diff --git a/.gitignore b/.gitignore index b2bdeba7a..136491a4b 100644 --- a/.gitignore +++ b/.gitignore @@ -71,4 +71,6 @@ docs/source/pythonapi/examples/*.xml docs/source/pythonapi/examples/*.png docs/source/pythonapi/examples/*.xls docs/source/pythonapi/examples/mgxs -docs/source/pythonapi/examples/tracks \ No newline at end of file +docs/source/pythonapi/examples/tracks +docs/source/pythonapi/examples/fission-rates +docs/source/pythonapi/examples/plots \ No newline at end of file diff --git a/CMakeLists.txt b/CMakeLists.txt index 36501c918..5ad537e10 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -235,6 +235,14 @@ if(NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/src/xml/fox/.git) endif() add_subdirectory(src/xml/fox) +#=============================================================================== +# RPATH information +#=============================================================================== + +# add the automatically determined parts of the RPATH +# which point to directories outside the build tree to the install RPATH +set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE) + #=============================================================================== # Build OpenMC executable #=============================================================================== @@ -278,14 +286,21 @@ target_link_libraries(${program} ${ldflags} ${HDF5_LIBRARIES} fox_dom) install(TARGETS ${program} RUNTIME DESTINATION bin) install(DIRECTORY src/relaxng DESTINATION share/openmc) install(FILES man/man1/openmc.1 DESTINATION share/man/man1) -install(FILES LICENSE DESTINATION "share/doc/${program}/copyright") +install(FILES LICENSE DESTINATION "share/doc/${program}" RENAME copyright) find_package(PythonInterp) if(PYTHONINTERP_FOUND) - install(CODE "execute_process( - COMMAND ${PYTHON_EXECUTABLE} setup.py install - --prefix=${CMAKE_INSTALL_PREFIX} - WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})") + if(debian) + install(CODE "execute_process( + COMMAND ${PYTHON_EXECUTABLE} setup.py install + --root=debian/openmc --install-layout=deb + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})") + else() + install(CODE "execute_process( + COMMAND ${PYTHON_EXECUTABLE} setup.py install + --prefix=${CMAKE_INSTALL_PREFIX} + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})") + endif() endif() #=============================================================================== diff --git a/docs/source/conf.py b/docs/source/conf.py index 05559aab0..118ff2c03 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -55,7 +55,7 @@ copyright = u'2011-2015, Massachusetts Institute of Technology' # The short X.Y version. version = "0.7" # The full version, including alpha/beta/rc tags. -release = "0.7.0" +release = "0.7.1" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -200,7 +200,7 @@ latex_elements = { \usepackage{enumitem} \usepackage{amsfonts} \usepackage{amsmath} -\setlistdepth{9} +\setlistdepth{99} \usepackage{tikz} \usetikzlibrary{shapes,snakes,shadows,arrows,calc,decorations.markings,patterns,fit,matrix,spy} \usepackage{fixltx2e} diff --git a/docs/source/publications.rst b/docs/source/publications.rst index 79d089605..369b9d977 100644 --- a/docs/source/publications.rst +++ b/docs/source/publications.rst @@ -26,6 +26,10 @@ Overviews Benchmarking ------------ +- Khurrum S. Chaudri and Sikander M. Mirza, "Burnup dependent Monte Carlo + neutron physics calculations of IAEA MTR benchmark," *Prog. Nucl. Energy*, + **81**, 43-52 (2015). ``_ + - Daniel J. Kelly, Brian N. Aviles, Paul K. Romano, Bryan R. Herman, Nicholas E. Horelik, and Benoit Forget, "Analysis of select BEAVRS PWR benchmark cycle 1 results using MC21 and OpenMC," *Proc. PHYSOR*, Kyoto, @@ -57,13 +61,8 @@ Coupling and Multi-physics - Bryan R. Herman, Benoit Forget, and Kord Smith, "Progress toward Monte Carlo-thermal hydraulic coupling using low-order nonlinear diffusion - acceleration methods." In press, *Ann. Nucl. Energy*, - (2014). ``_ - -- Adam G. Nelson and William R. Martin, "Improved Convergence of Monte Carlo - Generated Multi-Group Scattering Moments," *Proc. Int. Conf. Mathematics and - Computational Methods Applied to Nuclear Science and Engineering*, Sun Valley, - Idaho, May 5--9 (2013). + acceleration methods." *Ann. Nucl. Energy*, **84**, 63-72 + (2015). ``_ - Bryan R. Herman, Benoit Forget, and Kord Smith, "Utilizing CMFD in OpenMC to Estimate Dominance Ratio and Adjoint," *Trans. Am. Nucl. Soc.*, **109**, @@ -81,19 +80,65 @@ Geometry Miscellaneous ------------- +- William Boyd, Sterling Harper, and Paul K. Romano, "Equipping OpenMC for the + big data era," Accepted, *PHYSOR 2016*, Sun Valley, Idaho, May 1-5, 2016. + +- Qicang Shen, William Boyd, Benoit Forget, and Kord Smith, "Tally precision + triggers for the OpenMC Monte Carlo code," *Trans. Am. Nucl. Soc.*, **112**, + 637-640 (2015). + - Timothy P. Burke, Brian C. Kiedrowski, and William R. Martin, "Flux and Reaction Rate Kernel Density Estimators in OpenMC," *Trans. Am. Nucl. Soc.*, **109**, 683-686 (2013). +------------------------------------ +Multi-group Cross Section Generation +------------------------------------ + +- Adam G. Nelson and William R. Martin, "Improved Monte Carlo tallying of + multi-group scattering moments using the NDPP code," *Trans. Am. Nucl. Soc.*, + **113**, 645-648 (2015) + +- Adam G. Nelson and William R. Martin, "Improved Monte Carlo tallying of + multi-group scattering moment matrices," *Trans. Am. Nucl. Soc.*, **110**, + 217-220 (2014). + +- Adam G. Nelson and William R. Martin, "Improved Convergence of Monte Carlo + Generated Multi-Group Scattering Moments," *Proc. Int. Conf. Mathematics and + Computational Methods Applied to Nuclear Science and Engineering*, Sun Valley, + Idaho, May 5--9 (2013). + ------------ Nuclear Data ------------ +- Colin Josey, Pablo Ducru, Benoit Forget, and Kord Smith, "Windowed multipole + for cross section Doppler broadening," *J. Comput. Phys.*, In Press + (2016). ``_ + +- Colin Josey, Benoit Forget, and Kord Smith, "Windowed multipole sensitivity to + target accuracy of the optimization procedure," *J. Nucl. Sci. Technol.*, + **52**, 987-992 (2015). ``_ + +- Jonathan A. Walsh, Paul K. Romano, Benoit Forget, and Kord S. Smith, + "Optimizations of the energy grid search algorithm in continuous-energy Monte + Carlo particle transport codes", *Comput. Phys. Commun.*, **196**, 134-142 + (2015). ``_ + - Jonathan A. Walsh, Benoit Forget, Kord S. Smith, Brian C. Kiedrowski, and Forrest B. Brown, "Direct, on-the-fly calculation of unresolved resonance region cross sections in Monte Carlo simulations," *Proc. Joint Int. Conf. M&C+SNA+MC*, Nashville, Tennessee, Apr. 19--23 (2015). +- Amanda L. Lund, Andrew R. Siegel, Benoit Forget, Colin Josey, and + Paul K. Romano, "Using fractional cascading to accelerate cross section + lookups in Monte Carlo particle transport calculations," *Proc. Joint + Int. Conf. M&C+SNA+MC*, Nashville, Tennessee, Apr. 19--23 (2015). + +- Ronald O. Rahaman, Andrew R. Siegel, and Paul K. Romano, "Monte Carlo + performance analysis for varying cross section parameter regimes," + *Proc. Joint Int. Conf. M&C+SNA+MC*, Nashville, Tennessee, Apr. 19--23 (2015). + - Paul K. Romano and Timothy H. Trumbull, "Comparison of algorithms for Doppler broadening pointwise tabulated cross sections," *Ann. Nucl. Energy*, **75**, 358--364 (2015). ``_ @@ -114,6 +159,10 @@ Nuclear Data Parallelism ----------- +- Paul K. Romano, John R. Tramm, and Andrew R. Siegel, "Efficacy of hardware + threading for Monte Carlo particle transport calculations on multi- and + many-core systems," Accepted, *PHYSOR 2016*, Sun Valley, Idaho, May 1-5, 2016. + - David Ozog, Allen D. Malony, and Andrew R. Siegel, "A performance analysis of SIMD algorithms for Monte Carlo simulations of nuclear reactor cores," *Proc. IEEE Int. Parallel and Distributed Processing Symposium*, Hyderabad, diff --git a/docs/source/pythonapi/examples/images/mgxs.png b/docs/source/pythonapi/examples/images/mgxs.png new file mode 100644 index 000000000..3946a5b3c Binary files /dev/null and b/docs/source/pythonapi/examples/images/mgxs.png differ diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb new file mode 100644 index 000000000..897af8e3f --- /dev/null +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -0,0 +1,1195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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", + "\n", + "* **General equations** for scalar-flux averaged multi-group cross sections\n", + "* Creation of multi-group cross sections for an **infinite homogeneous medium**\n", + "* Use of **tally arithmetic** to manipulate multi-group cross sections\n", + "\n", + "**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/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Multi-Group Cross Sections (MGXS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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*. An example of U-235's continuous-energy fission cross section along with a 16-group cross section computed for a light water reactor spectrum is displayed below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Widi+P/wQePBAuhyEEOJoVEwTQogTkeJhQUv6jI0Fzp2zfwZCCJELFdOEEEII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id0CkRvp07At9/657mkrd9A4GCaiIjIQ9262W/du7ueQ1q3/v2dfx49OrCv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\njwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": { + "image/png": { + "width": 350 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='images/mgxs.png', width=350)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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", + "\n", + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introductory Notation\n", + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial and Energy Discretization\n", + "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", + "\n", + "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**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General Scalar-Flux Weighted MGXS\n", + "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", + "\n", + "$$\\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Group Scattering Matrices\n", + "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", + "\n", + "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", + "\n", + "$$\\sigma_{n,s,k,g\\rightarrow g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,s}(\\mathbf{r},E'\\rightarrow E'')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "This scalar flux-weighted multi-group microscopic scattering matrix is computed using OpenMC tallies with both energy in and energy out filters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Group 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", + "\n", + "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", + "\n", + "Similar to before, spatial homogenization and energy condensation are used to find the multi-group fission spectrum $\\chi_{n,k,g}$ as follows:\n", + "\n", + "$$\\chi_{n,k,g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "The fission production-weighted multi-group fission spectrum is computed using OpenMC tallies with both energy in and energy out filters.\n", + "\n", + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs\n", + "\n", + "%matplotlib inline" + ] + }, + { + "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('H-1')\n", + "o16 = openmc.Nuclide('O-16')\n", + "u235 = openmc.Nuclide('U-235')\n", + "u238 = openmc.Nuclide('U-238')\n", + "zr90 = openmc.Nuclide('Zr-90')" + ] + }, + { + "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": true + }, + "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, 0.028999667)\n", + "inf_medium.add_nuclide(o16, 0.01450188)\n", + "inf_medium.add_nuclide(u235, 0.000114142)\n", + "inf_medium.add_nuclide(u238, 0.006886019)\n", + "inf_medium.add_nuclide(zr90, 0.002116053)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our material, we can now create a `MaterialsFile` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a MaterialsFile, register all Materials, and export to XML\n", + "materials_file = openmc.MaterialsFile()\n", + "materials_file.default_xs = '71c'\n", + "materials_file.add_material(inf_medium)\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": true + }, + "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, put the geometry into a `GeometryFile` object, 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", + "# Instantiate a GeometryFile\n", + "geometry_file = openmc.GeometryFile()\n", + "geometry_file.geometry = openmc_geometry\n", + "\n", + "# Export to \"geometry.xml\"\n", + "geometry_file.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": true + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 2500\n", + "\n", + "# Instantiate a SettingsFile\n", + "settings_file = openmc.SettingsFile()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': True, 'summary': True}\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "settings_file.set_source_space('fission', bounds)\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 2-group structure using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a 2-group EnergyGroups object\n", + "groups = mgxs.EnergyGroups()\n", + "groups.group_edges = np.array([0., 0.625e-6, 20.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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", + "\n", + "* `TotalXS`\n", + "* `TransportXS`\n", + "* `AbsorptionXS`\n", + "* `CaptureXS`\n", + "* `FissionXS`\n", + "* `NuFissionXS`\n", + "* `ScatterXS`\n", + "* `NuScatterXS`\n", + "* `ScatterMatrixXS`\n", + "* `NuScatterMatrixXS`\n", + "* `Chi`\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 total, absorption and scattering cross sections with our 2-group structure." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a few different sections\n", + "total = mgxs.TotalXS(domain=cell, domain_type='cell', groups=groups)\n", + "absorption = mgxs.AbsorptionXS(domain=cell, domain_type='cell', groups=groups)\n", + "scattering = mgxs.ScatterXS(domain=cell, domain_type='cell', groups=groups)" + ] + }, + { + "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 `Absorption` object as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('flux', Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t['flux']\n", + "\tEstimator =\ttracklength\n", + "), ('absorption', Tally\n", + "\tID =\t10001\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t['absorption']\n", + "\tEstimator =\ttracklength\n", + ")])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "absorption.tallies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate an empty TalliesFile\n", + "tallies_file = openmc.TalliesFile()\n", + "\n", + "# Add total tallies to the tallies file\n", + "for tally in total.tallies.values():\n", + " tallies_file.add_tally(tally)\n", + "\n", + "# Add absorption tallies to the tallies file\n", + "for tally in absorption.tallies.values():\n", + " tallies_file.add_tally(tally)\n", + "\n", + "# Add scattering tallies to the tallies file\n", + "for tally in scattering.tallies.values():\n", + " tallies_file.add_tally(tally)\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", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.0\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-12-02 09:11:05\n", + " MPI Processes: 1\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Maximum neutron transport energy: 20.0000 MeV for 1001.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.19804 \n", + " 2/1 1.12945 \n", + " 3/1 1.15573 \n", + " 4/1 1.13929 \n", + " 5/1 1.16300 \n", + " 6/1 1.22117 \n", + " 7/1 1.19012 \n", + " 8/1 1.11299 \n", + " 9/1 1.16066 \n", + " 10/1 1.12566 \n", + " 11/1 1.20854 \n", + " 12/1 1.14691 1.17773 +/- 0.03082\n", + " 13/1 1.17204 1.17583 +/- 0.01789\n", + " 14/1 1.14148 1.16724 +/- 0.01529\n", + " 15/1 1.17272 1.16834 +/- 0.01189\n", + " 16/1 1.18575 1.17124 +/- 0.01014\n", + " 17/1 1.20498 1.17606 +/- 0.00983\n", + " 18/1 1.14754 1.17249 +/- 0.00923\n", + " 19/1 1.18141 1.17348 +/- 0.00820\n", + " 20/1 1.15074 1.17121 +/- 0.00768\n", + " 21/1 1.15914 1.17011 +/- 0.00703\n", + " 22/1 1.14586 1.16809 +/- 0.00673\n", + " 23/1 1.18999 1.16978 +/- 0.00642\n", + " 24/1 1.15101 1.16844 +/- 0.00609\n", + " 25/1 1.13791 1.16640 +/- 0.00602\n", + " 26/1 1.19791 1.16837 +/- 0.00597\n", + " 27/1 1.19818 1.17012 +/- 0.00587\n", + " 28/1 1.14160 1.16854 +/- 0.00576\n", + " 29/1 1.11487 1.16571 +/- 0.00614\n", + " 30/1 1.17538 1.16620 +/- 0.00584\n", + " 31/1 1.20210 1.16791 +/- 0.00581\n", + " 32/1 1.20078 1.16940 +/- 0.00574\n", + " 33/1 1.14624 1.16839 +/- 0.00558\n", + " 34/1 1.14618 1.16747 +/- 0.00542\n", + " 35/1 1.16866 1.16752 +/- 0.00520\n", + " 36/1 1.18565 1.16821 +/- 0.00504\n", + " 37/1 1.16824 1.16821 +/- 0.00485\n", + " 38/1 1.18299 1.16874 +/- 0.00471\n", + " 39/1 1.21418 1.17031 +/- 0.00480\n", + " 40/1 1.11167 1.16835 +/- 0.00504\n", + " 41/1 1.11545 1.16665 +/- 0.00516\n", + " 42/1 1.11114 1.16491 +/- 0.00529\n", + " 43/1 1.14227 1.16423 +/- 0.00517\n", + " 44/1 1.14104 1.16355 +/- 0.00506\n", + " 45/1 1.16756 1.16366 +/- 0.00492\n", + " 46/1 1.13065 1.16274 +/- 0.00487\n", + " 47/1 1.11251 1.16139 +/- 0.00492\n", + " 48/1 1.14731 1.16101 +/- 0.00481\n", + " 49/1 1.16691 1.16117 +/- 0.00469\n", + " 50/1 1.19679 1.16206 +/- 0.00465\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.1700E-01 seconds\n", + " Reading cross sections = 8.9000E-02 seconds\n", + " Total time in simulation = 1.4728E+01 seconds\n", + " Time in transport only = 1.4712E+01 seconds\n", + " Time in inactive batches = 1.7890E+00 seconds\n", + " Time in active batches = 1.2939E+01 seconds\n", + " Time synchronizing fission bank = 5.0000E-03 seconds\n", + " Sampling source sites = 3.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", + " Total time for finalization = 1.0000E-03 seconds\n", + " Total time elapsed = 1.5155E+01 seconds\n", + " Calculation Rate (inactive) = 13974.3 neutrons/second\n", + " Calculation Rate (active) = 7728.57 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.16131 +/- 0.00453\n", + " k-effective (Track-length) = 1.16206 +/- 0.00465\n", + " k-effective (Absorption) = 1.16096 +/- 0.00364\n", + " Combined k-effective = 1.16120 +/- 0.00325\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", + "executor = openmc.Executor()\n", + "executor.run_simulation()" + ] + }, + { + "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. 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." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the summary file and link it with the statepoint\n", + "su = openmc.Summary('summary.h5')\n", + "sp.link_with_summary(su)" + ] + }, + { + "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": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the tallies from the statepoint into each MGXS object\n", + "total.load_from_statepoint(sp)\n", + "absorption.load_from_statepoint(sp)\n", + "scattering.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 total cross section by printing it to the screen." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\ttotal\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t1\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t6.81e-01 +/- 1.88e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.40e+00 +/- 5.91e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "total.print_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": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "1 1 1 total 0.668323 0.001264\n", + "0 1 2 total 1.293258 0.007624" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = scattering.get_pandas_dataframe()\n", + "df.head(10)" + ] + }, + { + "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": true + }, + "outputs": [], + "source": [ + "absorption.export_xs_data(filename='absorption-xs', format='excel')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code snippet shows how to export all three `MGXS` to the same HDF5 binary data store." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "total.build_hdf5_store(filename='mgxs', append=True)\n", + "absorption.build_hdf5_store(filename='mgxs', append=True)\n", + "scattering.build_hdf5_store(filename='mgxs', append=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing MGXS with Tally Arithmetic" + ] + }, + { + "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 confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy [MeV]nuclidescoremeanstd. dev.
01(0.0e+00 - 6.3e-07)total(((total / flux) - (absorption / flux)) - (sca...4.884981e-150.011274
11(6.3e-07 - 2.0e+01)total(((total / flux) - (absorption / flux)) - (sca...1.221245e-150.001802
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide \\\n", + "0 1 (0.0e+00 - 6.3e-07) total \n", + "1 1 (6.3e-07 - 2.0e+01) total \n", + "\n", + " score mean std. dev. \n", + "0 (((total / flux) - (absorption / flux)) - (sca... 4.884981e-15 0.011274 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... 1.221245e-15 0.001802 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the difference between the total, absorption and scattering\n", + "difference = total.xs_tally - absorption.xs_tally - scattering.xs_tally\n", + "\n", + "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + "difference.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can use tally arithmetic to compute the ratio of `AbsorptionXS` and `ScatterXS` to the `TotalXS`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy [MeV]nuclidescoremeanstd. dev.
01(0.0e+00 - 6.3e-07)total((absorption / flux) / (total / flux))0.0762190.000651
11(6.3e-07 - 2.0e+01)total((absorption / flux) / (total / flux))0.0193190.000086
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide score \\\n", + "0 1 (0.0e+00 - 6.3e-07) total ((absorption / flux) / (total / flux)) \n", + "1 1 (6.3e-07 - 2.0e+01) total ((absorption / flux) / (total / flux)) \n", + "\n", + " mean std. dev. \n", + "0 0.076219 0.000651 \n", + "1 0.019319 0.000086 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the absorption-to-total MGXS ratio\n", + "absorption_to_total = absorption.xs_tally / total.xs_tally\n", + "\n", + "# The absorption-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "absorption_to_total.get_pandas_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy [MeV]nuclidescoremeanstd. dev.
01(0.0e+00 - 6.3e-07)total((scatter / flux) / (total / flux))0.9237810.007714
11(6.3e-07 - 2.0e+01)total((scatter / flux) / (total / flux))0.9806810.002617
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide score \\\n", + "0 1 (0.0e+00 - 6.3e-07) total ((scatter / flux) / (total / flux)) \n", + "1 1 (6.3e-07 - 2.0e+01) total ((scatter / flux) / (total / flux)) \n", + "\n", + " mean std. dev. \n", + "0 0.923781 0.007714 \n", + "1 0.980681 0.002617 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the scattering-to-total MGXS ratio\n", + "scattering_to_total = scattering.xs_tally / total.xs_tally\n", + "\n", + "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "scattering_to_total.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we sum the derived scatter-to-total and absorption-to-total ratios to confirm that they sum to unity." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy [MeV]nuclidescoremeanstd. dev.
