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All MGXS IPython Notebooks are up-to-date and ready to go!
This commit is contained in:
wbinventor@gmail.com
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"source": [
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"This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-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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"* **General equations** for scalar-flux averaged multi-group cross sections\n",
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"* Creation of multi-group cross sections for an **infinite homogeneous medium**\n",
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"* Use of **tally arithmetic** to manipulate multi-group cross sections\n",
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"\n",
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"**Note:** This Notebook illustrates the use of Pandas DataFrames to containerize multi-group cross section data. We recommend using Pandas >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases."
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"**Note:** This Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)."
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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-Group Cross Sections (MGXS)"
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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-based neutron 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*. An example of U-235's fission continuous energy cross section along with a 16-group cross section computed for a light water reactor spectrum is displayed below:\n",
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"\n",
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"<img src=\"images/mgxs.png\" style=\"width: 350px;\">\n",
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"\n",
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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 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-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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"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, or an unstructured mesh such as 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 MGXS\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 $\\sigma_{n,x,k,g}$ as follows:\n",
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"\n",
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"$$\\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
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"\n",
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"This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for most multi-group cross sections, including total, absorption, and fission reaction types. 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 or universe) define the bounds of integration for both numerator and denominator."
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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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"### Multi-Group Scattering Matrices\n",
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"The general multi-group cross section $\\sigma_{n,x,k,g}$ is a vector of $G$ values for each energy group $g$. The equation presented above only discretizes the energy of the incoming neutron and neglects the outgoing energy of the neutron (if any). Hence, this formulation must be extended to account for the outgoing energy of neutrons in the discretized scattering matrix cross section used by deterministic neutron transport codes. \n",
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"\n",
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"We denote the incoming and outgoing neutron energy groups as $g$ and $g'$ for the microscopic scattering matrix cross section $\\sigma_{n,s}(\\mathbf{r},E)$. As before, spatial homogenization and energy condensation are used to find the multi-group scattering matrix cross section $\\sigma_{n,s,k,g \\to g'}$ as follows:\n",
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"\n",
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"$$\\sigma_{n,s,k,g\\rightarrow g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,s}(\\mathbf{r},E'\\rightarrow E'')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
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"\n",
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"This scalar flux-weighted multi-group microscopic scattering matrix is computed using OpenMC tallies with both energy in and energy out filters."
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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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"### Multi-Group Fission Spectrum\n",
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"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",
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"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 $\\nu_{n}(\\mathbf{r},E)$. The multi-group fission spectrum $\\chi_{n,k,g}$ is then the probability of fission neutrons emitted into energy group $g$. \n",
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"\n",
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"Similar to before, spatial homogenization and energy condensation are used to find the multi-group fission spectrum $\\chi_{n,k,g}$ as follows:\n",
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"\n",
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"$$\\chi_{n,k,g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n",
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"\n",
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"The fission production-weighted multi-group fission spectrum is computed using OpenMC tallies with both energy in and energy out filters.\n",
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"\n",
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"This concludes our brief overview on the methodology to compute multi-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."
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate Input Files"
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]
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{
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We first construct a simple homogeneous infinite medium problem to illustrate use of the `openmc.mgxs` module to generate multi-group cross sections."
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate Input Files"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -95,7 +171,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With our material, we can now create a materials file object that can be exported to an actual XML file."
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"With our material, we can now create a `MaterialsFile` object that can be exported to an actual XML file."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML."
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"We now must create a geometry that is assigned a root universe, put the geometry into a `GeometryFile` object, and export it to XML."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can now use the fine and coarse `EnergyGroups` objects, 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",
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"We can now use the `EnergyGroups` object, 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",
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"\n",
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"* `TotalXS`\n",
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"* `TransportXS`\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `Absorption` object includes tracklength tallies for the 'absorption' and 'flux' scores in the 2-group structure in cell 1. Now that each multi-group cross section object contains the tallies that it needs, we must add these tallies to a `TalliesFile` object to generate the \"tallies.xml\" input file for OpenMC."
