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test_fvm.py
Markus Holzer authored and
Jan Hönig
committed
test_fvm.py 26.02 KiB
import sympy as sp
import pystencils as ps
import numpy as np
import pytest
from itertools import product
from pystencils.rng import random_symbol
from pystencils.astnodes import SympyAssignment
from pystencils.node_collection import NodeCollection
def advection_diffusion(dim: int):
# parameters
if dim == 2:
L = (32, 32)
elif dim == 3:
L = (16, 16, 16)
dh = ps.create_data_handling(domain_size=L, periodicity=True, default_target=ps.Target.CPU)
n_field = dh.add_array('n', values_per_cell=1)
j_field = dh.add_array('j', values_per_cell=3 ** dim // 2, field_type=ps.FieldType.STAGGERED_FLUX)
velocity_field = dh.add_array('v', values_per_cell=dim)
D = 0.0666
time = 100
def grad(f):
return sp.Matrix([ps.fd.diff(f, i) for i in range(dim)])
flux_eq = - D * grad(n_field)
fvm_eq = ps.fd.FVM1stOrder(n_field, flux=flux_eq)
vof_adv = ps.fd.VOF(j_field, velocity_field, n_field)
# merge calculation of advection and diffusion terms
flux = []
for adv, div in zip(vof_adv, fvm_eq.discrete_flux(j_field)):
assert adv.lhs == div.lhs
flux.append(ps.Assignment(adv.lhs, adv.rhs + div.rhs))
flux_kernel = ps.create_staggered_kernel(flux).compile()
pde_kernel = ps.create_kernel(fvm_eq.discrete_continuity(j_field)).compile()
sync_conc = dh.synchronization_function([n_field.name])
# analytical density calculation
def density(pos: np.ndarray, time: int, D: float):
return (4 * np.pi * D * time)**(-dim / 2) * \
np.exp(-np.sum(np.square(pos), axis=-1) / (4 * D * time))
pos = np.zeros((*L, dim))
xpos = np.arange(-L[0] // 2, L[0] // 2)
ypos = np.arange(-L[1] // 2, L[1] // 2)
if dim == 2:
pos[..., 1], pos[..., 0] = np.meshgrid(xpos, ypos)
elif dim == 3:
zpos = np.arange(-L[2] // 2, L[2] // 2)
pos[..., 2], pos[..., 1], pos[..., 0] = np.meshgrid(xpos, ypos, zpos)
pos += 0.5
def run(velocity: np.ndarray, time: int):
dh.fill(n_field.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
dh.fill(j_field.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
# set initial values for velocity and density
for i in range(dim):
dh.fill(velocity_field.name, velocity[i], i, ghost_layers=True, inner_ghost_layers=True)
dh.fill(n_field.name, 0)
if dim == 2:
start = ps.make_slice[L[0] // 2 - 1:L[0] // 2 + 1, L[1] // 2 - 1:L[1] // 2 + 1]
else:
start = ps.make_slice[L[0] // 2 - 1:L[0] // 2 + 1, L[1] // 2 - 1:L[1] // 2 + 1,
L[2] // 2 - 1:L[2] // 2 + 1]
dh.fill(n_field.name, 2**-dim, slice_obj=start)
sync_conc()
for i in range(time):
dh.run_kernel(flux_kernel)
dh.run_kernel(pde_kernel)
sync_conc()
sim_density = dh.gather_array(n_field.name)
# check that mass was conserved
assert np.isclose(sim_density.sum(), 1)
assert np.all(sim_density > 0)
# check that the maximum is in the right place
peak = np.unravel_index(np.argmax(sim_density, axis=None), sim_density.shape)
assert np.allclose(peak, np.array(L) // 2 - 0.5 + velocity * time, atol=0.5)
