - " Time in inactive batches = 9.8414e-01 seconds\n", - " Time in active batches = 2.5072e+01 seconds\n", - " Time synchronizing fission bank = 1.1907e-02 seconds\n", - " Sampling source sites = 9.8847e-03 seconds\n", - " SEND/RECV source sites = 1.9926e-03 seconds\n", - " Time accumulating tallies = 1.1168e-03 seconds\n", - " Time writing statepoints = 7.9423e-03 seconds\n", - " Total time for finalization = 6.1998e-03 seconds\n", - " Total time elapsed = 2.6333e+01 seconds\n", - " Calculation Rate (inactive) = 50805.8 particles/second\n", - " Calculation Rate (active) = 7976.89 particles/second\n", + " Total time for initialization = 1.4223e-01 seconds\n", + " Reading cross sections = 1.3814e-01 seconds\n", + " Total time in simulation = 1.4361e+01 seconds\n", + " Time in transport only = 1.4343e+01 seconds\n", + " Time in inactive batches = 5.7629e-01 seconds\n", + " Time in active batches = 1.3785e+01 seconds\n", + " Time synchronizing fission bank = 1.0628e-02 seconds\n", + " Sampling source sites = 9.2148e-03 seconds\n", + " SEND/RECV source sites = 1.3998e-03 seconds\n", + " Time accumulating tallies = 7.9017e-04 seconds\n", + " Time writing statepoints = 3.9794e-03 seconds\n", + " Total time for finalization = 2.6361e-03 seconds\n", + " Total time elapsed = 1.4510e+01 seconds\n", + " Calculation Rate (inactive) = 86762.3 particles/second\n", + " Calculation Rate (active) = 14508.6 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.23688 +/- 0.00324\n", - " k-effective (Track-length) = 1.23630 +/- 0.00330\n", - " k-effective (Absorption) = 1.23291 +/- 0.00216\n", - " Combined k-effective = 1.23399 +/- 0.00202\n", + " k-effective (Collision) = 1.23445 +/- 0.00332\n", + " k-effective (Track-length) = 1.23495 +/- 0.00355\n", + " k-effective (Absorption) = 1.23293 +/- 0.00238\n", + " Combined k-effective = 1.23332 +/- 0.00230\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -598,7 +605,7 @@ ], "source": [ "# Run OpenMC\n", - "openmc.run()" + "statepoint_filename = model.run()" ] }, { @@ -617,12 +624,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Load the last statepoint file\n", - "sp = openmc.StatePoint('statepoint.50.h5')" + "sp = openmc.StatePoint(statepoint_filename)" ] }, { @@ -641,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -679,26 +686,26 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[[5.15913423e-06, 1.16842422e-06]],\n", + "array([[[5.14603413e-06, 1.16498857e-06]],\n", "\n", - " [[2.66298632e-05, 7.60931827e-06]],\n", + " [[2.65622446e-05, 7.58694377e-06]],\n", "\n", - " [[2.54231809e-05, 5.75848069e-06]],\n", + " [[2.53586263e-05, 5.74154841e-06]],\n", "\n", - " [[5.70008690e-05, 1.05132542e-05]],\n", + " [[5.68561321e-05, 1.04823410e-05]],\n", "\n", - " [[2.33695515e-05, 5.47610066e-06]],\n", + " [[2.33102114e-05, 5.45999869e-06]],\n", "\n", - " [[9.78942387e-06, 1.65741521e-06]]])" + " [[9.76456653e-06, 1.65254173e-06]]])" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -716,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -757,8 +764,8 @@ " 1\n", " 1\n", " U235\n", - " 9.493755e-08\n", - " 9.395351e-08\n", + " 9.479196e-08\n", + " 5.863577e-08\n", " \n", " \n", " 199\n", @@ -766,8 +773,8 @@ " 1\n", " 1\n", " Pu239\n", - " 1.602797e-08\n", - " 1.586127e-08\n", + " 1.600306e-08\n", + " 9.891470e-09\n", " \n", " \n", " 398\n", @@ -775,8 +782,8 @@ " 2\n", " 1\n", " U235\n", - " 4.900384e-07\n", - " 4.849591e-07\n", + " 4.892869e-07\n", + " 3.026598e-07\n", " \n", " \n", " 399\n", @@ -784,8 +791,8 @@ " 2\n", " 1\n", " Pu239\n", - " 1.043816e-07\n", - " 1.032959e-07\n", + " 1.042193e-07\n", + " 6.441782e-08\n", " \n", " \n", " 598\n", @@ -793,8 +800,8 @@ " 3\n", " 1\n", " U235\n", - " 4.678332e-07\n", - " 4.629841e-07\n", + " 4.671158e-07\n", + " 2.889454e-07\n", " \n", " \n", " 599\n", @@ -802,8 +809,8 @@ " 3\n", " 1\n", " Pu239\n", - " 7.899251e-08\n", - " 7.817094e-08\n", + " 7.886975e-08\n", + " 4.874928e-08\n", " \n", " \n", " 798\n", @@ -811,8 +818,8 @@ " 4\n", " 1\n", " U235\n", - " 1.048921e-06\n", - " 1.038048e-06\n", + " 1.047312e-06\n", + " 6.478393e-07\n", " \n", " \n", " 799\n", @@ -820,8 +827,8 @@ " 4\n", " 1\n", " Pu239\n", - " 1.442166e-07\n", - " 1.427166e-07\n", + " 1.439925e-07\n", + " 8.900152e-08\n", " \n", " \n", " 998\n", @@ -829,8 +836,8 @@ " 5\n", " 1\n", " U235\n", - " 4.300427e-07\n", - " 4.255852e-07\n", + " 4.293832e-07\n", + " 2.656050e-07\n", " \n", " \n", " 999\n", @@ -838,8 +845,8 @@ " 5\n", " 1\n", " Pu239\n", - " 7.511894e-08\n", - " 7.433765e-08\n", + " 7.500220e-08\n", + " 4.635875e-08\n", " \n", " \n", "\n", @@ -847,19 +854,19 @@ ], "text/plain": [ " cell delayedgroup group in nuclide mean std. dev.\n", - "198 1 1 1 U235 9.493755e-08 9.395351e-08\n", - "199 1 1 1 Pu239 1.602797e-08 1.586127e-08\n", - "398 1 2 1 U235 4.900384e-07 4.849591e-07\n", - "399 1 2 1 Pu239 1.043816e-07 1.032959e-07\n", - "598 1 3 1 U235 4.678332e-07 4.629841e-07\n", - "599 1 3 1 Pu239 7.899251e-08 7.817094e-08\n", - "798 1 4 1 U235 1.048921e-06 1.038048e-06\n", - "799 1 4 1 Pu239 1.442166e-07 1.427166e-07\n", - "998 1 5 1 U235 4.300427e-07 4.255852e-07\n", - "999 1 5 1 Pu239 7.511894e-08 7.433765e-08" + "198 1 1 1 U235 9.479196e-08 5.863577e-08\n", + "199 1 1 1 Pu239 1.600306e-08 9.891470e-09\n", + "398 1 2 1 U235 4.892869e-07 3.026598e-07\n", + "399 1 2 1 Pu239 1.042193e-07 6.441782e-08\n", + "598 1 3 1 U235 4.671158e-07 2.889454e-07\n", + "599 1 3 1 Pu239 7.886975e-08 4.874928e-08\n", + "798 1 4 1 U235 1.047312e-06 6.478393e-07\n", + "799 1 4 1 Pu239 1.439925e-07 8.900152e-08\n", + "998 1 5 1 U235 4.293832e-07 2.656050e-07\n", + "999 1 5 1 Pu239 7.500220e-08 4.635875e-08" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -871,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -909,7 +916,7 @@ " 1\n", " U235\n", " 0.013336\n", - " 0.000056\n", + " 0.000061\n", " \n", " \n", " 1\n", @@ -917,7 +924,7 @@ " 1\n", " Pu239\n", " 0.013271\n", - " 0.000050\n", + " 0.000059\n", " \n", " \n", " 2\n", @@ -925,7 +932,7 @@ " 2\n", " U235\n", " 0.032739\n", - " 0.000137\n", + " 0.000150\n", " \n", " \n", " 3\n", @@ -933,7 +940,7 @@ " 2\n", " Pu239\n", " 0.030881\n", - " 0.000116\n", + " 0.000136\n", " \n", " \n", " 4\n", @@ -941,7 +948,7 @@ " 3\n", " U235\n", " 0.120780\n", - " 0.000504\n", + " 0.000552\n", " \n", " \n", " 5\n", @@ -949,7 +956,7 @@ " 3\n", " Pu239\n", " 0.113370\n", - " 0.000427\n", + " 0.000501\n", " \n", " \n", " 6\n", @@ -957,7 +964,7 @@ " 4\n", " U235\n", " 0.302780\n", - " 0.001264\n", + " 0.001383\n", " \n", " \n", " 7\n", @@ -965,7 +972,7 @@ " 4\n", " Pu239\n", " 0.292500\n", - " 0.001101\n", + " 0.001292\n", " \n", " \n", " 8\n", @@ -973,7 +980,7 @@ " 5\n", " U235\n", " 0.849490\n", - " 0.003545\n", + " 0.003880\n", " \n", " \n", " 9\n", @@ -981,7 +988,7 @@ " 5\n", " Pu239\n", " 0.857490\n", - " 0.003228\n", + " 0.003787\n", " \n", " \n", " 10\n", @@ -989,7 +996,7 @@ " 6\n", " U235\n", " 2.853000\n", - " 0.011907\n", + " 0.013029\n", " \n", " \n", " 11\n", @@ -997,7 +1004,7 @@ " 6\n", " Pu239\n", " 2.729700\n", - " 0.010276\n", + " 0.012056\n", " \n", " \n", "\n", @@ -1005,21 +1012,21 @@ ], "text/plain": [ " cell delayedgroup nuclide mean std. dev.\n", - "0 1 1 U235 0.013336 0.000056\n", - "1 1 1 Pu239 0.013271 0.000050\n", - "2 1 2 U235 0.032739 0.000137\n", - "3 1 2 Pu239 0.030881 0.000116\n", - "4 1 3 U235 0.120780 0.000504\n", - "5 1 3 Pu239 0.113370 0.000427\n", - "6 1 4 U235 0.302780 0.001264\n", - "7 1 4 Pu239 0.292500 0.001101\n", - "8 1 5 U235 0.849490 0.003545\n", - "9 1 5 Pu239 0.857490 0.003228\n", - "10 1 6 U235 2.853000 0.011907\n", - "11 1 6 Pu239 2.729700 0.010276" + "0 1 1 U235 0.013336 0.000061\n", + "1 1 1 Pu239 0.013271 0.000059\n", + "2 1 2 U235 0.032739 0.000150\n", + "3 1 2 Pu239 0.030881 0.000136\n", + "4 1 3 U235 0.120780 0.000552\n", + "5 1 3 Pu239 0.113370 0.000501\n", + "6 1 4 U235 0.302780 0.001383\n", + "7 1 4 Pu239 0.292500 0.001292\n", + "8 1 5 U235 0.849490 0.003880\n", + "9 1 5 Pu239 0.857490 0.003787\n", + "10 1 6 U235 2.853000 0.013029\n", + "11 1 6 Pu239 2.729700 0.012056" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1038,9 +1045,18 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mgxs/mdgxs.py:749: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n", + " df.to_excel(filename + '.xls', index=False)\n" + ] + } + ], "source": [ "beta.export_xs_data(filename='beta', format='excel')" ] @@ -1054,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1084,22 +1100,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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/kAzDMAzD/GPoLRDvBTcABVrLhep1/RqI7774KoqVXDUsrlvAClYwghGMYQwjGKEc5ahDHYQQYuTNfwFoG4gXJiSgurkM1rBGAALgBjeYq/8zhjEAgIAAAP4IH9Sh/KX/FwTjZqJe8kaTEGgyAprV/7eqBngUUPKB5GnSDscP/XU6eEqCBlMgZRhQZQvUmXGvWnPApB6osANu2QNby261Oba6qhrTP/yOW6AUJo2NMGtshGlTE0yammDU1IQPXF3BV6lg3NyMX6qrMRCtgfjNK3/h19kbYdHQAPvqavz7jz9gLa+CdW0trGtrYdbQAAWPB+eqChg3lQJft627bdxeNH8rASVArRC4Yn8GdUKgTgjUGgFCBRBQBjQIgevWPABtA/HraXvx4o0DKDMDrtoCf9oB9UIAmo/TqXXfsnoHcPd+rXZcWYqi0f8DAPwJ4AjAfegKE0BhChg3A4EygPJxXPoAXkZim+MXfbseRRY/AE3mwPkQoCSMe99kATTYAMYygCiBGjeM9PHC+dNWbY4PCQHS0wEeD3By4l4mJoCpKfcqLuaCeVNT4MUXgZdeavv5ffUVcOsW4OwMBAYCAwYA1tbcjQPDMAzDMHe/e+JPNiHkeXDpK3B3d9fpuRW0ueW9XP1fV4ob8zusO9p8CDWQdbo/H3wAgAoqCCDAuJLpWIWFbfb5RrkPFEoMxEAMx3DYN9vDqLmTkwEQtATsrcJSWuPOUee6rDoAIP914zbL8uYmfLEQqLUAZFYESaNNcMvBBBV2XPBe7QKo+Op9AfCN2v643BTy8GtYWMvyl1OmdFk2X6FAE6XgkdZruGlphoc++ACO1dXwLirCtN9/x4CKCriVlsO0qanN8YMrVR3O+chf6Xj6Qtt1zTygwpR7UQADZVxgn+wsA95pu+8AWQUG1gCF1sBNc6BZAEDQxL3afKcKKE1vdCi/yuEEYHuJW/A42+W1A0Bm9f0A2n5BOaFRoBF/QNlgg5Lr41BS+ABQ7QDUOgK1zgAlQLMZoDDFuXMdA/Hly7lAvD0zMy4gr68HKOWW33oLeOaZtvudOAHY2wMBAdw+DMMwDMP0L0MG4kUAtJuIB6rXdUAp/RTApwAwfPhwqstKNBs1Aw2929fOzabj8bxmoGOMCABQQtm6H5ph62zZYZ9Y1TdQQNGybA5z2Kv/c4AD8pAHYxjDBS6YLXq2dxXtQkCEqM2yOc8Gg/NaI7n2gbySACBAk5BrXXf9w7rNdldfb/AUgKoXP0WUkDZBOAA0+/siKSSkZfnduXNb3tvU1MC2pgY3bW1hVVsLcV4ufm13ziALiw7lCFWAcy330rBqAkbc7Phjszk5C/9K595XmAB/ugIlFkChFXDdFhAoAcsm4JILUODA71iWeV3PF65myrPpsK7R8Rxgqb65c73U8SAVD+CpACUfGfUvA9jaZnOt3XmAZwFUDeECdrW6Ou6lUV0NlHTynGnmTKBB/bNvbw94eHCt6ppXcTHg5QXcdx/wwANcCg7DMAzDMLpjyED8KICXCCEHAIwEUN3f+eEAUCa/gbq6OlRVVUGhUEChUKChoQH19fVoaGiARCJBSUkJqqurMXv27A7Hh0eMQk5ODurq6uDs7IyGhgZUV1dDJpOhsbGxzb6TXhjb4XglUXJNt2q16v/y0bb1PR3pWCha2GadSqXCIstFsOHbYIhwCBZNWAQ7pR2UMiWUNUo0VzajLquOu1GgQHDYkDbHe9RboaKbz4ZPueNMG7mX3SDbNttDVf74ZUIOlAKg1pqg8EEjFHsS5HoAOe4qXLFToIpydylGneRLKCdPAkpLOy27ytISVZbcjUutqSlqra077HNk/tMIf20tbORyDLt6FQt++QXeeXnwz82FRUPbuytnV88Ox//LyhOaez+7BmD89a4/i9qApg7r9jiNQvZ1I2S6CqDwcIexqTnkTXLIm+SobqhG9q1sVDdWQ6FS4OFwxw7HWzrIUdXYYXUrnvoOj6+Ep3fHxyRNcyYDRlUAAGHxg+AVjUJTiTdohRdQ4Q3wGwC5K9BsjkEds6Kg/eN56xb3Sk3tvCpffw088UTbdW+8wQXpEyawFnWGYRiGuR16C8QJIbEAHgLgQAgpBPAmACEAUEo/BhAHYAqAHAB1AJ7WV126w+fzYWlpCUvLjq3VAPDggw92e/wvv/zS5bbGxkZUVFSgoKAAubm5iIiI6LDPkCFDUFVVBblcDpVKhebmLvJSANwfcX+b5Vu3bkFaw+WN/4E/ELs/Fh4eHggKCkLwg8EIDAyEVCrFQw89hKDAIAhs2n7dRm5GcJzjiOZbzaDNFMbuxmgqbWp5KW61ttQTIQHPuO0gOy5ZSlwHIFAA1rcorL9vRIDWdmJCQAQ8CF2NYTzcHGh3HzLK3h5j6utRqVCAD8CEx0NJUxOKm5qgoG1bsG06af1ODQtDU0UFbtra4sTw4TgxfHjLNi8eD/bNzahpakJgZSXmEoKZ7U/Qh2Rq84GDO6yb/fFpICuLWwiuB6ZN4/I8ggMAPz8u0jUzAwgBpR1b5DdGrMel0ksolZfC1tQWdc11KKsrQ1ltGUrlpahurG7Zd8JQnw7HE+Oalpu45gG/AQN+a7sdPFCoYEStQUQHoD2IUXMzl4/e2Aiouniio23EiLbLVVXA5s2ty8HBQGgoEBTU+nJ2BvgdHyQwDMMwDKOmz1FT5vawnQJYoq/y7wbGxsZwdXWFq6sr7rvvvk73ycnJaXlPKUVFRQWKi4tRXFyMvLw8/PrrrygoKEBZWVmH/Phz5zomhefl5SEvLw/Hjx9vWbdhwwbweDwUFRXBxcWlZb2ppykCvgvocA4NhVwB+SU56rLqQJs6BpL12fVdXzwA2kBBQdGYXQ/FzY4tyuOvG+P+dBc4znaEwLr1R1FFKcqbmyGtq8MluRzZ9fXwMjXtcHxuQ9c5RVdVKlzl8wFTU2SamuKKuXmHQFz644+w5vHgUlPD5W6UlnL/z88Hrl8Hfv8dKCzk8jz8/TsWUlzc+v6vv7iXhpkZ18uyvh7w9wd57z3goYfaHP6fkf/psv4AIG+So6C6AJnlmRg1aFSbbU3KJhBC2jxNaY+qc6aaSDUGOAvbbBMKAdG2EAQ4BWDy4OkYZTcT5TeMUFzMXdbVq8APPwDl5Vz6yuB29yG//952uf3la9jacp1Sjx0DOrmXYhiGYZh/NNJZS93dbPjw4TQ5OdnQ1bgrpKenY926dbh27RrKy8tRVlbWZYu6QCBAU5M6eFNLSEjA0qVL8eCDDyIqKqrTFvvuUErRWNQI2R8yNJc3gzZR1EnqUJtZizpJHZrLWutiFmiG+9Lb3oz8NeUvVPzEJccIXYRwmOoA6wetYTfRDkaORj2Wn1tfj9+qqpAql6NOpUJ5czOy6upwpa5OKzuf86SzM/b5+bVZ53X+PK41NMCSz8e/XV2xaMAAiExN23xGALjmY6WSa0LWNmBA58nXndm0CVizpu26U6cAX9/bHteQUoryunJIy6W4XnUdORU5yKnMQU5FDjLLMlHTVNOyb83qGlgYtUbCl0ovYegnQ1uWJ3lPQoRnBB70eBBhrmEQ8ARQURX4vM6btI8f5zp/VlRwH013eDygqalt63hhIfcaORJo/3EzDNMWISSFUjq85z0ZhrnXsED8b6SpqQlXrlzBX3/9hfT0dJw+fRppaWmoq6uDr68vsjRpFGrz58/HN998AwAQCoWYOnUqpk2bhsjISDg4ONxxfeSX5aiIq4DsDxlsxtpg4NKBbbb/7vY7moo7tpSDAJbDLWERagHbCbZwnOUIwu99tNagVCKzrg4/3rqFs9XVyKyrw3teXpjj5NRmP+Hp0x1SYJyEQkTY2GC6gwMGGBtjmIUFLLtLYamrAzIzAYkEyMjgXpcucVGmtsuXubQVjeZmrsVcqeR6SZ46xQ1GriOUUhTICnCh6ALSb6RjQ8SGNtvfOPkGNp/Z3OmxZkIzBDkF4a8bfyHCMwJPD30as/079o/QuHmT+wjS01tfqamtOeju7kBeXttjliwBdu/msoPuv58b1SU8nA29yDCd+ScH4ikpKU4CgeAzAIHQ7ySEDKMPKgCXFQrFs2FhYTc724EF4v8AlZWVqKmp6ZDa4u3tjatXr3bYn8fjYfTo0Rg0aBCMjY0xf/58jB49GkKhsMO+dyJ5WDJq02tBFT38DPIAjzUeGLyhY5727SppbITbuXPdZXYA4IaG9DUzww8BAfAxN+99AUVFXP7GTz8BaWnAxYtc07DGt98CUVGty0IhMHYsMGUK9xKJgBs39DYL0Om801j761qklqSitrm2232tja1Ruaqy45OCbqhUwPnzQGws4O0NLGs7/D4CA7l7Fm22tkBkJJdqP2ECYGPT6+IY5m/tnxyIp6WlHXVxcfFzdHSU8Xi8eytgYf7xVCoVKSsrsy4tLZWEhIRM62wfFoj/g+3YsQP79+9Heno66uq6H4rv2WefxZ49e/RSj9rMWjQWNkJ2XoaK+ArIzsk6DAnp960fnB9zbrOOqigI7/bzGhpVKpyrrsbRW7dwpa4OZ2UyVCkUne4rve8++OhyaJBly4AdO7re7u4OFBRwzcRLlrQN2nWIUoqcihz8lvcbfsv/DadzTyOvum3z9XDX4bj4/MU2677880tIb0mxIHgBApy67mfQlYAA7iFCV3g8wMqKS6t/6622DxMY5p/mHx6IXwsKCqpkQThzr1KpVCQ9Pd02JCRkSGfbWSDOgFKKrKwsHD16FEePHsW5c+c6jPLx4YcfYvHixW3W1dTUdDnazJ1ormzGzQM3kb8lH42FjQABHqx/EDyj1hbl5opm/O76O8yDzOH+mjscH3XsU4ttZ1SU4i+5HMdu3UJMaSly1J1BbQUCVIwe3WbfP2tq8ERmJlYOGoQoJyeY9HV4EIUCOHcOOHKEazXPzOx63yFDuN6T/SSvKg+nck/hm/RvcCr3FHZM2oEXR7zYZh+37W4oruE6q258aCPWjl3b53Lkci495do1rjOndt9XbfPnc7OIMsw/1T88EM8NCQkpN3Q9GOZOpKWlOYSEhHh2to0F4kwHN27cwJEjR/DFF18gJSUFKpUKVVVVsLJqnaK9rq4ODg4OCAkJwfLly/Hoo4+Cr4ex6pQNStRcqIHNgzZt1mcvy0bRjtb5n0y8TeC60BXOC5xh4t6uU+Vtym9owL6SEpjy+XilXVrPhEuX8EtVFQDAx9QUx4OCILqTFvPCQi4gj4sDEhKAWq10kVmzgEOH2u7/88+Am5vem4rrm+vBIzwYC1pn86moq4D9e/Zt9nvE5xEsGbEED3s9DB7pexonpVxe+dGjXNaOVNq6raAAGNi2ewFycriUF4b5J2CBOAvEmXsbC8SZ21ZXV4dLly5h1Ki2w+e9/vrr2LJlCwBumMZdu3Zh7ty5MO9LHvUdOOd5Do15ncyGQwDHRx3hscYDFiH6GS9PqVLBJCmpQ0fP8TY2WOTmhmn29hDy7qBPUX098P33XHPx2bPAn39yg3RrUAo4OnIz8EydCnzwAddq3k/+uvEXHvzywTbjnGt423njhbAXcCLnBJ4IegKPBTwGc6O+/0xcugRs28bNCnr0aNttR48C06dz9yFLlwKvvspGXmH+3lggzgJx5t7GAnFG58RiMaTazZYArK2tER0djeXLl+slZUWbsl6J/PfyUbqnFM1VzVDJO85KYzLEBN47vOEQeecjwGhrVKmw/vp1fFxS0mlOuauREbxNTPCooyMWubnB6E6C8qIiLuLU9ssvXG9GgItAd+wAXnyxX4ccUaqUOJFzAjsv7ET81fgu9xtkNQjXll2DgKe7ug0dygXqGsOHA2+/zX0kLCBn/o5YIG7YQFwqlRo98sgjouzs7JYu5tHR0QMsLCyUGzduvKFZl5OTI3ziiScGl5eXCwkheOqpp8rWrl17EwDq6urIyJEjxU1NTUSpVJKpU6dWvv/++8UA4ObmFmRubq7k8XgQCAT08uXLneYqXr16Vfjrr79aPPfcc5X6vmZtXdVvzpw5nidPnrS2t7dXaH827W3atMkpJibGkVKKJ598smzdunU3AWDDhg1OX331lSMhBGKxuO7bb7/NNTMzu+uC0s6+677qLhBnQwExt+Wzzz7D9OnT26SjVFdX480334SXlxemTJmCxYsXo7irxN87xDflY/C6wXig4AGE3wiH3zd+sJ1g22afhmsNuPzIZVzf2M3c9bfBmMfDFi8v3AoPR1xQEKbZ27f5h1TS1IQkmQwvX70Kt3PnUN7UyRCNvdU+CAe41BUNSoH//IebcOirr7jc86Ym7v96xOfxEekTiRPzT+DKS1ew/P7lsDa27rCfrFGm87LbD92enAxMnAhERHAPEBiGYQxBKBRi27ZthVevXs24ePFi5ueff+6UkpJiAgAmJib0zJkzUqlUKsnIyJCcPHnS6uTJky2PC0+fPn0lKytL0lUQDgBxcXFWqampOhw1oPc6q98zzzxTfvTo0ezujrt48aJJTEyMY2pqamZmZmbGiRMnbC5fvmx8/fp14aeffup86dIlSXZ2doZSqSSfffaZnf6v5O7DAnHmtowePRpHjhxBaWkptm7dCm+thN2ysjL89NNP+Oijj+Dp6YmLFy92c6Y7xzfjw3meM0J+DkHYn2GwGKqVksID3BZ3EszqAI8QTLa3xw9BQbh+//1Y6+EBF6O2ExFVNTeDp+tm2tdeA9avbztVZXY28OSTgJ8f8Oyz3ERBMTFcoK5nInsRtk/cjqLoInzyyCfwc2idOGnL+C0dWsOl5VIoVT3MAtSN0lIuc2fECMC4NXUdp08Do0cDXl7AJ5/c9ukZhmFui4eHR/Po0aPrAMDW1lbl5eVVn5+fbwRwwwJbW1urAKCpqYkoFArSlwEG4uPjLdauXTvo+PHjtmKx2F8ikfQ8652eTZ48We7o6Nhtq096errp0KFD5ZaWliqhUIjw8PCaAwcO2ACAUqkktbW1vObmZtTX1/MGDhzYZkZCmUzGe+ihh7x9fX39RSJRwJ49e1pa23bv3m0XFBTkJxaL/efNm+ehUDc+7dq1y97Hx8ff19fXf8aMGYMBYP369c4ikShAJBIFbNy40QngnnIMGTIkICoqysPb2zsgPDxcJJfLW76QVatWuXh6egaGhYX5ZmdnG/dUnzvBAnHmjjg4OGDFihXIysrCl19+2WGs8ubmZhgZ9d/vC8tQS4SlhEG0WwSBvQD2U+1h5NC2/MbSRsj+1G1LrbuJCTYOHoy8++/H+15esFCnoyweMAB27cZfv9Xc3GFUmj6xtQXefJPrxbhhQ9sBt3NyuJbxa9eAp57iEq37ibmROZ4Pex4ZizPw28LfsChsEZ4Pe77NPvImOUZ+NhIhH4fgcObh2/4cZswALlzgBpNpn5Vz7Rq3zsUFKGeZpQzDGIBUKjWSSCRmY8eOlWvWKRQKiMVif2dn55CxY8fKxo0b19Irf/z48aKAgAC/rVu3dppLOXHiRHlQUFDt4cOHc7KysiT+/v63/ag1LCzMVywW+7d/HTlypMuc0p7q15XQ0ND6CxcuWJaWlvJramp4CQkJ1gUFBUaDBw9uXrJkSengwYODnZycQiwtLZWzZs1q84f58OHDVi4uLs1SqVSSnZ2dodmemppqcvDgQbvk5OSsrKwsCY/Hox9//LF9cnKyydatW11Pnz59RSqVSj755JP8pKQks/3799unpKRkJicnZ8bExDiePXvWFADy8/NNli5dejMnJyfD2tpaGRMTYwsASUlJZt9//71denq6JCEhITstLc28u/rcKRaIMzrB5/OxcOFCXLlyBR988EHLCCvh4eEICQlps69CobizQLQHhBC4LXJDeFk4Ag51HFUkfXI6UoelQvKkBKrmjrnld8KIx8PLgwahZNQobB48GFu8vNpsp5RiREoKhiYnQ9rD2O09srEB1q0DcnOBjRs7nwFHa6Sb/kIIwRiPMdj9yG7weW1H0ll2YhmqG6uRUZaBF46/gEZFJx1u+8DNDfjoIyArixvmUFtjI2D3j3zQyTB/X9HRGEAIwnrzmjsXHu2PnzsXHtr7REdjQE9ldtVy3dX66upq3qxZs7zeeeedAjs7u5Y/MgKBAFlZWZL8/Py/UlNTzS9evGgCAGfOnMmSSCSZP//8c/aePXucfvrpp05HGrh27ZpJaGhoAwBIJBKjxx57zGPSpEktPfV37dplv2bNGueoqCiPyMjIIYcPH+70D0BKSoo0KytL0v41Y8aMms727239OjNs2LCGZcuWlY4fP94nIiJCFBAQUMfn81FWVsb/8ccfbXJyctJLS0v/qqur4+3evduu3bH1SUlJVosWLXI7ceKEhb29vRIATpw4YXn58mWzkJAQP7FY7H/mzBmra9euGcfHx1tNnTq10tXVVQEAzs7OylOnTllMmTKlysrKSmVtba2KjIysTExMtAQANze3xlGjRtUDwNChQ+tyc3ONASAxMdFiypQpVZaWlio7OzvVww8/XNVdfe4UC8QZnTI2NsayZctQVFSEDRs24Msvv+ywz/PPP48ZM2agtLRUr3UhhIDHb/sjXvx5MeSXuAaKm1/dxAXfC5Bd0H0es4VAgNUeHjBrN6Tjx8XFuN7QgLTaWgRdvIi8+vo7L8zaGli7lgvIN21qDcgHDuRaxbWpVP2SrtIZFVUhPqe1Y2d5XTkmfjMRORU5d3xuLy/uQcDPPwMe6j+9773XdjJThmGY2+Hs7Kyorq5u88u8oqKC7+DgoNiyZYujpkU5NzdX2NjYSCIjI73mzJlT8dRTT1V1dj4HBwflmDFjao4dO2YNAIMHD24GADc3N0VkZGTVuXPnOgw1VVJSIrC0tFRqZrj29/dv+u6779rMvpacnGy2cePGGwcOHMjbu3dv3oEDBzpNnehri3hv6ted5cuXl2dkZGQmJydLbW1tlT4+Pg3Hjh2zcnd3bxwwYIDC2NiYzpgxo+r3339vE+AHBwc3pqamSoKCgurXrl3r9sorr7gCAKWUzJkz55bmBiI3N/fy9u3b+9whzcjIqOWPIZ/PpwqFottcoa7qc6fYnylGLywsLLBu3TqIRKI26xMSEvDll1/i6NGj8PX1RU7OnQdhfUGbKIig9d9aw/UGpN6fiuyXs6Go0W8HRwD4SKvzajOleFwiQfadtoxrWFsDa9ZwAfmiRcDBg22TqAFg61ZuyMPruu3A2htKlRIv3fcSLIxaf9f+lvcbhn4yFPsu7QOlFA2KhjsqY8IE7vJPnwb+/e+22ygFHnvMIJfOMMw9zNraWuXk5NR89OhRSwC4ceMG/9SpU9bjxo2Tr169ukwTELq7uzdHRUV5+Pj4NKxfv77NCBvFxcWC8vJyPgDI5XKSmJho5efn1yCTyXiVlZU8gMtBTkxMtAoODu7QQpOdnW3k7OzcZTpKY2MjEQgElKdufXj99dddly5dWtbZvn1pEe9t/bpTVFQk0FzDjz/+aPPss89WeHp6NqWmplrU1NTwVCoVfv31V0s/P782fwByc3OFlpaWqsWLF1dER0eXXrp0yQwAJk2aJDt+/Lit5rw3btzgX7lyxWjixImyY8eO2ZaWlvI16yMiIuRxcXE2NTU1PJlMxouLi7ONiIjotOVfY9y4cfK4uDgbuVxOKisreQkJCTbd1edO9d94ZwwDYNWqVS3va2pqcODAAbz22msQ9NPQe26L3ODwqAMuT7sM+SU5aCMFKFD03yKUHy6Hz0c+sI+07/lEtynW3x/zJRJcUk/Y80dNDUKTk7Hd2xvPu7re8eygALiAfPfujuvz8oDXXweUSuDXX4HERGDkyDsvr5eEfCFeG/0a/j3033jrt7ewO3k3FCoF5E1yLPxhIQ5nHkZycTKiH4jGy/e/3CGtpS8efLDjupdeAv73P+DwYS6T5/XX7+BiGIYxiO3bUbx9O257OK7YWOTFxiKv5z3b2rdv3/XFixe7r1y5chAArFq1qjggIKBNbl1CQoLFkSNH7EUiUb1YLPYHgA0bNhQ9/vjj1QUFBcKFCxcOViqVoJSS6dOnV8ydO7daIpEYzZw50xvgOi8++uijt2bPnt3hMW1ISEhDRUWFUCQSBezevTt3woQJtdrb4+PjLcaMGSNXqVRYsmSJW2RkZLWm4+idKCwsFHRVv6lTpw4+f/68ZWVlpcDZ2Tn4tddeK16+fHn52LFjvfft25fn6enZDADTpk3zqqqqEggEAvrBBx/kOzg4KMeNG1c7derUyuDgYD+BQICAgIC66OjoNjcOKSkppqtXrx6oGTZx9+7deQAQFhbWsGbNmqLx48f7qFQqCIVCumPHjvzx48fXrlixomTMmDFiHo9HAwMD6w4dOpQ7b968W8OGDfMDgAULFpSFh4fXS6XSLjuvjR49um7mzJkVgYGBAfb29s3BwcG13dXnTrFxxJl+QynFRx99hOXLl6NJa0i/kSNHIiYmBj4+Pv1an/rr9bjy4hVU/tx2SFaLUAuEJIZAaCPs4sg7o6QU7+XnY11uLpq1/v1NsrWFm7Ex3vDwwGBTU90X/PTTwN693HtTU+DMGWDYMN2X00spxSmYd3gerty60mHbvKB5+GbWNzor6/p1Ln1F+9fdypVcX1cT3UzEyjB6w8YRZxP6tFdaWsqPjo52S0pKspo/f365TCbjb968uWTnzp0OsbGx9iEhIbWhoaH1K1eu7LRVnOlfbEIf5q6SnZ2NBQsW4I8//mhZZ2pqivfeew/19fV49tlnYdNZx0M9oJTi5v6byHk5B83lrSMn8a34CEsOg5lIf0O2/llTg/mZmZC0S00x4fHwgS5byAEuAh0/nmsF1zAyAj7/vGMvx35U21SL5fHLsSd1T5v1y+9fju0Tt+usHEq5GTi3b28bjAcEcKM8GvB+hGF6xAJxFoj35Mknn3SPiYnJN3Q9mM6xCX2Yu4pIJMKZM2ewefNmaDqe1NfX46WXXsKrr76K+++/H9f7KZGXEALnJ5wxInMEjAZqPakigJGrfoddHGppiZSwMCwfOLDN+gaVCp/oeiIkQoD4eOCttwDNcIpNTcCCBVxeuUrFpa70M3Mjc3w69VMceuwQbIxtAAB8wsdzw57TaTmEcOnxf/0F3H9/6/qMDC4759NPdVocwzBMv2JB+L2LBeKMQQgEAqxevRoXLlxAYGBgm21SqRRLlizp1/oYORjh/mv3w3aiLYiQYNj5YRBY6D9v3YTPx3Zvb/wSEgInrfHGd4pEumsN1xAKgTfeANLSuJk4NTTzw/v6Aq+8ovdZOTszy28W0henI8IzArum7IKfo1+b7ZX1lbhVd+uOywkMBH7/HfjwQ8BM/bBDoQBeeAEICeGGPGQYhmGY/sICccagQkNDkZycjFdffbXN+qCgoH6vC0/IQ8iJEDxQ/ADMxW1HZ6KUQqXQ7Zjj2sbb2kJ6332IcnTEa4MGIdy643TxOuPnx0Wjkya1rvv1Vy4K3bYNWLZMf2V3Y6DVQPzy5C94IeyFNusbFY2YGjsVo74YhWuV1+64HEKAxYu5+xHtlJS//gIGDwbKWEYlwzAM009YIM4YnLGxMd59913ExMSAz+dj8uTJ2LJli8Hq034mTgDIWpiFP7z+QFPZbU9m1iMboRCxAQF4e8iQDts+LipCmlzeyVG3ydoaOHYMWLq07XpCgClTdFdOH/EIr8OTgDn/m4OzBWdx5dYVjPp8FKoaqnRSlrc3N8yhp2fruhs3DJKhwzAMw/xDsUCcuWssWLAASUlJOHz4MHjtZmJJTU1tM9JKfyrcWYgbMTfQmN+I857nIbuo+wmAtPHaBaKfFhdjUXY2RqWm4ueKCt0VJBAA//0vl6eh+bxtbYHQUN2VcYcKqgtwtuBsy7LYQQxrY909LbCwAK5cAcLCuOWvvwaG/yO7xDEMwzCGwAJx5q7ywAMPwKTdeHKpqakYPXo0Hn74YVToMhDtpRtft87LoKpT4dK4S6j8tbKbI3SnuKEBS7KzAQB1KhWic3Kg85GOFi8GTpwAXF25qSnd3NpuV+kvJacnVsZWCHRq7UNwOu80nj/2PJQqncwsDIBLnb94kRvNce5cnZ2WYRiGYXrEAnHmrnbr1i2MHTsW9fX1OH36NKKiovq9DsN+Hwb7aa2T/KjkKqRPS0f1+Wq9l12tVMKG3zqxTUZdHXbrekQVgOusee1aa9Owxs2b3DAjP/+s+zJ7wdrEGieeOIFpvtNa1n3252d4/ODjaFQ0QqlSQkXv/EaBECA8vOP6r7/m+rcyDMMwjD6wQJy5qx0/fhxyrdxogUDQ7ykqhE8Q9EMQQk6GwMiNyx9X1aqQPjkd8jQd5m13ws/cHBfDwuCl9ZTgpexsvHHtmu5bxtvPbFNSAjzwANdcPH0616HTAEyFpjj02CE8GfJky7pDmYcQuT8SLx5/EQuPLNRJMN5eUhLw1FPA5s3c/xmGYRhG11ggztzVHnvsMcyYMaNl+aeffsIzzzyj+yC0F2zH2SLklxAIHblhBhVVClwafwk1aTV6LdfT1BTnhw3DSEvLlnWb8/Pxck4OqpubuznyDp05w01JCXAjqpSW6q+sHgh4Anw5/Uu8PPLllnUnr5/EZ39+hq/++gorE1bqvMzZs1uzcmJigG+/1XkRDMMwzD8cC8SZu5qpqSkOHjyIxYsXt6z75ptvsHbtWoPUx1xsjuCfg8G35tJFFLcU+PP+P1GXU9fDkXfGwcgIJ0NDMcXOrmXdjqIiuJ8/jy9KSvRT6N69baehNDbWTzm9xCM8bJ+4HW9FvNVh24WiC2hU6HYQ8F9+AUxNW5effx5IT9dpEQzDMMw/HAvEmbsen8/Hrl278Pzzz7ese/vtt/Hf//4X77zzDlT93JnQMtQS/rH+gHpwE1WDCilhKVA16rce5nw+fggMxBxHx5Z1MqUSz0mliLt155PddBAb2zrQNqXAvHlcdGpAhBC88eAb2D1ld5v1owaNgrFAtzcKQUHAhQuA5uOWybiRHQsLdVoMwzD3AD6fHyYWi/1FIlHA5MmTh9TU1PQqfsrJyRGOHDnSx8vLK8Db2ztg06ZNTpptdXV1JCgoyM/X19ff29s7YPny5QM02zZt2uQkEokCvL29AzZu3OjU+dmBq1evCvfs2WN7Z1fXd25ubkE+Pj7+YrHYPzAw0A/o/lo1utqnu8/ibhMdHT1g3bp1zro6HwvEmXsCIQQffvghpmiNcf3yyy9j9erViI6O7vdUFfvJ9nCa2/o7xuUZF/CM9f/PScDjIUYsxn1aaSoqAIX6mBLSygr46SfAx4dbbmoCZszgolOFArh6Vfdl9tKiEYvwxbQvAAALQxZiY8RGvZQTGAgkJACaj7uwkAvGq/XfT5dhmLuIsbGxKisrS5KdnZ0hFArptm3bHHs+ChAKhdi2bVvh1atXMy5evJj5+eefO6WkpJgAgImJCT1z5oxUKpVKMjIyJCdPnrQ6efKk+cWLF01iYmIcU1NTMzMzMzNOnDhhc/ny5U5bGuLi4qxSU1PNdHmtvXX69OkrWVlZksuXL2cC3V+rRlf7dPVZGOK6+hsLxJl7hkAgwLfffoth2tMhAvjvf/+LP/74o9/r4/e1H+xn2MNzgydE74v6rVwTPh8/BgXBU925coSlJR5z7NXfhL5zcuIi0YEDueXaWmDyZGDaNGDkSG46SgN5eujTOL3wNL6Y/gWM+G0nYVKoFDorJyQEOHyYG3Yd4NJTAgIMmjLPMIwBjR49Wp6Tk2MslUqNRCJRgGb9unXrnKOjo9u05Hp4eDSPHj26DgBsbW1VXl5e9fn5+UYAwOPxYG1trQKApqYmolAoCCEE6enppkOHDpVbWlqqhEIhwsPDaw4cOGDTvh7x8fEWa9euHXT8+HFbsVjsL5FIOs5G14+6u9ae9unqs2hPJpPxHnroIW9fX19/kUgUoHkasHv3brugoCA/sVjsP2/ePA+FgvsbsGvXLnsfHx9/X19f/xkzZgzWnGf9+vXOIpEoQCQStTxxkEqlRkOGDAmIiory8Pb2DggPDxfJ5XICAKtWrXLx9PQMDAsL883OzjbuqT59wQJx5p5iYWGBH3/8Ee7u7i3rHB0dEdZ+2L1+QAhB0PdB8Fzn2e9lOxgZ4efgYCwZMABnhg6FjVCov8Lc3bnhC+3VQzhWVHAt5bduAZGRQHm5/sruwYMeD3aYifP4leMI+igIRbIinZXzr38BX3zRulxUBAQHA/X1OiuCYZh7QHNzM+Lj462CgoL6/K9fKpUaSSQSs7Fjx7YMt6VQKCAWi/2dnZ1Dxo4dKxs3blxtaGho/YULFyxLS0v5NTU1vISEBOuCgoIOQfbEiRPlQUFBtYcPH87JysqS+Pv73/aQYmFhYb5isdi//evIkSOWXR0zfvx4UUBAgN/WrVsdenOtPe3T2WfR/pjDhw9bubi4NEulUkl2dnbGrFmzZKmpqSYHDx60S05OzsrKypLweDz68ccf2ycnJ5ts3brV9fTp01ekUqnkk08+yQeApKQks/3799unpKRkJicnZ8bExDiePXvWFADy8/NNli5dejMnJyfD2tpaGRMTY5uUlGT2/fff26Wnp0sSEhKy09LSzLurT98+eRaIM/cgFxcX/PTTT7C2toaNjQ1+/PFHCPUZiPZR441GZD6Vqfd0GZGZGXb5+MCo3SykcoUCcoXuWoQBAH5+XPBt3u5J4f33t+3RaGCfp36OabHTkFWehSn7p0DWqLtZUBcs4NLkNcrKgGXLdHZ6hmF6IToaAwhBGCEIi45Ghzzi557DQM32N99EhzzeuXPhodm+dSs6BJBdaWxs5InFYv+goCD/gQMHNi1btqxPLRDV1dW8WbNmeb3zzjsFdnZ2LR2KBAIBsrKyJPn5+X+lpqaaX7x40WTYsGENy5YtKx0/frxPRESEKCAgoI6vNZ+EtmvXrpmEhoY2AIBEIjF67LHHPCZNmjREs33Xrl32a9ascY6KivKIjIwccvjwYavOzpOSkiLNysqStH/NmDGj02HBzpw5kyWRSDJ//vnn7D179jj99NNPFj1da0+fR2efRfvjhg0bVp+UlGS1aNEitxMnTljY29srT5w4YXn58mWzkJAQP7FY7H/mzBmra9euGcfHx1tNnTq10tXVVQEAzs7OSgA4deqUxZQpU6qsrKxU1tbWqsjIyMrExERLAHBzc2scNWpUPQAMHTq0Ljc31zgxMdFiypQpVZaWlio7OzvVww8/XNVdfTr9orrBAnHmnuTv74+4uDikpKRgxIgRhq5Oi/rr9bgguoAbMTdwdWX/51DfbGrCiJQUzMnIQLOuO7GOGAEcOQIYaTXMuLh0DM4NpFHRiP2X94OCuwGqbqhGeZ1uW+u//pq79wC4j2PnTp2enmGYu5QmRzwrK0uyb9++AhMTEyoQCKj2YAENDQ08ANiyZYujpkU5NzdX2NjYSCIjI73mzJlT8dRTT1V1dn4HBwflmDFjao4dO2YNAMuXLy/PyMjITE5Oltra2ip9fHwa2h9TUlIisLS0VGoaovz9/Zu+++67PO19kpOTzTZu3HjjwIEDeXv37s07cOBAp6kTfW0RHzx4cDMAuLm5KSIjI6vOnTtnDgC9udae9mn/WWgLDg5uTE1NlQQFBdWvXbvW7ZVXXnGllJI5c+bc0nw/ubm5l7dv335bM98ZGRm1tKDx+XyqUCg65sf0UJ++lskCceaeNWrUKAwZMqTNOkop9uzZg3IDpUtkzM6Asoa7IS7cWoiq36r6rewahQJ+Fy4gq74eJyor8eKVK7ov5F//Avbv5+aFf/NN4P33dV/GbcquyMb5wvMty66WrhhoNVCnZRAC/PYbsHEjcP68wUd0ZBjGgAYOHKioqKgQlJaW8uvr60l8fLw1AKxevbpMExS6u7s3R0VFefj4+DSsX7/+hvbxxcXFgvLycj4AyOVykpiYaOXn59cAAEVFRQIAyM7ONvrxxx9tnn322Yr25WdnZxs5Ozt3mY7S2NhIBAIB5amfmr7++uuuS5cuLets3760iMtkMl5lZSVP8z4xMdEqODi4XqVSoatr1ehqn+4+C225ublCS0tL1eLFiyuio6NLL126ZDZp0iTZ8ePHbTWf2Y0bN/hXrlwxmjhxouzYsWO2paWlfM16AIiIiJDHxcXZ1NTU8GQyGS8uLs42IiKiywlBxo0bJ4+Li7ORy+WksrKSl5CQYNNdfbo6T1cEfT2AYe5WN2/exOOPP45Tp07h+PHjOHLkSIf8YX3z+8oPF4MuckOZAMh6JgvD/xwOgaX+/6l9X16OCq2UlBtNTVBRCp6uP4NHHwVyc4EBnYwupVIBPMPc3wc6BeLrmV9j1nezAADnC8/jxeMv4vNpn+v050AoBAw0jD3D/ONt347i7dvRZWvnnj0o3LMHXQ4yGhuLvNhY5HW1vS+MjY3pihUrSkaMGOHn7Ozc7O3t3SFwTEhIsDhy5Ii9SCSqF4vF/gCwYcOGoscff7y6oKBAuHDhwsFKpRKUUjJ9+vSKuXPnVgPAtGnTvKqqqgQCgYB+8MEH+Q4ODh1SHkJCQhoqKiqEIpEoYPfu3bkTJkxok1MdHx9vMWbMGLlKpcKSJUvcIiMjqzUdJe9EYWGhYObMmd4AoFQqyaOPPnpr9uzZsvj4+C6vdezYsd779u3Lk0qlxp3t4+np2dTVZ6EtJSXFdPXq1QN5PB4EAgHdvXt3XlhYWMOaNWuKxo8f76NSqSAUCumOHTvyx48fX7tixYqSMWPGiHk8Hg0MDKw7dOhQ7ujRo+vmzZt3a9iwYX4AsGDBgrLw8PB6qVTaaWfX0aNH182cObMiMDAwwN7evjk4OLi2u/r09fMkhpih8E4MHz6cJicnG7oazF3ovffew8qVrTMsfvPNN5inndTbTwp3FeLaq9egauCicad5TvD72k/vNwXNKhUe+esv/FxVBQAwJgQpw4cjoD9SRyjl8jS+/57r2GnAnP3/O/N/eO3ka63L//o/rAzX/cyb2igFXn8dePVVQGvOJYbRCUJICqV0uKHrYQhpaWm5ISEhhusRfg8pLS3lR0dHuyUlJVnNnz+/XCaT8Tdv3lyyc+dOh9jYWPuQkJDa0NDQ+pUrV3baKs7oT1pamkNISIhnZ9tYIM78bUyePBknTpwAAFhbW+Pq1auw14z00c9ufHMDmfMzW5Z9P/eF6zN9Th3rM5lCgdF//on0Wu6GfZiFBc4PGwahPlupKQWeeIKbAAgAoqOBbdv0V16P1aF45ugz2HtpLwCAgOCrmV/ht7zfsHPKzg5DHd6pq1eBceOA/HwgNBT480+dnp5hWCDOAvHb8uSTT7rHxMTkG7oeTPeBOMsRZ/42PvnkE1iqZ16prq7GWgPmDzg/4QyXZ1xalrNfyoY8o8tRnHTGSiBArL8/jNWt76lyOTbn6/n3cEkJN8mPRlIS0NDhCW2/IYTgk0c+wYMeDwIAKCgWfL8An6Z+ik2nN+m8vLff5oJwALh0Cdi+XedFMAzD9BkLwu8NLBBn/jbc3d2xa9euluWPPvoI8fHxBquPaIcIZn5cvw1VvQqXxl6Csr7PIxv1WYC5Od4a3DJvATbl5uLprCz8WdNlX5Q7s3Nn6yybPB7w0UeASYdRp/qVEd8IsY/GwsbEBgBaRlJ57/f3UCrX7Uw8e/YA2vMpffwxUHfHWZgMwzDMPwELxJm/lQULFmDmzJkty08//TQqKipQW9thXgC945vz4fuFL6BODVfcUuDyjMv9UvbyQYMw2pob+UkJYG9pKZ7IzES9Ug83Am++yU0/CXCdNZ988q6IRAdYDsCOSTvarNsxeQdcLFy6OOL28PlcWrzm3iM7G1i1SqdFMAzDMH9TLBBn/lYIIfjkk0/g5OQEACgpKcGDDz4IsViMsrL+759iNdIKFiEt8xygJqWmX1rF+YRgr1gME60Oopl1dfhSH/Oym5hw+eGaiX0kEmDFCt2XcxvmB8/HIz6PAABcLFzgZeull3JCQwGthzHYtYsLzhmGYRimOywQZ/52HB0dsWfPnpbljIwMFBYW4tlnn9X7bJftEUIQ/EsweOY8WI+1xgOFD4Bv2vkMabrmZWqK7d7eLcsCQjBdX51X/fyA//63dfnjj4HDh4H0dOD33/VTZi9o8sUXD1+MzCWZGD9kvN7KeuYZYNq01uWnn+Zm32QYhmGYrrBAnPlbmjZtGp555pk268rKyiCX67/DZHtG9kYYdWMUhp4aCr5J/wThGi8OGIAJNjYQm5oiNSwMbvrM3X72WWD27NblefOAoUO5VBUDdt4cYDkAH0Z+2JIvrlHXXIeK+g5zZNw2QtrmixcXA/7+QFOX020wDMMw/3QsEGf+tt5//314enq2LM+ePbtlVJX+JjA3zNxZhBDEBgQgbcQIBFlY9HzAnRUGfPop4O7OLTc2Akol15Hz//5Pv2X3UXJxMoZ9MgwLjyzU6VMSJyfuI9AoLwfmztXZ6RmGYZi/GRaIM39bVlZW2Lt3L5ycnHDo0CFER0cbukotKKXIfy8fjTca9V6WvVAIo07GES/QRyu1rS3wzTdtZ9cUi4HHHtN9WbcpryoPD3z+AKS3pDh25Rg+S/1Mp+efMQMYMaJ1OS6OuydhGIZhmPZYIM78rY0dOxbXr1/HrFmzDF2VFpWnKvG76++4tvIaMqMyez5Ax6oVCsy6fBn+Fy8iXx/B+OjRwGutM1uCx+OC8btAo6IRG09vhEKlAACYCkxhItB9uo5mFBUvLyAzEzA21nkRDMMwzN8AC8SZvz0zM7MO6y5evIgq9VTw/a34o2I032gGAFSdqkJlUmW/lU0pxQOpqfi+vBxypRKrr13TT0Gvvw54egLLlgFnznBpK3cBI74RSuQlLctO5k541P9RnZdjYwNcucK9tLKjGIZhGKYNFogz/yiFhYWYP38+7rvvPrz11lsGqYPvHl8Q49bA9OrLV0FV/TOaS1J1NTK1xvi+Ul+PZpVK9wWZm3PDGH7wAZeucpcghODTqZ/CytgKAJBXnYc1v67RS1mDBrXN0GEY5t4klUqNRCJRgPa66OjoAevWrXPWXpeTkyMcOXKkj5eXV4C3t3fApk2bnDTb6urqSFBQkJ+vr6+/t7d3wPLlywdotrm5uQX5+Pj4i8Vi/8DAQL+u6nH16lXhnj17+vUXanfXNGfOHE87O7uQ9p9Ne53tl5aWZiwWi/01LwsLi6EbN2506u48htLZd61L7M8E849BKcX777+Pb775BgCwY8cOZGdn93s9BFYCeL/vDaj7b8pT5bjx9Y1+KftBGxvMd279fdKkUoGnr9Zqzbji2uLi2g5zaAADrQbi/Ynvtyx/cP4DnM0/2y9l5+b2SzEMwxiAUCjEtm3bCq9evZpx8eLFzM8//9wpJSXFBABMTEzomTNnpFKpVJKRkSE5efKk1cmTJ801x54+ffpKVlaW5PLly13mK8bFxVmlpqZ2fMSrR91d0zPPPFN+9OjRHv+IdrZfSEhIY1ZWlkR9zRITExNVVFRUlZ4u467GAnHmH0Mul2Pfvn0tyz4+PjAyMjJIXdwWucH9FfeW5WurrkEhU/RL2e8NGQJzdVPtX7W1+OZGP9wEyGTAzJlAZCTw6qtAVpb+y+zG06FPY6LXRAAABcXy+OWoaazBnyV/6qW8q1eB4cOBwYOB+Hi9FMEwjIF5eHg0jx49ug4AbG1tVV5eXvX5+flGAMDj8WBtba0CgKamJqJQKAjpQyNIfHy8xdq1awcdP37cViwW+0skkn7549XdNU2ePFnu6OjY4x+unvY7evSolbu7e6OPj0+bwV5lMhnvoYce8vb19fUXiUQB2k8Ddu/ebRcUFOQnFov9582b56FQcKfftWuXvY+Pj7+vr6//jBkzBgPA+vXrnUUiUYBIJArQtLpLpVKjIUOGBERFRXl4e3sHhIeHi+RyecsXsmrVKhdPT8/AsLAw3+zsbOOe6nMn9BqIE0ImEUKkhJAcQshrnWx3J4QkEkL+JIT8RQiZos/6MP9slpaWePPNN1uWi4qKDDacIQC4v+EOowHc79Km0ibkvZXXL+W6GBtjxaBBLctrrl9Hg1LPs33KZMBZdatzczPwxhv6La8Hmol+jPlcL8qLxRfh/oE7Hol9BHXNdT0c3Xfh4UBKCvf+6aeBfp5XimGYfiaVSo0kEonZ2LFjWyavUCgUEIvF/s7OziFjx46VjRs3rlazbfz48aKAgAC/rVu3OnR2vokTJ8qDgoJqDx8+nJOVlSXx9/e/7RkKwsLCfLXTQjSvI0eOdPsHsbNr0oXY2Fi72bNn32q//vDhw1YuLi7NUqlUkp2dnTFr1iwZAKSmppocPHjQLjk5OSsrK0vC4/Hoxx9/bJ+cnGyydetW19OnT1+RSqWSTz75JD8pKcls//799ikpKZnJycmZMTExjmfPnjUFgPz8fJOlS5fezMnJybC2tlbGxMTYAkBSUpLZ999/b5eeni5JSEjITktLM++uPndKb4E4IYQP4EMAkwH4A5hLCPFvt9saAN9RSocCiAKwW1/1YRgAeOGFF+Ctnm2yqqoKb7/9tsHqIrAQwOu91inXC7YXQH65fyYcemXQIDgJhVy5jY3YUVionxFUAG7svhEjWqeZvP9+buZNA/Ow8cBL973UslzVUIXimmJsP7dd52Vpd0coKQEuXtR5EQzzjxAdHz2AbCBhvXnNPTjXo/3xcw/O9dDeJzo+ekBn5WjrquW6q/XV1dW8WbNmeb3zzjsFdnZ2LZ1wBAIBsrKyJPn5+X+lpqaaX7x40QQAzpw5kyWRSDJ//vnn7D179jj99NNPnU76cO3aNZPQ0NAGAJBIJEaPPfaYx6RJk4Zotu/atct+zZo1zlFRUR6RkZFDDh8+bNXZeVJSUqSatBDt14wZM2q6+gy6uqY71dDQQH755RfrBQsWdBi1YNiwYfVJSUlWixYtcjtx4oSFvb29EgBOnDhhefnyZbOQkBA/sVjsf+bMGatr164Zx8fHW02dOrXS1dVVAQDOzs7KU6dOWUyZMqXKyspKZW1trYqMjKxMTEy0BAA3N7fGUaNG1QPA0KFD63Jzc40BIDEx0WLKlClVlpaWKjs7O9XDDz9c1V197pQ+W8TvA5BDKb1GKW0CcADA9Hb7UACaHxRrAMV6rA/DwMjICO+8807L8q5du3D9+nWD1cdprhPMQ9VpgkogY06GTieY6YqlQIA3tYbzeCM3FyHJyahobtZ9YcbGwNKlrctXrgD8/p1htCurR69u6bgJADYmNhhiO6SbI27Ps88CIlHr8oYNOi+CYRg9cXZ2VlRXV7f5pVVRUcF3cHBQbNmyxVHTopybmytsbGwkkZGRXnPmzKl46qmnqjo7n4ODg3LMmDE1x44dswaAwYMHNwOAm5ubIjIysurcuXPm7Y8pKSkRWFpaKoXqBhR/f/+m7777rs1j1OTkZLONGzfeOHDgQN7evXvzDhw40GnqRF9bxHtzTbfr4MGD1v7+/nWDBg3qkLoSHBzcmJqaKgkKCqpfu3at2yuvvOIKAJRSMmfOnFuaG4jc3NzL27dv73P8aGRk1PLHls/nU4VC0W2uUFf1uVP6DMTdABRoLReq12lbD2A+IaQQQByA/+ixPgwDAJg1axYeeOABAEBTUxNWr16NQ4cOQSbTyVOmPiGEwOZBm5bl+qx63PimfzpuPufqCm/1lPcKSlGlUOCtPD2lxyxfziVIA0BFxV0Tidqb2ePVUa+2LFsZW+HxgMf1UtbBg62jOMbFAb/8opdiGIbRMWtra5WTk1Pz0aNHLQHgxo0b/FOnTlmPGzdOvnr16jJNQOju7t4cFRXl4ePj07B+/fo2v8iLi4sF5eXlfACQy+UkMTHRys/Pr0Emk/EqKyt5AJeDnJiYaBUcHFzfvg7Z2dlGzs7OXaajNDY2EoFAQHnq/j+vv/6669KlS8s627cvLeIqlQpdXZMuHDhwwO6xxx6r6Gxbbm6u0NLSUrV48eKK6Ojo0kuXLpkBwKRJk2THjx+3LSoqEgDc93HlyhWjiRMnyo4dO2ZbWlrK16yPiIiQx8XF2dTU1PBkMhkvLi7ONiIiosuWfwAYN26cPC4uzkYul5PKykpeQkKCTXf1uVOGmXe71VwAeyml2wghDwD4ihASSClt89iDEPI8gOcBwN3dvZPTMEzvEUKwdetWhIeHAwC+/fZbfPvtt1i1alWb1vL+4vWuF0r3lkIpU8I8yBzWo6z7pVwhj4ctQ4ZgjkTSsq6yuRmU0i4fud42ExNg61bgUfWY3R9+CLzwAuDfPlut/718/8v48OKHeNDjQWyK2AQ+Tz+t9cHBwMKFwJdfcsuvvsrljbMhDhmm97ZP3F68fWLfWz81YmfH5sXOju1zi8O+ffuuL1682H3lypWDAGDVqlXFAQEBbebMTUhIsDhy5Ii9SCSqF4vF/gCwYcOGoscff7y6oKBAuHDhwsFKpRKUUjJ9+vSKuXPnVkskEqOZM2d6A4BSqSSPPvrordmzZ3doFQoJCWmoqKgQikSigN27d+dOmDChVnt7fHy8xZgxY+QqlQpLlixxi4yMrNZ0srwT3V3T1KlTB58/f96ysrJS4OzsHPzaa68VL1++vHzs2LHe+/bty/P09GwGgK72k8lkvDNnzljt27ev0+8jJSXFdPXq1QN5PB4EAgHdvXt3HgCEhYU1rFmzpmj8+PE+KpUKQqGQ7tixI3/8+PG1K1asKBkzZoyYx+PRwMDAukOHDuXOmzfv1rBhw/wAYMGCBWXh4eH1Uqm0y86uo0ePrps5c2ZFYGBggL29fXNwcHBtd/W5U0Rfj8HVgfV6SulE9fJqAKCUbtHaJwPAJEppgXr5GoD7KaU3uzrv8OHDaXJysl7qzPyzPProozh8+HDLspGRETIzMzFkiO5TE3pS/mM5VPUqOM3u32FUKaW4PyUFF+RyOAuF+C4gAA/a2OirMGDcOODUKW75X/8C5s0DLl8Gtm3TT5m9VNVQBRsTG72XU1TEpajUq9u73nsPeOUVvRfL3OMIISmU0uGGrochpKWl5YaEhJQbuh53m9LSUn50dLRbUlKS1fz588tlMhl/8+bNJTt37nSIjY21DwkJqQ0NDa1fuXJlp63iTP9KS0tzCAkJ8exsmz4DcQGAKwDGAygCcBHAPEpphtY+PwH4llK6lxDiB+AkADfaTaVYIM7oSnZ2Nvz8/KBUjxgyffp07N27Fzb6CkTvUmerq3GmuhpL3dxgqu/c7bQ0YNgwoP0kQunpQGCgfsvuo0JZIW7V3UKIS4hOz7tuHbBpU+vylStt88cZpj0WiLNAvCdPPvmke0xMTL6h68F0rrtAXG8PRSmlCgAvAYgHkAludJQMQshGQsg09W4rADxHCEkDEAtgYXdBOMPokkgkwqJFiwAAJiYmmDx58j8uCAeAcGtrrHJ3138QDgAhIcBzz3Vcv133I5XcrprGGqz5dQ1EO0V48siTUKp0O7TjK68AAq2kwMf1k5LOMMw/CAvC7116zU6klMZRSn0opV6U0rfV69ZRSo+q30sopeGU0hBKaSil9Gd91odh2lu3bh2WLFmCa9eu4YUXXjB0dVo0ljYiIyoDDcV6GlKwB80qFa7U6X48bQBcc7C1Vh78I48A77/f9f79rKSmBO+efRcNigb8deMvxKTF6PT8VlbAf7S6pRcWAor+mcuJYRiGucuwbkLMP5qjoyN27doFV1edjEKkE5IFEpwbcA5l35YhZ1lOv5ZNKcWx8nJ4//EHJqSloV4fE/04OnL5Gdqs+6eDak/yq/MR/mU4mlXcMI6BjoEQ2es+b+TddwFPT2DNGi5vXGDobvMMwzCMQbBAnGHaUSgUOHr0aL+M591p+ZUKboR9AOWHy9FcqYexvbtwUSbD7IwM5Dc2Ir+xEbuKivRT0IsvAlOnAj/+CBw9qp8ybsMgq0EQO4hblkNdQjHafbTOyxEIgGvXuIcD6mGBGYZhmH8gFogzjJZPP/0Unp6emD59Ok6ePGmQOoh2abXAqoDCHYX9VvbRW7fQpL4BIQBq9dEiDgBmZlwAPmVK6+DadwFCCLaMbxnYCd+kf4P0G+l6Kksvp2UYhmHuISwQZxi1srIy7Nq1C0XqVuB3333XIPUw9TTFwBUDW5aLdxVDWaengLidle7usFfnSVAANv2ZM1FdDbz9NpBv2D5Ho91H4xGfRwAAFBRv/PpGv5RLKVBV1S9FMQzDMHcJFogzjNrZs2eRns61fhJCcN9990HVfpi9fjJkyxAYexgDAJrLm1HyWUm/lGslEOBNT8+W5fcLC6Hoj89g3z7Aw4NLmjbQDZC2t8e9DQKuyfrYlWM4m38W1yuvQ6HSfa/Kpibg9dcBGxtg6FCdn55hGIa5i7FAnGHUpk6dCm9vbwBcp0UXFxfwDDTtIU/Ig/urrbPIFrxXAFVT/9wUPOfqCkd14nJ+YyMOl+t5CF+FAsjM5FrEAeCzz4Di2544TyeCnYPxRPATLcszv50J753eOCQ5pPOy/vc/YMsWQCYDcnOB8+d1XgTDMAxzl2KBOMOo8fl8vPzyyy3LH3zwQctkP4bg8owLhE5cQNxY2IjiT/onODXh87F4wICW5e0FBfptFV+5Evi//+Pem5sDn3zCjaxiYBsf2ggBj0vNKasrg4qqsPXcVp134p03D7C0bF3+7391enqGYRjmLsYCcYbRsnDhQtja2gIArl69imPHjhmsLnxTPlxfaB1W8fq666DK/hnJZZGbG4zUvQn/qKnBgHPnkFpTo5/Cnn229X1tLXDffXfFUCKDbQdjjv+cNutsTWxR06Tbz4GQtuOK//Ybl67CMAzD/P2xQJxhtJibm+PFF19sWd62bRtqampQo68gtAeWQ1ubSpVVSpTuK+2Xcp2NjPCEs3PLcllzM7YVFOinMH9/bihDjffe0085t2HZyGUt7wU8Ab6a+RWsjK10Xs66dYCLC/e+uBj49ludF8EwzB3g8/lhYrHYXyQSBUyePHlITU1Nr+KnnJwc4ciRI328vLwCvL29AzZt2uSk2VZXV0eCgoL8fH19/b29vQOWL1/e8ihy06ZNTiKRKMDb2ztg48aNTp2fHbh69apwz549tnd2dX3T1TV1d62dUSgU8PPz84+IiPDWrHNzcwvy8fHxF4vF/oGBgX76vpbbFR0dPWDdunXOPe/ZMxaIM0w7L730EoTqFtkzZ87Azc0NH3zwgUHq4jDDAUIXri5EQNBU3n9NpcsHDmyznFhVpZ8JfgDgtdda33/9NTfd5F1g5MCRGOk2Eu7W7tg8bjNMhaZ6KcfYGHjppdblbdu4UVQYhrk7GBsbq7KysiTZ2dkZQqGQbtu2rVf5c0KhENu2bSu8evVqxsWLFzM///xzp5SUFBMAMDExoWfOnJFKpVJJRkaG5OTJk1YnT540v3jxoklMTIxjampqZmZmZsaJEydsLl++bNzZ+ePi4qxSU1PNdHmtt3tN3V1rZ9566y1nb2/v+vbrT58+fSUrK0ty+fLlTP1eyd2BBeIM086AAQMQFRXVslxTU4MdO3agvr7D7wu9I4RAtEOEAYsHIFwWDo+VHv1WdpCFBf5l29rQsmLQIJjy+fopbNQoYLR64pzmZm7K+7w84Phx/ZTXBwcfO4irS6/i1fBX9dIarvHii4CpOs5PSwN+/VVvRTEMcwdGjx4tz8nJMZZKpUYikShAs37dunXO0dHRA7T39fDwaB49enQdANja2qq8vLzq8/PzjQCAx+PB2tpaBQBNTU1EoVAQQgjS09NNhw4dKre0tFQJhUKEh4fXHDhwwKZ9PeLj4y3Wrl076Pjx47ZisdhfIpEY6fXCe7im7q61vatXrwrj4+Otn3vuuT6PBiCTyXgPPfSQt6+vr79IJArQPBHYvXu3XVBQkJ9YLPafN2+eh0LBjXK1a9cuex8fH39fX1//GTNmDAaA9evXO4tEogCRSNTyxEEqlRoNGTIkICoqysPb2zsgPDxcJJfLW2Z8WLVqlYunp2dgWFiYb3Z2tnF3dekLFogzTCeio6PbLFtYWODatWsGqYvTHCf4fOgDgWn/z4P+yqBBmOvkhAvDhmHFoEH6LWzVqtb3O3YA3t7A/Pmto6kYyECrgS2dNrUpVbp9OmBvDzz9dOtyVBRgoNEzGYbpQnNzM+Lj462CgoL63DIjlUqNJBKJ2dixY+WadQqFAmKx2N/Z2Tlk7NixsnHjxtWGhobWX7hwwbK0tJRfU1PDS0hIsC4oKOgQ0E6cOFEeFBRUe/jw4ZysrCyJv7//bT8yDQsL8xWLxf7tX0eOHLHs7rjOrqm79RpLliwZ9O677xZ2NjLZ+PHjRQEBAX5bt2516OzYw4cPW7m4uDRLpVJJdnZ2xqxZs2SpqakmBw8etEtOTs7KysqS8Hg8+vHHH9snJyebbN261fX06dNXpFKp5JNPPslPSkoy279/v31KSkpmcnJyZkxMjOPZs2dNASA/P99k6dKlN3NycjKsra2VMTExtgCQlJRk9v3339ulp6dLEhISstPS0sy7qkuvPnAtLBBnmE6EhoZi3LhxLctPPfUUAgICujni72minR32+/tjhJX+WoJbTJkCaD5jhYJ7VVcDu3bpv+xeUlEVjl85jof2PoRNv23S+fmXLm19X14O7N6t8yIY5p4WHR89gGwgYWQDCYuOb9v6DADPHXtuoGb7m4lvdsjhnXtwrodm+9bfOw/0OtPY2MgTi8X+QUFB/gMHDmxatmxZn1pyq6urebNmzfJ65513Cuzs7FpusQUCAbKysiT5+fl/paamml+8eNFk2LBhDcuWLSsdP368T0REhCggIKCO38XTyGvXrpmEhoY2AIBEIjF67LHHPCZNmjREs33Xrl32a9ascY6KivKIjIwccvjw4U5/maekpEizsrIk7V8zZszosoNUV9fU1XqN2NhYawcHB8WYMWPq2m87c+ZMlkQiyfz555+z9+zZ4/TTTz9ZtN9n2LBh9UlJSVaLFi1yO3HihIW9vb3yxIkTlpcvXzYLCQnxE4vF/mfOnLG6du2acXx8vNXUqVMrXV1dFQDg7OysPHXqlMWUKVOqrKysVNbW1qrIyMjKxMRESwBwc3NrHDVqVD0ADB06tC43N9cYABITEy2mTJlSZWlpqbKzs1M9/PDDVV3VpavPqyssEGeYLkRHR2PUqFE4dOgQ1q5da+jqtCFP67SR4d7G47VtFQeA8HAgMNAw9enEUelRTI2ditN5p/HhxQ9R36zbdCVfX0Akal2+i/qtMsw/miZHPCsrS7Jv374CExMTKhAIqPakbw0NDTwA2LJli6OmRTk3N1fY2NhIIiMjvebMmVPx1FNPVXV2fgcHB+WYMWNqjh07Zg0Ay5cvL8/IyMhMTk6W2traKn18fBraH1NSUiKwtLRUavo0+fv7N3333Xd52vskJyebbdy48caBAwfy9u7dm3fgwIFOUyf62iLe1TX15lrPnDljkZCQYOPm5ha0cOHCIefPn7ecPn36YAAYPHhwMwC4ubkpIiMjq86dO2fe/vjg4ODG1NRUSVBQUP3atWvdXnnlFVdKKZkzZ84tzXeUm5t7efv27X0e89fIyKildw6fz6cKhYJ0t39ndelrmSwQZ5guTJkyBWfPnsWsWbPQVWtEfyv5ogS/u/6O5NBk1PzZ/yO5FDY04NWrV/G/mzf1U0BUFODeOpER5s0Dpk/XT1l9lFKcgvfPvd+yXFlfibMFZ3VezjvvcP8PCwO++krnp2cYRkcGDhyoqKioEJSWlvLr6+tJfHy8NQCsXr26TBMQuru7N0dFRXn4+Pg0rF+//ob28cXFxYLy8nI+AMjlcpKYmGjl5+fXAABFRUUCAMjOzjb68ccfbZ599tmK9uVnZ2cbOTs7d5mO0tjYSAQCAdWkf7z++uuuS5cuLets3760iKtUKnR2TV2tb+/DDz8sunHjxl9FRUXpe/fuvXb//ffX/PDDD9dlMhmvsrKSB3C514mJiVbBwcEdWjtyc3OFlpaWqsWLF1dER0eXXrp0yWzSpEmy48eP22o+txs3bvCvXLliNHHiRNmxY8dsS0tL+Zr1ERER8ri4OJuamhqeTCbjxcXF2UZERHT7B3XcuHHyuLg4G7lcTiorK3kJCQk2XdWlu/N0pv+TThnmHkFI5zfClNIut+mTqkmFK4uvgDZyN+w5S3MwNKn/5kT/9sYNzMvMhApAkLk5Zjs66v5zEAqB6Ghg9WrgueeAyEjdnv8OCPlC/Jb/GwCAgODsM2cxcuBInZczcyaQnw/oOyWfYe5F2yduL94+seuWzj1T9xTumbqny2GXYmfH5sXOjs3rantfGBsb0xUrVpSMGDHCz9nZudnb27tDq3VCQoLFkSNH7EUiUb1YLPYHgA0bNhQ9/vjj1QUFBcKFCxcOViqVoJSS6dOnV8ydO7caAKZNm+ZVVVUlEAgE9IMPPsh3cHDokPIQEhLSUFFRIRSJRAG7d+/OnTBhQq329vj4eIsxY8bIVSoVlixZ4hYZGVmt6Ux5J7q6JhsbG2VX1zp27Fjvffv25Xl6ejZ3dd7CwkLBzJkzvQFAqVSSRx999Nbs2bM75FynpKSYrl69eiCPx4NAIKC7d+/OCwsLa1izZk3R+PHjfVQqFYRCId2xY0f++PHja1esWFEyZswYMY/Ho4GBgXWHDh3KnTdv3q1hw4b5AcCCBQvKwsPD66VSaZedXUePHl03c+bMisDAwAB7e/vm4ODg2q7q0tfPk+h6ljh9Gz58OE1OTjZ0NZh/oKysLGzduhW3bt3C999/b5g6PJeF0s/UY4nzgPCycAjt9D/5Tb1SiYHnzqFC3QsdAH4NCUGErR6Gr62r414OvU7f7DcR+yJwKvcUAGD16NXYPH6zYSvE/CMQQlIopcMNXQ9DSEtLyw0JCenzyBr/RKWlpfzo6Gi3pKQkq/nz55fLZDL+5s2bS3bu3OkQGxtrHxISUhsaGlq/cuXKTlvFGf1JS0tzCAkJ8exsGwvEGaYXioqK4O7uDk0+YFpaGoKDg/u9Hk3lTbjgewGKCi4g9t7hjYH/GdjDUbrxZGYmvrrBPW180NoaiaGh4PXnk4HaWsC8Q7pgv/o+83vM+m4WAMDe1B4Fywv0NrY4w2iwQJwF4rfjySefdI+Jick3dD2Y7gNxliPOMD1QKBSYOHEitDvlxMTEGKQuRg5GGLK5pUM8SvaUoL9uprUn+PldJkNRY6P+C1UqgSNHgMmTuZ6MzV0+1ewX03ynwdPGEwBwq/4Wvkn/Ru9l5uQA06YBFR0yRBmGYbrGgvB7AwvEGaYHAoEAI0e25gI/+OCDePfddw1WH6e5TuCZcf90a9NrUXOxfzptDrW0xFhrawCAglLsKirSf6EqFfDCC8CJE0BREXDsmP7L7Aafx8dLI1qnwHz//Pv44s8v8MbJN/RS3oQJ3Cgqx45xqfMMwzDM3wsLxBmmF5YvX97y/syZM8jNzTVYXQRWAjg95tSyXPJZSb+VvVyrB+GnJSWQa+WM61xFBfDEE9yA2gBACPDnn/orr5f+PezfMBdyKTKSMgn+ffTf+L+z/4dCWZf9w26bQKs7fWwsN7Q6wzAM8/fBAnGG6YXAwEA8/PDDALghmnYZeJIZ1+dahyot3VcKhbx/IrRH7O3hrZ6HvUqhwLv5+Tivr5kvra2B8+dbp5fctQvYpPtJdPrKxsQGT4U81WadkirxVZruxxrU/jFragIkEp0XwTAMwxgQC8QZppe0W8W/+uorNDXd9mzCd8wizALEmOsoSZsoij7shzQRAHxCsMzNrWV5U34+/pOTo6fC+MDCha3Lx4/rp5zb8J+R/2mzvOKBFVgQskDn5Xh5AY880rocG6vzIhiGYRgDYoE4w/TShAkT4KYOQsvLy/HBBx8YbBhDvjEfpoNbR+so2tk/gTgALHRxgQWv9VdHck0NLsv1NNPn00+3vo+PBwp1n/5xO8QOYkz0mtiy7G7tjoFW+hm95rnnWt9/9RXXf5VhGIb5e2CBOMP0Ep/Px4IFra2eq1atwpIlS6AwUOLuoFe5fG1TH1MMfmtwv5VrIRDgcafWHHVPExOoutn/jgweDIwbx71XqYB9+/RVUp+teGAFFoYuxJ8v/ImlI5fqrZzJkwFHR+59URFw8qTeimIYhmH6GQvEGaYPnnqqbW5wSUkJfv75Z4PUxXmBM0aVjcJI6Ui4LnTt+QAdetLFBQBgLxDgSWdnBFtY6K+wf/+79f3nnwNffw2sWqW/8nppgtcEfDn9S4S6hOq1HKGQ67Oq8fHHei2OYRiG6UcsEGeYPhCLxW2GMnzggQfg4eFhkLrwhDwYOXQ5I69ejba2xk9BQSgZNQobBuu5NX7mTMDGhnt//TqwYAHw7rvA1av6Lfc21DbV4nrldZ2fVztV/vvvgQsXdF4EwzAMYwAsEGeYPtK0igsEAjz00EMICAgwcI36H48QTLK3h5DXD79CTE3bNglr7Nmj/7J76XrldTzzwzNw2eaCF398UefnDwnhBpHRePNNnRfBMAzDGAALxBmmj6KiorBz506UlpZi8+bNhq5Oi6aKJhR/VmzQOuhtls9nnmm7/MYbwFL95WX3VVVDFfZe2gt5kxwJVxP0Mqb4nDmt7++C4dQZ5h9DKpUaiUSiNi0u0dHRA9atW+esvS4nJ0c4cuRIHy8vrwBvb++ATZs2tXSmqaurI0FBQX6+vr7+3t7eAcuXLx+g2ebm5hbk4+PjLxaL/QMDA/26qsfVq1eFe/bssdXltfVGd/VLS0szFovF/pqXhYXF0I0bNzpp76NQKODn5+cfERHh3b81773Ovs/+Iuh5F4ZhtNna2uKll17qecd+0lzdjLRxaZD/KQcoYBNhAzMvs34rv6q5Gd+VleHL0lLMc3LCfwbqYfSQYcOA0FDg0iVu2cMDGDCguyP6zc9Xf0bk/khQcDchvg6+KKgu0PkoKuvXA7/+Crz4IvdiGObuIhQKsW3btsLRo0fXVVZW8oYOHeo/ZcoUWVhYWIOJiQk9c+aM1NraWtXY2EhGjBjhe/Lkyerx48fXAsDp06evuLq6dtvzPy4uzkoikZgAqOyXC9LSVf1CQkIas7KyJAAXcLu4uIRERUVVae/z1ltvOXt7e9fL5XJ+P1X3nsJaxBlGByilqK2tNUjZhE8gT+OCcADI25jXb2U3qVRYee0aXrhyBedlMnxZWqq/wl54AZg2DThypG3StIHdP/B+CHitbRrfPvotHhj0gM7LcXPj0uJffRWwtNT56RmGuUMeHh7No0ePrgMAW1tblZeXV31+fr4RAPB4PFhbW6sAoKmpiSgUCkII6fW54+PjLdauXTvo+PHjtmKx2F8ikRimg1A3jh49auXu7t7o4+PTMsnG1atXhfHx8dbPPfdceWfHyGQy3kMPPeTt6+vrLxKJArRb/Hfv3m0XFBTkJxaL/efNm+ehGaFs165d9j4+Pv6+vr7+M2bMGAwA69evdxaJRAEikShA0yIvlUqNhgwZEhAVFeXh7e0dEB4eLpLL5S0f+qpVq1w8PT0Dw8LCfLOzs417qo++sECcYe7AjRs3sGXLFvj6+mLlypUGqYPAQgDbca2/K8qPlkOl0NuAgm3UKZXYV1LSspxeW4u8hgb9FPbii8APPwDTp3NDidwlrIytMN13esvyN+nfGLA2DMPcDaRSqZFEIjEbO3ZsyyQLCoUCYrHY39nZOWTs2LGycePGtbTejB8/XhQQEOC3detWh87ON3HiRHlQUFDt4cOHc7KysiT+/v63PaNcWFiYr3Y6ieZ15MiRLm/xe6ofAMTGxtrNnj37lva6JUuWDHr33XcLeV30Jzp8+LCVi4tLs1QqlWRnZ2fMmjVLBgCpqakmBw8etEtOTs7KysqS8Hg8+vHHH9snJyebbN261fX06dNXpFKp5JNPPslPSkoy279/v31KSkpmcnJyZkxMjOPZs2dNASA/P99k6dKlN3NycjKsra2VMTExtgCQlJRk9v3339ulp6dLEhISstPS0sy7q48+sUCcYW4TpRTff/89Xn/9dWRnZ2P//v1o0FcQ2oMh7wwB35J76qesUqLiREW/lGsjFGKGZpBrAC+4usLDxKRfygYAyOXAxYv9V14XFgS3ji//Tfo3UKrYrDsMoxfR0QNASFivXnPndhzSau5cjzb7REf3mOPWVct1V+urq6t5s2bN8nrnnXcK7OzsWlpFBAIBsrKyJPn5+X+lpqaaX7x40QQAzpw5kyWRSDJ//vnn7D179jj99NNPnY4He+3aNZPQ0NAGAJBIJEaPPfaYx6RJk4Zotu/atct+zZo1zlFRUR6RkZFDDh8+bNXZeVJSUqRZWVmS9q8ZM2bUdLZ/b+rX0NBAfvnlF+sFCxa0pM3ExsZaOzg4KMaMGVPX6QcFYNiwYfVJSUlWixYtcjtx4oSFvb29EgBOnDhhefnyZbOQkBA/sVjsf+bMGatr164Zx8fHW02dOrVSkybj7OysPHXqlMWUKVOqrKysVNbW1qrIyMjKxMRESwBwc3NrHDVqVD0ADB06tC43N9cYABITEy2mTJlSZWlpqbKzs1M9/PDDVd3VR59YIM4wtykzMxOLFi1qWVapVEhPTzdIXSyHWWLAota/JyWflXSzt249pR5THAAOl5dDoeqH1niZDFi8mMsTj4wEmpv1X2Y3HvZ6GI5m3A1JUU0RTued1mt5tbXAunXAhAl6LYZhGADOzs6K6urqNvnNFRUVfAcHB8WWLVscNS3Kubm5wsbGRhIZGek1Z86ciqeeeqqqs/M5ODgox4wZU3Ps2DFrABg8eHAzALi5uSkiIyOrzp07Z97+mJKSEoGlpaVSqH4a6O/v3/Tdd9+1yUNMTk4227hx440DBw7k7d27N+/AgQOdplX0tUW8N/U7ePCgtb+/f92gQYNa8sjPnDljkZCQYOPm5ha0cOHCIefPn7ecPn16m/Fug4ODG1NTUyVBQUH1a9eudXvllVdcAYBSSubMmXNLc5OQm5t7efv27X0ejcDIyKhlBAE+n08VCkW3+UBd1UefWCDOMLfJ398foaGhLctvv/02RowYYbD6uP679ffFreO30FjS2C/lPmxrC2f1H4eSpiacrKrSf6FCIfDtt0BNDVBWZvDpJoV8IaICo1qW3z37LhYeWYjDmYd1XlZBAWBlBWzaBPzyC5CcrPMiGIbRYm1trXJycmo+evSoJQDcuHGDf+rUKetx48bJV69eXaYJFt3d3ZujoqI8fHx8GtavX39D+xzFxcWC8vJyPgDI5XKSmJho5efn1yCTyXiVlZU8gMtPTkxMtAoODq5vX4fs7GwjZ2fnLtNRGhsbiUAgoJoUkNdff9116dKlZZ3t25cW8d7W78CBA3aPPfZYm0exH374YdGNGzf+KioqSt+7d++1+++/v+aHH35oM9FCbm6u0NLSUrV48eKK6Ojo0kuXLpkBwKRJk2THjx+3LSoqEmg+8ytXrhhNnDhRduzYMdvS0lK+Zn1ERIQ8Li7OpqamhieTyXhxcXG2ERERnbbua4wbN04eFxdnI5fLSWVlJS8hIcGmu/roExs1hWHuwFNPPYVL6pE8Dhw4YNDRVMx8zGA91hrVp6sBJVC6rxQer+l/siEBj4cnnJ2xvZAbsi+mtBQT7ez0V+DPPwPz5wMV6t/5Q4ZwTcQGtiB4AXZe2AkAiL8aDwAolZdilt8snZYzcCBgYcE9FAC4McV//FGnRTDM3Wv79mLcRstoi9jYPMTG9rlH+759+64vXrzYfeXKlYMAYNWqVcUBAQFtWjsSEhIsjhw5Yi8SierFYrE/AGzYsKHo8ccfry4oKBAuXLhwsFKpBKWUTJ8+vWLu3LnVEonEaObMmd4AoFQqyaOPPnpr9uzZHfKSQ0JCGioqKoQikShg9+7duRMmTGjzSy8+Pt5izJgxcpVKhSVLlrhFRkZWazqO3onCwkJBV/UbO3as9759+/Ls7OyUZ86csdq3b1+fP9eUlBTT1atXD+TxeBAIBHT37t15ABAWFtawZs2aovHjx/uoVCoIhUK6Y8eO/PHjx9euWLGiZMyYMWIej0cDAwPrDh06lDtv3rxbw4YN8wOABQsWlIWHh9dLpdIuO7SOHj26bubMmRWBgYEB9vb2zcHBwbXd1UefiN7G/dWT4cOH02TWBMTcJW7evAk3NzdoenNnZ2fD29twQ6WWfl2KrAVZAACBnQDh5eFd5jHqUppcjlD1v0sTQvD2kCGY7+wMJyM9dOzPzwc8PQHN767r17llA6OUQvyhGFduXWlZR0BQFF0EV0vdPt185hngyy+59/fdB/zxh05Pz9xlCCEplNLhhq6HIaSlpeWGhIR0OuLGP1lpaSk/OjraLSkpyWr+/PnlMpmMv3nz5pKdO3c6xMbG2oeEhNSGhobWr1y5stNWcaZ/paWlOYSEhHh2tq3H1BRCyLBOXl6EENaazvzjOTk5YfLkyS3LMTExBqwNYOpl2vJeUaFA9e/V/VJuiIUFQsy5tMEGSrHi6lXsv3Gjh6Nuk7t72+RoA3/mGoSQNp02Paw9cOG5C3CxcOnmqNvz1luAZhCCCxe4YQ0ZhvnncHFxUe7fvz+/oKDg8pYtW0pramr41tbWqjVr1tzMyMjI3L9/fz4Lwu8NvckR3w3gPIBPAewBcA7A/wBICSEP67FuDHNP0Ex5DwBffPEFNmzYgPPnzxukLlYjrcAzb/1nXfi+7md47MqTLm0Dzi9LS/tnps0vvgD6o4NoLzwR9ETL+0ZlIwIcA/TyRGLAAEDr/u9uuRdhGMZAYmJi8g1dB+b29CYQLwYwlFI6nFIaBmAogGsAJgB4V5+VY5h7wSOPPAJbW65zelFREdavX4/du3cbpC6ER+AwzQFGLkYYsGgARP8V9VvZ85yc2vxCmeXgAL2FxzNmAOrPHHl5XLPwXWCw7WCsfXAtjkYdRf7L+TAVmvZ80G3SntMoJuauuRdhGIZh+qA3gbgPpTRDs0AplQAQU0qv6a9aDHPvMDY2xty5c9usO3ToEOrq7rifzG3x3++PUSWj4LPbB8Zuxv1WrouxMSbZ2cEIwBxHR0Q5O4Ovr/x0Y2Ng5szW5e3bgUWLgBMn9FNeH2yM2IipvlMh5Ot30qGpU1vvRXJzgdhYvRbHMAzD6EFvAvEMQshHhJCx6tduABJCiDEAww7eyzB3Ce30FD6fj23btvVLJ8m7zQ6RCKXh4fguIAC+Znoe9Wn27Nb3//sf8PHHrT0Y7yIqqsLJaydR39xhxK87YmzMBeMay5fr9PQMwzBMP+hNIL4QQA6Al9Wva+p1zQAi9FMthrm3jBgxAmKxGAAwfPhwjBs3Dqam+ktLuFt5mZrCtr+mnx8/HrCxabvu+PG7YihDjQ8vfIjB/x2Mf331L/wg/UHn53+iNSUdZWVAaqrOi2AYhmH0qMdAnFJaTyndRimdqX5tpZTWUUpVlFJ5f1SSYe52hBB8/PHHyMzMxPnz5+Hj42PoKgEAVM0q3PzuJuQSw/1TVemrw6aRETB9euvy0KFcaspdcgPUqGhEUn4S8qu5PlSHMg/pvIwJE7gxxTWOH9d5EQzDMIwe9TgEISEkHMB6AB7a+1NKh+ivWgxz7xk7dqyhq9CG9AUpSr8oBVVQOM52RMD/AvqtbIVKhcPl5fhfWRkuymTIGTkSAp4eJvKdPRvYt497L5cDY8bovozbQClFwO4AXK3kxhW0MraCi7nuhzEkBHjtNaC8HFiyBDDgEPYMwzDMbejNWOCfA1gOIAWAUr/VYZi/l7q6OpjpO1e6C003mkAVXGv0rRO3QCntt7z1S3I5nsnKQq16KI/fqqsxTtOzUJcmTAAeeQSIjGzbedPACCGYMGQCrqZwgfgc/znYOWWnXsp64w29nJZhGIbpB71poqqmlP5EKb1JKb2leem9Zgxzj2pubsaHH36IcePGwcPDA83NhunTPHD5wJb3KrkKNck1/Vb2wbKyliAcAE5VVemnIGNj4Ngx4MUXAWdn/ZRxmxaEtE7uc1ByEA2KBgPWhmEYhrkb9SYQTySEvEcIeUB7dk2914xh7lFXr17FG2+8gcTERJSXlyMxMdEg9bAZYwPLByxblsv+13+TrM1ydGx5b83nY627e7+VDUqBK1d63k/PHhj4AIbYchl81Y3V+PHKjwauEcMwDHO36U0gPhLAcACbAWxTv7bqs1IMcy974oknUF3NTS1PCMGff/5pkHoQHoHnG54ty2X/K9PfTJftDLe0hJuREQCgWqnEGZlM/4UqlcCqVcCQIUBgIFBZqf8yu0EIaTPT5uGsw3otT6EAvvoKGD0ayGdz7DGMTvH5/DCxWOwvEokCJk+ePKSmpqZXnV5ycnKEI0eO9PHy8grw9vYO2LRpk5NmW11dHQkKCvLz9fX19/b2Dli+fPkAzbZNmzY5iUSiAG9v74CNGzc6dX524OrVq8I9e/boIe+ve+Xl5fxJkyYNGTx4cMCQIUMCfvnlF/P2+8yZM8fTzs4uRCQSdeig1N22u0V0dPSAdevW6f1Ra29GTYno5DVO3xVjmHvVTK1c5SlTpmDVqlUGq4vtBFvwrfkAgIbcBtSk9E96Co8QzHBwaFk+XF7eD4XyuDSV3FyguRk4ckT/ZfbgUb9HW94fzTqKNb+uweakzXopy80NePJJ4OxZYNs2vRTBMP9YxsbGqqysLEl2dnaGUCik27Ztc+z5KEAoFGLbtm2FV69ezbh48WLm559/7pSSkmICACYmJvTMmTNSqVQqycjIkJw8edLq5MmT5hcvXjSJiYlxTE1NzczMzMw4ceKEzeXLlzudnS0uLs4qNTW13zsiPf/884Mefvhh2fXr1zMkEokkNDS0Q+7dM888U3706NHszo7vbts/TZeBOCFkvvr/0Z29+q+KDHNvmTVrVsv7xMRE1NfrdiKXvuAZ8eAwvTUgLvuu/9JTZmqlpxwpL9dva/zly1xLeGYmt2xjA6ifShhSsHMwPG08AQDyZjneTnob//3jv1CqdN/v3d+/9f0h3Y+UyDCM2ujRo+U5OTnGUqnUSLtFd926dc7R0dEDtPf18PBoHj16dB0A2Nraqry8vOrz8/ONAIDH48Ha2loFAE1NTUShUBBCCNLT002HDh0qt7S0VAmFQoSHh9ccOHDApn094uPjLdauXTvo+PHjtmKx2F8ikRjp9cLVbt26xf/jjz8sX3755XKAu6FwcHDo8Ett8uTJckdHR0Vn5+huGwDIZDLeQw895O3r6+svEokCNK3+u3fvtgsKCvITi8X+8+bN81AoWk+xa9cuex8fH39fX1//GTNmDAaA9evXO4tEogCRSNTyZEEqlRoNGTIkICoqysPb2zsgPDxcJJfLCQCsWrXKxdPTMzAsLMw3OzvbuKf66EJ3LeKaxwyWXbx6RAiZRAiREkJyCCGvdbHPY4QQCSEkgxCyvw91Z5i7kp+fH3x9fQFwo6b8/PPPBq2P45zWgLjk85J+S0950NoadgJuYKbCxkbsKirCNX3dlAwZAty82bqcmAi8/LJ+yuoDQghm+M5os+5m7U2cLTir87Jeeqn1fV0dl6nDMIxuNTc3Iz4+3iooKKjPv8ykUqmRRCIxGzt2bMvEDgqFAmKx2N/Z2Tlk7NixsnHjxtWGhobWX7hwwbK0tJRfU1PDS0hIsC4oKOgQZE+cOFEeFBRUe/jw4ZysrCyJv79/0+1eV1hYmK9YLPZv/zpy5EiHeE8qlRrZ2dkp5syZ4+nn5+f/+OOPe8hkMp2OT3v48GErFxeXZqlUKsnOzs6YNWuWLDU11eTgwYN2ycnJWVlZWRIej0c//vhjewBITk422bp1q+vp06evSKVSySeffJKflJRktn//fvuUlJTM5OTkzJiYGMezZ8+aAkB+fr7J0qVLb+bk5GRYW1srY2JibJOSksy+//57u/T0dElCQkJ2WlqaeXf10dW1dvnBUUo/Uf9/Q2evnk5MCOED+BDAZAD+AOYSQvzb7SMCsBpAOKU0ANzMnQxzTyOEtElP+f777w1YG8B4QOsTTUWFArIL/ZCvDUDI42GqvX3L8tKcHHxRUqKfwszMgClTWpePHtVPObdhhnhGy3tTgSm+nf0twlzDdF/ODEDzEKKyEjh/XudFMIzhRUcPACFhICQM7VqfAQDPPTewZfubb3bM750716Nl+9atDh22d6GxsZEnFov9g4KC/AcOHNi0bNmyPuXbVVdX82bNmuX1zjvvFNjZ2bUMKSUQCJCVlSXJz8//KzU11fzixYsmw4YNa1i2bFnp+PHjfSIiIkQBAQF1fD6/0/Neu3bNRJMWIpFIjB577DGPSZMmtczzsmvXLvs1a9Y4R0VFeURGRg45fPiwVWfnSUlJkWZlZUnav2bMmNEhn1GhUJDMzEyzJUuWlGVmZkrMzMxUa9eu1elECcOGDatPSkqyWrRokduJEycs7O3tlSdOnLC8fPmyWUhIiJ9YLPY/c+aM1bVr14wBID4+3mrq1KmVrq6uCgBwdnZWnjp1ymLKlClVVlZWKmtra1VkZGRlYmKiJQC4ubk1jho1qh4Ahg4dWpebm2ucmJhoMWXKlCpLS0uVnZ2d6uGHH67qrj66utYe72AIIY6EkNcJIZ8SQr7QvHpx7vsA5FBKr1FKmwAcADC93T7PAfiQUloJAJTSm2CYvwHt9JQffvgBe/fuRUZGhkHqYhFiAZ5p6z/1wvcL+61s7fQUADikz1zxOXNa3x88qL9y+ijcPRz2ptwNSaOyEYFOgTA36tCv6Y7x+W2HUj+s376hDPOPoskRz8rKkuzbt6/AxMSECgQCqtIaprWhoYEHAFu2bHHUtCjn5uYKGxsbSWRkpNecOXMqnnrqqarOzu/g4KAcM2ZMzbFjx6wBYPny5eUZGRmZycnJUltbW6WPj0+HHOySkhKBpaWlUigUAgD8/f2bvvvuuzztfZKTk802btx448CBA3l79+7NO3DgQKcpFX1pEff09GxydnZuGjduXC0APP7445VpaWk6zVMPDg5uTE1NlQQFBdWvXbvW7ZVXXnGllJI5c+bc0nwPubm5l7dv3158O+c3MjJqeTTM5/OpQqHodpKNzupzO+V2pjePEn4AYA3gFwA/ar164gagQGu5UL1Omw8AH0LIWULIeULIpM5ORAh5nhCSTAhJLivrvxxXhrldw4cPx8CB3DjeVVVVePrpp/H5558bpC6ET2A73hZ8Cz7sH7GH+6v9N5Tgw7a2MNWaROg+S0s0af3h0qkpUwATE+59ejogleqnnD4S8AR4c+yb+HL6l7jxyg34O/r3fNBt0rr/w+HD3EiODMPox8CBAxUVFRWC0tJSfn19PYmPj7cGgNWrV5dpgkV3d/fmqKgoDx8fn4b169ff0D6+uLhYUF5ezgcAuVxOEhMTrfz8/BoAoKioSAAA2dnZRj/++KPNs88+W9G+/OzsbCNnZ+cu01EaGxuJQCCgPPWsxq+//rrr0qVLOw2i+tIi7u7urnBxcWlKS0szBoCff/7ZytfXV6cTJeTm5gotLS1VixcvroiOji69dOmS2aRJk2THjx+31Xw2N27c4F+5csUIACZOnCg7duyYbWlpKV+zLSIiQh4XF2dTU1PDk8lkvLi4ONuIiIguRywYN26cPC4uzkYul5PKykpeQkKCTXf10dW19mZmTTNKqb6GfRAAEAF4CMBAAL8RQoIopVXaO1FKPwXwKQAMHz6c/Wlh7nqa9JSdO1tnUzx06BC2bdvWb7Nbagv4PgA8gR6mmO+BKZ+PKfb2OFRejvssLPDigAEw0sdU9wBgYQFMngxoUoE++ojrtOnvDzz2mH7K7KX/jPxPv5QTEQFYW3P9VHNzgd9/B8LD+6Vohukf27cXo7tW0D17CrFnT9eP/WJj8xAbm9fl9j4wNjamK1asKBkxYoSfs7Nzs7e3d4dgNCEhweLIkSP2IpGoXiwW+wPAhg0bih5//PHqgoIC4cKFCwcrlUpQSsn06dMr5s6dWw0A06ZN86qqqhIIBAL6wQcf5HfWGTIkJKShoqJCKBKJAnbv3p07YcKEWu3t8fHxFmPGjJGrVCosWbLELTIyslrTcfRO7dy5M/+JJ54Y0tTURNzd3RtjY2NzAWDs2LHe+/bty/P09GyeOnXq4PPnz1tWVlYKnJ2dg1977bXi5cuXlwNAd9sAICUlxXT16tUDeTweBAIB3b17d15YWFjDmjVrisaPH++jUqkgFArpjh078n18fJqGDx/esGLFipIxY8aIeTweDQwMrDt06FDuvHnzbg0bNswPABYsWFAWHh5eL5VKO+3UOnr06LqZM2dWBAYGBtjb2zcHBwfXdlcfXXyOAEB66rhFCHkLwO+U0rg+nZiQBwCsp5ROVC+vBgBK6RatfT4G8Ael9Ev18kkAr1FKL3Z13uHDh9Pk5OS+VIVhDOLUqVOIiIgAwA1htX79eqxYsQLGxp2OQvW3daWuDuZ8Ptz647r37weeeKLtugcfBE6f1n/ZfVRQXYCBVgN1fmM2c2bryI3+/oCBMqIYHSKEpFBKhxu6HoaQlpaWGxIS0g/jn977SktL+dHR0W5JSUlW8+fPL5fJZPzNmzeX7Ny50yE2NtY+JCSkNjQ0tH7lypUstaCfpaWlOYSEhHh2tq03gXgNuBFUGgE0AyAAKKW004R/reMEAK4AGA+gCMBFAPMopRla+0wCMJdS+hQhxAHAnwBCKaW3ujovC8SZe4VCoYCrqyvKy8vh6uqK33//HZ6enoau1t+bTAY4OQGNja3rCAEKC4EBHft1GcJ/z/8XX/31FVJKUpC+KB2BToE6Pf8HHwDLl3PveTygtrY1Y4e5N7FAnAXit+PJJ590j4mJYdN73QW6C8R7M6GPJaWURyk1pZRaqZe7DcLVxykAvAQgHkAmgO8opRmEkI2EkGnq3eIB3CKESAAkAni1uyCcYe4lAoEAu3fvxu+//47CwsK7JginlEJ2QYb6XMONb643VlbAxImty8HBQEwMt/4uoKIqHLtyDCklKQCAw5m671H53HPcvQcAqFRAXJ+eZTIM83fBgvB7Q5c54oQQMaU0ixAyrLPtlNLUnk6uTmeJa7dundZ7CiBa/WKYv5052iN53AWurbmGoh1FUNYo4fSEE/y/1l/HwfaUlOJsdTUOlZWhuKkJ/wvQ08zGs2e3Dl9oZATMn6+fcvqoUdEI753eKJRx6atCnhC36nTf7mBuzqXKGxsDS5dymTkMwzDM3am7zprRAJ4H0NlkyRQAm+aeYW4DpdQgHTYBoCalBsoars/PrWO3+rUuSVVVGJeWBk0yXFFjo37yxqdOBUJDgenTuaD8LmEsMIbITtQSiL834T0su3+ZXsr6sTfjWjEMwzAG192EPs+r/x/RyYsF4QzTB01NTTh06BDmzZuH+++/32D1GLh0YMt7pUwJ+Z/ybvbWrTeuX4d2j5Qf9DWmuI0N8OefwPr1QKBu86/vlPbkPvFX4w1XEYZhGOau0KtxxAghgeqp6J/UvPRdMYb5OyktLcW8efMQGxuLCxcuQCKRGKQethNsYR7cOplM+dH+6wM1S2tyn5GWlnjOVWfzIfROveFz4qf7ts5pdvL6Scga+2eWU4ZhGObu1JuZNd8EsFP9igDwLoBp3R7EMEyLxsZG+Pv7o6mpdd6F+HjDtIbyBDx4vOHRslzxY4c5IvRmpkPrbNKX5HI06GtiH211dcDmzcADDwAiEdd70YA8bDww1GUoAKBJ2YSfsn/Se5nnzwP//rfBL51hGIbpRG9axGeDG4KwlFL6NIAQcDNtMgzTC8bGxhg3rjWba/ny5Xj55ZcNVh/bh20BPve+JrkGjaWN3R+gI0NMTRFszrXGN1KKExX9cBNgbAxs3cpFo0VFXMqKgWmnp8RejsVHFz/CMekxnZejUAB2dtw9yBdfsNFTGIZh7ka9CcTrKaUqAApCiBWAmwAG6bdaDPP3Mktr7vHz588brLMmAAhthLAZY9OyXPGTYVrFD+srR1zjwgVg5EigspJb5vHuukD8B+kPWBy3GB/88YHOyxEIuPsQjR07dF4EwzAMc4d6E4gnE0JsAOwBkAIgFcA5fVaKYf5upk6dCj6fa4Y+d+4ciou7nqG5P9hF2rW8L91X2m/lztTKEz9eXo5kmQw1CoV+CnN0BFK48bohFAJ5ecCzz+qnrD4IcgrCYJvBbdadzj2N8jrd35hMb01Jxy02QwPDMMxdpzcT+iymlFZRSj8GMAHAU+oUFYZhesne3h5jx45tWf7hhx8MWBuAb8FveV99phqqpv5JIA42N8cQ9TSPcpUKI1JTcVRfEeLgwfj/9u48Lqp6/x/46zMLILKDrAoqDAzDKui1BEIlBSVTMQu9lq23q3bril1NU3O5pfd71dti3tIWtX5qXTVzIZEMStNKIE1BEEQEUZB932bm/P4YZhhkEXTODML7+XjMo7O/P+c0Du/5zGeBt7dquaUFuHCBnzi9xBhrVyvubO6Md6PehZHQSOexli5tW754Eais1HkIQggh96Gno6b4t86GGQTAgzEWc7dzCCHtaTdP2b9/P06fPo26ujqDlMV6knXbigKo+L5CL3EZY+2apwDAMT6raqOj25b7UCNp7USc4zgsHLMQFsa6n/1z5EhgdOvE6C0twNGjOg9BSL+XlZVlJJFI2s1AFhcX57x69WoH7W05OTnisWPHerq7u/t4eHj4rF+/3l69r76+nvn5+Xl7eXnJPDw8fBYvXuys3ufi4uLn6ekpk0qlMl9fX++uynH16lXxjh07rLvaz4fu7qknx6xfv95eIpH4eHh4+Kxbt67DuX1FZ/8/9aUno6Z8BuAzALMATGt9PcZzuQjpd2bMmKFZ/uGHHxAWFoaTJ08apCym7qYQWYvAxAxmo8wgsu1ubi/d0m6ewgAYC3pUH3Bvpk5tWz52DOC4ro/Vo5BhIYh0j8SGiA34Yf4PEDD+noHW9z8c0X2fUEJIK7FYjM2bN9+4evVq+rlz5y5/+umn9qmpqSYAYGJiwp0+fTorKysrIz09PePkyZMWJ0+e1Iwl++OPP17JzMzMuHTp0uWurh8fH2+RlpZmqo97Uevunu52zLlz50x27949JC0t7fLly5fTjx8/bnXp0iUeZnF7sPXk0/8hjuNGcxw3n+O451pfz/NeMkL6GRcXF4wdO7bdtngD1tKOyRiDR5oewei00bAcq7+BkB62sIC9SJX4DzMywltubnc54z6EhgIWrTXN168Dp04BO3cafExxoUCI4/OO443QNyC1k/Iaa9q0tuUTJ1SjqRBCdM/Nza0lNDS0HgCsra2V7u7uDfn5+UYAIBAIYGlpqQSA5uZmJpfLWW867SckJJitWrVq2NGjR62lUqksIyND923ZOtHdPd3tmIsXLw4aNWpUrbm5uVIsFiMkJKRm3759VtrnVldXC8aPH+/h5eUlk0gkPto1/tu2bbPx8/Pzlkqlsrlz57rJWz+8tm7dauvp6Snz8vKSzZgxYwQArFmzxkEikfhIJBJNzXtWVpbRyJEjfWJjY908PDx8QkJCJLW1tZqHvmzZMsfhw4f7BgcHe2VnZxvfrTx86Uk12FnGmIzjOMPMQEJIPxIdHY1ff/0VAGBqaoohWrXD+mbsaJiKCQFj2CGVYpixMQLNzPgdQUYsBiZPBvbvV62PH6+qFXdwAKZM4S/uPeI4TufPw8cHcHQEiopUbcTXrVO9CCH8ycrKMsrIyDANDw/XTF8sl8vh6+sry8/PN54/f/7tiRMnatomRkRESBhjeO6550pef/31Dj23IyMja/38/Oq2bNlSMGbMmMb7KVtwcLBXXV2d8M7tGzduLJgxY0ZNb+6pu2Py8vKM1q1b51JUVCQcPHgwl5iYaBkQENCuPebBgwctHB0dW5KTk3MAoKysTAgAaWlpJvv377dJSUnJNDY25ubNm+f60Ucf2T700EN1mzZtcjp79mymk5OTvLi4WHjq1CnTPXv22Kampl7mOA7BwcHeERERNXZ2dor8/HyTL7/8MnfcuHHXp06dOnL37t3WCxcuLD916pTpN998Y3Px4sWMlpYWBAYGykaNGlXfVXn41JNEfDdUyXgRgCaofk3mOI7z57VkhPRDU6ZMwerVqwEAVlZWWDdAM6LH72gnzqupU9sScXXTlCNH+kwiXt9Sj13ndyE+Jx6F1YVIezlNp9dnDHB1VSXigOpRDNC3HSH3pKsvx11tr6qqEsTExLhv3LixwMbGRtMTXiQSITMzM6O0tFQYHR3tfu7cOZMxY8Y0nj59OnPEiBEthYWFookTJ3r6+Pg0TpkypUOym5ubaxIYGNgIABkZGUZr1qxxqq6uFh4/fjwXUNUUFxUViXJyckxqamqEL7zwQmlMTEyH6XtTU1OzevsMurqn7o6xsbFpfO2114oiIiI8Bw0apPTx8alXjx6mFhQU1PDmm28OW7Bggcv06dOroqKiagHg+PHj5pcuXTINCAjwBoDGxkaBvb29vKqqSjht2rQKJycnOQA4ODgotm/fbjZ16tRKCwsLJQBER0dXJCUlmc+ePbvSxcWlady4cQ0AMGrUqPq8vDxjAEhKSjKbOnVqpbm5uRIAJk+eXNldefjUk0T8UwBPA7gIgOZmI+Q+BAUF4e9//zvCw8MRERFh0PHE76SoU0A4mPcv//p3Z8L98MNAUJBhytIJARNgyYklaJCrmstcLb8Kdxt3ncaYO1c1rDoAXLkCKBSAsB/+ryb9X1xOjvN/btxw6smxsfb2pXtlsuva2+ZkZLjtu31bUxOweOjQW1s8PLodT9bBwUFeVVXV7l9MeXm5cMSIEU0bNmwYsmvXriEAcPz48WwnJyd5dHS0++zZs8vnz59f2dn17OzsFGFhYTVHjhyxHDNmTOOIESNaAMDFxUUeHR1defbs2cF3JuK3bt0SmZubK8RiMQBAJpM1f/3119ejoqJGqo9JSUkx/eyzzwoEAgFKSkqEixYtGtpZIt7bGvGmpiZ2t3vq6pjFixeXLl68uBQAXnnlFZehQ4c2a5/n7+/flJaWlnHgwAHLVatWuXz//ffVmzZtusVxHJs9e3bZhx9+WKh9/Ntvv92rDp9GRkaajkFCoZBraGjotkl2V+XpTcze6kkb8RKO4w5zHHeN47jr6hefhSKkvxIIBPjPf/6DGTNmwNzc3NDFgaJZgayXsnDG5QxOWZ6CvEa/DYg5jkN6XR025ecjga+ZNh0dgeDgtvW//71PjCcOAH8U/4Gxn4zVJOEA8NP1n3Qe57nnAFNT4KGHgA8+UM1tRAjpGUtLS6W9vX3L4cOHzQGguLhYmJycbDlx4sTa5cuXl2RmZmZkZmZmuLq6tsTGxrp5eno2rlmzplj7Gjdv3hSVlpYKAaC2tpYlJSVZeHt7N1ZXVwsqKioEgKp9clJSkoW/v3+HTizZ2dlGDg4OzXduV2tqamIikYgTtP7jXrFihdOrr75a0tmxqampWeoya786S8KVSiW6uqeeHFNYWChSl//YsWNWL774YrsP+ry8PLG5ubly4cKF5XFxcUXnz583BYCoqKjqo0ePWqvPLy4uFl65csUoMjKy+siRI9ZFRUVC9fYJEybUxsfHW9XU1Aiqq6sF8fHx1hMmTOiyiQ0ATJw4sTY+Pt6qtraWVVRUCBITE626Kw+felIj/jtjbA+AI1A1TQEAcBx3kLdSEUL0QlGhwK1P2r7sl35TCsdnHPUWf/3163grLw8AMHvIEETa2HR/wr2Kjla1zZg6VTWmXx/hbO6Mi8UXAQAMDL+99BtGO4/WeRwLC6C2VtVMhRDSe7t27bq2cOFC16VLlw4DgGXLlt308fFp0j4mMTHR7NChQ7YSiaRBKpXKAGDt2rWFTz31VFVBQYH42WefHaFQKMBxHJs+fXr5nDlzqjIyMoxmzpzpAQAKhYLNmjWr7IknnuhQix0QENBYXl4ulkgkPtu2bcubNGlSu7bWCQkJZmFhYbVKpRKLFi1yiY6OrlJ3oLwf3d1TeHi4x65du65nZWUZd3XM448/7l5ZWSkSiUTcu+++m29nZ6fQvn5qauqg5cuXDxUIBBCJRNy2bduuA0BwcHDjypUrCyMiIjyVSiXEYjH3/vvv50dERNQtWbLkVlhYmFQgEHC+vr71Bw4cyJs7d25ZUFCQNwA8/fTTJSEhIQ1ZWVlddmgNDQ2tnzlzZrmvr6+Pra1ti7+/f1135eET4+4ynBdj7PNONnOGGjll9OjRXEpKiiFCE6JTLS0tOHPmDOLj4xEREYHJkycbpBxnnM+g+ZaqosVyvCVGJY3SS9yrDQ3waO24CgCWQiFKQ0Ig4qO6trFRNd97H8xEx34yFr8VqtqNfPXEV3jS50kDl4j0NYyxVI7jdP8N7QFw4cKFvICAAN1PO/uAKyoqEsbFxbmcOnXKYt68eaXV1dXCd95559YHH3xgt3fvXtuAgIC6wMDAhqVLl3ZaK07068KFC3YBAQHDO9t31xpxmkWTEH7885//1HTWLC4uNlgibv9ne9zYdAMAUH+5HpySAxPwn7CONDGBm7ExrjepKpX+4uQEBXr2M12vmZjc/RgDiXKP0iTix3OOUyJOCLkrR0dHxZ49e/LV688884yrpaWlcuXKlbdXrlx525BlI73Tkwl9PBljJxljl1rX/RljK/kvGiH91759+/Cvf/1Ls/7dd99BqTRMX2j3je4Q26k6ALUUt6AmrdumdTrDGMNUW9t267xO7qN2+zbw+efArFnAvn38x7uLKZK2zqTHc47jbr9S6kJJiWrKe0JI/7B79+78ux9F+qKe/NXbAWA5gBYA4DjuDwCxfBaKkP5OIpGgqbUm2MTEBDt27NBLAtYZJmSwiWprm11+jKdOk52I0moT/h1fnTXvtGsX8PzzwMGDwP/+p5+Y3RjjPAY2g1TP4VbtLfxW+BtO5p7k5f3wySfAkCGAvT3wxBM6vzwhhJBe6kkibspx3G93bKO52Qi5D6NGjYK9vWoUpsbGRjg5OeHO8VX1yfaxtprpsmNleos70coKRq3tti/W1eFG433NU3F3n34KaM9meuIE0NTU9fF6IBQIMdm9rVlS6OehePSLR5Fdnq3zWA0NQGlra9ucHKClRechCCGE9EJPEvFSxpg7AA4AGGNPAOB1TEVC+juBQICoqCjN+nfffWfA0gDWkdZA6/eAmnM1aC7ucpQsnTITiRBmaalZP15eDiWfvwzs3g0kJ6uWn3wSOHcOMNLLTNHdinJvey/Ilap6ju+ydf+emD+/bVmpBH7/XechCCGE9EJPEvFFAD4GIGWMFQL4O4C/8lkoQgaCKVoTzRg6ERdZimDk1JaQFu/pdLhYXkzRaif+Rm4uYjMy+AsWHd22rFQCUmmfGEkl0iOy3bqXrRcGGw3WeRwLC2C01tgbf/yh8xCEEEJ64a6JOMdxuRzHPQpgCAApgHAAoXwXjJD+bvLkyVBPvvDrr7+itLRU025c3xhjEBi3fRwUf6nHRFyrnXiZXI7vysvRzFfH1alT25ZPnOgzbTMczRwxyrFt2MiNj27Ei0H8TDoUq9XDx8Df/wghZMDrMhFnjFkwxpYzxrYyxiYBqAcwH0AOABpfi5D7ZGNjg4ceegiAaobJgIAAzNduO6BnQ2KGaJaZkf5qib1NTTFMq3lInUKB87W13ZxxH3x8AFdX1XJ1NfDzz/zEuQdRHlHwsPHAK2NewQirEbzF0f4ukpgINOunFRIhhJBOdFcj/gUALwAXAbwEIAnAbAAzOY6broeyEdLvaTdPuXnzJhISEiCXG6YvtMurLvB4zwNhdWEIPht89xN05M5hDF9xccGfLCz4Cta+ecqxY6rOmtd5nzztrtaOX4vsv2Xjg6kfIMAxgLc4Uing5qZarqkBzpzhLRQhhJC76C4RH8lx3LMcx30MYA4AGYBIjuPO66VkhAwA2ok4oBpBJSsryyBlMRlqgqGvDoXQVP+jtzxpb49XXFxwzM8PG/megl47Ef/4Y8DODnj2WX5j9oBYKNZLHMba14qvX6+XsIQQQjrRXSKuaTzJcZwCwA2O43geW4yQgUV7GEMAOHHiBHx8fAxYIsOYaG2NDyQSTLW1hSnfwzhOmNA202ZNDVBbq2qiwldzmHtQ11yHI1lHsODoAlwpu6Lz6wcFtS0nJ1PzFEIIMZTuEvEAxlh166sGgL96mTFWra8CEtKfCQQCzJo1C1OnTsUHH3wALy8vQxep/zM1VSXj2lxcgGvXDFOeTjz77bN4fN/j+Cj1Ixy9clTn19fusKlUAt98o/MQhPQrQqEwWCqVyiQSic+UKVNG1tTU9Gga4JycHPHYsWM93d3dfTw8PHzWr1+vqXmpr69nfn5+3l5eXjIPDw+fxYsXO6v3rV+/3l4ikfh4eHj4rFu3zr7zqwNXr14V79ixw/r+7q53ursnte7u7cKFC8ZSqVSmfpmZmY3q7h4NJS4uznn16tUOfMcRdbWD4zjDzS5CyACybds2QxehnebSZtz69BZu770N+6fs4bbcTe9lKGtpwaW6OoRbWfETIDq6bciQ8HAgKalPDGOo5JR4/9f3kVHSNoTjdznfIe7hOJ3GMTMDhg0DCgqAESNUwxoSQrpmbGyszMzMzACAxx9/fMTmzZuHrFmz5q7DS4nFYmzevPlGaGhofUVFhWDUqFGyqVOnVgcHBzeamJhwp0+fzrK0tFQ2NTWxMWPGeJ08ebLKwsJCsXv37iFpaWmXTUxMlOHh4Z4xMTFVvr6+HYbVio+Pt8jIyDABUMHDbff6ntTHdHVvERERdQEBAU3qZymXy+Ho6BgQGxtbqa/y9zU9+kZHCBk40mem49ob11B3oQ5Fu4r0GrtGLsfDaWmw//lnTP3jDzQqFPwEUjeSNjFRzfneRwiYAJ+kfaJJxGd5z8LqR1bzEuuHH4CqKiA3F7ijqwIhpBuhoaG1OTk5xllZWUYSiUTTlnD16tUOcXFxztrHurm5tYSGhtYDgLW1tdLd3b0hPz/fCFD9ImppaakEgObmZiaXyxljDBcvXhw0atSoWnNzc6VYLEZISEjNvn37rO4sR0JCgtmqVauGHT161FoqlcoyMjL0MjtZd/ek1tW93enw4cMWrq6uTZ6enu0ayFVXVwvGjx/v4eXlJZNIJD7qWv9t27bZ+Pn5eUulUtncuXPdtAc32Lp1q62np6fMy8tLNmPGjBEAsGbNGgeJROIjkUg0vyxkZWUZjRw50ic2NtbNw8PDJyQkRFJbW8sAYNmyZY7Dhw/3DQ4O9srOzja+W3l0ocsacUKIYWRnZyMzMxPTpk0zSHybqTaoOl0FAGjIboCiUQGhiX5+IHs1Jwe/VleDA1CvVOJUVRUmaY0zrjMjRgAnTwIPPwwMGqT769+HKI8opJekAwCczZ0R5hbGSxwPD14uS0i/1tLSgoSEBIvJkyf3uoluVlaWUUZGhml4eLimQ4pcLoevr68sPz/feP78+bcnTpxYl5aWpli3bp1LUVGRcPDgwVxiYqJlQEBA3Z3Xi4yMrPXz86vbsmVLwZgxY+6rD19wcLBXXV1dhw/6jRs3FsyYMaOmN/fU3b3deczevXttnnjiibI7tx88eNDC0dGxJTk5OQcAysrKhGlpaSb79++3SUlJyTQ2NubmzZvn+tFHH9m+8sorZSkpKSabNm1yOnv2bKaTk5O8uLhYeOrUKdM9e/bYpqamXuY4DsHBwd4RERE1dnZ2ivz8fJMvv/wyd9y4cdenTp06cvfu3dZ+fn6N33zzjc3FixczWlpaEBgYKBs1alR9V+Xp3RPuGiXihPQR5eXleOihh5CdnQ0zMzOUlpbC2Nj47ifqmMOfHXBt5TVACUAJVJ2ugs2jPCTDnTBiDOoJ7l2MjMDjZPfAxIl8Xv2eRXlEYfPZzQBUzVIIIYbX1NQkkEqlMgAYO3ZszWuvvVZ6/fr1Hg91VFVVJYiJiXHfuHFjgY2NjWbGMpFIhMzMzIzS0lJhdHS0+7lz50zGjBnT+NprrxVFRER4Dho0SOnj41Mv7KITe25urklgYGAjAGRkZBitWbPGqbq6Wnj8+PFcQFVLXFRUJMrJyTGpqakRvvDCC6UxMTEdvkSkpqb2eriuru7pbvem3t/Y2Mi+//57yy1btty489ygoKCGN998c9iCBQtcpk+fXhUVFVX78ccf21y6dMk0ICDAu/V8gb29vRwAEhISLKZNm1bh5OQkBwAHBwfF9u3bzaZOnVppYWGhBIDo6OiKpKQk89mzZ1e6uLg0jRs3rgEARo0aVZ+Xl2dcWloqmjp1aqW5ubkSACZPnlzZXXl6+7y6Qk1TCOkDampqsHz5clxr7TBYW1uL06dPG6QsJq4mcF7Q9utqxQm9NT1sN8vmECMjTOajNrwzDQ2qmTZ37tRPvG6EuYbBVGwKAMgpz0FOeY5e4nK8fushRDficnKcWXJyMEtODo7LyXG+c/9LWVlD1fvfunatQ0e7ORkZbur9m/Lz7XoaV91GPDMzM2PXrl0FJiYmnEgk4pRaswA3NjYKAGDDhg1D1B0R8/LyxE1NTSw6Otp99uzZ5fPnz6/s7Pp2dnaKsLCwmiNHjlgCwOLFi0vT09Mvp6SkZFlbWys8PT071HjfunVLZG5urhCLVd8HZDJZ89dff91uUoSUlBTTdevWFe/bt+/6zp07r+/bt6/TJhXBwcFe2h0o1a9Dhw6Zd3Z8T+6pq3tT279/v6VMJqsfNmxYh8kz/P39m9LS0jL8/PwaVq1a5fL66687cRzHZs+eXab+/5CXl3dpy5YtN7uL3RUjIyPNJ55QKOTkcnm3nYQ6K8+9xO3MXRNxxlgMYyybMVZFo6YQwo/Bgwfj22+/1UzmExoaCiMjvTT365RNVFsCXH6iXG9xJ1pbQ9TajvB8bS1uNXXom6R7+fmAjQ0QGQn8/e+AgSZUUjMWGWPiiLba+oScBHAch2aF7scYbG4Gli5VTfIzeLBqbiNCSM8MHTpUXl5eLioqKhI2NDSwhIQESwBYvnx5iTpZdHV1bYmNjXXz9PRsvLNz582bN0WlpaVCAKitrWVJSUkW3t7ejQBQWFgoAoDs7GyjY8eOWb344osdPoizs7ONHBwcuvxgaGpqYiKRiBMIVKneihUrnF599dWSzo5NTU3NUpdZ+9VZsxSlUomu7qkn96a2b98+myeffLLTPzB5eXlic3Nz5cKFC8vj4uKKzp8/bxoVFVV99OhRa/WzKS4uFl65csUIACIjI6uPHDliXVRUJFTvmzBhQm18fLxVTU2NoLq6WhAfH289YcKELpvZTJw4sTY+Pt6qtraWVVRUCBITE626K09X1+mtntSI/x+AxzmOs+Q4zoLjOHOO46iPPSE6JBAIEBUVpVl/9NFHERbGT9vgnrAabwUmViXEdRfq0Fysn4GmLUQihFq2VZocL+f5S0B1NfD774D6Z9+qKuC33/iN2QNR7m3vhX/9/C8M+88wvPfLezqPI5cD//43kJWl+lHgwAGdhyCk3zI2NuaWLFlya8yYMd5hYWGeHh4eHWqtExMTzQ4dOmR7+vRpc3Ut81dffWUJAAUFBeKwsDAvT09P2ahRo2QTJkyonjNnThUAPP744+7u7u4+jz32mMe7776bb2dn16HnekBAQGN5eblYIpH4JCYmDr5zf0JCgllYWFitUqnEggULXKKjo6vUnSzvR3f3FB4e7pGXlyfu7t4AVefH06dPW8ybN6+ysxipqamDAgMDvaVSqeztt992Xr169a3g4ODGlStXFkZERHh6enrKJk6c6FlQUCAGgNGjRzcuWbLkVlhYmNTLy0u2cOHCYaGhofVz584tCwoK8g4ODvZ++umnS0JCQhq6uq/Q0ND6mTNnlvv6+vo8+uijEn9//7ruynO/z1GNcXf5PZIx9jPHcSG6Cni/Ro8ezaWkpBi6GITo3FdffYXY1gGex44di19++cWg5fl9/O+o+lH1uen9pTcc/sz7cKoAgP/Lz8ey3FwAwOwhQ/A1nxMc/ec/QFzr0IDm5sBzzwELFqiqiA0otyIX7u+7t9s2YfgE/DD/B53HcnNT/SgAqAaTOXZM5yHIfWKMpXIcN9rQ5TCECxcu5AUEBJQauhwPgqKiImFcXJzLqVOnLObNm1daXV0tfOedd2598MEHdnv37rUNCAioCwwMbFi6dGmnteKEPxcuXLALCAgY3tm+ntSIpzDGvmKMzWltphLDGIvRbREJIZMnT4b6J8TffvsNpaWG/dtjNspMs3zz43tqhndPtNuJHysrw/zLl9Gk7NAPSDcmT25bFgqBLVsMnoQDwEjrkZDYSNpt+6P4D16ap8RofZrzPakpIYQ/jo6Oij179uQXFBRc2rBhQ1FNTY3Q0tJSuXLlytvp6emX9+zZk09JeN/Tk0TcAkA9gMkAprW+HuOzUIQMRNbW1nj44YcBABzHISEhwaDl4Zrbfi2rOVeDu/16piu+gwfDpbV9fL1Sid3FxThTVXWXs+6RTAY4t/b3qqwE+tCvbVEebc1TYn1jUfR6EYyEuu83sGBB23JSEtDSovMQhBAD2L17d76hy0Du7q6JOMdxz3Xyel4fhSNkoJmiNbPKu+++i9jYWL0lwHdyeLqtKYqyUYn6nPtuWtgjjDFMsbVtt+1EBU8jtzAGTJqkFegEP3HugToRNzMyg5OZE0QCfkablUhUzVMAoLYWMHCLKEIIGVB6MmrKUMbYN4yx262vA4yxofooHCEDzWStphIpKSn46quvcPHiRYOUxWKMBSxCLeDyNxeMTh+NwZIOfYF4o908xdnICH+2t+cvmHbzlMRE/uL00oThE5A8PxllS8uwJXILb3EY67OPgBBC+r2eNE35HMBhAM6tryOt2wghOhYUFARr6/bDvJ4wUC0tEzIEnQqC5H0JzGRmdz9BhyKsrWErEmGmnR1WDx8On8E8fgl49NG25Z9/Bv7yF9VkPwYeWHuQeBDCh4fz0hzlTto/Chw4APDVJJ8QQkh7PUnEh3Ac9znHcfLW104AQ3guFyEDklAoREREhGZ99uzZmpFUBhJLkQi3Q0Jw0NcXLzs7g7Fu51q4P/b2wKhRqmWlEtixQ9VY+soV/mLeAyWnxPmi8/ij+A+dX1t7ktGMDODUKZ2HIIQQ0omeJOJljLF5jDFh62segDK+C0bIQKVunuLv74+oqCgMHTowW4IJ+Ey+76TdNkPNwJ1ltR29chSOmxwx6uNReOfUOzq/vq2tak4jtR07dB6CEEJIJ3qSiD8P4EkARQBuAXgCwHN8FoqQgezJJ5/ErVu3cOHCBTz/fN/oF61sUqL0SCmK93Q6iZpeKDiOv46r2om4tTXw/feqJip9QFl9GTJKMlBSrxp17Pvc76HkdN92ZNy4tuVbOpuqghBCSHfu2g2f47jrAB7XQ1kIIQAsLS1hqTW7pKEVvFeAq3FXASUgthPDYa5+JvZR+3/FxThUUoKTlZU4GxQEL1OdzSzcJiQEGDRINb1kRQXg6QmYmOg+zj1Y9v0yfPr7pwAAU7EpJrlPQlVjFawHWd/lzF7GWQYEBwNz56pGUiGEEMK/LhNxxthSjuP+jzH2AYAO1VAcx73Ka8kIIRoNDQ0YNGiQQWIbuxgDrRWwLaUtaLrVBGMnY73E/qmyEityc5Hf1AQAOFFezk8ibmwMrF6tai8+aRIwbJjuY9yjye6TNYm4v70/9s7ay0uc0FDVixBCiP50VyN+ufW/fWeGC0IGmH//+984duwYzp49i/z8fDg46Lc2GgBsomwABs3X8bJjZXB+0VkvsS/W1WmScAD4raaGv2BvvMHfte9DxIgIMDBw4HDu5jlUNVbB0qTv/GJCCCHk3nXZRpzjuCOti/Ucx+3SfkE10yYhhEfnzp3Dtm3b8OOPP6K5uRnff/+9QcohMhPB8TlHzXplcqXeYk/WGsrRVCDAp56eeosNjgOuXdNfvC7YmtoiyCkIAKDgFEjKSzJwiQgZWLKysowkEomP9ra4uDjn1atXt6sZycnJEY8dO9bT3d3dx8PDw2f9+vWaCRDq6+uZn5+ft5eXl8zDw8Nn8eLFmtoMFxcXP09PT5lUKpX5+vp6d1WOq1evinfs2KHbNmk9sH//fovhw4f7urq6+q5YscKxs2PWrl1r7+Hh4SORSHymTZs2or6+nnX3PPqazv5/6ktPOmsu7+E2QogOvfbaa8jLy9Osp6enG6wsLgtdNMsViRXglPoZY9tj0CC4GauawdQrlfzWiKtxnGre9+HDgZEjgdu3+Y95F5NGtg30nXiV3xl36uuBTz9VtdC5cYPXUIT0K2KxGJs3b75x9erV9HPnzl3+9NNP7VNTU00AwMTEhDt9+nRWVlZWRnp6esbJkyctTp48qZkg4ccff7ySmZmZcenSpctdXT8+Pt4iLS2Nh7Z5XZPL5Vi8eLFrfHz8lStXrqQfOHDARn1PateuXRNv377d4fz58xnZ2dnpCoWCffLJJzbdPQ/SpstEnDE2pbV9uAtj7H2t104A8p5cnDEWxRjLYozlMMa6/N2XMTaLMcYxxkb3+g4I6ae0Z9mMjY3FO+/ofti6njIbZQaRraolW8vtFtT+UauXuIwxTNIaVy+Rr6nutXEc8OuvQH5+a1DDTzU5yb0tEf8261usTV6Lbee28RLL2Rl48UXVwDE0jCEhPefm5tYSGhpaDwDW1tZKd3f3hvz8fCMAEAgEsLS0VAJAc3Mzk8vlrDfzIyQkJJitWrVq2NGjR62lUqksIyOD/5m+ACQnJw92c3NrkslkzSYmJlxMTEz5/v37re48TqFQsLq6OkFLSwsaGhoEQ4cObenueWirrq4WjB8/3sPLy0smkUh81LX+27Zts/Hz8/OWSqWyuXPnusnlqtRz69attp6enjIvLy/ZjBkzRqivs2bNGgeJROIjkUh81q1bZw+ofs0YOXKkT2xsrJuHh4dPSEiIpLa2lgHAsmXLHIcPH+4bHBzslZ2dbXy38vCluxrxm1C1D28EkKr1Ogwg8m4XZowJAXwIYAoAGYA5jDFZJ8eZA3gNwK+9LTwh/dkkrekOf/75Z/6G7usBJmCwmdSWEFec0ENC3GqSVvMU3hPxM2cAJyfg999V6xYWQGkpvzF7IGRYCAaJVJ11C2sKsebHNfhvyn95ieXu3rb87be8hCCk38vKyjLKyMgwDQ8P19RayOVySKVSmYODQ0B4eHj1xIkT69T7IiIiJD4+Pt6bNm2y6+x6kZGRtX5+fnUHDx7MyczMzJDJZM33Wrbg4GAvqVQqu/N16NAh8zuPLSgoMHJxcdHEGjp0aHNhYWG7ZHrEiBEtixYtKhoxYoS/vb19gLm5uSImJqb6bs9D7eDBgxaOjo4tWVlZGdnZ2ekxMTHVaWlpJvv377dJSUnJzMzMzBAIBNxHH31km5KSYrJp0yanH3/88UpWVlbGxx9/nA8Ap06dMt2zZ49tamrq5ZSUlMu7d+8e8vPPPw8CgPz8fJNXX331dk5OTrqlpaVi9+7d1qdOnTL95ptvbC5evJiRmJiYfeHChcHdleden3VPdNdG/EJre3A/AF9qtQ//FkBTV+dp+ROAHI7jcjmOawawD8D0To5bD+BfUCX8hJBWf/rTn2BhYQEAKCgowBUDz/RoPbktIdbneOIR1tZQ1xv9Wl2NY6WlqJb36Ee53vPwaGuKIhQCubnAa6/xE6sXjEXGeMTtkXbbLt2+hJs1N3UeKyambVmh0PnlCXngdFVz3dX2qqoqQUxMjPvGjRsLbGxsNIP+i0QiZGZmZuTn5/+RlpY2+Ny5cyYAcPr06cyMjIzLJ06cyN6xY4f9d999Z9bZdXNzc00CAwMbASAjI8PoySefdIuKihqp3r9161bblStXOsTGxrpFR0ePPHjwoEVn10lNTc3KzMzMuPM1Y8aMe2r7V1JSIjx27JhVTk7OxaKioj/q6+sF27Zt09TcdPU81IKCghpOnTplsWDBApfjx4+b2draKo4fP25+6dIl04CAAG+pVCo7ffq0RW5urnFCQoLFtGnTKpycnOQA4ODgoACA5ORks6lTp1ZaWFgoLS0tldHR0RVJSUnmAODi4tI0bty4BgAYNWpUfV5ennFSUpLZ1KlTK83NzZU2NjbKyZMnV3ZXnnt5Lj3VkzbiJwBoj5s2CEBPeo25ACjQWr/Ruk2DMRYEYBjHccd6cD1CBhSxWIwJEyZo1k+cOGHA0gCcvK1Gvu6POijq9ZOl2YrFCDZXVdQoATx26RJ/NePa090rFMDPP/MT5x5Mdm9rqjTcajj2ztoLC+NO/87eF+15jC5fBqp5rQsipHdy4nKck1lycE9eGXMy3O48P2NOhpv2MTlxOXcdAsrBwUFeVVUl1N5WXl4utLOzk2/YsGGIukY5Ly9P3NTUxKKjo91nz55dPn/+/MrOrmdnZ6cICwurOXLkiCWgqlEGABcXF3l0dHTl2bNnB995zq1bt0Tm5uYKsVgMAJDJZM1ff/31de1jUlJSTNetW1e8b9++6zt37ry+b9++TptU9KZGfNiwYe1qwG/cuNGuhhwAjhw5YuHq6trk7OwsNzY25mbMmFF55swZMwDoyfPw9/dvSktLy/Dz82tYtWqVy+uvv+7EcRybPXt2mfpLQl5e3qUtW7bcU82DkZGR5o+XUCjk5HJ5t22COivPvcTtqZ4k4iYcx2l+Smhdvu/OAowxAYAtAJb04Ni/MMZSGGMpJSUl9xuakAeGdjvxY8eO4cCBA2hsNMyPRzZTtOZA54DyE+V6i63dPAVQjSfOX7C2JkEw8JcfbdodNqubqvGkz5MwM+q04uy+DBnS/rtIcrLOQxDyQLG0tFTa29u3HD582BwAiouLhcnJyZYTJ06sXb58eYk6WXR1dW2JjY118/T0bFyzZk27nw1v3rwpKi0tFQJAbW0tS0pKsvD29m6srq4WVFRUCABV2+SkpCQLf3//hjvLkJ2dbeTg4NBlc5SmpiYmEok4gUCV1q1YscLp1Vdf7TRh6k2NeHh4eF1eXp5JZmamUWNjIzt48KDNrFmzKrWPGT58eHNaWppZTU2NQKlU4ocffjD39vZuVCqV6Op5aMvLyxObm5srFy5cWB4XF1d0/vx506ioqOqjR49aFxYWitTP/MqVK0aRkZHVR44csS4qKhKqtwPAhAkTauPj461qamoE1dXVgvj4eOsJEyZ0WcM/ceLE2vj4eKva2lpWUVEhSExMtOquPF1dRxd6kojXtdZcAwAYY8EAOrxJOlEIQHtWjKGt29TMAfgCSGaM5QF4CMDhzjpschy3neO40RzHjR4yZEgPQhPSP2i3E09ISMATTzyBM2fOGKQsJkNNILYXQ2Qtgs0UG5h66q/zvnYiLmIMTkY89lPSnu6+D3TUVPO198WiMYvw5cwvkbEwAwLWk4/ve9NHv4sQYjC7du269vbbbztJpVJZeHi417Jly276+Pi0a6abmJhodujQIdvTp0+bq2uZv/rqK0sAKCgoEIeFhXl5enrKRo0aJZswYUL1nDlzqm7cuCF66KGHpF5eXrKgoCDvyZMnVz7xxBMdfocKCAhoLC8vF0skEp/ExMQONeYJCQlmYWFhtUqlEgsWLHCJjo6uUneUvB+tI5/kR0VFeUokEp8ZM2aUjx49uhEAwsPDPfLy8sQTJ06smzZtWoW/v7+3l5eXj1KpZHFxcSXdPQ9tqampgwIDA72lUqns7bffdl69evWt4ODgxpUrVxZGRER4enp6yiZOnOhZUFAgHj16dOOSJUtuhYWFSb28vGQLFy4cBgChoaH1c+fOLQsKCvIODg72fvrpp0tCQkK6zFVDQ0PrZ86cWe7r6+vz6KOPSvz9/eu6K8/9PsfusLt1AGOMjYGqffdNqKb1cATwFMdxqXc5TwTgCoAIqBLwcwDmchzX6RhsjLFkAK9zHNftBEKjR4/mUlJojiEyMHAch5EjR7YbxnDZsmXYuHGjQcqjlCshEPGXAHalSamE9alTaOA4BJmZITkwEOai7uYjuw+NjYCNjWq6ewA4dgxITwcWLQL4mNWzDzp5Enj0UdWypyeQmQn0YoAHomOMsVSO4wbkqGIXLlzICwgIMHyP6T6mqKhIGBcX53Lq1CmLefPmlVZXVwvfeeedWx988IHd3r17bQMCAuoCAwMbli5dSs0I+oALFy7YBQQEDO9s313/knEcd44xJgXg1bopi+O4lh6cJ2eMvQIgAYAQwGccx6UzxtYBSOE47nCP74CQAYoxhkmTJmFH6zhyzs7OGDFixF3O4o8hknAAMBYI8F1AAHxMTWHHZ204AJiYAOHhwPHjqvXoaNV/AwLa15b3EXKlHCKBbr+UhIQARkZAczNw5Qqwdy8wd65OQxBC7oOjo6Niz549+er1Z555xtXS0lK5cuXK2ytXrjT85Aekx3r6V9ULqiEIg6AahvCZnpzEcVw8x3GeHMe5cxz3duu21Z0l4RzHjb9bbTghA5F28xSZTIaXX37ZgKUxnHArK/6TcDXtthlqfaiZSmF1IZZ/vxzB24Mxbe80nV/fxARw0Jpj7osvdB6CEKJDu3fvzr/7UaQvums1CmPsLQDjoUrE46EaF/w0gN28lowQAgCIiIjA22+/jUmTJiEoKOjuJ+hRc2kzjOz0lBzrk3bNN2PA7NlAaKjhynOHZkUzNv6sap5kIjJBo7wRJiLdTlj36KPA55+rln/7TaeXJoQQ0qonNeJPQNXOu4jjuOcABADo0NieEMIPGxsbrFixAmPGjIFQKLz7CTxruN6A8xPO4yezn/DLsF/0Nt29Wr1CgYTycizJycF5vqa89/FRTewDqGbaXLIEmN7ZNAj6t/P8TgRtb/tC1qJowYWiCzqP89JLqtEcn3wS+PprnV+eEEIIelAjDqCB4zglY0zOGLMAcBvtR0MhhAwgLSUtqEyu1KzX/F4Di2Ddj2fdaWylEk+lp+No6/CFFiIRAs07DH17/xgDpkxRDaQ9aVL7dhoG5jDYAZWNlQCAYRbDcHHBRVia6L5u5OGHgWL9zdtECCEDUk9qxFMYY1YAdkA1xX0agLN8FooQ0rmioiJ8+eWXeOaZZ5CVlWWQMpgHmYMZtw2hUbxbf9na1yUlmiQcABL5HE/8k09UU96vXQu4dZgXxGAecXsERkJVc6CC6gLUtdTd5QxCCCF9VbeJOFPN37qB47hKjuM+AjAJwPzWJiqEED174YUX8PTTT+OLL77AcfWoHnrGBAy202w167UXa7s5WrcirKw0ywIArw/j8ce5Pjpe32CjwQgZFqJZ/z63JxMdE0II6Yu6TcQ51SDj8VrreRzH/cF7qQghHSxatAjx8Zp/jkhISDBYWSTvSjTL1T9X6226e0djY/gPVs1loQQgFuhpOMXUVGDdOiAsTDWmuIFpz7KZmMv/aC4cp3oEhYV3P5YQQkjP9eSvWFrrpD6EEAPy9PTULMtkMvzzn/80WFmMXYxh6qOa3IZr5lD5U6XeYmvPsslr0xRtGzcCb70FnD7dJ6aanOSulYhfTcSNqhs4nX+al1jz5qmGMxw9WvVdhBBCiO70JBEfC+AXxthVxtgfjLGLjDGqFSdEz7THE7958yb8/f0NWBrAZrKNZrniRIXe4k62aYt7ooLnuJWVwIoV7cfv6wPjiY9yHAXbQarmQcV1xRj27jA8tf8p3G2m5HtRVKSa2AfoE7dOCCH9SpeJOGPMtXUxEsBIABMBTAPwWOt/CSF65O3tDWdnZwBAZWUlUlIMO/+V9aS2munSo/qbgTrM0hLGre23L9fX40ZjI5Q8JKAAVFXB774L5LfOlfHee8DOnfzE6gWhQIiIkRHttt2suYmMkgydx9KeUVNfP0AQQshA0V2N+CEA4DjuOoAtHMdd137ppXSEEA31dPdqiQaunhw0chDQ2p+xMbsRTTeb9BNXKESoZdtwfcGpqdhx6xY/wdTT3WuCD1INrt0HaLcTZ2B4dOSjaJA36DxObCwgah3otqqK2okTQogudZeIaw8ZMJLvghBC7m6y1oyPJ06cgEKhQLO63YCeGQ81brd+++vbeos9Sat5yu2WFpzgs6pWe5bNPtA+XE07ETcSGuHInCMY7Txa53FMTYFHHmlb70OPgBC9EQqFwVKpVCaRSHymTJkysqampkc9xXNycsRjx471dHd39/Hw8PBZv3695pt8fX098/Pz8/by8pJ5eHj4LF682Fm9b/369fYSicTHw8PDZ926dV1++7969ap4x44d1l3t58v+/fsthg8f7uvq6uq7YsUKx86OWbt2rb2Hh4ePRCLxmTZt2oj6+noGALNnzx5uY2MTIJFIfPRb6t6Ji4tzXr16Ne+TSHT3RuK6WCaEGMijjz6qWf75559hZ2eH/fv3G6QswsFCmIxQTavOxAzyCrneYk+2bv9355fqal7aR6uCaSXi338PKPQzQszduFm5wdfeF+OHj8fq8NVoVvD3hSwysm2ZEnEyEBkbGyszMzMzsrOz08ViMbd58+YhPTlPLBZj8+bNN65evZp+7ty5y59++ql9amqqCQCYmJhwp0+fzsrKyspIT0/POHnypMXJkycHnzt3zmT37t1D0tLSLl++fDn9+PHjVpcuXTLu7Prx8fEWaWlpprq817uRy+VYvHixa3x8/JUrV66kHzhwwEZ9T2rXrl0Tb9++3eH8+fMZ2dnZ6QqFgn3yySc2APD888+XHj58OFufZe7LukvEAxhj1YyxGgD+rcvVjLEaxli1vgpICGljb2+PUaNGAQA4jkNlZSVOGDAz8tzuicAfA/FI0yMYsXaE3uIGmJnBTtQ2MfBBHx8wvsb9lsmA1rb5qKwEUlKA0lKgvp6feL1w/uXzSJqfhBVhK2BhzN/sptqJeGJin/kuQohBhIaG1ubk5BhnZWUZadfqrl692iEuLs5Z+1g3N7eW0NDQegCwtrZWuru7N+Tn5xsBgEAggKWlpRIAmpubmVwuZ4wxXLx4cdCoUaNqzc3NlWKxGCEhITX79u2zurMcCQkJZqtWrRp29OhRa6lUKsvIyDDi9cZbJScnD3Zzc2uSyWTNJiYmXExMTPn+/fs7lE+hULC6ujpBS0sLGhoaBEOHDm0BgClTptQOGTKk25qb6upqwfjx4z28vLxkEonER13rv23bNhs/Pz9vqVQqmzt3rptcrrrM1q1bbT09PWVeXl6yGTNmaP4YrVmzxkEikfhIJBLNLwtZWVlGI0eO9ImNjXXz8PDwCQkJkdTW1jIAWLZsmePw4cN9g4ODvbKzs43vVh5d6HKKe47jhLoKQgjRncjISPz++++a9YsXLxqsLDYRNnc/iAcCxrBk2DAIGEOkjY1mbHFeMKaqFVd30oyJAW7dAvbuBZ56ir+4PSAU6Odj2s9P1TT+9m2grAzYsQP461/1EpqQPqWlpQUJCQkWkydP7nWFZFZWllFGRoZpeHi4ZhY0uVwOX19fWX5+vvH8+fNvT5w4sS4tLU2xbt06l6KiIuHgwYO5xMREy4CAgA5T6EZGRtb6+fnVbdmypWDMmDGN93NfwcHBXnV1dR0+UDZu3FgwY8aMGu1tBQUFRi4uLpqf4IYOHdr866+/mmkfM2LEiJZFixYVjRgxwt/Y2FgZFhZWHRMT0+NndvDgQQtHR8eW5OTkHAAoKysTpqWlmezfv98mJSUl09jYmJs3b57rRx99ZPvQQw/Vbdq0yens2bOZTk5O8uLiYiEAnDp1ynTPnj22qamplzmOQ3BwsHdERESNnZ2dIj8/3+TLL7/MHTdu3PWpU6eO3L17t7Wfn1/jN998Y3Px4sWMlpYWBAYGykaNGlXfVXl683y7o6fZMAghuhKpVT3p4uKCc+fOGbA0hvOGmxuWuroiwMyMv9pwNa1Osrh5UzXDTR8byy+3Ihf/Pfdf/P3433V+bYEAGDq0bX37dp2HIKRHcuJynJNZcnAySw7OictxvnN/1ktZQ9X7r711rUP73ow5GW7q/fmb8u16GrepqUkglUplfn5+sqFDhza/9tprvRoqqqqqShATE+O+cePGAhsbG6V6u0gkQmZmZkZ+fv4faWlpg8+dO2cSFBTU+NprrxVFRER4TpgwQeLj41MvFHae9+Xm5poEBgY2AkBGRobRk08+6RYVFaXp17d161bblStXOsTGxrpFR0ePPHjwYKc/naWmpmZlZmZm3Pm6MwnvqZKSEuGxY8escnJyLhYVFf1RX18v2LZtW49rboKCghpOnTplsWDBApfjx4+b2draKo4fP25+6dIl04CAAG+pVCo7ffq0RW5urnFCQoLFtGnTKpycnOQA4ODgoACA5ORks6lTp1ZaWFgoLS0tldHR0RVJSUnmAODi4tI0bty4BgAYNWpUfV5ennFSUpLZ1KlTK83NzZU2NjbKyZMnV3ZXnnt5Lp3pskacENI3jRs3Dr6+vhg3bly7pJzwSKttPgBAKFQNIdJH1DXXQbpVihZlCwBgWcgyOJk76TTGtGlAWppqOT1d9V2E7+8/hPQV6jbi2ttEIhGnVGpyajQ2NgoAYMOGDUN27do1BACOHz+e7eTkJI+OjnafPXt2+fz58ys7u76dnZ0iLCys5siRI5ZjxoxpXLx4cenixYtLAeCVV15xGTp0aIdOILdu3RKZm5srxGIxAEAmkzV//fXX17UT8ZSUFNPPPvusQCAQoKSkRLho0aKhndVM96ZGfNiwYc2FhYWaZjA3btxoV0MOAEeOHLFwdXVtcnZ2lgPAjBkzKs+cOWO2cOHCHvWs9/f3b0pLS8s4cOCA5apVq1y+//77amtra8Xs2bPLPvzww3ZjN7399tu9HsrKyMhI06lIKBRyDQ0N3VZMd1aeTZs26WS4LqoRJ+QBY2RkhIsXL+Ljjz9GTEwMBPqa5r0bVWeqkPlCJn7z/k1v091r4zgOGXV1yOar3ba9PdDaNh8A8MUXwP/+x0+sXsopz8FbyW9BLBRrtp24qvt+Ay+/rPqvkZFqls2WFp2HIOSBMnToUHl5ebmoqKhI2NDQwBISEiwBYPny5SXqGmVXV9eW2NhYN09Pz8Y1a9YUa59/8+ZNUWlpqRAAamtrWVJSkoW3t3cjABQWFooAIDs72+jYsWNWL774YocENjs728jBwaHLXtpNTU1MJBJx6r8RK1ascHr11VdLOju2NzXi4eHhdXl5eSaZmZlGjY2N7ODBgzazZs2q1D5m+PDhzWlpaWY1NTUCpVKJH374wVx9bz2Rl5cnNjc3Vy5cuLA8Li6u6Pz586ZRUVHVR48etVY/m+LiYuGVK1eMIiMjq48cOWJdVFQkVG8HgAkTJtTGx8db1dTUCKqrqwXx8fHWEyZM6LKGf+LEibXx8fFWtbW1rKKiQpCYmGjVXXl6ei93QzXihJD70lLegt9Df9eMrVTxfQXsHu/xL7737VBJCRZmZ+NWczNecnLCdi8vfgJNngz8/jvg4tKneiuW1Zdh89nNAABjoTH+9ei/MGHEBJ3HcXICLl5U9V3tA9/9yADlscXjpscWj5td7ffa4XXDa4fXja72y/bKrsv2ynQyF4qxsTG3ZMmSW2PGjPF2cHBo8fDw6JBoJiYmmh06dMhWIpE0SKVSGQCsXbu28KmnnqoqKCgQP/vssyMUCgU4jmPTp08vnzNnThUAPP744+6VlZUikUjEvfvuu/l2dnYdPnQCAgIay8vLxRKJxGfbtm15kyZNateOPCEhwSwsLKxWqVRi0aJFLtHR0VXqjqP3o3UkmPyoqChPhUKBuXPnlo4ePboRAMLDwz127dp1feLEiXXTpk2r8Pf39xaJRPDx8amPi4srAYBp06aN+OWXX8wrKipEDg4O/m+88cZNde2/Wmpq6qDly5cPFQgEEIlE3LZt264HBwc3rly5sjAiIsJTqVRCLBZz77//fn5ERETdkiVLboWFhUkFAgHn6+tbf+DAgbzQ0ND6uXPnlgUFBXkDwNNPP10SEhLSkJWV1Wmn1tDQ0PqZM2eW+/r6+tja2rb4+/vXdVee+32Oaoy3Ib94Mnr0aM7QMwoS0pc0Nzfjl19+gZOTEyQSiUHKcNr2NOTlqt7rdjF28D3gq5e4OfX1iLhwAflNqsmE3IyNce2hh/hpM379umqkFKm0T7XJUCgVsN9kj/IGVYXZhb9egL+Dv4FLRXSJMZbKcZzuB4l/AFy4cCEvICBAf1P3PsCKioqEcXFxLqdOnbKYN29eaXV1tfCdd9659cEHH9jt3bvXNiAgoC4wMLBh6dKlndaKE/5cuHDBLiAgYHhn+6heg5AH2I4dO2Bra4vw8HDs2LHDYOWwDGub6bLuYofO/bwZZmKCEq0JjaSmpqjjq7bazQ3w9u5TSTigGjlFe3Kf4znHDVgaQoihODo6Kvbs2ZNfUFBwacOGDUU1NTVCS0tL5cqVK2+np6df3rNnTz4l4X0PJeKEPKCSkpKwfft21NaqRsJKSEgwWFmkO6VgYlWC2pDdgKZb+pnu3lggwAStyX2m29nBTKSnFneFhaohDTMy7noo36I8ojTLCVf19z54wH5QJWRA2b17d76hy0DujhJxQh5QlZWVUDfTMjIywsMPPwyFgdoui63EsAxpqxWvSKzQW+xIrenuE/ic6l7bqlWq8fyee041nriBTXZvm/3z1PVTqG2uhVzJz0ynv/2mGkp9yBDVSCqEEELuHSXihDygJk6cCPXYss3NzVi/fj26GmtWH6wnt9VMGyoR/6GyEi1aw4nxIjcXKCpqW+8D44k7mztr2oW3KFsQvjMcdv9nh9rm2ruc2Xuffw58841qctGfftL55QkhZEChRJyQB5SlpSUefvhhzXqigRNCm8ltCXF5Yjk4pX7aLXgOGgQ3Y9VMxDUKBc5W93rCu57jOOCRR4BPPlGtBwcDs2b1iTYake5tY8qn3UpDVVMVkvOSdR5HPYwhANTUqFroEEIIuTeUiBPyANOe0MeQbcQBYLD/YAjMVR8pLcUtqL2g+9rYzrDWae7V3rp2DZ/e0sk8C50FUw1jqPb448A//tEnOnBqtxNXS7mp+xGmAgIAK6u29dxcnYcghJABgxJxQh5gk7WSwhMnTsCQw5EyIQPX3Ba/aHdRN0frlnYinlxVhW18VtNqJ+IndD9xzr0KGRYCU3HbHBNJ85OwZvwancdhDJg9u23dwN//CCHkgUaJOCEPsODgYNi0JqFFRUV49tlnsXv3boOUhQkYzALMNOsVJ/TXTjzC2rrdh1labS1uN3c54dx9BotoW/7llz4z1b2xyBgThqsm8hlpPRIM/NXSa/0Q05e+ixBCyAOHEnFCHmBCoRCTJrWNIb17926DJeIA4LzAGRYhFvB4zwNBvwbpLa6lSISHLSw06y87OWEQX9M/DhkCBLXem0IBJCXxE+cevBPxDrL/lo2rr15F+PBw3uJERLTNrpmSouq4SQghpPcoESfkAafdThwATp06hfr6+57F+J44PeuEoNNBGPrqUIjM9DSed6tIGxt4DhqEV1xc8LKzM8z5HE9cu3nKf/4DTJ/e1oHTgPwd/OFh48F7HCsrYOxY1TLHAbt28R6SEEL6JUrECXnAadeICwQC7N27FyJ9TWrThyx3dUXW2LH4QCLBKHNzfoNpJ+I//QQcPgzs389vzHtQ21yLY1eO8TKmeEBA2/K//63zyxNCyIBAiTghD7ihQ4fCx8cHgwYNQmRkJIKDg2FkZGToYumdiK+mKJ0ZNw4wNW2/LTkZMNAvEZ3588E/w+ZfNnhs72P4rfA3nV//scfalouL+0xTeUJ0Lisry0gikfhob4uLi3NevXq1g/a2nJwc8dixYz3d3d19PDw8fNavX2+v3ldfX8/8/Py8vby8ZB4eHj6LFy92Vu9zcXHx8/T0lEmlUpmvr693V+W4evWqeMeOHdZd7efL/v37LYYPH+7r6urqu2LFCsc793d33wAgl8vh7e0tmzBhAv8/192jzv5/6gsl4oT0A4cOHUJFRQXi4+Ph5uZm6OIAABpvNSL/P/loLuGp02QPKPgaRcbYGBg/vm199mzg4sWOybkBcByHzNJMXC2/ihZlCwDgxFXd96iMjAS05486d07nIQh5oIjFYmzevPnG1atX08+dO3f5008/tU9NTTUBABMTE+706dNZWVlZGenp6RknT560OHny5GD1uT/++OOVzMzMjEuXLl3u6vrx8fEWaWlpev2QkcvlWLx4sWt8fPyVK1eupB84cMBGfU9q3d03APzzn/908PDwaNBnuR8klIgT0g94eHjAuHVSG0PjOA6/+fyGX5x/QW5cLoq/KNZr/KKmJqy5dg0Pp6XhsYsX+Quk3TyF4wCJhL9YvVDXUgf///rj18JfAQDedt6wH2x/l7N6TyQCVq4EvvwSqKwEHn1U5yEIeaC4ubm1hIaG1gOAtbW10t3dvSE/P98IUDUbtLS0VAJAc3Mzk8vljPVi/oGEhASzVatWDTt69Ki1VCqVZWRk6OVnz+Tk5MFubm5NMpms2cTEhIuJiSnfv3+/lfYx3d331atXxQkJCZYvvfRSl126q6urBePHj/fw8vKSSSQSH3Wt/7Zt22z8/Py8pVKpbO7cuW5yuaqJ3datW209PT1lXl5eshkzZowAgDVr1jhIJBIfiUTis27dOs0HXlZWltHIkSN9YmNj3Tw8PHxCQkIktbW1DACWLVvmOHz4cN/g4GCv7Oxs4+7KwqeB15CUkAGiqanJIMk5YwzKprZp5ov/XzGGxQ3TS2yO4/DhzZv45/XrAABjxtCgUGCQdtWtrkRHA9evqxLyRx7R/fXvkZmRGUJdQ5GUpxrNZWnIUjwb+Cwvsdas4eWyhDzwsrKyjDIyMkzDw8M1M5vJ5XL4+vrK8vPzjefPn3974sSJdep9EREREsYYnnvuuZLXX3+9Q9IaGRlZ6+fnV7dly5aCMWPGNN5P2YKDg73q6uo6fChu3LixYMaMGTXa2woKCoxcXFw0P2sOHTq0+ddffzW781y1O+970aJFw/7v//7vRlVVVZcfwgcPHrRwdHRsSU5OzgGAsrIyYVpamsn+/fttUlJSMo2Njbl58+a5fvTRR7YPPfRQ3aZNm5zOnj2b6eTkJC8uLhaeOnXKdM+ePbapqamXOY5DcHCwd0RERE1ISEgDAOTn55t8+eWXuePGjbs+derUkbt377b28/Nr/Oabb2wuXryY0dLSgsDAQNmoUaPqOytLb59vb1GNOCH9SHZ2Nv72t7/B09MTzz77rMHKMWTWEM1y3aW6dok5nxhj+KakRLPezHFIq+Vphk8PD2DLFiAqqk80SdGmPd19wlWacYf0H3Fxcc6MseCevObMmdOhnd6cOXPctI+Ji4tz7iyOtq5qrrvaXlVVJYiJiXHfuHFjgY2NjebDTyQSITMzMyM/P/+PtLS0wefOnTMBgNOnT2dmZGRcPnHiRPaOHTvsv/vuu04T3dzcXJPAwMBGAMjIyDB68skn3aKiokaq92/dutV25cqVDrGxsW7R0dEjDx48aNHZdVJTU7MyMzMz7nzdmYT31p33vXfvXks7Ozt5WFhYt51ngoKCGk6dOmWxYMECl+PHj5vZ2toqjh8/bn7p0iXTgIAAb6lUKjt9+rRFbm6ucUJCgsW0adMqnJyc5ADg4OCgSE5ONps6dWqlhYWF0tLSUhkdHV2RlJSk6bHv4uLSNG7cuAYAGDVqVH1eXp5xUlKS2dSpUyvNzc2VNjY2ysmTJ1d2VZb7eSY9QYk4If1EXV0djh49iq1btyI7OxuJiYlQKvWTAN/J5VUXiJ3FAACumUPlT5V6i609y+YLjo4IsbTUW2w0NAB5efqL1wXt6e5PXD0BhZL3vyWE9FsODg7yO2t0y8vLhXZ2dvINGzYMkUqlMqlUKsvLyxM3NTWx6Oho99mzZ5fPnz+/srPr2dnZKcLCwmqOHDliCQAjRoxoAQAXFxd5dHR05dmzZwffec6tW7dE5ubmCrFY9bkqk8mav/766+vax6SkpJiuW7eueN++fdd37tx5fd++fZ02qwgODvZSl1n7dejQoQ7DTQ0bNqy5sLBQ0wzmxo0b7WrI1Tq779OnT5slJiZaubi4+D377LMjf/nlF/Pp06ePuPNcf3//prS0tAw/P7+GVatWubz++utOHMex2bNnl6m/JOTl5V3asmXLzc7u526MjIw0nYWEQiEnl8u7bBPUWVnuJWZvUCJOSD9x7do1xMXFadabm5tx9epVg5TFxMUE9rPb2iWXHS3TW2ztRPwnfQ3lceWKahgRW1vgmWf0E7Mb/g7+cDRTDW5Q3lCO1FupvMbLywOWLQNeeonXMIQYhKWlpdLe3r7l8OHD5gBQXFwsTE5Otpw4cWLt8uXLS9TJoqura0tsbKybp6dn45o1a9p1jrl586aotLRUCAC1tbUsKSnJwtvbu7G6ulpQUVEhAFTtk5OSkiz8/f07dGzMzs42cnBw6LLne1NTExOJRJygdfSoFStWOL366qslnR3bmxrx8PDwury8PJPMzEyjxsZGdvDgQZtZs2ZVah+jVCrR2X1/+OGHhcXFxX8UFhZe3LlzZ+5DDz1U8+233167M0ZeXp7Y3NxcuXDhwvK4uLii8+fPm0ZFRVUfPXrUurCwUKR+5leuXDGKjIysPnLkiHVRUZFQvX3ChAm18fHxVjU1NYLq6mpBfHy89YQJE7qt3Z84cWJtfHy8VW1tLauoqBAkJiZadVWW7q6jC9RGnJB+wsfHBy4uLigsLAQAHDt2DBIDdiC0fcwWhe+pylJ2pAwe73p0+VOuLoVZWsJEIECjUokrDQ3Ia2jA8EGD+A0qFgPHjqmWz5wBKioAa72PMqbBGEOkeyR2XVDNtLPl7BZYGFtgachSnU/4c+gQMHOmalkgALZtUz0OQviwZcuWm/daMwoAe/fuvb53797rdz+yvV27dl1buHCh69KlS4cBwLJly276+Pg0aR+TmJhodujQIVuJRNIglUplALB27drCp556qqqgoED87LPPjlAoFOA4jk2fPr18zpw5VRkZGUYzZ870AACFQsFmzZpV9sQTT1TfGT8gIKCxvLxcLJFIfLZt25Y3adKkOu39CQkJZmFhYbVKpRKLFi1yiY6OrlJ3oLwfrSOi5EdFRXkqFArMnTu3dPTo0Y0AEB4e7rFr167rWVlZxl3dd09ipKamDlq+fPlQgUAAkUjEbdu27XpwcHDjypUrCyMiIjyVSiXEYjH3/vvv50dERNQtWbLkVlhYmFQgEHC+vr71Bw4cyJs7d25ZUFCQNwA8/fTTJer24V0JDQ2tnzlzZrmvr6+Pra1ti7+/f11XZbm/J3h3jONreC+ejB49mktJSTF0MQjpk55//nl8/vnnAIA1a9bgrbfeMlhZlM1K/Gz7MxS1qmYRYzLGYLB3h19ceRF14QISKioAAB95euJl57s2A713a9cCGzYATa1/k6VSYO9eIDCQv5g9sPfiXsw9OLfdtvei3sOrY1/VaZzaWkB7/qSDB9sSc6IbjLFUjuNGG7ochnDhwoW8gICALkfcGKiKioqEcXFxLqdOnbKYN29eaXV1tfCdd9659cEHH9jt3bvXNiAgoC4wMLBh6dKlndaKE/26cOGCXUBAwPDO9lHTFEL6kclaQ+olJBi2k57ASACryVaa9Vs7b+kttnbzlG9LS5FQXs5fMCentiT84YeBy5cNnoQDwCT3SWBo/wvE0StHdR7HzAxwcWlbv9bhh2dCiK45Ojoq9uzZk19QUHBpw4YNRTU1NUJLS0vlypUrb6enp1/es2dPPiXhDwZKxAnpRyZNmqRp/vHrr7+inM8EtAfk5W1Tq5f8T39/E7QT8e/KyzHj0iXUKXjqsBgd3bZ87lyfmWLSztQOY1zGaNZjvGOwIWIDL7H+9re25Z9+4iUEIaQbu3fvzjd0Gci9oUSckH7E1tYWY8eOBaDqQPPxxx9jz549BiuP9jCGTdea0FLRope43qamGKo1hnqjUonvW5uq6JyLCxAUpFqWywED/xKhTXsYwyGmQxDsHMxLHO2mKCdOAPX33TKVEEIGBkrECelnpk+frllesWIF5s+fjyoD1dLax9qDiRlMRpjAZbELBIP085HDGMMUrVrxYcbGGCTgMfa0aW3LR47wF6eXYrxjsCxkGZLmJ+H9Ke/zFsfTU9U0HlCN4Pj997yFIoSQfoUScUL6mRkzZrRbl8vlBmsvbmRnhEcaHsFDuQ9BskUCoQnvk5RpTLezAwCYCwSYY2+PyVqJuc5pJ+LHjgHffgu88w5/8Xoo0DEQGx/diPHDx8NIyO+M2Frf//DNN7yGIoSQfoMScUL6GalUCk9PT816SEgInJx4n5OgS0zI/5CFnYmwskKCvz9KQ0PxL3d3foMFBQHqkVkqKoAZM4A33wSKi7s9zRAUSgWqmzqMjnbftL//7doFFBXpPAQhhPQ7lIgT0g9p14pPmTIFYWFhhiuMgZgIhZhsYwMjPpukqDGmmtDnTt99x3/sHvqj+A88/+3zcNrshDdPvqnz6//pT23jh3Mc8D5/LWEIIaTfoESckH7o6aefxscff4ybN2/izTd1n3TdK0WjAnXZdXc/8EGk3TzFyAj4xz+AYH46R96LaxXX8Pn5z1FSX4LDVw5D13NICARtfVaBPvUdhBBC+iyaWZOQfsjX1xe+vr6GLoZG1dkqZL2QhfqseoisRQgtDdVr/NvNzThaVobDpaX4P3d3eJryMGvxxImAiQnQ2Ag0NwN/+QvgodtZLO/VssRl2Hx2s2a9WdGMmzU34WLh0s1ZvbdgAVBeDsTGqm6fEEJI9ygRJ4TwruV2C+ovq8a0k5fJUZ9bD9ORPCTDnaiVyzHt4kX8VlMDAAixtMQ/XF11H8jUVDWOX1OTqpnKkCF3P0dPnMydoOBU46g/NPQh/Pz8zxAw3f8gOn++6kUIIaRnqGkKIf1cZmYm/vWvfyEkJASZmZkGKYNNlA2gNWBK8Zf668T4RXGxJgkHgMNlZfwF27MHOHAAeO45wNKSvzi9NFPaNtB36s1UXjprEkII6T1eE3HGWBRjLIsxlsMYe6OT/XGMsQzG2B+MsZOMMTc+y0PIQMNxHBYuXIg33ngDZ86cwREDjXEtMBbAbrqdZr36jP4Swcft2uIKAPyH7xFU7qTjttj3ws3KDcFOqvbqLcoWHLtyzMAlIuTBIhQKg6VSqUwikfhMmTJlZE1NTY/yp5ycHPHYsWM93d3dfTw8PHzWr19vr95XX1/P/Pz8vL28vGQeHh4+ixcvdlbvW79+vb1EIvHx8PDwWbdunX3nVweuXr0q3rFjh/X93V3v7d+/32L48OG+rq6uvitWrHC8c3939+3i4uLn6ekpk0qlMl9fX2/9lrzn4uLinFevXu3AdxzeEnHGmBDAhwCmAJABmMMYk91x2O8ARnMc5w9gP4D/46s8hAxEL7/8MpKSkjTrhkrEAcBjS1t76cqkSshr5XqJ62JsjD+ZmwMAlAAy9TXt43ffAS+/DLi6Atev6ydmN2K8YzTLBzMP8h6voQHYuhU4d473UITwztjYWJmZmZmRnZ2dLhaLuc2bN/eo7ZlYLMbmzZtvXL16Nf3cuXOXP/30U/vU1FQTADAxMeFOnz6dlZWVlZGenp5x8uRJi5MnTw4+d+6cye7du4ekpaVdvnz5cvrx48etLl26ZNzZ9ePj4y3S0tL0086vlVwux+LFi13j4+OvXLlyJf3AgQM26ntS6+6+AeDHH3+8kpmZmXHp0qXL+ix7X8RnjfifAORwHJfLcVwzgH0ApmsfwHFcEsdx6r+KvwAYymN5CBlwHnnkEc3yyJEjceDAAYOVxcTNBIP9BgMAuGYOFd/zNOV8J2Zo1YofKi3VT9D33gO2bwdu3ADi4/UTsxvaiXj8lXgkXUvCJ2mf8BJrzhxg8GDgb38DVq3iJQQhBhMaGlqbk5NjnJWVZSSRSHzU21evXu0QFxfnrH2sm5tbS2hoaD0AWFtbK93d3Rvy8/ONAEAgEMDS0lIJAM3NzUwulzPGGC5evDho1KhRtebm5kqxWIyQkJCaffv2Wd1ZjoSEBLNVq1YNO3r0qLVUKpVlZGTwO2tXq+Tk5MFubm5NMpms2cTEhIuJiSnfv39/u/J1d989UV1dLRg/fryHl5eXTCKR+Khr/bdt22bj5+fnLZVKZXPnznWTy1UVOlu3brX19PSUeXl5yWbMmDFCfZ01a9Y4SCQSH4lEovllISsry2jkyJE+sbGxbh4eHj4hISGS2tpaBgDLli1zHD58uG9wcLBXdna2cXdl0RU+E3EXAAVa6zdat3XlBQA04BUhOjR16lQIharG2bm5uWhpaTFoeWwfs9Uslx3lsa32HbQT8ePl5WhQKPgLVlMDvPACcOZM27YffuAvXg9J7aSQ2qnmoW9UNGLi7olYeGwhqhqrdB5r8OC2FjmnT+v88oQYTEtLCxISEiz8/PwaentuVlaWUUZGhml4eHiteptcLodUKpU5ODgEhIeHV0+cOLEuMDCw4bfffjMvKioS1tTUCBITEy0LCgo6JLGRkZG1fn5+dQcPHszJzMzMkMlkzfd6X8HBwV5SqVR25+vQoUPmdx5bUFBg5OLiook1dOjQ5sLCwi6T7M7uOyIiQuLj4+O9adMmu87OOXjwoIWjo2NLVlZWRnZ2dnpMTEx1Wlqayf79+21SUlIyMzMzMwQCAffRRx/ZpqSkmGzatMnpxx9/vJKVlZXx8ccf5wPAqVOnTPfs2WObmpp6OSUl5fLu3buH/Pzzz4MAID8/3+TVV1+9nZOTk25paanYvXu39alTp0y/+eYbm4sXL2YkJiZmX7hwYXBXZbnX59yZPtFZkzE2D8BoAP/uYv9fGGMpjLGUkpIS/RaOkAeYjY0NwsPDNeuGbJoCtE/ES78pBafUT/tpqakpPAcNAgDUKZX4rqwMJc33/Dere2ZmwPHjqoQcAD74ANi7l59YvRQjjWm33qJswXc5uq//iItrW25sVA1pSIguxMXFOTPGghljwXfWPgPASy+9NFS9/6233urQvnfOnDlu6v1dJYGdaWpqEkilUpmfn59s6NChza+99lqvflqrqqoSxMTEuG/cuLHAxsZGqd4uEomQmZmZkZ+f/0daWtrgc+fOmQQFBTW+9tprRREREZ4TJkyQ+Pj41KsrVO6Um5trEhgY2AgAGRkZRk8++aRbVFTUSPX+rVu32q5cudIhNjbWLTo6euTBgwctOrtOampqVmZmZsadrxkzZtR0dvz93Pfp06czMzIyLp84cSJ7x44d9t99953ZnecFBQU1nDp1ymLBggUux48fN7O1tVUcP37c/NKlS6YBAQHeUqlUdvr0aYvc3FzjhIQEi2nTplU4OTnJAcDBwUEBAMnJyWZTp06ttLCwUFpaWiqjo6MrkpKSzAHAxcWlady4cQ0AMGrUqPq8vDzjpKQks6lTp1aam5srbWxslJMnT67sqiz380zuxGciXghgmNb60NZt7TDGHgXwJoDHOY5r6uxCHMdt5zhuNMdxo4f0oSHBCHkQTJ/e1iLs0KFDhisIgEHugzSDpsrL5aj+TT+dNhlj7WrFn7p8GW9eu8ZXsPazbObnA6K+MVKsdvMUBobnAp+Dh43uxzqXyQAvL9WyQgEkJ+s8BCF6pW4jnpmZmbFr164CExMTTiQScUqlJqdGY2OjAAA2bNgwRF2jnJeXJ25qamLR0dHus2fPLp8/f35lZ9e3s7NThIWF1Rw5csQSABYvXlyanp5+OSUlJcva2lrh6enZeOc5t27dEpmbmyvErVPaymSy5q+//rpdh5SUlBTTdevWFe/bt+/6zp07r+/bt6/TZhW9qREfNmxYuxrwGzdutKshV+vqvkeMGNECAC4uLvLo6OjKs2fPDr7zXH9//6a0tLQMPz+/hlWrVrm8/vrrThzHsdmzZ5ep/z/k5eVd2rJly83O7udujIyMNLVAQqGQk8vlrKtjOyvLvcTsCp+J+DkAEsbYCMaYEYBYAIe1D2CMjQLwMVRJ+G0ey0LIgKWdiP/www+4du0aCgs7fCfWC7G9GAKjto+dmx/f02foPZmplYjLOQ5Hysqg5GtEE+1ZNo8e5SfGPQhyCoKrpWoMdQ4cYn1jMdp5NC+xnniibfnbb3kJQYhBDR06VF5eXi4qKioSNjQ0sISEBEsAWL58eYk6WXR1dW2JjY118/T0bFyzZk27cVtv3rwpKi0tFQJAbW0tS0pKsvD29m4EgMLCQhEAZGdnGx07dszqxRdf7PC7UnZ2tpGDg0OXP+01NTUxkUjECQSqz9wVK1Y4vfrqq502K+hNjXh4eHhdXl6eSWZmplFjYyM7ePCgzaxZsyq1j1EqlejsvqurqwUVFRUC9XJSUpKFv79/h2Y+eXl5YnNzc+XChQvL4+Liis6fP28aFRVVffToUWv1sykuLhZeuXLFKDIysvrIkSPWRUVFQvV2AJgwYUJtfHy8VU1NjaC6uloQHx9vPWHChC5r+CdOnFgbHx9vVVtbyyoqKgSJiYlWXZWlq2vcC96qaTiOkzPGXgGQANUIwp9xHJfOGFsHIIXjuMNQNUUxA/A/xhgA5HMc9zhfZSJkIHJzc0NgYCDOnz+P5uZmuLu7Y+HChdi6davey8IYg+U4S1R8XwGRtQiDhg/SW+w/WVjAQSxGcWs7eTFjuNXcDBfjTgcjuD/as2xevgxcvQroe9jETjDG8Iz/M8guz0aMdwzGDRvHW6zp04G331YtHz0KyOV95ocB8gDbsmXLze5qQXfs2HFjx44dN7rav3fv3ut79+7VyTBGxsbG3JIlS26NGTPG28HBocXDw6NDrXViYqLZoUOHbCUSSYNUKpUBwNq1awufeuqpqoKCAvGzzz47QqFQgOM4Nn369PI5c+ZUAcDjjz/uXllZKRKJRNy7776bb2dn16E5REBAQGN5eblYIpH4bNu2LW/SpEl12vsTEhLMwsLCapVKJRYtWuQSHR1dpe5AeT9aR0TJj4qK8lQoFJg7d27p6NGjGwEgPDzcY9euXdezsrKMO7tvPz+/hpkzZ3oAgEKhYLNmzSp74oknOvw0mpqaOmj58uVDBQIBRCIRt23btuvBwcGNK1euLIyIiPBUKpUQi8Xc+++/nx8REVG3ZMmSW2FhYVKBQMD5+vrWHzhwIC80NLR+7ty5ZUFBQd4A8PTTT5eEhIQ0ZGVlddqePTQ0tH7mzJnlvr6+Pra2ti3+/v51XZXlfp+hNsb1gTFue2P06NFcSkqKoYtByANl7dq1WLNmjWbd1dUVeXl5aP0CrFf1OfUQGAlg4mpy94N17K9ZWfj41i0IALwvkWCRi26neG9n2rS22vDNm4GxY1Wzb44axV/MPkSpBIYNA262pkxJScD48QYt0gOLMZbKcRw/P130cRcuXMgLCAjQ01BHD7aioiJhXFycy6lTpyzmzZtXWl1dLXznnXduffDBB3Z79+61DQgIqAsMDGxYunQpdbbTswsXLtgFBAQM72wfJeKEDAAXLlxAYGCgZn3cuHE4cuQIbGxsDFcoA0itqcGlujo8ZmsL29Z2lbzZvl01jjjQVjs+ezbw9df8xu1DXn5Z9RgAwM8P+OMPw5bnQUWJOCXi9+KZZ55x3b17d76hy0G6T8T7xKgphBB++fv7w82tbeLaf/7znwMuCQeAYHNzzHd05D8JB9p32Gxs/cX6yBGguu9ML596MxUrTq6A7zZf/H7rd51ff+zYtuVLl4C6uq6PJYToFiXhDwZKxAkZABhjeO2117B27VqcP38e4/tQGwFlixKKOh7H9TYUZ2cgOLhtffBg4Omn+1Q2uunMJmw4vQHpJen4Nkv3PSr//GegtZ8YOA749FOdhyCEkAcaJeKEDBCLFy/G6tWrERAQYJC24XeqTqnG+cnncWrwKeSuyNV7/EaFAt+VlWFzQcHdD75Xj2v1PQ8PV7XTcNLpyFf37LXvXsORK23jyh/POa7zGMbGgL8/YGsLzJvXfjAZQgghPI6aQggh3cl5LQfVZ1TNNIq/LIbHux56+4JQ2dKCoWfPok6phBDAs3w1V/nzn4G33gIeegiYOVP3178P5Y3lqGtR1c7P9p6Nz2d8zkucX35RJeSEEEI6ohpxQgagmpoafPrpp3jxxRcNVoZhcW3zfcnL5ahJua8J3Hrlr1euoK51Ig4FgPiyMn4CubsDeXnA2bOAAZ91Z7Rn2UwvTcdgow5zaugEJeGEENI1SsQJGWCamprg5uaGF198EZ9++ikuXLhgkHLYPm4LsUNbLXTx/yvu5mjdmmDdNrmcm7ExHrGy4i+YVidZjT4wWlWkRyQGiVTjuGeUZCCrNMvAJSKkU0qlUmn4tnSE3KPW96+yq/2UiBMygHAch0mTJqGiokKzbefOnQYpi0AsgPeX3pr123tvQynv8rNKpx63tdUs32hqgplQqJe4yMwEVq9Wzf9+XadzQvSaqdgUUR5RmvVvMr/hPaZSCXzxhWpyH0J66FJJSYklJePkQaRUKllJSYklgEtdHUNtxAkZQBhjsLS01KxHRkZi+fLlBiuP9QRrGDkboflmM1put6DiRAVsp9re/cT75GRsjIcsLPBLdTUUAI6WlWG+oyPvcbF4MXC8tVPkvn3AsmX8x+xGjHeMJgE/ePkgpnlOw43qG4j0iNR5rHnzgP/9D2huBurr24ZYJ6Q7crn8xaKiok+Kiop8QZWH5MGjBHBJLpd32TaRJvQhZID56quvEBsbC0A1w2Zubi6E+qoR7sTVpVdR8G/VyCVDnhoCn30+eon7r/x8vJGrGq3lUWtrJAYE8BesoQH4z3+ADz4AiopU28aNA37+mb+YPVDZWIkh/x4CubKtitrV0hW5r+ZCKNDte0IiAXJyVMseHkB2tk4v368N5Al9COnv6NslIQPMjBkzYGdnBwDIz8/HiRMnDFoeh6cdNMulB0shr9JPu4U59vaaD8DvKyqQXluL283N/AQzMgK2bWtLwt94A/j+e35i9YKViRUiRkS025ZflY/vc3Vfttdfb1u+eVNVM04IIQMdJeKEDDDGxsaYP3++Zn27eg5yAxnsMxgia1UrOa6Fw80dN/US19XEBFO0Zhcdk5aGxeoqW10TClVtM9SuXAEGDeInVi/FeLeNnsLAMFs2G0MGD9F5nBdeANSPu74eOHZM5yEIIeSBQ4k4IQPQSy+9pFk+cuQIsrKycPHiRYOUhQkYxEPaRk+5uU0/iTgAvOzsrFluUCpxoKQEFS0t/ATT+vKDI0cAvoZM7KXpXtMhZKpmKBw4vBX+FoKcgnQeRyQCFixoW9+xQ+chCCHkgUOJOCEDkJeXFx555BEAgEKhgK+vL+bMmQND9RlxecUFACAwEcAixEJv5ZhiY4OhWgNdm4tEyGlo4CeYtzfwpz+plltaVJ01+wAHMwdMl07HGOcx2Dl9J0Zaj+Qt1gsvtC0fPw7k5/MWihBCHgiUiBMyQP3lL3/RLMvlcqSnpyM1NdUgZXF63gl+8X4Iqw+D7AuZ3mbYFAkEeFFryvkXHB0xxsKCv4DateI7dwJXrwIJCfzF66EvZn6B3176DfMD52OQmL8mMyNGAI8+qlrmOOCzz3gLRQghDwQaNYWQAaqxsRHOzs6aMcVHjhyJTz75BBMmTDBwyfTrRmMjtt28iZecnDCC73bb5eWAk1P7nooODsCNG6q2G31Mo7wRJiITnV7z66+Bp55SLQuFqo6b9vY6DdHv0KgphPRfVCNOyABlYmKCZ555RrMeHR094JJwABhqYoJ3Ro7kPwkHVL0VH3+8/bbiYiA5mf/YPVTVWIVt57Yh6OMgLDi24O4n9NL06W3fORQK1fxGhBAyUFEiTsgA9pe//AXz5s3DTz/9hPfee8/QxdHgOA5Nt5oMXQx+aDdPAYDHHgPMzAxTlk5klmZiUfwi/F70O75O/xpVjVU6vb6xMTB+fNv6wYM6vTwhhDxQKBEnZACTyWT44osvEBYWprd22d2R18qR+UImTlufxi8jf9HblPdqHMchtaYGC69cwcGSEn6CREa2b4uxaBHw0EP8xOql5LxkPPNN268kSk6JlJu6bwr4zjuAWAxMnQrEx+v88oQQ8sDoe40SCSEDVtP1JhR9VqRZLzlQAoenHLo5Q7c25OfjzWvXAACZ9fWIGaL78bQhFgN//jNw4ADwzDOATKb7GPdoiOkQXCm/AkA1pvi5l87B195X53HGjAFqalS144QQMpBRjTghBIBqGMOEhATExsZi8+bNBinDYJ/BMHI00qwXvluot9iVLS1Y25qEA0BSZSVy+RrKcP164No11X9dXfmJcQ987H0Q7hYOQDWm+NfpX/MWi5JwQgihRJwQ0urLL79EVFQUvvrqK3z00UcGG1Pc+ZW2SXZqfq+BvFo/U95bicV4zM5Osz7Lzg4jTHQ7YojG4MGA4I6PX4UCaB3BxpAWjVmkWd6euh3NCpqLnhBC+EKJOCEEt27dwqJFbQlYTk4O0tLSDFKW4W8Ox2D/wQAArolDyQGe2mp3QnumzZOVlWhU6qGNulwOfPEF4OsL/PWv/Me7ixnSGXA2Vz2H4rpiHLzMf2/KM2cArWHtCSFkwKBEnBACJycn+Pj4aNaXLFmC4OBgg5XH4em2duE3t93UW+38o9bWGNlaC14pl+N/fHXY1HbpkqqteGYm8L//AZcv8x+zG2KhGC8Hv6xZf/eXd/Gfs//Bd9nf6TxWXZ1qGPWQENWU92fO6DwEIYT0aZSIE0IAtJ9p8+jRowZrmgIADvMcwIxVo7jUpNSg7HCZXuIKGMNLWjNtfnzzJr8Bf/gBiItrWzc3B9LT+Y3ZAy8FvQSRQNWX/9fCXxF3Ig7/PvNvnccZPBhoaWlbf/NNnYcghJA+jRJxQggA4KmnnoK5uTkAICsrC6dOnTJYWYwdjeGy0EWzfmXBFXAK/XwxeM7JCaLWoRzPVFfjy6IilDbz1E66shJISlItGxsD588DTzzBT6xecDJ3wizvWe22JeUlIac8R+exnn66bTkvD3jAJnsmhJD7Qok4IQQAYGZmhrlz52rWt2/fbsDSAA7POACtQ5s332rGze08106r4xoZYaZWp82nMzOxs6iomzPuw7RpgKenarmpCfjwQ37i3APtTpsCJsB7Ue/B0cxR53HWrgXUk5rm5QG//abzEIQQ0mdRIk4I0dBunvK///0PmzZtwq5duwxSFjM/M4iHiDXreevy9BZbu9MmAHx48yaa+Oi4KRarZrdR++AD4Pp13ce5B6GuofCz99Osj7AaATMj3c8AamUFxMa2re/YofMQhBDSZ1EiTgjRCAoK0nTSbG5uxj/+8Q+8/vrrqKrS7TTnPcGEDB7vegAMsAyzRPBv+us8OsHKStNpEwACBw/mr818TAwwdqxqubkZWL1atSzXz7CNXWGM4Y3QN/Bm2JvIey0P07ym8RbrpZfalv/f/1PVjBNCyEBAiTghpJ2/3DGOXGlpKT744AODlMU+1h4P33wYo34aBZNhPI3p3QkBY/hra634CBMTxDo4wEQo5CcYY8C//tW2vns38NprwPDhQEEBPzF7aK7fXPxz4j8xzHIYr3EeeggYPVq13NgIzJ7dvhMnIYT0V8yQIyPci9GjR3MpKSmGLgYh/VZjYyN8fHyQm5sLABg9ejR++uknDFI35B0gylpacLG2FuFWVmCtnTd59dhjwLFj7be98oqquUof8vut35GYm4ilIUt1et2ffwZCQ9vWFywAtm3TaYgHFmMsleO40YYuByFE96hGnBDSjomJCd5//30AwJAhQ/D3v/+9zyXh+qhAsBWLMd7aWj9JOABs2KCqHdd24kSfqRqWK+X469G/Inh7MJZ9vwxnC87q9PohIUBQUNv6xx8DN27oNAQhhPQ5lIgTQjqIjo7Gjh07kJ2djT//+c+GLo5GzYUanAs4h+xXsg0Sv1oux9YbN/j5IuDnB8yf37Y+fDjwxx+qDp19QGVjJY5lHwMH1b0vObFE5zG++abtu4ira5/5DkIIIbyhRJwQ0qkXX3wRlpaWhi6GRv6mfKQGpqLujzrc2n4LLRX6zdJ2FRVh2Nmz+FtODvbevs1PkLVrVeOJA6pG001N/MTppWZFM0ZvH40b1aoqag8bD3w2/TOdx3F1BVauBLZuBXJzgREjdB6CEEL6FErECSE9UllZiaVLl+Lo0aMGiW871VazzMk5ZP9Nf7Xi2fX1WH71KqoVCgDAP65eRV3rsk65uqqy0F9+UU13b2Gh+xj3wEhohFfHvqpZv155HUqOh+EcAaxbByxa1LGVDiGE9EeUiBNC7urHH3+Em5sb/v3vf2PJkiVoMUCbgcGywbCaYKVZL/mmBM23eZrx8g4WIlG7xHusuTlMBTx9fL74Yttwhn3Ia2Nfw1gXVblalC14/tvnoVDy8GWEEEIGEErECSHdam5uxvvvv4/q6moAwJUrV7B//36DlMVnvw9MfUwBAFw9h/wN+XqJ62BkhI3u7pr1Y+XlyGlo0EtsFBSoRk/RnvjHAIQCIT59/FOIBao2678W/or3fn2P97hlZcD06UBlJe+hCCFE7ygRJ4R0y8jICEqtWSXd3Nwwe/Zsg5RFbCPGyLdHatYL/1uIxoJGvcT+i7MzxpibAwCaOQ4Ls7P5H73l3DnA3R348EPg7beB33/nN95d+Nj7YNUjqzTrb558Ex+lfISXDr/Ey7NYuxZwcAAOHwaefFLnlyeEEIOjRJwQclf/+c9/YNI60+T169fx8ccfG6wsto/bwvxPqoSYa+JwfZ1+poQXMoaPPD01H5rfV1TgK746bQKqmTV/+QVQfwlqbATOn+cvXg+9EfoG/B38AQCNikYsOLYAn/z+Cb7N+lbnsS5eBNQtghITgdRUnYcghBCDokScEHJXw4cPx5tvvqlZf/PNN3GbzyS0G4wxjHi7bTiNW5/cQv6/9dNEJcjcHK+4uGjWX83Jwd+zs6Hgo2acMdVg2upMdOpU4LnndB+nl8RCMT57/DMIWfuZRv/50z91Xiu+e3fbIDIAsGOHTi9PCCEGR4k4IaRHXn/9dXh4eAAAqqqq8MYbb6CwsBByuVzvZbGOsMYgr7ZJhnKX5uLWzlt6ib1+xAg4GRkBAEpaWvBeYSE+u8VDbKFQNcmP2tGjfWaqyWDnYLw+7nXNuligSs51PfmRqWn7iUW3bwfS0nQaghBCDIoScUJIj5iYmOADrazo888/R1BQEObNm6f3ZJwxBvd/uwNaeV9zsf5GUHm39QuJ2oeFhfwEe+wxICqqbX3RIuAz3Y/ffS/eCn8LnraeAIBIj0gMGTyElzgvvtj2CDhO1W/VAN/9CCGEF5SIE0J6LCoqCjNnztSs3759G1999RVeeOEFvZfFbpodpJ9LwcQMku0SuC1z01vs2UOG4FErK836EL5mv2QM+Oqr9sMZvvgi8PnnwLPPAocO8RO3BwaJB+Hz6Z9jT8weHI49DCdzJ17iMAa8917bBKNnzwKTJwNXr/ISjhBC9Irx3utfx0aPHs2lpKQYuhiEDFjXr1+Ht7c3GrSG79u6dSsWLVpkkPLIa+UQmYn0HvdqQwN8f/sNcxwc8J6HB8xFPJahshKIiOjYLmPwYOD0aSAwkL/Y92DDqQ0Y4zIGj458VGfXXLNGNYqKmrEx8OOPfXLIdZ1jjKVyHDfa0OUghOge1YgTQnrFzc0N33zzDUxNVeN5Dxs2DHPmzDFYeTpLwhuuNaDxBr/DGroPGoQb48bhM6m0QxKu81k3rayAEycAP7/22+vqVDNw9iHvnHoHK35Ygan/byq+Tv9aZ9d96y1g6dK29aYmICwMqKrSWQhCCNE7SsQJIb0WGRmJn376CYGBgfjpp59gY2Nj6CJpVKdV4zfv35Din4KWCn5nALXtpEnKxZoauJ09i91FRToOZqsaw08qbdsmFAIGaBbUlarGKqz7cR0A1eybO8/v1NlIKowB//oX8PLLbdvmzAEsLXVyeUIIMQhKxAkh9yQ4OBhpaWkYPnx4u+319fVYvXo1mpqa9F6mpptN+H3s7+CaOMgr5Djndw6KRv1Nw17Q2IiHfv8dZXI55mdm4r+67sTp4ACcPKma5AcANm8GRo7s/hw92nZuG5oUbf/f65rrUFJfotMYH30ErFsHTJkC7Nyp00sTQojeUSJOCLlndw5XV19fjz/96U9Yv349pk+fjsZG/cx6qWbkZASLhy00682FzUiflY7GfP2U44uiItRrzUJ6qLQUTVrrOuHsDPzwg2qQ7ddea78vPV012LaBhhV5efTLmCqZqln/Kf8nBH0chLMFZ3UaZ9UqID5eVUuuzQDf/Qgh5L5QIk4I0Zk1a9YgPT0dAJCQkID/9//+n17jM8YQmBQIs0Azzbby+HL86v4rMp/LRF1mHa/xF7q44BGLti8CJyoq8OiFCyht1vHQiq6uwNNPd9w+dy7wl78A/v7AmTO6jdkDNoNscGTOEawdvxasdWzJwppChO8Mx4e/fYif839GVSM/jbqrqgBHR9W8R7p+3IQQwhdKxAkhOmOsPQ0igKVLl+Lll19GcnIylEqlzmde7AwTMoz+fTSGLR2m2cbJORTtLMI573P4VforyhPKeYltJRbj+8BAvOjoqNl2uqoKQampWHPtGhZmZSGhrIyfmTgPHwb++EO1fPmyKhE3QM24gAmwOnw14v8cD2sTawCq9uKvfPcKHtn5CBw3O+KN79/QaczmZsDHRzW4zHffAUOGqJqv0BCHhJC+joYvJITo1LvvvovFixd32O7i4gInJyfY2NhgxYoVeOSRR3Q+E+Odyk+U4/o711H1Y8da2GHLhsF9ozsvcTmOw5YbN/CPq1fR2SeslUiEvLFjYanL8cfXrVMNLaLN1RX4299UY4+bmgKtM4LqS15lHmZ9PQtpt9oPu/gn5z/h15d+1VmcmzcBb2+gurrjvocfBubNA6ZPB1xcdBZSr2j4QkL6L15rxBljUYyxzK1I3wAADd9JREFULMZYDmOsQxUIY8yYMfZV6/5fGWPD+SwPIYR/f//733Ho0CG4ubWfYKewsBApKSk4ceIExo8fj//+97/t9iuVSih13J7aZrINRiWPwqifR7VrrgIALovaZ2WVP1fi2pprKEsog7z2/mqSGWNYMmwYDvr4wKqT8cWblEpY3LE9o64OV+rr0XCvQx8+/riquYr2dfPzgX/8Axg6VFVlbG4OBAQABw/eW4xeGm41HD8//zOeD3y+3fbZstkdjn35yMtY9+M6nLp+Ckqud+8DZ2fg2jVg9GhAcMdftbNnVROSDh2qqin/299UM3QSQkhfwFuNOGNMCOAKgEkAbgA4B2AOx3EZWscsBODPcdxfGWOxAGZyHPdUd9elGnFCHgxKpRJnzpzB3r178b///Q8lJe1Hz7hx4wZctKood+7cieeeew4mJiZwcXHBjBkzYGdnB7FYDJFIhJKSEly5cgVGRkbw8/PDsmXL2l3vu+++w4kTJwAAzs7OGDFiBIC2DqX5+fnIPJyJ6l+q4e/mj+WZy9udv2XcFvx6VlVLaw97ONk5wcjJCEYORhAYC3At/xpKKkvABAwR0RF45sNn2p2/6elNuHzpMhhj8PD0gL2zPcCAFk6J3MZGpKReQlllBQBg+OzJOPTP9sMOPjT+VTQUFQMABrmPgLWdNQYLBDAVCmEqEOBq2iU0NzZAAGD+shfw7NMT250fE7oItVXVgEIBLxsLmN4qBJqbwVo/4y82A/LW5ZXRfgjbuaXd+VOHTYNCLgcY4O85EiJjI1VWKxBACYbzl7I0x36a8B6GSodr1otyb2L+xAUAAIFAgMAAWbtrNzQ04WJGJuTKFnCMwxfntsPNse38H789iTcXblSdLxTA1EUEkaDtpWwESm9UgDEGY2MjnLh6pN31//vPbfj64yMAA4yNjGHj5ISaaqC+AYASgLIWTfWlAAAjI3OcvP51u46eC2etxcWff1Gdb2IKGwcHzX7GgNqKSlRXqf7fWdnZ4fAfX7SL/8KUJci+oPrTNniwGVZ8tBxhEUHQFaoRJ6Qf4ziOlxeAhwEkaK0vB7D8jmMSADzcuiwCUIrWLwddvYKDgzlCyIOlpaWFO378OPfYY49xYrGY8/Ly6nDMkiVLOAA9ejk4OHQ4Pzo6usfn+/r6djhfOkja4/PHWo/tcL6D0KHH50d7zOhwvikG9/j8mNBFHc4XQtTj89+YubzD+T09FwAXv+NIu3PPfPNTr84vzM5vd/7z0S/0+FwG1qHs032e7PH5JjDpcP44+0d7fL41s+lwvq9ZcLtj1iz6T4dj7geAFI6nv9X0ohe9DPvic15oFwAFWus3ANw5GbHmGI7j5IyxKgC2UCXkGoyxvwD4CwC4urryVV5CCE9EIhEiIyMRGRmJhoYG3L59u8Mx+fn5Pb6eUCjUZfEAAAITAdDQs2OZ4P7athuz+2sVqB6R5J5ZW7dfLyjo/Lie4u7vl1ULE/P7i99pS3x9nU0IIfeOz0RcZziO2w5gO6BqmmLg4hBC7sOgQYM6tB8HgE8++QRxcXG4ePEiqqqqoFAoUF5eDrlcDrlcjvz8fGRnZ0MulyMoqOPP/gEBAbhw4QIAwNHREW5ubuC0EsSCggIUtk6w09n5D8c8jMrvKgEArsNcYW9hD0WtAsoGJaAArt24hrK6MoAD/AP8O5wvs5dBUaxq3+1q7gobs9bZRluLcLX0KmrkNapjvWUdzvc0l+BG3Q0AgJvFSAwyMYOS46AAByUH3CjPQZNC9U1B4u3R4Xxn8TDUylW9FV2sJDAStR/BJr8sEwpO1fbd1VfS/mShEEMEDpC37nezkECgbmzNqX45zau+ojnczmVIu9MtbK1gzWwBqEZNcbNoXz65ogUFtdcAACImgvEgk3b7/WV+sD6oOl/MjOBiPlxrL4cmeSNu1au+LAwSmna4d8dhdrDOUJ0/SGAKezPndvsbmutxu/EmAMDGaEiH84fYD4F1ier8wSJz2A5yaLe/rqkGZc2qZkPOph3fu3aWjrCuU51vKbaG83CnDscQQkhn+Gwj/jCANRzHRbauLwcAjuM2aB2T0HrMWcaYCEARgCFcN4WiNuKEEEIGEmojTkj/xeeoKecASBhjIxhjRgBiARy+45jDAOa3Lj8B4IfuknBCCCGEEEL6C96aprS2+X4Fqg6ZQgCfcRyXzhhbB1XHk8MAPgXwBWMsB0A5VMk6IYQQQggh/R6vbcQ5josHEH/HttVay40AOg4oSwghhBBCSD9HU9wTQgghhBBiAJSIE0IIIYQQYgCUiBNCCCGEEGIAlIgTQgghhBBiAJSIE0IIIYQQYgCUiBNCCCGEEGIAlIgTQgghhBBiAJSIE0IIIYQQYgCUiBNCCCGEEGIAjOM4Q5ehVxhjJQCu37HZEkDVXbZpr2sv2wEo1XExOyvP/Rzf3f6e3Pud27p7Nrp+Hrp+Ft0d09PtvVnv689D1++NO9cH2r+V/vTe6O4YXfxbuXNfX38eD/K/FTeO44bo8HqEkL6C47gH/gVg+922aa/fsZyij/Lcz/Hd7e/JvXd3/3w/D10/i+6O6en23qz39eeh6/dGd++VgfBvpT+9N7o7Rhf/VjrZ16efx4P+b4Ve9KJX/3z1l6YpR3qw7Ug3+3Stt9e/2/Hd7e/Jvd+5rbtno2u6fhbdHdPT7b1d16W+/t64c32g/VvpT++N7o7Rxb+Vgfbe6GybPp8HIaQfeuCapugaYyyF47jRhi5HX0HPoz16Hm3oWbRHz6M9eh5t6FkQQnqqv9SI34/thi5AH0PPoz16Hm3oWbRHz6M9eh5t6FkQQnpkwNeIE0IIIYQQYghUI04IIYQQQogBUCJOCCGEEEKIAVAiTgghhBBCiAFQIt4Nxpg3Y+wjxth+xtgCQ5fH0BhjMxhjOxhjXzHGJhu6PIbEGBvJGPuUMbbf0GUxFMbYYMbYrtb3xJ8NXR5Do/dEG/qsaI/+lhBCutJvE3HG2GeMsduMsUt3bI9ijGUxxnIYY290dw2O4y5zHPdXAE8CCOGzvHzT0fM4xHHcSwD+CuApPsvLJx09i1yO417gt6T618tnEwNgf+t74nG9F1YPevM8+ut7Qq2Xz6JffFZ0p5fPo9/8LSGE6Fa/TcQB7AQQpb2BMSYE8CGAKQBkAOYwxmSMMT/G2NE7Xvat5zwO4BiAeP0WX+d2QgfPo9XK1vMeVDuhu2fR3+xED58NgKEACloPU+ixjPq0Ez1/Hv3dTvT+WTzonxXd2YlePI9+9LeEEKJDIkMXgC8cx/3EGBt+x+Y/AcjhOC4XABhj+wBM5zhuA4DHurjOYQCHGWPHAOzhsci80sXzYIwxABsBfMdxXBrPReaNrt4b/VFvng2AG1Al4+fRT7/U9/J5ZOi5eHrVm2fBGLuMfvBZ0Z3evjf6y98SQohu9cs/nt1wQVsNHqBKJFy6OpgxNp4x9j5j7GP0z1qMXj0PAH8D8CiAJxhjf+WzYAbQ2/eGLWPsIwCjGGPL+S6cgXX1bA4CmMUY+y8G1vTenT6PAfaeUOvqvdGfPyu609V7o7//LSGE3KN+WyOuCxzHJQNINnAx+gyO494H8L6hy9EXcBxXBlX71wGL47g6AM8Zuhx9Bb0n2tBnRXv0t4QQ0pWBViNeCGCY1vrQ1m0DFT2PNvQsukbPpj16Hm3oWbRHz4MQ0isDLRE/B0DCGBvBGDMCEAvgsIHLZEj0PNrQs+gaPZv26Hm0oWfRHj0PQkiv9NtEnDG2F8BZAF6MsRuMsRc4jpMDeAVAAoDLAL7mOC7dkOXUF3oebehZdI2eTXv0PNrQs2iPngchRBcYx3GGLgMhhBBCCCEDTr+tESeEEEIIIaQvo0ScEEIIIYQQA6BEnBBCCCGEEAOgRJwQQgghhBADoEScEEIIIYQQA6BEnBBCCCGEEAOgRJwQQgghhBADoESckH6KMWbLGDvf+ipijBW2LtcyxrbxFPPvjLFnutn/GGNsHR+xCSGEkAcNTehDyADAGFsDoJbjuE08xhABSAMQ1DrDYGfHsNZjQjiOq+erLIQQQsiDgGrECRlgGGPjGWNHW5fXMMZ2McZOMcauM8ZiGGP/xxi7yBg7zhgTtx4XzBj7kTGWyhhLYIw5dXLpiQDS1Ek4Y+xVxlgGY+wPxtg+AOBU3/yTATyml5slhBBC+jBKxAkh7lAl0Y8D+BJAEsdxfgAaAES3JuMfAHiC47hgAJ8BeLuT64QASNVafwPAKI7j/AH8VWt7CoAwnd8FIYQQ8oARGboAhBCD+47juBbG2EUAQgDHW7dfBDAcgBcAXwCJqpYlEAK41cl1nABc1lr/A8D/Y4wdAnBIa/ttAM66Kz4hhBDyYKJEnBDSBAAcxykZYy1cW8cRJVSfEQxAOsdxD9/lOg0ATLTWowE8AmAagDcZY36tzVZMWo8lhBBCBjRqmkIIuZssAEMYYw8DAGNMzBjz6eS4ywA8Wo8RABjGcVwSgGUALAGYtR7nCeAS76UmhBBC+jhKxAkh3eI4rhnAEwD+xRi7AOA8gHGdHPodVDXggKr5ypetzV1+B/A+x3GVrfsmADjGZ5kJIYSQBwENX0gI0RnG2DcAlnIcl93FfgcAeziOi9BvyQghhJC+hxJxQojOMMa8ADhwHPdTF/vHAGjhOO68XgtGCCGE9EGUiBNCCCGEEGIA1EacEEIIIYQQA6BEnBBCCCGEEAOgRJwQQgghhBADoEScEEIIIYQQA6BEnBBCCCGEEAP4/7+NVMvnDI5bAAAAAElFTkSuQmCC\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1146,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1185,8 +1201,8 @@ " 1\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 8.808003e-08\n", - " 4.352878e-10\n", + " 8.785604e-08\n", + " 4.659397e-10\n", " \n", " \n", " 1\n", @@ -1194,8 +1210,8 @@ " 1\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 7.175417e-09\n", - " 3.446159e-11\n", + " 7.154286e-09\n", + " 3.857161e-11\n", " \n", " \n", " 2\n", @@ -1203,8 +1219,8 @@ " 2\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 9.559207e-07\n", - " 4.724119e-09\n", + " 9.534897e-07\n", + " 5.056780e-09\n", " \n", " \n", " 3\n", @@ -1212,8 +1228,8 @@ " 2\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.307826e-07\n", - " 6.281133e-10\n", + " 1.303974e-07\n", + " 7.030245e-10\n", " \n", " \n", " 4\n", @@ -1221,8 +1237,8 @@ " 3\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.361643e-07\n", - " 1.167114e-09\n", + " 2.355637e-07\n", + " 1.249299e-09\n", " \n", " \n", " 5\n", @@ -1230,8 +1246,8 @@ " 3\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.040175e-08\n", - " 9.798408e-11\n", + " 2.034167e-08\n", + " 1.096700e-10\n", " \n", " \n", " 6\n", @@ -1239,8 +1255,8 @@ " 4\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 4.735711e-07\n", - " 2.340368e-09\n", + " 4.723668e-07\n", + " 2.505171e-09\n", " \n", " \n", " 7\n", @@ -1248,8 +1264,8 @@ " 4\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.635713e-08\n", - " 1.265862e-10\n", + " 2.627951e-08\n", + " 1.416833e-10\n", " \n", " \n", " 8\n", @@ -1257,8 +1273,8 @@ " 5\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.837213e-08\n", - " 1.402138e-10\n", + " 2.829997e-08\n", + " 1.500873e-10\n", " \n", " \n", " 9\n", @@ -1266,8 +1282,8 @@ " 5\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.439290e-09\n", - " 1.171525e-11\n", + " 2.432107e-09\n", + " 1.311246e-11\n", " \n", " \n", " 10\n", @@ -1275,8 +1291,8 @@ " 6\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.482388e-09\n", - " 7.325898e-12\n", + " 1.478618e-09\n", + " 7.841770e-12\n", " \n", " \n", " 11\n", @@ -1284,8 +1300,8 @@ " 6\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 7.019358e-11\n", - " 3.371208e-13\n", + " 6.998687e-11\n", + " 3.773271e-13\n", " \n", " \n", "\n", @@ -1307,21 +1323,21 @@ "11 1 6 Pu239 \n", "\n", " score mean std. dev. \n", - "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.81e-08 4.35e-10 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.18e-09 3.45e-11 \n", - "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.56e-07 4.72e-09 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.31e-07 6.28e-10 \n", - "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07 1.17e-09 \n", - "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.04e-08 9.80e-11 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.74e-07 2.34e-09 \n", - "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.64e-08 1.27e-10 \n", - "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.84e-08 1.40e-10 \n", - "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.44e-09 1.17e-11 \n", - "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.33e-12 \n", - "11 (((delayed-nu-fission / nu-fission) * (delayed... 7.02e-11 3.37e-13 " + "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.79e-08 4.66e-10 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.86e-11 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.06e-09 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 7.03e-10 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07 1.25e-09 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 1.10e-10 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.51e-09 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.42e-10 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 1.50e-10 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 1.31e-11 \n", + "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.84e-12 \n", + "11 (((delayed-nu-fission / nu-fission) * (delayed... 7.00e-11 3.77e-13 " ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1345,15 +1361,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Beta (U-235) : 0.006504 +/- 0.000006\n", - "Beta (Pu-239): 0.002245 +/- 0.000002\n" + "Beta (U-235) : 0.006504 +/- 0.000007\n", + "Beta (Pu-239): 0.002245 +/- 0.000003\n" ] }, { @@ -1362,13 +1378,13 @@ "(0.0, 7.0)" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1416,7 +1432,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1425,13 +1441,13 @@ "(1000.0, 20000000.0)" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1469,7 +1485,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1483,9 +1499,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.1" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/mdgxs-part-ii.ipynb b/mdgxs-part-ii.ipynb index a9102df..08d06b3 100644 --- a/mdgxs-part-ii.ipynb +++ b/mdgxs-part-ii.ipynb @@ -83,8 +83,7 @@ "outputs": [], "source": [ "# Create a materials collection and export to XML\n", - "materials = openmc.Materials((fuel, water, zircaloy))\n", - "materials.export_to_xml()" + "materials = openmc.Materials((fuel, water, zircaloy))" ] }, { @@ -267,8 +266,7 @@ "outputs": [], "source": [ "# Create Geometry and export to XML\n", - "geometry = openmc.Geometry(root_universe)\n", - "geometry.export_to_xml()" + "geometry = openmc.Geometry(root_universe)" ] }, { @@ -299,10 +297,7 @@ "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings.source = openmc.Source(space=uniform_dist)\n", - "\n", - "# Export to \"settings.xml\"\n", - "settings.export_to_xml()" + "settings.source = openmc.Source(space=uniform_dist)" ] }, { @@ -319,7 +314,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -383,26 +378,7 @@ "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n", - " warn(msg, IDWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# Instantiate a tally mesh \n", "mesh = openmc.RegularMesh(mesh_id=1)\n", @@ -429,8 +405,8 @@ "mgxs_lib.build_library()\n", "\n", "# Create a \"tallies.xml\" file for the MGXS Library\n", - "tallies_file = openmc.Tallies()\n", - "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)\n", + "tallies = openmc.Tallies()\n", + "mgxs_lib.add_to_tallies_file(tallies, merge=True)\n", "\n", "# Instantiate a current tally\n", "mesh_filter = openmc.MeshSurfaceFilter(mesh)\n", @@ -439,10 +415,7 @@ "current_tally.filters = [mesh_filter]\n", "\n", "# Add current tally to the tallies file\n", - "tallies_file.append(current_tally)\n", - "\n", - "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" + "tallies.append(current_tally)" ] }, { @@ -456,7 +429,38 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, + "outputs": [], + "source": [ + "# tie geometry, materials, settings, and tallies together into a model object\n", + "model = openmc.Model(geometry=geometry,\n", + " materials=materials,\n", + " settings=settings,\n", + " tallies=tallies)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=1.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=5.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=6.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=17.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=23.\n", + " warn(msg, IDWarning)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -485,120 +489,66 @@ " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2021 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.0-dev\n", - " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-26 13:44:53\n", - " OpenMP Threads | 2\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1-dev\n", + " Git SHA1 | 1be02f90bd0e9adb5a3311ee77c904a52ca7ebaa\n", + " Date/Time | 2022-05-11 17:40:59\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.0 K\n", - " Maximum neutron data temperature: 294.0 K\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading B10 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/B10.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for U235\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.03409\n", - " 2/1 1.02768\n", - " 3/1 1.01526\n", - " 4/1 1.03910\n", - " 5/1 1.00144\n", - " 6/1 1.00485\n", - " 7/1 1.03532\n", - " 8/1 1.01159\n", - " 9/1 0.97707\n", - " 10/1 1.06530\n", - " 11/1 1.08615\n", - " 12/1 1.00438 1.04527 +/- 0.04089\n", - " 13/1 1.03145 1.04066 +/- 0.02405\n", - " 14/1 1.04785 1.04246 +/- 0.01710\n", - " 15/1 1.04634 1.04323 +/- 0.01327\n", - " 16/1 1.02264 1.03980 +/- 0.01137\n", - " 17/1 1.00157 1.03434 +/- 0.01105\n", - " 18/1 1.09443 1.04185 +/- 0.01216\n", - " 19/1 1.02810 1.04032 +/- 0.01084\n", - " 20/1 1.01800 1.03809 +/- 0.00995\n", - " 21/1 1.02815 1.03719 +/- 0.00904\n", - " 22/1 1.00064 1.03414 +/- 0.00880\n", - " 23/1 0.99013 1.03076 +/- 0.00877\n", - " 24/1 1.01182 1.02940 +/- 0.00823\n", - " 25/1 1.07537 1.03247 +/- 0.00826\n", - " 26/1 1.02924 1.03227 +/- 0.00772\n", - " 27/1 1.01499 1.03125 +/- 0.00733\n", - " 28/1 1.01749 1.03049 +/- 0.00695\n", - " 29/1 1.04072 1.03102 +/- 0.00660\n", - " 30/1 0.99990 1.02947 +/- 0.00645\n", - " 31/1 1.03239 1.02961 +/- 0.00613\n", - " 32/1 1.02613 1.02945 +/- 0.00585\n", - " 33/1 1.04340 1.03006 +/- 0.00562\n", - " 34/1 1.05081 1.03092 +/- 0.00545\n", - " 35/1 1.02511 1.03069 +/- 0.00524\n", - " 36/1 0.99923 1.02948 +/- 0.00517\n", - " 37/1 0.97758 1.02756 +/- 0.00534\n", - " 38/1 0.99628 1.02644 +/- 0.00526\n", - " 39/1 1.06004 1.02760 +/- 0.00521\n", - " 40/1 1.08287 1.02944 +/- 0.00536\n", - " 41/1 1.02731 1.02937 +/- 0.00518\n", - " 42/1 1.02634 1.02928 +/- 0.00502\n", - " 43/1 1.04269 1.02968 +/- 0.00488\n", - " 44/1 1.05044 1.03029 +/- 0.00478\n", - " 45/1 1.05183 1.03091 +/- 0.00468\n", - " 46/1 1.02770 1.03082 +/- 0.00455\n", - " 47/1 1.07099 1.03191 +/- 0.00455\n", - " 48/1 1.04467 1.03224 +/- 0.00444\n", - " 49/1 1.02996 1.03218 +/- 0.00433\n", - " 50/1 1.03418 1.03223 +/- 0.00422\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 2.5756e-01 seconds\n", - " Reading cross sections = 2.4233e-01 seconds\n", - " Total time in simulation = 1.2099e+01 seconds\n", - " Time in transport only = 1.2046e+01 seconds\n", - " Time in inactive batches = 8.7582e-01 seconds\n", - " Time in active batches = 1.1223e+01 seconds\n", - " Time synchronizing fission bank = 4.8051e-03 seconds\n", - " Sampling source sites = 4.1028e-03 seconds\n", - " SEND/RECV source sites = 6.7903e-04 seconds\n", - " Time accumulating tallies = 3.0247e-02 seconds\n", - " Time writing statepoints = 1.4253e-02 seconds\n", - " Total time for finalization = 5.3700e-07 seconds\n", - " Total time elapsed = 1.2369e+01 seconds\n", - " Calculation Rate (inactive) = 28544.8 particles/second\n", - " Calculation Rate (active) = 8910.20 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.02658 +/- 0.00374\n", - " k-effective (Track-length) = 1.03223 +/- 0.00422\n", - " k-effective (Absorption) = 1.02640 +/- 0.00361\n", - " Combined k-effective = 1.02849 +/- 0.00321\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", - "\n" + " 1/1 0.99225\n", + " 2/1 0.99354\n", + " 3/1 1.02644\n", + " 4/1 1.06300\n", + " 5/1 1.03396\n", + " 6/1 1.00753\n", + " 7/1 1.04194\n", + " 8/1 1.04023\n", + " 9/1 1.03320\n", + " 10/1 1.04267\n", + " 11/1 1.02172\n", + " 12/1 1.07125 1.04648 +/- 0.02477\n", + " 13/1 1.04987 1.04761 +/- 0.01434\n", + " 14/1 1.01403 1.03922 +/- 0.01317\n", + " 15/1 1.04432 1.04024 +/- 0.01025\n", + " 16/1 1.06785 1.04484 +/- 0.00955\n", + " 17/1 1.04639 1.04506 +/- 0.00807\n", + " 18/1 1.06538 1.04760 +/- 0.00744\n", + " 19/1 1.00283 1.04263 +/- 0.00823\n", + " 20/1 1.00930 1.03929 +/- 0.00808\n", + " 21/1 1.02698 1.03817 +/- 0.00740\n", + " 22/1 1.04975 1.03914 +/- 0.00682\n", + " 23/1 1.03265 1.03864 +/- 0.00629\n" ] } ], "source": [ "# Run OpenMC\n", - "openmc.run()" + "statepoint_filename = model.run()" ] }, { @@ -617,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -634,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -668,197 +618,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2111x-max outtotalcurrent0.031540.000643
3111x-max intotalcurrent0.030300.000662
4111y-min outtotalcurrent0.000000.000000
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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Extract the energy-condensed delayed neutron fraction tally\n", "beta_by_group = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n", @@ -1164,7 +729,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1178,9 +743,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.1" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/mgxs-part-i.ipynb b/mgxs-part-i.ipynb index 10e02e9..64cd36a 100644 --- a/mgxs-part-i.ipynb +++ b/mgxs-part-i.ipynb @@ -144,6 +144,16 @@ "import openmc.mgxs as mgxs" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create a model object to tie geometry, materials, settings, and tallies together\n", + "model = openmc.Model()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -153,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -176,13 +186,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection and export to XML\n", - "materials_file = openmc.Materials([inf_medium])\n", - "materials_file.export_to_xml()" + "model.materials = openmc.Materials([inf_medium])" ] }, { @@ -194,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -254,15 +263,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry(root_universe)\n", - "\n", - "# Export to \"geometry.xml\"\n", - "openmc_geometry.export_to_xml()" + "model.geometry = openmc.Geometry(root_universe)" ] }, { @@ -274,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -284,19 +290,18 @@ "particles = 2500\n", "\n", "# Instantiate a Settings object\n", - "settings_file = openmc.Settings()\n", - "settings_file.batches = batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", - "settings_file.output = {'tallies': True}\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': True}\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings_file.source = openmc.Source(space=uniform_dist)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", "\n", - "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" + "model.settings = settings" ] }, { @@ -308,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -345,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -368,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -392,7 +397,7 @@ " \tEstimator =\ttracklength)])" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -410,35 +415,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", - " warn(msg, IDWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", - "tallies_file = openmc.Tallies()\n", + "tallies = openmc.Tallies()\n", "\n", "# Add total tallies to the tallies file\n", - "tallies_file += total.tallies.values()\n", + "tallies += total.tallies.values()\n", "\n", "# Add absorption tallies to the tallies file\n", - "tallies_file += absorption.tallies.values()\n", + "tallies += absorption.tallies.values()\n", "\n", "# Add scattering tallies to the tallies file\n", - "tallies_file += scattering.tallies.values()\n", + "tallies += scattering.tallies.values()\n", "\n", - "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" + "model.tallies = tallies" ] }, { @@ -450,9 +443,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -481,111 +484,112 @@ " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2021 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.0-dev\n", - " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-28 08:44:52\n", - " OpenMP Threads | 2\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1-dev\n", + " Git SHA1 | 1be02f90bd0e9adb5a3311ee77c904a52ca7ebaa\n", + " Date/Time | 2022-05-11 23:29:23\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", - " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", - " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.0 K\n", - " Maximum neutron data temperature: 294.0 K\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for H1\n", + " Maximum neutron transport energy: 20000000 eV for H1\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.18505\n", - " 2/1 1.17297\n", - " 3/1 1.16184\n", - " 4/1 1.14929\n", - " 5/1 1.09928\n", - " 6/1 1.18675\n", - " 7/1 1.19772\n", - " 8/1 1.17470\n", - " 9/1 1.17208\n", - " 10/1 1.09993\n", - " 11/1 1.14342\n", - " 12/1 1.10127 1.12234 +/- 0.02107\n", - " 13/1 1.19914 1.14794 +/- 0.02834\n", - " 14/1 1.18411 1.15698 +/- 0.02199\n", - " 15/1 1.14556 1.15470 +/- 0.01718\n", - " 16/1 1.20337 1.16281 +/- 0.01621\n", - " 17/1 1.13853 1.15934 +/- 0.01413\n", - " 18/1 1.18208 1.16218 +/- 0.01256\n", - " 19/1 1.11842 1.15732 +/- 0.01210\n", - " 20/1 1.15248 1.15684 +/- 0.01083\n", - " 21/1 1.14903 1.15613 +/- 0.00982\n", - " 22/1 1.23456 1.16266 +/- 0.01110\n", - " 23/1 1.18876 1.16467 +/- 0.01040\n", - " 24/1 1.13591 1.16262 +/- 0.00985\n", - " 25/1 1.19559 1.16481 +/- 0.00943\n", - " 26/1 1.16947 1.16511 +/- 0.00882\n", - " 27/1 1.13198 1.16316 +/- 0.00851\n", - " 28/1 1.15329 1.16261 +/- 0.00805\n", - " 29/1 1.16538 1.16275 +/- 0.00761\n", - " 30/1 1.18229 1.16373 +/- 0.00729\n", - " 31/1 1.15060 1.16311 +/- 0.00696\n", - " 32/1 1.15460 1.16272 +/- 0.00665\n", - " 33/1 1.13875 1.16168 +/- 0.00644\n", - " 34/1 1.13479 1.16056 +/- 0.00626\n", - " 35/1 1.21125 1.16258 +/- 0.00634\n", - " 36/1 1.15914 1.16245 +/- 0.00609\n", - " 37/1 1.10457 1.16031 +/- 0.00624\n", - " 38/1 1.17215 1.16073 +/- 0.00603\n", - " 39/1 1.18462 1.16155 +/- 0.00588\n", - " 40/1 1.15361 1.16129 +/- 0.00568\n", - " 41/1 1.14983 1.16092 +/- 0.00551\n", - " 42/1 1.14087 1.16029 +/- 0.00537\n", - " 43/1 1.18725 1.16111 +/- 0.00527\n", - " 44/1 1.19094 1.16199 +/- 0.00519\n", - " 45/1 1.17371 1.16232 +/- 0.00505\n", - " 46/1 1.18552 1.16297 +/- 0.00495\n", - " 47/1 1.14194 1.16240 +/- 0.00485\n", - " 48/1 1.12045 1.16130 +/- 0.00484\n", - " 49/1 1.18476 1.16190 +/- 0.00476\n", - " 50/1 1.17063 1.16212 +/- 0.00464\n", + " 1/1 1.15552\n", + " 2/1 1.16733\n", + " 3/1 1.17357\n", + " 4/1 1.12104\n", + " 5/1 1.15371\n", + " 6/1 1.12495\n", + " 7/1 1.19449\n", + " 8/1 1.20011\n", + " 9/1 1.16787\n", + " 10/1 1.14804\n", + " 11/1 1.10848\n", + " 12/1 1.13356 1.12102 +/- 0.01254\n", + " 13/1 1.15809 1.13337 +/- 0.01432\n", + " 14/1 1.15677 1.13922 +/- 0.01170\n", + " 15/1 1.17172 1.14572 +/- 0.01115\n", + " 16/1 1.24178 1.16173 +/- 0.01842\n", + " 17/1 1.12127 1.15595 +/- 0.01660\n", + " 18/1 1.14862 1.15503 +/- 0.01441\n", + " 19/1 1.19389 1.15935 +/- 0.01342\n", + " 20/1 1.13333 1.15675 +/- 0.01228\n", + " 21/1 1.11797 1.15322 +/- 0.01166\n", + " 22/1 1.15630 1.15348 +/- 0.01064\n", + " 23/1 1.19152 1.15641 +/- 0.01022\n", + " 24/1 1.15088 1.15601 +/- 0.00947\n", + " 25/1 1.18286 1.15780 +/- 0.00899\n", + " 26/1 1.13381 1.15630 +/- 0.00855\n", + " 27/1 1.15178 1.15604 +/- 0.00803\n", + " 28/1 1.15118 1.15577 +/- 0.00758\n", + " 29/1 1.12090 1.15393 +/- 0.00740\n", + " 30/1 1.18609 1.15554 +/- 0.00720\n", + " 31/1 1.20145 1.15773 +/- 0.00719\n", + " 32/1 1.12612 1.15629 +/- 0.00700\n", + " 33/1 1.17661 1.15717 +/- 0.00675\n", + " 34/1 1.17043 1.15773 +/- 0.00649\n", + " 35/1 1.16430 1.15799 +/- 0.00623\n", + " 36/1 1.13257 1.15701 +/- 0.00606\n", + " 37/1 1.11728 1.15554 +/- 0.00602\n", + " 38/1 1.18588 1.15662 +/- 0.00590\n", + " 39/1 1.19931 1.15809 +/- 0.00588\n", + " 40/1 1.13642 1.15737 +/- 0.00572\n", + " 41/1 1.17784 1.15803 +/- 0.00558\n", + " 42/1 1.15119 1.15782 +/- 0.00540\n", + " 43/1 1.16841 1.15814 +/- 0.00525\n", + " 44/1 1.18698 1.15899 +/- 0.00516\n", + " 45/1 1.10199 1.15736 +/- 0.00527\n", + " 46/1 1.13452 1.15672 +/- 0.00516\n", + " 47/1 1.11975 1.15573 +/- 0.00512\n", + " 48/1 1.15899 1.15581 +/- 0.00498\n", + " 49/1 1.12582 1.15504 +/- 0.00491\n", + " 50/1 1.12809 1.15437 +/- 0.00483\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.9038e-01 seconds\n", - " Reading cross sections = 2.7861e-01 seconds\n", - " Total time in simulation = 3.9987e+00 seconds\n", - " Time in transport only = 3.9829e+00 seconds\n", - " Time in inactive batches = 4.9461e-01 seconds\n", - " Time in active batches = 3.5041e+00 seconds\n", - " Time synchronizing fission bank = 5.2042e-03 seconds\n", - " Sampling source sites = 4.4431e-03 seconds\n", - " SEND/RECV source sites = 7.3804e-04 seconds\n", - " Time accumulating tallies = 3.6977e-04 seconds\n", - " Time writing statepoints = 6.6301e-03 seconds\n", - " Total time for finalization = 1.9945e-04 seconds\n", - " Total time elapsed = 4.2948e+00 seconds\n", - " Calculation Rate (inactive) = 50544.8 particles/second\n", - " Calculation Rate (active) = 28537.8 particles/second\n", + " Total time for initialization = 1.2841e-01 seconds\n", + " Reading cross sections = 1.2456e-01 seconds\n", + " Total time in simulation = 2.7131e+00 seconds\n", + " Time in transport only = 2.7038e+00 seconds\n", + " Time in inactive batches = 2.9116e-01 seconds\n", + " Time in active batches = 2.4219e+00 seconds\n", + " Time synchronizing fission bank = 5.1996e-03 seconds\n", + " Sampling source sites = 4.3432e-03 seconds\n", + " SEND/RECV source sites = 8.4443e-04 seconds\n", + " Time accumulating tallies = 2.4355e-04 seconds\n", + " Time writing statepoints = 2.3963e-03 seconds\n", + " Total time for finalization = 6.0680e-05 seconds\n", + " Total time elapsed = 2.8438e+00 seconds\n", + " Calculation Rate (inactive) = 85862.4 particles/second\n", + " Calculation Rate (active) = 41289.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16239 +/- 0.00461\n", - " k-effective (Track-length) = 1.16212 +/- 0.00464\n", - " k-effective (Absorption) = 1.15435 +/- 0.00325\n", - " Combined k-effective = 1.15666 +/- 0.00304\n", + " k-effective (Collision) = 1.15560 +/- 0.00487\n", + " k-effective (Track-length) = 1.15437 +/- 0.00483\n", + " k-effective (Absorption) = 1.15879 +/- 0.00318\n", + " Combined k-effective = 1.15743 +/- 0.00304\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -593,7 +597,7 @@ ], "source": [ "# Run OpenMC\n", - "openmc.run()" + "statepoint_filename = model.run()" ] }, { @@ -612,12 +616,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Load the last statepoint file\n", - "sp = openmc.StatePoint('statepoint.50.h5')" + "sp = openmc.StatePoint(statepoint_filename)" ] }, { @@ -636,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -671,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -683,8 +687,8 @@ "\tDomain Type =\tcell\n", "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t6.81e-01 +/- 2.69e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t1.40e+00 +/- 6.00e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t6.82e-01 +/- 2.16e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.40e+00 +/- 6.49e-01%\n", "\n", "\n", "\n" @@ -704,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -741,16 +745,16 @@ " 1\n", " 1\n", " total\n", - " 0.668083\n", - " 0.001798\n", + " 0.668519\n", + " 0.001449\n", " \n", " \n", " 0\n", " 1\n", " 2\n", " total\n", - " 1.292060\n", - " 0.007737\n", + " 1.292665\n", + " 0.008362\n", " \n", " \n", "\n", @@ -758,11 +762,11 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "1 1 1 total 0.668083 0.001798\n", - "0 1 2 total 1.292060 0.007737" + "1 1 1 total 0.668519 0.001449\n", + "0 1 2 total 1.292665 0.008362" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -781,9 +785,18 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mgxs/mgxs.py:1994: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n", + " df.to_excel(filename + '.xls', index=False)\n" + ] + } + ], "source": [ "absorption.export_xs_data(filename='absorption-xs', format='excel')" ] @@ -797,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -820,96 +833,6 @@ "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](../examples/tally-arithmetic.rst) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects." ] }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " cell energy low [eV] energy high [eV] nuclide \\\n", - "0 1 0.00e+00 6.25e-01 total \n", - "1 1 6.25e-01 2.00e+07 total \n", - "\n", - " score mean std. dev. \n", - "0 (((total / flux) - (absorption / flux)) - (sca... 4.44e-16 1.14e-02 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... -2.55e-15 2.57e-03 " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Use tally arithmetic to compute the difference between the total, absorption and scattering\n", - "difference = total.xs_tally - absorption.xs_tally - scattering.xs_tally\n", - "\n", - "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", - "difference.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, we can use tally arithmetic to compute the ratio of `AbsorptionXS` and `ScatterXS` to the `TotalXS`." - ] - }, { "cell_type": "code", "execution_count": 22, @@ -952,9 +875,9 @@ " 0.000\n", " 6.250000e-01\n", " total\n", - " ((absorption / flux) / (total / flux))\n", - " 0.076103\n", - " 0.000658\n", + " (((total / flux) - (absorption / flux)) - (sca...\n", + " -2.442491e-15\n", + " 0.012361\n", " \n", " \n", " 1\n", @@ -962,9 +885,9 @@ " 0.625\n", " 2.000000e+07\n", " total\n", - " ((absorption / flux) / (total / flux))\n", - " 0.019441\n", - " 0.000093\n", + " (((total / flux) - (absorption / flux)) - (sca...\n", + " 3.996803e-15\n", + " 0.002069\n", " \n", " \n", "\n", @@ -975,9 +898,9 @@ "0 1 0.00e+00 6.25e-01 total \n", "1 1 6.25e-01 2.00e+07 total \n", "\n", - " score mean std. dev. \n", - "0 ((absorption / flux) / (total / flux)) 7.61e-02 6.58e-04 \n", - "1 ((absorption / flux) / (total / flux)) 1.94e-02 9.26e-05 " + " score mean std. dev. \n", + "0 (((total / flux) - (absorption / flux)) - (sca... -2.44e-15 1.24e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... 4.00e-15 2.07e-03 " ] }, "execution_count": 22, @@ -986,11 +909,18 @@ } ], "source": [ - "# Use tally arithmetic to compute the absorption-to-total MGXS ratio\n", - "absorption_to_total = absorption.xs_tally / total.xs_tally\n", + "# Use tally arithmetic to compute the difference between the total, absorption and scattering\n", + "difference = total.xs_tally - absorption.xs_tally - scattering.xs_tally\n", "\n", - "# The absorption-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", - "absorption_to_total.get_pandas_dataframe()" + "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + "difference.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can use tally arithmetic to compute the ratio of `AbsorptionXS` and `ScatterXS` to the `TotalXS`." ] }, { @@ -1035,9 +965,9 @@ " 0.000\n", " 6.250000e-01\n", " total\n", - " ((scatter / flux) / (total / flux))\n", - " 0.923897\n", - " 0.007833\n", + " ((absorption / flux) / (total / flux))\n", + " 0.076155\n", + " 0.000712\n", " \n", " \n", " 1\n", @@ -1045,9 +975,9 @@ " 0.625\n", " 2.000000e+07\n", " total\n", - " ((scatter / flux) / (total / flux))\n", - " 0.980559\n", - " 0.003729\n", + " ((absorption / flux) / (total / flux))\n", + " 0.019411\n", + " 0.000091\n", " \n", " \n", "\n", @@ -1058,9 +988,9 @@ "0 1 0.00e+00 6.25e-01 total \n", "1 1 6.25e-01 2.00e+07 total \n", "\n", - " score mean std. dev. \n", - "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.83e-03 \n", - "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.73e-03 " + " score mean std. dev. \n", + "0 ((absorption / flux) / (total / flux)) 7.62e-02 7.12e-04 \n", + "1 ((absorption / flux) / (total / flux)) 1.94e-02 9.08e-05 " ] }, "execution_count": 23, @@ -1069,24 +999,107 @@ } ], "source": [ - "# Use tally arithmetic to compute the scattering-to-total MGXS ratio\n", - "scattering_to_total = scattering.xs_tally / total.xs_tally\n", + "# Use tally arithmetic to compute the absorption-to-total MGXS ratio\n", + "absorption_to_total = absorption.xs_tally / total.xs_tally\n", "\n", - "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", - "scattering_to_total.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly, we sum the derived scatter-to-total and absorption-to-total ratios to confirm that they sum to unity." + "# The absorption-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "absorption_to_total.get_pandas_dataframe()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide \\\n", + "0 1 0.00e+00 6.25e-01 total \n", + "1 1 6.25e-01 2.00e+07 total \n", + "\n", + " score mean std. dev. \n", + "0 ((scatter / flux) / (total / flux)) 9.24e-01 8.46e-03 \n", + "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.00e-03 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the scattering-to-total MGXS ratio\n", + "scattering_to_total = scattering.xs_tally / total.xs_tally\n", + "\n", + "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "scattering_to_total.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we sum the derived scatter-to-total and absorption-to-total ratios to confirm that they sum to unity." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { @@ -1127,7 +1140,7 @@ " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", " 1.0\n", - " 0.007861\n", + " 0.008492\n", " \n", " \n", " 1\n", @@ -1137,7 +1150,7 @@ " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", " 1.0\n", - " 0.003731\n", + " 0.003005\n", " \n", " \n", "\n", @@ -1149,11 +1162,11 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " score mean std. dev. \n", - "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 7.86e-03 \n", - "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.73e-03 " + "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 8.49e-03 \n", + "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.00e-03 " ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1169,7 +1182,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1183,9 +1196,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.1" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/mgxs-part-ii.ipynb b/mgxs-part-ii.ipynb index 495f012..c674d09 100644 --- a/mgxs-part-ii.ipynb +++ b/mgxs-part-ii.ipynb @@ -44,6 +44,16 @@ "%matplotlib inline" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# create a model object to tie together geometry, materials, settings, and tallies\n", + "model = openmc.Model()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -53,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -85,15 +95,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection\n", - "materials_file = openmc.Materials([fuel, water, zircaloy])\n", - "\n", - "# Export to \"materials.xml\"\n", - "materials_file.export_to_xml()" + "model.materials = openmc.Materials([fuel, water, zircaloy])" ] }, { @@ -105,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -161,15 +168,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry(pin_cell_universe)\n", - "\n", - "# Export to \"geometry.xml\"\n", - "openmc_geometry.export_to_xml()" + "model.geometry = openmc.Geometry(pin_cell_universe)" ] }, { @@ -181,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -191,23 +195,22 @@ "particles = 10000\n", "\n", "# Instantiate a Settings object\n", - "settings_file = openmc.Settings()\n", - "settings_file.batches = batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", - "settings_file.output = {'tallies': True}\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': True}\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings_file.source = openmc.Source(space=uniform_dist)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", "\n", "# Activate tally precision triggers\n", - "settings_file.trigger_active = True\n", - "settings_file.trigger_max_batches = settings_file.batches * 4\n", + "settings.trigger_active = True\n", + "settings.trigger_max_batches = settings.batches * 4\n", "\n", - "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" + "model.settings = settings" ] }, { @@ -219,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -240,12 +243,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Extract all Cells filled by Materials\n", - "openmc_cells = openmc_geometry.get_all_material_cells().values()\n", + "openmc_cells = model.geometry.get_all_material_cells().values()\n", "\n", "# Create dictionary to store multi-group cross sections for all cells\n", "xs_library = {}\n", @@ -269,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -291,33 +294,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=53.\n", - " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", - " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", - " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", - " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", - " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=41.\n", - " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n", - " warn(msg, IDWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", - "tallies_file = openmc.Tallies()\n", + "tallies = openmc.Tallies()\n", "\n", "# Iterate over all cells and cross section types\n", "for cell in openmc_cells:\n", @@ -331,10 +313,9 @@ " \n", " # Add OpenMC tallies to the tallies file for XML generation\n", " for tally in xs_library[cell.id][rxn_type].tallies.values():\n", - " tallies_file.append(tally, merge=True)\n", - "\n", - "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" + " tallies.append(tally, merge=True)\n", + " \n", + "model.tallies = tallies" ] }, { @@ -346,9 +327,29 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=53.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=21.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=41.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=15.\n", + " warn(msg, IDWarning)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -377,211 +378,226 @@ " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 07:08:16\n", - " OpenMP Threads | 4\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1-dev\n", + " Git SHA1 | 1be02f90bd0e9adb5a3311ee77c904a52ca7ebaa\n", + " Date/Time | 2022-05-11 23:33:34\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.20332\n", - " 2/1 1.22209\n", - " 3/1 1.24322\n", - " 4/1 1.21622\n", - " 5/1 1.25850\n", - " 6/1 1.22581\n", - " 7/1 1.21118\n", - " 8/1 1.23377\n", - " 9/1 1.24254\n", - " 10/1 1.21241\n", - " 11/1 1.21042\n", - " 12/1 1.23539 1.22290 +/- 0.01249\n", - " 13/1 1.22436 1.22339 +/- 0.00723\n", - " 14/1 1.22888 1.22476 +/- 0.00529\n", - " 15/1 1.22553 1.22491 +/- 0.00410\n", - " 16/1 1.24194 1.22775 +/- 0.00439\n", - " 17/1 1.24755 1.23058 +/- 0.00466\n", - " 18/1 1.21117 1.22815 +/- 0.00471\n", - " 19/1 1.22530 1.22784 +/- 0.00417\n", - " 20/1 1.20762 1.22582 +/- 0.00424\n", - " 21/1 1.20377 1.22381 +/- 0.00433\n", - " 22/1 1.24305 1.22541 +/- 0.00426\n", - " 23/1 1.22434 1.22533 +/- 0.00392\n", - " 24/1 1.22937 1.22562 +/- 0.00364\n", - " 25/1 1.22458 1.22555 +/- 0.00339\n", - " 26/1 1.18978 1.22332 +/- 0.00388\n", - " 27/1 1.20582 1.22229 +/- 0.00379\n", - " 28/1 1.22719 1.22256 +/- 0.00358\n", - " 29/1 1.21307 1.22206 +/- 0.00343\n", - " 30/1 1.20915 1.22141 +/- 0.00331\n", - " 31/1 1.22799 1.22173 +/- 0.00317\n", - " 32/1 1.21251 1.22131 +/- 0.00305\n", - " 33/1 1.20540 1.22062 +/- 0.00299\n", - " 34/1 1.20052 1.21978 +/- 0.00299\n", - " 35/1 1.24552 1.22081 +/- 0.00304\n", - " 36/1 1.21685 1.22066 +/- 0.00293\n", - " 37/1 1.22395 1.22078 +/- 0.00282\n", - " 38/1 1.22379 1.22089 +/- 0.00272\n", - " 39/1 1.20951 1.22049 +/- 0.00265\n", - " 40/1 1.25199 1.22154 +/- 0.00277\n", - " 41/1 1.23243 1.22190 +/- 0.00270\n", - " 42/1 1.20973 1.22152 +/- 0.00264\n", - " 43/1 1.24682 1.22228 +/- 0.00268\n", - " 44/1 1.20694 1.22183 +/- 0.00263\n", - " 45/1 1.22196 1.22183 +/- 0.00256\n", - " 46/1 1.20687 1.22142 +/- 0.00252\n", - " 47/1 1.22023 1.22139 +/- 0.00245\n", - " 48/1 1.22204 1.22140 +/- 0.00239\n", - " 49/1 1.22077 1.22139 +/- 0.00232\n", - " 50/1 1.23166 1.22164 +/- 0.00228\n", - " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 53\n", - " The estimated number of batches is 66\n", - " Creating state point statepoint.050.h5...