forked from crp/openmc-designs
3086 lines
678 KiB
Text
3086 lines
678 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Multigroup Cross Section Generation Part II: Advanced Features\n",
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"This IPython Notebook illustrates the use of the `openmc.mgxs` module to calculate multi-group cross sections for a heterogeneous fuel pin cell geometry. In particular, this Notebook illustrates the following features:\n",
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"\n",
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"* Creation of multi-group cross sections on a **heterogeneous geometry**\n",
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"* Calculation of cross sections on a **nuclide-by-nuclide basis**\n",
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"* The use of **[tally precision triggers](../io_formats/settings.rst#trigger-element)** with multi-group cross sections\n",
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"* Built-in features for **energy condensation** in downstream data processing\n",
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"* The use of the **`openmc.data`** module to plot continuous-energy vs. multi-group cross sections\n",
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"* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n",
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"\n",
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"**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system in order to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](https://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate Input Files"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"# plt.style.use('seaborn-dark')\n",
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"\n",
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"import openmoc\n",
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"\n",
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"import openmc\n",
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"import openmc.mgxs as mgxs\n",
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"import openmc.data\n",
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"from openmc.openmoc_compatible import get_openmoc_geometry\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# create a model object to tie together geometry, materials, settings, and tallies\n",
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"model = openmc.Model()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First we need to define materials that will be used in the problem. We'll create three distinct materials for water, clad and fuel."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 1.6% enriched fuel\n",
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"fuel = openmc.Material(name='1.6% Fuel')\n",
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"fuel.set_density('g/cm3', 10.31341)\n",
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"fuel.add_nuclide('U235', 3.7503e-4)\n",
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"fuel.add_nuclide('U238', 2.2625e-2)\n",
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"fuel.add_nuclide('O16', 4.6007e-2)\n",
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"\n",
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"# borated water\n",
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"water = openmc.Material(name='Borated Water')\n",
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"water.set_density('g/cm3', 0.740582)\n",
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"water.add_nuclide('H1', 4.9457e-2)\n",
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"water.add_nuclide('O16', 2.4732e-2)\n",
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"\n",
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"# zircaloy\n",
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"zircaloy = openmc.Material(name='Zircaloy')\n",
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"zircaloy.set_density('g/cm3', 6.55)\n",
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"zircaloy.add_nuclide('Zr90', 7.2758e-3)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With our materials, we can now create a `Materials` object that can be exported to an actual XML file."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Instantiate a Materials collection\n",
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"model.materials = openmc.Materials([fuel, water, zircaloy])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create cylinders for the fuel and clad\n",
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"fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
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"clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
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"\n",
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"# Create box to surround the geometry\n",
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"box = openmc.model.RectangularPrism(1.26, 1.26, boundary_type='reflective')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a Universe to encapsulate a fuel pin\n",
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"pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
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"\n",
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"# Create fuel Cell\n",
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"fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
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"fuel_cell.fill = fuel\n",
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"fuel_cell.region = -fuel_outer_radius\n",
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"pin_cell_universe.add_cell(fuel_cell)\n",
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"\n",
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"# Create a clad Cell\n",
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"clad_cell = openmc.Cell(name='1.6% Clad')\n",
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"clad_cell.fill = zircaloy\n",
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"clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
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"pin_cell_universe.add_cell(clad_cell)\n",
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"\n",
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"# Create a moderator Cell\n",
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"moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
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"moderator_cell.fill = water\n",
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"moderator_cell.region = +clad_outer_radius & box\n",
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"pin_cell_universe.add_cell(moderator_cell)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We now must create a geometry with the pin cell universe and export it to XML."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create Geometry and set root Universe\n",
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"model.geometry = openmc.Geometry(pin_cell_universe)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 10,000 particles."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# OpenMC simulation parameters\n",
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"batches = 50\n",
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"inactive = 10\n",
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"particles = 10000\n",
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"\n",
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"# Instantiate a Settings object\n",
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"settings = openmc.Settings()\n",
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"settings.batches = batches\n",
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"settings.inactive = inactive\n",
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"settings.particles = particles\n",
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"settings.output = {'tallies': True}\n",
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"\n",
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"# Create an initial uniform spatial source distribution over fissionable zones\n",
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"bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
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"uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
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"settings.source = openmc.IndependentSource(space=uniform_dist)\n",
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"\n",
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"# Activate tally precision triggers\n",
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"settings.trigger_active = True\n",
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"settings.trigger_max_batches = settings.batches * 4\n",
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"\n",
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"model.settings = settings"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define \"coarse\" 2-group and \"fine\" 8-group structures using the built-in `EnergyGroups` class."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Instantiate a \"coarse\" 2-group EnergyGroups object\n",
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"coarse_groups = mgxs.EnergyGroups([0., 0.625, 20.0e6])\n",
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"\n",
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"# Instantiate a \"fine\" 8-group EnergyGroups object\n",
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"fine_groups = mgxs.EnergyGroups([0., 0.058, 0.14, 0.28,\n",
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" 0.625, 4.0, 5.53e3, 821.0e3, 20.0e6])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we will instantiate a variety of `MGXS` objects needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we define transport, fission, nu-fission, nu-scatter and chi cross sections for each of the three cells in the fuel pin with the 8-group structure as our energy groups."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Extract all Cells filled by Materials\n",
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"openmc_cells = model.geometry.get_all_material_cells().values()\n",
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"\n",
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"# Create dictionary to store multi-group cross sections for all cells\n",
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"xs_library = {}\n",
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"\n",
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"# Instantiate 8-group cross sections for each cell\n",
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"for cell in openmc_cells:\n",
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" xs_library[cell.id] = {}\n",
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" xs_library[cell.id]['transport'] = mgxs.TransportXS(energy_groups=fine_groups)\n",
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" xs_library[cell.id]['fission'] = mgxs.FissionXS(energy_groups=fine_groups)\n",
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" xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(energy_groups=fine_groups, nu=True)\n",
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" xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(energy_groups=fine_groups, nu=True)\n",
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" xs_library[cell.id]['chi'] = mgxs.Chi(energy_groups=fine_groups)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Next, we showcase the use of OpenMC's [tally precision trigger](../io_formats/settings.rst#trigger-element) feature in conjunction with the `openmc.mgxs` module. In particular, we will assign a tally trigger of 1E-2 on the standard deviation for each of the tallies used to compute multi-group cross sections."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a tally trigger for +/- 0.01 on each tally used to compute the multi-group cross sections\n",
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"tally_trigger = openmc.Trigger('std_dev', 1e-2)\n",
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"\n",
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"# Add the tally trigger to each of the multi-group cross section tallies\n",
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"for cell in openmc_cells:\n",
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" for mgxs_type in xs_library[cell.id]:\n",
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" xs_library[cell.id][mgxs_type].tally_trigger = tally_trigger"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now, we must loop over all cells to set the cross section domains to the various cells - fuel, clad and moderator - included in the geometry. In addition, we will set each cross section to tally cross sections on a per-nuclide basis through the use of the `MGXS` class' boolean `by_nuclide` instance attribute. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Instantiate an empty Tallies object\n",
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"tallies = openmc.Tallies()\n",
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"\n",
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"# Iterate over all cells and cross section types\n",
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"for cell in openmc_cells:\n",
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" for rxn_type in xs_library[cell.id]:\n",
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"\n",
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" # Set the cross sections domain to the cell\n",
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" xs_library[cell.id][rxn_type].domain = cell\n",
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"\n",
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" # Tally cross sections by nuclide\n",
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" xs_library[cell.id][rxn_type].by_nuclide = True\n",
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"\n",
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" # Add OpenMC tallies to the tallies file for XML generation\n",
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" for tally in xs_library[cell.id][rxn_type].tallies.values():\n",