01(0.0e+00 - 6.3e-07)total(((absorption / flux) / (total / flux)) + ((sc...10.007741
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide \\\n", + "0 1 (0.0e+00 - 6.3e-07) total \n", + "1 1 (6.3e-07 - 2.0e+01) total \n", + "\n", + " score mean std. dev. \n", + "0 (((absorption / flux) / (total / flux)) + ((sc... 1 0.007741 \n", + "1 (((absorption / flux) / (total / flux)) + ((sc... 1 0.002619 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to ensure that the absorption- and scattering-to-total MGXS ratios sum to unity\n", + "sum_ratio = absorption_to_total + scattering_to_total\n", + "\n", + "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "sum_ratio.get_pandas_dataframe()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/mgxs-part-i.rst b/docs/source/pythonapi/examples/mgxs-part-i.rst new file mode 100644 index 000000000..8b29183f0 --- /dev/null +++ b/docs/source/pythonapi/examples/mgxs-part-i.rst @@ -0,0 +1,13 @@ +.. _notebook_mgxs_part_i: + +========================= +MGXS Part I: Introduction +========================= + +.. only:: html + + .. notebook:: mgxs-part-i.ipynb + +.. only:: latex + + IPython notebooks must be viewed in the online HTML documentation. diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb new file mode 100644 index 000000000..6194b154a --- /dev/null +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -0,0 +1,1947 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This IPython Notebook illustrates the use of the `openmc.mgxs` module to calculate multi-group cross sections for a heterogeneous fuel pin cell geometry. In particular, this Notebook illustrates the following features:\n", + "\n", + "* Creation of multi-group cross sections on a **heterogeneous geometry**\n", + "* Calculation of cross sections on a **nuclide-by-nuclide basis**\n", + "* The use of **[tally precision triggers](https://mit-crpg.github.io/openmc/usersguide/input.html#trigger-element)** with multi-group cross sections\n", + "* Built-in features for **energy condensation** in downstream data processing\n", + "* The use of **[PyNE](http://pyne.io/) to plot** continuous-energy vs. multi-group cross sections\n", + "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n", + "\n", + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, 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/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: UserWarning: This call to matplotlib.use() has no effect\n", + "because the backend has already been chosen;\n", + "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", + "or matplotlib.backends is imported for the first time.\n", + "\n", + " warnings.warn(_use_error_msg)\n", + "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.rxname is not yet QA compliant.\n", + "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.ace is not yet QA compliant.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs\n", + "import openmoc\n", + "from openmoc.compatible import get_openmoc_geometry\n", + "import pyne.ace\n", + "\n", + "%matplotlib inline" + ] + }, + { + "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": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate some Nuclides\n", + "h1 = openmc.Nuclide('H-1')\n", + "o16 = openmc.Nuclide('O-16')\n", + "u235 = openmc.Nuclide('U-235')\n", + "u238 = openmc.Nuclide('U-238')\n", + "zr90 = openmc.Nuclide('Zr-90')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the nuclides we defined, we will now create three distinct materials for water, clad and fuel." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# 1.6% enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide(u235, 3.7503e-4)\n", + "fuel.add_nuclide(u238, 2.2625e-2)\n", + "fuel.add_nuclide(o16, 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide(h1, 4.9457e-2)\n", + "water.add_nuclide(o16, 2.4732e-2)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide(zr90, 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our materials, we can now create a `MaterialsFile` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a MaterialsFile, add Materials\n", + "materials_file = openmc.MaterialsFile()\n", + "materials_file.add_material(fuel)\n", + "materials_file.add_material(water)\n", + "materials_file.add_material(zircaloy)\n", + "materials_file.default_xs = '71c'\n", + "\n", + "# Export to \"materials.xml\"\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", + "\n", + "# Create boundary planes to surround the geometry\n", + "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "pin_cell_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "pin_cell_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "pin_cell_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell')\n", + "root_cell.region = +min_x & -max_x & +min_y & -max_y\n", + "root_cell.fill = pin_cell_universe\n", + "\n", + "# Create root Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(root_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe, put the geometry into a `GeometryFile` object, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "openmc_geometry = openmc.Geometry()\n", + "openmc_geometry.root_universe = root_universe\n", + "\n", + "# Instantiate a GeometryFile\n", + "geometry_file = openmc.GeometryFile()\n", + "geometry_file.geometry = openmc_geometry\n", + "\n", + "# Export to \"geometry.xml\"\n", + "geometry_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 190 active batches each with 10,000 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 10000\n", + "\n", + "# Instantiate a SettingsFile\n", + "settings_file = openmc.SettingsFile()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': True, 'summary': True}\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "settings_file.set_source_space('fission', bounds)\n", + "\n", + "# Activate tally precision triggers\n", + "settings_file.trigger_active = True\n", + "settings_file.trigger_max_batches = settings_file.batches * 4\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define \"coarse\" 2-group and \"fine\" 8-group structures using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a \"coarse\" 2-group EnergyGroups object\n", + "coarse_groups = mgxs.EnergyGroups()\n", + "coarse_groups.group_edges = np.array([0., 0.625e-6, 20.])\n", + "\n", + "# Instantiate a \"fine\" 8-group EnergyGroups object\n", + "fine_groups = mgxs.EnergyGroups()\n", + "fine_groups.group_edges = np.array([0., 0.058e-6, 0.14e-6, 0.28e-6,\n", + " 0.625e-6, 4.e-6, 5.53e-3, 821.e-3, 20.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will instantiate a variety of `MGXS` objects needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we define transport, fission, nu-fission, nu-scatter and chi cross sections for each of the three cells in the fuel pin with the 8-group structure as our energy groups." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Extract all Cells filled by Materials\n", + "openmc_cells = openmc_geometry.get_all_material_cells()\n", + "\n", + "# Create dictionary to store multi-group cross sections for all cells\n", + "xs_library = {}\n", + "\n", + "# Instantiate 8-group cross sections for each cell\n", + "for cell in openmc_cells:\n", + " xs_library[cell.id] = {}\n", + " xs_library[cell.id]['transport'] = mgxs.TransportXS(groups=fine_groups)\n", + " xs_library[cell.id]['fission'] = mgxs.FissionXS(groups=fine_groups)\n", + " xs_library[cell.id]['nu-fission'] = mgxs.NuFissionXS(groups=fine_groups)\n", + " xs_library[cell.id]['nu-scatter'] = mgxs.NuScatterMatrixXS(groups=fine_groups)\n", + " xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we showcase the use of OpenMC's [tally precision trigger](https://mit-crpg.github.io/openmc/usersguide/input.html#trigger-element) feature in conjunction with the `openmc.mgxs` module. In particular, we will assign a tally trigger of 1E-2 on the standard deviation for each of the tallies used to compute multi-group cross sections." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create a tally trigger for +/- 0.01 on each tally used to compute the multi-group cross sections\n", + "tally_trigger = openmc.Trigger('std_dev', 1E-2)\n", + "\n", + "# Add the tally trigger to each of the multi-group cross section tallies\n", + "for cell in openmc_cells:\n", + " for mgxs_type in xs_library[cell.id]:\n", + " xs_library[cell.id][mgxs_type].tally_trigger = tally_trigger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we must loop over all cells to set the cross section domains to the various cells - fuel, clad and moderator - included in the geometry. In addition, we will set each cross section to tally cross sections on a per-nuclide basis through the use of the `MGXS` class' boolean `by_nuclide` instance attribute. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate an empty TalliesFile\n", + "tallies_file = openmc.TalliesFile()\n", + "\n", + "# Iterate over all cells and cross section types\n", + "for cell in openmc_cells:\n", + " for rxn_type in xs_library[cell.id]:\n", + "\n", + " # Set the cross sections domain type to the cell\n", + " xs_library[cell.id][rxn_type].domain = cell\n", + " xs_library[cell.id][rxn_type].domain_type = 'cell'\n", + " \n", + " # Tally cross sections by nuclide\n", + " xs_library[cell.id][rxn_type].by_nuclide = True\n", + " \n", + " # Add OpenMC tallies to the tallies file for XML generation\n", + " for tally in xs_library[cell.id][rxn_type].tallies.values():\n", + " tallies_file.add_tally(tally, merge=True)\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": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.0\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-12-02 09:13:42\n", + " MPI Processes: 3\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.22593 \n", + " 2/1 1.24245 \n", + " 3/1 1.24545 \n", + " 4/1 1.21868 \n", + " 5/1 1.22429 \n", + " 6/1 1.22607 \n", + " 7/1 1.21456 \n", + " 8/1 1.23816 \n", + " 9/1 1.25060 \n", + " 10/1 1.22806 \n", + " 11/1 1.19821 \n", + " 12/1 1.19897 1.19859 +/- 0.00038\n", + " 13/1 1.22119 1.20612 +/- 0.00754\n", + " 14/1 1.20701 1.20634 +/- 0.00533\n", + " 15/1 1.24784 1.21464 +/- 0.00927\n", + " 16/1 1.22413 1.21622 +/- 0.00773\n", + " 17/1 1.25050 1.22112 +/- 0.00817\n", + " 18/1 1.22006 1.22099 +/- 0.00707\n", + " 19/1 1.22813 1.22178 +/- 0.00629\n", + " 20/1 1.22791 1.22239 +/- 0.00566\n", + " 21/1 1.22729 1.22284 +/- 0.00514\n", + " 22/1 1.19867 1.22083 +/- 0.00510\n", + " 23/1 1.23796 1.22214 +/- 0.00488\n", + " 24/1 1.22412 1.22228 +/- 0.00452\n", + " 25/1 1.22638 1.22256 +/- 0.00421\n", + " 26/1 1.22181 1.22251 +/- 0.00394\n", + " 27/1 1.19055 1.22063 +/- 0.00415\n", + " 28/1 1.20683 1.21986 +/- 0.00399\n", + " 29/1 1.21689 1.21971 +/- 0.00378\n", + " 30/1 1.23670 1.22056 +/- 0.00368\n", + " 31/1 1.21396 1.22024 +/- 0.00352\n", + " 32/1 1.21389 1.21995 +/- 0.00337\n", + " 33/1 1.24649 1.22111 +/- 0.00342\n", + " 34/1 1.23204 1.22156 +/- 0.00330\n", + " 35/1 1.20768 1.22101 +/- 0.00322\n", + " 36/1 1.22271 1.22107 +/- 0.00309\n", + " 37/1 1.21796 1.22096 +/- 0.00298\n", + " 38/1 1.23842 1.22158 +/- 0.00293\n", + " 39/1 1.23080 1.22190 +/- 0.00285\n", + " 40/1 1.23572 1.22236 +/- 0.00279\n", + " 41/1 1.21691 1.22218 +/- 0.00271\n", + " 42/1 1.24616 1.22293 +/- 0.00272\n", + " 43/1 1.21903 1.22282 +/- 0.00264\n", + " 44/1 1.22967 1.22302 +/- 0.00257\n", + " 45/1 1.22053 1.22295 +/- 0.00250\n", + " 46/1 1.24087 1.22344 +/- 0.00248\n", + " 47/1 1.20251 1.22288 +/- 0.00248\n", + " 48/1 1.20331 1.22236 +/- 0.00246\n", + " 49/1 1.22724 1.22249 +/- 0.00240\n", + " 50/1 1.24798 1.22313 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 1.32110 for scatter-p1 in tally 10054\n", + " The estimated number of batches is 80\n", + " Creating state point statepoint.050.h5...\n", + " 51/1 1.22253 1.22311 +/- 0.00237\n", + " 52/1 1.24330 1.22359 +/- 0.00236\n", + " 53/1 1.23251 1.22380 +/- 0.00231\n", + " 54/1 1.21133 1.22352 +/- 0.00228\n", + " 55/1 1.24503 1.22399 +/- 0.00228\n", + " 56/1 1.22013 1.22391 +/- 0.00223\n", + " 57/1 1.23877 1.22423 +/- 0.00220\n", + " 58/1 1.23793 1.22451 +/- 0.00218\n", + " 59/1 1.21018 1.22422 +/- 0.00215\n", + " 60/1 1.22417 1.22422 +/- 0.00211\n", + " 61/1 1.23094 1.22435 +/- 0.00207\n", + " 62/1 1.23310 1.22452 +/- 0.00204\n", + " 63/1 1.22488 1.22453 +/- 0.00200\n", + " 64/1 1.22702 1.22457 +/- 0.00196\n", + " 65/1 1.18834 1.22391 +/- 0.00204\n", + " 66/1 1.23112 1.22404 +/- 0.00200\n", + " 67/1 1.21611 1.22390 +/- 0.00197\n", + " 68/1 1.22513 1.22392 +/- 0.00194\n", + " 69/1 1.21741 1.22381 +/- 0.00191\n", + " 70/1 1.22484 1.22383 +/- 0.00188\n", + " 71/1 1.19662 1.22338 +/- 0.00190\n", + " 72/1 1.23315 1.22354 +/- 0.00187\n", + " 73/1 1.22796 1.22361 +/- 0.00185\n", + " 74/1 1.21417 1.22346 +/- 0.00182\n", + " 75/1 1.21020 1.22326 +/- 0.00181\n", + " 76/1 1.23413 1.22343 +/- 0.00179\n", + " 77/1 1.22184 1.22340 +/- 0.00176\n", + " 78/1 1.20309 1.22310 +/- 0.00176\n", + " 79/1 1.23458 1.22327 +/- 0.00174\n", + " 80/1 1.20724 1.22304 +/- 0.00173\n", + " Triggers satisfied for batch 80\n", + " Creating state point statepoint.080.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 7.5700E-01 seconds\n", + " Reading cross sections = 1.5800E-01 seconds\n", + " Total time in simulation = 1.4921E+02 seconds\n", + " Time in transport only = 1.4336E+02 seconds\n", + " Time in inactive batches = 8.6210E+00 seconds\n", + " Time in active batches = 1.4059E+02 seconds\n", + " Time synchronizing fission bank = 5.6060E+00 seconds\n", + " Sampling source sites = 1.4000E-02 seconds\n", + " SEND/RECV source sites = 4.0000E-03 seconds\n", + " Time accumulating tallies = 6.0000E-03 seconds\n", + " Total time for finalization = 1.3000E-02 seconds\n", + " Total time elapsed = 1.5002E+02 seconds\n", + " Calculation Rate (inactive) = 11599.6 neutrons/second\n", + " Calculation Rate (active) = 2845.11 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.22327 +/- 0.00148\n", + " k-effective (Track-length) = 1.22304 +/- 0.00173\n", + " k-effective (Absorption) = 1.22407 +/- 0.00129\n", + " Combined k-effective = 1.22373 +/- 0.00113\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run OpenMC\n", + "executor = openmc.Executor()\n", + "executor.run_simulation(output=True, mpi_procs=3)" + ] + }, + { + "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": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint('statepoint.080.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. 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." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load the summary file and link it with the statepoint\n", + "su = openmc.Summary('summary.h5')\n", + "sp.link_with_summary(su)" + ] + }, + { + "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": [ + "# Iterate over all cells and cross section types\n", + "for cell in openmc_cells:\n", + " for rxn_type in xs_library[cell.id]:\n", + " xs_library[cell.id][rxn_type].load_from_statepoint(sp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Our multi-group cross sections are now ready for the big spotlight. This time we have cross sections in three distinct spatial zones - fuel, clad and moderator - on a per-nuclide basis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first inspect one of our cross sections by printing it to the screen as a microscopic cross section in units of barns." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [barns]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t3.31e+00 +/- 1.88e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t3.97e+00 +/- 1.24e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t5.50e+01 +/- 2.02e-01%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t8.83e+01 +/- 3.56e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t2.90e+02 +/- 4.54e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t4.49e+02 +/- 4.10e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t6.87e+02 +/- 2.56e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t1.44e+03 +/- 2.82e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [barns]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t1.06e+00 +/- 2.30e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t1.21e-03 +/- 2.25e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t5.82e-04 +/- 3.09e+00%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t6.54e-06 +/- 3.27e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.07e-05 +/- 4.39e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.55e-05 +/- 4.12e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.30e-05 +/- 2.57e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t4.24e-05 +/- 2.81e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "nufission = xs_library[fuel_cell.id]['nu-fission']\n", + "nufission.print_xs(xs_type='micro', nuclides=['U-235', 'U-238'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our multi-group cross sections are capable of summing across all nuclides to provide us with macroscopic cross sections as well." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t2.52e-02 +/- 2.19e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t1.51e-03 +/- 1.22e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t2.06e-02 +/- 2.02e-01%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t3.31e-02 +/- 3.56e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.09e-01 +/- 4.54e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.69e-01 +/- 4.10e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.58e-01 +/- 2.56e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t5.40e-01 +/- 2.82e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "nufission = xs_library[fuel_cell.id]['nu-fission']\n", + "nufission.print_xs(xs_type='macro', nuclides='sum')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although a printed report is nice, it is not scalable or flexible. Let's extract the microscopic cross section data for the moderator as a [Pandas](http://pandas.pydata.org/) `DataFrame` ." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell group in group out nuclide mean std. dev.\n", + "126 10002 1 1 H-1 0.234022 0.003645\n", + "127 10002 1 1 O-16 1.560305 0.006280\n", + "124 10002 1 2 H-1 1.588025 0.002815\n", + "125 10002 1 2 O-16 0.285147 0.001392\n", + "122 10002 1 3 H-1 0.010776 0.000186\n", + "123 10002 1 3 O-16 0.000000 0.000000\n", + "120 10002 1 4 H-1 0.000023 0.000010\n", + "121 10002 1 4 O-16 0.000000 0.000000\n", + "118 10002 1 5 H-1 0.000000 0.000000\n", + "119 10002 1 5 O-16 0.000000 0.000000" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", + "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we illustate how one can easily take multi-group cross sections and condense them down to a coarser energy group structure. The `MGXS` class includes a `get_condensed_xs(...)` method which takes an `EnergyGroups` parameter with a coarse(r) group structure and returns a new `MGXS` condensed to the coarse groups. We illustrate this process below using the 2-group structure created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Extract the 16-group transport cross section for the fuel\n", + "fine_xs = xs_library[fuel_cell.id]['transport']\n", + "\n", + "# Condense to the 2-group structure\n", + "condensed_xs = fine_xs.get_condensed_xs(coarse_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Group condensation is as simple as that! We now have a new coarse 2-group `TransportXS` in addition to our original 16-group `TransportXS`. Let's inspect the 2-group `TransportXS` by printing it to the screen and extracting a Pandas `DataFrame` as we have already learned how to do." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\ttransport\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t7.81e-03 +/- 4.75e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.82e-01 +/- 1.89e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t2.17e-01 +/- 1.31e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t2.53e-01 +/- 2.08e-01%\n", + "\n", + "\tNuclide =\tO-16\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t1.45e-01 +/- 1.50e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.74e-01 +/- 2.66e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "condensed_xs.print_xs()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup innuclidemeanstd. dev.
3100001U-23520.8281270.098842
4100001U-2389.5822950.012550
5100001O-163.1573580.004725
0100002U-235485.2176490.916465
1100002U-23811.1760810.023196
2100002O-163.7881670.010090
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "3 10000 1 U-235 20.828127 0.098842\n", + "4 10000 1 U-238 9.582295 0.012550\n", + "5 10000 1 O-16 3.157358 0.004725\n", + "0 10000 2 U-235 485.217649 0.916465\n", + "1 10000 2 U-238 11.176081 0.023196\n", + "2 10000 2 O-16 3.788167 0.010090" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = condensed_xs.get_pandas_dataframe(xs_type='micro')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification with OpenMOC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's verify our cross sections using OpenMOC. First, we use OpenCG construct an equivalent OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create an OpenMOC Geometry from the OpenCG Geometry\n", + "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we we can inject the multi-group cross sections into the equivalent fuel pin cell OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Get all OpenMOC cells in the gometry\n", + "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", + "\n", + "# Inject multi-group cross sections into OpenMOC Materials\n", + "for cell_id, cell in openmoc_cells.items():\n", + " \n", + " # Ignore the root cell\n", + " if cell.getName() == 'root cell':\n", + " continue\n", + " \n", + " # Get a reference to the Material filling this Cell\n", + " openmoc_material = cell.getFillMaterial()\n", + " \n", + " # Set the number of energy groups for the Material\n", + " openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n", + " \n", + " # Extract the appropriate cross section objects for this cell\n", + " transport = xs_library[cell_id]['transport']\n", + " nufission = xs_library[cell_id]['nu-fission']\n", + " nuscatter = xs_library[cell_id]['nu-scatter']\n", + " chi = xs_library[cell_id]['chi']\n", + " \n", + " # Inject NumPy arrays of cross section data into the Material\n", + " # NOTE: Sum across nuclides to get macro cross sections needed by OpenMOC\n", + " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.574633\tres = 1.959E-316\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.679931\tres = 4.254E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.660910\tres = 1.832E-01\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.658975\tres = 2.797E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.642976\tres = 2.928E-03\n", + "[ NORMAL ] Iteration 5:\tk_eff = 0.625710\tres = 2.428E-02\n", + "[ NORMAL ] Iteration 6:\tk_eff = 0.606520\tres = 2.685E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.587277\tres = 3.067E-02\n", + "[ NORMAL ] Iteration 8:\tk_eff = 0.568777\tres = 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"collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.223729\n", + "openmoc keff = 1.219868\n", + "bias [pcm]: -386.1\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.k_combined[0]\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a sanity check, let's run a simulation with the coarse 2-group cross sections to ensure that they also produce a reasonable result." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)\n", + "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", + "\n", + "# Inject multi-group cross sections into OpenMOC Materials\n", + "for cell_id, cell in openmoc_cells.items():\n", + " \n", + " # Ignore the root cell\n", + " if cell.getName() == 'root cell':\n", + " continue\n", + " \n", + " openmoc_material = cell.getFillMaterial()\n", + " openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n", + " \n", + " # Extract the appropriate cross section objects for this cell\n", + " transport = xs_library[cell_id]['transport']\n", + " nufission = xs_library[cell_id]['nu-fission']\n", + " nuscatter = xs_library[cell_id]['nu-scatter']\n", + " chi = xs_library[cell_id]['chi']\n", + " \n", + " # Perform group condensation\n", + " transport = transport.get_condensed_xs(coarse_groups)\n", + " nufission = nufission.get_condensed_xs(coarse_groups)\n", + " nuscatter = nuscatter.get_condensed_xs(coarse_groups)\n", + " chi = chi.get_condensed_xs(coarse_groups)\n", + " \n", + " # Inject NumPy arrays of cross section data into the Material\n", + " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.495594\tres = 1.959E-316\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.557312\tres = 5.044E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.518115\tres = 1.245E-01\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.509016\tres = 7.033E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.496279\tres = 1.756E-02\n", + "[ NORMAL ] Iteration 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NORMAL ] Iteration 212:\tk_eff = 1.222142\tres = 2.047E-05\n", + "[ NORMAL ] Iteration 213:\tk_eff = 1.222165\tres = 1.969E-05\n", + "[ NORMAL ] Iteration 214:\tk_eff = 1.222187\tres = 1.894E-05\n", + "[ NORMAL ] Iteration 215:\tk_eff = 1.222209\tres = 1.822E-05\n", + "[ NORMAL ] Iteration 216:\tk_eff = 1.222229\tres = 1.752E-05\n", + "[ NORMAL ] Iteration 217:\tk_eff = 1.222249\tres = 1.685E-05\n", + "[ NORMAL ] Iteration 218:\tk_eff = 1.222268\tres = 1.621E-05\n", + "[ NORMAL ] Iteration 219:\tk_eff = 1.222287\tres = 1.559E-05\n", + "[ NORMAL ] Iteration 220:\tk_eff = 1.222304\tres = 1.499E-05\n", + "[ NORMAL ] Iteration 221:\tk_eff = 1.222321\tres = 1.442E-05\n", + "[ NORMAL ] Iteration 222:\tk_eff = 1.222337\tres = 1.387E-05\n", + "[ NORMAL ] Iteration 223:\tk_eff = 1.222353\tres = 1.334E-05\n", + "[ NORMAL ] Iteration 224:\tk_eff = 1.222368\tres = 1.283E-05\n", + "[ NORMAL ] Iteration 225:\tk_eff = 1.222383\tres = 1.234E-05\n", + "[ NORMAL ] Iteration 226:\tk_eff = 1.222397\tres = 1.187E-05\n", + "[ NORMAL ] Iteration 227:\tk_eff = 1.222410\tres = 1.142E-05\n", + "[ NORMAL ] Iteration 228:\tk_eff = 1.222423\tres = 1.098E-05\n", + "[ NORMAL ] Iteration 229:\tk_eff = 1.222435\tres = 1.056E-05\n", + "[ NORMAL ] Iteration 230:\tk_eff = 1.222447\tres = 1.016E-05\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "openmoc_geometry.initializeFlatSourceRegions()\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, spacing=0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.223729\n", + "openmoc keff = 1.222447\n", + "bias [pcm]: -128.2\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.k_combined[0]\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a non-trivial bias in both the 2-group and 8-group cases. In the case of a pin cell, one can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", + "\n", + "* Appropriate transport-corrected cross sections\n", + "* Spatial discretization of OpenMOC's mesh\n", + "* Constant-in-angle multi-group cross sections" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Visualizing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is often insightful to generate visual depictions of multi-group cross sections. There are many different types of plots which may be useful for multi-group cross section visualization, only a few of which will be shown here for enrichment and inspiration.