\n", - " 51/1 1.20071 1.22113 +/- 0.00228\n", - " Triggers unsatisfied, max unc./thresh. is 1.26577 for flux in tally 53\n", - " The estimated number of batches is 76\n", - " 52/1 1.21423 1.22097 +/- 0.00223\n", - " Triggers unsatisfied, max unc./thresh. is 1.24 for flux in tally 53\n", - " The estimated number of batches is 75\n", - " 53/1 1.25595 1.22178 +/- 0.00233\n", - " Triggers unsatisfied, max unc./thresh. is 1.2112 for flux in tally 53\n", - " The estimated number of batches is 74\n", - " 54/1 1.21806 1.22170 +/- 0.00227\n", - " Triggers unsatisfied, max unc./thresh. is 1.18484 for flux in tally 53\n", - " The estimated number of batches is 72\n", - " 55/1 1.22911 1.22186 +/- 0.00223\n", - " Triggers unsatisfied, max unc./thresh. is 1.1596 for flux in tally 53\n", - " The estimated number of batches is 71\n", - " 56/1 1.23054 1.22205 +/- 0.00219\n", - " Triggers unsatisfied, max unc./thresh. is 1.13453 for flux in tally 53\n", - " The estimated number of batches is 70\n", - " 57/1 1.19384 1.22145 +/- 0.00222\n", - " Triggers unsatisfied, max unc./thresh. is 1.11914 for flux in tally 53\n", - " The estimated number of batches is 69\n", - " 58/1 1.20625 1.22114 +/- 0.00220\n", - " Triggers unsatisfied, max unc./thresh. is 1.11471 for flux in tally 53\n", - " The estimated number of batches is 70\n", - " 59/1 1.21977 1.22111 +/- 0.00216\n", - " Triggers unsatisfied, max unc./thresh. is 1.10334 for flux in tally 53\n", - " The estimated number of batches is 70\n", - " 60/1 1.20813 1.22085 +/- 0.00213\n", - " Triggers unsatisfied, max unc./thresh. is 1.09813 for flux in tally 53\n", - " The estimated number of batches is 71\n", - " 61/1 1.22077 1.22085 +/- 0.00209\n", - " Triggers unsatisfied, max unc./thresh. is 1.10221 for flux in tally 53\n", - " The estimated number of batches is 72\n", - " 62/1 1.21956 1.22082 +/- 0.00205\n", - " Triggers unsatisfied, max unc./thresh. is 1.11395 for flux in tally 53\n", - " The estimated number of batches is 75\n", - " 63/1 1.22360 1.22087 +/- 0.00201\n", - " Triggers unsatisfied, max unc./thresh. is 1.09283 for flux in tally 53\n", - " The estimated number of batches is 74\n", - " 64/1 1.23955 1.22122 +/- 0.00200\n", - " Triggers unsatisfied, max unc./thresh. is 1.07416 for flux in tally 53\n", - " The estimated number of batches is 73\n", - " 65/1 1.21143 1.22104 +/- 0.00197\n", - " Triggers unsatisfied, max unc./thresh. is 1.06461 for flux in tally 53\n", - " The estimated number of batches is 73\n", - " 66/1 1.21791 1.22099 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 53\n", - " The estimated number of batches is 82\n", - " 67/1 1.24897 1.22148 +/- 0.00196\n", - " Triggers unsatisfied, max unc./thresh. is 1.11277 for flux in tally 53\n", + " 1/1 1.23252\n", + " 2/1 1.21506\n", + " 3/1 1.20897\n", + " 4/1 1.23803\n", + " 5/1 1.22267\n", + " 6/1 1.20192\n", + " 7/1 1.23191\n", + " 8/1 1.21341\n", + " 9/1 1.23533\n", + " 10/1 1.21186\n", + " 11/1 1.24409\n", + " 12/1 1.24750 1.24579 +/- 0.00171\n", + " 13/1 1.22880 1.24013 +/- 0.00575\n", + " 14/1 1.22960 1.23750 +/- 0.00484\n", + " 15/1 1.21322 1.23264 +/- 0.00614\n", + " 16/1 1.21437 1.22960 +/- 0.00586\n", + " 17/1 1.23899 1.23094 +/- 0.00513\n", + " 18/1 1.23301 1.23120 +/- 0.00445\n", + " 19/1 1.22845 1.23089 +/- 0.00394\n", + " 20/1 1.20047 1.22785 +/- 0.00465\n", + " 21/1 1.21548 1.22673 +/- 0.00436\n", + " 22/1 1.23362 1.22730 +/- 0.00402\n", + " 23/1 1.20552 1.22563 +/- 0.00406\n", + " 24/1 1.24002 1.22665 +/- 0.00390\n", + " 25/1 1.19767 1.22472 +/- 0.00411\n", + " 26/1 1.23254 1.22521 +/- 0.00388\n", + " 27/1 1.20023 1.22374 +/- 0.00393\n", + " 28/1 1.22493 1.22381 +/- 0.00370\n", + " 29/1 1.24522 1.22493 +/- 0.00368\n", + " 30/1 1.22298 1.22484 +/- 0.00349\n", + " 31/1 1.26165 1.22659 +/- 0.00375\n", + " 32/1 1.21728 1.22617 +/- 0.00361\n", + " 33/1 1.20569 1.22528 +/- 0.00356\n", + " 34/1 1.21975 1.22505 +/- 0.00341\n", + " 35/1 1.19333 1.22378 +/- 0.00351\n", + " 36/1 1.20750 1.22315 +/- 0.00343\n", + " 37/1 1.22584 1.22325 +/- 0.00330\n", + " 38/1 1.21884 1.22309 +/- 0.00319\n", + " 39/1 1.20394 1.22243 +/- 0.00315\n", + " 40/1 1.20780 1.22194 +/- 0.00308\n", + " 41/1 1.27678 1.22371 +/- 0.00346\n", + " 42/1 1.22116 1.22363 +/- 0.00335\n", + " 43/1 1.22833 1.22378 +/- 0.00325\n", + " 44/1 1.20314 1.22317 +/- 0.00321\n", + " 45/1 1.21435 1.22292 +/- 0.00313\n", + " 46/1 1.22780 1.22305 +/- 0.00305\n", + " 47/1 1.22355 1.22307 +/- 0.00296\n", + " 48/1 1.22122 1.22302 +/- 0.00288\n", + " 49/1 1.21492 1.22281 +/- 0.00282\n", + " 50/1 1.21755 1.22268 +/- 0.00275\n", + " Triggers unsatisfied, max unc./thresh. is 1.3257681705281088 for scatter in\n", + " tally 44\n", " The estimated number of batches is 81\n", - " 68/1 1.22221 1.22149 +/- 0.00193\n", - " Triggers unsatisfied, max unc./thresh. is 1.09514 for flux in tally 53\n", + " Creating state point statepoint.050.h5...\n", + " 51/1 1.20449 1.22224 +/- 0.00272\n", + " Triggers unsatisfied, max unc./thresh. is 1.2974261552000377 for scatter in\n", + " tally 44\n", " The estimated number of batches is 80\n", - " 69/1 1.25627 1.22208 +/- 0.00199\n", - " Triggers unsatisfied, max unc./thresh. is 1.07653 for flux in tally 53\n", + " 52/1 1.21574 1.22208 +/- 0.00266\n", + " Triggers unsatisfied, max unc./thresh. is 1.27346043172062 for scatter in tally\n", + " 44\n", " The estimated number of batches is 79\n", - " 70/1 1.21493 1.22196 +/- 0.00196\n", - " Triggers unsatisfied, max unc./thresh. is 1.12831 for flux in tally 53\n", - " The estimated number of batches is 87\n", - " 71/1 1.23406 1.22216 +/- 0.00193\n", - " Triggers unsatisfied, max unc./thresh. is 1.11005 for flux in tally 53\n", + " 53/1 1.24344 1.22258 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 1.30084946898447 for scatter in tally\n", + " 44\n", + " The estimated number of batches is 83\n", + " 54/1 1.22925 1.22273 +/- 0.00258\n", + " Triggers unsatisfied, max unc./thresh. is 1.2803980121540603 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 83\n", + " 55/1 1.22680 1.22282 +/- 0.00253\n", + " Triggers unsatisfied, max unc./thresh. is 1.2610038284810392 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 82\n", + " 56/1 1.20922 1.22252 +/- 0.00249\n", + " Triggers unsatisfied, max unc./thresh. is 1.2485874299619153 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 82\n", + " 57/1 1.20023 1.22205 +/- 0.00248\n", + " Triggers unsatisfied, max unc./thresh. is 1.2657904522202317 for scatter in\n", + " tally 44\n", " The estimated number of batches is 86\n", - " 72/1 1.23842 1.22242 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 1.09352 for flux in tally 53\n", + " 58/1 1.24152 1.22246 +/- 0.00246\n", + " Triggers unsatisfied, max unc./thresh. is 1.2574813789485386 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 86\n", + " 59/1 1.23448 1.22270 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 1.2322770803354832 for scatter in\n", + " tally 44\n", " The estimated number of batches is 85\n", - " 73/1 1.24542 1.22279 +/- 0.00193\n", - " Triggers unsatisfied, max unc./thresh. is 1.08766 for flux in tally 53\n", + " 60/1 1.23315 1.22291 +/- 0.00239\n", + " Triggers unsatisfied, max unc./thresh. is 1.2177522287441334 for scatter in\n", + " tally 44\n", " The estimated number of batches is 85\n", - " 74/1 1.21314 1.22263 +/- 0.00190\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Triggers unsatisfied, max unc./thresh. is 1.07419 for flux in tally 53\n", + " 61/1 1.22615 1.22297 +/- 0.00234\n", + " Triggers unsatisfied, max unc./thresh. is 1.2140211030926062 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 86\n", + " 62/1 1.21352 1.22279 +/- 0.00230\n", + " Triggers unsatisfied, max unc./thresh. is 1.1990750546873084 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 85\n", + " 63/1 1.20786 1.22251 +/- 0.00227\n", + " Triggers unsatisfied, max unc./thresh. is 1.1785252216705007 for scatter in\n", + " tally 44\n", " The estimated number of batches is 84\n", - " 75/1 1.26484 1.22328 +/- 0.00198\n", - " Triggers unsatisfied, max unc./thresh. is 1.06788 for flux in tally 53\n", - " The estimated number of batches is 85\n", - " 76/1 1.22243 1.22327 +/- 0.00195\n", - " Triggers unsatisfied, max unc./thresh. is 1.05164 for flux in tally 53\n", + " 64/1 1.23456 1.22273 +/- 0.00224\n", + " Triggers unsatisfied, max unc./thresh. is 1.159682464507127 for scatter in\n", + " tally 44\n", " The estimated number of batches is 83\n", - " 77/1 1.21865 1.22320 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 1.04022 for flux in tally 53\n", - " The estimated number of batches is 83\n", - " 78/1 1.23500 1.22338 +/- 0.00190\n", - " Triggers unsatisfied, max unc./thresh. is 1.0275 for flux in tally 53\n", + " 65/1 1.20456 1.22240 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 1.1389064839511738 for scatter in\n", + " tally 44\n", " The estimated number of batches is 82\n", - " 79/1 1.22125 1.22334 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 1.0283 for flux in tally 53\n", + " 66/1 1.23242 1.22258 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 1.1387956990317125 for scatter in\n", + " tally 44\n", " The estimated number of batches is 83\n", - " 80/1 1.23793 1.22355 +/- 0.00186\n", - " Triggers unsatisfied, max unc./thresh. is 1.01363 for flux in tally 53\n", + " 67/1 1.22093 1.22255 +/- 0.00216\n", + " Triggers unsatisfied, max unc./thresh. is 1.1220389917124391 for scatter in\n", + " tally 44\n", " The estimated number of batches is 82\n", - " 81/1 1.24238 1.22382 +/- 0.00185\n", - " Triggers unsatisfied, max unc./thresh. is 1.01172 for flux in tally 53\n", + " 68/1 1.23100 1.22270 +/- 0.00212\n", + " Triggers unsatisfied, max unc./thresh. is 1.1027057175205282 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 81\n", + " 69/1 1.21663 1.22260 +/- 0.00209\n", + " Triggers unsatisfied, max unc./thresh. is 1.0855915700896739 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 80\n", + " 70/1 1.23880 1.22287 +/- 0.00207\n", + " Triggers unsatisfied, max unc./thresh. is 1.0998291692386097 for scatter in\n", + " tally 44\n", " The estimated number of batches is 83\n", - " 82/1 1.23493 1.22397 +/- 0.00183\n", - " Triggers satisfied for batch 82\n", - " Creating state point statepoint.082.h5...\n", + " 71/1 1.22575 1.22291 +/- 0.00204\n", + " Triggers unsatisfied, max unc./thresh. is 1.0819812352128337 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 82\n", + " 72/1 1.21793 1.22283 +/- 0.00201\n", + " Triggers unsatisfied, max unc./thresh. is 1.0689822896008951 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 81\n", + " 73/1 1.23328 1.22300 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 1.0583629318500747 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 81\n", + " 74/1 1.23432 1.22317 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 1.0438223698590827 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 80\n", + " 75/1 1.21756 1.22309 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 1.0305631836964941 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 80\n", + " 76/1 1.21626 1.22298 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 1.0150302883111983 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 78\n", + " 77/1 1.22540 1.22302 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 1.0011653901793245 for scatter in\n", + " tally 44\n", + " The estimated number of batches is 78\n", + " 78/1 1.20985 1.22283 +/- 0.00186\n", + " Triggers satisfied for batch 78\n", + " Creating state point statepoint.078.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 9.5644e-01 seconds\n", - " Reading cross sections = 9.0579e-01 seconds\n", - " Total time in simulation = 9.9887e+01 seconds\n", - " Time in transport only = 9.9333e+01 seconds\n", - " Time in inactive batches = 5.4841e+00 seconds\n", - " Time in active batches = 9.4403e+01 seconds\n", - " Time synchronizing fission bank = 7.3998e-02 seconds\n", - " Sampling source sites = 5.9021e-02 seconds\n", - " SEND/RECV source sites = 1.4787e-02 seconds\n", - " Time accumulating tallies = 1.2234e-03 seconds\n", - " Total time for finalization = 2.8416e-02 seconds\n", - " Total time elapsed = 1.0094e+02 seconds\n", - " Calculation Rate (inactive) = 18234.5 particles/second\n", - " Calculation Rate (active) = 7626.89 particles/second\n", + " Total time for initialization = 1.2097e-01 seconds\n", + " Reading cross sections = 1.1640e-01 seconds\n", + " Total time in simulation = 2.9732e+01 seconds\n", + " Time in transport only = 2.9681e+01 seconds\n", + " Time in inactive batches = 1.8751e+00 seconds\n", + " Time in active batches = 2.7857e+01 seconds\n", + " Time synchronizing fission bank = 3.2011e-02 seconds\n", + " Sampling source sites = 2.7749e-02 seconds\n", + " SEND/RECV source sites = 4.2411e-03 seconds\n", + " Time accumulating tallies = 1.1293e-03 seconds\n", + " Time writing statepoints = 7.8133e-03 seconds\n", + " Total time for finalization = 1.1651e-03 seconds\n", + " Total time elapsed = 2.9862e+01 seconds\n", + " Calculation Rate (inactive) = 53331.2 particles/second\n", + " Calculation Rate (active) = 24410.4 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.22348 +/- 0.00169\n", - " k-effective (Track-length) = 1.22397 +/- 0.00183\n", - " k-effective (Absorption) = 1.22467 +/- 0.00117\n", - " Combined k-effective = 1.22448 +/- 0.00108\n", + " k-effective (Collision) = 1.22330 +/- 0.00172\n", + " k-effective (Track-length) = 1.22283 +/- 0.00186\n", + " k-effective (Absorption) = 1.22474 +/- 0.00151\n", + " Combined k-effective = 1.22401 +/- 0.00125\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -589,7 +605,7 @@ ], "source": [ "# Run OpenMC\n", - "openmc.run()" + "sp_file = model.run()" ] }, { @@ -608,12 +624,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Load the last statepoint file\n", - "sp = openmc.StatePoint('statepoint.082.h5')" + "sp = openmc.StatePoint(sp_file)" ] }, { @@ -625,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -658,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -671,25 +687,25 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [barns]:\n", - " Group 1 [821000.0 - 20000000.0eV]:\t3.30e+00 +/- 2.14e-01%\n", - " Group 2 [5530.0 - 821000.0 eV]:\t3.96e+00 +/- 1.33e-01%\n", - " Group 3 [4.0 - 5530.0 eV]:\t5.50e+01 +/- 2.29e-01%\n", - " Group 4 [0.625 - 4.0 eV]:\t8.85e+01 +/- 3.10e-01%\n", - " Group 5 [0.28 - 0.625 eV]:\t2.90e+02 +/- 3.94e-01%\n", - " Group 6 [0.14 - 0.28 eV]:\t4.49e+02 +/- 4.12e-01%\n", - " Group 7 [0.058 - 0.14 eV]:\t6.87e+02 +/- 3.01e-01%\n", - " Group 8 [0.0 - 0.058 eV]:\t1.44e+03 +/- 2.79e-01%\n", + " Group 1 [821000.0 - 20000000.0eV]:\t3.30e+00 +/- 2.29e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t3.96e+00 +/- 1.41e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t5.50e+01 +/- 2.07e-01%\n", + " Group 4 [0.625 - 4.0 eV]:\t8.83e+01 +/- 3.15e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t2.90e+02 +/- 5.04e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t4.49e+02 +/- 4.81e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t6.87e+02 +/- 2.93e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t1.44e+03 +/- 2.76e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [barns]:\n", - " Group 1 [821000.0 - 20000000.0eV]:\t1.06e+00 +/- 2.53e-01%\n", - " Group 2 [5530.0 - 821000.0 eV]:\t1.21e-03 +/- 2.60e-01%\n", - " Group 3 [4.0 - 5530.0 eV]:\t5.73e-04 +/- 2.93e+00%\n", - " Group 4 [0.625 - 4.0 eV]:\t6.54e-06 +/- 2.72e-01%\n", - " Group 5 [0.28 - 0.625 eV]:\t1.07e-05 +/- 3.83e-01%\n", - " Group 6 [0.14 - 0.28 eV]:\t1.55e-05 +/- 4.13e-01%\n", - " Group 7 [0.058 - 0.14 eV]:\t2.30e-05 +/- 3.01e-01%\n", - " Group 8 [0.0 - 0.058 eV]:\t4.24e-05 +/- 2.79e-01%\n", + " Group 1 [821000.0 - 20000000.0eV]:\t1.06e+00 +/- 2.65e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t1.21e-03 +/- 2.93e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t5.56e-04 +/- 3.72e+00%\n", + " Group 4 [0.625 - 4.0 eV]:\t6.54e-06 +/- 2.87e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t1.07e-05 +/- 4.98e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t1.55e-05 +/- 4.79e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t2.30e-05 +/- 2.93e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t4.24e-05 +/- 2.75e-01%\n", "\n", "\n", "\n" @@ -710,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -722,14 +738,14 @@ "\tDomain Type =\tcell\n", "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [821000.0 - 20000000.0eV]:\t2.52e-02 +/- 2.41e-01%\n", - " Group 2 [5530.0 - 821000.0 eV]:\t1.51e-03 +/- 1.31e-01%\n", - " Group 3 [4.0 - 5530.0 eV]:\t2.07e-02 +/- 2.29e-01%\n", - " Group 4 [0.625 - 4.0 eV]:\t3.32e-02 +/- 3.10e-01%\n", - " Group 5 [0.28 - 0.625 eV]:\t1.09e-01 +/- 3.94e-01%\n", - " Group 6 [0.14 - 0.28 eV]:\t1.69e-01 +/- 4.12e-01%\n", - " Group 7 [0.058 - 0.14 eV]:\t2.58e-01 +/- 3.01e-01%\n", - " Group 8 [0.0 - 0.058 eV]:\t5.40e-01 +/- 2.79e-01%\n", + " Group 1 [821000.0 - 20000000.0eV]:\t2.52e-02 +/- 2.52e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t1.51e-03 +/- 1.39e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t2.06e-02 +/- 2.07e-01%\n", + " Group 4 [0.625 - 4.0 eV]:\t3.31e-02 +/- 3.15e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t1.09e-01 +/- 5.04e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t1.69e-01 +/- 4.81e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t2.58e-01 +/- 2.93e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t5.40e-01 +/- 2.76e-01%\n", "\n", "\n", "\n" @@ -750,7 +766,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -789,8 +805,8 @@ " 1\n", " 1\n", " H1\n", - " 0.233991\n", - " 0.003752\n", + " 0.234171\n", + " 0.003922\n", " \n", " \n", " 127\n", @@ -798,8 +814,8 @@ " 1\n", " 1\n", " O16\n", - " 1.569288\n", - " 0.006360\n", + " 1.559702\n", + " 0.005620\n", " \n", " \n", " 124\n", @@ -807,8 +823,8 @@ " 1\n", " 2\n", " H1\n", - " 1.587279\n", - " 0.003098\n", + " 1.589382\n", + " 0.002801\n", " \n", " \n", " 125\n", @@ -816,8 +832,8 @@ " 1\n", " 2\n", " O16\n", - " 0.285599\n", - " 0.001422\n", + " 0.285545\n", + " 0.001472\n", " \n", " \n", " 122\n", @@ -825,8 +841,8 @@ " 1\n", " 3\n", " H1\n", - " 0.010482\n", - " 0.000220\n", + " 0.010727\n", + " 0.000239\n", " \n", " \n", " 123\n", @@ -843,8 +859,8 @@ " 1\n", " 4\n", " H1\n", - " 0.000009\n", - " 0.000006\n", + " 0.000005\n", + " 0.000005\n", " \n", " \n", " 121\n", @@ -861,8 +877,8 @@ " 1\n", " 5\n", " H1\n", - " 0.000005\n", - " 0.000005\n", + " 0.000000\n", + " 0.000000\n", " \n", " \n", " 119\n", @@ -879,19 +895,19 @@ ], "text/plain": [ " cell group in group out nuclide mean std. dev.\n", - "126 3 1 1 H1 0.233991 0.003752\n", - "127 3 1 1 O16 1.569288 0.006360\n", - "124 3 1 2 H1 1.587279 0.003098\n", - "125 3 1 2 O16 0.285599 0.001422\n", - "122 3 1 3 H1 0.010482 0.000220\n", + "126 3 1 1 H1 0.234171 0.003922\n", + "127 3 1 1 O16 1.559702 0.005620\n", + "124 3 1 2 H1 1.589382 0.002801\n", + "125 3 1 2 O16 0.285545 0.001472\n", + "122 3 1 3 H1 0.010727 0.000239\n", "123 3 1 3 O16 0.000000 0.000000\n", - "120 3 1 4 H1 0.000009 0.000006\n", + "120 3 1 4 H1 0.000005 0.000005\n", "121 3 1 4 O16 0.000000 0.000000\n", - "118 3 1 5 H1 0.000005 0.000005\n", + "118 3 1 5 H1 0.000000 0.000000\n", "119 3 1 5 O16 0.000000 0.000000" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -911,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -931,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -944,18 +960,18 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t7.79e-03 +/- 2.12e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t1.82e-01 +/- 1.92e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.75e-03 +/- 2.12e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.82e-01 +/- 1.93e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t2.17e-01 +/- 1.12e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t2.53e-01 +/- 1.89e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t2.17e-01 +/- 1.20e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t2.54e-01 +/- 1.92e-01%\n", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t1.45e-01 +/- 1.12e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t1.74e-01 +/- 2.03e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t1.45e-01 +/- 1.17e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.74e-01 +/- 2.07e-01%\n", "\n", "\n", "\n" @@ -968,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1005,48 +1021,48 @@ " 1\n", " 1\n", " U235\n", - " 20.763062\n", - " 0.044093\n", + " 20.658286\n", + " 0.043889\n", " \n", " \n", " 4\n", " 1\n", " 1\n", " U238\n", - " 9.579086\n", - " 0.010757\n", + " 9.584869\n", + " 0.011511\n", " \n", " \n", " 5\n", " 1\n", " 1\n", " O16\n", - " 3.157274\n", - " 0.003531\n", + " 3.157267\n", + " 0.003680\n", " \n", " \n", " 0\n", " 1\n", " 2\n", " U235\n", - " 485.349036\n", - " 0.930937\n", + " 485.233252\n", + " 0.935407\n", " \n", " \n", " 1\n", " 1\n", " 2\n", " U238\n", - " 11.199167\n", - " 0.021167\n", + " 11.209561\n", + " 0.021499\n", " \n", " \n", " 2\n", " 1\n", " 2\n", " O16\n", - " 3.788383\n", - " 0.007676\n", + " 3.790416\n", + " 0.007835\n", " \n", " \n", "\n", @@ -1054,15 +1070,15 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 1 1 U235 20.763062 0.044093\n", - "4 1 1 U238 9.579086 0.010757\n", - "5 1 1 O16 3.157274 0.003531\n", - "0 1 2 U235 485.349036 0.930937\n", - "1 1 2 U238 11.199167 0.021167\n", - "2 1 2 O16 3.788383 0.007676" + "3 1 1 U235 20.658286 0.043889\n", + "4 1 1 U238 9.584869 0.011511\n", + "5 1 1 O16 3.157267 0.003680\n", + "0 1 2 U235 485.233252 0.935407\n", + "1 1 2 U238 11.209561 0.021499\n", + "2 1 2 O16 3.790416 0.007835" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1088,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1105,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1148,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1173,7 +1189,6 @@ "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", "[ NORMAL ] Initializing FSR lookup vectors\n", "[ NORMAL ] Total number of FSRs 3\n", - "[ RESULT ] Total Track Generation & Segmentation Time...........2.5566E-02 sec\n", "[ NORMAL ] Initializing MOC eigenvalue solver...\n", "[ NORMAL ] Initializing solver arrays...\n", "[ NORMAL ] Centering segments around FSR centroid...\n", @@ -1188,490 +1203,480 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0: k_eff = 0.423133 res = 5.671E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... -57686 D.R. = 0.00\n", - "[ NORMAL ] Iteration 1: k_eff = 0.475953 res = 2.442E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 5282 D.R. = 4.31\n", - "[ NORMAL ] Iteration 2: k_eff = 0.491468 res = 4.764E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1551 D.R. = 1.95\n", - "[ NORMAL ] Iteration 3: k_eff = 0.487446 res = 2.253E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -402 D.R. = 0.47\n", - "[ NORMAL ] Iteration 4: k_eff = 0.483930 res = 6.957E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... -351 D.R. = 0.31\n", - "[ NORMAL ] Iteration 5: k_eff = 0.477280 res = 3.902E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -665 D.R. = 5.61\n", - "[ NORMAL ] Iteration 6: k_eff = 0.468938 res = 3.161E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -834 D.R. = 0.81\n", - "[ NORMAL ] Iteration 7: k_eff = 0.460319 res = 2.480E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -861 D.R. = 0.78\n", - "[ NORMAL ] Iteration 8: k_eff = 0.450591 res = 9.377E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... -972 D.R. = 0.38\n", - "[ NORMAL ] Iteration 9: k_eff = 0.441377 res = 3.085E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -921 D.R. = 3.29\n", - "[ NORMAL ] Iteration 10: k_eff = 0.431990 res = 1.028E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -938 D.R. = 0.33\n", - "[ NORMAL ] Iteration 11: k_eff = 0.422932 res = 1.180E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -905 D.R. = 1.15\n", - "[ NORMAL ] Iteration 12: k_eff = 0.414487 res = 1.633E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -844 D.R. = 1.38\n", - "[ NORMAL ] Iteration 13: k_eff = 0.406708 res = 1.754E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -777 D.R. = 1.07\n", - "[ NORMAL ] Iteration 14: k_eff = 0.399378 res = 5.021E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -732 D.R. = 2.86\n", - "[ NORMAL ] Iteration 15: k_eff = 0.393067 res = 9.074E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... -631 D.R. = 0.18\n", - "[ NORMAL ] Iteration 16: k_eff = 0.387427 res = 4.840E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... -564 D.R. = 0.53\n", - "[ NORMAL ] Iteration 17: k_eff = 0.382668 res = 2.299E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -475 D.R. = 4.75\n", - "[ NORMAL ] Iteration 18: k_eff = 0.378741 res = 1.573E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -392 D.R. = 0.68\n", - "[ NORMAL ] Iteration 19: k_eff = 0.375642 res = 7.017E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -309 D.R. = 4.46\n", - "[ NORMAL ] Iteration 20: k_eff = 0.373489 res = 4.053E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -215 D.R. = 0.58\n", - "[ NORMAL ] Iteration 21: k_eff = 0.372357 res = 4.235E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -113 D.R. = 1.04\n", - "[ NORMAL ] Iteration 22: k_eff = 0.371974 res = 6.352E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -38 D.R. = 1.50\n", - "[ NORMAL ] Iteration 23: k_eff = 0.372581 res = 3.267E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 60 D.R. = 0.51\n", - "[ NORMAL ] Iteration 24: k_eff = 0.374056 res = 1.573E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 147 D.R. = 0.48\n", - "[ NORMAL ] Iteration 25: k_eff = 0.376384 res = 3.630E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 232 D.R. = 2.31\n", - "[ NORMAL ] Iteration 26: k_eff = 0.379563 res = 2.420E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 317 D.R. = 0.07\n", - "[ NORMAL ] Iteration 27: k_eff = 0.383583 res = 3.146E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 401 D.R. = 13.00\n", - "[ NORMAL ] Iteration 28: k_eff = 0.388380 res = 1.089E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 479 D.R. = 0.35\n", - "[ NORMAL ] Iteration 29: k_eff = 0.393938 res = 6.049E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 555 D.R. = 5.56\n", - "[ NORMAL ] Iteration 30: k_eff = 0.400234 res = 3.267E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 629 D.R. = 0.54\n", - "[ NORMAL ] Iteration 31: k_eff = 0.407235 res = 2.420E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 700 D.R. = 0.74\n", - "[ NORMAL ] Iteration 32: k_eff = 0.414884 res = 1.815E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 764 D.R. = 0.75\n", - "[ NORMAL ] Iteration 33: k_eff = 0.423172 res = 7.259E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 828 D.R. = 0.40\n", - "[ NORMAL ] Iteration 34: k_eff = 0.432051 res = 6.049E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 887 D.R. = 8.33\n", - "[ NORMAL ] Iteration 35: k_eff = 0.441471 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 942 D.R. = 0.96\n", - "[ NORMAL ] Iteration 36: k_eff = 0.451430 res = 2.662E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 995 D.R. = 0.46\n", - "[ NORMAL ] Iteration 37: k_eff = 0.461853 res = 8.227E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1042 D.R. = 3.09\n", - "[ NORMAL ] Iteration 38: k_eff = 0.472730 res = 5.928E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1087 D.R. = 0.72\n", - "[ NORMAL ] Iteration 39: k_eff = 0.484006 res = 2.299E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1127 D.R. = 0.39\n", - "[ NORMAL ] Iteration 40: k_eff = 0.495653 res = 2.299E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1164 D.R. = 1.00\n", - "[ NORMAL ] Iteration 41: k_eff = 0.507634 res = 7.017E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1198 D.R. = 3.05\n", - "[ NORMAL ] Iteration 42: k_eff = 0.519914 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1227 D.R. = 0.28\n", - "[ NORMAL ] Iteration 43: k_eff = 0.532458 res = 1.089E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1254 D.R. = 0.56\n", - "[ NORMAL ] Iteration 44: k_eff = 0.545234 res = 6.291E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1277 D.R. = 5.78\n", - "[ NORMAL ] Iteration 45: k_eff = 0.558210 res = 3.509E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1297 D.R. = 0.56\n", - "[ NORMAL ] Iteration 46: k_eff = 0.571353 res = 3.025E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1314 D.R. = 0.86\n", - "[ NORMAL ] Iteration 47: k_eff = 0.584635 res = 7.259E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 1328 D.R. = 0.24\n", - "[ NORMAL ] Iteration 48: k_eff = 0.598027 res = 4.961E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1339 D.R. = 6.83\n", - "[ NORMAL ] Iteration 49: k_eff = 0.611500 res = 9.014E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1347 D.R. = 1.82\n", - "[ NORMAL ] Iteration 50: k_eff = 0.625029 res = 5.203E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1352 D.R. = 0.58\n", - "[ NORMAL ] Iteration 51: k_eff = 0.638590 res = 1.512E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1356 D.R. = 0.29\n", - "[ NORMAL ] Iteration 52: k_eff = 0.652158 res = 2.359E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1356 D.R. = 1.56\n", - "[ NORMAL ] Iteration 53: k_eff = 0.665710 res = 4.598E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1355 D.R. = 1.95\n", - "[ NORMAL ] Iteration 54: k_eff = 0.679228 res = 2.783E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1351 D.R. = 0.61\n", - "[ NORMAL ] Iteration 55: k_eff = 0.692689 res = 2.117E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1346 D.R. = 0.76\n", - "[ NORMAL ] Iteration 56: k_eff = 0.706075 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1338 D.R. = 0.91\n", - "[ NORMAL ] Iteration 57: k_eff = 0.719370 res = 5.505E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1329 D.R. = 2.84\n", - "[ NORMAL ] Iteration 58: k_eff = 0.732556 res = 2.238E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1318 D.R. = 0.41\n", - "[ NORMAL ] Iteration 59: k_eff = 0.745619 res = 7.864E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 1306 D.R. = 0.35\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Iteration 60: k_eff = 0.758546 res = 4.477E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1292 D.R. = 5.69\n", - "[ NORMAL ] Iteration 61: k_eff = 0.771323 res = 4.658E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1277 D.R. = 1.04\n", - "[ NORMAL ] Iteration 62: k_eff = 0.783939 res = 9.679E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 1261 D.R. = 0.21\n", - "[ NORMAL ] Iteration 63: k_eff = 0.796383 res = 4.235E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 1244 D.R. = 0.44\n", - "[ NORMAL ] Iteration 64: k_eff = 0.808646 res = 9.074E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 1226 D.R. = 2.14\n", - "[ NORMAL ] Iteration 65: k_eff = 0.820719 res = 6.412E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1207 D.R. = 7.07\n", - "[ NORMAL ] Iteration 66: k_eff = 0.832594 res = 2.420E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 1187 D.R. = 0.04\n", - "[ NORMAL ] Iteration 67: k_eff = 0.844264 res = 2.299E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1167 D.R. = 9.50\n", - "[ NORMAL ] Iteration 68: k_eff = 0.855724 res = 5.928E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1146 D.R. = 2.58\n", - "[ NORMAL ] Iteration 69: k_eff = 0.866968 res = 6.049E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1124 D.R. = 1.02\n", - "[ NORMAL ] Iteration 70: k_eff = 0.877992 res = 2.722E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1102 D.R. = 0.45\n", - "[ NORMAL ] Iteration 71: k_eff = 0.888792 res = 1.633E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1079 D.R. = 0.60\n", - "[ NORMAL ] Iteration 72: k_eff = 0.899364 res = 2.117E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1057 D.R. = 1.30\n", - "[ NORMAL ] Iteration 73: k_eff = 0.909708 res = 3.327E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1034 D.R. = 1.57\n", - "[ NORMAL ] Iteration 74: k_eff = 0.919819 res = 4.477E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1011 D.R. = 1.35\n", - "[ NORMAL ] Iteration 75: k_eff = 0.929699 res = 3.569E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 987 D.R. = 0.80\n", - "[ NORMAL ] Iteration 76: k_eff = 0.939346 res = 4.840E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 964 D.R. = 0.14\n", - "[ NORMAL ] Iteration 77: k_eff = 0.948758 res = 4.840E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 941 D.R. = 1.00\n", - "[ NORMAL ] Iteration 78: k_eff = 0.957938 res = 6.049E-10 delta-k (pcm) =\n", - "[ NORMAL ] ... 917 D.R. = 0.12\n", - "[ NORMAL ] Iteration 79: k_eff = 0.966885 res = 2.057E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 894 D.R. = 34.00\n", - "[ NORMAL ] Iteration 80: k_eff = 0.975601 res = 4.235E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 871 D.R. = 2.06\n", - "[ NORMAL ] Iteration 81: k_eff = 0.984087 res = 5.868E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 848 D.R. = 1.39\n", - "[ NORMAL ] Iteration 82: k_eff = 0.992344 res = 7.259E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 825 D.R. = 0.12\n", - "[ NORMAL ] Iteration 83: k_eff = 1.000375 res = 1.512E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 803 D.R. = 2.08\n", - "[ NORMAL ] Iteration 84: k_eff = 1.008182 res = 7.864E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 780 D.R. = 0.52\n", - "[ NORMAL ] Iteration 85: k_eff = 1.015768 res = 7.259E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 758 D.R. = 0.92\n", - "[ NORMAL ] Iteration 86: k_eff = 1.023136 res = 3.025E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 736 D.R. = 0.42\n", - "[ NORMAL ] Iteration 87: k_eff = 1.030288 res = 1.210E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 715 D.R. = 4.00\n", - "[ NORMAL ] Iteration 88: k_eff = 1.037228 res = 3.690E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 693 D.R. = 3.05\n", - "[ NORMAL ] Iteration 89: k_eff = 1.043960 res = 5.203E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 673 D.R. = 1.41\n", - "[ NORMAL ] Iteration 90: k_eff = 1.050486 res = 6.231E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 652 D.R. = 1.20\n", - "[ NORMAL ] Iteration 91: k_eff = 1.056812 res = 6.775E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 632 D.R. = 1.09\n", - "[ NORMAL ] Iteration 92: k_eff = 1.062939 res = 2.964E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 612 D.R. = 0.44\n", - "[ NORMAL ] Iteration 93: k_eff = 1.068872 res = 5.505E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 593 D.R. = 1.86\n", - "[ NORMAL ] Iteration 94: k_eff = 1.074616 res = 4.235E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 574 D.R. = 0.08\n", - "[ NORMAL ] Iteration 95: k_eff = 1.080173 res = 2.541E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 555 D.R. = 6.00\n", - "[ NORMAL ] Iteration 96: k_eff = 1.085550 res = 1.996E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 537 D.R. = 0.79\n", - "[ NORMAL ] Iteration 97: k_eff = 1.090748 res = 3.388E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 519 D.R. = 1.70\n", - "[ NORMAL ] Iteration 98: k_eff = 1.095774 res = 3.085E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 502 D.R. = 0.91\n", - "[ NORMAL ] Iteration 99: k_eff = 1.100629 res = 3.267E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 485 D.R. = 1.06\n", - "[ NORMAL ] Iteration 100: k_eff = 1.105320 res = 3.025E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 469 D.R. = 0.09\n", - "[ NORMAL ] Iteration 101: k_eff = 1.109851 res = 6.654E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 453 D.R. = 2.20\n", - "[ NORMAL ] Iteration 102: k_eff = 1.114224 res = 3.569E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 437 D.R. = 5.36\n", - "[ NORMAL ] Iteration 103: k_eff = 1.118444 res = 5.203E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 421 D.R. = 1.46\n", - "[ NORMAL ] Iteration 104: k_eff = 1.122516 res = 1.452E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 407 D.R. = 0.28\n", - "[ NORMAL ] Iteration 105: k_eff = 1.126445 res = 5.445E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 392 D.R. = 0.38\n", - "[ NORMAL ] Iteration 106: k_eff = 1.130232 res = 2.783E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 378 D.R. = 5.11\n", - "[ NORMAL ] Iteration 107: k_eff = 1.133884 res = 1.996E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 365 D.R. = 0.72\n", - "[ NORMAL ] Iteration 108: k_eff = 1.137403 res = 4.235E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 351 D.R. = 2.12\n", - "[ NORMAL ] Iteration 109: k_eff = 1.140793 res = 3.751E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 339 D.R. = 0.89\n", - "[ NORMAL ] Iteration 110: k_eff = 1.144059 res = 4.174E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 326 D.R. = 1.11\n", - "[ NORMAL ] Iteration 111: k_eff = 1.147203 res = 4.416E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 314 D.R. = 1.06\n", - "[ NORMAL ] Iteration 112: k_eff = 1.150231 res = 3.025E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 302 D.R. = 0.68\n", - "[ NORMAL ] Iteration 113: k_eff = 1.153146 res = 5.384E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 291 D.R. = 1.78\n", - "[ NORMAL ] Iteration 114: k_eff = 1.155950 res = 3.569E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 280 D.R. = 0.66\n", - "[ NORMAL ] Iteration 115: k_eff = 1.158649 res = 5.142E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 269 D.R. = 1.44\n", - "[ NORMAL ] Iteration 116: k_eff = 1.161244 res = 2.843E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 259 D.R. = 0.55\n", - "[ NORMAL ] Iteration 117: k_eff = 1.163739 res = 3.267E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 249 D.R. = 1.15\n", - "[ NORMAL ] Iteration 118: k_eff = 1.166139 res = 5.505E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 239 D.R. = 1.69\n", - "[ NORMAL ] Iteration 119: k_eff = 1.168445 res = 4.719E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 230 D.R. = 0.86\n", - "[ NORMAL ] Iteration 120: k_eff = 1.170662 res = 6.170E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 221 D.R. = 1.31\n", - "[ NORMAL ] Iteration 121: k_eff = 1.172791 res = 5.384E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 212 D.R. = 0.87\n", - "[ NORMAL ] Iteration 122: k_eff = 1.174837 res = 1.331E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 204 D.R. = 0.25\n", - "[ NORMAL ] Iteration 123: k_eff = 1.176801 res = 1.391E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 196 D.R. = 1.05\n", - "[ NORMAL ] Iteration 124: k_eff = 1.178688 res = 1.089E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 188 D.R. = 0.78\n", - "[ NORMAL ] Iteration 125: k_eff = 1.180500 res = 3.448E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 181 D.R. = 3.17\n", - "[ NORMAL ] Iteration 126: k_eff = 1.182238 res = 1.041E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 173 D.R. = 3.02\n", - "[ NORMAL ] Iteration 127: k_eff = 1.183907 res = 3.690E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 166 D.R. = 0.35\n", - "[ NORMAL ] Iteration 128: k_eff = 1.185508 res = 2.722E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 160 D.R. = 0.74\n", - "[ NORMAL ] Iteration 129: k_eff = 1.187044 res = 9.074E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 153 D.R. = 0.33\n", - "[ NORMAL ] Iteration 130: k_eff = 1.188518 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 147 D.R. = 3.20\n", - "[ NORMAL ] Iteration 131: k_eff = 1.189930 res = 2.783E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 141 D.R. = 0.96\n", - "[ NORMAL ] Iteration 132: k_eff = 1.191285 res = 2.541E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 135 D.R. = 0.91\n", - "[ NORMAL ] Iteration 133: k_eff = 1.192584 res = 4.537E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 129 D.R. = 1.79\n", - "[ NORMAL ] Iteration 134: k_eff = 1.193829 res = 5.505E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 124 D.R. = 1.21\n", - "[ NORMAL ] Iteration 135: k_eff = 1.195022 res = 6.412E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 119 D.R. = 1.16\n", - "[ NORMAL ] Iteration 136: k_eff = 1.196166 res = 6.049E-10 delta-k (pcm)\n", - "[ NORMAL ] ... = 114 D.R. = 0.01\n", - "[ NORMAL ] Iteration 137: k_eff = 1.197262 res = 4.416E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 109 D.R. = 73.00\n", - "[ NORMAL ] Iteration 138: k_eff = 1.198311 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 104 D.R. = 0.88\n", - "[ NORMAL ] Iteration 139: k_eff = 1.199316 res = 2.420E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 100 D.R. = 0.06\n", - "[ NORMAL ] Iteration 140: k_eff = 1.200279 res = 4.053E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 96 D.R. = 16.75\n", - "[ NORMAL ] Iteration 141: k_eff = 1.201201 res = 1.028E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 92 D.R. = 0.25\n", - "[ NORMAL ] Iteration 142: k_eff = 1.202083 res = 3.509E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 88 D.R. = 3.41\n", - "[ NORMAL ] Iteration 143: k_eff = 1.202927 res = 1.815E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 84 D.R. = 0.52\n", - "[ NORMAL ] Iteration 144: k_eff = 1.203736 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 80 D.R. = 1.07\n", - "[ NORMAL ] Iteration 145: k_eff = 1.204510 res = 6.836E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 77 D.R. = 3.53\n", - "[ NORMAL ] Iteration 146: k_eff = 1.205251 res = 5.324E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 74 D.R. = 0.78\n", - "[ NORMAL ] Iteration 147: k_eff = 1.205959 res = 2.299E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 70 D.R. = 0.43\n", - "[ NORMAL ] Iteration 148: k_eff = 1.206637 res = 1.815E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 67 D.R. = 0.79\n", - "[ NORMAL ] Iteration 149: k_eff = 1.207285 res = 8.469E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 64 D.R. = 0.47\n", - "[ NORMAL ] Iteration 150: k_eff = 1.207905 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 61 D.R. = 2.29\n", - "[ NORMAL ] Iteration 151: k_eff = 1.208498 res = 9.074E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 59 D.R. = 0.47\n", - "[ NORMAL ] Iteration 152: k_eff = 1.209065 res = 2.178E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 56 D.R. = 2.40\n", - "[ NORMAL ] Iteration 153: k_eff = 1.209607 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 54 D.R. = 3.11\n", - "[ NORMAL ] Iteration 154: k_eff = 1.210125 res = 9.074E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 51 D.R. = 1.34\n", - "[ NORMAL ] Iteration 155: k_eff = 1.210621 res = 5.445E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 49 D.R. = 0.06\n", - "[ NORMAL ] Iteration 156: k_eff = 1.211094 res = 6.049E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 47 D.R. = 1.11\n", - "[ NORMAL ] Iteration 157: k_eff = 1.211546 res = 6.049E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 45 D.R. = 1.00\n", - "[ NORMAL ] Iteration 158: k_eff = 1.211978 res = 4.658E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 43 D.R. = 7.70\n", - "[ NORMAL ] Iteration 159: k_eff = 1.212391 res = 1.815E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 41 D.R. = 0.04\n", - "[ NORMAL ] Iteration 160: k_eff = 1.212786 res = 1.210E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 39 D.R. = 6.67\n", - "[ NORMAL ] Iteration 161: k_eff = 1.213162 res = 6.049E-10 delta-k (pcm)\n", - "[ NORMAL ] ... = 37 D.R. = 0.05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Iteration 162: k_eff = 1.213522 res = 1.996E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 35 D.R. = 33.00\n", - "[ NORMAL ] Iteration 163: k_eff = 1.213866 res = 4.658E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 34 D.R. = 2.33\n", - "[ NORMAL ] Iteration 164: k_eff = 1.214194 res = 1.996E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 32 D.R. = 0.43\n", - "[ NORMAL ] Iteration 165: k_eff = 1.214507 res = 1.210E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 31 D.R. = 0.06\n", - "[ NORMAL ] Iteration 166: k_eff = 1.214806 res = 1.875E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 29 D.R. = 15.50\n", - "[ NORMAL ] Iteration 167: k_eff = 1.215092 res = 4.961E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 28 D.R. = 2.65\n", - "[ NORMAL ] Iteration 168: k_eff = 1.215365 res = 6.049E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 27 D.R. = 1.22\n", - "[ NORMAL ] Iteration 169: k_eff = 1.215625 res = 2.964E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 26 D.R. = 0.49\n", - "[ NORMAL ] Iteration 170: k_eff = 1.215874 res = 5.505E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 24 D.R. = 1.86\n", - "[ NORMAL ] Iteration 171: k_eff = 1.216110 res = 2.117E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 23 D.R. = 0.38\n", - "[ NORMAL ] Iteration 172: k_eff = 1.216337 res = 4.174E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 22 D.R. = 1.97\n", - "[ NORMAL ] Iteration 173: k_eff = 1.216552 res = 3.509E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 21 D.R. = 0.84\n", - "[ NORMAL ] Iteration 174: k_eff = 1.216759 res = 2.722E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 20 D.R. = 0.78\n", - "[ NORMAL ] Iteration 175: k_eff = 1.216954 res = 2.601E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 19 D.R. = 0.96\n", - "[ NORMAL ] Iteration 176: k_eff = 1.217142 res = 4.295E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 18 D.R. = 1.65\n", - "[ NORMAL ] Iteration 177: k_eff = 1.217320 res = 4.598E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 17 D.R. = 1.07\n", - "[ NORMAL ] Iteration 178: k_eff = 1.217491 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 17 D.R. = 1.26\n", - "[ NORMAL ] Iteration 179: k_eff = 1.217654 res = 2.480E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 16 D.R. = 0.43\n", - "[ NORMAL ] Iteration 180: k_eff = 1.217809 res = 6.049E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 15 D.R. = 0.24\n", - "[ NORMAL ] Iteration 181: k_eff = 1.217956 res = 6.412E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 14 D.R. = 10.60\n", - "[ NORMAL ] Iteration 182: k_eff = 1.218098 res = 