{ "cells": [ { "cell_type": "markdown", "id": "4d724eae", "metadata": {}, "source": [ "# Getting Started\n", "\n", "It is relatively easy to get going with a quick simulation in OpenPNM.\n", "In fact the following code block produces a mercury intrusion simulation in\n", "just a few lines." ] }, { "cell_type": "markdown", "id": "d122c429", "metadata": {}, "source": [ "## Creating a Cubic Network" ] }, { "cell_type": "code", "execution_count": 1, "id": "1cc50017", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "══════════════════════════════════════════════════════════════════════════════\n", "net : \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " # Properties Valid Values\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " 2 pore.coords 1000 / 1000\n", " 3 throat.conns 2700 / 2700\n", " 4 pore.coordination_number 1000 / 1000\n", " 5 pore.max_size 1000 / 1000\n", " 6 throat.spacing 2700 / 2700\n", " 7 pore.seed 1000 / 1000\n", " 8 pore.diameter 1000 / 1000\n", " 9 throat.max_size 2700 / 2700\n", " 10 throat.diameter 2700 / 2700\n", " 11 throat.cross_sectional_area 2700 / 2700\n", " 12 throat.hydraulic_size_factors 2700 / 2700\n", " 13 throat.diffusive_size_factors 2700 / 2700\n", " 14 throat.lens_volume 2700 / 2700\n", " 15 throat.length 2700 / 2700\n", " 16 throat.total_volume 2700 / 2700\n", " 17 throat.volume 2700 / 2700\n", " 18 pore.volume 1000 / 1000\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " # Labels Assigned Locations\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " 2 pore.xmin 100\n", " 3 pore.xmax 100\n", " 4 pore.ymin 100\n", " 5 pore.ymax 100\n", " 6 pore.zmin 100\n", " 7 pore.zmax 100\n", " 8 pore.surface 488\n", " 9 throat.surface 972\n", " 10 pore.left 100\n", " 11 pore.right 100\n", " 12 pore.front 100\n", " 13 pore.back 100\n", " 14 pore.bottom 100\n", " 15 pore.top 100\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n" ] } ], "source": [ "import openpnm as op\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "op.visualization.set_mpl_style()\n", "\n", "Nx, Ny, Nz = 10, 10, 10\n", "Lc = 1e-4\n", "pn = op.network.Cubic([Nx, Ny, Nz], spacing=Lc)\n", "pn.add_model_collection(op.models.collections.geometry.spheres_and_cylinders)\n", "pn.regenerate_models()\n", "print(pn)" ] }, { "cell_type": "markdown", "id": "90c0fe86-4466-463c-a328-106e767ee421", "metadata": {}, "source": [ "## Defining a Phase" ] }, { "cell_type": "code", "execution_count": 2, "id": "39c7e190-2caf-4fa8-ba76-d56fae4d07b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "══════════════════════════════════════════════════════════════════════════════\n", "phase_01 : \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " # Properties Valid Values\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " 2 pore.temperature 1000 / 1000\n", " 3 pore.pressure 1000 / 1000\n", " 4 throat.contact_angle 2700 / 2700\n", " 5 pore.thermal_conductivity 1000 / 1000\n", " 6 pore.surface_tension 1000 / 1000\n", " 7 pore.viscosity 1000 / 1000\n", " 8 pore.density 1000 / 1000\n", " 9 pore.molar_density 1000 / 1000\n", " 10 throat.entry_pressure 2700 / 2700\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " # Labels Assigned Locations\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " 2 pore.all 1000\n", " 3 throat.all 2700\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n" ] } ], "source": [ "hg = op.phase.Mercury(network=pn)\n", "hg.add_model(propname='throat.entry_pressure',\n", " model=op.models.physics.capillary_pressure.washburn)\n", "hg.regenerate_models()\n", "print(hg)" ] }, { "cell_type": "markdown", "id": "1ce76fea-8224-4eec-812e-e20986068787", "metadata": {}, "source": [ "## Performing a Drainage Simulation" ] }, { "cell_type": "code", "execution_count": 3, "id": "cb0f3ee8-2d91-4a28-9bda-6e1abf363da9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a08646409dcb47d3a2d26aef010a5c20", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Performing drainage simulation: 0%| | 0/50 [00:00" ] }, "metadata": { "image/png": { "height": 383, "width": 534 } }, "output_type": "display_data" } ], "source": [ "data = mip.pc_curve()\n", "fig, ax = plt.subplots(figsize=(5.5, 4))\n", "ax.semilogx(data.pc, data.snwp, 'k-o')\n", "ax.set_xlabel('capillary pressure [Pa]')\n", "ax.set_ylabel('mercury saturation');" ] }, { "cell_type": "markdown", "id": "7f5c50c1", "metadata": {}, "source": [ "## Calculating Permeability Coefficient\n", "\n", "As another example, the permeability coefficient can be found as follows:" ] }, { "cell_type": "code", "execution_count": 5, "id": "aca961f1", "metadata": {}, "outputs": [], "source": [ "# Generate phase and physics\n", "water = op.phase.Water(network=pn)\n", "water.add_model(propname='throat.hydraulic_conductance',\n", " model=op.models.physics.hydraulic_conductance.generic_hydraulic)\n", "\n", "# Create algorithm, set boundary conditions and run simulation\n", "sf = op.algorithms.StokesFlow(network=pn, phase=water)\n", "Pin, Pout = (200_000, 101_325)\n", "sf.set_value_BC(pores=pn.pores('left'), values=Pin)\n", "sf.set_value_BC(pores=pn.pores('right'), values=Pout)\n", "sf.run()" ] }, { "cell_type": "markdown", "id": "010bcf56", "metadata": {}, "source": [ "The total flow rate into the domain through the boundary pores can be found\n", "using ``sf.rate(pores=pn.pores('xmin'))``. The permeability coefficient\n", "can be found by inserting known values into Darcy's law as follows:" ] }, { "cell_type": "code", "execution_count": 6, "id": "a9e72cc5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6.44364084e-13]\n" ] } ], "source": [ "Q = sf.rate(pores=pn.pores('left'))\n", "A = Ny*Nz*Lc**2\n", "L = Nx*Lc\n", "mu = water['pore.viscosity'].mean()\n", "K = Q*mu*L/(A*(Pin-Pout))\n", "print(K)" ] }, { "cell_type": "markdown", "id": "a4cdab94", "metadata": {}, "source": [ "## Adjusting Pore Size Distribution\n", "\n", "It's also worth explaining how to adjust the pore size distribution of the network, so that the capillary curve and permeability coefficient can be changed to match known values. The ``geo`` object controls the geometric properties, and it possess models to calculate values on demand. Let's change the pore size distribution to a Weibull distribution, but first let's store the existing values in a dummy variable so we can compare later." ] }, { "cell_type": "code", "execution_count": 7, "id": "ab23c414", "metadata": {}, "outputs": [], "source": [ "import openpnm.models.geometry as gmods\n", "\n", "pn['pore.old_diameter'] = pn.pop('pore.diameter')\n", "pn.add_model(propname='pore.diameter',\n", " model=gmods.pore_size.weibull,\n", " shape=0.5, loc=0, scale=1e-5)" ] }, { "cell_type": "markdown", "id": "252bdeb6", "metadata": {}, "source": [ "Now you can use `matplotlib.` to get a quick glance at the histograms of the two distributions." ] }, { "cell_type": "code", "execution_count": 8, "id": "1aae9dfd", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "image/png": { "height": 434, "width": 584 } }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots(figsize=(6, 4.5))\n", "ax.hist(pn['pore.diameter'], edgecolor='k', label='new diameter')\n", "ax.hist(pn['pore.old_diameter'], edgecolor='k', label='old_diameter', bins=20)\n", "ax.set_xlabel('diameter [m]')\n", "ax.set_ylabel('frequency')\n", "ax.legend();" ] }, { "cell_type": "markdown", "id": "0251bc1d", "metadata": {}, "source": [ "More complex tasks are explained in the [examples](../_examples/index) page." ] } ], "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }