4448 lines
330 KiB
Plaintext
4448 lines
330 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "87d52f49-f915-4ef9-8174-dff4e4abc4c3",
|
|
"metadata": {},
|
|
"source": [
|
|
"# OpenMC Tallies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "886ae4d1-1a57-4bff-a372-4398de06b25a",
|
|
"metadata": {},
|
|
"source": [
|
|
"In this tutorial, we will learn how to:\n",
|
|
"\n",
|
|
" - Understand application of filters and scores to create tallies\n",
|
|
" - Apply tallies to an OpenMC simulation\n",
|
|
" - Extract information from OpenMC statepoint files\n",
|
|
" - Understand tally units and normalization\n",
|
|
" - Plot tally results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "435868e4-2904-42e4-86ed-5fb8088ba992",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#model.settings.output = {'summary' : False}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "11dc05ba-523c-4379-b293-a7a8e059312a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import openmc\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "42c30480-d8c6-4ccf-b735-f72ca9cbeef1",
|
|
"metadata": {},
|
|
"source": [
|
|
"In this section, we'll be looking at how to extract custom information from an OpenMC simulation in what is known as a \"tally.\" A tally accumulates statistical information during the simulation about particles when they eneter regions of phase space specified on the tally. The limits of these regions are set by \"filters\" applied to the tally. Scores and nuclides can also be applied to tallies to indicate what type of information is kept about the particle (e.g. reaction types, flux, heat, etc.).\n",
|
|
"\n",
|
|
"Any tally in OpenMC can be described with the following form:\n",
|
|
"\n",
|
|
"$$ \n",
|
|
" X = \\underbrace{\\int d\\mathbf{r} \\int d\\mathbf{\\Omega} \\int\n",
|
|
" dE}_{\\text{filters}} \\underbrace{f(\\mathbf{r}, \\mathbf{\\Omega},\n",
|
|
" E)}_{\\text{scores}} \\underbrace{\\psi (\\mathbf{r}, \\mathbf{\\Omega}, E)}_{\\text{angular flux}}\n",
|
|
"$$\n",
|
|
"\n",
|
|
"where filters set the limits of the integrals and the scoring function is convolved with particle information (e.g. reaction type, current material, etc.). For example, if you wanted to calculate the fission reaction rate caused by fast neutrons in cell 3, your tally becomes\n",
|
|
"\n",
|
|
"$$ \n",
|
|
" X = \\int_\\text{cell 3} d\\mathbf{r} \\int_{4\\pi} d\\mathbf{\\Omega} \\int_{1 MeV}^{20 MeV}\n",
|
|
" dE \\ \\ \\Sigma_f(\\mathbf{r}, \\mathbf{\\Omega},\n",
|
|
" E) \\psi (\\mathbf{r}, \\mathbf{\\Omega}, E)\n",
|
|
"$$\n",
|
|
"\n",
|
|
"<div class=\"alert alert-block alert-info\">\n",
|
|
"A full list of scores and their meanings can be found <a href=https://docs.openmc.org/en/stable/usersguide/tallies.html#scores >here</a>.\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "31f3c102-bbdf-4b64-a7a3-7188aa97333a",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Pincell Model\n",
|
|
"\n",
|
|
"First we'll need a model to examine. OpenMC has a few basic models that we can use to look at tally setup. The function below generates a 2-D PWR pincell model with reflective boundary conditions on the X-Y planes. This function provides an `openmc.Model` object, which ties together materials, geometry, tallies, and settings in a single Python object with a full problem description."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "951705d5-4db5-4c27-9595-4b87c31d1fc1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = openmc.examples.pwr_pin_cell()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e189175b-e2d5-46a8-91da-efccbdfcd396",
|
|
"metadata": {},
|
|
"source": [
|
|
"To get a better idea of what this model looks like, we'll start by generating a plot and examining the materials used."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "13e90150-a82d-412f-a844-8c21a9c75682",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.geometry.root_universe.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "cc51643c-87bc-43d4-a74e-4d864b108473",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{1: Material\n",
|
|
" \tID =\t1\n",
|
|
" \tName =\tUO2 (2.4%)\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t10.29769 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tTrue\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tNuclides \n",
|
|
" \tU234 =\t4.4843e-06 [ao]\n",
|
|
" \tU235 =\t0.00055815 [ao]\n",
|
|
" \tU238 =\t0.022408 [ao]\n",
|
|
" \tO16 =\t0.045829 [ao],\n",
|
|
" 2: Material\n",
|
|
" \tID =\t2\n",
|
|
" \tName =\tZircaloy\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t6.55 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tFalse\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tNuclides \n",
|
|
" \tZr90 =\t0.021827 [ao]\n",
|
|
" \tZr91 =\t0.00476 [ao]\n",
|
|
" \tZr92 =\t0.0072758 [ao]\n",
|
|
" \tZr94 =\t0.0073734 [ao]\n",
|
|
" \tZr96 =\t0.0011879 [ao],\n",
|
|
" 3: Material\n",
|
|
" \tID =\t3\n",
|
|
" \tName =\tHot borated water\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t0.740582 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tFalse\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tS(a,b) =\t('c_H_in_H2O', 1.0)\n",
|
|
" \tNuclides \n",
|
|
" \tH1 =\t0.049457 [ao]\n",
|
|
" \tO16 =\t0.024672 [ao]\n",
|
|
" \tB10 =\t8.0042e-06 [ao]\n",
|
|
" \tB11 =\t3.2218e-05 [ao]}"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.geometry.get_all_materials()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "da00ed28-db4a-4cd6-b611-d041a4817716",
|
|
"metadata": {},
|
|
"source": [
|
|
"If we look at the tallies object on our pincell model, we'll see there aren't currently any custom tallies applied."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "4da56f67-e5cc-4cfe-aec6-3d08dd67a99e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[]"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.tallies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7b73d0d7-51d6-4c1d-8c44-e4848cc08ab3",
|
|
"metadata": {},
|
|
"source": [
|
|
"In this exercise we'll be adding tallies to perform a few different tasks:\n",
|
|
"\n",
|
|
"\n",
|
|
" **1. Determine the average energy produced per fission** \\\n",
|
|
" **2. Plot the flux spectrum of the pincell** \\\n",
|
|
" **3. Plot reaction types based on material**\n",
|
|
" \n",
|
|
"To do this we'll use a variety of different filters applied to different tallies.\n",
|
|
"\n",
|
|
"## An aside on units\n",
|
|
"\n",
|
|
"<div class=\"alert alert-block alert-info\">\n",
|
|
"\n",
|
|
"Geometry units specified in the model build are always in __cm__. Volumes computed by OpenMC will be in __cm<sup>3</sup>__. Tally values for energy will always be reported in __eV__.\n",
|
|
"\n",
|
|
"Tally values are always reported __per source particle__. A \"source particle\" is a physical particle which exists in the \"real world\" - *not* a simulated particle that you control with `settings.particles`. Tallies in OpenMC should be normalized by the source strength $S$, in $\\frac{\\text{src}}{\\text{s}}$. For example, \n",
|
|
"\n",
|
|
"Reacion rate: $r \\left\\lbrack\\frac{\\text{reactions}}{\\text{src}}\\right\\rbrack * S \\left\\lbrack\\frac{\\text{src}}{\\text{s}}\\right\\rbrack \\rightarrow \\left\\lbrack\\frac{\\text{reactions}}{\\text{s}}\\right\\rbrack$\n",
|
|
"\n",
|
|
"Flux tally: $t \\left\\lbrack\\frac{\\text{particle-cm}}{\\text{src}}\\right\\rbrack * \\frac{{\\text{S}}}{\\text{V}} \\left\\lbrack\\frac{\\text{src}}{\\text{s}} \\frac{1}{\\text{cm}^3}\\right\\rbrack \\rightarrow \\left\\lbrack\\frac{\\text{particle}}{\\text{cm}^2\\text{-s}}\\right\\rbrack$\n",
|
|
"</b>\n",
|
|
"\n",
|
|
"For example,\n",
|
|
"\n",
|
|
"- If you have a fixed source photon transport problem with a 1 Ci source of photons, then your source particle rate in the \"real world\" is $S=3.7\\times10^{10}$ src/s.\n",
|
|
"- If you have a fixed source photon transport problem with a 1 Ci source, but only 85% of the decays produce a photon, then your source particle rate in the \"real world\" is $S=0.85*3.7\\times10^{10}$ src/s.\n",
|
|
"- If you have an eigenvalue problem, then you need to impose some reaction rate from which you can determine $S$. For example, if you know your reactor produces $p$ Wth, then you would determine the source rate as\n",
|
|
"\n",
|
|
"Heating tally: $r \\left\\lbrack\\frac{\\text{eV}}{\\text{src}}\\right\\rbrack * \\textcolor{red}{S} \\left\\lbrack\\frac{\\text{src}}{\\text{s}}\\right\\rbrack = p \\left\\lbrack\\frac{J}{\\text{s}}\\right\\rbrack * \\frac{1}{1.602\\times10^{-19}} \\left\\lbrack\\frac{eV}{\\text{J}}\\right\\rbrack $\n",
|
|
"\n",
|
|
"- For eigenvalue problems, knowing the total power is the most common way to normalize, but is not strictly necessary - *any* reaction rate will do! You'll just construct a different equation to determine $S$. Suppose you had a radiation detector in the core from which you know the neutron-induced fission rate $d$. If you added this fission rate as a tally, then you can find $S$ by\n",
|
|
"\n",
|
|
"Fission reaction rate in detector: $r \\left\\lbrack\\frac{\\text{reaction}}{\\text{src}}\\right\\rbrack * \\textcolor{red}{S} \\left\\lbrack\\frac{\\text{src}}{\\text{s}}\\right\\rbrack = d \\left\\lbrack\\frac{\\text{reaction}}{\\text{s}}\\right\\rbrack $"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "15966abd-127d-4608-a732-0f5d7ecbcc6e",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Energy released per fission\n",
|
|
"\n",
|
|
"To compute the energy released per fission, we will use two different scores - the `kappa-fission` score, which tallies the recoverable energy release from fission [eV/src], and the `fission` score, which tallies the fission rate [fission reactions/src]. The energy released per fission, averaged over all fission events, is simply the `kappa-fission` score divided by the `fission` score. We start with this tally, because if your quantity of interest is a ratio of two other tallies, we may not have to do any normalization with a source rate.\n",
|
|
"\n",
|
|
"Because we want this information talllied throughout the model, a \"global\" tally, no filters need to be applied."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "edfd4a42-4fcf-4191-94ae-00890d0e45b6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Tally\n",
|
|
"\tID =\t1\n",
|
|
"\tName =\t\n",
|
|
"\tFilters =\t\n",
|
|
"\tNuclides =\t\n",
|
|
"\tScores =\t['fission', 'kappa-fission']\n",
|
|
"\tEstimator =\tNone\n",
|
|
"\tMultiply dens. =\tTrue"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"fission_tally = openmc.Tally()\n",
|
|
"fission_tally.scores = ['fission', 'kappa-fission']\n",
|
|
"fission_tally"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "418f543a-5705-483a-ac0b-0e66fd76a6cf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.tallies = openmc.Tallies([fission_tally])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6f7f618b-0406-49be-a6ad-13ed784e05c0",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n",
|
|
"After adjusting the default settings for number of particles and batches on the model we'll run it and examine the data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "ae72a8ed-8680-431d-b51a-3df11d3960aa",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
|
|
" License | https://docs.openmc.org/en/latest/license.html\n",
|
|
" Version | 0.15.3\n",
|
|
" Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
|
|
" Date/Time | 2025-11-25 12:55:16\n",
|
|
" OpenMP Threads | 2\n",
|
|
"\n",
|
|
" Reading model XML file 'model.xml' ...\n",
|
|
" Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
|
|
" Reading cross sections XML file...\n",
|
|
" Reading U234 from /home/ubuntu/data/endfb71_hdf5/U234.h5\n",
|
|
" Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
|
|
" Reading U238 from /home/ubuntu/data/endfb71_hdf5/U238.h5\n",
|
|
" Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
|
|
" Reading Zr90 from /home/ubuntu/data/endfb71_hdf5/Zr90.h5\n",
|
|
" Reading Zr91 from /home/ubuntu/data/endfb71_hdf5/Zr91.h5\n",
|
|
" Reading Zr92 from /home/ubuntu/data/endfb71_hdf5/Zr92.h5\n",
|
|
" Reading Zr94 from /home/ubuntu/data/endfb71_hdf5/Zr94.h5\n",
|
|
" Reading Zr96 from /home/ubuntu/data/endfb71_hdf5/Zr96.h5\n",
|
|
" Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
|
|
" Reading B10 from /home/ubuntu/data/endfb71_hdf5/B10.h5\n",
|
|
" Reading B11 from /home/ubuntu/data/endfb71_hdf5/B11.h5\n",
|
|
" Reading c_H_in_H2O from /home/ubuntu/data/endfb71_hdf5/c_H_in_H2O.h5\n",
|
|
" Minimum neutron data temperature: 294 K\n",
|
|
" Maximum neutron data temperature: 294 K\n",
|
|
" Preparing distributed cell instances...\n",
|
|
" Writing summary.h5 file...\n",
|
|
" Maximum neutron transport energy: 20000000 eV for U235\n",
|
|
" Initializing source particles...\n",
|
|
"\n",
|
|
" ====================> K EIGENVALUE SIMULATION <====================\n",
|
|
"\n",
|
|
" Bat./Gen. k Average k\n",
|
|
" ========= ======== ====================\n",
|
|
" 1/1 1.16175\n",
|
|
" 2/1 1.24360\n",
|
|
" 3/1 1.22054\n",
|
|
" 4/1 1.16121\n",
|
|
" 5/1 1.22086\n",
|
|
" 6/1 1.13842\n",
|
|
" 7/1 1.17810\n",
|
|
" 8/1 1.22061\n",
|
|
" 9/1 1.14009\n",
|
|
" 10/1 1.23238\n",
|
|
" 11/1 1.21093\n",
|
|
" 12/1 1.23695 1.22394 +/- 0.01301\n",
|
|
" 13/1 1.17767 1.20852 +/- 0.01716\n",
|
|
" 14/1 1.16627 1.19795 +/- 0.01609\n",
|
|
" 15/1 1.07831 1.17403 +/- 0.02698\n",
|
|
" 16/1 1.09983 1.16166 +/- 0.02526\n",
|
|
" 17/1 1.19357 1.16622 +/- 0.02183\n",
|
|
" 18/1 1.17591 1.16743 +/- 0.01894\n",
|
|
" 19/1 1.17271 1.16802 +/- 0.01672\n",
|
|
" 20/1 1.10673 1.16189 +/- 0.01616\n",
|
|
" 21/1 1.16555 1.16222 +/- 0.01462\n",
|
|
" 22/1 1.23973 1.16868 +/- 0.01483\n",
|
|
" 23/1 1.16415 1.16833 +/- 0.01364\n",
|
|
" 24/1 1.23280 1.17294 +/- 0.01344\n",
|
|
" 25/1 1.16381 1.17233 +/- 0.01253\n",
|
|
" 26/1 1.18951 1.17340 +/- 0.01177\n",
|
|
" 27/1 1.11374 1.16989 +/- 0.01160\n",
|
|
" 28/1 1.21397 1.17234 +/- 0.01121\n",
|
|
" 29/1 1.08256 1.16762 +/- 0.01161\n",
|
|
" 30/1 1.23904 1.17119 +/- 0.01158\n",
|
|
" 31/1 1.14425 1.16990 +/- 0.01109\n",
|
|
" 32/1 1.20935 1.17170 +/- 0.01072\n",
|
|
" 33/1 1.16389 1.17136 +/- 0.01025\n",
|
|
" 34/1 1.19602 1.17239 +/- 0.00987\n",
|
|
" 35/1 1.19306 1.17321 +/- 0.00950\n",
|
|
" 36/1 1.16684 1.17297 +/- 0.00913\n",
|
|
" 37/1 1.15748 1.17239 +/- 0.00880\n",
|
|
" 38/1 1.12755 1.17079 +/- 0.00863\n",
|
|
" 39/1 1.13591 1.16959 +/- 0.00842\n",
|
|
" 40/1 1.18270 1.17003 +/- 0.00814\n",
|
|
" 41/1 1.18595 1.17054 +/- 0.00789\n",
|
|
" 42/1 1.11832 1.16891 +/- 0.00782\n",
|
|
" 43/1 1.16320 1.16874 +/- 0.00758\n",
|
|
" 44/1 1.15004 1.16819 +/- 0.00737\n",
|
|
" 45/1 1.06354 1.16520 +/- 0.00776\n",
|
|
" 46/1 1.15839 1.16501 +/- 0.00754\n",
|
|
" 47/1 1.14766 1.16454 +/- 0.00735\n",
|
|
" 48/1 1.16228 1.16448 +/- 0.00715\n",
|
|
" 49/1 1.10533 1.16296 +/- 0.00713\n",
|
|
" 50/1 1.16454 1.16300 +/- 0.00695\n",
|
|
" Creating state point statepoint.50.h5...\n",
|
|
"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
|
"\n",
|
|
" Total time for initialization = 6.7147e-01 seconds\n",
|
|
" Reading cross sections = 4.8942e-01 seconds\n",
|
|
" Total time in simulation = 2.6130e+00 seconds\n",
|
|
" Time in transport only = 2.5971e+00 seconds\n",
|
|
" Time in inactive batches = 4.8042e-01 seconds\n",
|
|
" Time in active batches = 2.1325e+00 seconds\n",
|
|
" Time synchronizing fission bank = 2.4960e-03 seconds\n",
|
|
" Sampling source sites = 2.0921e-03 seconds\n",
|
|
" SEND/RECV source sites = 3.9472e-04 seconds\n",
|
|
" Time accumulating tallies = 7.8659e-03 seconds\n",
|
|
" Time writing statepoints = 2.0748e-03 seconds\n",
|
|
" Total time for finalization = 9.6125e-05 seconds\n",
|
|
" Total time elapsed = 3.2889e+00 seconds\n",
|
|
" Calculation Rate (inactive) = 20815.2 particles/second\n",
|
|
" Calculation Rate (active) = 18756.9 particles/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 1.16092 +/- 0.00560\n",
|
|
" k-effective (Track-length) = 1.16300 +/- 0.00695\n",
|
|
" k-effective (Absorption) = 1.15140 +/- 0.00505\n",
|
|
" Combined k-effective = 1.15553 +/- 0.00437\n",
|
|
" Leakage Fraction = 0.00000 +/- 0.00000\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.settings.batches = 50\n",
|
|
"model.settings.inactive = 10\n",
|
|
"model.settings.particles = 1000\n",
|
|
"statepoint = model.run(apply_tally_results=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "038ed849-0495-4fa4-9f85-51c9683d8526",
|
|
"metadata": {},
|
|
"source": [
|
|
"If we list our current directory, we see that several new files have been created as a result of this run: `summary.h5`, `tallies.out`, and `statepoint.50.h5`. The summary file contains information about the simulation's setup (geometry, materials, meshes, etc.) in an HDF5 format. The `tallies.out` file contains a text output of all user-specified tallies for the simulation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "b4a6d2aa-148c-4217-9682-c56758093d00",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" ============================> TALLY 1 <============================\n",
|
|
"\n",
|
|
" Total Material\n",
|
|
" Fission Rate 0.472831 +/- 0.00284146\n",
|
|
" Kappa-Fission Rate 9.15736e+07 +/- 549766\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"!cat tallies.out"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1ff60bff-aeb0-496c-b656-d712c5778045",
|
|
"metadata": {},
|
|
"source": [
|
|
"This can be useful to quickly look at simple tally results, but isn't a great format to post-process simulation data. For that we'll look to the statepoint file. The statepoint file contains information about simulation results including tally specifications and data. The location of this statepoint file was provided to us by the `model.run()` command."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "c53c5630-07eb-4308-be68-6ffc093f6131",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/home/ubuntu/openmc-nea-course/notebooks/tallies-i/statepoint.50.h5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(statepoint)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "99eed91b-5e91-4320-8e7a-51c68c28f4a6",
|
|
"metadata": {},
|
|
"source": [
|
|
"To extract information from the statepoint file we'll create an `openmc.StatePoint` object. The `statepoint.get_tally` function will search for tallies by scores, filters, nuclides, ids, and return the closest match. Exact matches can be specified as well."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "687cd96d-6035-4f01-b493-e42f0834fff3",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"with openmc.StatePoint(statepoint) as sp:\n",
|
|
" tally_by_scores = sp.get_tally(scores=['fission'])\n",
|
|
" tally_by_id = sp.get_tally(id=fission_tally.id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "de12a615-a9d1-4917-a80b-c75a1226797f",
|
|
"metadata": {},
|
|
"source": [
|
|
"If we print the tally objects returned, we see that they indeed match the tally specification we generated above. The `tally_by_scores` represents the tally object, so even though we only searched for the fission score, the quantity we extracted includes the tally as a whole entity (not just the one score)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "ffb2a7f3-f575-4104-b537-8d04607df314",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"openmc.tallies.Tally"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"type(fiss)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "4ee04505-a26f-4f1f-a21d-ee8520cea00a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "fa4f4cc8-7458-47a7-bd91-102b473c87d7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Tally\n",
