mirror of
https://github.com/openmc-dev/openmc.git
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1548 lines
240 KiB
Text
1548 lines
240 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-energy-group and multi-delayed-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n",
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"\n",
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"* Creation of multi-delayed-group cross sections for an **infinite homogeneous medium**\n",
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"* Calculation of delayed neutron precursor concentrations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Introduction to Multi-Delayed-Group Cross Sections (MDGXS)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. Furthermore, kinetics calculations typically separate out parameters that involve delayed neutrons into prompt and delayed components and further subdivide delayed components by delayed groups. An example is the energy spectrum for prompt and delayed neutrons for U-235 and Pu-239 computed for a light water reactor spectrum."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"image/png": 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wsGA3b95U+7jKepkRQkh1GqUEJpfLERERgYMHD+LSpUuIjY1VeMYEKG1gjY6Oxrvvvqv0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\nfmI+3IqsCwAAAABJRU5ErkJggg==\n",
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 1,
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"metadata": {
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"image/png": {
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"width": 350
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}
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},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"from IPython.display import Image\n",
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"Image(filename='images/mdgxs.png', width=350)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations and different delayed group models (e.g. 6, 7, or 8 delayed group models) for fine-mesh heterogeneous deterministic neutron transport applications.\n",
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"\n",
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"Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-energy-group and multi-delayed-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic."
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]
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},
|
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Introductory Notation\n",
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"The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Spatial and Energy Discretization\n",
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"The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n",
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"\n",
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"The delayed neutrons created from fissions are created from > 30 delayed neutron precursors. Modeling each of the delayed neutron precursors is possible, but this approach has not recieved much attention due to large uncertainties in certain precursors. Therefore, the delayed neutrons are often combined into \"delayed groups\" that have a set time constant, $\\lambda_d$. Some cross section libraries use the same group time constants for all nuclides (e.g. JEFF 3.1) while other libraries use different time constants for all nuclides (e.g. ENDF/B-VII.1). Multi-delayed-group cross sections can either be created with the entire delayed group set, a subset of delayed groups, or integrated over all delayed groups.\n",
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"\n",
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|
"Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**."
|
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]
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},
|
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{
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"cell_type": "markdown",
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|
"metadata": {},
|
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"source": [
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"### General Scalar-Flux Weighted MDGXS\n",
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|
"The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section. For instance, the delayed-nu-fission multi-energy-group and multi-delayed-group cross section, $\\nu_d \\sigma_{f,x,k,g}$, can be computed as follows:\n",