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"The `Absorption` object includes tracklength tallies for the 'absorption' and 'flux' scores in the 2-group structure in cell 1. Now that each `MGXS` object contains the tallies that it needs, we must add these tallies to a `TalliesFile` object to generate the \"tallies.xml\" input file for OpenMC."
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]
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},
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{
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" License: http://mit-crpg.github.io/openmc/license.html\n",
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" Version: 0.7.0\n",
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" Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n",
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" Date/Time: 2015-11-29 17:50:29\n",
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" Date/Time: 2015-11-30 20:15:33\n",
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" MPI Processes: 1\n",
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"\n",
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" ===========================================================================\n",
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"\n",
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" =======================> TIMING STATISTICS <=======================\n",
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"\n",
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" Total time for initialization = 4.2800E-01 seconds\n",
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" Reading cross sections = 8.9000E-02 seconds\n",
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" Total time in simulation = 1.4506E+01 seconds\n",
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" Time in transport only = 1.4496E+01 seconds\n",
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" Time in inactive batches = 1.7910E+00 seconds\n",
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" Time in active batches = 1.2715E+01 seconds\n",
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" Time synchronizing fission bank = 1.0000E-03 seconds\n",
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" Sampling source sites = 0.0000E+00 seconds\n",
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" Total time for initialization = 4.1200E-01 seconds\n",
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" Reading cross sections = 9.2000E-02 seconds\n",
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" Total time in simulation = 1.4213E+01 seconds\n",
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" Time in transport only = 1.4199E+01 seconds\n",
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" Time in inactive batches = 1.7980E+00 seconds\n",
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" Time in active batches = 1.2415E+01 seconds\n",
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" Time synchronizing fission bank = 5.0000E-03 seconds\n",
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" Sampling source sites = 3.0000E-03 seconds\n",
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" SEND/RECV source sites = 1.0000E-03 seconds\n",
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" Time accumulating tallies = 0.0000E+00 seconds\n",
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" Total time for finalization = 0.0000E+00 seconds\n",
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" Total time elapsed = 1.4943E+01 seconds\n",
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" Calculation Rate (inactive) = 13958.7 neutrons/second\n",
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" Calculation Rate (active) = 7864.73 neutrons/second\n",
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" Total time elapsed = 1.4634E+01 seconds\n",
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" Calculation Rate (inactive) = 13904.3 neutrons/second\n",
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" Calculation Rate (active) = 8054.77 neutrons/second\n",
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"\n",
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" ============================> RESULTS <============================\n",
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"\n",
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}
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],
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"source": [
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"# Remove old HDF5 (summary, statepoint) files\n",
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"!rm statepoint.*\n",
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"\n",
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"# Run OpenMC\n",
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"executor = openmc.Executor()\n",
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"executor.run_simulation()"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and \"reading\" the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge."
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"Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
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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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"In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry which is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a summary object and link it with the statepoint."
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"In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. This is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a `Summary` object and link it with the statepoint."
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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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"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."
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"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."
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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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"Since the `openmc.mgxs` module uses tally arithmetic under-the-hood, the cross section is stored as a \"derived\" tally. This means that it can be queried and manipulated using all of the same method supported for the `Tally` class in the OpenMC Python API. For example, we can construct a Pandas DataFrame of the multi-group cross section data."
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"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."
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
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"/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
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"/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1272: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
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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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"The following code snippet shows how to export all of three cross sections to the same HDF5 binary data store."
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"The following code snippet shows how to export all three `MGXS` to the same HDF5 binary data store."
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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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"Finally, we illustrate how one can leverage OpenMC's tally arithmetic 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 confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects."
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"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 confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects."
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]
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},
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{
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"scattering_to_total.get_pandas_dataframe()"
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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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"Lastly, we sum the derived scatter-to-total and absorption-to-total ratios to confirm that they sum to unity."
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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": 25,
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@ -126,7 +126,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
|
||||
"Now let's move on to the geometry. This problem will be a square array of fuel pins for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -192,7 +192,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26cm pitch."
|
||||
"Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -242,7 +242,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML."
|
||||
"We now must create a geometry that is assigned a root universe, put the geometry into a `GeometryFile` object, and export it to XML."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue