diff --git a/depletion.ipynb b/depletion.ipynb index f152972..3159db7 100644 --- a/depletion.ipynb +++ b/depletion.ipynb @@ -125,7 +125,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -134,7 +134,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -282,7 +282,16 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/deplete/coupled_operator.py:546: FutureWarning: The Operator(...) class has been renamed and will be removed in a future version of OpenMC. Use CoupledOperator(...) instead.\n", + " warn(\n" + ] + } + ], "source": [ "model = openmc.Model(geometry=geometry, settings=settings)\n", "operator = openmc.deplete.Operator(model, \"./chain_simple.xml\")" @@ -379,9 +388,9 @@ " | 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", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-03 22:39:53\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", @@ -476,21 +485,21 @@ "\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", + " Total time for initialization = 1.7021e+00 seconds\n", + " Reading cross sections = 1.6923e+00 seconds\n", + " Total time in simulation = 1.4132e+01 seconds\n", + " Time in transport only = 1.4123e+01 seconds\n", + " Time in inactive batches = 2.4396e+00 seconds\n", + " Time in active batches = 1.1692e+01 seconds\n", + " Time synchronizing fission bank = 4.0944e-03 seconds\n", + " Sampling source sites = 3.9258e-03 seconds\n", + " SEND/RECV source sites = 1.4190e-04 seconds\n", + " Time accumulating tallies = 2.1257e-04 seconds\n", + " Time writing statepoints = 1.4112e-03 seconds\n", + " Total time for finalization = 3.4840e-05 seconds\n", + " Total time elapsed = 1.5859e+01 seconds\n", + " Calculation Rate (inactive) = 4098.98 particles/second\n", + " Calculation Rate (active) = 3421.07 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -565,19 +574,19 @@ "\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", + " Total time in simulation = 1.4334e+01 seconds\n", + " Time in transport only = 1.4323e+01 seconds\n", + " Time in inactive batches = 2.4599e+00 seconds\n", + " Time in active batches = 1.1874e+01 seconds\n", + " Time synchronizing fission bank = 4.7284e-03 seconds\n", + " Sampling source sites = 4.3409e-03 seconds\n", + " SEND/RECV source sites = 3.5783e-04 seconds\n", + " Time accumulating tallies = 2.7686e-04 seconds\n", + " Time writing statepoints = 2.7450e-03 seconds\n", + " Total time for finalization = 6.6834e-03 seconds\n", + " Total time elapsed = 1.4365e+01 seconds\n", + " Calculation Rate (inactive) = 4065.28 particles/second\n", + " Calculation Rate (active) = 3368.77 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -652,19 +661,19 @@ "\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", + " Total time in simulation = 1.5356e+01 seconds\n", + " Time in transport only = 1.5345e+01 seconds\n", + " Time in inactive batches = 2.5600e+00 seconds\n", + " Time in active batches = 1.2796e+01 seconds\n", + " Time synchronizing fission bank = 4.8536e-03 seconds\n", + " Sampling source sites = 4.4341e-03 seconds\n", + " SEND/RECV source sites = 3.8411e-04 seconds\n", + " Time accumulating tallies = 3.1893e-04 seconds\n", + " Time writing statepoints = 2.9909e-03 seconds\n", + " Total time for finalization = 5.8300e-05 seconds\n", + " Total time elapsed = 1.5380e+01 seconds\n", + " Calculation Rate (inactive) = 3906.26 particles/second\n", + " Calculation Rate (active) = 3125.88 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -739,19 +748,19 @@ "\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", + " Total time in simulation = 1.5938e+01 seconds\n", + " Time in transport only = 1.5926e+01 seconds\n", + " Time in inactive batches = 2.7348e+00 seconds\n", + " Time in active batches = 1.3203e+01 seconds\n", + " Time synchronizing fission bank = 4.8009e-03 seconds\n", + " Sampling source sites = 4.3574e-03 seconds\n", + " SEND/RECV source sites = 4.0496e-04 seconds\n", + " Time accumulating tallies = 3.4334e-04 seconds\n", + " Time writing statepoints = 3.1199e-03 seconds\n", + " Total time for finalization = 5.7181e-05 seconds\n", + " Total time elapsed = 1.5963e+01 seconds\n", + " Calculation Rate (inactive) = 3656.52 particles/second\n", + " Calculation Rate (active) = 3029.69 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -826,19 +835,19 @@ "\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", + " Total time in simulation = 1.5839e+01 seconds\n", + " Time in transport only = 1.5827e+01 seconds\n", + " Time in inactive batches = 2.7033e+00 seconds\n", + " Time in active batches = 1.3136e+01 seconds\n", + " Time synchronizing fission bank = 4.9757e-03 seconds\n", + " Sampling source sites = 4.5464e-03 seconds\n", + " SEND/RECV source sites = 3.9214e-04 seconds\n", + " Time accumulating tallies = 3.2820e-04 seconds\n", + " Time writing statepoints = 3.0288e-03 seconds\n", + " Total time for finalization = 5.6741e-05 seconds\n", + " Total time elapsed = 1.5865e+01 seconds\n", + " Calculation Rate (inactive) = 3699.13 particles/second\n", + " Calculation Rate (active) = 3045.12 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -913,19 +922,19 @@ "\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", + " Total time in simulation = 1.5879e+01 seconds\n", + " Time in transport only = 1.5868e+01 seconds\n", + " Time in inactive batches = 2.7260e+00 seconds\n", + " Time in active batches = 1.3153e+01 seconds\n", + " Time synchronizing fission bank = 4.7604e-03 seconds\n", + " Sampling source sites = 4.3338e-03 seconds\n", + " SEND/RECV source sites = 3.9159e-04 seconds\n", + " Time accumulating tallies = 3.6340e-04 seconds\n", + " Time writing statepoints = 2.9921e-03 seconds\n", + " Total time for finalization = 5.7621e-05 seconds\n", + " Total time elapsed = 1.5905e+01 seconds\n", + " Calculation Rate (inactive) = 3668.45 particles/second\n", + " Calculation Rate (active) = 3041.07 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1000,19 +1009,19 @@ "\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", + " Total time in simulation = 1.4969e+01 seconds\n", + " Time in transport only = 1.4958e+01 seconds\n", + " Time in inactive batches = 2.7192e+00 seconds\n", + " Time in active batches = 1.2250e+01 seconds\n", + " Time synchronizing fission bank = 5.2067e-03 seconds\n", + " Sampling source sites = 4.8368e-03 seconds\n", + " SEND/RECV source sites = 3.3899e-04 seconds\n", + " Time accumulating tallies = 2.7359e-04 seconds\n", + " Time writing statepoints = 2.8293e-03 seconds\n", + " Total time for finalization = 5.0560e-05 seconds\n", + " Total time elapsed = 1.5008e+01 seconds\n", + " Calculation Rate (inactive) = 3677.55 particles/second\n", + " Calculation Rate (active) = 3265.28 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1070,7 +1079,16 @@ "cell_type": "code", "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/deplete/results.py:87: FutureWarning: The ResultsList.from_hdf5(...) method is no longer necessary and will be removed in a future version of OpenMC. Use Results(...) instead.\n", + " warn(\n" + ] + } + ], "source": [ "results = openmc.deplete.ResultsList.from_hdf5(\"./depletion_results.h5\")" ] @@ -1084,7 +1102,7 @@ "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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/deplete/results.py:240: 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" ] } @@ -1151,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1199,7 +1217,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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UEg+3h8tSKhGISMKi9Fm8zN0vMbNHAcLFaaJMMSFTaM1kqa+p5DVLm5IORURSLkqLYMjMKgEHMLNFwGisUaVAa6aDdataqK481aEcIiKzI8pvoc8D3wTOMLO/BP4D+HSsUZW5Q939ZA73an1iESkK094acvd7zWwb8CaCwWRvB56LOa6y1hbWBzS/kIgUg6lWKPu2ma0AcPen3f2L7v4FgqUkHytQfGWpdXeWeXVVXHD2vKRDERGZ8tbQ/cCPzexjZlZtZmeb2T8Dfwm8uzDhlafW9g4uW72AyoopZ+kWESmIvInA3e8lWHB+OfAU0EawDsF6d99WmPDKz97OPvZ2Htf4AREpGtMViy8A1gGPAAPAYqJ1OZU8VB8QkWIzVY3gToKBY3/k7r9J0DqYDzxuZr9aoPjKTlsmy4KGGs5b3Jh0KCIiwNQtgicIZh9tA3D3Xnf/CPAbwMcLEVy5cXfaMlnWr1mAmeoDIlIcppp07m/z7N8J/GJsEZWx5zt6ebm7X/UBESkqGtZaQK0Z1QdEpPgoERRQW3uWM+fVsXJBfdKhiIiMUyIokNFRZ3Mmy+WqD4hIkZlRIjCz7XEFUu6ePXSMbO+gpp0WkaIz0xaB/pQ9RW0ZrT8gIsVppong32KJIgVaM1mWt9SztFn1AREpLjNKBFOtYyz5jYw6m9uz6jYqIkVJxeIC2PXSUY71D+u2kIgUJSWCAhivD2ghGhEpQjPtNdRsZq+JeOxdZnbIzJ7I8/obzOyomT0WPm6dSSylpDWT5ZwzGjljXl3SoYiIvMK0icDMfmJm88ysBdgO/IOZfTbCue8GrprmmJ+7+8Xh45MRzllyhkZG2bKnU/UBESlaUVoE8929G7ge+Iq7Xwb8ynRvcvefAZ2nGV/J27Gvi77BEd0WEpGiFSURVJnZWcA7ge/M8udvMLPHzey7ZnbhLJ+7KLTuDuoD65UIRKRIRUkEnwS+B+x29y1mtprZWbx+O7DC3S8C/g74Vr4DzexmM9tqZlsPHz48Cx9dOK2ZLBecNY/mhpqkQxERmdS0icDdv+7ur3H3Pwqft7v7r5/uB7t7t7v3hNsPAtVmNum0nO6+0d3XuvvaRYsWne5HF0z/0AjbXjyibqMiUtSmXXbSzFYBHwRW5h7v7teezgeb2ZnAQXd3M1tHkJSyp3POYrP9xSMMDo+qUCwiRS3K+sPfAu4Evg2MRj2xmd0HvAFYaGb7gNuAagB3vwO4AXifmQ0Dx4Eb3d1nEnyxa8tkqaww1q1qSToUEZG8oiSCfnf//ExP7O43TfP6F4AvzPS8paQ1k+XVS+Yzt6466VBERPKKkgg+Z2a3Ad8HBsZ2urumpJ5C78Awj+/t4vdfvzrpUEREphQlEbwaeBfwRk7cGvLwueSxZU8nw6Ou+oCIFL0oieAdwGp3H4w7mHLSlslSXWmsXaH6gIgUtyjjCJ4AmmKOo+y0tWd57bJm5tRUJh2KiMiUorQImoCnzWwLJ9cITqv7aDk72jfEE/uP8sE3npt0KCIi04qSCG6LPYoy8/DzWUYd1QdEpCRMmwjc/admthi4NNz1iLsfijes0tbWnqW2qoKLlzclHYqIyLSiTEP9TuARgqLxO4GHzeyGuAMrZW2ZLJeubKG2SvUBESl+UW4NfQy4dKwVYGaLgB8CD8QZWKnK9gzw9MvH+JO3nJ10KCIikUTpNVQx4VZQNuL7Umlze7AEg+oDIlIqorQIHjKz7wH3hc9/A/hufCGVttZMB421Vbx6yfykQxERiSRKsfhPzOx64Mpw10Z3/2a8YZWutkyWdataqKpUo0lESkOUaahvd/ePAt+YZJ/kePloP+0dvfzmZcuTDkVEJLIof7a+eZJ9V892IOWgrb0D0LKUIlJa8rYIzOx9wB8Bq81sR85Lc4H/jDuwUtS6O8v8OdVccNa8pEMREYlsqltDXyUoCn8auCVn/zF374w1qhLVmsmyYfUCKios6VBERCLLe2vI3Y+6+x53v8ndXyBYRcyBRjPTTfAJ9nb2sb/ruNYnFpGSE2Vk8dvM7DngeeCnwB7UffQVWjNBfUDjB0Sk1EQpFv8FsB541t1XAW8CNscaVQlqzWRZ2FjLOWc0Jh2KiMiMREkEQ+6eBSrMrMLdfwysjTmukuLutGWyXL5mAWaqD4hIaYkysrjLzBqBnwH3mtkhoDfesEpL5nAvh44NqD4gIiUpSovgOqAP+B/AQ0AGeFucQZWaNtUHRKSERZliYuyv/1HgnnjDKU2tmSxLmuawvKU+6VBERGZME+KcptFRZ3N7lvWrVR8QkdKkRHCann75GEf6hnRbSERKVt5EYGb/Hn69vXDhlJ6x8QMqFItIqZqqRnCWmV0OXGtm9wMn3fdw9+2xRlYiNrdnWbmgnrOb5iQdiojIKZkqEdwKfBxYCnx2wmsOvDGuoErF8MgoD7d38taLtCyliJSuvInA3R8AHjCzj7v7pwoYU8l44qVujg0Mqz4gIiUtSvfRT5nZtcDrw10/cffvxBtWaWjLZAGtPyAipS3KpHOfBj4EPBk+PmRmfxXhfXeZ2SEzeyLP62Zmnzez3Wa2w8wumWnwSWvNdHDe4kYWza1NOhQRkVMWpfvorwFvdve73P0u4CrgrRHed3d4bD5XA+eGj5uBL0c4Z9EYHB5l654jXL5mYdKhiIiclqjjCJpytudHeYO7/wyYagGb64CveGAz0GRmZ0WMJ3GP7+vi+NCIuo2KSMmLMuncp4FHzezHBF1IX8/JK5adqiXA3pzn+8J9ByYeaGY3E7QaWL68ONbEad2dxQzWr1IiEJHSFqVYfJ+Z/QS4NNz1UXd/OdaoXhnDRmAjwNq1a72Qn51Pa6aDC8+ex/z66qRDERE5LVFaBLj7AWDTLH/2fmBZzvOl4b6i1z80wqMvdvGeK1YmHYqIyGlLcq6hTcBvh72H1gNHw4RT9La9cITBkVE2qNuoiJSBSC2CU2Fm9wFvABaa2T7gNqAawN3vAB4ErgF2E6x38N64YpltrZkOKiuMS1e1JB2KiMhpmzIRmFklsMvdz5/pid39pmled+D9Mz1vMWjNZLlo6Xwaa2PLoyIiBTPlrSF3HwGeMbPi6KpTBHoGhtmx76i6jYpI2YjyJ20zsMvMHiFnrWJ3vza2qIrYluc7GRl1DSQTkbIRJRF8PPYoSkhrpoOaygpet6I56VBERGZFlHEEPzWzFcC57v5DM6sHKuMPrTi1tWe5ZEUTddWpvQQiUmaiTDr3+8ADwN+Hu5YA34oxpqLV1TfIrpe62bBat4VEpHxEGUfwfuAKoBvA3Z8DzogzqGK1ub0Td7j8HBWKRaR8REkEA+4+OPbEzKoIVihLnbZMB3OqK7loaVPSoYiIzJooieCnZvZnwBwzezPwdeDb8YZVnNras6xd2UxNVZIDskVEZleU32i3AIeBncAfEIwI/vM4gypGh48N8OzBHnUbFZGyE6XX0KiZ3QM8THBL6JlwVHCqtLUHy1JqfWIRKTfTJgIz+zXgDiBDsB7BKjP7A3f/btzBFZO2TJa5tVVcePa8pEMREZlVUQaUfQb4ZXffDWBma4B/A1KWCDq4bHULVZWqD4hIeYnyW+3YWBIItQPHYoqnKL3UdZw92T42qD4gImUob4vAzK4PN7ea2YPAPxPUCN4BbClAbEWjLaP6gIiUr6luDb0tZ/sg8Evh9mFgTmwRFaHWTJbm+mpetXhu0qGIiMy6vInA3UtmoZg4uTttmQ42rFlARYUlHY6IyKyL0mtoFfBBYGXu8WmZhvrFzj5eOtrP+1QfEJEyFaXX0LeAOwlGE4/GGk0Rag3rA1qfWETKVZRE0O/un489kiLVmslyxtxa1ixqSDoUEZFYREkEnzOz24DvAwNjO919e2xRFYmgPpDlynMWYKb6gIiUpyiJ4NXAu4A3cuLWkIfPy9ruQz109AxofWIRKWtREsE7gNW5U1GnRev4+AEVikWkfEUZWfwE0BRzHEWpNdPB0uY5LGupTzoUEZHYRGkRNAFPm9kWTq4RlHX30dFRZ3N7J2+5cHHSoYiIxCpKIrgt9iiK0JMHujl6fEj1AREpe1HWI/hpIQIpNm3j4wdUHxCR8hZlZPExTqxRXANUA73uXtYT87e1Z1m9qIEz59clHYqISKyitAjGZ1qzoDP9dcD6OINK2tDIKA+3Z3n7a5ckHYqISOxmtMqKB74FvCWecIrDzv1H6R0cUbdREUmFKLeGrs95WgGsBfpji6gIjNUH1q9uSTgSEZH4RWkRvC3n8RaC1cmui3JyM7vKzJ4xs91mdsskr7/HzA6b2WPh4/dmEnxc2jJZzj9zLgsaa5MORUQkdlFqBKe0LoGZVQJfBN4M7AO2mNkmd39ywqFfc/cPnMpnxGFgeIQtezr5zcuWJx2KiEhBTLVU5a1TvM/d/VPTnHsdsNvd28Pz3U/QkpiYCIrKoy92MTA8qvqAiKTGVLeGeid5APwu8NEI514C7M15vi/cN9Gvm9kOM3vAzJZNdiIzu9nMtprZ1sOHD0f46FPXlslSYbBuleoDIpIOeROBu39m7AFsJFin+L3A/cDqWfr8bwMr3f01wA+Ae/LEstHd17r72kWLFs3SR0+uLZPlF5bMZ/6c6lg/R0SkWExZLDazFjP7C2AHwW2kS9z9o+5+KMK59wO5f+EvDfeNc/esu4/NX/R/gddFjjwGxwdHeHTvEU0rISKpkjcRmNnfAFsIegm92t0/4e5HZnDuLcC5ZrbKzGqAG4FNEz7jrJyn1wJPzeD8s27rC50MjbjqAyKSKlP1GvpjgtlG/xz4WM4KXUZQLJ5yigl3HzazDwDfAyqBu9x9l5l9Etjq7puA/25m1wLDQCfwntP5x5yu1kyWqgpj7YrmJMMQESmovInA3Wc06jjPOR4EHpyw79ac7T8F/vR0P2e2tGayXLysiYbaKJOyioiUh9P+ZV8uuvuH2Lmvi8tVHxCRlFEiCG15vpNRh/VKBCKSMkoEodZMlpqqCi5ZrvqAiKSLEkGoNZNl7Ypm6qorkw5FRKSglAiAI72DPHWgW/UBEUklJQJgc3u4LKUSgYikkBIBwW2h+ppKXrO0KelQREQKTomAYH3idataqK7U5RCR9En9b75D3f3sPtTDhtW6LSQi6ZT6RNAW1gc0v5CIpJUSQSbLvLoqLjh7yqmTRETKVuoTQWsmy2WrF1BZYdMfLCJShlKdCPZ29vFiZ5/GD4hIqqU6Eag+ICKS8kSwOZNlQUMN5y1uTDoUEZHEpDYRuDutmSzr1ywgZ9EdEZHUSW0ieL6jl5e7+1UfEJHUS20iUH1ARCSQ2kTQmsly5rw6Vi6oTzoUEZFEpTIRuDubM1kuV31ARCSdieDZgz1kewc17bSICClNBK2ZDkDrD4iIQGoTQZblLfUsbVZ9QEQkdYlgZNTZ3J5Vt1ERkVDqEsGTL3VzrH9Yt4VEREKpSwTj9QEtRCMiAqQyEWQ554xGzphXl3QoIiJFIVWJYGhklC17OlUfEBHJkapEsGNfF32DI7otJCKSI1WJoHV3ML/QeiUCEZFxsSYCM7vKzJ4xs91mdsskr9ea2dfC1x82s5VxxtPWnuWCs+bR3FAT58eIiJSU2BKBmVUCXwSuBi4AbjKzCyYc9rvAEXc/B/hb4Pa44ukfGmHrC0fUbVREZII4WwTrgN3u3u7ug8D9wHUTjrkOuCfcfgB4k8U0C9z2F48wODyqQrGIyARxJoIlwN6c5/vCfZMe4+7DwFHgFb+pzexmM9tqZlsPHz58SsFUV1bwy69axKWrWk7p/SIi5aokisXuvtHd17r72kWLFp3SOS5d2cL/e+865tVVz3J0IiKlLc5EsB9YlvN8abhv0mPMrAqYD2RjjElERCaIMxFsAc41s1VmVgPcCGyacMwm4N3h9g3Aj9zdY4xJREQmqIrrxO4+bGYfAL4HVAJ3ufsuM/sksNXdNwF3Av9oZruBToJkISIiBRRbIgBw9weBByfsuzVnux94R5wxiIjI1EqiWCwiIvFRIhARSTklAhGRlFMiEBFJOSu13ppmdhh44RTfvhDomMVw4lQqsSrO2VcqsSrO2RV3nCvcfdIRuSWXCE6HmW1197VJxxFFqcSqOGdfqcSqOGdXknHq1pCISMopEYiIpFzaEsHGpAOYgVKJVXHOvlKJVXHOrsTiTFWNQEREXiltLQIREZlAiUBEJOVSkwjM7Coze8bMdpvZLUnHM8bMlpnZj83sSTPbZWYfCvd/wsz2m9lj4eOaIoh1j5ntDOPZGu5rMbMfmNlz4dfmIojzVTnX7TEz6zazDxfDNTWzu8zskJk9kbNv0mtogc+HP7M7zOyShOP8GzN7Oozlm2bWFO5faWbHc67rHYWKc4pY836vzexPw2v6jJm9JeE4v5YT4x4zeyzcX9hr6u5l/yCYBjsDrAZqgMeBC5KOK4ztLOCScHsu8CxwAfAJ4CNJxzch1j3Awgn7/hq4Jdy+Bbg96Tgn+d6/DKwohmsKvB64BHhiumsIXAN8FzBgPfBwwnH+KlAVbt+eE+fK3OOK5JpO+r0O/289DtQCq8LfC5VJxTnh9c8AtyZxTdPSIlgH7Hb3dncfBO4Hrks4JgDc/YC7bw+3jwFP8cq1nYvZdcA94fY9wNuTC2VSbwIy7n6qo9Fnlbv/jGDtjVz5ruF1wFc8sBloMrOzkorT3b/vwdriAJsJVh1MXJ5rms91wP3uPuDuzwO7CX4/xG6qOM3MgHcC9xUilonSkgiWAHtznu+jCH/ZmtlK4LXAw+GuD4TN8LuK4ZYL4MD3zWybmd0c7lvs7gfC7ZeBxcmElteNnPyfq9iuKeS/hsX8c/s7BK2VMavM7FEz+6mZ/WJSQU0w2fe6WK/pLwIH3f25nH0Fu6ZpSQRFz8wagX8BPuzu3cCXgTXAxcABgmZj0q5090uAq4H3m9nrc1/0oE1bNP2RwyVSrwW+Hu4qxmt6kmK7hpMxs48Bw8C94a4DwHJ3fy3wP4Gvmtm8pOILFf33eoKbOPkPloJe07Qkgv3AspznS8N9RcHMqgmSwL3u/g0Adz/o7iPuPgr8AwVqvk7F3feHXw8B3ySI6eDY7Yrw66HkInyFq4Ht7n4QivOahvJdw6L7uTWz9wBvBX4rTFqEt1my4fY2gvvu5yUWJFN+r4vxmlYB1wNfG9tX6GualkSwBTjXzFaFfyXeCGxKOCZg/N7gncBT7v7ZnP2594L/K/DExPcWkpk1mNncsW2CwuETBNfx3eFh7wb+NZkIJ3XSX1nFdk1z5LuGm4DfDnsPrQeO5txCKjgzuwr4X8C17t6Xs3+RmVWG26uBc4H2ZKIcjynf93oTcKOZ1ZrZKoJYHyl0fBP8CvC0u+8b21Hwa1qoqnTSD4IeGM8SZNaPJR1PTlxXEtwK2AE8Fj6uAf4R2Bnu3wSclXCcqwl6WzwO7Bq7hsAC4N+B54AfAi1JX9MwrgYgC8zP2Zf4NSVITAeAIYL707+b7xoS9Bb6YvgzuxNYm3Ccuwnur4/9nN4RHvvr4c/EY8B24G1FcE3zfq+Bj4XX9Bng6iTjDPffDfzhhGMLek01xYSISMql5daQiIjkoUQgIpJySgQiIimnRCAiknJKBCIiKadEICKSckoEklpmtiBnmt+Xc6Yt7jGzL8XweXeb2fNm9od5Xu8Jv64Zi2O2YxCZjMYRiBDMXw/0uPv/jvEz7ga+4+4P5Hm9x90b8z0XiYtaBCITmNkbzOw74fYnzOweM/u5mb1gZteb2V9bsEDPQ+E8UZjZ68JZIreZ2feiTBcdTnnSFp7rL+L+d4nko0QgMr01wBsJZjL9J+DH7v5q4Djwa2Ey+DvgBnd/HXAX8JcRzvs54MvhuRKbQ0ikKukARErAd919yMx2Eqx49lC4fyfBSlKvAn4B+EEwhyCVRPvFfgXBnDIQzI1z+yzGLBKZEoHI9AYA3H3UzIb8RGFtlOD/kAG73H3DKZxbRTpJnG4NiZy+Z4BFZrYBgvUlzOzCCO/7T4Ip0QF+K67gRKajRCBymjxYB/sG4HYze5xg6uDLI7z1QwQrve2kOJZLlJRS91GRApmu++gkx6v7qBSEWgQihXMU+FS+AWVjxgaUAQcLEpWknloEIiIppxaBiEjKKRGIiKScEoGISMopEYiIpNz/Bz+nMSDHtUBuAAAAAElFTkSuQmCC\n", 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" ] @@ -1263,7 +1281,7 @@ "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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" ] @@ -1411,7 +1429,16 @@ "cell_type": "code", "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/deplete/coupled_operator.py:546: FutureWarning: The Operator(...) class has been renamed and will be removed in a future version of OpenMC. Use CoupledOperator(...) instead.