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" tallies.append(tally, merge=True)\n",
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"\n",
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"model.tallies = tallies"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we a have a complete set of inputs, so we can go ahead and run our simulation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" warn(msg, IDWarning)\n",
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"/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",
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" warn(msg, IDWarning)\n",
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"/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",
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" warn(msg, IDWarning)\n",
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"/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",
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" warn(msg, IDWarning)\n",
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"/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",
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" warn(msg, IDWarning)\n",
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"/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",
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" warn(msg, IDWarning)\n",
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"/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",
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" warn(msg, IDWarning)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" %%%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%%%%%%%%%%%%%%\n",
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" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
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" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
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" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
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" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
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" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
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" ###################### %%%%%%%%%%%%%%%%%%%%\n",
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" ####################### %%%%%%%%%%%%%%%%%%\n",
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" ####################### %%%%%%%%%%%%%%%%%\n",
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" ###################### %%%%%%%%%%%%%%%%%\n",
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" #################### %%%%%%%%%%%%%%%%%\n",
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" ################# %%%%%%%%%%%%%%%%%\n",
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" ############### %%%%%%%%%%%%%%%%\n",
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" ############ %%%%%%%%%%%%%%%\n",
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" ######## %%%%%%%%%%%%%%\n",
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" %%%%%%%%%%%\n",
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"\n",
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" | The OpenMC Monte Carlo Code\n",
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" Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
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" License | https://docs.openmc.org/en/latest/license.html\n",
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" Version | 0.13.3\n",
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" Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
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" Date/Time | 2023-11-07 12:05:29\n",
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" OpenMP Threads | 32\n",
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"\n",
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" Reading settings XML file...\n",
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" Reading cross sections XML file...\n",
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" Reading materials XML file...\n",
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" Reading geometry XML file...\n",
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" Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n",
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" Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n",
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" Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n",
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" Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
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" Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n",
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" Minimum neutron data temperature: 294 K\n",
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" Maximum neutron data temperature: 294 K\n",
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" Reading tallies XML file...\n",
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" Preparing distributed cell instances...\n",
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" Reading plot XML file...\n",
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" Writing summary.h5 file...\n",
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" Maximum neutron transport energy: 20000000 eV for U235\n",
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" Initializing source particles...\n",
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"\n",
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" ====================> K EIGENVALUE SIMULATION <====================\n",
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"\n",
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" Bat./Gen. k Average k\n",
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" ========= ======== ====================\n",
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" 1/1 1.22394\n",
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" 2/1 1.22784\n",
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" 3/1 1.25162\n",
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" 4/1 1.18933\n",
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" 5/1 1.24249\n",
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" 6/1 1.22774\n",
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" 7/1 1.22083\n",
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" 8/1 1.21236\n",
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" 9/1 1.24216\n",
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" 10/1 1.20525\n",
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" 11/1 1.22801\n",
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" 12/1 1.24750 1.23775 +/- 0.00975\n",
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" 13/1 1.23962 1.23838 +/- 0.00566\n",
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" 14/1 1.24706 1.24055 +/- 0.00455\n",
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" 15/1 1.22279 1.23700 +/- 0.00500\n",
|
|
" 16/1 1.18058 1.22759 +/- 0.01025\n",
|
|
" 17/1 1.21435 1.22570 +/- 0.00887\n",
|
|
" 18/1 1.23701 1.22712 +/- 0.00781\n",
|
|
" 19/1 1.23145 1.22760 +/- 0.00690\n",
|
|
" 20/1 1.21727 1.22656 +/- 0.00626\n",
|
|
" 21/1 1.22156 1.22611 +/- 0.00568\n",
|
|
" 22/1 1.25529 1.22854 +/- 0.00573\n",
|
|
" 23/1 1.23090 1.22872 +/- 0.00527\n",
|
|
" 24/1 1.23231 1.22898 +/- 0.00489\n",
|
|
" 25/1 1.23425 1.22933 +/- 0.00456\n",
|
|
" 26/1 1.22500 1.22906 +/- 0.00428\n",
|
|
" 27/1 1.21126 1.22801 +/- 0.00415\n",
|
|
" 28/1 1.21880 1.22750 +/- 0.00395\n",
|
|
" 29/1 1.25221 1.22880 +/- 0.00395\n",
|
|
" 30/1 1.23143 1.22893 +/- 0.00375\n",
|
|
" 31/1 1.24257 1.22958 +/- 0.00363\n",
|
|
" 32/1 1.23452 1.22981 +/- 0.00347\n",
|
|
" 33/1 1.20175 1.22859 +/- 0.00353\n",
|
|
" 34/1 1.22931 1.22862 +/- 0.00338\n",
|
|
" 35/1 1.23860 1.22902 +/- 0.00327\n",
|
|
" 36/1 1.18381 1.22728 +/- 0.00359\n",
|
|
" 37/1 1.20017 1.22627 +/- 0.00360\n",
|
|
" 38/1 1.23447 1.22657 +/- 0.00348\n",
|
|
" 39/1 1.20672 1.22588 +/- 0.00342\n",
|
|
" 40/1 1.20439 1.22516 +/- 0.00339\n",
|
|
" 41/1 1.21524 1.22484 +/- 0.00329\n",
|
|
" 42/1 1.21467 1.22453 +/- 0.00320\n",
|
|
" 43/1 1.21988 1.22439 +/- 0.00311\n",
|
|
" 44/1 1.21610 1.22414 +/- 0.00302\n",
|
|
" 45/1 1.20887 1.22371 +/- 0.00297\n",
|
|
" 46/1 1.23855 1.22412 +/- 0.00291\n",
|
|
" 47/1 1.21390 1.22384 +/- 0.00285\n",
|
|
" 48/1 1.21694 1.22366 +/- 0.00278\n",
|
|
" 49/1 1.24311 1.22416 +/- 0.00275\n",
|
|
" 50/1 1.22343 1.22414 +/- 0.00268\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" Creating state point statepoint.050.h5...\n",
|
|
" 51/1 1.22495 1.22416 +/- 0.00261\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 52/1 1.21607 1.22397 +/- 0.00256\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 53/1 1.21531 1.22377 +/- 0.00251\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 54/1 1.22888 1.22388 +/- 0.00245\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 55/1 1.20765 1.22352 +/- 0.00242\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 56/1 1.22969 1.22366 +/- 0.00237\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 57/1 1.24581 1.22413 +/- 0.00237\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 58/1 1.20849 1.22380 +/- 0.00234\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 59/1 1.23903 1.22411 +/- 0.00232\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 60/1 1.20733 1.22378 +/- 0.00229\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 61/1 1.23517 1.22400 +/- 0.00226\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 62/1 1.22390 1.22400 +/- 0.00222\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 63/1 1.22695 1.22405 +/- 0.00217\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 64/1 1.20634 1.22373 +/- 0.00216\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 65/1 1.22490 1.22375 +/- 0.00212\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 66/1 1.21709 1.22363 +/- 0.00208\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 67/1 1.22494 1.22365 +/- 0.00205\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 68/1 1.21584 1.22352 +/- 0.00202\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 69/1 1.21189 1.22332 +/- 0.00199\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 70/1 1.21044 1.22310 +/- 0.00197\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 71/1 1.21279 1.22294 +/- 0.00194\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 72/1 1.24979 1.22337 +/- 0.00196\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 73/1 1.21196 1.22319 +/- 0.00194\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 74/1 1.23868 1.22343 +/- 0.00192\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 75/1 1.22632 1.22347 +/- 0.00189\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 76/1 1.20763 1.22323 +/- 0.00188\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 77/1 1.20654 1.22299 +/- 0.00187\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 78/1 1.21582 1.22288 +/- 0.00184\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 79/1 1.22156 1.22286 +/- 0.00182\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 80/1 1.26424 1.22345 +/- 0.00189\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 81/1 1.23856 1.22366 +/- 0.00187\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 82/1 1.22635 1.22370 +/- 0.00185\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 83/1 1.23682 1.22388 +/- 0.00183\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 84/1 1.21823 1.22381 +/- 0.00181\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 85/1 1.24048 1.22403 +/- 0.00180\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 86/1 1.22127 1.22399 +/- 0.00177\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 87/1 1.22862 1.22405 +/- 0.00175\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 88/1 1.24011 1.22426 +/- 0.00174\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 89/1 1.22234 1.22423 +/- 0.00172\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 90/1 1.23789 1.22440 +/- 0.00170\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 91/1 1.21690 1.22431 +/- 0.00169\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 92/1 1.23422 1.22443 +/- 0.00167\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 93/1 1.21595 1.22433 +/- 0.00165\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 94/1 1.23509 1.22446 +/- 0.00164\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 95/1 1.19459 1.22411 +/- 0.00166\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 96/1 1.22999 1.22417 +/- 0.00164\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 97/1 1.22043 1.22413 +/- 0.00162\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 98/1 1.22169 1.22410 +/- 0.00160\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 99/1 1.22003 1.22406 +/- 0.00158\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 100/1 1.20257 1.22382 +/- 0.00158\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 101/1 1.21000 1.22367 +/- 0.00157\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 102/1 1.23888 1.22383 +/- 0.00157\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 103/1 1.21211 1.22371 +/- 0.00155\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 104/1 1.20659 1.22352 +/- 0.00155\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 105/1 1.21947 1.22348 +/- 0.00153\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 106/1 1.23843 1.22364 +/- 0.00152\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 107/1 1.22764 1.22368 +/- 0.00151\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 108/1 1.23291 1.22377 +/- 0.00150\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 109/1 1.22325 1.22377 +/- 0.00148\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 110/1 1.21052 1.22364 +/- 0.00147\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 111/1 1.23348 1.22373 +/- 0.00146\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 112/1 1.22456 1.22374 +/- 0.00145\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 113/1 1.20578 1.22357 +/- 0.00144\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 114/1 1.22042 1.22354 +/- 0.00143\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 115/1 1.22143 1.22352 +/- 0.00142\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 116/1 1.23161 1.22359 +/- 0.00140\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 117/1 1.22951 1.22365 +/- 0.00139\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 118/1 1.21103 1.22353 +/- 0.00138\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 119/1 1.24296 1.22371 +/- 0.00138\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 120/1 1.22464 1.22372 +/- 0.00137\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 121/1 1.24168 1.22388 +/- 0.00137\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 122/1 1.22327 1.22387 +/- 0.00136\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 123/1 1.22344 1.22387 +/- 0.00134\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 124/1 1.21236 1.22377 +/- 0.00134\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 125/1 1.25017 1.22400 +/- 0.00134\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 126/1 1.22034 1.22397 +/- 0.00133\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 127/1 1.20737 1.22383 +/- 0.00133\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 128/1 1.22039 1.22380 +/- 0.00132\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 129/1 1.19783 1.22358 +/- 0.00132\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 130/1 1.20356 1.22341 +/- 0.00132\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 131/1 1.24384 1.22358 +/- 0.00132\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 132/1 1.22925 1.22363 +/- 0.00131\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 133/1 1.22562 1.22364 +/- 0.00130\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 134/1 1.21676 1.22359 +/- 0.00129\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 135/1 1.20402 1.22343 +/- 0.00129\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 136/1 1.21821 1.22339 +/- 0.00128\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 137/1 1.25992 1.22368 +/- 0.00131\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 138/1 1.21551 1.22361 +/- 0.00130\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 139/1 1.21593 1.22355 +/- 0.00129\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 140/1 1.20545 1.22341 +/- 0.00129\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 141/1 1.23441 1.22350 +/- 0.00128\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 142/1 1.21756 1.22345 +/- 0.00127\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 143/1 1.24155 1.22359 +/- 0.00127\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 144/1 1.25159 1.22380 +/- 0.00127\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 145/1 1.21877 1.22376 +/- 0.00127\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 146/1 1.21616 1.22371 +/- 0.00126\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 147/1 1.23678 1.22380 +/- 0.00125\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 148/1 1.20358 1.22365 +/- 0.00125\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 149/1 1.21250 1.22357 +/- 0.00125\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 150/1 1.20860 1.22347 +/- 0.00124\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 151/1 1.22502 1.22348 +/- 0.00123\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 152/1 1.21802 1.22344 +/- 0.00122\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 153/1 1.24871 1.22362 +/- 0.00123\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 154/1 1.22950 1.22366 +/- 0.00122\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 155/1 1.19887 1.22349 +/- 0.00122\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 156/1 1.21147 1.22340 +/- 0.00122\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 157/1 1.20278 1.22326 +/- 0.00122\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 158/1 1.21427 1.22320 +/- 0.00121\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 159/1 1.21654 1.22316 +/- 0.00120\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 160/1 1.24186 1.22328 +/- 0.00120\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 161/1 1.21032 1.22320 +/- 0.00120\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 162/1 1.19739 1.22303 +/- 0.00120\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 163/1 1.22975 1.22307 +/- 0.00119\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 164/1 1.21494 1.22302 +/- 0.00119\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 165/1 1.20907 1.22293 +/- 0.00118\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 166/1 1.20143 1.22279 +/- 0.00118\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 167/1 1.22043 1.22278 +/- 0.00118\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 168/1 1.22880 1.22281 +/- 0.00117\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 169/1 1.24968 1.22298 +/- 0.00117\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 170/1 1.22392 1.22299 +/- 0.00117\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 171/1 1.23658 1.22307 +/- 0.00116\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 172/1 1.22377 1.22308 +/- 0.00116\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 173/1 1.21977 1.22306 +/- 0.00115\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 174/1 1.21514 1.22301 +/- 0.00114\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 175/1 1.20258 1.22288 +/- 0.00114\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 176/1 1.21552 1.22284 +/- 0.00114\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 177/1 1.20676 1.22274 +/- 0.00113\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 178/1 1.23262 1.22280 +/- 0.00113\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 179/1 1.20232 1.22268 +/- 0.00113\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 180/1 1.22200 1.22268 +/- 0.00112\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 181/1 1.21723 1.22265 +/- 0.00112\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 182/1 1.22808 1.22268 +/- 0.00111\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 183/1 1.22492 1.22269 +/- 0.00110\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 184/1 1.22204 1.22269 +/- 0.00110\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 185/1 1.22431 1.22270 +/- 0.00109\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 186/1 1.22800 1.22273 +/- 0.00109\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 187/1 1.22258 1.22273 +/- 0.00108\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 188/1 1.24411 1.22285 +/- 0.00108\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 189/1 1.23613 1.22292 +/- 0.00108\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 190/1 1.22197 1.22291 +/- 0.00107\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 191/1 1.21940 1.22289 +/- 0.00106\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 192/1 1.21190 1.22283 +/- 0.00106\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 193/1 1.21577 1.22280 +/- 0.00106\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 194/1 1.22491 1.22281 +/- 0.00105\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 195/1 1.21509 1.22277 +/- 0.00104\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 196/1 1.22257 1.22276 +/- 0.00104\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 197/1 1.21065 1.22270 +/- 0.00104\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 198/1 1.23695 1.22278 +/- 0.00103\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 199/1 1.21822 1.22275 +/- 0.00103\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" 200/1 1.21757 1.22272 +/- 0.00102\n",
|
|
" Triggers unsatisfied, no result tallied for score total in tally 23\n",
|
|
" The estimated number of batches is -2147483637\n",
|
|
" Creating state point statepoint.200.h5...\n",
|
|
"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
|
"\n",
|
|
" Total time for initialization = 5.0144e-01 seconds\n",
|
|
" Reading cross sections = 4.5672e-01 seconds\n",
|
|
" Total time in simulation = 8.5119e+01 seconds\n",
|
|
" Time in transport only = 3.2873e+01 seconds\n",
|
|
" Time in inactive batches = 7.6939e-01 seconds\n",
|
|
" Time in active batches = 8.4350e+01 seconds\n",
|
|
" Time synchronizing fission bank = 1.9959e-01 seconds\n",
|
|
" Sampling source sites = 1.7881e-01 seconds\n",
|
|
" SEND/RECV source sites = 2.0610e-02 seconds\n",
|
|
" Time accumulating tallies = 5.1680e+01 seconds\n",
|
|
" Time writing statepoints = 5.8805e-02 seconds\n",
|
|
" Total time for finalization = 4.1537e-03 seconds\n",
|
|
" Total time elapsed = 8.5672e+01 seconds\n",
|
|
" Calculation Rate (inactive) = 129973 particles/second\n",
|
|
" Calculation Rate (active) = 22525.3 particles/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 1.22328 +/- 0.00091\n",
|
|
" k-effective (Track-length) = 1.22272 +/- 0.00102\n",
|
|
" k-effective (Absorption) = 1.22396 +/- 0.00079\n",
|
|
" Combined k-effective = 1.22359 +/- 0.00070\n",
|
|
" Leakage Fraction = 0.00000 +/- 0.00000\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Run OpenMC\n",
|
|
"sp_file = model.run()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Tally Data Processing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load the last statepoint file\n",
|
|
"sp = openmc.StatePoint(sp_file)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Iterate over all cells and cross section types\n",
|
|
"for cell in openmc_cells:\n",
|
|
" for rxn_type in xs_library[cell.id]:\n",
|
|
" xs_library[cell.id][rxn_type].load_from_statepoint(sp)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"That's it! Our multi-group cross sections are now ready for the big spotlight. This time we have cross sections in three distinct spatial zones - fuel, clad and moderator - on a per-nuclide basis."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Extracting and Storing MGXS Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's first inspect one of our cross sections by printing it to the screen as a microscopic cross section in units of barns."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Multi-Group XS\n",
|
|
"\tReaction Type =\tnu-fission\n",
|
|
"\tDomain Type =\tcell\n",
|
|
"\tDomain ID =\t1\n",
|
|
"\tNuclide =\tU235\n",
|
|
"\tCross Sections [barns]:\n",
|
|
" Group 1 [821000.0 - 20000000.0eV]:\t3.30e+00 +/- 1.30e-01%\n",
|
|
" Group 2 [5530.0 - 821000.0 eV]:\t3.97e+00 +/- 8.86e-02%\n",
|
|
" Group 3 [4.0 - 5530.0 eV]:\t5.50e+01 +/- 1.26e-01%\n",
|
|
" Group 4 [0.625 - 4.0 eV]:\t8.84e+01 +/- 1.94e-01%\n",
|
|
" Group 5 [0.28 - 0.625 eV]:\t2.90e+02 +/- 2.66e-01%\n",
|
|
" Group 6 [0.14 - 0.28 eV]:\t4.49e+02 +/- 2.50e-01%\n",
|
|
" Group 7 [0.058 - 0.14 eV]:\t6.87e+02 +/- 1.76e-01%\n",
|
|
" Group 8 [0.0 - 0.058 eV]:\t1.44e+03 +/- 1.62e-01%\n",
|
|
"\n",
|
|
"\tNuclide =\tU238\n",
|
|
"\tCross Sections [barns]:\n",
|
|
" Group 1 [821000.0 - 20000000.0eV]:\t1.06e+00 +/- 1.57e-01%\n",
|
|
" Group 2 [5530.0 - 821000.0 eV]:\t1.21e-03 +/- 1.56e-01%\n",
|
|
" Group 3 [4.0 - 5530.0 eV]:\t5.81e-04 +/- 2.04e+00%\n",
|
|
" Group 4 [0.625 - 4.0 eV]:\t6.54e-06 +/- 1.72e-01%\n",
|
|
" Group 5 [0.28 - 0.625 eV]:\t1.07e-05 +/- 2.64e-01%\n",
|
|
" Group 6 [0.14 - 0.28 eV]:\t1.55e-05 +/- 2.51e-01%\n",
|
|
" Group 7 [0.058 - 0.14 eV]:\t2.30e-05 +/- 1.76e-01%\n",
|
|
" Group 8 [0.0 - 0.058 eV]:\t4.24e-05 +/- 1.61e-01%\n",
|
|
"\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"nufission = xs_library[fuel_cell.id]['nu-fission']\n",
|
|
"nufission.print_xs(xs_type='micro', nuclides=['U235', 'U238'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Our multi-group cross sections are capable of summing across all nuclides to provide us with macroscopic cross sections as well."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Multi-Group XS\n",
|
|
"\tReaction Type =\tnu-fission\n",
|
|
"\tDomain Type =\tcell\n",
|
|
"\tDomain ID =\t1\n",
|
|
"\tCross Sections [cm^-1]:\n",
|
|
" Group 1 [821000.0 - 20000000.0eV]:\t2.52e-02 +/- 1.50e-01%\n",
|
|
" Group 2 [5530.0 - 821000.0 eV]:\t1.51e-03 +/- 8.70e-02%\n",
|
|
" Group 3 [4.0 - 5530.0 eV]:\t2.07e-02 +/- 1.26e-01%\n",
|
|
" Group 4 [0.625 - 4.0 eV]:\t3.32e-02 +/- 1.94e-01%\n",
|
|
" Group 5 [0.28 - 0.625 eV]:\t1.09e-01 +/- 2.66e-01%\n",
|
|
" Group 6 [0.14 - 0.28 eV]:\t1.69e-01 +/- 2.50e-01%\n",
|
|
" Group 7 [0.058 - 0.14 eV]:\t2.58e-01 +/- 1.76e-01%\n",
|
|
" Group 8 [0.0 - 0.058 eV]:\t5.40e-01 +/- 1.62e-01%\n",
|
|
"\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"nufission = xs_library[fuel_cell.id]['nu-fission']\n",
|
|
"nufission.print_xs(xs_type='macro', nuclides='sum')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Although a printed report is nice, it is not scalable or flexible. Let's extract the microscopic cross section data for the moderator as a [Pandas](https://pandas.pydata.org/) `DataFrame` ."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
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" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>cell</th>\n",
|
|
" <th>group in</th>\n",
|
|
" <th>group out</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>126</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>H1</td>\n",
|
|
" <td>0.233843</td>\n",
|
|
" <td>0.002374</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>127</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>O16</td>\n",
|
|
" <td>1.563140</td>\n",
|
|
" <td>0.003891</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>124</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>H1</td>\n",
|
|
" <td>1.590210</td>\n",
|
|
" <td>0.001807</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>125</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>O16</td>\n",
|
|
" <td>0.285848</td>\n",
|
|
" <td>0.001017</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>122</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>H1</td>\n",
|
|
" <td>0.010769</td>\n",
|
|
" <td>0.000138</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>123</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>O16</td>\n",
|
|
" <td>0.000003</td>\n",
|
|
" <td>0.000003</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>120</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>H1</td>\n",
|
|
" <td>0.000012</td>\n",
|
|
" <td>0.000004</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>121</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>O16</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>118</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>H1</td>\n",
|
|
" <td>0.000002</td>\n",
|
|
" <td>0.000002</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>119</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>O16</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" cell group in group out nuclide mean std. dev.\n",
|
|
"126 3 1 1 H1 0.233843 0.002374\n",
|
|
"127 3 1 1 O16 1.563140 0.003891\n",
|
|
"124 3 1 2 H1 1.590210 0.001807\n",