\n", + "\n", + "One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the open source [PyNE](http://pyne.io/) library to parse continuous-energy cross sections from the cross section data library provided with OpenMC. First, we instantiate a `pyne.ace.Library` object for U-235 as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a PyNE ACE continuous-energy cross sections library\n", + "pyne_lib = pyne.ace.Library('../../../../data/nndc/293.6K/U_235_293.6K.ace')\n", + "pyne_lib.read('92235.71c')\n", + "\n", + "# Extract the U-235 data from the library\n", + "u235 = pyne_lib.tables['92235.71c']\n", + "\n", + "# Extract the continuous-energy U-235 fission cross section data\n", + "fission = u235.reactions[18]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we use [`matplotlib`](http://matplotlib.org/) and [`seaborn`](http://stanford.edu/~mwaskom/software/seaborn/) to plot the continuous-energy and multi-group cross sections on a single plot." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.9999999999999994e-12, 20.0)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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NiQo+A2zbVhpS7/HHs7juuuDB588+s3PyyQURB6bT7T6mu1ai9ZoTfG5s5bMg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\nCIEvuNyxY/AO25faIpyd2Joi3KfhVM/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a loglog plot of the U-235 continuous-energy fission cross section \n", + "plt.loglog(u235.energy, fission.sigma, color='b', linewidth=1)\n", + "\n", + "# Extract energy group bounds and MGXS values to plot\n", + "nufission = xs_library[fuel_cell.id]['fission']\n", + "energy_groups = nufission.energy_groups\n", + "x = energy_groups.group_edges\n", + "y = nufission.get_xs(nuclides=['U-235'], order_groups='decreasing', xs_type='micro')\n", + "\n", + "# Fix low energy bound to the value defined by the ACE library\n", + "x[0] = u235.energy[0]\n", + "\n", + "# Extend the mgxs values array for matplotlib's step plot\n", + "y = np.insert(y, 0, y[0])\n", + "\n", + "# Create a step plot for the MGXS\n", + "plt.plot(x, y, drawstyle='steps', color='r', linewidth=3)\n", + "\n", + "plt.title('U-235 Fission Cross Section')\n", + "plt.xlabel('Energy [MeV]')\n", + "plt.ylabel('Micro Fission XS')\n", + "plt.legend(['Continuous', 'Multi-Group'])\n", + "plt.xlim((x.min(), x.max()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful type of illustration is scattering matrix sparsity structures. First, we extract Pandas `DataFrames` for the H-1 and O-16 scattering matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n", + "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", + "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", + "\n", + "# Slice DataFrame in two for each nuclide's mean values\n", + "h1 = df[df['nuclide'] == 'H-1']['mean']\n", + "o16 = df[df['nuclide'] == 'O-16']['mean']\n", + "\n", + "# Cast DataFrames as NumPy arrays\n", + "h1 = h1.as_matrix()\n", + "o16 = o16.as_matrix()\n", + "\n", + "# Reshape arrays to 2D matrix for plotting\n", + "h1.shape = (fine_groups.num_groups, fine_groups.num_groups)\n", + "o16.shape = (fine_groups.num_groups, fine_groups.num_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib's `imshow` routine can be used to plot the matrices to illustrate their sparsity structures." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plot of the H-1 scattering matrix\n", + "fig = plt.subplot(121)\n", + "fig.imshow(h1, interpolation='nearest', cmap='jet')\n", + "plt.title('H-1 Scattering Matrix')\n", + "plt.xlabel('Group Out')\n", + "plt.ylabel('Group In')\n", + "plt.grid()\n", + "\n", + "# Create plot of the O-16 scattering matrix\n", + "fig2 = plt.subplot(122)\n", + "fig2.imshow(o16, interpolation='nearest', cmap='jet')\n", + "plt.title('O-16 Scattering Matrix')\n", + "plt.xlabel('Group Out')\n", + "plt.ylabel('Group In')\n", + "plt.grid()\n", + "\n", + "# Show the plot on screen\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.rst b/docs/source/pythonapi/examples/mgxs-part-ii.rst new file mode 100644 index 000000000..1f6dd2214 --- /dev/null +++ b/docs/source/pythonapi/examples/mgxs-part-ii.rst @@ -0,0 +1,13 @@ +.. _notebook_mgxs_part_ii: + +=============================== +MGXS Part II: Advanced Features +=============================== + +.. only:: html + + .. notebook:: mgxs-part-ii.ipynb + +.. only:: latex + + IPython notebooks must be viewed in the online HTML documentation. diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb new file mode 100644 index 000000000..930203660 --- /dev/null +++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb @@ -0,0 +1,1653 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n", + "\n", + "* Calculation of multi-group cross sections for a **fuel assembly**\n", + "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n", + "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n", + "* Steady-state pin-by-pin **fission rates comparison** between OpenMC and [OpenMOC](https://mit-crpg.github.io/OpenMOC/)\n", + "\n", + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, 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/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: UserWarning: This call to matplotlib.use() has no effect\n", + "because the backend has already been chosen;\n", + "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", + "or matplotlib.backends is imported for the first time.\n", + "\n", + " warnings.warn(_use_error_msg)\n" + ] + } + ], + "source": [ + "import math\n", + "import pickle\n", + "from IPython.display import Image\n", + "import matplotlib.pylab as pylab\n", + "import numpy as np\n", + "\n", + "import openmc\n", + "import openmc.mgxs\n", + "from openmc.statepoint import StatePoint\n", + "from openmc.summary import Summary\n", + "\n", + "import openmoc\n", + "import openmoc.process\n", + "from openmoc.compatible import get_openmoc_geometry\n", + "from openmoc.materialize import load_openmc_mgxs_lib\n", + "\n", + "%matplotlib inline" + ] + }, + { + "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": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate some Nuclides\n", + "h1 = openmc.Nuclide('H-1')\n", + "b10 = openmc.Nuclide('B-10')\n", + "o16 = openmc.Nuclide('O-16')\n", + "u235 = openmc.Nuclide('U-235')\n", + "u238 = openmc.Nuclide('U-238')\n", + "zr90 = openmc.Nuclide('Zr-90')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pins." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide(u235, 3.7503e-4)\n", + "fuel.add_nuclide(u238, 2.2625e-2)\n", + "fuel.add_nuclide(o16, 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide(h1, 4.9457e-2)\n", + "water.add_nuclide(o16, 2.4732e-2)\n", + "water.add_nuclide(b10, 8.0042e-6)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide(zr90, 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we can now create a `MaterialsFile` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a MaterialsFile, add Materials\n", + "materials_file = openmc.MaterialsFile()\n", + "materials_file.add_material(fuel)\n", + "materials_file.add_material(water)\n", + "materials_file.add_material(zircaloy)\n", + "materials_file.default_xs = '71c'\n", + "\n", + "# Export to \"materials.xml\"\n", + "materials_file.export_to_xml()" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", + "\n", + "# Create boundary planes to surround the geometry\n", + "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "fuel_pin_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "fuel_pin_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "fuel_pin_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Likewise, we can construct a control rod guide tube with the same surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a control rod guide tube\n", + "guide_tube_universe = openmc.Universe(name='Guide Tube')\n", + "\n", + "# Create guide tube Cell\n", + "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n", + "guide_tube_cell.fill = water\n", + "guide_tube_cell.region = -fuel_outer_radius\n", + "guide_tube_universe.add_cell(guide_tube_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='Guide Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "guide_tube_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "guide_tube_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create fuel assembly Lattice\n", + "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n", + "assembly.dimension = (17, 17)\n", + "assembly.pitch = (1.26, 1.26)\n", + "assembly.lower_left = [-1.26 * 17. / 2.0] * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create array indices for guide tube locations in lattice\n", + "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n", + " 11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n", + "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n", + " 8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n", + "\n", + "# Initialize an empty 17x17 array of the lattice universes\n", + "universes = np.empty((17, 17), dtype=openmc.Universe)\n", + "\n", + "# Fill the array with the fuel pin and guide tube universes\n", + "universes[:,:] = fuel_pin_universe\n", + "universes[template_x, template_y] = guide_tube_universe\n", + "\n", + "# Store the array of universes in the lattice\n", + "assembly.universes = universes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell')\n", + "root_cell.fill = assembly\n", + "\n", + "# Add boundary planes\n", + "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", + "\n", + "# Create root Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(root_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe, put the geometry into a `GeometryFile` object, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "geometry = openmc.Geometry()\n", + "geometry.root_universe = root_universe" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a GeometryFile\n", + "geometry_file = openmc.GeometryFile()\n", + "geometry_file.geometry = geometry\n", + "\n", + "# Export to \"geometry.xml\"\n", + "geometry_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the geometry and materials finished, we now just need to 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": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 2500\n", + "\n", + "# Instantiate a SettingsFile\n", + "settings_file = openmc.SettingsFile()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': False, 'summary': True}\n", + "source_bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", + "settings_file.set_source_space('fission', source_bounds)\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a `PlotsFile` that we can use to verify that our fuel assembly geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a Plot\n", + "plot = openmc.Plot(plot_id=1)\n", + "plot.filename = 'materials-xy'\n", + "plot.origin = [0, 0, 0]\n", + "plot.width = [21.5, 21.5]\n", + "plot.pixels = [250, 250]\n", + "plot.color = 'mat'\n", + "\n", + "# Instantiate a PlotsFile, add Plot, and export to \"plots.xml\"\n", + "plot_file = openmc.PlotsFile()\n", + "plot_file.add_plot(plot)\n", + "plot_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run openmc in plotting mode\n", + "executor = openmc.Executor()\n", + "executor.plot_geometry(output=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nMS0zMFQyMToyMDowNy0wNTowMOEGeWEAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert OpenMC's funky ppm to png\n", + "!convert materials-xy.ppm materials-xy.png\n", + "\n", + "# Display the materials plot inline\n", + "Image(filename='materials-xy.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the plot, we have a nice array of fuel and guide tube pin cells with fuel, cladding, and water!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an MGXS Library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a 2-group EnergyGroups object\n", + "groups = openmc.mgxs.EnergyGroups()\n", + "groups.group_edges = np.array([0., 0.625e-6, 20.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will instantiate an `openmc.mgxs.Library` for the energy groups with our the fuel assembly geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize an 2-group MGXS Library for OpenMOC\n", + "mgxs_lib = openmc.mgxs.Library(geometry)\n", + "mgxs_lib.energy_groups = groups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we must specify to the `Library` which types of cross sections to compute. In particular, the following are the multi-group cross section `MGXS` subclasses that are mapped to string codes accepted by the `Library` class:\n", + "\n", + "* `TotalXS` (`\"total\"`)\n", + "* `TransportXS` (`\"transport\"`)\n", + "* `AbsorptionXS` (`\"absorption\"`)\n", + "* `CaptureXS` (`\"capture\"`)\n", + "* `FissionXS` (`\"fission\"`)\n", + "* `NuFissionXS` (`\"nu-fission\"`)\n", + "* `ScatterXS` (`\"scatter\"`)\n", + "* `NuScatterXS` (`\"nu-scatter\"`)\n", + "* `ScatterMatrixXS` (`\"scatter matrix\"`)\n", + "* `NuScatterMatrixXS` (`\"nu-scatter matrix\"`)\n", + "* `Chi` (`\"chi\"`)\n", + "\n", + "In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define `\"transport\"`, `\"nu-fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n", + "\n", + "**Note**: A variety of different approximate transport-corrected total multi-group cross sections (and corresponding scattering matrices) can be found in the literature. At the present time, the `openmc.mgxs` module only supports the `\"P0\"` transport correction. This correction can be turned on and off through the boolean `Library.correction` property which may take values of `\"P0\"` (default) or `None`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify multi-group cross section types to compute\n", + "mgxs_lib.mgxs_types = ['transport', 'nu-fission', 'nu-scatter matrix', 'chi']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material,\"` `\"cell,\"` and `\"universe\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n", + "\n", + "**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property. In our case, we wish to compute multi-group cross sections in each and every cell since they will be needed in our downstream OpenMOC calculation on the identical combinatorial geometry mesh." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify a \"cell\" domain type for the cross section tally filters\n", + "mgxs_lib.domain_type = \"cell\"\n", + "\n", + "# Specify the cell domains over which to compute multi-group cross sections\n", + "mgxs_lib.domains = geometry.get_all_material_cells()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily instruct the `Library` to compute multi-group cross sections on a nuclide-by-nuclide basis with the boolean `Library.by_nuclide` property. By default, `by_nuclide` is set to `False`, but we will set it to `True` here." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Compute cross sections on a nuclide-by-nuclide basis\n", + "mgxs_lib.by_nuclide = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we use the `Library` to construct the tallies needed to compute all of the requested multi-group cross sections in each domain and nuclide." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct all tallies needed for the multi-group cross section library\n", + "mgxs_lib.build_library()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tallies can now be export to a \"tallies.xml\" input file for OpenMC. \n", + "\n", + "**NOTE**: At this point the `Library` has constructed nearly 100 distinct `Tally` objects. The overhead to tally in OpenMC scales as $O(N)$ for $N$ tallies, which can become a bottleneck for large tally datasets. To compensate for this, the Python API's `Tally`, `Filter` and `TalliesFile` classes allow for the smart *merging* of tallies when possible. The `Library` class supports this runtime optimization with the use of the optional `merge` paramter (`False` by default) for the `Library.add_to_tallies_file(...)` method, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a \"tallies.xml\" file for the MGXS Library\n", + "tallies_file = openmc.TalliesFile()\n", + "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we instantiate a fission rate mesh tally to compare with OpenMOC." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a tally Mesh\n", + "mesh = openmc.Mesh(mesh_id=1)\n", + "mesh.type = 'regular'\n", + "mesh.dimension = [17, 17]\n", + "mesh.lower_left = [-10.71, -10.71]\n", + "mesh.width = [1.26, 1.26]\n", + "\n", + "# Instantiate tally Filter\n", + "mesh_filter = openmc.Filter()\n", + "mesh_filter.mesh = mesh\n", + "\n", + "# Instantiate the Tally\n", + "tally = openmc.Tally(name='mesh tally')\n", + "tally.add_filter(mesh_filter)\n", + "tally.add_score('fission')\n", + "tally.add_score('nu-fission')\n", + "\n", + "# Add mesh and Tally to TalliesFile\n", + "tallies_file.add_mesh(mesh)\n", + "tallies_file.add_tally(tally)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Export all tallies to a \"tallies.xml\" file\n", + "tallies_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.0\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-11-30 21:20:07\n", + " MPI Processes: 1\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 5010.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.02650 \n", + " 2/1 1.01386 \n", + " 3/1 1.01045 \n", + " 4/1 1.05511 \n", + " 5/1 1.04873 \n", + " 6/1 1.04558 \n", + " 7/1 1.03840 \n", + " 8/1 1.02086 \n", + " 9/1 1.08845 \n", + " 10/1 1.03932 \n", + " 11/1 1.01271 \n", + " 12/1 1.03448 1.02360 +/- 0.01088\n", + " 13/1 1.04395 1.03038 +/- 0.00925\n", + " 14/1 1.05477 1.03648 +/- 0.00894\n", + " 15/1 1.00485 1.03015 +/- 0.00938\n", + " 16/1 1.04523 1.03267 +/- 0.00806\n", + " 17/1 1.01328 1.02990 +/- 0.00735\n", + " 18/1 1.01476 1.02800 +/- 0.00664\n", + " 19/1 1.01490 1.02655 +/- 0.00604\n", + " 20/1 1.00926 1.02482 +/- 0.00567\n", + " 21/1 0.98504 1.02120 +/- 0.00627\n", + " 22/1 1.00397 1.01977 +/- 0.00591\n", + " 23/1 1.02556 1.02021 +/- 0.00545\n", + " 24/1 0.99808 1.01863 +/- 0.00529\n", + " 25/1 0.99638 1.01715 +/- 0.00514\n", + " 26/1 0.99615 1.01584 +/- 0.00499\n", + " 27/1 1.01843 1.01599 +/- 0.00469\n", + " 28/1 1.00315 1.01528 +/- 0.00447\n", + " 29/1 1.00633 1.01480 +/- 0.00426\n", + " 30/1 1.02159 1.01514 +/- 0.00405\n", + " 31/1 1.03395 1.01604 +/- 0.00396\n", + " 32/1 1.02672 1.01652 +/- 0.00381\n", + " 33/1 1.03778 1.01745 +/- 0.00375\n", + " 34/1 1.03807 1.01831 +/- 0.00369\n", + " 35/1 1.07854 1.02072 +/- 0.00428\n", + " 36/1 1.03524 1.02128 +/- 0.00415\n", + " 37/1 1.03100 1.02164 +/- 0.00401\n", + " 38/1 1.03853 1.02224 +/- 0.00391\n", + " 39/1 1.04089 1.02288 +/- 0.00383\n", + " 40/1 1.02150 1.02284 +/- 0.00370\n", + " 41/1 0.98470 1.02161 +/- 0.00379\n", + " 42/1 1.00658 1.02114 +/- 0.00370\n", + " 43/1 0.98652 1.02009 +/- 0.00373\n", + " 44/1 1.02787 1.02032 +/- 0.00363\n", + " 45/1 0.98800 1.01939 +/- 0.00364\n", + " 46/1 1.00286 1.01893 +/- 0.00357\n", + " 47/1 1.02559 1.01911 +/- 0.00348\n", + " 48/1 1.03729 1.01959 +/- 0.00342\n", + " 49/1 1.02538 1.01974 +/- 0.00333\n", + " 50/1 1.01478 1.01962 +/- 0.00325\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.2800E-01 seconds\n", + " Reading cross sections = 9.1000E-02 seconds\n", + " Total time in simulation = 4.1240E+01 seconds\n", + " Time in transport only = 4.1215E+01 seconds\n", + " Time in inactive batches = 4.0230E+00 seconds\n", + " Time in active batches = 3.7217E+01 seconds\n", + " Time synchronizing fission bank = 8.0000E-03 seconds\n", + " Sampling source sites = 6.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", + " Time accumulating tallies = 2.0000E-03 seconds\n", + " Total time for finalization = 0.0000E+00 seconds\n", + " Total time elapsed = 4.1683E+01 seconds\n", + " Calculation Rate (inactive) = 6214.27 neutrons/second\n", + " Calculation Rate (active) = 2686.94 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.01805 +/- 0.00261\n", + " k-effective (Track-length) = 1.01962 +/- 0.00325\n", + " k-effective (Absorption) = 1.01554 +/- 0.00339\n", + " Combined k-effective = 1.01711 +/- 0.00235\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run OpenMC\n", + "executor.run_simulation()" + ] + }, + { + "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": 27, + "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. 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." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "su = openmc.Summary('summary.h5')\n", + "sp.link_with_summary(su)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1514: RuntimeWarning: invalid value encountered in true_divide\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1515: RuntimeWarning: invalid value encountered in true_divide\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1516: RuntimeWarning: invalid value encountered in true_divide\n" + ] + } + ], + "source": [ + "# Initialize MGXS Library with OpenMC statepoint data\n", + "mgxs_lib.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": [ + "The `Library` supports a rich API to automate a variety of tasks, including multi-group cross section data retrieval and storage. We will highlight a few of these features here. First, the `Library.get_mgxs(...)` method allows one to extract an `MGXS` object from the `Library` for a particular domain and cross section type. The following cell illustrates how one may extract the `NuFissionXS` object for the fuel cell.\n", + "\n", + "**Note:** The `MGXS.get_mgxs(...)` method will accept either the domain *or* the integer domain ID of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Retrieve the NuFissionXS object for the fuel cell from the library\n", + "fuel_mgxs = mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `NuFissionXS` object supports all of the methods described previously the `openmc.mgxs` tutorials, such as [Pandas](http://pandas.pydata.org/) `DataFrames`:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "3 10000 1 U-235 8.063513e-03 4.062984e-05\n", + "4 10000 1 U-238 7.335515e-03 4.459335e-05\n", + "5 10000 1 O-16 0.000000e+00 0.000000e+00\n", + "0 10000 2 U-235 3.613274e-01 1.902492e-03\n", + "1 10000 2 U-238 6.738424e-07 3.536787e-09\n", + "2 10000 2 O-16 0.000000e+00 0.000000e+00" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = fuel_mgxs.get_pandas_dataframe()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can use the `MGXS.print_xs(...)` method to view a string representation of the multi-group cross section data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t8.06e-03 +/- 5.04e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t3.61e-01 +/- 5.27e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t7.34e-03 +/- 6.08e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t6.74e-07 +/- 5.25e-01%\n", + "\n", + "\tNuclide =\tO-16\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t0.00e+00 +/- nan%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t0.00e+00 +/- nan%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "fuel_mgxs.print_xs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can export the entire `Library` to HDF5 with the `Library.build_hdf5_store(...)` method as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store the cross section data in an \"mgxs/mgxs.h5\" HDF5 binary file\n", + "mgxs_lib.build_hdf5_store(filename='mgxs.h5', directory='mgxs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The HDF5 store will contain the numerical multi-group cross section data indexed by domain, nuclide and cross section type. Some data workflows may be optimized by storing and retrieving binary representations of the `MGXS` objects in the `Library`. This feature is supported through the `Library.dump_to_file(...)` and `Library.load_from_file(...)` routines which use Python's [`pickle`](https://docs.python.org/2/library/pickle.html) module. This is illustrated as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store a Library and its MGXS objects in a pickled binary file \"mgxs/mgxs.pkl\"\n", + "mgxs_lib.dump_to_file(filename='mgxs', directory='mgxs')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a new MGXS Library from the pickled binary file \"mgxs/mgxs.pkl\"\n", + "mgxs_lib = openmc.mgxs.Library.load_from_file(filename='mgxs', directory='mgxs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Library` class may be used to leverage the energy condensation features supported by the `MGXS` class. In particular, one can use the `Library.get_condensed_library(...)` with a coarse group structure which is a subset of the original \"fine\" group structure as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a 1-group structure\n", + "coarse_groups = openmc.mgxs.EnergyGroups(group_edges=[0., 20.])\n", + "\n", + "# Create a new MGXS Library on the coarse 1-group structure\n", + "coarse_mgxs_lib = mgxs_lib.get_condensed_library(coarse_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup innuclidemeanstd. dev.