# check the concentration profile
if np.linalg.norm(velocity) == 0:
calc_density = density(pos - velocity * time, time, D)
target = [time, D]
pytest.importorskip('scipy.optimize')
from scipy.optimize import curve_fit
popt, _ = curve_fit(lambda x, t, D: density(x - velocity * time, t, D),
pos.reshape(-1, dim),
sim_density.reshape(-1),
p0=target)
assert np.isclose(popt[0], time, rtol=0.1)
assert np.isclose(popt[1], D, rtol=0.1)
assert np.allclose(calc_density, sim_density, atol=1e-4)
return lambda v: run(np.array(v), time)
advection_diffusion.runners = {}
@pytest.mark.parametrize("velocity", list(product([0, -0.047, 0.041], [0, -0.031, 0.023])))
def test_advection_diffusion_2d(velocity):
if 2 not in advection_diffusion.runners:
advection_diffusion.runners[2] = advection_diffusion(2)
advection_diffusion.runners[2](velocity)
@pytest.mark.parametrize("velocity", list(product([0, -0.047, 0.041], [0, -0.031, 0.023], [0, -0.017, 0.011])))
@pytest.mark.longrun
def test_advection_diffusion_3d(velocity):
if 3 not in advection_diffusion.runners:
advection_diffusion.runners[3] = advection_diffusion(3)
advection_diffusion.runners[3](velocity)
def advection_diffusion_fluctuations(dim: int):
# parameters
if dim == 2:
L = (32, 32)
stencil_factor = np.sqrt(1 / (1 + np.sqrt(2)))
elif dim == 3:
L = (16, 16, 16)
stencil_factor = np.sqrt(1 / (1 + 2 * np.sqrt(2) + 4.0 / 3.0 * np.sqrt(3)))
dh = ps.create_data_handling(domain_size=L, periodicity=True, default_target=ps.Target.CPU)
n_field = dh.add_array('n', values_per_cell=1)
j_field = dh.add_array('j', values_per_cell=3 ** dim // 2, field_type=ps.FieldType.STAGGERED_FLUX)
velocity_field = dh.add_array('v', values_per_cell=dim)
D = 0.00666
time = 10000
def grad(f):
return sp.Matrix([ps.fd.diff(f, i) for i in range(dim)])
flux_eq = - D * grad(n_field)
fvm_eq = ps.fd.FVM1stOrder(n_field, flux=flux_eq)
vof_adv = ps.fd.VOF(j_field, velocity_field, n_field)
# merge calculation of advection and diffusion terms
flux = []
for adv, div in zip(vof_adv, fvm_eq.discrete_flux(j_field)):
assert adv.lhs == div.lhs
flux.append(ps.Assignment(adv.lhs, adv.rhs + div.rhs))
flux = ps.AssignmentCollection(flux)
rng_symbol_gen = random_symbol(flux.subexpressions, dim=dh.dim)
for i in range(len(flux.main_assignments)):
n = j_field.staggered_stencil[i]
assert flux.main_assignments[i].lhs == j_field.staggered_access(n)
# calculate mean density
dens = (n_field.neighbor_vector(n) + n_field.center_vector)[0] / 2
# multyply by smoothed haviside function so that fluctuation will not get bigger that the density
dens *= sp.Max(0, sp.Min(1.0, n_field.neighbor_vector(n)[0]) * sp.Min(1.0, n_field.center_vector[0]))
# lenght of the vector
length = sp.sqrt(len(j_field.staggered_stencil[i]))
# amplitude of the random fluctuations
fluct = sp.sqrt(2 * dens * D) * sp.sqrt(1 / length) * stencil_factor
# add fluctuations
fluct *= 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
flux.main_assignments[i] = ps.Assignment(flux.main_assignments[i].lhs, flux.main_assignments[i].rhs + fluct)
