\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ " # Properties Valid Values\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ " 2 throat.conns 2510 / 2510\n",
+ " 3 pore.coords 1420 / 1420\n",
+ " 4 pore.region_label 1420 / 1420\n",
+ " 5 pore.phase 1420 / 1420\n",
+ " 6 throat.phases 2510 / 2510\n",
+ " 7 pore.region_volume 1420 / 1420\n",
+ " 8 pore.equivalent_diameter 1420 / 1420\n",
+ " 9 pore.local_peak 1420 / 1420\n",
+ " 10 pore.global_peak 1420 / 1420\n",
+ " 11 pore.geometric_centroid 1420 / 1420\n",
+ " 12 throat.global_peak 2510 / 2510\n",
+ " 13 pore.inscribed_diameter 1420 / 1420\n",
+ " 14 pore.extended_diameter 1420 / 1420\n",
+ " 15 throat.inscribed_diameter 2510 / 2510\n",
+ " 16 throat.total_length 2510 / 2510\n",
+ " 17 throat.direct_length 2510 / 2510\n",
+ " 18 throat.perimeter 2510 / 2510\n",
+ " 19 pore.volume 1420 / 1420\n",
+ " 20 pore.surface_area 1420 / 1420\n",
+ " 21 throat.cross_sectional_area 2510 / 2510\n",
+ " 22 throat.equivalent_diameter 2510 / 2510\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ " # Labels Assigned Locations\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ " 2 pore.all 1420\n",
+ " 3 throat.all 2510\n",
+ " 4 pore.boundary 180\n",
+ " 5 pore.xmin 103\n",
+ " 6 pore.xmax 77\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(pn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dcd1152d",
+ "metadata": {},
+ "source": [
+ "When we update the SNOW algorithm to `snow2` we removed many of the opinionated decision that the original version made. For instance, `snow2` does not pick which diameter should be the *definitive* one, so this decision needs to be made by the user once they can the network into OpenPNM. This is illustrated below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "f1aa6c19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pn['pore.diameter'] = pn['pore.equivalent_diameter']\n",
+ "pn['throat.diameter'] = pn['throat.inscribed_diameter']\n",
+ "pn['throat.spacing'] = pn['throat.total_length']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6cc07911",
+ "metadata": {},
+ "source": [
+ "The user also needs to decide which 'shape' to assume for the pores and throats, which impacts how the transport conductance values are computed. Here we use the `pyramids_and_cuboid` models:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "f32e2f79",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pn.add_model(propname='throat.hydraulic_size_factors',\n",
+ " model=op.models.geometry.hydraulic_size_factors.pyramids_and_cuboids)\n",
+ "pn.add_model(propname='throat.diffusive_size_factors',\n",
+ " model=op.models.geometry.diffusive_size_factors.pyramids_and_cuboids)\n",
+ "pn.regenerate_models()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20104b10",
+ "metadata": {},
+ "source": [
+ "More information about the \"size factors\" can be found in [this example](https://openpnm.org/examples/reference/simulations/size_factors_and_transport_conductance.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "590326c1",
+ "metadata": {},
+ "source": [
+ "One issue that can happen in extracted networks is that some pores can be isolated from the remainder of the network. The problem with 'disconnected networks' is discussed in detail [here](https://openpnm.org/examples/reference/networks/managing_clustered_networks.html). The end result is that we need to first check the network health, then deal with any problems:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "7868024f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ "Key Value\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ "headless_throats []\n",
+ "looped_throats []\n",
+ "isolated_pores [456, 574, 783, 1023, 1115, 1158, 1179]\n",
+ "disconnected_pores [227, 246, 247, 319, 362, 456, 574, 783, 1023, 1115, 1158, 1179, 1181, 1189, 1204, 1345, 1346, 1347]\n",
+ "duplicate_throats []\n",
+ "bidirectional_throats []\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
+ ]
+ }
+ ],
+ "source": [
+ "h = op.utils.check_network_health(pn)\n",
+ "print(h)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6247f065",
+ "metadata": {},
+ "source": [
+ "The above output shows there are both 'single' isolated pores as well as 'groups' of pores that are disconnected. These all need to be \"trimmed\". We can use the list provided by `h['disconnected_pores']` directly in the `trim` function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "e6975f8c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ "Key Value\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
+ "headless_throats []\n",
+ "looped_throats []\n",
+ "isolated_pores []\n",
+ "disconnected_pores []\n",
+ "duplicate_throats []\n",
+ "bidirectional_throats []\n",
+ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
+ ]
+ }
+ ],
+ "source": [
+ "op.topotools.trim(network=pn, pores=h['disconnected_pores'])\n",
+ "h = op.utils.check_network_health(pn)\n",
+ "print(h)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bdb57f8c",
+ "metadata": {},
+ "source": [
+ "Now we are ready to perform some simulations, so let's create a phase object to compute the thermophysical properties and transport conductances:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "ebe5084d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "[17:21:55] WARNING throat.entry_pressure was not run since the following property is missing: _models.py:480 \n",
+ " 'throat.surface_tension' \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m[17:21:55]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m throat.entry_pressure was not run since the following property is missing: \u001b[2m_models.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m480\u001b[0m\n",
+ "\u001b[2;36m \u001b[0m \u001b[32m'throat.surface_tension'\u001b[0m \u001b[2m \u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gas = op.phase.Phase(network=pn)\n",
+ "gas['pore.diffusivity'] = 1.0\n",
+ "gas['pore.viscosity'] = 1.0\n",
+ "gas.add_model_collection(op.models.collections.physics.basic)\n",
+ "gas.regenerate_models()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "609a0594",
+ "metadata": {},
+ "source": [
+ "Finally we can do a Fickian diffusion simulation to find the formation factor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "1943e69d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The Formation factor of the extracted network is 25.098142684548733\n",
+ "The compares to a value of 23.12 from DNS\n"
+ ]
+ }
+ ],
+ "source": [
+ "fd = op.algorithms.FickianDiffusion(network=pn, phase=gas)\n",
+ "fd.set_value_BC(pores=pn.pores('xmin'), values=1.0)\n",
+ "fd.set_value_BC(pores=pn.pores('xmax'), values=0.0)\n",
+ "fd.run()\n",
+ "dC = 1.0\n",
+ "L = (data['shape']['x'] + 6)*data['resolution']\n",
+ "A = data['shape']['y']*data['shape']['z']*data['resolution']**2\n",
+ "Deff = fd.rate(pores=pn.pores('xmin'))*(L/A)/dC\n",
+ "F = 1/Deff\n",
+ "print(f\"The Formation factor of the extracted network is {F[0]}\")\n",
+ "print(f\"The compares to a value of {data['formation factor']['Fx']} from DNS\")\n",
+ "np.testing.assert_allclose(F, data['formation factor']['Fx'], rtol=0.09)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "89681eea",
+ "metadata": {},
+ "source": [
+ "And a Stokes flow simulation to find Permeability coefficient:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "4a500b65",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Permeability coefficient is 1.3181940680110902 Darcy\n",
+ "The compares to a value of 1.36 from DNS\n"
+ ]
+ }
+ ],
+ "source": [
+ "sf = op.algorithms.StokesFlow(network=pn, phase=gas)\n",
+ "sf.set_value_BC(pores=pn.pores('xmin'), values=1.0)\n",
+ "sf.set_value_BC(pores=pn.pores('xmax'), values=0.0)\n",
+ "sf.run()\n",
+ "dP = 1.0\n",
+ "L = (data['shape']['x'] + 6)*data['resolution']\n",
+ "A = data['shape']['y']*data['shape']['z']*data['resolution']**2\n",
+ "K = sf.rate(pores=pn.pores('xmin'))*(L/A)/dP*1e12\n",
+ "print(f'Permeability coefficient is {K[0]} Darcy')\n",
+ "print(f\"The compares to a value of {data['permeability']['Kx']/1000} from DNS\")\n",
+ "np.testing.assert_allclose(K, data['permeability']['Kx']/1000, rtol=0.05)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8597118",
+ "metadata": {},
+ "source": [
+ "Both of the above simulations agree quite well with the known values for this sample. This is not always the case because network extraction is not always perfect. One problem that can occur is that the pore sizes are much too small due to anisotropic materials. In other cases the pores are too large and overlap each other too much. Basically, the user really need to double, then triple check that their extraction is 'sane'. It is almost mandatory to compare the extraction to some known values as we have done above. It's also a good idea visualize the network in Paraview, as explained [here](https://openpnm.org/examples/tutorials/10_visualization_options.html), which can reveal problems. "
+ ]
+ }
+ ],
+ "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": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/applications/relative_diffusivity.ipynb b/examples/applications/relative_diffusivity.ipynb
index 692e1bd6b4..a4ddd0769b 100644
--- a/examples/applications/relative_diffusivity.ipynb
+++ b/examples/applications/relative_diffusivity.ipynb
@@ -148,7 +148,7 @@
"id": "3c85f5fa",
"metadata": {},
"source": [
- "To update calculate the saturation based on occupancy from each pressure point, we created a costum function:"
+ "To update the saturation based on occupancy from each pressure point, we created a custom function:"
]
},
{
@@ -184,7 +184,7 @@
"id": "7e4dc401",
"metadata": {},
"source": [
- "Similar to the relative permeability example, the geometrical parameters in relative diffusivity can cancell out. Therefore, the remaining fraction includes diffusion rates as follows:\n",
+ "Similar to the relative permeability example, the geometrical parameters in relative diffusivity can cancel out. Therefore, the remaining fraction includes diffusion rates as follows:\n",
"\n",
"$$\n",
"D_{r}=\\frac {D_\\text{eff-multiphase}}{D_\\text{eff-single phase}}=\\frac {Rate_\\text{multiphase}}{Rate_\\text{single phase}}\n",
@@ -193,7 +193,7 @@
"Where $Rate_\\text{multiphase}$ is the inlet diffusion rate of the phase (water/air) for a Fickian diffusion algorithm with `multiphase conduit_conductance` assigned as conduit diffusive conductance model to account for the existence of the other phase. Whereas $Rate_\\text{single phase}$ is the inlet diffusion rate of the phase (water/air) for a Fickian diffusion algorithm with `generic_diffusive_conductance` assigned as diffusive conductance model for a single phase flow.\n",
"\n",
"\n",
- "Note that $D_\\text{eff-single phase}$ is a property of the porous material, not the fluid. However, here to cancel out diffusivity in the $D_{r}$ fraction, the same phase must be used to calculate $Rate_\\text{single phase}$. Therefore, we only need to find the rate values for calcuating the relative permeability.\n",
+ "Note that $D_\\text{eff-single phase}$ is a property of the porous material, not the fluid. However, here to cancel out diffusivity in the $D_{r}$ fraction, the same phase must be used to calculate $Rate_\\text{single phase}$. Therefore, we only need to find the rate values for calculating the relative permeability.\n",
"We define a function `Rate_calc`, which simulates a Fickian diffusion given the diffusive conductance keyword and returns the rate diffusion at inlet pores. "
]
},
@@ -374,7 +374,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/examples/applications/relative_permeability.ipynb b/examples/applications/relative_permeability.ipynb
index faf2599117..6cfd719830 100644
--- a/examples/applications/relative_permeability.ipynb
+++ b/examples/applications/relative_permeability.ipynb
@@ -99,7 +99,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The next step is to apply an invasion percolation algorithm to obtain the invasion sequence. Asumming a drainage process, the air(invading/non-wetting phase) will be invading the medium. The IP algorithm can be impplemented through a user-defined inlet face. Here, we use the left surface pores in the x direction. By updating the air phase, the invasion sequence can then be found using the phase occupancy which is a property of the phase."
+ "The next step is to apply an invasion percolation algorithm to obtain the invasion sequence. Assuming a drainage process, the air(invading/non-wetting phase) will be invading the medium. The IP algorithm can be implemented through a user-defined inlet face. Here, we use the left surface pores in the x direction. By updating the air phase, the invasion sequence can then be found using the phase occupancy which is a property of the phase."
]
},
{
@@ -133,7 +133,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To update the occupancy of phases at each saturation point, we created a costum function:"
+ "To update the occupancy of phases at each saturation point, we created a custom function:"
]
},
{
@@ -189,7 +189,7 @@
"\n",
"\n",
"Note that $K_{abs}$ is a property of the porous material, not the fluid. However, here to cancel out viscosity in the $K_{rphase}$ fraction, the same phase must be used to calculate $Q_{abs-phase}$. Therefore, naming `abs-phase` was used here to indicate the flow rate from a single phase flow algorithm for the same phase that relative permeability is being calculated.\n",
- "Therefore, we only need to find the rate values for calcuating the relative permeability. \n",
+ "Therefore, we only need to find the rate values for calculating the relative permeability. \n",
"\n",
"\n",
"We define a function `Rate_calc`, which simulates a stokes flow given the hydraulic conductance keyword and returns the rate of inlet fluid flow. "
@@ -216,7 +216,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "In this example we are focusing on finding the relativep permeabilities in x direction. A similar procedure can be applied on y and z directions. Let's define inlet and outlet pores:"
+ "In this example we are focusing on finding the relative permeabilities in x direction. A similar procedure can be applied on y and z directions. Let's define inlet and outlet pores:"
]
},
{
@@ -240,7 +240,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Assigning multiphase conductance models to the phases. The multiphase conduit conductance accounts for the presence of the other phase in effective permeability calculations. For more details, plesase see the model's code in OpenPNM package."
+ "Assigning multiphase conductance models to the phases. The multiphase conduit conductance accounts for the presence of the other phase in effective permeability calculations. For more details, please see the model's code in OpenPNM package."