|
|
"\tID =\t1\n",
|
|
"\tName =\t\n",
|
|
"\tFilters =\t\n",
|
|
"\tNuclides =\ttotal\n",
|
|
"\tScores =\t['fission', 'kappa-fission']\n",
|
|
"\tEstimator =\tNone\n",
|
|
"\tMultiply dens. =\tTrue"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"fission_tally"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "4338ff09-ac43-4896-bef6-9d0bdee19145",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "dab472ae",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(1, 1, 2)"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# filter, nuclide, score\n",
|
|
"tally_by_id.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "90855429-1b53-414a-a3a7-f7bdb51fb6fd",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2e102428-a43f-4dd7-8f27-84abf9f7ea67",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-info\">\n",
|
|
"<b>A quick aside on how statepoint objects interact with summary files:</b>\n",
|
|
"\n",
|
|
"\n",
|
|
"The `openmc.statepoint` object will read information from the `summary.h5` file if one is present, keeping that file open in the Python interpreter. The open `summary.h5` file can interfere with the initialization of subsequent OpenMC simulations. It is recommended that information be extracted from statepoints within a [context manager](https://book.pythontips.com/en/latest/context_managers.html) as we do here. Alternatively, making sure to call the `openmc.StatePoint.close` method will work also. For more details please look to the [relevant section in the user's guide](https://docs.openmc.org/en/stable/usersguide/troubleshoot.html#runtimeerror-failed-to-open-hdf5-file-with-mode-w-summary-h5). \n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "eade3a16-86ce-4717-a0ef-c403e9bce2fe",
|
|
"metadata": {},
|
|
"source": [
|
|
"To compute the energy released per fission event, we can simply take the tallied energy released per fission and divide it by the fission rate. `squeeze()` is a python function that will eliminate axes of length 1 (for us, these are the slices pertaining to material filters or nuclides)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "677fd032",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fission_rate = fission_tally.get_values(scores=['fission']).squeeze()\n",
|
|
"kappa_fission_rate = fission_tally.get_values(scores=['kappa-fission']).squeeze()\n",
|
|
"\n",
|
|
"ev_per_fission = kappa_fission_rate / fission_rate"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "3ce27d43-ebd2-44c1-a00d-f963d38ee294",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" ============================> TALLY 1 <============================\n",
|
|
"\n",
|
|
" Total Material\n",
|
|
" Fission Rate 0.472831 +/- 0.00284146\n",
|
|
" Kappa-Fission Rate 9.15736e+07 +/- 549766\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"!cat tallies.out"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "64d927da-11af-4dea-9478-c3fb77f77c29",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"MeV per fission: 193.67064439662153\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"mev_per_fission = ev_per_fission * 1e-6\n",
|
|
"print('MeV per fission: ', mev_per_fission)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2dcfdf54",
|
|
"metadata": {},
|
|
"source": [
|
|
"For a water reactor with U235 as the only fissioning isotope this is about what we would expect: ~193 MeV! \n",
|
|
"\n",
|
|
"### Uncertainties\n",
|
|
"\n",
|
|
"When dealing with tallies, we must remember that every output of a Monte Carlo simulation is uncertain -- it is associated with a mean and a standard deviation. Whenever you present results from a Monte Carlo simulation, you should *always* present the mean value *and* its standard deviation. To obtain the standard deviation associated with our MeV/fission estimation, we can use the Python [uncertainties](https://pythonhosted.org/uncertainties/) module, while also using the `value='std_dev'` option when fetching the tally values to get the standard deviations."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "f9863a49",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fission_rate_std_dev = fission_tally.get_values(scores=['fission'], value='std_dev').squeeze()\n",
|
|
"kappa_fission_rate_std_dev = fission_tally.get_values(scores=['kappa-fission'], value='std_dev').squeeze()\n",
|
|
"\n",
|
|
"from uncertainties import ufloat\n",
|
|
"f = ufloat(fission_rate, fission_rate_std_dev)\n",
|
|
"kf = ufloat(kappa_fission_rate, kappa_fission_rate_std_dev)\n",
|
|
"\n",
|
|
"ratio = kf / f *1e-6"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "e49255f3-ff6c-4f34-97a9-0fe757130859",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"MeV per fission: 193.670644+/-1.645129\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print('MeV per fission: {:.6f}'.format(ratio))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ec85bfe1-c305-4154-908c-d0e6af45f565",
|
|
"metadata": {},
|
|
"source": [
|
|
"It is common to communicate these uncertainties in terms of a \"relative error,\" or the standard deviation divided by the mean."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "ba76d87c-f6e9-4c30-925a-f7358aed05a0",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Relative error (%): 0.85\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"rel_err = ratio.std_dev / ratio.nominal_value\n",
|
|
"print('Relative error (%): {:.2f}'.format(100 * rel_err))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0fa947ba-b916-4f6e-bad1-c2d7e8f72da6",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plot the neutron flux spectrum\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "09db31d5-08c6-49d3-be36-97db7ef98cd1",
|
|
"metadata": {},
|
|
"source": [
|
|
"Plotting a neutron flux spectrum is a very useful way to understand the physical processes happening to neutrons - the energy at which they exist is determined by scattering reactions (to lower energies), capture reactions (removing them from the population), as well as their birth distribution (such as from fission or fusion). It is often an engineer's objective to control the energies at which neutrons are predominantly at in their system in order to encourage certain reactions.\n",
|
|
"\n",
|
|
"To plot the neutron flux spectrum, we'll be applying a tally with an energy filter and a score. OpenMC's data module contains different group structures. For this problem we'll use the CASMO-70 group structure. An energy filter can easily be created from a pre-defined group structure in OpenMC as follows:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "45919f51-f2bf-4654-8a8a-fe9f315376d2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"dict_keys(['CASMO-2', 'CASMO-4', 'CASMO-8', 'CASMO-16', 'CASMO-25', 'ECCO-33', 'CASMO-40', 'VITAMIN-J-42', 'SCALE-44', 'MPACT-51', 'MPACT-60', 'MPACT-69', 'CASMO-70', 'XMAS-172', 'VITAMIN-J-175', 'SCALE-252', 'TRIPOLI-315', 'SHEM-361', 'LLNL-616', 'CCFE-709', 'SCALE-999', 'UKAEA-1102', 'ECCO-1968'])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(openmc.mgxs.GROUP_STRUCTURES.keys())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "a9aaae47-c6b5-42e0-8be5-a1fdf84180c2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"energy_filter = openmc.EnergyFilter.from_group_structure('CASMO-70')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "5f7b3f68-19d6-4928-962a-ce7032a38c14",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"70"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(energy_filter.bins)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "190b2aa0-bc1c-499d-a8e0-f9e531a33b8e",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now we'll apply this tally and re-run the problem. We can leave the other tally we added earlier by appending our additional tally."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "8e8e5be2-3055-4027-9ee2-e9eb19fccaa2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"spectrum_tally = openmc.Tally()\n",
|
|
"spectrum_tally.scores = ['flux']\n",
|
|
"spectrum_tally.filters = [energy_filter]\n",
|
|
"model.tallies += [spectrum_tally]\n",
|
|
"\n",
|
|
"statepoint = model.run(output=False, apply_tally_results=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c8b2b3bb-5205-40b1-a084-eca719f6c595",
|
|
"metadata": {},
|
|
"source": [
|
|
"As before, we can fetch our tally of interest by finding the closest match based on the ID, scores, and/or filters."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "00fb7c45-1e73-44e7-8f3f-0dee18cb381e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"spectrum = spectrum_tally.mean.squeeze()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "0f754d3d-fce3-43d7-bae1-36a3a700962c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b0796bc9-60a9-4499-8765-939fd1376ba3",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now to plot the spectrum, we will plot the neutron flux per unit lethargy (a common way to visualize neutron flux). We will plot the flux spectrum with a point at the lower energy bin value of each bin (you could alternatively plot in the midpoint or the right point defining each bin).\n",
|
|
"\n",
|
|
"$\\text{lethargy bin width}\\equiv\\ln\\frac{E_i}{E_{i-1}}$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"id": "90a02970-3251-4713-a3ff-85cd14a71c8e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/home/ubuntu/openmc/openmc/filter.py:1653: RuntimeWarning: divide by zero encountered in divide\n",
|
|
" return np.log10(self.bins[:, 1]/self.bins[:, 0])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"unit_lethargy = energy_filter.lethargy_bin_width"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "391dfc59",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "fefb9e46",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.step(np.unique(energy_filter.bins)[:-1], spectrum / unit_lethargy)\n",
|
|
"plt.xscale('log')\n",
|
|
"plt.xlabel('Energy (eV)')\n",
|
|
"plt.ylabel('Flux per unit lethargy (particle-cm/src/lethargy)')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "79d7963e-cc07-4df4-8299-0871f327da9f",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Normalizing Tallies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c2283976-8e63-4f88-a65c-40c8a041db47",
|
|
"metadata": {},
|
|
"source": [
|
|
"Note that the units of flux in the above plot are in $\\frac{\\text{particle-cm}}{\\text{src}}$ per unit lethargy. As is the case with many values tallied by Monte Carlo codes, the value of the flux does not account for volume and is in terms of the number of source particles emitted in the \"real world\". To generate this same plot in terms of absolute flux units ($\\frac{\\text{particle}}{\\text{cm}^{2}-\\text{s}}$) we'll need to normalize this tally by:\n",
|
|
"\n",
|
|
" - the volume of the region the tally covers\n",
|
|
" - the number of source particle emitted per second, $S$\n",
|
|
"\n",
|
|
"In this case, the volume of the region is the volume of the entire pincell, because we did not add any spatial filters. Because we're working with a 2-D model, we'll get units that give us the flux per unit length of the pincell in the axial direction. For simplicity, we'll assume that our pincell is 1 cm in height to make life easier.\n",
|
|
"\n",
|
|
"In order to obtain the volumes of the cells, we can\n",
|
|
"\n",
|
|
"- Code in the volume of the region analytically; this is trivial for our pincell ($p^2H$, where $p$ is the pitch), but may not be possible for more general shapes.\n",
|
|
"- If it is relevant to your problem, you can use a bounding box which covers the relevant domain.\n",
|
|
"- Perform a stochastic volume calculation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "db2002fd-7734-4b10-b2e7-bcf26c29805b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bb = model.geometry.bounding_box"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "be6b78c4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"np.float64(1.5876000000000001)"
|
|
]
|
|
},
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"bb.lower_left[-1] = 0\n",
|
|
"bb.upper_right[-1] = 1\n",
|
|
"volume = np.prod(bb.upper_right - bb.lower_left)\n",
|
|
"volume"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"id": "ebf90614-8543-48f2-abc6-5df40cb5992b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1.5876000000000001"
|
|
]
|
|
},
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"1.26**2*1\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"id": "2095eac7-f1d4-4207-bb30-749da16d84ce",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"np.float64(1.5876000000000001)"
|
|
]
|
|
},
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"bb.volume"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6de0b475-9fb5-42da-987c-f13c7182f1e6",
|
|
"metadata": {},
|
|
"source": [
|
|
"Determining the number of source particles per second requires us to impose knowledge of some reaction rate. Let's choose to specify the total power produced in the pincell. The quantity $S$ is what we seek in order to multiply against our flux tally. So, we need to have a heating tally over the same domain as the flux tally to give us $r$. Luckily, we already have this tally added to our simulation from the earlier portion.\n",
|
|
"\n",
|
|
"Heating tally: $r \\left\\lbrack\\frac{\\text{eV}}{\\text{src}}\\right\\rbrack * \\textcolor{red}{S} \\left\\lbrack\\frac{\\text{src}}{\\text{s}}\\right\\rbrack = p \\left\\lbrack\\frac{J}{\\text{s}}\\right\\rbrack * \\frac{1}{1.602\\times10^{-19}} \\left\\lbrack\\frac{eV}{\\text{J}}\\right\\rbrack $"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"id": "38246dd3-b34a-4058-a516-ea25f502169f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"r = fission_tally.get_values(scores=['kappa-fission']).squeeze()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 60,
|
|
"id": "8303295a-dcc0-48bf-b42b-e1ad61592df8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"openmc.tallies.Tally"
|
|
]
|
|
},
|
|
"execution_count": 60,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"new_t = fission_tally.get_slice(scores=['kappa-fission'])\n",
|
|
"type(new_t)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"id": "b90636e8-b154-4ba9-98f9-08c644077920",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"27266368593435.87\n",
|
|
"Neutron source: 2.73e+13 n/s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"neutron_source = 400 / 1.602e-19 / r\n",
|
|
"print(neutron_source)\n",
|
|
"\n",
|
|
"print(f'Neutron source: {neutron_source:.2e} n/s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "394d3cf1-d913-4b84-a9be-30319916b5ef",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now use this information to normalize our flux values and reproduce our plot in more standard units. The shape of the plot is identical to what we obtained earlier, as all that we've done is scale the y-axis into more conventional units for flux. We can also plot $\\pm3\\sigma$ on our plot, though the error bars are visibly small due to the large range in values shown on the y-axis."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 63,
|
|
"id": "b55565bb-736e-4f98-a4dc-5accedba6ca8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"normalized_spectrum = spectrum * neutron_source / volume"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 64,
|
|
"id": "b5d071ed-cfd8-4bac-bfc4-8d6a30440602",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"spectrum_std_dev = spectrum_tally.std_dev.squeeze() * neutron_source / volume"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 65,
|
|
"id": "408bf824-47ef-4413-b2b8-778615cc75f6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"e = np.unique(energy_filter.bins)\n",
|
|
"plt.step(e[:-1], normalized_spectrum / unit_lethargy, where='mid')\n",
|
|
"\n",
|
|
"energy_midpoints = [e[i] for i in range(len(e) - 1)]\n",
|
|
"plt.errorbar(e[:-1], normalized_spectrum / unit_lethargy, yerr=3*spectrum_std_dev, capsize=2, fmt='None')\n",
|
|
"plt.xscale('log')\n",
|
|
"plt.xlabel('Energy (eV)')\n",
|
|
"plt.ylabel('Flux per unit lethargy (1/cm$^2$/s/lethargy)')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "00c2f4af-3370-4f4d-9d1b-4b7278bc55e0",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Reaction Types by Material"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2889624c-c7bb-4224-b2cb-e13204da10f1",
|
|
"metadata": {},
|
|
"source": [
|
|
"Looking at the different reaction types by material will require a material filter and the set of reaction types we want to score. For this example, we'll be scoring absorption, scattering and fission in each material. To start, we'll create a material filter. We'll add all our materials to this filter so that we obtain reaction rates separately for each."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"id": "b270ef21-84d1-4ec7-9b7e-f83ae1813687",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[Material\n",
|
|
" \tID =\t1\n",
|
|
" \tName =\tUO2 (2.4%)\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t10.29769 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tTrue\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tNuclides \n",
|
|
" \tU234 =\t4.4843e-06 [ao]\n",
|
|
" \tU235 =\t0.00055815 [ao]\n",
|
|
" \tU238 =\t0.022408 [ao]\n",
|
|
" \tO16 =\t0.045829 [ao],\n",
|
|
" Material\n",
|
|
" \tID =\t2\n",
|
|
" \tName =\tZircaloy\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t6.55 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tFalse\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tNuclides \n",
|
|
" \tZr90 =\t0.021827 [ao]\n",
|
|
" \tZr91 =\t0.00476 [ao]\n",
|
|
" \tZr92 =\t0.0072758 [ao]\n",
|
|
" \tZr94 =\t0.0073734 [ao]\n",
|
|
" \tZr96 =\t0.0011879 [ao],\n",
|
|
" Material\n",
|
|
" \tID =\t3\n",
|
|
" \tName =\tHot borated water\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t0.740582 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tFalse\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tS(a,b) =\t('c_H_in_H2O', 1.0)\n",
|
|
" \tNuclides \n",
|
|
" \tH1 =\t0.049457 [ao]\n",
|
|
" \tO16 =\t0.024672 [ao]\n",
|
|
" \tB10 =\t8.0042e-06 [ao]\n",
|
|
" \tB11 =\t3.2218e-05 [ao]]"
|
|
]
|
|
},
|
|
"execution_count": 66,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.materials"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 68,
|
|
"id": "7a8b416a-4183-4266-880a-fb32f85d6508",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"material_filter = openmc.MaterialFilter(model.materials)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 69,
|
|
"id": "e7a1e5b4-1851-4042-80da-cfbd7df66dca",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"material_tally = openmc.Tally()\n",
|
|
"material_tally.filters = [material_filter]\n",
|
|
"material_tally.scores = ['absorption', 'scatter', 'fission']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 70,
|
|
"id": "15c27cc5-0dd8-48d9-8115-08f57c4447c0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.tallies += [material_tally]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"id": "49b068fc-9bdc-4bdc-9567-5f55ec16bf7d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"statepoint = model.run(apply_tally_results=True, output=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a26a49e4-8d30-4362-b897-6c6263e5592a",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now we'll gather information from the statepoint file about each score we applied to the tally. With multiple scores and materials, we'll use a Pandas data frame to view the results in a more coherent manner."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 72,