# Add the folding to the flux, so that the random numbers persist through the ghostlayers.
fold = {ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i):
ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i) % L[i] for i in range(len(L))}
flux.subs(fold)
flux_kernel = ps.create_staggered_kernel(flux).compile()
pde_kernel = ps.create_kernel(fvm_eq.discrete_continuity(j_field)).compile()
sync_conc = dh.synchronization_function([n_field.name])
# analytical density distribution calculation
def P(rho, density_init):
res = []
for r in rho:
res.append(np.power(density_init, r) * np.exp(-density_init) / np.math.gamma(r + 1))
return np.array(res)
def run(density_init: float, velocity: np.ndarray, time: int):
dh.fill(n_field.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
dh.fill(j_field.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
# set initial values for velocity and density
for i in range(dim):
dh.fill(velocity_field.name, velocity[i], i, ghost_layers=True, inner_ghost_layers=True)
dh.fill(n_field.name, density_init)
measurement_intervall = 10
warm_up = 1000
data = []
sync_conc()
for i in range(warm_up):
dh.run_kernel(flux_kernel, seed=42, time_step=i)
dh.run_kernel(pde_kernel)
sync_conc()
for i in range(time):
dh.run_kernel(flux_kernel, seed=42, time_step=i + warm_up)
dh.run_kernel(pde_kernel)
sync_conc()
if(i % measurement_intervall == 0):
data = np.append(data, dh.gather_array(n_field.name).ravel(), 0)
# test mass conservation
np.testing.assert_almost_equal(dh.gather_array(n_field.name).mean(), density_init)
n_bins = 50
density_value, bins = np.histogram(data, density=True, bins=n_bins)
bins_mean = bins[:-1] + (bins[1:] - bins[:-1]) / 2
analytical_value = P(bins_mean, density_init)
print(density_value - analytical_value)
np.testing.assert_allclose(density_value, analytical_value, atol=2e-3)
return lambda density_init, v: run(density_init, np.array(v), time)
advection_diffusion_fluctuations.runners = {}
@pytest.mark.parametrize("velocity", list(product([0, 0.00041], [0, -0.00031])))
@pytest.mark.parametrize("density", [27.0, 56.5])
@pytest.mark.longrun
def test_advection_diffusion_fluctuation_2d(density, velocity):
if 2 not in advection_diffusion_fluctuations.runners:
advection_diffusion_fluctuations.runners[2] = advection_diffusion_fluctuations(2)
advection_diffusion_fluctuations.runners[2](density, velocity)
@pytest.mark.parametrize("velocity", [(0.0, 0.0, 0.0), (0.00043, -0.00017, 0.00028)])
@pytest.mark.parametrize("density", [27.0, 56.5])
@pytest.mark.longrun
def test_advection_diffusion_fluctuation_3d(density, velocity):
if 3 not in advection_diffusion_fluctuations.runners:
advection_diffusion_fluctuations.runners[3] = advection_diffusion_fluctuations(3)
advection_diffusion_fluctuations.runners[3](density, velocity)
def diffusion_reaction(fluctuations: bool):
# parameters
L = (32, 32)
stencil_factor = np.sqrt(1 / (1 + np.sqrt(2)))
dh = ps.create_data_handling(domain_size=L, periodicity=True, default_target=ps.Target.CPU)
species = 2
n_fields = []
j_fields = []
r_flux_fields = []
for i in range(species):
n_fields.append(dh.add_array(f'n_{i}', values_per_cell=1))
j_fields.append(dh.add_array(f'j_{i}', values_per_cell=3 ** dh.dim // 2,
field_type=ps.FieldType.STAGGERED_FLUX))
r_flux_fields.append(dh.add_array(f'r_{i}', values_per_cell=1))
velocity_field = dh.add_array('v', values_per_cell=dh.dim)
D = 0.00666
time = 1000
r_order = [2.0, 0.0]
r_rate_const = 0.00001
r_coefs = [-2, 1]
def grad(f):
return sp.Matrix([ps.fd.diff(f, i) for i in range(dh.dim)])
flux_eq = - D * grad(n_fields[0])
fvm_eq = ps.fd.FVM1stOrder(n_fields[0], flux=flux_eq)
vof_adv = ps.fd.VOF(j_fields[0], velocity_field, n_fields[0])
continuity_assignments = fvm_eq.discrete_continuity(j_fields[0])
# merge calculation of advection and diffusion terms
flux = []
for adv, div in zip(vof_adv, fvm_eq.discrete_flux(j_fields[0])):
assert adv.lhs == div.lhs
flux.append(ps.Assignment(adv.lhs, adv.rhs + div.rhs))
flux = ps.AssignmentCollection(flux)
if(fluctuations):
rng_symbol_gen = random_symbol(flux.subexpressions, dim=dh.dim)
for i in range(len(flux.main_assignments)):
n = j_fields[0].staggered_stencil[i]
assert flux.main_assignments[i].lhs == j_fields[0].staggered_access(n)
# calculate mean density
dens = (n_fields[0].neighbor_vector(n) + n_fields[0].center_vector)[0] / 2
# multyply by smoothed haviside function so that fluctuation will not get bigger that the density
dens *= sp.Max(0,
sp.Min(1.0, n_fields[0].neighbor_vector(n)[0]) * sp.Min(1.0, n_fields[0].center_vector[0]))
# lenght of the vector
length = sp.sqrt(len(j_fields[0].staggered_stencil[i]))
# amplitude of the random fluctuations
fluct = sp.sqrt(2 * dens * D) * sp.sqrt(1 / length) * stencil_factor
# add fluctuations
fluct *= 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
flux.main_assignments[i] = ps.Assignment(flux.main_assignments[i].lhs, flux.main_assignments[i].rhs + fluct)