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"\n",
|
|
"$$\\nu_d \\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\nu_d \\sigma_{f,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
|
|
"\n",
|
|
"This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the moment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](https://mit-crpg.github.io/openmc/pythonapi/filter.html) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Multi-Group Prompt and Delayed Fission Spectrum\n",
|
|
"The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n",
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"\n",
|
|
"Computing the cumulative energy spectrum of emitted neutrons, $\\chi_{n}(\\mathbf{r},E)$, has been presented in the `mgxs-part-i.ipynb` notebook. Here, we will present the energy spectrum of prompt and delayed emission neutrons, $\\chi_{n,p}(\\mathbf{r},E)$ and $\\chi_{n,d}(\\mathbf{r},E)$, respectively. Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n,p}(\\mathbf{r},E)$ and $\\nu_{n,d}(\\mathbf{r},E)$ for prompt and delayed neutrons, respectively. The multi-group fission spectrum $\\chi_{n,k,g,d}$ is then the probability of fission neutrons emitted into energy group $g$ and delayed group $d$. There are not prompt groups, so inserting $p$ in place of $d$ just denotes all prompt neutrons. \n",
|
|
"\n",
|
|
"Similar to before, spatial homogenization and energy condensation are used to find the multi-energy-group and multi-delayed-group fission spectrum $\\chi_{n,k,g,d}$ as follows:\n",
|
|
"\n",
|
|
"$$\\chi_{n,k,g',d} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n,d}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n",
|
|
"\n",
|
|
"The fission production-weighted multi-energy-group and multi-delayed-group fission spectrum for delayed neutrons is computed using OpenMC tallies with energy in, energy out, and delayed group filters. Alternatively, the delayed group filter can be omitted to compute the fission spectrum integrated over all delayed groups.\n",
|
|
"\n",
|
|
"This concludes our brief overview on the methodology to compute multi-energy-group and multi-delayed-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Generate Input Files"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%matplotlib inline\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"import openmc\n",
|
|
"import openmc.mgxs as mgxs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate some Nuclides\n",
|
|
"h1 = openmc.Nuclide('H1')\n",
|
|
"o16 = openmc.Nuclide('O16')\n",
|
|
"u235 = openmc.Nuclide('U235')\n",
|
|
"u238 = openmc.Nuclide('U238')\n",
|
|
"pu239 = openmc.Nuclide('Pu239')\n",
|
|
"zr90 = openmc.Nuclide('Zr90')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"With the nuclides we defined, we will now create a material for the homogeneous medium."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate a Material and register the Nuclides\n",
|
|
"inf_medium = openmc.Material(name='moderator')\n",
|
|
"inf_medium.set_density('g/cc', 5.)\n",
|
|
"inf_medium.add_nuclide(h1, 0.03)\n",
|
|
"inf_medium.add_nuclide(o16, 0.015)\n",
|
|
"inf_medium.add_nuclide(u235 , 0.0001)\n",
|
|
"inf_medium.add_nuclide(u238 , 0.007)\n",
|
|
"inf_medium.add_nuclide(pu239, 0.00003)\n",
|
|
"inf_medium.add_nuclide(zr90, 0.002)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"With our material, we can now create a `Materials` object that can be exported to an actual XML file."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate a Materials collection and export to XML\n",
|
|
"materials_file = openmc.Materials([inf_medium])\n",
|
|
"materials_file.default_xs = '71c'\n",
|
|
"materials_file.export_to_xml()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate boundary Planes\n",
|
|
"min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n",
|
|
"max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n",
|
|
"min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n",
|
|
"max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate a Cell\n",
|
|
"cell = openmc.Cell(cell_id=1, name='cell')\n",
|
|
"\n",
|
|
"# Register bounding Surfaces with the Cell\n",
|
|
"cell.region = +min_x & -max_x & +min_y & -max_y\n",
|
|
"\n",
|
|
"# Fill the Cell with the Material\n",
|
|
"cell.fill = inf_medium"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"OpenMC requires that there is a \"root\" universe. Let us create a root universe and add our square cell to it."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate Universe\n",
|
|
"root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
|
|
"root_universe.add_cell(cell)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We now must create a geometry that is assigned a root universe and export it to XML."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Create Geometry and set root Universe\n",
|
|
"openmc_geometry = openmc.Geometry()\n",
|
|
"openmc_geometry.root_universe = root_universe\n",
|
|
"\n",
|
|
"# Export to \"geometry.xml\"\n",
|
|
"openmc_geometry.export_to_xml()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# OpenMC simulation parameters\n",
|
|
"batches = 50\n",
|
|
"inactive = 10\n",
|
|
"particles = 5000\n",
|
|
"\n",
|
|
"# Instantiate a Settings object\n",
|
|
"settings_file = openmc.Settings()\n",
|
|
"settings_file.batches = batches\n",
|
|
"settings_file.inactive = inactive\n",
|
|
"settings_file.particles = particles\n",
|
|
"settings_file.output = {'tallies': True}\n",
|
|
"\n",
|
|
"# Create an initial uniform spatial source distribution over fissionable zones\n",
|
|
"bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
|
|
"uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
|
|
"settings_file.source = openmc.source.Source(space=uniform_dist)\n",
|
|
"\n",
|
|
"# Export to \"settings.xml\"\n",
|
|
"settings_file.export_to_xml()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now we are ready to generate multi-group cross sections! First, let's define a 100-energy-group structure and 1-energy-group structure using the built-in `EnergyGroups` class. We will also create a 6-delayed-group list."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate a 100-group EnergyGroups object\n",
|
|
"energy_groups = mgxs.EnergyGroups()\n",
|
|
"energy_groups.group_edges = np.logspace(-3, 7.3, 101)\n",
|
|
"\n",
|
|
"# Instantiate a 1-group EnergyGroups object\n",
|
|
"one_group = mgxs.EnergyGroups()\n",
|
|
"one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])\n",
|
|
"\n",
|
|
"delayed_groups = list(range(1,7))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now use the `EnergyGroups` object and delayed group list, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n",
|
|
"\n",
|
|
"* `TotalXS`\n",
|
|
"* `TransportXS`\n",
|
|
"* `NuTransportXS`\n",
|
|
"* `AbsorptionXS`\n",
|
|
"* `CaptureXS`\n",
|
|
"* `FissionXS`\n",
|
|
"* `NuFissionXS`\n",
|
|
"* `KappaFissionXS`\n",
|
|
"* `ScatterXS`\n",
|
|
"* `NuScatterXS`\n",
|
|
"* `ScatterMatrixXS`\n",
|
|
"* `NuScatterMatrixXS`\n",
|
|
"* `Chi`\n",
|
|
"* `ChiPrompt`\n",
|
|
"* `InverseVelocity`\n",
|
|
"* `PromptNuFissionXS`\n",
|
|
"\n",
|
|
"A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n",
|
|
"\n",
|
|
"* `DelayedNuFissionXS`\n",
|
|
"* `ChiDelayed`\n",
|
|
"* `Beta`\n",
|
|
"* `DecayRate`\n",
|
|
"\n",
|
|
"These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate a few different sections\n",
|
|
"chi_prompt = mgxs.ChiPrompt(domain=cell, groups=energy_groups, by_nuclide=True)\n",
|
|
"prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=energy_groups, by_nuclide=True)\n",
|
|
"chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n",
|
|
"delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
|
|
"beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
|
|
"decay_rate = mgxs.DecayRate(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n",
|
|
"\n",
|
|
"chi_prompt.nuclides = ['U235', 'Pu239']\n",
|
|
"prompt_nu_fission.nuclides = ['U235', 'Pu239']\n",
|
|
"chi_delayed.nuclides = ['U235', 'Pu239']\n",
|
|
"delayed_nu_fission.nuclides = ['U235', 'Pu239']\n",
|
|
"beta.nuclides = ['U235', 'Pu239']\n",
|
|
"decay_rate.nuclides = ['U235', 'Pu239']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Decay Rate` object as follows. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"OrderedDict([('delayed-nu-fission', Tally\n",
|
|
" \tID =\t10000\n",
|
|
" \tName =\t\n",
|
|
" \tFilters =\t\n",
|
|
" \t\tCellFilter\t[1]\n",
|
|
" \t\tDelayedGroupFilter\t[1 2 3 4 5 6]\n",
|
|
" \t\tEnergyFilter\t[ 1.00000000e-03 1.99526231e+07]\n",
|
|
" \tNuclides =\tU235 Pu239 \n",
|
|
" \tScores =\t['delayed-nu-fission']\n",
|
|
" \tEstimator =\tanalog), ('decay-rate', Tally\n",
|
|
" \tID =\t10001\n",
|
|
" \tName =\t\n",
|
|
" \tFilters =\t\n",
|
|
" \t\tCellFilter\t[1]\n",
|
|
" \t\tDelayedGroupFilter\t[1 2 3 4 5 6]\n",
|
|
" \t\tEnergyFilter\t[ 1.00000000e-03 1.99526231e+07]\n",
|
|
" \tNuclides =\tU235 Pu239 \n",
|
|
" \tScores =\t['decay-rate']\n",
|
|
" \tEstimator =\tanalog)])"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"decay_rate.tallies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The `Beta` object includes tracklength tallies for the 'nu-fission' and 'delayed-nu-fission' scores in the 100-energy-group and 6-delayed-group structure in cell 1. Now that each `MGXS` and `MDGXS` object contains the tallies that it needs, we must add these tallies to a `Tallies` object to generate the \"tallies.xml\" input file for OpenMC."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate an empty Tallies object\n",
|
|
"tallies_file = openmc.Tallies()\n",
|
|
"\n",
|
|
"# Add chi-prompt tallies to the tallies file\n",
|
|
"tallies_file += chi_prompt.tallies.values()\n",
|
|
"\n",
|
|
"# Add prompt-nu-fission tallies to the tallies file\n",
|
|
"tallies_file += prompt_nu_fission.tallies.values()\n",
|
|
"\n",
|
|
"# Add chi-delayed tallies to the tallies file\n",
|
|
"tallies_file += chi_delayed.tallies.values()\n",
|
|
"\n",
|
|
"# Add delayed-nu-fission tallies to the tallies file\n",
|
|
"tallies_file += delayed_nu_fission.tallies.values()\n",
|
|
"\n",
|
|
"# Add beta tallies to the tallies file\n",
|
|
"tallies_file += beta.tallies.values()\n",
|
|
"\n",
|
|
"# Add decay rate tallies to the tallies file\n",
|
|
"tallies_file += decay_rate.tallies.values()\n",
|
|
"\n",
|
|
"# Export to \"tallies.xml\"\n",
|
|
"tallies_file.export_to_xml()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now we a have a complete set of inputs, so we can go ahead and run our simulation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2016 Massachusetts Institute of Technology\n",
|
|
" License | http://openmc.readthedocs.io/en/latest/license.html\n",
|
|
" Version | 0.8.0\n",
|
|
" Git SHA1 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n",
|
|
" Date/Time | 2016-10-31 14:07:44\n",
|
|
" OpenMP Threads | 4\n",
|
|
"\n",
|
|
" ===========================================================================\n",
|
|
" ========================> INITIALIZATION <=========================\n",
|
|
" ===========================================================================\n",
|
|
"\n",
|
|
" Reading settings XML file...\n",
|
|
" Reading geometry XML file...\n",
|
|
" Reading materials XML file...\n",
|
|
" Reading cross sections XML file...\n",
|
|
" Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n",
|
|
" Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n",
|
|
" Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n",
|
|
" Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n",
|
|
" Reading Pu239 from /home/romano/openmc/scripts/nndc_hdf5/Pu239.h5\n",
|
|
" Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
|
|
" Maximum neutron transport energy: 2.00000E+07 eV for H1\n",
|
|
" Reading tallies XML file...\n",
|
|
" Building neighboring cells lists for each surface...\n",
|
|
" Initializing source particles...\n",
|
|
"\n",
|
|
" ===========================================================================\n",
|
|
" ====================> K EIGENVALUE SIMULATION <====================\n",
|
|
" ===========================================================================\n",
|
|
"\n",
|
|
" Bat./Gen. k Average k \n",
|
|
" ========= ======== ==================== \n",
|
|
" 1/1 1.21670 \n",
|
|
" 2/1 1.24155 \n",
|
|
" 3/1 1.21924 \n",
|
|
" 4/1 1.22486 \n",
|
|
" 5/1 1.21719 \n",
|
|
" 6/1 1.24330 \n",
|
|
" 7/1 1.22322 \n",
|
|
" 8/1 1.24133 \n",
|
|
" 9/1 1.21840 \n",
|
|
" 10/1 1.25141 \n",
|
|
" 11/1 1.21217 \n",
|
|
" 12/1 1.25625 1.23421 +/- 0.02204\n",
|
|
" 13/1 1.22056 1.22966 +/- 0.01351\n",
|
|
" 14/1 1.21757 1.22664 +/- 0.01002\n",
|
|
" 15/1 1.24571 1.23045 +/- 0.00865\n",
|
|
" 16/1 1.26489 1.23619 +/- 0.00910\n",
|
|
" 17/1 1.22323 1.23434 +/- 0.00791\n",
|
|
" 18/1 1.26108 1.23768 +/- 0.00762\n",
|
|
" 19/1 1.23145 1.23699 +/- 0.00676\n",
|
|
" 20/1 1.23548 1.23684 +/- 0.00605\n",
|
|
" 21/1 1.20446 1.23390 +/- 0.00621\n",
|
|
" 22/1 1.20533 1.23152 +/- 0.00615\n",
|
|
" 23/1 1.22520 1.23103 +/- 0.00568\n",
|
|
" 24/1 1.18367 1.22765 +/- 0.00625\n",
|
|
" 25/1 1.23614 1.22821 +/- 0.00585\n",
|
|
" 26/1 1.23746 1.22879 +/- 0.00550\n",
|
|
" 27/1 1.23626 1.22923 +/- 0.00518\n",
|
|
" 28/1 1.21334 1.22835 +/- 0.00497\n",
|
|
" 29/1 1.25169 1.22958 +/- 0.00486\n",
|
|
" 30/1 1.25579 1.23089 +/- 0.00479\n",
|
|
" 31/1 1.23828 1.23124 +/- 0.00457\n",
|
|
" 32/1 1.26911 1.23296 +/- 0.00468\n",
|
|
" 33/1 1.20090 1.23157 +/- 0.00469\n",
|
|
" 34/1 1.28606 1.23384 +/- 0.00503\n",
|
|
" 35/1 1.23129 1.23374 +/- 0.00483\n",
|
|
" 36/1 1.22535 1.23341 +/- 0.00465\n",
|
|
" 37/1 1.20367 1.23231 +/- 0.00461\n",
|
|
" 38/1 1.22886 1.23219 +/- 0.00444\n",
