From bbaad4ed93f0c6351d31aebe69afeee601815681 Mon Sep 17 00:00:00 2001 From: amandalund Date: Wed, 14 Nov 2018 16:03:44 -0600 Subject: [PATCH] Updated TRISO example notebook --- docs/source/pythonapi/index.rst | 2 +- docs/source/pythonapi/model.rst | 2 +- examples/jupyter/triso.ipynb | 115 +++++++++++++++++--------------- 3 files changed, 63 insertions(+), 56 deletions(-) diff --git a/docs/source/pythonapi/index.rst b/docs/source/pythonapi/index.rst index 3c28a2185..8abb52528 100644 --- a/docs/source/pythonapi/index.rst +++ b/docs/source/pythonapi/index.rst @@ -22,7 +22,7 @@ there are many substantial benefits to using the Python API, including: - Ability to plot individual universes as geometry is being created - A :math:`k_\text{eff}` search function (:func:`openmc.search_for_keff`) - Random sphere packing for generating TRISO particle locations - (:func:`openmc.model.pack_trisos`) + (:func:`openmc.model.pack_spheres`) - Ability to create materials based on natural elements or uranium enrichment For those new to Python, there are many good tutorials available online. We diff --git a/docs/source/pythonapi/model.rst b/docs/source/pythonapi/model.rst index 9ed77f366..90e8b779d 100644 --- a/docs/source/pythonapi/model.rst +++ b/docs/source/pythonapi/model.rst @@ -37,7 +37,7 @@ Functions :template: myfunction.rst openmc.model.create_triso_lattice - openmc.model.pack_trisos + openmc.model.pack_spheres Model Container --------------- diff --git a/examples/jupyter/triso.ipynb b/examples/jupyter/triso.ipynb index bc7801e2d..36e0c1f14 100644 --- a/examples/jupyter/triso.ipynb +++ b/examples/jupyter/triso.ipynb @@ -101,7 +101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have a universe that can be used for each TRISO particle, we need to randomly select locations. In this example, we will select locations at random within a 1 cm x 1 cm x 1 cm box centered at the origin with a packing fraction of 30%. Note that `pack_trisos` can handle up to the theoretical maximum of 60% (it will just be slow)." + "Next, we need a region to pack the TRISO particles in. We will use a 1 cm x 1 cm x 1 cm box centered at the origin." ] }, { @@ -111,16 +111,49 @@ "collapsed": false }, "outputs": [], + "source": [ + "min_x = openmc.XPlane(x0=-0.5, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=0.5, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-0.5, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=0.5, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-0.5, boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=0.5, boundary_type='reflective')\n", + "region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to randomly select locations for the TRISO particles. In this example, we will select locations at random within the box with a packing fraction of 30%. Note that `pack_spheres` can handle up to the theoretical maximum of 60% (it will just be slow)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "outer_radius = 425.*1e-4\n", - "\n", - "trisos = openmc.model.pack_trisos(\n", - " radius=outer_radius,\n", - " fill=triso_univ,\n", - " domain_shape='cube',\n", - " domain_length=1,\n", - " packing_fraction=0.3\n", - ")" + "centers = openmc.model.pack_spheres(radius=outer_radius, region=region, pf=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have the locations of the TRISO particles determined and a universe that can be used for each particle, we can create the TRISO particles." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trisos = [openmc.model.TRISO(outer_radius, triso_univ, c) for c in centers]" ] }, { @@ -132,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -142,10 +175,10 @@ "output_type": "stream", "text": [ "Cell\n", - "\tID =\t10005\n", + "\tID =\t6\n", "\tName =\t\n", - "\tFill =\t10000\n", - "\tRegion =\t-10004\n", + "\tFill =\t1\n", + "\tRegion =\t-11\n", "\tRotation =\tNone\n", "\tTranslation =\t[-0.33455672 0.31790187 0.24135378]\n", "\n" @@ -165,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -175,7 +208,7 @@ "output_type": "stream", "text": [ "[-0.45718713 -0.45730405 -0.45725048]\n", - "[ 0.45705454 0.45743843 0.45741142]\n" + "[0.45705454 0.45743843 0.45741142]\n" ] } ], @@ -194,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -205,7 +238,7 @@ "0.2996893513959326" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -218,42 +251,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have our TRISO particles created, we need to place them in a lattice to provide optimal tracking performance in OpenMC. We'll start by creating a box that the lattice will be placed within." + "Now that we have our TRISO particles created, we need to place them in a lattice to provide optimal tracking performance in OpenMC. We can use the box we created above to place the lattice in. Actually creating a lattice containing TRISO particles can be done with the `model.create_triso_lattice()` function. This function requires that we give it a list of TRISO particles, the lower-left coordinates of the lattice, the pitch of each lattice cell, the overall shape of the lattice (number of cells in each direction), and a background material." ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "min_x = openmc.XPlane(x0=-0.5, boundary_type='reflective')\n", - "max_x = openmc.XPlane(x0=0.5, boundary_type='reflective')\n", - "min_y = openmc.YPlane(y0=-0.5, boundary_type='reflective')\n", - "max_y = openmc.YPlane(y0=0.5, boundary_type='reflective')\n", - "min_z = openmc.ZPlane(z0=-0.5, boundary_type='reflective')\n", - "max_z = openmc.ZPlane(z0=0.5, boundary_type='reflective')\n", - "box = openmc.Cell(region=+min_x & -max_x & +min_y & -max_y & +min_z & -max_z)" - ] - }, - { - "cell_type": "markdown", + "execution_count": 10, "metadata": {}, - "source": [ - "Our last step is to actually create a lattice containing TRISO particles which can be done with `model.create_triso_lattice()` function. This function requires that we give it a list of TRISO particles, the lower-left coordinates of the lattice, the pitch of each lattice cell, the overall shape of the lattice (number of cells in each direction), and a background material." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, "outputs": [], "source": [ + "box = openmc.Cell(region=region)\n", "lower_left, upper_right = box.region.bounding_box\n", "shape = (3, 3, 3)\n", "pitch = (upper_right - lower_left)/shape\n", @@ -270,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -288,14 +295,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Adn4TX7cECe2vv85+gtPx0PyCoTMWBLihhEBCdXIgQuqUBNk0tkbfNojtauAQ68BKJ1r0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\n", 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\n", "text/plain": [ "" ] @@ -330,14 +337,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "" ] @@ -356,7 +363,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -370,9 +377,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }