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openmc-designs/Depletion/depletion.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pincell Depletion\n",
"This notebook is intended to introduce the reader to the depletion interface contained in OpenMC. It is recommended that you are moderately familiar with building models using the OpenMC Python API. The earlier examples are excellent starting points, as this notebook will not focus heavily on model building.\n",
"\n",
"If you have a real power reactor, the fuel composition is constantly changing as fission events produce energy, remove some fissile isotopes, and produce fission products. Other reactions, like $(n, \\alpha)$ and $(n, \\gamma)$ will alter the composition as well. Furthermore, some nuclides undergo spontaneous decay with widely ranging frequencies. Depletion is the process of modeling this behavior.\n",
"\n",
"In this notebook, we will model a simple fuel pin in an infinite lattice using the Python API. We will then build and examine some of the necessary components for performing depletion analysis. Then, we will use the depletion interface in OpenMC to simulate the fuel pin producing power over several months. Lastly, we will wrap up with some helpful tips to improve the fidelity of depletion simulations."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import math\n",
"import openmc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the Geometry\n",
"\n",
"Much of this section is borrowed from the \"Modeling a Pin-Cell\" example. If you find yourself not understanding some aspects of this section, feel free to refer to that example, as some details may be glossed over for brevity.\n",
"\n",
"First, we will create our fuel, cladding, and water materials to represent a typical PWR."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fuel = openmc.Material(name=\"uo2\")\n",
"fuel.add_element(\"U\", 1, percent_type=\"ao\", enrichment=4.25)\n",
"fuel.add_element(\"O\", 2)\n",
"fuel.set_density(\"g/cc\", 10.4)\n",
"\n",
"clad = openmc.Material(name=\"clad\")\n",
"clad.add_element(\"Zr\", 1)\n",
"clad.set_density(\"g/cc\", 6)\n",
"\n",
"water = openmc.Material(name=\"water\")\n",
"water.add_element(\"O\", 1)\n",
"water.add_element(\"H\", 2)\n",
"water.set_density(\"g/cc\", 1.0)\n",
"water.add_s_alpha_beta(\"c_H_in_H2O\")\n",
"materials = openmc.Materials([fuel, clad, water])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we are going to use the `openmc.model.pin` function to build our pin cell. The `pin` function anticipates concentric cylinders and materials to fill the inner regions. One additional material is needed than the number of cylinders to cover the domain outside the final ring. \n",
"\n",
"To do this, we define two radii for the outer radius of our fuel pin, and the outer radius of the cladding."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"radii = [0.42, 0.45]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using these radii, we define concentric `ZCylinder` objects. So long as the cylinders are concentric and increasing in radius, any orientation can be used. We also take advantage of the fact that the `openmc.Materials` object is a subclass of the `list` object to assign materials to the regions defined by the surfaces."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pin_surfaces = [openmc.ZCylinder(r=r) for r in radii]\n",
"pin_univ = openmc.model.pin(pin_surfaces, materials)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first material, in our case `fuel`, is placed inside the first cylinder in the inner-most region. The second material, `clad`, fills the space between our cylinders, while `water` is placed outside the last ring. The `pin` function returns an `openmc.Universe` object, and has some additional features we will mention later. Finally, we need to place the fuel pin universe in a bounding cell."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"bound_box = openmc.model.RectangularPrism(1.24, 1.24, boundary_type=\"reflective\")\n",
"root_cell = openmc.Cell(fill=pin_univ, region=bound_box)\n",
"geometry = openmc.Geometry([root_cell])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To ensure our geometry looks right, let's plot it."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f78e7d2a890>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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kpCSkpKSgpKQEJSUlSElJwfz5881CJTIyEocOHQIA3L17Fz/5yU9w9uxZ7N27F52dnTAajTAajWhvb1eqqkQkmaKfY9m7dy8mTJiAxMREJCYm4oknnsB7771nVubixYtobm4GAFy9ehX/+Mc/cPXqVTz55JMICgoy3Ry5kqQUnmshmbR4bqWbYlOhwaLkVKgbp0Q0UIMVKqqfChGR+2Kw9AOnRDQQWp4CdWOw9BPDhfrDHUIFYLAohuFCPblTn2CwDICWjjA0uLTWlxgsA8QpEdnDXaZA3RgsCmO4kDv2AQaLBH0dcdyxY9E9fe17LY5WAAaLNAwX6sldQwVgsEjFcKFu7hwqAINFOoYLuXuoAAyWQcFw0S7u23sYLAqw54jEDqg99uxTdxitAAwWxTBc3AtDxRyDRUEMF/fAULHEYFEYw0XbGCrWMVicgOGiTQwV2xgsTsJw0RaGSu/41ZRO1tvXWt6PX3HpmuwNf1cNFX41pUbZ2+E4enE9ag8VZ+KIZRDZM3rhyMU1aGXq46wRC4NlkHFq5Nq0NkrhVMhNcGrkurQWKs7EEYuLsHfkAnD0ojRHQlxtocKpUD+pNVi6MWAGj5YDpRunQm7KkQ7L6ZE87hAqzsQRiwvj6EV57hYonAr1k5aCBXAsXAAGjL0cHe1pIVQABku/aS1YujFg5HDXQOmmiXMst27dgsFggF6vh16vh8FgQFNTk93rv/jii9DpdMjNzVWsjmrhaAc/NW+D6ebu+vtaaC1UnMlTyY0vWbIEV69eRUFBAQDg5z//OQwGA/75z3/2ue7HH3+M0tJSBAcHK1lFVenu6I6OXrrfUO42iulvqDJQBk6xYKmqqkJBQQFKSkoQFxcHANi+fTsSEhJw8eJFRERE2Fz32rVrSEtLw9GjRzFv3rxen6etrQ1tbW2m+y0tLXIa4MIGGjCAdkNmICM0Boo8igVLcXEx9Hq9KVQAID4+Hnq9HkVFRTaDpaurCwaDAa+88grGjx/f5/Pk5OQgOztbWr3VpL8BA2grZAY63WOgyKdYsBiNRgQEBFgsDwgIgNFotLnexo0b4enpidWrV9v1PJmZmUhPTzfdb2lpQUhIiOMVVrGBBAxg+cZ09aCRdd6IgaIch4MlKyurzxHCmTNnAAA6nc7iMSGE1eUAUFZWhrfffhvl5eU2y/Tk4+MDHx8fu8pq3UADppurBY3sE9AMFOU5HCxpaWlITk7utcyYMWNw/vx53Lhxw+KxmzdvIjAw0Op6p0+fRn19PUJDQ03LOjs78dJLLyE3NxfffPONo9V1S7ICpltvb2xZoeOMq1cMFOdR7HMsVVVVGDduHEpLSzF58mQAQGlpKeLj43HhwgWr51gaGxtRV1dntmzOnDkwGAxYsWJFryd8u2n1cywDJStk1IZhYs5Zn2NR7BxLVFQUkpKSkJKSgm3btgG4d7l