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "0 10000 1 U-235 0.074383 0.000280\n", + "1 10000 1 U-238 0.005959 0.000036\n", + "2 10000 1 O-16 0.000000 0.000000" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Retrieve the NuFissionXS object for the fuel cell from the 1-group library\n", + "coarse_fuel_mgxs = coarse_mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')\n", + "\n", + "# Show the Pandas DataFrame for the 1-group MGXS\n", + "coarse_fuel_mgxs.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification with OpenMOC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code [OpenMOC](https://mit-crpg.github.io/OpenMOC/). We will extract an OpenCG geometry from the summary file and convert it into an equivalent OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create an OpenMOC Geometry from the OpenCG Geometry\n", + "openmoc_geometry = get_openmoc_geometry(mgxs_lib.opencg_geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can inject the multi-group cross sections into the equivalent fuel assembly OpenMOC geometry. The `openmoc.materialize` module supports the loading of `Library` objects from OpenMC as illustrated below." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the library into the OpenMOC geometry\n", + "materials = load_openmc_mgxs_lib(mgxs_lib, openmoc_geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Ray tracing for track segmentation...\n", + "[ NORMAL ] Dumping tracks to file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.854316\tres = 0.000E+00\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.801593\tres = 1.522E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.761131\tres = 6.380E-02\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.731467\tres = 5.066E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.709897\tres = 3.910E-02\n", + "[ NORMAL ] Iteration 5:\tk_eff = 0.695110\tres = 2.954E-02\n", + "[ NORMAL ] Iteration 6:\tk_eff = 0.685966\tres = 2.085E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.681511\tres = 1.317E-02\n", + "[ NORMAL ] Iteration 8:\tk_eff = 0.680926\tres = 6.520E-03\n", + "[ NORMAL ] Iteration 9:\tk_eff = 0.683509\tres = 1.046E-03\n", + "[ NORMAL ] Iteration 10:\tk_eff = 0.688659\tres = 3.848E-03\n", + "[ NORMAL ] Iteration 11:\tk_eff = 0.695860\tres = 7.565E-03\n", + "[ NORMAL ] Iteration 12:\tk_eff = 0.704674\tres = 1.048E-02\n", + "[ NORMAL ] Iteration 13:\tk_eff = 0.714726\tres = 1.269E-02\n", + "[ NORMAL ] Iteration 14:\tk_eff = 0.725700\tres = 1.428E-02\n", + "[ NORMAL ] Iteration 15:\tk_eff = 0.737329\tres = 1.537E-02\n", + "[ NORMAL ] Iteration 16:\tk_eff = 0.749388\tres = 1.604E-02\n", + "[ 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+ "[ NORMAL ] Iteration 32:\tk_eff = 0.916523\tres = 8.745E-03\n", + "[ NORMAL ] Iteration 33:\tk_eff = 0.923546\tres = 8.194E-03\n", + "[ NORMAL ] Iteration 34:\tk_eff = 0.930162\tres = 7.669E-03\n", + "[ NORMAL ] Iteration 35:\tk_eff = 0.936387\tres = 7.171E-03\n", + "[ NORMAL ] Iteration 36:\tk_eff = 0.942236\tres = 6.698E-03\n", + "[ NORMAL ] Iteration 37:\tk_eff = 0.947725\tres = 6.252E-03\n", + "[ NORMAL ] Iteration 38:\tk_eff = 0.952869\tres = 5.830E-03\n", + "[ NORMAL ] Iteration 39:\tk_eff = 0.957687\tres = 5.433E-03\n", + "[ NORMAL ] Iteration 40:\tk_eff = 0.962193\tres = 5.060E-03\n", + "[ NORMAL ] Iteration 41:\tk_eff = 0.966404\tres = 4.710E-03\n", + "[ NORMAL ] Iteration 42:\tk_eff = 0.970337\tres = 4.381E-03\n", + "[ NORMAL ] Iteration 43:\tk_eff = 0.974006\tres = 4.073E-03\n", + "[ NORMAL ] Iteration 44:\tk_eff = 0.977426\tres = 3.785E-03\n", + "[ NORMAL ] Iteration 45:\tk_eff = 0.980613\tres = 3.515E-03\n", + "[ NORMAL ] Iteration 46:\tk_eff = 0.983580\tres = 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"[ NORMAL ] Iteration 106:\tk_eff = 1.020551\tres = 2.526E-05\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.020573\tres = 2.319E-05\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.020593\tres = 2.134E-05\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.020611\tres = 1.960E-05\n", + "[ NORMAL ] Iteration 110:\tk_eff = 1.020628\tres = 1.800E-05\n", + "[ NORMAL ] Iteration 111:\tk_eff = 1.020643\tres = 1.652E-05\n", + "[ NORMAL ] Iteration 112:\tk_eff = 1.020657\tres = 1.518E-05\n", + "[ NORMAL ] Iteration 113:\tk_eff = 1.020670\tres = 1.398E-05\n", + "[ NORMAL ] Iteration 114:\tk_eff = 1.020682\tres = 1.283E-05\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.020693\tres = 1.178E-05\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.020704\tres = 1.083E-05\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "openmoc_geometry.initializeFlatSourceRegions()\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=32, spacing=0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.017105\n", + "openmoc keff = 1.020704\n", + "bias [pcm]: 359.8\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.k_combined[0]\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a non-trivial bias between the eigenvalues computed by OpenMC and OpenMOC. One can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", + "\n", + "* Appropriate transport-corrected cross sections\n", + "* Spatial discretization of OpenMOC's mesh\n", + "* Constant-in-angle multi-group cross sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flux and Pin Power Visualizations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will conclude this tutorial by illustrating how to visualize the fission rates computed by OpenMOC and OpenMC. First, we extract volume-integrated fission rates from OpenMC's mesh fission rate tally for each pin cell in the fuel assembly." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Get the OpenMC fission rate mesh tally data\n", + "mesh_tally = sp.get_tally(name='mesh tally')\n", + "openmc_fission_rates = mesh_tally.get_values(scores=['nu-fission'])\n", + "\n", + "# Reshape array to 2D for plotting\n", + "openmc_fission_rates.shape = (17,17)\n", + "\n", + "# Normalize to the average pin power\n", + "openmc_fission_rates /= np.mean(openmc_fission_rates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we extract OpenMOC's volume-averaged fission rates into a 2D 17x17 NumPy array." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Export OpenMOC's fission rates for each pin cell instance in the fuel assembly\n", + "openmoc.process.compute_fission_rates(solver)\n", + "\n", + "# Open the pickle file with the fission rates\n", + "fission_rates = pickle.load(open('fission-rates/fission-rates.pkl', 'rb' ))\n", + "\n", + "# Allocate array for fission rates in each fuel pin\n", + "openmoc_fission_rates = np.zeros((17, 17))\n", + "\n", + "# Extract fission rates for each fuel pin\n", + "for key, value in fission_rates.items():\n", + " lat_x = int(key.split(':')[1].split()[3][1:-1])\n", + " lat_y = int(key.split(':')[1].split()[4][:-1]) \n", + " openmoc_fission_rates[lat_x, lat_y] = value\n", + "\n", + "# Normalize to the average pin fission rate\n", + "openmoc_fission_rates /= np.mean(openmoc_fission_rates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can easily use Matplotlib to visualize the fission rates from OpenMC and OpenMOC side-by-side." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot OpenMC's fission rates in the left subplot\n", + "fig = pylab.subplot(121)\n", + "pylab.imshow(openmc_fission_rates, interpolation='none', cmap='jet')\n", + "pylab.title('OpenMC Fission Rates')\n", + "\n", + "# Plot OpenMOC's fission rates in the right subplot\n", + "fig2 = pylab.subplot(122)\n", + "pylab.imshow(openmoc_fission_rates, interpolation='none', cmap='jet')\n", + "pylab.title('OpenMOC Fission Rates')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.rst b/docs/source/pythonapi/examples/mgxs-part-iii.rst new file mode 100644 index 000000000..f44102862 --- /dev/null +++ b/docs/source/pythonapi/examples/mgxs-part-iii.rst @@ -0,0 +1,13 @@ +.. _notebook_mgxs_part_iii: + +======================== +MGXS Part III: Libraries +======================== + +.. only:: html + + .. notebook:: mgxs-part-iii.ipynb + +.. only:: latex + + IPython notebooks must be viewed in the online HTML documentation. diff --git a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb b/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb deleted file mode 100644 index f4e4b59ef..000000000 --- a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb +++ /dev/null @@ -1,2454 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook demonstrates how to use the **``openmc.mgxs``** module to generate multi-group cross sections with OpenMC.\n", - "\n", - "**Note:** that this Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: UserWarning: This call to matplotlib.use() has no effect\n", - "because the backend has already been chosen;\n", - "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", - "or matplotlib.backends is imported for the first time.\n", - "\n", - " warnings.warn(_use_error_msg)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import openmc\n", - "import openmc.mgxs as mgxs\n", - "import openmoc\n", - "from openmoc.compatible import get_openmoc_geometry\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Infinite Homogeneous Medium" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first construct a simple homogeneous infinite medium problem to illustrate use of the `openmc.mgxs` module to generate multi-group cross sections." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate Inputs" - ] - }, - { - "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": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" - ] - }, - { - "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": 3, - "metadata": { - "collapsed": true - }, - "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, 0.028999667)\n", - "inf_medium.add_nuclide(o16, 0.01450188)\n", - "inf_medium.add_nuclide(u235, 0.000114142)\n", - "inf_medium.add_nuclide(u238, 0.006886019)\n", - "inf_medium.add_nuclide(zr90, 0.002116053)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our material, we can now create a materials file object that can be exported to an actual XML file." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate a MaterialsFile, register all Materials, and export to XML\n", - "materials_file = openmc.MaterialsFile()\n", - "materials_file.default_xs = '71c'\n", - "materials_file.add_material(inf_medium)\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": 5, - "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": 6, - "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": 7, - "metadata": { - "collapsed": true - }, - "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, put the geometry into a geometry file, and export it to XML." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "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", - "# Instantiate a GeometryFile\n", - "geometry_file = openmc.GeometryFile()\n", - "geometry_file.geometry = openmc_geometry\n", - "\n", - "# Export to \"geometry.xml\"\n", - "geometry_file.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": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# OpenMC simulation parameters\n", - "batches = 50\n", - "inactive = 10\n", - "particles = 2500\n", - "\n", - "# Instantiate a SettingsFile\n", - "settings_file = openmc.SettingsFile()\n", - "settings_file.batches = batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", - "settings_file.output = {'tallies': True, 'summary': True}\n", - "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", - "settings_file.set_source_space('box', bounds)\n", - "\n", - "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define a \"fine\" 8-group and \"coarse\" 2-group structures using the built-in `EnergyGroups` class." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate a \"fine\" 8-group EneryGroups object\n", - "fine_groups = mgxs.EnergyGroups()\n", - "fine_groups.group_edges = np.array([0., 0.058e-6, 0.14e-6, 0.28e-6,\n", - " 0.625e-6, 4.e-6, 5.53e-3, 821.e-3, 20.])\n", - "\n", - "# Instantiate a \"coarse\" 2-group EneryGroups object\n", - "coarse_groups = mgxs.EnergyGroups()\n", - "coarse_groups.group_edges = np.array([0., 0.625e-6, 20.])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "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", - "\n", - "* `TotalXS`\n", - "* `TransportXS`\n", - "* `AbsorptionXS`\n", - "* `CaptureXS`\n", - "* `FissionXS`\n", - "* `NuFissionXS`\n", - "* `ScatterXS`\n", - "* `NuScatterXS`\n", - "* `ScatterMatrixXS`\n", - "* `NuScatterMatrixXS`\n", - "* `Chi`\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 cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define total, nu-fission, nu-scatter and chi cross sections for our infinite medium cell as the domain and our fine 8-group structure as our energy groups." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate cross sections needed for an OpenMOC simulation\n", - "transport = mgxs.TransportXS(domain=cell, domain_type='cell', groups=fine_groups)\n", - "nufission = mgxs.NuFissionXS(domain=cell, domain_type='cell', groups=fine_groups)\n", - "nuscatter = mgxs.NuScatterMatrixXS(domain=cell, domain_type='cell', groups=fine_groups)\n", - "chi = mgxs.Chi(domain=cell, domain_type='cell', groups=fine_groups)" - ] - }, - { - "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 `NuFission` object as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('flux', Tally\n", - "\tID =\t10000\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 5.80000000e-08 1.40000000e-07 2.80000000e-07\n", - " 6.25000000e-07 4.00000000e-06 5.53000000e-03 8.21000000e-01\n", - " 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n", - "), ('nu-fission', Tally\n", - "\tID =\t10001\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 5.80000000e-08 1.40000000e-07 2.80000000e-07\n", - " 6.25000000e-07 4.00000000e-06 5.53000000e-03 8.21000000e-01\n", - " 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['nu-fission']\n", - "\tEstimator =\ttracklength\n", - ")])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nufission.tallies" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `NuFission` object includes tracklength tallies for the 'nu-fission' and 'flux' scores in the 8-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." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate an empty TalliesFile\n", - "tallies_file = openmc.TalliesFile()\n", - "\n", - "# Add transport tallies to the tallies file\n", - "for tally in transport.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - "\n", - "# Add nu-fission tallies to the tallies file\n", - "for tally in nufission.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - "\n", - "# Add nu-scatter tallies to the tallies file\n", - "for tally in nuscatter.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - "\n", - "# Add chi tallies to the tallies file \n", - "for tally in chi.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\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": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " .d88888b. 888b d888 .d8888b.\n", - " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", - " 888 888 88888b.d88888 888 888\n", - " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", - " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", - " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", - " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", - " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", - "__________________888______________________________________________________\n", - " 888\n", - " 888\n", - "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", - " Version: 0.7.0\n", - " Git SHA1: 21738db07debeabde824c9b955bd3bf0c9a16366\n", - " Date/Time: 2015-11-01 21:28:30\n", - " MPI Processes: 1\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", - " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 1001.71c\n", - " Initializing source particles...\n", - "\n", - " ===========================================================================\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", - "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.19804 \n", - " 2/1 1.12945 \n", - " 3/1 1.15573 \n", - " 4/1 1.13929 \n", - " 5/1 1.16300 \n", - " 6/1 1.22117 \n", - " 7/1 1.19012 \n", - " 8/1 1.11299 \n", - " 9/1 1.16066 \n", - " 10/1 1.12566 \n", - " 11/1 1.20854 \n", - " 12/1 1.14691 1.17773 +/- 0.03082\n", - " 13/1 1.17204 1.17583 +/- 0.01789\n", - " 14/1 1.14148 1.16724 +/- 0.01529\n", - " 15/1 1.17272 1.16834 +/- 0.01189\n", - " 16/1 1.18575 1.17124 +/- 0.01014\n", - " 17/1 1.20498 1.17606 +/- 0.00983\n", - " 18/1 1.14754 1.17249 +/- 0.00923\n", - " 19/1 1.18141 1.17348 +/- 0.00820\n", - " 20/1 1.15074 1.17121 +/- 0.00768\n", - " 21/1 1.15914 1.17011 +/- 0.00703\n", - " 22/1 1.14586 1.16809 +/- 0.00673\n", - " 23/1 1.18999 1.16978 +/- 0.00642\n", - " 24/1 1.15101 1.16844 +/- 0.00609\n", - " 25/1 1.13791 1.16640 +/- 0.00602\n", - " 26/1 1.19791 1.16837 +/- 0.00597\n", - " 27/1 1.19818 1.17012 +/- 0.00587\n", - " 28/1 1.14160 1.16854 +/- 0.00576\n", - " 29/1 1.11487 1.16571 +/- 0.00614\n", - " 30/1 1.17538 1.16620 +/- 0.00584\n", - " 31/1 1.20210 1.16791 +/- 0.00581\n", - " 32/1 1.20078 1.16940 +/- 0.00574\n", - " 33/1 1.14624 1.16839 +/- 0.00558\n", - " 34/1 1.14618 1.16747 +/- 0.00542\n", - " 35/1 1.16866 1.16752 +/- 0.00520\n", - " 36/1 1.18565 1.16821 +/- 0.00504\n", - " 37/1 1.16824 1.16821 +/- 0.00485\n", - " 38/1 1.18299 1.16874 +/- 0.00471\n", - " 39/1 1.21418 1.17031 +/- 0.00480\n", - " 40/1 1.11167 1.16835 +/- 0.00504\n", - " 41/1 1.11545 1.16665 +/- 0.00516\n", - " 42/1 1.11114 1.16491 +/- 0.00529\n", - " 43/1 1.14227 1.16423 +/- 0.00517\n", - " 44/1 1.14104 1.16355 +/- 0.00506\n", - " 45/1 1.16756 1.16366 +/- 0.00492\n", - " 46/1 1.13065 1.16274 +/- 0.00487\n", - " 47/1 1.11251 1.16139 +/- 0.00492\n", - " 48/1 1.14731 1.16101 +/- 0.00481\n", - " 49/1 1.16691 1.16117 +/- 0.00469\n", - " 50/1 1.19679 1.16206 +/- 0.00465\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.9120E+00 seconds\n", - " Reading cross sections = 4.8600E-01 seconds\n", - " Total time in simulation = 6.1145E+01 seconds\n", - " Time in transport only = 6.0977E+01 seconds\n", - " Time in inactive batches = 8.1390E+00 seconds\n", - " Time in active batches = 5.3006E+01 seconds\n", - " Time synchronizing fission bank = 1.6000E-02 seconds\n", - " Sampling source sites = 1.2000E-02 seconds\n", - " SEND/RECV source sites = 3.0000E-03 seconds\n", - " Time accumulating tallies = 1.0000E-03 seconds\n", - " Total time for finalization = 1.0000E-02 seconds\n", - " Total time elapsed = 6.3104E+01 seconds\n", - " Calculation Rate (inactive) = 3071.63 neutrons/second\n", - " Calculation Rate (active) = 1886.58 neutrons/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.16131 +/- 0.00453\n", - " k-effective (Track-length) = 1.16206 +/- 0.00465\n", - " k-effective (Absorption) = 1.16096 +/- 0.00364\n", - " Combined k-effective = 1.16120 +/- 0.00325\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Run OpenMC!\n", - "executor = openmc.Executor()\n", - "executor.run_simulation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tally Data Processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "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 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." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the summary file and link it with the statepoint\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, - { - "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": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1514: RuntimeWarning: invalid value encountered in true_divide\n" - ] - } - ], - "source": [ - "# Load the tallies from the statepoint into each MGXS object\n", - "transport.load_from_statepoint(sp)\n", - "nufission.load_from_statepoint(sp)\n", - "nuscatter.load_from_statepoint(sp)\n", - "chi.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": [ - "### Cross Section Data Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's first inspect our fission production cross section by printing it to the screen." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t1\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t1.11e-02 +/- 7.69e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t6.59e-04 +/- 2.97e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t8.95e-03 +/- 5.12e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t1.45e-02 +/- 7.10e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t4.71e-02 +/- 1.02e+00%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t7.29e-02 +/- 8.86e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t1.11e-01 +/- 6.67e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t2.38e-01 +/- 7.71e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "nufission.print_xs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1264: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " cell group in group out nuclide mean std. dev.\n", - "63 1 1 1 total 0.076970 0.001012\n", - "62 1 1 2 total 0.087876 0.000344\n", - "61 1 1 3 total 0.000418 0.000023\n", - "60 1 1 4 total 0.000000 0.000000\n", - "59 1 1 5 total 0.000000 0.000000\n", - "58 1 1 6 total 0.000000 0.000000\n", - "57 1 1 7 total 0.000000 0.000000\n", - "56 1 1 8 total 0.000000 0.000000\n", - "55 1 2 1 total 0.000000 0.000000\n", - "54 1 2 2 total 0.266499 0.001265" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = nuscatter.get_pandas_dataframe()\n", - "df.head(10)" - ] - }, - { - "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": 20, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "transport.export_xs_data(filename='transport-xs', format='excel')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following code snippet shows how to export all of four cross sections to the same HDF5 binary data store." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "transport.build_hdf5_store(filename='mgxs', append=True)\n", - "nufission.build_hdf5_store(filename='mgxs', append=True)\n", - "nuscatter.build_hdf5_store(filename='mgxs', append=True)\n", - "chi.build_hdf5_store(filename='mgxs', append=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verification with OpenMOC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code OpenMOC. We will extract an OpenCG geometry from the summary file and convert it into an equivalent OpenMOC geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create an OpenMOC Geometry from the OpenCG Geometry\n", - "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can inject the multi-group cross sections into the equivalent infinite homogeneous medium OpenMOC geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Get all OpenMOC cells in the gometry\n", - "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", - "\n", - "# Inject multi-group cross sections into OpenMOC Materials\n", - "for cell_id, cell in openmoc_cells.items():\n", - " \n", - " # Get a reference to the Material filling this Cell\n", - " openmoc_material = cell.getFillMaterial()\n", - " \n", - " # Set the number of energy groups for the Material\n", - " openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n", - " \n", - " # Inject NumPy arrays of cross section data into the Material\n", - " openmoc_material.setSigmaT(transport.get_xs().flatten())\n", - " openmoc_material.setNuSigmaF(nufission.get_xs().flatten())\n", - " openmoc_material.setSigmaS(nuscatter.get_xs().flatten())\n", - " openmoc_material.setChi(chi.get_xs().flatten())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Ray tracing for track segmentation...\n", - "[ NORMAL ] Dumping tracks to file...\n", - "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.685184\tres = 1.483E-316\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.785642\tres = 3.148E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.750185\tres = 1.466E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.728846\tres = 4.513E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.695632\tres = 2.844E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.663357\tres = 4.557E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.632339\tres = 4.640E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.604187\tres = 4.676E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.579451\tres = 4.452E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.558474\tres = 4.094E-02\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.541436\tres = 3.620E-02\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.528380\tres = 3.051E-02\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.519273\tres = 2.411E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.513991\tres = 1.724E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.512364\tres = 1.017E-02\n", - "[ NORMAL ] Iteration 15:\tk_eff = 0.514171\tres = 3.165E-03\n", - "[ NORMAL ] Iteration 16:\tk_eff = 0.519155\tres = 3.527E-03\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.527038\tres = 9.693E-03\n", - "[ NORMAL ] Iteration 18:\tk_eff = 0.537524\tres = 1.518E-02\n", - "[ NORMAL ] Iteration 19:\tk_eff = 0.550310\tres = 1.990E-02\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.565096\tres = 2.379E-02\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.581585\tres = 2.687E-02\n", - "[ NORMAL ] Iteration 22:\tk_eff = 0.599493\tres = 2.918E-02\n", - "[ NORMAL ] Iteration 23:\tk_eff = 0.618548\tres = 3.079E-02\n", - "[ NORMAL ] Iteration 24:\tk_eff = 0.638497\tres = 3.179E-02\n", - "[ NORMAL ] Iteration 25:\tk_eff = 0.659105\tres = 3.225E-02\n", - "[ NORMAL ] Iteration 26:\tk_eff = 0.680156\tres = 3.228E-02\n", - "[ NORMAL ] Iteration 27:\tk_eff = 0.701457\tres = 3.194E-02\n", - "[ NORMAL ] Iteration 28:\tk_eff = 0.722832\tres = 3.132E-02\n", - "[ NORMAL ] Iteration 29:\tk_eff = 0.744127\tres = 3.047E-02\n", - "[ NORMAL ] Iteration 30:\tk_eff = 0.765209\tres = 2.946E-02\n", - "[ NORMAL ] Iteration 31:\tk_eff = 0.785961\tres = 2.833E-02\n", - "[ NORMAL ] Iteration 32:\tk_eff = 0.806283\tres = 2.712E-02\n", - "[ NORMAL ] Iteration 33:\tk_eff = 0.826093\tres = 2.586E-02\n", - "[ NORMAL ] Iteration 34:\tk_eff = 0.845324\tres = 2.457E-02\n", - "[ NORMAL ] Iteration 35:\tk_eff = 0.863921\tres = 2.328E-02\n", - "[ NORMAL ] Iteration 36:\tk_eff = 0.881841\tres = 2.200E-02\n", - "[ NORMAL ] Iteration 37:\tk_eff = 0.899055\tres = 2.074E-02\n", - "[ NORMAL ] Iteration 38:\tk_eff = 0.915540\tres = 1.952E-02\n", - "[ NORMAL ] Iteration 39:\tk_eff = 0.931284\tres = 1.834E-02\n", - "[ NORMAL ] Iteration 40:\tk_eff = 0.946283\tres = 1.720E-02\n", - "[ NORMAL ] Iteration 41:\tk_eff = 0.960536\tres = 1.610E-02\n", - "[ NORMAL ] Iteration 42:\tk_eff = 0.974052\tres = 1.506E-02\n", - "[ NORMAL ] Iteration 43:\tk_eff = 0.986841\tres = 1.407E-02\n", - "[ NORMAL ] Iteration 44:\tk_eff = 0.998920\tres = 1.313E-02\n", - "[ NORMAL ] Iteration 45:\tk_eff = 1.010307\tres = 1.224E-02\n", - "[ NORMAL ] Iteration 46:\tk_eff = 1.021023\tres = 1.140E-02\n", - "[ NORMAL ] Iteration 47:\tk_eff = 1.031091\tres = 1.061E-02\n", - "[ NORMAL ] Iteration 48:\tk_eff = 1.040537\tres = 9.861E-03\n", - "[ NORMAL ] Iteration 49:\tk_eff = 1.049386\tres = 9.161E-03\n", - "[ NORMAL ] Iteration 50:\tk_eff = 1.057664\tres = 8.504E-03\n", - "[ NORMAL ] Iteration 51:\tk_eff = 1.065399\tres = 7.889E-03\n", - "[ NORMAL ] Iteration 52:\tk_eff = 1.072617\tres = 7.313E-03\n", - "[ NORMAL ] Iteration 53:\tk_eff = 1.079345\tres = 6.775E-03\n", - "[ NORMAL ] Iteration 54:\tk_eff = 1.085610\tres = 6.273E-03\n", - "[ NORMAL ] Iteration 55:\tk_eff = 1.091437\tres = 5.804E-03\n", - "[ NORMAL ] Iteration 56:\tk_eff = 1.096851\tres = 5.367E-03\n", - "[ NORMAL ] Iteration 57:\tk_eff = 1.101878\tres = 4.961E-03\n", - "[ NORMAL ] Iteration 58:\tk_eff = 1.106540\tres = 4.583E-03\n", - "[ NORMAL ] Iteration 59:\tk_eff = 1.110861\tres = 4.231E-03\n", - "[ NORMAL ] Iteration 60:\tk_eff = 1.114862\tres = 3.905E-03\n", - "[ NORMAL ] Iteration 61:\tk_eff = 1.118564\tres = 3.602E-03\n", - "[ NORMAL ] Iteration 62:\tk_eff = 1.121987\tres = 3.321E-03\n", - "[ NORMAL ] Iteration 63:\tk_eff = 1.125150\tres = 3.060E-03\n", - "[ NORMAL ] Iteration 64:\tk_eff = 1.128070\tres = 2.819E-03\n", - "[ NORMAL ] Iteration 65:\tk_eff = 1.130764\tres = 2.595E-03\n", - "[ NORMAL ] Iteration 66:\tk_eff = 1.133249\tres = 2.389E-03\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.135539\tres = 2.197E-03\n", - "[ NORMAL ] Iteration 68:\tk_eff = 1.137649\tres = 2.021E-03\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.139591\tres = 1.858E-03\n", - "[ NORMAL ] Iteration 70:\tk_eff = 1.141378\tres = 1.707E-03\n", - "[ NORMAL ] Iteration 71:\tk_eff = 1.143021\tres = 1.568E-03\n", - "[ NORMAL ] Iteration 72:\tk_eff = 1.144532\tres = 1.440E-03\n", - "[ NORMAL ] Iteration 73:\tk_eff = 1.145920\tres = 1.322E-03\n", - "[ NORMAL ] Iteration 74:\tk_eff = 1.147196\tres = 1.213E-03\n", - "[ NORMAL ] Iteration 75:\tk_eff = 1.148366\tres = 1.113E-03\n", - "[ NORMAL ] Iteration 76:\tk_eff = 1.149440\tres = 1.020E-03\n", - "[ NORMAL ] Iteration 77:\tk_eff = 1.150426\tres = 9.355E-04\n", - "[ NORMAL ] Iteration 78:\tk_eff = 1.151330\tres = 8.574E-04\n", - "[ NORMAL ] Iteration 79:\tk_eff = 1.152158\tres = 7.856E-04\n", - "[ NORMAL ] Iteration 80:\tk_eff = 1.152918\tres = 7.196E-04\n", - "[ NORMAL ] Iteration 81:\tk_eff = 1.153613\tres = 6.590E-04\n", - "[ NORMAL ] Iteration 82:\tk_eff = 1.154250\tres = 6.033E-04\n", - "[ NORMAL ] Iteration 83:\tk_eff = 1.154833\tres = 5.522E-04\n", - "[ NORMAL ] Iteration 84:\tk_eff = 1.155367\tres = 5.053E-04\n", - "[ NORMAL ] Iteration 85:\tk_eff = 1.155856\tres = 4.623E-04\n", - "[ NORMAL ] Iteration 86:\tk_eff = 1.156303\tres = 4.228E-04\n", - "[ NORMAL ] Iteration 87:\tk_eff = 1.156711\tres = 3.866E-04\n", - "[ NORMAL ] Iteration 88:\tk_eff = 1.157085\tres = 3.535E-04\n", - "[ NORMAL ] Iteration 89:\tk_eff = 1.157427\tres = 3.231E-04\n", - "[ NORMAL ] Iteration 90:\tk_eff = 1.157739\tres = 2.952E-04\n", - "[ NORMAL ] Iteration 91:\tk_eff = 1.158024\tres = 2.697E-04\n", - "[ NORMAL ] Iteration 92:\tk_eff = 1.158285\tres = 2.464E-04\n", - "[ NORMAL ] Iteration 93:\tk_eff = 1.158523\tres = 2.251E-04\n", - "[ NORMAL ] Iteration 94:\tk_eff = 1.158740\tres = 2.055E-04\n", - "[ NORMAL ] Iteration 95:\tk_eff = 1.158939\tres = 1.876E-04\n", - "[ NORMAL ] Iteration 96:\tk_eff = 1.159120\tres = 1.713E-04\n", - "[ NORMAL ] Iteration 97:\tk_eff = 1.159285\tres = 1.563E-04\n", - "[ NORMAL ] Iteration 98:\tk_eff = 1.159436\tres = 1.427E-04\n", - "[ NORMAL ] Iteration 99:\tk_eff = 1.159574\tres = 1.302E-04\n", - "[ NORMAL ] Iteration 100:\tk_eff = 1.159699\tres = 1.188E-04\n", - "[ NORMAL ] Iteration 101:\tk_eff = 1.159814\tres = 1.083E-04\n", - "[ NORMAL ] Iteration 102:\tk_eff = 1.159918\tres = 9.880E-05\n", - "[ NORMAL ] Iteration 103:\tk_eff = 1.160014\tres = 9.009E-05\n", - "[ NORMAL ] Iteration 104:\tk_eff = 1.160101\tres = 8.215E-05\n", - "[ NORMAL ] Iteration 105:\tk_eff = 1.160180\tres = 7.490E-05\n", - "[ NORMAL ] Iteration 106:\tk_eff = 1.160252\tres = 6.827E-05\n", - "[ NORMAL ] Iteration 107:\tk_eff = 1.160318\tres = 6.223E-05\n", - "[ NORMAL ] Iteration 108:\tk_eff = 1.160378\tres = 5.672E-05\n", - "[ NORMAL ] Iteration 109:\tk_eff = 1.160432\tres = 5.169E-05\n", - "[ NORMAL ] Iteration 110:\tk_eff = 1.160482\tres = 4.710E-05\n", - "[ NORMAL ] Iteration 111:\tk_eff = 1.160528\tres = 4.291E-05\n", - "[ NORMAL ] Iteration 112:\tk_eff = 1.160569\tres = 3.909E-05\n", - "[ NORMAL ] Iteration 113:\tk_eff = 1.160607\tres = 3.561E-05\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.160641\tres = 3.244E-05\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.160672\tres = 2.954E-05\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.160700\tres = 2.691E-05\n", - "[ NORMAL ] Iteration 117:\tk_eff = 1.160726\tres = 2.450E-05\n", - "[ NORMAL ] Iteration 118:\tk_eff = 1.160750\tres = 2.231E-05\n", - "[ NORMAL ] Iteration 119:\tk_eff = 1.160771\tres = 2.031E-05\n", - "[ NORMAL ] Iteration 120:\tk_eff = 1.160791\tres = 1.849E-05\n", - "[ NORMAL ] Iteration 121:\tk_eff = 1.160809\tres = 1.684E-05\n", - "[ NORMAL ] Iteration 122:\tk_eff = 1.160825\tres = 1.533E-05\n", - "[ NORMAL ] Iteration 123:\tk_eff = 1.160840\tres = 1.395E-05\n", - "[ NORMAL ] Iteration 124:\tk_eff = 1.160853\tres = 1.270E-05\n", - "[ NORMAL ] Iteration 125:\tk_eff = 1.160865\tres = 1.156E-05\n", - "[ NORMAL ] Iteration 126:\tk_eff = 1.160876\tres = 1.052E-05\n" - ] - } - ], - "source": [ - "# Generate tracks for OpenMOC\n", - "openmoc_geometry.initializeFlatSourceRegions()\n", - "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", - "track_generator.generateTracks()\n", - "\n", - "# Run OpenMOC\n", - "solver = openmoc.CPUSolver(track_generator)\n", - "solver.computeEigenvalue()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.161200\n", - "openmoc keff = 1.160876\n", - "bias [pcm]: -32.4\n" - ] - } - ], - "source": [ - "# Print report of keff and bias with OpenMC\n", - "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", - "bias = (openmoc_keff - openmc_keff) * 1e5\n", - "\n", - "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", - "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", - "print('bias [pcm]: {0:1.1f}'.format(bias))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although there is a non-trivial bias, one can easily run the preceding code with more particle histories to show that both codes converge to the same eigenvalue with <10 pcm bias. It should be noted that this discrepancy is due to use of tracklength tallies for `NuFission`, while one must use more slowly converging analog tallies for `TransportXS`, `NuScatterMatrixXS` and `Chi` (which require an 'energyout' filter)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fuel Pin Cell" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section we show how to compute multi-group cross sections for a fuel pin cell. In addition, we will illustrate how to use some of the more advanced features in `openmc.mgxs` such as nuclide-by-nuclide microscopic cross section tallies and downstream energy group condensation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate Inputs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "this time we separate our nuclides into three distinct materials for water, clad and fuel." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# 1.6 enriched fuel\n", - "fuel = openmc.Material(name='1.6% Fuel')\n", - "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide(u235, 3.7503e-4)\n", - "fuel.add_nuclide(u238, 2.2625e-2)\n", - "fuel.add_nuclide(o16, 4.6007e-2)\n", - "\n", - "# borated water\n", - "water = openmc.Material(name='Borated Water')\n", - "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide(h1, 4.9457e-2)\n", - "water.add_nuclide(o16, 2.4732e-2)\n", - "\n", - "# zircaloy\n", - "zircaloy = openmc.Material(name='Zircaloy')\n", - "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide(zr90, 7.2758e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our materials, we can now create a materials file object that can be exported to an actual XML file." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate a MaterialsFile, add Materials\n", - "materials_file = openmc.MaterialsFile()\n", - "materials_file.add_material(fuel)\n", - "materials_file.add_material(water)\n", - "materials_file.add_material(zircaloy)\n", - "materials_file.default_xs = '71c'\n", - "\n", - "# Export to \"materials.xml\"\n", - "materials_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create cylinders for the fuel and clad\n", - "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", - "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", - "\n", - "# Create boundary planes to surround the geometry\n", - "# Use both reflective and vacuum boundaries to make life interesting\n", - "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", - "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", - "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", - "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", - "min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n", - "max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create a Universe to encapsulate a fuel pin\n", - "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", - "\n", - "# Create fuel Cell\n", - "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", - "fuel_cell.fill = fuel\n", - "fuel_cell.region = -fuel_outer_radius\n", - "pin_cell_universe.add_cell(fuel_cell)\n", - "\n", - "# Create a clad Cell\n", - "clad_cell = openmc.Cell(name='1.6% Clad')\n", - "clad_cell.fill = zircaloy\n", - "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", - "pin_cell_universe.add_cell(clad_cell)\n", - "\n", - "# Create a moderator Cell\n", - "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", - "moderator_cell.fill = water\n", - "moderator_cell.region = +clad_outer_radius\n", - "pin_cell_universe.add_cell(moderator_cell)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create root Cell\n", - "root_cell = openmc.Cell(name='root cell')\n", - "root_cell.region = +min_x & -max_x & +min_y & -max_y\n", - "root_cell.fill = pin_cell_universe\n", - "\n", - "# Create root Universe\n", - "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", - "root_universe.add_cell(root_cell)" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry()\n", - "openmc_geometry.root_universe = root_universe\n", - "\n", - "# Instantiate a GeometryFile\n", - "geometry_file = openmc.GeometryFile()\n", - "geometry_file.geometry = openmc_geometry\n", - "\n", - "# Export to \"geometry.xml\"\n", - "geometry_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will reuse our settings from the previous simulation. Now, we let's create transport, nu-fission, nu-scatter and chi multi-group cross sections for each cell." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Extract all Cells filled by Materials\n", - "openmc_cells = openmc_geometry.get_all_material_cells()\n", - "\n", - "# Create dictionary to store multi-group cross sections for all cells\n", - "xs_library = {}\n", - "\n", - "# Instantiate 8-group cross sections for each cell\n", - "for cell in openmc_cells:\n", - " xs_library[cell.id] = {}\n", - " xs_library[cell.id]['transport'] = mgxs.TransportXS(groups=fine_groups)\n", - " xs_library[cell.id]['nu-fission'] = mgxs.NuFissionXS(groups=fine_groups)\n", - " xs_library[cell.id]['nu-scatter'] = mgxs.NuScatterMatrixXS(groups=fine_groups)\n", - " xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create a tally trigger set to +/- 0.01 for each tally\n", - "# used to compute the multi-group cross sections\n", - "tally_trigger = openmc.Trigger('std_dev', 1E-2)\n", - "\n", - "# Add the tally trigger to each of the multi-group cross section tallies\n", - "for cell in openmc_cells:\n", - " for mgxs_type in xs_library[cell.id]:\n", - " xs_library[cell.id][mgxs_type].tally_trigger = tally_trigger\n", - " \n", - "# Set the trigger to active in the \"settings.xml\" file\n", - "settings_file.trigger_active = True\n", - "settings_file.particles *= 4\n", - "settings_file.trigger_max_batches = settings_file.batches * 4\n", - "settings_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we did not give our cross sections a spatial domain in their constructors. Instead, we will loop over all cells to set each cross sections domain. In addition, we will set each cross section to tally cross sections on a per-nuclide basis through the use of the `by_nuclide` instance attribute. " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate an empty TalliesFile\n", - "tallies_file = openmc.TalliesFile()\n", - "\n", - "# Iterate over all cells and cross section types\n", - "for cell in openmc_cells:\n", - " for rxn_type in xs_library[cell.id]:\n", - "\n", - " # Set the cross sections domain type to the cell\n", - " xs_library[cell.id][rxn_type].domain = cell\n", - " xs_library[cell.id][rxn_type].domain_type = 'cell'\n", - " \n", - " # Tally cross sections by nuclide (e.g., micro cross sections)\n", - " xs_library[cell.id][rxn_type].by_nuclide = True\n", - " \n", - " # Add OpenMC tallies to the tallies file for XML generation\n", - " for tally in xs_library[cell.id][rxn_type].tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\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": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " .d88888b. 888b d888 .d8888b.\n", - " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", - " 888 888 88888b.d88888 888 888\n", - " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", - " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", - " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", - " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", - " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", - "__________________888______________________________________________________\n", - " 888\n", - " 888\n", - "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", - " Version: 0.7.0\n", - " Git SHA1: 21738db07debeabde824c9b955bd3bf0c9a16366\n", - " Date/Time: 2015-11-01 21:29:37\n", - " MPI Processes: 1\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", - " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", - " Initializing source particles...\n", - "\n", - " ===========================================================================\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", - "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.23985 \n", - " 2/1 1.24082 \n", - " 3/1 1.22031 \n", - " 4/1 1.21649 \n", - " 5/1 1.23229 \n", - " 6/1 1.21957 \n", - " 7/1 1.22515 \n", - " 8/1 1.21309 \n", - " 9/1 1.23939 \n", - " 10/1 1.23865 \n", - " 11/1 1.22776 \n", - " 12/1 1.21661 1.22219 +/- 0.00558\n", - " 13/1 1.22202 1.22213 +/- 0.00322\n", - " 14/1 1.23251 1.22473 +/- 0.00345\n", - " 15/1 1.23965 1.22771 +/- 0.00401\n", - " 16/1 1.21441 1.22549 +/- 0.00395\n", - " 17/1 1.23348 1.22663 +/- 0.00353\n", - " 18/1 1.21121 1.22471 +/- 0.00361\n", - " 19/1 1.20506 1.22252 +/- 0.00386\n", - " 20/1 1.22275 1.22255 +/- 0.00346\n", - " 21/1 1.21700 1.22204 +/- 0.00317\n", - " 22/1 1.20841 1.22091 +/- 0.00311\n", - " 23/1 1.21302 1.22030 +/- 0.00292\n", - " 24/1 1.22504 1.22064 +/- 0.00272\n", - " 25/1 1.22325 1.22081 +/- 0.00254\n", - " 26/1 1.22988 1.22138 +/- 0.00244\n", - " 27/1 1.21374 1.22093 +/- 0.00234\n", - " 28/1 1.21434 1.22056 +/- 0.00224\n", - " 29/1 1.24678 1.22194 +/- 0.00253\n", - " 30/1 1.22600 1.22215 +/- 0.00240\n", - " 31/1 1.22783 1.22242 +/- 0.00230\n", - " 32/1 1.23107 1.22281 +/- 0.00223\n", - " 33/1 1.23041 1.22314 +/- 0.00216\n", - " 34/1 1.21147 1.22266 +/- 0.00212\n", - " 35/1 1.23184 1.22302 +/- 0.00207\n", - " 36/1 1.22513 1.22310 +/- 0.00199\n", - " 37/1 1.22969 1.22335 +/- 0.00193\n", - " 38/1 1.21288 1.22297 +/- 0.00190\n", - " 39/1 1.23967 1.22355 +/- 0.00192\n", - " 40/1 1.21419 1.22324 +/- 0.00188\n", - " 41/1 1.23212 1.22352 +/- 0.00184\n", - " 42/1 1.20703 1.22301 +/- 0.00185\n", - " 43/1 1.24153 1.22357 +/- 0.00188\n", - " 44/1 1.23561 1.22392 +/- 0.00186\n", - " 45/1 1.20369 1.22335 +/- 0.00190\n", - " 46/1 1.24517 1.22395 +/- 0.00194\n", - " 47/1 1.22985 1.22411 +/- 0.00189\n", - " 48/1 1.23570 1.22442 +/- 0.00187\n", - " 49/1 1.22288 1.22438 +/- 0.00182\n", - " 50/1 1.20470 1.22389 +/- 0.00184\n", - " Triggers unsatisfied, max unc./thresh. is 1.18932 for flux in tally 10080\n", - " The estimated number of batches is 67\n", - " Creating state point statepoint.050.h5...\n", - " 51/1 1.24158 1.22432 +/- 0.00185\n", - " 52/1 1.24407 1.22479 +/- 0.00186\n", - " 53/1 1.23412 1.22500 +/- 0.00183\n", - " 54/1 1.25172 1.22561 +/- 0.00189\n", - " 55/1 1.22653 1.22563 +/- 0.00185\n", - " 56/1 1.24741 1.22610 +/- 0.00187\n", - " 57/1 1.24342 1.22647 +/- 0.00186\n", - " 58/1 1.20365 1.22600 +/- 0.00189\n", - " 59/1 1.23576 1.22620 +/- 0.00186\n", - " 60/1 1.21398 1.22595 +/- 0.00184\n", - " 61/1 1.22186 1.22587 +/- 0.00180\n", - " 62/1 1.23502 1.22605 +/- 0.00178\n", - " 63/1 1.23328 1.22618 +/- 0.00175\n", - " 64/1 1.23990 1.22644 +/- 0.00173\n", - " 65/1 1.23283 1.22655 +/- 0.00171\n", - " 66/1 1.21605 1.22637 +/- 0.00169\n", - " 67/1 1.22322 1.22631 +/- 0.00166\n", - " Triggers satisfied for batch 67\n", - " Creating state point statepoint.067.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 2.0080E+00 seconds\n", - " Reading cross sections = 4.3400E-01 seconds\n", - " Total time in simulation = 9.0060E+02 seconds\n", - " Time in transport only = 9.0020E+02 seconds\n", - " Time in inactive batches = 7.3259E+01 seconds\n", - " Time in active batches = 8.2734E+02 seconds\n", - " Time synchronizing fission bank = 6.9000E-02 seconds\n", - " Sampling source sites = 4.7000E-02 seconds\n", - " SEND/RECV source sites = 2.2000E-02 seconds\n", - " Time accumulating tallies = 5.0000E-03 seconds\n", - " Total time for finalization = 7.7000E-02 seconds\n", - " Total time elapsed = 9.0285E+02 seconds\n", - " Calculation Rate (inactive) = 1365.02 neutrons/second\n", - " Calculation Rate (active) = 483.479 neutrons/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.22548 +/- 0.00143\n", - " k-effective (Track-length) = 1.22631 +/- 0.00166\n", - " k-effective (Absorption) = 1.22204 +/- 0.00138\n", - " Combined k-effective = 1.22386 +/- 0.00114\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Delete old HDF5 files\n", - "!rm *.h5\n", - "\n", - "# Run OpenMC with the output throttled!\n", - "executor = openmc.Executor()\n", - "executor.run_simulation(output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tally Data Processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our simulation ran successfully and created a statepoint file with all the tally data in it. As before, we begin our analysis here loading the statepoint file." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the last statepoint and summary files\n", - "sp = openmc.StatePoint('statepoint.067.h5')\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The statepoint is now ready to be analyzed by our multi-group cross sections. Next, we load the tallies from the statepoint into each object and to compute the cross sections using tally arithmetic." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Iterate over all cells and cross section types\n", - "for cell in openmc_cells:\n", - " for rxn_type in xs_library[cell.id]:\n", - " xs_library[cell.id][rxn_type].load_from_statepoint(sp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it! Our multi-group cross sections are now ready for the big spotlight. This time we have cross sections in three distinct spatial zones - fuel, clad and moderator - on a per-nuclide basis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cross Section Data Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's first inspect one of our cross sections by printing it to the screen as a microscopic cross section in units of barns." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", - "\tCross Sections [barns]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t3.31e+00 +/- 2.13e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t3.96e+00 +/- 1.54e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t5.51e+01 +/- 2.36e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t8.83e+01 +/- 3.76e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t2.89e+02 +/- 4.10e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t4.49e+02 +/- 4.94e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t6.87e+02 +/- 3.44e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t1.44e+03 +/- 2.37e-01%\n", - "\n", - "\tNuclide =\tU-238\n", - "\tCross Sections [barns]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t1.06e+00 +/- 2.47e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t1.21e-03 +/- 3.07e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t5.72e-04 +/- 3.47e+00%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t6.54e-06 +/- 3.29e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.07e-05 +/- 4.20e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.55e-05 +/- 4.94e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.30e-05 +/- 3.44e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t4.24e-05 +/- 2.37e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "nufission = xs_library[fuel_cell.id]['nu-fission']\n", - "nufission.print_xs(xs_type='micro', nuclides=['U-235', 'U-238'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our multi-group cross sections are capable of summing across all nuclides to provide us with macroscopic cross sections as well." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t2.53e-02 +/- 2.35e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t1.51e-03 +/- 1.51e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t2.07e-02 +/- 2.36e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t3.31e-02 +/- 3.76e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.09e-01 +/- 4.10e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.69e-01 +/- 4.94e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.58e-01 +/- 3.44e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t5.40e-01 +/- 2.37e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "nufission = xs_library[fuel_cell.id]['nu-fission']\n", - "nufission.print_xs(xs_type='macro', nuclides='sum')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although a printed report is nice, it is not scalable or flexible. Let's extract the cross section data for the moderator as a Pandas DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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cellgroup ingroup outnuclidemeanstd. dev.
1261000211H-10.2338960.004410
1271000211O-161.5644880.007478
1241000212H-11.5899750.003196
1251000212O-160.2836970.001986
1221000213H-10.0108460.000225
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" - ], - "text/plain": [ - " cell group in group out nuclide mean std. dev.\n", - "126 10002 1 1 H-1 0.233896 0.004410\n", - "127 10002 1 1 O-16 1.564488 0.007478\n", - "124 10002 1 2 H-1 1.589975 0.003196\n", - "125 10002 1 2 O-16 0.283697 0.001986\n", - "122 10002 1 3 H-1 0.010846 0.000225\n", - "123 10002 1 3 O-16 0.000000 0.000000\n", - "120 10002 1 4 H-1 0.000000 0.000000\n", - "121 10002 1 4 O-16 0.000000 0.000000\n", - "118 10002 1 5 H-1 0.000000 0.000000\n", - "119 10002 1 5 O-16 0.000000 0.000000" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", - "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can easily use the Pandas DataFrame to extract the H-1 and O-16 scattering matrices separately." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Slice DataFrame in two for each nuclide's mean values\n", - "h1 = df[df['nuclide'] == 'H-1']['mean']\n", - "o16 = df[df['nuclide'] == 'O-16']['mean']\n", - "\n", - "# Cast DataFrames as NumPy arrays\n", - "h1 = h1.as_matrix()\n", - "o16 = o16.as_matrix()\n", - "\n", - "# Reshape arrays to 2D matrix for plotting\n", - "h1.shape = (fine_groups.num_groups, fine_groups.num_groups)\n", - "o16.shape = (fine_groups.num_groups, fine_groups.num_groups)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Matplotlib's `imshow` routine can be used to plot the matrices to illustrate their sparsity structures." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create plot of the H-1 scattering matrix\n", - "fig = plt.subplot(121)\n", - "fig.imshow(h1, interpolation='nearest')\n", - "plt.title('H-1 Scattering Matrix')\n", - "\n", - "# Create plot of the O-16 scattering matrix\n", - "fig2 = plt.subplot(122)\n", - "fig2.imshow(o16, interpolation='nearest')\n", - "plt.title('O-16 Scattering Matrix')\n", - "\n", - "# Show the plot on screen\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we illustate how one can easily take multi-group cross sections and condense them down to a coarser energy group structure using. The `get_condensed_xs(...)` class method takes in as a parameter an `EnergyGroups` object with a coarse(r) group structure and returns a new multi-group cross section condensed to the coarse groups. We illustrate this process below using the 2-group structure created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Extract the 16-group transport cross section for the fuel\n", - "fine_xs = xs_library[fuel_cell.id]['transport']\n", - "\n", - "# Condense to the 2-group structure\n", - "condense_xs = fine_xs.get_condensed_xs(coarse_groups)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Group condensation is as simple as that! We now have a new coarse 2-group cross section in addition to our original 16-group cross section. Let's inspect the 2-group cross section by printing it to the screen and extracting a Pandas DataFrame as we have already learned how to do." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\ttransport\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t7.81e-03 +/- 4.72e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t1.82e-01 +/- 2.09e-01%\n", - "\n", - "\tNuclide =\tU-238\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t2.17e-01 +/- 1.58e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t2.54e-01 +/- 2.46e-01%\n", - "\n", - "\tNuclide =\tO-16\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t1.45e-01 +/- 1.73e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t1.75e-01 +/- 2.64e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "condense_xs.print_xs()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " cell group in nuclide mean std. dev.\n", - "3 10000 1 U-235 20.832704 0.098310\n", - "4 10000 1 U-238 9.574435 0.015117\n", - "5 10000 1 O-16 3.161919 0.005466\n", - "0 10000 2 U-235 484.133513 1.011870\n", - "1 10000 2 U-238 11.215152 0.027565\n", - "2 10000 2 O-16 3.798833 0.010031" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = condense_xs.get_pandas_dataframe(xs_type='micro')\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verification with OpenMOC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's verify our cross sections using OpenMOC. First, we use OpenCG construct an equivalent OpenMOC geometry just as we did before." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create an OpenMOC Geometry from the OpenCG Geometry\n", - "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Likewise, we can inject the multi-group cross sections into the equivalent fuel pin cell OpenMOC geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Get all OpenMOC cells in the gometry\n", - "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", - "\n", - "# Inject multi-group cross sections into OpenMOC Materials\n", - "# NOTE: This code will work for 1, 10, or 1,000s of cells\n", - "# as is the case for a complicated geometry like BEAVRS\n", - "for cell_id, cell in openmoc_cells.items():\n", - " \n", - " # Ignore the root cell\n", - " if cell.getName() == 'root cell':\n", - " continue\n", - " \n", - " # Get a reference to the Material filling this Cell\n", - " openmoc_material = cell.getFillMaterial()\n", - " \n", - " # Set the number of energy groups for the Material\n", - " openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n", - " \n", - " # Extract the appropriate cross section objects for this cell\n", - " transport = xs_library[cell_id]['transport']\n", - " nufission = xs_library[cell_id]['nu-fission']\n", - " nuscatter = xs_library[cell_id]['nu-scatter']\n", - " chi = xs_library[cell_id]['chi']\n", - " \n", - " # Inject NumPy arrays of cross section data into the Material\n", - " # NOTE: In each case we must sum across nuclides to get the\n", - " # macroscopic cross sections needed by OpenMOC\n", - " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Throttle OpenMOC output to screen\n", - "openmoc.log.set_log_level('WARNING')\n", - "\n", - "# Generate tracks for OpenMOC\n", - "openmoc_geometry.initializeFlatSourceRegions()\n", - "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", - "track_generator.generateTracks()\n", - "\n", - "# Run OpenMOC\n", - "solver = openmoc.CPUSolver(track_generator)\n", - "solver.computeEigenvalue()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.223863\n", - "openmoc keff = 1.222517\n", - "bias [pcm]: -134.7\n" - ] - } - ], - "source": [ - "# Print report of keff and bias with OpenMC\n", - "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", - "bias = (openmoc_keff - openmc_keff) * 1e5\n", - "\n", - "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", - "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", - "print('bias [pcm]: {0:1.1f}'.format(bias))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a sanity check, let's run a simulation with the coarse 2-group cross sections to ensure that they produce a reasonable result." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)\n", - "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", - "\n", - "# Inject multi-group cross sections into OpenMOC Materials\n", - "for cell_id, cell in openmoc_cells.items():\n", - " \n", - " # Ignore the root cell\n", - " if cell.getName() == 'root cell':\n", - " continue\n", - " \n", - " openmoc_material = cell.getFillMaterial()\n", - " openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n", - " \n", - " # Extract the appropriate cross section objects for this cell\n", - " transport = xs_library[cell_id]['transport']\n", - " nufission = xs_library[cell_id]['nu-fission']\n", - " nuscatter = xs_library[cell_id]['nu-scatter']\n", - " chi = xs_library[cell_id]['chi']\n", - " \n", - " # Perform group condensation\n", - " transport = transport.get_condensed_xs(coarse_groups)\n", - " nufission = nufission.get_condensed_xs(coarse_groups)\n", - " nuscatter = nuscatter.get_condensed_xs(coarse_groups)\n", - " chi = chi.get_condensed_xs(coarse_groups)\n", - " \n", - " # Inject NumPy arrays of cross section data into the Material\n", - " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Generate tracks for OpenMOC\n", - "openmoc_geometry.initializeFlatSourceRegions()\n", - "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", - "track_generator.generateTracks()\n", - "\n", - "# Run OpenMOC\n", - "solver = openmoc.CPUSolver(track_generator)\n", - "solver.computeEigenvalue()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.223863\n", - "openmoc keff = 1.225691\n", - "bias [pcm]: 182.7\n" - ] - } - ], - "source": [ - "# Print report of keff and bias with OpenMC\n", - "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", - "bias = (openmoc_keff - openmc_keff) * 1e5\n", - "\n", - "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", - "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", - "print('bias [pcm]: {0:1.1f}'.format(bias))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a non-trivial bias in both the 2-group and 8-group cases. In the case of the pin cell, one can show that these biases do not converge to <100 pcm with more particle histories. In the case of heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", - "\n", - "* Appropriate transport-corrected cross sections\n", - "* Spatial discretization of OpenMOC's mesh\n", - "* Constant-in-angle multi-group cross sections" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/docs/source/pythonapi/examples/multi-group-cross-sections.rst b/docs/source/pythonapi/examples/multi-group-cross-sections.rst deleted file mode 100644 index b2da0e1bc..000000000 --- a/docs/source/pythonapi/examples/multi-group-cross-sections.rst +++ /dev/null @@ -1,11 +0,0 @@ -==================================== -Multi-Group Cross Section Generation -==================================== - -.. only:: html - - .. notebook:: multi-group-cross-sections.ipynb - -.. only:: latex - - IPython notebooks must be viewed in the online HTML documentation. diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 1e4c3c9cd..1b05f8207 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -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, which we can use OpenMC's lattice/universe feature for. 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." ] }, { @@ -155,7 +155,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces." ] }, { @@ -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." ] }, { diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb index 87ae42b41..6e2dd9429 100644 --- a/docs/source/pythonapi/examples/post-processing.ipynb +++ b/docs/source/pythonapi/examples/post-processing.ipynb @@ -347,7 +347,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -413,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -432,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true @@ -458,8 +458,8 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", - " Git SHA1: 21738db07debeabde824c9b955bd3bf0c9a16366\n", - " Date/Time: 2015-10-28 21:04:43\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-11-29 16:46:53\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -520,8 +520,116 @@ " 31/1 1.04883 1.04094 +/- 0.00388\n", " 32/1 1.03557 1.04070 +/- 0.00371\n", " 33/1 1.02947 1.04021 +/- 0.00358\n", - " 34/1 1.03651 1.04006 +/- 0.00343\n" + " 34/1 1.03651 1.04006 +/- 0.00343\n", + " 35/1 1.03331 1.03979 +/- 0.00330\n", + " 36/1 1.05947 1.04054 +/- 0.00326\n", + " 37/1 1.05093 1.04093 +/- 0.00316\n", + " 38/1 1.06787 1.04189 +/- 0.00319\n", + " 39/1 1.01451 1.04095 +/- 0.00322\n", + " 40/1 1.02351 1.04037 +/- 0.00317\n", + " 41/1 1.04826 1.04062 +/- 0.00307\n", + " 42/1 1.04228 1.04067 +/- 0.00298\n", + " 43/1 1.03214 1.04041 +/- 0.00290\n", + " 44/1 1.04950 1.04068 +/- 0.00282\n", + " 45/1 1.06616 1.04141 +/- 0.00284\n", + " 46/1 1.07039 1.04221 +/- 0.00287\n", + " 47/1 1.00292 1.04115 +/- 0.00299\n", + " 48/1 1.04477 1.04125 +/- 0.00291\n", + " 49/1 1.03360 1.04105 +/- 0.00284\n", + " 50/1 1.04783 1.04122 +/- 0.00277\n", + " 51/1 1.03985 1.04119 +/- 0.00271\n", + " 52/1 1.02507 1.04080 +/- 0.00267\n", + " 53/1 1.03477 1.04066 +/- 0.00261\n", + " 54/1 1.00412 1.03983 +/- 0.00268\n", + " 55/1 1.02239 1.03945 +/- 0.00265\n", + " 56/1 1.04308 1.03952 +/- 0.00259\n", + " 57/1 1.05534 1.03986 +/- 0.00256\n", + " 58/1 1.06667 1.04042 +/- 0.00257\n", + " 59/1 1.06458 1.04091 +/- 0.00256\n", + " 60/1 1.00304 1.04015 +/- 0.00262\n", + " 61/1 1.05038 1.04036 +/- 0.00258\n", + " 62/1 1.02904 1.04014 +/- 0.00254\n", + " 63/1 1.00249 1.03943 +/- 0.00259\n", + " 64/1 1.01779 1.03903 +/- 0.00257\n", + " 65/1 1.05335 1.03929 +/- 0.00254\n", + " 66/1 1.06231 1.03970 +/- 0.00253\n", + " 67/1 1.02382 1.03942 +/- 0.00250\n", + " 68/1 1.03796 1.03939 +/- 0.00245\n", + " 69/1 1.03672 1.03935 +/- 0.00241\n", + " 70/1 1.02926 1.03918 +/- 0.00238\n", + " 71/1 1.05834 1.03950 +/- 0.00236\n", + " 72/1 1.04332 1.03956 +/- 0.00232\n", + " 73/1 1.05613 1.03982 +/- 0.00230\n", + " 74/1 1.01963 1.03950 +/- 0.00228\n", + " 75/1 1.02228 1.03924 +/- 0.00226\n", + " 76/1 1.04842 1.03938 +/- 0.00223\n", + " 77/1 1.02157 1.03911 +/- 0.00222\n", + " 78/1 1.02810 1.03895 +/- 0.00219\n", + " 79/1 1.05030 1.03912 +/- 0.00216\n", + " 80/1 1.02391 1.03890 +/- 0.00214\n", + " 81/1 1.02488 1.03870 +/- 0.00212\n", + " 82/1 1.04957 1.03885 +/- 0.00210\n", + " 83/1 1.03499 1.03880 +/- 0.00207\n", + " 84/1 1.05922 1.03907 +/- 0.00206\n", + " 85/1 1.05898 1.03934 +/- 0.00205\n", + " 86/1 1.02242 1.03912 +/- 0.00204\n", + " 87/1 1.03278 1.03904 +/- 0.00201\n", + " 88/1 1.06134 1.03932 +/- 0.00201\n", + " 89/1 1.04521 1.03940 +/- 0.00198\n", + " 90/1 1.04277 1.03944 +/- 0.00196\n", + " 91/1 1.04214 1.03947 +/- 0.00193\n", + " 92/1 1.05610 1.03967 +/- 0.00192\n", + " 93/1 1.04531 1.03974 +/- 0.00190\n", + " 94/1 1.01534 1.03945 +/- 0.00190\n", + " 95/1 1.03971 1.03945 +/- 0.00187\n", + " 96/1 1.07183 1.03983 +/- 0.00189\n", + " 97/1 1.07214 1.04020 +/- 0.00191\n", + " 98/1 1.03710 1.04017 +/- 0.00188\n", + " 99/1 1.02532 1.04000 +/- 0.00187\n", + " 100/1 1.03965 1.04000 +/- 0.00185\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.7900E-01 seconds\n", + " Reading cross sections = 8.7000E-02 seconds\n", + " Total time in simulation = 2.2064E+02 seconds\n", + " Time in transport only = 2.2060E+02 seconds\n", + " Time in inactive batches = 8.7100E+00 seconds\n", + " Time in active batches = 2.1193E+02 seconds\n", + " Time synchronizing fission bank = 1.4000E-02 seconds\n", + " Sampling source sites = 8.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", + " Time accumulating tallies = 1.3000E-02 seconds\n", + " Total time for finalization = 1.6600E-01 seconds\n", + " Total time elapsed = 2.2120E+02 seconds\n", + " Calculation Rate (inactive) = 5740.53 neutrons/second\n", + " Calculation Rate (active) = 2123.37 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.03912 +/- 0.00160\n", + " k-effective (Track-length) = 1.04000 +/- 0.00185\n", + " k-effective (Absorption) = 1.04240 +/- 0.00156\n", + " Combined k-effective = 1.04078 +/- 0.00127\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -545,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false, "scrolled": true @@ -565,11 +673,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tmesh\t[10000]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t[u'flux', u'fission']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], "source": [ "tally = sp.get_tally(scores=['flux'])\n", "print(tally)" @@ -584,11 +708,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0.4107676 , 0. ]],\n", + "\n", + " [[ 0.40849402, 0. ]],\n", + "\n", + " [[ 0.41014343, 0. ]],\n", + "\n", + " ..., \n", + " [[ 0.41049467, 0. ]],\n", + "\n", + " [[ 0.40982242, 0. ]],\n", + "\n", + " [[ 0.40996987, 0. ]]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tally.sum" ] @@ -602,11 +748,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 1, 2)\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([[[ 0.00456408, 0. ]],\n", + " \n", + " [[ 0.00453882, 0. ]],\n", + " \n", + " [[ 0.00455715, 0. ]],\n", + " \n", + " ..., \n", + " [[ 0.00456105, 0. ]],\n", + " \n", + " [[ 0.00455358, 0. ]],\n", + " \n", + " [[ 0.00455522, 0. ]]]),\n", + " array([[[ 1.95085625e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.78129859e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.89709648e-05, 0.00000000e+00]],\n", + " \n", + " ..., \n", + " [[ 1.56286612e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.65813279e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.67530331e-05, 0.00000000e+00]]]))" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(tally.mean.shape)\n", "(tally.mean, tally.std_dev)" @@ -621,11 +808,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tmesh\t[10000]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t[u'flux']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], "source": [ "flux = tally.get_slice(scores=['flux'])\n", "fission = tally.get_slice(scores=['fission'])\n", @@ -641,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -655,11 +858,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PFI0uXxK+SsPl49ro4/zR5SHsnEyg3WQksIGs9WhIPrqoPON+mVOjt7jx7GPs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\nunTp0qVLly5dunTp0qVLl585/j/rdUfNxuwKnQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.subplot(121)\n", "fig.imshow(flux.mean)\n", @@ -676,11 +900,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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7EWD5LB9DklSidgznjNN4Om39eqmi/CqqqmO2XzsdBa4ETgBLgddj+WvAisx2V8WyBgMD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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Determine relative error\n", "relative_error = np.zeros_like(flux.std_dev)\n", @@ -707,11 +942,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ (1.0, [0.2712169917165897, -0.04844236597355761, -0.1887902218343974], [0.3889598463000694, 0.8470657529949065, 0.36220139158953857], 2.2746035619924734, 0),\n", + " (1.0, [0.080729018085932, 0.19838688738571317, -0.38053428394017363], [-0.6604834049157511, -0.6893239101986768, 0.2976478097673534], 0.7833467555325838, 0),\n", + " (1.0, [0.019430574216787868, 0.06594180627832635, 0.23329810254580194], [-0.7472138923667574, 0.13227244377548197, -0.651287493870243], 1.1632342240714935, 0),\n", + " ...,\n", + " (1.0, [0.18544614514351207, -0.0113070561851496, 0.5468392238881264], [-0.8006491411918817, 0.43855795172388223, -0.4082007786475368], 1.4358240241589555, 0),\n", + " (1.0, [0.18544614514351207, -0.0113070561851496, 0.5468392238881264], [-0.5150076397044656, -0.34922134026850293, 0.7828228321575105], 1.5771133724329802, 0),\n", + " (1.0, [-0.2722999793764598, 0.22680062445008103, 0.2987060438567475], [0.9207818175032396, -0.2884020326181676, 0.26265017063984586], 2.932342523379745, 0)], \n", + " dtype=[('wgt', '" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GlUqYsKkZZ/mbetoFju0tliVNMWOz/EnQvh/v08Djdeszgd8lyzuJY2Y+3+OQuiCW9w/K\nLpZ8LHJY9cFOLypJ6l9pBjmUJOkZBg5JUiYGDklSJgYOSUDocjsw0Pxjl1vV67hVPRL2qpJyEkuP\nn34WSx53YwZASZKeYeCQJGVi4JAkZWLgkCRlYuCQJGVi4JCkLhkaat3luVLpderSszuuJCCerqJT\nVTfz3+64kqSuMnBIkjIxcEiSMjFwSJIyMXBIkjIxcEiSMjFwSJIyMXBIkjIxcEiSMjFwSJIy6Ubg\nOALYANwPnNrimHOS/euAQ5Jtc4HrgfXA3cDJxSZTkpRG0YFjEFhFCB4HAsuA+Q3HLAb2A/YHTgDO\nTbY/BfwN8HJgIXBik3MlSV1WdOBYAGwENhMCwWpgacMxS4BLkuWbgFnAPsAvgTuS7b8F7gX2LTa5\nkqSJFB04ZgNb6ta3JtsmOmZOwzHDhCqsm3JOnyQpo+kFXz/tIMGNw/vWn/ds4ArgFELJY5yRkZFn\nlqvVKtVqNVMCpamkUoHR0eb7hoa6mxZ1T61Wo1ar5Xa9oufjWAiMENo4AE4DdgBn1h1zHlAjVGNB\naEhfBDwE7AH8O/A94Owm13c+DikD59yIl/Nx7HIrodF7GJgBHAOsaThmDXBcsrwQ+DUhaAwAFwH3\n0DxoSJJ6oOiqqu3AScC1hB5WFxEauVck+88Hrib0rNoIPAYsT/a9FngPcCdwe7LtNOCagtMsSWrD\nqWOlKcSqqnhN1P60bVt+95psVZWBQ5pCDBzllPf/W+xtHJKkPmPgkCRlYuCQJGVi4JAkZWLgkPpM\npRIaU5t9fDtcebBXldRn7DnVf+xVJUkqNQOHJCkTA4ckKRMDh1RSrRrBbQBX0Wwcl0rKRvCpw8Zx\nSVKpGTgkSZkYOCRJmRg4JEmZGDgkSZkYOCRJmRg4JEmZGDgkKXJDQ81f9qxUepMeXwCUSsoXANXp\nz4AvAEp9zLk1FCNLHFLELFWoHUsc0hRlqUJlY+CQuqBdcIDwV2Ozz7ZtvU231IxVVVIXWOWkIlhV\nJUkqBQOHlBPbKjRVWFUl5cTqKHWbVVWSpFKY3usESJI6MzYUSStFlYCtqpJyYlWVysKqKqmLbACX\nLHFImViqUD+wxCEVoFXJwlKFVHzgOALYANwPnNrimHOS/euAQzKeKxVidNQhQKRWigwcg8AqQgA4\nEFgGzG84ZjGwH7A/cAJwboZzlbNardbrJPQV8zM/5mVcigwcC4CNwGbgKWA1sLThmCXAJcnyTcAs\n4IUpz1XOyvrL2a7ButNPHlVSZc3PGJmXcSkycMwGttStb022pTlm3xTndk2nP7RZzpvo2Fb7s2xv\n3FbkL2Prh3mt5XSX7QJApdI6vY3VStdfX2s52uxEx4xtb6yS6nV+tjKZe6Y9t9OfzVb7JrOtaDH/\nrrfa14ufzSIDR9q+J9H37Ir5hynt9koFDjusNu5h3Li+cmW2v8rbzXfcqo3g9NNDurIOLw67p7dV\n6SBNvpctELdi4MhXzL/rrfbF+rPZqYXANXXrp7F7I/d5wLF16xuAfVKeC6E6a6cfP378+Mn02Uik\npgObgGFgBnAHzRvHr06WFwI/zXCuJKkPHQncR4hupyXbViSfMauS/euAQyc4V5IkSZIkSZIkqZ+9\njPAW+reA9/c4Lf1gKXAB4UXMw3uclrJ7CXAhcHmvE1JyzyK8PHwB8K4ep6Uf+HNZZxoheCgfswg/\nXJo8f0En573AW5Ll1b1MSJ9J9XPZz6PjHgVchT9UefokoRec1Gv1o0483cuETEWxB46LgYeAuxq2\nNxs5973AWYThSgC+S+jS+77ik1kanebnAHAm8D3COzWa3M+mmsuSp1uBucly7M+xXsmSn33ldYSh\n1uu/+CDh3Y5hYA+avxy4CPgCcD7wkcJTWR6d5ufJwK2EdqMVCDrPywphxIS+/aWdhCx5ujfhwfgl\nwujZ2l2W/Oy7n8thxn/x1zB+OJKPJx+lM4z5mZdhzMu8DWOe5mmYAvKzjEW8NKPuKj3zMz/mZf7M\n03zlkp9lDBw7e52APmN+5se8zJ95mq9c8rOMgeMBdjWKkSxv7VFa+oH5mR/zMn/mab6mTH4OM76O\nzpFzJ2cY8zMvw5iXeRvGPM3TMFMwPy8DHgSeJNTLLU+2O3JuZ8zP/JiX+TNP82V+SpIkSZIkSZIk\nSZIkSZIkSZIkSYrS08DtdZ+P9TY541wHPCdZ3gF8vW7fdOBhwnwyrewN/KruGmO+A7wTWAJ8KpeU\nStIU8mgB15yewzVeD/xL3fqjwFpgr2T9SEKgWzPBdS4Fjqtbfx4h4OxFGIPuDsJ8C1LuyjjIoTQZ\nm4ER4DbgTuClyfZnESYGuonwIF+SbD+e8BD/T+D7wEzCPPbrgSuBnwKvJAzncFbdfT4I/HOT+78L\n+LeGbVeza/7sZYShIgYmSNdlwLF113gbYZ6FJwilmJ8Ab2qWAZKk5rYzvqrqHcn2/wZOTJY/BHw5\nWf4s8O5keRZhLJ+9CYFjS7IN4KOEmRABXg48BRxKeMBvJMywBvCjZH+jewmzrY15FDgIuBzYM0nr\nInZVVTVL10zCAHW/BIaSfdcAi+uuu5ww3a+UuzyK3lKMfkeYNrOZK5N/1wJHJ8tvAo4iBAYID/EX\nEeYv+D7w62T7a4Gzk+X1hFILwGPAD5JrbCBUE61vcu99gW0N2+4ijFa6DLiqYV+rdN1HKAm9I/k+\nfwpcW3feg4S5paXcGTg0FT2Z/Ps0438HjibMuVzv1YSgUG+A5i4EPkEoVVycMU1rgM8TShsvaNjX\nLF0Qqqs+laTnO4TvM2YaToKkgtjGIQXXAifXrY+VVhqDxI8IPZcADiRUM425GZhDaMe4rMV9HgSe\n32T7xYS2l8ZSSqt0AdSAAwhVb433+yPgFy3SIE2KgUP9aibj2zg+2+SYnez6q/wzhOqlO4G7gZVN\njgH4EqFEsD45Zz3wSN3+bwE3NmyrdyPwqoY0QJiZbVWGdI0ddzmhzeSGhvssAH7YIg2SpC6aRmhn\nAJgH/Jzx1V3fBQ5rc36VXY3rRRnrjmtVtCRF4DnALYQH8zrgzcn2sR5P30xxjfoXAIuwBPhkgdeX\nJEmSJEmSJEmSJEmSJEmSJHXm/wHv3X8uqw7iTQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Create log-spaced energy bins from 1 keV to 100 MeV\n", "energy_bins = np.logspace(-3,1)\n", @@ -773,11 +1066,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 0.5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == 'face':\n" + ] + }, + { + "data": { + "image/png": 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XE9lKiVrnA2uehFuXwuE18Pb90D4Qer4CVXWefJuKPX3Q5wqh70BIbA1RFkRB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\nD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O/JQ8OBPXYkplAEh0GlUdpJ7oZsNHUQwSu8+LIOE39Gj+TZAjRBqAaR0h/zqEnIQ8ai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\nNhv274ZnHobEthAVB0NHnls6VRWFC6eEXwHSULwkjgEPnq6xqoR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.quiver(sp.source['xyz'][:,0], sp.source['xyz'][:,1],\n", " sp.source['uvw'][:,0], sp.source['uvw'][:,1],\n", diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index fce805f18..3ec974e05 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -363,7 +363,26 @@ "outputs": [ { "data": { - "image/png": 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===========================================================================\n", @@ -615,13 +634,13 @@ " 11/1 1.07867 1.05536 +/- 0.01277\n", " 12/1 1.04203 1.05345 +/- 0.01096\n", " 13/1 1.04482 1.05237 +/- 0.00955\n", - " 14/1 1.04117 1.05113 +/- 0.00852\n", - " 15/1 1.07581 1.05360 +/- 0.00801\n", - " 16/1 1.04235 1.05257 +/- 0.00731\n", - " 17/1 1.02710 1.05045 +/- 0.00701\n", - " 18/1 1.01970 1.04809 +/- 0.00687\n", - " 19/1 1.01022 1.04538 +/- 0.00691\n", - " 20/1 1.01449 1.04332 +/- 0.00675\n", + " 14/1 1.04116 1.05113 +/- 0.00852\n", + " 15/1 1.07569 1.05358 +/- 0.00800\n", + " 16/1 1.04188 1.05252 +/- 0.00732\n", + " 17/1 1.03775 1.05129 +/- 0.00679\n", + " 18/1 0.98462 1.04616 +/- 0.00808\n", + " 19/1 1.08613 1.04902 +/- 0.00801\n", + " 20/1 1.00571 1.04613 +/- 0.00800\n", " Creating state point statepoint.20.h5...\n", "\n", " ===========================================================================\n", @@ -631,27 +650,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.3800E-01 seconds\n", - " Reading cross sections = 1.3500E-01 seconds\n", - " Total time in simulation = 2.3556E+01 seconds\n", - " Time in transport only = 2.3532E+01 seconds\n", - " Time in inactive batches = 3.1100E+00 seconds\n", - " Time in active batches = 2.0446E+01 seconds\n", + " Total time for initialization = 7.9600E-01 seconds\n", + " Reading cross sections = 2.1200E-01 seconds\n", + " Total time in simulation = 1.8740E+01 seconds\n", + " Time in transport only = 1.8727E+01 seconds\n", + " Time in inactive batches = 2.5970E+00 seconds\n", + " Time in active batches = 1.6143E+01 seconds\n", " Time synchronizing fission bank = 2.0000E-03 seconds\n", " Sampling source sites = 1.0000E-03 seconds\n", " SEND/RECV source sites = 1.0000E-03 seconds\n", - " Time accumulating tallies = 1.0000E-03 seconds\n", - " Total time for finalization = 3.0000E-03 seconds\n", - " Total time elapsed = 2.4210E+01 seconds\n", - " Calculation Rate (inactive) = 4019.29 neutrons/second\n", - " Calculation Rate (active) = 1834.10 neutrons/second\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for finalization = 2.0000E-03 seconds\n", + " Total time elapsed = 1.9553E+01 seconds\n", + " Calculation Rate (inactive) = 4813.25 neutrons/second\n", + " Calculation Rate (active) = 2322.99 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.03935 +/- 0.00682\n", - " k-effective (Track-length) = 1.04332 +/- 0.00675\n", - " k-effective (Absorption) = 1.03845 +/- 0.00598\n", - " Combined k-effective = 1.04024 +/- 0.00523\n", + " k-effective (Collision) = 1.04597 +/- 0.00663\n", + " k-effective (Track-length) = 1.04613 +/- 0.00800\n", + " k-effective (Absorption) = 1.04087 +/- 0.00627\n", + " Combined k-effective = 1.04322 +/- 0.00570\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -742,7 +761,7 @@ { 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" ], "text/plain": [ - " cell energy [MeV] nuclide score \\\n", - "0 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (nu-fission / flux) \n", - "1 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (scatter / flux) \n", - "2 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (nu-fission / flux) \n", - "3 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (scatter / flux) \n", - "4 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (nu-fission / flux) \n", - "5 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (scatter / flux) \n", - "6 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (nu-fission / flux) \n", - "7 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (scatter / flux) \n", + " cell energy [MeV] nuclide score mean \\\n", + "0 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (nu-fission / flux) 0.000001 \n", + "1 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (scatter / flux) 0.209990 \n", + "2 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (nu-fission / flux) 0.356117 \n", + "3 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (scatter / flux) 0.005555 \n", + "4 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (nu-fission / flux) 0.007190 \n", + "5 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (scatter / flux) 0.227843 \n", + "6 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (nu-fission / flux) 0.008086 \n", + "7 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (scatter / flux) 0.003365 \n", "\n", - " mean std. dev. \n", - "0 6.657029e-07 7.377419e-09 \n", - "1 2.099891e-01 2.303838e-03 \n", - "2 3.564204e-01 3.951669e-03 \n", - "3 5.555330e-03 6.101004e-05 \n", - "4 7.154887e-03 8.053460e-05 \n", - "5 2.277701e-01 1.079289e-03 \n", - "6 8.066738e-03 5.254797e-05 \n", - "7 3.366802e-03 1.647058e-05 " + " std. dev. \n", + "0 8.078651e-09 \n", + "1 2.449396e-03 \n", + "2 4.364366e-03 \n", + "3 6.495710e-05 \n", + "4 7.596666e-05 \n", + "5 1.024510e-03 \n", + "6 6.251590e-05 \n", + "7 1.646663e-05 " ] }, "execution_count": 33, @@ -1258,11 +1286,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 6.65702880e-07]\n", - " [ 3.56420449e-01]]\n", + "[[[ 6.65302296e-07]\n", + " [ 3.56116716e-01]]\n", "\n", - " [[ 7.15488656e-03]\n", - " [ 8.06673774e-03]]]\n" + " [[ 7.19004460e-03]\n", + " [ 8.08598751e-03]]]\n" ] } ], @@ -1290,9 +1318,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00555533]]\n", + "[[[ 0.00555516]]\n", "\n", - " [[ 0.0033668 ]]]\n" + " [[ 0.00336498]]]\n" ] } ], @@ -1314,8 +1342,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.22777006]\n", - " [ 0.0033668 ]]]\n" + "[[[ 0.22784316]\n", + " [ 0.00336498]]]\n" ] } ], @@ -1344,7 +1372,7 @@ { "data": { "text/html": [ - "
