| import logging |
| from openpnm.algorithms import ReactiveTransport |
| from openpnm.utils import Docorator |
|
|
|
|
| docstr = Docorator() |
| logger = logging.getLogger(__name__) |
|
|
|
|
| __all__ = ['FickianDiffusion'] |
|
|
|
|
| @docstr.dedent |
| class FickianDiffusionSettings(): |
| r""" |
| |
| Parameters |
| ---------- |
| %(ReactiveTransportSettings.parameters)s |
| |
| """ |
| quantity = 'pore.concentration' |
| conductance = 'throat.diffusive_conductance' |
|
|
|
|
| @docstr.dedent |
| class FickianDiffusion(ReactiveTransport): |
| r""" |
| A class to simulate binary diffusion with reactions |
| |
| Parameters |
| ---------- |
| %(ReactiveTransport.parameters)s |
| |
| Notes |
| ----- |
| Fickian diffusion in porous materials occurs in the void space, but |
| becuase the diffusion is confined to pores it is impacted by the porosity |
| and tortuosity of the network. Thus the total diffusive flux through the |
| network is reduced. This class can be used to simualte diffusion-reaction |
| in domains with specified boundary conditions, or it can be used |
| to calculate the effective diffusivity of the network by applying |
| controlled boundary conditions on opposing faces, calculating the |
| diffusion rate, and inverting Fick's first law as follows: |
| |
| .. math:: |
| |
| D_{eff} = N_{A}*L/(A*\Delta C_{A}) |
| |
| """ |
|
|
| def __init__(self, name='fick_?', **kwargs): |
| super().__init__(name=name, **kwargs) |
| self.settings._update(FickianDiffusionSettings()) |
| self.name = name |
|
|