5/vz5ZgERGRmJnJwcPPvssxgxYgRGjBhhth0vLy+MGjXKrlAh2+5/g2k9ZBgmg0/Rz7Hs3bsXq1evRmJiIgBgwYIF+POf/2xW5uLFi2hublayGtSDFkOGYeJa+JF+MqOWoGGQ9I/qp0KkTtbesIMdNgwR9WGwUJ96e2PLCh2Gh7YwWGhAGAhkDb9BjoikY7AQkXQMFiKSjsFCRNIxWIhIOgYLEUnHYCEi6RgsRCSd5j4g1/2vT+L2IFeEyAV1vy+U/hdBzQVLY2PjvT9WAJr670oiiRobG6HX6xXbvuaCZfjw4QCAmpoaRV84pXV/d29tba2i/4WqNLbDtTQ3NyM0NNT0PlGK5oJlyJB7p430er2qO0A3Pz8/tsOFaKUd3e8Txbav6NaJyC0xWIhIOs0Fi4+PD373u9+p/idB2A7XwnY4RnNfTUlEg09zIxYiGnwMFiKSjsFCRNIxWIhIOgYLEUmnymC5desWDAYD9Ho99Ho9DAYDmpqael3n+eefh06nM7vFx8eblWlra8OvfvUr+Pv7Y+jQoViwYAGuXr3qMu3o6OjAq6++igkTJmDo0KEIDg7GsmXLcP36dbNyP/jBDyzampycLK3eeXl5CA8Ph6+vL2JjY3H69Oley588eRKxsbHw9fXFI488gq1bt1qUOXDgAMaNGwcfHx+MGzcOhw4dklZfWxxpx8GDBzF79myMHDkSfn5+SEhIwNGjR83K7Nmzx+J11+l0uHPnjsu048SJE1breOHCBbNyA94fQoWSkpJEdHS0KCoqEkVFRSI6OlrMnz+/13WWL18ukpKSRF1dnenW2NhoViY1NVU8/PDDorCwUJSXl4sZM2aI7373u+Lu3bsu0Y6mpiYxa9YskZ+fLy5cuCCKi4tFXFyciI2NNSs3ffp0kZKSYtbWpqYmKXX+4IMPhJeXl9i+fbuorKwUa9asEUOHDhVXrlyxWv7f//63eOCBB8SaNWtEZWWl2L59u/Dy8hIfffSRqUxRUZHw8PAQb7zxhqiqqhJvvPGG8PT0FCUlJVLqLKMda9asERs3bhSfffaZuHTpksjMzBReXl6ivLzcVGb37t3Cz8/P7HWvq6tTrA39acfx48cFAHHx4kWzOt7fx2XsD9UFS2VlpQBg1sji4mIBQFy4cMHmesuXLxfPPPOMzcebmpqEl5eX+OCDD0zLrl27JoYMGSIKCgqk1P1+/W1HT5999pkAYNaRpk+fLtasWSOzuiaTJ08WqampZssiIyNFRkaG1fK//vWvRWRkpNmyF198UcTHx5vuL1q0SCQlJZmVmTNnjkhOTpZUa0uOtsOacePGiezsbNP93bt3C71eL6uKdnG0Hd3BcuvWLZvblLE/VDcVKi4uhl6vR1xcnGlZfHw89Ho9ioqKel33xIkTCAgIwOOPP46UlBTU19ebHisrK0NHR4fpB+wBIDg4GNHR0X1u19ntuF9zczN0Oh0efPBBs+V79+6Fv78/xo8fj5dffhmtra0DrnN7ezvKysrMXiMASExMtFnn4uJii/Jz5szB2bNn0dHR0WsZJV53oH/t6Kmrqwutra0W/yX8n//8B2FhYRg9ejTmz5+PiooKafXuaSDtmDhxIoKCgvD000/j+PHjZo/J2B+q++9mo9GIgIAAi+UBAQEwGo0215s7dy5++tOfIiwsDNXV1Xj99dcxc+ZMlJWVwcfHB0ajEd7e3njooYfM1gsMDOx1u85ux/3u3LmDjIwMLFmyxOw/bpcuXYrw8HCMGjUKX375JTIzM/H555+jsLBwQHVuaGhAZ2cnAgMDzZb39hoZjUar5e/evYuGhgYEBQXZLKPE6w70rx09bdq0Cd9++y0WLVpkWhYZGYk9e/ZgwoQJaGlpwdtvv42pU6fi888/x9ixY6W2AehfO4KCgvDuu+8iNjYWbW1teO+99/D000/jxIkTeOqppwDY3meO7A+XCZasrCxkZ2f3WubMmTMAAJ1OZ/GYEMLq8m6LFy82/R0dHY1JkyYhLCwMhw8fxo9//GOb6/W13Z6Ubke3jo4OJCcno6urC3l5eWaPpaSkmP6Ojo7G2LFjMWnSJJSXlyMmJsaeZvSqZ/36qrO18j2XO7pNGfr7nPv370dWVhb+/ve/mx0c4uPjzS4ITJ06FTExMfjTn/6Ed955R17Fe3CkHREREYiIiDDdT0hIQG1tLd566y1TsDi6TWtcJljS0tL6vHIxZswYnD9/Hjdu3LB47ObNmxYp25ugoCCEhYXh8uXLAIBRo0ahvb0dt27dMhu11NfXY8qUKXZv1xnt6OjowKJFi1BdXY1jx471+f0gMTEx8PLywuXLlwcULP7+/vDw8LA4ctXX19us86hRo6yW9/T0xIgRI3ot48j+dER/2tEtPz8fK1euxIcffohZs2b1WnbIkCH43ve+Z+pjsg2kHfeLj4/H+++/b7ovZX/YfTbGRXSf9CwtLTUtKykpcfikZ0NDg/Dx8RF/+ctfhBD/f/I2Pz/fVOb69euKn7x1tB3t7e1i4cKFYvz48aK+vt6u5/riiy8EAHHy5MkB13vy5MniF7/4hdmyqKioXk/eRkVFmS1LTU21OHk7d+5cszJJSUmKn7x1pB1CCLFv3z7h6+srDh06ZNdzdHV1iUmTJokVK1YMpKq96k87enruuefEjBkzTPdl7A/VBYsQ9xr5xBNPiOLiYlFcXCwmTJhgcZk2IiJCHDx4UAghRGtrq3jppZdEUVGRqK6uFsePHxcJCQni4YcfFi0tLaZ1UlNTxejRo8W//vUvUV5eLmbOnKn45WZH2tHR0SEWLFggRo8eLc6dO2d2ubCtrU0IIcRXX30lsrOzxZkzZ0R1dbU4fPiwiIyMFBMnTpTSju7Lmzt37hSVlZVi7dq1YujQoeKbb74RQgiRkZEhDAaDqXz35eZ169aJyspKsXPnTovLzZ9++qnw8PAQGzZsEFVVVWLDhg1Ou9xsbzv27dsnPD09xebNm21exs/KyhIFBQXi66+/FhUVFWLFihXC09PT7OAx2O344x//KA4dOiQuXbokvvzyS5GRkSEAiAMHDpjKyNgfqgyWxsZGsXTpUjFs2DAxbNgwsXTpUovLZwDE7t27hRBC3L59WyQmJoqRI0cKLy8vERoaKpYvXy5qamrM1vnvf/8r0tLSxPDhw8V3vvMdMX/+fIsyg9mO6upqgXvfEW5xO378uBBCiJqaGvHUU0+J4cOHC29vb/Hoo4+K1atXW3xmZyA2b94swsLChLe3t4iJiTEbCS1fvlxMnz7drPyJEyfExIkThbe3txgzZozYsmWLxTY//PBDERERIby8vERkZKRZR1eKI+2YPn261dd9+fLlpjJr164VoaGhwtvbW4wcOVIkJiaKoqIil2rHxo0bxaOPPip8fX3FQw89JL7//e+Lw4cPW2xzoPuD38dCRNKp7nMsROT6GCxEJB2DhYikY7AQkXQMFiKSjsFCRNIxWIhIOgYLEUnHYCEi6RgsRCQdg4WIpPs/+NfNrXua6kIAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 258.065x259.74 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"geometry.root_universe.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly we construct our settings. For the sake of time, a relatively low number of particles will be used."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"settings = openmc.Settings()\n",
"settings.particles = 1000\n",
"settings.inactive = 10\n",
"settings.batches = 50"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The depletion interface relies on `OpenMC` to perform the transport simulation and obtain reaction rates and other important information. Normally, we would need to export XML files before running OpenMC, but the depletion interface takes care of this for us."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, we must first 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": 8,
"metadata": {},
"outputs": [],
"source": [
"fuel.volume = math.pi * radii[0] ** 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setting up for depletion\n",
"\n",
"The OpenMC depletion interface can be accessed from the `openmc.deplete` module, and has a variety of classes that will help us."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import openmc.deplete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to run the depletion calculation we need the following information:\n",
"\n",
"1. Nuclide decay, fission yield, and reaction data\n",
"2. Operational power or power density\n",
"3. Desired depletion schedule\n",
"4. Desired time integration scheme\n",
"\n",
"The first item is necessary to determine the paths by which nuclides transmute over the depletion simulation. This includes spontaneous decay, fission product yield distributions, and nuclides produced through neutron-reactions. For example,\n",
"* Te129 decays to I129 with a half life of ~70 minutes\n",
"* A fission event for U-235 produces fission products like Xe135 according to a distribution\n",
"* For thermal problems, Am241 will produce metastable Am242 about 8% of the time during an $(n,\\gamma)$ reaction. The other 92% of capture reactions will produce ground state Am242\n",
"\n",
"These data are often distributed with other nuclear data, like incident neutron cross sections with ENDF/B-VII.\n",
"OpenMC uses the [`openmc.deplete.Chain`](https://docs.openmc.org/en/latest/pythonapi/generated/openmc.deplete.Chain.html#openmc.deplete.Chain) to collect represent the various decay and transmutation pathways in a single object.\n",
"While a complete `Chain` can be created using nuclear data files, users may prefer to download pre-generated XML-representations instead.\n",
"Such files can be found at https://openmc.org/depletion-chains/ and include full and compressed chains, with capture branching ratios derived using PWR- or SFR-spectra.\n",
"\n",
"For this problem, we will be using a much smaller depletion chain that contains very few nuclides. In a realistic problem, over 1000 isotopes may be included in the depletion chain."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([('I135', 0),\n",
" ('Xe135', 1),\n",
" ('Xe136', 2),\n",
" ('Cs135', 3),\n",
" ('Gd157', 4),\n",
" ('Gd156', 5),\n",
" ('U234', 6),\n",
" ('U235', 7),\n",
" ('U238', 8)])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = openmc.deplete.Chain.from_xml(\"./chain_simple.xml\")\n",
"chain.nuclide_dict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The primary entry point for depletion is the `openmc.deplete.Operator`. It relies on the `openmc.deplete.Chain` and helper classes to run `openmc`, retrieve and normalize reaction rates, and other perform other tasks. For a thorough description, please see the full API documentation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will create our Operator using the geometry and settings from above, and our simple chain file. The materials are read in automatically using the `materials.xml` file."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"model = openmc.Model(geometry=geometry, settings=settings)\n",
"operator = openmc.deplete.CoupledOperator(model, \"./chain_simple.xml\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will then simulate our fuel pin operating at linear power of 174 W/cm, or 174 W given a unit height for our problem."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"power = 174"
]
},
{
"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 cumulative."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"time_steps = [30] * 6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And lastly, we will use the basic predictor, or forward Euler, time integration scheme. Other, more advanced methods are provided to the user through `openmc.deplete`"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"integrator = openmc.deplete.PredictorIntegrator(operator, time_steps, power, timestep_units='d')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To perform the simulation, we use the `integrate` method, and let `openmc` take care of the rest."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" %%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
" ####################### %%%%%%%%%%%%%%%%%%\n",
" ####################### %%%%%%%%%%%%%%%%%\n",
" ###################### %%%%%%%%%%%%%%%%%\n",
" #################### %%%%%%%%%%%%%%%%%\n",
" ################# %%%%%%%%%%%%%%%%%\n",
" ############### %%%%%%%%%%%%%%%%\n",
" ############ %%%%%%%%%%%%%%%\n",
" ######## %%%%%%%%%%%%%%\n",
" %%%%%%%%%%%\n",
"\n",
" | The OpenMC Monte Carlo Code\n",
" Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
" License | https://docs.openmc.org/en/latest/license.html\n",
" Version | 0.13.3\n",
" Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
" Date/Time | 2023-10-26 01:26:50\n",
" OpenMP Threads | 32\n",
"\n",
" Reading settings XML file...\n",
" Reading cross sections XML file...\n",
" Reading materials XML file...\n",
" Reading geometry XML file...\n",
" Reading U234 from /opt/xdata/endfb-vii.1-hdf5/neutron/U234.h5\n",
" Reading U235 from /opt/xdata/endfb-vii.1-hdf5/neutron/U235.h5\n",
" Reading U238 from /opt/xdata/endfb-vii.1-hdf5/neutron/U238.h5\n",
" Reading O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.h5\n",
" Reading O17 from /opt/xdata/endfb-vii.1-hdf5/neutron/O17.h5\n",
" Reading U236 from /opt/xdata/endfb-vii.1-hdf5/neutron/U236.h5\n",
" Reading Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n",
" Reading Zr91 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr91.h5\n",
" Reading Zr92 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr92.h5\n",
" Reading Zr94 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr94.h5\n",
" Reading Zr96 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr96.h5\n",
" Reading H1 from /opt/xdata/endfb-vii.1-hdf5/neutron/H1.h5\n",
" Reading H2 from /opt/xdata/endfb-vii.1-hdf5/neutron/H2.h5\n",
" Reading c_H_in_H2O from /opt/xdata/endfb-vii.1-hdf5/neutron/c_H_in_H2O.h5\n",
" Minimum neutron data temperature: 294 K\n",
" Maximum neutron data temperature: 294 K\n",
" Preparing distributed cell instances...\n",
" Reading plot XML file...\n",
" Writing summary.h5 file...\n",
"[openmc.deplete] t=0.0 s, dt=2592000 s, source=174\n",
" Reading I135 from /opt/xdata/endfb-vii.1-hdf5/neutron/I135.h5\n",
" Reading Xe135 from /opt/xdata/endfb-vii.1-hdf5/neutron/Xe135.h5\n",
" Reading Xe136 from /opt/xdata/endfb-vii.1-hdf5/neutron/Xe136.h5\n",
" Reading Cs135 from /opt/xdata/endfb-vii.1-hdf5/neutron/Cs135.h5\n",
" Reading Gd157 from /opt/xdata/endfb-vii.1-hdf5/neutron/Gd157.h5\n",
" Reading Gd156 from /opt/xdata/endfb-vii.1-hdf5/neutron/Gd156.h5\n",
" Maximum neutron transport energy: 20000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 1.53702\n",
" 2/1 1.43876\n",
" 3/1 1.43346\n",
" 4/1 1.44635\n",
" 5/1 1.61133\n",
" 6/1 1.49998\n",
" 7/1 1.44172\n",
" 8/1 1.47214\n",
" 9/1 1.43927\n",
" 10/1 1.45062\n",
" 11/1 1.36761\n",
" 12/1 1.47828 1.42295 +/- 0.05533\n",
" 13/1 1.49637 1.44742 +/- 0.04024\n",
" 14/1 1.42753 1.44245 +/- 0.02889\n",
" 15/1 1.45598 1.44516 +/- 0.02254\n",
" 16/1 1.45223 1.44634 +/- 0.01844\n",
" 17/1 1.39662 1.43923 +/- 0.01713\n",
" 18/1 1.43137 1.43825 +/- 0.01487\n",
" 19/1 1.45607 1.44023 +/- 0.01326\n",
" 20/1 1.37620 1.43383 +/- 0.01348\n",
" 21/1 1.45783 1.43601 +/- 0.01238\n",
" 22/1 1.50132 1.44145 +/- 0.01255\n",
" 23/1 1.48660 1.44493 +/- 0.01205\n",
" 24/1 1.46315 1.44623 +/- 0.01123\n",
" 25/1 1.38739 1.44231 +/- 0.01117\n",
" 26/1 1.38844 1.43894 +/- 0.01098\n",
" 27/1 1.50351 1.44274 +/- 0.01099\n",
" 28/1 1.44082 1.44263 +/- 0.01036\n",
" 29/1 1.56234 1.44893 +/- 0.01165\n",
" 30/1 1.39650 1.44631 +/- 0.01136\n",
" 31/1 1.40645 1.44441 +/- 0.01097\n",
" 32/1 1.45086 1.44471 +/- 0.01046\n",
" 33/1 1.41487 1.44341 +/- 0.01008\n",
" 34/1 1.41254 1.44212 +/- 0.00974\n",
" 35/1 1.44422 1.44221 +/- 0.00934\n",
" 36/1 1.51714 1.44509 +/- 0.00943\n",
" 37/1 1.38374 1.44282 +/- 0.00935\n",
" 38/1 1.43018 1.44236 +/- 0.00902\n",
" 39/1 1.45851 1.44292 +/- 0.00872\n",
" 40/1 1.54850 1.44644 +/- 0.00913\n",
" 41/1 1.38747 1.44454 +/- 0.00904\n",
" 42/1 1.46092 1.44505 +/- 0.00876\n",
" 43/1 1.48960 1.44640 +/- 0.00860\n",
" 44/1 1.39648 1.44493 +/- 0.00847\n",
" 45/1 1.54765 1.44787 +/- 0.00873\n",
" 46/1 1.42142 1.44713 +/- 0.00852\n",
" 47/1 1.49596 1.44845 +/- 0.00839\n",
" 48/1 1.46689 1.44894 +/- 0.00818\n",
" 49/1 1.43829 1.44866 +/- 0.00797\n",
" 50/1 1.47259 1.44926 +/- 0.00779\n",
" Creating state point statepoint.50.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 9.6620e-01 seconds\n",
" Reading cross sections = 9.5103e-01 seconds\n",
" Total time in simulation = 1.9522e+00 seconds\n",
" Time in transport only = 1.9279e+00 seconds\n",
" Time in inactive batches = 3.6865e-01 seconds\n",
" Time in active batches = 1.5836e+00 seconds\n",
" Time synchronizing fission bank = 4.2209e-03 seconds\n",
" Sampling source sites = 3.7456e-03 seconds\n",
" SEND/RECV source sites = 4.5376e-04 seconds\n",
" Time accumulating tallies = 9.2936e-03 seconds\n",
" Time writing statepoints = 5.0476e-03 seconds\n",
" Total time for finalization = 1.2903e-04 seconds\n",
" Total time elapsed = 2.9312e+00 seconds\n",
" Calculation Rate (inactive) = 27126.1 particles/second\n",
" Calculation Rate (active) = 25259 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.45079 +/- 0.00728\n",
" k-effective (Track-length) = 1.44926 +/- 0.00779\n",
" k-effective (Absorption) = 1.45541 +/- 0.00477\n",
" Combined k-effective = 1.45490 +/- 0.00474\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n",
" Creating state point openmc_simulation_n0.h5...\n",
"[openmc.deplete] t=2592000.0 s, dt=2592000 s, source=174\n",
" Maximum neutron transport energy: 20000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 1.38610\n",
" 2/1 1.48168\n",
" 3/1 1.50267\n",
" 4/1 1.47834\n",
" 5/1 1.45348\n",
" 6/1 1.38505\n",
" 7/1 1.36547\n",
" 8/1 1.42814\n",
" 9/1 1.44743\n",
" 10/1 1.43416\n",
" 11/1 1.45512\n",
" 12/1 1.34085 1.39798 +/- 0.05714\n",
" 13/1 1.43011 1.40869 +/- 0.03468\n",
" 14/1 1.47060 1.42417 +/- 0.02900\n",
" 15/1 1.43763 1.42686 +/- 0.02262\n",
" 16/1 1.39925 1.42226 +/- 0.01904\n",
" 17/1 1.50656 1.43430 +/- 0.02010\n",
" 18/1 1.42710 1.43340 +/- 0.01743\n",
" 19/1 1.44105 1.43425 +/- 0.01539\n",
" 20/1 1.31871 1.42270 +/- 0.01797\n",
" 21/1 1.44718 1.42492 +/- 0.01641\n",
" 22/1 1.42535 1.42496 +/- 0.01498\n",
" 23/1 1.56508 1.43574 +/- 0.01749\n",
" 24/1 1.43354 1.43558 +/- 0.01620\n",
" 25/1 1.38408 1.43215 +/- 0.01547\n",
" 26/1 1.47748 1.43498 +/- 0.01474\n",
" 27/1 1.51403 1.43963 +/- 0.01461\n",
" 28/1 1.41745 1.43840 +/- 0.01383\n",
" 29/1 1.43331 1.43813 +/- 0.01308\n",
" 30/1 1.39084 1.43577 +/- 0.01263\n",
" 31/1 1.47378 1.43758 +/- 0.01215\n",
" 32/1 1.42902 1.43719 +/- 0.01159\n",
" 33/1 1.43219 1.43697 +/- 0.01108\n",
" 34/1 1.46083 1.43796 +/- 0.01065\n",
" 35/1 1.49512 1.44025 +/- 0.01047\n",
" 36/1 1.41634 1.43933 +/- 0.01010\n",
" 37/1 1.36758 1.43667 +/- 0.01008\n",
" 38/1 1.47189 1.43793 +/- 0.00979\n",
" 39/1 1.41503 1.43714 +/- 0.00948\n",
" 40/1 1.41165 1.43629 +/- 0.00920\n",
" 41/1 1.40983 1.43544 +/- 0.00894\n",
" 42/1 1.40339 1.43444 +/- 0.00871\n",
" 43/1 1.38040 1.43280 +/- 0.00860\n",
" 44/1 1.44208 1.43307 +/- 0.00835\n",
" 45/1 1.48910 1.43467 +/- 0.00826\n",
" 46/1 1.36484 1.43273 +/- 0.00826\n",
" 47/1 1.44490 1.43306 +/- 0.00804\n",
" 48/1 1.45630 1.43367 +/- 0.00785\n",
" 49/1 1.43474 1.43370 +/- 0.00765\n",
" 50/1 1.46575 1.43450 +/- 0.00750\n",
" Creating state point statepoint.50.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 0.0000e+00 seconds\n",
" Reading cross sections = 0.0000e+00 seconds\n",
" Total time in simulation = 2.1220e+00 seconds\n",
" Time in transport only = 2.0960e+00 seconds\n",
" Time in inactive batches = 4.0429e-01 seconds\n",
" Time in active batches = 1.7177e+00 seconds\n",
" Time synchronizing fission bank = 4.2377e-03 seconds\n",
" Sampling source sites = 3.7539e-03 seconds\n",
" SEND/RECV source sites = 4.6137e-04 seconds\n",
" Time accumulating tallies = 7.6969e-03 seconds\n",
" Time writing statepoints = 1.1371e-02 seconds\n",
" Total time for finalization = 1.3024e-04 seconds\n",
" Total time elapsed = 2.1331e+00 seconds\n",
" Calculation Rate (inactive) = 24734.9 particles/second\n",
" Calculation Rate (active) = 23286.3 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.42726 +/- 0.00588\n",
" k-effective (Track-length) = 1.43450 +/- 0.00750\n",
" k-effective (Absorption) = 1.44447 +/- 0.00447\n",
" Combined k-effective = 1.43943 +/- 0.00475\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n",
" Creating state point openmc_simulation_n1.h5...\n",
"[openmc.deplete] t=5184000.0 s, dt=2592000 s, source=174\n",
" Maximum neutron transport energy: 20000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 1.50898\n",
" 2/1 1.55378\n",
" 3/1 1.48344\n",
" 4/1 1.41734\n",
" 5/1 1.45680\n",
" 6/1 1.42132\n",
" 7/1 1.45812\n",
" 8/1 1.62452\n",
" 9/1 1.45545\n",
" 10/1 1.47973\n",
" 11/1 1.45058\n",
" 12/1 1.38818 1.41938 +/- 0.03120\n",
" 13/1 1.42619 1.42165 +/- 0.01816\n",
" 14/1 1.41350 1.41961 +/- 0.01300\n",
" 15/1 1.52301 1.44029 +/- 0.02300\n",
" 16/1 1.45366 1.44252 +/- 0.01891\n",
" 17/1 1.46955 1.44638 +/- 0.01644\n",
" 18/1 1.44131 1.44575 +/- 0.01425\n",
" 19/1 1.48535 1.45015 +/- 0.01332\n",
" 20/1 1.40243 1.44538 +/- 0.01283\n",
" 21/1 1.43354 1.44430 +/- 0.01166\n",
" 22/1 1.42089 1.44235 +/- 0.01082\n",
" 23/1 1.48037 1.44527 +/- 0.01037\n",
" 24/1 1.38331 1.44085 +/- 0.01057\n",
" 25/1 1.43077 1.44018 +/- 0.00987\n",
" 26/1 1.42727 1.43937 +/- 0.00927\n",
" 27/1 1.47006 1.44117 +/- 0.00889\n",
" 28/1 1.43249 1.44069 +/- 0.00839\n",
" 29/1 1.40403 1.43876 +/- 0.00817\n",
" 30/1 1.41033 1.43734 +/- 0.00788\n",
" 31/1 1.48052 1.43940 +/- 0.00777\n",
" 32/1 1.48062 1.44127 +/- 0.00764\n",
" 33/1 1.47116 1.44257 +/- 0.00742\n",
" 34/1 1.39372 1.44054 +/- 0.00739\n",
" 35/1 1.44398 1.44067 +/- 0.00709\n",
" 36/1 1.49633 1.44281 +/- 0.00714\n",
" 37/1 1.49206 1.44464 +/- 0.00711\n",
" 38/1 1.42788 1.44404 +/- 0.00688\n",
" 39/1 1.41317 1.44297 +/- 0.00672\n",
" 40/1 1.46586 1.44374 +/- 0.00654\n",
" 41/1 1.37584 1.44155 +/- 0.00669\n",
" 42/1 1.42766 1.44111 +/- 0.00649\n",
" 43/1 1.43667 1.44098 +/- 0.00629\n",
" 44/1 1.46639 1.44173 +/- 0.00615\n",
" 45/1 1.38801 1.44019 +/- 0.00617\n",
" 46/1 1.42058 1.43965 +/- 0.00602\n",
" 47/1 1.39808 1.43852 +/- 0.00596\n",
" 48/1 1.40581 1.43766 +/- 0.00586\n",
" 49/1 1.42390 1.43731 +/- 0.00572\n",
" 50/1 1.34860 1.43509 +/- 0.00600\n",
" Creating state point statepoint.50.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 0.0000e+00 seconds\n",
" Reading cross sections = 0.0000e+00 seconds\n",
" Total time in simulation = 2.2633e+00 seconds\n",
" Time in transport only = 2.2198e+00 seconds\n",
" Time in inactive batches = 3.6496e-01 seconds\n",
" Time in active batches = 1.8983e+00 seconds\n",
" Time synchronizing fission bank = 4.3325e-03 seconds\n",
" Sampling source sites = 3.8357e-03 seconds\n",
" SEND/RECV source sites = 4.7343e-04 seconds\n",
" Time accumulating tallies = 2.5058e-02 seconds\n",
" Time writing statepoints = 8.2730e-03 seconds\n",
" Total time for finalization = 1.3693e-04 seconds\n",
" Total time elapsed = 2.2745e+00 seconds\n",
" Calculation Rate (inactive) = 27399.9 particles/second\n",
" Calculation Rate (active) = 21071.1 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.43309 +/- 0.00659\n",
" k-effective (Track-length) = 1.43509 +/- 0.00600\n",
" k-effective (Absorption) = 1.42600 +/- 0.00359\n",
" Combined k-effective = 1.42781 +/- 0.00354\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n",
" Creating state point openmc_simulation_n2.h5...\n",
"[openmc.deplete] t=7776000.0 s, dt=2592000 s, source=174\n",
" Maximum neutron transport energy: 20000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 1.44203\n",
" 2/1 1.37132\n",
" 3/1 1.42001\n",
" 4/1 1.42945\n",
" 5/1 1.44713\n",
" 6/1 1.46065\n",
" 7/1 1.44657\n",
" 8/1 1.38157\n",
" 9/1 1.46971\n",
" 10/1 1.55415\n",
" 11/1 1.34500\n",
" 12/1 1.40011 1.37255 +/- 0.02756\n",
" 13/1 1.47993 1.40834 +/- 0.03917\n",
" 14/1 1.48390 1.42723 +/- 0.03352\n",
" 15/1 1.45548 1.43288 +/- 0.02658\n",
" 16/1 1.44571 1.43502 +/- 0.02180\n",
" 17/1 1.43232 1.43463 +/- 0.01843\n",
" 18/1 1.50338 1.44323 +/- 0.01813\n",
" 19/1 1.53546 1.45348 +/- 0.01899\n",
" 20/1 1.41146 1.44927 +/- 0.01750\n",
" 21/1 1.39360 1.44421 +/- 0.01662\n",
" 22/1 1.46111 1.44562 +/- 0.01523\n",
" 23/1 1.53882 1.45279 +/- 0.01574\n",
" 24/1 1.39733 1.44883 +/- 0.01510\n",
" 25/1 1.42802 1.44744 +/- 0.01413\n",
" 26/1 1.39609 1.44423 +/- 0.01360\n",
" 27/1 1.43661 1.44378 +/- 0.01278\n",
" 28/1 1.38011 1.44025 +/- 0.01256\n",
" 29/1 1.41518 1.43893 +/- 0.01195\n",
" 30/1 1.45954 1.43996 +/- 0.01139\n",
" 31/1 1.44620 1.44025 +/- 0.01083\n",
" 32/1 1.50544 1.44322 +/- 0.01075\n",
" 33/1 1.47331 1.44453 +/- 0.01035\n",
" 34/1 1.38227 1.44193 +/- 0.01025\n",
" 35/1 1.43997 1.44185 +/- 0.00983\n",
" 36/1 1.39731 1.44014 +/- 0.00960\n",
" 37/1 1.49595 1.44221 +/- 0.00946\n",
" 38/1 1.40348 1.44082 +/- 0.00922\n",
" 39/1 1.33228 1.43708 +/- 0.00965\n",
" 40/1 1.40644 1.43606 +/- 0.00938\n",
" 41/1 1.41400 1.43535 +/- 0.00910\n",
" 42/1 1.38516 1.43378 +/- 0.00895\n",
" 43/1 1.38802 1.43239 +/- 0.00879\n",
" 44/1 1.39526 1.43130 +/- 0.00859\n",
" 45/1 1.30116 1.42758 +/- 0.00914\n",
" 46/1 1.43647 1.42783 +/- 0.00888\n",
" 47/1 1.38574 1.42669 +/- 0.00871\n",
" 48/1 1.38079 1.42549 +/- 0.00857\n",
" 49/1 1.45147 1.42615 +/- 0.00837\n",
" 50/1 1.41959 1.42599 +/- 0.00816\n",
" Creating state point statepoint.50.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 0.0000e+00 seconds\n",
" Reading cross sections = 0.0000e+00 seconds\n",
" Total time in simulation = 2.1898e+00 seconds\n",
" Time in transport only = 2.1562e+00 seconds\n",
" Time in inactive batches = 3.8682e-01 seconds\n",
" Time in active batches = 1.8030e+00 seconds\n",
" Time synchronizing fission bank = 5.1998e-03 seconds\n",
" Sampling source sites = 4.5969e-03 seconds\n",
" SEND/RECV source sites = 5.7608e-04 seconds\n",
" Time accumulating tallies = 7.0743e-03 seconds\n",
" Time writing statepoints = 9.8496e-03 seconds\n",
" Total time for finalization = 1.5070e-04 seconds\n",
" Total time elapsed = 2.2008e+00 seconds\n",
" Calculation Rate (inactive) = 25851.7 particles/second\n",
" Calculation Rate (active) = 22185.4 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.43114 +/- 0.00639\n",
" k-effective (Track-length) = 1.42599 +/- 0.00816\n",
" k-effective (Absorption) = 1.42830 +/- 0.00470\n",
" Combined k-effective = 1.42849 +/- 0.00451\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n",
" Creating state point openmc_simulation_n3.h5...\n",
"[openmc.deplete] t=10368000.0 s, dt=2592000 s, source=174\n",
" Maximum neutron transport energy: 20000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 1.37033\n",
" 2/1 1.37110\n",