]
},
{
@@ -359,7 +359,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.9.13 ('sci')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -373,7 +373,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.10.13"
},
"vscode": {
"interpreter": {
diff --git a/examples/tutorials/01_numerical_python_primer.ipynb b/examples/tutorials/01_numerical_python_primer.ipynb
index eeb01f3b30..6937fd9c8f 100644
--- a/examples/tutorials/01_numerical_python_primer.ipynb
+++ b/examples/tutorials/01_numerical_python_primer.ipynb
@@ -279,7 +279,7 @@
"metadata": {},
"source": [
"```{seealso}\n",
- "There are many resources for learning and understaning Numpy arrays, such as the [official numpy user guide](https://numpy.org/doc/stable/user/index.html#user) and this fun [illustrated guide](https://betterprogramming.pub/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d).\n",
+ "There are many resources for learning and understanding Numpy arrays, such as the [official numpy user guide](https://numpy.org/doc/stable/user/index.html#user) and this fun [illustrated guide](https://betterprogramming.pub/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d).\n",
"```"
]
},
@@ -308,7 +308,7 @@
"id": "dfa588e4",
"metadata": {},
"source": [
- "This is an example of the 'mini-language' that you needs to learn, since ``arange`` is the Numpy version of ``range``. The Numpy package has hundreds of functions available, and to be proficient with Numpy you need to at least be aware of most of them. You can see a list by typing ``dir(np)``. "
+ "This is an example of the 'mini-language' that you need to learn, since ``arange`` is the Numpy version of ``range``. The Numpy package has hundreds of functions available, and to be proficient with Numpy you need to at least be aware of most of them. You can see a list by typing ``dir(np)``. "
]
},
{
@@ -959,7 +959,7 @@
"source": [
"In the above cell block the statement within the ``try`` block raises and exception, which is caught in the ``except`` block, and the error message is printed indicating clearly what the problem is.\n",
"\n",
- "We can implememnt this behavior in our ``NewDict`` class above as follows:"
+ "We can implement this behavior in our ``NewDict`` class above as follows:"
]
},
{
@@ -1135,7 +1135,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/examples/tutorials/02_network_generation_and_visualization.ipynb b/examples/tutorials/02_network_generation_and_visualization.ipynb
index 5944829408..e4d6fe7a53 100644
--- a/examples/tutorials/02_network_generation_and_visualization.ipynb
+++ b/examples/tutorials/02_network_generation_and_visualization.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Generating Networks and Quick Visualization\n",
"\n",
- "OpenPNM includes a variety of network generators. This notebook cover the use of a few commons ones which is a good primer for for the rest of this tutorial series. It also illustrates how to quickly visualize networks for inspection while developing and troubleshoot. Full visualization is best done in a dedicated software like paraview or draongfly."
+ "OpenPNM includes a variety of network generators. This notebook covers the use of a few commons ones which is a good primer for the rest of this tutorial series. It also illustrates how to quickly visualize networks for inspection while developing and troubleshooting. Full visualization is best done in a dedicated software like paraview or Dragonfly."
]
},
{
@@ -226,22 +226,24 @@
"id": "971cf3b8",
"metadata": {},
"source": [
- "The above printout reveals for instance that `pn` has only two \"properties\": 'pore.coords' and 'throat.conns'. These are discussed further below. Also shown are a several \"labels\" that were added to the network during generation. These are just `boolean` arrays with `True` values indicating where the label is found, and `False` elsewhere. This convention is discussed at length in the next tutorial. It is worth illustrating here how these can be used. Each OpenPNM object has a `pores` and a `throats` method, which returns the pore or throat indices where a given label or set of labels is present:"
+ "The above printout reveals for instance that `pn` has only two \"properties\": 'pore.coords' and 'throat.conns'. These are discussed further below. Also shown are several \"labels\" that were added to the network during generation. These are just `boolean` arrays with `True` values indicating where the label is found, and `False` elsewhere. This convention is discussed at length in the next tutorial. It is worth illustrating here how these can be used. Each OpenPNM object has a `pores` and a `throats` method, which returns the pore or throat indices where a given label or set of labels is present:"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 1,
"id": "d080aa29",
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The following pores are labelled 'back': [12 13 14 15 28 29 30 31 44 45 46 47 60 61 62 63]\n",
- "The following pores are labelled 'back' AND 'left': [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 28 29 30 31 44 45 46 47\n",
- " 60 61 62 63]\n"
+ "ename": "NameError",
+ "evalue": "name 'pn' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Ps \u001b[38;5;241m=\u001b[39m \u001b[43mpn\u001b[49m\u001b[38;5;241m.\u001b[39mpores(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mback\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe following pores are labelled \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mback\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m, Ps)\n\u001b[0;32m 3\u001b[0m Ps \u001b[38;5;241m=\u001b[39m pn\u001b[38;5;241m.\u001b[39mpores([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mback\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'pn' is not defined"
]
}
],
@@ -249,7 +251,7 @@
"Ps = pn.pores('back')\n",
"print(\"The following pores are labelled 'back':\", Ps)\n",
"Ps = pn.pores(['back', 'left'])\n",
- "print(\"The following pores are labelled 'back' AND 'left':\", Ps)"
+ "print(\"The following pores are labelled 'back' OR 'left':\", Ps)"
]
},
{
@@ -282,7 +284,7 @@
"metadata": {},
"source": [
"## CubicTemplates\n",
- "It's possible to make a cubic network with an arbitrary shape, like a sphere or cylinder, or even more complex, though it wil still have a cubic lattice topology. This might be useful for simulating transport in a catalyst pellet for instance. This requires creating a boolean image with `True` values indicating the shape of the network. OpenPNM has a few helper functions, but you are free to create any shape. "
+ "It's possible to make a cubic network with an arbitrary shape, like a sphere or cylinder, or even more complex, though it will still have a cubic lattice topology. This might be useful for simulating transport in a catalyst pellet for instance. This requires creating a boolean image with `True` values indicating the shape of the network. OpenPNM has a few helper functions, but you are free to create any shape. "
]
},
{
@@ -318,7 +320,7 @@
"source": [
"## Other Cubic Lattices\n",
"\n",
- "Another useful and related set of networks are `FaceCenteredCubic` and `BodyCenteredCubic` lattices. These are very helpful for creating higher porosity networks since it jams extra pores into the interstial spaces:"
+ "Another useful and related set of networks are `FaceCenteredCubic` and `BodyCenteredCubic` lattices. These are very helpful for creating higher porosity networks since it jams extra pores into the interstitial spaces:"
]
},
{
@@ -376,7 +378,7 @@
"id": "47c6d38c",
"metadata": {},
"source": [
- "The fcc and bcc lattices include labels indicating whether pores are are center, corner, or face pores, as well as labels for throats indidcating which type of pores they connect. These labels can be used in the `plot_connections` and `plot_coordinates` functions to show only a portion of the pores and throats or to color them each differently."
+ "The fcc and bcc lattices include labels indicating whether pores are center, corner, or face pores, as well as labels for throats indicating which type of pores they connect. These labels can be used in the `plot_connections` and `plot_coordinates` functions to show only a portion of the pores and throats or to color them each differently."
]
},
{
@@ -576,7 +578,7 @@
"id": "eb2e85e5",
"metadata": {},
"source": [
- "It is also possible to create these lattices in cylindrical and spherical shapes. This is done via the `shape` argument such that [x, y, z] assumes cartesian, [r, z] assumes cylindrical and [r] assumes spherical:"
+ "It is also possible to create these lattices in cylindrical and spherical shapes. This is done via the `shape` argument such that [x, y, z] assumes Cartesian, [r, z] assumes cylindrical and [r] assumes spherical:"
]
},
{
@@ -765,7 +767,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.12"
},
"toc-autonumbering": true,
"toc-showcode": false,
diff --git a/examples/tutorials/03_data_and_topology_storage.ipynb b/examples/tutorials/03_data_and_topology_storage.ipynb
index 8931188d6c..e20c677b05 100644
--- a/examples/tutorials/03_data_and_topology_storage.ipynb
+++ b/examples/tutorials/03_data_and_topology_storage.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Storage of Network Data and Topology\n",
"\n",
- "As discussed in the first tutorial, OpenPNM uses Numpy ``ndarrays`` to store all data, and these arrays store all stored in ``dict``s, because all OpenPNM objects are subclassed python dictionaries. In this tutorial will take a deper dive into the details of the OpenPNM data storage scheme. The sections to be covered are:\n",
+ "As discussed in the first tutorial, OpenPNM uses Numpy ``ndarrays`` to store all data, and these arrays are all stored in ``dict``s, because all OpenPNM objects are subclassed python dictionaries. In this tutorial will take a deeper dive into the details of the OpenPNM data storage scheme. The sections to be covered are:\n",
"\n",
"- The Spreadsheet Analogy \n",
"- Maintaining Data Integrity\n",
@@ -24,7 +24,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 32,
"metadata": {
"tags": []
},
@@ -41,12 +41,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's use [pandas](https://pandas.pydata.org/) to express the geometric properties as a 'spreadsheet' and pull out all pore properties that are are a single column wide"
+ "Let's use [pandas](https://pandas.pydata.org/) to express the geometric properties as a 'spreadsheet' and pull out all pore properties that are as wide as a single column."
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 33,
"metadata": {
"tags": []
},
@@ -65,7 +65,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 34,
"metadata": {
"tags": []
},
@@ -149,7 +149,7 @@
"3 0.055213 "
]
},
- "execution_count": 4,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -174,7 +174,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 35,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.060457Z",
@@ -210,7 +210,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 36,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.071219Z",
@@ -237,7 +237,7 @@
"metadata": {},
"source": [
"```{warning}\n",
- "The spreadsheet analogy is very apt, but it breaks down slightly when one considers data that is multiple colums wide such as **'pore.coords'**. The is stored as an Np-by-3 array in the network dictionary, but spreadsheets usually require that all data is only a single column wide. \n",
+ "The spreadsheet analogy is very apt, but it breaks down slightly when one considers data that is multiple columns wide such as **'pore.coords'**. The is stored as an Np-by-3 array in the network dictionary, but spreadsheets usually require that all data is only a single column wide. \n",
"```"
]
},
@@ -247,7 +247,7 @@
"source": [
"### Rules to Maintain Data Integrity\n",
"\n",
- "Several rules have been implemented to control the integrity of the data. This is implemented by subclassing the `__setitem__` method of the the `dict`. Each time data is assigned to a dictionary key, OpenPNM first checks the data type and dictionary key to ensure the rules are followed. Each of these checks are explored below.\n",
+ "Several rules have been implemented to control the integrity of the data. This is implemented by subclassing the `__setitem__` method of the `dict`. Each time data is assigned to a dictionary key, OpenPNM first checks the data type and dictionary key to ensure the rules are followed. Each of these checks are explored below.\n",
"\n",
"#### All Values are Converted to Numpy Arrays\n",
"Only Numpy arrays can be stored in an OpenPNM object, and any data that is written to an OpenPNM object (i.e. dictionary) will be converted to a Numpy array. This is done to ensure that all mathematical operations throughout the code can be consistently done using vectorization. "
@@ -255,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 37,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.176510Z",
@@ -295,7 +295,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 38,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.204358Z",
@@ -329,7 +329,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
@@ -357,7 +357,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 40,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.191724Z",
@@ -397,7 +397,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 41,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.218609Z",
@@ -426,12 +426,12 @@
"source": [
"The above rule about expanding the scalar values to a numpy array have been applied. \n",
"\n",
- "Requesting the top level of dictionary key returns both concentrations, but they can accessed directly too:"
+ "Requesting the top level of dictionary key returns both concentrations, but they can be accessed directly too:"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 42,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.231472Z",
@@ -457,12 +457,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can also retreive a dictionary by requesting only the element ('pore' or 'throat') and the propname ('concentration'):"
+ "You can also retrieve a dictionary by requesting only the element ('pore' or 'throat') and the propname ('concentration'):"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 43,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.244270Z",
@@ -478,7 +478,7 @@
"{'species_A': array([0, 0, 0, 0]), 'species_B': array([1, 1, 1, 1])}"
]
},
- "execution_count": 13,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -491,12 +491,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The above retuns a 'subdictionary' which can be indexed into:"
+ "The above returns a 'subdictionary' which can be indexed into:"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -505,7 +505,7 @@
"array([0, 0, 0, 0])"
]
},
- "execution_count": 14,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -524,7 +524,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 45,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.257992Z",
@@ -538,7 +538,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "['pore.label', 'pore.left', 'pore.right', 'pore.surface']\n"
+ "['pore.label', 'pore.left', 'pore.right', 'pore.surface', 'pore.xmax', 'pore.xmin']\n"
]
}
],
@@ -556,7 +556,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
@@ -565,7 +565,7 @@
"array([], dtype=int32)"
]
},
- "execution_count": 16,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -583,7 +583,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -608,7 +608,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
@@ -629,12 +629,12 @@
"metadata": {},
"source": [
"#### Dictionary Keys with a Leading Underscore are Hidden\n",
- "Following the Python convention, if a piece of data is not really meant to be seen or used by the user, it can be pre-pended with an underscore and it will no appear in any output."
+ "Following the Python convention, if a piece of data is not really meant to be seen or used by the user, it can be pre-pended with an underscore and it will not appear in any output."