# Add the folding to the flux, so that the random numbers persist through the ghostlayers.
fold = {ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i):
ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i) % L[i] for i in range(len(L))}
flux.subs(fold)
r_flux = NodeCollection([SympyAssignment(j_fields[i].center, 0) for i in range(species)])
reaction = r_rate_const
for i in range(species):
reaction *= sp.Pow(n_fields[i].center, r_order[i])
new_assignments = []
if fluctuations:
rng_symbol_gen = random_symbol(new_assignments, dim=dh.dim)
reaction_fluctuations = sp.sqrt(sp.Abs(reaction)) * 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
reaction_fluctuations *= sp.Min(1, sp.Abs(reaction**2))
else:
reaction_fluctuations = 0.0
for i in range(species):
r_flux.all_assignments[i] = SympyAssignment(
r_flux_fields[i].center, (reaction + reaction_fluctuations) * r_coefs[i])
[r_flux.all_assignments.insert(0, new) for new in new_assignments]
continuity_assignments = [SympyAssignment(*assignment.args) for assignment in continuity_assignments]
continuity_assignments.append(SympyAssignment(n_fields[0].center, n_fields[0].center + r_flux_fields[0].center))
flux_kernel = ps.create_staggered_kernel(flux).compile()
reaction_kernel = ps.create_kernel(r_flux).compile()
config = ps.CreateKernelConfig(allow_double_writes=True)
pde_kernel = ps.create_kernel(continuity_assignments, config=config).compile()
sync_conc = dh.synchronization_function([n_fields[0].name, n_fields[1].name])
def f(t, r, n0, fac, fluctuations):
"""Calculates the amount of product created after a certain time of a reaction with form xA -> B
Args:
t: Time of the reation
r: Reaction rate constant
n0: Initial density of the
fac: Reaction order of A (this in most cases equals the stochometric coefficient x)
fluctuations: Boolian whether fluctuations were included during the reaction.
"""
if fluctuations:
return 1 / fac * (n0 + n0 / (n0 - (n0 + 1) * np.exp(fac * r * t)))
return 1 / fac * (n0 - (1 / (fac * r * t + (1 / n0))))
def run(density_init: float, velocity: np.ndarray, time: int):
for i in range(species):
dh.fill(n_fields[i].name, np.nan, ghost_layers=True, inner_ghost_layers=True)
dh.fill(j_fields[i].name, 0.0, ghost_layers=True, inner_ghost_layers=True)
dh.fill(r_flux_fields[i].name, 0.0, ghost_layers=True, inner_ghost_layers=True)
# set initial values for velocity and density
for i in range(dh.dim):
dh.fill(velocity_field.name, velocity[i], i, ghost_layers=True, inner_ghost_layers=True)
dh.fill(n_fields[0].name, density_init)
dh.fill(n_fields[1].name, 0.0)
measurement_intervall = 10
data = []
sync_conc()
for i in range(time):
if(i % measurement_intervall == 0):
data.append([i, dh.gather_array(n_fields[1].name).mean(), dh.gather_array(n_fields[0].name).mean()])
dh.run_kernel(reaction_kernel, seed=41, time_step=i)
for s_idx in range(species):
flux_kernel(n_0=dh.cpu_arrays[n_fields[s_idx].name],
j_0=dh.cpu_arrays[j_fields[s_idx].name],
v=dh.cpu_arrays[velocity_field.name], seed=42 + s_idx, time_step=i)