\n", + " warn(\n" + ] + } + ], "source": [ "model = openmc.Model(geometry=geometry, settings=settings)\n", "new_op = openmc.deplete.Operator(model, \"./chain_simple.xml\")" diff --git a/hexagonal-lattice.ipynb b/hexagonal-lattice.ipynb index 07c3ab8..87f2a2a 100644 --- a/hexagonal-lattice.ipynb +++ b/hexagonal-lattice.ipynb @@ -247,7 +247,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -291,7 +291,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -362,7 +362,7 @@ "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "" ] @@ -389,7 +389,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -403,9 +403,9 @@ "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/mdgxs-part-i.ipynb b/mdgxs-part-i.ipynb index d474e1e..b311465 100644 --- a/mdgxs-part-i.ipynb +++ b/mdgxs-part-i.ipynb @@ -326,8 +326,8 @@ "outputs": [], "source": [ "# Instantiate a few different sections\n", - "chi_prompt = mgxs.Chi(domain=cell, groups=energy_groups, by_nuclide=True, prompt=True)\n", - "prompt_nu_fission = mgxs.FissionXS(domain=cell, groups=energy_groups, by_nuclide=True, nu=True, prompt=True)\n", + "chi_prompt = mgxs.Chi(domain=cell, energy_groups=energy_groups, by_nuclide=True, prompt=True)\n", + "prompt_nu_fission = mgxs.FissionXS(domain=cell, energy_groups=energy_groups, by_nuclide=True, nu=True, prompt=True)\n", "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n", "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", @@ -399,15 +399,15 @@ "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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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=5.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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=4.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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=7.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=7.\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=13.\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=13.\n", " warn(msg, IDWarning)\n" ] } @@ -494,9 +494,9 @@ " | 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", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-03 23:19:19\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", @@ -576,21 +576,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\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", + " Total time for initialization = 8.7385e-01 seconds\n", + " Reading cross sections = 8.6672e-01 seconds\n", + " Total time in simulation = 1.3402e+02 seconds\n", + " Time in transport only = 1.3397e+02 seconds\n", + " Time in inactive batches = 4.7414e+00 seconds\n", + " Time in active batches = 1.2927e+02 seconds\n", + " Time synchronizing fission bank = 2.1579e-02 seconds\n", + " Sampling source sites = 1.9879e-02 seconds\n", + " SEND/RECV source sites = 1.6605e-03 seconds\n", + " Time accumulating tallies = 1.0377e-02 seconds\n", + " Time writing statepoints = 4.2323e-03 seconds\n", + " Total time for finalization = 1.3320e-02 seconds\n", + " Total time elapsed = 1.3492e+02 seconds\n", + " Calculation Rate (inactive) = 10545.3 particles/second\n", + " Calculation Rate (active) = 1547.09 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1106,7 +1106,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -1115,7 +1115,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] diff --git a/mdgxs-part-ii.ipynb b/mdgxs-part-ii.ipynb index 08d06b3..91d0285 100644 --- a/mdgxs-part-ii.ipynb +++ b/mdgxs-part-ii.ipynb @@ -304,17 +304,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us also create a plot to verify that our fuel assembly geometry was created successfully." + "We'll tie the materials, geometry, and settings into a single model object and export the necessary XML files." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, + "outputs": [], + "source": [ + "model = openmc.Model(geometry, materials, settings)\n", + "model.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a plot to verify that our fuel assembly geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -354,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -376,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -427,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -440,24 +457,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=23.\n", " warn(msg, IDWarning)\n" ] }, @@ -492,9 +509,9 @@ " | 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", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-03 23:30:12\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", @@ -542,7 +559,62 @@ " 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" + " 23/1 1.03265 1.03864 +/- 0.00629\n", + " 24/1 0.98957 1.03513 +/- 0.00680\n", + " 25/1 1.01457 1.03376 +/- 0.00648\n", + " 26/1 1.00560 1.03200 +/- 0.00631\n", + " 27/1 1.00933 1.03067 +/- 0.00608\n", + " 28/1 1.01275 1.02967 +/- 0.00581\n", + " 29/1 1.04347 1.03040 +/- 0.00555\n", + " 30/1 1.03126 1.03044 +/- 0.00526\n", + " 31/1 1.05165 1.03145 +/- 0.00511\n", + " 32/1 1.02594 1.03120 +/- 0.00488\n", + " 33/1 1.00047 1.02987 +/- 0.00485\n", + " 34/1 1.04045 1.03031 +/- 0.00466\n", + " 35/1 1.01414 1.02966 +/- 0.00452\n", + " 36/1 1.03056 1.02970 +/- 0.00434\n", + " 37/1 1.05870 1.03077 +/- 0.00431\n", + " 38/1 0.97655 1.02883 +/- 0.00458\n", + " 39/1 1.05223 1.02964 +/- 0.00450\n", + " 40/1 1.08089 1.03135 +/- 0.00467\n", + " 41/1 1.05155 1.03200 +/- 0.00456\n", + " 42/1 1.01221 1.03138 +/- 0.00446\n", + " 43/1 1.03906 1.03161 +/- 0.00433\n", + " 44/1 1.00455 1.03082 +/- 0.00427\n", + " 45/1 1.00183 1.02999 +/- 0.00423\n", + " 46/1 1.05348 1.03064 +/- 0.00416\n", + " 47/1 1.06321 1.03152 +/- 0.00414\n", + " 48/1 1.05862 1.03224 +/- 0.00410\n", + " 49/1 1.04610 1.03259 +/- 0.00401\n", + " 50/1 1.02808 1.03248 +/- 0.00391\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 7.2704e-01 seconds\n", + " Reading cross sections = 7.1697e-01 seconds\n", + " Total time in simulation = 6.0982e+01 seconds\n", + " Time in transport only = 6.0103e+01 seconds\n", + " Time in inactive batches = 4.4142e+00 seconds\n", + " Time in active batches = 5.6568e+01 seconds\n", + " Time synchronizing fission bank = 1.0706e-02 seconds\n", + " Sampling source sites = 9.8511e-03 seconds\n", + " SEND/RECV source sites = 8.2068e-04 seconds\n", + " Time accumulating tallies = 8.5453e-01 seconds\n", + " Time writing statepoints = 8.0261e-03 seconds\n", + " Total time for finalization = 2.0600e-06 seconds\n", + " Total time elapsed = 6.1735e+01 seconds\n", + " Calculation Rate (inactive) = 5663.53 particles/second\n", + " Calculation Rate (active) = 1767.79 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.03096 +/- 0.00285\n", + " k-effective (Track-length) = 1.03248 +/- 0.00391\n", + " k-effective (Absorption) = 1.02246 +/- 0.00327\n", + " Combined k-effective = 1.02749 +/- 0.00267\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" ] } ], @@ -567,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "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", diff --git a/mgxs-part-i.ipynb b/mgxs-part-i.ipynb index 64cd36a..1257424 100644 --- a/mgxs-part-i.ipynb +++ b/mgxs-part-i.ipynb @@ -355,13 +355,13 @@ "outputs": [], "source": [ "# Instantiate a few different sections\n", - "total = mgxs.TotalXS(domain=cell, groups=groups)\n", - "absorption = mgxs.AbsorptionXS(domain=cell, groups=groups)\n", - "scattering = mgxs.ScatterXS(domain=cell, groups=groups)\n", + "total = mgxs.TotalXS(domain=cell, energy_groups=groups)\n", + "absorption = mgxs.AbsorptionXS(domain=cell, energy_groups=groups)\n", + "scattering = mgxs.ScatterXS(domain=cell, energy_groups=groups)\n", "\n", "# Note that if we wanted to incorporate neutron multiplication in the\n", "# scattering cross section we would write the previous line as:\n", - "# scattering = mgxs.ScatterXS(domain=cell, groups=groups, nu=True)" + "# scattering = mgxs.ScatterXS(domain=cell, energy_groups=groups, nu=True)" ] }, { @@ -450,9 +450,9 @@ "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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n" ] }, @@ -487,20 +487,20 @@ " | 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", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-04 12:30:35\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 /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", + " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n", " Minimum neutron data temperature: 294 K\n", " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", @@ -514,82 +514,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\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", + " 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", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\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", + " Total time for initialization = 7.5024e-01 seconds\n", + " Reading cross sections = 7.4182e-01 seconds\n", + " Total time in simulation = 1.9857e+01 seconds\n", + " Time in transport only = 1.9835e+01 seconds\n", + " Time in inactive batches = 2.4766e+00 seconds\n", + " Time in active batches = 1.7380e+01 seconds\n", + " Time synchronizing fission bank = 1.1568e-02 seconds\n", + " Sampling source sites = 1.0777e-02 seconds\n", + " SEND/RECV source sites = 7.5224e-04 seconds\n", + " Time accumulating tallies = 3.8921e-04 seconds\n", + " Time writing statepoints = 3.0584e-03 seconds\n", + " Total time for finalization = 1.9732e-04 seconds\n", + " Total time elapsed = 2.0619e+01 seconds\n", + " Calculation Rate (inactive) = 10094.3 particles/second\n", + " Calculation Rate (active) = 5753.6 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\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", + " 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", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -687,8 +687,8 @@ "\tDomain Type =\tcell\n", "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\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", + " 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", "\n", "\n", "\n" @@ -745,16 +745,16 @@ " 1\n", " 1\n", " total\n", - " 0.668519\n", - " 0.001449\n", + " 0.668083\n", + " 0.001798\n", " \n", " \n", " 0\n", " 1\n", " 2\n", " total\n", - " 1.292665\n", - " 0.008362\n", + " 1.292060\n", + " 0.007737\n", " \n", " \n", "\n", @@ -762,8 +762,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "1 1 1 total 0.668519 0.001449\n", - "0 1 2 total 1.292665 0.008362" + "1 1 1 total 0.668083 0.001798\n", + "0 1 2 total 1.292060 0.007737" ] }, "execution_count": 19, @@ -792,7 +792,7 @@ "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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mgxs/mgxs.py:2007: 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" ] } @@ -876,8 +876,8 @@ " 6.250000e-01\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " -2.442491e-15\n", - " 0.012361\n", + " 1.110223e-15\n", + " 0.011435\n", " \n", " \n", " 1\n", @@ -886,8 +886,8 @@ " 2.000000e+07\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " 3.996803e-15\n", - " 0.002069\n", + " -2.997602e-15\n", + " 0.002567\n", " \n", " \n", "\n", @@ -899,8 +899,8 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " 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 " + "0 (((total / flux) - (absorption / flux)) - (sca... 1.11e-15 1.14e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... -3.00e-15 2.57e-03 " ] }, "execution_count": 22, @@ -966,8 +966,8 @@ " 6.250000e-01\n", " total\n", " ((absorption / flux) / (total / flux))\n", - " 0.076155\n", - " 0.000712\n", + " 0.076103\n", + " 0.000658\n", " \n", " \n", " 1\n", @@ -976,8 +976,8 @@ " 2.000000e+07\n", " total\n", " ((absorption / flux) / (total / flux))\n", - " 0.019411\n", - " 0.000091\n", + " 0.019441\n", + " 0.000093\n", " \n", " \n", "\n", @@ -989,8 +989,8 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " 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 " + "0 ((absorption / flux) / (total / flux)) 7.61e-02 6.58e-04 \n", + "1 ((absorption / flux) / (total / flux)) 1.94e-02 9.26e-05 " ] }, "execution_count": 23, @@ -1049,8 +1049,8 @@ " 6.250000e-01\n", " total\n", " ((scatter / flux) / (total / flux))\n", - " 0.923845\n", - " 0.008463\n", + " 0.923897\n", + " 0.007833\n", " \n", " \n", " 1\n", @@ -1059,8 +1059,8 @@ " 2.000000e+07\n", " total\n", " ((scatter / flux) / (total / flux))\n", - " 0.980589\n", - " 0.003003\n", + " 0.980559\n", + " 0.003729\n", " \n", " \n", "\n", @@ -1072,8 +1072,8 @@ "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 " + "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.83e-03 \n", + "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.73e-03 " ] }, "execution_count": 24, @@ -1140,7 +1140,7 @@ " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", " 1.0\n", - " 0.008492\n", + " 0.007861\n", " \n", " \n", " 1\n", @@ -1150,7 +1150,7 @@ " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", " 1.0\n", - " 0.003005\n", + " 0.003731\n", " \n", " \n", "\n", @@ -1162,8 +1162,8 @@ "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 8.49e-03 \n", - "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.00e-03 " + "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 " ] }, "execution_count": 25, diff --git a/mgxs-part-ii.ipynb b/mgxs-part-ii.ipynb index c674d09..e6c458c 100644 --- a/mgxs-part-ii.ipynb +++ b/mgxs-part-ii.ipynb @@ -256,11 +256,11 @@ "# Instantiate 8-group cross sections for each cell\n", "for cell in openmc_cells:\n", " xs_library[cell.id] = {}\n", - " xs_library[cell.id]['transport'] = mgxs.TransportXS(groups=fine_groups)\n", - " xs_library[cell.id]['fission'] = mgxs.FissionXS(groups=fine_groups)\n", - " xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(groups=fine_groups, nu=True)\n", - " xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(groups=fine_groups, nu=True)\n", - " xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)" + " xs_library[cell.id]['transport'] = mgxs.TransportXS(energy_groups=fine_groups)\n", + " xs_library[cell.id]['fission'] = mgxs.FissionXS(energy_groups=fine_groups)\n", + " xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(energy_groups=fine_groups, nu=True)\n", + " xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(energy_groups=fine_groups, nu=True)\n", + " xs_library[cell.id]['chi'] = mgxs.Chi(energy_groups=fine_groups)" ] }, { @@ -334,19 +334,19 @@ "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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=15.