diffusive_size_factor_DNS

PoreSpy’s diffusive_size_factor_DNS includes the steps for predicting the diffusive size factors of the conduit images decribed here. Note that the diffusive conductance of the conduits can be then calculated by multiplying the size factor by diffusivity of the phase. The function takes in the images of segmented porous medium and returns an array of diffusive size factors for all conduits in the image. Therefore, the framework can be applied to both one conduit image as well as a segmented image of porous medium.

import inspect
import warnings

import numpy as np
import scipy as sp
from matplotlib import pyplot as plt

import porespy as ps

ps.visualization.set_mpl_style()
warnings.filterwarnings("ignore")
inspect.signature(ps.networks.diffusive_size_factor_DNS)

<Signature (regions, throat_conns, voxel_size=1)>

regions

We can create a 3D image using PoreSpy’s poly_disperese_spheres generator and segment the image using snow_partitioning method or snow2 network extraction method.

np.random.seed(10)
shape = [40, 40, 40]
dist = sp.stats.norm(loc=7, scale=5)
im = ps.generators.polydisperse_spheres(shape=shape,
                                        porosity=0.7,
                                        dist=dist,
                                        r_min=3)

results = ps.filters.snow_partitioning(im=im.astype(bool))
regions = results['regions']
fig, ax = plt.subplots(1, 1, figsize=[4, 4])
ax.imshow(regions[:, :, 5], origin='lower', interpolation='none')
ax.axis(False);

throat_conns

PoreSpy’s diffusive_size_factor_DNS method takes in the segmented image and the conncetions of regions in the segmented image (throat conns). In this example we have created an image with voxel_size=1. For a different voxel size, the voxel_size argument needs to be passed to the method.

Note that find_conns method returns the connections in the segmented region. The order of values in conns is similar to the network extraction conns. Therefore, the region with label=1 in the segmented image is mapped to indice 0 in conns.

conns = ps.networks.find_conns(regions)
size_factors = ps.networks.diffusive_size_factor_DNS(regions,
                                                    throat_conns=conns)

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[3], line 2
      1 conns = ps.networks.find_conns(regions)
----> 2 size_factors = ps.networks.diffusive_size_factor_DNS(regions,
      3                                                     throat_conns=conns)

File ~/work/porespy/porespy/src/porespy/networks/_size_factors.py:127, in diffusive_size_factor_DNS(regions, throat_conns, voxel_size)
    125     # label two pore regions as 1,2
    126     roi_masked = _create_labeled_pair(cn, roi_crop)
--> 127     DNS_size_factor.append(_calc_g_val(roi_masked))
    128 diff_size_factor = np.array(DNS_size_factor) * voxel_size
    129 return diff_size_factor

File ~/work/porespy/porespy/src/porespy/networks/_size_factors.py:148, in _calc_g_val(im)
    132 def _calc_g_val(im):
    133     """
    134     Calculates the diffusive size factor of conduit image (ROI) using finite
    135     difference method. The finite difference nodes are created using OpenPNM's
   (...)    146         Diffusive size factor of the conduit.
    147     """
--> 148     import openpnm as op
    150     c1 = 20
    151     c2 = 10

ModuleNotFoundError: No module named 'openpnm'

voxel_size

By default, the diffusive_size_factor_DNS uses voxel_size=1. For an image with a different resolution, the voxel size needs to be passed as an argument. Let’s assume that voxel size for the generated image is 1e-6 [m]:

voxel_size = 1e-6
size_factors = ps.networks.diffusive_size_factor_DNS(regions,
                                                    throat_conns=conns,
                                                    voxel_size=1e-6)