" 3/1 1.38802\n",
" 4/1 1.36630\n",
" 5/1 1.46033\n",
" 6/1 1.38151\n",
" 7/1 1.47543\n",
" 8/1 1.27561\n",
" 9/1 1.43995\n",
" 10/1 1.47347\n",
" 11/1 1.44212\n",
" 12/1 1.43502 1.43857 +/- 0.00355\n",
" 13/1 1.33714 1.40476 +/- 0.03387\n",
" 14/1 1.39828 1.40314 +/- 0.02401\n",
" 15/1 1.39016 1.40055 +/- 0.01877\n",
" 16/1 1.52402 1.42112 +/- 0.02566\n",
" 17/1 1.39956 1.41804 +/- 0.02191\n",
" 18/1 1.43515 1.42018 +/- 0.01909\n",
" 19/1 1.42497 1.42071 +/- 0.01684\n",
" 20/1 1.39614 1.41826 +/- 0.01527\n",
" 21/1 1.43771 1.42003 +/- 0.01392\n",
" 22/1 1.47738 1.42480 +/- 0.01358\n",
" 23/1 1.48097 1.42913 +/- 0.01322\n",
" 24/1 1.37766 1.42545 +/- 0.01278\n",
" 25/1 1.40317 1.42396 +/- 0.01199\n",
" 26/1 1.41995 1.42371 +/- 0.01121\n",
" 27/1 1.41925 1.42345 +/- 0.01054\n",
" 28/1 1.51134 1.42833 +/- 0.01107\n",
" 29/1 1.43506 1.42869 +/- 0.01048\n",
" 30/1 1.43915 1.42921 +/- 0.00995\n",
" 31/1 1.47963 1.43161 +/- 0.00977\n",
" 32/1 1.38891 1.42967 +/- 0.00951\n",
" 33/1 1.44461 1.43032 +/- 0.00911\n",
" 34/1 1.39490 1.42884 +/- 0.00885\n",
" 35/1 1.39509 1.42749 +/- 0.00859\n",
" 36/1 1.33191 1.42382 +/- 0.00904\n",
" 37/1 1.43944 1.42440 +/- 0.00872\n",
" 38/1 1.38917 1.42314 +/- 0.00849\n",
" 39/1 1.30703 1.41913 +/- 0.00912\n",
" 40/1 1.40722 1.41874 +/- 0.00882\n",
" 41/1 1.42532 1.41895 +/- 0.00853\n",
" 42/1 1.40705 1.41858 +/- 0.00827\n",
" 43/1 1.43005 1.41893 +/- 0.00802\n",
" 44/1 1.53167 1.42224 +/- 0.00846\n",
" 45/1 1.43357 1.42257 +/- 0.00822\n",
" 46/1 1.42545 1.42265 +/- 0.00799\n",
" 47/1 1.35218 1.42074 +/- 0.00800\n",
" 48/1 1.38210 1.41972 +/- 0.00785\n",
" 49/1 1.37741 1.41864 +/- 0.00773\n",
" 50/1 1.36010 1.41718 +/- 0.00767\n",
" Creating state point statepoint.50.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 0.0000e+00 seconds\n",
" Reading cross sections = 0.0000e+00 seconds\n",
" Total time in simulation = 2.1741e+00 seconds\n",
" Time in transport only = 2.1341e+00 seconds\n",
" Time in inactive batches = 3.8098e-01 seconds\n",
" Time in active batches = 1.7932e+00 seconds\n",
" Time synchronizing fission bank = 4.3005e-03 seconds\n",
" Sampling source sites = 3.8080e-03 seconds\n",
" SEND/RECV source sites = 4.6940e-04 seconds\n",
" Time accumulating tallies = 1.6604e-02 seconds\n",
" Time writing statepoints = 8.4289e-03 seconds\n",
" Total time for finalization = 1.1203e-04 seconds\n",
" Total time elapsed = 2.1851e+00 seconds\n",
" Calculation Rate (inactive) = 26248.3 particles/second\n",
" Calculation Rate (active) = 22307 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.41664 +/- 0.00557\n",
" k-effective (Track-length) = 1.41718 +/- 0.00767\n",
" k-effective (Absorption) = 1.41504 +/- 0.00530\n",
" Combined k-effective = 1.41572 +/- 0.00495\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n",
" Creating state point openmc_simulation_n4.h5...\n",
"[openmc.deplete] t=12960000.0 s, dt=2592000 s, source=174\n",
" Maximum neutron transport energy: 20000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 1.46306\n",
" 2/1 1.40413\n",
" 3/1 1.38221\n",
" 4/1 1.39894\n",
" 5/1 1.41542\n",
" 6/1 1.39772\n",
" 7/1 1.45022\n",
" 8/1 1.44785\n",
" 9/1 1.48213\n",
" 10/1 1.46955\n",
" 11/1 1.42723\n",
" 12/1 1.34964 1.38843 +/- 0.03879\n",
" 13/1 1.42135 1.39940 +/- 0.02494\n",
" 14/1 1.37438 1.39315 +/- 0.01871\n",
" 15/1 1.38311 1.39114 +/- 0.01463\n",
" 16/1 1.41977 1.39591 +/- 0.01286\n",
" 17/1 1.33150 1.38671 +/- 0.01424\n",
" 18/1 1.42436 1.39142 +/- 0.01320\n",
" 19/1 1.38779 1.39101 +/- 0.01165\n",
" 20/1 1.38721 1.39063 +/- 0.01043\n",
" 21/1 1.36568 1.38837 +/- 0.00970\n",
" 22/1 1.46070 1.39439 +/- 0.01071\n",
" 23/1 1.33710 1.38999 +/- 0.01080\n",
" 24/1 1.36294 1.38805 +/- 0.01018\n",
" 25/1 1.42953 1.39082 +/- 0.00987\n",
" 26/1 1.38685 1.39057 +/- 0.00924\n",
" 27/1 1.38880 1.39047 +/- 0.00868\n",
" 28/1 1.40920 1.39151 +/- 0.00825\n",
" 29/1 1.42561 1.39330 +/- 0.00800\n",
" 30/1 1.37126 1.39220 +/- 0.00767\n",
" 31/1 1.41916 1.39348 +/- 0.00741\n",
" 32/1 1.43876 1.39554 +/- 0.00736\n",
" 33/1 1.46095 1.39839 +/- 0.00759\n",
" 34/1 1.45793 1.40087 +/- 0.00767\n",
" 35/1 1.50240 1.40493 +/- 0.00841\n",
" 36/1 1.48791 1.40812 +/- 0.00869\n",
" 37/1 1.48119 1.41083 +/- 0.00878\n",
" 38/1 1.41484 1.41097 +/- 0.00847\n",
" 39/1 1.37848 1.40985 +/- 0.00825\n",
" 40/1 1.44824 1.41113 +/- 0.00807\n",
" 41/1 1.39708 1.41068 +/- 0.00782\n",
" 42/1 1.53692 1.41462 +/- 0.00854\n",
" 43/1 1.46509 1.41615 +/- 0.00841\n",
" 44/1 1.37780 1.41502 +/- 0.00824\n",
" 45/1 1.38167 1.41407 +/- 0.00806\n",
" 46/1 1.37516 1.41299 +/- 0.00790\n",
" 47/1 1.35518 1.41143 +/- 0.00784\n",
" 48/1 1.43825 1.41213 +/- 0.00767\n",
" 49/1 1.47710 1.41380 +/- 0.00765\n",
" 50/1 1.29075 1.41072 +/- 0.00807\n",
" Creating state point statepoint.50.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 0.0000e+00 seconds\n",
" Reading cross sections = 0.0000e+00 seconds\n",
" Total time in simulation = 2.2692e+00 seconds\n",
" Time in transport only = 2.2311e+00 seconds\n",
" Time in inactive batches = 3.7992e-01 seconds\n",
" Time in active batches = 1.8893e+00 seconds\n",
" Time synchronizing fission bank = 4.1443e-03 seconds\n",
" Sampling source sites = 3.6657e-03 seconds\n",
" SEND/RECV source sites = 4.5581e-04 seconds\n",
" Time accumulating tallies = 1.1112e-02 seconds\n",
" Time writing statepoints = 9.1483e-03 seconds\n",
" Total time for finalization = 1.8265e-04 seconds\n",
" Total time elapsed = 2.2804e+00 seconds\n",
" Calculation Rate (inactive) = 26321.7 particles/second\n",
" Calculation Rate (active) = 21172.1 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.41804 +/- 0.00536\n",
" k-effective (Track-length) = 1.41072 +/- 0.00807\n",
" k-effective (Absorption) = 1.41232 +/- 0.00474\n",
" Combined k-effective = 1.41568 +/- 0.00449\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n",