\n", " warn(msg, IDWarning)\n" ] }, @@ -381,20 +381,20 @@ " | 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", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-04 12:38:13\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/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", + " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n", " Minimum neutron data temperature: 294 K\n", " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", @@ -408,196 +408,180 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\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", + " 1/1 1.23300\n", + " 2/1 1.22712\n", + " 3/1 1.23403\n", + " 4/1 1.23023\n", + " 5/1 1.20250\n", + " 6/1 1.20565\n", + " 7/1 1.23034\n", + " 8/1 1.21931\n", + " 9/1 1.23826\n", + " 10/1 1.25090\n", + " 11/1 1.21111\n", + " 12/1 1.20786 1.20948 +/- 0.00162\n", + " 13/1 1.23784 1.21894 +/- 0.00950\n", + " 14/1 1.22692 1.22093 +/- 0.00701\n", + " 15/1 1.20622 1.21799 +/- 0.00617\n", + " 16/1 1.20024 1.21503 +/- 0.00584\n", + " 17/1 1.20624 1.21378 +/- 0.00510\n", + " 18/1 1.20491 1.21267 +/- 0.00455\n", + " 19/1 1.20434 1.21174 +/- 0.00412\n", + " 20/1 1.22590 1.21316 +/- 0.00395\n", + " 21/1 1.20102 1.21206 +/- 0.00374\n", + " 22/1 1.23117 1.21365 +/- 0.00376\n", + " 23/1 1.22446 1.21448 +/- 0.00356\n", + " 24/1 1.22469 1.21521 +/- 0.00338\n", + " 25/1 1.23859 1.21677 +/- 0.00351\n", + " 26/1 1.25129 1.21893 +/- 0.00393\n", + " 27/1 1.22524 1.21930 +/- 0.00371\n", + " 28/1 1.22082 1.21938 +/- 0.00350\n", + " 29/1 1.21838 1.21933 +/- 0.00331\n", + " 30/1 1.24252 1.22049 +/- 0.00335\n", + " 31/1 1.23912 1.22138 +/- 0.00330\n", + " 32/1 1.22704 1.22163 +/- 0.00316\n", + " 33/1 1.22330 1.22171 +/- 0.00302\n", + " 34/1 1.20966 1.22120 +/- 0.00294\n", + " 35/1 1.20998 1.22076 +/- 0.00285\n", + " 36/1 1.21069 1.22037 +/- 0.00277\n", + " 37/1 1.20250 1.21971 +/- 0.00274\n", + " 38/1 1.20573 1.21921 +/- 0.00269\n", + " 39/1 1.23647 1.21980 +/- 0.00266\n", + " 40/1 1.22719 1.22005 +/- 0.00258\n", + " 41/1 1.23410 1.22050 +/- 0.00254\n", + " 42/1 1.23220 1.22087 +/- 0.00249\n", + " 43/1 1.23399 1.22127 +/- 0.00244\n", + " 44/1 1.23143 1.22156 +/- 0.00239\n", + " 45/1 1.23144 1.22185 +/- 0.00234\n", + " 46/1 1.22653 1.22198 +/- 0.00227\n", + " 47/1 1.21239 1.22172 +/- 0.00223\n", + " 48/1 1.22858 1.22190 +/- 0.00218\n", + " 49/1 1.20952 1.22158 +/- 0.00214\n", + " 50/1 1.20497 1.22117 +/- 0.00213\n", + " Triggers unsatisfied, max unc./thresh. is 1.258443881396485 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", + " 51/1 1.22506 1.22126 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 1.2360657756715019 for flux in tally\n", + " 53\n", + " The estimated number of batches is 73\n", + " 52/1 1.22892 1.22144 +/- 0.00204\n", + " Triggers unsatisfied, max unc./thresh. is 1.2066960139701686 for flux in tally\n", + " 53\n", + " The estimated number of batches is 72\n", + " 53/1 1.20425 1.22104 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 1.179403356279039 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 54/1 1.18761 1.22028 +/- 0.00212\n", + " Triggers unsatisfied, max unc./thresh. is 1.174509036327948 for flux in tally\n", + " 53\n", + " The estimated number of batches is 71\n", + " 55/1 1.21624 1.22019 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 1.1488291965006945 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 56/1 1.22056 1.22020 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 1.1253755542270594 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 57/1 1.22213 1.22024 +/- 0.00199\n", + " Triggers unsatisfied, max unc./thresh. is 1.1013090664481884 for flux in tally\n", + " 53\n", + " The estimated number of batches is 68\n", + " 58/1 1.23192 1.22049 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 1.0814142069510033 for flux in tally\n", + " 53\n", + " The estimated number of batches is 67\n", + " 59/1 1.23142 1.22071 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 1.0688144272740103 for flux in tally\n", + " 53\n", + " The estimated number of batches is 66\n", + " 60/1 1.23744 1.22104 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 1.0845694915767077 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 61/1 1.24681 1.22155 +/- 0.00195\n", + " Triggers unsatisfied, max unc./thresh. is 1.0650160561431934 for flux in tally\n", + " 53\n", + " The estimated number of batches is 68\n", + " 62/1 1.23159 1.22174 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 1.062883546252306 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 63/1 1.21588 1.22163 +/- 0.00189\n", + " Triggers unsatisfied, max unc./thresh. is 1.0440750211709433 for flux in tally\n", + " 53\n", + " The estimated number of batches is 68\n", + " 64/1 1.21867 1.22158 +/- 0.00186\n", + " Triggers unsatisfied, max unc./thresh. is 1.0258844139300165 for flux in tally\n", + " 53\n", + " The estimated number of batches is 67\n", + " 65/1 1.22410 1.22162 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 1.0384126882352387 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 66/1 1.20786 1.22138 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 1.0213633609662338 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 67/1 1.21064 1.22119 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 1.0191822472678496 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 68/1 1.23857 1.22149 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 1.012860968303348 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 69/1 1.21603 1.22139 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 1.011310730467471 for flux in tally\n", + " 53\n", + " The estimated number of batches is 71\n", + " 70/1 1.19730 1.22099 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 1.025023774534994 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\n", + " 71/1 1.22459 1.22105 +/- 0.00174\n", + " Triggers unsatisfied, max unc./thresh. is 1.0084011441774638 for flux in tally\n", + " 53\n", + " The estimated number of batches is 73\n", + " 72/1 1.24564 1.22145 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 1.0083223109546262 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\n", + " 73/1 1.21948 1.22142 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 1.0051824390044617 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\n", + " 74/1 1.20923 1.22123 +/- 0.00171\n", + " Triggers satisfied for batch 74\n", + " Creating state point statepoint.074.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\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", + " Total time for initialization = 6.5906e-01 seconds\n", + " Reading cross sections = 6.5113e-01 seconds\n", + " Total time in simulation = 2.8372e+02 seconds\n", + " Time in transport only = 2.8357e+02 seconds\n", + " Time in inactive batches = 1.4161e+01 seconds\n", + " Time in active batches = 2.6956e+02 seconds\n", + " Time synchronizing fission bank = 7.3528e-02 seconds\n", + " Sampling source sites = 5.6344e-02 seconds\n", + " SEND/RECV source sites = 1.7125e-02 seconds\n", + " Time accumulating tallies = 8.9480e-03 seconds\n", + " Time writing statepoints = 1.7131e-02 seconds\n", + " Total time for finalization = 6.5114e-03 seconds\n", + " Total time elapsed = 2.8444e+02 seconds\n", + " Calculation Rate (inactive) = 7061.79 particles/second\n", + " Calculation Rate (active) = 2374.27 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\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", + " k-effective (Collision) = 1.22070 +/- 0.00153\n", + " k-effective (Track-length) = 1.22123 +/- 0.00171\n", + " k-effective (Absorption) = 1.22336 +/- 0.00157\n", + " Combined k-effective = 1.22204 +/- 0.00135\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -687,25 +671,25 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [barns]:\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", + " Group 1 [821000.0 - 20000000.0eV]:\t3.30e+00 +/- 2.34e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t3.96e+00 +/- 1.53e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t5.52e+01 +/- 2.15e-01%\n", + " Group 4 [0.625 - 4.0 eV]:\t8.83e+01 +/- 3.51e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t2.90e+02 +/- 5.28e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t4.49e+02 +/- 4.05e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t6.87e+02 +/- 3.36e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t1.44e+03 +/- 2.54e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [barns]:\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", + " Group 1 [821000.0 - 20000000.0eV]:\t1.06e+00 +/- 2.64e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t1.21e-03 +/- 2.83e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t5.51e-04 +/- 2.99e+00%\n", + " Group 4 [0.625 - 4.0 eV]:\t6.54e-06 +/- 3.28e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t1.07e-05 +/- 5.12e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t1.55e-05 +/- 4.08e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t2.30e-05 +/- 3.35e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t4.24e-05 +/- 2.53e-01%\n", "\n", "\n", "\n" @@ -738,14 +722,14 @@ "\tDomain Type =\tcell\n", "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\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", + " Group 1 [821000.0 - 20000000.0eV]:\t2.52e-02 +/- 2.51e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t1.51e-03 +/- 1.50e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t2.07e-02 +/- 2.14e-01%\n", + " Group 4 [0.625 - 4.0 eV]:\t3.31e-02 +/- 3.51e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t1.09e-01 +/- 5.28e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t1.69e-01 +/- 4.05e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t2.58e-01 +/- 3.36e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t5.40e-01 +/- 2.54e-01%\n", "\n", "\n", "\n" @@ -805,8 +789,8 @@ " 1\n", " 1\n", " H1\n", - " 0.234171\n", - " 0.003922\n", + " 0.233979\n", + " 0.003921\n", " \n", " \n", " 127\n", @@ -814,8 +798,8 @@ " 1\n", " 1\n", " O16\n", - " 1.559702\n", - " 0.005620\n", + " 1.566955\n", + " 0.006599\n", " \n", " \n", " 124\n", @@ -823,8 +807,8 @@ " 1\n", " 2\n", " H1\n", - " 1.589382\n", - " 0.002801\n", + " 1.589223\n", + " 0.003224\n", " \n", " \n", " 125\n", @@ -832,8 +816,8 @@ " 1\n", " 2\n", " O16\n", - " 0.285545\n", - " 0.001472\n", + " 0.286382\n", + " 0.001478\n", " \n", " \n", " 122\n", @@ -841,8 +825,8 @@ " 1\n", " 3\n", " H1\n", - " 0.010727\n", - " 0.000239\n", + " 0.011405\n", + " 0.000244\n", " \n", " \n", " 123\n", @@ -859,8 +843,8 @@ " 1\n", " 4\n", " H1\n", - " 0.000005\n", - " 0.000005\n", + " 0.000015\n", + " 0.000009\n", " \n", " \n", " 121\n", @@ -877,8 +861,8 @@ " 1\n", " 5\n", " H1\n", - " 0.000000\n", - " 0.000000\n", + " 0.000005\n", + " 0.000005\n", " \n", " \n", " 119\n", @@ -895,15 +879,15 @@ ], "text/plain": [ " cell group in group out nuclide mean std. dev.\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", + "126 3 1 1 H1 0.233979 0.003921\n", + "127 3 1 1 O16 1.566955 0.006599\n", + "124 3 1 2 H1 1.589223 0.003224\n", + "125 3 1 2 O16 0.286382 0.001478\n", + "122 3 1 3 H1 0.011405 0.000244\n", "123 3 1 3 O16 0.000000 0.000000\n", - "120 3 1 4 H1 0.000005 0.000005\n", + "120 3 1 4 H1 0.000015 0.000009\n", "121 3 1 4 O16 0.000000 0.000000\n", - "118 3 1 5 H1 0.000000 0.000000\n", + "118 3 1 5 H1 0.000005 0.000005\n", "119 3 1 5 O16 0.000000 0.000000" ] }, @@ -960,18 +944,18 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\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", + " Group 1 [0.625 - 20000000.0eV]:\t7.77e-03 +/- 2.18e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.82e-01 +/- 1.85e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\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", + " Group 1 [0.625 - 20000000.0eV]:\t2.17e-01 +/- 1.32e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t2.53e-01 +/- 1.91e-01%\n", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\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", + " Group 1 [0.625 - 20000000.0eV]:\t1.45e-01 +/- 1.26e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.74e-01 +/- 2.02e-01%\n", "\n", "\n", "\n" @@ -1021,48 +1005,48 @@ " 1\n", " 1\n", " U235\n", - " 20.658286\n", - " 0.043889\n", + " 20.715441\n", + " 0.045146\n", " \n", " \n", " 4\n", " 1\n", " 1\n", " U238\n", - " 9.584869\n", - " 0.011511\n", + " 9.579757\n", + " 0.012606\n", " \n", " \n", " 5\n", " 1\n", " 1\n", " O16\n", - " 3.157267\n", - " 0.003680\n", + " 3.155966\n", + " 0.003977\n", " \n", " \n", " 0\n", " 1\n", " 2\n", " U235\n", - " 485.233252\n", - " 0.935407\n", + " 485.656482\n", + " 0.899766\n", " \n", " \n", " 1\n", " 1\n", " 2\n", " U238\n", - " 11.209561\n", - " 0.021499\n", + " 11.191961\n", + " 0.021372\n", " \n", " \n", " 2\n", " 1\n", " 2\n", " O16\n", - " 3.790416\n", - " 0.007835\n", + " 3.790699\n", + " 0.007656\n", " \n", " \n", "\n", @@ -1070,12 +1054,12 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "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" + "3 1 1 U235 20.715441 0.045146\n", + "4 1 1 U238 9.579757 0.012606\n", + "5 1 1 O16 3.155966 0.003977\n", + "0 1 2 U235 485.656482 0.899766\n", + "1 1 2 U238 11.191961 0.021372\n", + "2 1 2 O16 3.790699 0.007656" ] }, "execution_count": 21, @@ -1203,480 +1187,480 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\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" + "[ NORMAL ] Iteration 0: k_eff = 0.423006 res = 1.792E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -57699 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 1: k_eff = 0.475905 res = 3.879E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 5289 D.R. = 2.1646\n", + "[ NORMAL ] Iteration 2: k_eff = 0.491392 res = 3.463E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1548 D.R. = 0.8928\n", + "[ NORMAL ] Iteration 3: k_eff = 0.487335 res = 3.478E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -405 D.R. = 0.1004\n", + "[ NORMAL ] Iteration 4: k_eff = 0.483788 res = 8.318E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -354 D.R. = 2.3913\n", + "[ NORMAL ] Iteration 5: k_eff = 0.477110 res = 1.437E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -667 D.R. = 1.7273\n", + "[ NORMAL ] Iteration 6: k_eff = 0.468745 res = 1.966E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -836 D.R. = 0.1368\n", + "[ NORMAL ] Iteration 7: k_eff = 0.460108 res = 5.656E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -863 D.R. = 28.7692\n", + "[ NORMAL ] Iteration 8: k_eff = 0.450367 res = 1.612E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... -974 D.R. = 2.8503\n", + "[ NORMAL ] Iteration 9: k_eff = 0.441143 res = 6.685E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -922 D.R. = 0.4146\n", + "[ NORMAL ] Iteration 10: k_eff = 0.431749 res = 3.902E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -939 D.R. = 0.5837\n", + "[ NORMAL ] Iteration 11: k_eff = 0.422685 res = 2.208E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -906 D.R. = 0.5659\n", + "[ NORMAL ] Iteration 12: k_eff = 0.414237 res = 2.964E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -844 D.R. = 1.3425\n", + "[ NORMAL ] Iteration 13: k_eff = 0.406456 res = 2.238E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -778 D.R. = 0.7551\n", + "[ NORMAL ] Iteration 14: k_eff = 0.399125 res = 2.117E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -733 