From 86c081b28b16827d29416dc3a08eb7d07b25bd31 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 4 Oct 2019 13:59:19 -0500 Subject: [PATCH] Update MG mode part 3 notebook --- examples/jupyter/mg-mode-part-iii.ipynb | 433 ++++++++++++------------ 1 file changed, 210 insertions(+), 223 deletions(-) diff --git a/examples/jupyter/mg-mode-part-iii.ipynb b/examples/jupyter/mg-mode-part-iii.ipynb index 2cf9981889..e953d64d33 100644 --- a/examples/jupyter/mg-mode-part-iii.ipynb +++ b/examples/jupyter/mg-mode-part-iii.ipynb @@ -30,7 +30,6 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "\n", "import openmc\n", "\n", "%matplotlib inline" @@ -40,7 +39,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will be running a rodded 8x8 assembly with Gadolinia fuel pins. Let's create all the elemental data we would need for this case." + "We will be running a rodded 8x8 assembly with Gadolinia fuel pins. Let's start by creating the materials that we will use later.\n", + "\n", + "Material Definition Simplifications:\n", + "\n", + "- This model will be run at room temperature so the NNDC ENDF-B/VII.1 data set can be used but the water density will be representative of a module with around 20% voiding. This water density will be non-physically used in all regions of the problem.\n", + "- Steel is composed of more than just iron, but we will only treat it as such here." ] }, { @@ -48,67 +52,43 @@ "execution_count": 2, "metadata": {}, "outputs": [], - "source": [ - "# Instantiate some elements\n", - "elements = {}\n", - "for elem in ['H', 'O', 'U', 'Zr', 'Gd', 'B', 'C', 'Fe']:\n", - " elements[elem] = openmc.Element(elem)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the elements we defined, we will now create the materials we will use later.\n", - "\n", - "Material Definition Simplifications:\n", - "\n", - "- This model will be run at room temperature so the NNDC ENDF-B/VII.1 data set can be used but the water density will be representative of a module with around 20% voiding. This water density will be non-physically used in all regions of the problem.\n", - "- Steel is composed of more than just iron, but we will only treat it as such here.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], "source": [ "materials = {}\n", "\n", "# Fuel\n", "materials['Fuel'] = openmc.Material(name='Fuel')\n", "materials['Fuel'].set_density('g/cm3', 10.32)\n", - "materials['Fuel'].add_element(elements['O'], 2)\n", - "materials['Fuel'].add_element(elements['U'], 1, enrichment=3.)\n", + "materials['Fuel'].add_element('O', 2)\n", + "materials['Fuel'].add_element('U', 1, enrichment=3.)\n", "\n", "# Gadolinia bearing fuel\n", "materials['Gad'] = openmc.Material(name='Gad')\n", "materials['Gad'].set_density('g/cm3', 10.23)\n", - "materials['Gad'].add_element(elements['O'], 2)\n", - "materials['Gad'].add_element(elements['U'], 1, enrichment=3.)\n", - "materials['Gad'].add_element(elements['Gd'], .02)\n", + "materials['Gad'].add_element('O', 2)\n", + "materials['Gad'].add_element('U', 1, enrichment=3.)\n", + "materials['Gad'].add_element('Gd', .02)\n", "\n", "# Zircaloy\n", "materials['Zirc2'] = openmc.Material(name='Zirc2')\n", "materials['Zirc2'].set_density('g/cm3', 6.55)\n", - "materials['Zirc2'].add_element(elements['Zr'], 1)\n", + "materials['Zirc2'].add_element('Zr', 1)\n", "\n", "# Boiling Water\n", "materials['Water'] = openmc.Material(name='Water')\n", "materials['Water'].set_density('g/cm3', 0.6)\n", - "materials['Water'].add_element(elements['H'], 2)\n", - "materials['Water'].add_element(elements['O'], 1)\n", + "materials['Water'].add_element('H', 2)\n", + "materials['Water'].add_element('O', 1)\n", "\n", "# Boron Carbide for the Control Rods\n", "materials['B4C'] = openmc.Material(name='B4C')\n", "materials['B4C'].set_density('g/cm3', 0.7 * 2.52)\n", - "materials['B4C'].add_element(elements['B'], 4)\n", - "materials['B4C'].add_element(elements['C'], 1)\n", + "materials['B4C'].add_element('B', 4)\n", + "materials['B4C'].add_element('C', 1)\n", "\n", "# Steel \n", "materials['Steel'] = openmc.Material(name='Steel')\n", "materials['Steel'].set_density('g/cm3', 7.75)\n", - "materials['Steel'].add_element(elements['Fe'], 1)" + "materials['Steel'].add_element('Fe', 1)" ] }, { @@ -120,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -164,27 +144,27 @@ "surfaces = {}\n", "\n", "# Create boundary planes to surround the geometry\n", - "surfaces['Global x-'] = openmc.XPlane(x0=0., boundary_type='reflective')\n", - "surfaces['Global x+'] = openmc.XPlane(x0=length, boundary_type='reflective')\n", - "surfaces['Global y-'] = openmc.YPlane(y0=0., boundary_type='reflective')\n", - "surfaces['Global y+'] = openmc.YPlane(y0=length, boundary_type='reflective')\n", + "surfaces['Global x-'] = openmc.XPlane(0., boundary_type='reflective')\n", + "surfaces['Global x+'] = openmc.XPlane(length, boundary_type='reflective')\n", + "surfaces['Global y-'] = openmc.YPlane(0., boundary_type='reflective')\n", + "surfaces['Global y+'] = openmc.YPlane(length, boundary_type='reflective')\n", "\n", "# Create cylinders for the fuel and clad\n", - "surfaces['Fuel Radius'] = openmc.ZCylinder(R=fuel_rad)\n", - "surfaces['Clad Radius'] = openmc.ZCylinder(R=clad_rad)\n", + "surfaces['Fuel Radius'] = openmc.ZCylinder(r=fuel_rad)\n", + "surfaces['Clad Radius'] = openmc.ZCylinder(r=clad_rad)\n", "\n", - "surfaces['Assembly x-'] = openmc.XPlane(x0=pin_pitch)\n", - "surfaces['Assembly x+'] = openmc.XPlane(x0=length - pin_pitch)\n", - "surfaces['Assembly y-'] = openmc.YPlane(y0=pin_pitch)\n", - "surfaces['Assembly y+'] = openmc.YPlane(y0=length - pin_pitch)\n", + "surfaces['Assembly x-'] = openmc.XPlane(pin_pitch)\n", + "surfaces['Assembly x+'] = openmc.XPlane(length - pin_pitch)\n", + "surfaces['Assembly y-'] = openmc.YPlane(pin_pitch)\n", + "surfaces['Assembly y+'] = openmc.YPlane(length - pin_pitch)\n", "\n", "# Set surfaces for the control blades\n", - "surfaces['Top Blade y-'] = openmc.YPlane(y0=length - rod_thick)\n", - "surfaces['Top Blade x-'] = openmc.XPlane(x0=pin_pitch)\n", - "surfaces['Top Blade x+'] = openmc.XPlane(x0=rod_span)\n", - "surfaces['Left Blade x+'] = openmc.XPlane(x0=rod_thick)\n", - "surfaces['Left Blade y-'] = openmc.YPlane(y0=length - rod_span)\n", - "surfaces['Left Blade y+'] = openmc.YPlane(y0=9. * pin_pitch)" + "surfaces['Top Blade y-'] = openmc.YPlane(length - rod_thick)\n", + "surfaces['Top Blade x-'] = openmc.XPlane(pin_pitch)\n", + "surfaces['Top Blade x+'] = openmc.XPlane(rod_span)\n", + "surfaces['Left Blade x+'] = openmc.XPlane(rod_thick)\n", + "surfaces['Left Blade y-'] = openmc.YPlane(length - rod_span)\n", + "surfaces['Left Blade y+'] = openmc.YPlane(9. * pin_pitch)" ] }, { @@ -196,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -280,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -319,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -375,27 +355,29 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -422,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -444,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -481,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -540,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -572,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -632,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -652,14 +634,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mgxs/mgxs.py:4116: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", + "/home/romano/openmc/openmc/mgxs/mgxs.py:4144: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", " warnings.warn(msg)\n" ] } @@ -679,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -698,26 +680,26 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=1.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=11.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=11.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=22.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=22.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=12.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=12.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=18.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=18.\n", " warn(msg, IDWarning)\n" ] } @@ -749,57 +731,58 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-24 19:15:17\n", - " OpenMP Threads | 8\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", + " Date/Time | 2019-10-04 10:54:35\n", + " OpenMP Threads | 4\n", "\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83880 +/- 0.00101\n", - " k-effective (Track-length) = 0.83917 +/- 0.00118\n", - " k-effective (Absorption) = 0.83859 +/- 0.00104\n", - " Combined k-effective = 0.83879 +/- 0.00086\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 0.83866 +/- 0.00102\n", + " k-effective (Track-length) = 0.83799 +/- 0.00118\n", + " k-effective (Absorption) = 0.83968 +/- 0.00103\n", + " Combined k-effective = 0.83900 +/- 0.00085\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -818,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -841,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -858,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -875,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -912,14 +895,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] } @@ -947,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -967,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -990,27 +973,29 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1031,45 +1016,44 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-24 19:16:03\n", - " OpenMP Threads | 8\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", + " Date/Time | 2019-10-04 10:56:58\n", + " OpenMP Threads | 4\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1077,11 +1061,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.82313 +/- 0.00100\n", - " k-effective (Track-length) = 0.82363 +/- 0.00102\n", - " k-effective (Absorption) = 0.82532 +/- 0.00076\n", - " Combined k-effective = 0.82484 +/- 0.00067\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 0.82471 +/- 0.00104\n", + " k-effective (Track-length) = 0.82466 +/- 0.00103\n", + " k-effective (Absorption) = 0.82482 +/- 0.00075\n", + " Combined k-effective = 0.82477 +/- 0.00068\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1100,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1125,14 +1109,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] } @@ -1153,45 +1137,44 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-24 19:16:12\n", - " OpenMP Threads | 8\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", + " Date/Time | 2019-10-04 10:57:24\n", + " OpenMP Threads | 4\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1199,11 +1182,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83745 +/- 0.00108\n", - " k-effective (Track-length) = 0.83712 +/- 0.00108\n", - " k-effective (Absorption) = 0.83694 +/- 0.00078\n", - " Combined k-effective = 0.83695 +/- 0.00070\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 0.83564 +/- 0.00103\n", + " k-effective (Track-length) = 0.83572 +/- 0.00104\n", + " k-effective (Absorption) = 0.83478 +/- 0.00073\n", + " Combined k-effective = 0.83504 +/- 0.00066\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1225,7 +1208,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1248,7 +1231,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1257,8 +1240,8 @@ "angle_mg_keff = angle_mgsp.k_combined\n", "\n", "# Find eigenvalue bias\n", - "iso_bias = 1.0E5 * (ce_keff - iso_mg_keff)\n", - "angle_bias = 1.0E5 * (ce_keff - angle_mg_keff)" + "iso_bias = 1.0e5 * (ce_keff - iso_mg_keff)\n", + "angle_bias = 1.0e5 * (ce_keff - angle_mg_keff)" ] }, { @@ -1270,15 +1253,15 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Isotropic to CE Bias [pcm]: 1394.6\n", - "Angle to CE Bias [pcm]: 183.4\n" + "Isotropic to CE Bias [pcm]: 1423.5\n", + "Angle to CE Bias [pcm]: 396.6\n" ] } ], @@ -1305,19 +1288,21 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "scrolled": false }, "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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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1430,7 +1417,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4,