" Creating state point openmc_simulation_n5.h5...\n",
"[openmc.deplete] t=15552000.0 (final operator evaluation)\n",
" Maximum neutron transport energy: 20000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 1.44678\n",
" 2/1 1.34716\n",
" 3/1 1.36739\n",
" 4/1 1.39101\n",
" 5/1 1.49718\n",
" 6/1 1.51489\n",
" 7/1 1.44260\n",
" 8/1 1.46439\n",
" 9/1 1.44700\n",
" 10/1 1.38851\n",
" 11/1 1.46101\n",
" 12/1 1.47356 1.46728 +/- 0.00627\n",
" 13/1 1.40065 1.44507 +/- 0.02251\n",
" 14/1 1.35718 1.42310 +/- 0.02713\n",
" 15/1 1.38017 1.41451 +/- 0.02270\n",
" 16/1 1.37180 1.40739 +/- 0.01986\n",
" 17/1 1.52455 1.42413 +/- 0.02370\n",
" 18/1 1.45525 1.42802 +/- 0.02089\n",
" 19/1 1.44822 1.43026 +/- 0.01856\n",
" 20/1 1.37248 1.42449 +/- 0.01758\n",
" 21/1 1.42099 1.42417 +/- 0.01590\n",
" 22/1 1.42216 1.42400 +/- 0.01452\n",
" 23/1 1.45655 1.42650 +/- 0.01359\n",
" 24/1 1.36976 1.42245 +/- 0.01322\n",
" 25/1 1.44990 1.42428 +/- 0.01244\n",
" 26/1 1.32784 1.41825 +/- 0.01310\n",
" 27/1 1.41348 1.41797 +/- 0.01231\n",
" 28/1 1.34169 1.41374 +/- 0.01236\n",
" 29/1 1.41758 1.41394 +/- 0.01169\n",
" 30/1 1.36275 1.41138 +/- 0.01138\n",
" 31/1 1.49931 1.41557 +/- 0.01161\n",
" 32/1 1.45097 1.41717 +/- 0.01118\n",
" 33/1 1.51481 1.42142 +/- 0.01150\n",
" 34/1 1.37432 1.41946 +/- 0.01118\n",
" 35/1 1.48161 1.42194 +/- 0.01101\n",
" 36/1 1.40184 1.42117 +/- 0.01061\n",
" 37/1 1.29647 1.41655 +/- 0.01120\n",
" 38/1 1.45697 1.41800 +/- 0.01089\n",
" 39/1 1.38990 1.41703 +/- 0.01055\n",
" 40/1 1.40550 1.41664 +/- 0.01020\n",
" 41/1 1.46331 1.41815 +/- 0.00998\n",
" 42/1 1.43932 1.41881 +/- 0.00969\n",
" 43/1 1.38469 1.41777 +/- 0.00945\n",
" 44/1 1.42941 1.41812 +/- 0.00917\n",
" 45/1 1.35216 1.41623 +/- 0.00910\n",
" 46/1 1.36468 1.41480 +/- 0.00896\n",
" 47/1 1.38988 1.41413 +/- 0.00874\n",
" 48/1 1.58585 1.41865 +/- 0.00963\n",
" 49/1 1.30513 1.41574 +/- 0.00982\n",
" 50/1 1.33485 1.41371 +/- 0.00979\n",
" Creating state point statepoint.50.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 0.0000e+00 seconds\n",
" Reading cross sections = 0.0000e+00 seconds\n",
" Total time in simulation = 2.2627e+00 seconds\n",
" Time in transport only = 2.2212e+00 seconds\n",
" Time in inactive batches = 3.7561e-01 seconds\n",
" Time in active batches = 1.8871e+00 seconds\n",
" Time synchronizing fission bank = 4.1635e-03 seconds\n",
" Sampling source sites = 3.6738e-03 seconds\n",
" SEND/RECV source sites = 4.6676e-04 seconds\n",
" Time accumulating tallies = 1.9553e-02 seconds\n",
" Time writing statepoints = 9.2177e-03 seconds\n",
" Total time for finalization = 1.9956e-04 seconds\n",
" Total time elapsed = 2.2739e+00 seconds\n",
" Calculation Rate (inactive) = 26623.6 particles/second\n",
" Calculation Rate (active) = 21196.5 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.41716 +/- 0.00736\n",
" k-effective (Track-length) = 1.41371 +/- 0.00979\n",
" k-effective (Absorption) = 1.42694 +/- 0.00479\n",
" Combined k-effective = 1.42526 +/- 0.00491\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n",
" Creating state point openmc_simulation_n6.h5...\n"
]
}
],
"source": [
"integrator.integrate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Processing the outputs\n",
"\n",
"The depletion simulation produces a few output files. First, the statepoint files from each individual transport simulation are written to `openmc_simulation_n<N>.h5`, where `<N>` indicates the current depletion step. Any tallies that we defined in `tallies.xml` will be included in these files across our simulations. We have 7 such files, one for each our of 6 depletion steps and the initial state."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"depletion_results.h5\t openmc_simulation_n3.h5 statepoint.50.h5\n",
"openmc_simulation_n0.h5 openmc_simulation_n4.h5 summary.h5\n",
"openmc_simulation_n1.h5 openmc_simulation_n5.h5\n",
"openmc_simulation_n2.h5 openmc_simulation_n6.h5\n"
]
}
],
"source": [
"!ls *.h5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `depletion_results.h5` file contains information that is aggregated over all time steps through depletion. This includes the multiplication factor, as well as concentrations. We can process this file using the `openmc.deplete.Results` object"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"results = openmc.deplete.Results(\"./depletion_results.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"time, k = results.get_keff()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"time /= (24 * 60 * 60) # convert back to days from seconds"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.45490147, 0.00474236],\n",
" [1.43943243, 0.00475038],\n",
" [1.42781404, 0.0035448 ],\n",
" [1.42849313, 0.00451031],\n",
" [1.41571572, 0.00495453],\n",
" [1.41567621, 0.00449157],\n",
" [1.42525626, 0.00491492]])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first column of `k` is the value of `k-combined` at each point in our simulation, while the second column contains the associated uncertainty. We can plot this using `matplotlib`"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyplot.errorbar(time, k[:, 0], yerr=k[:, 1])\n",
"pyplot.xlabel(\"Time [d]\")\n",
"pyplot.ylabel(\"$k_{eff}\\pm \\sigma$\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Due to the low number of particles selected, the uncertainty on each value is rather high. However, we can still see the decline in `k` over time due to fuel consumption."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then examine concentrations of atoms in each of our materials. This requires knowing the material ID, which can be obtained from the `materials.xml` file."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"_, u235 = results.get_atoms(\"1\", \"U235\")\n",
"_, xe135 = results.get_atoms(\"1\", \"Xe135\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyplot.plot(time, u235, label=\"U235\")\n",
"pyplot.xlabel(\"Time [d]\")\n",
"pyplot.ylabel(\"Number of atoms - U235\");"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyplot.plot(time, xe135, label=\"Xe135\")\n",
"pyplot.xlabel(\"Time [d]\")\n",
"pyplot.ylabel(\"Number of atoms - Xe135\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also examine reaction rates over time using the `Results` object."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"_, u235_fission = results.get_reaction_rate(\"1\", \"U235\", \"fission\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyplot.plot(time, u235_fission)\n",
"pyplot.xlabel(\"Time [d]\")\n",
"pyplot.ylabel(\"Fission reactions / s\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Helpful tips\n",
"\n",
"Depletion is a tricky task to get correct. Use too short of a time step, and you will incur a steep cost in computational time due to excessive transport simulations. Use too long of a time step, and you may miss physics happening on shorter timescales, leading to 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 it 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. Pins experiencing different flux will deplete at different rates. 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",
"Fuel pins with integrated burnable absorbers, like gadolinia, experience strong flux gradients until the absorbers are mostly burned away. This means that the spectrum and magnitude of the flux at the edge of the fuel pin can be vastly different than that in the interior. The helper `pin` function can be used to subdivide regions into equal volume segments, as follows."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"div_surfs_1 = [openmc.ZCylinder(r=1)]\n",
"div_1 = openmc.model.pin(div_surfs_1, [fuel, water], subdivisions={0: 10})"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fb9dfe7a7c0>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 258.065x259.74 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"div_1.plot(width=(2.0, 2.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The innermost region has been divided into 10 equal volume regions. We can pass additional arguments to divide multiple regions, except for the region outside the last cylinder."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register depletion chain\n",
"\n",
"The depletion chain we created can be registered into the OpenMC `cross_sections.xml` file, so we don't have to always pass the `chain_file` argument to the `Operator`. To do this, we create a `DataLibrary` using `openmc.data`. Without any arguments, the `from_xml` method will look for the file located at `OPENMC_CROSS_SECTIONS`. For this example, we will just create a bare library."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"data_lib = openmc.data.DataLibrary()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"data_lib.register_file(\"./chain_simple.xml\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"data_lib.export_to_xml()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<?xml version='1.0' encoding='utf-8'?>\n",
"<cross_sections>\n",
" <depletion_chain path=\"chain_simple.xml\" type=\"depletion_chain\" />\n",
"</cross_sections>\n"
]
}
],
"source": [
"!cat cross_sections.xml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This allows us to make an `Operator` simply with the geometry and settings arguments, provided we exported our library to `OPENMC_CROSS_SECTIONS`. For a problem where we built and registered a `Chain` using all the available nuclear data, we might see something like the following."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"model = openmc.Model(geometry=geometry, settings=settings)\n",
"new_op = openmc.deplete.CoupledOperator(model, \"./chain_simple.xml\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_op.chain.nuclide_dict)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[nuc.name for nuc in new_op.chain.nuclides[:10]]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[nuc.name for nuc in new_op.chain.nuclides[-10:]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Choice of depletion step size\n",
"\n",
"A general rule of thumb is to use depletion step sizes around 2 MWd/kgHM, where kgHM is really the initial heavy metal mass in kg. If your problem includes integral burnable absorbers, these typically require shorter time steps at or below 1 MWd/kgHM. These are typically valid for the predictor scheme, as the point of recent schemes is to extend this step size. A good convergence study, where the step size is decreased until some convergence metric is satisfied, is a beneficial exercise.\n",
"\n",
"We can use the `Operator` to determine our maximum step size using this recommendation. The `heavy_metal` attribute returns the mass of initial heavy metal in g, which, using our power, can be used to compute this step size. $$\\frac{2\\,MWd}{kgHM} = \\frac{P\\times\\Delta}{hm_{op}}$$"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.080339195444219"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"operator.heavy_metal"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"max_step = 2 * operator.heavy_metal / power * 1E3"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"Maximum\" depletion step: 58.4 [d]\n"
]
}
],
"source": [
"print(\"\\\"Maximum\\\" depletion step: {:5.3} [d]\".format(max_step))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, if we were provided the power density of our problem, we can provide this directly with `openmc.deplete.PredictorIntegrator(operator, time_steps, power_density=pdens)`. The values of `power` and `power_density` do not have to be scalars. For problems with variable power, we can provide an iterable with the same number of elements as `time_steps`."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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