|
|
" 39/1 1.24056 1.23248 +/- 0.00429\n",
|
|
" 40/1 1.25038 1.23307 +/- 0.00419\n",
|
|
" 41/1 1.21504 1.23249 +/- 0.00410\n",
|
|
" 42/1 1.20762 1.23171 +/- 0.00404\n",
|
|
" 43/1 1.20597 1.23093 +/- 0.00399\n",
|
|
" 44/1 1.24424 1.23133 +/- 0.00389\n",
|
|
" 45/1 1.24767 1.23179 +/- 0.00381\n",
|
|
" 46/1 1.22998 1.23174 +/- 0.00370\n",
|
|
" 47/1 1.26352 1.23260 +/- 0.00370\n",
|
|
" 48/1 1.23155 1.23257 +/- 0.00360\n",
|
|
" 49/1 1.22059 1.23227 +/- 0.00352\n",
|
|
" 50/1 1.24724 1.23264 +/- 0.00345\n",
|
|
" Creating state point statepoint.50.h5...\n",
|
|
"\n",
|
|
" ===========================================================================\n",
|
|
" ======================> SIMULATION FINISHED <======================\n",
|
|
" ===========================================================================\n",
|
|
"\n",
|
|
"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
|
"\n",
|
|
" Total time for initialization = 6.3901E-01 seconds\n",
|
|
" Reading cross sections = 4.7650E-01 seconds\n",
|
|
" Total time in simulation = 3.8488E+01 seconds\n",
|
|
" Time in transport only = 3.8300E+01 seconds\n",
|
|
" Time in inactive batches = 2.6205E+00 seconds\n",
|
|
" Time in active batches = 3.5868E+01 seconds\n",
|
|
" Time synchronizing fission bank = 1.0040E-02 seconds\n",
|
|
" Sampling source sites = 7.0596E-03 seconds\n",
|
|
" SEND/RECV source sites = 2.8789E-03 seconds\n",
|
|
" Time accumulating tallies = 1.9011E-03 seconds\n",
|
|
" Total time for finalization = 4.0220E-02 seconds\n",
|
|
" Total time elapsed = 3.9193E+01 seconds\n",
|
|
" Calculation Rate (inactive) = 19080.5 neutrons/second\n",
|
|
" Calculation Rate (active) = 5576.04 neutrons/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 1.23260 +/- 0.00309\n",
|
|
" k-effective (Track-length) = 1.23264 +/- 0.00345\n",
|
|
" k-effective (Absorption) = 1.23111 +/- 0.00186\n",
|
|
" Combined k-effective = 1.23135 +/- 0.00185\n",
|
|
" Leakage Fraction = 0.00000 +/- 0.00000\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Run OpenMC\n",
|
|
"openmc.run()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Tally Data Processing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load the last statepoint file\n",
|
|
"sp = openmc.StatePoint('statepoint.50.h5')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load the tallies from the statepoint into each MGXS object\n",
|
|
"chi_prompt.load_from_statepoint(sp)\n",
|
|
"prompt_nu_fission.load_from_statepoint(sp)\n",
|
|
"chi_delayed.load_from_statepoint(sp)\n",
|
|
"delayed_nu_fission.load_from_statepoint(sp)\n",
|
|
"beta.load_from_statepoint(sp)\n",
|
|
"decay_rate.load_from_statepoint(sp)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Voila! Our multi-group cross sections are now ready to rock 'n roll!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Extracting and Storing MGXS Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's first inspect our delayed-nu-fission section by printing it to the screen after condensing the cross section down to one group."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[ 5.14239169e-06, 1.16429778e-06],\n",
|
|
" [ 2.65434434e-05, 7.58244504e-06],\n",
|
|
" [ 2.53406770e-05, 5.73814391e-06],\n",
|
|
" [ 5.68158884e-05, 1.04761254e-05],\n",
|
|
" [ 2.32937121e-05, 5.45676114e-06],\n",
|
|
" [ 9.75765501e-06, 1.65156185e-06]])"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"delayed_nu_fission.get_condensed_xs(one_group).get_xs()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Since the `openmc.mgxs` module uses [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](http://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>cell</th>\n",
|
|
" <th>delayedgroup</th>\n",
|
|
" <th>group in</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>198</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>9.533842e-11</td>\n",
|
|
" <td>4.789050e-11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>199</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>1.606499e-11</td>\n",
|
|
" <td>8.071081e-12</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>398</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>1.224131e-09</td>\n",
|
|
" <td>6.149449e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>399</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>2.602518e-10</td>\n",
|
|
" <td>1.307590e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>598</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>9.033000e-10</td>\n",
|
|
" <td>4.537601e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>599</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>1.522295e-10</td>\n",
|
|
" <td>7.648264e-11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>798</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>1.749138e-09</td>\n",
|
|
" <td>8.786432e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>799</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>2.400317e-10</td>\n",
|
|
" <td>1.205943e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>998</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>2.724017e-10</td>\n",
|
|
" <td>1.368376e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>999</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>4.749191e-11</td>\n",
|
|
" <td>2.386080e-11</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" cell delayedgroup group in nuclide mean std. dev.\n",
|
|
"198 1 1 1 U235 9.533842e-11 4.789050e-11\n",
|
|
"199 1 1 1 Pu239 1.606499e-11 8.071081e-12\n",
|
|
"398 1 2 1 U235 1.224131e-09 6.149449e-10\n",
|
|
"399 1 2 1 Pu239 2.602518e-10 1.307590e-10\n",
|
|
"598 1 3 1 U235 9.033000e-10 4.537601e-10\n",
|
|
"599 1 3 1 Pu239 1.522295e-10 7.648264e-11\n",
|
|
"798 1 4 1 U235 1.749138e-09 8.786432e-10\n",
|
|
"799 1 4 1 Pu239 2.400317e-10 1.205943e-10\n",
|
|
"998 1 5 1 U235 2.724017e-10 1.368376e-10\n",
|
|
"999 1 5 1 Pu239 4.749191e-11 2.386080e-11"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df = delayed_nu_fission.get_pandas_dataframe()\n",
|
|
"df.head(10)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>cell</th>\n",
|
|
" <th>delayedgroup</th>\n",
|
|
" <th>group in</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>0.013336</td>\n",
|
|
" <td>0.003509</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>0.013271</td>\n",
|
|
" <td>0.007154</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>0.032739</td>\n",
|
|
" <td>0.003864</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>0.030881</td>\n",
|
|
" <td>0.008109</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>0.120780</td>\n",
|
|
" <td>0.017277</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>0.113370</td>\n",
|
|
" <td>0.031354</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>0.302780</td>\n",
|
|
" <td>0.027190</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>0.292500</td>\n",
|
|
" <td>0.057372</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>0.849490</td>\n",
|
|
" <td>0.120338</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>0.857490</td>\n",
|
|
" <td>0.231893</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>2.853000</td>\n",
|
|
" <td>0.659168</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>2.729700</td>\n",
|
|
" <td>1.342167</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" cell delayedgroup group in nuclide mean std. dev.\n",
|
|
"0 1 1 1 U235 0.013336 0.003509\n",
|
|
"1 1 1 1 Pu239 0.013271 0.007154\n",
|
|
"2 1 2 1 U235 0.032739 0.003864\n",
|
|
"3 1 2 1 Pu239 0.030881 0.008109\n",
|
|
"4 1 3 1 U235 0.120780 0.017277\n",
|
|
"5 1 3 1 Pu239 0.113370 0.031354\n",
|
|
"6 1 4 1 U235 0.302780 0.027190\n",
|
|
"7 1 4 1 Pu239 0.292500 0.057372\n",
|
|
"8 1 5 1 U235 0.849490 0.120338\n",
|
|
"9 1 5 1 Pu239 0.857490 0.231893\n",
|
|
"10 1 6 1 U235 2.853000 0.659168\n",
|
|
"11 1 6 1 Pu239 2.729700 1.342167"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df = decay_rate.get_pandas_dataframe()\n",
|
|
"df.head(12)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"beta.export_xs_data(filename='beta', format='excel')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The following code snippet shows how to export the chi-prompt and chi-delayed `MGXS` to the same HDF5 binary data store."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"chi_prompt.build_hdf5_store(filename='mdgxs', append=True)\n",
|
|
"chi_delayed.build_hdf5_store(filename='mdgxs', append=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta`, `DelayedNuFissionXS`, and `DecayRate` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
|
|
"\n",
|
|
"$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
|
|
"\n",
|
|
"$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$\n",
|
|
"\n",
|
|
"First, let's investigate the decay rates for U235 and Pu235. The fraction of the delayed neutron precursors remaining as a function of time after fission for each delayed group and fissioning isotope have been plotted below."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x7fc336841080>"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
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48WI8PDyw2Wx4eHiQmZnJgw8+yAsvvIDFYqFFixb2bCuO5uzsswT1DsJkMZGV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VEmVXRcCPGl4dSSSB4t3nnnHd555x2WLVuGj48PzZs354svvqgVxnfjmY3xmuVlUh0szczo7uzM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\nQgjhJP8f17k2kT9pikIAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fc336841048>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Get the decay rate data\n",
|
|
"dr_tally = decay_rate.xs_tally\n",
|
|
"dr_u235 = dr_tally.get_values(nuclides=['U235']).flatten()\n",
|
|
"dr_pu239 = dr_tally.get_values(nuclides=['Pu239']).flatten()\n",
|
|
"\n",
|
|
"# Compute the exponential decay of the precursors\n",
|
|
"time = np.logspace(-3,3)\n",
|
|
"dr_u235_points = np.exp(-np.outer(dr_u235, time))\n",
|
|
"dr_pu239_points = np.exp(-np.outer(dr_pu239, time))\n",
|
|
"\n",
|
|
"# Create a plot of the fraction of the precursors remaining as a f(time)\n",
|
|
"colors = ['b', 'g', 'r', 'c', 'm', 'k']\n",
|
|
"legend = []\n",
|
|
"fig = plt.figure(figsize=(8,6))\n",
|
|
"for g,c in enumerate(colors):\n",
|
|
" plt.semilogx(time, dr_u235_points [g,:], color=c, linestyle='--', linewidth=3)\n",
|
|
" plt.semilogx(time, dr_pu239_points[g,:], color=c, linestyle=':' , linewidth=3)\n",
|
|
" legend.append('U-235 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_u235[g]))\n",
|
|
" legend.append('Pu-239 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_pu239[g]))\n",
|
|
"\n",
|
|
"plt.title('Delayed Neutron Precursor Decay Rates')\n",
|
|
"plt.xlabel('Time (s)')\n",
|
|
"plt.ylabel('Fraction Remaining')\n",
|
|
"plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now let's compute the initial concentration of the delayed neutron precursors:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>cell</th>\n",
|
|
" <th>delayedgroup</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>1</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>8.779406e-08</td>\n",
|
|
" <td>2.310240e-08</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>7.150041e-09</td>\n",
|
|
" <td>3.854534e-09</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>9.528171e-07</td>\n",
|
|
" <td>1.124883e-07</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>1.303200e-07</td>\n",
|
|
" <td>3.422243e-08</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>2.353975e-07</td>\n",
|
|
" <td>3.367779e-08</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>2.032960e-08</td>\n",
|
|
" <td>5.622830e-09</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>4.720335e-07</td>\n",
|
|
" <td>4.240950e-08</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>2.626392e-08</td>\n",
|
|
" <td>5.152078e-09</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>2.828001e-08</td>\n",
|
|
" <td>4.006859e-09</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>2.430664e-09</td>\n",
|
|
" <td>6.573695e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>U235</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>1.477575e-09</td>\n",
|
|
" <td>3.414084e-10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>Pu239</td>\n",
|
|
" <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
|
|
" <td>6.994534e-11</td>\n",
|
|
" <td>3.439208e-11</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" cell delayedgroup nuclide \\\n",
|
|
"0 1 1 U235 \n",
|
|
"1 1 1 Pu239 \n",
|
|
"2 1 2 U235 \n",
|
|
"3 1 2 Pu239 \n",
|
|
"4 1 3 U235 \n",
|
|
"5 1 3 Pu239 \n",
|
|
"6 1 4 U235 \n",
|
|
"7 1 4 Pu239 \n",
|
|
"8 1 5 U235 \n",
|
|
"9 1 5 Pu239 \n",
|
|
"10 1 6 U235 \n",
|
|
"11 1 6 Pu239 \n",
|
|
"\n",
|
|
" score mean std. dev. \n",
|
|
"0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 2.31e-08 \n",
|
|
"1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.85e-09 \n",
|
|
"2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 1.12e-07 \n",
|
|
"3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 3.42e-08 \n",
|
|
"4 (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07 3.37e-08 \n",
|
|
"5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 5.62e-09 \n",
|
|
"6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 4.24e-08 \n",
|
|
"7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 5.15e-09 \n",
|
|
"8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 4.01e-09 \n",
|
|
"9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 6.57e-10 \n",
|
|
"10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 3.41e-10 \n",
|
|
"11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.44e-11 "
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Use tally arithmetic to compute the precursor concentrations\n",
