def test_periodic_bc(self):
"""
We set up the test case P
|--------|
| |
D | g2 | D
| |
|--------|
P
where D are dirichlet boundaries and P the periodic boundaries.
We construct periodic solution
p = sin(pi/xmax * (x - x0)) * cos(2*pi/ymax * (y - y0))
which gives a source term on the rhs.
"""
n = 8
xmax = 1
ymax = 1
gb = self.generate_2d_grid(n, xmax, ymax)
tol = 1e-6
def analytic_p(x):
x = x.copy()
shiftx = xmax / 4
shifty = ymax / 3
x[0] = x[0] - shiftx
x[1] = x[1] - shifty
p = np.sin(np.pi / xmax * x[0]) * np.cos(2 * np.pi / ymax * x[1])
px = (
(np.pi / xmax)
* np.cos(np.pi / xmax * x[0])
* np.cos(2 * np.pi / ymax * x[1])
)
py = (
2
* np.pi
/ ymax
* np.sin(np.pi / xmax * x[0])
* np.sin(2 * np.pi / ymax * x[1])
)
pxx = -(np.pi / xmax) ** 2 * p
pyy = -(2 * np.pi / ymax) ** 2 * p
return p, np.vstack([px, py]), pxx + pyy
for g, d in gb:
left = g.face_centers[0] < tol
right = g.face_centers[0] > xmax - tol
dir_bc = left + right
bound = pp.BoundaryCondition(g, dir_bc, "dir")
bc_val = np.zeros(g.num_faces)
bc_val[dir_bc], _, _ = analytic_p(g.face_centers[:, dir_bc])
pa, _, lpc = analytic_p(g.cell_centers)
src = -lpc * g.cell_volumes
specified_parameters = {"bc": bound, "bc_values": bc_val, "source": src}
pp.initialize_default_data(g, d, "flow", specified_parameters)
for _, d in gb.edges():
pp.params.data.add_discretization_matrix_keyword(d, "flow")
self.solve(gb, analytic_p)
def setup_cart_2d(nx):
frac1 = np.array([[0.2, 0.8], [0.5, 0.5]])
frac2 = np.array([[0.5, 0.5], [0.8, 0.2]])
fracs = [frac1, frac2]
gb = pp.meshing.cart_grid(fracs, nx, physdims=[1, 1])
gb.compute_geometry()
gb.assign_node_ordering()
kw = "flow"
aperture = 0.01 / np.max(nx)
for g, d in gb:
a = np.power(aperture, gb.dim_max() - g.dim) * np.ones(g.num_cells)
kxx = np.ones(g.num_cells) * a
perm = pp.SecondOrderTensor(kxx)
specified_parameters = {"second_order_tensor": perm}
if g.dim == 2:
bound_faces = g.tags["domain_boundary_faces"].nonzero()[0]
bound = pp.BoundaryCondition(g, bound_faces.ravel("F"),
["dir"] * bound_faces.size)
bc_val = np.zeros(g.num_faces)
bc_val[bound_faces] = g.face_centers[1, bound_faces]
specified_parameters.update({"bc": bound, "bc_values": bc_val})
# Initialize data and matrix dictionaries in d
pp.initialize_default_data(g, d, kw, specified_parameters)
for e, d in gb.edges():
# Compute normal permeability
gl, _ = gb.nodes_of_edge(e)
kn = 1.0 / aperture
data = {"normal_diffusivity": kn}
# Add parameters
d[pp.PARAMETERS] = pp.Parameters(keywords=[kw], dictionaries=[data])
# Initialize matrix dictionary
d[pp.DISCRETIZATION_MATRICES] = {kw: {}}
return gb
def setup_biot(self):
g = pp.CartGrid([5, 5])
g.compute_geometry()
stiffness = pp.FourthOrderTensor(np.ones(g.num_cells),
np.ones(g.num_cells))
bnd = pp.BoundaryConditionVectorial(g)
specified_data = {
"fourth_order_tensor": stiffness,
"bc": bnd,
"inverter": "python",
"biot_alpha": 1,
}
keyword_mech = "mechanics"
keyword_flow = "flow"
data = pp.initialize_default_data(g, {},
keyword_mech,
specified_parameters=specified_data)
data = pp.initialize_default_data(g, data, keyword_flow)
discr = pp.Biot()
discr.discretize(g, data)
div_u = data[pp.DISCRETIZATION_MATRICES][keyword_flow][
discr.div_u_matrix_key]
bound_div_u = data[pp.DISCRETIZATION_MATRICES][keyword_flow][
discr.bound_div_u_matrix_key]
stab = data[pp.DISCRETIZATION_MATRICES][keyword_flow][
discr.stabilization_matrix_key]
grad_p = data[pp.DISCRETIZATION_MATRICES][keyword_mech][
discr.grad_p_matrix_key]
bound_pressure = data[pp.DISCRETIZATION_MATRICES][keyword_mech][
discr.bound_pressure_matrix_key]
return g, stiffness, bnd, div_u, bound_div_u, grad_p, stab, bound_pressure
def test_two_cart_grids(self):
"""
We set up the test case -----|---------
| | |
| g1 | g2 |
| | |
-----|---------
with a linear pressure increase from left to right
"""
n = 2
xmax = 3
ymax = 1
split = 2
gb = self.generate_grids(n, xmax, ymax, split)
tol = 1e-6
for g, d in gb:
left = g.face_centers[0] < tol
right = g.face_centers[0] > xmax - tol
dir_bc = left + right
bound = pp.BoundaryCondition(g, dir_bc, "dir")
bc_val = np.zeros(g.num_faces)
bc_val[left] = xmax
bc_val[right] = 0
specified_parameters = {"bc": bound, "bc_values": bc_val}
pp.initialize_default_data(g, d, "flow", specified_parameters)
for e, d in gb.edges():
mg = d["mortar_grid"]
d[pp.PARAMETERS] = pp.Parameters(mg, ["flow"], [{}])
pp.params.data.add_discretization_matrix_keyword(d, "flow")
# assign discretization
data_key = "flow"
tpfa = pp.Tpfa(data_key)
coupler = pp.FluxPressureContinuity(data_key, tpfa)
assembler = test_utils.setup_flow_assembler(gb,
tpfa,
data_key,
coupler=coupler)
test_utils.solve_and_distribute_pressure(gb, assembler)
# test pressure
for g, d in gb:
self.assertTrue(
np.allclose(d[pp.STATE]["pressure"], xmax - g.cell_centers[0]))
# test mortar solution
for e, d_e in gb.edges():
mg = d_e["mortar_grid"]
g2, g1 = gb.nodes_of_edge(e)
master_to_m = mg.master_to_mortar_avg()
slave_to_m = mg.slave_to_mortar_avg()
master_area = master_to_m * g1.face_areas
slave_area = slave_to_m * g2.face_areas
self.assertTrue(
np.allclose(d_e[pp.STATE]["mortar_flux"] / master_area, 1))
self.assertTrue(
np.allclose(d_e[pp.STATE]["mortar_flux"] / slave_area, 1))
def set_parameters_cell_basis(self, gb: pp.GridBucket, data: Dict):
"""
Assign parameters for the micro gb. Very simple for now, this must be improved.
Args:
gb (TYPE): the micro gb.
Returns:
None.
"""
# First initialize data
for g, d in gb:
d["Aavatsmark_transmissibilities"] = True
domain_boundary = np.logical_and(
g.tags["domain_boundary_faces"],
np.logical_not(g.tags["fracture_faces"]),
)
boundary_faces = np.where(domain_boundary)[0]
if domain_boundary.size > 0:
bc_type = boundary_faces.size * ["dir"]
else:
bc_type = np.empty(0)
bc = pp.BoundaryCondition(g, boundary_faces, bc_type)
if hasattr(g, "face_on_macro_bound"):
micro_ind = g.face_on_macro_bound
macro_ind = g.macro_face_ind
bc.is_neu[micro_ind] = data["bc_macro"]["bc"].is_neu[macro_ind]
bc.is_dir[micro_ind] = data["bc_macro"]["bc"].is_dir[macro_ind]
param = {"bc": bc}
perm = data["g_data"](g)["second_order_tensor"]
param["second_order_tensor"] = perm
param["specific_volume"] = data["g_data"](g)["specific_volume"]
# Use python inverter for mpfa for small problems, where it does not pay off
# to fire up numba. The set threshold value is somewhat randomly picked.
if g.num_cells < 100:
param["mpfa_inverter"] = "python"
pp.initialize_default_data(g, d, self.keyword, param)
for e, d in gb.edges():
mg = d["mortar_grid"]
g1, g2 = gb.nodes_of_edge(e)
param = {}
if not hasattr(g1, "is_auxiliary") or not g1.is_auxiliary:
check_P = mg.secondary_to_mortar_avg()
param.update(data["e_data"](mg, g1, g2, check_P))
pp.initialize_data(mg, d, self.keyword, param)
def add_data(gb, domain, kf):
"""
Define the permeability, apertures, boundary conditions
"""
gb.add_node_props(["param", "is_tangential"])
tol = 1e-5
a = 1e-4
for g, d in gb:
# Assign aperture
a_dim = np.power(a, gb.dim_max() - g.dim)
