EffectiveDiffusivity

class openpnm.metrics.EffectiveDiffusivity(*args, **kwargs)

This class works by applying ‘value’ boundary conditions across the domain to find molar flow, then using Fick’s law to back-calculate the effective diffusivity of the domain.

\[D_{eff} = \frac{n_{A} L }{\Delta C_{A} A }\]

and

\[D_{eff} = D_{AB} \frac{\varepsilon}{\tau}\]

Examples

>>> import openpnm as op
>>> import numpy as np
>>> np.random.seed(5)
>>> pn = op.network.Cubic(shape=[10, 10, 10], spacing=1e-5)
>>> geo = op.geometry.SpheresAndCylinders(network=pn, pores=pn.Ps, throats=pn.Ts)

Now find the effective diffusivity of the network:

>>> ED = op.metrics.EffectiveDiffusivity(network=pn)
>>> Deff = ED.run()
>>> print(np.round(Deff,decimals=3))
0.049

Attributes

Np

A shortcut to query the total number of pores on the object

Nt

A shortcut to query the total number of throats on the object

Ps

A shortcut to get a list of all pores on the object

Ts

A shortcut to get a list of all throats on the object

name

String representing the name of the object

network

A shortcut to get a handle to the associated network.

project

A shortcut to get a handle to the associated project.

settings

Dictionary containing object settings.

Methods

get_conduit_data(poreprop[, throatprop, mode])	Combines requested data into a single 3-column array.
interleave_data(prop)	Retrieves requested property from associated objects, to produce a full Np or Nt length array.
interpolate_data(propname[, mode])	Determines a pore (or throat) property as the average of it's neighboring throats (or pores)
props([element, mode, deep])	Returns a list containing the names of all defined pore or throat properties.
run()	Execute the diffusion simulations in the principle directions.
show_hist([props, bins, fontsize])	Shows a quick plot of key property distributions.
to_indices(mask)	Converts a boolean mask to a list of pore or throat indices.
to_mask([pores, throats])	Convert a list of pore or throat indices into a boolean mask of the correct length.