D.R. = 0.9459\n", + "[ NORMAL ] Iteration 15: k_eff = 0.392814 res = 3.569E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -631 D.R. = 1.6857\n", + "[ NORMAL ] Iteration 16: k_eff = 0.387174 res = 3.206E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -564 D.R. = 0.8983\n", + "[ NORMAL ] Iteration 17: k_eff = 0.382416 res = 5.263E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -475 D.R. = 1.6415\n", + "[ NORMAL ] Iteration 18: k_eff = 0.378489 res = 4.537E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -392 D.R. = 0.8621\n", + "[ NORMAL ] Iteration 19: k_eff = 0.375392 res = 3.085E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -309 D.R. = 0.6800\n", + "[ NORMAL ] Iteration 20: k_eff = 0.373239 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -215 D.R. = 0.3137\n", + "[ NORMAL ] Iteration 21: k_eff = 0.372107 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -113 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 22: k_eff = 0.371724 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -38 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 23: k_eff = 0.372330 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 4.5714\n", + "[ NORMAL ] Iteration 24: k_eff = 0.373804 res = 5.686E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 147 D.R. = 2.9375\n", + "[ NORMAL ] Iteration 25: k_eff = 0.376130 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 232 D.R. = 0.8511\n", + "[ NORMAL ] Iteration 26: k_eff = 0.379307 res = 3.509E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 317 D.R. = 0.7250\n", + "[ NORMAL ] Iteration 27: k_eff = 0.383324 res = 3.993E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 401 D.R. = 1.1379\n", + "[ NORMAL ] Iteration 28: k_eff = 0.388118 res = 8.469E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 479 D.R. = 0.2121\n", + "[ NORMAL ] Iteration 29: k_eff = 0.393671 res = 1.452E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 1.7143\n", + "[ NORMAL ] Iteration 30: k_eff = 0.399962 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 629 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 31: k_eff = 0.406957 res = 3.146E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 699 D.R. = 1.0833\n", + "[ NORMAL ] Iteration 32: k_eff = 0.414600 res = 6.896E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 764 D.R. = 2.1923\n", + "[ NORMAL ] Iteration 33: k_eff = 0.422881 res = 1.815E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 828 D.R. = 0.2632\n", + "[ NORMAL ] Iteration 34: k_eff = 0.431752 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 887 D.R. = 0.5333\n", + "[ NORMAL ] Iteration 35: k_eff = 0.441164 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 941 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 36: k_eff = 0.451113 res = 8.469E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 994 D.R. = 0.2917\n", + "[ NORMAL ] Iteration 37: k_eff = 0.461526 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1041 D.R. = 4.5714\n", + "[ NORMAL ] Iteration 38: k_eff = 0.472392 res = 7.501E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1086 D.R. = 1.9375\n", + "[ NORMAL ] Iteration 39: k_eff = 0.483657 res = 6.533E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1126 D.R. = 0.8710\n", + "[ NORMAL ] Iteration 40: k_eff = 0.495292 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1163 D.R. = 0.5926\n", + "[ NORMAL ] Iteration 41: k_eff = 0.507260 res = 3.388E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1196 D.R. = 0.8750\n", + "[ NORMAL ] Iteration 42: k_eff = 0.519528 res = 4.598E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1226 D.R. = 1.3571\n", + "[ NORMAL ] Iteration 43: k_eff = 0.532058 res = 4.598E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1253 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 44: k_eff = 0.544820 res = 8.469E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1276 D.R. = 0.1842\n", + "[ NORMAL ] Iteration 45: k_eff = 0.557781 res = 1.089E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1296 D.R. = 1.2857\n", + "[ NORMAL ] Iteration 46: k_eff = 0.570908 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1312 D.R. = 6.2222\n", + "[ NORMAL ] Iteration 47: k_eff = 0.584174 res = 7.864E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1326 D.R. = 1.1607\n", + "[ NORMAL ] Iteration 48: k_eff = 0.597549 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1337 D.R. = 0.1154\n", + "[ NORMAL ] Iteration 49: k_eff = 0.611006 res = 2.662E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1345 D.R. = 2.9333\n", + "[ NORMAL ] Iteration 50: k_eff = 0.624518 res = 3.085E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1351 D.R. = 1.1591\n", + "[ NORMAL ] Iteration 51: k_eff = 0.638062 res = 7.501E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1354 D.R. = 2.4314\n", + "[ NORMAL ] Iteration 52: k_eff = 0.651612 res = 6.654E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1355 D.R. = 0.8871\n", + "[ NORMAL ] Iteration 53: k_eff = 0.665146 res = 2.057E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1353 D.R. = 0.3091\n", + "[ NORMAL ] Iteration 54: k_eff = 0.678645 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1349 D.R. = 0.7353\n", + "[ NORMAL ] Iteration 55: k_eff = 0.692087 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1344 D.R. = 0.0800\n", + "[ NORMAL ] Iteration 56: k_eff = 0.705453 res = 1.028E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1336 D.R. = 8.5000\n", + "[ NORMAL ] Iteration 57: k_eff = 0.718729 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1327 D.R. = 0.8824\n", + "[ NORMAL ] Iteration 58: k_eff = 0.731896 res = 6.049E-10 delta-k (pcm) =\n", + "[ NORMAL ] ... 1316 D.R. = 0.0667\n", + "[ NORMAL ] Iteration 59: k_eff = 0.744940 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1304 D.R. = 19.0000\n", + "[ NORMAL ] Iteration 60: k_eff = 0.757847 res = 2.057E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1290 D.R. = 1.7895\n", + "[ NORMAL ] Iteration 61: k_eff = 0.770604 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1275 D.R. = 0.5588\n", + "[ NORMAL ] Iteration 62: k_eff = 0.783200 res = 3.146E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1259 D.R. = 2.7368\n", + "[ NORMAL ] Iteration 63: k_eff = 0.795624 res = 3.448E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1242 D.R. = 1.0962\n", + "[ NORMAL ] Iteration 64: k_eff = 0.807866 res = 2.964E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1224 D.R. = 0.8596\n", + "[ NORMAL ] Iteration 65: k_eff = 0.819919 res = 6.715E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1205 D.R. = 2.2653\n", + "[ NORMAL ] Iteration 66: k_eff = 0.831774 res = 2.238E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1185 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 67: k_eff = 0.843424 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1165 D.R. = 0.7297\n", + "[ NORMAL ] Iteration 68: k_eff = 0.854864 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1143 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 69: k_eff = 0.866087 res = 2.359E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1122 D.R. = 1.4444\n", + "[ NORMAL ] Iteration 70: k_eff = 0.877091 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1100 D.R. = 0.9744\n", + "[ NORMAL ] Iteration 71: k_eff = 0.887870 res = 1.875E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1077 D.R. = 0.8158\n", + "[ NORMAL ] Iteration 72: k_eff = 0.898423 res = 2.420E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1055 D.R. = 0.1290\n", + "[ NORMAL ] Iteration 73: k_eff = 0.908747 res = 7.864E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1032 D.R. = 3.2500\n", + "[ NORMAL ] Iteration 74: k_eff = 0.918839 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1009 D.R. = 2.0769\n", + "[ NORMAL ] Iteration 75: k_eff = 0.928699 res = 4.174E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 986 D.R. = 2.5556\n", + "[ NORMAL ] Iteration 76: k_eff = 0.938326 res = 4.779E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 962 D.R. = 1.1449\n", + "[ NORMAL ] Iteration 77: k_eff = 0.947719 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 939 D.R. = 0.0253\n", + "[ NORMAL ] Iteration 78: k_eff = 0.956880 res = 6.049E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 916 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 79: k_eff = 0.965808 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 892 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 80: k_eff = 0.974505 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 869 D.R. = 1.2800\n", + "[ NORMAL ] Iteration 81: k_eff = 0.982972 res = 1.452E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 846 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 82: k_eff = 0.991211 res = 5.445E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 823 D.R. = 0.3750\n", + "[ NORMAL ] Iteration 83: k_eff = 0.999224 res = 3.630E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 801 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 84: k_eff = 1.007014 res = 5.445E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 778 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 85: k_eff = 1.014581 res = 4.537E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 756 D.R. = 8.3333\n", + "[ NORMAL ] Iteration 86: k_eff = 1.021932 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 735 D.R. = 1.2800\n", + "[ NORMAL ] Iteration 87: k_eff = 1.029067 res = 6.049E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 713 D.R. = 1.0417\n", + "[ NORMAL ] Iteration 88: k_eff = 1.035990 res = 7.078E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 692 D.R. = 1.1700\n", + "[ NORMAL ] Iteration 89: k_eff = 1.042705 res = 2.662E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 671 D.R. = 0.3761\n", + "[ NORMAL ] Iteration 90: k_eff = 1.049215 res = 3.569E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 650 D.R. = 1.3409\n", + "[ NORMAL ] Iteration 91: k_eff = 1.055524 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 630 D.R. = 0.3220\n", + "[ NORMAL ] Iteration 92: k_eff = 1.061635 res = 3.327E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 611 D.R. = 2.8947\n", + "[ NORMAL ] Iteration 93: k_eff = 1.067553 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 591 D.R. = 0.3455\n", + "[ NORMAL ] Iteration 94: k_eff = 1.073282 res = 1.815E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 572 D.R. = 0.1579\n", + "[ NORMAL ] Iteration 95: k_eff = 1.078825 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 554 D.R. = 16.0000\n", + "[ NORMAL ] Iteration 96: k_eff = 1.084186 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 536 D.R. = 0.1458\n", + "[ NORMAL ] Iteration 97: k_eff = 1.089370 res = 1.694E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 518 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 98: k_eff = 1.094381 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 501 D.R. = 1.3571\n", + "[ NORMAL ] Iteration 99: k_eff = 1.099224 res = 3.569E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 484 D.R. = 1.5526\n", + "[ NORMAL ] Iteration 100: k_eff = 1.103901 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 467 D.R. = 0.6441\n", + "[ NORMAL ] Iteration 101: k_eff = 1.108418 res = 4.779E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 451 D.R. = 2.0789\n", + "[ NORMAL ] Iteration 102: k_eff = 1.112777 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 435 D.R. = 0.0886\n", + "[ NORMAL ] Iteration 103: k_eff = 1.116986 res = 2.480E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 420 D.R. = 5.8571\n", + "[ NORMAL ] Iteration 104: k_eff = 1.121046 res = 1.573E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 405 D.R. = 0.6341\n", + "[ NORMAL ] Iteration 105: k_eff = 1.124962 res = 7.320E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 391 D.R. = 4.6538\n", + "[ NORMAL ] Iteration 106: k_eff = 1.128738 res = 5.082E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 377 D.R. = 0.6942\n", + "[ NORMAL ] Iteration 107: k_eff = 1.132377 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 363 D.R. = 0.0119\n", + "[ NORMAL ] Iteration 108: k_eff = 1.135884 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 350 D.R. = 48.0000\n", + "[ NORMAL ] Iteration 109: k_eff = 1.139264 res = 3.509E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 337 D.R. = 1.2083\n", + "[ NORMAL ] Iteration 110: k_eff = 1.142519 res = 1.633E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 325 D.R. = 0.4655\n", + "[ NORMAL ] Iteration 111: k_eff = 1.145654 res = 5.505E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 313 D.R. = 3.3704\n", + "[ NORMAL ] Iteration 112: k_eff = 1.148671 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 301 D.R. = 0.3956\n", + "[ NORMAL ] Iteration 113: k_eff = 1.151575 res = 9.074E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 290 D.R. = 0.4167\n", + "[ NORMAL ] Iteration 114: k_eff = 1.154371 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 279 D.R. = 0.4667\n", + "[ NORMAL ] Iteration 115: k_eff = 1.157059 res = 1.512E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 268 D.R. = 3.5714\n", + "[ NORMAL ] Iteration 116: k_eff = 1.159645 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 258 D.R. = 1.8800\n", + "[ NORMAL ] Iteration 117: k_eff = 1.162132 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 248 D.R. = 1.6383\n", + "[ NORMAL ] Iteration 118: k_eff = 1.164522 res = 3.025E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 239 D.R. = 0.0649\n", + "[ NORMAL ] Iteration 119: k_eff = 1.166820 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 229 D.R. = 16.0000\n", + "[ NORMAL ] Iteration 120: k_eff = 1.169029 res = 3.630E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 220 D.R. = 0.0750\n", + "[ NORMAL ] Iteration 121: k_eff = 1.171150 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 212 D.R. = 2.1667\n", + "[ NORMAL ] Iteration 122: k_eff = 1.173188 res = 1.331E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 203 D.R. = 1.6923\n", + "[ NORMAL ] Iteration 123: k_eff = 1.175145 res = 2.057E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 195 D.R. = 1.5455\n", + "[ NORMAL ] Iteration 124: k_eff = 1.177024 res = 1.452E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 187 D.R. = 0.7059\n", + "[ NORMAL ] Iteration 125: k_eff = 1.178828 res = 1.149E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 180 D.R. = 0.7917\n", + "[ NORMAL ] Iteration 126: k_eff = 1.180560 res = 3.388E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 173 D.R. = 2.9474\n", + "[ NORMAL ] Iteration 127: k_eff = 1.182222 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 166 D.R. = 0.5714\n", + "[ NORMAL ] Iteration 128: k_eff = 1.183817 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 159 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 129: k_eff = 1.185346 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 152 D.R. = inf\n", + "[ NORMAL ] Iteration 130: k_eff = 1.186813 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 146 D.R. = 2.4839\n", + "[ NORMAL ] Iteration 131: k_eff = 1.188220 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 140 D.R. = 0.2727\n", + "[ NORMAL ] Iteration 132: k_eff = 1.189569 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 134 D.R. = 1.8095\n", + "[ NORMAL ] Iteration 133: k_eff = 1.190862 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 129 D.R. = 1.2368\n", + "[ NORMAL ] Iteration 134: k_eff = 1.192102 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 123 D.R. = 1.6383\n", + "[ NORMAL ] Iteration 135: k_eff = 1.193290 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 118 D.R. = 0.4675\n", + "[ NORMAL ] Iteration 136: k_eff = 1.194428 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 113 D.R. = 0.0278\n", + "[ NORMAL ] Iteration 137: k_eff = 1.195519 res = 2.420E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 109 D.R. = 40.0000\n", + "[ NORMAL ] Iteration 138: k_eff = 1.196563 res = 3.932E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 104 D.R. = 1.6250\n", + "[ NORMAL ] Iteration 139: k_eff = 1.197564 res = 5.505E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 140: k_eff = 1.198522 res = 2.420E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 95 D.R. = 0.0440\n", + "[ NORMAL ] Iteration 141: k_eff = 1.199440 res = 8.348E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 91 D.R. = 34.5000\n", + "[ NORMAL ] Iteration 142: k_eff = 1.200317 res = 3.993E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 87 D.R. = 0.4783\n", + "[ NORMAL ] Iteration 143: k_eff = 1.201159 res = 5.263E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 84 D.R. = 1.3182\n", + "[ NORMAL ] Iteration 144: k_eff = 1.201963 res = 6.352E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 80 D.R. = 1.2069\n", + "[ NORMAL ] Iteration 145: k_eff = 1.202733 res = 5.686E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 77 D.R. = 0.8952\n", + "[ NORMAL ] Iteration 146: k_eff = 1.203470 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 73 D.R. = 0.1170\n", + "[ NORMAL ] Iteration 147: k_eff = 1.204175 res = 3.448E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 70 D.R. = 5.1818\n", + "[ NORMAL ] Iteration 148: k_eff = 1.204849 res = 2.541E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 67 D.R. = 0.7368\n", + "[ NORMAL ] Iteration 149: k_eff = 1.205494 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 64 D.R. = 0.8571\n", + "[ NORMAL ] Iteration 150: k_eff = 1.206111 res = 3.993E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 61 D.R. = 1.8333\n", + "[ NORMAL ] Iteration 151: k_eff = 1.206700 res = 5.324E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 58 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 152: k_eff = 1.207264 res = 1.391E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 56 D.R. = 0.2614\n", + "[ NORMAL ] Iteration 153: k_eff = 1.207804 res = 1.210E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 53 D.R. = 0.0870\n", + "[ NORMAL ] Iteration 154: k_eff = 1.208319 res = 5.021E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 51 D.R. = 41.5000\n", + "[ NORMAL ] Iteration 155: k_eff = 1.208811 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 49 D.R. = 0.1325\n", + "[ NORMAL ] Iteration 156: k_eff = 1.209282 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 1.4545\n", + "[ NORMAL ] Iteration 157: k_eff = 1.209732 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 44 D.R. = 0.0625\n", + "[ NORMAL ] Iteration 158: k_eff = 1.210162 res = 3.690E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 42 D.R. = 61.0000\n", + "[ NORMAL ] Iteration 159: k_eff = 1.210573 res = 1.149E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.3115\n", + "[ NORMAL ] Iteration 160: k_eff = 1.210964 res = 7.259E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 6.3158\n", + "[ NORMAL ] Iteration 161: k_eff = 1.211339 res = 1.010E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 1.3917\n", + "[ NORMAL ] Iteration 162: k_eff = 1.211697 res = 5.263E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 0.5210\n", + "[ NORMAL ] Iteration 163: k_eff = 1.212039 res = 6.110E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = 1.1609\n", + "[ NORMAL ] Iteration 164: k_eff = 1.212365 res = 4.356E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 0.7129\n", + "[ NORMAL ] Iteration 165: k_eff = 1.212676 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 166: k_eff = 1.212973 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 0.2187\n", + "[ NORMAL ] Iteration 167: k_eff = 1.213258 res = 1.815E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 4.2857\n", + "[ NORMAL ] Iteration 168: k_eff = 1.213529 res = 5.747E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 3.1667\n", + "[ NORMAL ] Iteration 169: k_eff = 1.213787 res = 8.530E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = 1.4842\n", + "[ NORMAL ] Iteration 170: k_eff = 1.214034 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 24 D.R. = 0.0071\n", + "[ NORMAL ] Iteration 171: k_eff = 1.214270 res = 8.469E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 14.0000\n", + "[ NORMAL ] Iteration 172: k_eff = 1.214494 res = 3.206E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 3.7857\n", + "[ NORMAL ] Iteration 173: k_eff = 1.214709 res = 3.751E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 1.1698\n", + "[ NORMAL ] Iteration 174: k_eff = 1.214913 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 0.2903\n", + "[ NORMAL ] Iteration 175: k_eff = 1.215108 res = 4.537E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 4.1667\n", + "[ NORMAL ] Iteration 176: k_eff = 1.215295 res = 3.267E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 0.7200\n", + "[ NORMAL ] Iteration 177: k_eff = 1.215472 res = 3.690E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 1.1296\n", + "[ NORMAL ] Iteration 178: k_eff = 1.215641 res = 3.085E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.8361\n", + "[ NORMAL ] Iteration 179: k_eff = 1.215803 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.7059\n", + "[ NORMAL ] Iteration 180: k_eff = 1.215957 res = 3.932E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 1.8056\n", + "[ NORMAL ] Iteration 181: k_eff = 1.216103 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.2462\n", + "[ NORMAL ] Iteration 182: k_eff = 1.216243 res = 8.469E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.8750\n", + "[ NORMAL ] Iteration 183: k_eff = 1.216377 res = 6.049E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 0.7143\n", + "[ NORMAL ] Iteration 184: k_eff = 1.216504 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 3.8000\n", + "[ NORMAL ] Iteration 185: k_eff = 1.216625 res = 4.053E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 1.7632\n", + "[ NORMAL ] Iteration 186: k_eff = 1.216741 res = 2.238E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.5522\n", + "[ NORMAL ] Iteration 187: k_eff = 1.216851 res = 5.565E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 2.4865\n", + "[ NORMAL ] Iteration 188: k_eff = 1.216955 res = 6.049E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 1.0870\n", + "[ NORMAL ] Iteration 189: k_eff = 1.217056 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.0900\n", + "[ NORMAL ] Iteration 190: k_eff = 1.217151 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 1.4444\n", + "[ NORMAL ] Iteration 191: k_eff = 1.217242 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 2.9231\n", + "[ NORMAL ] Iteration 192: k_eff = 1.217328 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 0.0263\n", + "[ NORMAL ] Iteration 193: k_eff = 1.217410 res = 4.114E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 68.0000\n", + "[ NORMAL ] Iteration 194: k_eff = 1.217489 res = 2.420E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.5882\n", + "[ NORMAL ] Iteration 195: k_eff = 1.217563 res = 3.569E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 1.4750\n", + "[ NORMAL ] Iteration 196: k_eff = 1.217635 res = 1.210E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.0339\n", + "[ NORMAL ] Iteration 197: k_eff = 1.217703 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 6.5000\n", + "[ NORMAL ] Iteration 198: k_eff = 1.217768 res = 1.149E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 1.4615\n", + "[ NORMAL ] Iteration 199: k_eff = 1.217829 res = 2.238E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 1.9474\n", + "[ NORMAL ] Iteration 200: k_eff = 1.217887 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.1892\n", + "[ NORMAL ] Iteration 201: k_eff = 1.217943 res = 3.993E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 9.4286\n", + "[ NORMAL ] Iteration 202: k_eff = 1.217996 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.7121\n", + "[ NORMAL ] Iteration 203: k_eff = 1.218047 res = 7.441E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 2.6170\n", + "[ NORMAL ] Iteration 204: k_eff = 1.218095 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.1463\n", + "[ NORMAL ] Iteration 205: k_eff = 1.218141 res = 1.754E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.6111\n", + "[ NORMAL ] Iteration 206: k_eff = 1.218184 res = 4.356E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 2.4828\n", + "[ NORMAL ] Iteration 207: k_eff = 1.218225 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.2222\n", + "[ NORMAL ] Iteration 208: k_eff = 1.218265 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 209: k_eff = 1.218302 res = 3.146E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.6000\n", + "[ NORMAL ] Iteration 210: k_eff = 1.218338 res = 5.626E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.7885\n", + "[ NORMAL ] Iteration 211: k_eff = 1.218372 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.0323\n", + "[ NORMAL ] Iteration 212: k_eff = 1.218404 res = 6.291E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 34.6667\n", + "[ NORMAL ] Iteration 213: k_eff = 1.218435 res = 4.356E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 214: k_eff = 1.218465 res = 6.836E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.5694\n", + "[ NORMAL ] Iteration 215: k_eff = 1.218493 res = 4.719E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6903\n", + "[ NORMAL ] Iteration 216: k_eff = 1.218519 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.1410\n", + "[ NORMAL ] Iteration 217: k_eff = 1.218544 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.8182\n", + "[ NORMAL ] Iteration 218: k_eff = 1.218568 res = 1.573E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.3000\n", + "[ NORMAL ] Iteration 219: k_eff = 1.218591 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1923\n", + "[ NORMAL ] Iteration 220: k_eff = 1.218613 res = 3.388E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.8065\n", + "[ NORMAL ] Iteration 221: k_eff = 1.218634 res = 3.751E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1071\n", + "[ NORMAL ] Iteration 222: k_eff = 1.218654 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0484\n", + "[ NORMAL ] Iteration 223: k_eff = 1.218672 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 224: k_eff = 1.218690 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 225: k_eff = 1.218707 res = 6.291E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 34.6667\n", + "[ NORMAL ] Iteration 226: k_eff = 1.218723 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 227: k_eff = 1.218738 res = 6.896E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 8.7692\n", + "[ NORMAL ] Iteration 228: k_eff = 1.218753 res = 6.715E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9737\n", + "[ NORMAL ] Iteration 229: k_eff = 1.218766 res = 1.512E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2252\n", + "[ NORMAL ] Iteration 230: k_eff = 1.218779 res = 1.573E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.0400\n", + "[ NORMAL ] Iteration 231: k_eff = 1.218792 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.2308\n", + "[ NORMAL ] Iteration 232: k_eff = 1.218804 res = 4.840E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 233: k_eff = 1.218815 res = 4.174E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 8.6250\n", + "[ NORMAL ] Iteration 234: k_eff = 1.218826 res = 4.114E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9855\n", + "[ NORMAL ] Iteration 235: k_eff = 1.218836 res = 3.630E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.8824\n", + "[ NORMAL ] Iteration 236: k_eff = 1.218846 res = 7.804E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 2.1500\n" ] } ], @@ -1706,9 +1690,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.224010\n", - "openmoc keff = 1.220549\n", - "bias [pcm]: -346.1\n" + "openmc keff = 1.222044\n", + "openmoc keff = 1.218846\n", + "bias [pcm]: -319.8\n" ] }, { @@ -1817,704 +1801,706 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\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 ] Iteration 0: k_eff = 0.366644 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -63335 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 1: k_eff = 0.390955 res = 1.210E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 2431 D.R. = 12.5000\n", + "[ NORMAL ] Iteration 2: k_eff = 0.392706 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 175 D.R. = 0.0800\n", + "[ NORMAL ] Iteration 3: k_eff = 0.380770 res = 8.711E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -1193 D.R. = 9.0000\n", + "[ NORMAL ] Iteration 4: k_eff = 0.374646 res = 5.324E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -612 D.R. = 0.6111\n", + "[ NORMAL ] Iteration 5: k_eff = 0.369186 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -546 D.R. = 1.2727\n", + "[ NORMAL ] Iteration 6: k_eff = 0.365104 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -408 D.R. = 1.4286\n", + "[ NORMAL ] Iteration 7: k_eff = 0.362589 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -251 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 8: k_eff = 0.360985 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -160 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 9: k_eff = 0.360771 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -21 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 10: k_eff = 0.361478 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 70 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 11: k_eff = 0.363179 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 170 D.R. = inf\n", + "[ NORMAL ] Iteration 12: k_eff = 0.365787 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 260 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 13: k_eff = 0.369244 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 345 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 14: k_eff = 0.373421 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 417 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 15: k_eff = 0.378350 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 492 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 16: k_eff = 0.383902 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 17: k_eff = 0.390057 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 615 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 18: k_eff = 0.396756 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 669 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 19: k_eff = 0.403951 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 719 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 20: k_eff = 0.411603 res = 1.355E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 