aperture = np.ones(g.num_cells) * a_dim
# Effective permeability, scaled with aperture.
kxx = np.ones(g.num_cells) * np.power(kf,
g.dim < gb.dim_max()) * aperture
if g.dim == 2:
perm = pp.SecondOrderTensor(kxx=kxx, kyy=kxx, kzz=1)
else:
perm = pp.SecondOrderTensor(kxx=kxx, kyy=1, kzz=1)
specified_parameters = {"second_order_tensor": perm}
# Boundaries
bound_faces = g.tags["domain_boundary_faces"].nonzero()[0]
if bound_faces.size != 0:
bound_face_centers = g.face_centers[:, bound_faces]
left = bound_face_centers[0, :] < domain["xmin"] + tol
right = bound_face_centers[0, :] > domain["xmax"] - tol
labels = np.array(["neu"] * bound_faces.size)
labels[right] = "dir"
bc_val = np.zeros(g.num_faces)
bc_val[
bound_faces[left]] = -a_dim * g.face_areas[bound_faces[left]]
bc_val[bound_faces[right]] = 1
bound = pp.BoundaryCondition(g, bound_faces, labels)
specified_parameters.update({"bc": bound, "bc_values": bc_val})
else:
bound = pp.BoundaryCondition(g, np.empty(0), np.empty(0))
specified_parameters.update({"bc": bound})
d["is_tangential"] = True
pp.initialize_default_data(g, d, "flow", specified_parameters)
# Assign coupling permeability
for _, d in gb.edges():
mg = d["mortar_grid"]
kn = 2 * kf * np.ones(mg.num_cells) / a
d[pp.PARAMETERS] = pp.Parameters(mg, ["flow"],
[{
"normal_diffusivity": kn
}])
d[pp.DISCRETIZATION_MATRICES] = {"flow": {}}
def test_mpsa(self):
self.setup()
g, g_larger = self.g, self.g_larger
specified_data = {
"inverter": "python",
}
keyword = "mechanics"
data_small = pp.initialize_default_data(
g, {}, keyword, specified_parameters=specified_data)
discr = pp.Mpsa(keyword)
# Discretization on a small problem
discr.discretize(g, data_small)
# Perturb one node
g_larger.nodes[0, 2] += 0.2
# Faces that have their geometry changed
update_faces = np.array([2, 21, 22])
# Perturb the permeability in some cells on the larger grid
mu, lmbda = np.ones(g_larger.num_cells), np.ones(g_larger.num_cells)
high_coeff_cells = np.array([7, 12])
stiff_larger = pp.FourthOrderTensor(mu, lmbda)
specified_data_larger = {"fourth_order_tensor": stiff_larger}
# Do a full discretization on the larger grid
data_full = pp.initialize_default_data(
g_larger, {}, keyword, specified_parameters=specified_data_larger)
discr.discretize(g_larger, data_full)
# Cells that will be marked as updated, either due to changed parameters or
# the newly defined topology
update_cells = np.union1d(self.new_cells, high_coeff_cells)
updates = {
"modified_cells": update_cells,
# "modified_faces": update_faces,
"map_cells": self.cell_map,
"map_faces": self.face_map,
}
# Data dictionary for the two-step discretization
data_partial = pp.initialize_default_data(
g_larger, {}, keyword, specified_parameters=specified_data_larger)
data_partial["update_discretization"] = updates
self._update_and_compare(data_small, data_partial, data_full, g_larger,
[keyword], discr)
def test_inv_mass_matrix_1d(self):
"""
Test the inverse of mass matrix in 1d for a simple geometry
"""
g = pp.CartGrid(3, 1)
g.compute_geometry()
# Mass weight is scaled by aperture 0.01
specified_parameters = {"mass_weight": 0.5 * 1e-2}
data = pp.initialize_default_data(g, {}, "flow", specified_parameters)
discr = pp.MixedInvMassMatrix()
discr.discretize(g, data)
lhs, rhs = discr.assemble_matrix_rhs(g, data)
lhs_known = 600 * np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
]
)
self.assertTrue(np.allclose(lhs.toarray(), lhs_known))
self.assertTrue(np.allclose(rhs, 0))
def test_convergence_rt0_2d_iso_simplex_exact(self):
p_ex = lambda pt: 2 * pt[0, :] - 3 * pt[1, :] - 9
for i in np.arange(5):
g = pp.simplex.StructuredTriangleGrid([3 + i] * 2, [1, 1])
g.compute_geometry()
kxx = np.ones(g.num_cells)
perm = pp.SecondOrderTensor(kxx=kxx, kyy=kxx, kzz=1)
bf = g.get_boundary_faces()
bc = pp.BoundaryCondition(g, bf, bf.size * ["dir"])
bc_val = np.zeros(g.num_faces)
bc_val[bf] = p_ex(g.face_centers[:, bf])
solver = pp.RT0(keyword="flow")
specified_parameters = {
"bc": bc,
"bc_values": bc_val,
"second_order_tensor": perm,
}
data = pp.initialize_default_data(g, {}, "flow",
specified_parameters)
solver.discretize(g, data)
M, rhs = solver.assemble_matrix_rhs(g, data)
up = sps.linalg.spsolve(M, rhs)
p = solver.extract_pressure(g, up, data)
err = np.sum(np.abs(p - p_ex(g.cell_centers)))
self.assertTrue(np.isclose(err, 0))
def test_upwind_2d_cart_darcy_flux_positive(self):
g = pp.CartGrid([3, 2], [1, 1])
g.compute_geometry()
solver = pp.Upwind()