|
|
"125 3 1 2 O16 0.285848 0.001017\n",
|
|
"122 3 1 3 H1 0.010769 0.000138\n",
|
|
"123 3 1 3 O16 0.000003 0.000003\n",
|
|
"120 3 1 4 H1 0.000012 0.000004\n",
|
|
"121 3 1 4 O16 0.000000 0.000000\n",
|
|
"118 3 1 5 H1 0.000002 0.000002\n",
|
|
"119 3 1 5 O16 0.000000 0.000000"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n",
|
|
"df = nuscatter.get_pandas_dataframe(xs_type='micro')\n",
|
|
"df.head(10)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Next, we illustate how one can easily take multi-group cross sections and condense them down to a coarser energy group structure. The `MGXS` class includes a `get_condensed_xs(...)` method which takes an `EnergyGroups` parameter with a coarse(r) group structure and returns a new `MGXS` condensed to the coarse groups. We illustrate this process below using the 2-group structure created earlier."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Extract the 8-group transport cross section for the fuel\n",
|
|
"fine_xs = xs_library[fuel_cell.id]['transport']\n",
|
|
"\n",
|
|
"# Condense to the 2-group structure\n",
|
|
"condensed_xs = fine_xs.get_condensed_xs(coarse_groups)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Group condensation is as simple as that! We now have a new coarse 2-group `TransportXS` in addition to our original 8-group `TransportXS`. Let's inspect the 2-group `TransportXS` by printing it to the screen and extracting a Pandas `DataFrame` as we have already learned how to do."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Multi-Group XS\n",
|
|
"\tReaction Type =\ttransport\n",
|
|
"\tDomain Type =\tcell\n",
|
|
"\tDomain ID =\t1\n",
|
|
"\tNuclide =\tU235\n",
|
|
"\tCross Sections [cm^-1]:\n",
|
|
" Group 1 [0.625 - 20000000.0eV]:\t7.79e-03 +/- 1.22e-01%\n",
|
|
" Group 2 [0.0 - 0.625 eV]:\t1.82e-01 +/- 1.12e-01%\n",
|
|
"\n",
|
|
"\tNuclide =\tU238\n",
|
|
"\tCross Sections [cm^-1]:\n",
|
|
" Group 1 [0.625 - 20000000.0eV]:\t2.17e-01 +/- 7.27e-02%\n",
|
|
" Group 2 [0.0 - 0.625 eV]:\t2.53e-01 +/- 1.11e-01%\n",
|
|
"\n",
|
|
"\tNuclide =\tO16\n",
|
|
"\tCross Sections [cm^-1]:\n",
|
|
" Group 1 [0.625 - 20000000.0eV]:\t1.45e-01 +/- 6.93e-02%\n",
|
|
" Group 2 [0.0 - 0.625 eV]:\t1.74e-01 +/- 1.22e-01%\n",
|
|
"\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"condensed_xs.print_xs()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>cell</th>\n",
|
|
" <th>group in</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>20.780474</td>\n",
|
|
" <td>0.025302</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U238</td>\n",
|
|
" <td>9.584864</td>\n",
|
|
" <td>0.006964</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>O16</td>\n",
|
|
" <td>3.159245</td>\n",
|
|
" <td>0.002191</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>485.324537</td>\n",
|
|
" <td>0.544794</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>U238</td>\n",
|
|
" <td>11.195219</td>\n",
|
|
" <td>0.012455</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>O16</td>\n",
|
|
" <td>3.788412</td>\n",
|
|
" <td>0.004606</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" cell group in nuclide mean std. dev.\n",
|
|
"3 1 1 U235 20.780474 0.025302\n",
|
|
"4 1 1 U238 9.584864 0.006964\n",
|
|
"5 1 1 O16 3.159245 0.002191\n",
|
|
"0 1 2 U235 485.324537 0.544794\n",
|
|
"1 1 2 U238 11.195219 0.012455\n",
|
|
"2 1 2 O16 3.788412 0.004606"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df = condensed_xs.get_pandas_dataframe(xs_type='micro')\n",
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Verification with OpenMOC"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now, let's verify our cross sections using OpenMOC. First, we construct an equivalent OpenMOC geometry."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Create an OpenMOC Geometry from the OpenMC Geometry\n",
|
|
"openmoc_geometry = get_openmoc_geometry(sp.summary.geometry)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Next, we we can inject the multi-group cross sections into the equivalent fuel pin cell OpenMOC geometry."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Get all OpenMOC cells in the gometry\n",
|
|
"openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n",
|
|
"\n",
|
|
"# Inject multi-group cross sections into OpenMOC Materials\n",
|
|
"for cell_id, cell in openmoc_cells.items():\n",
|
|
"\n",
|
|
" # Ignore the root cell\n",
|
|
" if cell.getName() == 'root cell':\n",
|
|
" continue\n",
|
|
"\n",
|
|
" # Get a reference to the Material filling this Cell\n",
|
|
" openmoc_material = cell.getFillMaterial()\n",
|
|
"\n",
|
|
" # Set the number of energy groups for the Material\n",
|
|
" openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n",
|
|
"\n",
|
|
" # Extract the appropriate cross section objects for this cell\n",
|
|
" transport = xs_library[cell_id]['transport']\n",
|
|
" nufission = xs_library[cell_id]['nu-fission']\n",
|
|
" nuscatter = xs_library[cell_id]['nu-scatter']\n",
|
|
" chi = xs_library[cell_id]['chi']\n",
|
|
"\n",
|
|
" # Inject NumPy arrays of cross section data into the Material\n",
|
|
" # NOTE: Sum across nuclides to get macro cross sections needed by OpenMOC\n",
|
|
" openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n",
|
|
" openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n",
|
|
" openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n",
|
|
" openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We are now ready to run OpenMOC to verify our cross-sections from OpenMC."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[ NORMAL ] Initializing a default angular quadrature...\n",
|
|
"[ NORMAL ] Initializing 2D tracks...\n",
|
|
"[ NORMAL ] Initializing 2D tracks reflections...\n",
|
|
"[ NORMAL ] Initializing 2D tracks array...\n",
|
|
"[ NORMAL ] Ray tracing for 2D track segmentation...\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 0.09 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 19.94 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 29.87 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 39.80 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 49.72 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 59.65 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 69.58 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 79.50 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 89.43 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n",
|
|
"[ NORMAL ] Initializing FSR lookup vectors\n",
|
|
"[ NORMAL ] Total number of FSRs 3\n",
|
|
"[ NORMAL ] Initializing MOC eigenvalue solver...\n",
|
|
"[ NORMAL ] Initializing solver arrays...\n",
|
|
"[ NORMAL ] Centering segments around FSR centroid...\n",
|
|
"[ NORMAL ] Max boundary angular flux storage per domain = 0.42 MB\n",
|
|
"[ NORMAL ] Max scalar flux storage per domain = 0.00 MB\n",
|
|
"[ NORMAL ] Max source storage per domain = 0.00 MB\n",
|
|
"[ NORMAL ] Number of azimuthal angles = 128\n",
|
|
"[ NORMAL ] Azimuthal ray spacing = 0.100000\n",
|
|
"[ NORMAL ] Number of polar angles = 6\n",
|
|
"[ NORMAL ] Source type = Flat\n",
|
|
"[ NORMAL ] MOC transport undamped\n",
|
|
"[ NORMAL ] CMFD acceleration: OFF\n",
|
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"[ NORMAL ] Using 1 threads\n",
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"[ NORMAL ] Computing the eigenvalue...\n",
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"[ NORMAL ] Iteration 0: k_eff = 0.422947 res = 4.915E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... -57705 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 1: k_eff = 0.475805 res = 2.518E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 5285 D.R. = 5.1231\n",
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"[ NORMAL ] Iteration 2: k_eff = 0.491328 res = 4.930E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1552 D.R. = 1.9580\n",
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"[ NORMAL ] Iteration 3: k_eff = 0.487304 res = 8.137E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -402 D.R. = 1.6503\n",
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"[ NORMAL ] Iteration 4: k_eff = 0.483787 res = 2.904E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -351 D.R. = 0.3569\n",
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"[ NORMAL ] Iteration 5: k_eff = 0.477137 res = 2.435E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -664 D.R. = 0.8385\n",
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"[ NORMAL ] Iteration 6: k_eff = 0.468798 res = 3.811E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -833 D.R. = 1.5652\n",
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"[ NORMAL ] Iteration 7: k_eff = 0.460184 res = 1.231E-07 delta-k (pcm) =\n",
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"[ NORMAL ] ... -861 D.R. = 3.2302\n",
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"[ NORMAL ] Iteration 8: k_eff = 0.450461 res = 4.870E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -972 D.R. = 0.3956\n",
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"[ NORMAL ] Iteration 9: k_eff = 0.441253 res = 2.027E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -920 D.R. = 0.4161\n",
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"[ NORMAL ] Iteration 10: k_eff = 0.431872 res = 4.507E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -938 D.R. = 2.2239\n",
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"[ NORMAL ] Iteration 11: k_eff = 0.422820 res = 3.781E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -905 D.R. = 0.8389\n",
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"[ NORMAL ] Iteration 12: k_eff = 0.414380 res = 2.450E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -843 D.R. = 0.6480\n",
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"[ NORMAL ] Iteration 13: k_eff = 0.406607 res = 3.085E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -777 D.R. = 1.2593\n",
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"[ NORMAL ] Iteration 14: k_eff = 0.399282 res = 4.537E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -732 D.R. = 1.4706\n",
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"[ NORMAL ] Iteration 15: k_eff = 0.392976 res = 1.815E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... -630 D.R. = 0.0400\n",
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"[ NORMAL ] Iteration 16: k_eff = 0.387339 res = 0.000E+00 delta-k (pcm) =\n",
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"[ NORMAL ] ... -563 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 17: k_eff = 0.382584 res = 4.235E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... -475 D.R. = inf\n",
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"[ NORMAL ] Iteration 18: k_eff = 0.378660 res = 1.149E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -392 D.R. = 2.7143\n",
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"[ NORMAL ] Iteration 19: k_eff = 0.375564 res = 0.000E+00 delta-k (pcm) =\n",
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"[ NORMAL ] ... -309 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 20: k_eff = 0.373412 res = 1.875E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -215 D.R. = inf\n",
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"[ NORMAL ] Iteration 21: k_eff = 0.372281 res = 1.875E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -113 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 22: k_eff = 0.371898 res = 1.633E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... -38 D.R. = 0.8710\n",
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"[ NORMAL ] Iteration 23: k_eff = 0.372505 res = 1.028E-07 delta-k (pcm) =\n",
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"[ NORMAL ] ... 60 D.R. = 6.2963\n",
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"[ NORMAL ] Iteration 24: k_eff = 0.373979 res = 6.170E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 147 D.R. = 0.6000\n",
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"[ NORMAL ] Iteration 25: k_eff = 0.376305 res = 2.783E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 232 D.R. = 0.4510\n",
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"[ NORMAL ] Iteration 26: k_eff = 0.379483 res = 2.178E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 317 D.R. = 0.7826\n",
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"[ NORMAL ] Iteration 27: k_eff = 0.383500 res = 2.662E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 401 D.R. = 1.2222\n",
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"[ NORMAL ] Iteration 28: k_eff = 0.388295 res = 2.541E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 479 D.R. = 0.9545\n",
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"[ NORMAL ] Iteration 29: k_eff = 0.393849 res = 3.630E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 555 D.R. = 1.4286\n",
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"[ NORMAL ] Iteration 30: k_eff = 0.400141 res = 3.630E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 629 D.R. = 0.1000\n",
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"[ NORMAL ] Iteration 31: k_eff = 0.407137 res = 4.719E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 699 D.R. = 13.0000\n",
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"[ NORMAL ] Iteration 32: k_eff = 0.414782 res = 5.686E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 764 D.R. = 1.2051\n",
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"[ NORMAL ] Iteration 33: k_eff = 0.423064 res = 3.267E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 828 D.R. = 0.5745\n",
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"[ NORMAL ] Iteration 34: k_eff = 0.431936 res = 1.936E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 887 D.R. = 0.5926\n",
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"[ NORMAL ] Iteration 35: k_eff = 0.441350 res = 1.452E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 941 D.R. = 0.7500\n",
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"[ NORMAL ] Iteration 36: k_eff = 0.451302 res = 1.210E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 995 D.R. = 0.0833\n",
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"[ NORMAL ] Iteration 37: k_eff = 0.461718 res = 1.694E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1041 D.R. = 14.0000\n",
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"[ NORMAL ] Iteration 38: k_eff = 0.472586 res = 1.089E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1086 D.R. = 0.6429\n",