|
|
"precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n",
|
|
" delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / \\\n",
|
|
" decay_rate.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True)\n",
|
|
"\n",
|
|
"# Get the Pandas DataFrames for inspection\n",
|
|
"precursor_conc.get_pandas_dataframe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can plot the delayed neutron fractions for each nuclide."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Beta (U-235) : 0.006504 +/- 0.000007\n",
|
|
"Beta (Pu-239): 0.002245 +/- 0.000002\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(0, 7)"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
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dFaQusznp/QRJr5LNwjyhfNt77qnt0dPDhw+vvNEazPE3bcSI4o9R5L7bIn7/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\nQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fc334382fd0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"energy_filter = [f for f in beta.xs_tally.filters if type(f) is openmc.EnergyFilter]\n",
|
|
"beta_integrated = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True)\n",
|
|
"beta_u235 = beta_integrated.get_values(nuclides=['U235'])\n",
|
|
"beta_pu239 = beta_integrated.get_values(nuclides=['Pu239'])\n",
|
|
"\n",
|
|
"# Reshape the betas\n",
|
|
"beta_u235.shape = (beta_u235.shape[0])\n",
|
|
"beta_pu239.shape = (beta_pu239.shape[0])\n",
|
|
"\n",
|
|
"df = beta_integrated.summation(filter_type=openmc.DelayedGroupFilter, remove_filter=True).get_pandas_dataframe()\n",
|
|
"print('Beta (U-235) : {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'U235']['mean'][0], df[df['nuclide'] == 'U235']['std. dev.'][0]))\n",
|
|
"print('Beta (Pu-239): {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'Pu239']['mean'][1], df[df['nuclide'] == 'Pu239']['std. dev.'][1]))\n",
|
|
"\n",
|
|
"beta_u235 = np.append(beta_u235[0], beta_u235)\n",
|
|
"beta_pu239 = np.append(beta_pu239[0], beta_pu239)\n",
|
|
"\n",
|
|
"# Create a step plot for the MGXS\n",
|
|
"plt.plot(np.arange(0.5, 7.5, 1), beta_u235, drawstyle='steps', color='b', linewidth=3)\n",
|
|
"plt.plot(np.arange(0.5, 7.5, 1), beta_pu239, drawstyle='steps', color='g', linewidth=3)\n",
|
|
"\n",
|
|
"plt.title('Delayed Neutron Fraction (beta)')\n",
|
|
"plt.xlabel('Delayed Group')\n",
|
|
"plt.ylabel('Beta(fraction total neutrons)')\n",
|
|
"plt.legend(['U-235', 'Pu-239'])\n",
|
|
"plt.xlim([0,7])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also plot the energy spectrum for fission emission of prompt and delayed neutrons."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(1000.0, 20000000.0)"
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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sFnh1BeK8V1ATsr3GlwrMWFOdE1YxiEgBcJWd9lPgOxFZo6p3hcxoMNSCI0eO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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fc3368feb70>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"chi_d_u235 = np.squeeze(chi_delayed.get_xs(nuclides=['U235'], order_groups='decreasing'))\n",
|
|
"chi_d_pu239 = np.squeeze(chi_delayed.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n",
|
|
"chi_p_u235 = np.squeeze(chi_prompt.get_xs(nuclides=['U235'], order_groups='decreasing'))\n",
|
|
"chi_p_pu239 = np.squeeze(chi_prompt.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n",
|
|
"\n",
|
|
"chi_d_u235 = np.append(chi_d_u235 , chi_d_u235[0])\n",
|
|
"chi_d_pu239 = np.append(chi_d_pu239, chi_d_pu239[0])\n",
|
|
"chi_p_u235 = np.append(chi_p_u235 , chi_p_u235[0])\n",
|
|
"chi_p_pu239 = np.append(chi_p_pu239, chi_p_pu239[0])\n",
|
|
"\n",
|
|
"# Create a step plot for the MGXS\n",
|
|
"plt.semilogx(energy_groups.group_edges, chi_d_u235 , drawstyle='steps', color='b', linestyle='--', linewidth=3)\n",
|
|
"plt.semilogx(energy_groups.group_edges, chi_d_pu239, drawstyle='steps', color='g', linestyle='--', linewidth=3)\n",
|
|
"plt.semilogx(energy_groups.group_edges, chi_p_u235 , drawstyle='steps', color='b', linestyle=':', linewidth=3)\n",
|
|
"plt.semilogx(energy_groups.group_edges, chi_p_pu239, drawstyle='steps', color='g', linestyle=':', linewidth=3)\n",
|
|
"\n",
|
|
"plt.title('Energy Spectrum for Fission Neutrons')\n",
|
|
"plt.xlabel('Energy (eV)')\n",
|
|
"plt.ylabel('Fraction on emitted neutrons')\n",
|
|
"plt.legend(['U-235 delayed', 'Pu-239 delayed', 'U-235 prompt', 'Pu-239 prompt'],loc=2)\n",
|
|
"plt.xlim(1.0e3, 20.0e6)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.5.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|