dis = solver.darcy_flux(g, [2, 0, 0])
bf = g.tags["domain_boundary_faces"].nonzero()[0]
bc = pp.BoundaryCondition(g, bf, bf.size * ["neu"])
specified_parameters = {"bc": bc, "darcy_flux": dis}
data = pp.initialize_default_data(g, {}, "transport", specified_parameters)
solver.discretize(g, data)
M = solver.assemble_matrix_rhs(g, data)[0].todense()
deltaT = solver.cfl(g, data)
M_known = np.array(
[
[1, 0, 0, 0, 0, 0],
[-1, 1, 0, 0, 0, 0],
[0, -1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0],
[0, 0, 0, -1, 1, 0],
[0, 0, 0, 0, -1, 0],
]
)
deltaT_known = 1 / 12
rtol = 1e-15
atol = rtol
self.assertTrue(np.allclose(M, M_known, rtol, atol))
self.assertTrue(np.allclose(deltaT, deltaT_known, rtol, atol))
def test_upwind_1d_darcy_flux_negative_bc_neu(self):
g = pp.CartGrid(3, 1)
g.compute_geometry()
solver = pp.Upwind()
dis = solver.darcy_flux(g, [-2, 0, 0])
bf = g.tags["domain_boundary_faces"].nonzero()[0]
bc = pp.BoundaryCondition(g, bf, bf.size * ["neu"])
bc_val = np.array([2, 0, 0, -2]).ravel("F")
specified_parameters = {"bc": bc, "bc_values": bc_val, "darcy_flux": dis}
data = pp.initialize_default_data(g, {}, "transport", specified_parameters)
solver.discretize(g, data)
M, rhs = solver.assemble_matrix_rhs(g, data)
deltaT = solver.cfl(g, data)
M_known = np.array([[0, -2, 0], [0, 2, -2], [0, 0, 2]])
rhs_known = np.array([-2, 0, 2])
deltaT_known = 1 / 12
rtol = 1e-15
atol = rtol
self.assertTrue(np.allclose(M.todense(), M_known, rtol, atol))
self.assertTrue(np.allclose(rhs, rhs_known, rtol, atol))
self.assertTrue(np.allclose(deltaT, deltaT_known, rtol, atol))
def test_upwind_2d_simplex_surf_darcy_flux_negative(self):
g = pp.StructuredTriangleGrid([2, 1], [1, 1])
R = pp.map_geometry.rotation_matrix(-np.pi / 5.0, [1, 1, -1])
g.nodes = np.dot(R, g.nodes)
g.compute_geometry()
solver = pp.Upwind()
dis = solver.darcy_flux(g, np.dot(R, [-1, 0, 0]))
bf = g.tags["domain_boundary_faces"].nonzero()[0]
bc = pp.BoundaryCondition(g, bf, bf.size * ["neu"])
specified_parameters = {"bc": bc, "darcy_flux": dis}
data = pp.initialize_default_data(g, {}, "transport", specified_parameters)
solver.discretize(g, data)
M = solver.assemble_matrix_rhs(g, data)[0].todense()
deltaT = solver.cfl(g, data)
M_known = np.array([[1, 0, 0, -1], [-1, 0, 0, 0], [0, 0, 1, 0], [0, 0, -1, 1]])
deltaT_known = 1 / 6
rtol = 1e-15
atol = rtol
self.assertTrue(np.allclose(M, M_known, rtol, atol))
self.assertTrue(np.allclose(deltaT, deltaT_known, rtol, atol))
def add_data(self, g):
"""
Define the permeability, apertures, boundary conditions
"""
# Permeability
kxx = np.array([self.permeability(*pt) for pt in g.cell_centers.T])
perm = pp.SecondOrderTensor(kxx)
# Source term
source = g.cell_volumes * np.array(
[self.rhs(*pt) for pt in g.cell_centers.T])
# Boundaries
bound_faces = g.tags["domain_boundary_faces"].nonzero()[0]
bound_face_centers = g.face_centers[:, bound_faces]
labels = np.array(["dir"] * bound_faces.size)
bc_val = np.zeros(g.num_faces)
bc_val[bound_faces] = np.array(
[self.solution(*pt) for pt in bound_face_centers.T])
bound = pp.BoundaryCondition(g, bound_faces, labels)
specified_parameters = {
"second_order_tensor": perm,
"source": source,
"bc": bound,
"bc_values": bc_val,
}
return pp.initialize_default_data(g, {}, "flow", specified_parameters)
def setup_3d_grid():
g = pp.CartGrid([2, 2, 2], physdims=[1, 1, 1])
g.compute_geometry()
src = np.zeros(g.num_cells)
src[4] = 1
data = pp.initialize_default_data(g, {}, "flow", {"source": src})
return g, data
def setup(self):
g = pp.CartGrid([5, 5])
g.compute_geometry()
stiffness = pp.FourthOrderTensor(np.ones(g.num_cells),
np.ones(g.num_cells))
bnd = pp.BoundaryConditionVectorial(g)
specified_data = {
"fourth_order_tensor": stiffness,
"bc": bnd,
"inverter": "python",
}
keyword = "mechanics"
data = pp.initialize_default_data(g, {},
keyword,
specified_parameters=specified_data)
discr = pp.Mpsa(keyword)
discr.discretize(g, data)
stress = data[pp.DISCRETIZATION_MATRICES][keyword][
discr.stress_matrix_key]
bound_stress = data[pp.DISCRETIZATION_MATRICES][keyword][
discr.bound_stress_matrix_key]
return g, stiffness, bnd, stress, bound_stress
def test_symmetry_periodic_pressure_field_2d(method):
"""
Test that we obtain a symmetric solution accross the periodic boundary.
The test consider the unit square with periodic boundary conditions
on the top and bottom boundary. A source is added to the bottom row of
cells and we test that the solution is periodic.