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 49,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.271759Z",
@@ -688,7 +688,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 50,
"metadata": {},
"outputs": [
{
@@ -697,7 +697,7 @@
"array([1, 1, 1, 1])"
]
},
- "execution_count": 20,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
@@ -708,7 +708,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 51,
"metadata": {},
"outputs": [
{
@@ -729,12 +729,12 @@
"source": [
"## The `param` prefix\n",
"\n",
- "Expanding all scalar values to arrays that are either Np or Nt long is done as a convenience or shorthand to mean \"place this value of X in all pores\". This them means that you can later ask \"what is the value of X in pore Y\" and get an answer. This implies that X might take on different values in each pore/throat. However, there are properties which we know are constant, such as the molecular weight or critical temperature of a pure species. In V3 we have introduced the `params` attribute to all object, such that `pn.params` is a dictionary containing such scalar values. For convenience we have also added the ability to read and write to the `params` dictionary via the main get/set method. This is illustrated below:"
+ "Expanding all scalar values to arrays that are either Np or Nt long is done as a convenience or shorthand to mean \"place this value of X in all pores\". It means that you can later ask \"what is the value of X in pore Y\" and get an answer. This implies that X might take on different values in each pore/throat. However, there are properties which we know are constant, such as the molecular weight or critical temperature of a pure species. In V3 we have introduced the `params` attribute to all object, such that `pn.params` is a dictionary containing such scalar values. For convenience we have also added the ability to read and write to the `params` dictionary via the main get/set method. This is illustrated below:"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 52,
"metadata": {},
"outputs": [
{
@@ -766,7 +766,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 53,
"metadata": {},
"outputs": [
{
@@ -806,7 +806,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 54,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.285814Z",
@@ -818,15 +818,15 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {
"image/png": {
- "height": 960,
- "width": 348
+ "height": 1329,
+ "width": 482
}
},
"output_type": "display_data"
@@ -846,12 +846,12 @@
"\n",
"**The pores on either end of a throat are just another property to be stored, along with diameter, length, etc.**\n",
"\n",
- "In other words, referring to the above diagram, throat No. 5 has pores 1 and 4 on it's ends. Using the spreadsheet analogy, this implies a new colunm that stores the pair of pores connected by each throat. OpenPNM calls this property ``'throat.conns'``: "
+ "In other words, referring to the above diagram, throat No. 5 has pores 1 and 4 on its ends. Using the spreadsheet analogy, this implies a new column that stores the pair of pores connected by each throat. OpenPNM calls this property ``'throat.conns'``: "
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 55,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:06.713161Z",
@@ -894,7 +894,7 @@
" 2. Throats are non-directional, meaning that flow in either direction is equal\n",
" Other general, but non-essential rules are:\n",
" 3. Pores can have an arbitrary number of throats, including zero; however, pores with zero throats lead to singular matrices and other problems so should be avoided.\n",
- " 4. Two pores are generally connected by no more than one throat. It is technically possible in OpenPNM to have multiple throats between a pair of pores, but it is not rigorosly supported so unintended results may arise. For instance, when converting to CSR format the dual throats are merged into one, so re-converting back results in loss of such throats.\n",
+ " 4. Two pores are generally connected by no more than one throat. It is technically possible in OpenPNM to have multiple throats between a pair of pores, but it is not rigorously supported so unintended results may arise. For instance, when converting to CSR format the dual throats are merged into one, so re-converting back results in loss of such throats.\n",
"```"
]
},
@@ -918,7 +918,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 56,
"metadata": {},
"outputs": [
{
@@ -947,7 +947,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
@@ -977,7 +977,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "An important feature of the adjacency matrix is that it is highly sparse (mostly zeros) and can be stored with a variety of [sparse storage schemes](https://docs.scipy.org/doc/scipy/reference/sparse.html) offered by Scipy. OpenPNM stores the adjacency matrix in the 'COO' format, which essentially stores the coordinates (I,J) of the nonzero elements in an two-colume wide array.\n",
+ "An important feature of the adjacency matrix is that it is highly sparse (mostly zeros) and can be stored with a variety of [sparse storage schemes](https://docs.scipy.org/doc/scipy/reference/sparse.html) offered by Scipy. OpenPNM stores the adjacency matrix in the 'COO' format, which essentially stores the coordinates (I,J) of the nonzero elements in an two-column wide array.\n",
"\n",
"Thus the throat property called ``'throat.conns'`` is an *Nt*-by-2 array that gives the index of the two pores on either end of a given throat. "
]
@@ -1002,12 +1002,12 @@
"source": [
"## ``conduit`` data\n",
"\n",
- "A *conduit* in OpenPNM refers to a 'pore-throat-pore' connection, where the pores and throats are each called *elements*. These *elements* act as resistors (or conductors) in series. Each *conduit* therefor contains exactly 1 throat and 2 pores. Transport within the network occurs via these interconnected *conduits*, so the geometric properties of these elements are often need for computing things like diffusive conductance. This is common enough that OpenPNM V3 includes a new helper function for obtaining this data, as illustrated below:"
+ "A *conduit* in OpenPNM refers to a 'pore-throat-pore' connection, where the pores and throats are each called *elements*. These *elements* act as resistors (or conductors) in series. Each *conduit* therefor contains exactly 1 throat and 2 pores. Transport within the network occurs via these interconnected *conduits*, so the geometric properties of these elements are often needed for computing things like diffusive conductance. This is common enough that OpenPNM V3 includes a new helper function for obtaining this data, as illustrated below:"
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
@@ -1034,7 +1034,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 59,
"metadata": {},
"outputs": [
{
@@ -1058,7 +1058,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 60,
"metadata": {},
"outputs": [
{
@@ -1084,7 +1084,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 61,
"metadata": {},
"outputs": [
{
@@ -1130,7 +1130,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.13"
},
"toc": {
"base_numbering": 1,
diff --git a/examples/tutorials/04_graph_queries_and_manipulation.ipynb b/examples/tutorials/04_graph_queries_and_manipulation.ipynb
index d8a9e8cfb2..f9eeb71989 100644
--- a/examples/tutorials/04_graph_queries_and_manipulation.ipynb
+++ b/examples/tutorials/04_graph_queries_and_manipulation.ipynb
@@ -8,7 +8,7 @@
"source": [
"# Network Representation, Queries, and Manipulation\n",
"\n",
- "To understand how to query and manipulate the network, we'll first cover the various forms of network representation. Specifically, OpenPNM uses an adjacency matrix to represent topology, but occassionally invokes the incidence matrix since this makes it easier for some queries. "
+ "To understand how to query and manipulate the network, we'll first cover the various forms of network representation. Specifically, OpenPNM uses an adjacency matrix to represent topology, but occasionally invokes the incidence matrix since this makes it easier for some queries. "
]
},
{
@@ -77,7 +77,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The adjacency matrix can also be plotted as a image for a helpful visualization:"
+ "The adjacency matrix can also be plotted as an image for a helpful visualization:"
]
},
{
@@ -109,7 +109,7 @@
"source": [
"There are a few notable features to point out:\n",
"* The matrix is symmetrical, since pore `i` connects to `j` and `j` connects to `i`\n",
- "* Each row contains only a few entries since a given pores only has a few neighbors. It may not be obvious above since the network is small, but as the network grows to millions of pores, each pore only has on the order of 10 neighbors.\n",
+ "* Each row contains only a few entries since a given pore only has a few neighbors. It may not be obvious above since the network is small, but as the network grows to millions of pores, each pore only has on the order of 10 neighbors.\n",
"* You can find which pores are neighbors of pore `i` by finding the locations on non-zeros in row `i` of the adjacency matrix\n",
"* No entries are found in the diagonal since this would indicate that a pore connects with itself which is not physically meaningful.\n",
"* Since each pair of pores is connected by a single throat then each nonzero entry in the matrix corresponds to a throat"
@@ -268,7 +268,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Another query we might wish to do is determine which throat connects which pair of pores. The \"Dictionary of Keys\" format is useful for this. It is basically the COO format, but the `(i, j)` values are used as the dictionary keys. This is helpful since dicionary lookups are actually quite fast thanks to some sophisticated data strutures used by python. Consider the following:"
+ "Another query we might wish to do is determine which throat connects which pair of pores. The \"Dictionary of Keys\" format is useful for this. It is basically the COO format, but the `(i, j)` values are used as the dictionary keys. This is helpful since dictionary lookups are actually quite fast thanks to some sophisticated data structures used by python. Consider the following:"
]
},
{
@@ -377,7 +377,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's start by finding all pores on the 'left' and 'bottom'. These labels are predefined on `Cubic` networks, and we we cn use the `pores` method to find all pores with these labels:"
+ "Let's start by finding all pores on the 'left' and 'bottom'. These labels are predefined on `Cubic` networks, and we can use the `pores` method to find all pores with these labels:"
]
},
{
@@ -511,7 +511,7 @@
"source": [
"`mode='xor' finds all pores with exactly one connection to the input pores\n",
"\n",
- "Given a set of pores find the pores that are neighbors of one and only one of the input pores. This is called **XOR**, or 'exclusve_or' because it finds the pores that are neigbhors to the 'bottom' *or* the 'left', but *not* both. "
+ "Given a set of pores find the pores that are neighbors of one and only one of the input pores. This is called **XOR**, or 'exclusive_or' because it finds the pores that are neigbhors to the 'bottom' *or* the 'left', but *not* both. "
]
},
{
@@ -575,7 +575,7 @@
"source": [
"`mode='xnor'` finds all the pores with 2 or more connections to the input pores\n",
"\n",
- "This finds pores that are common to both 'left' and 'bottom' pores. It is called **XNOR** since it is the opposite of **XOR** , incidated by the *N for not* . Note that **XNOR** and **NXOR** are interchangable."
+ "This finds pores that are common to both 'left' and 'bottom' pores. It is called **XNOR** since it is the opposite of **XOR** , indicated by the *N for not* . Note that **XNOR** and **NXOR** are interchangeable."
]
},
{
@@ -980,7 +980,7 @@
"source": [
"## Removing Throats\n",
"\n",
- "Removing throats may be useful for a number of reasons such as making a cubic network more heterogeneous, or to study the effect of blockages on flow. Throat deletion is actualy trivial and requires simply removing the row(s) corresponding to the to-be deleted throats from all throat arrays. For instance, let's manually delete throats `0` and `3`:"
+ "Removing throats may be useful for a number of reasons such as making a cubic network more heterogeneous, or to study the effect of blockages on flow. Throat deletion is actually trivial and requires simply removing the row(s) corresponding to the to-be deleted throats from all throat arrays. For instance, let's manually delete throats `0` and `3`:"
]
},
{
@@ -1147,7 +1147,7 @@
"Removing pores is *almost* as easy as removing throats, with **two significant complications**. \n",
"\n",
"1. When a pore is removed, the values in the `'throat.conns'` array must be updated. If pore 2 is removed, the pore 3 becomes the new pore 2. This means that any throats which were pointing to pore 3 (i.e. [1, 3]) must now be updated to point to pore 2 instead (i.e. [1, 2]). This can be done manually as shown below, but OpenPNM's `trim` function should be used.\n",
- "2. A throat cannot point to nothing, so when a pore is deleted, all of it's neighboring throats must also be deleted.\n",
+ "2. A throat cannot point to nothing, so when a pore is deleted, all of its neighboring throats must also be deleted.\n",
"\n",
"First we'll see how to do this manually, then we'll demonstrate the `trim` function, this time for pores."
]
@@ -1566,7 +1566,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.13"
},
"toc": {
"base_numbering": 1,
diff --git a/examples/tutorials/05_defining_geometry.ipynb b/examples/tutorials/05_defining_geometry.ipynb
index f959298f55..24b6ad2f1d 100644
--- a/examples/tutorials/05_defining_geometry.ipynb
+++ b/examples/tutorials/05_defining_geometry.ipynb
@@ -7,9 +7,9 @@
"source": [
"# Assigning Geometric Properties\n",
"\n",
- "Geometric properties refer to the physical sizes of the pores and throats, such as pore diameter and throat lengths. The values are essential to the pore network modeling enterprize since they control all the transport and percolation behavior of the network. Unlike phase properties which can be normalized for, geometric properties define the network even more so than topological properties like connectivity. \n",
+ "Geometric properties refer to the physical sizes of the pores and throats, such as pore diameter and throat lengths. The values are essential to the pore network modeling enterprize since they control all the transport and percolation behavior of the network. Unlike phase properties which can be normalized for, geometric properties define the network even more than topological properties like connectivity. \n",
"\n",
- "Geometric properties *can* be calculated by various means, ranging from assigning random values to pores, extracting values from tomographic images. OpenPNM provides a library of functions, or 'pore-scale models' which can calcuate these geometric properties automatically. This tutorial will cover the following subjects:\n",
+ "Geometric properties *can* be calculated by various means, ranging from assigning random values to pores, to extracting values from tomographic images. OpenPNM provides a library of functions, or 'pore-scale models' which can calculate these geometric properties automatically. This tutorial will cover the following subjects:\n",
"\n",
"- Manually Calculating Pore and Throat Properties (⚠ Not Recommended!) \n",
"- Using Pore-scale Models from the Library \n",
@@ -38,7 +38,7 @@
"id": "f63bbaee",
"metadata": {},
"source": [
- "Let's start by creating a blank Cubic network. As we can see by printing it, there only coordinates, connections, and some labels, but no geometric properties:"
+ "Let's start by creating a blank Cubic network. As we can see by printing it, there are only coordinates, connections, and some labels, but no geometric properties:"
]
},
{
@@ -109,7 +109,7 @@
"\n",
"```{warning} **Manual Calculation is Not Recommended**\n",
"\n",
- " This is *not* the preferred way to do things, but does illustrate the processes very well. The preferred way is using pore-scale models, which allow for the automatic regeneration of dependent properities when something changes. \n",
+ " This is *not* the preferred way to do things, but does illustrate the processes very well. The preferred way is using pore-scale models, which allow for the automatic regeneration of dependent properties when something changes. \n",
" \n",
"```\n",
"\n",
@@ -325,7 +325,7 @@
"source": [
"```{tip}\n",
"\n",
- " **Indexing pore properties by conns:** Using the `conns` array to index into a pore property returns an Nt-by-2 array with the properties of the pores on the end of a throat in each column. For instance, so see the diameter of the pore on each end of a throat, use `pn['pore.diameter'][pn.conns]`. This approach is very powerful. If you ever feel tempted to use a for-loop to scan over each pore, then inspect the properties of the neighboring throats, consider instead if you can \"loop\" over the throats then interogate each pore. If yes, then you can use this conns-indexing trick.\n",
+ " **Indexing pore properties by conns:** Using the `conns` array to index into a pore property returns an Nt-by-2 array with the properties of the pores on the end of a throat in each column. For instance, see the diameter of the pore on each end of a throat, use `pn['pore.diameter'][pn.conns]`. This approach is very powerful. If you ever feel tempted to use a for-loop to scan over each pore, then inspect the properties of the neighboring throats, consider instead if you can \"loop\" over the throats then interrogate each pore. If yes, then you can use this conns-indexing trick.\n",
" \n",
"```"
]
@@ -431,7 +431,7 @@
"metadata": {},
"source": [
"```{tip}\n",
- " Setting the `lo` and `hi` values of the the seeds prevents values that lie far out in the tail of a distribution from occurring which can lead to overly large pores or throats.\n",
+ " Setting the `lo` and `hi` values of the seeds prevents values that lie far out in the tail of a distribution from occurring which can lead to overly large pores or throats.\n",
"```"
]
},
@@ -570,7 +570,7 @@
"The above cell contains 3 complicated but powerful steps:\n",
"1. The radii of the pores on each end of each throat are retrieved. `R1` and `R2` contain duplicates since each pore has numerous throats, so they appear once for each throat. This fact is used in step 3.\n",
"2. The pore-to-pore distance is found by retrieving the coordinates of each pore, in the same was as was done for the radii. The Euclidean distance between each pair of pores is then computed by finding their difference (`np.diff`), squaring it, summing it (`np.sum`), then taking the square root (`np.sqrt`) of each. The `axis` argument is used throughout to tell numpy which way to apply the operation. This line is an example of how you must learn a *mini-language* to use numpy effectively. \n",
- "3. The length of each throat is found as the spacing between pores, less the radii the pore on each end. This is where the duplicate values in `R1` and `R2` are necessary, since the radii of pore *i* must be subtracted from length of each of it's neighboring throats.\n",
+ "3. The length of each throat is found as the spacing between pores, less the radii of the pore on each end. This is where the duplicate values in `R1` and `R2` are necessary, since the radii of pore *i* must be subtracted from length of each of its neighboring throats.\n",
"\n",
"The main take-home message here is that throat properties can be computed in a vectorized way, even if some pore-properties are required. This avoids the use of a for-loop to scan through a list of throats followed by a nested for-loop to scan through each neighboring pore."
]
@@ -614,7 +614,7 @@
"source": [
"The above cell contains 2 lines of importance, which each do almost the same thing:\n",
"1. The first line subtracts the throat cross-sectional area from each pore listed in the first column of the `pn.conns` array. Since a given pore is connected to potentially many throats, the subtraction must be done using `np.substract.at` which is a special version of the subtract function that subtracts every value in `At` from the locations in `SAp` indicated by `pn.conn[:, 0]`, and it does so in a cumulative fashion. Thus if pore *i* appears in `pn.conns[:, 0]` more than once, then more than one value of `At` is removed. The operation is performed *in-place* so that no array is returned.\n",
- "2. Recall that `pn.conns` contains only the upper-triangular entries of the adjacency matrix, so this process only gets half of the connections. This means that the pore connectioned found in `pn.conns[:, 1]` must also be analyzed."
+ "2. Recall that `pn.conns` contains only the upper-triangular entries of the adjacency matrix, so this process only gets half of the connections. This means that the pore connections found in `pn.conns[:, 1]` must also be analyzed."
]
},
{
@@ -877,7 +877,7 @@
"source": [
"## Using Predefined Collections of Models\n",
"\n",
- "The process of selecting all the correct models from the `models` library can be tedious and error prone. In OpenPNM V3 we have introduced the concept of model *collections*, which are predfined dictionaries of models that form a complete and correct geometry. For instance, there is a collection called `spheres_and_cylinders`, which contains all the needed models to describe this geometry. \n",
+ "The process of selecting all the correct models from the `models` library can be tedious and error prone. In OpenPNM V3 we have introduced the concept of model *collections*, which are predefined dictionaries of models that form a complete and correct geometry. For instance, there is a collection called `spheres_and_cylinders`, which contains all the needed models to describe this geometry. \n",
"\n",
"These collections are found under `openpnm.models.collections`, and their use is outlined below:"
]
@@ -1225,7 +1225,7 @@
"source": [
"## Introduction to the Dependency Handler\n",
"\n",
- "Pore-scale models clearly make life easier since you don't have to produce the complex numpy functions by hand each time, but this is not even their best feature! The main benefit of pore-scale models is that they can be *recomputed* automatically. For instance, if we re-run the `'pore.seed'` model, then all the other models that depend on `'pore.seed'` will automatically be reomcomputed as well. This all occurs when we call `regenerate_models`, as demonstrated below:"
+ "Pore-scale models clearly make life easier since you don't have to produce the complex numpy functions by hand each time, but this is not even their best feature! The main benefit of pore-scale models is that they can be *recomputed* automatically. For instance, if we re-run the `'pore.seed'` model, then all the other models that depend on `'pore.seed'` will automatically be recomputed as well. This all occurs when we call `regenerate_models`, as demonstrated below:"
]
},
{
@@ -1292,7 +1292,7 @@
"id": "8ed8615a",
"metadata": {},
"source": [
- "As we can see above the initial values of `'pore.seed'` and `'throat.diameter'` are both changed after calling `regenerate_models()`. OpenPNM does this automaticall by inspecting the arguments to each function. \n",
+ "As we can see above the initial values of `'pore.seed'` and `'throat.diameter'` are both changed after calling `regenerate_models()`. OpenPNM does this automatically by inspecting the arguments to each function. \n",
"\n",
"Inspecting the `add_model` calls above we can see:\n",
"\n",
@@ -1382,7 +1382,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/examples/tutorials/06_labels_and_domains.ipynb b/examples/tutorials/06_labels_and_domains.ipynb
index f38577a578..4b23f1c19c 100644
--- a/examples/tutorials/06_labels_and_domains.ipynb
+++ b/examples/tutorials/06_labels_and_domains.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Using Labels and Defining Domains\n",
"\n",
- "Labels are a way to 'bookmark' certain pores for easier lookup later, such as specifying boundary conditions. When networks are generated they include a set of relevent labels that have been added for convenience. It's also possible for users to add their own labels. This tutorial will cover how they work, how to add them, and how to use them."
+ "Labels are a way to 'bookmark' certain pores for easier lookup later, such as specifying boundary conditions. When networks are generated they include a set of relevant labels that have been added for convenience. It's also possible for users to add their own labels. This tutorial will cover how they work, how to add them, and how to use them."
]
},
{
@@ -133,7 +133,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "One of the advantages of using boolean masks as labels is that its easy to use boolean logic to find pores and throats with a certain combination of labels. For instance, to find all pores that are labelled both `'left'` and `'top'` we just multiply these label arrays together:"
+ "One of the advantages of using boolean masks as labels is that it's easy to use boolean logic to find pores and throats with a certain combination of labels. For instance, to find all pores that are labelled both `'left'` and `'top'` we just multiply these label arrays together:"
]
},
{
@@ -191,7 +191,7 @@
"source": [
"```{note} **Points to Note about Using Labels**\n",
"\n",
- " * Labels can be applied to throats in exactly the same way as decribed here for pores.\n",
+ " * Labels can be applied to throats in exactly the same way as described here for pores.\n",
" * Every OpenPNM object has a ``pores`` and a ``throats`` method that can be used to retrieve the pore or throat indices where a label or combination of labels has been applied.\n",
" * The format of creating an entire ND-array to label just a few locations may seem a bit wasteful of data storage since many ``False`` values are created to indicate pores do *not* have a label. Worse, Boolean arrays are actually stored as 8-bit integer arrays by Numpy, so they consume 8x more memory than needed. Nonetheless, the investment is worth it since Boolean arrays are so convenient. They can be used to index into Numpy arrays (e.g. ``arr[mask]``) and great for combining logic (e.g. ``locs = mask1 * mask2``).\n",
" * Labels are used throughout any given script, particularly for specifying boundary conditions, so are an important feature to understand.\n",
@@ -271,7 +271,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "`mode='nor'` means \"retrieve all the pores indices that do no possess the 'left' or 'back' label. `'not'` is also accepted:"
+ "`mode='nor'` means \"retrieve all the pores indices that do not possess the 'left' or 'back' label. `'not'` is also accepted:"
]
},
{
@@ -352,7 +352,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Lastly, `'xnor'` means \"retieve pore indices that have more than one of the given label\":"
+ "Lastly, `'xnor'` means \"retrieve pore indices that have more than one of the given label\":"
]
},
{
@@ -519,7 +519,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Using the @ sytax to read and write data\n",
+ "## Using the @ syntax to read and write data\n",
"The @ syntax provides a shortcut for reading and writing data to certain locations, defined by labels. The @ syntax is also used to define \"domains\" where pore network properties differ, but first let's look at using @:"
]
},
@@ -553,7 +553,7 @@
"source": [
"This certainly saves some typing! It's also pretty intuitive since ``@`` is usually read as \"at\", inferring a location. \n",
"\n",
- "The @ sytax can also be used to write data as well. Let's create an array of 1.0s, then use the @ syntax to change them:"
+ "The @ syntax can also be used to write data as well. Let's create an array of 1.0s, then use the @ syntax to change them:"
]
},
{
@@ -708,7 +708,7 @@
"\n",
"Using the @ symbol for data read/write as shown above is actually a side effect of a *major conceptual shift* made in V3. The ``Geometry`` and ``Physics`` objects are now *gone*. There was essentially only one use case for these, which was to model heterogeneous domains, like bimodal pore size distributions or layered structures. \n",
"\n",
- "In V2 this was accomplished by using 2 (or more) ``Geometry`` objects to represent each class of pores, with unique pore-scale models attached to each. Without getting lost in the details, it is sufficient to say that having separate objects for managing each class of pores (and/or throats) created a *lot* of complications, both to the user and to the maintenence of the backend. \n",
+ "In V2 this was accomplished by using 2 (or more) ``Geometry`` objects to represent each class of pores, with unique pore-scale models attached to each. Without getting lost in the details, it is sufficient to say that having separate objects for managing each class of pores (and/or throats) created a *lot* of complications, both to the user and to the maintenance of the backend. \n",
"\n",
"In V3 we have developed what we think is a *much tidier approach* to managing heterogeneous domains. Instead of creating multiple ``Geometry`` objects (and consequently multiple ``Physics`` objects), you now add all the pore-scale models to the ``Network`` and ``Phase`` objects directly. The trick is that when adding models you specify one additional argument: the ``domain`` (i.e. pores or throats) to which the model applies, as follows:"
]
@@ -882,7 +882,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Appending ``@`` to the model name creates a unique dictionary key. OpenPNM recoginizes that the models in ``'pore.seed@left'`` and ``'pore.seed@right'`` both compute values of ``'pore.seed'``, and directs the outputs of each function to the correct locations, which it can infer from the ``@right/left`` portion of the key."
+ "Appending ``@`` to the model name creates a unique dictionary key. OpenPNM recognizes that the models in ``'pore.seed@left'`` and ``'pore.seed@right'`` both compute values of ``'pore.seed'``, and directs the outputs of each function to the correct locations, which it can infer from the ``@right/left`` portion of the key."
]
},
{
@@ -973,7 +973,7 @@
"metadata": {},
"source": [
"### Mixing Full Domain and Subdomain Models\n",
- "When defining two separate subdomains, the pore and throat sizes are often the only thing that is different. In V2, however, it was recommended practice to include ALL the additional models on each subdomain object as well, such as volume calculations. With the @ syntax, only models that actualy differ between the domains need to be specifically dealt with.\n",
+ "When defining two separate subdomains, the pore and throat sizes are often the only thing that is different. In V2, however, it was recommended practice to include ALL the additional models on each subdomain object as well, such as volume calculations. With the @ syntax, only models that actually differ between the domains need to be specifically dealt with.\n",
"\n",
"This is demonstrated below by first deleting the individual ``'pore.seed'`` models applied above, and replacing them with a single model that applies uniform values on all locations, then applying two different normal distributions to the ``'left'`` and ``'right'`` domains."
]
@@ -1134,7 +1134,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we can see that the throat diameters have beed added to the network:"
+ "Now we can see that the throat diameters have been added to the network:"
]
},
{
@@ -1197,7 +1197,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/examples/tutorials/07_phases_and_mixtures.ipynb b/examples/tutorials/07_phases_and_mixtures.ipynb
index eb63737165..a504005109 100644
--- a/examples/tutorials/07_phases_and_mixtures.ipynb
+++ b/examples/tutorials/07_phases_and_mixtures.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Creating Phases\n",
"\n",
- "All simulations in OpenPNM eventually require knowing the physical and thermodynamic properties of the fluids and phases. OpenPNM provides a framework for computing these values in the ``phase`` submodule, along with a *basic* set of functions for predicting the properties pure phases and mixtures. Since we can't possibly support an exhaustive physical property library, our policy is to provide a set of reasonable default functions for use as first approximations. There are a number of other packages that can be used when more exacting values are required, such as ``chemicals`` and ``cantera``, which can be used within the framework of OpenPNM.\n",
+ "All simulations in OpenPNM eventually require knowing the physical and thermodynamic properties of the fluids and phases. OpenPNM provides a framework for computing these values in the ``phase`` submodule, along with a *basic* set of functions for predicting the properties of pure phases and mixtures. Since we can't possibly support an exhaustive physical property library, our policy is to provide a set of reasonable default functions for use as first approximations. There are a number of other packages that can be used when more exact values are required, such as ``chemicals`` and ``cantera``, which can be used within the framework of OpenPNM.\n",
"\n",
"In the first part of this notebook, we will cover: \n",
"\n",
@@ -37,7 +37,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 4,
"id": "d0583d45",
"metadata": {},
"outputs": [],
@@ -57,7 +57,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 5,
"id": "41da6fd0",
"metadata": {},
"outputs": [],
@@ -75,7 +75,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 6,
"id": "41924bb8",
"metadata": {},
"outputs": [
@@ -85,17 +85,17 @@
"text": [
"\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "phase_01 : \n",
+ "phase_01 : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.pressure 4 / 4\n",
" 2 pore.temperature 4 / 4\n",
+ " 3 pore.pressure 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all 4\n",
- " 2 throat.all 4\n",
+ " 2 pore.all 4\n",
+ " 3 throat.all 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -123,7 +123,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 7,
"id": "8f98fb81",
"metadata": {},
"outputs": [],
@@ -144,7 +144,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 8,
"id": "50b97f81",
"metadata": {},
"outputs": [
@@ -154,7 +154,7 @@
"array([0.001, 0.001, 0.001, 0.001])"
]
},
- "execution_count": 5,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -177,7 +177,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 9,
"id": "0a61bc2a",
