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f421c2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | # %% Initializations
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
from openpnm.utils import tic, toc
from openpnm.algorithms.legacy import TransientReactiveTransport
np.random.seed(10)
ws = op.Workspace()
ws.clear()
ws.settings["loglevel"] = 40
# %% Set the stage up for algorithms
Nx = 10
shape = [Nx, Nx, Nx]
spacing = 1/Nx
net = op.network.Cubic(shape=shape, spacing=spacing)
air = op.phase.Air(network=net)
air["throat.diffusive_conductance"] = np.random.rand(net.Nt)
# Add reactions
mod = op.models.physics.generic_source_term.standard_kinetics
k, n = -1e-1, 2
air["pore.k"], air["pore.n"] = k, n
air.add_model(
X="pore.concentration",
propname="pore.rxn",
model=mod, prefactor="pore.k", exponent="pore.n",
regen_mode="deferred"
)
alg_settings = {
"conductance": "throat.diffusive_conductance",
"quantity": "pore.concentration"
}
pardiso = op.solvers.PardisoSpsolve()
rk45 = op.integrators.ScipyRK45(verbose=True)
# %% Try w/ GenericTransport
print(f"\n{'+'*65}", "\n--> Using GenericTransport", f"\n{'+'*65}\n", flush=True)
gt = op.algorithms.GenericTransport(network=net, phase=air)
gt.settings._update(alg_settings)
gt.set_value_BC(net.pores("left"), 1.0)
gt.set_value_BC(net.pores("right"), 0.4)
gt.run(solver=pardiso)
print(gt["pore.concentration"].mean())
# %% Try w/ ReactiveTransport
print(f"\n{'+'*65}", "\n--> Using ReactiveTransport", f"\n{'+'*65}\n", flush=True)
rt = op.algorithms.ReactiveTransport(network=net, phase=air)
rt.settings._update(alg_settings)
rt.set_value_BC(net.pores("left"), 1.0)
rt.set_value_BC(net.pores("right"), 0.4)
rt.set_source(propname="pore.rxn", pores=net.pores("surface", mode="not"))
rt.run(solver=pardiso)
print(rt["pore.concentration"].mean())
# %% Try w/ FickianDiffusion
print(f"\n{'+'*65}", "\n--> Using FickianDiffusion", f"\n{'+'*65}\n", flush=True)
fd = op.algorithms.FickianDiffusion(network=net, phase=air)
fd.set_value_BC(net.pores("left"), 1.0)
fd.set_value_BC(net.pores("right"), 0.4)
fd.run(solver=pardiso)
print(fd["pore.concentration"].mean())
# %% Playing around w/ Transient stuff
print(f"\n{'+'*65}", "\n--> Using TransientReactiveTransport", f"\n{'+'*65}\n", flush=True)
# air["throat.diffusive_conductance"] = np.ones(net.Nt, dtype=float) * spacing
air["throat.diffusive_conductance"] = np.random.rand(net.Nt) * spacing
V = net["pore.volume"] = spacing**3
trt = op.algorithms.TransientReactiveTransport(network=net, phase=air)
trt.settings._update(alg_settings)
trt.set_value_BC(net.pores("left"), 1.0)
trt.set_value_BC(net.pores("right"), 0.4)
c0 = np.zeros(trt.Np)
tspan = [0, 0.4]
tout = np.linspace(tspan[0], tspan[1], 11)
dt = (tspan[1] - tspan[0]) / 200
# Adding source term
trt.set_source("pore.rxn", pores=net.pores("surface", mode="not"))
# air.remove_model("pore.rxn")
tic()
sol = trt.run(x0=c0, tspan=tspan, integrator=rk45, saveat=tout)
toc()
# %% Hardcoded solution using solve_ivp
# trt._build_A()
# trt._build_b()
# trt._apply_BCs()
# trt._apply_sources()
# A = trt.A.tocsc()
# b = trt.b
# from scipy.integrate import solve_ivp
# def ode_func(t, y, A, b, V):
# return (-A.dot(y) + b) / V
# f = lambda t, y: ode_func(t, y, A, b, V)
# tic()
# # TODO: implicit methods: pass lband/uband for drastically better performance
# # TODO: lband/uband = 1 is probably wrong!
# sol = solve_ivp(fun=f, t_span=tspan, y0=c0, method="RK23", rtol=1e-5)
# toc()
# %% TransientReactiveTransport (legacy)
trt_legacy = TransientReactiveTransport(network=net, phase=air)
dt_avg = np.diff(sol.t).mean()
t_settings = {
"t_initial": tspan[0],
"t_final": tspan[1],
"t_scheme": "implicit",
"t_step": dt,
"t_output": tout.tolist()
}
trt_legacy.settings._update({**alg_settings, **t_settings})
trt_legacy.set_value_BC(net.pores("left"), 1.0)
trt_legacy.set_value_BC(net.pores("right"), 0.4)
trt_legacy.set_IC(0.0)
# Add source term
trt_legacy.set_source("pore.rxn", pores=net.pores("surface", mode="not"))
tic()
trt_legacy.run(solver=pardiso)
toc()
# %% Compare new implementation vs legacy
c_new = sol.reshape((*shape, sol.t.size))
c_legacy = np.zeros((trt_legacy.Np, tout.size))
for i, t in enumerate(tout):
t_fmt = trt_legacy._nbr_to_str(t)
c_legacy[..., i] = trt_legacy[f"pore.concentration@{t_fmt}"]
c_legacy = c_legacy.reshape((*shape, tout.size))
fig, ax = plt.subplots(ncols=2)
ax[0].imshow(c_new[..., -1].mean(axis=2))
ax[0].set_title("new implementation")
ax[1].set_title("legacy")
h = ax[1].imshow(c_legacy[..., -1].mean(axis=2))
fig, ax = plt.subplots()
ax.plot(sol.t, c_new.mean(axis=(0, 1, 2)), "g-", label="new implementation")
ax.plot(tout, c_legacy.mean(axis=(0, 1, 2)), "rx", label="legacy")
ax.legend()
# Ensure correctness
np.testing.assert_allclose(c_new[..., -1], c_legacy[..., -1], rtol=1e-3)
# %% Continue integration
# # Integrate from 0 to 0.2 at once
# sol1 = trt.run(x0=c0, tspan=[0, 0.2], solver=rk45)
# # Integrate from 0 to 0.1 and then from 0.1 to 0.2
# sol2 = trt.run(x0=c0, tspan=[0, 0.1], solver=rk45)
# sol3 = trt.run(x0=sol2(0.1), tspan=[0.1, 0.2], solver=rk45)
# # Ensure correctness
# np.testing.assert_allclose(sol1(0.2), sol3(0.2), rtol=1e-5)
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