Setup, with x denoting the source:
--------
| |
p = 0 | | p = 0
| x |
-------
"""
# Structured Cartesian grid
g, kxx = setup_cart_2d(np.array([5, 5]), [1, 1])
bot_faces = np.argwhere(g.face_centers[1] < 1e-5).ravel()
top_faces = np.argwhere(g.face_centers[1] > 1 - 1e-5).ravel()
left_faces = np.argwhere(g.face_centers[0] < 1e-5).ravel()
right_faces = np.argwhere(g.face_centers[0] > 1 - 1e-5).ravel()
dir_faces = np.hstack((left_faces, right_faces))
g.set_periodic_map(np.vstack((bot_faces, top_faces)))
bound = pp.BoundaryCondition(g, dir_faces, "dir")
# Solve
key = "flow"
d = pp.initialize_default_data(
g, {}, key, {
"second_order_tensor": pp.SecondOrderTensor(kxx),
"bc": bound
})
if method == "tpfa":
discr = pp.Tpfa(key)
elif method == "mpfa":
discr = pp.Mpfa(key)
else:
assert False
discr.discretize(g, d)
matrix_dictionary = d[pp.DISCRETIZATION_MATRICES][key]
flux, bound_flux = matrix_dictionary["flux"], matrix_dictionary[
"bound_flux"]
div = g.cell_faces.T
a = div * flux
pr_bound = np.zeros(g.num_faces)
src = np.zeros(g.num_cells)
src[2] = 1
rhs = -div * bound_flux * pr_bound + src
pr = np.linalg.solve(a.todense(), rhs)
p_diff = pr[5:15] - np.hstack((pr[-5:], pr[-10:-5]))
assert np.max(np.abs(p_diff)) < 1e-10
def _tpfa_matrix(g, perm=None):
"""
Compute a two-point flux approximation matrix useful related to a call of
create_partition.
Parameters
----------
g: the grid
perm: (optional) permeability, the it is not given unitary tensor is assumed
Returns
-------
out: sparse matrix
Two-point flux approximation matrix
"""
if isinstance(g, grid_bucket.GridBucket):
g = g.get_grids(lambda g_: g_.dim == g.dim_max())[0]
if perm is None:
perm = pp.SecondOrderTensor(np.ones(g.num_cells))
solver = pp.Tpfa("flow")
specified_parameters = {
"second_order_tensor": perm,
"bc": pp.BoundaryCondition(g, np.empty(0), ""),
}
data = pp.initialize_default_data(g, {}, "flow", specified_parameters)
solver.discretize(g, data)
return solver.assemble_matrix(g, data)
def test_upwind_example_2(self, if_export=False):
#######################
# Simple 2d upwind problem with implicit Euler scheme in time
#######################
T = 1
Nx, Ny = 10, 1
g = pp.CartGrid([Nx, Ny], [1, 1])
g.compute_geometry()
advect = pp.Upwind("transport")
dis = advect.darcy_flux(g, [1, 0, 0])
b_faces = g.get_all_boundary_faces()
bc = pp.BoundaryCondition(g, b_faces, ["dir"] * b_faces.size)
bc_val = np.hstack(([1], np.zeros(g.num_faces - 1)))
specified_parameters = {
"bc": bc,
"bc_values": bc_val,
"darcy_flux": dis
}
data = pp.initialize_default_data(g, {}, "transport",
specified_parameters)
time_step = advect.cfl(g, data)
data[pp.PARAMETERS]["transport"]["time_step"] = time_step
advect.discretize(g, data)
U, rhs = advect.assemble_matrix_rhs(g, data)
rhs = time_step * rhs
U = time_step * U
mass = pp.MassMatrix("transport")
mass.discretize(g, data)
M, _ = mass.assemble_matrix_rhs(g, data)
conc = np.zeros(g.num_cells)
# Perform an LU factorization to speedup the solver
IE_solver = sps.linalg.factorized((M + U).tocsc())
# Loop over the time
Nt = int(T / time_step)
time = np.empty(Nt)
for i in np.arange(Nt):
# Update the solution
# Backward and forward substitution to solve the system
conc = IE_solver(M.dot(conc) + rhs)
time[i] = time_step * i
known = np.array([
0.99969927,
0.99769441,
0.99067741,
0.97352474,
0.94064879,
0.88804726,
0.81498958,
0.72453722,
0.62277832,
0.51725056,
])
assert np.allclose(conc, known)
def test_initialize_default_data(self):
""" Test default flow data initialization with default values.
initialize_data returns a data dictionary d with default "keyword" Parameters
stored in d["parameters"].
"""
data = pp.initialize_default_data(self.g, {}, parameter_type="flow")
self.check_default_flow_dictionary(data[pp.PARAMETERS]["flow"])
def _matrix(self, g, perm, bc):
solver = pp.RT0(keyword="flow")
data = pp.initialize_default_data(g, {}, "flow", {
"second_order_tensor": perm,
"bc": bc
})
solver.discretize(g, data)
return solver.assemble_matrix(g, data).todense()
def test_matrix_rhs(self):
g_list = setup_grids.setup_2d()
kw = "mechanics"
for g in g_list:
solver = pp.numerics.fv.mpsa.Mpsa(kw)
data = pp.initialize_default_data(g, {}, kw)
A, b = solver.assemble_matrix_rhs(g, data)
self.assertTrue(
np.all(A.shape == (g.dim * g.num_cells, g.dim * g.num_cells)))
self.assertTrue(b.size == g.dim * g.num_cells)
def setup_2d_1d(nx, simplex_grid=False):
if not simplex_grid:
frac1 = np.array([[0.2, 0.8], [0.5, 0.5]])
frac2 = np.array([[0.5, 0.5], [0.8, 0.2]])
fracs = [frac1, frac2]
gb = pp.meshing.cart_grid(fracs, nx, physdims=[1, 1])
else:
p = np.array([[0.2, 0.8, 0.5, 0.5], [0.5, 0.5, 0.8, 0.2]])
e = np.array([[0, 2], [1, 3]])
domain = {"xmin": 0, "ymin": 0, "xmax": 1, "ymax": 1}
network = pp.FractureNetwork2d(p, e, domain)
mesh_size = 0.08
mesh_kwargs = {"mesh_size_frac": mesh_size, "mesh_size_min": mesh_size / 20}
gb = network.mesh(mesh_kwargs)
gb.compute_geometry()
gb.assign_node_ordering()
aperture = 0.01 / np.max(nx)
for g, d in gb:
a = np.power(aperture, gb.dim_max() - g.dim) * np.ones(g.num_cells)
kxx = np.ones(g.num_cells) * a
perm = pp.SecondOrderTensor(kxx)
specified_parameters = {"second_order_tensor": perm}
if g.dim == 2:
bound_faces = g.tags["domain_boundary_faces"].nonzero()[0]
bound = pp.BoundaryCondition(
g, bound_faces.ravel("F"), ["dir"] * bound_faces.size
)
bc_val = np.zeros(g.num_faces)
bc_val[bound_faces] = g.face_centers[1, bound_faces]
specified_parameters.update({"bc": bound, "bc_values": bc_val})
pp.initialize_default_data(g, d, "flow", specified_parameters)
for e, d in gb.edges():
gl, _ = gb.nodes_of_edge(e)
mg = d["mortar_grid"]
kn = 1.0 / aperture
d[pp.PARAMETERS] = pp.Parameters(mg, ["flow"], [{"normal_diffusivity": kn}])
d[pp.DISCRETIZATION_MATRICES] = {"flow": {}}
return gb
def test_upwind_example_1(self, if_export=False):
#######################
# Simple 2d upwind problem with explicit Euler scheme in time
#######################
T = 1
Nx, Ny = 4, 1
g = pp.CartGrid([Nx, Ny], [1, 1])
g.compute_geometry()
advect = pp.Upwind("transport")
dis = advect.darcy_flux(g, [1, 0, 0])
b_faces = g.get_all_boundary_faces()
bc = pp.BoundaryCondition(g, b_faces, ["dir"] * b_faces.size)
bc_val = np.hstack(([1], np.zeros(g.num_faces - 1)))
specified_parameters = {
"bc": bc,
"bc_values": bc_val,
"darcy_flux": dis
}
data = pp.initialize_default_data(g, {}, "transport",
specified_parameters)
time_step = advect.cfl(g, data)
data[pp.PARAMETERS]["transport"]["time_step"] = time_step
advect.discretize(g, data)
U, rhs = advect.assemble_matrix_rhs(g, data)
rhs = time_step * rhs
U = time_step * U
OF = advect.outflow(g, data)
mass = pp.MassMatrix("transport")
mass.discretize(g, data)
M, _ = mass.assemble_matrix_rhs(g, data)
conc = np.zeros(g.num_cells)
M_minus_U = M - U
inv_mass = pp.InvMassMatrix("transport")
inv_mass.discretize(g, data)
invM, _ = inv_mass.assemble_matrix_rhs(g, data)
# Loop over the time
Nt = int(T / time_step)
time = np.empty(Nt)
production = np.zeros(Nt)
for i in np.arange(Nt):
# Update the solution
production[i] = np.sum(OF.dot(conc))
conc = invM.dot((M_minus_U).dot(conc) + rhs)
time[i] = time_step * i
known = 1.09375
assert np.sum(production) == known
def test_fv_cart_2d_periodic(method):
""" Apply TPFA and MPFA on a periodic Cartesian grid, should obtain Laplacian stencil."""