pde_kernel(n_0=dh.cpu_arrays[n_fields[s_idx].name],
j_0=dh.cpu_arrays[j_fields[s_idx].name],
r_0=dh.cpu_arrays[r_flux_fields[s_idx].name])
sync_conc()
data = np.array(data).transpose()
x = data[0]
analytical_value = f(x, r_rate_const, density_init, abs(r_coefs[0]), fluctuations)
# test mass conservation
np.testing.assert_almost_equal(
dh.gather_array(n_fields[0].name).mean() + 2 * dh.gather_array(n_fields[1].name).mean(), density_init)
r_tol = 2e-3
if fluctuations:
r_tol = 3e-2
np.testing.assert_allclose(data[1], analytical_value, rtol=r_tol)
return lambda density_init, v: run(density_init, np.array(v), time)
advection_diffusion_fluctuations.runners = {}
@pytest.mark.parametrize("velocity", list(product([0, 0.0041], [0, -0.0031])))
@pytest.mark.parametrize("density", [27.0, 56.5])
@pytest.mark.parametrize("fluctuations", [False, True])
@pytest.mark.longrun
def test_diffusion_reaction(fluctuations, density, velocity):
diffusion_reaction.runner = diffusion_reaction(fluctuations)
diffusion_reaction.runner(density, velocity)
def VOF2(j: ps.field.Field, v: ps.field.Field, ρ: ps.field.Field, simplify=True):
"""Volume-of-fluid discretization of advection
Args:
j: the staggered field to write the fluxes to. Should have a D2Q9/D3Q27 stencil. Other stencils work too, but
incur a small error (D2Q5/D3Q7: v^2, D3Q19: v^3).
v: the flow velocity field
ρ: the quantity to advect
simplify: whether to simplify the generated expressions (slow, but makes them much more readable and faster)
"""
dim = j.spatial_dimensions
assert ps.FieldType.is_staggered(j)
def assume_velocity(e):
if not simplify:
return e
repl = {}
for c in e.atoms(sp.StrictGreaterThan, sp.GreaterThan):
if isinstance(c.lhs, ps.field.Field.Access) and c.lhs.field == v and isinstance(c.rhs, sp.Number):
if c.rhs <= -1:
repl[c] = True
elif c.rhs >= 1:
repl[c] = False
for c in e.atoms(sp.StrictLessThan, sp.LessThan):
if isinstance(c.lhs, ps.field.Field.Access) and c.lhs.field == v and isinstance(c.rhs, sp.Number):
if c.rhs >= 1:
repl[c] = True
elif c.rhs <= -1:
repl[c] = False
for c in e.atoms(sp.Equality):
if isinstance(c.lhs, ps.field.Field.Access) and c.lhs.field == v and isinstance(c.rhs, sp.Number):
if c.rhs <= -1 or c.rhs >= 1:
repl[c] = False
return e.subs(repl)
class AABB:
def __init__(self, corner0, corner1):
self.dim = len(corner0)
self.minCorner = sp.zeros(self.dim, 1)
self.maxCorner = sp.zeros(self.dim, 1)
for i in range(self.dim):
self.minCorner[i] = sp.Piecewise((corner0[i], corner0[i] < corner1[i]), (corner1[i], True))
self.maxCorner[i] = sp.Piecewise((corner1[i], corner0[i] < corner1[i]), (corner0[i], True))
def intersect(self, other):
minCorner = [sp.Max(self.minCorner[d], other.minCorner[d]) for d in range(self.dim)]
maxCorner = [sp.Max(minCorner[d], sp.Min(self.maxCorner[d], other.maxCorner[d]))
for d in range(self.dim)]
return AABB(minCorner, maxCorner)
@property
def volume(self):
v = sp.prod([self.maxCorner[d] - self.minCorner[d] for d in range(self.dim)])
if simplify:
return sp.simplify(assume_velocity(v.rewrite(sp.Piecewise)))
else:
return v
fluxes = []
cell = AABB([-0.5] * dim, [0.5] * dim)
cell_s = AABB(sp.Matrix([-0.5] * dim) + v.center_vector, sp.Matrix([0.5] * dim) + v.center_vector)
for d, neighbor in enumerate(j.staggered_stencil):
c = sp.Matrix(ps.stencil.direction_string_to_offset(neighbor)[:dim])
cell_n = AABB(sp.Matrix([-0.5] * dim) + c, sp.Matrix([0.5] * dim) + c)
cell_ns = AABB(sp.Matrix([-0.5] * dim) + c + v.neighbor_vector(neighbor),
sp.Matrix([0.5] * dim) + c + v.neighbor_vector(neighbor))
fluxes.append(assume_velocity(ρ.center_vector * cell_s.intersect(cell_n).volume
- ρ.neighbor_vector(neighbor) * cell_ns.intersect(cell).volume))
assignments = []
for i, d in enumerate(j.staggered_stencil):
for lhs, rhs in zip(j.staggered_vector_access(d).values(), fluxes[i].values()):
assignments.append(ps.Assignment(lhs, rhs))
return assignments
@pytest.mark.parametrize("dim", [2, 3])
def test_advection(dim):
L = (8,) * dim
dh = ps.create_data_handling(L, periodicity=True, default_target=ps.Target.CPU)
c = dh.add_array('c', values_per_cell=1)
j = dh.add_array('j', values_per_cell=3 ** dh.dim // 2, field_type=ps.FieldType.STAGGERED_FLUX)
u = dh.add_array('u', values_per_cell=dh.dim)
dh.cpu_arrays[c.name][:] = (np.random.random([l + 2 for l in L]))
dh.cpu_arrays[u.name][:] = (np.random.random([l + 2 for l in L] + [dim]) - 0.5) / 5
vof1 = ps.create_kernel(ps.fd.VOF(j, u, c)).compile()
dh.fill(j.name, np.nan, ghost_layers=True)
dh.run_kernel(vof1)
j1 = dh.gather_array(j.name).copy()
vof2 = ps.create_kernel(VOF2(j, u, c, simplify=False)).compile()
dh.fill(j.name, np.nan, ghost_layers=True)
dh.run_kernel(vof2)
j2 = dh.gather_array(j.name)
assert np.allclose(j1, j2)
@pytest.mark.parametrize("stencil", ["D2Q5", "D2Q9"])
def test_ek(stencil):
# parameters
L = (40, 40)
D = sp.Symbol("D")
z = sp.Symbol("z")
# data structures
dh = ps.create_data_handling(L, periodicity=True, default_target=ps.Target.CPU)
c = dh.add_array('c', values_per_cell=1)
j = dh.add_array('j', values_per_cell=int(stencil[-1]) // 2, field_type=ps.FieldType.STAGGERED_FLUX)
Phi = dh.add_array('Φ', values_per_cell=1)
# perform automatic discretization
def Gradient(f):
return sp.Matrix([ps.fd.diff(f, i) for i in range(dh.dim)])
flux_eq = -D * Gradient(c) + D * z * c.center * Gradient(Phi)
disc = ps.fd.FVM1stOrder(c, flux_eq)
flux_assignments = disc.discrete_flux(j)
continuity_assignments = disc.discrete_continuity(j)
# manual discretization
x_staggered = - c[-1, 0] + c[0, 0] + z * (c[-1, 0] + c[0, 0]) / 2 * (Phi[-1, 0] - Phi[0, 0])
y_staggered = - c[0, -1] + c[0, 0] + z * (c[0, -1] + c[0, 0]) / 2 * (Phi[0, -1] - Phi[0, 0])
xy_staggered = (- c[-1, -1] + c[0, 0]) / sp.sqrt(2) + \
z * (c[-1, -1] + c[0, 0]) / 2 * (Phi[-1, -1] - Phi[0, 0]) / sp.sqrt(2)
xY_staggered = (- c[-1, 1] + c[0, 0]) / sp.sqrt(2) + \
z * (c[-1, 1] + c[0, 0]) / 2 * (Phi[-1, 1] - Phi[0, 0]) / sp.sqrt(2)
A0 = (1 + sp.sqrt(2) if j.index_shape[0] == 4 else 1)
jj = j.staggered_access
divergence = -1 * sum([jj(d) for d in j.staggered_stencil
+ [ps.stencil.inverse_direction_string(d) for d in j.staggered_stencil]])
update = [ps.Assignment(c.center, c.center + divergence)]
flux = [ps.Assignment(j.staggered_access("W"), D * x_staggered / A0),
ps.Assignment(j.staggered_access("S"), D * y_staggered / A0)]
if j.index_shape[0] == 4:
flux += [ps.Assignment(j.staggered_access("SW"), D * xy_staggered / A0),
ps.Assignment(j.staggered_access("NW"), D * xY_staggered / A0)]
# compare
for a, b in zip(flux, flux_assignments):
assert a.lhs == b.lhs
assert sp.simplify(a.rhs - b.rhs) == 0
for a, b in zip(update, continuity_assignments):
assert a.lhs == b.lhs
assert a.rhs == b.rhs
# TODO: test source
@pytest.mark.parametrize("stencil", ["D2Q5", "D2Q9", "D3Q7", "D3Q19", "D3Q27"])
@pytest.mark.parametrize("derivative", [0, 1])
def test_flux_stencil(stencil, derivative):
L = (40, ) * int(stencil[1])
dh = ps.create_data_handling(L, periodicity=True, default_target=ps.Target.CPU)
c = dh.add_array('c', values_per_cell=1)
j = dh.add_array('j', values_per_cell=int(stencil[3:]) // 2, field_type=ps.FieldType.STAGGERED_FLUX)
def Gradient(f):
return sp.Matrix([ps.fd.diff(f, i) for i in range(dh.dim)])
eq = [sp.Matrix([sp.Symbol(f"a_{i}") * c.center for i in range(dh.dim)]), Gradient(c)][derivative]
disc = ps.fd.FVM1stOrder(c, flux=eq)
# check the continuity
continuity_assignments = disc.discrete_continuity(j)
assert [len(a.rhs.atoms(ps.field.Field.Access)) for a in continuity_assignments] == \
[int(stencil[3:])] * len(continuity_assignments)
# check the flux
flux_assignments = disc.discrete_flux(j)
assert [len(a.rhs.atoms(ps.field.Field.Access)) for a in flux_assignments] == [2] * len(flux_assignments)
@pytest.mark.parametrize("stencil", ["D2Q5", "D2Q9", "D3Q7", "D3Q19", "D3Q27"])
def test_source_stencil(stencil):
L = (40, ) * int(stencil[1])
dh = ps.create_data_handling(L, periodicity=True, default_target=ps.Target.CPU)
c = dh.add_array('c', values_per_cell=1)
j = dh.add_array('j', values_per_cell=int(stencil[3:]) // 2, field_type=ps.FieldType.STAGGERED_FLUX)
continuity_ref = ps.fd.FVM1stOrder(c).discrete_continuity(j)
for eq in [c.center] + [ps.fd.diff(c, i) for i in range(dh.dim)]:
disc = ps.fd.FVM1stOrder(c, source=eq)
diff = sp.simplify(disc.discrete_continuity(j)[0].rhs - continuity_ref[0].rhs)
if type(eq) is ps.field.Field.Access:
assert len(diff.atoms(ps.field.Field.Access)) == 1
else:
assert len(diff.atoms(ps.field.Field.Access)) == 2
def test_fvm_staggered_simplification():
D = sp.Symbol("D")
data_type = "float64"
c = ps.fields(f"c: {data_type}[2D]", layout='fzyx')
j = ps.fields(f"j(2): {data_type}[2D]", layout='fzyx', field_type=ps.FieldType.STAGGERED_FLUX)
grad_c = sp.Matrix([ps.fd.diff(c, i) for i in range(c.spatial_dimensions)])
ek = ps.fd.FVM1stOrder(c, flux=-D * grad_c)
ast = ps.create_staggered_kernel(ek.discrete_flux(j))
code = ps.get_code_str(ast)
assert '_size_c_0 - 1 < _size_c_0 - 1' not in code