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"[ NORMAL ] Iteration 39: k_eff = 0.483854 res = 3.751E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1126 D.R. = 3.4444\n",
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"[ NORMAL ] Iteration 40: k_eff = 0.495492 res = 7.259E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1163 D.R. = 0.1935\n",
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"[ NORMAL ] Iteration 41: k_eff = 0.507463 res = 3.025E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1197 D.R. = 4.1667\n",
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"[ NORMAL ] Iteration 42: k_eff = 0.519734 res = 3.751E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1227 D.R. = 1.2400\n",
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"[ NORMAL ] Iteration 43: k_eff = 0.532267 res = 3.025E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1253 D.R. = 0.8065\n",
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"[ NORMAL ] Iteration 44: k_eff = 0.545033 res = 5.445E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1276 D.R. = 1.8000\n",
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"[ NORMAL ] Iteration 45: k_eff = 0.557997 res = 2.904E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1296 D.R. = 0.5333\n",
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"[ NORMAL ] Iteration 46: k_eff = 0.571128 res = 2.178E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1313 D.R. = 0.7500\n",
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"[ NORMAL ] Iteration 47: k_eff = 0.584397 res = 7.259E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1326 D.R. = 0.3333\n",
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"[ NORMAL ] Iteration 48: k_eff = 0.597776 res = 9.074E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1337 D.R. = 1.2500\n",
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"[ NORMAL ] Iteration 49: k_eff = 0.611237 res = 4.235E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1346 D.R. = 0.4667\n",
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"[ NORMAL ] Iteration 50: k_eff = 0.624752 res = 5.263E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1351 D.R. = 12.4286\n",
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"[ NORMAL ] Iteration 51: k_eff = 0.638299 res = 4.779E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1354 D.R. = 0.9080\n",
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"[ NORMAL ] Iteration 52: k_eff = 0.651853 res = 3.993E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1355 D.R. = 0.8354\n",
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"[ NORMAL ] Iteration 53: k_eff = 0.665390 res = 4.053E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1353 D.R. = 1.0152\n",
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"[ NORMAL ] Iteration 54: k_eff = 0.678893 res = 3.025E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1350 D.R. = 0.0746\n",
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"[ NORMAL ] Iteration 55: k_eff = 0.692338 res = 2.904E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1344 D.R. = 9.6000\n",
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"[ NORMAL ] Iteration 56: k_eff = 0.705709 res = 3.448E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1337 D.R. = 1.1875\n",
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"[ NORMAL ] Iteration 57: k_eff = 0.718987 res = 4.840E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1327 D.R. = 0.1404\n",
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"[ NORMAL ] Iteration 58: k_eff = 0.732158 res = 3.327E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1317 D.R. = 6.8750\n",
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"[ NORMAL ] Iteration 59: k_eff = 0.745205 res = 1.754E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1304 D.R. = 0.5273\n",
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"[ NORMAL ] Iteration 60: k_eff = 0.758114 res = 1.512E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1290 D.R. = 0.8621\n",
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"[ NORMAL ] Iteration 61: k_eff = 0.770875 res = 3.267E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1276 D.R. = 2.1600\n",
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"[ NORMAL ] Iteration 62: k_eff = 0.783473 res = 2.541E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1259 D.R. = 0.7778\n",
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"[ NORMAL ] Iteration 63: k_eff = 0.795900 res = 1.815E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1242 D.R. = 0.7143\n",
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"[ NORMAL ] Iteration 64: k_eff = 0.808146 res = 1.089E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1224 D.R. = 0.6000\n",
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"[ NORMAL ] Iteration 65: k_eff = 0.820201 res = 1.391E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1205 D.R. = 1.2778\n",
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"[ NORMAL ] Iteration 66: k_eff = 0.832058 res = 2.057E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1185 D.R. = 1.4783\n",
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"[ NORMAL ] Iteration 67: k_eff = 0.843711 res = 2.601E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1165 D.R. = 1.2647\n",
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"[ NORMAL ] Iteration 68: k_eff = 0.855153 res = 2.783E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1144 D.R. = 1.0698\n",
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"[ NORMAL ] Iteration 69: k_eff = 0.866379 res = 3.327E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1122 D.R. = 1.1957\n",
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"[ NORMAL ] Iteration 70: k_eff = 0.877384 res = 6.049E-10 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1100 D.R. = 0.0182\n",
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"[ NORMAL ] Iteration 71: k_eff = 0.888166 res = 4.477E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1078 D.R. = 74.0000\n",
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"[ NORMAL ] Iteration 72: k_eff = 0.898721 res = 1.452E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1055 D.R. = 0.3243\n",
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"[ NORMAL ] Iteration 73: k_eff = 0.909046 res = 4.174E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1032 D.R. = 2.8750\n",
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"[ NORMAL ] Iteration 74: k_eff = 0.919140 res = 1.754E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 1009 D.R. = 0.4203\n",
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"[ NORMAL ] Iteration 75: k_eff = 0.929002 res = 2.117E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 986 D.R. = 1.2069\n",
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"[ NORMAL ] Iteration 76: k_eff = 0.938631 res = 2.783E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 962 D.R. = 1.3143\n",
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"[ NORMAL ] Iteration 77: k_eff = 0.948026 res = 5.263E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 939 D.R. = 1.8913\n",
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"[ NORMAL ] Iteration 78: k_eff = 0.957187 res = 2.722E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 916 D.R. = 0.5172\n",
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"[ NORMAL ] Iteration 79: k_eff = 0.966117 res = 7.864E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 892 D.R. = 0.2889\n",
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"[ NORMAL ] Iteration 80: k_eff = 0.974815 res = 2.904E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 869 D.R. = 3.6923\n",
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"[ NORMAL ] Iteration 81: k_eff = 0.983284 res = 3.085E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 846 D.R. = 1.0625\n",
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"[ NORMAL ] Iteration 82: k_eff = 0.991523 res = 2.420E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 823 D.R. = 0.0784\n",
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"[ NORMAL ] Iteration 83: k_eff = 0.999538 res = 6.049E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 801 D.R. = 2.5000\n",
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"[ NORMAL ] Iteration 84: k_eff = 1.007328 res = 2.904E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 779 D.R. = 4.8000\n",
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"[ NORMAL ] Iteration 85: k_eff = 1.014897 res = 1.633E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 756 D.R. = 0.5625\n",
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"[ NORMAL ] Iteration 86: k_eff = 1.022248 res = 2.662E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 735 D.R. = 1.6296\n",
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"[ NORMAL ] Iteration 87: k_eff = 1.029384 res = 1.573E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 713 D.R. = 0.5909\n",
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"[ NORMAL ] Iteration 88: k_eff = 1.036308 res = 1.936E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 692 D.R. = 1.2308\n",
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"[ NORMAL ] Iteration 89: k_eff = 1.043024 res = 1.512E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 671 D.R. = 0.7812\n",
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"[ NORMAL ] Iteration 90: k_eff = 1.049535 res = 2.964E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 651 D.R. = 1.9600\n",
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"[ NORMAL ] Iteration 91: k_eff = 1.055844 res = 5.384E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 630 D.R. = 1.8163\n",
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"[ NORMAL ] Iteration 92: k_eff = 1.061956 res = 3.267E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 611 D.R. = 0.6067\n",
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"[ NORMAL ] Iteration 93: k_eff = 1.067875 res = 2.299E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 591 D.R. = 0.7037\n",
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"[ NORMAL ] Iteration 94: k_eff = 1.073603 res = 3.630E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 572 D.R. = 0.1579\n",
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"[ NORMAL ] Iteration 95: k_eff = 1.079147 res = 4.114E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 554 D.R. = 11.3333\n",
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"[ NORMAL ] Iteration 96: k_eff = 1.084509 res = 7.804E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 536 D.R. = 1.8971\n",
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"[ NORMAL ] Iteration 97: k_eff = 1.089693 res = 3.146E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 518 D.R. = 0.4031\n",
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"[ NORMAL ] Iteration 98: k_eff = 1.094704 res = 6.836E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 501 D.R. = 2.1731\n",
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"[ NORMAL ] Iteration 99: k_eff = 1.099547 res = 4.598E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 484 D.R. = 0.6726\n",
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"[ NORMAL ] Iteration 100: k_eff = 1.104225 res = 2.480E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 467 D.R. = 0.5395\n",
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"[ NORMAL ] Iteration 101: k_eff = 1.108742 res = 5.505E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 451 D.R. = 2.2195\n",
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"[ NORMAL ] Iteration 102: k_eff = 1.113102 res = 5.082E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 436 D.R. = 0.9231\n",
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"[ NORMAL ] Iteration 103: k_eff = 1.117310 res = 4.779E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 420 D.R. = 0.9405\n",
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"[ NORMAL ] Iteration 104: k_eff = 1.121371 res = 1.089E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 406 D.R. = 0.2278\n",
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"[ NORMAL ] Iteration 105: k_eff = 1.125287 res = 3.569E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 391 D.R. = 3.2778\n",
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"[ NORMAL ] Iteration 106: k_eff = 1.129062 res = 3.630E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 377 D.R. = 0.1017\n",
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"[ NORMAL ] Iteration 107: k_eff = 1.132703 res = 6.654E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 364 D.R. = 1.8333\n",
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"[ NORMAL ] Iteration 108: k_eff = 1.136210 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] ... = 350 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 109: k_eff = 1.139590 res = 1.149E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 337 D.R. = inf\n",
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"[ NORMAL ] Iteration 110: k_eff = 1.142845 res = 2.904E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 325 D.R. = 2.5263\n",
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"[ NORMAL ] Iteration 111: k_eff = 1.145979 res = 2.178E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 313 D.R. = 0.7500\n",
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"[ NORMAL ] Iteration 112: k_eff = 1.148997 res = 1.210E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 301 D.R. = 0.5556\n",
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"[ NORMAL ] Iteration 113: k_eff = 1.151901 res = 4.840E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 290 D.R. = 0.4000\n",
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"[ NORMAL ] Iteration 114: k_eff = 1.154696 res = 2.117E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 279 D.R. = 4.3750\n",
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"[ NORMAL ] Iteration 115: k_eff = 1.157385 res = 1.210E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 131: k_eff = 1.188547 res = 6.231E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 132: k_eff = 1.189896 res = 4.840E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 133: k_eff = 1.191189 res = 5.082E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 134: k_eff = 1.192429 res = 1.815E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 135: k_eff = 1.193617 res = 6.231E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 137: k_eff = 1.195846 res = 5.021E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 138: k_eff = 1.196890 res = 6.473E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 139: k_eff = 1.197891 res = 8.409E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 100 D.R. = 1.2991\n",