# Set up 3 X 3 Cartesian grid
nx = np.array([3, 3])
g = pp.CartGrid(nx)
g.compute_geometry()
kxx = np.ones(g.num_cells)
perm = pp.SecondOrderTensor(kxx)
left_faces = [0, 4, 8, 12, 13, 14]
right_faces = [3, 7, 11, 21, 22, 23]
periodic_face_map = np.vstack((left_faces, right_faces))
g.set_periodic_map(periodic_face_map)
bound = pp.BoundaryCondition(g)
key = "flow"
d = pp.initialize_default_data(g, {}, key, {
"second_order_tensor": perm,
"bc": bound
})
if method == "tpfa":
discr = pp.Tpfa(key)
elif method == "mpfa":
discr = pp.Mpfa(key)
else:
assert False
discr.discretize(g, d)
matrix_dictionary = d[pp.DISCRETIZATION_MATRICES][key]
trm, bound_flux = matrix_dictionary["flux"], matrix_dictionary[
"bound_flux"]
div = g.cell_faces.T
a = div * trm
b = -(div * bound_flux).A
# Create laplace matrix
A_lap = np.array([
[4.0, -1.0, -1.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0],
[-1.0, 4.0, -1.0, 0.0, -1.0, 0.0, 0.0, -1.0, 0.0],
[-1.0, -1.0, 4.0, 0.0, 0.0, -1.0, 0.0, 0.0, -1.0],
[-1.0, 0.0, 0.0, 4.0, -1.0, -1.0, -1.0, 0.0, 0.0],
[0.0, -1.0, 0.0, -1.0, 4.0, -1.0, 0.0, -1.0, 0.0],
[0.0, 0.0, -1.0, -1.0, -1.0, 4.0, 0.0, 0.0, -1.0],
[-1.0, 0.0, 0.0, -1.0, 0.0, 0.0, 4.0, -1.0, -1.0],
[0.0, -1.0, 0.0, 0.0, -1.0, 0.0, -1.0, 4.0, -1.0],
[0.0, 0.0, -1.0, 0.0, 0.0, -1.0, -1.0, -1.0, 4.0],
])
assert np.allclose(a.A, A_lap)
assert np.allclose(b, 0)
return a
def _matrix(self, g, perm, bc, vect):
solver = pp.MVEM(keyword="flow")
data = pp.initialize_default_data(
g,
{},
"flow",
{"second_order_tensor": perm, "bc": bc, "vector_source": vect},
)
solver.discretize(g, data)
return solver.assemble_matrix_rhs(g, data)[1]
def test_convergence_rt0_2d_iso_simplex(self):
a = 8 * np.pi**2
rhs_ex = lambda pt: np.multiply(np.sin(2 * np.pi * pt[0, :]),
np.sin(2 * np.pi * pt[1, :]))
p_ex = lambda pt: rhs_ex(pt) / a
errs_known = np.array([
0.00128247705764,
0.000770088925769,
0.00050939369071,
0.000360006145403,
0.000267209318912,
])
for i, err_known in zip(np.arange(5), errs_known):
g = pp.simplex.StructuredTriangleGrid([3 + i] * 2, [1, 1])
g.compute_geometry()
kxx = np.ones(g.num_cells)
perm = pp.SecondOrderTensor(kxx=kxx, kyy=kxx, kzz=1)
bf = g.get_boundary_faces()
bc = pp.BoundaryCondition(g, bf, bf.size * ["dir"])
bc_val = np.zeros(g.num_faces)
bc_val[bf] = p_ex(g.face_centers[:, bf])
# Minus sign to move to rhs
source = np.multiply(g.cell_volumes, rhs_ex(g.cell_centers))
solver = pp.RT0(keyword="flow")
solver_rhs = pp.DualScalarSource(keyword="flow")
specified_parameters = {
"bc": bc,
"bc_values": bc_val,
"second_order_tensor": perm,
"source": source,
}
data = pp.initialize_default_data(g, {}, "flow",
specified_parameters)
solver.discretize(g, data)
solver_rhs.discretize(g, data)
M, rhs_bc = solver.assemble_matrix_rhs(g, data)
_, rhs = solver_rhs.assemble_matrix_rhs(g, data)
up = sps.linalg.spsolve(M, rhs_bc + rhs)
p = solver.extract_pressure(g, up, data)
err = np.sqrt(
np.sum(
np.multiply(g.cell_volumes,
np.power(p - p_ex(g.cell_centers), 2))))
self.assertTrue(np.isclose(err, err_known))
def setup_2d_1d(nx, simplex_grid=False):
if not simplex_grid:
mesh_args = nx
else:
mesh_size = 0.08
mesh_args = {
"mesh_size_frac": mesh_size,
"mesh_size_min": mesh_size / 20
}
end_x = [0.2, 0.8]
end_y = [0.8, 0.2]
gb, _ = pp.grid_buckets_2d.two_intersecting(mesh_args, end_x, end_y,
simplex_grid)
aperture = 0.01 / np.max(nx)
for g, d in gb:
a = np.power(aperture, gb.dim_max() - g.dim) * np.ones(g.num_cells)
kxx = np.ones(g.num_cells) * a
perm = pp.SecondOrderTensor(kxx)
specified_parameters = {"second_order_tensor": perm}
if g.dim == 2:
bound_faces = g.tags["domain_boundary_faces"].nonzero()[0]
bound = pp.BoundaryCondition(g, bound_faces.ravel("F"),
["dir"] * bound_faces.size)
bc_val = np.zeros(g.num_faces)
bc_val[bound_faces] = g.face_centers[1, bound_faces]
specified_parameters.update({"bc": bound, "bc_values": bc_val})
pp.initialize_default_data(g, d, "flow", specified_parameters)
for e, d in gb.edges():
gl, _ = gb.nodes_of_edge(e)
mg = d["mortar_grid"]
kn = 1.0 / aperture
d[pp.PARAMETERS] = pp.Parameters(mg, ["flow"],
[{
"normal_diffusivity": kn
}])
d[pp.DISCRETIZATION_MATRICES] = {"flow": {}}
return gb
def test_convergence_rt0_2d_ani_simplex(self):
rhs_ex = lambda pt: 14
p_ex = (lambda pt: 2 * np.power(pt[0, :], 2) - 6 * np.power(
pt[1, :], 2) + np.multiply(pt[0, :], pt[1, :]))