|
|
"id": "a191ffb7-5349-4522-bc0f-8a6405295606",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df = material_tally.get_pandas_dataframe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0fa5d495-d3ac-46bf-aabe-2aa5c6ac72c7",
|
|
"metadata": {},
|
|
"source": [
|
|
"Each score has three values -- one for each material in the model."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 73,
|
|
"id": "7c1ebec5-aa76-484c-9470-c06018eb2b07",
|
|
"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>material</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>score</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.846019</td>\n",
|
|
" <td>0.003977</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>5.219461</td>\n",
|
|
" <td>0.013513</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.472831</td>\n",
|
|
" <td>0.002841</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.010171</td>\n",
|
|
" <td>0.000113</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>1.489722</td>\n",
|
|
" <td>0.004175</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.149320</td>\n",
|
|
" <td>0.000868</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>23.177875</td>\n",
|
|
" <td>0.069286</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" material nuclide score mean std. dev.\n",
|
|
"0 1 total absorption 8.46e-01 3.98e-03\n",
|
|
"1 1 total scatter 5.22e+00 1.35e-02\n",
|
|
"2 1 total fission 4.73e-01 2.84e-03\n",
|
|
"3 2 total absorption 1.02e-02 1.13e-04\n",
|
|
"4 2 total scatter 1.49e+00 4.18e-03\n",
|
|
"5 2 total fission 0.00e+00 0.00e+00\n",
|
|
"6 3 total absorption 1.49e-01 8.68e-04\n",
|
|
"7 3 total scatter 2.32e+01 6.93e-02\n",
|
|
"8 3 total fission 0.00e+00 0.00e+00"
|
|
]
|
|
},
|
|
"execution_count": 73,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "44e26c58-107f-4a37-a170-534a410459aa",
|
|
"metadata": {},
|
|
"source": [
|
|
"First, we'll add a new column to the data frame with normalized results."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 75,
|
|
"id": "06f78164-f9c4-41f4-8070-535656e0f66e",
|
|
"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>material</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>score</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" <th>normalized mean (rxn/s)</th>\n",
|
|
" <th>normalized mean (rxn/s/cm3)</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.846019</td>\n",
|
|
" <td>0.003977</td>\n",
|
|
" <td>2.306787e+13</td>\n",
|
|
" <td>1.453003e+13</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>5.219461</td>\n",
|
|
" <td>0.013513</td>\n",
|
|
" <td>1.423158e+14</td>\n",
|
|
" <td>8.964208e+13</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.472831</td>\n",
|
|
" <td>0.002841</td>\n",
|
|
" <td>1.289240e+13</td>\n",
|
|
" <td>8.120684e+12</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.010171</td>\n",
|
|
" <td>0.000113</td>\n",
|
|
" <td>2.773140e+11</td>\n",
|
|
" <td>1.746750e+11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>1.489722</td>\n",
|
|
" <td>0.004175</td>\n",
|
|
" <td>4.061930e+13</td>\n",
|
|
" <td>2.558535e+13</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.149320</td>\n",
|
|
" <td>0.000868</td>\n",
|
|
" <td>4.071405e+12</td>\n",
|
|
" <td>2.564503e+12</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>23.177875</td>\n",
|
|
" <td>0.069286</td>\n",
|
|
" <td>6.319765e+14</td>\n",
|
|
" <td>3.980703e+14</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" material nuclide score mean std. dev. normalized mean (rxn/s) \\\n",
|
|
"0 1 total absorption 8.46e-01 3.98e-03 2.31e+13 \n",
|
|
"1 1 total scatter 5.22e+00 1.35e-02 1.42e+14 \n",
|
|
"2 1 total fission 4.73e-01 2.84e-03 1.29e+13 \n",
|
|
"3 2 total absorption 1.02e-02 1.13e-04 2.77e+11 \n",
|
|
"4 2 total scatter 1.49e+00 4.18e-03 4.06e+13 \n",
|
|
"5 2 total fission 0.00e+00 0.00e+00 0.00e+00 \n",
|
|
"6 3 total absorption 1.49e-01 8.68e-04 4.07e+12 \n",
|
|
"7 3 total scatter 2.32e+01 6.93e-02 6.32e+14 \n",
|
|
"8 3 total fission 0.00e+00 0.00e+00 0.00e+00 \n",
|
|
"\n",
|
|
" normalized mean (rxn/s/cm3) \n",
|
|
"0 1.45e+13 \n",
|
|
"1 8.96e+13 \n",
|
|
"2 8.12e+12 \n",
|
|
"3 1.75e+11 \n",
|
|
"4 2.56e+13 \n",
|
|
"5 0.00e+00 \n",
|
|
"6 2.56e+12 \n",
|
|
"7 3.98e+14 \n",
|
|
"8 0.00e+00 "
|
|
]
|
|
},
|
|
"execution_count": 75,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df['normalized mean (rxn/s/cm3)'] = df['mean'] * neutron_source / volume\n",
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4104a62e-7039-4acb-b6d9-19513611ed23",
|
|
"metadata": {},
|
|
"source": [
|
|
"We'll add a new entry in the dataframe for our material names to make plotting easier."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"id": "65f57455-efee-4fc5-b8af-67b4c6ebff89",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[Material\n",
|
|
" \tID =\t1\n",
|
|
" \tName =\tUO2 (2.4%)\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t10.29769 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tTrue\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tNuclides \n",
|
|
" \tU234 =\t4.4843e-06 [ao]\n",
|
|
" \tU235 =\t0.00055815 [ao]\n",
|
|
" \tU238 =\t0.022408 [ao]\n",
|
|
" \tO16 =\t0.045829 [ao],\n",
|
|
" Material\n",
|
|
" \tID =\t2\n",
|
|
" \tName =\tZircaloy\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t6.55 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tFalse\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tNuclides \n",
|
|
" \tZr90 =\t0.021827 [ao]\n",
|
|
" \tZr91 =\t0.00476 [ao]\n",
|
|
" \tZr92 =\t0.0072758 [ao]\n",
|
|
" \tZr94 =\t0.0073734 [ao]\n",
|
|
" \tZr96 =\t0.0011879 [ao],\n",
|
|
" Material\n",
|
|
" \tID =\t3\n",
|
|
" \tName =\tHot borated water\n",
|
|
" \tTemperature =\tNone\n",
|
|
" \tDensity =\t0.740582 [g/cm3]\n",
|
|
" \tVolume =\tNone [cm^3]\n",
|
|
" \tDepletable =\tFalse\n",
|
|
" \tS(a,b) Tables \n",
|
|
" \tS(a,b) =\t('c_H_in_H2O', 1.0)\n",
|
|
" \tNuclides \n",
|
|
" \tH1 =\t0.049457 [ao]\n",
|
|
" \tO16 =\t0.024672 [ao]\n",
|
|
" \tB10 =\t8.0042e-06 [ao]\n",
|
|
" \tB11 =\t3.2218e-05 [ao]]"
|
|
]
|
|
},
|
|
"execution_count": 76,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
" model.materials"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 78,
|
|
"id": "88ed5b3f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for mat_id, material in model.geometry.get_all_materials().items():\n",
|
|
" df.loc[df['material'] == mat_id, 'mat_name'] = material.name"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 79,
|
|
"id": "0580efe8-46ab-4324-8642-c255ebc1c26e",
|
|
"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>material</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>score</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" <th>normalized mean (rxn/s)</th>\n",
|
|
" <th>normalized mean (rxn/s/cm3)</th>\n",
|
|
" <th>mat_name</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.846019</td>\n",
|
|
" <td>0.003977</td>\n",
|
|
" <td>2.306787e+13</td>\n",
|
|
" <td>1.453003e+13</td>\n",
|
|
" <td>UO2 (2.4%)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>5.219461</td>\n",
|
|
" <td>0.013513</td>\n",
|
|
" <td>1.423158e+14</td>\n",
|
|
" <td>8.964208e+13</td>\n",
|
|
" <td>UO2 (2.4%)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.472831</td>\n",
|
|
" <td>0.002841</td>\n",
|
|
" <td>1.289240e+13</td>\n",
|
|
" <td>8.120684e+12</td>\n",
|
|
" <td>UO2 (2.4%)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.010171</td>\n",
|
|
" <td>0.000113</td>\n",
|
|
" <td>2.773140e+11</td>\n",
|
|
" <td>1.746750e+11</td>\n",
|
|
" <td>Zircaloy</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>1.489722</td>\n",
|
|
" <td>0.004175</td>\n",
|
|
" <td>4.061930e+13</td>\n",
|
|
" <td>2.558535e+13</td>\n",
|
|
" <td>Zircaloy</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>2</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" <td>Zircaloy</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>absorption</td>\n",
|
|
" <td>0.149320</td>\n",
|
|
" <td>0.000868</td>\n",
|
|
" <td>4.071405e+12</td>\n",
|
|
" <td>2.564503e+12</td>\n",
|
|
" <td>Hot borated water</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>scatter</td>\n",
|
|
" <td>23.177875</td>\n",
|
|
" <td>0.069286</td>\n",
|
|
" <td>6.319765e+14</td>\n",
|
|
" <td>3.980703e+14</td>\n",
|
|
" <td>Hot borated water</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>3</td>\n",
|
|
" <td>total</td>\n",
|
|
" <td>fission</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" <td>Hot borated water</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" material nuclide score mean std. dev. normalized mean (rxn/s) \\\n",
|
|
"0 1 total absorption 8.46e-01 3.98e-03 2.31e+13 \n",
|
|
"1 1 total scatter 5.22e+00 1.35e-02 1.42e+14 \n",
|
|
"2 1 total fission 4.73e-01 2.84e-03 1.29e+13 \n",
|
|
"3 2 total absorption 1.02e-02 1.13e-04 2.77e+11 \n",
|
|
"4 2 total scatter 1.49e+00 4.18e-03 4.06e+13 \n",
|
|
"5 2 total fission 0.00e+00 0.00e+00 0.00e+00 \n",
|
|
"6 3 total absorption 1.49e-01 8.68e-04 4.07e+12 \n",
|
|
"7 3 total scatter 2.32e+01 6.93e-02 6.32e+14 \n",
|
|
"8 3 total fission 0.00e+00 0.00e+00 0.00e+00 \n",
|
|
"\n",
|
|
" normalized mean (rxn/s/cm3) mat_name \n",
|
|
"0 1.45e+13 UO2 (2.4%) \n",
|
|
"1 8.96e+13 UO2 (2.4%) \n",
|
|
"2 8.12e+12 UO2 (2.4%) \n",
|
|
"3 1.75e+11 Zircaloy \n",
|
|
"4 2.56e+13 Zircaloy \n",
|
|
"5 0.00e+00 Zircaloy \n",
|
|
"6 2.56e+12 Hot borated water \n",
|
|
"7 3.98e+14 Hot borated water \n",
|
|
"8 0.00e+00 Hot borated water "
|
|
]
|
|
},
|
|
"execution_count": 79,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 83,
|
|
"id": "83127c2a-3fcd-480c-9868-895124159238",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='mat_name', ylabel='fissions / s'>"
|
|
]
|
|
},
|
|
"execution_count": 83,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fission_df = df[df['score'] == 'fission']\n",
|
|
"fission_df.plot('mat_name', 'normalized mean (rxn/s)', kind='bar', ylabel='fissions / s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 84,
|
|
"id": "93ce1de1-f9d2-4df9-882a-e25181dea02a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='mat_name', ylabel='scatters / s'>"
|
|
]
|
|
},
|
|
"execution_count": 84,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"scatter_df = df[df['score'] == 'scatter']\n",
|
|
"scatter_df.plot('mat_name', 'mean', kind='bar', ylabel='scatters / s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 85,
|
|
"id": "7d77acb4-176c-4f9a-b177-42bd565dff60",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='mat_name', ylabel='absorptions / s'>"
|
|
]
|
|
},
|
|
"execution_count": 85,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"absorption_df = df[df['score'] == 'absorption']\n",
|
|
"absorption_df.plot('mat_name', 'mean', kind='bar', ylabel='absorptions / s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "20d0e66c-5e25-43e1-aca0-bcf948e5d6ea",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Tally Triggers\n",
|
|
"\n",
|
|
"When running OpenMC, you usually want to run enough computational resources (particles, batches) to adequately reduce the statistical error in your predictions. If the tally realizations are independent of one another, then the standard deviation decreases as\n",
|
|
"\n",
|
|
"$\\sigma\\propto\\frac{1}{\\sqrt{N}}$\n",
|
|
"\n",
|
|
"You can use this approximate relationship to sketch out how many batches are required to reach a given statistical threshold - but this can be tedious and requires running OpenMC at least twice. A better way to proceed is to use a `Trigger`, which will continue running batches in OpenMC until a desired condition is met on the standard deviation, variance, and/or relative error.\n",
|
|
"\n",
|
|
"- $\\sigma<\\sigma_{tol}$\n",
|
|
"- $\\sigma^2<v_{tol}$\n",
|
|
"- $\\frac{\\sigma}{\\mu}<r_{tol}$\n",
|
|
"\n",
|
|
"Triggers can be applied to (i) any tally you create or (ii) the automatically-applied $k_{eff}$ tally which OpenMC creates internally. The approach is slightly different for each.\n",
|
|
"Let's start with a trigger on $k$. For $k$, we add a trigger using the `model.settings.keff_trigger` parameter. OpenMC will re-evaluate the projected number of batches required, assuming the central limit theorem holds, every `model.settings.trigger_batch_interval` batches. Since OpenMC could run forever, you should set the `model.settings.trigger_max_batches` to be the maximum number of batches to run (terminating at that point even if the trigger is not met); the minimum number of batches which will be run is the `model.settings.batches`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 86,
|
|
"id": "c261debc-fd91-45eb-aec1-c1e7811313d9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
|
|
" License | https://docs.openmc.org/en/latest/license.html\n",
|
|
" Version | 0.15.3\n",
|
|
" Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
|
|
" Date/Time | 2025-11-25 13:38:35\n",
|
|
" OpenMP Threads | 2\n",
|
|
"\n",
|
|
" Reading model XML file 'model.xml' ...\n",
|
|
" Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
|
|
" Reading cross sections XML file...\n",
|
|
" Reading U234 from /home/ubuntu/data/endfb71_hdf5/U234.h5\n",
|
|
" Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
|
|
" Reading U238 from /home/ubuntu/data/endfb71_hdf5/U238.h5\n",
|
|
" Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
|
|
" Reading Zr90 from /home/ubuntu/data/endfb71_hdf5/Zr90.h5\n",
|
|
" Reading Zr91 from /home/ubuntu/data/endfb71_hdf5/Zr91.h5\n",
|
|
" Reading Zr92 from /home/ubuntu/data/endfb71_hdf5/Zr92.h5\n",
|
|
" Reading Zr94 from /home/ubuntu/data/endfb71_hdf5/Zr94.h5\n",
|
|
" Reading Zr96 from /home/ubuntu/data/endfb71_hdf5/Zr96.h5\n",
|
|
" Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
|
|
" Reading B10 from /home/ubuntu/data/endfb71_hdf5/B10.h5\n",
|
|
" Reading B11 from /home/ubuntu/data/endfb71_hdf5/B11.h5\n",
|
|
" Reading c_H_in_H2O from /home/ubuntu/data/endfb71_hdf5/c_H_in_H2O.h5\n",
|
|
" Minimum neutron data temperature: 294 K\n",
|
|
" Maximum neutron data temperature: 294 K\n",
|
|
" Preparing distributed cell instances...\n",
|
|
" Writing summary.h5 file...\n",
|
|
" Maximum neutron transport energy: 20000000 eV for U235\n",
|
|
" Initializing source particles...\n",
|
|
"\n",
|
|
" ====================> K EIGENVALUE SIMULATION <====================\n",
|
|
"\n",
|
|
" Bat./Gen. k Average k\n",
|
|
" ========= ======== ====================\n",
|
|
" 1/1 1.16175\n",
|
|
" 2/1 1.24360\n",
|
|
" 3/1 1.22054\n",
|
|
" 4/1 1.16121\n",
|
|
" 5/1 1.22086\n",
|
|
" 6/1 1.13842\n",
|
|
" 7/1 1.17810\n",
|
|
" 8/1 1.22061\n",
|
|
" 9/1 1.14009\n",
|
|
" 10/1 1.23238\n",
|
|
" 11/1 1.21093\n",
|
|
" 12/1 1.23695 1.22394 +/- 0.01301\n",
|
|
" 13/1 1.17767 1.20852 +/- 0.01716\n",
|
|
" 14/1 1.16627 1.19795 +/- 0.01609\n",
|
|
" 15/1 1.07831 1.17403 +/- 0.02698\n",
|
|
" 16/1 1.09983 1.16166 +/- 0.02526\n",
|
|
" 17/1 1.19357 1.16622 +/- 0.02183\n",
|
|
" 18/1 1.17591 1.16743 +/- 0.01894\n",
|
|
" 19/1 1.17271 1.16802 +/- 0.01672\n",
|
|
" 20/1 1.10673 1.16189 +/- 0.01616\n",
|
|
" 21/1 1.16555 1.16222 +/- 0.01462\n",
|
|
" 22/1 1.23973 1.16868 +/- 0.01483\n",
|
|
" 23/1 1.16415 1.16833 +/- 0.01364\n",
|
|
" 24/1 1.23280 1.17294 +/- 0.01344\n",
|
|
" 25/1 1.16381 1.17233 +/- 0.01253\n",
|
|
" 26/1 1.18951 1.17340 +/- 0.01177\n",
|
|
" 27/1 1.11374 1.16989 +/- 0.01160\n",
|
|
" 28/1 1.21397 1.17234 +/- 0.01121\n",
|
|
" 29/1 1.08256 1.16762 +/- 0.01161\n",
|
|
" 30/1 1.23904 1.17119 +/- 0.01158\n",
|
|
" 31/1 1.14425 1.16990 +/- 0.01109\n",
|
|
" 32/1 1.20935 1.17170 +/- 0.01072\n",
|
|
" 33/1 1.16389 1.17136 +/- 0.01025\n",
|
|
" 34/1 1.19602 1.17239 +/- 0.00987\n",
|
|
" 35/1 1.19306 1.17321 +/- 0.00950\n",
|
|
" 36/1 1.16684 1.17297 +/- 0.00913\n",
|
|
" 37/1 1.15748 1.17239 +/- 0.00880\n",
|
|
" 38/1 1.12755 1.17079 +/- 0.00863\n",
|
|
" 39/1 1.13591 1.16959 +/- 0.00842\n",
|
|
" 40/1 1.18270 1.17003 +/- 0.00814\n",
|
|
" 41/1 1.18595 1.17054 +/- 0.00789\n",
|
|
" 42/1 1.11832 1.16891 +/- 0.00782\n",
|
|
" 43/1 1.16320 1.16874 +/- 0.00758\n",
|
|
" 44/1 1.15004 1.16819 +/- 0.00737\n",
|
|
" 45/1 1.06354 1.16520 +/- 0.00776\n",
|
|
" 46/1 1.15839 1.16501 +/- 0.00754\n",
|
|
" 47/1 1.14766 1.16454 +/- 0.00735\n",
|
|
" 48/1 1.16228 1.16448 +/- 0.00715\n",
|
|
" 49/1 1.10533 1.16296 +/- 0.00713\n",
|
|
" 50/1 1.16454 1.16300 +/- 0.00695\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 4.368987504189191 for eigenvalue\n",
|
|
" Creating state point statepoint.0050.h5...\n",
|
|
" 51/1 1.16891 1.16315 +/- 0.00678\n",
|
|
" 52/1 1.15034 1.16284 +/- 0.00662\n",
|
|
" 53/1 1.15664 1.16270 +/- 0.00647\n",
|
|
" 54/1 1.16504 1.16275 +/- 0.00632\n",
|
|
" 55/1 1.14136 1.16227 +/- 0.00620\n",
|
|
" 56/1 1.17928 1.16264 +/- 0.00607\n",
|
|
" 57/1 1.12541 1.16185 +/- 0.00599\n",
|
|
" 58/1 1.11615 1.16090 +/- 0.00594\n",
|
|
" 59/1 1.15534 1.16079 +/- 0.00582\n",
|
|
" 60/1 1.09757 1.15952 +/- 0.00584\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.9498034108740447 for eigenvalue\n",
|
|
" 61/1 1.26620 1.16161 +/- 0.00610\n",
|
|
" 62/1 1.20238 1.16240 +/- 0.00603\n",
|
|
" 63/1 1.16499 1.16245 +/- 0.00592\n",
|
|
" 64/1 1.10054 1.16130 +/- 0.00592\n",
|
|
" 65/1 1.20794 1.16215 +/- 0.00587\n",
|
|
" 66/1 1.06515 1.16042 +/- 0.00602\n",
|
|
" 67/1 1.18871 1.16091 +/- 0.00593\n",
|
|
" 68/1 1.20047 1.16159 +/- 0.00587\n",
|
|
" 69/1 1.24030 1.16293 +/- 0.00592\n",
|
|
" 70/1 1.21042 1.16372 +/- 0.00588\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.829979378169924 for eigenvalue\n",
|
|
" 71/1 1.13997 1.16333 +/- 0.00579\n",
|
|
" 72/1 1.15732 1.16323 +/- 0.00570\n",
|
|
" 73/1 1.17533 1.16343 +/- 0.00561\n",
|
|
" 74/1 1.19163 1.16387 +/- 0.00554\n",
|
|
" 75/1 1.20901 1.16456 +/- 0.00550\n",
|
|
" 76/1 1.18692 1.16490 +/- 0.00542\n",
|
|
" 77/1 1.18630 1.16522 +/- 0.00535\n",
|
|
" 78/1 1.11168 1.16443 +/- 0.00533\n",
|
|
" 79/1 1.11102 1.16366 +/- 0.00531\n",
|
|
" 80/1 1.14841 1.16344 +/- 0.00524\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.612151883090217 for eigenvalue\n",
|
|
" 81/1 1.14892 1.16324 +/- 0.00517\n",
|
|
" 82/1 1.10115 1.16237 +/- 0.00517\n",
|
|
" 83/1 1.10318 1.16156 +/- 0.00516\n",
|
|
" 84/1 1.18776 1.16192 +/- 0.00510\n",
|
|
" 85/1 1.21406 1.16261 +/- 0.00508\n",
|
|
" 86/1 1.15678 1.16253 +/- 0.00502\n",
|
|
" 87/1 1.19323 1.16293 +/- 0.00497\n",
|
|
" 88/1 1.14028 1.16264 +/- 0.00491\n",
|
|
" 89/1 1.17943 1.16286 +/- 0.00485\n",
|
|
" 90/1 1.16974 1.16294 +/- 0.00479\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.5215493934080953 for eigenvalue\n",
|
|
" 91/1 1.18421 1.16320 +/- 0.00474\n",
|
|
" 92/1 1.13505 1.16286 +/- 0.00469\n",
|
|
" 93/1 1.14121 1.16260 +/- 0.00465\n",
|
|
" 94/1 1.15743 1.16254 +/- 0.00459\n",
|
|
" 95/1 1.11594 1.16199 +/- 0.00457\n",
|
|
" 96/1 1.14144 1.16175 +/- 0.00452\n",
|
|
" 97/1 1.19388 1.16212 +/- 0.00448\n",
|
|
" 98/1 1.11209 1.16155 +/- 0.00447\n",
|
|
" 99/1 1.24527 1.16249 +/- 0.00452\n",
|
|
" 100/1 1.20605 1.16298 +/- 0.00449\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.2151379021305027 for eigenvalue\n",
|
|
" 101/1 1.26435 1.16409 +/- 0.00458\n",
|
|
" 102/1 1.09150 1.16330 +/- 0.00460\n",
|
|
" 103/1 1.28257 1.16458 +/- 0.00473\n",
|
|
" 104/1 1.11718 1.16408 +/- 0.00470\n",
|
|
" 105/1 1.22022 1.16467 +/- 0.00469\n",
|
|
" 106/1 1.15208 1.16454 +/- 0.00464\n",
|
|
" 107/1 1.17786 1.16468 +/- 0.00460\n",
|
|
" 108/1 1.13425 1.16437 +/- 0.00456\n",
|
|
" 109/1 1.17876 1.16451 +/- 0.00452\n",
|
|
" 110/1 1.12187 1.16409 +/- 0.00449\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.1630624286002362 for eigenvalue\n",