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"[ NORMAL ] Iteration 140: k_eff = 1.198849 res = 8.106E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 141: k_eff = 1.199767 res = 5.445E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 142: k_eff = 1.200645 res = 5.868E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 143: k_eff = 1.201486 res = 4.416E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 144: k_eff = 1.202291 res = 1.149E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 80 D.R. = 0.2603\n",
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"[ NORMAL ] Iteration 145: k_eff = 1.203060 res = 1.996E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 146: k_eff = 1.203797 res = 7.743E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 147: k_eff = 1.204502 res = 4.598E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 148: k_eff = 1.205177 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 149: k_eff = 1.205822 res = 5.928E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 150: k_eff = 1.206439 res = 4.053E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 151: k_eff = 1.207028 res = 6.049E-10 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 152: k_eff = 1.207592 res = 2.843E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 153: k_eff = 1.208132 res = 3.085E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 154: k_eff = 1.208647 res = 4.235E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 155: k_eff = 1.209139 res = 1.512E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 156: k_eff = 1.209611 res = 2.117E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 47 D.R. = 1.4000\n",
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"[ NORMAL ] Iteration 157: k_eff = 1.210060 res = 5.989E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 158: k_eff = 1.210490 res = 4.053E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 159: k_eff = 1.210901 res = 1.089E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 160: k_eff = 1.211293 res = 6.049E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 161: k_eff = 1.211668 res = 2.541E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 162: k_eff = 1.212026 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 163: k_eff = 1.212367 res = 7.864E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 34 D.R. = inf\n",
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"[ NORMAL ] Iteration 164: k_eff = 1.212693 res = 2.783E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 32 D.R. = 3.5385\n",
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"[ NORMAL ] Iteration 165: k_eff = 1.213005 res = 1.815E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 167: k_eff = 1.213587 res = 1.149E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 169: k_eff = 1.214116 res = 3.932E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 171: k_eff = 1.214598 res = 5.445E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 172: k_eff = 1.214823 res = 5.142E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 173: k_eff = 1.215038 res = 3.146E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 175: k_eff = 1.215438 res = 3.025E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 176: k_eff = 1.215624 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 177: k_eff = 1.215801 res = 8.469E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 178: k_eff = 1.215971 res = 6.352E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 180: k_eff = 1.216286 res = 6.231E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 184: k_eff = 1.216834 res = 2.541E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 190: k_eff = 1.217481 res = 2.117E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 193: k_eff = 1.217741 res = 2.420E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 200: k_eff = 1.218218 res = 6.654E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 204: k_eff = 1.218425 res = 2.541E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 210: k_eff = 1.218669 res = 7.864E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 216: k_eff = 1.218850 res = 9.982E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 220: k_eff = 1.218944 res = 3.085E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 224: k_eff = 1.219021 res = 5.686E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 227: k_eff = 1.219070 res = 2.904E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 231: k_eff = 1.219123 res = 2.904E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 233: k_eff = 1.219146 res = 6.654E-09 delta-k (pcm)\n",
|
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"[ NORMAL ] ... = 1 D.R. = 0.4074\n",
|
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"[ NORMAL ] Iteration 234: k_eff = 1.219157 res = 1.028E-08 delta-k (pcm)\n",
|
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"[ NORMAL ] ... = 1 D.R. = 1.5455\n",
|
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"[ NORMAL ] Iteration 235: k_eff = 1.219167 res = 6.654E-09 delta-k (pcm)\n",
|
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"[ NORMAL ] ... = 1 D.R. = 0.6471\n",
|
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"[ NORMAL ] Iteration 236: k_eff = 1.219177 res = 5.445E-08 delta-k (pcm)\n",
|
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"[ NORMAL ] ... = 0 D.R. = 8.1818\n"
|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Generate tracks for OpenMOC\n",
|
|
"track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, azim_spacing=0.1)\n",
|
|
"track_generator.generateTracks()\n",
|
|
"\n",
|
|
"# Run OpenMOC\n",
|
|
"solver = openmoc.CPUSolver(track_generator)\n",
|
|
"solver.computeEigenvalue()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"openmc keff = 1.223594\n",
|
|
"openmoc keff = 1.219177\n",
|
|
"bias [pcm]: -441.7\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Print report of keff and bias with OpenMC\n",
|
|
"openmoc_keff = solver.getKeff()\n",
|
|
"openmc_keff = sp.keff.n\n",
|
|
"bias = (openmoc_keff - openmc_keff) * 1e5\n",
|
|
"\n",
|
|
"print('openmc keff = {0:1.6f}'.format(openmc_keff))\n",
|
|
"print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n",
|
|
"print('bias [pcm]: {0:1.1f}'.format(bias))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"As a sanity check, let's run a simulation with the coarse 2-group cross sections to ensure that they also produce a reasonable result."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"openmoc_geometry = get_openmoc_geometry(sp.summary.geometry)\n",
|
|
"openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n",
|
|
"\n",
|
|
"# Inject multi-group cross sections into OpenMOC Materials\n",
|
|
"for cell_id, cell in openmoc_cells.items():\n",
|
|
"\n",
|
|
" # Ignore the root cell\n",
|
|
" if cell.getName() == 'root cell':\n",
|
|
" continue\n",
|
|
"\n",
|
|
" openmoc_material = cell.getFillMaterial()\n",
|
|
" openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n",
|
|
"\n",
|
|
" # Extract the appropriate cross section objects for this cell\n",
|
|
" transport = xs_library[cell_id]['transport']\n",
|
|
" nufission = xs_library[cell_id]['nu-fission']\n",
|
|
" nuscatter = xs_library[cell_id]['nu-scatter']\n",
|
|
" chi = xs_library[cell_id]['chi']\n",
|
|
"\n",
|
|
" # Perform group condensation\n",
|
|
" transport = transport.get_condensed_xs(coarse_groups)\n",
|
|
" nufission = nufission.get_condensed_xs(coarse_groups)\n",
|
|
" nuscatter = nuscatter.get_condensed_xs(coarse_groups)\n",
|
|
" chi = chi.get_condensed_xs(coarse_groups)\n",
|
|
"\n",
|
|
" # Inject NumPy arrays of cross section data into the Material\n",
|
|
" openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n",
|
|
" openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n",
|
|
" openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n",
|
|
" openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[ NORMAL ] Initializing a default angular quadrature...\n",
|
|
"[ NORMAL ] Initializing 2D tracks...\n",
|
|
"[ NORMAL ] Initializing 2D tracks reflections...\n",
|
|
"[ NORMAL ] Initializing 2D tracks array...\n",
|
|
"[ NORMAL ] Ray tracing for 2D track segmentation...\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 0.09 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 19.94 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 29.87 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 39.80 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 49.72 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 59.65 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 69.58 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 79.50 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 89.43 %\n",
|
|
"[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n",
|
|
"[ NORMAL ] Initializing FSR lookup vectors\n",
|
|
"[ NORMAL ] Total number of FSRs 3\n",
|
|
"[ NORMAL ] Initializing MOC eigenvalue solver...\n",
|
|
"[ NORMAL ] Initializing solver arrays...\n",
|
|
"[ NORMAL ] Centering segments around FSR centroid...\n",
|
|
"[ NORMAL ] Max boundary angular flux storage per domain = 0.10 MB\n",
|
|
"[ NORMAL ] Max scalar flux storage per domain = 0.00 MB\n",
|
|
"[ NORMAL ] Max source storage per domain = 0.00 MB\n",
|
|
"[ NORMAL ] Number of azimuthal angles = 128\n",
|
|
"[ NORMAL ] Azimuthal ray spacing = 0.100000\n",
|
|
"[ NORMAL ] Number of polar angles = 6\n",
|
|
"[ NORMAL ] Source type = Flat\n",
|
|
"[ NORMAL ] MOC transport undamped\n",
|
|
"[ NORMAL ] CMFD acceleration: OFF\n",
|
|
"[ NORMAL ] Using 1 threads\n",
|
|
"[ NORMAL ] Computing the eigenvalue...\n",
|
|
"[ NORMAL ] Iteration 0: k_eff = 0.366745 res = 6.049E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -63325 D.R. = 0.0000\n",
|
|
"[ NORMAL ] Iteration 1: k_eff = 0.391062 res = 5.324E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 2431 D.R. = 0.8800\n",
|
|
"[ NORMAL ] Iteration 2: k_eff = 0.392876 res = 4.840E-09 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 181 D.R. = 0.0909\n",
|
|
"[ NORMAL ] Iteration 3: k_eff = 0.380986 res = 5.324E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -1189 D.R. = 11.0000\n",
|
|
"[ NORMAL ] Iteration 4: k_eff = 0.374905 res = 2.420E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -608 D.R. = 0.4545\n",
|
|
"[ NORMAL ] Iteration 5: k_eff = 0.369482 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -542 D.R. = 0.8000\n",
|
|
"[ NORMAL ] Iteration 6: k_eff = 0.365432 res = 4.840E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -404 D.R. = 2.5000\n",
|
|
"[ NORMAL ] Iteration 7: k_eff = 0.362942 res = 6.775E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -248 D.R. = 1.4000\n",
|
|
"[ NORMAL ] Iteration 8: k_eff = 0.361361 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -158 D.R. = 0.8571\n",
|
|
"[ NORMAL ] Iteration 9: k_eff = 0.361166 res = 6.775E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... -19 D.R. = 1.1667\n",
|
|
"[ NORMAL ] Iteration 10: k_eff = 0.361886 res = 1.549E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 72 D.R. = 2.2857\n",
|
|
"[ NORMAL ] Iteration 11: k_eff = 0.363597 res = 9.679E-09 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 171 D.R. = 0.0625\n",
|
|
"[ NORMAL ] Iteration 12: k_eff = 0.366212 res = 9.679E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 261 D.R. = 10.0000\n",
|
|
"[ NORMAL ] Iteration 13: k_eff = 0.369672 res = 9.679E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 346 D.R. = 1.0000\n",
|
|
"[ NORMAL ] Iteration 14: k_eff = 0.373850 res = 1.355E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 417 D.R. = 1.4000\n",
|
|
"[ NORMAL ] Iteration 15: k_eff = 0.378775 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 492 D.R. = 0.5714\n",
|
|
"[ NORMAL ] Iteration 16: k_eff = 0.384321 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 554 D.R. = 0.5000\n",
|
|
"[ NORMAL ] Iteration 17: k_eff = 0.390468 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 614 D.R. = 0.5000\n",
|
|
"[ NORMAL ] Iteration 18: k_eff = 0.397157 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 668 D.R. = 4.0000\n",
|
|
"[ NORMAL ] Iteration 19: k_eff = 0.404338 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 718 D.R. = 1.0000\n",
|
|
"[ NORMAL ] Iteration 20: k_eff = 0.411974 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 763 D.R. = 0.2500\n",
|
|
"[ NORMAL ] Iteration 21: k_eff = 0.420027 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 805 D.R. = 1.0000\n",
|
|
"[ NORMAL ] Iteration 22: k_eff = 0.428443 res = 0.000E+00 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 841 D.R. = 0.0000\n",
|
|
"[ NORMAL ] Iteration 23: k_eff = 0.437200 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 875 D.R. = inf\n",
|
|
"[ NORMAL ] Iteration 24: k_eff = 0.446255 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 905 D.R. = 2.0000\n",
|
|
"[ NORMAL ] Iteration 25: k_eff = 0.455580 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 932 D.R. = 1.5000\n",
|
|
"[ NORMAL ] Iteration 26: k_eff = 0.465142 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 956 D.R. = 0.6667\n",
|
|
"[ NORMAL ] Iteration 27: k_eff = 0.474915 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 977 D.R. = 1.5000\n",
|
|
"[ NORMAL ] Iteration 28: k_eff = 0.484870 res = 1.742E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 995 D.R. = 3.0000\n",
|
|
"[ NORMAL ] Iteration 29: k_eff = 0.494984 res = 1.549E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1011 D.R. = 0.8889\n",
|
|
"[ NORMAL ] Iteration 30: k_eff = 0.505232 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1024 D.R. = 0.2500\n",
|
|
"[ NORMAL ] Iteration 31: k_eff = 0.515593 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1036 D.R. = 2.0000\n",
|
|
"[ NORMAL ] Iteration 32: k_eff = 0.526047 res = 9.679E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1045 D.R. = 1.2500\n",
|
|
"[ NORMAL ] Iteration 33: k_eff = 0.536574 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1052 D.R. = 0.2000\n",
|
|
"[ NORMAL ] Iteration 34: k_eff = 0.547157 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1058 D.R. = 4.0000\n",
|
|
"[ NORMAL ] Iteration 35: k_eff = 0.557779 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1062 D.R. = 0.5000\n",
|
|
"[ NORMAL ] Iteration 36: k_eff = 0.568425 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1064 D.R. = 1.5000\n",
|
|