errs_known = np.array([
0.014848639601,
0.00928479234915,
0.00625096095775,
0.00446722560521,
0.00334170283883,
])
for i, err_known in zip(np.arange(5), errs_known):
g = pp.simplex.StructuredTriangleGrid([3 + i] * 2, [1, 1])
g.compute_geometry()
kxx = 2 * np.ones(g.num_cells)
kxy = np.ones(g.num_cells)
perm = pp.SecondOrderTensor(kxx=kxx, kyy=kxx, kxy=kxy, kzz=1)
bf = g.get_boundary_faces()
bc = pp.BoundaryCondition(g, bf, bf.size * ["dir"])
bc_val = np.zeros(g.num_faces)
bc_val[bf] = p_ex(g.face_centers[:, bf])
# Minus sign to move to rhs
source = np.multiply(g.cell_volumes, rhs_ex(g.cell_centers))
solver = pp.RT0(keyword="flow")
solver_rhs = pp.DualScalarSource(keyword="flow")
specified_parameters = {
"bc": bc,
"bc_values": bc_val,
"second_order_tensor": perm,
"source": source,
}
data = pp.initialize_default_data(g, {}, "flow",
specified_parameters)
solver.discretize(g, data)
solver_rhs.discretize(g, data)
M, rhs_bc = solver.assemble_matrix_rhs(g, data)
_, rhs = solver_rhs.assemble_matrix_rhs(g, data)
up = sps.linalg.spsolve(M, rhs_bc + rhs)
p = solver.extract_pressure(g, up, data)
err = np.sqrt(
np.sum(
np.multiply(g.cell_volumes,
np.power(p - p_ex(g.cell_centers), 2))))
self.assertTrue(np.isclose(err, err_known))
def test_bound_cell_node_keyword(self):
# Compute update for a single cell on the boundary
g, perm, bnd, flux, bound_flux, vector_source = self.setup()
# cell = 10
nodes_of_cell = np.array([12, 13, 18, 19])
faces_of_cell = np.array([12, 13, 40, 45])
specified_data = {
"second_order_tensor": perm,
"bc": bnd,
"inverter": "python",
"specified_nodes": nodes_of_cell,
}
keyword = "flow"
data = pp.initialize_default_data(g, {},
keyword,
specified_parameters=specified_data)
discr = pp.Mpfa(keyword)
discr.discretize(g, data)
partial_flux = data[pp.DISCRETIZATION_MATRICES][keyword][
discr.flux_matrix_key]
partial_bound = data[pp.DISCRETIZATION_MATRICES][keyword][
discr.bound_flux_matrix_key]
partial_vector_source = data[pp.DISCRETIZATION_MATRICES][keyword][
discr.vector_source_matrix_key]
active_faces = data[pp.PARAMETERS][keyword]["active_faces"]
self.assertTrue(faces_of_cell.size == active_faces.size)
self.assertTrue(
np.all(np.sort(faces_of_cell) == np.sort(active_faces)))
diff_flux = (flux - partial_flux).todense()
diff_bound = (bound_flux - partial_bound).todense()
diff_vc = (vector_source - partial_vector_source).todense()
self.assertTrue(np.max(np.abs(diff_flux[faces_of_cell])) == 0)
self.assertTrue(np.max(np.abs(diff_bound[faces_of_cell])) == 0)
self.assertTrue(np.max(np.abs(diff_vc[faces_of_cell])) == 0)
# Only the faces of the central cell should be zero
pp.fvutils.zero_out_sparse_rows(partial_flux, faces_of_cell)
pp.fvutils.zero_out_sparse_rows(partial_bound, faces_of_cell)
pp.fvutils.zero_out_sparse_rows(partial_vector_source, faces_of_cell)
self.assertTrue(np.max(np.abs(partial_flux.data)) == 0)
self.assertTrue(np.max(np.abs(partial_bound.data)) == 0)
self.assertTrue(np.max(np.abs(partial_vector_source.data)) == 0)
def test_periodic_pressure_field_2d(method):
"""
Test that TPFA approximate an analytical periodic solution by imposing
periodic boundary conditions to the bottom and top faces of the unit square.
"""
# Structured Cartesian grid
g, kxx = setup_cart_2d(np.array([10, 10]), [1, 1])
bot_faces = np.argwhere(g.face_centers[1] < 1e-5).ravel()
top_faces = np.argwhere(g.face_centers[1] > 1 - 1e-5).ravel()
left_faces = np.argwhere(g.face_centers[0] < 1e-5).ravel()
right_faces = np.argwhere(g.face_centers[0] > 1 - 1e-5).ravel()
dir_faces = np.hstack((left_faces, right_faces))
g.set_periodic_map(np.vstack((bot_faces, top_faces)))
bound = pp.BoundaryCondition(g, dir_faces, "dir")
# Solve
key = "flow"
d = pp.initialize_default_data(
g, {}, key, {
"second_order_tensor": pp.SecondOrderTensor(kxx),
"bc": bound
})
if method == "tpfa":
discr = pp.Tpfa(key)
elif method == "mpfa":
discr = pp.Mpfa(key)
else:
assert False
discr.discretize(g, d)
matrix_dictionary = d[pp.DISCRETIZATION_MATRICES][key]
flux, bound_flux = matrix_dictionary["flux"], matrix_dictionary[
"bound_flux"]
div = g.cell_faces.T
a = div * flux
pr_bound, pr_cell, src = setup_periodic_pressure_field(g, kxx)
rhs = -div * bound_flux * pr_bound + src * g.cell_volumes
pr = np.linalg.solve(a.todense(), rhs)
p_diff = pr - pr_cell
assert np.max(np.abs(p_diff)) < 0.06