|
|
" 111/1 1.10143 1.16347 +/- 0.00449\n",
|
|
" 112/1 1.06473 1.16250 +/- 0.00455\n",
|
|
" 113/1 1.12448 1.16213 +/- 0.00452\n",
|
|
" 114/1 1.18992 1.16240 +/- 0.00449\n",
|
|
" 115/1 1.18972 1.16266 +/- 0.00445\n",
|
|
" 116/1 1.16327 1.16266 +/- 0.00441\n",
|
|
" 117/1 1.15589 1.16260 +/- 0.00437\n",
|
|
" 118/1 1.13818 1.16237 +/- 0.00433\n",
|
|
" 119/1 1.15698 1.16232 +/- 0.00429\n",
|
|
" 120/1 1.18506 1.16253 +/- 0.00426\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.9978382496926397 for eigenvalue\n",
|
|
" 121/1 1.18601 1.16274 +/- 0.00423\n",
|
|
" 122/1 1.14438 1.16258 +/- 0.00419\n",
|
|
" 123/1 1.12646 1.16226 +/- 0.00417\n",
|
|
" 124/1 1.24083 1.16295 +/- 0.00419\n",
|
|
" 125/1 1.14156 1.16276 +/- 0.00415\n",
|
|
" 126/1 1.11405 1.16234 +/- 0.00414\n",
|
|
" 127/1 1.12817 1.16205 +/- 0.00411\n",
|
|
" 128/1 1.13402 1.16181 +/- 0.00409\n",
|
|
" 129/1 1.18585 1.16201 +/- 0.00406\n",
|
|
" 130/1 1.16845 1.16207 +/- 0.00402\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.7875827910968787 for eigenvalue\n",
|
|
" 131/1 1.15291 1.16199 +/- 0.00399\n",
|
|
" 132/1 1.19398 1.16225 +/- 0.00397\n",
|
|
" 133/1 1.20030 1.16256 +/- 0.00395\n",
|
|
" 134/1 1.13483 1.16234 +/- 0.00392\n",
|
|
" 135/1 1.17713 1.16246 +/- 0.00389\n",
|
|
" 136/1 1.21106 1.16284 +/- 0.00388\n",
|
|
" 137/1 1.16494 1.16286 +/- 0.00385\n",
|
|
" 138/1 1.22902 1.16338 +/- 0.00385\n",
|
|
" 139/1 1.20082 1.16367 +/- 0.00383\n",
|
|
" 140/1 1.10710 1.16323 +/- 0.00383\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.6510788529694485 for eigenvalue\n",
|
|
" 141/1 1.25141 1.16390 +/- 0.00386\n",
|
|
" 142/1 1.17977 1.16402 +/- 0.00383\n",
|
|
" 143/1 1.17530 1.16411 +/- 0.00380\n",
|
|
" 144/1 1.18781 1.16429 +/- 0.00378\n",
|
|
" 145/1 1.21974 1.16470 +/- 0.00377\n",
|
|
" 146/1 1.17995 1.16481 +/- 0.00375\n",
|
|
" 147/1 1.15608 1.16475 +/- 0.00372\n",
|
|
" 148/1 1.22180 1.16516 +/- 0.00372\n",
|
|
" 149/1 1.11726 1.16481 +/- 0.00371\n",
|
|
" 150/1 1.19554 1.16503 +/- 0.00369\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.5110179929292893 for eigenvalue\n",
|
|
" 151/1 1.13584 1.16483 +/- 0.00367\n",
|
|
" 152/1 1.16186 1.16481 +/- 0.00364\n",
|
|
" 153/1 1.11305 1.16444 +/- 0.00363\n",
|
|
" 154/1 1.12952 1.16420 +/- 0.00361\n",
|
|
" 155/1 1.14201 1.16405 +/- 0.00359\n",
|
|
" 156/1 1.14834 1.16394 +/- 0.00357\n",
|
|
" 157/1 1.14876 1.16384 +/- 0.00355\n",
|
|
" 158/1 1.13988 1.16368 +/- 0.00353\n",
|
|
" 159/1 1.12279 1.16340 +/- 0.00351\n",
|
|
" 160/1 1.12990 1.16318 +/- 0.00350\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.37281311481808 for eigenvalue\n",
|
|
" 161/1 1.25819 1.16381 +/- 0.00353\n",
|
|
" 162/1 1.13722 1.16363 +/- 0.00351\n",
|
|
" 163/1 1.18786 1.16379 +/- 0.00349\n",
|
|
" 164/1 1.12051 1.16351 +/- 0.00348\n",
|
|
" 165/1 1.16969 1.16355 +/- 0.00346\n",
|
|
" 166/1 1.21385 1.16387 +/- 0.00345\n",
|
|
" 167/1 1.14988 1.16378 +/- 0.00343\n",
|
|
" 168/1 1.20043 1.16401 +/- 0.00342\n",
|
|
" 169/1 1.18816 1.16417 +/- 0.00340\n",
|
|
" 170/1 1.22607 1.16455 +/- 0.00340\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.266697834062024 for eigenvalue\n",
|
|
" 171/1 1.12366 1.16430 +/- 0.00339\n",
|
|
" 172/1 1.18831 1.16445 +/- 0.00337\n",
|
|
" 173/1 1.14369 1.16432 +/- 0.00335\n",
|
|
" 174/1 1.15635 1.16427 +/- 0.00333\n",
|
|
" 175/1 1.20488 1.16452 +/- 0.00332\n",
|
|
" 176/1 1.03945 1.16376 +/- 0.00339\n",
|
|
" 177/1 1.13604 1.16360 +/- 0.00337\n",
|
|
" 178/1 1.16035 1.16358 +/- 0.00335\n",
|
|
" 179/1 1.18764 1.16372 +/- 0.00333\n",
|
|
" 180/1 1.11916 1.16346 +/- 0.00332\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.18139484215554 for eigenvalue\n",
|
|
" 181/1 1.07092 1.16292 +/- 0.00335\n",
|
|
" 182/1 1.17784 1.16300 +/- 0.00333\n",
|
|
" 183/1 1.20457 1.16324 +/- 0.00332\n",
|
|
" 184/1 1.16469 1.16325 +/- 0.00330\n",
|
|
" 185/1 1.22644 1.16361 +/- 0.00330\n",
|
|
" 186/1 1.07347 1.16310 +/- 0.00332\n",
|
|
" 187/1 1.11038 1.16280 +/- 0.00332\n",
|
|
" 188/1 1.17576 1.16288 +/- 0.00330\n",
|
|
" 189/1 1.12903 1.16269 +/- 0.00328\n",
|
|
" 190/1 1.18657 1.16282 +/- 0.00327\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.1309598337696767 for eigenvalue\n",
|
|
" 191/1 1.10463 1.16250 +/- 0.00327\n",
|
|
" 192/1 1.17064 1.16254 +/- 0.00325\n",
|
|
" 193/1 1.19327 1.16271 +/- 0.00324\n",
|
|
" 194/1 1.11209 1.16244 +/- 0.00323\n",
|
|
" 195/1 1.20609 1.16267 +/- 0.00322\n",
|
|
" 196/1 1.22887 1.16303 +/- 0.00322\n",
|
|
" 197/1 1.13621 1.16289 +/- 0.00321\n",
|
|
" 198/1 1.16535 1.16290 +/- 0.00319\n",
|
|
" 199/1 1.17375 1.16296 +/- 0.00318\n",
|
|
" 200/1 1.16857 1.16299 +/- 0.00316\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.0441351713813902 for eigenvalue\n",
|
|
" 201/1 1.04481 1.16237 +/- 0.00320\n",
|
|
" 202/1 1.16853 1.16240 +/- 0.00319\n",
|
|
" 203/1 1.17614 1.16247 +/- 0.00317\n",
|
|
" 204/1 1.16960 1.16251 +/- 0.00315\n",
|
|
" 205/1 1.22812 1.16284 +/- 0.00316\n",
|
|
" 206/1 1.21946 1.16313 +/- 0.00315\n",
|
|
" 207/1 1.13503 1.16299 +/- 0.00314\n",
|
|
" 208/1 1.13558 1.16285 +/- 0.00313\n",
|
|
" 209/1 1.19099 1.16299 +/- 0.00312\n",
|
|
" 210/1 1.17668 1.16306 +/- 0.00310\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.9975649288509387 for eigenvalue\n",
|
|
" 211/1 1.16634 1.16308 +/- 0.00308\n",
|
|
" 212/1 1.17239 1.16312 +/- 0.00307\n",
|
|
" 213/1 1.15321 1.16307 +/- 0.00306\n",
|
|
" 214/1 1.16245 1.16307 +/- 0.00304\n",
|
|
" 215/1 1.12638 1.16289 +/- 0.00303\n",
|
|
" 216/1 1.13920 1.16278 +/- 0.00302\n",
|
|
" 217/1 1.23143 1.16311 +/- 0.00302\n",
|
|
" 218/1 1.17752 1.16318 +/- 0.00301\n",
|
|
" 219/1 1.13855 1.16306 +/- 0.00300\n",
|
|
" 220/1 1.13699 1.16294 +/- 0.00298\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.9166401863641809 for eigenvalue\n",
|
|
" 221/1 1.09028 1.16259 +/- 0.00299\n",
|
|
" 222/1 1.20738 1.16280 +/- 0.00298\n",
|
|
" 223/1 1.13367 1.16267 +/- 0.00297\n",
|
|
" 224/1 1.19861 1.16283 +/- 0.00296\n",
|
|
" 225/1 1.18219 1.16292 +/- 0.00295\n",
|
|
" 226/1 1.17352 1.16297 +/- 0.00294\n",
|
|
" 227/1 1.16419 1.16298 +/- 0.00292\n",
|
|
" 228/1 1.06489 1.16253 +/- 0.00294\n",
|
|
" 229/1 1.10396 1.16226 +/- 0.00294\n",
|
|
" 230/1 1.20727 1.16247 +/- 0.00294\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.8959554840220951 for eigenvalue\n",
|
|
" 231/1 1.31170 1.16314 +/- 0.00300\n",
|
|
" 232/1 1.23017 1.16344 +/- 0.00300\n",
|
|
" 233/1 1.23467 1.16376 +/- 0.00301\n",
|
|
" 234/1 1.23445 1.16408 +/- 0.00301\n",
|
|
" 235/1 1.20194 1.16425 +/- 0.00300\n",
|
|
" 236/1 1.20380 1.16442 +/- 0.00299\n",
|
|
" 237/1 1.19482 1.16456 +/- 0.00298\n",
|
|
" 238/1 1.17085 1.16458 +/- 0.00297\n",
|
|
" 239/1 1.19742 1.16473 +/- 0.00296\n",
|
|
" 240/1 1.21437 1.16494 +/- 0.00295\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.9126130441087148 for eigenvalue\n",
|
|
" 241/1 1.14930 1.16487 +/- 0.00294\n",
|
|
" 242/1 1.23030 1.16516 +/- 0.00294\n",
|
|
" 243/1 1.15810 1.16513 +/- 0.00293\n",
|
|
" 244/1 1.18390 1.16521 +/- 0.00292\n",
|
|
" 245/1 1.11676 1.16500 +/- 0.00291\n",
|
|
" 246/1 1.22214 1.16524 +/- 0.00291\n",
|
|
" 247/1 1.18791 1.16534 +/- 0.00290\n",
|
|
" 248/1 1.13853 1.16523 +/- 0.00289\n",
|
|
" 249/1 1.10678 1.16498 +/- 0.00289\n",
|
|
" 250/1 1.06902 1.16458 +/- 0.00291\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.8917804344235318 for eigenvalue\n",
|
|
" 251/1 1.23736 1.16488 +/- 0.00291\n",
|
|
" 252/1 1.12551 1.16472 +/- 0.00290\n",
|
|
" 253/1 1.16054 1.16470 +/- 0.00289\n",
|
|
" 254/1 1.20512 1.16487 +/- 0.00288\n",
|
|
" 255/1 1.21619 1.16508 +/- 0.00288\n",
|
|
" 256/1 1.19007 1.16518 +/- 0.00287\n",
|
|
" 257/1 1.12877 1.16503 +/- 0.00286\n",
|
|
" 258/1 1.18121 1.16510 +/- 0.00285\n",
|
|
" 259/1 1.13717 1.16499 +/- 0.00284\n",
|
|
" 260/1 1.11622 1.16479 +/- 0.00284\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.843830822004332 for eigenvalue\n",
|
|
" 261/1 1.18732 1.16488 +/- 0.00283\n",
|
|
" 262/1 1.10882 1.16466 +/- 0.00282\n",
|
|
" 263/1 1.13125 1.16453 +/- 0.00282\n",
|
|
" 264/1 1.17960 1.16459 +/- 0.00280\n",
|
|
" 265/1 1.13617 1.16447 +/- 0.00280\n",
|
|
" 266/1 1.28415 1.16494 +/- 0.00282\n",
|
|
" 267/1 1.14000 1.16484 +/- 0.00281\n",
|
|
" 268/1 1.11971 1.16467 +/- 0.00281\n",
|
|
" 269/1 1.13799 1.16457 +/- 0.00280\n",
|
|
" 270/1 1.15869 1.16454 +/- 0.00279\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.810445699828465 for eigenvalue\n",
|
|
" 271/1 1.12184 1.16438 +/- 0.00278\n",
|
|
" 272/1 1.13897 1.16428 +/- 0.00277\n",
|
|
" 273/1 1.18275 1.16435 +/- 0.00276\n",
|
|
" 274/1 1.10584 1.16413 +/- 0.00276\n",
|
|
" 275/1 1.16501 1.16414 +/- 0.00275\n",
|
|
" 276/1 1.17552 1.16418 +/- 0.00274\n",
|
|
" 277/1 1.12953 1.16405 +/- 0.00274\n",
|
|
" 278/1 1.21081 1.16422 +/- 0.00273\n",
|
|
" 279/1 1.10369 1.16400 +/- 0.00273\n",
|
|
" 280/1 1.21266 1.16418 +/- 0.00273\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.7775983749987805 for eigenvalue\n",
|
|
" 281/1 1.17926 1.16423 +/- 0.00272\n",
|
|
" 282/1 1.12323 1.16408 +/- 0.00271\n",
|
|
" 283/1 1.27269 1.16448 +/- 0.00273\n",
|
|
" 284/1 1.15900 1.16446 +/- 0.00272\n",
|
|
" 285/1 1.21715 1.16465 +/- 0.00272\n",
|
|
" 286/1 1.14302 1.16457 +/- 0.00271\n",
|
|
" 287/1 1.30478 1.16508 +/- 0.00275\n",
|
|
" 288/1 1.21330 1.16525 +/- 0.00274\n",
|
|
" 289/1 1.15232 1.16521 +/- 0.00273\n",
|
|
" 290/1 1.18861 1.16529 +/- 0.00272\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.752660986357896 for eigenvalue\n",
|
|
" 291/1 1.15765 1.16526 +/- 0.00271\n",
|
|
" 292/1 1.18449 1.16533 +/- 0.00270\n",
|
|
" 293/1 1.16366 1.16533 +/- 0.00270\n",
|
|
" 294/1 1.21120 1.16549 +/- 0.00269\n",
|
|
" 295/1 1.17288 1.16551 +/- 0.00268\n",
|
|
" 296/1 1.17344 1.16554 +/- 0.00267\n",
|
|
" 297/1 1.11360 1.16536 +/- 0.00267\n",
|
|
" 298/1 1.20650 1.16550 +/- 0.00266\n",
|
|
" 299/1 1.10803 1.16530 +/- 0.00266\n",
|
|
" 300/1 1.07080 1.16498 +/- 0.00267\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.7119817884104997 for eigenvalue\n",
|
|
" 301/1 1.14932 1.16492 +/- 0.00266\n",
|
|
" 302/1 1.18480 1.16499 +/- 0.00266\n",
|
|
" 303/1 1.21204 1.16515 +/- 0.00265\n",
|
|
" 304/1 1.13567 1.16505 +/- 0.00264\n",
|
|
" 305/1 1.13442 1.16495 +/- 0.00264\n",
|
|
" 306/1 1.22448 1.16515 +/- 0.00264\n",
|
|
" 307/1 1.11349 1.16498 +/- 0.00263\n",
|
|
" 308/1 1.23700 1.16522 +/- 0.00263\n",
|
|
" 309/1 1.13264 1.16511 +/- 0.00263\n",
|
|
" 310/1 1.13838 1.16502 +/- 0.00262\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6722538543569145 for eigenvalue\n",
|
|
" 311/1 1.14002 1.16494 +/- 0.00261\n",
|
|
" 312/1 1.09636 1.16471 +/- 0.00261\n",
|
|
" 313/1 1.14255 1.16464 +/- 0.00261\n",
|
|
" 314/1 1.16193 1.16463 +/- 0.00260\n",
|
|
" 315/1 1.11510 1.16446 +/- 0.00260\n",
|
|
" 316/1 1.17565 1.16450 +/- 0.00259\n",
|
|
" 317/1 1.12394 1.16437 +/- 0.00258\n",
|
|
" 318/1 1.18535 1.16444 +/- 0.00257\n",
|
|
" 319/1 1.07502 1.16415 +/- 0.00258\n",
|
|
" 320/1 1.23118 1.16436 +/- 0.00258\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6320860969261237 for eigenvalue\n",
|
|
" 321/1 1.23835 1.16460 +/- 0.00259\n",
|
|
" 322/1 1.19264 1.16469 +/- 0.00258\n",
|
|
" 323/1 1.18433 1.16475 +/- 0.00257\n",
|
|
" 324/1 1.07886 1.16448 +/- 0.00258\n",
|
|
" 325/1 1.07599 1.16420 +/- 0.00258\n",
|
|
" 326/1 1.20753 1.16434 +/- 0.00258\n",
|
|
" 327/1 1.21453 1.16450 +/- 0.00258\n",
|
|
" 328/1 1.18711 1.16457 +/- 0.00257\n",
|
|
" 329/1 1.19171 1.16465 +/- 0.00256\n",
|
|
" 330/1 1.14063 1.16458 +/- 0.00256\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6012385926034651 for eigenvalue\n",
|
|
" 331/1 1.08068 1.16432 +/- 0.00256\n",
|
|
" 332/1 1.15438 1.16428 +/- 0.00255\n",
|
|
" 333/1 1.12259 1.16416 +/- 0.00255\n",
|
|
" 334/1 1.14925 1.16411 +/- 0.00254\n",
|
|
" 335/1 1.21415 1.16426 +/- 0.00254\n",
|
|
" 336/1 1.15623 1.16424 +/- 0.00253\n",
|
|
" 337/1 1.12122 1.16411 +/- 0.00253\n",
|
|
" 338/1 1.16103 1.16410 +/- 0.00252\n",
|
|
" 339/1 1.15116 1.16406 +/- 0.00251\n",
|
|
" 340/1 1.20378 1.16418 +/- 0.00251\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.567096731850776 for eigenvalue\n",
|
|
" 341/1 1.19251 1.16426 +/- 0.00250\n",
|
|
" 342/1 1.18666 1.16433 +/- 0.00249\n",
|
|
" 343/1 1.18725 1.16440 +/- 0.00249\n",
|
|
" 344/1 1.12901 1.16429 +/- 0.00248\n",
|
|
" 345/1 1.18783 1.16437 +/- 0.00248\n",
|
|
" 346/1 1.05974 1.16405 +/- 0.00249\n",
|
|
" 347/1 1.15304 1.16402 +/- 0.00248\n",
|
|
" 348/1 1.08504 1.16379 +/- 0.00248\n",
|
|
" 349/1 1.18229 1.16384 +/- 0.00248\n",
|
|
" 350/1 1.11558 1.16370 +/- 0.00247\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5354081226428917 for eigenvalue\n",
|
|
" 351/1 1.13541 1.16362 +/- 0.00247\n",
|
|
" 352/1 1.14824 1.16357 +/- 0.00246\n",
|
|
" 353/1 1.19013 1.16365 +/- 0.00246\n",
|
|
" 354/1 1.17910 1.16369 +/- 0.00245\n",
|
|
" 355/1 1.10042 1.16351 +/- 0.00245\n",
|
|
" 356/1 1.11773 1.16338 +/- 0.00245\n",
|
|
" 357/1 1.19694 1.16348 +/- 0.00244\n",
|
|
" 358/1 1.10998 1.16332 +/- 0.00244\n",
|
|
" 359/1 1.16082 1.16331 +/- 0.00243\n",
|
|
" 360/1 1.22355 1.16349 +/- 0.00243\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5065219360403737 for eigenvalue\n",
|
|
" 361/1 1.15034 1.16345 +/- 0.00242\n",
|
|
" 362/1 1.14772 1.16340 +/- 0.00242\n",
|
|
" 363/1 1.13379 1.16332 +/- 0.00241\n",
|
|
" 364/1 1.05434 1.16301 +/- 0.00242\n",
|
|
" 365/1 1.27294 1.16332 +/- 0.00244\n",
|
|
" 366/1 1.15636 1.16330 +/- 0.00243\n",
|
|
" 367/1 1.20710 1.16343 +/- 0.00243\n",
|
|
" 368/1 1.14897 1.16339 +/- 0.00242\n",
|
|
" 369/1 1.03904 1.16304 +/- 0.00244\n",
|
|
" 370/1 1.12377 1.16293 +/- 0.00243\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4909451313605837 for eigenvalue\n",
|
|
" 371/1 1.16101 1.16292 +/- 0.00243\n",
|
|
" 372/1 1.18106 1.16297 +/- 0.00242\n",
|
|
" 373/1 1.15295 1.16295 +/- 0.00241\n",
|
|
" 374/1 1.06662 1.16268 +/- 0.00242\n",
|
|
" 375/1 1.07844 1.16245 +/- 0.00243\n",
|
|
" 376/1 1.07999 1.16223 +/- 0.00243\n",
|
|
" 377/1 1.25591 1.16248 +/- 0.00244\n",
|
|
" 378/1 1.19816 1.16258 +/- 0.00243\n",
|
|
" 379/1 1.21543 1.16272 +/- 0.00243\n",
|
|
" 380/1 1.13144 1.16264 +/- 0.00243\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4727310701711616 for eigenvalue\n",
|
|
" 381/1 1.16981 1.16266 +/- 0.00242\n",
|
|
" 382/1 1.18403 1.16271 +/- 0.00241\n",
|
|
" 383/1 1.19586 1.16280 +/- 0.00241\n",
|
|
" 384/1 1.17815 1.16284 +/- 0.00240\n",
|
|
" 385/1 1.15841 1.16283 +/- 0.00240\n",
|
|
" 386/1 1.13726 1.16276 +/- 0.00239\n",
|
|
" 387/1 1.19484 1.16285 +/- 0.00239\n",
|
|
" 388/1 1.17278 1.16288 +/- 0.00238\n",
|
|
" 389/1 1.15332 1.16285 +/- 0.00237\n",
|
|
" 390/1 1.18398 1.16291 +/- 0.00237\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4444471316594256 for eigenvalue\n",
|
|
" 391/1 1.16987 1.16292 +/- 0.00236\n",
|
|
" 392/1 1.15710 1.16291 +/- 0.00236\n",
|
|
" 393/1 1.15398 1.16289 +/- 0.00235\n",
|
|
" 394/1 1.10557 1.16274 +/- 0.00235\n",
|
|
" 395/1 1.16672 1.16275 +/- 0.00234\n",
|
|
" 396/1 1.24188 1.16295 +/- 0.00234\n",
|
|
" 397/1 1.13297 1.16287 +/- 0.00234\n",
|
|
" 398/1 1.18487 1.16293 +/- 0.00233\n",
|
|
" 399/1 1.16811 1.16294 +/- 0.00233\n",
|
|
" 400/1 1.10119 1.16279 +/- 0.00233\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.42693486232336 for eigenvalue\n",
|
|
" 401/1 1.14653 1.16274 +/- 0.00232\n",
|
|
" 402/1 1.15484 1.16272 +/- 0.00232\n",
|
|
" 403/1 1.18756 1.16279 +/- 0.00231\n",
|
|
" 404/1 1.19890 1.16288 +/- 0.00231\n",
|
|
" 405/1 1.17167 1.16290 +/- 0.00230\n",
|
|
" 406/1 1.11278 1.16277 +/- 0.00230\n",
|
|
" 407/1 1.19779 1.16286 +/- 0.00230\n",
|
|
" 408/1 1.18729 1.16292 +/- 0.00229\n",
|
|
" 409/1 1.08649 1.16273 +/- 0.00229\n",
|
|
" 410/1 1.12321 1.16263 +/- 0.00229\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.419187258466732 for eigenvalue\n",
|
|
" 411/1 1.13250 1.16256 +/- 0.00228\n",
|
|
" 412/1 1.18418 1.16261 +/- 0.00228\n",
|
|
" 413/1 1.12214 1.16251 +/- 0.00228\n",
|
|
" 414/1 1.15114 1.16248 +/- 0.00227\n",
|
|
" 415/1 1.14440 1.16244 +/- 0.00227\n",
|
|
" 416/1 1.15170 1.16241 +/- 0.00226\n",
|
|
" 417/1 1.20651 1.16252 +/- 0.00226\n",
|
|
" 418/1 1.20156 1.16262 +/- 0.00225\n",
|
|
" 419/1 1.08415 1.16242 +/- 0.00226\n",
|
|
" 420/1 1.13462 1.16236 +/- 0.00225\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.404396746070045 for eigenvalue\n",
|
|
" 421/1 1.09184 1.16219 +/- 0.00225\n",
|
|
" 422/1 1.18240 1.16223 +/- 0.00225\n",
|
|
" 423/1 1.12267 1.16214 +/- 0.00224\n",
|
|
" 424/1 1.17295 1.16216 +/- 0.00224\n",
|
|
" 425/1 1.13013 1.16209 +/- 0.00224\n",
|
|
" 426/1 1.12039 1.16199 +/- 0.00223\n",
|
|
" 427/1 1.13246 1.16192 +/- 0.00223\n",
|
|
" 428/1 1.09958 1.16177 +/- 0.00223\n",
|
|
" 429/1 1.09499 1.16161 +/- 0.00223\n",
|
|
" 430/1 1.14284 1.16156 +/- 0.00222\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.387946026058316 for eigenvalue\n",
|
|
" 431/1 1.09517 1.16141 +/- 0.00222\n",
|
|
" 432/1 1.15377 1.16139 +/- 0.00222\n",
|
|
" 433/1 1.14639 1.16135 +/- 0.00221\n",
|
|
" 434/1 1.12643 1.16127 +/- 0.00221\n",
|
|
" 435/1 1.10707 1.16114 +/- 0.00221\n",
|
|
" 436/1 1.17022 1.16116 +/- 0.00220\n",
|
|
" 437/1 1.23111 1.16133 +/- 0.00220\n",
|
|
" 438/1 1.17739 1.16136 +/- 0.00220\n",
|
|
" 439/1 1.20559 1.16147 +/- 0.00220\n",
|
|
" 440/1 1.11235 1.16135 +/- 0.00219\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.362032395101567 for eigenvalue\n",
|
|
" 441/1 1.16611 1.16136 +/- 0.00219\n",
|
|
" 442/1 1.16788 1.16138 +/- 0.00218\n",
|
|
" 443/1 1.14426 1.16134 +/- 0.00218\n",
|
|
" 444/1 1.13486 1.16128 +/- 0.00218\n",
|
|
" 445/1 1.09802 1.16113 +/- 0.00217\n",
|
|
" 446/1 1.13375 1.16107 +/- 0.00217\n",
|
|
" 447/1 1.12262 1.16098 +/- 0.00217\n",
|
|
" 448/1 1.18268 1.16103 +/- 0.00216\n",
|
|
" 449/1 1.17696 1.16107 +/- 0.00216\n",
|
|
" 450/1 1.15987 1.16107 +/- 0.00215\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3459899676758298 for eigenvalue\n",
|
|
" 451/1 1.10426 1.16094 +/- 0.00215\n",
|
|
" 452/1 1.17587 1.16097 +/- 0.00215\n",
|
|
" 453/1 1.17434 1.16100 +/- 0.00214\n",
|
|
" 454/1 1.14973 1.16098 +/- 0.00214\n",
|
|
" 455/1 1.23963 1.16115 +/- 0.00214\n",
|
|
" 456/1 1.13257 1.16109 +/- 0.00214\n",
|
|
" 457/1 1.11564 1.16099 +/- 0.00214\n",
|
|
" 458/1 1.19687 1.16107 +/- 0.00213\n",
|
|
" 459/1 1.24497 1.16125 +/- 0.00214\n",
|
|
" 460/1 1.16467 1.16126 +/- 0.00213\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3398209446044176 for eigenvalue\n",
|
|
" 461/1 1.14423 1.16122 +/- 0.00213\n",
|
|
" 462/1 1.14730 1.16119 +/- 0.00212\n",
|
|
" 463/1 1.19990 1.16128 +/- 0.00212\n",
|
|
" 464/1 1.07663 1.16109 +/- 0.00212\n",
|
|
" 465/1 1.24485 1.16128 +/- 0.00213\n",
|
|
" 466/1 1.17122 1.16130 +/- 0.00212\n",
|
|
" 467/1 1.16253 1.16130 +/- 0.00212\n",
|
|
" 468/1 1.20279 1.16139 +/- 0.00211\n",
|
|
" 469/1 1.18902 1.16145 +/- 0.00211\n",
|
|
" 470/1 1.15365 1.16143 +/- 0.00211\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3259440481605855 for eigenvalue\n",
|
|
" 471/1 1.14887 1.16141 +/- 0.00210\n",
|
|
" 472/1 1.16247 1.16141 +/- 0.00210\n",
|
|
" 473/1 1.19680 1.16149 +/- 0.00209\n",
|
|
" 474/1 1.10829 1.16137 +/- 0.00209\n",
|
|
" 475/1 1.18312 1.16142 +/- 0.00209\n",
|
|
" 476/1 1.24695 1.16160 +/- 0.00209\n",
|
|
" 477/1 1.20131 1.16169 +/- 0.00209\n",
|
|
" 478/1 1.23078 1.16183 +/- 0.00209\n",
|
|
" 479/1 1.18544 1.16188 +/- 0.00209\n",
|
|
" 480/1 1.06279 1.16167 +/- 0.00209\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.313881747021619 for eigenvalue\n",
|
|
" 481/1 1.10744 1.16156 +/- 0.00209\n",
|
|
" 482/1 1.22556 1.16169 +/- 0.00209\n",
|
|
" 483/1 1.16646 1.16170 +/- 0.00209\n",
|
|
" 484/1 1.10452 1.16158 +/- 0.00209\n",
|
|
" 485/1 1.09338 1.16144 +/- 0.00209\n",
|
|
" 486/1 1.14164 1.16140 +/- 0.00208\n",
|
|
" 487/1 1.22290 1.16153 +/- 0.00208\n",
|
|
" 488/1 1.17238 1.16155 +/- 0.00208\n",
|
|
" 489/1 1.11149 1.16145 +/- 0.00208\n",
|
|
" 490/1 1.23177 1.16159 +/- 0.00208\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3039110499680273 for eigenvalue\n",
|
|
" 491/1 1.08750 1.16144 +/- 0.00208\n",
|
|
" 492/1 1.14094 1.16140 +/- 0.00207\n",
|
|
" 493/1 1.13697 1.16134 +/- 0.00207\n",
|
|
" 494/1 1.18943 1.16140 +/- 0.00207\n",
|
|
" 495/1 1.10949 1.16130 +/- 0.00207\n",
|
|
" 496/1 1.21709 1.16141 +/- 0.00206\n",
|
|
" 497/1 1.23317 1.16156 +/- 0.00207\n",
|
|
" 498/1 1.14435 1.16152 +/- 0.00206\n",
|
|
" 499/1 1.13050 1.16146 +/- 0.00206\n",
|
|
" 500/1 1.17809 1.16149 +/- 0.00205\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2969023583787707 for eigenvalue\n",
|
|
" 501/1 1.13696 1.16144 +/- 0.00205\n",
|
|
" 502/1 1.10476 1.16133 +/- 0.00205\n",
|
|
" 503/1 1.14726 1.16130 +/- 0.00205\n",
|
|
" 504/1 1.20398 1.16139 +/- 0.00204\n",
|
|
" 505/1 1.15900 1.16138 +/- 0.00204\n",
|
|
" 506/1 1.15435 1.16137 +/- 0.00204\n",
|
|
" 507/1 1.14232 1.16133 +/- 0.00203\n",
|
|
" 508/1 1.11295 1.16123 +/- 0.00203\n",
|
|
" 509/1 1.13737 1.16118 +/- 0.00203\n",
|
|
" 510/1 1.16504 1.16119 +/- 0.00202\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2805196174071767 for eigenvalue\n",
|
|
" 511/1 1.15605 1.16118 +/- 0.00202\n",
|
|
" 512/1 1.12061 1.16110 +/- 0.00202\n",
|
|
" 513/1 1.19703 1.16117 +/- 0.00201\n",
|
|
" 514/1 1.21467 1.16128 +/- 0.00201\n",
|
|
" 515/1 1.13571 1.16123 +/- 0.00201\n",
|
|
" 516/1 1.14999 1.16121 +/- 0.00200\n",
|
|
" 517/1 1.26413 1.16141 +/- 0.00201\n",
|
|
" 518/1 1.11595 1.16132 +/- 0.00201\n",
|
|
" 519/1 1.05228 1.16110 +/- 0.00202\n",
|
|
" 520/1 1.22667 1.16123 +/- 0.00202\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2685250534649957 for eigenvalue\n",
|
|
" 521/1 1.11825 1.16115 +/- 0.00201\n",
|
|
" 522/1 1.13121 1.16109 +/- 0.00201\n",
|
|
" 523/1 1.21762 1.16120 +/- 0.00201\n",
|
|
" 524/1 1.14812 1.16118 +/- 0.00201\n",
|
|
" 525/1 1.15251 1.16116 +/- 0.00200\n",
|
|
" 526/1 1.19712 1.16123 +/- 0.00200\n",
|
|
" 527/1 1.16838 1.16124 +/- 0.00200\n",
|
|
" 528/1 1.18182 1.16128 +/- 0.00199\n",
|
|
" 529/1 1.13730 1.16124 +/- 0.00199\n",
|
|
" 530/1 1.16183 1.16124 +/- 0.00199\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2544509814665465 for eigenvalue\n",
|
|
" 531/1 1.18585 1.16128 +/- 0.00198\n",
|
|
" 532/1 1.12970 1.16122 +/- 0.00198\n",
|
|
" 533/1 1.14698 1.16120 +/- 0.00198\n",
|
|
" 534/1 1.17010 1.16121 +/- 0.00197\n",
|
|
" 535/1 1.18752 1.16126 +/- 0.00197\n",
|
|
" 536/1 1.17409 1.16129 +/- 0.00197\n",
|
|
" 537/1 1.19596 1.16135 +/- 0.00196\n",
|
|
" 538/1 1.18038 1.16139 +/- 0.00196\n",
|
|
" 539/1 1.09778 1.16127 +/- 0.00196\n",
|
|
" 540/1 1.10666 1.16117 +/- 0.00196\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2374371862273332 for eigenvalue\n",
|
|
" 541/1 1.14910 1.16114 +/- 0.00195\n",
|
|
" 542/1 1.12376 1.16107 +/- 0.00195\n",
|
|
" 543/1 1.11472 1.16099 +/- 0.00195\n",
|
|
" 544/1 1.16239 1.16099 +/- 0.00195\n",
|
|
" 545/1 1.14658 1.16096 +/- 0.00194\n",
|
|
" 546/1 1.12005 1.16089 +/- 0.00194\n",
|
|
" 547/1 1.20728 1.16097 +/- 0.00194\n",
|
|
" 548/1 1.09924 1.16086 +/- 0.00194\n",
|
|
" 549/1 1.14762 1.16083 +/- 0.00194\n",
|
|
" 550/1 1.13528 1.16079 +/- 0.00193\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2222873135902872 for eigenvalue\n",
|
|
" 551/1 1.21751 1.16089 +/- 0.00193\n",
|
|
" 552/1 1.17411 1.16091 +/- 0.00193\n",
|
|
" 553/1 1.20750 1.16100 +/- 0.00193\n",
|
|
" 554/1 1.15452 1.16099 +/- 0.00192\n",
|
|
" 555/1 1.18309 1.16103 +/- 0.00192\n",
|
|
" 556/1 1.16603 1.16104 +/- 0.00192\n",
|
|
" 557/1 1.19836 1.16111 +/- 0.00191\n",
|
|
" 558/1 1.13889 1.16107 +/- 0.00191\n",
|
|
" 559/1 1.21587 1.16117 +/- 0.00191\n",
|
|
" 560/1 1.13648 1.16112 +/- 0.00191\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2176862541090068 for eigenvalue\n",
|
|
" 561/1 1.13388 1.16107 +/- 0.00191\n",
|
|
" 562/1 1.07022 1.16091 +/- 0.00191\n",
|
|
" 563/1 1.24461 1.16106 +/- 0.00191\n",
|
|
" 564/1 1.16955 1.16107 +/- 0.00191\n",
|
|
" 565/1 1.12131 1.16100 +/- 0.00191\n",
|
|
" 566/1 1.14360 1.16097 +/- 0.00190\n",
|
|
" 567/1 1.17025 1.16099 +/- 0.00190\n",
|
|
" 568/1 1.10661 1.16089 +/- 0.00190\n",
|
|
" 569/1 1.20036 1.16096 +/- 0.00190\n",
|
|
" 570/1 1.19922 1.16103 +/- 0.00189\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2018184822719058 for eigenvalue\n",
|
|
" 571/1 1.17230 1.16105 +/- 0.00189\n",
|
|
" 572/1 1.10514 1.16095 +/- 0.00189\n",
|
|
" 573/1 1.10126 1.16084 +/- 0.00189\n",
|
|
" 574/1 1.12266 1.16078 +/- 0.00189\n",
|
|
" 575/1 1.19516 1.16084 +/- 0.00189\n",
|
|
" 576/1 1.16273 1.16084 +/- 0.00188\n",
|
|
" 577/1 1.16497 1.16085 +/- 0.00188\n",
|
|
" 578/1 1.25239 1.16101 +/- 0.00188\n",
|
|
" 579/1 1.17643 1.16104 +/- 0.00188\n",
|
|
" 580/1 1.22696 1.16115 +/- 0.00188\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.189263781246433 for eigenvalue\n",
|
|
" 581/1 1.16206 1.16115 +/- 0.00188\n",
|
|
" 582/1 1.18767 1.16120 +/- 0.00187\n",
|
|
" 583/1 1.14592 1.16117 +/- 0.00187\n",
|
|
" 584/1 1.11676 1.16109 +/- 0.00187\n",
|
|
" 585/1 1.10888 1.16100 +/- 0.00187\n",
|
|
" 586/1 1.10989 1.16092 +/- 0.00187\n",
|
|
" 587/1 1.21990 1.16102 +/- 0.00187\n",
|
|
" 588/1 1.15177 1.16100 +/- 0.00186\n",
|
|
" 589/1 1.26370 1.16118 +/- 0.00187\n",
|
|
" 590/1 1.12452 1.16112 +/- 0.00187\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1853719434719088 for eigenvalue\n",
|
|
" 591/1 1.26458 1.16129 +/- 0.00187\n",
|
|
" 592/1 1.17242 1.16131 +/- 0.00187\n",
|
|
" 593/1 1.20642 1.16139 +/- 0.00187\n",
|
|
" 594/1 1.08012 1.16125 +/- 0.00187\n",
|
|
" 595/1 1.21647 1.16135 +/- 0.00187\n",
|
|
" 596/1 1.10155 1.16124 +/- 0.00187\n",
|
|
" 597/1 1.14499 1.16122 +/- 0.00186\n",
|
|
" 598/1 1.17835 1.16124 +/- 0.00186\n",
|
|
" 599/1 1.10458 1.16115 +/- 0.00186\n",
|
|
" 600/1 1.14884 1.16113 +/- 0.00186\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.180467352493672 for eigenvalue\n",
|
|
" 601/1 1.11358 1.16105 +/- 0.00186\n",
|
|
" 602/1 1.15446 1.16104 +/- 0.00185\n",
|
|
" 603/1 1.17109 1.16105 +/- 0.00185\n",
|
|
" 604/1 1.17753 1.16108 +/- 0.00185\n",
|
|
" 605/1 1.06318 1.16092 +/- 0.00185\n",
|
|
" 606/1 1.20619 1.16099 +/- 0.00185\n",
|
|
" 607/1 1.16786 1.16100 +/- 0.00185\n",
|
|
" 608/1 1.24683 1.16115 +/- 0.00185\n",
|
|
" 609/1 1.20217 1.16122 +/- 0.00185\n",
|
|
" 610/1 1.18625 1.16126 +/- 0.00184\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1714646160582372 for eigenvalue\n",
|
|
" 611/1 1.19768 1.16132 +/- 0.00184\n",
|
|
" 612/1 1.15788 1.16131 +/- 0.00184\n",
|
|
" 613/1 1.22393 1.16142 +/- 0.00184\n",
|
|
" 614/1 1.25470 1.16157 +/- 0.00184\n",
|
|
" 615/1 1.07942 1.16143 +/- 0.00185\n",
|
|
" 616/1 1.14314 1.16140 +/- 0.00184\n",
|
|
" 617/1 1.14214 1.16137 +/- 0.00184\n",
|
|
" 618/1 1.19555 1.16143 +/- 0.00184\n",
|
|
" 619/1 1.18395 1.16147 +/- 0.00183\n",
|
|
" 620/1 1.22862 1.16158 +/- 0.00183\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1597261038175937 for eigenvalue\n",
|
|
" 621/1 1.13093 1.16153 +/- 0.00183\n",
|
|
" 622/1 1.14693 1.16150 +/- 0.00183\n",
|
|
" 623/1 1.12634 1.16144 +/- 0.00183\n",
|
|
" 624/1 1.14764 1.16142 +/- 0.00182\n",
|
|
" 625/1 1.17260 1.16144 +/- 0.00182\n",
|
|
" 626/1 1.19704 1.16150 +/- 0.00182\n",
|
|
" 627/1 1.20063 1.16156 +/- 0.00182\n",
|
|
" 628/1 1.17572 1.16158 +/- 0.00182\n",
|
|
" 629/1 1.05932 1.16142 +/- 0.00182\n",
|
|
" 630/1 1.21534 1.16151 +/- 0.00182\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1485547893456707 for eigenvalue\n",
|
|
" 631/1 1.13716 1.16147 +/- 0.00182\n",
|
|
" 632/1 1.13822 1.16143 +/- 0.00181\n",
|
|
" 633/1 1.05504 1.16126 +/- 0.00182\n",
|
|
" 634/1 1.11201 1.16118 +/- 0.00182\n",
|
|
" 635/1 1.17350 1.16120 +/- 0.00182\n",
|
|
" 636/1 1.20003 1.16126 +/- 0.00181\n",
|
|
" 637/1 1.13587 1.16122 +/- 0.00181\n",
|
|
" 638/1 1.21392 1.16131 +/- 0.00181\n",
|
|
" 639/1 1.27695 1.16149 +/- 0.00182\n",
|
|
" 640/1 1.11612 1.16142 +/- 0.00181\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1425765711462703 for eigenvalue\n",
|
|
" 641/1 1.14264 1.16139 +/- 0.00181\n",
|
|
" 642/1 1.09105 1.16128 +/- 0.00181\n",
|
|
" 643/1 1.30403 1.16150 +/- 0.00182\n",
|
|
" 644/1 1.12746 1.16145 +/- 0.00182\n",
|
|
" 645/1 1.20823 1.16152 +/- 0.00182\n",
|
|
" 646/1 1.18503 1.16156 +/- 0.00182\n",
|
|
" 647/1 1.10371 1.16147 +/- 0.00182\n",
|
|
" 648/1 1.17045 1.16148 +/- 0.00181\n",
|
|
" 649/1 1.18263 1.16151 +/- 0.00181\n",
|
|
" 650/1 1.12497 1.16146 +/- 0.00181\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.139289798450086 for eigenvalue\n",
|
|
" 651/1 1.22107 1.16155 +/- 0.00181\n",
|
|
" 652/1 1.09687 1.16145 +/- 0.00181\n",
|
|
" 653/1 1.16296 1.16145 +/- 0.00181\n",
|
|
" 654/1 1.06545 1.16130 +/- 0.00181\n",
|
|
" 655/1 1.14759 1.16128 +/- 0.00181\n",
|
|
" 656/1 1.18125 1.16131 +/- 0.00180\n",
|
|
" 657/1 1.17878 1.16134 +/- 0.00180\n",
|
|
" 658/1 1.21262 1.16142 +/- 0.00180\n",
|
|
" 659/1 1.22850 1.16152 +/- 0.00180\n",
|
|
" 660/1 1.19269 1.16157 +/- 0.00180\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1308238272411224 for eigenvalue\n",
|
|
" 661/1 1.13966 1.16154 +/- 0.00180\n",
|
|
" 662/1 1.21690 1.16162 +/- 0.00180\n",
|
|
" 663/1 1.11293 1.16155 +/- 0.00179\n",
|
|
" 664/1 1.21468 1.16163 +/- 0.00179\n",
|
|
" 665/1 1.24769 1.16176 +/- 0.00180\n",
|
|
" 666/1 1.20529 1.16183 +/- 0.00179\n",
|
|
" 667/1 1.18311 1.16186 +/- 0.00179\n",
|
|
" 668/1 1.16552 1.16186 +/- 0.00179\n",
|
|
" 669/1 1.17113 1.16188 +/- 0.00179\n",
|
|
" 670/1 1.11940 1.16181 +/- 0.00179\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1203022820681046 for eigenvalue\n",
|
|
" 671/1 1.18897 1.16185 +/- 0.00178\n",
|
|
" 672/1 1.26250 1.16201 +/- 0.00179\n",
|
|
" 673/1 1.21984 1.16209 +/- 0.00179\n",
|
|
" 674/1 1.14885 1.16207 +/- 0.00178\n",
|
|
" 675/1 1.10876 1.16199 +/- 0.00178\n",
|
|
" 676/1 1.22072 1.16208 +/- 0.00178\n",
|
|
" 677/1 1.09677 1.16198 +/- 0.00178\n",
|
|
" 678/1 1.09926 1.16189 +/- 0.00178\n",
|
|
" 679/1 1.16297 1.16189 +/- 0.00178\n",
|
|
" 680/1 1.14245 1.16186 +/- 0.00178\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1194740242758798 for eigenvalue\n",
|
|
" 681/1 1.11547 1.16179 +/- 0.00178\n",
|
|
" 682/1 1.18794 1.16183 +/- 0.00177\n",
|
|
" 683/1 1.14462 1.16181 +/- 0.00177\n",
|
|
" 684/1 1.10043 1.16172 +/- 0.00177\n",
|
|
" 685/1 1.10244 1.16163 +/- 0.00177\n",
|
|
" 686/1 1.19471 1.16168 +/- 0.00177\n",
|
|
" 687/1 1.07344 1.16155 +/- 0.00177\n",
|
|
" 688/1 1.18756 1.16159 +/- 0.00177\n",
|
|
" 689/1 1.15734 1.16158 +/- 0.00177\n",
|
|
" 690/1 1.13140 1.16153 +/- 0.00176\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1082363057673894 for eigenvalue\n",
|
|
" 691/1 1.09892 1.16144 +/- 0.00176\n",
|
|
" 692/1 1.14985 1.16143 +/- 0.00176\n",
|
|
" 693/1 1.10939 1.16135 +/- 0.00176\n",
|
|
" 694/1 1.19824 1.16140 +/- 0.00176\n",
|
|
" 695/1 1.18510 1.16144 +/- 0.00176\n",
|
|
" 696/1 1.14073 1.16141 +/- 0.00175\n",
|
|
" 697/1 1.29724 1.16161 +/- 0.00176\n",
|
|
" 698/1 1.08614 1.16150 +/- 0.00176\n",
|
|
" 699/1 1.16962 1.16151 +/- 0.00176\n",
|
|
" 700/1 1.13559 1.16147 +/- 0.00176\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.101253550066511 for eigenvalue\n",
|
|
" 701/1 1.09457 1.16137 +/- 0.00176\n",
|
|
" 702/1 1.13782 1.16134 +/- 0.00176\n",
|
|
" 703/1 1.19167 1.16138 +/- 0.00175\n",
|
|
" 704/1 1.14099 1.16135 +/- 0.00175\n",
|
|
" 705/1 1.23445 1.16146 +/- 0.00175\n",
|
|
" 706/1 1.14546 1.16144 +/- 0.00175\n",
|
|
" 707/1 1.14464 1.16141 +/- 0.00175\n",
|
|
" 708/1 1.22340 1.16150 +/- 0.00175\n",
|
|
" 709/1 1.14018 1.16147 +/- 0.00175\n",
|
|
" 710/1 1.16188 1.16147 +/- 0.00174\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0880812392130488 for eigenvalue\n",
|
|
" 711/1 1.09789 1.16138 +/- 0.00174\n",
|
|
" 712/1 1.07486 1.16126 +/- 0.00174\n",
|
|
" 713/1 1.13099 1.16121 +/- 0.00174\n",
|
|
" 714/1 1.17445 1.16123 +/- 0.00174\n",
|
|
" 715/1 1.11858 1.16117 +/- 0.00174\n",
|
|
" 716/1 1.13483 1.16113 +/- 0.00174\n",
|
|
" 717/1 1.16654 1.16114 +/- 0.00173\n",
|
|
" 718/1 1.16729 1.16115 +/- 0.00173\n",
|
|
" 719/1 1.15158 1.16114 +/- 0.00173\n",
|
|
" 720/1 1.13281 1.16110 +/- 0.00173\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0793035284362018 for eigenvalue\n",
|
|
" 721/1 1.11431 1.16103 +/- 0.00173\n",
|
|
" 722/1 1.12470 1.16098 +/- 0.00172\n",
|
|
" 723/1 1.13957 1.16095 +/- 0.00172\n",
|
|
" 724/1 1.12052 1.16089 +/- 0.00172\n",
|
|
" 725/1 1.25474 1.16103 +/- 0.00172\n",
|
|
" 726/1 1.17506 1.16104 +/- 0.00172\n",
|
|
" 727/1 1.12692 1.16100 +/- 0.00172\n",
|
|
" 728/1 1.20193 1.16105 +/- 0.00172\n",
|
|
" 729/1 1.22776 1.16115 +/- 0.00172\n",
|
|
" 730/1 1.17163 1.16116 +/- 0.00172\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.068130566005907 for eigenvalue\n",
|
|
" 731/1 1.11036 1.16109 +/- 0.00172\n",
|
|
" 732/1 1.04671 1.16093 +/- 0.00172\n",
|
|
" 733/1 1.17491 1.16095 +/- 0.00172\n",
|
|
" 734/1 1.16586 1.16096 +/- 0.00172\n",
|
|
" 735/1 1.10386 1.16088 +/- 0.00172\n",
|
|
" 736/1 1.12384 1.16083 +/- 0.00171\n",
|
|
" 737/1 1.15327 1.16082 +/- 0.00171\n",
|
|
" 738/1 1.08366 1.16071 +/- 0.00171\n",
|
|
" 739/1 1.10048 1.16063 +/- 0.00171\n",
|
|
" 740/1 1.09526 1.16054 +/- 0.00171\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.065678944462061 for eigenvalue\n",
|
|
" 741/1 1.15676 1.16054 +/- 0.00171\n",
|
|
" 742/1 1.18877 1.16057 +/- 0.00171\n",
|
|
" 743/1 1.15092 1.16056 +/- 0.00171\n",
|
|
" 744/1 1.08072 1.16045 +/- 0.00171\n",
|
|
" 745/1 1.11758 1.16039 +/- 0.00170\n",
|
|
" 746/1 1.14565 1.16037 +/- 0.00170\n",
|
|
" 747/1 1.13127 1.16033 +/- 0.00170\n",
|
|
" 748/1 1.24808 1.16045 +/- 0.00170\n",
|
|
" 749/1 1.16994 1.16047 +/- 0.00170\n",
|
|
" 750/1 1.12790 1.16042 +/- 0.00170\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0573369339838317 for eigenvalue\n",
|
|
" 751/1 1.22544 1.16051 +/- 0.00170\n",
|
|
" 752/1 1.10956 1.16044 +/- 0.00170\n",
|
|
" 753/1 1.17965 1.16047 +/- 0.00170\n",
|
|
" 754/1 1.12730 1.16042 +/- 0.00169\n",
|
|
" 755/1 1.19312 1.16047 +/- 0.00169\n",
|
|
" 756/1 1.20855 1.16053 +/- 0.00169\n",
|
|
" 757/1 1.10655 1.16046 +/- 0.00169\n",
|
|
" 758/1 1.08072 1.16035 +/- 0.00169\n",
|
|
" 759/1 1.20657 1.16041 +/- 0.00169\n",
|
|
" 760/1 1.19064 1.16045 +/- 0.00169\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0479453025302448 for eigenvalue\n",
|
|
" 761/1 1.14384 1.16043 +/- 0.00169\n",
|
|
" 762/1 1.13858 1.16040 +/- 0.00168\n",
|
|
" 763/1 1.18619 1.16044 +/- 0.00168\n",
|
|
" 764/1 1.20132 1.16049 +/- 0.00168\n",
|
|
" 765/1 1.17576 1.16051 +/- 0.00168\n",
|
|
" 766/1 1.14625 1.16049 +/- 0.00168\n",
|
|
" 767/1 1.17175 1.16051 +/- 0.00168\n",
|
|
" 768/1 1.18898 1.16054 +/- 0.00167\n",
|
|
" 769/1 1.14051 1.16052 +/- 0.00167\n",
|
|
" 770/1 1.13652 1.16049 +/- 0.00167\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0400051223242148 for eigenvalue\n",
|
|
" 771/1 1.15824 1.16048 +/- 0.00167\n",
|
|
" 772/1 1.10704 1.16041 +/- 0.00167\n",
|
|
" 773/1 1.19213 1.16046 +/- 0.00166\n",
|
|
" 774/1 1.15520 1.16045 +/- 0.00166\n",
|
|
" 775/1 1.13123 1.16041 +/- 0.00166\n",
|
|
" 776/1 1.14837 1.16039 +/- 0.00166\n",
|
|
" 777/1 1.10166 1.16032 +/- 0.00166\n",
|
|
" 778/1 1.24197 1.16042 +/- 0.00166\n",
|
|
" 779/1 1.16782 1.16043 +/- 0.00166\n",
|
|
" 780/1 1.24036 1.16054 +/- 0.00166\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0330996148868279 for eigenvalue\n",
|
|
" 781/1 1.10765 1.16047 +/- 0.00166\n",
|
|
" 782/1 1.16934 1.16048 +/- 0.00166\n",
|
|
" 783/1 1.21380 1.16055 +/- 0.00166\n",
|
|
" 784/1 1.15004 1.16054 +/- 0.00165\n",
|
|
" 785/1 1.17317 1.16055 +/- 0.00165\n",
|
|
" 786/1 1.14633 1.16053 +/- 0.00165\n",
|
|
" 787/1 1.15997 1.16053 +/- 0.00165\n",
|
|
" 788/1 1.19370 1.16058 +/- 0.00165\n",
|
|
" 789/1 1.14902 1.16056 +/- 0.00164\n",
|
|
" 790/1 1.19165 1.16060 +/- 0.00164\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0259157644677854 for eigenvalue\n",
|
|
" 791/1 1.17482 1.16062 +/- 0.00164\n",
|
|
" 792/1 1.22696 1.16070 +/- 0.00164\n",
|
|
" 793/1 1.11359 1.16064 +/- 0.00164\n",
|
|
" 794/1 1.16946 1.16066 +/- 0.00164\n",
|
|
" 795/1 1.14679 1.16064 +/- 0.00163\n",
|
|
" 796/1 1.09798 1.16056 +/- 0.00163\n",
|
|
" 797/1 1.20727 1.16062 +/- 0.00163\n",
|
|
" 798/1 1.07710 1.16051 +/- 0.00163\n",
|
|
" 799/1 1.14357 1.16049 +/- 0.00163\n",
|
|
" 800/1 1.17809 1.16051 +/- 0.00163\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0167097251563104 for eigenvalue\n",
|
|
" 801/1 1.15506 1.16050 +/- 0.00163\n",
|
|
" 802/1 1.14992 1.16049 +/- 0.00163\n",
|
|
" 803/1 1.10918 1.16043 +/- 0.00163\n",
|
|
" 804/1 1.11235 1.16037 +/- 0.00163\n",
|
|
" 805/1 1.16903 1.16038 +/- 0.00162\n",
|
|
" 806/1 1.17159 1.16039 +/- 0.00162\n",
|
|
" 807/1 1.14350 1.16037 +/- 0.00162\n",
|
|
" 808/1 1.14684 1.16035 +/- 0.00162\n",
|
|
" 809/1 1.19901 1.16040 +/- 0.00162\n",
|
|
" 810/1 1.16581 1.16041 +/- 0.00161\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0122459782539301 for eigenvalue\n",
|
|
" 811/1 1.19056 1.16045 +/- 0.00161\n",
|
|
" 812/1 1.17979 1.16047 +/- 0.00161\n",
|
|
" 813/1 1.14071 1.16045 +/- 0.00161\n",
|
|
" 814/1 1.17123 1.16046 +/- 0.00161\n",
|
|
" 815/1 1.09156 1.16037 +/- 0.00161\n",
|
|
" 816/1 1.23674 1.16047 +/- 0.00161\n",
|
|
" 817/1 1.19487 1.16051 +/- 0.00161\n",
|
|
" 818/1 1.10640 1.16044 +/- 0.00161\n",
|
|
" 819/1 1.18735 1.16048 +/- 0.00160\n",
|
|
" 820/1 1.15671 1.16047 +/- 0.00160\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.009208315064328 for eigenvalue\n",
|
|
" 821/1 1.20932 1.16053 +/- 0.00160\n",
|
|
" 822/1 1.20855 1.16059 +/- 0.00160\n",
|
|
" 823/1 1.19483 1.16063 +/- 0.00160\n",
|
|
" 824/1 1.08965 1.16055 +/- 0.00160\n",
|
|
" 825/1 1.25639 1.16066 +/- 0.00160\n",
|
|
" 826/1 1.23409 1.16075 +/- 0.00160\n",
|
|
" 827/1 1.12800 1.16071 +/- 0.00160\n",
|
|
" 828/1 1.10317 1.16064 +/- 0.00160\n",
|
|
" 829/1 1.15621 1.16064 +/- 0.00160\n",
|
|
" 830/1 1.16214 1.16064 +/- 0.00160\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.0024712048742856 for eigenvalue\n",
|
|
" 831/1 1.19790 1.16069 +/- 0.00160\n",
|
|
" 832/1 1.13307 1.16065 +/- 0.00159\n",
|
|
" 833/1 1.15022 1.16064 +/- 0.00159\n",
|
|
" 834/1 1.21210 1.16070 +/- 0.00159\n",
|
|
" 835/1 1.16329 1.16070 +/- 0.00159\n",
|
|
" 836/1 1.13296 1.16067 +/- 0.00159\n",
|
|
" 837/1 1.16582 1.16068 +/- 0.00159\n",
|
|
" 838/1 1.13101 1.16064 +/- 0.00158\n",
|
|
" 839/1 1.17725 1.16066 +/- 0.00158\n",
|
|
" 840/1 1.14832 1.16065 +/- 0.00158\n",
|
|
" Triggers satisfied for batch 840\n",
|
|
" Creating state point statepoint.0840.h5...\n",
|
|
"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
|
"\n",
|
|
" Total time for initialization = 6.7754e-01 seconds\n",
|
|
" Reading cross sections = 4.9230e-01 seconds\n",
|
|
" Total time in simulation = 5.7780e+01 seconds\n",
|
|
" Time in transport only = 5.7491e+01 seconds\n",
|
|
" Time in inactive batches = 4.8799e-01 seconds\n",
|
|
" Time in active batches = 5.7292e+01 seconds\n",
|
|
" Time synchronizing fission bank = 4.2732e-02 seconds\n",
|
|
" Sampling source sites = 3.5676e-02 seconds\n",
|
|
" SEND/RECV source sites = 6.8696e-03 seconds\n",
|
|
" Time accumulating tallies = 1.7679e-02 seconds\n",
|
|
" Time writing statepoints = 6.1653e-03 seconds\n",
|
|
" Total time for finalization = 2.4888e-04 seconds\n",
|
|
" Total time elapsed = 5.8462e+01 seconds\n",
|
|
" Calculation Rate (inactive) = 20492.1 particles/second\n",
|
|
" Calculation Rate (active) = 14487.1 particles/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 1.16083 +/- 0.00131\n",
|
|
" k-effective (Track-length) = 1.16065 +/- 0.00158\n",
|
|
" k-effective (Absorption) = 1.16211 +/- 0.00116\n",
|
|
" Combined k-effective = 1.16157 +/- 0.00100\n",
|
|
" Leakage Fraction = 0.00000 +/- 0.00000\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.settings.keff_trigger = {'type': 'std_dev', 'threshold': 0.00100}\n",
|
|
"model.settings.trigger_batch_interval = 10\n",
|
|
"model.settings.trigger_max_batches = 1000\n",
|
|
"model.settings.trigger_active = True\n",
|
|
"\n",
|
|
"statepoint = model.run()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2d892b9b-b362-4506-bfd5-5845cc1939eb",
|
|
"metadata": {},
|
|
"source": [
|
|
"To add a trigger now for a generic tally, we create an `openmc.Trigger` object and apply it to a tally. When multiple triggers are used, OpenMC prints out the trigger which is the furthest from convergence."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"id": "718c7acc-8a24-4786-a16b-dbc04eac46d7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.settings.seed = 43782\n",
|
|
"rel_err_trig = openmc.Trigger(trigger_type='rel_err', threshold=1e-3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 88,
|
|
"id": "d039d985-a446-486e-b5a3-f5ac4acc0c3e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fission_tally.triggers = [rel_err_trig]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 89,
|
|
"id": "f517dde5-cb81-43cc-8497-bf017ea45cf1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
|
|
" License | https://docs.openmc.org/en/latest/license.html\n",
|
|
" Version | 0.15.3\n",
|
|
" Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
|
|
" Date/Time | 2025-11-25 13:44:13\n",
|
|
" OpenMP Threads | 2\n",
|
|
"\n",
|
|
" Reading model XML file 'model.xml' ...\n",
|
|
" Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
|
|
" Reading cross sections XML file...\n",
|
|
" Reading U234 from /home/ubuntu/data/endfb71_hdf5/U234.h5\n",
|
|
" Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
|
|
" Reading U238 from /home/ubuntu/data/endfb71_hdf5/U238.h5\n",
|
|
" Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
|
|
" Reading Zr90 from /home/ubuntu/data/endfb71_hdf5/Zr90.h5\n",
|
|
" Reading Zr91 from /home/ubuntu/data/endfb71_hdf5/Zr91.h5\n",
|
|
" Reading Zr92 from /home/ubuntu/data/endfb71_hdf5/Zr92.h5\n",
|
|
" Reading Zr94 from /home/ubuntu/data/endfb71_hdf5/Zr94.h5\n",
|
|
" Reading Zr96 from /home/ubuntu/data/endfb71_hdf5/Zr96.h5\n",
|
|
" Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
|
|
" Reading B10 from /home/ubuntu/data/endfb71_hdf5/B10.h5\n",
|
|
" Reading B11 from /home/ubuntu/data/endfb71_hdf5/B11.h5\n",
|
|
" Reading c_H_in_H2O from /home/ubuntu/data/endfb71_hdf5/c_H_in_H2O.h5\n",
|
|
" Minimum neutron data temperature: 294 K\n",
|
|
" Maximum neutron data temperature: 294 K\n",
|
|
" Preparing distributed cell instances...\n",
|
|
" Writing summary.h5 file...\n",
|
|
" Maximum neutron transport energy: 20000000 eV for U235\n",
|
|
" Initializing source particles...\n",
|
|
"\n",
|
|
" ====================> K EIGENVALUE SIMULATION <====================\n",
|
|
"\n",
|
|
" Bat./Gen. k Average k\n",
|
|
" ========= ======== ====================\n",
|
|
" 1/1 1.18452\n",
|
|
" 2/1 1.22548\n",
|
|
" 3/1 1.28047\n",
|
|
" 4/1 1.14837\n",
|
|
" 5/1 1.22085\n",
|
|
" 6/1 1.18884\n",
|
|
" 7/1 1.11509\n",
|
|
" 8/1 1.14305\n",
|
|
" 9/1 1.22502\n",
|
|
" 10/1 1.17772\n",
|
|
" 11/1 1.14896\n",
|
|
" 12/1 1.16620 1.15758 +/- 0.00862\n",
|
|
" 13/1 1.15707 1.15741 +/- 0.00498\n",
|
|
" 14/1 1.25468 1.18173 +/- 0.02457\n",
|
|
" 15/1 1.13982 1.17335 +/- 0.02080\n",
|
|
" 16/1 1.18070 1.17457 +/- 0.01702\n",
|
|
" 17/1 1.14706 1.17064 +/- 0.01491\n",
|
|
" 18/1 1.15201 1.16831 +/- 0.01312\n",
|
|
" 19/1 1.16009 1.16740 +/- 0.01161\n",
|
|
" 20/1 1.12090 1.16275 +/- 0.01138\n",
|
|
" 21/1 1.10365 1.15738 +/- 0.01161\n",
|
|
" 22/1 1.10135 1.15271 +/- 0.01158\n",
|
|
" 23/1 1.11882 1.15010 +/- 0.01097\n",
|
|
" 24/1 1.06606 1.14410 +/- 0.01180\n",
|
|
" 25/1 1.20696 1.14829 +/- 0.01175\n",
|
|
" 26/1 1.16543 1.14936 +/- 0.01105\n",
|
|
" 27/1 1.20803 1.15281 +/- 0.01094\n",
|
|
" 28/1 1.08879 1.14925 +/- 0.01091\n",
|
|
" 29/1 1.13871 1.14870 +/- 0.01033\n",
|
|
" 30/1 1.20238 1.15138 +/- 0.01016\n",
|
|
" 31/1 1.19080 1.15326 +/- 0.00985\n",
|
|
" 32/1 1.09579 1.15065 +/- 0.00975\n",
|
|
" 33/1 1.13753 1.15008 +/- 0.00933\n",
|
|
" 34/1 1.18237 1.15142 +/- 0.00903\n",
|
|
" 35/1 1.22722 1.15445 +/- 0.00918\n",
|
|
" 36/1 1.12858 1.15346 +/- 0.00888\n",
|
|
" 37/1 1.14605 1.15318 +/- 0.00854\n",
|
|
" 38/1 1.05665 1.14974 +/- 0.00893\n",
|
|
" 39/1 1.15425 1.14989 +/- 0.00861\n",
|
|
" 40/1 1.16060 1.15025 +/- 0.00833\n",
|
|
" 41/1 1.15793 1.15050 +/- 0.00806\n",
|
|
" 42/1 1.14749 1.15040 +/- 0.00781\n",
|
|
" 43/1 1.14546 1.15025 +/- 0.00757\n",
|
|
" 44/1 1.12049 1.14938 +/- 0.00739\n",
|
|
" 45/1 1.12890 1.14879 +/- 0.00720\n",
|
|
" 46/1 1.08365 1.14698 +/- 0.00723\n",
|
|
" 47/1 1.13417 1.14664 +/- 0.00704\n",
|
|
" 48/1 1.19137 1.14781 +/- 0.00695\n",
|
|
" 49/1 1.16111 1.14816 +/- 0.00678\n",
|
|
" 50/1 1.19854 1.14941 +/- 0.00673\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 5.90298751398992 for fission in tally\n",
|
|
" 1\n",
|
|
" Creating state point statepoint.0050.h5...\n",
|
|
" 51/1 1.11453 1.14856 +/- 0.00662\n",
|
|
" 52/1 1.18518 1.14944 +/- 0.00652\n",
|
|
" 53/1 1.15197 1.14949 +/- 0.00636\n",
|
|
" 54/1 1.09047 1.14815 +/- 0.00636\n",
|
|
" 55/1 1.19298 1.14915 +/- 0.00630\n",
|
|
" 56/1 1.26858 1.15175 +/- 0.00668\n",
|
|
" 57/1 1.18570 1.15247 +/- 0.00658\n",
|
|
" 58/1 1.15932 1.15261 +/- 0.00644\n",
|
|
" 59/1 1.06608 1.15085 +/- 0.00655\n",
|
|
" 60/1 1.15129 1.15085 +/- 0.00642\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 5.6185795369779905 for fission in\n",
|
|
" tally 1\n",
|
|
" 61/1 1.14538 1.15075 +/- 0.00629\n",
|
|
" 62/1 1.20699 1.15183 +/- 0.00626\n",
|
|
" 63/1 1.14872 1.15177 +/- 0.00615\n",
|
|
" 64/1 1.20119 1.15268 +/- 0.00610\n",
|
|
" 65/1 1.08699 1.15149 +/- 0.00611\n",
|
|
" 66/1 1.23721 1.15302 +/- 0.00619\n",
|
|
" 67/1 1.14060 1.15280 +/- 0.00608\n",
|
|
" 68/1 1.14888 1.15274 +/- 0.00598\n",
|
|
" 69/1 1.20757 1.15366 +/- 0.00595\n",
|
|
" 70/1 1.11371 1.15300 +/- 0.00589\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 5.148289541056272 for fission in\n",
|
|
" tally 1\n",
|
|
" 71/1 1.13618 1.15272 +/- 0.00579\n",
|
|
" 72/1 1.13332 1.15241 +/- 0.00571\n",
|
|
" 73/1 1.14890 1.15235 +/- 0.00562\n",
|
|
" 74/1 1.15285 1.15236 +/- 0.00553\n",
|
|
" 75/1 1.15833 1.15245 +/- 0.00544\n",
|
|
" 76/1 1.15880 1.15255 +/- 0.00536\n",
|
|
" 77/1 1.08873 1.15160 +/- 0.00537\n",
|
|
" 78/1 1.18297 1.15206 +/- 0.00531\n",
|
|
" 79/1 1.22571 1.15313 +/- 0.00534\n",
|
|
" 80/1 1.16156 1.15325 +/- 0.00526\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 4.601865390370014 for fission in\n",
|
|
" tally 1\n",
|
|
" 81/1 1.15362 1.15325 +/- 0.00519\n",
|
|
" 82/1 1.17224 1.15352 +/- 0.00512\n",
|
|
" 83/1 1.13445 1.15325 +/- 0.00506\n",
|
|
" 84/1 1.12432 1.15286 +/- 0.00500\n",
|
|
" 85/1 1.14191 1.15272 +/- 0.00494\n",
|
|
" 86/1 1.15460 1.15274 +/- 0.00487\n",
|
|
" 87/1 1.16814 1.15294 +/- 0.00481\n",
|
|
" 88/1 1.23487 1.15399 +/- 0.00487\n",
|
|
" 89/1 1.19847 1.15456 +/- 0.00484\n",
|
|
" 90/1 1.16287 1.15466 +/- 0.00478\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 4.177263762230337 for fission in\n",
|
|
" tally 1\n",
|
|
" 91/1 1.10939 1.15410 +/- 0.00475\n",
|
|
" 92/1 1.14942 1.15404 +/- 0.00469\n",
|
|
" 93/1 1.22490 1.15490 +/- 0.00472\n",
|
|
" 94/1 1.19655 1.15539 +/- 0.00468\n",
|
|
" 95/1 1.17625 1.15564 +/- 0.00464\n",
|
|
" 96/1 1.21259 1.15630 +/- 0.00463\n",
|
|
" 97/1 1.19327 1.15673 +/- 0.00460\n",
|
|
" 98/1 1.18918 1.15709 +/- 0.00456\n",
|
|
" 99/1 1.11493 1.15662 +/- 0.00453\n",
|
|
" 100/1 1.18450 1.15693 +/- 0.00449\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.9186051399950115 for fission in\n",
|
|
" tally 1\n",
|
|
" 101/1 1.17733 1.15715 +/- 0.00445\n",
|
|
" 102/1 1.16348 1.15722 +/- 0.00440\n",
|
|
" 103/1 1.18500 1.15752 +/- 0.00436\n",
|
|
" 104/1 1.17818 1.15774 +/- 0.00432\n",
|
|
" 105/1 1.14522 1.15761 +/- 0.00428\n",
|
|
" 106/1 1.14781 1.15751 +/- 0.00423\n",
|
|
" 107/1 1.13661 1.15729 +/- 0.00420\n",
|
|
" 108/1 1.19392 1.15767 +/- 0.00417\n",
|
|
" 109/1 1.16912 1.15778 +/- 0.00413\n",
|
|
" 110/1 1.19043 1.15811 +/- 0.00410\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.573691611481584 for fission in\n",
|
|
" tally 1\n",
|
|
" 111/1 1.16911 1.15822 +/- 0.00406\n",
|
|
" 112/1 1.04742 1.15713 +/- 0.00416\n",
|
|
" 113/1 1.15928 1.15715 +/- 0.00412\n",
|
|
" 114/1 1.18388 1.15741 +/- 0.00409\n",
|
|
" 115/1 1.09071 1.15677 +/- 0.00410\n",
|
|
" 116/1 1.24256 1.15758 +/- 0.00414\n",
|
|
" 117/1 1.18973 1.15788 +/- 0.00412\n",
|
|
" 118/1 1.19583 1.15823 +/- 0.00409\n",
|
|
" 119/1 1.17313 1.15837 +/- 0.00406\n",
|
|
" 120/1 1.13578 1.15817 +/- 0.00403\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.50702543097018 for fission in tally\n",
|
|
" 1\n",
|
|
" 121/1 1.12207 1.15784 +/- 0.00400\n",
|
|
" 122/1 1.17815 1.15802 +/- 0.00397\n",
|
|
" 123/1 1.11236 1.15762 +/- 0.00396\n",
|
|
" 124/1 1.16978 1.15772 +/- 0.00392\n",
|
|
" 125/1 1.15373 1.15769 +/- 0.00389\n",
|
|
" 126/1 1.08754 1.15709 +/- 0.00390\n",
|
|
" 127/1 1.10487 1.15664 +/- 0.00389\n",
|
|
" 128/1 1.15228 1.15660 +/- 0.00386\n",
|
|
" 129/1 1.19133 1.15689 +/- 0.00384\n",
|
|
" 130/1 1.08008 1.15625 +/- 0.00386\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.3732588080774466 for fission in\n",
|
|
" tally 1\n",
|
|
" 131/1 1.11900 1.15595 +/- 0.00384\n",
|
|
" 132/1 1.14149 1.15583 +/- 0.00381\n",
|
|
" 133/1 1.24631 1.15656 +/- 0.00385\n",
|
|
" 134/1 1.14356 1.15646 +/- 0.00382\n",
|
|
" 135/1 1.10477 1.15604 +/- 0.00381\n",
|
|
" 136/1 1.17208 1.15617 +/- 0.00378\n",
|
|
" 137/1 1.12407 1.15592 +/- 0.00376\n",
|
|
" 138/1 1.14460 1.15583 +/- 0.00373\n",
|
|
" 139/1 1.21541 1.15629 +/- 0.00373\n",
|
|
" 140/1 1.15645 1.15629 +/- 0.00371\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.236447996855347 for fission in\n",
|
|
" tally 1\n",
|
|
" 141/1 1.27185 1.15718 +/- 0.00378\n",
|
|
" 142/1 1.14132 1.15706 +/- 0.00375\n",
|
|
" 143/1 1.17703 1.15721 +/- 0.00373\n",
|
|
" 144/1 1.17418 1.15733 +/- 0.00370\n",
|
|
" 145/1 1.13828 1.15719 +/- 0.00368\n",
|
|
" 146/1 1.17212 1.15730 +/- 0.00365\n",
|
|
" 147/1 1.18293 1.15749 +/- 0.00363\n",
|
|
" 148/1 1.21184 1.15788 +/- 0.00363\n",
|
|
" 149/1 1.20422 1.15822 +/- 0.00362\n",
|
|
" 150/1 1.20621 1.15856 +/- 0.00361\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.1419947833710276 for fission in\n",
|
|
" tally 1\n",
|
|
" 151/1 1.09338 1.15810 +/- 0.00361\n",
|
|
" 152/1 1.13872 1.15796 +/- 0.00359\n",
|
|
" 153/1 1.12279 1.15771 +/- 0.00357\n",
|
|
" 154/1 1.16041 1.15773 +/- 0.00355\n",
|
|
" 155/1 1.05883 1.15705 +/- 0.00359\n",
|
|
" 156/1 1.23203 1.15756 +/- 0.00360\n",
|
|
" 157/1 1.05980 1.15690 +/- 0.00364\n",
|
|
" 158/1 1.13778 1.15677 +/- 0.00361\n",
|
|
" 159/1 1.15132 1.15673 +/- 0.00359\n",
|
|
" 160/1 1.23253 1.15724 +/- 0.00360\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 3.1411211593483683 for fission in\n",
|
|
" tally 1\n",
|
|
" 161/1 1.19327 1.15748 +/- 0.00358\n",
|
|
" 162/1 1.15700 1.15747 +/- 0.00356\n",
|
|
" 163/1 1.13119 1.15730 +/- 0.00354\n",
|
|
" 164/1 1.17702 1.15743 +/- 0.00352\n",
|
|
" 165/1 1.15712 1.15743 +/- 0.00350\n",
|
|
" 166/1 1.15724 1.15743 +/- 0.00348\n",
|
|
" 167/1 1.13308 1.15727 +/- 0.00346\n",
|
|
" 168/1 1.12910 1.15709 +/- 0.00344\n",
|
|
" 169/1 1.09441 1.15670 +/- 0.00344\n",
|
|
" 170/1 1.18159 1.15686 +/- 0.00342\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.986240776773681 for fission in\n",
|
|
" tally 1\n",
|
|
" 171/1 1.11216 1.15658 +/- 0.00341\n",
|
|
" 172/1 1.20735 1.15689 +/- 0.00341\n",
|
|
" 173/1 1.19752 1.15714 +/- 0.00339\n",
|
|
" 174/1 1.24902 1.15770 +/- 0.00342\n",
|
|
" 175/1 1.22839 1.15813 +/- 0.00343\n",
|
|
" 176/1 1.17283 1.15822 +/- 0.00341\n",
|
|
" 177/1 1.18381 1.15837 +/- 0.00339\n",
|
|
" 178/1 1.17378 1.15846 +/- 0.00337\n",
|
|
" 179/1 1.12623 1.15827 +/- 0.00336\n",
|
|
" 180/1 1.17677 1.15838 +/- 0.00334\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.9071867577554067 for fission in\n",
|
|
" tally 1\n",
|
|
" 181/1 1.25349 1.15894 +/- 0.00336\n",
|
|
" 182/1 1.11057 1.15866 +/- 0.00336\n",
|
|
" 183/1 1.19360 1.15886 +/- 0.00334\n",
|
|
" 184/1 1.14001 1.15875 +/- 0.00333\n",
|
|
" 185/1 1.13846 1.15863 +/- 0.00331\n",
|
|
" 186/1 1.14713 1.15857 +/- 0.00329\n",
|
|
" 187/1 1.10419 1.15826 +/- 0.00329\n",
|
|
" 188/1 1.14969 1.15821 +/- 0.00327\n",
|
|
" 189/1 1.17563 1.15831 +/- 0.00325\n",
|
|
" 190/1 1.19935 1.15854 +/- 0.00324\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.8235971776926623 for fission in\n",
|
|
" tally 1\n",
|
|
" 191/1 1.17659 1.15864 +/- 0.00322\n",
|
|
" 192/1 1.12423 1.15845 +/- 0.00321\n",
|
|
" 193/1 1.16069 1.15846 +/- 0.00319\n",
|
|
" 194/1 1.15890 1.15846 +/- 0.00318\n",
|
|
" 195/1 1.18529 1.15861 +/- 0.00316\n",
|
|
" 196/1 1.11095 1.15835 +/- 0.00316\n",
|
|
" 197/1 1.17296 1.15843 +/- 0.00314\n",
|
|
" 198/1 1.17555 1.15852 +/- 0.00313\n",
|
|
" 199/1 1.22248 1.15886 +/- 0.00313\n",
|
|
" 200/1 1.18555 1.15900 +/- 0.00311\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.7118226927106233 for fission in\n",
|
|
" tally 1\n",
|
|
" 201/1 1.15348 1.15897 +/- 0.00310\n",
|
|
" 202/1 1.17501 1.15905 +/- 0.00308\n",
|
|
" 203/1 1.22354 1.15939 +/- 0.00308\n",
|
|
" 204/1 1.20392 1.15962 +/- 0.00308\n",
|
|
" 205/1 1.15772 1.15961 +/- 0.00306\n",
|
|
" 206/1 1.27764 1.16021 +/- 0.00311\n",
|
|
" 207/1 1.17312 1.16028 +/- 0.00309\n",
|
|
" 208/1 1.10507 1.16000 +/- 0.00309\n",
|
|
" 209/1 1.12795 1.15984 +/- 0.00308\n",
|
|
" 210/1 1.14587 1.15977 +/- 0.00306\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.6627295926210737 for fission in\n",
|
|
" tally 1\n",
|
|
" 211/1 1.20106 1.15997 +/- 0.00305\n",
|
|
" 212/1 1.12269 1.15979 +/- 0.00304\n",
|
|
" 213/1 1.17329 1.15985 +/- 0.00303\n",
|
|
" 214/1 1.12702 1.15969 +/- 0.00302\n",
|
|
" 215/1 1.20944 1.15994 +/- 0.00301\n",
|
|
" 216/1 1.13599 1.15982 +/- 0.00300\n",
|
|
" 217/1 1.15183 1.15978 +/- 0.00299\n",
|
|
" 218/1 1.17120 1.15984 +/- 0.00297\n",
|
|
" 219/1 1.24916 1.16026 +/- 0.00299\n",
|
|
" 220/1 1.12741 1.16011 +/- 0.00298\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.590077441709246 for fission in\n",
|
|
" tally 1\n",
|
|
" 221/1 1.15101 1.16006 +/- 0.00297\n",
|
|
" 222/1 1.17056 1.16011 +/- 0.00295\n",
|
|
" 223/1 1.11638 1.15991 +/- 0.00294\n",
|
|
" 224/1 1.23526 1.16026 +/- 0.00295\n",
|
|
" 225/1 1.11248 1.16004 +/- 0.00295\n",
|
|
" 226/1 1.18779 1.16017 +/- 0.00294\n",
|
|
" 227/1 1.21695 1.16043 +/- 0.00293\n",
|
|
" 228/1 1.19032 1.16057 +/- 0.00292\n",
|
|
" 229/1 1.17807 1.16064 +/- 0.00291\n",
|
|
" 230/1 1.21159 1.16088 +/- 0.00291\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.5254882720286242 for fission in\n",
|
|
" tally 1\n",
|
|
" 231/1 1.24427 1.16125 +/- 0.00292\n",
|
|
" 232/1 1.17208 1.16130 +/- 0.00291\n",
|
|
" 233/1 1.12334 1.16113 +/- 0.00290\n",
|
|
" 234/1 1.07195 1.16073 +/- 0.00291\n",
|
|
" 235/1 1.17503 1.16080 +/- 0.00290\n",
|
|
" 236/1 1.15681 1.16078 +/- 0.00289\n",
|
|
" 237/1 1.15201 1.16074 +/- 0.00287\n",
|
|
" 238/1 1.14855 1.16069 +/- 0.00286\n",
|
|
" 239/1 1.17723 1.16076 +/- 0.00285\n",
|
|
" 240/1 1.14278 1.16068 +/- 0.00284\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.4662576052907617 for fission in\n",
|
|
" tally 1\n",
|
|
" 241/1 1.17489 1.16074 +/- 0.00283\n",
|
|
" 242/1 1.14959 1.16070 +/- 0.00282\n",
|
|
" 243/1 1.09629 1.16042 +/- 0.00282\n",
|
|
" 244/1 1.11259 1.16021 +/- 0.00281\n",
|
|
" 245/1 1.17466 1.16028 +/- 0.00280\n",
|
|
" 246/1 1.20285 1.16046 +/- 0.00280\n",
|
|
" 247/1 1.16155 1.16046 +/- 0.00278\n",
|
|
" 248/1 1.16199 1.16047 +/- 0.00277\n",
|
|
" 249/1 1.16635 1.16049 +/- 0.00276\n",
|
|
" 250/1 1.06466 1.16009 +/- 0.00278\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.413735899152342 for fission in\n",
|
|
" tally 1\n",
|
|
" 251/1 1.16609 1.16012 +/- 0.00277\n",
|
|
" 252/1 1.20279 1.16029 +/- 0.00276\n",
|
|
" 253/1 1.15422 1.16027 +/- 0.00275\n",
|
|
" 254/1 1.16743 1.16030 +/- 0.00274\n",
|
|
" 255/1 1.07761 1.15996 +/- 0.00275\n",
|
|
" 256/1 1.14600 1.15990 +/- 0.00274\n",
|
|
" 257/1 1.12890 1.15978 +/- 0.00273\n",
|
|
" 258/1 1.23836 1.16010 +/- 0.00274\n",
|
|
" 259/1 1.16033 1.16010 +/- 0.00273\n",
|
|
" 260/1 1.24614 1.16044 +/- 0.00274\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.3751588720509864 for fission in\n",
|
|
" tally 1\n",
|
|
" 261/1 1.19071 1.16056 +/- 0.00273\n",
|
|
" 262/1 1.19850 1.16071 +/- 0.00272\n",
|
|
" 263/1 1.13904 1.16063 +/- 0.00271\n",
|
|
" 264/1 1.06931 1.16027 +/- 0.00272\n",
|
|
" 265/1 1.13795 1.16018 +/- 0.00272\n",
|
|
" 266/1 1.14625 1.16012 +/- 0.00271\n",
|
|
" 267/1 1.22590 1.16038 +/- 0.00271\n",
|
|
" 268/1 1.18575 1.16048 +/- 0.00270\n",
|
|
" 269/1 1.03090 1.15998 +/- 0.00273\n",
|
|
" 270/1 1.18177 1.16006 +/- 0.00272\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.3655089886086205 for fission in\n",
|
|
" tally 1\n",
|
|
" 271/1 1.20009 1.16022 +/- 0.00272\n",
|
|
" 272/1 1.14914 1.16017 +/- 0.00271\n",
|
|
" 273/1 1.24798 1.16051 +/- 0.00272\n",
|
|
" 274/1 1.14138 1.16043 +/- 0.00271\n",
|
|
" 275/1 1.14413 1.16037 +/- 0.00270\n",
|
|
" 276/1 1.17418 1.16043 +/- 0.00269\n",
|
|
" 277/1 1.17611 1.16048 +/- 0.00268\n",
|
|
" 278/1 1.15292 1.16046 +/- 0.00267\n",
|
|
" 279/1 1.13202 1.16035 +/- 0.00266\n",
|
|
" 280/1 1.15832 1.16034 +/- 0.00265\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.3031419298720373 for fission in\n",
|
|
" tally 1\n",
|
|
" 281/1 1.09626 1.16011 +/- 0.00265\n",
|
|
" 282/1 1.18556 1.16020 +/- 0.00265\n",
|
|
" 283/1 1.20186 1.16035 +/- 0.00264\n",
|
|
" 284/1 1.20995 1.16053 +/- 0.00264\n",
|
|
" 285/1 1.18790 1.16063 +/- 0.00263\n",
|
|
" 286/1 1.12241 1.16049 +/- 0.00262\n",
|
|
" 287/1 1.11485 1.16033 +/- 0.00262\n",
|
|
" 288/1 1.15829 1.16032 +/- 0.00261\n",
|
|
" 289/1 1.11508 1.16016 +/- 0.00261\n",
|
|
" 290/1 1.13552 1.16007 +/- 0.00260\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.25610899014523 for fission in tally\n",
|
|
" 1\n",
|
|
" 291/1 1.10896 1.15989 +/- 0.00259\n",
|
|
" 292/1 1.16790 1.15992 +/- 0.00259\n",
|
|
" 293/1 1.14526 1.15987 +/- 0.00258\n",
|
|
" 294/1 1.20359 1.16002 +/- 0.00257\n",
|
|
" 295/1 1.15116 1.15999 +/- 0.00256\n",
|
|
" 296/1 1.11065 1.15982 +/- 0.00256\n",
|
|
" 297/1 1.19470 1.15994 +/- 0.00255\n",
|
|
" 298/1 1.13197 1.15984 +/- 0.00255\n",
|
|
" 299/1 1.16638 1.15986 +/- 0.00254\n",
|
|
" 300/1 1.13176 1.15977 +/- 0.00253\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.199393221038069 for fission in\n",
|
|
" tally 1\n",
|
|
" 301/1 1.20857 1.15994 +/- 0.00253\n",
|
|
" 302/1 1.17180 1.15998 +/- 0.00252\n",
|
|
" 303/1 1.18304 1.16005 +/- 0.00251\n",
|
|
" 304/1 1.16096 1.16006 +/- 0.00250\n",
|
|
" 305/1 1.14775 1.16002 +/- 0.00250\n",
|
|
" 306/1 1.17141 1.16005 +/- 0.00249\n",
|
|
" 307/1 1.13349 1.15996 +/- 0.00248\n",
|
|
" 308/1 1.13595 1.15988 +/- 0.00247\n",
|
|
" 309/1 1.11001 1.15972 +/- 0.00247\n",
|
|
" 310/1 1.18558 1.15980 +/- 0.00247\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.1407213414802904 for fission in\n",
|
|
" tally 1\n",
|
|
" 311/1 1.19739 1.15993 +/- 0.00246\n",
|
|
" 312/1 1.14192 1.15987 +/- 0.00245\n",
|
|
" 313/1 1.19164 1.15997 +/- 0.00245\n",
|
|
" 314/1 1.16355 1.15999 +/- 0.00244\n",
|
|
" 315/1 1.08338 1.15973 +/- 0.00244\n",
|
|
" 316/1 1.14686 1.15969 +/- 0.00244\n",
|
|
" 317/1 1.18801 1.15978 +/- 0.00243\n",
|
|
" 318/1 1.12935 1.15969 +/- 0.00242\n",
|
|
" 319/1 1.17350 1.15973 +/- 0.00242\n",
|
|
" 320/1 1.17136 1.15977 +/- 0.00241\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.0925802762161987 for fission in\n",
|
|
" tally 1\n",
|
|
" 321/1 1.17381 1.15981 +/- 0.00240\n",
|
|
" 322/1 1.13758 1.15974 +/- 0.00239\n",
|
|
" 323/1 1.20270 1.15988 +/- 0.00239\n",
|
|
" 324/1 1.20626 1.16003 +/- 0.00239\n",
|
|
" 325/1 1.14374 1.15998 +/- 0.00238\n",
|
|
" 326/1 1.17204 1.16001 +/- 0.00237\n",
|
|
" 327/1 1.22259 1.16021 +/- 0.00237\n",
|
|
" 328/1 1.23092 1.16043 +/- 0.00238\n",
|
|
" 329/1 1.16972 1.16046 +/- 0.00237\n",
|
|
" 330/1 1.09760 1.16027 +/- 0.00237\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.05857822548654 for fission in tally\n",
|
|
" 1\n",
|
|
" 331/1 1.19178 1.16036 +/- 0.00237\n",
|
|
" 332/1 1.12923 1.16027 +/- 0.00236\n",
|
|
" 333/1 1.12597 1.16016 +/- 0.00236\n",
|
|
" 334/1 1.16429 1.16017 +/- 0.00235\n",
|
|
" 335/1 1.11297 1.16003 +/- 0.00235\n",
|
|
" 336/1 1.08790 1.15981 +/- 0.00235\n",
|
|
" 337/1 1.21909 1.15999 +/- 0.00235\n",
|
|
" 338/1 1.11992 1.15987 +/- 0.00234\n",
|
|
" 339/1 1.19799 1.15998 +/- 0.00234\n",
|
|
" 340/1 1.16001 1.15998 +/- 0.00233\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.025819386631143 for fission in\n",
|
|
" tally 1\n",
|
|
" 341/1 1.11743 1.15985 +/- 0.00233\n",
|
|
" 342/1 1.09815 1.15967 +/- 0.00233\n",
|
|
" 343/1 1.13220 1.15959 +/- 0.00232\n",
|
|
" 344/1 1.15567 1.15957 +/- 0.00232\n",
|
|
" 345/1 1.15096 1.15955 +/- 0.00231\n",
|
|
" 346/1 1.10683 1.15939 +/- 0.00231\n",
|
|
" 347/1 1.21587 1.15956 +/- 0.00231\n",
|
|
" 348/1 1.15898 1.15956 +/- 0.00230\n",
|
|
" 349/1 1.28272 1.15992 +/- 0.00232\n",
|
|
" 350/1 1.19508 1.16002 +/- 0.00232\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 2.0141469887906767 for fission in\n",
|
|
" tally 1\n",
|
|
" 351/1 1.14170 1.15997 +/- 0.00231\n",
|
|
" 352/1 1.22611 1.16016 +/- 0.00231\n",
|
|
" 353/1 1.12683 1.16007 +/- 0.00231\n",
|
|
" 354/1 1.10289 1.15990 +/- 0.00231\n",
|
|
" 355/1 1.11031 1.15976 +/- 0.00231\n",
|
|
" 356/1 1.18864 1.15984 +/- 0.00230\n",
|
|
" 357/1 1.22910 1.16004 +/- 0.00230\n",
|
|
" 358/1 1.16602 1.16006 +/- 0.00230\n",
|
|
" 359/1 1.14841 1.16002 +/- 0.00229\n",
|
|
" 360/1 1.13831 1.15996 +/- 0.00228\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.9842676365758125 for fission in\n",
|
|
" tally 1\n",
|
|
" 361/1 1.06087 1.15968 +/- 0.00230\n",
|
|
" 362/1 1.26760 1.15999 +/- 0.00231\n",
|
|
" 363/1 1.13288 1.15991 +/- 0.00230\n",
|
|
" 364/1 1.14441 1.15986 +/- 0.00230\n",
|
|
" 365/1 1.13699 1.15980 +/- 0.00229\n",
|
|
" 366/1 1.19447 1.15990 +/- 0.00229\n",
|
|
" 367/1 1.17922 1.15995 +/- 0.00228\n",
|
|
" 368/1 1.16830 1.15998 +/- 0.00228\n",
|
|
" 369/1 1.16121 1.15998 +/- 0.00227\n",
|
|
" 370/1 1.11381 1.15985 +/- 0.00227\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.969452143129258 for fission in\n",
|
|
" tally 1\n",
|
|
" 371/1 1.15286 1.15983 +/- 0.00226\n",
|
|
" 372/1 1.15448 1.15982 +/- 0.00225\n",
|
|
" 373/1 1.16178 1.15982 +/- 0.00225\n",
|
|
" 374/1 1.13665 1.15976 +/- 0.00224\n",
|
|
" 375/1 1.18406 1.15982 +/- 0.00224\n",
|
|
" 376/1 1.07108 1.15958 +/- 0.00224\n",
|
|
" 377/1 1.22134 1.15975 +/- 0.00225\n",
|
|
" 378/1 1.23027 1.15994 +/- 0.00225\n",
|
|
" 379/1 1.17410 1.15998 +/- 0.00224\n",
|
|
" 380/1 1.15233 1.15996 +/- 0.00224\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.9419992404114979 for fission in\n",
|
|
" tally 1\n",
|
|
" 381/1 1.14279 1.15991 +/- 0.00223\n",
|
|
" 382/1 1.16558 1.15993 +/- 0.00222\n",
|
|
" 383/1 1.12547 1.15984 +/- 0.00222\n",
|
|
" 384/1 1.16077 1.15984 +/- 0.00221\n",
|
|
" 385/1 1.16331 1.15985 +/- 0.00221\n",
|
|
" 386/1 1.20534 1.15997 +/- 0.00221\n",
|
|
" 387/1 1.15820 1.15996 +/- 0.00220\n",
|
|
" 388/1 1.10060 1.15981 +/- 0.00220\n",
|
|
" 389/1 1.10784 1.15967 +/- 0.00220\n",
|
|
" 390/1 1.11001 1.15954 +/- 0.00220\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.9081458359286168 for fission in\n",
|
|
" tally 1\n",
|
|
" 391/1 1.21094 1.15967 +/- 0.00219\n",
|
|
" 392/1 1.16716 1.15969 +/- 0.00219\n",
|
|
" 393/1 1.15071 1.15967 +/- 0.00218\n",
|
|
" 394/1 1.20053 1.15978 +/- 0.00218\n",
|
|
" 395/1 1.14569 1.15974 +/- 0.00217\n",
|
|
" 396/1 1.08472 1.15955 +/- 0.00218\n",
|
|
" 397/1 1.18673 1.15962 +/- 0.00217\n",
|
|
" 398/1 1.20326 1.15973 +/- 0.00217\n",
|
|
" 399/1 1.18226 1.15979 +/- 0.00217\n",
|
|
" 400/1 1.20164 1.15989 +/- 0.00216\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.8790349237581148 for fission in\n",
|
|
" tally 1\n",
|
|
" 401/1 1.21470 1.16003 +/- 0.00216\n",
|
|
" 402/1 1.10287 1.15989 +/- 0.00216\n",
|
|
" 403/1 1.18410 1.15995 +/- 0.00216\n",
|
|
" 404/1 1.14040 1.15990 +/- 0.00215\n",
|
|
" 405/1 1.21836 1.16005 +/- 0.00215\n",
|
|
" 406/1 1.18402 1.16011 +/- 0.00215\n",
|
|
" 407/1 1.14508 1.16007 +/- 0.00214\n",
|
|
" 408/1 1.10827 1.15994 +/- 0.00214\n",
|
|
" 409/1 1.19408 1.16003 +/- 0.00214\n",
|
|
" 410/1 1.15274 1.16001 +/- 0.00213\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.8513346282167602 for fission in\n",
|
|
" tally 1\n",
|
|
" 411/1 1.17759 1.16005 +/- 0.00213\n",
|
|
" 412/1 1.23909 1.16025 +/- 0.00213\n",
|
|
" 413/1 1.18765 1.16032 +/- 0.00213\n",
|
|
" 414/1 1.16861 1.16034 +/- 0.00212\n",
|
|
" 415/1 1.26025 1.16058 +/- 0.00213\n",
|
|
" 416/1 1.11862 1.16048 +/- 0.00213\n",
|
|
" 417/1 1.17194 1.16051 +/- 0.00212\n",
|
|
" 418/1 1.20228 1.16061 +/- 0.00212\n",
|
|
" 419/1 1.17482 1.16065 +/- 0.00211\n",
|
|
" 420/1 1.15517 1.16063 +/- 0.00211\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.8309212828253167 for fission in\n",
|
|
" tally 1\n",
|
|
" 421/1 1.24498 1.16084 +/- 0.00211\n",
|
|
" 422/1 1.20507 1.16094 +/- 0.00211\n",
|
|
" 423/1 1.17854 1.16099 +/- 0.00211\n",
|
|
" 424/1 1.14704 1.16095 +/- 0.00210\n",
|
|
" 425/1 1.20011 1.16105 +/- 0.00210\n",
|
|
" 426/1 1.16379 1.16105 +/- 0.00209\n",
|
|
" 427/1 1.22255 1.16120 +/- 0.00209\n",
|
|
" 428/1 1.14377 1.16116 +/- 0.00209\n",
|
|
" 429/1 1.18765 1.16122 +/- 0.00209\n",
|
|
" 430/1 1.21945 1.16136 +/- 0.00209\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.809216340694154 for fission in\n",
|
|
" tally 1\n",
|
|
" 431/1 1.16168 1.16136 +/- 0.00208\n",
|
|
" 432/1 1.21358 1.16149 +/- 0.00208\n",
|
|
" 433/1 1.11040 1.16137 +/- 0.00208\n",
|
|
" 434/1 1.15668 1.16136 +/- 0.00207\n",
|
|
" 435/1 1.18634 1.16141 +/- 0.00207\n",
|
|
" 436/1 1.17987 1.16146 +/- 0.00206\n",
|
|
" 437/1 1.17723 1.16149 +/- 0.00206\n",
|
|
" 438/1 1.16566 1.16150 +/- 0.00206\n",
|
|
" 439/1 1.18712 1.16156 +/- 0.00205\n",
|
|
" 440/1 1.15007 1.16154 +/- 0.00205\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.7754384982433196 for fission in\n",
|
|
" tally 1\n",
|
|
" 441/1 1.18786 1.16160 +/- 0.00204\n",
|
|
" 442/1 1.16612 1.16161 +/- 0.00204\n",
|
|
" 443/1 1.16671 1.16162 +/- 0.00203\n",
|
|
" 444/1 1.14235 1.16158 +/- 0.00203\n",
|
|
" 445/1 1.11463 1.16147 +/- 0.00203\n",
|
|
" 446/1 1.11531 1.16136 +/- 0.00203\n",
|
|
" 447/1 1.15363 1.16134 +/- 0.00202\n",
|
|
" 448/1 1.16119 1.16134 +/- 0.00202\n",
|
|
" 449/1 1.18941 1.16141 +/- 0.00201\n",
|
|
" 450/1 1.18376 1.16146 +/- 0.00201\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.7428488361519452 for fission in\n",
|
|
" tally 1\n",
|
|
" 451/1 1.28486 1.16174 +/- 0.00202\n",
|
|
" 452/1 1.22236 1.16188 +/- 0.00202\n",
|
|
" 453/1 1.13089 1.16181 +/- 0.00202\n",
|
|
" 454/1 1.16372 1.16181 +/- 0.00202\n",
|
|
" 455/1 1.11461 1.16170 +/- 0.00201\n",
|
|
" 456/1 1.19295 1.16177 +/- 0.00201\n",
|
|
" 457/1 1.18854 1.16183 +/- 0.00201\n",
|
|
" 458/1 1.14395 1.16179 +/- 0.00200\n",
|
|
" 459/1 1.20998 1.16190 +/- 0.00200\n",
|
|
" 460/1 1.14960 1.16187 +/- 0.00200\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.7324481148700657 for fission in\n",
|
|
" tally 1\n",
|
|
" 461/1 1.12403 1.16179 +/- 0.00199\n",
|
|
" 462/1 1.15049 1.16177 +/- 0.00199\n",
|
|
" 463/1 1.12938 1.16169 +/- 0.00199\n",
|
|
" 464/1 1.09753 1.16155 +/- 0.00199\n",
|
|
" 465/1 1.21438 1.16167 +/- 0.00199\n",
|
|
" 466/1 1.11592 1.16157 +/- 0.00199\n",
|
|
" 467/1 1.14666 1.16154 +/- 0.00198\n",
|
|
" 468/1 1.13763 1.16148 +/- 0.00198\n",
|
|
" 469/1 1.01143 1.16116 +/- 0.00200\n",
|
|
" 470/1 1.20108 1.16124 +/- 0.00200\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.7338469753297434 for fission in\n",
|
|
" tally 1\n",
|
|
" 471/1 1.20162 1.16133 +/- 0.00200\n",
|
|
" 472/1 1.21000 1.16144 +/- 0.00199\n",
|
|
" 473/1 1.11418 1.16133 +/- 0.00199\n",
|
|
" 474/1 1.12373 1.16125 +/- 0.00199\n",
|
|
" 475/1 1.15058 1.16123 +/- 0.00199\n",
|
|
" 476/1 1.21586 1.16135 +/- 0.00198\n",
|
|
" 477/1 1.14505 1.16131 +/- 0.00198\n",
|
|
" 478/1 1.15757 1.16130 +/- 0.00198\n",
|
|
" 479/1 1.14999 1.16128 +/- 0.00197\n",
|
|
" 480/1 1.15901 1.16128 +/- 0.00197\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.7082050758295246 for fission in\n",
|
|
" tally 1\n",
|
|
" 481/1 1.11569 1.16118 +/- 0.00197\n",
|
|
" 482/1 1.18604 1.16123 +/- 0.00196\n",
|
|
" 483/1 1.17109 1.16125 +/- 0.00196\n",
|
|
" 484/1 1.10726 1.16114 +/- 0.00196\n",
|
|
" 485/1 1.19309 1.16121 +/- 0.00195\n",
|
|
" 486/1 1.11021 1.16110 +/- 0.00195\n",
|
|
" 487/1 1.25568 1.16130 +/- 0.00196\n",
|
|
" 488/1 1.18761 1.16135 +/- 0.00196\n",
|
|
" 489/1 1.17015 1.16137 +/- 0.00195\n",
|
|
" 490/1 1.24594 1.16155 +/- 0.00196\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6970072328088472 for fission in\n",
|
|
" tally 1\n",
|
|
" 491/1 1.11876 1.16146 +/- 0.00195\n",
|
|
" 492/1 1.13745 1.16141 +/- 0.00195\n",
|
|
" 493/1 1.18639 1.16146 +/- 0.00195\n",
|
|
" 494/1 1.19407 1.16153 +/- 0.00194\n",
|
|
" 495/1 1.10880 1.16142 +/- 0.00194\n",
|
|
" 496/1 1.18138 1.16146 +/- 0.00194\n",
|
|
" 497/1 1.20048 1.16154 +/- 0.00194\n",
|
|
" 498/1 1.18218 1.16158 +/- 0.00193\n",
|
|
" 499/1 1.15178 1.16156 +/- 0.00193\n",
|
|
" 500/1 1.14402 1.16153 +/- 0.00193\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6714385453733254 for fission in\n",
|
|
" tally 1\n",
|
|
" 501/1 1.09845 1.16140 +/- 0.00193\n",
|
|
" 502/1 1.09396 1.16126 +/- 0.00193\n",
|
|
" 503/1 1.17951 1.16130 +/- 0.00192\n",
|
|
" 504/1 1.16466 1.16130 +/- 0.00192\n",
|
|
" 505/1 1.18263 1.16135 +/- 0.00192\n",
|
|
" 506/1 1.16104 1.16135 +/- 0.00191\n",
|
|
" 507/1 1.21103 1.16145 +/- 0.00191\n",
|
|
" 508/1 1.09983 1.16132 +/- 0.00191\n",
|
|
" 509/1 1.20840 1.16142 +/- 0.00191\n",
|
|
" 510/1 1.29302 1.16168 +/- 0.00192\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.66993689419649 for fission in tally\n",
|
|
" 1\n",
|
|
" 511/1 1.22824 1.16181 +/- 0.00193\n",
|
|
" 512/1 1.19923 1.16189 +/- 0.00192\n",
|
|
" 513/1 1.10386 1.16177 +/- 0.00192\n",
|
|
" 514/1 1.15286 1.16175 +/- 0.00192\n",
|
|
" 515/1 1.14736 1.16173 +/- 0.00192\n",
|
|
" 516/1 1.14271 1.16169 +/- 0.00191\n",
|
|
" 517/1 1.16090 1.16169 +/- 0.00191\n",
|
|
" 518/1 1.12926 1.16162 +/- 0.00191\n",
|
|
" 519/1 1.15718 1.16161 +/- 0.00190\n",
|
|
" 520/1 1.21454 1.16172 +/- 0.00190\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6491359877683698 for fission in\n",
|
|
" tally 1\n",
|
|
" 521/1 1.15091 1.16170 +/- 0.00190\n",
|
|
" 522/1 1.15650 1.16169 +/- 0.00189\n",
|
|
" 523/1 1.15937 1.16168 +/- 0.00189\n",
|
|
" 524/1 1.20908 1.16177 +/- 0.00189\n",
|
|
" 525/1 1.17480 1.16180 +/- 0.00189\n",
|
|
" 526/1 1.11071 1.16170 +/- 0.00188\n",
|
|
" 527/1 1.16608 1.16171 +/- 0.00188\n",
|
|
" 528/1 1.13321 1.16165 +/- 0.00188\n",
|
|
" 529/1 1.10734 1.16155 +/- 0.00188\n",
|
|
" 530/1 1.14109 1.16151 +/- 0.00187\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6254449047410648 for fission in\n",
|
|
" tally 1\n",
|
|
" 531/1 1.18482 1.16155 +/- 0.00187\n",
|
|
" 532/1 1.12048 1.16148 +/- 0.00187\n",
|
|
" 533/1 1.15231 1.16146 +/- 0.00187\n",
|
|
" 534/1 1.13134 1.16140 +/- 0.00186\n",
|
|
" 535/1 1.18586 1.16145 +/- 0.00186\n",
|
|
" 536/1 1.21003 1.16154 +/- 0.00186\n",
|
|
" 537/1 1.19771 1.16161 +/- 0.00186\n",
|
|
" 538/1 1.13865 1.16157 +/- 0.00185\n",
|
|
" 539/1 1.17853 1.16160 +/- 0.00185\n",
|
|
" 540/1 1.10924 1.16150 +/- 0.00185\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.6039109268365503 for fission in\n",
|
|
" tally 1\n",
|
|
" 541/1 1.22326 1.16161 +/- 0.00185\n",
|
|
" 542/1 1.12913 1.16155 +/- 0.00185\n",
|
|
" 543/1 1.11790 1.16147 +/- 0.00184\n",
|
|
" 544/1 1.11222 1.16138 +/- 0.00184\n",
|
|
" 545/1 1.11181 1.16129 +/- 0.00184\n",
|
|
" 546/1 1.16659 1.16130 +/- 0.00184\n",
|
|
" 547/1 1.09324 1.16117 +/- 0.00184\n",
|
|
" 548/1 1.10936 1.16107 +/- 0.00184\n",
|
|
" 549/1 1.12117 1.16100 +/- 0.00184\n",
|
|
" 550/1 1.17136 1.16102 +/- 0.00183\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5915976258178324 for fission in\n",
|
|
" tally 1\n",
|
|
" 551/1 1.12202 1.16095 +/- 0.00183\n",
|
|
" 552/1 1.27838 1.16116 +/- 0.00184\n",
|
|
" 553/1 1.18521 1.16121 +/- 0.00184\n",
|
|
" 554/1 1.12863 1.16115 +/- 0.00184\n",
|
|
" 555/1 1.18074 1.16118 +/- 0.00183\n",
|
|
" 556/1 1.18727 1.16123 +/- 0.00183\n",
|
|
" 557/1 1.15282 1.16122 +/- 0.00183\n",
|
|
" 558/1 1.15092 1.16120 +/- 0.00182\n",
|
|
" 559/1 1.15433 1.16119 +/- 0.00182\n",
|
|
" 560/1 1.20693 1.16127 +/- 0.00182\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5781421962245008 for fission in\n",
|
|
" tally 1\n",
|
|
" 561/1 1.15914 1.16126 +/- 0.00182\n",
|
|
" 562/1 1.17745 1.16129 +/- 0.00181\n",
|
|
" 563/1 1.20059 1.16136 +/- 0.00181\n",
|
|
" 564/1 1.17679 1.16139 +/- 0.00181\n",
|
|
" 565/1 1.13101 1.16134 +/- 0.00181\n",
|
|
" 566/1 1.08991 1.16121 +/- 0.00181\n",
|
|
" 567/1 1.17916 1.16124 +/- 0.00180\n",
|
|
" 568/1 1.10608 1.16114 +/- 0.00180\n",
|
|
" 569/1 1.21938 1.16125 +/- 0.00180\n",
|
|
" 570/1 1.20095 1.16132 +/- 0.00180\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.562231879103241 for fission in\n",
|
|
" tally 1\n",
|
|
" 571/1 1.15109 1.16130 +/- 0.00180\n",
|
|
" 572/1 1.17190 1.16132 +/- 0.00180\n",
|
|
" 573/1 1.23084 1.16144 +/- 0.00180\n",
|
|
" 574/1 1.13051 1.16139 +/- 0.00179\n",
|
|
" 575/1 1.14321 1.16135 +/- 0.00179\n",
|
|
" 576/1 1.09362 1.16124 +/- 0.00179\n",
|
|
" 577/1 1.15294 1.16122 +/- 0.00179\n",
|
|
" 578/1 1.09700 1.16111 +/- 0.00179\n",
|
|
" 579/1 1.13506 1.16106 +/- 0.00179\n",
|
|
" 580/1 1.11114 1.16097 +/- 0.00179\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5490080791402718 for fission in\n",
|
|
" tally 1\n",
|
|
" 581/1 1.22617 1.16109 +/- 0.00179\n",
|
|
" 582/1 1.13880 1.16105 +/- 0.00178\n",
|
|
" 583/1 1.17648 1.16108 +/- 0.00178\n",
|
|
" 584/1 1.14060 1.16104 +/- 0.00178\n",
|
|
" 585/1 1.14303 1.16101 +/- 0.00177\n",
|
|
" 586/1 1.14707 1.16099 +/- 0.00177\n",
|
|
" 587/1 1.22793 1.16110 +/- 0.00177\n",
|
|
" 588/1 1.14471 1.16107 +/- 0.00177\n",
|
|
" 589/1 1.19508 1.16113 +/- 0.00177\n",
|
|
" 590/1 1.09867 1.16102 +/- 0.00177\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5337156036757251 for fission in\n",
|
|
" tally 1\n",
|
|
" 591/1 1.16026 1.16102 +/- 0.00176\n",
|
|
" 592/1 1.09619 1.16091 +/- 0.00177\n",
|
|
" 593/1 1.21297 1.16100 +/- 0.00176\n",
|
|
" 594/1 1.15066 1.16098 +/- 0.00176\n",
|
|
" 595/1 1.09601 1.16087 +/- 0.00176\n",
|
|
" 596/1 1.16136 1.16087 +/- 0.00176\n",
|
|
" 597/1 1.11783 1.16080 +/- 0.00176\n",
|
|
" 598/1 1.16141 1.16080 +/- 0.00175\n",
|
|
" 599/1 1.21882 1.16090 +/- 0.00175\n",
|
|
" 600/1 1.17923 1.16093 +/- 0.00175\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.51979456239908 for fission in tally\n",
|
|
" 1\n",
|
|
" 601/1 1.13526 1.16089 +/- 0.00175\n",
|
|
" 602/1 1.12905 1.16083 +/- 0.00175\n",
|
|
" 603/1 1.13957 1.16080 +/- 0.00174\n",
|
|
" 604/1 1.17465 1.16082 +/- 0.00174\n",
|
|
" 605/1 1.22832 1.16093 +/- 0.00174\n",
|
|
" 606/1 1.13549 1.16089 +/- 0.00174\n",
|
|
" 607/1 1.17406 1.16091 +/- 0.00174\n",
|
|
" 608/1 1.16471 1.16092 +/- 0.00173\n",
|
|
" 609/1 1.10480 1.16083 +/- 0.00173\n",
|
|
" 610/1 1.20017 1.16089 +/- 0.00173\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5033169977130132 for fission in\n",
|
|
" tally 1\n",
|
|
" 611/1 1.13327 1.16085 +/- 0.00173\n",
|
|
" 612/1 1.09935 1.16074 +/- 0.00173\n",
|
|
" 613/1 1.08867 1.16062 +/- 0.00173\n",
|
|
" 614/1 1.05858 1.16045 +/- 0.00174\n",
|
|
" 615/1 1.08089 1.16032 +/- 0.00174\n",
|
|
" 616/1 1.28984 1.16054 +/- 0.00175\n",
|
|
" 617/1 1.11315 1.16046 +/- 0.00175\n",
|
|
" 618/1 1.17814 1.16049 +/- 0.00175\n",
|
|
" 619/1 1.23956 1.16062 +/- 0.00175\n",
|
|
" 620/1 1.25577 1.16077 +/- 0.00175\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.5200855096647965 for fission in\n",
|
|
" tally 1\n",
|
|
" 621/1 1.15402 1.16076 +/- 0.00175\n",
|
|
" 622/1 1.14942 1.16074 +/- 0.00175\n",
|
|
" 623/1 1.24116 1.16088 +/- 0.00175\n",
|
|
" 624/1 1.22415 1.16098 +/- 0.00175\n",
|
|
" 625/1 1.14101 1.16095 +/- 0.00175\n",
|
|
" 626/1 1.12119 1.16088 +/- 0.00174\n",
|
|
" 627/1 1.11715 1.16081 +/- 0.00174\n",
|
|
" 628/1 1.19555 1.16087 +/- 0.00174\n",
|
|
" 629/1 1.16649 1.16088 +/- 0.00174\n",
|
|
" 630/1 1.17160 1.16089 +/- 0.00174\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.505605375139108 for fission in\n",
|
|
" tally 1\n",
|
|
" 631/1 1.19994 1.16096 +/- 0.00173\n",
|
|
" 632/1 1.15646 1.16095 +/- 0.00173\n",
|
|
" 633/1 1.13847 1.16091 +/- 0.00173\n",
|
|
" 634/1 1.21278 1.16100 +/- 0.00173\n",
|
|
" 635/1 1.08957 1.16088 +/- 0.00173\n",
|
|
" 636/1 1.14896 1.16086 +/- 0.00173\n",
|
|
" 637/1 1.21507 1.16095 +/- 0.00173\n",
|
|
" 638/1 1.11514 1.16088 +/- 0.00172\n",
|
|
" 639/1 1.22792 1.16098 +/- 0.00172\n",
|
|
" 640/1 1.17035 1.16100 +/- 0.00172\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.494317424461375 for fission in\n",
|
|
" tally 1\n",
|
|
" 641/1 1.17236 1.16102 +/- 0.00172\n",
|
|
" 642/1 1.15463 1.16101 +/- 0.00172\n",
|
|
" 643/1 1.20041 1.16107 +/- 0.00172\n",
|
|
" 644/1 1.11922 1.16100 +/- 0.00171\n",
|
|
" 645/1 1.10346 1.16091 +/- 0.00171\n",
|
|
" 646/1 1.17591 1.16093 +/- 0.00171\n",
|
|
" 647/1 1.20213 1.16100 +/- 0.00171\n",
|
|
" 648/1 1.19149 1.16105 +/- 0.00171\n",
|
|
" 649/1 1.08740 1.16093 +/- 0.00171\n",
|
|
" 650/1 1.23331 1.16104 +/- 0.00171\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4832725822769874 for fission in\n",
|
|
" tally 1\n",
|
|
" 651/1 1.12278 1.16098 +/- 0.00171\n",
|
|
" 652/1 1.15425 1.16097 +/- 0.00171\n",
|
|
" 653/1 1.14841 1.16095 +/- 0.00170\n",
|
|
" 654/1 1.17249 1.16097 +/- 0.00170\n",
|
|
" 655/1 1.10515 1.16089 +/- 0.00170\n",
|
|
" 656/1 1.13158 1.16084 +/- 0.00170\n",
|
|
" 657/1 1.14694 1.16082 +/- 0.00170\n",
|
|
" 658/1 1.17377 1.16084 +/- 0.00169\n",
|
|
" 659/1 1.11772 1.16077 +/- 0.00169\n",
|
|
" 660/1 1.21459 1.16086 +/- 0.00169\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.46710337098001 for fission in tally\n",
|
|
" 1\n",
|
|
" 661/1 1.12383 1.16080 +/- 0.00169\n",
|
|
" 662/1 1.19084 1.16084 +/- 0.00169\n",
|
|
" 663/1 1.18932 1.16089 +/- 0.00169\n",
|
|
" 664/1 1.14566 1.16087 +/- 0.00168\n",
|
|
" 665/1 1.20804 1.16094 +/- 0.00168\n",
|
|
" 666/1 1.19460 1.16099 +/- 0.00168\n",
|
|
" 667/1 1.17121 1.16100 +/- 0.00168\n",
|
|
" 668/1 1.13607 1.16097 +/- 0.00168\n",
|
|
" 669/1 1.21071 1.16104 +/- 0.00167\n",
|
|
" 670/1 1.07264 1.16091 +/- 0.00168\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4552920574941686 for fission in\n",
|
|
" tally 1\n",
|
|
" 671/1 1.01393 1.16069 +/- 0.00169\n",
|
|
" 672/1 1.15908 1.16068 +/- 0.00169\n",
|
|
" 673/1 1.16244 1.16069 +/- 0.00168\n",
|
|
" 674/1 1.13062 1.16064 +/- 0.00168\n",
|
|
" 675/1 1.12760 1.16059 +/- 0.00168\n",
|
|
" 676/1 1.14234 1.16056 +/- 0.00168\n",
|
|
" 677/1 1.09400 1.16046 +/- 0.00168\n",
|
|
" 678/1 1.15709 1.16046 +/- 0.00168\n",
|
|
" 679/1 1.15327 1.16045 +/- 0.00167\n",
|
|
" 680/1 1.11572 1.16038 +/- 0.00167\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.452079599385895 for fission in\n",
|
|
" tally 1\n",
|
|
" 681/1 1.11686 1.16032 +/- 0.00167\n",
|
|
" 682/1 1.13217 1.16027 +/- 0.00167\n",
|
|
" 683/1 1.15776 1.16027 +/- 0.00167\n",
|
|
" 684/1 1.07610 1.16015 +/- 0.00167\n",
|
|
" 685/1 1.11701 1.16008 +/- 0.00167\n",
|
|
" 686/1 1.20318 1.16015 +/- 0.00167\n",
|
|
" 687/1 1.14859 1.16013 +/- 0.00166\n",
|
|
" 688/1 1.09866 1.16004 +/- 0.00166\n",
|
|
" 689/1 1.11496 1.15997 +/- 0.00166\n",
|
|
" 690/1 1.12370 1.15992 +/- 0.00166\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4431456143551507 for fission in\n",
|
|
" tally 1\n",
|
|
" 691/1 1.14367 1.15989 +/- 0.00166\n",
|
|
" 692/1 1.18960 1.15994 +/- 0.00166\n",
|
|
" 693/1 1.13696 1.15990 +/- 0.00166\n",
|
|
" 694/1 1.18980 1.15995 +/- 0.00165\n",
|
|
" 695/1 1.10521 1.15987 +/- 0.00165\n",
|
|
" 696/1 1.18973 1.15991 +/- 0.00165\n",
|
|
" 697/1 1.18943 1.15995 +/- 0.00165\n",
|
|
" 698/1 1.22309 1.16005 +/- 0.00165\n",
|
|
" 699/1 1.12886 1.16000 +/- 0.00165\n",
|
|
" 700/1 1.09826 1.15991 +/- 0.00165\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.431235913998896 for fission in\n",
|
|
" tally 1\n",
|
|
" 701/1 1.15866 1.15991 +/- 0.00165\n",
|
|
" 702/1 1.14574 1.15989 +/- 0.00164\n",
|
|
" 703/1 1.17929 1.15992 +/- 0.00164\n",
|
|
" 704/1 1.14183 1.15989 +/- 0.00164\n",
|
|
" 705/1 1.11444 1.15983 +/- 0.00164\n",
|
|
" 706/1 1.16592 1.15983 +/- 0.00164\n",
|
|
" 707/1 1.12979 1.15979 +/- 0.00163\n",
|
|
" 708/1 1.10944 1.15972 +/- 0.00163\n",
|
|
" 709/1 1.19960 1.15978 +/- 0.00163\n",
|
|
" 710/1 1.18503 1.15981 +/- 0.00163\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4157359322369343 for fission in\n",
|
|
" tally 1\n",
|
|
" 711/1 1.10359 1.15973 +/- 0.00163\n",
|
|
" 712/1 1.19498 1.15978 +/- 0.00163\n",
|
|
" 713/1 1.22028 1.15987 +/- 0.00163\n",
|
|
" 714/1 1.15091 1.15986 +/- 0.00163\n",
|
|
" 715/1 1.09995 1.15977 +/- 0.00163\n",
|
|
" 716/1 1.14758 1.15975 +/- 0.00162\n",
|
|
" 717/1 1.17465 1.15977 +/- 0.00162\n",
|
|
" 718/1 1.09413 1.15968 +/- 0.00162\n",
|
|
" 719/1 1.17868 1.15971 +/- 0.00162\n",
|
|
" 720/1 1.15967 1.15971 +/- 0.00162\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.4048728565705424 for fission in\n",
|
|
" tally 1\n",
|
|
" 721/1 1.11092 1.15964 +/- 0.00162\n",
|
|
" 722/1 1.11125 1.15957 +/- 0.00162\n",
|
|
" 723/1 1.15718 1.15957 +/- 0.00161\n",
|
|
" 724/1 1.21531 1.15965 +/- 0.00161\n",
|
|
" 725/1 1.18654 1.15968 +/- 0.00161\n",
|
|
" 726/1 1.21811 1.15977 +/- 0.00161\n",
|
|
" 727/1 1.15595 1.15976 +/- 0.00161\n",
|
|
" 728/1 1.08940 1.15966 +/- 0.00161\n",
|
|
" 729/1 1.20896 1.15973 +/- 0.00161\n",
|
|
" 730/1 1.15090 1.15972 +/- 0.00161\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3957302262241922 for fission in\n",
|
|
" tally 1\n",
|
|
" 731/1 1.12774 1.15967 +/- 0.00160\n",
|
|
" 732/1 1.20262 1.15973 +/- 0.00160\n",
|
|
" 733/1 1.14559 1.15971 +/- 0.00160\n",
|
|
" 734/1 1.09162 1.15962 +/- 0.00160\n",
|
|
" 735/1 1.14839 1.15960 +/- 0.00160\n",
|
|
" 736/1 1.13091 1.15957 +/- 0.00160\n",
|
|
" 737/1 1.14311 1.15954 +/- 0.00160\n",
|
|
" 738/1 1.18561 1.15958 +/- 0.00159\n",
|
|
" 739/1 1.21040 1.15965 +/- 0.00159\n",
|
|
" 740/1 1.07445 1.15953 +/- 0.00160\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.386584615078833 for fission in\n",
|
|
" tally 1\n",
|
|
" 741/1 1.19980 1.15959 +/- 0.00159\n",
|
|
" 742/1 1.24116 1.15970 +/- 0.00160\n",
|
|
" 743/1 1.14247 1.15967 +/- 0.00159\n",
|
|
" 744/1 1.16392 1.15968 +/- 0.00159\n",
|
|
" 745/1 1.17031 1.15969 +/- 0.00159\n",
|
|
" 746/1 1.14855 1.15968 +/- 0.00159\n",
|
|
" 747/1 1.22727 1.15977 +/- 0.00159\n",
|
|
" 748/1 1.21553 1.15985 +/- 0.00159\n",
|
|
" 749/1 1.07678 1.15973 +/- 0.00159\n",
|
|
" 750/1 1.12757 1.15969 +/- 0.00159\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3799409012204495 for fission in\n",
|
|
" tally 1\n",
|
|
" 751/1 1.23165 1.15979 +/- 0.00159\n",
|
|
" 752/1 1.12898 1.15975 +/- 0.00159\n",
|
|
" 753/1 1.17368 1.15977 +/- 0.00159\n",
|
|
" 754/1 1.11160 1.15970 +/- 0.00158\n",
|
|
" 755/1 1.16678 1.15971 +/- 0.00158\n",
|
|
" 756/1 1.12368 1.15966 +/- 0.00158\n",
|
|
" 757/1 1.18002 1.15969 +/- 0.00158\n",
|
|
" 758/1 1.15642 1.15968 +/- 0.00158\n",
|
|
" 759/1 1.06130 1.15955 +/- 0.00158\n",
|
|
" 760/1 1.11361 1.15949 +/- 0.00158\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3727081188949644 for fission in\n",
|
|
" tally 1\n",
|
|
" 761/1 1.19924 1.15955 +/- 0.00158\n",
|
|
" 762/1 1.15110 1.15953 +/- 0.00158\n",
|
|
" 763/1 1.21770 1.15961 +/- 0.00158\n",
|
|
" 764/1 1.19655 1.15966 +/- 0.00158\n",
|
|
" 765/1 1.21490 1.15973 +/- 0.00157\n",
|
|
" 766/1 1.16181 1.15974 +/- 0.00157\n",
|
|
" 767/1 1.18279 1.15977 +/- 0.00157\n",
|
|
" 768/1 1.21632 1.15984 +/- 0.00157\n",
|
|
" 769/1 1.10509 1.15977 +/- 0.00157\n",
|
|
" 770/1 1.18613 1.15980 +/- 0.00157\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.362408106952381 for fission in\n",
|
|
" tally 1\n",
|
|
" 771/1 1.27709 1.15996 +/- 0.00157\n",
|
|
" 772/1 1.09338 1.15987 +/- 0.00157\n",
|
|
" 773/1 1.15383 1.15986 +/- 0.00157\n",
|
|
" 774/1 1.11459 1.15980 +/- 0.00157\n",
|
|
" 775/1 1.13827 1.15977 +/- 0.00157\n",
|
|
" 776/1 1.09860 1.15970 +/- 0.00157\n",
|
|
" 777/1 1.22327 1.15978 +/- 0.00157\n",
|
|
" 778/1 1.23371 1.15987 +/- 0.00157\n",
|
|
" 779/1 1.09397 1.15979 +/- 0.00157\n",
|
|
" 780/1 1.17554 1.15981 +/- 0.00157\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3628385840884694 for fission in\n",
|
|
" tally 1\n",
|
|
" 781/1 1.17280 1.15983 +/- 0.00157\n",
|
|
" 782/1 1.18832 1.15986 +/- 0.00157\n",
|
|
" 783/1 1.15944 1.15986 +/- 0.00156\n",
|
|
" 784/1 1.12304 1.15981 +/- 0.00156\n",
|
|
" 785/1 1.16417 1.15982 +/- 0.00156\n",
|
|
" 786/1 1.22011 1.15990 +/- 0.00156\n",
|
|
" 787/1 1.23435 1.15999 +/- 0.00156\n",
|
|
" 788/1 1.19518 1.16004 +/- 0.00156\n",
|
|
" 789/1 1.13863 1.16001 +/- 0.00156\n",
|
|
" 790/1 1.14629 1.15999 +/- 0.00156\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3512577957474567 for fission in\n",
|
|
" tally 1\n",
|
|
" 791/1 1.15289 1.15998 +/- 0.00155\n",
|
|
" 792/1 1.19497 1.16003 +/- 0.00155\n",
|
|
" 793/1 1.20018 1.16008 +/- 0.00155\n",
|
|
" 794/1 1.09230 1.15999 +/- 0.00155\n",
|
|
" 795/1 1.20153 1.16005 +/- 0.00155\n",
|
|
" 796/1 1.22555 1.16013 +/- 0.00155\n",
|
|
" 797/1 1.14532 1.16011 +/- 0.00155\n",
|
|
" 798/1 1.16174 1.16011 +/- 0.00155\n",
|
|
" 799/1 1.10984 1.16005 +/- 0.00155\n",
|
|
" 800/1 1.21615 1.16012 +/- 0.00155\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3427240330535484 for fission in\n",
|
|
" tally 1\n",
|
|
" 801/1 1.17779 1.16014 +/- 0.00154\n",
|
|
" 802/1 1.17957 1.16017 +/- 0.00154\n",
|
|
" 803/1 1.22500 1.16025 +/- 0.00154\n",
|
|
" 804/1 1.24404 1.16036 +/- 0.00154\n",
|
|
" 805/1 1.17423 1.16037 +/- 0.00154\n",
|
|
" 806/1 1.13200 1.16034 +/- 0.00154\n",
|
|
" 807/1 1.15070 1.16033 +/- 0.00154\n",
|
|
" 808/1 1.18665 1.16036 +/- 0.00154\n",
|
|
" 809/1 1.07662 1.16025 +/- 0.00154\n",
|
|
" 810/1 1.14446 1.16023 +/- 0.00154\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.334863147038607 for fission in\n",
|
|
" tally 1\n",
|
|
" 811/1 1.18513 1.16026 +/- 0.00154\n",
|
|
" 812/1 1.13535 1.16023 +/- 0.00153\n",
|
|
" 813/1 1.14716 1.16022 +/- 0.00153\n",
|
|
" 814/1 1.10937 1.16015 +/- 0.00153\n",
|
|
" 815/1 1.18216 1.16018 +/- 0.00153\n",
|
|
" 816/1 1.14602 1.16016 +/- 0.00153\n",
|
|
" 817/1 1.16435 1.16017 +/- 0.00153\n",
|
|
" 818/1 1.14113 1.16015 +/- 0.00152\n",
|
|
" 819/1 1.15498 1.16014 +/- 0.00152\n",
|
|
" 820/1 1.15304 1.16013 +/- 0.00152\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.320712365236563 for fission in\n",
|
|
" tally 1\n",
|
|
" 821/1 1.16003 1.16013 +/- 0.00152\n",
|
|
" 822/1 1.20782 1.16019 +/- 0.00152\n",
|
|
" 823/1 1.15064 1.16018 +/- 0.00152\n",
|
|
" 824/1 1.06911 1.16007 +/- 0.00152\n",
|
|
" 825/1 1.20257 1.16012 +/- 0.00152\n",
|
|
" 826/1 1.19410 1.16016 +/- 0.00152\n",
|
|
" 827/1 1.12573 1.16012 +/- 0.00152\n",
|
|
" 828/1 1.09537 1.16004 +/- 0.00152\n",
|
|
" 829/1 1.18463 1.16007 +/- 0.00151\n",
|
|
" 830/1 1.18507 1.16010 +/- 0.00151\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.31330699710732 for fission in tally\n",
|
|
" 1\n",
|
|
" 831/1 1.17839 1.16012 +/- 0.00151\n",
|
|
" 832/1 1.13437 1.16009 +/- 0.00151\n",
|
|
" 833/1 1.20079 1.16014 +/- 0.00151\n",
|
|
" 834/1 1.06651 1.16003 +/- 0.00151\n",
|
|
" 835/1 1.12780 1.15999 +/- 0.00151\n",
|
|
" 836/1 1.15558 1.15998 +/- 0.00151\n",
|
|
" 837/1 1.10195 1.15991 +/- 0.00151\n",
|
|
" 838/1 1.20500 1.15996 +/- 0.00151\n",
|
|
" 839/1 1.15776 1.15996 +/- 0.00150\n",
|
|
" 840/1 1.27191 1.16010 +/- 0.00151\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.3102167432260823 for fission in\n",
|
|
" tally 1\n",
|
|
" 841/1 1.11659 1.16004 +/- 0.00151\n",
|
|
" 842/1 1.17900 1.16007 +/- 0.00151\n",
|
|
" 843/1 1.16503 1.16007 +/- 0.00150\n",
|
|
" 844/1 1.12335 1.16003 +/- 0.00150\n",
|
|
" 845/1 1.15827 1.16003 +/- 0.00150\n",
|
|
" 846/1 1.14530 1.16001 +/- 0.00150\n",
|
|
" 847/1 1.21065 1.16007 +/- 0.00150\n",
|
|
" 848/1 1.17817 1.16009 +/- 0.00150\n",
|
|
" 849/1 1.11710 1.16004 +/- 0.00150\n",
|
|
" 850/1 1.20963 1.16010 +/- 0.00150\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2991530981321877 for fission in\n",
|
|
" tally 1\n",
|
|
" 851/1 1.18059 1.16012 +/- 0.00149\n",
|
|
" 852/1 1.17393 1.16014 +/- 0.00149\n",
|
|
" 853/1 1.17516 1.16016 +/- 0.00149\n",
|
|
" 854/1 1.09588 1.16008 +/- 0.00149\n",
|
|
" 855/1 1.19774 1.16013 +/- 0.00149\n",
|
|
" 856/1 1.22727 1.16021 +/- 0.00149\n",
|
|
" 857/1 1.12324 1.16016 +/- 0.00149\n",
|
|
" 858/1 1.22766 1.16024 +/- 0.00149\n",
|
|
" 859/1 1.12675 1.16020 +/- 0.00149\n",
|
|
" 860/1 1.15201 1.16019 +/- 0.00149\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2910308076092192 for fission in\n",
|
|
" tally 1\n",
|
|
" 861/1 1.10263 1.16013 +/- 0.00149\n",
|
|
" 862/1 1.16537 1.16013 +/- 0.00149\n",
|
|
" 863/1 1.09632 1.16006 +/- 0.00149\n",
|
|
" 864/1 1.14743 1.16004 +/- 0.00148\n",
|
|
" 865/1 1.19051 1.16008 +/- 0.00148\n",
|
|
" 866/1 1.11492 1.16002 +/- 0.00148\n",
|
|
" 867/1 1.21362 1.16009 +/- 0.00148\n",
|
|
" 868/1 1.14251 1.16007 +/- 0.00148\n",
|
|
" 869/1 1.18122 1.16009 +/- 0.00148\n",
|
|
" 870/1 1.14356 1.16007 +/- 0.00148\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2817756874394233 for fission in\n",
|
|
" tally 1\n",
|
|
" 871/1 1.13895 1.16005 +/- 0.00147\n",
|
|
" 872/1 1.23043 1.16013 +/- 0.00148\n",
|
|
" 873/1 1.11831 1.16008 +/- 0.00147\n",
|
|
" 874/1 1.19878 1.16013 +/- 0.00147\n",
|
|
" 875/1 1.18750 1.16016 +/- 0.00147\n",
|
|
" 876/1 1.14293 1.16014 +/- 0.00147\n",
|
|
" 877/1 1.17856 1.16016 +/- 0.00147\n",
|
|
" 878/1 1.08291 1.16007 +/- 0.00147\n",
|
|
" 879/1 1.23737 1.16016 +/- 0.00147\n",
|
|
" 880/1 1.13461 1.16013 +/- 0.00147\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2759981744454836 for fission in\n",
|
|
" tally 1\n",
|
|
" 881/1 1.13974 1.16011 +/- 0.00147\n",
|
|
" 882/1 1.18666 1.16014 +/- 0.00147\n",
|
|
" 883/1 1.13577 1.16011 +/- 0.00147\n",
|
|
" 884/1 1.12592 1.16007 +/- 0.00146\n",
|
|
" 885/1 1.12454 1.16003 +/- 0.00146\n",
|
|
" 886/1 1.18080 1.16005 +/- 0.00146\n",
|
|
" 887/1 1.15357 1.16004 +/- 0.00146\n",
|
|
" 888/1 1.16811 1.16005 +/- 0.00146\n",
|
|
" 889/1 1.13953 1.16003 +/- 0.00146\n",
|
|
" 890/1 1.13669 1.16000 +/- 0.00146\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2637434484607197 for fission in\n",
|
|
" tally 1\n",
|
|
" 891/1 1.18170 1.16003 +/- 0.00145\n",
|
|
" 892/1 1.13083 1.16000 +/- 0.00145\n",
|
|
" 893/1 1.19231 1.16003 +/- 0.00145\n",
|
|
" 894/1 1.23467 1.16012 +/- 0.00145\n",
|
|
" 895/1 1.21650 1.16018 +/- 0.00145\n",
|
|
" 896/1 1.15187 1.16017 +/- 0.00145\n",
|
|
" 897/1 1.18856 1.16020 +/- 0.00145\n",
|
|
" 898/1 1.16829 1.16021 +/- 0.00145\n",
|
|
" 899/1 1.18516 1.16024 +/- 0.00145\n",
|
|
" 900/1 1.11196 1.16019 +/- 0.00145\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2551014422557492 for fission in\n",
|
|
" tally 1\n",
|
|
" 901/1 1.23544 1.16027 +/- 0.00145\n",
|
|
" 902/1 1.20011 1.16032 +/- 0.00145\n",
|
|
" 903/1 1.07407 1.16022 +/- 0.00145\n",
|
|
" 904/1 1.17082 1.16023 +/- 0.00145\n",
|
|
" 905/1 1.09464 1.16016 +/- 0.00145\n",
|
|
" 906/1 1.20295 1.16020 +/- 0.00145\n",
|
|
" 907/1 1.21090 1.16026 +/- 0.00144\n",
|
|
" 908/1 1.15570 1.16026 +/- 0.00144\n",
|
|
" 909/1 1.15800 1.16025 +/- 0.00144\n",
|
|
" 910/1 1.14001 1.16023 +/- 0.00144\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2500895991438328 for fission in\n",
|
|
" tally 1\n",
|
|
" 911/1 1.14349 1.16021 +/- 0.00144\n",
|
|
" 912/1 1.10423 1.16015 +/- 0.00144\n",
|
|
" 913/1 1.18961 1.16018 +/- 0.00144\n",
|
|
" 914/1 1.12681 1.16015 +/- 0.00144\n",
|
|
" 915/1 1.13251 1.16012 +/- 0.00143\n",
|
|
" 916/1 1.08269 1.16003 +/- 0.00144\n",
|
|
" 917/1 1.07886 1.15994 +/- 0.00144\n",
|
|
" 918/1 1.13762 1.15992 +/- 0.00144\n",
|
|
" 919/1 1.22738 1.15999 +/- 0.00144\n",
|
|
" 920/1 1.13570 1.15996 +/- 0.00143\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2454842617255577 for fission in\n",
|
|
" tally 1\n",
|
|
" 921/1 1.21771 1.16003 +/- 0.00143\n",
|
|
" 922/1 1.13286 1.16000 +/- 0.00143\n",
|
|
" 923/1 1.15516 1.15999 +/- 0.00143\n",
|
|
" 924/1 1.18136 1.16002 +/- 0.00143\n",
|
|
" 925/1 1.17765 1.16003 +/- 0.00143\n",
|
|
" 926/1 1.13259 1.16000 +/- 0.00143\n",
|
|
" 927/1 1.13984 1.15998 +/- 0.00143\n",
|
|
" 928/1 1.17732 1.16000 +/- 0.00142\n",
|
|
" 929/1 1.20753 1.16005 +/- 0.00142\n",
|
|
" 930/1 1.11605 1.16001 +/- 0.00142\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2355886854038638 for fission in\n",
|
|
" tally 1\n",
|
|
" 931/1 1.20897 1.16006 +/- 0.00142\n",
|
|
" 932/1 1.13631 1.16003 +/- 0.00142\n",
|
|
" 933/1 1.18982 1.16007 +/- 0.00142\n",
|
|
" 934/1 1.16926 1.16008 +/- 0.00142\n",
|
|
" 935/1 1.15440 1.16007 +/- 0.00142\n",
|
|
" 936/1 1.16562 1.16008 +/- 0.00142\n",
|
|
" 937/1 1.08499 1.15999 +/- 0.00142\n",
|
|
" 938/1 1.12772 1.15996 +/- 0.00142\n",
|
|
" 939/1 1.13451 1.15993 +/- 0.00141\n",
|
|
" 940/1 1.17647 1.15995 +/- 0.00141\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2265590803544175 for fission in\n",
|
|
" tally 1\n",
|
|
" 941/1 1.14566 1.15993 +/- 0.00141\n",
|
|
" 942/1 1.07649 1.15984 +/- 0.00141\n",
|
|
" 943/1 1.20215 1.15989 +/- 0.00141\n",
|
|
" 944/1 1.18790 1.15992 +/- 0.00141\n",
|
|
" 945/1 1.13568 1.15989 +/- 0.00141\n",
|
|
" 946/1 1.16919 1.15990 +/- 0.00141\n",
|
|
" 947/1 1.17433 1.15992 +/- 0.00141\n",
|
|
" 948/1 1.16120 1.15992 +/- 0.00140\n",
|
|
" 949/1 1.12831 1.15989 +/- 0.00140\n",
|
|
" 950/1 1.24966 1.15998 +/- 0.00141\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2203407845150798 for fission in\n",
|
|
" tally 1\n",
|
|
" 951/1 1.18087 1.16000 +/- 0.00140\n",
|
|
" 952/1 1.13030 1.15997 +/- 0.00140\n",
|
|
" 953/1 1.20477 1.16002 +/- 0.00140\n",
|
|
" 954/1 1.20146 1.16006 +/- 0.00140\n",
|
|
" 955/1 1.15341 1.16006 +/- 0.00140\n",
|
|
" 956/1 1.11411 1.16001 +/- 0.00140\n",
|
|
" 957/1 1.13927 1.15999 +/- 0.00140\n",
|
|
" 958/1 1.20682 1.16004 +/- 0.00140\n",
|
|
" 959/1 1.14671 1.16002 +/- 0.00140\n",
|
|
" 960/1 1.08807 1.15995 +/- 0.00140\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.212778609221525 for fission in\n",
|
|
" tally 1\n",
|
|
" 961/1 1.18497 1.15997 +/- 0.00140\n",
|
|
" 962/1 1.13784 1.15995 +/- 0.00139\n",
|
|
" 963/1 1.16559 1.15996 +/- 0.00139\n",
|
|
" 964/1 1.16856 1.15996 +/- 0.00139\n",
|
|
" 965/1 1.15318 1.15996 +/- 0.00139\n",
|
|
" 966/1 1.08336 1.15988 +/- 0.00139\n",
|
|
" 967/1 1.10349 1.15982 +/- 0.00139\n",
|
|
" 968/1 1.16221 1.15982 +/- 0.00139\n",
|
|
" 969/1 1.10782 1.15977 +/- 0.00139\n",
|
|
" 970/1 1.13606 1.15974 +/- 0.00139\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2049930137963756 for fission in\n",
|
|
" tally 1\n",
|
|
" 971/1 1.09196 1.15967 +/- 0.00139\n",
|
|
" 972/1 1.19009 1.15970 +/- 0.00139\n",
|
|
" 973/1 1.13965 1.15968 +/- 0.00139\n",
|
|
" 974/1 1.13666 1.15966 +/- 0.00138\n",
|
|
" 975/1 1.05480 1.15955 +/- 0.00139\n",
|
|
" 976/1 1.18612 1.15958 +/- 0.00139\n",
|
|
" 977/1 1.17708 1.15960 +/- 0.00138\n",
|
|
" 978/1 1.25250 1.15969 +/- 0.00139\n",
|
|
" 979/1 1.06988 1.15960 +/- 0.00139\n",
|
|
" 980/1 1.24613 1.15969 +/- 0.00139\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2067534650569471 for fission in\n",
|
|
" tally 1\n",
|
|
" 981/1 1.17294 1.15970 +/- 0.00139\n",
|
|
" 982/1 1.15596 1.15970 +/- 0.00139\n",
|
|
" 983/1 1.13361 1.15967 +/- 0.00139\n",
|
|
" 984/1 1.21250 1.15973 +/- 0.00139\n",
|
|
" 985/1 1.12190 1.15969 +/- 0.00138\n",
|
|
" 986/1 1.10932 1.15964 +/- 0.00138\n",
|
|
" 987/1 1.09516 1.15957 +/- 0.00138\n",
|
|
" 988/1 1.22316 1.15963 +/- 0.00138\n",
|
|
" 989/1 1.14082 1.15961 +/- 0.00138\n",
|
|
" 990/1 1.22382 1.15968 +/- 0.00138\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.2011270428604999 for fission in\n",
|
|
" tally 1\n",
|
|
" 991/1 1.13586 1.15966 +/- 0.00138\n",
|
|
" 992/1 1.12337 1.15962 +/- 0.00138\n",
|
|
" 993/1 1.18942 1.15965 +/- 0.00138\n",
|
|
" 994/1 1.08739 1.15958 +/- 0.00138\n",
|
|
" 995/1 1.12248 1.15954 +/- 0.00138\n",
|
|
" 996/1 1.16129 1.15954 +/- 0.00138\n",
|
|
" 997/1 1.16713 1.15955 +/- 0.00138\n",
|
|
" 998/1 1.12868 1.15952 +/- 0.00138\n",
|
|
" 999/1 1.18506 1.15954 +/- 0.00137\n",
|
|
" 1000/1 1.12451 1.15951 +/- 0.00137\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.1932161707863758 for fission in\n",
|
|
" tally 1\n",
|
|
" Creating state point statepoint.1000.h5...\n",
|
|
"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
|
"\n",
|
|
" Total time for initialization = 6.7246e-01 seconds\n",
|
|
" Reading cross sections = 4.8824e-01 seconds\n",
|
|
" Total time in simulation = 6.8352e+01 seconds\n",
|
|
" Time in transport only = 6.8002e+01 seconds\n",
|
|
" Time in inactive batches = 4.8541e-01 seconds\n",
|
|
" Time in active batches = 6.7867e+01 seconds\n",
|
|
" Time synchronizing fission bank = 5.0812e-02 seconds\n",
|
|
" Sampling source sites = 4.2335e-02 seconds\n",
|
|
" SEND/RECV source sites = 8.2458e-03 seconds\n",
|
|
" Time accumulating tallies = 4.7912e-02 seconds\n",
|
|
" Time writing statepoints = 5.1404e-03 seconds\n",
|
|
" Total time for finalization = 2.5354e-04 seconds\n",
|
|
" Total time elapsed = 6.9030e+01 seconds\n",
|
|
" Calculation Rate (inactive) = 20601 particles/second\n",
|
|
" Calculation Rate (active) = 14587.4 particles/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 1.16009 +/- 0.00121\n",
|
|
" k-effective (Track-length) = 1.15951 +/- 0.00137\n",
|
|
" k-effective (Absorption) = 1.16171 +/- 0.00105\n",
|
|
" Combined k-effective = 1.16094 +/- 0.00090\n",
|
|
" Leakage Fraction = 0.00000 +/- 0.00000\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"statepoint = model.run()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 90,
|
|
"id": "bafd8d8b-ff59-47c6-8a79-1020ebe267f2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[0.00119322 0.00119178]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"with openmc.StatePoint(statepoint) as sp:\n",
|
|
" means = sp.get_tally(scores=['kappa-fission']).get_values(value='mean').squeeze()\n",
|
|
" std_devs = sp.get_tally(scores=['kappa-fission']).get_values(value='std_dev').squeeze()\n",
|
|
"\n",
|
|
"rel_errs = std_devs / means\n",
|
|
"print(rel_errs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "9647ce91-a67c-4063-8c96-3a8f7c4b76e6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|