"[ NORMAL ] Iteration 37: k_eff = 0.579080 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1065 D.R. = 0.6667\n",
|
|
"[ NORMAL ] Iteration 38: k_eff = 0.589731 res = 1.161E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1065 D.R. = 3.0000\n",
|
|
"[ NORMAL ] Iteration 39: k_eff = 0.600366 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1063 D.R. = 0.6667\n",
|
|
"[ NORMAL ] Iteration 40: k_eff = 0.610974 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1060 D.R. = 0.2500\n",
|
|
"[ NORMAL ] Iteration 41: k_eff = 0.621542 res = 9.679E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1056 D.R. = 5.0000\n",
|
|
"[ NORMAL ] Iteration 42: k_eff = 0.632063 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1052 D.R. = 0.6000\n",
|
|
"[ NORMAL ] Iteration 43: k_eff = 0.642526 res = 0.000E+00 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1046 D.R. = 0.0000\n",
|
|
"[ NORMAL ] Iteration 44: k_eff = 0.652925 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1039 D.R. = inf\n",
|
|
"[ NORMAL ] Iteration 45: k_eff = 0.663250 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1032 D.R. = 2.0000\n",
|
|
"[ NORMAL ] Iteration 46: k_eff = 0.673494 res = 0.000E+00 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1024 D.R. = 0.0000\n",
|
|
"[ NORMAL ] Iteration 47: k_eff = 0.683653 res = 0.000E+00 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1015 D.R. = -nan\n",
|
|
"[ NORMAL ] Iteration 48: k_eff = 0.693719 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 1006 D.R. = inf\n",
|
|
"[ NORMAL ] Iteration 49: k_eff = 0.703687 res = 1.355E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 996 D.R. = 1.7500\n",
|
|
"[ NORMAL ] Iteration 50: k_eff = 0.713552 res = 9.679E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 986 D.R. = 0.7143\n",
|
|
"[ NORMAL ] Iteration 51: k_eff = 0.723311 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 975 D.R. = 0.6000\n",
|
|
"[ NORMAL ] Iteration 52: k_eff = 0.732959 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 964 D.R. = 0.3333\n",
|
|
"[ NORMAL ] Iteration 53: k_eff = 0.742493 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 953 D.R. = 2.0000\n",
|
|
"[ NORMAL ] Iteration 54: k_eff = 0.751909 res = 0.000E+00 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 941 D.R. = 0.0000\n",
|
|
"[ NORMAL ] Iteration 55: k_eff = 0.761205 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 929 D.R. = inf\n",
|
|
"[ NORMAL ] Iteration 56: k_eff = 0.770378 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 917 D.R. = 0.5000\n",
|
|
"[ NORMAL ] Iteration 57: k_eff = 0.779427 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 904 D.R. = 2.0000\n",
|
|
"[ NORMAL ] Iteration 58: k_eff = 0.788350 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 892 D.R. = 0.7500\n",
|
|
"[ NORMAL ] Iteration 59: k_eff = 0.797144 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 879 D.R. = 0.6667\n",
|
|
"[ NORMAL ] Iteration 60: k_eff = 0.805809 res = 1.355E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 866 D.R. = 3.5000\n",
|
|
"[ NORMAL ] Iteration 61: k_eff = 0.814344 res = 1.161E-07 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 853 D.R. = 0.8571\n",
|
|
"[ NORMAL ] Iteration 62: k_eff = 0.822748 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 840 D.R. = 0.3333\n",
|
|
"[ NORMAL ] Iteration 63: k_eff = 0.831020 res = 9.679E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 827 D.R. = 2.5000\n",
|
|
"[ NORMAL ] Iteration 64: k_eff = 0.839161 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 814 D.R. = 0.2000\n",
|
|
"[ NORMAL ] Iteration 65: k_eff = 0.847169 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 800 D.R. = 1.0000\n",
|
|
"[ NORMAL ] Iteration 66: k_eff = 0.855045 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 787 D.R. = 4.0000\n",
|
|
"[ NORMAL ] Iteration 67: k_eff = 0.862789 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 774 D.R. = 1.0000\n",
|
|
"[ NORMAL ] Iteration 68: k_eff = 0.870401 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 761 D.R. = 1.0000\n",
|
|
"[ NORMAL ] Iteration 69: k_eff = 0.877882 res = 9.679E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 748 D.R. = 1.2500\n",
|
|
"[ NORMAL ] Iteration 70: k_eff = 0.885232 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 735 D.R. = 0.2000\n",
|
|
"[ NORMAL ] Iteration 71: k_eff = 0.892452 res = 5.807E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 722 D.R. = 3.0000\n",
|
|
"[ NORMAL ] Iteration 72: k_eff = 0.899542 res = 7.743E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 709 D.R. = 1.3333\n",
|
|
"[ NORMAL ] Iteration 73: k_eff = 0.906504 res = 3.872E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 696 D.R. = 0.5000\n",
|
|
"[ NORMAL ] Iteration 74: k_eff = 0.913339 res = 1.936E-08 delta-k (pcm) =\n",
|
|
"[ NORMAL ] ... 683 D.R. = 0.5000\n",
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"[ NORMAL ] Iteration 75: k_eff = 0.920047 res = 0.000E+00 delta-k (pcm) =\n",
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"[ NORMAL ] ... 670 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 76: k_eff = 0.926629 res = 0.000E+00 delta-k (pcm) =\n",
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"[ NORMAL ] ... 658 D.R. = -nan\n",
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"[ NORMAL ] Iteration 77: k_eff = 0.933088 res = 9.679E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 645 D.R. = inf\n",
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"[ NORMAL ] Iteration 78: k_eff = 0.939423 res = 5.807E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 633 D.R. = 0.6000\n",
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"[ NORMAL ] Iteration 79: k_eff = 0.945637 res = 7.743E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 621 D.R. = 1.3333\n",
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"[ NORMAL ] Iteration 80: k_eff = 0.951730 res = 3.872E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 609 D.R. = 0.5000\n",
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"[ NORMAL ] Iteration 81: k_eff = 0.957705 res = 3.872E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 597 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 82: k_eff = 0.963562 res = 2.904E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 585 D.R. = 0.7500\n",
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"[ NORMAL ] Iteration 83: k_eff = 0.969304 res = 7.743E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 574 D.R. = 2.6667\n",
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"[ NORMAL ] Iteration 84: k_eff = 0.974931 res = 9.679E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 562 D.R. = 0.1250\n",
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"[ NORMAL ] Iteration 85: k_eff = 0.980444 res = 3.872E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 551 D.R. = 4.0000\n",
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"[ NORMAL ] Iteration 86: k_eff = 0.985847 res = 1.936E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 540 D.R. = 0.5000\n",
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"[ NORMAL ] Iteration 87: k_eff = 0.991140 res = 9.679E-09 delta-k (pcm) =\n",
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"[ NORMAL ] ... 529 D.R. = 0.5000\n",
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"[ NORMAL ] Iteration 88: k_eff = 0.996324 res = 1.936E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 518 D.R. = 2.0000\n",
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"[ NORMAL ] Iteration 89: k_eff = 1.001402 res = 1.161E-07 delta-k (pcm) =\n",
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"[ NORMAL ] ... 507 D.R. = 6.0000\n",
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"[ NORMAL ] Iteration 90: k_eff = 1.006375 res = 1.258E-07 delta-k (pcm) =\n",
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"[ NORMAL ] ... 497 D.R. = 1.0833\n",
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"[ NORMAL ] Iteration 91: k_eff = 1.011244 res = 0.000E+00 delta-k (pcm) =\n",
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"[ NORMAL ] ... 486 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 92: k_eff = 1.016012 res = 0.000E+00 delta-k (pcm) =\n",
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"[ NORMAL ] ... 476 D.R. = -nan\n",
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"[ NORMAL ] Iteration 93: k_eff = 1.020679 res = 1.258E-07 delta-k (pcm) =\n",
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"[ NORMAL ] ... 466 D.R. = inf\n",
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"[ NORMAL ] Iteration 94: k_eff = 1.025249 res = 2.904E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 456 D.R. = 0.2308\n",
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"[ NORMAL ] Iteration 95: k_eff = 1.029722 res = 3.872E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 447 D.R. = 1.3333\n",
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"[ NORMAL ] Iteration 96: k_eff = 1.034099 res = 1.936E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 437 D.R. = 0.5000\n",
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"[ NORMAL ] Iteration 97: k_eff = 1.038383 res = 5.807E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 428 D.R. = 3.0000\n",
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"[ NORMAL ] Iteration 98: k_eff = 1.042575 res = 7.743E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 419 D.R. = 1.3333\n",
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"[ NORMAL ] Iteration 99: k_eff = 1.046677 res = 4.840E-08 delta-k (pcm) =\n",
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"[ NORMAL ] ... 410 D.R. = 0.6250\n",
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"[ NORMAL ] Iteration 100: k_eff = 1.050690 res = 7.743E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 401 D.R. = 1.6000\n",
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"[ NORMAL ] Iteration 101: k_eff = 1.054616 res = 1.065E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 392 D.R. = 1.3750\n",
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"[ NORMAL ] Iteration 102: k_eff = 1.058457 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 384 D.R. = 0.4545\n",
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"[ NORMAL ] Iteration 103: k_eff = 1.062214 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] ... = 375 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 104: k_eff = 1.065888 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 367 D.R. = inf\n",
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"[ NORMAL ] Iteration 105: k_eff = 1.069481 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 359 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 106: k_eff = 1.072995 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 351 D.R. = 1.2500\n",
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"[ NORMAL ] Iteration 107: k_eff = 1.076432 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 343 D.R. = 1.2000\n",
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"[ NORMAL ] Iteration 108: k_eff = 1.079792 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 335 D.R. = 0.1667\n",
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"[ NORMAL ] Iteration 109: k_eff = 1.083078 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 328 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 110: k_eff = 1.086291 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 321 D.R. = 2.0000\n",
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"[ NORMAL ] Iteration 111: k_eff = 1.089431 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] ... = 314 D.R. = 0.0000\n",
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"[ NORMAL ] Iteration 112: k_eff = 1.092501 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 307 D.R. = inf\n",
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"[ NORMAL ] Iteration 113: k_eff = 1.095502 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 300 D.R. = 0.2500\n",
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"[ NORMAL ] Iteration 114: k_eff = 1.098435 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 293 D.R. = 5.0000\n",
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"[ NORMAL ] Iteration 115: k_eff = 1.101302 res = 7.743E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 286 D.R. = 1.6000\n",
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"[ NORMAL ] Iteration 116: k_eff = 1.104105 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 280 D.R. = 0.5000\n",
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"[ NORMAL ] Iteration 117: k_eff = 1.106844 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 118: k_eff = 1.109520 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 119: k_eff = 1.112135 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 261 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 120: k_eff = 1.114691 res = 7.743E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 255 D.R. = 1.3333\n",
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"[ NORMAL ] Iteration 121: k_eff = 1.117188 res = 2.904E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 249 D.R. = 0.3750\n",
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"[ NORMAL ] Iteration 122: k_eff = 1.119629 res = 9.679E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 123: k_eff = 1.122013 res = 9.679E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 124: k_eff = 1.124342 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 232 D.R. = 0.4000\n",
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"[ NORMAL ] Iteration 125: k_eff = 1.126617 res = 1.065E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 227 D.R. = 2.7500\n",