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Non-simple cell regions can now be defined through the ``|`` (union) and +``~`` (complement) operators. Similar changes in the Python API also allow +complex cell regions to be defined. A true secondary particle bank now exists; +this is crucial for photon transport (to be added in the next minor release). A +rich API for multi-group cross section generation has been added via the +``openmc.mgxs`` Python module. + +Various improvements to tallies have also been made. It is now possible to +explicitly specify that a collision estimator be used in a tally. A new +``delayedgroup`` filter and ``delayed-nu-fission`` score allow a user to obtain +delayed fission neutron production rates filtered by delayed group. Finally, the +new ``inverse-velocity`` score may be useful for calculating kinetics +parameters. + +.. caution:: In previous versions, depending on how OpenMC was compiled binary + output was either given in HDF5 or a flat binary format. With this + version, all binary output is now HDF5 which means you **must** + have HDF5 in order to install OpenMC. Please consult the user's + guide for instructions on how to compile with HDF5. + ------------------- System Requirements ------------------- @@ -17,36 +38,41 @@ the problem at hand (mostly on the number of nuclides in the problem). New Features ------------ -- Complete Python API -- Python 3 compatability for all scripts -- All scripts consistently named openmc-* and installed together -- New 'distribcell' tally filter for repeated cells -- Ability to specify outer lattice universe -- XML input validation utility (openmc-validate-xml) -- Support for hexagonal lattices -- Material union energy grid method -- Tally triggers -- Remove dependence on PETSc -- Significant OpenMP performance improvements -- Support for Fortran 2008 MPI interface -- Use of Travis CI for continuous integration -- Simplifications and improvements to test suite +- Support for complex cell regions (union and complement operators) +- Generic quadric surface type +- Improved handling of secondary particles +- Binary output is now solely HDF5 +- ``openmc.mgxs`` Python module enabling multi-group cross section generation +- Collision estimator for tallies +- Delayed fission neutron production tallies with ability to filter by delayed + group +- Inverse velocity tally score +- Performance improvements for binary search +- Performance improvements for reaction rate tallies --------- Bug Fixes --------- -- b5f712_: Fix bug in spherical harmonics tallies -- e6675b_: Ensure all constants are double precision -- 04e2c1_: Fix potential bug in sample_nuclide routine -- 6121d9_: Fix bugs related to particle track files -- 2f0e89_: Fixes for nuclide specification in tallies +- 299322_: Bug with material filter when void material present +- d74840_: Fix triggers on tallies with multiple filters +- c29a81_: Correctly handle maximum transport energy +- 3edc23_: Fixes in the nu-scatter score +- 629e3b_: Assume unspecified surface coefficients are zero in Python API +- 5dbe8b_: Fix energy filters for openmc-plot-mesh-tally +- ff66f4_: Fixes in the openmc-plot-mesh-tally script +- 441fd4_: Fix bug in kappa-fission score +- 7e5974_: Allow fixed source simulations from Python API -.. _b5f712: https://github.com/mit-crpg/openmc/commit/b5f712 -.. _e6675b: https://github.com/mit-crpg/openmc/commit/e6675b -.. _04e2c1: https://github.com/mit-crpg/openmc/commit/04e2c1 -.. _6121d9: https://github.com/mit-crpg/openmc/commit/6121d9 -.. _2f0e89: https://github.com/mit-crpg/openmc/commit/2f0e89 +.. _299322: https://github.com/mit-crpg/openmc/commit/299322 +.. _d74840: https://github.com/mit-crpg/openmc/commit/d74840 +.. _c29a81: https://github.com/mit-crpg/openmc/commit/c29a81 +.. _3edc23: https://github.com/mit-crpg/openmc/commit/3edc23 +.. _629e3b: https://github.com/mit-crpg/openmc/commit/629e3b +.. _5dbe8b: https://github.com/mit-crpg/openmc/commit/5dbe8b +.. _ff66f4: https://github.com/mit-crpg/openmc/commit/ff66f4 +.. _441fd4: https://github.com/mit-crpg/openmc/commit/441fd4 +.. _7e5974: https://github.com/mit-crpg/openmc/commit/7e5974 ------------ Contributors @@ -55,13 +81,11 @@ Contributors This release contains new contributions from the following people: - `Will Boyd `_ -- `Matt Ellis `_ - `Sterling Harper `_ -- `Bryan Herman `_ -- `Nicholas Horelik `_ +- `Bryan Herman `_ - `Colin Josey `_ -- `William Lyu `_ - `Adam Nelson `_ - `Paul Romano `_ -- `Anthony Scopatz `_ +- `Kelly Rowland `_ +- `Sam Shaner `_ - `Jon Walsh `_ diff --git a/docs/source/usersguide/input.rst b/docs/source/usersguide/input.rst index 069d483e3..754f815de 100644 --- a/docs/source/usersguide/input.rst +++ b/docs/source/usersguide/input.rst @@ -1147,10 +1147,12 @@ Each ``material`` element can have the following attributes or sub-elements: :density: An element with attributes/sub-elements called ``value`` and ``units``. The ``value`` attribute is the numeric value of the density while the ``units`` - can be "g/cm3", "kg/m3", "atom/b-cm", "atom/cm3", "sum", or "macro". - The "sum" unit indicates that the density should be calculated as the sum - of the atom fractions for each nuclide in the material. This should not be - used in conjunction with weight percents. The "macro" unit is used with + can be "g/cm3", "kg/m3", "atom/b-cm", "atom/cm3", or "sum". The "sum" unit + indicates that values appearing in ``ao`` attributes for ```` and + ```` sub-elements are to be interpreted as nuclide/element + densities in atom/b-cm, and the total density of the material is taken as + the sum of all nuclides/elements. The "sum" option cannot be used in + conjunction with weight percents. The "macro" unit is used with a ``macroscopic`` quantity to indicate that the density is already included in the library and thus not needed here. However, if a value is provided for the ``value``, then this is treated as a number density multiplier on @@ -1964,9 +1966,6 @@ attributes/sub-elements: automatically assumes a one energy group calculation over the entire energy range. - .. note:: When running in the multi-group :ref:`energy_mode`, the - energy bins must match the data library's group boundaries. - :albedo: Surface ratio of incoming to outgoing partial currents on global boundary conditions. They are listed in the following order: -x +x -y +y -z +z. diff --git a/docs/source/usersguide/install.rst b/docs/source/usersguide/install.rst index 246e343c1..2bfdbc582 100644 --- a/docs/source/usersguide/install.rst +++ b/docs/source/usersguide/install.rst @@ -8,7 +8,7 @@ Installation and Configuration Installing on Ubuntu with PPA ----------------------------- -For users with Ubuntu 11.10 or later, a binary package for OpenMC is available +For users with Ubuntu 15.04 or later, a binary package for OpenMC is available through a Personal Package Archive (PPA) and can be installed through the APT package manager. First, add the following PPA to the repository sources: @@ -28,6 +28,9 @@ Now OpenMC should be recognized within the repository and can be installed: sudo apt-get install openmc +Binary packages from this PPA may exist for earlier versions of Ubuntu, but they +are no longer supported. + -------------------- Building from Source -------------------- @@ -74,6 +77,12 @@ Prerequisites You may omit ``--enable-parallel`` if you want to compile HDF5_ in serial. + .. important:: + + OpenMC uses various parts of the HDF5 Fortran 2003 API; as such you + must include ``--enable-fortran2003`` or else OpenMC will not be able + to compile. + On Debian derivatives, HDF5 and/or parallel HDF5 can be installed through the APT package manager: diff --git a/examples/python/boxes/build-xml.py b/examples/python/boxes/build-xml.py index 4bac9fff4..9c28d37bb 100644 --- a/examples/python/boxes/build-xml.py +++ b/examples/python/boxes/build-xml.py @@ -1,3 +1,5 @@ +import numpy as np + import openmc ############################################################################### @@ -115,7 +117,7 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.set_source_space('point', [0., 0., 0.]) +settings_file.set_source_space('box', np.concatenate(outer_cube.bounding_box)) settings_file.export_to_xml() ############################################################################### diff --git a/examples/python/reflective/build-xml.py b/examples/python/reflective/build-xml.py index 9eab4aec3..44b544d20 100644 --- a/examples/python/reflective/build-xml.py +++ b/examples/python/reflective/build-xml.py @@ -1,3 +1,5 @@ +import numpy as np + import openmc ############################################################################### @@ -82,5 +84,5 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.set_source_space('box', [-1, -1, -1, 1, 1, 1]) +settings_file.set_source_space('box', np.concatenate(cell.region.bounding_box)) settings_file.export_to_xml() diff --git a/examples/xml/boxes/settings.xml b/examples/xml/boxes/settings.xml index 0ac26ec4d..eff7c1c10 100644 --- a/examples/xml/boxes/settings.xml +++ b/examples/xml/boxes/settings.xml @@ -10,7 +10,7 @@ - + diff --git a/openmc/clean_xml.py b/openmc/clean_xml.py index aefd30ac7..564281a5c 100644 --- a/openmc/clean_xml.py +++ b/openmc/clean_xml.py @@ -1,7 +1,7 @@ def sort_xml_elements(tree): # Retrieve all children of the root XML node in the tree - elements = tree.getchildren() + elements = list(tree) # Initialize empty lists for the sorted and comment elements sorted_elements = [] diff --git a/openmc/cross.py b/openmc/cross.py index 435557ede..c03ee5188 100644 --- a/openmc/cross.py +++ b/openmc/cross.py @@ -52,7 +52,7 @@ class CrossScore(object): self.binary_op = binary_op def __hash__(self): - return hash(str(self)) + return hash(repr(self)) def __eq__(self, other): return str(other) == str(self) @@ -152,7 +152,7 @@ class CrossNuclide(object): self.binary_op = binary_op def __hash__(self): - return hash(str(self)) + return hash(repr(self)) def __eq__(self, other): return str(other) == str(self) @@ -309,7 +309,7 @@ class CrossFilter(object): clone._right_filter = self.right_filter clone._binary_op = self.binary_op clone._type = self.type - clone._bins = self.bins + clone._bins = self._bins clone._num_bins = self.num_bins clone._stride = self.stride @@ -356,7 +356,7 @@ class CrossFilter(object): def type(self, filter_type): if filter_type not in _FILTER_TYPES.values(): msg = 'Unable to set Filter type to "{0}" since it is not one ' \ - 'of the supported types'.format(type) + 'of the supported types'.format(filter_type) raise ValueError(msg) self._type = filter_type diff --git a/openmc/element.py b/openmc/element.py index d395b434f..9f04abfda 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -58,7 +58,7 @@ class Element(object): return not self == other def __hash__(self): - return hash((self._name, self._xs)) + return hash(repr(self)) def __repr__(self): string = 'Element - {0}\n'.format(self._name) diff --git a/openmc/filter.py b/openmc/filter.py index 2c4915f51..04935b8ed 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -81,7 +81,7 @@ class Filter(object): return not self == other def __hash__(self): - return hash((self.type, tuple(self.bins))) + return hash(repr(self)) def __deepcopy__(self, memo): existing = memo.get(id(self)) diff --git a/openmc/material.py b/openmc/material.py index 4ccc9485e..4da4e0ae3 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -12,17 +12,13 @@ from openmc.checkvalue import check_type, check_value, check_greater_than from openmc.clean_xml import * -# A list of all IDs for all Materials created -MATERIAL_IDS = [] - # A static variable for auto-generated Material IDs AUTO_MATERIAL_ID = 10000 def reset_auto_material_id(): - global AUTO_MATERIAL_ID, MATERIAL_IDS + global AUTO_MATERIAL_ID AUTO_MATERIAL_ID = 10000 - MATERIAL_IDS = [] # Units for density supported by OpenMC @@ -89,6 +85,33 @@ class Material(object): # If specified, this file will be used instead of composition values self._distrib_otf_file = None + def __eq__(self, other): + if not isinstance(other, Material): + return False + elif self.id != other.id: + return False + elif self.name != other.name: + return False + # FIXME: We cannot compare densities since OpenMC outputs densities + # in atom/b-cm in summary.h5 irregardless of input units, and we + # cannot compute the sum percent in Python since we lack AWR + #elif self.density != other.density: + # return False + #elif self._nuclides != other._nuclides: + # return False + #elif self._elements != other._elements: + # return False + elif self._sab != other._sab: + return False + else: + return True + + def __ne__(self, other): + return not self == other + + def __hash__(self): + return hash(repr(self)) + def __repr__(self): string = 'Material\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) @@ -176,26 +199,15 @@ class Material(object): @id.setter def id(self, material_id): - global AUTO_MATERIAL_ID, MATERIAL_IDS - - # If the Material already has an ID, remove it from global list - if hasattr(self, '_id') and self._id is not None: - MATERIAL_IDS.remove(self._id) if material_id is None: + global AUTO_MATERIAL_ID self._id = AUTO_MATERIAL_ID - MATERIAL_IDS.append(AUTO_MATERIAL_ID) AUTO_MATERIAL_ID += 1 else: check_type('material ID', material_id, Integral) - if material_id in MATERIAL_IDS: - msg = 'Unable to set Material ID to "{0}" since a Material with ' \ - 'this ID was already initialized'.format(material_id) - raise ValueError(msg) check_greater_than('material ID', material_id, 0, equality=True) - self._id = material_id - MATERIAL_IDS.append(material_id) @name.setter def name(self, name): diff --git a/openmc/mesh.py b/openmc/mesh.py index 3b66076b7..8bad6c537 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -189,6 +189,9 @@ class Mesh(object): cv.check_length('mesh width', width, 2, 3) self._width = width + def __hash__(self): + return hash(repr(self)) + def __repr__(self): string = 'Mesh\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py index 87c6665b2..ef3e90fc4 100644 --- a/openmc/mgxs/library.py +++ b/openmc/mgxs/library.py @@ -67,6 +67,9 @@ class Library(object): sp_filename : str The filename of the statepoint with tally data used to the compute cross sections + keff : Real or None + The combined keff from the statepoint file with tally data used to + compute cross sections (for eigenvalue calculations only) name : str, optional Name of the multi-group cross section library. Used as a label to identify tallies in OpenMC 'tallies.xml' file. @@ -88,6 +91,7 @@ class Library(object): self._tally_trigger = None self._all_mgxs = OrderedDict() self._sp_filename = None + self._keff = None self.name = name self.openmc_geometry = openmc_geometry @@ -114,6 +118,7 @@ class Library(object): clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo) clone._all_mgxs = self.all_mgxs clone._sp_filename = self._sp_filename + clone._keff = self._keff clone._all_mgxs = OrderedDict() for domain in self.domains: @@ -199,10 +204,15 @@ class Library(object): def sp_filename(self): return self._sp_filename + @property + def keff(self): + return self._keff + @openmc_geometry.setter def openmc_geometry(self, openmc_geometry): cv.check_type('openmc_geometry', openmc_geometry, openmc.Geometry) self._openmc_geometry = openmc_geometry + self._opencg_geometry = None @name.setter def name(self, name): @@ -361,6 +371,10 @@ class Library(object): raise ValueError(msg) self._sp_filename = statepoint._f.filename + self._openmc_geometry = statepoint.summary.openmc_geometry + + if statepoint.run_mode == 'k-eigenvalue': + self._keff = statepoint.k_combined[0] # Load tallies for each MGXS for each domain and mgxs type for domain in self.domains: @@ -380,9 +394,7 @@ class Library(object): ---------- domain : Material or Cell or Universe or Integral The material, cell, or universe object of interest (or its ID) - mgxs_type : {'total', 'transport', 'absorption', 'capture', 'fission', - 'nu-fission', 'scatter', 'nu-scatter', 'scatter matrix', - 'nu-scatter matrix', 'chi'} + mgxs_type : {'total', 'transport', 'absorption', 'capture', 'fission', 'nu-fission', 'scatter', 'nu-scatter', 'scatter matrix', 'nu-scatter matrix', 'chi'} The type of multi-group cross section object to return Returns diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 96fb6e07e..635c822e3 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -1225,28 +1225,29 @@ class MGXS(object): df = df.drop('score', axis=1) # Override energy groups bounds with indices - groups = np.arange(self.num_groups, 0, -1, dtype=np.int) - groups = np.repeat(groups, self.num_nuclides) + all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int) + all_groups = np.repeat(all_groups, self.num_nuclides) if 'energy [MeV]' in df and 'energyout [MeV]' in df: df.rename(columns={'energy [MeV]': 'group in'}, inplace=True) - in_groups = np.tile(groups, self.num_subdomains) + in_groups = np.tile(all_groups, self.num_subdomains) in_groups = np.repeat(in_groups, self.num_groups) df['group in'] = in_groups df.rename(columns={'energyout [MeV]': 'group out'}, inplace=True) - out_groups = np.tile(groups, self.num_subdomains * self.num_groups) + out_groups = \ + np.tile(all_groups, self.num_subdomains * self.num_groups) df['group out'] = out_groups columns = ['group in', 'group out'] elif 'energyout [MeV]' in df: df.rename(columns={'energyout [MeV]': 'group out'}, inplace=True) - in_groups = np.tile(groups, self.num_subdomains) + in_groups = np.tile(all_groups, self.num_subdomains) df['group out'] = in_groups columns = ['group out'] elif 'energy [MeV]' in df: df.rename(columns={'energy [MeV]': 'group in'}, inplace=True) - in_groups = np.tile(groups, self.num_subdomains) + in_groups = np.tile(all_groups, self.num_subdomains) df['group in'] = in_groups columns = ['group in'] diff --git a/openmc/nuclide.py b/openmc/nuclide.py index 2dd8eb153..01fb2aa45 100644 --- a/openmc/nuclide.py +++ b/openmc/nuclide.py @@ -61,7 +61,7 @@ class Nuclide(object): return not self == other def __hash__(self): - return hash((self._name, self._xs)) + return hash(repr(self)) def __repr__(self): string = 'Nuclide - {0}\n'.format(self._name) diff --git a/openmc/opencg_compatible.py b/openmc/opencg_compatible.py index 93c0e5fae..afa57c78d 100644 --- a/openmc/opencg_compatible.py +++ b/openmc/opencg_compatible.py @@ -726,7 +726,7 @@ def get_openmc_cell(opencg_cell): openmc_cell.fill = get_openmc_material(fill) if opencg_cell.rotation: - rotation = np.asarray(opencg_cell.rotation, dtype=np.int) + rotation = np.asarray(opencg_cell.rotation, dtype=np.float64) openmc_cell.rotation = rotation if opencg_cell.translation: @@ -882,7 +882,7 @@ def get_opencg_lattice(openmc_lattice): outer = openmc_lattice.outer if len(pitch) == 2: - new_pitch = np.ones(3, dtype=np.float64) + new_pitch = np.ones(3, dtype=np.float64) * np.inf new_pitch[:2] = pitch pitch = new_pitch diff --git a/openmc/region.py b/openmc/region.py index 97069d797..7589184aa 100644 --- a/openmc/region.py +++ b/openmc/region.py @@ -1,6 +1,8 @@ from abc import ABCMeta, abstractmethod from collections import Iterable +import numpy as np + from openmc.checkvalue import check_type @@ -29,6 +31,17 @@ class Region(object): def __str__(self): return '' + def __eq__(self, other): + if not isinstance(other, type(self)): + return False + elif str(self) != str(other): + return False + else: + return True + + def __ne__(self, other): + return not self == other + @staticmethod def from_expression(expression, surfaces): """Generate a region given an infix expression. @@ -207,6 +220,8 @@ class Intersection(Region): ---------- nodes : tuple of Region Regions to take the intersection of + bounding_box : tuple of numpy.array + Lower-left and upper-right coordinates of an axis-aligned bounding box """ @@ -220,6 +235,16 @@ class Intersection(Region): def nodes(self): return self._nodes + @property + def bounding_box(self): + lower_left = np.array([-np.inf, -np.inf, -np.inf]) + upper_right = np.array([np.inf, np.inf, np.inf]) + for n in self.nodes: + lower_left_n, upper_right_n = n.bounding_box + lower_left[:] = np.maximum(lower_left, lower_left_n) + upper_right[:] = np.minimum(upper_right, upper_right_n) + return lower_left, upper_right + @nodes.setter def nodes(self, nodes): check_type('nodes', nodes, Iterable, Region) @@ -246,6 +271,8 @@ class Union(Region): ---------- nodes : tuple of Region Regions to take the union of + bounding_box : tuple of numpy.array + Lower-left and upper-right coordinates of an axis-aligned bounding box """ @@ -259,6 +286,16 @@ class Union(Region): def nodes(self): return self._nodes + @property + def bounding_box(self): + lower_left = np.array([np.inf, np.inf, np.inf]) + upper_right = np.array([-np.inf, -np.inf, -np.inf]) + for n in self.nodes: + lower_left_n, upper_right_n = n.bounding_box + lower_left[:] = np.minimum(lower_left, lower_left_n) + upper_right[:] = np.maximum(upper_right, upper_right_n) + return lower_left, upper_right + @nodes.setter def nodes(self, nodes): check_type('nodes', nodes, Iterable, Region) @@ -289,6 +326,8 @@ class Complement(Region): ---------- node : Region Regions to take the complement of + bounding_box : tuple of numpy.array + Lower-left and upper-right coordinates of an axis-aligned bounding box """ @@ -306,3 +345,18 @@ class Complement(Region): def node(self, node): check_type('node', node, Region) self._node = node + + @property + def bounding_box(self): + # Use De Morgan's laws to distribute the complement operator so that it + # only applies to surface half-spaces, thus allowing us to calculate the + # bounding box in the usual recursive manner. + if isinstance(self.node, Union): + temp_region = Intersection(*[~n for n in self.node.nodes]) + elif isinstance(self.node, Intersection): + temp_region = Union(*[~n for n in self.node.nodes]) + elif isinstance(self.node, Complement): + temp_region = self.node.node + else: + temp_region = ~self.node + return temp_region.bounding_box diff --git a/openmc/summary.py b/openmc/summary.py index f7077efab..aff749200 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -570,7 +570,7 @@ class Summary(object): """ for index, material in self.materials.items(): - if material._id == material_id: + if material.id == material_id: return material return None @@ -591,7 +591,7 @@ class Summary(object): """ for index, surface in self.surfaces.items(): - if surface._id == surface_id: + if surface.id == surface_id: return surface return None @@ -612,7 +612,7 @@ class Summary(object): """ for index, cell in self.cells.items(): - if cell._id == cell_id: + if cell.id == cell_id: return cell return None @@ -633,7 +633,7 @@ class Summary(object): """ for index, universe in self.universes.items(): - if universe._id == universe_id: + if universe.id == universe_id: return universe return None @@ -654,7 +654,7 @@ class Summary(object): """ for index, lattice in self.lattices.items(): - if lattice._id == lattice_id: + if lattice.id == lattice_id: return lattice return None diff --git a/openmc/surface.py b/openmc/surface.py index 279246b02..8dc45209b 100644 --- a/openmc/surface.py +++ b/openmc/surface.py @@ -3,6 +3,8 @@ from numbers import Real, Integral from xml.etree import ElementTree as ET import sys +import numpy as np + from openmc.checkvalue import check_type, check_value, check_greater_than from openmc.region import Region @@ -136,6 +138,33 @@ class Surface(object): check_value('boundary type', boundary_type, _BC_TYPES) self._boundary_type = boundary_type + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. If the half-space is + unbounded in a particular direction, numpy.inf is used to represent + infinity. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([np.inf, np.inf, np.inf])) + def create_xml_subelement(self): element = ET.Element("surface") element.set("id", str(self._id)) @@ -194,6 +223,10 @@ class Plane(Surface): self._type = 'plane' self._coeff_keys = ['A', 'B', 'C', 'D'] + self._coeffs['A'] = 1. + self._coeffs['B'] = 0. + self._coeffs['C'] = 0. + self._coeffs['D'] = 0. if A is not None: self.a = A @@ -276,6 +309,7 @@ class XPlane(Plane): self._type = 'x-plane' self._coeff_keys = ['x0'] + self._coeffs['x0'] = 0. if x0 is not None: self.x0 = x0 @@ -289,6 +323,37 @@ class XPlane(Plane): check_type('x0 coefficient', x0, Real) self._coeffs['x0'] = x0 + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. For the x-plane surface, the + half-spaces are unbounded in their y- and z- directions. To represent + infinity, numpy.inf is used. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + if side == '-': + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([self.x0, np.inf, np.inf])) + elif side == '+': + return (np.array([self.x0, -np.inf, -np.inf]), + np.array([np.inf, np.inf, np.inf])) + class YPlane(Plane): """A plane perpendicular to the y axis, i.e. a surface of the form :math:`y - @@ -322,6 +387,7 @@ class YPlane(Plane): self._type = 'y-plane' self._coeff_keys = ['y0'] + self._coeffs['y0'] = 0. if y0 is not None: self.y0 = y0 @@ -335,6 +401,37 @@ class YPlane(Plane): check_type('y0 coefficient', y0, Real) self._coeffs['y0'] = y0 + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. For the y-plane surface, the + half-spaces are unbounded in their x- and z- directions. To represent + infinity, numpy.inf is used. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + if side == '-': + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([np.inf, self.y0, np.inf])) + elif side == '+': + return (np.array([-np.inf, self.y0, -np.inf]), + np.array([np.inf, np.inf, np.inf])) + class ZPlane(Plane): """A plane perpendicular to the z axis, i.e. a surface of the form :math:`z - @@ -368,6 +465,7 @@ class ZPlane(Plane): self._type = 'z-plane' self._coeff_keys = ['z0'] + self._coeffs['z0'] = 0. if z0 is not None: self.z0 = z0 @@ -381,6 +479,37 @@ class ZPlane(Plane): check_type('z0 coefficient', z0, Real) self._coeffs['z0'] = z0 + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. For the z-plane surface, the + half-spaces are unbounded in their x- and y- directions. To represent + infinity, numpy.inf is used. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + if side == '-': + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([np.inf, np.inf, self.z0])) + elif side == '+': + return (np.array([-np.inf, -np.inf, self.z0]), + np.array([np.inf, np.inf, np.inf])) + class Cylinder(Surface): """A cylinder whose length is parallel to the x-, y-, or z-axis. @@ -415,6 +544,7 @@ class Cylinder(Surface): super(Cylinder, self).__init__(surface_id, boundary_type, name=name) self._coeff_keys = ['R'] + self._coeffs['R'] = 1. if R is not None: self.r = R @@ -468,6 +598,8 @@ class XCylinder(Cylinder): self._type = 'x-cylinder' self._coeff_keys = ['y0', 'z0', 'R'] + self._coeffs['y0'] = 0. + self._coeffs['z0'] = 0. if y0 is not None: self.y0 = y0 @@ -493,6 +625,38 @@ class XCylinder(Cylinder): check_type('z0 coefficient', z0, Real) self._coeffs['z0'] = z0 + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. For the x-cylinder surface, + the negative half-space is unbounded in the x- direction and the + positive half-space is unbounded in all directions. To represent + infinity, numpy.inf is used. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + if side == '-': + return (np.array([-np.inf, self.y0 - self.r, self.z0 - self.r]), + np.array([np.inf, self.y0 + self.r, self.z0 + self.r])) + elif side == '+': + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([np.inf, np.inf, np.inf])) + class YCylinder(Cylinder): """An infinite cylinder whose length is parallel to the y-axis. This is a @@ -533,6 +697,8 @@ class YCylinder(Cylinder): self._type = 'y-cylinder' self._coeff_keys = ['x0', 'z0', 'R'] + self._coeffs['x0'] = 0. + self._coeffs['z0'] = 0. if x0 is not None: self.x0 = x0 @@ -558,6 +724,38 @@ class YCylinder(Cylinder): check_type('z0 coefficient', z0, Real) self._coeffs['z0'] = z0 + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. For the y-cylinder surface, + the negative half-space is unbounded in the y- direction and the + positive half-space is unbounded in all directions. To represent + infinity, numpy.inf is used. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + if side == '-': + return (np.array([self.x0 - self.r, -np.inf, self.z0 - self.r]), + np.array([self.x0 + self.r, np.inf, self.z0 + self.r])) + elif side == '+': + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([np.inf, np.inf, np.inf])) + class ZCylinder(Cylinder): """An infinite cylinder whose length is parallel to the z-axis. This is a @@ -598,6 +796,8 @@ class ZCylinder(Cylinder): self._type = 'z-cylinder' self._coeff_keys = ['x0', 'y0', 'R'] + self._coeffs['x0'] = 0. + self._coeffs['y0'] = 0. if x0 is not None: self.x0 = x0 @@ -623,6 +823,38 @@ class ZCylinder(Cylinder): check_type('y0 coefficient', y0, Real) self._coeffs['y0'] = y0 + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. For the z-cylinder surface, + the negative half-space is unbounded in the z- direction and the + positive half-space is unbounded in all directions. To represent + infinity, numpy.inf is used. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + if side == '-': + return (np.array([self.x0 - self.r, self.y0 - self.r, -np.inf]), + np.array([self.x0 + self.r, self.y0 + self.r, np.inf])) + elif side == '+': + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([np.inf, np.inf, np.inf])) + class Sphere(Surface): """A sphere of the form :math:`(x - x_0)^2 + (y - y_0)^2 + (z - z_0)^2 = R^2`. @@ -667,6 +899,10 @@ class Sphere(Surface): self._type = 'sphere' self._coeff_keys = ['x0', 'y0', 'z0', 'R'] + self._coeffs['x0'] = 0. + self._coeffs['y0'] = 0. + self._coeffs['z0'] = 0. + self._coeffs['R'] = 1. if x0 is not None: self.x0 = x0 @@ -716,6 +952,39 @@ class Sphere(Surface): check_type('R coefficient', R, Real) self._coeffs['R'] = R + def bounding_box(self, side): + """Determine an axis-aligned bounding box. + + An axis-aligned bounding box for surface half-spaces is represented by + its lower-left and upper-right coordinates. The positive half-space of a + sphere is unbounded in all directions. To represent infinity, numpy.inf + is used. + + Parameters + ---------- + side : {'+', '-'} + Indicates the negative or positive half-space + + Returns + ------- + numpy.array + Lower-left coordinates of the axis-aligned bounding box for the + desired half-space + numpy.array + Upper-right coordinates of the axis-aligned bounding box for the + desired half-space + + """ + + if side == '-': + return (np.array([self.x0 - self.r, self.y0 - self.r, + self.z0 - self.r]), + np.array([self.x0 + self.r, self.y0 + self.r, + self.z0 + self.r])) + elif side == '+': + return (np.array([-np.inf, -np.inf, -np.inf]), + np.array([np.inf, np.inf, np.inf])) + class Cone(Surface): """A conical surface parallel to the x-, y-, or z-axis. @@ -761,6 +1030,10 @@ class Cone(Surface): super(Cone, self).