How to fix "KeyError: 'throat.hydraulic_conductance'"

[jiahui185]

import numpy as np
import openpnm as op
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as spst

op.visualization.set_mpl_style()
np.random.seed(10)
ws = op.Workspace()
ws.settings["loglevel"] = 40
np.set_printoptions(precision=5)
df = pd.read_csv('Sandstone.csv')
print(df)
df['R'] = -2*0.480*np.cos(np.deg2rad(140))/df['Pc [Pa]']
fig, ax = plt.subplots()
ax.semilogx(2*df['R']*1e6, df['S_Hg'], 'b.')
ax.set_xlabel('Pore Diameter [um]');
pn = op.network.Cubic(shape=[4, 4, 4], connectivity=26)
np.random.seed(0)
drop = np.random.randint(0, pn.Nt, 500)
op.topotools.trim(network=pn, throats=drop)
ax = op.visualization.plot_coordinates(pn)
ax = op.visualization.plot_connections(pn, ax=ax)
np.random.seed(0)  # Set the state of the random number generator to "0"
pn['pore.seed'] = np.random.rand(pn.Np)
from scipy.stats import dweibull
dst = dweibull(c=2.9, loc=15e-6, scale=8e-6)

fig, ax = plt.subplots()
ax.hist(dst.rvs(10000)*1e6, bins=50, edgecolor='k')
ax.tick_params(axis='x', labelrotation=45)
pn['pore.diameter'] = dst.rvs(pn.Np)
fig, ax = plt.subplots()
ax.hist(pn['pore.diameter']*1e6, edgecolor='k')
ax.tick_params(axis='x', labelrotation=45)
ax.set_xlabel('Pore Diameter [um]');
f = op.models.geometry.pore_seed.random
pn.add_model(propname='pore.seed',
             model=f,
             num_range=[0.0, 0.98])
pn.add_model(propname='pore.diameter',
             model=op.models.geometry.pore_size.generic_distribution,
             func=dst,
             seeds='pore.seed')
pn.regenerate_models()
fig, ax = plt.subplots()
ax.hist(pn['pore.diameter'], edgecolor='k')
ax.tick_params(axis='x', labelrotation=45);
pn.add_model(propname='throat.diameter_1', 
             model=op.models.misc.from_neighbor_pores,
             prop='pore.diameter',
             mode='min')
pn.add_model(propname='throat.seed', 
             model=op.models.misc.from_neighbor_pores,
             prop='pore.seed',
             mode='min')
pn.add_model(propname='throat.diameter_2',
             model=op.models.geometry.throat_size.generic_distribution,
             func=dst)
pn.add_model(propname='throat.diameter',
             model=op.models.misc.scaled,
             prop='throat.diameter_2',  # This could also be 'throat.diameter_2'
             factor=0.6,  # This could be 1.0 if no scaling is desired
)
fig, ax = plt.subplots()
ax.hist(pn['pore.diameter']*1e6, edgecolor='k', density=True, alpha=0.5, label='pores')
ax.hist(pn['throat.diameter']*1e6, edgecolor='k', density=True, alpha=0.5, label='throats')
ax.tick_params(axis='x', labelrotation=45)
ax.legend()
ax.set_xlabel('Pore diameter [um]');
pn.add_model(propname='pore.volume',
             model=op.models.geometry.pore_volume.sphere)
pn.add_model(propname='throat.length',
             model=op.models.geometry.throat_length.spheres_and_cylinders)
pn.add_model(propname='throat.total_volume',
             model=op.models.geometry.throat_volume.cylinder)
pn.add_model(propname='throat.lens_volume', 
            model=op.models.geometry.throat_volume.lens)
pn.add_model(propname='throat.volume', 
             model=op.models.misc.difference,
             props=['throat.total_volume', 'throat.lens_volume'])
hg = op.phase.Mercury(network=pn)
hg.add_model(propname='throat.entry_pressure',
             model=op.models.physics.capillary_pressure.washburn)
hg.regenerate_models()
mip = op.algorithms.Drainage(network=pn, phase=hg)
mip.set_inlet_BC(pores=pn.pores('surface'))
mip.run()
pc_sim = mip.pc_curve()
fig, ax = plt.subplots()
ax.semilogx(*pc_sim, 'b-', label='simulation')
ax.semilogx(df['Pc [Pa]'], df['S_Hg'], 'r.', label='experiment')
ax.legend();
pn.models['throat.diameter@all']['factor'] = 0.5
pn.regenerate_models()
hg.regenerate_models()
mip = op.algorithms.Drainage(network=pn, phase=hg)
mip.set_inlet_BC(pores=pn.pores('surface'))
mip.run()
pc_sim = mip.pc_curve()
fig, ax = plt.subplots()
ax.semilogx(*pc_sim, 'b-', label='simulation')
ax.semilogx(df['Pc [Pa]'], df['S_Hg'], 'r.', label='experiment')
ax.legend();
water = op.phase.Phase(network=pn)
water.add_model(propname='pore.viscosity',
                model=op.models.phase.viscosity.water_correlation)
print(water)
R = pn['throat.diameter']/2
L = pn['throat.length']
mu = water['throat.viscosity']  # See ProTip below
water['throat.hydraulic_conductance'] = np.pi*R**4/(8*mu*L)
print(water['throat.hydraulic_conductance'])
gas = op.phase.Phase(network=pn)
gas['pore.diffusivity'] = 1.0
gas['pore.viscosity'] = 1.0
gas.add_model_collection(op.models.collections.physics.basic)
gas.regenerate_models()
sf = op.algorithms.StokesFlow(network=pn, phase=gas)
sf.set_value_BC(pores=pn.pores('xmin'), values=1.0)
sf.set_value_BC(pores=pn.pores('xmax'), values=0.0)
sf.run()
dP = 1.0
L = (4 + 6)*1
A = 4*4*1
K = sf.rate(pores=pn.pores('xmin'))*(L/A)/dP*1e12
print(f'Permeability coefficient is {K[0]} Darcy')

[jgostick]

It's not clear where the error occurred in this large script. The message is pretty clear though...you don't have hydraulic conductance on the phase you pass to the StokesFlow. So probably the gas.add_model_collection call is not working, possibly because some other dependency is not found.