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"[ NORMAL ] Iteration 126: k_eff = 1.128841 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 127: k_eff = 1.131013 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 128: k_eff = 1.133134 res = 1.161E-07 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 129: k_eff = 1.135206 res = 8.711E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 130: k_eff = 1.137231 res = 1.258E-07 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 131: k_eff = 1.139208 res = 1.065E-07 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 132: k_eff = 1.141139 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 133: k_eff = 1.143025 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 134: k_eff = 1.144867 res = 8.711E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 135: k_eff = 1.146666 res = 1.549E-07 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 136: k_eff = 1.148423 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 137: k_eff = 1.150139 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 138: k_eff = 1.151815 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 139: k_eff = 1.153451 res = 2.904E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 140: k_eff = 1.155049 res = 1.355E-07 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 141: k_eff = 1.156609 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 142: k_eff = 1.158132 res = 1.452E-07 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 143: k_eff = 1.159620 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 144: k_eff = 1.161073 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 146: k_eff = 1.163875 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 147: k_eff = 1.165227 res = 9.679E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 148: k_eff = 1.166546 res = 8.711E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 149: k_eff = 1.167835 res = 9.679E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 150: k_eff = 1.169094 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 157: k_eff = 1.177100 res = 1.065E-07 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 162: k_eff = 1.182044 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 165: k_eff = 1.184735 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 171: k_eff = 1.189563 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 172: k_eff = 1.190302 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 174: k_eff = 1.191726 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 178: k_eff = 1.194374 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 181: k_eff = 1.196197 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 183: k_eff = 1.197340 res = 0.000E+00 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 184: k_eff = 1.197890 res = 2.904E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 185: k_eff = 1.198428 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 53 D.R. = 2.3333\n",
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"[ NORMAL ] Iteration 186: k_eff = 1.198953 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 188: k_eff = 1.199964 res = 7.743E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 189: k_eff = 1.200452 res = 2.904E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 191: k_eff = 1.201391 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 193: k_eff = 1.202285 res = 8.711E-08 delta-k (pcm)\n",
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"[ NORMAL ] Iteration 312: k_eff = 1.219045 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 2 D.R. = 0.2500\n",
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"[ NORMAL ] Iteration 313: k_eff = 1.219067 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 2 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 314: k_eff = 1.219090 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 2 D.R. = 5.0000\n",
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"[ NORMAL ] Iteration 315: k_eff = 1.219111 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 2 D.R. = 1.4000\n",
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"[ NORMAL ] Iteration 316: k_eff = 1.219133 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 2 D.R. = 0.1429\n",
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"[ NORMAL ] Iteration 317: k_eff = 1.219153 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 2 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 318: k_eff = 1.219173 res = 7.743E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 2 D.R. = 8.0000\n",
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"[ NORMAL ] Iteration 319: k_eff = 1.219193 res = 9.679E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 1.2500\n",
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"[ NORMAL ] Iteration 320: k_eff = 1.219212 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.1000\n",
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"[ NORMAL ] Iteration 321: k_eff = 1.219231 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 7.0000\n",
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"[ NORMAL ] Iteration 322: k_eff = 1.219249 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.7143\n",
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"[ NORMAL ] Iteration 323: k_eff = 1.219267 res = 1.452E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 3.0000\n",
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"[ NORMAL ] Iteration 324: k_eff = 1.219284 res = 1.549E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 1.0667\n",
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"[ NORMAL ] Iteration 325: k_eff = 1.219301 res = 1.258E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.8125\n",
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"[ NORMAL ] Iteration 326: k_eff = 1.219318 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.5385\n",
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"[ NORMAL ] Iteration 327: k_eff = 1.219334 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.7143\n",
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"[ NORMAL ] Iteration 328: k_eff = 1.219349 res = 1.645E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 3.4000\n",
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"[ NORMAL ] Iteration 329: k_eff = 1.219365 res = 1.258E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.7647\n",
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"[ NORMAL ] Iteration 330: k_eff = 1.219380 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.0769\n",
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"[ NORMAL ] Iteration 331: k_eff = 1.219394 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 6.0000\n",
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"[ NORMAL ] Iteration 332: k_eff = 1.219409 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.6667\n",
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"[ NORMAL ] Iteration 333: k_eff = 1.219422 res = 1.161E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 3.0000\n",
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"[ NORMAL ] Iteration 334: k_eff = 1.219436 res = 1.065E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.9167\n",
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"[ NORMAL ] Iteration 335: k_eff = 1.219449 res = 1.065E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 1.0000\n",
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"[ NORMAL ] Iteration 336: k_eff = 1.219462 res = 1.452E-07 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 1.3636\n",
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"[ NORMAL ] Iteration 337: k_eff = 1.219475 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.4667\n",
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"[ NORMAL ] Iteration 338: k_eff = 1.219487 res = 5.807E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.8571\n",
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"[ NORMAL ] Iteration 339: k_eff = 1.219499 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.1667\n",
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"[ NORMAL ] Iteration 340: k_eff = 1.219510 res = 6.775E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 7.0000\n",
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"[ NORMAL ] Iteration 341: k_eff = 1.219522 res = 1.936E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.2857\n",
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"[ NORMAL ] Iteration 342: k_eff = 1.219533 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 2.5000\n",
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"[ NORMAL ] Iteration 343: k_eff = 1.219543 res = 3.872E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.8000\n",
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"[ NORMAL ] Iteration 344: k_eff = 1.219554 res = 8.711E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 2.2500\n",
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"[ NORMAL ] Iteration 345: k_eff = 1.219564 res = 4.840E-08 delta-k (pcm)\n",
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"[ NORMAL ] ... = 1 D.R. = 0.5556\n",
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"[ NORMAL ] Iteration 346: k_eff = 1.219574 res = 9.679E-09 delta-k (pcm)\n",
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"[ NORMAL ] ... = 0 D.R. = 0.2000\n"
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]
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}
|
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],
|
|
"source": [
|
|
"# Generate tracks for OpenMOC\n",
|
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"track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, azim_spacing=0.1)\n",
|
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"track_generator.generateTracks()\n",
|
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"\n",
|
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"# Run OpenMOC\n",
|
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"solver = openmoc.CPUSolver(track_generator)\n",
|
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"solver.computeEigenvalue()"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 28,
|
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"metadata": {},
|
|
"outputs": [
|
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{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"openmc keff = 1.223594\n",
|
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"openmoc keff = 1.219574\n",
|
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"bias [pcm]: -402.0\n"
|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Print report of keff and bias with OpenMC\n",
|
|
"openmoc_keff = solver.getKeff()\n",
|
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"openmc_keff = sp.keff.n\n",
|
|
"bias = (openmoc_keff - openmc_keff) * 1e5\n",
|
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"\n",
|
|
"print('openmc keff = {0:1.6f}'.format(openmc_keff))\n",
|
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"print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n",
|
|
"print('bias [pcm]: {0:1.1f}'.format(bias))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
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{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"There is a non-trivial bias in both the 2-group and 8-group cases. In the case of a pin cell, one can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n",
|
|
"\n",
|
|
"* Appropriate transport-corrected cross sections\n",
|
|
"* Spatial discretization of OpenMOC's mesh\n",
|
|
"* Constant-in-angle multi-group cross sections"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Visualizing MGXS Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"It is often insightful to generate visual depictions of multi-group cross sections. There are many different types of plots which may be useful for multi-group cross section visualization, only a few of which will be shown here for enrichment and inspiration.\n",
|
|
"\n",
|
|
"One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the `openmc.plotter` module to plot continuous-energy cross sections from the openly available cross section library distributed by NNDC.\n",
|
|
"\n",
|
|
"The MGXS data can also be plotted using the openmc.plot_xs command, however we will do this manually here to show how the openmc.Mgxs.get_xs method can be used to obtain data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(1e-05, 20000000.0)"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 3200x2400 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create a figure of the U-235 continuous-energy fission cross section\n",
|
|
"fig = openmc.plot_xs('U235', ['fission'])\n",
|
|
"\n",
|
|
"# Get the axis to use for plotting the MGXS\n",
|
|
"ax = fig.gca()\n",
|
|
"\n",
|
|
"# Extract energy group bounds and MGXS values to plot\n",
|
|
"fission = xs_library[fuel_cell.id]['fission']\n",
|
|
"energy_groups = fission.energy_groups\n",
|
|
"x = energy_groups.group_edges\n",
|
|
"y = fission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n",
|
|
"y = np.squeeze(y)\n",
|
|
"\n",
|
|
"# Fix low energy bound\n",
|
|
"x[0] = 1.e-5\n",
|
|
"\n",
|
|
"# Extend the mgxs values array for matplotlib's step plot\n",
|
|
"y = np.insert(y, 0, y[0])\n",
|
|
"\n",
|
|
"# Create a step plot for the MGXS\n",
|
|
"ax.plot(x, y, drawstyle='steps', color='r', linewidth=3)\n",
|
|
"\n",
|
|
"ax.set_title('U-235 Fission Cross Section')\n",
|
|
"ax.legend(['Continuous', 'Multi-Group'])\n",
|
|
"ax.set_xlim((x.min(), x.max()))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Another useful type of illustration is scattering matrix sparsity structures. First, we extract Pandas `DataFrames` for the H-1 and O-16 scattering matrices."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n",
|
|
"nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n",
|
|
"df = nuscatter.get_pandas_dataframe(xs_type='micro')\n",
|
|
"\n",
|
|
"# Slice DataFrame in two for each nuclide's mean values\n",
|
|
"h1 = df[df['nuclide'] == 'H1']['mean']\n",
|
|
"o16 = df[df['nuclide'] == 'O16']['mean']\n",
|
|
"\n",
|
|
"# Cast DataFrames as NumPy arrays\n",
|
|
"h1 = h1.values\n",
|
|
"o16 = o16.values\n",
|
|
"\n",
|
|
"# Reshape arrays to 2D matrix for plotting\n",
|
|
"h1.shape = (fine_groups.num_groups, fine_groups.num_groups)\n",
|
|
"o16.shape = (fine_groups.num_groups, fine_groups.num_groups)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Matplotlib's `imshow` routine can be used to plot the matrices to illustrate their sparsity structures."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 3200x2400 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create plot of the H-1 scattering matrix\n",
|
|
"fig = plt.subplot(121)\n",
|
|
"fig.imshow(h1, interpolation='nearest', cmap='jet')\n",
|
|
"plt.title('H-1 Scattering Matrix')\n",
|
|
"plt.xlabel('Group Out')\n",
|
|
"plt.ylabel('Group In')\n",
|
|
"\n",
|
|
"# Create plot of the O-16 scattering matrix\n",
|
|
"fig2 = plt.subplot(122)\n",
|
|
"fig2.imshow(o16, interpolation='nearest', cmap='jet')\n",
|
|
"plt.title('O-16 Scattering Matrix')\n",
|
|
"plt.xlabel('Group Out')\n",
|
|
"plt.ylabel('Group In')\n",
|
|
"\n",
|
|
"# Show the plot on screen\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# close statepoint file to release HDF5 file handles\n",
|
|
"sp.close()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"anaconda-cloud": {},
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|