__init__(surface_id, boundary_type, name=name) self._coeff_keys = ['x0', 'y0', 'z0', 'R2'] + self._coeffs['x0'] = 0. + self._coeffs['y0'] = 0. + self._coeffs['z0'] = 0. + self._coeffs['R2'] = 1. if x0 is not None: self.x0 = x0 @@ -982,6 +1255,8 @@ class Quadric(Surface): self._type = 'quadric' self._coeff_keys = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'j', 'k'] + for key in self._coeff_keys: + self._coeffs[key] = 0. if a is not None: self.a = a @@ -1127,6 +1402,8 @@ class Halfspace(Region): Surface which divides Euclidean space. side : {'+', '-'} Indicates whether the positive or negative half-space is used. + bounding_box : tuple of numpy.array + Lower-left and upper-right coordinates of an axis-aligned bounding box """ @@ -1155,6 +1432,10 @@ class Halfspace(Region): check_value('side', side, ('+', '-')) self._side = side + @property + def bounding_box(self): + return self.surface.bounding_box(self.side) + def __str__(self): return '-' + str(self.surface.id) if self.side == '-' \ else str(self.surface.id) diff --git a/openmc/tallies.py b/openmc/tallies.py index 0661ab67b..13a219ded 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -190,21 +190,7 @@ class Tally(object): return not self == other def __hash__(self): - hashable = [] - - for filter in self.filters: - hashable.append((filter.type, tuple(filter.bins))) - - for nuclide in self.nuclides: - hashable.append(nuclide.name) - - for score in self.scores: - hashable.append(score) - - hashable.append(self.estimator) - hashable.append(self.name) - - return hash(tuple(hashable)) + return hash(repr(self)) def __repr__(self): string = 'Tally\n' @@ -1482,19 +1468,31 @@ class Tally(object): new_name = '({0} {1} {2})'.format(self.name, binary_op, other.name) new_tally.name = new_name + # Create copies of self and other tallies to rearrange for tally + # arithmetic + self_copy = copy.deepcopy(self) + other_copy = copy.deepcopy(other) + # Find any shared filters between the two tallies - self_filters = set(self.filters) - other_filters = set(other.filters) - filter_intersect = self_filters.intersection(other_filters) + filter_intersect = [] + for filter in self_copy.filters: + if filter in other_copy.filters: + filter_intersect.append(filter) - # Align the shared filters to follow in each tally operand + # Align the shared filters in successive order for i, filter in enumerate(filter_intersect): - self_index = self.filters.index(filter) - other_filter = other.filters[self_index] - if other_filter != filter: - other = other.swap_filters(filter, other_filter) + self_index = self_copy.filters.index(filter) + other_index = other_copy.filters.index(filter) - data = self._align_tally_data(other) + # If necessary, swap self filter + if self_index != i: + self_copy.swap_filters(filter, self_copy.filters[i], inplace=True) + + # If necessary, swap other filter + if other_index != i: + other_copy.swap_filters(filter, other_copy.filters[i], inplace=True) + + data = self_copy._align_tally_data(other_copy) if binary_op == '+': new_tally._mean = data['self']['mean'] + data['other']['mean'] @@ -1525,16 +1523,16 @@ class Tally(object): new_tally._std_dev = np.abs(new_tally.mean) * \ np.sqrt(first_term**2 + second_term**2) - if self.estimator == other.estimator: - new_tally.estimator = self.estimator - if self.with_summary and other.with_summary: - new_tally.with_summary = self.with_summary - if self.num_realizations == other.num_realizations: - new_tally.num_realizations = self.num_realizations + if self_copy.estimator == other_copy.estimator: + new_tally.estimator = self_copy.estimator + if self_copy.with_summary and other_copy.with_summary: + new_tally.with_summary = self_copy.with_summary + if self_copy.num_realizations == other_copy.num_realizations: + new_tally.num_realizations = self_copy.num_realizations # If filters are identical, simply reuse them in derived tally - if self.filters == other.filters: - for self_filter in self.filters: + if self_copy.filters == other_copy.filters: + for self_filter in self_copy.filters: new_tally.add_filter(self_filter) # Generate filter "outer products" for non-identical filters @@ -1542,24 +1540,24 @@ class Tally(object): # Find the common longest sequence of shared filters match = 0 - for self_filter, other_filter in zip(self.filters, other.filters): + for self_filter, other_filter in zip(self_copy.filters, other_copy.filters): if self_filter == other_filter: match += 1 else: break - match_filters = self.filters[:match] - cross_filters = [self.filters[match:], other.filters[match:]] + match_filters = self_copy.filters[:match] + cross_filters = [self_copy.filters[match:], other_copy.filters[match:]] # Simply reuse shared filters in derived tally for filter in match_filters: new_tally.add_filter(filter) # Use cross filters to combine non-shared filters in derived tally - if len(self.filters) != match and len(other.filters) == match: + if len(self_copy.filters) != match and len(other_copy.filters) == match: for filter in cross_filters[0]: new_tally.add_filter(filter) - elif len(other.filters) == match and len(other.filters) != match: + elif len(self_copy.filters) == match and len(other_copy.filters) != match: for filter in cross_filters[1]: new_tally.add_filter(filter) else: @@ -1568,23 +1566,23 @@ class Tally(object): new_tally.add_filter(new_filter) # Generate score "outer products" - if self.scores == other.scores: - new_tally.num_score_bins = self.num_score_bins - for self_score in self.scores: + if self_copy.scores == other_copy.scores: + new_tally.num_score_bins = self_copy.num_score_bins + for self_score in self_copy.scores: new_tally.add_score(self_score) else: - new_tally.num_score_bins = self.num_score_bins * other.num_score_bins - all_scores = [self.scores, other.scores] + new_tally.num_score_bins = self_copy.num_score_bins * other_copy.num_score_bins + all_scores = [self_copy.scores, other_copy.scores] for self_score, other_score in itertools.product(*all_scores): new_score = CrossScore(self_score, other_score, binary_op) new_tally.add_score(new_score) # Generate nuclide "outer products" - if self.nuclides == other.nuclides: - for self_nuclide in self.nuclides: + if self_copy.nuclides == other_copy.nuclides: + for self_nuclide in self_copy.nuclides: new_tally.nuclides.append(self_nuclide) else: - all_nuclides = [self.nuclides, other.nuclides] + all_nuclides = [self_copy.nuclides, other_copy.nuclides] for self_nuclide, other_nuclide in itertools.product(*all_nuclides): new_nuclide = CrossNuclide(self_nuclide, other_nuclide, binary_op) new_tally.add_nuclide(new_nuclide) @@ -1644,8 +1642,8 @@ class Tally(object): self_repeat_factor *= filter.num_bins # Tile / repeat the tally data for the tally outer product - self_shape = list(self.mean.shape) - other_shape = list(other.mean.shape) + self_shape = list(self_mean.shape) + other_shape = list(other_mean.shape) self_shape[0] *= self_repeat_factor self_mean = np.repeat(self_mean, self_repeat_factor) self_std_dev = np.repeat(self_std_dev, self_repeat_factor) @@ -1653,7 +1651,8 @@ class Tally(object): if self_repeat_factor == 1: other_shape[0] *= other_tile_factor other_mean = np.repeat(other_mean, other_tile_factor, axis=0) - other_std_dev = np.repeat(other_std_dev, other_tile_factor, axis=0) + other_std_dev = np.repeat(other_std_dev, other_tile_factor, + axis=0) else: other_mean = np.tile(other_mean, (other_tile_factor, 1, 1)) other_std_dev = np.tile(other_std_dev, (other_tile_factor, 1, 1)) @@ -1672,7 +1671,11 @@ class Tally(object): self_repeat_factor = other.num_nuclides other_tile_factor = self.num_nuclides - # Replicate the data + # Tile / repeat the tally data for the tally outer product + self_shape = list(self_mean.shape) + other_shape = list(other_mean.shape) + self_shape[1] *= self_repeat_factor + other_shape[1] *= other_tile_factor self_mean = np.repeat(self_mean, self_repeat_factor, axis=1) other_mean = np.tile(other_mean, (1, other_tile_factor, 1)) self_std_dev = np.repeat(self_std_dev, self_repeat_factor, axis=1) @@ -1680,10 +1683,10 @@ class Tally(object): # NumPy repeat and tile routines return 1D flattened arrays # Reshape arrays as 3D with filters, nuclides and scores axes - self_shape = list(self.mean.shape) - self_shape[1] *= self_repeat_factor self_mean.shape = tuple(self_shape) self_std_dev.shape = tuple(self_shape) + other_mean.shape = tuple(other_shape) + other_std_dev.shape = tuple(other_shape) if self.scores != other.scores: @@ -1692,7 +1695,11 @@ class Tally(object): self_repeat_factor = other.num_score_bins other_tile_factor = self.num_score_bins - # Replicate the data + # Tile / repeat the tally data for the tally outer product + self_shape = list(self_mean.shape) + other_shape = list(other_mean.shape) + self_shape[2] *= self_repeat_factor + other_shape[2] *= other_tile_factor self_mean = np.repeat(self_mean, self_repeat_factor, axis=2) other_mean = np.tile(other_mean, (1, 1, other_tile_factor)) self_std_dev = np.repeat(self_std_dev, self_repeat_factor, axis=2) @@ -1700,10 +1707,10 @@ class Tally(object): # NumPy repeat and tile routines return 1D flattened arrays # Reshape arrays as 3D with filters, nuclides and scores axes - self_shape = list(self.mean.shape) - self_shape[2] *= self_repeat_factor self_mean.shape = tuple(self_shape) self_std_dev.shape = tuple(self_shape) + other_mean.shape = tuple(other_shape) + other_std_dev.shape = tuple(other_shape) data = {} data['self'] = {} @@ -1714,7 +1721,7 @@ class Tally(object): data['other']['std. dev.'] = other_std_dev return data - def swap_filters(self, filter1, filter2): + def swap_filters(self, filter1, filter2, inplace=False): """Reverse the ordering of two filters in this tally This is a helper method for tally arithmetic which helps align the data @@ -1729,10 +1736,15 @@ class Tally(object): filter2 : Filter The filter to swap with filter1 + inplace : bool, optional + Whether to perform operation inplace or return new tally with the + filters swapped. + Returns ------- swap_tally - A copy of this tally with the filters swapped + If inplace is false, a copy of this tally with the filters swapped. + Otherwise, nothing is returned. Raises ------ @@ -1763,7 +1775,15 @@ class Tally(object): 'does not contain such a filter'.format(filter2.type, self.id) raise ValueError(msg) - swap_tally = copy.deepcopy(self) + # Create a copy of the tally that preserves the original data formatting + # throughout swapping process + tally_copy = copy.deepcopy(self) + + # Set the swap tally + if inplace: + swap_tally = self + else: + swap_tally = copy.deepcopy(self) # Swap the filters in the copied version of this Tally filter1_index = swap_tally.filters.index(filter1) @@ -1790,42 +1810,43 @@ class Tally(object): filter2_bins = [filter2.get_bin(i) for i in range(filter2.num_bins)] # Adjust the sum data array to relect the new filter order - if self.sum is not None: + if swap_tally.sum is not None: for bin1, bin2 in itertools.product(filter1_bins, filter2_bins): filter_bins = [(bin1,), (bin2,)] - data = self.get_values(filters=filters, - filter_bins=filter_bins, value='sum') + data = tally_copy.get_values( + filters=filters, filter_bins=filter_bins, value='sum') indices = swap_tally.get_filter_indices(filters, filter_bins) swap_tally.sum[indices, :, :] = data # Adjust the sum_sq data array to relect the new filter order - if self.sum_sq is not None: + if swap_tally.sum_sq is not None: for bin1, bin2 in itertools.product(filter1_bins, filter2_bins): filter_bins = [(bin1,), (bin2,)] - data = self.get_values(filters=filters, - filter_bins=filter_bins, value='sum_sq') + data = tally_copy.get_values( + filters=filters, filter_bins=filter_bins, value='sum_sq') indices = swap_tally.get_filter_indices(filters, filter_bins) swap_tally.sum_sq[indices, :, :] = data # Adjust the mean data array to relect the new filter order - if self.mean is not None: + if swap_tally.mean is not None: for bin1, bin2 in itertools.product(filter1_bins, filter2_bins): filter_bins = [(bin1,), (bin2,)] - data = self.get_values(filters=filters, - filter_bins=filter_bins, value='mean') + data = tally_copy.get_values( + filters=filters, filter_bins=filter_bins, value='mean') indices = swap_tally.get_filter_indices(filters, filter_bins) swap_tally._mean[indices, :, :] = data # Adjust the std_dev data array to relect the new filter order - if self.std_dev is not None: + if swap_tally.std_dev is not None: for bin1, bin2 in itertools.product(filter1_bins, filter2_bins): filter_bins = [(bin1,), (bin2,)] - data = self.get_values(filters=filters, - filter_bins=filter_bins, value='std_dev') + data = tally_copy.get_values( + filters=filters, filter_bins=filter_bins, value='std_dev') indices = swap_tally.get_filter_indices(filters, filter_bins) swap_tally._std_dev[indices, :, :] = data - return swap_tally + if not inplace: + return swap_tally def __add__(self, other): """Adds this tally to another tally or scalar value. @@ -2415,7 +2436,7 @@ class Tally(object): return new_tally def summation(self, scores=[], filter_type=None, - filter_bins=[], nuclides=[]): + filter_bins=[], nuclides=[], remove_filter=False): """Vectorized sum of tally data across scores, filter bins and/or nuclides using tally addition. @@ -2444,6 +2465,9 @@ class Tally(object): nuclides : list of str A list of nuclide name strings to sum across (e.g., ['U-235', 'U-238']; default is []) + remove_filter : bool + If a filter is being summed over, this bool indicates whether to + remove that filter in the returned tally. Default is False. Returns ------- @@ -2467,7 +2491,14 @@ class Tally(object): # Sum across any filter bins specified by the user if filter_type in _FILTER_TYPES: - filter_bins = [[(filter_bin,)] for filter_bin in filter_bins] + + # If user did not specify filter bins, sum across all bins + if len(filter_bins) == 0: + filter = self.find_filter(filter_type) + filter_bins = [[(filter.get_bin(i),)] for i in range(filter.num_bins)] + else: + filter_bins = [[(filter_bin,)] for filter_bin in filter_bins] + filters = [[filter_type]] # If user did not specify a filter type, do not sum across filter bins else: @@ -2492,12 +2523,17 @@ class Tally(object): # Accumulate this Tally slice into the Tally sum tally_sum += tally_slice - # Add back the filter(s) which were summed across to derived tally - for filter_type in summed_filters: - filters = summed_filters[filter_type] - for i in range(1, len(filters)): - filters[i] = CrossFilter(filters[i-1], filters[i], '+') - tally_sum.add_filter(filters[-1]) + # Add back the filter(s) which were summed across to derived tally, + # if filter bins were input; otherwise, leave out summed filter(s) + if remove_filter and filter_type is not None: + # Rename tally sum indicating a summation over a particular filter + tally_sum.name = 'sum({0}, {1})'.format(self.name, filter_type) + else: + for summed_filter_type in summed_filters: + filters = summed_filters[summed_filter_type] + for i in range(1, len(filters)): + filters[i] = CrossFilter(filters[i-1], filters[i], '+') + tally_sum.add_filter(filters[-1]) return tally_sum diff --git a/openmc/universe.py b/openmc/universe.py index b74dde656..98367c381 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -15,6 +15,10 @@ from openmc.region import Region, Intersection, Complement if sys.version_info[0] >= 3: basestring = str + +# DeprecationWarning filter for the Cell.add_surface(...) method +warnings.simplefilter('always', DeprecationWarning) + # A static variable for auto-generated Cell IDs AUTO_CELL_ID = 10000 @@ -73,6 +77,30 @@ class Cell(object): self._translation = None self._offsets = None + def __eq__(self, other): + if not isinstance(other, Cell): + return False + elif self.id != other.id: + return False + elif self.name != other.name: + return False + elif self.fill != other.fill: + return False + elif self.region != other.region: + return False + elif self.rotation != other.rotation: + return False + elif self.translation != other.translation: + return False + else: + return True + + def __ne__(self, other): + return not self == other + + def __hash__(self): + return hash(repr(self)) + def __repr__(self): string = 'Cell\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) @@ -216,7 +244,6 @@ class Cell(object): """ - warnings.simplefilter('always', DeprecationWarning) warnings.warn("Cell.add_surface(...) has been deprecated and may be " "removed in a future version. The region for a Cell " "should be defined using the region property directly.", @@ -443,6 +470,34 @@ class Universe(object): self._cell_offsets = OrderedDict() self._num_regions = 0 + def __eq__(self, other): + if not isinstance(other, Universe): + return False + elif self.id != other.id: + return False + elif self.name != other.name: + return False + elif self.cells != other.cells: + return False + else: + return True + + def __ne__(self, other): + return not self == other + + def __hash__(self): + return hash(repr(self)) + + def __repr__(self): + string = 'Universe\n' + string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) + string += '{0: <16}{1}{2}\n'.format('\tName', '=\t', self._name) + string += '{0: <16}{1}{2}\n'.format('\tCells', '=\t', + list(self._cells.keys())) + string += '{0: <16}{1}{2}\n'.format('\t# Regions', '=\t', + self._num_regions) + return string + @property def id(self): return self._id @@ -630,16 +685,6 @@ class Universe(object): return universes - def __repr__(self): - string = 'Universe\n' - string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) - string += '{0: <16}{1}{2}\n'.format('\tName', '=\t', self._name) - string += '{0: <16}{1}{2}\n'.format('\tCells', '=\t', - list(self._cells.keys())) - string += '{0: <16}{1}{2}\n'.format('\t# Regions', '=\t', - self._num_regions) - return string - def create_xml_subelement(self, xml_element): # Iterate over all Cells @@ -695,6 +740,25 @@ class Lattice(object): self._outer = None self._universes = None + def __eq__(self, other): + if not isinstance(other, Lattice): + return False + elif self.id != other.id: + return False + elif self.name != other.name: + return False + elif self.pitch != other.pitch: + return False + elif self.outer != other.outer: + return False + elif self.universes != other.universes: + return False + else: + return True + + def __ne__(self, other): + return not self == other + @property def id(self): return self._id @@ -894,6 +958,24 @@ class RectLattice(Lattice): self._lower_left = None self._offsets = None + def __eq__(self, other): + if not isinstance(other, RectLattice): + return False + elif not super(RectLattice, self).__eq__(other): + return False + elif self.dimension != other.dimension: + return False + elif self.lower_left != other.lower_left: + return False + else: + return True + + def __ne__(self, other): + return not self == other + + def __hash__(self): + return hash(repr(self)) + def __repr__(self): string = 'RectLattice\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) @@ -1111,6 +1193,26 @@ class HexLattice(Lattice): self._num_axial = None self._center = None + def __eq__(self, other): + if not isinstance(other, HexLattice): + return False + elif not super(HexLattice, self).__eq__(other): + return False + elif self.num_rings != other.num_rings: + return False + elif self.num_axial != other.num_axial: + return False + elif self.center != other.center: + return False + else: + return True + + def __ne__(self, other): + return not self == other + + def __hash__(self): + return hash(repr(self)) + def __repr__(self): string = 'HexLattice\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) diff --git a/setup.py b/setup.py index 0c3d5c116..907d80e31 100644 --- a/setup.py +++ b/setup.py @@ -10,7 +10,7 @@ except ImportError: have_setuptools = False kwargs = {'name': 'openmc', - 'version': '0.7.0', + 'version': '0.7.1', 'packages': ['openmc', 'openmc.mgxs'], 'scripts': glob.glob('scripts/openmc-*'), diff --git a/src/constants.F90 b/src/constants.F90 index b7eb1d0be..e08fdf8b6 100644 --- a/src/constants.F90 +++ b/src/constants.F90 @@ -8,7 +8,7 @@ module constants ! OpenMC major, minor, and release numbers integer, parameter :: VERSION_MAJOR = 0 integer, parameter :: VERSION_MINOR = 7 - integer, parameter :: VERSION_RELEASE = 0 + integer, parameter :: VERSION_RELEASE = 1 ! Revision numbers for binary files integer, parameter :: REVISION_STATEPOINT = 14 diff --git a/src/geometry.F90 b/src/geometry.F90 index ea43fea4b..1f7582004 100644 --- a/src/geometry.F90 +++ b/src/geometry.F90 @@ -120,6 +120,7 @@ contains stack(i_stack) = (actual_sense .eqv. (token > 0)) end if end select + end do if (i_stack == 1) then @@ -598,8 +599,9 @@ contains real(8) :: d_lat ! distance to lattice boundary real(8) :: d_surf ! distance to surface real(8) :: x0,y0,z0 ! coefficients for surface + real(8) :: xyz_cross(3) ! coordinates at projected surface crossing logical :: coincident ! is particle on surface? - type(Cell), pointer :: cl + type(Cell), pointer :: c class(Surface), pointer :: surf class(Lattice), pointer :: lat @@ -615,7 +617,7 @@ contains LEVEL_LOOP: do j = 1, p % n_coord ! get pointer to cell on this level - cl => cells(p % coord(j) % cell) + c => cells(p % coord(j) % cell) ! copy directional cosines u = p % coord(j) % uvw(1) @@ -625,8 +627,8 @@ contains ! ======================================================================= ! FIND MINIMUM DISTANCE TO SURFACE IN THIS CELL - SURFACE_LOOP: do i = 1, size(cl%region) - index_surf = cl%region(i) + SURFACE_LOOP: do i = 1, size(c % region) + index_surf = c % region(i) coincident = (index_surf == p % surface) ! ignore this token if it corresponds to an operator rather than a @@ -635,14 +637,14 @@ contains if (index_surf >= OP_UNION) cycle ! Calculate distance to surface - surf => surfaces(index_surf)%obj - d = surf%distance(p%coord(j)%xyz, p%coord(j)%uvw, coincident) + surf => surfaces(index_surf) % obj + d = surf % distance(p % coord(j) % xyz, p % coord(j) % uvw, coincident) ! Check if calculated distance is new minimum if (d < d_surf) then if (abs(d - d_surf)/d_surf >= FP_PRECISION) then d_surf = d - level_surf_cross = -cl % region(i) + level_surf_cross = -c % region(i) end if end if end do SURFACE_LOOP @@ -848,14 +850,31 @@ contains if (d_surf < d_lat) then if ((dist - d_surf)/dist >= FP_REL_PRECISION) then dist = d_surf - surface_crossed = level_surf_cross + + ! If the cell is not simple, it is possible that both the negative and + ! positive half-space were given in the region specification. Thus, we + ! have to explicitly check which half-space the particle would be + ! traveling into if the surface is crossed + if (.not. c % simple) then + xyz_cross(:) = p % coord(j) % xyz + d_surf*p % coord(j) % uvw + surf => surfaces(abs(level_surf_cross)) % obj + if (dot_product(p % coord(j) % uvw, & + surf % normal(xyz_cross)) > ZERO) then + surface_crossed = abs(level_surf_cross) + else + surface_crossed = -abs(level_surf_cross) + end if + else + surface_crossed = level_surf_cross + end if + lattice_translation(:) = [0, 0, 0] next_level = j end if else if ((dist - d_lat)/dist >= FP_REL_PRECISION) then dist = d_lat - surface_crossed = None + surface_crossed = NONE lattice_translation(:) = level_lat_trans next_level = j end if diff --git a/src/hdf5_interface.F90 b/src/hdf5_interface.F90 index 656039d19..fc7a462e6 100644 --- a/src/hdf5_interface.F90 +++ b/src/hdf5_interface.F90 @@ -1483,7 +1483,7 @@ contains integer(HID_T) :: dspace ! data or file space handle integer(HID_T) :: filetype integer(HID_T) :: memtype - integer(HSIZE_T) :: n + integer(SIZE_T) :: n type(c_ptr) :: f_ptr ! Set up collective vs. independent I/O @@ -1544,8 +1544,8 @@ contains integer(HID_T) :: dspace ! data or file space handle integer(HID_T) :: filetype integer(HID_T) :: memtype - integer(HSIZE_T) :: size - integer(HSIZE_T) :: n + integer(SIZE_T) :: size + integer(SIZE_T) :: n type(c_ptr) :: f_ptr ! Set up collective vs. independent I/O @@ -1628,7 +1628,7 @@ contains integer(HID_T) :: dspace ! data or file space handle integer(HID_T) :: filetype integer(HID_T) :: memtype - integer(HSIZE_T) :: n + integer(SIZE_T) :: n type(c_ptr) :: f_ptr ! Set up collective vs. independent I/O @@ -1644,7 +1644,7 @@ contains ! Create datatype in memory based on Fortran character call h5tcopy_f(H5T_FORTRAN_S1, memtype, hdf5_err) - call h5tset_size_f(memtype, int(len(buffer(1)), HSIZE_T), hdf5_err) + call h5tset_size_f(memtype, int(len(buffer(1)), SIZE_T), hdf5_err) ! Create dataspace/dataset call h5screate_simple_f(1, dims, dspace, hdf5_err) @@ -1706,8 +1706,8 @@ contains integer(HID_T) :: dspace ! data or file space handle integer(HID_T) :: filetype integer(HID_T) :: memtype - integer(HSIZE_T) :: size - integer(HSIZE_T) :: n + integer(SIZE_T) :: size + integer(SIZE_T) :: n type(c_ptr) :: f_ptr ! Set up collective vs. independent I/O diff --git a/src/state_point.F90 b/src/state_point.F90 index 883df4932..0a818ed29 100644 --- a/src/state_point.F90 +++ b/src/state_point.F90 @@ -890,7 +890,6 @@ contains integer(HSIZE_T) :: dims(1) type(c_ptr) :: f_ptr #ifdef PHDF5 - integer :: data_xfer_mode integer(HID_T) :: plist ! property list #else integer :: i @@ -1009,7 +1008,6 @@ contains integer(HSIZE_T) :: offset(1) ! offset of data type(c_ptr) :: f_ptr #ifdef PHDF5 - integer :: data_xfer_mode integer(HID_T) :: plist ! property list #endif diff --git a/tests/test_complex_cell/geometry.xml b/tests/test_complex_cell/geometry.xml index 18e304fe0..a695396e0 100644 --- a/tests/test_complex_cell/geometry.xml +++ b/tests/test_complex_cell/geometry.xml @@ -15,10 +15,11 @@ + - - + + diff --git a/tests/test_complex_cell/results_true.dat b/tests/test_complex_cell/results_true.dat index 56e7e409f..97f228e3e 100644 --- a/tests/test_complex_cell/results_true.dat +++ b/tests/test_complex_cell/results_true.dat @@ -1,11 +1,11 @@ k-combined: -2.651570E-01 2.116381E-03 +2.565769E-01 8.980879E-04 tally 1: -2.639097E+00 -1.394398E+00 -2.743740E+00 -1.506124E+00 -1.041248E+00 -2.177204E-01 -1.087210E-01 -2.365126E-03 +2.584080E+00 +1.335682E+00 +2.763580E+00 +1.528633E+00 +1.007148E+00 +2.031543E-01 +1.113696E-01 +2.485351E-03