765 D.R. = 7.0000\n", + "[ NORMAL ] Iteration 21: k_eff = 0.419673 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 807 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 22: k_eff = 0.428109 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 843 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 23: k_eff = 0.436887 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 877 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 24: k_eff = 0.445966 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 907 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 25: k_eff = 0.455315 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 934 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 26: k_eff = 0.464905 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 958 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 27: k_eff = 0.474705 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 980 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 28: k_eff = 0.484690 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 998 D.R. = inf\n", + "[ NORMAL ] Iteration 29: k_eff = 0.494835 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1014 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 30: k_eff = 0.505115 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1028 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 31: k_eff = 0.515510 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1039 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 32: k_eff = 0.525998 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1048 D.R. = inf\n", + "[ NORMAL ] Iteration 33: k_eff = 0.536561 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1056 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 34: k_eff = 0.547180 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1061 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 35: k_eff = 0.557840 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1065 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 36: k_eff = 0.568524 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1068 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 37: k_eff = 0.579219 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1069 D.R. = -nan\n", + "[ NORMAL ] Iteration 38: k_eff = 0.589911 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1069 D.R. = -nan\n", + "[ NORMAL ] Iteration 39: k_eff = 0.600586 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1067 D.R. = inf\n", + "[ NORMAL ] Iteration 40: k_eff = 0.611235 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1064 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 41: k_eff = 0.621847 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1061 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 42: k_eff = 0.632410 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 ] Iteration 43: k_eff = 0.642917 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1050 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 44: k_eff = 0.653360 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1044 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 45: k_eff = 0.663729 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1036 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 46: k_eff = 0.674019 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1028 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 47: k_eff = 0.684223 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1020 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 48: k_eff = 0.694335 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1011 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 49: k_eff = 0.704350 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1001 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 50: k_eff = 0.714262 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 991 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 51: k_eff = 0.724068 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 980 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 52: k_eff = 0.733764 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 969 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 53: k_eff = 0.743346 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 958 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 54: k_eff = 0.752809 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 946 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 55: k_eff = 0.762154 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 934 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 56: k_eff = 0.771376 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 922 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 57: k_eff = 0.780473 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 909 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 58: k_eff = 0.789445 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 897 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 59: k_eff = 0.798288 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 884 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 60: k_eff = 0.807003 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 871 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 61: k_eff = 0.815587 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 858 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 62: k_eff = 0.824040 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 845 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 63: k_eff = 0.832361 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 832 D.R. = inf\n", + "[ NORMAL ] Iteration 64: k_eff = 0.840551 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 818 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 65: k_eff = 0.848608 res = 5.807E-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 = 0.935312 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", - "[ NORMAL ] ... = 224 D.R. = 2.4000\n", - "[ NORMAL ] Iteration 127: k_eff = 1.134930 res = 1.161E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 219 D.R. = 1.0000\n", - "[ NORMAL ] Iteration 128: k_eff = 1.137076 res = 1.452E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 214 D.R. = 1.2500\n", - "[ NORMAL ] Iteration 129: k_eff = 1.139173 res = 1.065E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 209 D.R. = 0.7333\n", - "[ NORMAL ] Iteration 130: k_eff = 1.141221 res = 0.000E+00 delta-k (pcm)\n", - "[ NORMAL ] ... = 204 D.R. = 0.0000\n", - "[ NORMAL ] Iteration 131: k_eff = 1.143222 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 200 D.R. = inf\n", - "[ NORMAL ] Iteration 132: k_eff = 1.145176 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 195 D.R. = 2.0000\n", - "[ NORMAL ] Iteration 133: k_eff = 1.147086 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 190 D.R. = 1.0000\n", - "[ NORMAL ] Iteration 134: k_eff = 1.148951 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 186 D.R. = 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 ] Iteration 66: k_eff = 0.856534 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 792 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 67: k_eff = 0.864327 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 779 D.R. = inf\n", + "[ NORMAL ] Iteration 68: k_eff = 0.871988 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 766 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 69: k_eff = 0.879518 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 752 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 70: k_eff = 0.886917 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 739 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 71: k_eff = 0.894186 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 726 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 72: k_eff = 0.901326 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 713 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 73: k_eff = 0.908336 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 701 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 74: k_eff = 0.915219 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 688 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 75: k_eff = 0.921976 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 675 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 76: k_eff = 0.928607 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 663 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 77: k_eff = 0.935114 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 650 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 78: k_eff = 0.941498 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 638 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 79: k_eff = 0.947759 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 626 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 80: k_eff = 0.953900 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 614 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 81: k_eff = 0.959922 res = 8.711E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 602 D.R. = 0.9000\n", + "[ NORMAL ] Iteration 82: k_eff = 0.965827 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 590 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 83: k_eff = 0.971615 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 578 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 84: k_eff = 0.977288 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 567 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 85: k_eff = 0.982848 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 86: k_eff = 0.988297 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 544 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 87: k_eff = 0.993635 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 533 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 88: k_eff = 0.998865 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 523 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 89: k_eff = 1.003988 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 512 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 90: k_eff = 1.009006 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 501 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 91: k_eff = 1.013921 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 491 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 92: k_eff = 1.018732 res = 8.711E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 481 D.R. = 1.2857\n", + "[ NORMAL ] Iteration 93: k_eff = 1.023444 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 471 D.R. = 0.7778\n", + "[ NORMAL ] Iteration 94: k_eff = 1.028057 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 461 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 95: k_eff = 1.032573 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 451 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 96: k_eff = 1.036994 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 442 D.R. = 0.4286\n", + "[ NORMAL ] Iteration 97: k_eff = 1.041321 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 432 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 98: k_eff = 1.045554 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 423 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 99: k_eff = 1.049699 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 414 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 100: k_eff = 1.053753 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 405 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 101: k_eff = 1.057721 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 396 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 102: k_eff = 1.061602 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 388 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 103: k_eff = 1.065399 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 379 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 104: k_eff = 1.069113 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 371 D.R. = 0.8889\n", + "[ NORMAL ] Iteration 105: k_eff = 1.072747 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 363 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 106: k_eff = 1.076301 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 355 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 107: k_eff = 1.079776 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 347 D.R. = 0.1111\n", + "[ NORMAL ] Iteration 108: k_eff = 1.083176 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 339 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 109: k_eff = 1.086499 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 332 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 110: k_eff = 1.089750 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 325 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 111: k_eff = 1.092928 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 317 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 112: k_eff = 1.096035 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 310 D.R. = 2.2000\n", + "[ NORMAL ] Iteration 113: k_eff = 1.099073 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 303 D.R. = 0.2727\n", + "[ NORMAL ] Iteration 114: k_eff = 1.102043 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 297 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 115: k_eff = 1.104946 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 290 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 116: k_eff = 1.107783 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 283 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 117: k_eff = 1.110557 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 277 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 118: k_eff = 1.113268 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 271 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 119: k_eff = 1.115919 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 265 D.R. = inf\n", + "[ NORMAL ] Iteration 120: k_eff = 1.118508 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 258 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 121: k_eff = 1.121039 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 253 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 122: k_eff = 1.123513 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 247 D.R. = inf\n", + "[ NORMAL ] Iteration 123: k_eff = 1.125930 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 241 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 124: k_eff = 1.128291 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 236 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 125: k_eff = 1.130599 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 230 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 126: k_eff = 1.132854 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 225 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 127: k_eff = 1.135057 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 220 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 128: k_eff = 1.137210 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 215 D.R. = 3.3333\n", + "[ NORMAL ] Iteration 129: k_eff = 1.139313 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 210 