diff --git a/examples/jupyter/pincell_depletion.ipynb b/examples/jupyter/pincell_depletion.ipynb index 2599290e3a..63b644a9df 100644 --- a/examples/jupyter/pincell_depletion.ipynb +++ b/examples/jupyter/pincell_depletion.ipynb @@ -18,6 +18,7 @@ "outputs": [], "source": [ "%matplotlib inline\n", + "import math\n", "import openmc" ] }, @@ -167,7 +168,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -176,7 +177,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -281,63 +282,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we write the material file, we must add one bit of information: the volume of our fuel. In order to translate the reaction rates obtained by `openmc` to meaningful units for depletion, we have to normalize them to a correct power. This requires us to know, or be able to calculate, how much fuel is in our problem. Correctly setting the volumes is a critical step, and incorrect answers, as the fuel is over- or under-depleted due to poor normalization.\n", + "Before we write the material file, we must add one bit of information: the volume of our fuel. In order to translate the reaction rates obtained by `openmc` to meaningful units for depletion, we have to normalize them to a correct power. This requires us to know, or be able to calculate, how much fuel is in our problem. Correctly setting the volumes is a critical step, and can lead to incorrect answers, as the fuel is over- or under-depleted due to poor normalization.\n", "\n", "For our problem, we can assign the \"volume\" to be the cross-sectional area of our fuel. This is identical to modeling our fuel pin inside a box with height of 1 cm." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "fuel.volume = 3.14159 * radii[0] ** 2" + "fuel.volume = math.pi * radii[0] ** 2" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" - ] - } - ], + "outputs": [], "source": [ - "materials.export_to_xml()\n", - "!cat materials.xml" + "materials.export_to_xml()" ] }, { @@ -351,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -407,7 +372,7 @@ " ('U238', 8)])" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -432,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -448,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -459,12 +424,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For this problem, we will take depletion step sizes of 30 days, and instruct OpenMC to re-run a transport simulation every 30 days until we have modeled the problem over a six month cycle. The depletion interface expects the time to be given in seconds, so we will have to convert. Note that these values are not cummulative." + "For this problem, we will take depletion step sizes of 30 days, and instruct OpenMC to re-run a transport simulation every 30 days until we have modeled the problem over a six month cycle. The depletion interface expects the time to be given in seconds, so we will have to convert. Note that these values are not cumulative." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -514,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -541,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -550,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -559,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -568,22 +533,22 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.76882937, 0.00982155],\n", - " [0.75724032, 0.00827689],\n", - " [0.75532241, 0.01031746],\n", - " [0.74796854, 0.00919769],\n", - " [0.74066559, 0.01157708],\n", - " [0.7318449 , 0.00971503],\n", - " [0.72072927, 0.00703074]])" + " [0.75724033, 0.00827689],\n", + " [0.75532242, 0.01031746],\n", + " [0.74796855, 0.00919769],\n", + " [0.74066561, 0.01157708],\n", + " [0.73184492, 0.00971504],\n", + " [0.7207293 , 0.00703074]])" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -601,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -610,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -646,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -656,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -678,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -707,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -716,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -742,9 +707,9 @@ "source": [ "# Helpful tips\n", "\n", - "Depletion is a tricky task to get correct. Too short of time steps and you may never get your results due to running many transport simulations. Too long of time steps and you may get incorrect answers. Take the plot of xenon from above. Xenon is a fission product with a thermal absorption cross section on the order of millions of barns, but has a half life of ~9 hours. Taking smaller time steps at the beginning of your simulation to build up some equilibrium in your fission products is highly recommended.\n", + "Depletion is a tricky task to get correct. Use too short of time steps and you may never get your results due to running many transport simulations. Use long of time steps and you may get incorrect answers. Consider the xenon plot from above. Xenon-135 is a fission product with a thermal absorption cross section on the order of millions of barns, but has a half life of ~9 hours. Taking smaller time steps at the beginning of your simulation to build up some equilibrium in your fission products is highly recommended.\n", "\n", - "When possible, differentiate materials that reappear in multiple places. If we had built an entire core with the single `fuel` material, every pin would be depleted using the same averaged spectrum and reaction rates which is incorrect. The `Operator` can differentiate these materials using the `diff_burnable_mats` argument, but not that the volumes will be copied from the original material.\n", + "When possible, differentiate materials that reappear in multiple places. If we had built an entire core with the single `fuel` material, every pin would be depleted using the same averaged spectrum and reaction rates which is incorrect. The `Operator` can differentiate these materials using the `diff_burnable_mats` argument, but note that the volumes will be copied from the original material.\n", "\n", "Using higher-order integrators, like the `CECMIntegrator`, `EPCRK4Integrator` with a fourth order Runge-Kutta, or the `LEQIIntegrator`, can improve the accuracy of a simulation, or at least allow you to take longer depletion steps between transport simulations with similar accuracy.\n", "\n", @@ -753,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -763,22 +728,22 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -809,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -818,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -827,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -836,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -863,33 +828,13 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "new_op = openmc.deplete.Operator(geometry, settings)" ] }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3820" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(new_op.chain.nuclide_dict)" - ] - }, { "cell_type": "code", "execution_count": 52, @@ -898,7 +843,7 @@ { "data": { "text/plain": [ - "['H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7', 'He3', 'He4', 'He5']" + "3820" ] }, "execution_count": 52, @@ -906,13 +851,33 @@ "output_type": "execute_result" } ], + "source": [ + "len(new_op.chain.nuclide_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7', 'He3', 'He4', 'He5']" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "[nuc.name for nuc in new_op.chain.nuclides[:10]]" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -930,7 +895,7 @@ " 'Rg272']" ] }, - "execution_count": 53, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -952,16 +917,16 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "5.080334904405777" + "5.080339195584719" ] }, - "execution_count": 56, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -972,7 +937,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -981,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 57, "metadata": {}, "outputs": [ {