"metadata": {},
"outputs": [],
@@ -199,19 +199,16 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 10,
"id": "8c13fabf",
"metadata": {},
"outputs": [
{
- "ename": "NameError",
- "evalue": "name 'phase1' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23108/3066581887.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Before:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphase1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pore.viscosity'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolynomial\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m phase1.add_model(propname='pore.viscosity', \n\u001b[0;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ma0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mNameError\u001b[0m: name 'phase1' is not defined"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Before: [0.001 0.001 0.001 0.001]\n",
+ "After: [0.00091476 0.00091476 0.00091476 0.00091476]\n"
]
}
],
@@ -246,7 +243,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 11,
"id": "57f24409",
"metadata": {},
"outputs": [
@@ -255,7 +252,7 @@
"output_type": "stream",
"text": [
"Before: [0.00091476 0.00091476 0.00091476 0.00091476]\n",
- "After: [0.00062288 0.00043031 0.00043805 0.00066168]\n"
+ "After: [0.00060174 0.00077504 0.00059527 0.00048215]\n"
]
}
],
@@ -278,7 +275,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 12,
"id": "97a5b4ec",
"metadata": {},
"outputs": [
@@ -286,8 +283,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Before: [0.00062288 0.00043031 0.00043805 0.00066168]\n",
- "After: [0.00059036 0.00039567 0.00040532 0.00063039]\n"
+ "Before: [0.00060174 0.00077504 0.00059527 0.00048215]\n",
+ "After: [0.00056884 0.00074893 0.00056231 0.00045147]\n"
]
}
],
@@ -333,7 +330,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 13,
"id": "a30c7773",
"metadata": {},
"outputs": [
@@ -341,7 +338,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[0.00062288 0.00043031 0.00043805 0.00066168]\n"
+ "[0.00060174 0.00077504 0.00059527 0.00048215]\n"
]
}
],
@@ -376,12 +373,12 @@
"source": [
"### Specific Classes for Common Fluids\n",
"\n",
- "Air, water, and mercury are used commonly enough that OpenPNM not only has pore-scale models for their propertie (i.e. ``op.models.viscosity.water_correlation``), but we have also created pre-defined classes with all the appropriate models already attached:"
+ "Air, water, and mercury are used commonly enough that OpenPNM not only has pore-scale models for their properties (i.e. ``op.models.viscosity.water_correlation``), but we have also created pre-defined classes with all the appropriate models already attached:"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 14,
"id": "d7232b92",
"metadata": {},
"outputs": [
@@ -391,24 +388,24 @@
"text": [
"\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "phase_02 : \n",
+ "phase_02 : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.contact_angle 4 / 4\n",
- " 2 pore.density 4 / 4\n",
- " 3 pore.molar_density 4 / 4\n",
- " 4 pore.pressure 4 / 4\n",
- " 5 pore.surface_tension 4 / 4\n",
- " 6 pore.temperature 4 / 4\n",
- " 7 pore.thermal_conductivity 4 / 4\n",
- " 8 pore.vapor_pressure 4 / 4\n",
- " 9 pore.viscosity 4 / 4\n",
+ " 2 pore.temperature 4 / 4\n",
+ " 3 pore.pressure 4 / 4\n",
+ " 4 pore.contact_angle 4 / 4\n",
+ " 5 pore.density 4 / 4\n",
+ " 6 pore.molar_density 4 / 4\n",
+ " 7 pore.surface_tension 4 / 4\n",
+ " 8 pore.thermal_conductivity 4 / 4\n",
+ " 9 pore.vapor_pressure 4 / 4\n",
+ " 10 pore.viscosity 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all 4\n",
- " 2 throat.all 4\n",
+ " 2 pore.all 4\n",
+ " 3 throat.all 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -428,7 +425,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 15,
"id": "f9f8f4df",
"metadata": {},
"outputs": [
@@ -465,7 +462,6 @@
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"6 pore.vapor_pressure@all model: liquid_pure_antoine\n",
" T: pore.temperature\n",
- " Tc: param.critical_temperature\n",
" regeneration mode: normal\n",
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"7 pore.viscosity@all model: water_correlation\n",
@@ -506,7 +502,7 @@
"source": [
"## Part 2: Using Species and Mixtures\n",
"\n",
- "In some cases a user may wish to compute the thermophysical properties of unusual species, or even mixtures of species. There are packages who's entire purpose is computing these values, so OpenPNM does not attempt to reproduce this functionality. However, it is needed often enough that OpenPNM has some base level support for making 'first-approximation' estimates of species properties, and also has a defined API for dealing with mixtures of individual species. "
+ "In some cases a user may wish to compute the thermophysical properties of unusual species, or even mixtures of species. There are packages whose entire purpose is computing these values, so OpenPNM does not attempt to reproduce this functionality. However, it is needed often enough that OpenPNM has some base level support for making 'first-approximation' estimates of species properties, and also has a defined API for dealing with mixtures of individual species. "
]
},
{
@@ -516,7 +512,7 @@
"source": [
"```{caution} **Mixtures might be an unnecessary complication for you**\n",
"\n",
- " In most cases you probably do not need to use the `Mixture` class. For instance, air is a gas mixture with a well known set of properties. This is sufficient for computing things like permeability. On the other hand, if you have a 4 component gas mixture at a variety of compositions, and the properties are unknown, the perhaps the `Mixture` feature is the right choice.\n",
+ " In most cases you probably do not need to use the `Mixture` class. For instance, air is a gas mixture with a well-known set of properties. This is sufficient for computing things like permeability. On the other hand, if you have a 4 component gas mixture at a variety of compositions, and the properties are unknown, then perhaps the `Mixture` feature is the right choice.\n",
"```"
]
},
@@ -532,7 +528,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 16,
"id": "06659362",
"metadata": {},
"outputs": [
@@ -542,17 +538,17 @@
"text": [
"\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "phase_03 : \n",
+ "phase_03 : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.pressure 4 / 4\n",
" 2 pore.temperature 4 / 4\n",
+ " 3 pore.pressure 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all 4\n",
- " 2 throat.all 4\n",
+ " 2 pore.all 4\n",
+ " 3 throat.all 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -583,7 +579,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 17,
"id": "271081b5",
"metadata": {},
"outputs": [
@@ -598,21 +594,21 @@
"common_name oxygen\n",
"charge 0\n",
"formula O2\n",
- "boiling_temperature 90.188\n",
+ "boiling_temperature 90.1878078805\n",
"melting_temperature 54.36\n",
- "triple_point_temperature 54.33\n",
- "triple_point_pressure 148.9796864589355\n",
+ "triple_point_temperature 54.361\n",
+ "triple_point_pressure 146.277647044\n",
"dipole_moment 0.0\n",
"LJ_diameter 3.29728\n",
"LJ_energy 1.6520845934e-21\n",
- "surface_tension_Tb 0.013145633010272155\n",
- "molar_volume_Tb 2.802254619756072e-05\n",
+ "surface_tension_Tb 0.013145681055529814\n",
+ "molar_volume_Tb 2.8039990920723867e-05\n",
"molecular_weight 31.9988\n",
- "critical_temperature 154.58\n",
- "critical_pressure 5042945.25\n",
- "critical_volume 7.34e-05\n",
- "critical_compressibilty_factor 0.2880002236716698\n",
- "acentric_factor 0.021\n",
+ "critical_temperature 154.581\n",
+ "critical_pressure 5043000.0\n",
+ "critical_volume 7.33675715334e-05\n",
+ "critical_compressibilty_factor 0.28787424687871216\n",
+ "acentric_factor 0.0222\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -634,7 +630,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 18,
"id": "abb267b0",
"metadata": {},
"outputs": [
@@ -644,7 +640,7 @@
"'7782-44-7'"
]
},
- "execution_count": 15,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -655,7 +651,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 19,
"id": "f4fa075f",
"metadata": {},
"outputs": [
@@ -665,7 +661,7 @@
"'7782-44-7'"
]
},
- "execution_count": 16,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -684,7 +680,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 20,
"id": "0d372fb3",
"metadata": {},
"outputs": [
@@ -694,7 +690,7 @@
"'bar'"
]
},
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -714,7 +710,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 21,
"id": "4eb23ca6",
"metadata": {},
"outputs": [
@@ -722,7 +718,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[2.01023694e-05 2.01023694e-05 2.01023694e-05 2.01023694e-05]\n"
+ "[2.0102393e-05 2.0102393e-05 2.0102393e-05 2.0102393e-05]\n"
]
}
],
@@ -743,7 +739,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 22,
"id": "4c994288",
"metadata": {},
"outputs": [
@@ -778,7 +774,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 23,
"id": "9f9e8ac3",
"metadata": {},
"outputs": [],
@@ -789,7 +785,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 24,
"id": "1409b0ef",
"metadata": {},
"outputs": [
@@ -824,7 +820,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 25,
"id": "ddc5d20b",
"metadata": {},
"outputs": [
@@ -832,7 +828,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[2.01023694e-05 2.01023694e-05 2.01023694e-05 2.01023694e-05]\n"
+ "[2.0102393e-05 2.0102393e-05 2.0102393e-05 2.0102393e-05]\n"
]
}
],
@@ -864,12 +860,12 @@
"source": [
"### Gas and Liquid Species\n",
"\n",
- "OpenPNM has a suite of functions for computing the properties of pure phases, but these functions differ for gases and liquids. For this reason we offer two classes for gas and liquid with the appropriate models already defined. These are referred to as ``StandardLiquid`` and ``StandardGas`` to indicate that the models being used are the standard selection which provide a first-approximatation:"
+ "OpenPNM has a suite of functions for computing the properties of pure phases, but these functions differ for gases and liquids. For this reason we offer two classes for gas and liquid with the appropriate models already defined. These are referred to as ``StandardLiquid`` and ``StandardGas`` to indicate that the models being used are the standard selection which provide a first-approximation:"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 26,
"id": "5f02a314",
"metadata": {},
"outputs": [],
@@ -888,7 +884,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 27,
"id": "bce39721",
"metadata": {},
"outputs": [
@@ -898,22 +894,22 @@
"text": [
"\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "phase_04 : \n",
+ "phase_04 : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.density 4 / 4\n",
- " 2 pore.heat_capacity 4 / 4\n",
- " 3 pore.heat_capacity_gas 4 / 4\n",
- " 4 pore.pressure 4 / 4\n",
- " 5 pore.temperature 4 / 4\n",
+ " 2 pore.temperature 4 / 4\n",
+ " 3 pore.pressure 4 / 4\n",
+ " 4 pore.heat_capacity_gas 4 / 4\n",
+ " 5 pore.heat_capacity 4 / 4\n",
" 6 pore.thermal_conductivity 4 / 4\n",
" 7 pore.viscosity 4 / 4\n",
+ " 8 pore.density 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all 4\n",
- " 2 throat.all 4\n",
+ " 2 pore.all 4\n",
+ " 3 throat.all 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -924,7 +920,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 28,
"id": "21e4fad1",
"metadata": {},
"outputs": [
@@ -934,23 +930,23 @@
"text": [
"\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "phase_05 : \n",
+ "phase_05 : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.density 4 / 4\n",
- " 2 pore.heat_capacity 4 / 4\n",
- " 3 pore.heat_capacity_gas 4 / 4\n",
- " 4 pore.pressure 4 / 4\n",
- " 5 pore.temperature 4 / 4\n",
+ " 2 pore.temperature 4 / 4\n",
+ " 3 pore.pressure 4 / 4\n",
+ " 4 pore.heat_capacity_gas 4 / 4\n",
+ " 5 pore.heat_capacity 4 / 4\n",
" 6 pore.thermal_conductivity 4 / 4\n",
" 7 pore.vapor_pressure 4 / 4\n",
" 8 pore.viscosity 4 / 4\n",
+ " 9 pore.density 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all 4\n",
- " 2 throat.all 4\n",
+ " 2 pore.all 4\n",
+ " 3 throat.all 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -969,7 +965,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 29,
"id": "3b3376ff",
"metadata": {},
"outputs": [
@@ -1018,7 +1014,6 @@
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"6 pore.vapor_pressure@all model: liquid_pure_antoine\n",
" T: pore.temperature\n",
- " Tc: param.critical_temperature\n",
" regeneration mode: deferred\n",
"―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
@@ -1046,7 +1041,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 30,
"id": "9faf6146",
"metadata": {},
"outputs": [],
@@ -1066,7 +1061,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 31,
"id": "555e09fa",
"metadata": {},
"outputs": [],
@@ -1080,12 +1075,12 @@
"id": "083bfb4c",
"metadata": {},
"source": [
- "Now that the compositios are known we can run the models:"
+ "Now that the compositions are known, we can run the models:"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 32,
"id": "5e4f682d",
"metadata": {},
"outputs": [
@@ -1095,24 +1090,23 @@
"text": [
"\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "mixture_01 : \n",
+ "mixture_01 : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.density 4 / 4\n",
- " 2 pore.diffusivity 4 / 4\n",
- " 3 pore.heat_capacity 4 / 4\n",
- " 4 pore.mole_fraction.nitrogen 4 / 4\n",
- " 5 pore.mole_fraction.oxygen 4 / 4\n",
- " 6 pore.pressure 4 / 4\n",
- " 7 pore.temperature 4 / 4\n",
- " 8 pore.thermal_conductivity 4 / 4\n",
- " 9 pore.viscosity 4 / 4\n",
+ " 2 pore.temperature 4 / 4\n",
+ " 3 pore.pressure 4 / 4\n",
+ " 4 pore.mole_fraction.oxygen 4 / 4\n",
+ " 5 pore.mole_fraction.nitrogen 4 / 4\n",
+ " 6 pore.heat_capacity 4 / 4\n",
+ " 7 pore.thermal_conductivity 4 / 4\n",
+ " 8 pore.viscosity 4 / 4\n",
+ " 9 pore.density 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all 4\n",
- " 2 throat.all 4\n",
+ " 2 pore.all 4\n",
+ " 3 throat.all 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -1132,7 +1126,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 33,
"id": "85a2ed10",
"metadata": {},
"outputs": [
@@ -1140,8 +1134,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Before: [1.78543425e-05 1.78543425e-05 1.78543425e-05 1.78543425e-05]\n",
- "After: [2.01881831e-05 2.01881831e-05 2.01881831e-05 2.01881831e-05]\n"
+ "Before: [1.78599451e-05 1.78599451e-05 1.78599451e-05 1.78599451e-05]\n",
+ "After: [2.01895979e-05 2.01895979e-05 2.01895979e-05 2.01895979e-05]\n"
]
}
],
@@ -1166,7 +1160,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 34,
"id": "9cfb0801",
"metadata": {},
"outputs": [
@@ -1192,7 +1186,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 35,
"id": "fa02fd83",
"metadata": {},
"outputs": [
@@ -1213,12 +1207,12 @@
"id": "a799d459",
"metadata": {},
"source": [
- "Alternatively the returned dictionary can be used to get a list of components that are currently part of the mixture:"
+ "Alternatively, the returned dictionary can be used to get a list of components that are currently part of the mixture:"
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 36,
"id": "abc8bffe",
"metadata": {},
"outputs": [
@@ -1228,7 +1222,7 @@
"dict_keys(['oxygen', 'nitrogen'])"
]
},
- "execution_count": 33,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -1247,18 +1241,18 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 37,
"id": "e102fe27",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'oxygen': oxygen : ,\n",
- " 'nitrogen': nitrogen : }"
+ "{'oxygen': oxygen : ,\n",
+ " 'nitrogen': nitrogen : }"
]
},
- "execution_count": 34,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -1279,7 +1273,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 38,
"id": "ff18e855",
"metadata": {},
"outputs": [
@@ -1290,7 +1284,7 @@
" 'nitrogen': array([0.2, 0.2, 0.2, 0.2])}"
]
},
- "execution_count": 35,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -1301,7 +1295,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 39,
"id": "fa400400",
"metadata": {},
"outputs": [
@@ -1311,7 +1305,7 @@
"array([0.8, 0.8, 0.8, 0.8])"
]
},
- "execution_count": 36,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
@@ -1340,17 +1334,17 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 40,
"id": "8d1dcc11",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'nitrogen': nitrogen : }"
+ "{'nitrogen': nitrogen : }"
]
},
- "execution_count": 37,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -1373,7 +1367,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 41,
"id": "781001d6",
"metadata": {},
"outputs": [
@@ -1383,7 +1377,7 @@
"{}"
]
},
- "execution_count": 38,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -1403,17 +1397,17 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 42,
"id": "413dace1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'oxygen': oxygen : }"
+ "{'oxygen': oxygen : }"
]
},
- "execution_count": 39,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
@@ -1433,18 +1427,18 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 43,
"id": "99f85168",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'oxygen': oxygen : ,\n",
- " 'nitrogen': nitrogen : }"
+ "{'oxygen': oxygen : ,\n",
+ " 'nitrogen': nitrogen : }"
]
},
- "execution_count": 40,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -1465,7 +1459,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 44,
"id": "9e7473ec",
"metadata": {},
"outputs": [
@@ -1475,60 +1469,59 @@
"text": [
"\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "mixture_01 : \n",
+ "mixture_01 : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.density 4 / 4\n",
- " 2 pore.diffusivity 4 / 4\n",
- " 3 pore.heat_capacity 4 / 4\n",
- " 4 pore.mole_fraction.nitrogen 4 / 4\n",
- " 5 pore.mole_fraction.oxygen 4 / 4\n",
- " 6 pore.pressure 4 / 4\n",
- " 7 pore.temperature 4 / 4\n",
- " 8 pore.thermal_conductivity 4 / 4\n",
- " 9 pore.viscosity 4 / 4\n",
+ " 2 pore.temperature 4 / 4\n",
+ " 3 pore.pressure 4 / 4\n",
+ " 4 pore.heat_capacity 4 / 4\n",
+ " 5 pore.thermal_conductivity 4 / 4\n",
+ " 6 pore.viscosity 4 / 4\n",
+ " 7 pore.density 4 / 4\n",
+ " 8 pore.mole_fraction.oxygen 4 / 4\n",
+ " 9 pore.mole_fraction.nitrogen 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all 4\n",
- " 2 throat.all 4\n",
+ " 2 pore.all 4\n",
+ " 3 throat.all 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"Component Phases\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "oxygen : \n",
+ "oxygen : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.density.oxygen 4 / 4\n",
- " 2 pore.heat_capacity.oxygen 4 / 4\n",
- " 3 pore.heat_capacity_gas.oxygen 4 / 4\n",
- " 4 pore.pressure.oxygen 4 / 4\n",
- " 5 pore.temperature.oxygen 4 / 4\n",
+ " 2 pore.temperature.oxygen 4 / 4\n",
+ " 3 pore.pressure.oxygen 4 / 4\n",
+ " 4 pore.heat_capacity_gas.oxygen 4 / 4\n",
+ " 5 pore.heat_capacity.oxygen 4 / 4\n",
" 6 pore.thermal_conductivity.oxygen 4 / 4\n",
" 7 pore.viscosity.oxygen 4 / 4\n",
+ " 8 pore.density.oxygen 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all.oxygen 4\n",
- " 2 throat.all.oxygen 4\n",
+ " 2 pore.all.oxygen 4\n",
+ " 3 throat.all.oxygen 4\n",
"══════════════════════════════════════════════════════════════════════════════\n",
- "nitrogen : \n",
+ "nitrogen : \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.density.nitrogen 4 / 4\n",
- " 2 pore.heat_capacity.nitrogen 4 / 4\n",
- " 3 pore.heat_capacity_gas.nitrogen 4 / 4\n",
- " 4 pore.pressure.nitrogen 4 / 4\n",
- " 5 pore.temperature.nitrogen 4 / 4\n",
+ " 2 pore.temperature.nitrogen 4 / 4\n",
+ " 3 pore.pressure.nitrogen 4 / 4\n",
+ " 4 pore.heat_capacity_gas.nitrogen 4 / 4\n",
+ " 5 pore.heat_capacity.nitrogen 4 / 4\n",
" 6 pore.thermal_conductivity.nitrogen 4 / 4\n",
" 7 pore.viscosity.nitrogen 4 / 4\n",
+ " 8 pore.density.nitrogen 4 / 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
" # Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
- " 1 pore.all.nitrogen 4\n",
- " 2 throat.all.nitrogen 4\n",
+ " 2 pore.all.nitrogen 4\n",
+ " 3 throat.all.nitrogen 4\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
@@ -1549,7 +1542,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 45,
"id": "5636fdfa",
"metadata": {},
"outputs": [
@@ -1560,7 +1553,7 @@
" 'nitrogen': array([0.79, 0.79, 0.79, 0.79])}"
]
},
- "execution_count": 42,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -1580,7 +1573,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 46,
"id": "692448e5",
"metadata": {},
"outputs": [
@@ -1603,12 +1596,12 @@
"metadata": {},
"source": [
"#### Using ``get_comp_vals`` \n",
- "Since the mixture is made from several components, it is often desired get the values of a specific property from each component. This method provides a convenient way to do this:"
+ "Since the mixture is made from several components, it is often desired to get the values of a specific property from each component. This method provides a convenient way to do this:"
]
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 61,
"id": "e1a60168",
"metadata": {},
"outputs": [
@@ -1616,7 +1609,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "{'oxygen': array([2.09391006e-05, 2.09391006e-05, 2.09391006e-05, 2.09391006e-05]), 'nitrogen': array([1.69779528e-05, 1.69779528e-05, 1.69779528e-05, 1.69779528e-05])}\n"
+ "{'oxygen': array([2.09391681e-05, 2.09391681e-05, 2.09391681e-05, 2.09391681e-05]), 'nitrogen': array([1.6985128e-05, 1.6985128e-05, 1.6985128e-05, 1.6985128e-05])}\n"
]
}
],
@@ -1630,22 +1623,22 @@
"id": "7bfab499",
"metadata": {},
"source": [
- "It is also possible to retrieve the properteis of a component by asking the mixture and appending the component name, as follows:"
+ "It is also possible to retrieve the properties of a component by asking the mixture and appending the component name, as follows:"
]
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 48,
"id": "87dfb7ab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([2.09391006e-05, 2.09391006e-05, 2.09391006e-05, 2.09391006e-05])"
+ "array([2.09391681e-05, 2.09391681e-05, 2.09391681e-05, 2.09391681e-05])"
]
},
- "execution_count": 45,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -1669,12 +1662,12 @@
"source": [
"#### Checking consistency with ``check_mixture_health``\n",
"\n",
- "You can also check the health of the mixture, such as whether all the mole fractions add to 1.0 each each pore:"
+ "You can also check the health of the mixture, such as whether all the mole fractions add to 1.0 each pore:"
]
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 49,
"id": "a5bd5ca5",
"metadata": {},
"outputs": [
@@ -1697,7 +1690,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 50,
"id": "b809ec4c",
"metadata": {},
"outputs": [
@@ -1731,18 +1724,18 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 63,
"id": "b0a699ad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'oxygen': array([2.09391006e-05, 2.09391006e-05, 2.09391006e-05, 2.09391006e-05]),\n",
- " 'nitrogen': array([1.69779528e-05, 1.69779528e-05, 1.69779528e-05, 1.69779528e-05])}"
+ "{'oxygen': array([2.09391681e-05, 2.09391681e-05, 2.09391681e-05, 2.09391681e-05]),\n",
+ " 'nitrogen': array([1.6985128e-05, 1.6985128e-05, 1.6985128e-05, 1.6985128e-05])}"
]
},
- "execution_count": 48,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
@@ -1758,7 +1751,7 @@
"source": [
"#### Using the wildcard (``*``) syntax\n",
"\n",
- "One more feature that has been added is the ability to fetch requested property arrays from *all* the components by replacing the component name with the universal *wildcard* symbmol: ``*`` as follows:"
+ "One more feature that has been added is the ability to fetch requested property arrays from *all* the components by replacing the component name with the universal *wildcard* symbol: ``*`` as follows:"
]
},
{
@@ -1864,7 +1857,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/examples/tutorials/08_simulating_transport.ipynb b/examples/tutorials/08_simulating_transport.ipynb
index e5f9daa834..d322f5653a 100644
--- a/examples/tutorials/08_simulating_transport.ipynb
+++ b/examples/tutorials/08_simulating_transport.ipynb
@@ -226,7 +226,7 @@
"id": "7720d25b",
"metadata": {},
"source": [
- "```{tip} Phase can do auotmatic interpolation to get throat values\n",
+ "```{admonition} Phase can do automatic interpolation to get throat values\n",
" The ``Phase`` class has a special ability to interpolate pore values to throats, and vice-versa. In PNM simulations all the balances are solved for each pore, so the thermodynamic properties like temperature, pressure, etc. are all defined on pores. Consequently, the physical properties are also defined in pores, like viscosity. However, as shown above we often want viscosity values in the throats. OpenPNM provides a shortcut for this, such that if you request a throat property that does not exist, it will attempt to fetch the pores values and do a linear interpolation of values to produce an array of throat values. There is also a function for this, ``water.interpolate_data('throat.viscosity')``, but the ``water['throat.viscosity']`` shortcut is very convenient. The automatic interpolation can be disabled in the `phase.settings`.\n",
"```"
]
@@ -377,9 +377,8 @@
"id": "e81f2e4b",
"metadata": {},
"source": [
- "```{tip} All boundary conditions are preceeded with 'pore.bc'\n",
- " All boundary conditions are stored as 'pore.bc.', which means that OpenPNM's dictionary lookup tricks can be used to see all types and values of bc's using: `sf['pore.bc']` which will return a `dict`. This can be used as shown below:\n",
- "```"
+ "```{admonition} All boundary conditions are preceeded with 'pore.bc'\n",
+ " All boundary conditions are stored as 'pore.bc.', which means that OpenPNM's dictionary lookup tricks can be used to see all types and values of bc's using: `sf['pore.bc']` which will return a `dict`. This can be used as shown below:\n"
]
},
{
@@ -465,7 +464,7 @@
"id": "bd75fd50",
"metadata": {},
"source": [
- "The reason for the dict format is to provide a consistent API for single components and multiphysics, where multiple different quanties might be solved for. However, these ``'pore.pressure'`` values are also written to the dictionary of the algorithm object as well:"
+ "The reason for the dict format is to provide a consistent API for single components and multiphysics, where multiple different quantities might be solved for. However, these ``'pore.pressure'`` values are also written to the dictionary of the algorithm object as well:"
]
},
{
@@ -864,7 +863,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.12"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/examples/tutorials/09_simulating_invasion.ipynb b/examples/tutorials/09_simulating_invasion.ipynb
index 779a31bdf5..3f71d41470 100644
--- a/examples/tutorials/09_simulating_invasion.ipynb
+++ b/examples/tutorials/09_simulating_invasion.ipynb
@@ -9,8 +9,9 @@
"\n",
"Studying the impact of multiphase conditions on transport in porous materials was the original motivation for pore network modeling. OpenPNM includes a set of algorithms for simulating drainage (i.e. non-wetting phase invasion) using both ordinary and invasion percolation. These will be covered in this notebook.\n",
"\n",
- "```{note} On simulating imbibition\n",
- " Imbibtion is quite a bit more challenging to simulate than drainage, since the menisci movement mechanisms are more complicated. OpenPNM plans to offer some imbibition algorithms, but these need to be carefully developed so are still 'works in progress'.\n",
+ "```{admonition} On simulating imbibition\n",
+ "\n",
+ " Imbibition is quite a bit more challenging to simulate than drainage, since the menisci movement mechanisms are more complicated. OpenPNM plans to offer some imbibition algorithms, but these need to be carefully developed so there are still 'works in progress'.\n",
"```"
]
},
@@ -87,7 +88,7 @@
"id": "858011f7",
"metadata": {},
"source": [
- "### Manual Implementation of the Drainge Process\n",
+ "### Manual Implementation of the Drainage Process\n",
"\n",
"Before outlining the `Drainage` algorithm in OpenPNM, let's illustrate the process by hand. This requires the following steps:\n",
"\n",
@@ -194,7 +195,7 @@
"id": "2622d4cb",
"metadata": {},
"source": [
- "The 'black' cluster extends from the left edge thoughout the domain, while the other colors are not connected to the left edge. Physically speaking only a pores that are connected to the left edge can be filled, so all the other cluster numbers should be set to uninvaded. This is illustrated in the following block where the yellow pores are invaded:"
+ "The 'black' cluster extends from the left edge throughout the domain, while the other colors are not connected to the left edge. Physically speaking only pores that are connected to the left edge can be filled, so all the other cluster numbers should be set to uninvaded. This is illustrated in the following block where the yellow pores are invaded:"
]
},
{
@@ -231,7 +232,7 @@
"id": "e66bb817",
"metadata": {},
"source": [
- "The above process can yeild a capillary pressure or drainage curve be repeating for increaingly high values of 'throat.entry_pressure' until the entire domain is invaded. This is the process that is used in the `Drainage` algorithm."
+ "The above process can yield a capillary pressure or drainage curve be repeating for increasingly high values of 'throat.entry_pressure' until the entire domain is invaded. This is the process that is used in the `Drainage` algorithm."
]
},
{
@@ -323,7 +324,7 @@
"id": "de021df9",
"metadata": {},
"source": [
- "The `'invasion_pressure'` and `'invasion_sequence'` arrays are quite useful because the actaully contain the entire invasion history in a single array. We can obtain the invasion pattern at any applied pressure by doing:"
+ "The `'invasion_pressure'` and `'invasion_sequence'` arrays are quite useful because the actually contain the entire invasion history in a single array. We can obtain the invasion pattern at any applied pressure by doing:"
]
},
{
@@ -418,7 +419,7 @@
"id": "0118228b",
"metadata": {},
"source": [
- "One of the main uses of the `Drainage` algorithm is to simulate capillary pressure curves, and specifical mercury intrusion porosimetry. One of the main ways to calibrate a PNM is to ensure that the simulated MIP curve matches experimental data. Let's simulate this:"
+ "One of the main uses of the `Drainage` algorithm is to simulate capillary pressure curves, and specifically mercury intrusion porosimetry. One of the main ways to calibrate a PNM is to ensure that the simulated MIP curve matches experimental data. Let's simulate this:"
]
},
{
@@ -452,7 +453,7 @@
" contact_angle='throat.contact_angle',\n",
" diameter='throat.diameter',)\n",
"mip = op.algorithms.Drainage(network=pn, phase=hg)\n",
- "mip.set_inlet_BC(pores=pn.pores('surface')) # mercury invades froma all sides\n",
+ "mip.set_inlet_BC(pores=pn.pores('surface')) # mercury invades from all sides\n",
"mip.run()"
]
},
@@ -549,8 +550,9 @@
"id": "1f6224b9",
"metadata": {},
"source": [
- "```{warning} Inlet pores start invaded\n",
- " The curve shown above starts at a non-wetting phase saturation slightly above 0. This is because the inlet pores are set to invaded to begin the algorithm. If the domain is large this is not really noticable. To be fully rigorous, one should actually add \"boundary pores\" to the domain, which are fictitious pores that have no volume. Another solution is to set the pore volume of the inlet pores to 0, with `pn['pore.volume@left'] = 0`.\n",
+ "```{warning} \n",
+ " **Inlet pores start invaded**\n",
+ " The curve shown above starts at a non-wetting phase saturation slightly above 0. This is because the inlet pores are set to invaded to begin the algorithm. If the domain is large this is not really noticeable. To be fully rigorous, one should actually add \"boundary pores\" to the domain, which are fictitious pores that have no volume. Another solution is to set the pore volume of the inlet pores to 0, with `pn['pore.volume@left'] = 0`.\n",
"```"
]
},
@@ -560,7 +562,7 @@
"metadata": {},
"source": [
"## Invasion Percolation\n",
- "Invasion percolation is performed by invading individual throats based on the next most easily invadedable option, rather than applying a fixed pressure like the drainage algorithm does (which is based on ordinary percoaltion, with access limitations). IP therefore provides a lot more information since each individual invasion event is tracked. IP can be thought of as a volume-controlled injection where drainage is a pressure-based process. In other words, in drainage the pressure is increased in steps and all invaded locations are found. In invasion percolation, some incremental amount of volume is injected, corresponding to the volume required to invaded the next available throat and the pore beyond it."
+ "Invasion percolation is performed by invading individual throats based on the next most easily invadedable option, rather than applying a fixed pressure like the drainage algorithm does (which is based on ordinary percolation, with access limitations). IP therefore provides a lot more information since each individual invasion event is tracked. IP can be thought of as a volume-controlled injection where drainage is a pressure-based process. In other words, in drainage the pressure is increased in steps and all invaded locations are found. In invasion percolation, some incremental amount of volume is injected, corresponding to the volume required to invaded the next available throat and the pore beyond it."
]
},
{
@@ -573,8 +575,8 @@
"Invasion percolation is more computationally involved than drainage, mainly because there is not a dedicated function for this in `scipy.sparse.csgraph`. OpenPNM uses a [binary heap](https://en.wikipedia.org/wiki/Binary_heap), which is available in python's standard library. It is a `list` where the elements are sorted in special way, such that the first element always has the smallest value, which is convenient if the list is sorted by 'throat.entry_pressure'. It is then trivial to determine which throat should be invaded next. The process works by the following steps:\n",
"\n",
"1. Identify the next most easily invadable throat\n",
- "2. Set this throat to invaded and remove it from the the queue\n",
- "3. Identify which pore is now accessbible via this newly invaded throat\n",
+ "2. Set this throat to invaded and remove it from the queue\n",
+ "3. Identify which pore is now accessible via this newly invaded throat\n",
"4. If it is not already invaded then:\n",
" 1. Set it to invaded\n",
" 2. Find all of its neighboring throats and add them to the queue\n",
@@ -665,7 +667,7 @@
"id": "43e3128d",
"metadata": {},
"source": [
- "Printing the queue shows that the first element is indeed the most easiliy invaded one. Note that the rest of the elements are NOT in sequential order but are in a special order dictated by the `binary heap` data structure. We only care about the first entry. We can 'invade' this throat by \"popping\" the first entry off the queue using `heappop`:"
+ "Printing the queue shows that the first element is indeed the most easily invaded one. Note that the rest of the elements are NOT in sequential order but are in a special order dictated by the `binary heap` data structure. We only care about the first entry. We can 'invade' this throat by \"popping\" the first entry off the queue using `heappop`:"
]
},
{
@@ -884,7 +886,7 @@
"source": [
"```{note} **A note about computational performance**\n",
"\n",
- " The OpenPNM implementation uses 'just-in-time' compilation offer by the `numba` package to accelerate the for-loop so the process demonstrated abovec is actually quite fast\n",
+ " The OpenPNM implementation uses 'just-in-time' compilation offer by the `numba` package to accelerate the for-loop so the process demonstrated above is actually quite fast\n",
"```"
]
},
@@ -1094,7 +1096,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/examples/tutorials/10_visualization_options.ipynb b/examples/tutorials/10_visualization_options.ipynb
index b46d6a168c..cca5f0313a 100644
--- a/examples/tutorials/10_visualization_options.ipynb
+++ b/examples/tutorials/10_visualization_options.ipynb
@@ -64,7 +64,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We create a cubic network and visualize the network pores and throats using `plot_coordinates` and `plot_connections`. These functions are based on `matplotlib` functions and costumized for pore network visualization. Here the pores and throats are colored by the pores and throats diameter. The throats are shown as connections in a wireframe. We used `alpha` to create a more transparent color for throats. :"
+ "We create a cubic network and visualize the network pores and throats using `plot_coordinates` and `plot_connections`. These functions are based on `matplotlib` functions and costumized for pore network visualization. Here the pores and throats are colored by the pores and throats diameter. The throats are shown as connections in a wireframe. We used `alpha` to create a more transparent color for throats:"
]
},
{
@@ -131,7 +131,7 @@
"source": [
"## Interactive plots in Jupyter notebook\n",
"\n",
- "The plots in Spyder and Jupyter notebook shown in previous examples are static.To create an interactive plot in Jupyter notebook we use [Plotly Python](https://plotly.com/python/), which is an interactive open-source library for plotting in Python. Plotly has different methods to plot data such as histogram, scatter plot, etc. When using Jupyter notebooks for pore network visualization, OpenPNM's costumized function `plot_notebook` (based on Plotly) can be used. Following shows an example. The resulting plot is interactive to (rotate, zoom, etc) and by hovering over pores their indice and coordinates will be shown in a box."
+ "The plots in Spyder and Jupyter notebook shown in previous examples are static. To create an interactive plot in Jupyter notebook we use [Plotly Python](https://plotly.com/python/), which is an interactive open-source library for plotting in Python. Plotly has different methods to plot data such as histogram, scatter plot, etc. When using Jupyter notebooks for pore network visualization, OpenPNM's costumized function `plot_notebook` (based on Plotly) can be used. Following shows an example. The resulting plot is interactive to (rotate, zoom, etc) and by hovering over pores their indices and coordinates will be shown in a box."
]
},
{
@@ -19100,85059 +19100,85059 @@
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]
}
],
@@ -125444,9 +125444,9 @@
"\n",
"4) Click on Glyph button in the tool bar\n",
"\n",
- "5) Change pore shapes to spheres , adjust size, color, and other settings\n",
+ "5) Change pore shapes to spheres, adjust size, color, and other settings\n",
"\n",
- "7) Click on Apply to visualize this settings"
+ "7) Click on Apply to visualize this setting"
]
},
{
@@ -125571,7 +125571,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/openpnm/__version__.py b/openpnm/__version__.py
index ad5a2688e5..799a81fb86 100644
--- a/openpnm/__version__.py
+++ b/openpnm/__version__.py
@@ -1 +1 @@
-__version__ = '3.4.0.dev0'
+__version__ = '3.4.0.dev3'
diff --git a/openpnm/algorithms/__init__.py b/openpnm/algorithms/__init__.py
index 8e7e915ad9..0a4a1fd704 100644
--- a/openpnm/algorithms/__init__.py
+++ b/openpnm/algorithms/__init__.py
@@ -1,3 +1,12 @@
+r"""
+Algorithms
+==========
+
+This module contains various algorithms for performing transport simulations
+
+
+"""
+
from ._algorithm import *
from ._transport import *
diff --git a/openpnm/core/__init__.py b/openpnm/core/__init__.py
index f064ace70b..ca385e27ab 100644
--- a/openpnm/core/__init__.py
+++ b/openpnm/core/__init__.py
@@ -2,8 +2,7 @@
Main classes of OpenPNM
=======================
-This module contains the main classes from which all other major objects
-(Network, Geometry, Physics, Phase, and Algorithm) derive.
+This module contains the main classes from which all other major objects derive.
The Base class
--------------
@@ -12,37 +11,6 @@
and throats, applying labels, and managing the stored data. All OpenPNM
object inherit from ``Base`` so possess these methods.
-----
-
-``Base`` objects, Networks, Phase, Algorithms, are assigned to all locations
-in the domain. The ``Subdomain`` class is a direct descendent of ``Base``
-which has the added ability to be assigned to a subset of the domain. Objects
-that inherit from ``Subdomain`` are Geomery and Physics.
-
-Boss objects refer to the Full Domain object it is associated with. For
-Geomery objects this is the Network, and for Physics objects this is the
-Phase that was specified during instantiation.
-
-The associations between an object and it's boss are tracked using labels in
-the boss. So a Geometry object named ``geom1`` will put labels 'pore.geom1'
-and 'throat.geom1' into the Network dictionary, with ``True`` values indicating
-where ``geom1`` applies.
-
-The ModelsMixin class
----------------------
-
-`Mixins `_ are a useful feature of Python
-that allow a few methods to be added to a class that needs them. In OpenPNM,
-the ability to store and run 'pore-scale' models is not needed by some objects
-(Network, Algorithms), but is essential to Geometry, Physics, and Phase
-objects.
-
-In addition to these methods, the ``ModelsMixin`` also adds a ``models``
-attribute to each object. This is a dictionary that stores the pore-scale
-models and their associated parameters. When ``regenerate_models`` is called
-the function and all the given parameters are retrieved from this dictionary
-and run.
-
"""
from ._models import *
diff --git a/openpnm/models/geometry/conduit_lengths/_funcs.py b/openpnm/models/geometry/conduit_lengths/_funcs.py
index 3355ba44fd..d9db7423c3 100644
--- a/openpnm/models/geometry/conduit_lengths/_funcs.py
+++ b/openpnm/models/geometry/conduit_lengths/_funcs.py
@@ -51,15 +51,31 @@ def spheres_and_cylinders(
# Handle the case where Dt > Dp
if (Dt > D1).any() or (Dt > D2).any():
_raise_incompatible_data()
+
+ # If spheres do not overlap:
L1 = np.sqrt(D1**2 - Dt**2) / 2
L2 = np.sqrt(D2**2 - Dt**2) / 2
+ Lt = L_ctc - (L1 + L2)
+
+ if np.any(Lt < 0): # Find pores that touch/overlap
+ # Find diameter of overlap between spheres
+ d = L_ctc
+ R1 = D1/2
+ R2 = D2/2
+ # Check distance to the intersection
+ L1_int = (d**2 - R2**2 + R1**2) / (2*d)
+ if np.any(L1_int < 0) or np.any(L1_int > L_ctc):
+ raise Exception('The pores overlap too much')
+ D_int = 2/(2*d) * np.sqrt(4*d**2 * R1**2 - (d**2 - R2**2 + R1**2)**2)
+ mask = D_int > Dt
+ if np.any(mask):
+ d = d[mask]
+ R1 = R1[mask]
+ R2 = R2[mask]
+ L1[mask] = (d**2 - R2**2 + R1**2) / (2*d)
+ L2[mask] = L_ctc[mask] - L1[mask]
+ Lt[mask] = 1e-15
- # Handle throats w/ overlapping pores
- _L1 = (4 * L_ctc**2 + D1**2 - D2**2) / (8 * L_ctc)
- mask = L_ctc - 0.5 * (D1 + D2) < 0
- L1[mask] = _L1[mask]
- L2[mask] = (L_ctc - L1)[mask]
- Lt = np.maximum(L_ctc - (L1 + L2), 1e-15)
return np.vstack((L1, Lt, L2)).T
diff --git a/openpnm/models/geometry/pore_surface_area/_funcs.py b/openpnm/models/geometry/pore_surface_area/_funcs.py
index 0fc9846e66..655d870a38 100644
--- a/openpnm/models/geometry/pore_surface_area/_funcs.py
+++ b/openpnm/models/geometry/pore_surface_area/_funcs.py
@@ -40,7 +40,9 @@ def sphere(
R = network[pore_diameter] / 2
value = 4 * _np.pi * R**2
Tca = network[throat_cross_sectional_area]
- _np.subtract.at(value, network.conns.T.flatten(), _np.repeat(Tca, repeats=2))
+ _np.subtract.at(value, network.conns[:, 0], Tca)
+ _np.subtract.at(value, network.conns[:, 1], Tca)
+ value = _np.clip(value, 1e-12, _np.inf)
return value
@@ -69,7 +71,9 @@ def circle(
"""
value = _np.pi * network[pore_diameter]
Tca = network[throat_cross_sectional_area]
- _np.subtract.at(value, network.conns.T.flatten(), _np.repeat(Tca, repeats=2))
+ _np.subtract.at(value, network.conns[:, 0], Tca)
+ _np.subtract.at(value, network.conns[:, 1], Tca)
+ value = _np.clip(value, 1e-12, _np.inf)
return value
@@ -95,7 +99,9 @@ def cube(
D = network[pore_diameter]
value = 6.0 * D**2
Tca = network[throat_cross_sectional_area]
- _np.subtract.at(value, network.conns.T.flatten(), _np.repeat(Tca, repeats=2))
+ _np.subtract.at(value, network.conns[:, 0], Tca)
+ _np.subtract.at(value, network.conns[:, 1], Tca)
+ value = _np.clip(value, 1e-12, _np.inf)
return value
@@ -121,5 +127,7 @@ def square(
D = network[pore_diameter]
value = 4.0 * D
Tca = network[throat_cross_sectional_area]
- _np.subtract.at(value, network.conns.T.flatten(), _np.repeat(Tca, repeats=2))
+ _np.subtract.at(value, network.conns[:, 0], Tca)
+ _np.subtract.at(value, network.conns[:, 1], Tca)
+ value = _np.clip(value, 1e-12, _np.inf)
return value
diff --git a/openpnm/network/__init__.py b/openpnm/network/__init__.py
index ab5365e06a..2090014e4d 100644
--- a/openpnm/network/__init__.py
+++ b/openpnm/network/__init__.py
@@ -1,3 +1,11 @@
+r"""
+Network
+=======
+
+Contains network generators and the basic Network class
+
+"""
+
from ._network import Network
from ._cubic import Cubic
from ._demo import Demo
diff --git a/openpnm/network/_bcc.py b/openpnm/network/_bcc.py
index 0cf2578f45..dabe8254d6 100644
--- a/openpnm/network/_bcc.py
+++ b/openpnm/network/_bcc.py
@@ -69,7 +69,6 @@ def __init__(self, shape, mode='sc', spacing=1, **kwargs):
Ps = self.pores(['left', 'right', 'top', 'bottom', 'front', 'back'])
Ps = self.to_mask(pores=Ps)
self['pore.surface'] = Ps
- self['pore.coords'] *= np.array(spacing)
def add_boundary_pores(self, labels, spacing):
r"""
diff --git a/openpnm/phase/__init__.py b/openpnm/phase/__init__.py
index 5e87026bf6..fee8d12be4 100644
--- a/openpnm/phase/__init__.py
+++ b/openpnm/phase/__init__.py
@@ -1,3 +1,12 @@
+r"""
+Phase
+=====
+
+This module contains classes generating and handling thermophysical properties
+
+"""
+
+
def _fetch_chemical_props(a):
temp = {}
k = 1.380649e-23 # Boltzmann constant
diff --git a/requirements/docs.txt b/requirements/docs.txt
index 913eda84c6..1badbc29f5 100644
--- a/requirements/docs.txt
+++ b/requirements/docs.txt
@@ -8,3 +8,4 @@ pydata-sphinx-theme
sphinx
sphinx-copybutton
sphinx-design
+porespy
diff --git a/setup.cfg b/setup.cfg
index 52a6b0c4ac..5ab6bfc322 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
[bumpversion]
-current_version = 3.4.0.dev0
+current_version = 3.4.0.dev3
parse = (?P\d+)\.(?P\d+)\.(?P\d+)\.(?P\D+)(?P\d+)?
serialize = {major}.{minor}.{patch}.{release}{build}
diff --git a/tests/integration/Flow_in_straight_conduit.py b/tests/integration/Flow_in_straight_conduit.py
new file mode 100644
index 0000000000..8042049ab1
--- /dev/null
+++ b/tests/integration/Flow_in_straight_conduit.py
@@ -0,0 +1,55 @@
+# This test checks to ensure that pores which overlap give the same transport
+# rates as pores which do not but are the same size
+if __name__ == "__main__":
+
+ import openpnm as op
+ import numpy as np
+
+ mu = 1.0
+ Pin = 1.0
+ Pout = 0.0
+ diameters = [0.9, 1.0, 1.1, 1.5]
+ Q = np.pi*(np.array(diameters)/2)**4/(8*mu*4) * (Pin - Pout)
+
+ for i, D in enumerate(diameters):
+ pn5 = op.network.Cubic(shape=[5, 1, 1], spacing=1)
+ pn5['pore.diameter'] = D
+ pn5['throat.diameter'] = D
+ pn5.regenerate_models()
+ pn5.add_model(
+ propname='throat.hydraulic_size_factors',
+ model=op.models.geometry.hydraulic_size_factors.spheres_and_cylinders)
+
+ pn3 = op.network.Cubic(shape=[3, 1, 1], spacing=2)
+ pn3['pore.diameter'] = D
+ pn3['throat.diameter'] = D
+ pn3.add_model(
+ propname='throat.hydraulic_size_factors',
+ model=op.models.geometry.hydraulic_size_factors.spheres_and_cylinders)
+ pn3.regenerate_models()
+
+ ph1 = op.phase.Phase(network=pn3)
+ ph1['pore.viscosity'] = mu
+ ph1.add_model(
+ propname='throat.hydraulic_conductance',
+ model=op.models.physics.hydraulic_conductance.generic_hydraulic)
+
+ ph2 = op.phase.Phase(network=pn5)
+ ph2['pore.viscosity'] = mu
+ ph2.add_model(
+ propname='throat.hydraulic_conductance',
+ model=op.models.physics.hydraulic_conductance.generic_hydraulic)
+
+ sf1 = op.algorithms.StokesFlow(network=pn3, phase=ph1)
+ sf1.set_value_BC(pores=pn3.pores('xmin'), values=Pin)
+ sf1.set_value_BC(pores=pn3.pores('xmax'), values=Pout)
+ sf1.run()
+ r1 = sf1.rate(pores=pn3.pores('xmin'))
+
+ sf2 = op.algorithms.StokesFlow(network=pn5, phase=ph1)
+ sf2.set_value_BC(pores=pn5.pores('xmin'), values=Pin)
+ sf2.set_value_BC(pores=pn5.pores('xmax'), values=Pout)
+ sf2.run()
+ r2 = sf2.rate(pores=pn5.pores('xmin'))
+ assert np.allclose(r1, r2, atol=0, rtol=1e-10)
+ assert np.allclose(r1, Q[i], atol=0, rtol=1e-10)
diff --git a/tests/unit/models/geometry/ConduitDiffusiveSizeFactorsTest.py b/tests/unit/models/geometry/ConduitDiffusiveSizeFactorsTest.py
index 874f690de2..6b8fe24605 100644
--- a/tests/unit/models/geometry/ConduitDiffusiveSizeFactorsTest.py
+++ b/tests/unit/models/geometry/ConduitDiffusiveSizeFactorsTest.py
@@ -21,10 +21,9 @@ def test_spheres_and_cylinders(self):
'throat': SF[:, 1],
'pore2': SF[:, 2]}
S_desired = {
- 'pore1': array([0.93875728, 0.97459088]),
- 'throat': array([1.25663706e+14, 4.05813214e-01]),
- 'pore2': array([0.82884551, 0.94886745])
- }
+ 'pore1': array([1.06932839, 0.97459088]),
+ 'throat': array([4.02746504, 0.40581321]),
+ 'pore2': array([0.97459088, 0.94886745])}
for k, v in S_actual.items():
assert_allclose(v, S_desired[k])
@@ -34,10 +33,9 @@ def test_circles_and_rectangles(self):
'throat': SF[:, 1],
'pore2': SF[:, 2]}
S_desired = {
- 'pore1': array([1.53390798, 1.80140852]),
- 'throat': array([4.00000000e+14, 1.29174358e+00]),
- 'pore2': array([1.65097536, 2.07781253])
- }
+ 'pore1': array([1.62474893, 1.80140852]),
+ 'throat': array([12.81981939, 1.29174358]),
+ 'pore2': array([1.80140852, 2.07781253])}
for k, v in S_actual.items():
assert_allclose(v, S_desired[k])
@@ -112,10 +110,9 @@ def test_ncylinders_in_series(self):
'throat': SF[:, 1],
'pore2': SF[:, 2]}
S_desired = {
- 'pore1': array([0.49260457, 0.77443401]),
- 'throat': array([1.25663706e+14, 4.05813214e-01]),
- 'pore2': array([0.563723, 0.84600371])
- }
+ 'pore1': array([0.69688454, 0.77443401]),
+ 'throat': array([4.02746504, 0.40581321]),
+ 'pore2': array([0.77443401, 0.84600371])}
for k, v in S_actual.items():
assert_allclose(v, S_desired[k])
diff --git a/tests/unit/models/geometry/ConduitHydraulicSizeFactorsTest.py b/tests/unit/models/geometry/ConduitHydraulicSizeFactorsTest.py
index 5242a83ad0..77cf3bf7cc 100644
--- a/tests/unit/models/geometry/ConduitHydraulicSizeFactorsTest.py
+++ b/tests/unit/models/geometry/ConduitHydraulicSizeFactorsTest.py
@@ -21,10 +21,9 @@ def test_spheres_and_cylinders(self):
'throat': SF[:, 1],
'pore2': SF[:, 2]}
S_desired = {
- 'pore1': array([0.01068901, 0.01195694]),
- 'throat': array([6.28318531e+11, 2.02906607e-03]),
- 'pore2': array([0.00771764, 0.00917032])
- }
+ 'pore1': array([0.01655401, 0.01195694]),
+ 'throat': array([0.02013733, 0.00202907]),
+ 'pore2': array([0.01195694, 0.00917032])}
for k, v in S_actual.items():
assert_allclose(v, S_desired[k], rtol=1e-5)
@@ -34,10 +33,9 @@ def test_circles_and_rectangles(self):
'throat': SF[:, 1],
'pore2': SF[:, 2]}
S_desired = {
- 'pore1': array([0.06563123, 0.06697876]),
- 'throat': array([5.33333333e+12, 1.72232477e-02]),
- 'pore2': array([0.05072058, 0.05686537])
- }
+ 'pore1': array([0.08485281, 0.06697876]),
+ 'throat': array([0.17093093, 0.01722325]),
+ 'pore2': array([0.06697876, 0.05686537])}
for k, v in S_actual.items():
assert_allclose(v, S_desired[k], rtol=1e-5)
@@ -112,10 +110,9 @@ def test_ncylinders_in_series(self):
'throat': SF[:, 1],
'pore2': SF[:, 2]}
S_desired = {
- 'pore1': array([0.0020471, 0.00612218]),
- 'throat': array([6.28318531e+11, 2.02906607e-03]),
- 'pore2': array([0.00261812, 0.00661558])
- }
+ 'pore1': array([0.00506726, 0.00612218]),
+ 'throat': array([0.02013733, 0.00202907]),
+ 'pore2': array([0.00612218, 0.00661558])}
for k, v in S_actual.items():
assert_allclose(v, S_desired[k], rtol=1e-5)
diff --git a/tests/unit/models/geometry/ConduitLengthsTest.py b/tests/unit/models/geometry/ConduitLengthsTest.py
index 2efd319576..f4d951cfdf 100644
--- a/tests/unit/models/geometry/ConduitLengthsTest.py
+++ b/tests/unit/models/geometry/ConduitLengthsTest.py
@@ -24,12 +24,23 @@ def test_spheres_and_cylinders(self):
with pytest.raises(Exception):
L_actual = mods.spheres_and_cylinders(self.net)
self.net["pore.diameter"][1] = 0.9
- # Overlapping pores
+ # Slightly Overlapping pores
self.net["pore.diameter"][0] = 1.2
L_actual = mods.spheres_and_cylinders(self.net)
+ L_desired = np.array([[0.56568542, 0.03120169, 0.40311289],
+ [0.40311289, 0.30965898, 0.28722813]])
+ assert_allclose(L_actual, L_desired)
+ # Moderately overlapping pores
+ self.net["throat.diameter"][0] = 0.2
+ L_actual = mods.spheres_and_cylinders(self.net)
L_desired = np.array([[5.78750000e-01, 1.00000000e-15, 4.21250000e-01],
[4.03112887e-01, 3.09658980e-01, 2.87228132e-01]])
assert_allclose(L_actual, L_desired)
+ self.net["throat.diameter"][0] = 0.4
+ # Strongly overlapping pores
+ self.net["pore.diameter"][0] = 2.5
+ with pytest.raises(Exception):
+ mods.spheres_and_cylinders(self.net)
self.net["pore.diameter"][0] = 0.5
def test_circles_and_rectangles(self):
@@ -40,14 +51,25 @@ def test_circles_and_rectangles(self):
# Incompatible data with model assumptions
self.net["pore.diameter"][1] = 0.3
with pytest.raises(Exception):
- L_actual = mods.circles_and_squares(self.net)
+ L_actual = mods.spheres_and_cylinders(self.net)
self.net["pore.diameter"][1] = 0.9
- # Overlapping pores
+ # Slightly Overlapping pores
self.net["pore.diameter"][0] = 1.2
- L_actual = mods.circles_and_rectangles(self.net)
+ L_actual = mods.spheres_and_cylinders(self.net)
+ L_desired = np.array([[0.56568542, 0.03120169, 0.40311289],
+ [0.40311289, 0.30965898, 0.28722813]])
+ assert_allclose(L_actual, L_desired)
+ # Moderately overlapping pores
+ self.net["throat.diameter"][0] = 0.2
+ L_actual = mods.spheres_and_cylinders(self.net)
L_desired = np.array([[5.78750000e-01, 1.00000000e-15, 4.21250000e-01],
[4.03112887e-01, 3.09658980e-01, 2.87228132e-01]])
assert_allclose(L_actual, L_desired)
+ self.net["throat.diameter"][0] = 0.4
+ # Strongly overlapping pores
+ self.net["pore.diameter"][0] = 2.5
+ with pytest.raises(Exception):
+ mods.spheres_and_cylinders(self.net)
self.net["pore.diameter"][0] = 0.5
def test_cones_and_cylinders(self):
diff --git a/tests/unit/models/geometry/PoreSurfaceAreaTest.py b/tests/unit/models/geometry/PoreSurfaceAreaTest.py
index fc4c52354d..af7e0d938e 100644
--- a/tests/unit/models/geometry/PoreSurfaceAreaTest.py
+++ b/tests/unit/models/geometry/PoreSurfaceAreaTest.py
@@ -23,8 +23,8 @@ def test_sphere(self):
def test_circle(self):
self.net2D.add_model(propname='pore.surface_area',
- model=mods.circle,
- regen_mode='normal')
+ model=mods.circle,
+ regen_mode='normal')
a = np.array([2.74159265, 2.84159265, 2.94159265])
b = np.unique(self.net2D['pore.surface_area'])
assert_allclose(a, b)
@@ -39,8 +39,8 @@ def test_cube(self):
def test_square(self):
self.net2D.add_model(propname='pore.surface_area',
- model=mods.square,
- regen_mode='normal')
+ model=mods.square,
+ regen_mode='normal')
a = np.array([3.6, 3.7, 3.8])
b = np.unique(self.net2D['pore.surface_area'])
assert_allclose(a, b)
@@ -54,16 +54,16 @@ def test_circle_multi_geom(self):
net['throat.cross_sectional_area'][[0, 1, 2]] = 0.3
net['pore.right'] = ~net['pore.left']
net.add_model(propname='pore.surface_area',
- model=mods.circle,
- domain='left',
- regen_mode='normal')
+ model=mods.circle,
+ domain='left',
+ regen_mode='normal')
net.add_model(propname='pore.surface_area',
model=mods.circle,
domain='right',
regen_mode='normal')
- a = np.array([2.84159265, 2.74159265, 2.74159265])
+ a = np.array([2.84159265, 2.54159265, 2.54159265])
b = net['pore.surface_area@left']
- c = np.array([2.74159265, 2.74159265, 2.74159265, 2.94159265,
+ c = np.array([2.74159265, 2.94159265, 2.94159265, 2.94159265,
2.94159265, 2.94159265, 3.04159265])
d = net['pore.surface_area@right']
assert_allclose(a, b)