7.138E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 14 D.R. = 1.11\n", - "[ NORMAL ] Iteration 183: k_eff = 1.218232 res = 1.633E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 13 D.R. = 0.23\n", - "[ NORMAL ] Iteration 184: k_eff = 1.218360 res = 2.541E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 12 D.R. = 1.56\n", - "[ NORMAL ] Iteration 185: k_eff = 1.218482 res = 1.028E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 12 D.R. = 0.40\n", - "[ NORMAL ] Iteration 186: k_eff = 1.218599 res = 7.864E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 0.76\n", - "[ NORMAL ] Iteration 187: k_eff = 1.218709 res = 4.053E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 5.15\n", - "[ NORMAL ] Iteration 188: k_eff = 1.218815 res = 2.299E-08 delta-k (pcm)\n", - "[ 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Iteration 197: k_eff = 1.219567 res = 2.057E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.76\n", - "[ NORMAL ] Iteration 198: k_eff = 1.219633 res = 1.331E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.65\n", - "[ NORMAL ] Iteration 199: k_eff = 1.219695 res = 3.932E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 2.95\n", - "[ NORMAL ] Iteration 200: k_eff = 1.219753 res = 1.996E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.51\n", - "[ NORMAL ] Iteration 201: k_eff = 1.219810 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 2.42\n", - "[ NORMAL ] Iteration 202: k_eff = 1.219863 res = 1.270E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.26\n", - "[ NORMAL ] Iteration 203: k_eff = 1.219914 res = 2.662E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 2.10\n", - "[ NORMAL ] Iteration 204: k_eff = 1.219962 res = 5.445E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.20\n", - "[ NORMAL ] Iteration 205: k_eff = 1.220009 res = 3.327E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 6.11\n", - "[ NORMAL ] Iteration 206: k_eff = 1.220052 res = 4.658E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 1.40\n", - "[ NORMAL ] Iteration 207: k_eff = 1.220094 res = 3.025E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.65\n", - "[ NORMAL ] Iteration 208: k_eff = 1.220134 res = 2.117E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.70\n", - "[ NORMAL ] Iteration 209: k_eff = 1.220172 res = 1.875E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.89\n", - "[ NORMAL ] Iteration 210: k_eff = 1.220208 res = 1.028E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.55\n", - "[ NORMAL ] Iteration 211: k_eff = 1.220243 res = 5.263E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 5.12\n", - "[ NORMAL ] Iteration 212: k_eff = 1.220275 res = 2.480E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.47\n", - "[ NORMAL ] Iteration 213: k_eff = 1.220306 res = 4.598E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 1.85\n", - "[ 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9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.42\n", - "[ NORMAL ] Iteration 223: k_eff = 1.220545 res = 7.078E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 7.31\n", - "[ NORMAL ] Iteration 224: k_eff = 1.220563 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.14\n", - "[ NORMAL ] Iteration 225: k_eff = 1.220580 res = 3.146E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 3.25\n", - "[ NORMAL ] Iteration 226: k_eff = 1.220596 res = 1.633E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.52\n", - "[ NORMAL ] Iteration 227: k_eff = 1.220612 res = 3.569E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 2.19\n", - "[ NORMAL ] Iteration 228: k_eff = 1.220627 res = 2.722E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.76\n", - "[ NORMAL ] Iteration 229: k_eff = 1.220641 res = 1.875E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.69\n", - "[ NORMAL ] Iteration 230: k_eff = 1.220655 res = 3.448E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.84\n", - "[ NORMAL ] Iteration 231: k_eff = 1.220667 res = 8.046E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 2.33\n", - "[ NORMAL ] Iteration 232: k_eff = 1.220679 res = 3.751E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.47\n", - "[ NORMAL ] Iteration 233: k_eff = 1.220690 res = 5.686E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.52\n", - "[ NORMAL ] Iteration 234: k_eff = 1.220701 res = 1.270E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.22\n", - "[ NORMAL ] Iteration 235: k_eff = 1.220711 res = 6.715E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 0 D.R. = 5.29\n" + "[ NORMAL ] Iteration 0: k_eff = 0.422963 res = 2.412E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -57703 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 1: k_eff = 0.475837 res = 5.240E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 5287 D.R. = 2.1724\n", + "[ NORMAL ] Iteration 2: k_eff = 0.491381 res = 7.698E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1554 D.R. = 1.4690\n", + "[ NORMAL ] Iteration 3: k_eff = 0.487386 res = 1.437E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -399 D.R. = 0.1866\n", + "[ NORMAL ] Iteration 4: k_eff = 0.483892 res = 4.945E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -349 D.R. = 3.4421\n", + "[ NORMAL ] Iteration 5: k_eff = 0.477262 res = 5.263E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -662 D.R. = 1.0642\n", + "[ NORMAL ] Iteration 6: k_eff = 0.468938 res = 1.149E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... -832 D.R. = 2.1839\n", + "[ NORMAL ] Iteration 7: k_eff = 0.460335 res = 7.108E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -860 D.R. = 0.6184\n", + "[ NORMAL ] Iteration 8: k_eff = 0.450622 res = 3.055E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -971 D.R. = 0.4298\n", + "[ NORMAL ] Iteration 9: k_eff = 0.441422 res = 6.352E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -920 D.R. = 0.2079\n", + "[ NORMAL ] Iteration 10: k_eff = 0.432048 res = 2.390E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -937 D.R. = 3.7619\n", + "[ NORMAL ] Iteration 11: k_eff = 0.423001 res = 2.117E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -904 D.R. = 0.8861\n", + "[ NORMAL ] Iteration 12: k_eff = 0.414566 res = 1.422E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -843 D.R. = 0.6714\n", + "[ NORMAL ] Iteration 13: k_eff = 0.406798 res = 2.480E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -776 D.R. = 1.7447\n", + "[ NORMAL ] Iteration 14: k_eff = 0.399476 res = 1.815E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -732 D.R. = 0.0732\n", + "[ NORMAL ] Iteration 15: k_eff = 0.393174 res = 6.715E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -630 D.R. = 37.0000\n", + "[ NORMAL ] Iteration 16: k_eff = 0.387541 res = 8.953E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -563 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 17: k_eff = 0.382789 res = 3.751E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -475 D.R. = 0.4189\n", + "[ NORMAL ] Iteration 18: k_eff = 0.378867 res = 5.686E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -392 D.R. = 1.5161\n", + "[ NORMAL ] Iteration 19: k_eff = 0.375774 res = 5.686E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -309 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 20: k_eff = 0.373624 res = 3.025E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -214 D.R. = 0.0532\n", + "[ NORMAL ] Iteration 21: k_eff = 0.372496 res = 3.751E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -112 D.R. = 12.4000\n", + "[ NORMAL ] Iteration 22: k_eff = 0.372116 res = 6.715E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -37 D.R. = 1.7903\n", + "[ NORMAL ] Iteration 23: k_eff = 0.372725 res = 7.017E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 1.0450\n", + "[ NORMAL ] Iteration 24: k_eff = 0.374202 res = 4.235E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 147 D.R. = 0.6034\n", + "[ NORMAL ] Iteration 25: k_eff = 0.376531 res = 3.630E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 232 D.R. = 0.0857\n", + "[ NORMAL ] Iteration 26: k_eff = 0.379711 res = 3.630E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 318 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 27: k_eff = 0.383732 res = 2.420E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 402 D.R. = 6.6667\n", + "[ NORMAL ] Iteration 28: k_eff = 0.388530 res = 5.082E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 479 D.R. = 2.1000\n", + "[ NORMAL ] Iteration 29: k_eff = 0.394087 res = 1.573E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.3095\n", + "[ NORMAL ] Iteration 30: k_eff = 0.400383 res = 2.783E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 629 D.R. = 1.7692\n", + "[ NORMAL ] Iteration 31: k_eff = 0.407383 res = 2.420E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 700 D.R. = 0.0870\n", + "[ NORMAL ] Iteration 32: k_eff = 0.415032 res = 2.178E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 764 D.R. = 9.0000\n", + "[ NORMAL ] Iteration 33: k_eff = 0.423318 res = 7.138E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 828 D.R. = 3.2778\n", + "[ NORMAL ] Iteration 34: k_eff = 0.432196 res = 3.993E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 887 D.R. = 0.5593\n", + "[ NORMAL ] Iteration 35: k_eff = 0.441615 res = 4.235E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 941 D.R. = 1.0606\n", + "[ NORMAL ] Iteration 36: k_eff = 0.451573 res = 7.622E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 995 D.R. = 1.8000\n", + "[ NORMAL ] Iteration 37: k_eff = 0.461994 res = 2.541E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1042 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 38: k_eff = 0.472869 res = 2.662E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1087 D.R. = 1.0476\n", + "[ NORMAL ] Iteration 39: k_eff = 0.484143 res = 7.259E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1127 D.R. = 0.2727\n", + "[ NORMAL ] Iteration 40: k_eff = 0.495789 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1164 D.R. = 6.6667\n", + "[ NORMAL ] Iteration 41: k_eff = 0.507768 res = 7.138E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1197 D.R. = 1.4750\n", + "[ NORMAL ] Iteration 42: k_eff = 0.520046 res = 2.783E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1227 D.R. = 0.3898\n", + "[ NORMAL ] Iteration 43: k_eff = 0.532588 res = 3.025E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1254 D.R. = 1.0870\n", + "[ NORMAL ] Iteration 44: k_eff = 0.545363 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1277 D.R. = 0.0400\n", + "[ NORMAL ] Iteration 45: k_eff = 0.558337 res = 3.146E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1297 D.R. = 26.0000\n", + "[ NORMAL ] Iteration 46: k_eff = 0.571477 res = 3.751E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1314 D.R. = 1.1923\n", + "[ NORMAL ] Iteration 47: k_eff = 0.584757 res = 1.694E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1328 D.R. = 0.4516\n", + "[ NORMAL ] Iteration 48: k_eff = 0.598147 res = 1.754E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1338 D.R. = 1.0357\n", + "[ NORMAL ] Iteration 49: k_eff = 0.611618 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1347 D.R. = 1.1034\n", + "[ NORMAL ] Iteration 50: k_eff = 0.625145 res = 6.049E-10 delta-k (pcm) =\n", + "[ NORMAL ] ... 1352 D.R. = 0.0312\n", + "[ NORMAL ] Iteration 51: k_eff = 0.638704 res = 1.815E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1355 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 52: k_eff = 0.652270 res = 2.238E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1356 D.R. = 12.3333\n", + "[ NORMAL ] Iteration 53: k_eff = 0.665820 res = 6.654E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1355 D.R. = 0.2973\n", + "[ NORMAL ] Iteration 54: k_eff = 0.679335 res = 3.327E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1351 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 55: k_eff = 0.692794 res = 3.025E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1345 D.R. = 0.0909\n", + "[ NORMAL ] Iteration 56: k_eff = 0.706178 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1338 D.R. = 3.2000\n", + "[ NORMAL ] Iteration 57: k_eff = 0.719471 res = 4.719E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1329 D.R. = 4.8750\n", + "[ NORMAL ] Iteration 58: k_eff = 0.732655 res = 3.025E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1318 D.R. = 0.0641\n", + "[ NORMAL ] Iteration 59: k_eff = 0.745716 res = 1.270E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1306 D.R. = 4.2000\n", + "[ NORMAL ] Iteration 60: k_eff = 0.758640 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1292 D.R. = 1.1905\n", + "[ NORMAL ] Iteration 61: k_eff = 0.771415 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1277 D.R. = 0.2800\n", + "[ NORMAL ] Iteration 62: k_eff = 0.784028 res = 3.630E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1261 D.R. = 8.5714\n", + "[ NORMAL ] Iteration 63: k_eff = 0.796470 res = 6.412E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1244 D.R. = 1.7667\n", + "[ NORMAL ] Iteration 64: k_eff = 0.808730 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1226 D.R. = 0.2358\n", + "[ NORMAL ] Iteration 65: k_eff = 0.820801 res = 4.053E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1207 D.R. = 2.6800\n", + "[ NORMAL ] Iteration 66: k_eff = 0.832673 res = 4.416E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1187 D.R. = 1.0896\n", + "[ NORMAL ] Iteration 67: k_eff = 0.844341 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1166 D.R. = 0.2055\n", + "[ NORMAL ] Iteration 68: k_eff = 0.855798 res = 3.267E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1145 D.R. = 3.6000\n", + "[ NORMAL ] Iteration 69: k_eff = 0.867039 res = 2.117E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1124 D.R. = 0.6481\n", + "[ NORMAL ] Iteration 70: k_eff = 0.878060 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1102 D.R. = 0.9143\n", + "[ NORMAL ] Iteration 71: k_eff = 0.888857 res = 5.142E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1079 D.R. = 2.6563\n", + "[ NORMAL ] Iteration 72: k_eff = 0.899427 res = 1.996E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1056 D.R. = 0.3882\n", + "[ NORMAL ] Iteration 73: k_eff = 0.909767 res = 3.327E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1034 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 74: k_eff = 0.919876 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1010 D.R. = 0.8727\n", + "[ NORMAL ] Iteration 75: k_eff = 0.929752 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 987 D.R. = 0.0417\n", + "[ NORMAL ] Iteration 76: k_eff = 0.939396 res = 2.964E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 964 D.R. = 24.5000\n", + "[ NORMAL ] Iteration 77: k_eff = 0.948806 res = 2.238E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 940 D.R. = 0.7551\n", + "[ NORMAL ] Iteration 78: k_eff = 0.957982 res = 4.719E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 917 D.R. = 2.1081\n", + "[ NORMAL ] Iteration 79: k_eff = 0.966926 res = 1.452E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 894 D.R. = 0.3077\n", + "[ NORMAL ] Iteration 80: k_eff = 0.975638 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 871 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 81: k_eff = 0.984121 res = 1.089E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 848 D.R. = 0.3750\n", + "[ NORMAL ] Iteration 82: k_eff = 0.992375 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 825 D.R. = 0.3889\n", + "[ NORMAL ] Iteration 83: k_eff = 1.000403 res = 3.993E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 802 D.R. = 9.4286\n", + "[ NORMAL ] Iteration 84: k_eff = 1.008207 res = 3.751E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 780 D.R. = 0.9394\n", + "[ NORMAL ] Iteration 85: k_eff = 1.015790 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 758 D.R. = 0.0323\n", + "[ NORMAL ] Iteration 86: k_eff = 1.023154 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 736 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 87: k_eff = 1.030303 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 714 D.R. = inf\n", + "[ NORMAL ] Iteration 88: k_eff = 1.037240 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 693 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 89: k_eff = 1.043969 res = 1.996E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 672 D.R. = 2.2000\n", + "[ NORMAL ] Iteration 90: k_eff = 1.050491 res = 1.452E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 652 D.R. = 0.7273\n", + "[ NORMAL ] Iteration 91: k_eff = 1.056813 res = 4.114E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 632 D.R. = 2.8333\n", + "[ NORMAL ] Iteration 92: k_eff = 1.062937 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 612 D.R. = 0.5588\n", + "[ NORMAL ] Iteration 93: k_eff = 1.068867 res = 5.445E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 592 D.R. = 0.2368\n", + "[ NORMAL ] Iteration 94: k_eff = 1.074608 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 574 D.R. = 0.7778\n", + "[ NORMAL ] Iteration 95: k_eff = 1.080162 res = 1.391E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 3.2857\n", + "[ NORMAL ] Iteration 96: k_eff = 1.085535 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 537 D.R. = 0.8261\n", + "[ NORMAL ] Iteration 97: k_eff = 1.090730 res = 3.509E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 519 D.R. = 3.0526\n", + "[ NORMAL ] Iteration 98: k_eff = 1.095752 res = 7.078E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 502 D.R. = 2.0172\n", + "[ NORMAL ] Iteration 99: k_eff = 1.100605 res = 2.420E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 485 D.R. = 0.0342\n", + "[ NORMAL ] Iteration 100: k_eff = 1.105293 res = 4.416E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 468 D.R. = 18.2500\n", + "[ NORMAL ] Iteration 101: k_eff = 1.109819 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 452 D.R. = 0.1781\n", + "[ NORMAL ] Iteration 102: k_eff = 1.114189 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 437 D.R. = 1.5385\n", + "[ NORMAL ] Iteration 103: k_eff = 1.118407 res = 1.633E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 421 D.R. = 1.3500\n", + "[ NORMAL ] Iteration 104: k_eff = 1.122476 res = 1.754E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 406 D.R. = 1.0741\n", + "[ NORMAL ] Iteration 105: k_eff = 1.126402 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 392 D.R. = 0.3103\n", + "[ NORMAL ] Iteration 106: k_eff = 1.130186 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 378 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 107: k_eff = 1.133834 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 364 D.R. = 4.2222\n", + "[ NORMAL ] Iteration 108: k_eff = 1.137350 res = 4.537E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 351 D.R. = 1.9737\n", + "[ NORMAL ] Iteration 109: k_eff = 1.140737 res = 3.509E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 338 D.R. = 0.7733\n", + "[ NORMAL ] Iteration 110: k_eff = 1.144000 res = 2.420E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 326 D.R. = 0.0690\n", + "[ NORMAL ] Iteration 111: k_eff = 1.147142 res = 1.815E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 314 D.R. = 7.5000\n", + "[ NORMAL ] Iteration 112: k_eff = 1.150167 res = 1.149E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 302 D.R. = 0.6333\n", + "[ NORMAL ] Iteration 113: k_eff = 1.153079 res = 3.448E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 291 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 114: k_eff = 1.155881 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 280 D.R. = 1.3509\n", + "[ NORMAL ] Iteration 115: k_eff = 1.158576 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 269 D.R. = 0.6234\n", + "[ NORMAL ] Iteration 116: k_eff = 1.161168 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 259 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 117: k_eff = 1.163661 res = 4.235E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 249 D.R. = 1.4583\n", + "[ NORMAL ] Iteration 118: k_eff = 1.166058 res = 1.028E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 239 D.R. = 0.2429\n", + "[ NORMAL ] Iteration 119: k_eff = 1.168362 res = 5.324E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 230 D.R. = 5.1765\n", + "[ NORMAL ] Iteration 120: k_eff = 1.170576 res = 3.932E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 221 D.R. = 0.7386\n", + "[ NORMAL ] Iteration 121: k_eff = 1.172703 res = 2.117E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 212 D.R. = 0.5385\n", + "[ NORMAL ] Iteration 122: k_eff = 1.174746 res = 8.469E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 204 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 123: k_eff = 1.176709 res = 3.690E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 196 D.R. = 4.3571\n", + "[ NORMAL ] Iteration 124: k_eff = 1.178593 res = 5.142E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 188 D.R. = 1.3934\n", + "[ NORMAL ] Iteration 125: k_eff = 1.180403 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 180 D.R. = 1.1294\n", + "[ NORMAL ] Iteration 126: k_eff = 1.182139 res = 1.694E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 173 D.R. = 0.2917\n", + "[ NORMAL ] Iteration 127: k_eff = 1.183805 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 166 D.R. = 0.3929\n", + "[ NORMAL ] Iteration 128: k_eff = 1.185404 res = 3.751E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 159 D.R. = 5.6364\n", + "[ NORMAL ] Iteration 129: k_eff = 1.186938 res = 6.957E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 153 D.R. = 1.8548\n", + "[ NORMAL ] Iteration 130: k_eff = 1.188409 res = 2.057E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 147 D.R. = 0.2957\n", + "[ NORMAL ] Iteration 131: k_eff = 1.189820 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 141 D.R. = 0.0882\n", + "[ NORMAL ] Iteration 132: k_eff = 1.191173 res = 4.356E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 135 D.R. = 24.0000\n", + "[ NORMAL ] Iteration 133: k_eff = 1.192470 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 129 D.R. = 0.1806\n", + "[ NORMAL ] Iteration 134: k_eff = 1.193714 res = 1.210E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 124 D.R. = 0.1538\n", + "[ NORMAL ] Iteration 135: k_eff = 1.194905 res = 3.811E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 119 D.R. = 31.5000\n", + "[ NORMAL ] Iteration 136: k_eff = 1.196046 res = 1.694E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 114 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 137: k_eff = 1.197140 res = 2.238E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 109 D.R. = 1.3214\n", + "[ NORMAL ] Iteration 138: k_eff = 1.198188 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 104 D.R. = 0.8378\n", + "[ NORMAL ] Iteration 139: k_eff = 1.199192 res = 5.928E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 3.1613\n", + "[ NORMAL ] Iteration 140: k_eff = 1.200153 res = 7.441E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 96 D.R. = 1.2551\n", + "[ NORMAL ] Iteration 141: k_eff = 1.201073 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 91 D.R. = 1.0407\n", + "[ NORMAL ] Iteration 142: k_eff = 1.201954 res = 6.473E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 88 D.R. = 0.8359\n", + "[ NORMAL ] Iteration 143: k_eff = 1.202797 res = 2.964E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 84 D.R. = 0.4579\n", + "[ NORMAL ] Iteration 144: k_eff = 1.203604 res = 1.754E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 80 D.R. = 0.5918\n", + "[ NORMAL ] Iteration 145: k_eff = 1.204377 res = 3.569E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 77 D.R. = 2.0345\n", + "[ NORMAL ] Iteration 146: k_eff = 1.205116 res = 8.106E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 73 D.R. = 2.2712\n", + "[ NORMAL ] Iteration 147: k_eff = 1.205823 res = 1.815E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 70 D.R. = 0.2239\n", + "[ NORMAL ] Iteration 148: k_eff = 1.206499 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 67 D.R. = 1.0667\n", + "[ NORMAL ] Iteration 149: k_eff = 1.207147 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 64 D.R. = 0.2813\n", + "[ NORMAL ] Iteration 150: k_eff = 1.207766 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 61 D.R. = 0.7778\n", + "[ NORMAL ] Iteration 151: k_eff = 1.208357 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 59 D.R. = 6.8571\n", + "[ NORMAL ] Iteration 152: k_eff = 1.208923 res = 7.259E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 56 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 153: k_eff = 1.209464 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 54 D.R. = 0.5833\n", + "[ NORMAL ] Iteration 154: k_eff = 1.209981 res = 3.630E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 51 D.R. = 0.8571\n", + "[ NORMAL ] Iteration 155: k_eff = 1.210475 res = 4.840E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 49 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 156: k_eff = 1.210947 res = 2.601E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 5.3750\n", + "[ NORMAL ] Iteration 157: k_eff = 1.211399 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 45 D.R. = 0.8837\n", + "[ NORMAL ] Iteration 158: k_eff = 1.211830 res = 4.295E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 43 D.R. = 1.8684\n", + "[ NORMAL ] Iteration 159: k_eff = 1.212242 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.9014\n", + "[ NORMAL ] Iteration 160: k_eff = 1.212635 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 0.1406\n", + "[ NORMAL ] Iteration 161: k_eff = 1.213011 res = 2.662E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 4.8889\n", + "[ NORMAL ] Iteration 162: k_eff = 1.213371 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 0.8636\n", + "[ NORMAL ] Iteration 163: k_eff = 1.213713 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = 0.0263\n", + "[ NORMAL ] Iteration 164: k_eff = 1.214040 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 11.0000\n", + "[ NORMAL ] Iteration 165: k_eff = 1.214353 res = 3.267E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 4.9091\n", + "[ NORMAL ] Iteration 166: k_eff = 1.214652 res = 5.445E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 167: k_eff = 1.214936 res = 4.537E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 168: k_eff = 1.215209 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 169: k_eff = 1.215468 res = 1.391E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = inf\n", + "[ NORMAL ] Iteration 170: k_eff = 1.215715 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 24 D.R. = 2.0435\n", + "[ NORMAL ] Iteration 171: k_eff = 1.215952 res = 1.633E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 0.5745\n", + "[ NORMAL ] Iteration 172: k_eff = 1.216178 res = 4.840E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 0.2963\n", + "[ NORMAL ] Iteration 173: k_eff = 1.216393 res = 2.420E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 174: k_eff = 1.216598 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 0.1750\n", + "[ NORMAL ] Iteration 175: k_eff = 1.216794 res = 5.989E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 14.1429\n", + "[ NORMAL ] Iteration 176: k_eff = 1.216980 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 177: k_eff = 1.217159 res = 3.025E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = inf\n", + "[ NORMAL ] Iteration 178: k_eff = 1.217329 res = 7.078E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 2.3400\n", + "[ NORMAL ] Iteration 179: k_eff = 1.217490 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.6581\n", + "[ NORMAL ] Iteration 180: k_eff = 1.217645 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 0.1688\n", + "[ NORMAL ] Iteration 181: k_eff = 1.217793 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 1.6154\n", + "[ NORMAL ] Iteration 182: k_eff = 1.217934 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 183: k_eff = 1.218067 res = 2.359E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 1.8571\n", + "[ NORMAL ] Iteration 184: k_eff = 1.218195 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 0.4615\n", + "[ NORMAL ] Iteration 185: k_eff = 1.218316 res = 2.420E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 0.2222\n", + "[ NORMAL ] Iteration 186: k_eff = 1.218433 res = 5.868E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 24.2500\n", + "[ NORMAL ] Iteration 187: k_eff = 1.218543 res = 1.512E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.2577\n", + "[ NORMAL ] Iteration 188: k_eff = 1.218648 res = 1.512E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 189: k_eff = 1.218749 res = 2.117E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 190: k_eff = 1.218845 res = 2.722E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 1.2857\n", + "[ NORMAL ] Iteration 191: k_eff = 1.218936 res = 3.025E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 0.1111\n", + "[ NORMAL ] Iteration 192: k_eff = 1.219022 res = 2.057E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 6.8000\n", + "[ NORMAL ] Iteration 193: k_eff = 1.219105 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 0.5882\n", + "[ NORMAL ] Iteration 194: k_eff = 1.219184 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.9000\n", + "[ NORMAL ] Iteration 195: k_eff = 1.219259 res = 2.662E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 2.4444\n", + "[ NORMAL ] Iteration 196: k_eff = 1.219330 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.2045\n", + "[ NORMAL ] Iteration 197: k_eff = 1.219399 res = 3.630E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 198: k_eff = 1.219463 res = 1.996E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 5.5000\n", + "[ NORMAL ] Iteration 199: k_eff = 1.219525 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 200: k_eff = 1.219584 res = 1.996E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 201: k_eff = 1.219640 res = 3.267E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 1.6364\n", + "[ NORMAL ] Iteration 202: k_eff = 1.219693 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 203: k_eff = 1.219744 res = 1.452E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 204: k_eff = 1.219793 res = 3.146E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 2.1667\n", + "[ NORMAL ] Iteration 205: k_eff = 1.219839 res = 2.662E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.8462\n", + "[ NORMAL ] Iteration 206: k_eff = 1.219882 res = 6.049E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 2.2727\n", + "[ NORMAL ] Iteration 207: k_eff = 1.219924 res = 4.719E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.7800\n", + "[ NORMAL ] Iteration 208: k_eff = 1.219964 res = 3.327E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.7051\n", + "[ NORMAL ] Iteration 209: k_eff = 1.220002 res = 1.754E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.5273\n", + "[ NORMAL ] Iteration 210: k_eff = 1.220038 res = 5.445E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 3.1034\n", + "[ NORMAL ] Iteration 211: k_eff = 1.220072 res = 3.509E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.6444\n", + "[ NORMAL ] Iteration 212: k_eff = 1.220104 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.1207\n", + "[ NORMAL ] Iteration 213: k_eff = 1.220135 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.8571\n", + "[ NORMAL ] Iteration 214: k_eff = 1.220165 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.6000\n", + "[ NORMAL ] Iteration 215: k_eff = 1.220192 res = 1.210E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.0625\n", + "[ NORMAL ] Iteration 216: k_eff = 1.220219 res = 9.014E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 74.5000\n", + "[ NORMAL ] Iteration 217: k_eff = 1.220245 res = 4.174E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.4631\n", + "[ NORMAL ] Iteration 218: k_eff = 1.220269 res = 3.025E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.7246\n", + "[ NORMAL ] Iteration 219: k_eff = 1.220292 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 2.5600\n", + "[ NORMAL ] Iteration 220: k_eff = 1.220313 res = 8.106E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.0469\n", + "[ NORMAL ] Iteration 221: k_eff = 1.220334 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.1567\n", + "[ NORMAL ] Iteration 222: k_eff = 1.220354 res = 3.690E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 2.9048\n", + "[ NORMAL ] Iteration 223: k_eff = 1.220373 res = 1.633E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.4426\n", + "[ NORMAL ] Iteration 224: k_eff = 1.220391 res = 2.420E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.4815\n", + "[ NORMAL ] Iteration 225: k_eff = 1.220408 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0750\n", + "[ NORMAL ] Iteration 226: k_eff = 1.220424 res = 7.380E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 40.6667\n", + "[ NORMAL ] Iteration 227: k_eff = 1.220440 res = 4.295E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5820\n", + "[ NORMAL ] Iteration 228: k_eff = 1.220454 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.1831\n", + "[ NORMAL ] Iteration 229: k_eff = 1.220468 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 230: k_eff = 1.220482 res = 5.626E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 231: k_eff = 1.220494 res = 4.235E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.7527\n", + "[ NORMAL ] Iteration 232: k_eff = 1.220506 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.1571\n", + "[ NORMAL ] Iteration 233: k_eff = 1.220518 res = 2.420E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3636\n", + "[ NORMAL ] Iteration 234: k_eff = 1.220529 res = 1.512E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 6.2500\n", + "[ NORMAL ] Iteration 235: k_eff = 1.220539 res = 2.359E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.5600\n", + "[ NORMAL ] Iteration 236: k_eff = 1.220549 res = 3.146E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 1.3333\n" ] } ], @@ -1694,16 +1699,24 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.224484\n", - "openmoc keff = 1.220711\n", - "bias [pcm]: -377.3\n" + "openmc keff = 1.224010\n", + "openmoc keff = 1.220549\n", + "bias [pcm]: -346.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/statepoint.py:277: FutureWarning: The 'k_combined' property has been renamed to 'keff' and will be removed in a future version of OpenMC.\n", + " warnings.warn(\n" ] } ], @@ -1727,7 +1740,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1765,7 +1778,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1790,7 +1803,6 @@ "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", "[ NORMAL ] Initializing FSR lookup vectors\n", "[ NORMAL ] Total number of FSRs 3\n", - "[ RESULT ] Total Track Generation & Segmentation Time...........3.9517E-02 sec\n", "[ NORMAL ] Initializing MOC eigenvalue solver...\n", "[ NORMAL ] Initializing solver arrays...\n", "[ NORMAL ] Centering segments around FSR centroid...\n", @@ -1805,720 +1817,704 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0: k_eff = 0.366880 res = 4.840E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -63312 D.R. = 0.00\n", - "[ NORMAL ] Iteration 1: k_eff = 0.391184 res = 9.679E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 2430 D.R. = 0.20\n", - "[ NORMAL ] Iteration 2: k_eff = 0.392990 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 180 D.R. = 6.00\n", - "[ NORMAL ] Iteration 3: k_eff = 0.381099 res = 9.195E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -1189 D.R. = 1.58\n", - "[ NORMAL ] Iteration 4: k_eff = 0.375018 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... -608 D.R. = 0.00\n", - "[ NORMAL ] Iteration 5: k_eff = 0.369593 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -542 D.R. = inf\n", - "[ NORMAL ] Iteration 6: k_eff = 0.365543 res = 1.065E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... -405 D.R. = 5.50\n", - "[ NORMAL ] Iteration 7: k_eff = 0.363054 res = 1.065E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... -248 D.R. = 1.00\n", - "[ NORMAL ] Iteration 8: k_eff = 0.361473 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -158 D.R. = 0.18\n", - "[ NORMAL ] Iteration 9: k_eff = 0.361280 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... -19 D.R. = 3.00\n", - "[ NORMAL ] Iteration 10: k_eff = 0.362003 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 72 D.R. = 0.33\n", - "[ NORMAL ] Iteration 11: k_eff = 0.363718 res = 4.840E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 171 D.R. = 2.50\n", - "[ NORMAL ] Iteration 12: k_eff = 0.366338 res = 1.258E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... 262 D.R. = 2.60\n", - "[ NORMAL ] Iteration 13: k_eff = 0.369804 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 346 D.R. = 0.77\n", - "[ NORMAL ] Iteration 14: k_eff = 0.373989 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 418 D.R. = 0.40\n", - "[ NORMAL ] Iteration 15: k_eff = 0.378923 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... 493 D.R. = 0.00\n", - "[ NORMAL ] Iteration 16: k_eff = 0.384479 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 555 D.R. = inf\n", - "[ NORMAL ] Iteration 17: k_eff = 0.390637 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 615 D.R. = 1.00\n", - "[ NORMAL ] Iteration 18: k_eff = 0.397338 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... 670 D.R. = 0.00\n", - "[ NORMAL ] Iteration 19: k_eff = 0.404533 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 719 D.R. = inf\n", - "[ NORMAL ] Iteration 20: k_eff = 0.412184 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 765 D.R. = 1.67\n", - "[ NORMAL ] Iteration 21: k_eff = 0.420253 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 806 D.R. = 0.80\n", - "[ NORMAL ] Iteration 22: k_eff = 0.428686 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 843 D.R. = 0.75\n", - "[ NORMAL ] Iteration 23: k_eff = 0.437462 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 877 D.R. = 1.67\n", - "[ NORMAL ] Iteration 24: k_eff = 0.446538 res = 1.161E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... 907 D.R. = 1.20\n", - "[ NORMAL ] Iteration 25: k_eff = 0.455883 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 934 D.R. = 0.17\n", - "[ NORMAL ] Iteration 26: k_eff = 0.465469 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 958 D.R. = 1.00\n", - "[ NORMAL ] Iteration 27: k_eff = 0.475265 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 979 D.R. = 2.00\n", - "[ NORMAL ] Iteration 28: k_eff = 0.485246 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 998 D.R. = 1.00\n", - "[ NORMAL ] Iteration 29: k_eff = 0.495385 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1013 D.R. = 0.50\n", - "[ NORMAL ] Iteration 30: k_eff = 0.505661 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1027 D.R. = 4.00\n", - "[ NORMAL ] Iteration 31: k_eff = 0.516051 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1038 D.R. = 1.25\n", - "[ NORMAL ] Iteration 32: k_eff = 0.526534 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1048 D.R. = 1.00\n", - "[ NORMAL ] Iteration 33: k_eff = 0.537092 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1055 D.R. = 1.00\n", - "[ NORMAL ] Iteration 34: k_eff = 0.547706 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1061 D.R. = 0.60\n", - "[ NORMAL ] Iteration 35: k_eff = 0.558361 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1065 D.R. = 0.33\n", - "[ NORMAL ] Iteration 36: k_eff = 0.569040 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1067 D.R. = 3.00\n", - "[ NORMAL ] Iteration 37: k_eff = 0.579730 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1068 D.R. = 1.67\n", - "[ NORMAL ] Iteration 38: k_eff = 0.590416 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... 1068 D.R. = 0.00\n", - "[ NORMAL ] Iteration 39: k_eff = 0.601087 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1067 D.R. = inf\n", - "[ NORMAL ] Iteration 40: k_eff = 0.611731 res = 1.549E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... 1064 D.R. = 1.60\n", - "[ NORMAL ] Iteration 41: k_eff = 0.622338 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1060 D.R. = 0.50\n", - "[ NORMAL ] Iteration 42: k_eff = 0.632897 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1055 D.R. = 1.00\n", - "[ NORMAL ] Iteration 43: k_eff = 0.643400 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1050 D.R. = 0.75\n", - "[ NORMAL ] Iteration 44: k_eff = 0.653837 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... 1043 D.R. = 0.00\n", - "[ NORMAL ] Iteration 45: k_eff = 0.664203 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1036 D.R. = inf\n", - "[ NORMAL ] Iteration 46: k_eff = 0.674488 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1028 D.R. = 1.25\n", - "[ NORMAL ] Iteration 47: k_eff = 0.684688 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1020 D.R. = 1.00\n", - "[ NORMAL ] Iteration 48: k_eff = 0.694796 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1010 D.R. = 0.40\n", - "[ NORMAL ] Iteration 49: k_eff = 0.704807 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 1001 D.R. = 1.00\n", - "[ NORMAL ] Iteration 50: k_eff = 0.714715 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 990 D.R. = 0.50\n", - "[ NORMAL ] Iteration 51: k_eff = 0.724517 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 980 D.R. = 1.00\n", - "[ NORMAL ] Iteration 52: k_eff = 0.734209 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 969 D.R. = 1.00\n", - "[ NORMAL ] Iteration 53: k_eff = 0.743787 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... 957 D.R. = 0.00\n", - "[ NORMAL ] Iteration 54: k_eff = 0.753247 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 946 D.R. = inf\n", - "[ NORMAL ] Iteration 55: k_eff = 0.762588 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 934 D.R. = 1.50\n", - "[ NORMAL ] Iteration 56: k_eff = 0.771806 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 921 D.R. = 0.33\n", - "[ NORMAL ] Iteration 57: k_eff = 0.780901 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 909 D.R. = 2.00\n", - "[ NORMAL ] Iteration 58: k_eff = 0.789868 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 896 D.R. = 0.50\n", - "[ NORMAL ] Iteration 59: k_eff = 0.798708 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 884 D.R. = 1.00\n", - "[ NORMAL ] Iteration 60: k_eff = 0.807419 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 871 D.R. = 4.00\n", - "[ NORMAL ] Iteration 61: k_eff = 0.816000 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 858 D.R. = 0.50\n", - "[ NORMAL ] Iteration 62: k_eff = 0.824450 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 844 D.R. = 1.00\n", - "[ NORMAL ] Iteration 63: k_eff = 0.832768 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 831 D.R. = 2.00\n", - "[ NORMAL ] Iteration 64: k_eff = 0.840954 res = 1.742E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... 818 D.R. = 2.25\n", - "[ NORMAL ] Iteration 65: k_eff = 0.849008 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 805 D.R. = 0.44\n", - "[ NORMAL ] Iteration 66: k_eff = 0.856930 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 792 D.R. = 0.50\n", - "[ NORMAL ] Iteration 67: k_eff = 0.864720 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 778 D.R. = 1.00\n", - "[ NORMAL ] Iteration 68: k_eff = 0.872378 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 765 D.R. = 1.00\n", - "[ NORMAL ] Iteration 69: k_eff = 0.879905 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 752 D.R. = 0.50\n", - "[ NORMAL ] Iteration 70: k_eff = 0.887301 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 739 D.R. = 2.00\n", - "[ NORMAL ] Iteration 71: k_eff = 0.894566 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 726 D.R. = 1.50\n", - "[ NORMAL ] Iteration 72: k_eff = 0.901702 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 713 D.R. = 0.33\n", - "[ NORMAL ] Iteration 73: k_eff = 0.908710 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 700 D.R. = 2.00\n", - "[ NORMAL ] Iteration 74: k_eff = 0.915590 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 687 D.R. = 1.50\n", - "[ NORMAL ] Iteration 75: k_eff = 0.922342 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... 675 D.R. = 0.00\n", - "[ NORMAL ] Iteration 76: k_eff = 0.928971 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] Iteration 0: k_eff = 0.366745 res = 5.082E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -63325 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 1: k_eff = 0.391042 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 2429 D.R. = 0.9524\n", + "[ NORMAL ] Iteration 2: k_eff = 0.392839 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 179 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 3: k_eff = 0.380943 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -1189 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 4: k_eff = 0.374852 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -609 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 5: k_eff = 0.369423 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -542 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 6: k_eff = 0.365368 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -405 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 7: k_eff = 0.362876 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -249 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 8: k_eff = 0.361293 res = 1.452E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... -158 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 9: k_eff = 0.361099 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -19 D.R. = 0.1333\n", + "[ NORMAL ] Iteration 10: k_eff = 0.361821 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 72 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 11: k_eff = 0.363536 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 171 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 12: k_eff = 0.366157 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 262 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 13: k_eff = 0.369624 res = 8.711E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 346 D.R. = 1.2857\n", + "[ NORMAL ] Iteration 14: k_eff = 0.373811 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 418 D.R. = 0.8889\n", + "[ NORMAL ] Iteration 15: k_eff = 0.378747 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 493 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 16: k_eff = 0.384305 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 17: k_eff = 0.390466 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 616 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 18: k_eff = 0.397170 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 670 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 19: k_eff = 0.404368 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 719 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 20: k_eff = 0.412022 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 765 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 21: k_eff = 0.420095 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 807 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 22: k_eff = 0.428531 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 843 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 23: k_eff = 0.437310 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 877 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 24: k_eff = 0.446389 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 907 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 25: k_eff = 0.455738 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 934 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 26: k_eff = 0.465326 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 958 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 27: k_eff = 0.475126 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 979 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 28: k_eff = 0.485109 res = 1.549E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 998 D.R. = 1.6000\n", + "[ NORMAL ] Iteration 29: k_eff = 0.495252 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1014 D.R. = 0.3750\n", + "[ NORMAL ] Iteration 30: k_eff = 0.505530 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1027 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 31: k_eff = 0.515922 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1039 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 32: k_eff = 0.526408 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1048 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 33: k_eff = 0.536968 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1056 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 34: k_eff = 0.547584 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1061 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 35: k_eff = 0.558241 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1065 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 36: k_eff = 0.568922 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1068 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 37: k_eff = 0.579613 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1069 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 38: k_eff = 0.590301 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1068 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 39: k_eff = 0.600973 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1067 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 40: k_eff = 0.611619 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1064 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 41: k_eff = 0.622226 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1060 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 42: k_eff = 0.632786 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1055 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 43: k_eff = 0.643289 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1050 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 44: k_eff = 0.653727 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1043 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 45: k_eff = 0.664093 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 1036 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 46: k_eff = 0.674379 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1028 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 47: k_eff = 0.684578 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1019 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 48: k_eff = 0.694686 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1010 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 49: k_eff = 0.704696 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1001 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 50: k_eff = 0.714604 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 990 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 51: k_eff = 0.724405 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 980 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 52: k_eff = 0.734096 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 969 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 53: k_eff = 0.743673 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 957 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 54: k_eff = 0.753133 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 945 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 55: k_eff = 0.762472 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 933 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 56: k_eff = 0.771690 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 921 D.R. = inf\n", + "[ NORMAL ] Iteration 57: k_eff = 0.780782 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 909 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 58: k_eff = 0.789749 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 896 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 59: k_eff = 0.798587 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 883 D.R. = inf\n", + "[ NORMAL ] Iteration 60: k_eff = 0.807296 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 870 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 61: k_eff = 0.815875 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 857 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 62: k_eff = 0.824323 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 844 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 63: k_eff = 0.832639 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 831 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 64: k_eff = 0.840824 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 818 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 65: k_eff = 0.848875 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 805 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 66: k_eff = 0.856796 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 792 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 67: k_eff = 0.864583 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 778 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 68: k_eff = 0.872239 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 765 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 69: k_eff = 0.879764 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 752 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 70: k_eff = 0.887157 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 739 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 71: k_eff = 0.894420 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 726 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 72: k_eff = 0.901554 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 713 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 73: k_eff = 0.908559 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 700 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 74: k_eff = 0.915436 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 687 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 75: k_eff = 0.922187 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 675 D.R. = -nan\n", + "[ NORMAL ] Iteration 76: k_eff = 0.928811 res = 1.936E-08 delta-k (pcm) =\n", "[ NORMAL ] ... 662 D.R. = inf\n", - "[ NORMAL ] Iteration 77: k_eff = 0.935474 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 650 D.R. = 0.17\n", - "[ NORMAL ] Iteration 78: k_eff = 0.941853 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 637 D.R. = 1.00\n", - "[ NORMAL ] Iteration 79: k_eff = 0.948112 res = 9.679E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 625 D.R. = 5.00\n", - "[ NORMAL ] Iteration 80: k_eff = 0.954249 res = 1.065E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... 613 D.R. = 1.10\n", - "[ NORMAL ] Iteration 81: k_eff = 0.960267 res = 9.679E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 601 D.R. = 0.09\n", - "[ NORMAL ] Iteration 82: k_eff = 0.966168 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 590 D.R. = 8.00\n", - "[ NORMAL ] Iteration 83: k_eff = 0.971953 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 578 D.R. = 0.75\n", - "[ NORMAL ] Iteration 84: k_eff = 0.977623 res = 1.936E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 566 D.R. = 0.33\n", - "[ NORMAL ] Iteration 85: k_eff = 0.983179 res = 2.904E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 555 D.R. = 1.50\n", - "[ NORMAL ] Iteration 86: k_eff = 0.988624 res = 0.000E+00 delta-k (pcm) =\n", - "[ NORMAL ] ... 544 D.R. = 0.00\n", - "[ NORMAL ] Iteration 87: k_eff = 0.993958 res = 6.775E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 533 D.R. = inf\n", - "[ NORMAL ] Iteration 88: k_eff = 0.999184 res = 9.679E-09 delta-k (pcm) =\n", - "[ NORMAL ] ... 522 D.R. = 0.14\n", - "[ NORMAL ] Iteration 89: k_eff = 1.004304 res = 4.840E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 511 D.R. = 5.00\n", - "[ NORMAL ] Iteration 90: k_eff = 1.009318 res = 3.872E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 501 D.R. = 0.80\n", - "[ NORMAL ] Iteration 91: k_eff = 1.014228 res = 4.840E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 491 D.R. = 1.25\n", - "[ NORMAL ] Iteration 92: k_eff = 1.019037 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 480 D.R. = 1.20\n", - "[ NORMAL ] Iteration 93: k_eff = 1.023744 res = 2.904E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 470 D.R. = 0.50\n", - "[ NORMAL ] Iteration 94: k_eff = 1.028353 res = 6.775E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 460 D.R. = 2.33\n", - "[ NORMAL ] Iteration 95: k_eff = 1.032865 res = 7.743E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 451 D.R. = 1.14\n", - "[ NORMAL ] Iteration 96: k_eff = 1.037281 res = 1.355E-07 delta-k (pcm) =\n", - "[ NORMAL ] ... 441 D.R. = 1.75\n", - "[ NORMAL ] Iteration 97: k_eff = 1.041604 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 432 D.R. = 0.43\n", - "[ NORMAL ] Iteration 98: k_eff = 1.045834 res = 4.840E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 422 D.R. = 0.83\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Iteration 99: k_eff = 1.049974 res = 5.807E-08 delta-k (pcm) =\n", - "[ NORMAL ] ... 413 D.R. = 1.20\n", - "[ NORMAL ] Iteration 100: k_eff = 1.054024 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 405 D.R. = 1.00\n", - "[ NORMAL ] Iteration 101: k_eff = 1.057987 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 396 D.R. = 0.83\n", - "[ NORMAL ] Iteration 102: k_eff = 1.061864 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 387 D.R. = 0.20\n", - "[ NORMAL ] Iteration 103: k_eff = 1.065657 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 379 D.R. = 3.00\n", - "[ NORMAL ] Iteration 104: k_eff = 1.069367 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 370 D.R. = 1.33\n", - "[ NORMAL ] Iteration 105: k_eff = 1.072996 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 362 D.R. = 1.00\n", - "[ NORMAL ] Iteration 106: k_eff = 1.076545 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 354 D.R. = 0.50\n", - "[ NORMAL ] Iteration 107: k_eff = 1.080016 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 347 D.R. = 3.50\n", - "[ NORMAL ] Iteration 108: k_eff = 1.083411 res = 1.161E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 339 D.R. = 1.71\n", - "[ NORMAL ] Iteration 109: k_eff = 1.086731 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 331 D.R. = 0.50\n", - "[ NORMAL ] Iteration 110: k_eff = 1.089976 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 324 D.R. = 0.33\n", - "[ NORMAL ] Iteration 111: k_eff = 1.093150 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 317 D.R. = 4.00\n", - "[ NORMAL ] Iteration 112: k_eff = 1.096253 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 310 D.R. = 1.00\n", - "[ NORMAL ] Iteration 113: k_eff = 1.099286 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 303 D.R. = 0.50\n", - "[ NORMAL ] Iteration 114: k_eff = 1.102251 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 296 D.R. = 1.25\n", - "[ NORMAL ] Iteration 115: k_eff = 1.105150 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 289 D.R. = 0.20\n", - "[ NORMAL ] Iteration 116: k_eff = 1.107983 res = 1.452E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 283 D.R. = 15.00\n", - "[ NORMAL ] Iteration 117: k_eff = 1.110753 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 276 D.R. = 0.33\n", - "[ NORMAL ] Iteration 118: k_eff = 1.113459 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 270 D.R. = 1.80\n", - "[ NORMAL ] Iteration 119: k_eff = 1.116105 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 264 D.R. = 1.22\n", - "[ NORMAL ] Iteration 120: k_eff = 1.118690 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 258 D.R. = 0.36\n", - "[ NORMAL ] Iteration 121: k_eff = 1.121216 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 252 D.R. = 0.25\n", - "[ NORMAL ] Iteration 122: k_eff = 1.123685 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 246 D.R. = 1.00\n", - "[ NORMAL ] Iteration 123: k_eff = 1.126097 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 241 D.R. = 4.00\n", - "[ NORMAL ] Iteration 124: k_eff = 1.128454 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 235 D.R. = 0.00\n", - "[ NORMAL ] Iteration 125: k_eff = 1.130758 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 230 D.R. = inf\n", - "[ NORMAL ] Iteration 126: k_eff = 1.133008 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 224 D.R. = 5.50\n", - "[ NORMAL ] Iteration 127: k_eff = 1.135207 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 219 D.R. = 0.45\n", - "[ NORMAL ] Iteration 128: k_eff = 1.137354 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 214 D.R. = 0.40\n", - "[ NORMAL ] Iteration 129: k_eff = 1.139453 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 209 D.R. = 0.00\n", - "[ NORMAL ] Iteration 130: k_eff = 1.141503 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 204 D.R. = inf\n", - "[ NORMAL ] Iteration 131: k_eff = 1.143505 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 200 D.R. = 1.50\n", - "[ NORMAL ] Iteration 132: k_eff = 1.145461 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 195 D.R. = 2.00\n", - "[ NORMAL ] Iteration 133: k_eff = 1.147372 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 191 D.R. = 1.50\n", - "[ NORMAL ] Iteration 134: k_eff = 1.149238 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 186 D.R. = 1.22\n", - "[ NORMAL ] Iteration 135: k_eff = 1.151061 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 182 D.R. = 0.18\n", - "[ NORMAL ] Iteration 136: k_eff = 1.152842 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 178 D.R. = 1.00\n", - "[ NORMAL ] Iteration 137: k_eff = 1.154581 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 173 D.R. = 2.00\n", - "[ NORMAL ] Iteration 138: k_eff = 1.156279 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 169 D.R. = 2.50\n", - "[ NORMAL ] Iteration 139: k_eff = 1.157938 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 165 D.R. = 0.80\n", - "[ NORMAL ] Iteration 140: k_eff = 1.159557 res = 1.161E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 161 D.R. = 1.50\n", - "[ NORMAL ] Iteration 141: k_eff = 1.161139 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 158 D.R. = 0.33\n", - "[ NORMAL ] Iteration 142: k_eff = 1.162684 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 154 D.R. = 1.00\n", - "[ NORMAL ] Iteration 143: k_eff = 1.164193 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 150 D.R. = 2.75\n", - "[ NORMAL ] Iteration 144: k_eff = 1.165666 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 147 D.R. = 0.09\n", - "[ NORMAL ] Iteration 145: k_eff = 1.167105 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 143 D.R. = 2.00\n", - "[ NORMAL ] Iteration 146: k_eff = 1.168509 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 140 D.R. = 4.00\n", - "[ NORMAL ] Iteration 147: k_eff = 1.169881 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 137 D.R. = 1.25\n", - "[ NORMAL ] Iteration 148: k_eff = 1.171220 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 133 D.R. = 0.10\n", - "[ NORMAL ] Iteration 149: k_eff = 1.172528 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 130 D.R. = 6.00\n", - "[ NORMAL ] Iteration 150: k_eff = 1.173804 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 127 D.R. = 1.67\n", - "[ NORMAL ] Iteration 151: k_eff = 1.175051 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 124 D.R. = 0.80\n", - "[ NORMAL ] Iteration 152: k_eff = 1.176268 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 121 D.R. = 1.00\n", - "[ NORMAL ] Iteration 153: k_eff = 1.177456 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 118 D.R. = 1.00\n", - "[ NORMAL ] Iteration 154: k_eff = 1.178616 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 115 D.R. = 0.63\n", - "[ NORMAL ] Iteration 155: k_eff = 1.179749 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 113 D.R. = 1.20\n", - "[ NORMAL ] Iteration 156: k_eff = 1.180855 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 110 D.R. = 0.83\n", - "[ NORMAL ] Iteration 157: k_eff = 1.181935 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 107 D.R. = 0.20\n", - "[ NORMAL ] Iteration 158: k_eff = 1.182988 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 105 D.R. = 3.00\n", - "[ NORMAL ] Iteration 159: k_eff = 1.184017 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 102 D.R. = 0.67\n", - "[ NORMAL ] Iteration 160: k_eff = 1.185021 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 100 D.R. = 1.50\n", - "[ NORMAL ] Iteration 161: k_eff = 1.186002 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 98 D.R. = 0.67\n", - "[ NORMAL ] Iteration 162: k_eff = 1.186959 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 95 D.R. = 2.50\n", - "[ NORMAL ] Iteration 163: k_eff = 1.187893 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 93 D.R. = 1.40\n", - "[ NORMAL ] Iteration 164: k_eff = 1.188805 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 91 D.R. = 1.29\n", - "[ NORMAL ] Iteration 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(pcm)\n", - "[ NORMAL ] ... = 73 D.R. = 0.67\n", - "[ NORMAL ] Iteration 174: k_eff = 1.196808 res = 1.355E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 71 D.R. = 3.50\n", - "[ NORMAL ] Iteration 175: k_eff = 1.197507 res = 1.161E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 69 D.R. = 0.86\n", - "[ NORMAL ] Iteration 176: k_eff = 1.198190 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 68 D.R. = 0.33\n", - "[ NORMAL ] Iteration 177: k_eff = 1.198855 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 66 D.R. = 0.75\n", - "[ NORMAL ] Iteration 178: k_eff = 1.199505 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 64 D.R. = 0.67\n", - "[ NORMAL ] Iteration 179: k_eff = 1.200139 res = 1.258E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 63 D.R. = 6.50\n", - "[ NORMAL ] Iteration 180: k_eff = 1.200757 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 61 D.R. = 0.62\n", - "[ NORMAL ] Iteration 181: k_eff = 1.201361 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 60 D.R. = 0.25\n", - "[ NORMAL ] Iteration 182: k_eff = 1.201951 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 58 D.R. = 1.50\n", - "[ NORMAL ] Iteration 183: k_eff = 1.202526 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 57 D.R. = 0.33\n", - "[ NORMAL ] Iteration 184: k_eff = 1.203088 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 56 D.R. = 5.00\n", - "[ NORMAL ] Iteration 185: k_eff = 1.203636 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 54 D.R. = 0.00\n", - "[ NORMAL ] Iteration 186: k_eff = 1.204171 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 53 D.R. = inf\n", - "[ NORMAL ] Iteration 187: k_eff = 1.204692 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 52 D.R. = 2.33\n", - "[ NORMAL ] Iteration 188: k_eff = 1.205202 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 50 D.R. = 0.57\n", - "[ NORMAL ] Iteration 189: k_eff = 1.205698 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 49 D.R. = 0.50\n", - "[ NORMAL ] Iteration 190: k_eff = 1.206183 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 48 D.R. = 0.50\n", - "[ NORMAL ] Iteration 191: k_eff = 1.206656 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 47 D.R. = 6.00\n", - "[ NORMAL ] Iteration 192: k_eff = 1.207118 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 46 D.R. = 0.00\n", - "[ NORMAL ] Iteration 193: k_eff = 1.207570 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 45 D.R. = inf\n", - "[ NORMAL ] Iteration 194: k_eff = 1.208010 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 44 D.R. = 0.33\n", - "[ NORMAL ] Iteration 195: k_eff = 1.208439 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 42 D.R. = 1.33\n", - "[ NORMAL ] Iteration 196: k_eff = 1.208858 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 41 D.R. = 0.50\n", - "[ NORMAL ] Iteration 197: k_eff = 1.209267 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 40 D.R. = 1.50\n", - "[ NORMAL ] Iteration 198: k_eff = 1.209665 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 39 D.R. = 1.33\n", - "[ NORMAL ] Iteration 199: k_eff = 1.210055 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 38 D.R. = 1.50\n", - "[ NORMAL ] Iteration 200: k_eff = 1.210435 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 37 D.R. = 0.17\n", - "[ NORMAL ] Iteration 201: k_eff = 1.210805 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 37 D.R. = 9.00\n", - "[ NORMAL ] Iteration 202: k_eff = 1.211167 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 36 D.R. = 0.11\n", - "[ NORMAL ] Iteration 203: k_eff = 1.211520 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 35 D.R. = 5.00\n", - "[ NORMAL ] Iteration 204: k_eff = 1.211864 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 34 D.R. = 0.60\n", - "[ NORMAL ] Iteration 205: k_eff = 1.212200 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 33 D.R. = 0.67\n", - "[ NORMAL ] Iteration 206: k_eff = 1.212528 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 32 D.R. = 2.50\n", - "[ NORMAL ] Iteration 207: k_eff = 1.212848 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 32 D.R. = 1.60\n", - "[ NORMAL ] Iteration 208: k_eff = 1.213160 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 31 D.R. = 0.25\n", - "[ NORMAL ] Iteration 209: k_eff = 1.213466 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 30 D.R. = 5.50\n", - "[ NORMAL ] Iteration 210: k_eff = 1.213763 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 29 D.R. = 0.55\n", - "[ NORMAL ] Iteration 211: k_eff = 1.214053 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 29 D.R. = 1.67\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Iteration 212: k_eff = 1.214337 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 28 D.R. = 0.30\n", - "[ NORMAL ] Iteration 213: k_eff = 1.214614 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 27 D.R. = 2.33\n", - "[ NORMAL ] Iteration 214: k_eff = 1.214883 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 26 D.R. = 1.29\n", - "[ NORMAL ] Iteration 215: k_eff = 1.215145 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 26 D.R. = 0.56\n", - "[ NORMAL ] Iteration 216: k_eff = 1.215402 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 25 D.R. = 0.40\n", - "[ NORMAL ] Iteration 217: k_eff = 1.215653 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 25 D.R. = 3.00\n", - "[ NORMAL ] Iteration 218: k_eff = 1.215897 res = 1.258E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 24 D.R. = 2.17\n", - "[ NORMAL ] Iteration 219: k_eff = 1.216136 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 23 D.R. = 0.77\n", - "[ NORMAL ] Iteration 220: k_eff = 1.216368 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 23 D.R. = 0.80\n", - "[ NORMAL ] Iteration 221: k_eff = 1.216595 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 22 D.R. = 0.25\n", - "[ NORMAL ] Iteration 222: k_eff = 1.216817 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 22 D.R. = 3.00\n", - "[ NORMAL ] Iteration 223: k_eff = 1.217033 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 21 D.R. = 0.33\n", - "[ NORMAL ] Iteration 224: k_eff = 1.217244 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 21 D.R. = 4.00\n", - "[ NORMAL ] Iteration 225: k_eff = 1.217450 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 20 D.R. = 0.75\n", - "[ NORMAL ] Iteration 226: k_eff = 1.217651 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 20 D.R. = 0.17\n", - "[ NORMAL ] Iteration 227: k_eff = 1.217847 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 19 D.R. = 4.00\n", - "[ NORMAL ] Iteration 228: k_eff = 1.218039 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 19 D.R. = 2.50\n", - "[ NORMAL ] Iteration 229: k_eff = 1.218225 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 18 D.R. = 0.20\n", - "[ NORMAL ] Iteration 230: k_eff = 1.218407 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 18 D.R. = 2.50\n", - "[ NORMAL ] Iteration 231: k_eff = 1.218585 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 17 D.R. = 1.00\n", - "[ NORMAL ] Iteration 232: k_eff = 1.218758 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 17 D.R. = 1.40\n", - "[ NORMAL ] Iteration 233: k_eff = 1.218927 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 16 D.R. = 0.71\n", - "[ NORMAL ] Iteration 234: k_eff = 1.219092 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 16 D.R. = 1.00\n", - "[ NORMAL ] Iteration 235: k_eff = 1.219253 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 16 D.R. = 1.40\n", - "[ NORMAL ] Iteration 236: k_eff = 1.219410 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 15 D.R. = 0.57\n", - "[ NORMAL ] Iteration 237: k_eff = 1.219563 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 15 D.R. = 0.50\n", - "[ NORMAL ] Iteration 238: k_eff = 1.219712 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 14 D.R. = 0.00\n", - "[ NORMAL ] Iteration 239: k_eff = 1.219858 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 14 D.R. = -nan\n", - "[ NORMAL ] Iteration 240: 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(pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 3.50\n", - "[ NORMAL ] Iteration 249: k_eff = 1.221135 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 1.43\n", - "[ NORMAL ] Iteration 250: k_eff = 1.221246 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 0.80\n", - "[ NORMAL ] Iteration 251: k_eff = 1.221355 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 10 D.R. = 0.87\n", - "[ NORMAL ] Iteration 252: k_eff = 1.221461 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 10 D.R. = 0.43\n", - "[ NORMAL ] Iteration 253: k_eff = 1.221564 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 10 D.R. = 1.67\n", - "[ NORMAL ] Iteration 254: k_eff = 1.221665 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 10 D.R. = 0.40\n", - "[ NORMAL ] Iteration 255: k_eff = 1.221763 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 9 D.R. = 0.00\n", - "[ NORMAL ] Iteration 256: k_eff = 1.221859 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 9 D.R. = inf\n", - "[ NORMAL ] Iteration 257: k_eff = 1.221953 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 9 D.R. = 1.00\n", - "[ NORMAL ] Iteration 258: k_eff = 1.222044 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 9 D.R. = 0.40\n", - "[ NORMAL ] Iteration 259: k_eff = 1.222133 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 1.50\n", - "[ NORMAL ] Iteration 260: k_eff = 1.222220 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 0.00\n", - "[ NORMAL ] Iteration 261: k_eff = 1.222305 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = inf\n", - "[ NORMAL ] Iteration 262: k_eff = 1.222387 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 1.17\n", - "[ NORMAL ] Iteration 263: k_eff = 1.222468 res = 1.839E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 2.71\n", - "[ NORMAL ] Iteration 264: k_eff = 1.222547 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 7 D.R. = 0.16\n", - "[ NORMAL ] Iteration 265: k_eff = 1.222624 res = 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1.33\n", - "[ NORMAL ] Iteration 274: k_eff = 1.223238 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 1.00\n", - "[ NORMAL ] Iteration 275: k_eff = 1.223298 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 1.00\n", - "[ NORMAL ] Iteration 276: k_eff = 1.223357 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 1.00\n", - "[ NORMAL ] Iteration 277: k_eff = 1.223414 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.50\n", - "[ NORMAL ] Iteration 278: k_eff = 1.223470 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 1.50\n", - "[ NORMAL ] Iteration 279: k_eff = 1.223524 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 1.67\n", - "[ NORMAL ] Iteration 280: k_eff = 1.223577 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.20\n", - "[ NORMAL ] Iteration 281: k_eff = 1.223629 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 5.00\n", - "[ NORMAL ] Iteration 282: k_eff = 1.223680 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1.224373 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 2.00\n", - "[ NORMAL ] Iteration 300: k_eff = 1.224406 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 5.50\n", - "[ NORMAL ] Iteration 301: k_eff = 1.224438 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.82\n", - "[ NORMAL ] Iteration 302: k_eff = 1.224468 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.78\n", - "[ NORMAL ] Iteration 303: k_eff = 1.224498 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.71\n", - "[ NORMAL ] Iteration 304: k_eff = 1.224528 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.40\n", - "[ NORMAL ] Iteration 305: k_eff = 1.224557 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 3.00\n", - "[ NORMAL ] Iteration 306: k_eff = 1.224585 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.83\n", - "[ NORMAL ] Iteration 307: k_eff = 1.224612 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.20\n", - "[ NORMAL ] Iteration 308: k_eff = 1.224639 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 3.00\n", - "[ NORMAL ] Iteration 309: k_eff = 1.224665 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 3.33\n", - "[ NORMAL ] Iteration 310: k_eff = 1.224691 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.80\n", - "[ NORMAL ] Iteration 311: k_eff = 1.224716 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.50\n", - "[ NORMAL ] Iteration 312: k_eff = 1.224740 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.75\n", - "[ NORMAL ] Iteration 313: k_eff = 1.224764 res = 1.161E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 4.00\n", - "[ NORMAL ] Iteration 314: k_eff = 1.224786 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.17\n", - "[ NORMAL ] Iteration 315: k_eff = 1.224809 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 1.50\n", - "[ NORMAL ] Iteration 316: k_eff = 1.224831 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 3.67\n", - "[ NORMAL ] Iteration 317: k_eff = 1.224852 res = 1.161E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 1.09\n", - "[ NORMAL ] Iteration 318: k_eff = 1.224873 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.00\n", - "[ NORMAL ] Iteration 319: k_eff = 1.224893 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = inf\n", - "[ NORMAL ] Iteration 320: k_eff = 1.224913 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.63\n", - "[ NORMAL ] Iteration 321: k_eff = 1.224932 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.40\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Iteration 322: k_eff = 1.224951 res = 8.711E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.29\n", - "[ NORMAL ] Iteration 323: k_eff = 1.224969 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.11\n", - "[ NORMAL ] Iteration 324: k_eff = 1.224987 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 8.00\n", - "[ NORMAL ] Iteration 325: k_eff = 1.225005 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.25\n", - "[ NORMAL ] Iteration 326: k_eff = 1.225022 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.50\n", - "[ NORMAL ] Iteration 327: k_eff = 1.225039 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 2.00\n", - "[ NORMAL ] Iteration 328: k_eff = 1.225055 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 3.00\n", - "[ NORMAL ] Iteration 329: k_eff = 1.225071 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.83\n", - "[ NORMAL ] Iteration 330: k_eff = 1.225086 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.27\n", - "[ NORMAL ] Iteration 331: k_eff = 1.225102 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.00\n", - "[ NORMAL ] Iteration 332: k_eff = 1.225116 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.00\n", - "[ NORMAL ] Iteration 333: k_eff = 1.225131 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 2.00\n", - "[ NORMAL ] Iteration 334: k_eff = 1.225145 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.83\n", - "[ NORMAL ] Iteration 335: k_eff = 1.225159 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.20\n", - "[ NORMAL ] Iteration 336: k_eff = 1.225172 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 6.00\n", - "[ NORMAL ] Iteration 337: k_eff = 1.225185 res = 1.355E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 2.33\n", - "[ NORMAL ] Iteration 338: k_eff = 1.225198 res = 1.258E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.93\n", - "[ NORMAL ] Iteration 339: k_eff = 1.225210 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.00\n", - "[ NORMAL ] Iteration 340: k_eff = 1.225222 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] Iteration 77: k_eff = 0.935312 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 650 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 78: k_eff = 0.941689 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 637 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 79: k_eff = 0.947945 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 625 D.R. = inf\n", + "[ NORMAL ] Iteration 80: k_eff = 0.954080 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 613 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 81: k_eff = 0.960096 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 601 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 82: k_eff = 0.965994 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 589 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 83: k_eff = 0.971776 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 578 D.R. = inf\n", + "[ NORMAL ] Iteration 84: k_eff = 0.977442 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 566 D.R. = 1.7143\n", + "[ NORMAL ] Iteration 85: k_eff = 0.982996 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 86: k_eff = 0.988439 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 544 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 87: k_eff = 0.993771 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 533 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 88: k_eff = 0.998994 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 522 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 89: k_eff = 1.004111 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 511 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 90: k_eff = 1.009122 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 501 D.R. = 0.8571\n", + "[ NORMAL ] Iteration 91: k_eff = 1.014031 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 490 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 92: k_eff = 1.018836 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 480 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 93: k_eff = 1.023541 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 470 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 94: k_eff = 1.028147 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 460 D.R. = 0.4286\n", + "[ NORMAL ] Iteration 95: k_eff = 1.032657 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 450 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 96: k_eff = 1.037070 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 441 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 97: k_eff = 1.041390 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 431 D.R. = 0.7143\n", + "[ NORMAL ] Iteration 98: k_eff = 1.045618 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 422 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 99: k_eff = 1.049755 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 413 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 100: k_eff = 1.053803 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 404 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 101: k_eff = 1.057764 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 396 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 102: k_eff = 1.061639 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 387 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 103: k_eff = 1.065429 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 379 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 104: k_eff = 1.069137 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 370 D.R. = 2.3333\n", + "[ NORMAL ] Iteration 105: k_eff = 1.072764 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 362 D.R. = 0.5714\n", + "[ NORMAL ] Iteration 106: k_eff = 1.076311 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 354 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 107: k_eff = 1.079780 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 346 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 108: k_eff = 1.083172 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 339 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 109: k_eff = 1.086490 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 331 D.R. = 10.0000\n", + "[ NORMAL ] Iteration 110: k_eff = 1.089733 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 324 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 111: k_eff = 1.092904 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 317 D.R. = inf\n", + "[ NORMAL ] Iteration 112: k_eff = 1.096005 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 310 D.R. = 0.8571\n", + "[ NORMAL ] Iteration 113: k_eff = 1.099036 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 303 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 114: k_eff = 1.102000 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 296 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 115: k_eff = 1.104896 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 289 D.R. = 0.5714\n", + "[ NORMAL ] Iteration 116: k_eff = 1.107727 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 283 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 117: k_eff = 1.110494 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 276 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 118: k_eff = 1.113199 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 270 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 119: k_eff = 1.115842 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 264 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 120: k_eff = 1.118425 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 258 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 121: k_eff = 1.120950 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 252 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 122: k_eff = 1.123417 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 246 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 123: k_eff = 1.125828 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 241 D.R. = inf\n", + "[ NORMAL ] Iteration 124: k_eff = 1.128183 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 235 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 125: k_eff = 1.130485 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 230 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 126: k_eff = 1.132733 res = 1.161E-07 delta-k (pcm)\n", + "[ 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0.2500\n", + "[ NORMAL ] Iteration 135: k_eff = 1.150772 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 182 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 136: k_eff = 1.152551 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 177 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 137: k_eff = 1.154288 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 173 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 138: k_eff = 1.155985 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 169 D.R. = inf\n", + "[ NORMAL ] Iteration 139: k_eff = 1.157643 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 165 D.R. = 0.8571\n", + "[ NORMAL ] Iteration 140: k_eff = 1.159261 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 161 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 141: k_eff = 1.160842 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 158 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 142: k_eff = 1.162386 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 154 D.R. = 2.2000\n", + "[ NORMAL ] Iteration 143: k_eff = 1.163892 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 150 D.R. = 0.5455\n", + "[ NORMAL ] Iteration 144: k_eff = 1.165364 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 147 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 145: k_eff = 1.166802 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 143 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 146: k_eff = 1.168206 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 140 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 147: k_eff = 1.169576 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 136 D.R. = 2.2000\n", + "[ NORMAL ] Iteration 148: k_eff = 1.170913 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 133 D.R. = 0.7273\n", + "[ NORMAL ] Iteration 149: k_eff = 1.172220 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 130 D.R. = 0.6250\n", + "[ NORMAL ] Iteration 150: k_eff = 1.173496 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 127 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 151: k_eff = 1.174741 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 124 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 152: k_eff = 1.175957 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 121 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 153: k_eff = 1.177144 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 118 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 154: k_eff = 1.178303 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 115 D.R. = 1.8000\n", + "[ NORMAL ] Iteration 155: k_eff = 1.179435 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 113 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 156: k_eff = 1.180540 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 110 D.R. = 0.0833\n", + "[ NORMAL ] Iteration 157: k_eff = 1.181618 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 107 D.R. = 7.0000\n", + "[ NORMAL ] Iteration 158: k_eff = 1.182671 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 105 D.R. = 1.4286\n", + "[ NORMAL ] Iteration 159: k_eff = 1.183699 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 102 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 160: k_eff = 1.184702 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 161: k_eff = 1.185682 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 97 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 162: k_eff = 1.186638 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 95 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 163: k_eff = 1.187572 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 93 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 164: k_eff = 1.188483 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 91 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 165: k_eff = 1.189372 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 88 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 166: k_eff = 1.190240 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 86 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 167: k_eff = 1.191088 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 84 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 168: k_eff = 1.191914 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 82 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 169: k_eff = 1.192722 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 80 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 170: k_eff = 1.193510 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 78 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 171: k_eff = 1.194280 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 76 D.R. = 8.0000\n", + "[ NORMAL ] Iteration 172: k_eff = 1.195030 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 75 D.R. = 1.3750\n", + "[ NORMAL ] Iteration 173: k_eff = 1.195763 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 73 D.R. = 0.4545\n", + "[ NORMAL ] Iteration 174: k_eff = 1.196478 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 71 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 175: k_eff = 1.197176 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 69 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 176: k_eff = 1.197858 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 68 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 177: k_eff = 1.198523 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 66 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 178: k_eff = 1.199171 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 64 D.R. = inf\n", + "[ NORMAL ] Iteration 179: k_eff = 1.199805 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 63 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 180: k_eff = 1.200423 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 61 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 181: k_eff = 1.201027 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 60 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 182: k_eff = 1.201615 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 58 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 183: k_eff = 1.202190 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 57 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 184: k_eff = 1.202751 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 56 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 185: k_eff = 1.203299 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 54 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 186: k_eff = 1.203833 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 53 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 187: k_eff = 1.204354 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 52 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 188: k_eff = 1.204863 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 50 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 189: k_eff = 1.205359 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 49 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 190: k_eff = 1.205844 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 48 D.R. = 3.3333\n", + "[ NORMAL ] Iteration 191: k_eff = 1.206316 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 1.3000\n", + "[ NORMAL ] Iteration 192: k_eff = 1.206778 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 46 D.R. = 0.4615\n", + "[ NORMAL ] Iteration 193: k_eff = 1.207229 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 45 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 194: k_eff = 1.207668 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 43 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 195: k_eff = 1.208097 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 42 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 196: k_eff = 1.208516 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.3000\n", + "[ NORMAL ] Iteration 197: k_eff = 1.208924 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 40 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 198: k_eff = 1.209322 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 1.8750\n", + "[ NORMAL ] Iteration 199: k_eff = 1.209711 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 38 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 200: k_eff = 1.210091 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 201: k_eff = 1.210461 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 202: k_eff = 1.210823 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 36 D.R. = 3.2500\n", + "[ NORMAL ] Iteration 203: k_eff = 1.211176 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 0.5385\n", + "[ NORMAL ] Iteration 204: k_eff = 1.211520 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 205: k_eff = 1.211856 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 33 D.R. = 1.5714\n", + "[ NORMAL ] Iteration 206: k_eff = 1.212183 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 0.7273\n", + "[ NORMAL ] Iteration 207: k_eff = 1.212503 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 208: k_eff = 1.212815 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 209: k_eff = 1.213119 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 30 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 210: k_eff = 1.213416 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 211: k_eff = 1.213706 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 212: k_eff = 1.213989 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 213: k_eff = 1.214265 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 214: k_eff = 1.214535 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 215: k_eff = 1.214798 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 26 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 216: k_eff = 1.215054 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 217: k_eff = 1.215304 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 218: k_eff = 1.215548 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 24 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 219: k_eff = 1.215786 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 220: k_eff = 1.216019 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 221: k_eff = 1.216246 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = inf\n", + "[ NORMAL ] Iteration 222: k_eff = 1.216467 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 223: k_eff = 1.216683 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 224: k_eff = 1.216894 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 225: k_eff = 1.217100 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 226: k_eff = 1.217300 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 0.7143\n", + "[ NORMAL ] Iteration 227: k_eff = 1.217496 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 228: k_eff = 1.217687 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 229: k_eff = 1.217873 res = 1.549E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 230: k_eff = 1.218055 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 0.6875\n", + "[ NORMAL ] Iteration 231: k_eff = 1.218233 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.5455\n", + "[ NORMAL ] Iteration 232: k_eff = 1.218406 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 233: k_eff = 1.218574 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 234: k_eff = 1.218739 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = inf\n", + "[ NORMAL ] Iteration 235: k_eff = 1.218900 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 236: k_eff = 1.219057 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 237: k_eff = 1.219209 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 238: k_eff = 1.219359 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 3.2500\n", + "[ NORMAL ] Iteration 239: k_eff = 1.219505 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.9231\n", + "[ NORMAL ] Iteration 240: k_eff = 1.219647 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.0833\n", + "[ NORMAL ] Iteration 241: k_eff = 1.219786 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 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9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 251: k_eff = 1.221000 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 8.0000\n", + "[ NORMAL ] Iteration 252: k_eff = 1.221106 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 253: k_eff = 1.221209 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 254: k_eff = 1.221310 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 255: k_eff = 1.221409 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 3.3333\n", + "[ NORMAL ] Iteration 256: k_eff = 1.221504 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 257: k_eff = 1.221598 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 258: k_eff = 1.221689 res = 3.872E-08 delta-k (pcm)\n", + "[ 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res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 276: k_eff = 1.223000 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = -nan\n", + "[ NORMAL ] Iteration 277: k_eff = 1.223057 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = inf\n", + "[ NORMAL ] Iteration 278: k_eff = 1.223113 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 11.0000\n", + "[ NORMAL ] Iteration 279: k_eff = 1.223168 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.8182\n", + "[ NORMAL ] Iteration 280: k_eff = 1.223221 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 281: k_eff = 1.223272 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 282: k_eff = 1.223323 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 283: k_eff = 1.223372 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 284: k_eff = 1.223420 res = 1.645E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 17.0000\n", + "[ NORMAL ] Iteration 285: k_eff = 1.223467 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.1765\n", + "[ NORMAL ] Iteration 286: k_eff = 1.223513 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 287: k_eff = 1.223557 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 288: k_eff = 1.223601 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 289: k_eff = 1.223644 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 290: k_eff = 1.223685 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 291: k_eff = 1.223726 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 292: k_eff = 1.223765 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 293: k_eff = 1.223804 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 294: k_eff = 1.223841 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 295: k_eff = 1.223878 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 296: k_eff = 1.223914 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = inf\n", + "[ NORMAL ] Iteration 297: k_eff = 1.223948 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 298: k_eff = 1.223982 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = inf\n", + "[ NORMAL ] Iteration 299: k_eff = 1.224016 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 300: k_eff = 1.224048 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = inf\n", + "[ NORMAL ] Iteration 301: k_eff = 1.224080 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 302: k_eff = 1.224111 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 303: k_eff = 1.224141 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 304: k_eff = 1.224170 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 305: k_eff = 1.224199 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.4286\n", + "[ NORMAL ] Iteration 306: k_eff = 1.224227 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 307: k_eff = 1.224254 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 308: k_eff = 1.224281 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 309: k_eff = 1.224307 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 310: k_eff = 1.224332 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 311: k_eff = 1.224357 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 2.8000\n", + "[ NORMAL ] Iteration 312: k_eff = 1.224381 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.4286\n", + "[ NORMAL ] Iteration 313: k_eff = 1.224405 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 314: k_eff = 1.224428 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 315: k_eff = 1.224450 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 316: k_eff = 1.224472 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.7143\n", + "[ NORMAL ] Iteration 317: k_eff = 1.224494 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 318: k_eff = 1.224514 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.8000\n", + "[ NORMAL ] Iteration 319: k_eff = 1.224535 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 320: k_eff = 1.224554 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 3.3333\n", + "[ NORMAL ] Iteration 321: k_eff = 1.224574 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 322: k_eff = 1.224592 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 323: k_eff = 1.224611 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 324: k_eff = 1.224629 res = 9.679E-09 delta-k (pcm)\n", "[ NORMAL ] ... = 1 D.R. = inf\n", - "[ NORMAL ] Iteration 341: k_eff = 1.225233 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 4.00\n", - "[ NORMAL ] Iteration 342: k_eff = 1.225245 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.50\n", - "[ NORMAL ] Iteration 343: k_eff = 1.225256 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.00\n", - "[ NORMAL ] Iteration 344: k_eff = 1.225267 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] Iteration 325: k_eff = 1.224646 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 8.0000\n", + "[ NORMAL ] Iteration 326: k_eff = 1.224663 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.1250\n", + "[ NORMAL ] Iteration 327: k_eff = 1.224680 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.7778\n", + "[ NORMAL ] Iteration 328: k_eff = 1.224696 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5714\n", + "[ NORMAL ] Iteration 329: k_eff = 1.224712 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 330: k_eff = 1.224727 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 331: k_eff = 1.224742 res = 2.904E-08 delta-k (pcm)\n", "[ NORMAL ] ... = 1 D.R. = inf\n", - "[ NORMAL ] Iteration 345: k_eff = 1.225278 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.38\n", - "[ NORMAL ] Iteration 346: k_eff = 1.225288 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 1.33\n", - "[ NORMAL ] Iteration 347: k_eff = 1.225298 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 0 D.R. = 0.50\n" + "[ NORMAL ] Iteration 332: k_eff = 1.224757 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 333: k_eff = 1.224771 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 334: k_eff = 1.224785 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 335: k_eff = 1.224799 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 336: k_eff = 1.224812 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.6000\n", + "[ NORMAL ] Iteration 337: k_eff = 1.224825 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.7692\n", + "[ NORMAL ] Iteration 338: k_eff = 1.224838 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.1000\n", + "[ NORMAL ] Iteration 339: k_eff = 1.224851 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 340: k_eff = 1.224863 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 341: k_eff = 1.224874 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 342: k_eff = 1.224886 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 343: k_eff = 1.224897 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 8.0000\n", + "[ NORMAL ] Iteration 344: k_eff = 1.224908 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 345: k_eff = 1.224919 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 346: k_eff = 1.224929 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 347: k_eff = 1.224940 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 348: k_eff = 1.224949 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.3333\n" ] } ], @@ -2534,16 +2530,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.224484\n", - "openmoc keff = 1.225298\n", - "bias [pcm]: 81.4\n" + "openmc keff = 1.224010\n", + "openmoc keff = 1.224949\n", + "bias [pcm]: 94.0\n" ] } ], @@ -2571,9 +2567,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "## Visualizing MGXS Data" ] @@ -2591,7 +2585,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2600,13 +2594,13 @@ "(1e-05, 20000000.0)" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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07twVgNWrVzJggHXs3Ny2ZGRk1krmnjlp//37k5qaSlpaOt2796gqNtdQTaGf41igABhsjDlbRN6OxEEHdGzOlFED+fenSxj/1TLmrNvJP4/pRbOUpEgcXqmEVXr12Dq7ZMKpQ4eOlJaW8s47b3DFFdeyfr2VrN0VMlNSUqoqZHqy2ew1knFBwQCeeGJy1f1WrVrz0EMT6Nt3f9q0aQNYF5Q9q3y6b3fp0o158+bSu3cftmzZzO7du8jJaU5qaipFRVtp1649y5cvpWvXboB1/aCystJVvXNFVQXRhgpr4jfGHAI8ICJHGmPswCSsJF8GXCYiy0XkDNdzp0Qq6bu1ykjlsTMO4IWf1/DszNUs3lTMAyfvR7fWGZEMQykVYUcffSxffPEpnTt3qUr8vipkeiooGMDNN1/HJZeM8bnPPn36cvXV1zF+/J1UVFSwd28pbdq05Z57Hqj13AsvvJgJE+7mu+++oaysjFtvvZ3k5GTOP/9CbrnlevLz25OdnV31/IqKCm6++Tp27tzJ6NGX0qJFi0adf9jG8RtjbgVGAXtE5FBjzBnAKSJykTHmUOA2ETk1mH01dBx/ffyyejv/9+kSSvZV8q/jenFC38guFKGUUr7Mnv2bz4vJdYnWOP5C4AyP+8OAzwFEZBYQthE8DTG4S0umjBpI3/xs/v2pcN9XSymr0OUdlVLxJ2yJX0TeBco9NuUAOz3uVxpjmsI1hiq5WWlMOrs/Fw3uxPvzN3LJa3NYuz0i15yVUsqngQMPrndrvy6RHNWzC8j2uG8XkYoIHj8oyXYb1xzejUdO78fG3WWMmjKbb5duiXZYSikVMpFM/DOAEQCuPv4FETx2vQ3r3popowbSrXUG//hoMROnFVJeqV0/SqnYF8nE/z6w1xgzE3gEuDGCx26QdjnpTD6ngPMGduCN2esY8+Y8Nu7aG+2wlFKqUeK6Omcofbt0C3d/sZRku427TjAM69462iEppZRfCVudM5SG987llQsGkpedxo3vL+KJH1ZSocs7KqVikLb466mswsHD0wp5b/4GDuzYnPEn9iE3Ky3aYSmlVA0JtfRipHz6xyYmfLWMZilJ3HNiHw7p0jLaISmlVBXt6gmDEfvl8dIFB9IiI4Wx7yzg2Z9WU6ldP0qpGKAt/kYqLa/k/q+X8ekfmzmkSwvuHtGHVhmp0Q5LKZXgtKsnzJxOJ1MXbOShaYXkpCdz34l9GdCxebTDUkolMO3qCTObzcZp/dvxwnkDSE+2c+Vb83jl17U4YuBDVSmVePy2+I0xr9XxWqeI/K2O54REU2/xeyouq+DeL5fyzdKtHN69FXceb2jeLCXaYSmlEkyDunqMMXOAG/y9DnhERHwvXxNisZT4wer6eWvOeh6dvoLcrFQmnNSXfu1yoh2WUiqBNDTxHyYiMzzutxaRIn+Ph1OsJX63RRt2cdvHi9lSvI8b/tKdkQe2r7Eaj1JKhUujLu4aY0YAT2CVVM4ExojId6EMsC6xmvgBdpaWc9fnwo8rtnFM7zbcflxvstKaVDVqpVQcauzF3TuBQ1zdOkcA94cqsETQvFkKE0/rx3VHdGPasq2MfnUOSzcXRzsspVQCCybx7xaRLQAishHYE96Q4o/dZmPUoE48PbKA0vJKLnl9LlMXbCAWhtIqpeJPoD7+CYATGAoUAz8Cg4F0ERkRsQiJ7a4eb9tK9vF/nyzhlzU7OHG/tvzjmF40S0mKdlhKqTgTqKsnUGfzEtdv8dg2NSQRJbBWGan898wDeGHWGp79aTV/bCrmgZP3o1vrjGiHppRKEIFa/C+KyEX+XljX46EUTy1+Tz+v3s7/fbKEvRWV/OvY3hzft220Q1JKxYmGDufcCSzy9zqgr4i0aHx4dYvXxA+wpbiM2z9ezJx1uzijfzv+flQP0pJ1QrVSqnEa2tXTPwyxKC+5WWlMGlnA0zNW8dIva1m4YRcPnLIfHVs0i3ZoSqk4pUXampAfVxRx52dCpcPJv483DO/VJtohKaVilFbnjCEbdu3lto8Ws2jjbi44uCPXHN6NZLvO9lVK1Y8m/hizr8LBI98V8s48a3nH+07qS5tMrfGvlApeY0s2HAv8HahaWFZEhocsuiAkWuJ3+/SPTdz31TKy05KZcJLW+FdKBa+xiX8hVpXOte5tIiL+XxF6iZr4AZZv2cOtHy5i/a4yrjuiG+cN7KCF3pRSdWps4v800jN1vSVy4gerxv+4z4XvlhdxrMnljuN6k5Gqs32VUv41NvG/COwF5mCVcEBEJocwvjoleuIHq8b/y7/+yaQfV9KlZQb/OWU/uupsX6WUH42tzrkS2ADkA+1cPyrCbDYbowd34omzDmBHaTmjX53D11btPKWUqpegRvUYY04E+mF170e8Xo+2+GvatLuM2z76gwUbdnP+QR0Ye3g3kpN0tq9Sqlpju3omAL2wqnMeAawQkZtDGmEdNPHXVl7p4LHpK3hzznoO7JBjDfnMSqv7hUqphNDYrp4jROQsEXkUOBM4PGSRqQZLSbJz8/Ce3DOiD4s3FXPBlDnM+XNntMNSSsWAYBJ/ijHG/Twbrgu8oWKMOcgY86Ix5iVjTF4o950Iju/blv/97UAyU5O46q15vPrbn7rAi1IqoGAS/5vADGPMI1jdPW+GOIZ0rHkCnwBDQrzvhNCzTSYv/e1AjujZhkenr+C2jxezZ19FtMNSSjVRwV7c3R/oAywRkYWhDsIYMwR4DBgpIqu8H9c+/uA4nU6m/PYnT/zgGvJ56n50baVDPpVKRA3q4zfGXOb6PQE4HxgInG+MuS+UwRljBgG/AydglYZQDWRzre375Fn92V5azkWvzuH7wqJoh6WUamIC1eN3l2hY4rU96Na3MeYQ4AEROdJ1nWASUACUAZeJyHIgB3gB2AdEdGJYvDq4cwteueBAbpn6Bzd9sIgxQ7pw6ZDO2LXUg1KKAIlfRL5w3RwkIte6txtjXgZermvHxphbgVHAHtem07AWah9ijDkUmAicKiLfAN80MH7lR35OOs+eW8D9Xy9j8k+rWbK5mHEnGLLSAn3WK6USQaCunmuMMRuAy40x610/G4EOQe67EDjD4/4w4HMAEZkFHNzAmFWQ0lOSuPN4w81H9WDGym1c9OocVhWVRDsspVSU+U38IvKkiLQDxolIe9dPvogcHcyOReRdoNxjUw7gOdC80hijzc8ws9lsnDOwA5POPoDdZRVc9Nocvlu2NdphKaWiKJjhnAuNMeMAjDGfG2OOa+CxdgHZnscWER1zGCEDO7bg5QsG0qVVBrd8+AdPz1iFQ8f7K5WQgkn8dwEPu26f47rfEDOAEQCuPv4FDdyPaqC87DQmn1PAyf3yeH7WGm76YBG79+pnr1KJJpjEXy4iOwFcvysbeKz3gb3GmJnAI8CNDdyPaoS0ZDv/99fe/OPonvy0ajsXvTaHFUV76n6hUipuBFOk7XGgNfATMBjYLiLXRSC2KjqBKzzm/rmTf3z0B3vLHdx5gmF4rzbRDqlJ2lpcxuuz13PN4V0DDon9obCI/u1zaN4sJYLRKeVbo4q0ichY4C2gGfBWpJO+Cp8BHZvzygUD6dEmg398+AdPab+/T/d8uZSXf10bsAjejpJy/v7BIm758I8IRqZUw9SZ+I0x2VhDL/tgFWzrGfaoVMS0zU7j6ZEFnLp/Pi/MWsO/Pl7M3vKG9ubFp/JK68OwwuH/Q7Hc4QBg7fbSiMSkVGMEM5zyBeAz4C/ARuB51+2Iad2tPfY9xZE8ZMJ5zPVTH47MLEpuuY3Sq8eGI6Qmw+76whxMXSv9vqRiQTAXd1uLyAtYF3lnBvmakNKk3zTZ9xST8eCEaIcRdjZXv36ABj9aDEPFkqCSuDGmj+t3RyDi4/8cmVmRPqQKUiJ8KFe3+Ot+rq6FoGJBMF091wH/A/oC7wBXhzUiH4pWro/0IRPe1j37uHXqIhZs2M3Yw7sxalDHqpYvQG7bnChGF1n2qhZ/oCZ/4Db/wg27aN88nVYZqaEMTakGCWZUz0IRGQJ0A44VkdnhD0tFW5vMVJ4aWcBxJpfHf1jJxGmFCT/iJ1BXT10ufm0uF06ZE7pglGoEvy1+Y8xArAu5g4GTgGeA7caYm0XkowjFp6IoLdnOPSf2ITcrjVd//5OiPeWMO8GQmhzxyzxRVd2Wb9wH36bdZY0NRamQCPQ/+EFgtIiUA+OxFkoZBPwzEoGppsFus3HDkd257ohufL10C9e/v5DissQq8+Du4krwLzwqjgRK/EkiMt8Y0x7IFJHfRWQX4IhQbKoJGTWoE+NOMMz5cydXvjU/2uFE1aINuxg08Xv+3KFj9lVsCpT43SWVjwe+BjDGpFCzwqZKICP2y+Ph0/qxelti1fR3d/W4G/wfL9oEwMyV26qf5Po6oN8KVCwIlPi/NsbMwKrG+bgxpgfwIfBmJAJTTdPQbq2YdHb/aIcRUe4BO86q+7W7fjTfq1gSaCGWB4DLgENFZK5r82QRif8ZOyqgA9rXHMq5JlHKFLgyvfc3AI+HlIoJAcfxi8hij9uFWMspKlXDFW/OY9LZ/enWOiPaoYRFVQu/6j417nve1vyvYkFijctTYeFwOrnyrXks3xKfdf2rWvheWd1zlq7O2FWxRNe8VY02+86/Wjfu9P14rBdz89fH31jllQ7m/LmTwV1ahmR/SgUrmLLM/Y0xQ4wxhxhjvjHGBLXYuopv9amfFOvF3Kpb/P5b9c4Az/H3usemr+CadxawZNPuRkaoVP0E09XzNFAG3AHcjt92nUokJbfcVu/kH7tqVuf01d4P1NPj76GVRdaw2B2l5X6eoVR4BJP49wKLgFQRmUXD19xVcaT06rEUrVzPls27avzMW7aZofd9zf7//ozv5v0Z7TBDwt2z465V5CuRO11bK53OWi18zxo/npO+qrqOtKizirBgEr8TeBn41BgzkuqJXUrV0r55Os+M7E+LZilc886CaIcTUoGK1LkfKi6r5NHpK2o8ttJjMfvTn/+1+jWu3yG6ZKBU0IJJ/OcALwH/BTYD54Y1IhXz8nPSeXpkAa0z46MEsTsvO4IsVvLuvA1Vt3fvreD8l2sXtF1VVMJva3ZY+9fEryIsmMSfBqwCegGjgM7hDEjFh7zsNJ4eGdwM3027y9hX0XRLQLkTc2UQLX7rdvWdUj/rF49+tbpEs10zv4qwYBL/a0AecB/wFfBIWCNScSM3K63G/Q8XbKxxv7isgn99vJiTJv/MmS/8yoZdeyMZXr0F7OoJ4ranEo8PBM37KtKCGcfvAL4HbheRN4wxl4c5JhWnLj26d437ucCzrtt7Upvx0bzLaPfk+IjHVRf3uP3KAF9KGjOBSy/uqkgLpsWfAvwH+N4YcxQQHx23KiKCHfKZua+Uk6Y+x6omWPmzqo8/yBa/r9cGYte8ryIsmMR/MVaNnvuxGmmjwxqRiiv1Ge+fta+UjxZuCnNE9ec9nNMnPw8F8z0gVDOBlQpWMIl/BVbD5RGgHRAfg7NVRPgb7+/54+nTPzZR2ZjFbcPAnZYDxdWYSp2a9lWkBZP4JwPdsS7sdgWeC2dAKrFt3bOP39buiHYYNdlqztz1xemR+uv7saVdPSrSgkn8vUTkJhH5QERuBHqGOyiVuDJTk/h88eaoxrCtZB+DJn7P9OVbgdp9/L7r8VTfrnQ4WVyP+js2m42zXviVs174te4nAx8v2sgMz9W/lKqnYBJ/ujEmA8AY0wxICmUAxpijjTHPGmNeNcYUhHLfKvYM79WGacu2stfP+PdIWOYqL/3GnPU1tnt39Xj2zXt/FFw4ZQ7Bstlg9fZSVvtY0MbhdDJ9+dYaHzbjPl/KDe8trLq/alsJgyZ+z/Kt8VkWW4VeMIn/UWCeMeZ9YC6hH8efAYwBHgKOC/G+VYx5/MJBLLrnBDp1aElu25waP627tafZpMfDHkNVOvdecStM9fjtAXr535m7npun/sGnf/j/FvTtUuubyZdLovtNScWOYBL/BuAQYDwwVETeCGUAIvIRVvK/Dqs0hEowwY76iVR5Z18rbAFM/mm139f4y/v1/UDwfv6m3WWAde3D72t03S9VT8Ek/nEisk1EfhORolAHYIxpAzwO/FtEtMmSgOoz5NNXeedvlm7h2Zn+k3Kz2du2AAAgAElEQVRj+Zu3Fairpz48S0GM/3IZqz3mMujCXiocgpm563R18wiu/wMi8q9gdm6MOQR4QESONMbYgUlAAVZ9/8tEZDnwMNb8gAnGmA9E5J0GnIeKYaVXj62xOteSTbsZNWUOtx3TkzMK2gOQ2zbH38v550fW0tCjBnUkPaXxl6Cq6u6719oNJvv6eMoj3xWyYVdZre3rdpbWep7b1IUbmbpwI7/edESN5zzxw0oO7doS07b6A3LQxO/55pohdcemlJdgEv8LDdmxMeZWrKJu7itOpwHpIjLEGHMoMBE4VUQubMj+VfwybbPo2qoZny3eXJX4/fGcVLVhV1loFnx37dL9ddjfMM4affw+Mv9rv6/z+bpzXvy9xv2563b5fJ63a99ZwFdX10z0f+6orm+ko0JVsAJ29RhjjgNeFpGXgHnAJtftYBQCZ3jcHwZ8DuBa0OXg+oerEoHNZuPkfvnMXbcL2Rx45a7Nu6tb1Lv21m+piGvens8zM1bV2u5wJfFf1uxg4rRCvy1+z66e1dtqj8jxp6welUiD6elxh/fCz2uD3q9KbH4TvzHmKuAuwP3d0gHcaYwZE8yOReRdai7akgPs9LhfaYzRxd6VT6f3b0dGShJTfgs8Udwz4RaX1W8I6C9rdvDcrDW1tnu28N+Yvc5vi/+5n1ZXjaS549Ml9Tq2UtEUqMV/ETBcRHYDiMh84FigodU5dwHZnscWkYoG7kvFuez0ZE7rn89XSzazfqf/cs2eY993l4Xmz8m7hV/7vvV7194Kbv9kCee9VLPrJlx8deVc/fZ8SvbpaqiqfgIl/hIRqfE/TkSKgeCnJNY0AxgB4Orjj691+VTInTewA0l2G5NnrvL7nDXbq0fAFIcs8de8793i/2FFzcFt4Zg49d689bW2bS8tZ/76mtcD9uyr5BWPb0UVDicPfL2sRheYUt4CJf5y11DLKq77De2eeR/Ya4yZiTUJ7MYG7kcliPycdM4d2CHg5KU120vp1sq6oBuqFr93ove8gFxR6fA5UifUJny9nJs/WMTuvTXP6c3Zvi8Yu/28ejvvzNvAfV8tC2d4KsYFSuL3AF8aY17CqtDZGbgUuDXYnYvIKuBQ120HcGWDI1UJ6aLBnZnqtXKXpxVFJRS0z2HN9pKQdXn469qBwIXaQm16Ye1pM0vquNjtjl0ndalA/Lb4ReQH4EygOXAi1sXZ00Xk6wjFphTZ6cnccGR3n49tKS5j0+4y+rXLJiM12e/6tvXlPebG3eJPbgJlNNf4qOfjyf0hpev4qkACdtuIyErg7gjFopRPJ+6X53P77LXWILH+7XNolmJnTz1a/AFr63u3+F2/KxzOwIuxNAFNbCkD1UQFU7JBqajyXqHKPcrns8WbaZOZSt+8bDJTk+vV1VMRIEN6fyjU6ONv4pnVXf7Bs8U/fXkRgyZ+z4oird6pLDqOXsWcC6fMZki3VsxYuY0rD+tCkt1GRmpS0Im/rMLB2gBdJpVO78Rffbuismkn/oenWeUf3Gn/qRmreME1V+GPjbvp3jozSpGppqTOFr8x5mRjzDjX7c9ds3mViprebbP4btlWju/bllEHdwKwEn+QffwPfL2M8172P/beO7k7PDJ/uSP4WbfRsMlrGOfrv1cP9Swuq2TjLv9zIlTiCKbFPw44ynX7HOAz4MuwRaRUHSad3b/WtoyUJLaXBFey4Ys66tZ7d+d4dvWUN/EWv5vNZi3QUlpe/UE1cVohE6cV1ioApxJPMH385SKyE8D1W6cJqibH6uoJbhx/Xd305ZU1W/Weud77sabKZrNx9v9+8/v4jyuK6l3bSMWPYFr8vxhjXgN+AgYDwa8pp1SEZKQmBT2qp66ROfsCdvXERot/2rKtfh/bVrKPG99fxMGdmvPUSF3tNBHV2eIXkbHAW1irZL0lIteFPSql6ikzNSnocfx15e59XtUzPT8o3plbu5RCrHGf329rdzZqyUgVuwJV5zzJ9XsM0BbYDrQLtjqnUpHULCWJfZVOKkLQFVPmtQ/PPv93521o9P6bkl/W7Ih2CCoKArX4W7t+t3P95HvcVqpJyUi1Vt6qzyQuf8rKvVv8jd5lk3L9ewurbi/dXIzD6WTeup088cPKKEalIslW11c9Y4wNa/UsAywUkY8jEZinLVt2x9l/PVVfgZZebCxHZhYlt9xWtfzjf75ZztseXTq9cjNZtiV+Jz9dPawrk35cBaAjfuJIbm6237odwYzqeRZrGGcpcKEx5uFQBaZUsIJdjL0h7HuKyXhwQtV97z7+Soczrpc1XLShoZXWVawKJvEfICLnishjIjISGBruoJTyVnLLbWFP/m7effwOZ3zXutR6boknmOGcy40x3URkpTGmLVB7rTqlwqz06rFVXTG+zPlzJ2PenMcTZx7AIV1bBtzXoInfV91e9cBJtR6vPaoHOrdsVmdlzFjlWQvp40UbOalffhSjUZEQTIt/CLDEGLMMWAUca4zZYIyJ/XFtKm5UXdwNQWnmfT5G9eRlpzV6v02V55j/cZ8vjWIkKlLqbPGLiO9i6Eo1IZmuxF/qNarn40Ubkc17uOmoHkDdk7fAKuLmyeFw0iwlKUSRKhV9dSZ+Y8wBwAtAR2AjcImI6Oxd1aT4G87pbsG6E7/3Uoa++JrAlZacOBXMS8sraZaSxJuz1zG8dxtys+L3206iCqaP/7/AZSIyzxgzAHgSOCy8YSlVPxkp7sQfOLFvL627Po13i7/SSUIl/otenUP75un8uGIbny/ZzP/OPzDaIakQCybx20RkHoCIzDXGhGZFa6VCKC3ZTquMFFZvK/H5uNPpxGazBazg6Z4r8Lmfx59obJCx6oaGvcx7foRqOoJpxlQaY04yxjQ3xpwMlNX5CqUizGazsV9+Nn9s9L0YuXtVLe8Wf3Fqs7DHlqi850eopiOYxH8JMBqYAYwCLg9rREo1UL/8bFZtK6G4rPaX0r2u7pvtJftqbP/vYeeFdX5AovOcH6GajmC6eoqBySLylTHmWmBnmGNSqkH2y8/GCSzZVMzBnVvUeMw9RHObV1fPs4PP4PI3H+XKt+bx+9qdTDr7AG798A+Ky6ovEjdLsXN6/3a89vu6sJ9DU/b8eQPo3z640hnhLLGhGi+YFv8bgPuy/jZgSvjCUarh9svLBqy1Zb3tdRVeKy6rICMlib/0sGoQOqnu/wdrspavCVxJOr01rstWJJpgEn+muzCbiLyGVZdfqSanRUYKHZqns8hH4nfX6i/ZV0lGahIPnrofY4Z0AaxRO+6k9sh3hbUWYql0OLHbNe2p+BFMV88+Y8yxwCysFbhiY+05lZAGdMhhemGR1bJPrZ505U78peVW4rfZbCQnuVr5HkXYCrfWHhXkcDpJ0sTf5BeaV8ELpsV/GXAN8AtwNXBFWCNSqhHOHdiB4rJKnvxhJSUek7nct0v2VVbNwrVXde84AxYqs7p6whdzrLjizfm6YlecCGbpxeXAmcABwKPA2nAHpVRD9cnL5vyDOvDOvA08PK2wartni79ZivVn727EO5xg8+jB7tkms9Z+7TYbZxboGkTxWqgu0dSZ+I0xj2K1+u8Gbseqz69UkzX2iO78tU8uHy3aVLWtpCrxO3y2+D2vXB7VqzXekuw2jurVpsa2cw5sH+rQm7wVRb4nyKnYEkxXzyAReQYYIiLHY9XsCTljzHBjzHPh2LdKLMl2G+NO6MM/j+nJ5UM6A7BplzXvsMTVxw9UXbD1XmglI7X2pS+7zVarUJv3B0EiuPXDP6IdggqBYC7uJhljDgJWGWNSgexQB2GM6QkcCKSHet8qMSXZbZxZYLXIP1y4icWbrIlEpR59/ElVXT01+60zU2tX4rTboE/b6oleH485hPU794YjdKXCLpgW/8vAJOAh4D/AM6EOQkSWi8jEUO9XKYDDu7dixspt7Npbbo3q8erqqXTWXIXKV+JPSbKTmmznsTP2Z8R+bWmblYoO9FGxKpiLu5OAEVjj9+8VkefDHpVSIXRa/3aUVTj4aOEmSsoraebV1eN0Omtc3M300dWT7Hru0G6tGHdCH2w2W42VqwA6ttAvrCo2BHNxdyQwE+vC7ixjzAX1OYAx5hBjzHeu23ZjzNPGmJ+MMd+5uniUCivTNosBHXJ4ffY6yiud1aN6XI9XOmoO58zw2eKv3bz33nTK/vlkpemCLarpC6ar50bgIBE5Dasf/vpgd26MuRV4juq++9OAdBEZAvwTqNG9IyL1+lBRKlhnD2jPpt3WBd6qUT326jINnnx19STba/9X8W7xA7gvF/ztoLCMgVAqJIJJ/A4RKQYQkd1Afa5oFQJneNwfhqvcuYjMAg6ux76UarCh3VpV3XYn9iTPCVwezw22xe/dx+95jfiA9tncdox+oVVNUzCjelYYYyYC3wNHYCXzoIjIu8aYrh6bcqhZ3bPSGJMsIrq4iwqrrLRkkmzWhVz3cE13I77Sq8mf5auPP6l2G8m7po+T6vs2IOB0YKWiKJgW/6XACuBY1+/G1OPfRc3hoHZN+ipSOrey6gtm1Grx1+y28dXiT/YxhMe7fo/dYx82m02rWaomK5gW/8ciclyIjjcDOBl4yxhzKLAgRPtVqk6nHZDPI9+tIC/bqjJu89PVk+pjfV1fXT2e4/oBKhxeLX6lmqhgEv92Y8ypgOCqzCkiSxt4vPeBY40xM7H+b1zcwP0oVW8jD+zAQZ1aVNXi8ZzAVVevjL8Wf2ZqEntcBeBqJP4gW/x52WlVF52VipRgEn9bai637ASGB3sAEVkFHOq67QCurEd8SoVMst2G8WilV9XqCaLacIqPUT3eKiqrd2Sz1e7iH39iH7q0zOCCKbMB+OG6w7DbbBz22I9BRK9U6AST+E8A+orIHGPMacAnYY5JqYioqtUTRKnh5CDqMlc4nFUje+y2mhU/AY7r07bG/fQUHfOvoiOYi7tTgAGu272Bl8IXjlKR4zmc03ssvzd/LX7PVn15pWcfv007+lWTFUzi7yAi/wMQkf8AWpRcxQVbVR9/7UJt3oJp8ack2ar2Y7fHb96f8tuf0Q5BNVIwid9pjOkNYIzpAej3UxUXqlr8Hl00h3Rp4fO5vi7uehs9uFPVBd4Uuz1uh/E/Nn0F20v2RTsM1QjBlmx40xizAXgT+Ht4Q1IqMqomcDmdVf38d4/o4/O5KT4mcEHN2bqtMlKrJoMl2221+vjjyS+rd1BR6WDT7jJWFpXw06pt0Q5J1UOdF3dF5GesGj1KxRXPFbjKyis5qFNzWmWkAnDbMT2Z8PXyquf6a/F79xC576Yk2eK2xd+iWQr3f7OMR6avoGhPdcv/jP7tuOHI7rUWrFFNj9/Eb4x5R0TOcrX03X/PNsApIom35pyKO0kewzl37K2gm2tmL8AZrkVc3Mnf1wQuqJ69+8SZB9TY7quom9vXVw+p9YERSx47Y38m/biSn1fvqLH9vfkb6N46g3MGdohSZCpYfhO/iJzl+q0Xc1VccrfIK51OtpeUU9C+5n+HnPSUqtv+EvnVw7rywDfLOahzzWsDgS4GN2+W4vexWLBffjYPndqPv70ym52l5ezcW111Zefe8ihGpoIVqMX/gr/HROSS8ISjVOS4W+t/7tjLjtJyeuXWLMHQwiNB+2vxnzWgPWcNqP0FOCUpfi/ugjUH4e2LD6bS4WToo9UT0KYu2MjlQ7pEMTIVjEAXdw/Gqsa5BngD68Ku+0epmOfu45/9p9VlMbBj8xqP52alVt32Vb8nkKy0pLi+uAvW+5eSZOfH64dVbdtcvI+J04Iu4KuixO9fs4j0x7VwCtaiKUOAQhH5IkKxKRVW7j7+39bsICc9me5tMmo87tnitwfZfHdfBM5KTY7ztF8tLdnOh5cPZvyJ1oioN+esj3JEqi4BmzEislBE/ikiw4FvgQnGmFmRCU2p8Epydd/s3FtBQfucWsk9rZ6tfICnR/bnqsO6kpoc31093trlpHNcn7ac6+PC7jkv/kaJq5CdahrqHM5pjMnGWkXrPCATq4SDUjEv3SOx92uXXevxhiT+gg7NKejQvO4nxqmbjurBTUf1gAeqt60oKmH++p0c2rUVFZUOn4vaqMgKdHF3JHAu0AV4F7jSVWlTqbjgmdiNV2198L2mbn009vXxZOy7Czm9fz7vz9/I+BP71CpYpyIrUIv/DWAJMA84ALjPGAOAiJwf/tCUCq/05OqJRu7FWUJJ035N78/fCMDtnyzhhxXbGDmgPRUOJy2apdCtdUYdr1ahFCjxHxWxKJSKgvSU6hZ/68zUAM9UjfHU2f256u35NbZ9vngzny/eXHV/+tjDfC55qcIj0ASu6ZEMRKlI8yzD0Dw99JOqGjo5t1duJsu27AlpLNF0cGffhe88/eXxGfzy98O1eyxCglmIRam45JlkvBdOV6F14aBOvPzrWsYM6cJRvdtQUelg1JQ5NZ6zuXgfC9bvoqzCQdfWGaQl26uWyVShpYlfqSYmHj+Crj28K9cc3rXGkNneuZks3bKHm4/qwUPTCjlp8s+1XvfrTUdEMsyEoYlfJbzMOvqW22Y1rP9/cBBdHL7EY3eHr8XnJ53dnz37KslMTeIhP7N9f169nUO6tAx/gAlGE79KaA+c3JcWGf7796ddOzSoRVh88SzGNqBDTtCvq+tow7q34scVsV//vnmzlKr36NebjmDyzFUk2W08PWN11XOufWcBL5w3gH7tsoOePZ2oZHMxX8sWTNssju7dJuBzNfGrhDa8d27Ax7PSQvNfZPTgTkE/1zu//e2gjrz6e/wvdzhmaFcANuwsY+rCjVXbL3l9Lq0yUhjarRWn92/HAe2y4/JbUWO8+PManvxxVdX9fvnZfHKD/24yTfxKRUBjCrbVN8f1zcti8abiBh8v2m4/rhdXH96Vx79fyZkF7VizvZSZK7cxbdlWPl60ib55WZxzYAeONbn1Lp4Xj3aUlvPiL2sZ2q0ldx1vmLa8iLfmrAv4Gk38SkVCkMl79OBO/LrGqhY6vFcbvl22NahFW7LSkigus+rhDOnWKqYTv81mo1VGKnceb00Y3b9dDiP2y6NkXyWf/LGJt+es567PhUenr2Bw5xbs3z6HA9pl0zs3K+4/CGRzMet27qVH6ww6tmhGkt3GS7+spWRfJWOP6E7LjFTO6N+OM/oHXkZFE79SYXRol5bMWr09YN7v2CKdP3fsZUjXllx7eDcufs0a5lifeQB987KrPjCCcUzvNny9dGs9jhB9GalJnD2gPWcVtOOXNTv4cMFG5q3fxZeyBbBKcBzfty0XHNyRrq3ibybwJ4s2cc+XS6vWdU5LttM+J52V20o4uV9evYa+auJXKoycrvQdqLvm7AHteeS7FXRu2cx6rvu1fpr67sfb5aSxYVdZjW3BGtK1VcQSf27b4C9sB+sk109T4cjMouSW2yi9emxY9r95dxn3frmUAzvkcNWwbqzeVsLSLXv4cUUR7XPSuObwbvXanyZ+pcLInbsDJeZT9s9nzp87ufiQzvXa901H9WDc50vZXVZR95O9HN6jVb1fUx+OzCzse2K3u6m+7HuKyXhwQtgS/+uz1+F0Ornjr73p0LwZ/dtbH6Y3Htkdh8NZ74qn8d0hplSUudvsgS7uZqUl8+Cp/arqBbnHrbfxUz/I14gWz01NYbxLyS234cisXfE0noXrg87pdPLlks0c3qM1HZo3q3lMm61BZa61xa9UGFV11tQjG18+tAunHpDPV66+62A0tWUeS68eG7bWb305nU5mrtzOF0s2M2PlNnbtrSA1ycaQrq0Y1LkFf+nZmvyc9AbvPxxdWZ6WbtnD5uJ9XNmjdcj2GfXEb4wZClzhunu9iAR/hUqpps7V11OftGy32WolooyUJErKa65i5W+0j69jvTH6IPJz0jjy8ZnWawMc/9CuLZm1ans9Im7abDYbh3VvxWHdrYVg5q3fxfTlRXy2eDPTC4t4aFohffOyOK5PW047ID9kczfcnE4nG3aVsWxLMTtKy8nPSadD83Tys9OCaq3/uKIIgKHdQtc9F/XED4zBSvyDgXOAZ6IbjlKhU9XV04gGuRMn08YO5awXfmXtjr01Eruv/bZrXrv12kOLnQGQnGTnoE4tOKhTC248sjuFW0v4vrCI137/k8emr+DZmasZ2q0lKUl2MlOT6Ncum375OXRp1SyomcP+Wv9tgYIGxvxP14/nqmZBCTAOuCkk/iQR2WuM2QAMj3YwSoXSKfvn8/vanY0eXmi32Xh99MFUOBzc+akA1oeKs/oigut4eZzcL497vlgacH+B5gb4S2/jT+zDgDhaVtJms9EzN5OeuZlccmhnFm/azZtz1jNv3U4cTthZWs478zYA0D4njUuHdGFQ5xbkZafV+BCIxQvZTSHxlxhj0oB2wMa6nqxULBmxXx4j9ssLyb7Sku2kYSfZtUh8jZa/6/fBnVsEVc4gKcivIAM65DB33S6AuF8usW9eNne5Jo0BOJxOVm8rZcGGXbw9Z33Vh2lKko22WWkk2W3s2VfJqMPP57JvXyFrX2m0Qq+3sCZ+Y8whwAMicqQxxg5MwvrGUwZcJiLLgclY3TspVPf1K5Xw3MtBevf33zy8J60yUhnWvVWtrp5gZvkCAQvTATx2xv5c/97CoGONR3abjW6tM+jWOoOT+uWxYP0uVhSV8OeOUjbtLsPhtCaVreh2OZOuuY7zBnYgPaXhq4iVVTiYtWob3y0v4vPFm0lJsrGv0knrjBQ+GnNIvYvUBapCFbbEb4y5FRgFuJcSOg1IF5EhxphDgYnAqSLyO3BRuOJQKlYda3JplpLEYd1rXtRrk5nKrUf3rLGtIdcQWjZLYXtpuc/HBnRoTrucNK4e1o0xb86r/87jjN1mo6BDcwrC2NWVlmznLz3b8Jeebbj00M488cNKfl+7k8uHdAl5ZdJwtvgLgTOAV1z3hwGfA4jILGPMwWE8tlIxz2azcXiQQ/jcwzmDbfEDvD76IN6Zu57nZq2psT01yU5GahIfXn5I8DtTIdWxRTPuP3m/sO0/bBO4RORdwLM5kQPs9LhfaYxpCtcYlEpIrTNTMW2rJ1lNvWwwowd34rZje0UxKhUJkUy8u4Bsj/t2Ean/XHOlVC35Odb1gJz0wP+lT+6Xx196Vi/Skeu6jgDWRctrfdR8ef3Cg8hMa3jftWp6Ipn4ZwAnA2+5+vgXRPDYSsW1y1xDDYd1DzzJ598eo1bAWrDDzd9ooJ65Ogcg3kQy8b8PHGuMmYk1+uziCB5bqbjkTtXJdhtH17GamD8fXT6Y7wuL/NYGUvEnrIlfRFYBh7puO4Arw3k8pVRt7nLP/uTnpDPywA4RikY1BXpxVakYdnzftrw5Zz3pflaemnrZ4Dr7/VXi0b8IpWLYjUf24MrDuvqdONTeR90epbQev1IxLMluC3k1SRX/NPErpVSC0cSvlFIJRhO/UkolGE38SsWhM/q3i3YIqgmzOetT1SlKtmzZ3fSDVEqpJiQ3N9tvSU9t8SulVILRxK+UUglGE79SSiUYTfxKKZVgNPErpVSC0cSvlFIJRhO/UkolGE38SimVYGJiApdSSqnQ0Ra/UkolGE38SimVYDTxK6VUgtHEr5RSCSZm12wzxhQAjwMrgJdEZFqUQwoZY8xBwFjABtwqIpuiHFLIGGOGA+eLyGXRjiVU4vSc4vJvMM7zxg3AAKAX8KqITPL33Fhu8R8CbAQqgUVRjiXU0oEbgE+AIVGOJWSMMT2BA7HOLy7E4zm5xOXfIHGcN0TkUWAM1nk9Hei5MdPid32aHeO6+xPwPvAmkAfcDNwapdAazfvcRGS8MWYI1nmNjF5kjePrvICJxpgpUQwrpERkOXF2TgAiMiMe/gZ9+JE4yRt+nAe8JyKOQE+KmcTv+jR71H3fGHM+sAHYTgydhy8+zm0Q8DtwAnAncF2UQmsU7/NSsSNe/gZ9GECc5A0/jgDq7HJsEidujDkEeEBEjjTG2IFJQAFQBlzmalV5W4XVV1cO3B2pWOurgeeWA7wA7AMmRyzYemjgecWUeD3HIM+ryf8NegvyvFYRA3nDWz3+FjNEpM5ZuVFP/MaYW4FRwB7XptOAdBEZYow5FJgInOr9OhGZCcyMWKAN0Ihz+wb4JmKB1lNDz8tNRC4If5SNU99zjIVzguDPq6n/DXqrx3k1+bzhrT5/iyJyXjD7bAoXdwuBMzzuDwM+BxCRWcDB0QgqROL13OL1vDzF6znqecWekJ9b1BO/iLyL9bXLLQfY6XG/0hgT9W8mDRGv5xav5+UpXs9Rzyv2hOPcop74fdgFZHvct4tIRbSCCbF4Pbd4PS9P8XqOel6xp9Hn1hQT/wxgBICr/2pBdMMJqXg9t3g9L0/xeo56XrGn0efWFL/6vA8ca4yZiTVr8OIoxxNK8Xpu8XpenuL1HPW8Yk+jz03r8SulVIJpil09SimlwkgTv1JKJRhN/EoplWA08SulVILRxK+UUglGE79SSiUYTfxKKZVgmuIELqUazRhzJPAW8IfH5i0icnYUYlkFrAFOE5FtXo9lA8uBHiJS7LF9DvAacCnwgYj8M2IBq7iniV/Fs29F5NxoB+FynIjs9d4oIruNMR8BZwEvQtV6t9tF5EFjzBagT0QjVXFPE79KOMaY74C5wP5YlQ7PFpHVxpixwPmAE3hDRP5rjHkRaO36OREYj1UGdyPQDasO+pfAYBHZZoy5CsgWkf/4OXatYwDPAhNwJX7gEmJk8RMVm7SPX8Wz4caY7zx+bvF47BcROQb4CjjPGLMfcA5WrfPDgdOMMcb13G9FZCjWsnatRWQwVhdMJ8ABvAq4v1lcALzkKxh/xxCRn4FWxphOxpg0rHWK3wvVm6CUN23xq3gWqKtnjuv3WiAfq/XfhepVp1oCvVy3xfW7L/ATgIhsMcYscW1/AXjDGPM9sElENvk5pr9jCPA81ofGSuBDEdkX7EkqVV/a4leJyrs6oaLvCcQAAAEBSURBVACLgKNE5Eisbpf5rsccrt8LgSEAxpiWQG8AEVkN7ABux0rg/gQ6xhTgdKxuIO3mUWGlLX4Vz4a7+vM9neDriSIyzxjzDfCjq7vlF2Cd19M+AU5wlcPdCJRQvTLSs8B/sVrtPgU6hohsd32DyBeRZfU4R6XqTcsyKxUkY0wfYICIvGGMaY3Veu8iImXGmLOBA0Tk3z5etwro42tUTxDHvMj1Wh3OqUJGu3qUCt5arAvBs7AWu/6HK+nfB/wdeCzAa780xrSqz8GMMWcBmvBVyGmLXymlEoy2+JVSKsFo4ldKqQSjiV8ppRKMJn6llEowmviVUirBaOJXSqkE8/8cKOvSe6RXGQAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -2654,7 +2648,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -2684,12 +2678,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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Kz+1fckJlbW3JbZW1lVxVcXvN098/qWNuu4duZlaIEZ3lEhFLAEsCs7cKPk3LSuDctpIMW9Aj4jxgR2AabyZ9P7BJjXGZ1c65baUZSQ99Y2ANSTPrDsasy5zbVpSRjKH/Fli67kDMesC5bUUZSQ99beCRiHgQeIO0a9ovybulNto5t60oIyno+wO+3rOVyLltRRm0oEfEet0MxKxbnNtWqqF66GcP8Vo/MLHiWMy6xbltRRq0oEua0M1AzLrFuW2l8i9FzcwK4YJuZlaIkf70/13AWqTxxYclvVRnUGbd4ty2kgzbQ4+ILwO3AycAJwG3R8RRdQdmVjfntpVmJD30PYBxkl4DiIhFgKnAaXUGZtYFzm0rykjG0B+n7Up0+fHj9YRj1lXObSvKSHroiwGPRcRtpIQfDygifgAgac8a4zOrk3PbijKSgv51wFejsxI5t60oIynoq3aYNkbS+RXHUp7Dq2tqt77NqmsMmPX8NpW1dcrSh1fW1n+/u8pb7W0/3AzO7Xm0JadU1lb/FhtV1hZA3/MV3gHw/knVtdUFIynofzVg/vHA74Hz6wjIrIuc21aUYQu6pC8MnJbv9GI2qjm3rTQjuQXdYgMmLQO8r55wzLrHuW2lGcmQy71tj/uBFxn6anVmo4Vz24oykiGXNQEiYilglqTptUdl1gXObSvNSIZctgX+FXgN6IsIgL+XdGPNsZnVyrltpRnJkMtXga0kPQ0QEasD3wc+ONyCEfEO4B7gRJ8KZg3k3LaijOSn/6+3Eh5A0hOM/McYxwIvzEtgZl3g3LaijKSH/nBEnANcT/p59IeAh4dbKCLWBcYBV7+tCM3q49y2ooykh/73wE3AlsAWwC/ztOGcBhwx76GZ1c65bUUZsoceEX3AJZL+GvjuSBuNiH2BGyQ9lg80mTWKc9tKNGRBl9QfEc9HxMnAraSzAVqvXTPEojsBa0bEHqTrZbwaEU9Kuq6KoM3eLue2lWgkY+gLASsDu7ZN6wcGTXpJe7UeR8Qk4DEnvDWQc9uKMpIfFu0PEBGLksbcZ0p6te7AzOrm3LbSDFrQI2IZ4AxgP0n9wF15/sUjYkdJt45kBZImVRGoWVWc21aqoc5yOQu4Oyc8wFOS1gJ2ASbVHZhZjZzbVqShCvqakr7R9nw6gKSpwAq1RmVWL+e2FWmogv6W235I2q3t6WuYjV7ObSvSUAX92YjYYuDEiNgBeKq+kMxq59y2Ig11lsvngcsi4l7gbmABYCNgTWDHLsRm7W6bVGlzY5ZZurK2+s89rLK21jnoocraGuKeos7tt+3lylrqu3FyZW0B9J/aV1lbfY9WeH/ScydV19YgBu2hS3oY2BD4HvAK8CpwHrCxpGm1R2ZWE+e2lWq4X4rOAn6S/5kVw7ltJRrJxbnMzGwUcEE3MyuEC7qZWSFc0M3MCuGCbmZWCBd0M7NCuKCbmRXCBd3MrBAu6GZmhXBBNzMrhAu6mVkhXNDNzArhgm5mVggXdDOzQrigm5kVwgXdzKwQQ97gwhpkRrXNLfriJytrq+/Mz1XWVv+46m4fxn3VNWV1qvY2rn3HXFZZW/1HVng7u20rvJ3dINxDNzMrhAu6mVkhXNDNzArhgm5mVggXdDOzQtR6lktE7AMcCfQBX5F0dZ3rM+sG57U1VW099IgYS0r6LYCdgd3qWpdZtzivrcnq7KFvB1wt6RXgD8CBNa7LrFuc19ZYdRb01YDlIuJaYCxwvKSf1bg+s25wXltj1XlQdBFgBdJu6aeB8yPCB2FttHNeW2PVmYhPA1MlzZT0IPASsGyN6zPrBue1NVadBf06YGJE9EXE8sDiwHM1rs+sG5zX1li1FXRJTwFXAD8HrgU+K2lWXesz6wbntTVZreehS5oMTK5zHWbd5ry2pvLBHDOzQrigm5kVwgXdzKwQLuhmZoXo6++v/7ZIHVc8hd6s2JKx1TV1/YabV9bWDX1TK2trUn9/hfezG7m+vknO7WJ8ubKWfsrClbX14UFy2z10M7NCuKCbmRXCBd3MrBAu6GZmhXBBNzMrhAu6mVkhXNDNzArhgm5mVggXdDOzQrigm5kVwgXdzKwQLuhmZoVwQTczK4QLuplZIVzQzcwK4YJuZlYIF3Qzs0K4oJuZFaJnt6AzM7NquYduZlYIF3Qzs0K4oJuZFWLBXgcwmIg4EZgILAocJOm2Hoc0W0R8DZgALAScKunSHocEQES8A7gHOFHS+T0OZ7aI2Ac4EugDviLp6h6H1FNNze2m5jU0M7ebmNeN7KFHxARgI0lbAPsBp/c4pNkiYitgA0mbAR8BzuhxSO2OBV7odRDtImIsKem3AHYGdutpQD3W1NxueF5Dw3K7qXndyIJO6iVcCSDpHmDliFistyHNdhOwZ348HVg4Inr+d4yIdYFxQM97CQNsB1wt6RVJf5B0YK8D6rGm5nYj8xoam9uNzOumDrmsBNzZ9vxZYAXg0d6E8yZJbwAz8tMDgGskzephSC2nAYcCn+pxHAOtBiwXEdcCY4HjJf2sxzH1UiNzu8F5Dc3M7UbmdSO2wB28NuB5H9CoE+YjYlfgQODwHodCROwL3CDpsV7H0sEipIK1M/Bp4Pym9Px6pNG53aS8hkbndiPzuqk99GnA8m3PlwP+2KNY5hAR2wHHAR+R9GKPwwHYCVgzIvYAVgVejYgnJV3X47gAngamSpoJPBgRLwHLAs/0NqyeaWxuNzCvobm53ci8bmpBvxY4GTgnIsYDj0h6uccxARARS5IOZE2U9Hyv4wGQtFfrcURMAh5rQMK3XAd8OyJOIxWvxYHnehtSTzUyt5uY19Do3G5kXjeyoEu6PSLujIjfAG+QxvSaYi9gKeCSiGhN21fSE70LqbkkPRURVwA/JyX9Zxs0Ntt1Dc5t5/VcaGpe+1ouZmaF6PkgvpmZVcMF3cysEC7oZmaFcEE3MyuEC7qZWSEaedriaBYR65AubLQ86VeAjwKHSKr1HNWIWAD4KrAj8HL+d8xQV/KLiNWBFSXdUmdsVgbndvO5h16hnHiXAd+QtKmkTYDbgTMHzFfH3/0o0hdtI0mbk659cVFErDjEMhOBTWqIxQrj3B4dfB56hSJiB+BvJe3TNm0M0CdpZkScD7xO+onwnsBkYG1gYeA4ST+NiMeAv5Q0I/8K7R7g98DRwJ+BNYDLJZ00YN2PA+tLmt427TjgFeAW4FBJH8vTnyNdve7WHM+Rkn5U8Z/DCuLcHh3cQ69WAHe3T5A0K1/voeU5SbsDHwdek7QVsDtw9hDtzgQ2AD4BfAD4dEQsPXulEe8CXmlP+OwOYL1ODUp6Fjgf+Of5KeFtnjm3RwGPoVdrQWCB1pOIuBJYknRRofXz5Fvz/xsB1wNImhYRr7cncge3Sfpzbvc+YC3evOD/W9bbZgwNupKfjWrO7VHAPfRq3QNs2HoiaVdJ25CSsvW3bl0+tZ90YKmlD5jFW5N0sA3uwEuuPg8sFBHLDZhvgxzTwMT3htzmlnN7FHBBr9b1wFoRsVNrQkRsRrp4z8wB894KfCjPszpAvmTpdGCZPM/4tvk3jIjFImIR0u7vw60XJPUD5wFnRMSCuc31SLu+321vMyLWJvWsIH3J5usvgI2Yc3sUcEGvkKTXSadW7R8Rv85X1DsO2KnDJVIvIfU8bgAuBg7K088CLo2IfwOe5M3P6H7gIuBmYHKH61WfAgi4JSJuAr4F7C3pGeAu0nWkLyfdvODxfEBrKnBUvtmt2aCc26ODz3IZBSJiG9qO5JuVwrldLffQzcwK4R66mVkh3EM3MyuEC7qZWSFc0M3MCuGCbmZWCBd0M7NCuKCbmRXi/wE4+TCPtPmsAQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -2718,12 +2712,22 @@ "# Show the plot on screen\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# close statepoint file to release HDF5 file handles\n", + "sp.close()" + ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2737,9 +2741,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.1" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/mgxs-part-iii.ipynb b/mgxs-part-iii.ipynb index 60f44ac..b861aac 100644 --- a/mgxs-part-iii.ipynb +++ b/mgxs-part-iii.ipynb @@ -45,6 +45,16 @@ "%matplotlib inline" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# create a model object to tie together geometry, materials, settings, and tallies\n", + "model = openmc.Model()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -54,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -87,15 +97,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials object\n", - "materials_file = openmc.Materials([fuel, water, zircaloy])\n", - "\n", - "# Export to \"materials.xml\"\n", - "materials_file.export_to_xml()" + "model.materials = openmc.Materials([fuel, water, zircaloy])" ] }, { @@ -107,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -203,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -252,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -275,24 +282,14 @@ "We now must create a geometry that is assigned a root universe and export it to XML." ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Create Geometry and set root Universe\n", - "geometry = openmc.Geometry(root_universe)" - ] - }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "# Export to \"geometry.xml\"\n", - "geometry.export_to_xml()" + "# Create Geometry and set root Universe\n", + "model.geometry = openmc.Geometry(root_universe)" ] }, { @@ -314,19 +311,18 @@ "particles = 10000\n", "\n", "# Instantiate a Settings object\n", - "settings_file = openmc.Settings()\n", - "settings_file.batches = batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", - "settings_file.output = {'tallies': False}\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': False}\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings_file.source = openmc.Source(space=uniform_dist)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", "\n", - "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" + "model.settings = settings" ] }, { @@ -343,7 +339,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "" ] @@ -355,7 +351,8 @@ ], "source": [ "# Instantiate a Plot\n", - "plot = openmc.Plot.from_geometry(geometry)\n", + "model.export_to_xml()\n", + "plot = openmc.Plot.from_geometry(model.geometry)\n", "plot.pixels = (250, 250)\n", "plot.color_by = 'material'\n", "plot.to_ipython_image()" @@ -407,7 +404,7 @@ "outputs": [], "source": [ "# Initialize a 2-group MGXS Library for OpenMOC\n", - "mgxs_lib = openmc.mgxs.Library(geometry)\n", + "mgxs_lib = openmc.mgxs.Library(model.geometry)\n", "mgxs_lib.energy_groups = groups" ] }, @@ -467,7 +464,7 @@ "mgxs_lib.domain_type = 'cell'\n", "\n", "# Specify the cell domains over which to compute multi-group cross sections\n", - "mgxs_lib.domains = geometry.get_all_material_cells().values()" + "mgxs_lib.domains = model.geometry.get_all_material_cells().values()" ] }, { @@ -520,8 +517,8 @@ "outputs": [], "source": [ "# Create a \"tallies.xml\" file for the MGXS Library\n", - "tallies_file = openmc.Tallies()\n", - "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)" + "tallies = openmc.Tallies()\n", + "mgxs_lib.add_to_tallies_file(tallies, merge=True)" ] }, { @@ -552,40 +549,16 @@ "tally.scores = ['fission', 'nu-fission']\n", "\n", "# Add tally to collection\n", - "tallies_file.append(tally)" + "tallies.append(tally)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=126.