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 130: k_eff = 1.141367 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 205 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 131: k_eff = 1.143374 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 200 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 132: k_eff = 1.145334 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 195 D.R. = inf\n", + "[ NORMAL ] Iteration 133: k_eff = 1.147250 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 191 D.R. = 0.4545\n", + "[ NORMAL ] Iteration 134: k_eff = 1.149120 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 187 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 135: k_eff = 1.150948 res = 1.549E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 182 D.R. = 1.6000\n", + "[ NORMAL ] Iteration 136: k_eff = 1.152733 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 178 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 137: k_eff = 1.154476 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 174 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 138: k_eff = 1.156179 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 170 D.R. = 1.1250\n", + "[ NORMAL ] Iteration 139: k_eff = 1.157842 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 166 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 140: k_eff = 1.159466 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 162 D.R. = 1.7500\n", + "[ NORMAL ] Iteration 141: k_eff = 1.161053 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 158 D.R. = 1.4286\n", + "[ NORMAL ] Iteration 142: k_eff = 1.162602 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 154 D.R. = 0.1000\n", + "[ NORMAL ] Iteration 143: k_eff = 1.164115 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 151 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 144: k_eff = 1.165592 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 147 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 145: k_eff = 1.167035 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 144 D.R. = 0.7143\n", + "[ NORMAL ] Iteration 146: k_eff = 1.168444 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 140 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 147: k_eff = 1.169819 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 137 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 148: k_eff = 1.171163 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 134 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 149: k_eff = 1.172475 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 131 D.R. = 0.6250\n", + "[ NORMAL ] Iteration 150: k_eff = 1.173756 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 128 D.R. = 1.6000\n", + "[ NORMAL ] Iteration 151: k_eff = 1.175006 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 125 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 152: k_eff = 1.176227 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 122 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 153: k_eff = 1.177420 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 119 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 154: k_eff = 1.178584 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 116 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 155: k_eff = 1.179720 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 113 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 156: k_eff = 1.180830 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 111 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 157: k_eff = 1.181914 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 108 D.R. = 13.0000\n", + "[ NORMAL ] Iteration 158: k_eff = 1.182971 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 105 D.R. = 0.4615\n", + "[ NORMAL ] Iteration 159: k_eff = 1.184004 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 103 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 160: k_eff = 1.185012 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 161: k_eff = 1.185997 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 98 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 162: k_eff = 1.186957 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 96 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 163: k_eff = 1.187895 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 93 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 164: k_eff = 1.188811 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 91 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 165: k_eff = 1.189705 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 89 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 166: k_eff = 1.190578 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 87 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 167: k_eff = 1.191430 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 85 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 168: k_eff = 1.192261 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 83 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 169: k_eff = 1.193073 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 81 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 170: k_eff = 1.193865 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 79 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 171: k_eff = 1.194638 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 77 D.R. = 0.8889\n", + "[ NORMAL ] Iteration 172: k_eff = 1.195394 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 75 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 173: k_eff = 1.196130 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 73 D.R. = 7.0000\n", + "[ NORMAL ] Iteration 174: k_eff = 1.196849 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 71 D.R. = 1.0714\n", + "[ NORMAL ] Iteration 175: k_eff = 1.197551 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 70 D.R. = 0.1333\n", + "[ NORMAL ] Iteration 176: k_eff = 1.198236 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 68 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 177: k_eff = 1.198905 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 66 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 178: k_eff = 1.199558 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 65 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 179: k_eff = 1.200195 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 63 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 180: k_eff = 1.200818 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 62 D.R. = inf\n", + "[ NORMAL ] Iteration 181: k_eff = 1.201424 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 60 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 182: k_eff = 1.202017 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 59 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 183: k_eff = 1.202595 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 57 D.R. = inf\n", + "[ NORMAL ] Iteration 184: k_eff = 1.203159 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 56 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 185: k_eff = 1.203710 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 55 D.R. = 0.7333\n", + "[ NORMAL ] Iteration 186: k_eff = 1.204247 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 53 D.R. = 0.6364\n", + "[ NORMAL ] Iteration 187: k_eff = 1.204772 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 52 D.R. = 1.5714\n", + "[ NORMAL ] Iteration 188: k_eff = 1.205284 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 51 D.R. = 1.0909\n", + "[ NORMAL ] Iteration 189: k_eff = 1.205784 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 50 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 190: k_eff = 1.206273 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 48 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 191: k_eff = 1.206748 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 192: k_eff = 1.207212 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 46 D.R. = inf\n", + "[ NORMAL ] Iteration 193: k_eff = 1.207666 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 45 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 194: k_eff = 1.208109 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 44 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 195: k_eff = 1.208540 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 43 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 196: k_eff = 1.208962 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 42 D.R. = 6.5000\n", + "[ NORMAL ] Iteration 197: k_eff = 1.209373 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 198: k_eff = 1.209775 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 40 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 199: k_eff = 1.210167 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 200: k_eff = 1.210549 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 38 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 201: k_eff = 1.210922 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 202: k_eff = 1.211286 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 36 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 203: k_eff = 1.211641 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 204: k_eff = 1.211988 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = inf\n", + "[ NORMAL ] Iteration 205: k_eff = 1.212326 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 33 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 206: k_eff = 1.212656 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 33 D.R. = 2.3333\n", + "[ NORMAL ] Iteration 207: k_eff = 1.212979 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 1.5714\n", + "[ NORMAL ] Iteration 208: k_eff = 1.213293 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 0.4545\n", + "[ NORMAL ] Iteration 209: k_eff = 1.213600 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 30 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 210: k_eff = 1.213899 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 211: k_eff = 1.214192 res = 9.679E-09 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", - "[ 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(pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 214: k_eff = 1.215027 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 215: k_eff = 1.215292 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 26 D.R. = -nan\n", + "[ NORMAL ] Iteration 216: k_eff = 1.215551 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = inf\n", + "[ NORMAL ] Iteration 217: k_eff = 1.215803 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 218: k_eff = 1.216049 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 24 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 219: k_eff = 1.216289 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 220: k_eff = 1.216524 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 221: k_eff = 1.216753 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 222: k_eff = 1.216976 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 223: k_eff = 1.217193 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 224: k_eff = 1.217406 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 225: k_eff = 1.217613 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 226: k_eff = 1.217816 res = 1.645E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 2.1250\n", + "[ NORMAL ] Iteration 227: k_eff = 1.218013 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 0.3529\n", + "[ NORMAL ] Iteration 228: k_eff = 1.218206 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 229: k_eff = 1.218394 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 230: k_eff = 1.218578 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 2.1667\n", + "[ NORMAL ] Iteration 231: k_eff = 1.218758 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.7692\n", + "[ NORMAL ] Iteration 232: k_eff = 1.218932 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.3000\n", + "[ NORMAL ] Iteration 233: k_eff = 1.219103 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 234: k_eff = 1.219269 res = 3.872E-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 242: k_eff = 1.219921 res = 9.679E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 13 D.R. = 5.0000\n", - "[ NORMAL ] Iteration 243: k_eff = 1.220053 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 13 D.R. = 0.6000\n", - "[ NORMAL ] Iteration 244: k_eff = 1.220181 res = 2.904E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 12 D.R. = 0.5000\n", - "[ NORMAL ] Iteration 245: k_eff = 1.220307 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 12 D.R. = 0.6667\n", - "[ NORMAL ] Iteration 246: k_eff = 1.220430 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] Iteration 235: k_eff = 1.219431 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 236: k_eff = 1.219590 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 7.0000\n", + "[ NORMAL ] Iteration 237: k_eff = 1.219745 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 238: k_eff = 1.219896 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 239: k_eff = 1.220043 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 3.3333\n", + "[ NORMAL ] Iteration 240: k_eff = 1.220187 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 241: k_eff = 1.220327 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 242: k_eff = 1.220464 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 243: k_eff = 1.220597 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 244: k_eff = 1.220727 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 245: k_eff = 1.220854 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 246: k_eff = 1.220978 res = 3.872E-08 delta-k (pcm)\n", "[ NORMAL ] ... = 12 D.R. = 1.0000\n", - "[ NORMAL ] Iteration 247: k_eff = 1.220549 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 2.0000\n", - "[ NORMAL ] Iteration 248: k_eff = 1.220666 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 1.2500\n", - "[ NORMAL ] Iteration 249: k_eff = 1.220780 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 11 D.R. = 0.4000\n", - "[ NORMAL ] Iteration 250: k_eff = 1.220892 res = 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", + "[ NORMAL ] Iteration 247: k_eff = 1.221099 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 1.7500\n", + "[ NORMAL ] Iteration 248: k_eff = 1.221217 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 1.8571\n", + "[ NORMAL ] Iteration 249: k_eff = 1.221333 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.9231\n", + "[ NORMAL ] Iteration 250: k_eff = 1.221445 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 251: k_eff = 1.221555 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 252: k_eff = 1.221662 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 253: k_eff = 1.221766 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 254: k_eff = 1.221868 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 255: k_eff = 1.221968 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 256: k_eff = 1.222064 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 257: k_eff = 1.222159 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 258: k_eff = 1.222251 res = 3.872E-08 delta-k (pcm)\n", "[ NORMAL ] ... = 9 D.R. = 2.0000\n", - "[ NORMAL ] Iteration 259: k_eff = 1.221778 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 0.2500\n", - "[ NORMAL ] Iteration 260: k_eff = 1.221865 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] Iteration 259: k_eff = 1.222342 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 2.2500\n", + "[ NORMAL ] Iteration 260: k_eff = 1.222429 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 0.5556\n", + "[ NORMAL ] Iteration 261: k_eff = 1.222515 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 262: k_eff = 1.222599 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 263: k_eff = 1.222681 res = 9.679E-09 delta-k (pcm)\n", "[ NORMAL ] ... = 8 D.R. = 1.0000\n", - "[ NORMAL ] Iteration 261: k_eff = 1.221949 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 2.0000\n", - "[ NORMAL ] Iteration 262: k_eff = 1.222032 res = 1.936E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 1.0000\n", - "[ NORMAL ] Iteration 263: k_eff = 1.222113 res = 4.840E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 8 D.R. = 2.5000\n", - "[ NORMAL ] Iteration 264: k_eff = 1.222192 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 7 D.R. = 0.2000\n", - "[ NORMAL ] Iteration 265: k_eff = 1.222268 res = 9.679E-09 delta-k (pcm)\n", - "[ NORMAL ] ... = 7 D.R. = 1.0000\n", - "[ NORMAL ] Iteration 266: k_eff = 1.222343 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 7 D.R. = 6.0000\n", - "[ NORMAL ] Iteration 267: k_eff = 1.222417 res = 3.872E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 7 D.R. = 0.6667\n", - "[ NORMAL ] Iteration 268: k_eff = 1.222488 res = 6.775E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 7 D.R. = 1.7500\n", - "[ NORMAL ] Iteration 269: k_eff = 1.222558 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 1.1429\n", - "[ NORMAL ] Iteration 270: k_eff = 1.222626 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] Iteration 264: k_eff = 1.222760 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 265: k_eff = 1.222838 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 266: k_eff = 1.222914 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 267: k_eff = 1.222988 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 9.0000\n", + "[ NORMAL ] Iteration 268: k_eff = 1.223060 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.7778\n", + "[ NORMAL ] Iteration 269: k_eff = 1.223130 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 270: k_eff = 1.223199 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 13.0000\n", + "[ NORMAL ] Iteration 271: k_eff = 1.223266 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 272: k_eff = 1.223331 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.2222\n", + "[ NORMAL ] Iteration 273: k_eff = 1.223395 res = 9.679E-09 delta-k (pcm)\n", "[ NORMAL ] ... = 6 D.R. = 0.5000\n", - "[ NORMAL ] Iteration 271: k_eff = 1.222692 res = 1.161E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 3.0000\n", - "[ NORMAL ] Iteration 272: k_eff = 1.222757 res = 5.807E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.5000\n", - "[ NORMAL ] Iteration 273: k_eff = 1.222820 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 1.3333\n", - "[ NORMAL ] Iteration 274: k_eff = 1.222881 res = 7.743E-08 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 1.0000\n", - "[ NORMAL ] Iteration 275: k_eff = 1.222941 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 ] 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Iteration 292: k_eff = 1.223765 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] Iteration 274: k_eff = 1.223457 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 275: k_eff = 1.223518 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 276: k_eff = 1.223578 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 277: k_eff = 1.223636 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 278: k_eff = 1.223692 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.5833\n", + "[ NORMAL ] Iteration 279: k_eff = 1.223747 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 280: k_eff = 1.223801 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 13.0000\n", + "[ NORMAL ] Iteration 281: k_eff = 1.223853 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] 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Iteration 290: k_eff = 1.224272 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 291: k_eff = 1.224313 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 292: k_eff = 1.224352 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.8000\n", + "[ NORMAL ] Iteration 293: k_eff = 1.224392 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.1111\n", + "[ NORMAL ] Iteration 294: k_eff = 1.224430 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 295: k_eff = 1.224467 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 296: k_eff = 1.224503 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 297: k_eff = 1.224538 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 298: k_eff = 1.224573 res = 2.904E-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 ] Iteration 299: k_eff = 1.224607 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 ] Iteration 300: k_eff = 1.224640 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 301: k_eff = 1.224672 res = 1.645E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.4286\n", + "[ NORMAL ] Iteration 302: k_eff = 1.224703 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.8235\n", + "[ NORMAL ] Iteration 303: k_eff = 1.224734 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 304: k_eff = 1.224763 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = inf\n", + "[ NORMAL ] Iteration 305: k_eff = 1.224792 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 306: k_eff = 1.224821 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 307: k_eff = 1.224849 res = 1.549E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.7778\n", + "[ NORMAL ] Iteration 308: k_eff = 1.224876 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 309: k_eff = 1.224902 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1250\n", + "[ NORMAL ] Iteration 310: k_eff = 1.224928 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.2222\n", + "[ NORMAL ] Iteration 311: k_eff = 1.224953 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.5455\n", + "[ NORMAL ] Iteration 312: k_eff = 1.224977 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 313: k_eff = 1.225001 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 314: k_eff = 1.225025 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 315: k_eff = 1.225047 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 316: k_eff = 1.225069 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 317: k_eff = 1.225091 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 318: k_eff = 1.225112 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 ] Iteration 319: k_eff = 1.225133 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 320: k_eff = 1.225153 res = 1.161E-07 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 ] Iteration 321: k_eff = 1.225173 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 ] Iteration 322: k_eff = 1.225192 res = 2.904E-08 delta-k (pcm)\n", "[ NORMAL ] ... = 1 D.R. = inf\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 ] Iteration 323: k_eff = 1.225210 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 324: k_eff = 1.225228 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.2500\n", + "[ NORMAL ] Iteration 325: k_eff = 1.225246 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5556\n", + "[ NORMAL ] Iteration 326: k_eff = 1.225264 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 327: k_eff = 1.225280 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 328: k_eff = 1.225297 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 ] Iteration 329: k_eff = 1.225313 res = 8.711E-08 delta-k (pcm)\n", "[ NORMAL ] ... = 1 D.R. = inf\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 ] Iteration 330: k_eff = 1.225329 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5556\n", + "[ NORMAL ] Iteration 331: k_eff = 1.225344 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 332: k_eff = 1.225359 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 333: k_eff = 1.225374 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 334: k_eff = 1.225388 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 ] Iteration 335: k_eff = 1.225402 res = 5.807E-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" + "[ NORMAL ] Iteration 336: k_eff = 1.225416 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 337: k_eff = 1.225429 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 338: k_eff = 1.225442 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 339: k_eff = 1.225455 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.4667\n", + "[ NORMAL ] Iteration 340: k_eff = 1.225467 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 341: k_eff = 1.225479 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 342: k_eff = 1.225491 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 343: k_eff = 1.225502 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 344: k_eff = 1.225513 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 9.0000\n", + "[ NORMAL ] Iteration 345: k_eff = 1.225524 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2222\n", + "[ NORMAL ] Iteration 346: k_eff = 1.225535 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 347: k_eff = 1.225545 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 348: k_eff = 1.225555 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3636\n", + "[ NORMAL ] Iteration 349: k_eff = 1.225565 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.2500\n" ] } ], @@ -2537,9 +2523,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.224010\n", - "openmoc keff = 1.224949\n", - "bias [pcm]: 94.0\n" + "openmc keff = 1.222044\n", + "openmoc keff = 1.225565\n", + "bias [pcm]: 352.2\n" ] } ], @@ -2600,7 +2586,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2683,7 +2669,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] diff --git a/mgxs-part-iii.ipynb b/mgxs-part-iii.ipynb index b861aac..778bbc8 100644 --- a/mgxs-part-iii.ipynb +++ b/mgxs-part-iii.ipynb @@ -570,21 +570,21 @@ "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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: 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", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=114.\n", " warn(msg, IDWarning)\n" ] }, @@ -619,21 +619,21 @@ " | 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", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-04 12:49:47\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/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", + " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/pshriwise/data/xs/openmc/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n", " Minimum neutron data temperature: 294 K\n", " Maximum neutron data temperature: 294 K\n", " Reading tallies XML file...\n", @@ -647,82 +647,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\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", + " 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", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\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", + " Total time for initialization = 7.0587e-01 seconds\n", + " Reading cross sections = 6.9497e-01 seconds\n", + " Total time in simulation = 2.6490e+02 seconds\n", + " Time in transport only = 2.6482e+02 seconds\n", + " Time in inactive batches = 1.8031e+01 seconds\n", + " Time in active batches = 2.4687e+02 seconds\n", + " Time synchronizing fission bank = 5.0873e-02 seconds\n", + " Sampling source sites = 3.9332e-02 seconds\n", + " SEND/RECV source sites = 1.1498e-02 seconds\n", + " Time accumulating tallies = 4.3525e-03 seconds\n", + " Time writing statepoints = 6.7551e-03 seconds\n", + " Total time for finalization = 2.3500e-06 seconds\n", + " Total time elapsed = 2.6569e+02 seconds\n", + " Calculation Rate (inactive) = 5546.05 particles/second\n", + " Calculation Rate (active) = 1620.29 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\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", + " 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", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -865,16 +865,16 @@ " 1\n", " 1\n", " U235\n", - " 8.082113e-03\n", - " 1.791995e-05\n", + " 8.099261e-03\n", + " 1.626934e-05\n", " \n", " \n", " 4\n", " 1\n", " 1\n", " U238\n", - " 7.351550e-03\n", - " 2.067911e-05\n", + " 7.326723e-03\n", + " 2.168273e-05\n", " \n", " \n", " 5\n", @@ -889,16 +889,16 @@ " 1\n", " 2\n", " U235\n", - " 3.618776e-01\n", - " 1.051518e-03\n", + " 3.613773e-01\n", + " 1.025247e-03\n", " \n", " \n", " 1\n", " 1\n", " 2\n", " U238\n", - " 6.747305e-07\n", - " 1.939403e-09\n", + " 6.739270e-07\n", + " 1.911057e-09\n", " \n", " \n", " 2\n", @@ -914,11 +914,11 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 1 1 U235 8.082113e-03 1.791995e-05\n", - "4 1 1 U238 7.351550e-03 2.067911e-05\n", + "3 1 1 U235 8.099261e-03 1.626934e-05\n", + "4 1 1 U238 7.326723e-03 2.168273e-05\n", "5 1 1 O16 0.000000e+00 0.000000e+00\n", - "0 1 2 U235 3.618776e-01 1.051518e-03\n", - "1 1 2 U238 6.747305e-07 1.939403e-09\n", + "0 1 2 U235 3.613773e-01 1.025247e-03\n", + "1 1 2 U238 6.739270e-07 1.911057e-09\n", "2 1 2 O16 0.000000e+00 0.000000e+00" ] }, @@ -954,13 +954,13 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\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", + " 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", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\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", + " 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", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", @@ -1087,15 +1087,15 @@ " 1\n", " 1\n", " U235\n", - " 0.074339\n", - " 0.000161\n", + " 0.074479\n", + " 0.000151\n", " \n", " \n", " 1\n", " 1\n", " 1\n", " U238\n", - " 0.005975\n", + " 0.005950\n", " 0.000017\n", " \n", " \n", @@ -1112,8 +1112,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 1 1 U235 0.074339 0.000161\n", - "1 1 1 U238 0.005975 0.000017\n", + "0 1 1 U235 0.074479 0.000151\n", + "1 1 1 U238 0.005950 0.000017\n", "2 1 1 O16 0.000000 0.000000" ] }, @@ -1234,256 +1234,258 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\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 ] 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", + "[ 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 ] ... = 2 D.R. = 0.9302\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" + "[ 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" ] } ], @@ -1513,9 +1515,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.023841\n", - "openmoc keff = 1.023311\n", - "bias [pcm]: -53.0\n" + "openmc keff = 1.023625\n", + "openmoc keff = 1.030508\n", + "bias [pcm]: 688.3\n" ] } ], @@ -1628,7 +1630,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/post-processing.ipynb b/post-processing.ipynb index 8684cca..65d83bc 100644 --- a/post-processing.ipynb +++ b/post-processing.ipynb @@ -970,7 +970,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -984,9 +984,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.1" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 }