| import matplotlib.pyplot as plt |
| import numpy as np |
| import openpnm as op |
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| |
| shape = np.array([10, 10, 10]) |
| spacing = 1.0 |
| net = op.network.Cubic(shape=[10, 10, 10], spacing=spacing) |
| geom = op.geometry.SpheresAndCylinders(network=net, pores=net.Ps, throats=net.Ts) |
| air = op.phase.Air(network=net) |
| phys = op.physics.Standard(network=net, geometry=geom, phase=air) |
| odiff = op.models.physics.diffusive_conductance.ordinary_diffusion |
| phys.add_model(propname="throat.diffusive_conductance", model=odiff) |
| fd = op.algorithms.FickianDiffusion(network=net, phase=air) |
| fd.set_value_BC(pores=net.pores("left"), values=1.0) |
| fd.set_value_BC(pores=net.pores("right"), values=0.0) |
| fd.run() |
| L = (shape * spacing)[1] |
| A = (shape * spacing)[[0, 2]].prod() |
| Mdot = fd.rate(pores=net.pores("left")).squeeze() |
| Deff0 = Mdot * L / A |
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| |
| mdiff = op.models.physics.diffusive_conductance.mixed_diffusion |
| phys.add_model(propname="throat.diffusive_conductance", model=mdiff) |
| spacings = np.linspace(1e-9, 1e-4, 20) |
| spacings = np.logspace(-9, -3, 25) |
| Deff = [] |
|
|
| for spacing in spacings: |
| np.random.seed(10) |
| net = op.network.Cubic(shape=[10, 10, 10], spacing=spacing) |
| geom = op.geometry.SpheresAndCylinders(network=net, pores=net.Ps, throats=net.Ts) |
| air = op.phase.Air(network=net) |
| phys = op.physics.Standard(network=net, geometry=geom, phase=air) |
| phys.add_model(propname="throat.diffusive_conductance", model=mdiff) |
| fd = op.algorithms.FickianDiffusion(network=net, phase=air) |
| fd.set_value_BC(pores=net.pores("left"), values=1.0) |
| fd.set_value_BC(pores=net.pores("right"), values=0.0) |
| fd.run() |
| L = (shape * spacing)[1] |
| A = (shape * spacing)[[0, 2]].prod() |
| Mdot = fd.rate(pores=net.pores("left")).squeeze() |
| Deff.append(Mdot * L / A) |
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| |
| Deff = np.array(Deff) |
| plt.figure() |
| plt.plot(spacings, Deff/Deff0) |
| plt.xscale("log") |
| plt.xlabel("spacing (m)") |
| plt.ylabel("Deff/Deff0") |
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