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=96.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=15.\n", - " warn(msg, IDWarning)\n", - "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=114.\n", - " warn(msg, IDWarning)\n" - ] - } - ], + "outputs": [], "source": [ - "# Export all tallies to a \"tallies.xml\" file\n", - "tallies_file.export_to_xml()" + "model.tallies = tallies" ] }, { @@ -593,6 +566,28 @@ "execution_count": 23, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=126.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=21.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=96.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=15.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:67: IDWarning: Another Filter instance already exists with id=114.\n", + " warn(msg, IDWarning)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -621,112 +616,113 @@ " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2021 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.0-dev\n", - " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-26 16:01:58\n", - " OpenMP Threads | 2\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1-dev\n", + " Git SHA1 | 1be02f90bd0e9adb5a3311ee77c904a52ca7ebaa\n", + " Date/Time | 2022-05-11 23:48:07\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", - " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", - " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", - " Reading B10 from /opt/data/xs/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.0 K\n", - " Maximum neutron data temperature: 294.0 K\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading B10 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/B10.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for U235\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.04638\n", - " 2/1 1.00498\n", - " 3/1 1.00535\n", - " 4/1 1.02695\n", - " 5/1 1.00781\n", - " 6/1 1.02035\n", - " 7/1 1.03808\n", - " 8/1 1.02532\n", - " 9/1 1.02783\n", - " 10/1 1.01371\n", - " 11/1 1.03205\n", - " 12/1 1.01947 1.02576 +/- 0.00629\n", - " 13/1 1.01843 1.02332 +/- 0.00438\n", - " 14/1 1.03269 1.02566 +/- 0.00388\n", - " 15/1 1.03879 1.02829 +/- 0.00399\n", - " 16/1 1.02251 1.02732 +/- 0.00340\n", - " 17/1 1.01274 1.02524 +/- 0.00355\n", - " 18/1 1.02454 1.02515 +/- 0.00307\n", - " 19/1 1.01993 1.02457 +/- 0.00277\n", - " 20/1 1.00665 1.02278 +/- 0.00306\n", - " 21/1 1.02200 1.02271 +/- 0.00277\n", - " 22/1 1.03748 1.02394 +/- 0.00281\n", - " 23/1 1.02803 1.02425 +/- 0.00260\n", - " 24/1 1.03052 1.02470 +/- 0.00245\n", - " 25/1 1.02027 1.02441 +/- 0.00230\n", - " 26/1 1.02597 1.02450 +/- 0.00216\n", - " 27/1 1.01776 1.02411 +/- 0.00206\n", - " 28/1 1.02652 1.02424 +/- 0.00195\n", - " 29/1 1.01063 1.02353 +/- 0.00198\n", - " 30/1 1.03300 1.02400 +/- 0.00194\n", - " 31/1 1.02020 1.02382 +/- 0.00185\n", - " 32/1 1.03720 1.02443 +/- 0.00187\n", - " 33/1 1.02797 1.02458 +/- 0.00179\n", - " 34/1 1.01994 1.02439 +/- 0.00172\n", - " 35/1 1.02626 1.02446 +/- 0.00166\n", - " 36/1 1.03183 1.02475 +/- 0.00162\n", - " 37/1 1.03410 1.02509 +/- 0.00159\n", - " 38/1 1.00919 1.02452 +/- 0.00164\n", - " 39/1 1.04388 1.02519 +/- 0.00171\n", - " 40/1 1.01254 1.02477 +/- 0.00171\n", - " 41/1 1.01584 1.02448 +/- 0.00168\n", - " 42/1 1.01002 1.02403 +/- 0.00169\n", - " 43/1 1.00277 1.02339 +/- 0.00176\n", - " 44/1 1.00672 1.02289 +/- 0.00177\n", - " 45/1 1.03974 1.02338 +/- 0.00179\n", - " 46/1 1.00460 1.02285 +/- 0.00181\n", - " 47/1 1.03954 1.02331 +/- 0.00182\n", - " 48/1 1.01414 1.02306 +/- 0.00179\n", - " 49/1 1.02016 1.02299 +/- 0.00174\n", - " 50/1 0.99791 1.02236 +/- 0.00181\n", + " 1/1 1.03383\n", + " 2/1 1.02103\n", + " 3/1 1.02661\n", + " 4/1 1.02341\n", + " 5/1 1.02463\n", + " 6/1 1.01733\n", + " 7/1 1.01773\n", + " 8/1 1.03460\n", + " 9/1 1.03867\n", + " 10/1 1.03875\n", + " 11/1 1.03794\n", + " 12/1 1.02392 1.03093 +/- 0.00701\n", + " 13/1 1.01093 1.02426 +/- 0.00780\n", + " 14/1 1.02124 1.02351 +/- 0.00557\n", + " 15/1 1.02591 1.02399 +/- 0.00434\n", + " 16/1 1.01028 1.02170 +/- 0.00421\n", + " 17/1 1.01545 1.02081 +/- 0.00367\n", + " 18/1 1.03571 1.02267 +/- 0.00369\n", + " 19/1 1.02461 1.02289 +/- 0.00326\n", + " 20/1 1.02540 1.02314 +/- 0.00292\n", + " 21/1 1.01637 1.02253 +/- 0.00272\n", + " 22/1 1.01729 1.02209 +/- 0.00252\n", + " 23/1 1.04243 1.02365 +/- 0.00279\n", + " 24/1 0.99941 1.02192 +/- 0.00311\n", + " 25/1 1.02386 1.02205 +/- 0.00290\n", + " 26/1 1.01231 1.02144 +/- 0.00278\n", + " 27/1 1.01426 1.02102 +/- 0.00265\n", + " 28/1 1.02632 1.02131 +/- 0.00251\n", + " 29/1 1.01444 1.02095 +/- 0.00240\n", + " 30/1 1.01224 1.02052 +/- 0.00232\n", + " 31/1 1.01147 1.02009 +/- 0.00225\n", + " 32/1 1.02152 1.02015 +/- 0.00215\n", + " 33/1 1.04380 1.02118 +/- 0.00229\n", + " 34/1 1.04399 1.02213 +/- 0.00239\n", + " 35/1 1.01848 1.02198 +/- 0.00230\n", + " 36/1 1.00573 1.02136 +/- 0.00230\n", + " 37/1 1.04575 1.02226 +/- 0.00239\n", + " 38/1 1.03570 1.02274 +/- 0.00235\n", + " 39/1 1.01804 1.02258 +/- 0.00227\n", + " 40/1 1.01908 1.02246 +/- 0.00220\n", + " 41/1 1.01397 1.02219 +/- 0.00214\n", + " 42/1 1.02454 1.02226 +/- 0.00208\n", + " 43/1 1.02417 1.02232 +/- 0.00201\n", + " 44/1 1.04336 1.02294 +/- 0.00205\n", + " 45/1 1.02288 1.02294 +/- 0.00199\n", + " 46/1 1.00995 1.02258 +/- 0.00197\n", + " 47/1 1.00308 1.02205 +/- 0.00199\n", + " 48/1 1.02382 1.02210 +/- 0.00193\n", + " 49/1 0.99800 1.02148 +/- 0.00198\n", + " 50/1 1.02179 1.02149 +/- 0.00193\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.4372e-01 seconds\n", - " Reading cross sections = 2.2745e-01 seconds\n", - " Total time in simulation = 4.7933e+01 seconds\n", - " Time in transport only = 4.7887e+01 seconds\n", - " Time in inactive batches = 3.4521e+00 seconds\n", - " Time in active batches = 4.4481e+01 seconds\n", - " Time synchronizing fission bank = 2.0174e-02 seconds\n", - " Sampling source sites = 1.7952e-02 seconds\n", - " SEND/RECV source sites = 2.1985e-03 seconds\n", - " Time accumulating tallies = 1.1888e-03 seconds\n", - " Time writing statepoints = 1.2929e-02 seconds\n", - " Total time for finalization = 3.9800e-07 seconds\n", - " Total time elapsed = 4.8193e+01 seconds\n", - " Calculation Rate (inactive) = 28967.6 particles/second\n", - " Calculation Rate (active) = 8992.65 particles/second\n", + " Total time for initialization = 1.3755e-01 seconds\n", + " Reading cross sections = 1.3208e-01 seconds\n", + " Total time in simulation = 2.8830e+01 seconds\n", + " Time in transport only = 2.8794e+01 seconds\n", + " Time in inactive batches = 2.2402e+00 seconds\n", + " Time in active batches = 2.6589e+01 seconds\n", + " Time synchronizing fission bank = 2.2248e-02 seconds\n", + " Sampling source sites = 1.8977e-02 seconds\n", + " SEND/RECV source sites = 3.2519e-03 seconds\n", + " Time accumulating tallies = 9.7806e-04 seconds\n", + " Time writing statepoints = 5.8152e-03 seconds\n", + " Total time for finalization = 1.3000e-07 seconds\n", + " Total time elapsed = 2.8977e+01 seconds\n", + " Calculation Rate (inactive) = 44639.1 particles/second\n", + " Calculation Rate (active) = 15043.6 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02393 +/- 0.00174\n", - " k-effective (Track-length) = 1.02236 +/- 0.00181\n", - " k-effective (Absorption) = 1.02412 +/- 0.00167\n", - " Combined k-effective = 1.02362 +/- 0.00128\n", + " k-effective (Collision) = 1.02289 +/- 0.00161\n", + " k-effective (Track-length) = 1.02149 +/- 0.00193\n", + " k-effective (Absorption) = 1.02480 +/- 0.00172\n", + " Combined k-effective = 1.02384 +/- 0.00136\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -734,7 +730,7 @@ ], "source": [ "# Run OpenMC\n", - "openmc.run()" + "statepoint_filename = model.run()" ] }, { @@ -758,7 +754,7 @@ "outputs": [], "source": [ "# Load the last statepoint file\n", - "sp = openmc.StatePoint('statepoint.50.h5')" + "sp = openmc.StatePoint(statepoint_filename)" ] }, { @@ -772,7 +768,16 @@ "cell_type": "code", "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/statepoint.py:277: FutureWarning: The 'k_combined' property has been renamed to 'keff' and will be removed in a future version of OpenMC.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Initialize MGXS Library with OpenMC statepoint data\n", "mgxs_lib.load_from_statepoint(sp)\n", @@ -860,16 +865,16 @@ " 1\n", " 1\n", " U235\n", - " 8.099261e-03\n", - " 1.626934e-05\n", + " 8.082113e-03\n", + " 1.791995e-05\n", " \n", " \n", " 4\n", " 1\n", " 1\n", " U238\n", - " 7.326723e-03\n", - " 2.168273e-05\n", + " 7.351550e-03\n", + " 2.067911e-05\n", " \n", " \n", " 5\n", @@ -884,16 +889,16 @@ " 1\n", " 2\n", " U235\n", - " 3.613773e-01\n", - " 1.025247e-03\n", + " 3.618776e-01\n", + " 1.051518e-03\n", " \n", " \n", " 1\n", " 1\n", " 2\n", " U238\n", - " 6.739270e-07\n", - " 1.911057e-09\n", + " 6.747305e-07\n", + " 1.939403e-09\n", " \n", " \n", " 2\n", @@ -909,11 +914,11 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 1 1 U235 8.099261e-03 1.626934e-05\n", - "4 1 1 U238 7.326723e-03 2.168273e-05\n", + "3 1 1 U235 8.082113e-03 1.791995e-05\n", + "4 1 1 U238 7.351550e-03 2.067911e-05\n", "5 1 1 O16 0.000000e+00 0.000000e+00\n", - "0 1 2 U235 3.613773e-01 1.025247e-03\n", - "1 1 2 U238 6.739270e-07 1.911057e-09\n", + "0 1 2 U235 3.618776e-01 1.051518e-03\n", + "1 1 2 U238 6.747305e-07 1.939403e-09\n", "2 1 2 O16 0.000000e+00 0.000000e+00" ] }, @@ -949,13 +954,13 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t8.10e-03 +/- 2.01e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t3.61e-01 +/- 2.84e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t8.08e-03 +/- 2.22e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t3.62e-01 +/- 2.91e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t7.33e-03 +/- 2.96e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 2.84e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.35e-03 +/- 2.81e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t6.75e-07 +/- 2.87e-01%\n", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", @@ -970,7 +975,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/opt/openmc/openmc/openmc/tallies.py:1216: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/tallies.py:1255: RuntimeWarning: invalid value encountered in true_divide\n", " data = self.std_dev[indices] / self.mean[indices]\n" ] } @@ -1082,15 +1087,15 @@ " 1\n", " 1\n", " U235\n", - " 0.074479\n", - " 0.000151\n", + " 0.074339\n", + " 0.000161\n", " \n", " \n", " 1\n", " 1\n", " 1\n", " U238\n", - " 0.005950\n", + " 0.005975\n", " 0.000017\n", " \n", " \n", @@ -1107,8 +1112,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 1 1 U235 0.074479 0.000151\n", - "1 1 1 U238 0.005950 0.000017\n", + "0 1 1 U235 0.074339 0.000161\n", + "1 1 1 U238 0.005975 0.000017\n", "2 1 1 O16 0.000000 0.000000" ] }, @@ -1229,264 +1234,256 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0: k_eff = 0.823342 res = 9.831E-02 delta-k (pcm) =\n", - "[ NORMAL ] ... -17665 D.R. = 0.0983\n", - "[ NORMAL ] Iteration 1: k_eff = 0.780160 res = 4.646E-02 delta-k (pcm) =\n", - "[ NORMAL ] ... -4318 D.R. = 0.4725\n", - "[ NORMAL ] Iteration 2: k_eff = 0.739336 res = 9.631E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -4082 D.R. = 0.2073\n", - "[ NORMAL ] Iteration 3: k_eff = 0.710750 res = 8.566E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -2858 D.R. = 0.8894\n", - "[ NORMAL ] Iteration 4: k_eff = 0.689598 res = 5.205E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -2115 D.R. = 0.6076\n", - "[ NORMAL ] Iteration 5: k_eff = 0.675031 res = 3.605E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -1456 D.R. = 0.6926\n", - "[ NORMAL ] Iteration 6: k_eff = 0.665895 res = 2.538E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -913 D.R. = 0.7040\n", - "[ NORMAL ] Iteration 7: k_eff = 0.661318 res = 1.889E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -457 D.R. = 0.7443\n", - "[ NORMAL ] Iteration 8: k_eff = 0.660529 res = 1.493E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -78 D.R. = 0.7904\n", - "[ NORMAL ] Iteration 9: k_eff = 0.662872 res = 1.268E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 234 D.R. = 0.8490\n", - "[ NORMAL ] Iteration 10: k_eff = 0.667780 res = 1.140E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 490 D.R. = 0.8995\n", - "[ NORMAL ] Iteration 11: k_eff = 0.674766 res = 1.065E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 698 D.R. = 0.9337\n", - "[ NORMAL ] Iteration 12: k_eff = 0.683410 res = 1.014E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 864 D.R. = 0.9527\n", - "[ NORMAL ] Iteration 13: k_eff = 0.693354 res = 9.759E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 994 D.R. = 0.9623\n", - "[ NORMAL ] Iteration 14: k_eff = 0.704290 res = 9.438E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1093 D.R. = 0.9671\n", - "[ NORMAL ] Iteration 15: k_eff = 0.715957 res = 9.154E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1166 D.R. = 0.9698\n", - "[ NORMAL ] Iteration 16: k_eff = 0.728134 res = 8.892E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1217 D.R. = 0.9714\n", - "[ NORMAL ] Iteration 17: k_eff = 0.740633 res = 8.644E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1249 D.R. = 0.9721\n", - "[ NORMAL ] Iteration 18: k_eff = 0.753297 res = 8.404E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1266 D.R. = 0.9722\n", - "[ NORMAL ] Iteration 19: k_eff = 0.765995 res = 8.167E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1269 D.R. = 0.9719\n", - "[ NORMAL ] Iteration 20: k_eff = 0.778619 res = 7.930E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1262 D.R. = 0.9710\n", - "[ NORMAL ] Iteration 21: k_eff = 0.791079 res = 7.691E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1246 D.R. = 0.9698\n", - "[ NORMAL ] Iteration 22: k_eff = 0.803304 res = 7.447E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1222 D.R. = 0.9683\n", - "[ NORMAL ] Iteration 23: k_eff = 0.815235 res = 7.199E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1193 D.R. = 0.9667\n", - "[ NORMAL ] Iteration 24: k_eff = 0.826828 res = 6.947E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1159 D.R. = 0.9650\n", - "[ NORMAL ] Iteration 25: k_eff = 0.838047 res = 6.693E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1121 D.R. = 0.9634\n", - "[ NORMAL ] Iteration 26: k_eff = 0.848868 res = 6.436E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1082 D.R. = 0.9617\n", - "[ NORMAL ] Iteration 27: k_eff = 0.859271 res = 6.179E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1040 D.R. = 0.9600\n", - "[ NORMAL ] Iteration 28: k_eff = 0.869247 res = 5.922E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 997 D.R. = 0.9585\n", - "[ NORMAL ] Iteration 29: k_eff = 0.878788 res = 5.667E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 954 D.R. = 0.9570\n", - "[ NORMAL ] Iteration 30: k_eff = 0.887894 res = 5.415E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 910 D.R. = 0.9556\n", - "[ NORMAL ] Iteration 31: k_eff = 0.896566 res = 5.168E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 867 D.R. = 0.9543\n", - "[ NORMAL ] Iteration 32: k_eff = 0.904810 res = 4.925E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 824 D.R. = 0.9530\n", - "[ NORMAL ] Iteration 33: k_eff = 0.912635 res = 4.688E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 782 D.R. = 0.9519\n", - "[ NORMAL ] Iteration 34: k_eff = 0.920049 res = 4.457E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 741 D.R. = 0.9508\n", - "[ NORMAL ] Iteration 35: k_eff = 0.927066 res = 4.233E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 701 D.R. = 0.9498\n", - "[ NORMAL ] Iteration 36: k_eff = 0.933696 res = 4.016E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 663 D.R. = 0.9488\n", - "[ NORMAL ] Iteration 37: k_eff = 0.939954 res = 3.807E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 625 D.R. = 0.9479\n", - "[ NORMAL ] Iteration 38: k_eff = 0.945855 res = 3.606E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 590 D.R. = 0.9471\n", - "[ NORMAL ] Iteration 39: k_eff = 0.951412 res = 3.413E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 555 D.R. = 0.9464\n", - "[ NORMAL ] Iteration 40: k_eff = 0.956641 res = 3.227E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 522 D.R. = 0.9456\n", - "[ NORMAL ] Iteration 41: k_eff = 0.961556 res = 3.049E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 491 D.R. = 0.9448\n", - "[ NORMAL ] Iteration 42: k_eff = 0.966174 res = 2.879E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 461 D.R. = 0.9443\n", - "[ NORMAL ] Iteration 43: k_eff = 0.970507 res = 2.716E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 433 D.R. = 0.9435\n", - "[ NORMAL ] Iteration 44: k_eff = 0.974571 res = 2.561E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 406 D.R. = 0.9430\n", - "[ NORMAL ] Iteration 45: k_eff = 0.978380 res = 2.414E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 380 D.R. = 0.9423\n", - "[ NORMAL ] Iteration 46: k_eff = 0.981948 res = 2.273E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 356 D.R. = 0.9417\n", - "[ NORMAL ] Iteration 47: k_eff = 0.985287 res = 2.140E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 333 D.R. = 0.9413\n", - "[ NORMAL ] Iteration 48: k_eff = 0.988410 res = 2.013E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 312 D.R. = 0.9409\n", - "[ NORMAL ] Iteration 49: k_eff = 0.991331 res = 1.893E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 292 D.R. = 0.9402\n", - "[ NORMAL ] Iteration 50: k_eff = 0.994060 res = 1.779E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 272 D.R. = 0.9399\n", - "[ NORMAL ] Iteration 51: k_eff = 0.996609 res = 1.671E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 254 D.R. = 0.9393\n", - "[ NORMAL ] Iteration 52: k_eff = 0.998988 res = 1.569E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 237 D.R. = 0.9390\n", - "[ NORMAL ] Iteration 53: k_eff = 1.001209 res = 1.473E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 222 D.R. = 0.9386\n", - "[ NORMAL ] Iteration 54: k_eff = 1.003280 res = 1.382E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 207 D.R. = 0.9380\n", - "[ NORMAL ] Iteration 55: k_eff = 1.005211 res = 1.296E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 193 D.R. = 0.9378\n", - "[ NORMAL ] Iteration 56: k_eff = 1.007012 res = 1.215E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 180 D.R. = 0.9374\n", - "[ NORMAL ] Iteration 57: k_eff = 1.008689 res = 1.138E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 167 D.R. = 0.9369\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Iteration 58: k_eff = 1.010251 res = 1.066E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 156 D.R. = 0.9369\n", - "[ NORMAL ] Iteration 59: k_eff = 1.011706 res = 9.981E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 145 D.R. = 0.9362\n", - "[ NORMAL ] Iteration 60: k_eff = 1.013060 res = 9.344E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 135 D.R. = 0.9361\n", - "[ NORMAL ] Iteration 61: k_eff = 1.014320 res = 8.743E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 125 D.R. = 0.9357\n", - "[ NORMAL ] Iteration 62: k_eff = 1.015492 res = 8.177E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 117 D.R. = 0.9353\n", - "[ NORMAL ] Iteration 63: k_eff = 1.016582 res = 7.648E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 109 D.R. = 0.9353\n", - "[ NORMAL ] Iteration 64: k_eff = 1.017596 res = 7.147E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 101 D.R. = 0.9345\n", - "[ NORMAL ] Iteration 65: k_eff = 1.018538 res = 6.680E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 94 D.R. = 0.9346\n", - "[ NORMAL ] Iteration 66: k_eff = 1.019414 res = 6.237E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 87 D.R. = 0.9338\n", - "[ NORMAL ] Iteration 67: k_eff = 1.020227 res = 5.828E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 81 D.R. = 0.9343\n", - "[ NORMAL ] Iteration 68: k_eff = 1.020983 res = 5.442E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 75 D.R. = 0.9338\n", - "[ NORMAL ] Iteration 69: k_eff = 1.021685 res = 5.080E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 70 D.R. = 0.9336\n", - "[ NORMAL ] Iteration 70: k_eff = 1.022337 res = 4.739E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 65 D.R. = 0.9328\n", - "[ NORMAL ] Iteration 71: k_eff = 1.022942 res = 4.422E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 60 D.R. = 0.9331\n", - "[ NORMAL ] Iteration 72: k_eff = 1.023504 res = 4.124E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 56 D.R. = 0.9327\n", - "[ NORMAL ] Iteration 73: k_eff = 1.024026 res = 3.843E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 52 D.R. = 0.9317\n", - "[ NORMAL ] Iteration 74: k_eff = 1.024510 res = 3.586E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 48 D.R. = 0.9331\n", - "[ NORMAL ] Iteration 75: k_eff = 1.024959 res = 3.341E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 44 D.R. = 0.9317\n", - "[ NORMAL ] Iteration 76: k_eff = 1.025375 res = 3.115E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 41 D.R. = 0.9325\n", - "[ NORMAL ] Iteration 77: k_eff = 1.025762 res = 2.898E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 38 D.R. = 0.9303\n", - "[ NORMAL ] Iteration 78: k_eff = 1.026120 res = 2.703E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 35 D.R. = 0.9327\n", - "[ NORMAL ] Iteration 79: k_eff = 1.026452 res = 2.519E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 33 D.R. = 0.9318\n", - "[ NORMAL ] Iteration 80: k_eff = 1.026760 res = 2.341E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 30 D.R. = 0.9295\n", - "[ NORMAL ] Iteration 81: k_eff = 1.027046 res = 2.180E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 28 D.R. = 0.9312\n", - "[ NORMAL ] Iteration 82: k_eff = 1.027311 res = 2.028E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 26 D.R. = 0.9301\n", - "[ NORMAL ] Iteration 83: k_eff = 1.027556 res = 1.889E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 24 D.R. = 0.9315\n", - "[ NORMAL ] Iteration 84: k_eff = 1.027783 res = 1.757E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 22 D.R. = 0.9305\n", - "[ NORMAL ] Iteration 85: k_eff = 1.027994 res = 1.632E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 21 D.R. = 0.9284\n", - "[ NORMAL ] Iteration 86: k_eff = 1.028189 res = 1.521E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 19 D.R. = 0.9322\n", - "[ NORMAL ] Iteration 87: k_eff = 1.028370 res = 1.412E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 18 D.R. = 0.9285\n", - "[ NORMAL ] Iteration 88: k_eff = 1.028538 res = 1.311E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 16 D.R. = 0.9287\n", - "[ NORMAL ] Iteration 89: k_eff = 1.028693 res = 1.222E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 15 D.R. = 0.9316\n", - "[ NORMAL ] Iteration 90: k_eff = 1.028837 res = 1.134E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 14 D.R. = 0.9279\n", - "[ NORMAL ] Iteration 91: k_eff = 1.028970 res = 1.056E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 13 D.R. = 0.9314\n", - "[ NORMAL ] Iteration 92: k_eff = 1.029093 res = 9.786E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 12 D.R. = 0.9268\n", - "[ NORMAL ] Iteration 93: k_eff = 1.029207 res = 9.093E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 11 D.R. = 0.9292\n", - "[ NORMAL ] Iteration 94: k_eff = 1.029313 res = 8.445E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 10 D.R. = 0.9287\n", - "[ NORMAL ] Iteration 95: k_eff = 1.029411 res = 7.848E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 9 D.R. = 0.9294\n", - "[ NORMAL ] Iteration 96: k_eff = 1.029502 res = 7.272E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 9 D.R. = 0.9265\n", - "[ NORMAL ] Iteration 97: k_eff = 1.029586 res = 6.769E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 8 D.R. = 0.9309\n", - "[ NORMAL ] Iteration 98: k_eff = 1.029663 res = 6.294E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 7 D.R. = 0.9298\n", - "[ NORMAL ] Iteration 99: k_eff = 1.029735 res = 5.827E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 7 D.R. = 0.9259\n", - "[ NORMAL ] Iteration 100: k_eff = 1.029802 res = 5.413E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.9290\n", - "[ NORMAL ] Iteration 101: k_eff = 1.029863 res = 5.032E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.9295\n", - "[ NORMAL ] Iteration 102: k_eff = 1.029920 res = 4.648E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.9237\n", - "[ NORMAL ] Iteration 103: k_eff = 1.029973 res = 4.328E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.9313\n", - "[ NORMAL ] Iteration 104: k_eff = 1.030022 res = 4.004E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.9249\n", - "[ NORMAL ] Iteration 105: k_eff = 1.030067 res = 3.734E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.9326\n", - "[ NORMAL ] Iteration 106: k_eff = 1.030109 res = 3.452E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.9246\n", - "[ NORMAL ] Iteration 107: k_eff = 1.030148 res = 3.195E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.9253\n", - "[ NORMAL ] Iteration 108: k_eff = 1.030184 res = 2.979E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.9325\n", - "[ NORMAL ] Iteration 109: k_eff = 1.030217 res = 2.750E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.9230\n", - "[ NORMAL ] Iteration 110: k_eff = 1.030247 res = 2.541E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.9240\n", - "[ NORMAL ] Iteration 111: k_eff = 1.030276 res = 2.364E-06 delta-k (pcm)\n", + "[ NORMAL ] Iteration 0: k_eff = 0.822779 res = 9.829E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -17722 D.R. = 0.0983\n", + "[ NORMAL ] Iteration 1: k_eff = 0.779130 res = 4.644E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -4364 D.R. = 0.4725\n", + "[ NORMAL ] Iteration 2: k_eff = 0.737947 res = 9.632E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -4118 D.R. = 0.2074\n", + "[ NORMAL ] Iteration 3: k_eff = 0.709058 res = 8.560E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2888 D.R. = 0.8887\n", + "[ NORMAL ] Iteration 4: k_eff = 0.687649 res = 5.199E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2140 D.R. = 0.6073\n", + "[ NORMAL ] Iteration 5: k_eff = 0.672859 res = 3.599E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -1478 D.R. = 0.6924\n", + "[ NORMAL ] Iteration 6: k_eff = 0.663528 res = 2.534E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -933 D.R. = 0.7039\n", + "[ NORMAL ] Iteration 7: k_eff = 0.658776 res = 1.887E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -475 D.R. = 0.7447\n", + "[ NORMAL ] Iteration 8: k_eff = 0.657829 res = 1.493E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -94 D.R. = 0.7913\n", + "[ NORMAL ] Iteration 9: k_eff = 0.660026 res = 1.269E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 219 D.R. = 0.8502\n", + "[ NORMAL ] Iteration 10: k_eff = 0.664797 res = 1.143E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 477 D.R. = 0.9006\n", + "[ NORMAL ] Iteration 11: k_eff = 0.671653 res = 1.068E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 685 D.R. = 0.9345\n", + "[ NORMAL ] Iteration 12: k_eff = 0.680173 res = 1.018E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 851 D.R. = 0.9532\n", + "[ NORMAL ] Iteration 13: k_eff = 0.689996 res = 9.800E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 982 D.R. = 0.9625\n", + "[ NORMAL ] Iteration 14: k_eff = 0.700814 res = 9.479E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1081 D.R. = 0.9672\n", + "[ NORMAL ] Iteration 15: k_eff = 0.712366 res = 9.192E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1155 D.R. = 0.9698\n", + "[ NORMAL ] Iteration 16: k_eff = 0.724428 res = 8.928E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1206 D.R. = 0.9712\n", + "[ NORMAL ] Iteration 17: k_eff = 0.736814 res = 8.677E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1238 D.R. = 0.9719\n", + "[ NORMAL ] Iteration 18: k_eff = 0.749366 res = 8.434E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1255 D.R. = 0.9720\n", + "[ NORMAL ] Iteration 19: k_eff = 0.761953 res = 8.194E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1258 D.R. = 0.9715\n", + "[ NORMAL ] Iteration 20: k_eff = 0.774466 res = 7.954E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1251 D.R. = 0.9707\n", + "[ NORMAL ] Iteration 21: k_eff = 0.786817 res = 7.711E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1235 D.R. = 0.9694\n", + "[ NORMAL ] Iteration 22: k_eff = 0.798933 res = 7.464E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1211 D.R. = 0.9680\n", + "[ NORMAL ] Iteration 23: k_eff = 0.810758 res = 7.212E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1182 D.R. = 0.9663\n", + "[ NORMAL ] Iteration 24: k_eff = 0.822245 res = 6.958E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1148 D.R. = 0.9647\n", + "[ NORMAL ] Iteration 25: k_eff = 0.833360 res = 6.700E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1111 D.R. = 0.9629\n", + "[ NORMAL ] Iteration 26: k_eff = 0.844079 res = 6.440E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1071 D.R. = 0.9613\n", + "[ NORMAL ] Iteration 27: k_eff = 0.854382 res = 6.180E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1030 D.R. = 0.9596\n", + "[ NORMAL ] Iteration 28: k_eff = 0.864259 res = 5.921E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 987 D.R. = 0.9581\n", + "[ NORMAL ] Iteration 29: k_eff = 0.873704 res = 5.664E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 944 D.R. = 0.9566\n", + "[ NORMAL ] Iteration 30: k_eff = 0.882716 res = 5.410E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 901 D.R. = 0.9552\n", + "[ NORMAL ] Iteration 31: k_eff = 0.891297 res = 5.161E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 858 D.R. = 0.9539\n", + "[ NORMAL ] Iteration 32: k_eff = 0.899453 res = 4.917E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 815 D.R. = 0.9527\n", + "[ NORMAL ] Iteration 33: k_eff = 0.907191 res = 4.678E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 773 D.R. = 0.9515\n", + "[ NORMAL ] Iteration 34: k_eff = 0.914523 res = 4.447E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 733 D.R. = 0.9504\n", + "[ NORMAL ] Iteration 35: k_eff = 0.921458 res = 4.222E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 693 D.R. = 0.9495\n", + "[ NORMAL ] Iteration 36: k_eff = 0.928010 res = 4.004E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 655 D.R. = 0.9485\n", + "[ NORMAL ] Iteration 37: k_eff = 0.934193 res = 3.794E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 618 D.R. = 0.9476\n", + "[ NORMAL ] Iteration 38: k_eff = 0.940021 res = 3.593E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 582 D.R. = 0.9469\n", + "[ NORMAL ] Iteration 39: k_eff = 0.945509 res = 3.398E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 548 D.R. = 0.9459\n", + "[ NORMAL ] Iteration 40: k_eff = 0.950671 res = 3.212E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 516 D.R. = 0.9453\n", + "[ NORMAL ] Iteration 41: k_eff = 0.955522 res = 3.034E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 485 D.R. = 0.9446\n", + "[ NORMAL ] Iteration 42: k_eff = 0.960078 res = 2.864E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 455 D.R. = 0.9438\n", + "[ NORMAL ] Iteration 43: k_eff = 0.964353 res = 2.701E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 427 D.R. = 0.9432\n", + "[ NORMAL ] Iteration 44: k_eff = 0.968360 res = 2.546E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 400 D.R. = 0.9426\n", + "[ NORMAL ] Iteration 45: k_eff = 0.972115 res = 2.399E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 375 D.R. = 0.9422\n", + "[ NORMAL ] Iteration 46: k_eff = 0.975631 res = 2.258E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 351 D.R. = 0.9413\n", + "[ NORMAL ] Iteration 47: k_eff = 0.978922 res = 2.126E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 329 D.R. = 0.9412\n", + "[ NORMAL ] Iteration 48: k_eff = 0.981999 res = 1.999E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 307 D.R. = 0.9404\n", + "[ NORMAL ] Iteration 49: k_eff = 0.984875 res = 1.879E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 287 D.R. = 0.9399\n", + "[ NORMAL ] Iteration 50: k_eff = 0.987561 res = 1.766E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 268 D.R. = 0.9398\n", + "[ NORMAL ] Iteration 51: k_eff = 0.990070 res = 1.658E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 250 D.R. = 0.9390\n", + "[ NORMAL ] Iteration 52: k_eff = 0.992412 res = 1.556E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 234 D.R. = 0.9387\n", + "[ NORMAL ] Iteration 53: k_eff = 0.994596 res = 1.460E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 218 D.R. = 0.9381\n", + "[ NORMAL ] Iteration 54: k_eff = 0.996634 res = 1.369E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 203 D.R. = 0.9380\n", + "[ NORMAL ] Iteration 55: k_eff = 0.998533 res = 1.284E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 189 D.R. = 0.9375\n", + "[ NORMAL ] Iteration 56: k_eff = 1.000302 res = 1.203E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 176 D.R. = 0.9370\n", + "[ NORMAL ] Iteration 57: k_eff = 1.001950 res = 1.127E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 164 D.R. = 0.9369\n", + "[ NORMAL ] Iteration 58: k_eff = 1.003485 res = 1.055E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 153 D.R. = 0.9363\n", + "[ NORMAL ] Iteration 59: k_eff = 1.004914 res = 9.877E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 142 D.R. = 0.9360\n", + "[ NORMAL ] Iteration 60: k_eff = 1.006243 res = 9.244E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 132 D.R. = 0.9358\n", + "[ NORMAL ] Iteration 61: k_eff = 1.007480 res = 8.644E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 123 D.R. = 0.9351\n", + "[ NORMAL ] Iteration 62: k_eff = 1.008631 res = 8.084E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 115 D.R. = 0.9352\n", + "[ NORMAL ] Iteration 63: k_eff = 1.009700 res = 7.560E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 106 D.R. = 0.9352\n", + "[ NORMAL ] Iteration 64: k_eff = 1.010695 res = 7.062E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 99 D.R. = 0.9341\n", + "[ NORMAL ] Iteration 65: k_eff = 1.011619 res = 6.597E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 92 D.R. = 0.9342\n", + "[ NORMAL ] Iteration 66: k_eff = 1.012477 res = 6.161E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 85 D.R. = 0.9339\n", + "[ NORMAL ] Iteration 67: k_eff = 1.013275 res = 5.753E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 79 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 68: k_eff = 1.014016 res = 5.372E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 74 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 69: k_eff = 1.014703 res = 5.012E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 68 D.R. = 0.9330\n", + "[ NORMAL ] Iteration 70: k_eff = 1.015342 res = 4.674E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 63 D.R. = 0.9327\n", + "[ NORMAL ] Iteration 71: k_eff = 1.015935 res = 4.360E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 59 D.R. = 0.9328\n", + "[ NORMAL ] Iteration 72: k_eff = 1.016485 res = 4.062E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 55 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 73: k_eff = 1.016995 res = 3.791E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 51 D.R. = 0.9332\n", + "[ NORMAL ] Iteration 74: k_eff = 1.017469 res = 3.532E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 47 D.R. = 0.9316\n", + "[ NORMAL ] Iteration 75: k_eff = 1.017908 res = 3.292E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 43 D.R. = 0.9322\n", + "[ NORMAL ] Iteration 76: k_eff = 1.018315 res = 3.065E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 40 D.R. = 0.9309\n", + "[ NORMAL ] Iteration 77: k_eff = 1.018693 res = 2.855E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 37 D.R. = 0.9316\n", + "[ NORMAL ] Iteration 78: k_eff = 1.019043 res = 2.659E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 35 D.R. = 0.9312\n", + "[ NORMAL ] Iteration 79: k_eff = 1.019368 res = 2.476E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 32 D.R. = 0.9313\n", + "[ NORMAL ] Iteration 80: k_eff = 1.019669 res = 2.302E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 30 D.R. = 0.9296\n", + "[ NORMAL ] Iteration 81: k_eff = 1.019948 res = 2.144E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 27 D.R. = 0.9315\n", + "[ NORMAL ] Iteration 82: k_eff = 1.020206 res = 1.993E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 25 D.R. = 0.9296\n", + "[ NORMAL ] Iteration 83: k_eff = 1.020446 res = 1.856E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 23 D.R. = 0.9309\n", + "[ NORMAL ] Iteration 84: k_eff = 1.020668 res = 1.726E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 22 D.R. = 0.9301\n", + "[ NORMAL ] Iteration 85: k_eff = 1.020873 res = 1.606E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 20 D.R. = 0.9304\n", + "[ NORMAL ] Iteration 86: k_eff = 1.021064 res = 1.492E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 19 D.R. = 0.9289\n", + "[ NORMAL ] Iteration 87: k_eff = 1.021240 res = 1.385E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 17 D.R. = 0.9286\n", + "[ NORMAL ] Iteration 88: k_eff = 1.021404 res = 1.291E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 16 D.R. = 0.9318\n", + "[ NORMAL ] Iteration 89: k_eff = 1.021555 res = 1.197E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 15 D.R. = 0.9275\n", + "[ NORMAL ] Iteration 90: k_eff = 1.021695 res = 1.111E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 14 D.R. = 0.9282\n", + "[ NORMAL ] Iteration 91: k_eff = 1.021825 res = 1.036E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 12 D.R. = 0.9320\n", + "[ NORMAL ] Iteration 92: k_eff = 1.021945 res = 9.595E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 12 D.R. = 0.9266\n", + "[ NORMAL ] Iteration 93: k_eff = 1.022056 res = 8.925E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 11 D.R. = 0.9301\n", + "[ NORMAL ] Iteration 94: k_eff = 1.022159 res = 8.275E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 10 D.R. = 0.9272\n", + "[ NORMAL ] Iteration 95: k_eff = 1.022255 res = 7.682E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.9283\n", + "[ NORMAL ] Iteration 96: k_eff = 1.022343 res = 7.170E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 8 D.R. = 0.9334\n", + "[ NORMAL ] Iteration 97: k_eff = 1.022425 res = 6.616E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 8 D.R. = 0.9227\n", + "[ NORMAL ] Iteration 98: k_eff = 1.022500 res = 6.120E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.9251\n", + "[ NORMAL ] Iteration 99: k_eff = 1.022570 res = 5.719E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 6 D.R. = 0.9345\n", + "[ NORMAL ] Iteration 100: k_eff = 1.022635 res = 5.291E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.9250\n", + "[ NORMAL ] Iteration 101: k_eff = 1.022695 res = 4.914E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9288\n", + "[ NORMAL ] Iteration 102: k_eff = 1.022750 res = 4.571E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9302\n", + "[ NORMAL ] Iteration 103: k_eff = 1.022802 res = 4.206E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9202\n", + "[ NORMAL ] Iteration 104: k_eff = 1.022849 res = 3.934E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9352\n", + "[ NORMAL ] Iteration 105: k_eff = 1.022893 res = 3.653E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9286\n", + "[ NORMAL ] Iteration 106: k_eff = 1.022933 res = 3.355E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9184\n", + "[ NORMAL ] Iteration 107: k_eff = 1.022971 res = 3.106E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9257\n", + "[ NORMAL ] Iteration 108: k_eff = 1.023006 res = 2.902E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9343\n", + "[ NORMAL ] Iteration 109: k_eff = 1.023038 res = 2.703E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 110: k_eff = 1.023068 res = 2.485E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9191\n", + "[ NORMAL ] Iteration 111: k_eff = 1.023095 res = 2.311E-06 delta-k (pcm)\n", "[ NORMAL ] ... = 2 D.R. = 0.9302\n", - "[ NORMAL ] Iteration 112: k_eff = 1.030302 res = 2.178E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.9215\n", - "[ NORMAL ] Iteration 113: k_eff = 1.030326 res = 2.022E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.9285\n", - "[ NORMAL ] Iteration 114: k_eff = 1.030349 res = 1.896E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.9374\n", - "[ NORMAL ] Iteration 115: k_eff = 1.030369 res = 1.753E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.9246\n", - "[ NORMAL ] Iteration 116: k_eff = 1.030389 res = 1.619E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9236\n", - "[ NORMAL ] Iteration 117: k_eff = 1.030406 res = 1.500E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9266\n", - "[ NORMAL ] Iteration 118: k_eff = 1.030423 res = 1.406E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9375\n", - "[ NORMAL ] Iteration 119: k_eff = 1.030438 res = 1.287E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9154\n", - "[ NORMAL ] Iteration 120: k_eff = 1.030452 res = 1.193E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9269\n", - "[ NORMAL ] Iteration 121: k_eff = 1.030465 res = 1.095E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9174\n", - "[ NORMAL ] Iteration 122: k_eff = 1.030477 res = 1.033E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9437\n", - "[ NORMAL ] Iteration 123: k_eff = 1.030488 res = 9.328E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9029\n", - "[ NORMAL ] Iteration 124: k_eff = 1.030498 res = 8.716E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.9344\n", - "[ NORMAL ] Iteration 125: k_eff = 1.030508 res = 8.527E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 0 D.R. = 0.9783\n" + "[ NORMAL ] Iteration 112: k_eff = 1.023121 res = 2.137E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9244\n", + "[ NORMAL ] Iteration 113: k_eff = 1.023144 res = 1.973E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9233\n", + "[ NORMAL ] Iteration 114: k_eff = 1.023166 res = 1.834E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9298\n", + "[ NORMAL ] Iteration 115: k_eff = 1.023186 res = 1.708E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9314\n", + "[ NORMAL ] Iteration 116: k_eff = 1.023205 res = 1.587E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9287\n", + "[ NORMAL ] Iteration 117: k_eff = 1.023222 res = 1.469E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9257\n", + "[ NORMAL ] Iteration 118: k_eff = 1.023238 res = 1.340E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9124\n", + "[ NORMAL ] Iteration 119: k_eff = 1.023252 res = 1.259E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9398\n", + "[ NORMAL ] Iteration 120: k_eff = 1.023266 res = 1.169E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9282\n", + "[ NORMAL ] Iteration 121: k_eff = 1.023279 res = 1.071E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9162\n", + "[ NORMAL ] Iteration 122: k_eff = 1.023290 res = 9.844E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9192\n", + "[ NORMAL ] Iteration 123: k_eff = 1.023301 res = 9.342E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9490\n", + "[ NORMAL ] Iteration 124: k_eff = 1.023311 res = 8.550E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.9152\n" ] } ], @@ -1516,9 +1513,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.023625\n", - "openmoc keff = 1.030508\n", - "bias [pcm]: 688.3\n" + "openmc keff = 1.023841\n", + "openmoc keff = 1.023311\n", + "bias [pcm]: -53.0\n" ] } ], @@ -1631,7 +1628,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1657,12 +1654,22 @@ "plt.imshow(openmoc_fission_rates, interpolation='none', cmap='jet')\n", "plt.title('OpenMOC Fission Rates')" ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# close statepoint file to release HDF5 file handles\n", + "sp.close()" + ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1676,9 +1683,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.1" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 }